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  1. Mar 2025
    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      1. General Statements

      We thank the editor for handling our manuscript and the reviewers for their constructive critiques. We are deeply convinced that the reviewers’ suggestions have substantially raised the quality and possible impact of our manuscript. We also like to thank the reviewers for their judgements that the subject of our manuscript is biologically and clinically significant and of high importance, and that our manuscript might help to increase focus and visibility for affected individuals.

      New text passages in the manuscript are colored in red. Below is a point-by-point response to the reviewers’ comments.

      2. Point-by-point description of the revisions

      Response to reviewer 1 comments

      Major comments

      Point 1-1

      The authors performed qRT-PCR validation for markers of differentiation and hypoxia, with a major absence of VEGF and HIF1a. The paper would be strengthened by mention of these factors, especially by qRT-PCR or Western blot.

      We thank the reviewer for the suggestion to include the bona fide hypoxia markers Vegfa and Hif1-alpha. We followed the suggestion and performed qRT-PCR on Vegfa transcripts at each tested condition (Figs. 1A,2A,3A,4A,5A,5D,5I,5N). As Hif1α is rather regulated on protein than on transcript level, we followed the advice to perform Western blots. We analyzed Hif1α protein levels on proliferating cells and quantified by normalization to actin (Figs. 1B,C and 5 B,C).

      Point 1-2

      Please provide justification of selection 0.5% as their hypoxic condition or perhaps repeat experiments in a less extreme environment to see if their conclusions still hold true.

      We admit that our approach to use 0.5% hypoxia was a drastic challenge for the cells. It should be noted, however, that physiologic oxygen levels during pregnancy at times drop to lower than 1% (Hansen et al, 2020; Ng et al, 2017). In the first place, we had used oxygen levels lower than this, because we had wanted to ensure that we can detect responses by bulk RNA-seq with a limited number of samples. As we had many conditions to compare, we did not want to use more than 3-4 samples per condition. The fact that the cells showed normal proliferation underscores the fact that 0.5% O2 per se was not so low that it would be overly stressful to the cells.

      Nevertheless, we are very grateful to the reviewer for the suggestion to include a milder hypoxic condition. We chose 2% O2, because this equals the physiological oxygen concentration shortly before the onset of cranial neural crest cell (CNCC) differentiation. We could recapitulate the phenomenon of impaired differentiation to chondrocytes, osteoblasts and smooth muscle cells at these mild hypoxic conditions, as shown by qRT-PCR and immunofluorescence of typical markers (Figs. 5D-R). Moreover, the differentiation-specific induction of the two central hypoxia-attenuated risk genes associated with orofacial clefts that we had identified by our bioinformatic analyses at 0.5% O2 (Boc and Cdo1), was still observable at 2% O2 (Figs. S6C,D). Interestingly, in some rare cases, the attenuation of induction was lost or not as drastic as in 0.5% O2.

      We are convinced that the experiments at 2% O2 strongly increased the relevance of our manuscript, because we thus detected that oxygen levels prevailing shortly before the onset of CNCC differentiation still can influence their differentiation. This leads to the conclusion that only slight decreases of intra-uterine oxygen levels indeed might interfere with correct differentiation of CNCC.

      Point 1-3

      Standard immunohistochemistry or histology of differentiated cells would strengthen the authors' claims of reduced differentiation under hypoxic conditions, e.g., Alcian blue, alk-phos or Alizarin red, and smooth muscle actin or other indicator.

      We are grateful to the reviewer for the suggestion to include stainings of cells, as these stainings visualized the drastic effects of hypoxia on the cells. We performed immunofluorescent stainings against at least one marker protein for each differentiation paradigm. At 0.5% O2, each protein signals were nearly completely absent and cell morphology was disrupted (Figs. 2E,F, 3E, 4E). At 2% O2, we detected some more protein deposition than at 0.5%. Importantly, cells had retained their normal shape at mild hypoxia (Figs. 5H,M,R, S5A).

      Point 1-4

      The authors identify a few genes that appear down-regulated in all three differentiation conditions. If it is within the scope of the study, it would strengthen the claim of these genes' function to show the effect of knock-down or knock-out for validation.

      We thank the reviewer for the suggestion of gene knock-down or knock-out in order to prove functional relevance of our findings. As this would have been too much effort and beyond the scope of our study, we rather followed the suggestion of reviewer 2 (cf. points 2-6, and 2-8) that headed to the same direction: we mined publicly available sequence data on orofacial development for gene expression or marks of active enhancers. We found robust expression of the two central hypoxia-attenuated OFC risk genes Boc and Cdo1 during human craniofacial development (Fig. 7A) and we identified enhancers that are active in embryonic craniofacial mouse tissue (Fig. 7B). Moreover, we detected expression of both genes during murine craniofacial development in undifferentiated mesenchymal cells, osteoblasts, chondrocytes and smooth muscle cells with the help of a single cell RNA-seq dataset (Figs. 7C-E, S6B).

      Thus, we found evidence for the in vivo relevance of Boc and Cdo1 and could rule out a possible important role of Actg2, the third gene we had identified. We therefore are grateful for the suggestion to circumvent gene knockouts by reviewer 2, as we think these data strongly emphasized the importance of our findings.

      Point 1-5

      Another major critique lies in the initial claim that proliferation of O9-1 cells is not significantly impacted by hypoxia. In figures 1E-H, photograms of the cells cultured 24 -72 hours and quantifications of live vs dead cells are shown as evidence for this argument. However, the increased density of cells in normoxic conditions may be a confounding variable in this assay. It would be interesting for the researchers to assess the percent of dead vs alive cells between normoxic and hypoxic conditions when the plates reach equivalent densities.

      We apologize for the use of image sections from photographs with different cell densities. Of course, as demonstrated by our quantification, cell densities between 0.5% and 21% O2 in total were equal (cf. Figs. 1D,E). We therefore replaced the formerly used sections with new image sections with equal cell numbers.

      We thank the reviewer for the suggestion to examine if cell numbers influence cell death rates. We followed this advice by several approaches: first, we seeded cells at different densities, incubated them for 72 h (the same time span where a minimal difference had been detected) and performed live/dead stainings (Fig. S1B). The seeding density did not affect percentages of dead cells and the values were in the same range as in our initial experiment (Fig. 1J). Moreover, we performed TUNEL stainings of apoptotic cells at different time points to have an additional readout of cell death (Figs. 1K,L). As expected, the percentages of TUNEL-positive cells were identical between hypoxic and normoxic cells at all analyzed time points.

      We therefore concluded that hypoxia does not influence the rate of cell death of proliferating CNCC and accordingly specified our wording in the results section.

      Point 1-6

      At end of Fig 1 section authors attempt to tie phenotypes observed in a cell line in vitro to the complex biological processes. They are not comparable and in vivo models would be better suited for these types of comparisons.

      We apologize for the overconfident wording in our manuscript. Of course, our in vitro experiments cannot fully simulate the complex developmental processes taking place in vivo. We therefore changed the text to a more careful formulation. Moreover, we kept the wording in the discussion section that we cannot exclude that in the in vivo situation proliferation of CNCC is also affected by low oxygen levels because nutrients might not be available in such excess as they are in cell culture.

      Point 1-7

      Fig 2: if qRT-PCR did not show statistically different results between experimental and control groups why move on to bulk RNA seq?

      We apologize that the sentence about statistical significance was misleading. What we wanted to express is that there was only a little difference (if any at all) between differentiated cells at 0.5% O2 and proliferating cells at 0.5% O2 or 21% O2. For the sake of clarity and readability, we deleted this misleading sentence.

      Point 1-8

      Fig 5: hypoxia this intense is going to affect broad range of biological processes and genes. Finding a few genes that are affected in extreme hypoxia that are also risk genes is highly unlikely. How can the authors be assured that these overlaps are actually significant and not just by chance?

      We thank the reviewer for the suggestion to test for statistical significance. We tested significance of the overlap of respective gene sets (nsOFC vs. hyp-a; OFC vs. hyp-a) by Fisher’s exact test. We included Venn diagrams depicting the overlap and present the exact p-values (Figs. S5C,D). In each case where overlap of genes occurred, p-values indicated significance.

      Point 1-9

      Would appreciate discussion on how examination of neural crest is relevant for OFC, as most animal models of OFC demonstrate the pathogenesis in embryonic epithelium or periderm, not in the neural crest. Defects in neural crest are associated with other congenital craniofacial anomalies such as craniosynostosis or complex (Tessier) clefts, not the typical orofacial cleft. Please revise rationale of study, interpretation of data and Discussion to specifically state how neural crest cells are involved in the pathogenesis of orofacial cleft.

      We apologize for not pointing out enough the role of epithelial cells in the emergence of orofacial clefts. We revised our introduction, results and discussion sections in this regard and emphasized the role of epithelial cells. Importantly, we addressed the possible influence of the results gained in CNCC on epithelial cells by analyzing scRNA-seq data with the algorithm CellChat, as suggested by reviewer 2 (cf. point 2-8). We detected several cell communication pathways from CNCC to epithelial cells which contain components that are misexpressed upon hypoxia in our dataset (Figs. 7F-I). Therefore, during hypoxia, these pathways might influence epithelial cells and therefore indirectly cause orofacial clefts. We outlined this possible interplay in the discussion and briefly mentioned it in the abstract.

      We have not discussed more strongly the role of CNCC in the emergence of OFC in the revised manuscript, because we did not want to put even more emphasis on this matter. Numerous studies have proven the contribution of cranial neural crest tissue to the emergence of orofacial clefts. This fact is also pointed out in several review articles about orofacial clefts. In most cases, this knowledge was achieved by mouse models, because tissue-specific conditional knockouts are feasible (in contrast to genetic studies on patients), usually via deletion with the Wnt1-Cre driver. Funato et al. give an excellent (but quite old) overview of mouse models in which the neural crest-specific knockout of a gene leads to emergence of OFC and lists 17 genes for which this is the case (Funato et al, 2015). Moreover, several recent studies also report on the emergence of orofacial clefts upon neural crest-specific deletion (Forman et al, 2024; Li et al, 2025). These include genes responsible for DNA methylation (Ulschmid et al, 2024), and a study on subunits of chromatin remodeling complexes that are necessary for correct transcription of their target genes, which was conducted by our group (Gehlen-Breitbach et al, 2023).

      Minor comments

      __Point 1-10 __

      The author should replace "Final proof" in the introduction with "further evidence supporting."

      We apologize for the incorrect wording. Of course, it is highly questionable if there is such a thing as final proof in life sciences. We re-phrased the text according to the reviewer’s suggestion.

      Point 1-11

      Authors are inconsistent when referring to Figures- sometimes they capitalize (i.e. 1J) and other times they leave lower case (i.e. 1i). Needs to be consistent throughout. Figures are not numbered.

      We apologize for the inconsistency. We corrected the references to figures. Moreover, we apologize for the missing figure numbers. We also corrected this and included figure numbers.

      Point 1-12

      In figures authors would sometimes list 21% O2 first then 0.5% O2 or vice versa. (i.e. Fig on page 21 panels I, J, K). Needs to be consistent.

      We again apologize for being inconsistent. We corrected the inconsistency in Fig. 1D. Now, 21% O2 is presented before/above 0.5% O2.

      Point 1-13

      Figures on pages 28, 29, 30 panel J and page 31 panel F: there is no legend on what the scale/measurement is for the difference in expression level other than it ranges from -1 to +3.

      We thank the reviewer for the hint. We are aware that from the heatmaps we used one cannot infer relative expression rates of different genes or similar. If we would have considered expression strength of single genes, many of the gene-specific differing expression rates under the different conditions would have been hard to detect, as presentation would have been dominated by the differences in expression rates between genes. We therefore plotted gene-wise scaled expression.

      We included an explanation of the procedure in the materials and methods section.

      Point 1-14

      Will the authors please comment on the one normoxic sample in Figure 1I that did not cluster with the others? Did this meet the standards to merit exclusion as an outlier?

      We regret that the default scale of our plot of the principal component analysis is a bit misleading. This is the case because x-axis accounts for 80.3% of variance and y-axis only accounts for 6.1%. Therefore, the sample that might seem as an outlier actually met our standards. Nevertheless, we decided to keep the default scaling as is, in order not to embellish the graph (Fig. 1M).

      Point 1-15

      The authors refer to DEG as deregulated genes; while not strictly incorrect, the more standard usage is "differentially expressed genes." Please address.

      We apologize for the incorrect explanation of the acronym. Of course, this was corrected in the revised manuscript.

      Significance

      This work on neural crest cells and hypoxia are biologically and clinically significant.

      We are deeply grateful to the reviewer for considering our manuscript significant for both biologists and clinicians. We are convinced that the additional data we gathered in the course of the revision has significantly increased the importance of our work. Therefore, we once again express our gratitude to the reviewer for the valuable suggestions.

      Response to reviewer 2 comments

      Major comments

      Point 2-1

      The conclusions drawn from the experimental data are carefully formulated for the most part. One of the main concerns is that the cells were subjected to extreme hypoxic conditions, while it may be more biologically relevant to include a condition representing more mild hypoxia (e.g. 10%).

      Please refer to the response to point 1-2.

      Point 2-2

      One of the opening claims regarding severe hypoxia only mildly affecting cell proliferation is not shown clearly, since no mitotic markers have been analyzed (i.e. KI67 or PCNA staining or a simple EdU incorporation assay). Thus, the claim that they assessed cell proliferation is not very convincing, even though cell death was analyzed.

      We appreciate the reviewer’s suggestion to include a more thorough analysis of proliferation rates. We followed the advice and performed immunofluorescent stainings against Ki67 (accounting for cells in proliferative state) and phospho-histone H3 (accounting for cells undergoing mitosis). We performed this assay at different time points of culture in order to address the question if cell density might influence proliferation rates (Figs. 1F-H). Neither for Ki67 nor for pHH3 a difference was detected between 21% and 0.5% O2.

      We are convinced that these analyses strengthened our initial findings and provide strong evidence that hypoxia does not influence proliferation rates of CNCC.

      Point 2-3

      Additionally, cellular morphology of the cells could be assessed (brightfield images), since previous studies observed that hypoxia can be an inducive factor in cranial neural crest and driving EMT (Scully et al. 2016; Barriga et al. 2013).

      We thank the reviewer’s hint and followed the advice. We analyzed cellular morphology by the parameters cell length, total number of pseudopodia, number of filopodia and number of lobopodia (Figs. S1C-F). As outlined in the results section, we did not detect a difference in these parameters between 21% and 0.5% O2.

      We included the second reference mentioned by the reviewer (Barriga et al, 2013) additionally to Scully et al. 2016 that had already been cited.

      Point 2-4

      Furthermore, in the RNA seq analysis of chondrogenic fate biased cells the authors draw a conclusion based on the proximity of the samples on the PCA plot, which is not very convincing. More careful analysis of the bulk RNA seq data sets they have generated for key marker genes will be more convincing (for example, a heatmap with selected genes would be a helpful representation).

      We apologize for the rash and inaccurate conclusion based on proximity on PCA plots. We are grateful to the reviewer for the suggestion to include heatmaps with selected marker genes. Following this advice, we generated heatmaps on our bulk RNA-seq data with the GO terms specific for each differentiation paradigm (Figs. S2F, S3F, S4F).

      We are convinced that these maps are perfect additions to the heatmaps of the 200 top differentially-expressed genes that already had been included in the manuscript (Figs. 2K, 3J, 4J) and helped to strengthen our findings. For chondrocytes and smooth muscle cells, the new, GO-specific heatmaps perfectly recapitulated the phenomenon of hypoxia-attenuated induction. Interestingly, for osteoblasts, about half of the induced genes were hypoxia-attenuated, while the other half was induced stronger than under normoxia. This pointed to gene-specific mechanisms of hypoxia-dependent attenuation of transcription. Moreover, it shed light on a hypoxia-evoked complete dysregulation of transcriptional induction in osteoblasts, as nearly none of the genes was induced similar to normoxia.

      __ __

      Point 2-5

      As mentioned above, a straight-forward and not time consuming experiment (given that it was assessed for a maximum of 72 hrs) would be to repeat the culture of NCCs and stain for mitotic markers, and quantify the number of positively stained cells over total cell numbers. Furthermore, it is not that demanding to add an experimental condition of less severe hypoxia in this assay.

      We thank the reviewer for the suggestion and followed the advice (cf. point 2-2). The conducted experiments straightened our results, because the initially detected slight tendency to lower cell numbers at 0.5% O2 could thus be falsified: We did not detect any difference for Ki67 and pHH3 between 0.5% and 21% O2 at any analyzed time point (Figs. 1F-H). Moreover, percentages of dead or apoptotic cells at 0.5% O2 did not vary from 21% (Figs. 1I-L, S1B). As we could not detect any difference in proliferation between 21% and 0.5% O2, we skipped the analysis of proliferating cells at 2% O2.

      Point 2-6

      Without underestimating how time consuming this would be, a major lack of experimental validation of the key genes they identify as important across all conditions may be the limitation of the study (this would be the difference between correlation and a probable underlying mechanism). This can be circumvented by more extensive reference to in situ data sets from mouse or existing data sets of single cell and spatial transcriptomics. A suggested targeted knock-down (for example with siRNA, shRNA or CRISPR) to validate a few of the key genes revealed as important could take a few months, with an estimated cost up to 5,000 euros per targeted gene and replicate.

      We thank the reviewer for the notion that targeted knockdowns are beyond the scope of our manuscript. We are deeply grateful for the reviewer’s constructive criticism and for the suggestion to analyze publicly available data sets in order to gather data depicting in vivo relevance of our identified central hypoxia-attenuated OFC risk genes Boc, Cdo1 and Actg2 (cf. point 1-4). We detected robust expression of Boc and Cdo1 during human craniofacial development (Fig. 7A) and we identified enhancers that are active in embryonic craniofacial mouse tissue (Fig. 7B). Moreover, we detected expression of both genes during murine craniofacial development in undifferentiated mesenchymal cells, osteoblasts, chondrocytes and smooth muscle cells by reanalysis of a scRNA-seq dataset (Figs. 7C-E, S6B). This data comprised scRNA-seq of mouse embryonic maxillary prominence at stages E11.5 and E14.5 (Sun et al, 2023).

      Thus, we found evidence for the in vivo relevance of Boc and Cdo1 and could rule out a possible important role of Actg2, the third gene we had identified. We therefore are deeply grateful for the suggestion, as we think these data strongly emphasize the importance of our findings.

      Point 2-7

      On methods, replicates and statistics: The experimental methods and approach are described efficiently and seem reproducible. All biological and technical replicates are of a minimum of N=3 from independent experiments and statistical tests have been run in all cases.

      We thank the reviewer for the appreciation of our methodology, descriptions and statistical analyses.

      Minor points

      Point 2-8

      One of the key implications of NCCs in palate formation is interaction with orofacial epithelial cells, which the authors also mention. It may be interesting to check if any signaling pathways involved in this crosstalk are affected under hypoxic conditions in their existing data sets of bulk RNA SEQ. This can be done by using available algorithms such as CellChat (Jin et al. 2021; Jin, Plikus, and Nie 2023), which has been reported to work also in bulk RNA seq data analysis (according to GitHub). The authors could mine the literature for existing RNA sequencing data that include osteoblasts, chondrocytes and epithelial cells (Ozekin, O'Rourke, and Bates 2023; Piña et al. 2023).

      We are very grateful to the reviewer for this suggestion. Moreover, we like to thank the reviewer for mentioning exemplary references. We followed the advice by the methodology lined out in results and materials and methods sections: we applied the CellChat algorithm on a scRNA-seq dataset (Pina et al, 2023; Sun et al., 2023) to identify pathways containing components that are hypoxia-attenuated (and associated with a risk for OFC) in our bulk RNA-seq dataset (Figs. 7F-I). We did not use the datasets the reviewer had suggested, because the data were not available for us or the file format was not well-suited for the analysis with CellChat. Importantly, the dataset from Sun et al. has the following advantages over the suggested references: the complete maxillary prominence was used (instead of palatal shelves only), and different time points were included. Thus, we were able to follow the expression of genes of interest at different developmental stages before the onset of differentiation and after (Figs. 7C-E and S6B). By our approach, we identified several OFC-related pathways that contain hypoxia-attenuated components such as BMP and FGF signaling and deposition of collagen and fibronectin (Figs. 7F-I). Importantly, the named pathways (and others) send outgoing communication patterns to epithelial cells. Therefore, hypoxia-attenuated gene induction in CNCC could influence epithelial cells via these pathways.

      We believe that the use of the CellChat algorithm has brought a deeper understanding of how hypoxia can have indirect consequences on the important topic of epithelial cells and thus could also evoke OFC. We therefore once again like to express our gratitude to the reviewer.

      Point 2-9

      Additionally, another process that may be affected is EMT (epithelial-to-mesenchymal-transition) and is possible to assess by re-analysis of bulk RNA-seq data while focusing on key genes implicated in this process (i.e. E-cadherin, vimentin, EpCAM, Snail, Twist, PRRX1).

      We thank the reviewer for the advice. We followed the advice and analyzed cellular morphology by the parameters cell length, total number of pseudopodia, number of filopodia and number of lobopodia (Figs. S1C-F) (cf. point 2-3). As we did not detect any differences between 21% and 0.5% O2, and because the cells we used for our analyses represent mesenchymal cells, i.e. cells that had already undergone EMT, we did not re-analyze our dataset with the focus on EMT.

      Point 2-10

      Lastly, when the authors report on the significantly up- or down-regulated genes, it may be interesting to categorize them by ligands, receptors, intracellular molecules and transcription factors (and use separate plots to visualize them). While a big focus of the manuscript are down-regulated genes, less emphasis was given in upregulated genes (other than the response to hypoxia gene module).

      We thank the reviewer for the advice. Following this advice, we categorized genes according to Panther protein classes "intercellular signal molecule" (PC00207), "transmembrane signal receptor" (PC00197) and "gene-specific transcriptional regulator" (PC00264) and depicted the results with violin plots (Fig. S5B). We could not analyze intracellular molecules, because this protein class does not exist in the Panther database. We had not focused on the genes with stronger induction in hypoxic condition, because the number of genes was low in each differentiation paradigm (7 in chondrocytes, less than 30 in osteoblasts, none in smooth muscle cells) and the transcriptional changes were mostly not as drastic as for the attenuated genes. In order to achieve a broader overview of deregulated processes, we now included GO term analyses of genes downregulated during the differentiation regimes both at 21% and 0.5% O2 (Figs. S2D,E, S3D,E, S4D,E).

      Point 2-11

      The authors are referencing extensively and accurately existing studies in the field and the manuscript is exceptionally well-written, with only a few points of limited clarity or increased complexity. Such an example is when the authors refer to OFC risk genes, because it is not clearly stated how the referenced studies reached their conclusions (for example, are they mouse studies, do they involve mutants, are any of these studies based on GWAS on human cohorts). This matter would significantly improve the flow of the text and highlight the importance of the study and their findings.

      We would like to thank the reviewer very much for the appreciation of our scientific writing. We apologize for not explaining exactly how our OFC risk gene lists had been curated. We included this information for both non-syndromic and other OFC risk genes at the respective sites in the results section. Moreover, we included the Human Phenotype Ontology terms that had been used in the search in the materials and methods section.

      We thank the reviewer for this suggestion, as we agree that this information significantly highlights the importance of our findings.

      Point 2-12

      The figures could be redesigned to be more intuitive to interpret. For example, using violin plots and heatmaps, as discussed, and including references or re-analysis/re-use of existing spatial transcriptomics and in situs for marker genes.

      In all cases where there is a comparison of gene expression levels, violin plots would be a better representation of up- and down-regulated genes (i.e. selected genes from Fig1K, comparison of gene expression between normoxic and hypoxic NCCs, Fig 2G when analyzing chondrogenesis and the respective analysis for osteoblasts and smooth muscle cells, as well as when comparing the three fate-biasing conditions to identify common genes that are misregulated).

      We thank the reviewer for the advice and for the appreciation of the usage of heatmaps (Figs. 2K, 3J, 4J, 6F). Unfortunately, as the number of biological replicates is only three to four, the visualization of gene expression data from our bulk RNA-seq data with violin plots was not intuitive. We therefore retained the heatmaps rather than choosing bar graphs, because they are much clearer when presenting expression data of several to many genes. We included violin plots whenever possible due to high numbers of data points (Figs. S1C, S1D, S1E, S1F, S5B). Moreover, we added additional heatmaps to depict transcriptional changes of genes associated with GO terms with the various differentiation regimes (Figs. S2F, S3F, S4F). Unfortunately, we did not detect the three central hypoxia-attenuated genes in spatial transcriptomics data on craniofacial development. But we used scRNA-seq data of different stages of orofacial mouse tissue where we could identify expression of Boc and Cdo1 (cf. points 1-4 and 2-6). These data helped, together with other in vivo data to gain evidence for the in vivo function of Boc and Cdo1 during CNCC differentiation and helped to dismiss Actg2 as another central player.

      Significance

      Several pieces of evidence have pointed to hypoxia as an environmental factor contributing to congenital orofacial clefts, ranging from studies in mouse to observations in human. The authors are doing an excellent job in putting this information together and the question they are trying to answer is of high importance, given the prevalence of such congenital syndromes.

      We are deeply grateful to the reviewer for the appreciation of our work and for classifying our research topic as highly important.

      In terms of the methods and model employed, there are some limitations, related to the choice of a mouse cell line over one from human, the severe hypoxia induced (over a more mild), and the conditions of directed differentiation not allowing for simultaneous examination of more complex lineage transitions. The methods as a whole are not that up-to-date, given the single cell and multiplexed transcriptomic advances the last couple of decades, advanced bioinformatics that could be used in combination with in vitro lineage tracing methods.

      We thank the reviewer for the honest evaluation of our methods, especially for the constructive suggestions that were given to address our hypotheses with more up-to-date methods and at milder hypoxic conditions. As outlined above, we followed the advice and re-analyzed existing scRNA-seq datasets (cf. points 2-6 and 2-8) and checked our central hypotheses at milder hypoxic conditions (cf. response to point 1-3).

      We are deeply convinced that both significantly increased the biological relevance of our results, because we thus (1) gathered evidence for the in vivo function of Boc and Cdo1 and (2) were able to show that the phenomenon of hypoxia-attenuated gene induction still holds true at biologically relevant hypoxic conditions.

      The audience this work will reach are neural crest experts, developmental biologists, and potentially clinical doctors. The general public outreach of such a paper is also diverse, as more focus and visibility is required for the individuals affected by those syndromes and their families.

      We thank the reviewer for the judgement that our manuscript will not only reach neural crest experts, but also developmental biologists in general and potentially also clinicians. We are very much pleased that the reviewer shares our opinion that affected individuals should be more in the focus of public attention. We like to express our gratitude for the judgement that our manuscript might help to increase focus and visibility for them.

      References

      Barriga EH, Maxwell PH, Reyes AE, Mayor R (2013) The hypoxia factor Hif-1α controls neural crest chemotaxis and epithelial to mesenchymal transition. The Journal of cell biology 201: 759-776, 10.1083/jcb.201212100.

      Forman TE, Sajek MP, Larson ED, Mukherjee N, Fantauzzo KA (2024) PDGFRα signaling regulates Srsf3 transcript binding to affect PI3K signaling and endosomal trafficking. Elife 13, 10.7554/eLife.98531.

      Funato N, Nakamura M, Yanagisawa H (2015) Molecular basis of cleft palates in mice. World journal of biological chemistry 6: 121-138, 10.4331/wjbc.v6.i3.121.

      Gehlen-Breitbach S, Schmid T, Fröb F, Rodrian G, Weider M, Wegner M, Gölz L (2023) The Tip60/Ep400 chromatin remodeling complex impacts basic cellular functions in cranial neural crest-derived tissue during early orofacial development. International Journal of Oral Science 15: 16, 10.1038/s41368-023-00222-7.

      Hansen JM, Jones DP, Harris C (2020) The Redox Theory of Development. Antioxid Redox Signal 32: 715-740, 10.1089/ars.2019.7976.

      Li D, Tian Y, Vona B, Yu X, Lin J, Ma L, Lou S, Li X, Zhu G, Wang Y et al (2025) A TAF11 variant contributes to non-syndromic cleft lip only through modulating neural crest cell migration. Hum Mol Genet 34: 392-401, 10.1093/hmg/ddae188.

      Ng KYB, Mingels R, Morgan H, Macklon N, Cheong Y (2017) In vivo oxygen, temperature and pH dynamics in the female reproductive tract and their importance in human conception: a systematic review. Human Reproduction Update 24: 15-34, 10.1093/humupd/dmx028.

      Pina JO, Raju R, Roth DM, Winchester EW, Chattaraj P, Kidwai F, Faucz FR, Iben J, Mitra A, Campbell K et al (2023) Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis. Nature communications 14: 5687, 10.1038/s41467-023-41349-9.

      Sun J, Lin Y, Ha N, Zhang J, Wang W, Wang X, Bian Q (2023) Single-cell RNA-Seq reveals transcriptional regulatory networks directing the development of mouse maxillary prominence. J Genet Genomics 50: 676-687, 10.1016/j.jgg.2023.02.008.

      Ulschmid CM, Sun MR, Jabbarpour CR, Steward AC, Rivera-González KS, Cao J, Martin AA, Barnes M, Wicklund L, Madrid A et al (2024) Disruption of DNA methylation-mediated cranial neural crest proliferation and differentiation causes orofacial clefts in mice. Proc Natl Acad Sci U S A 121: e2317668121, 10.1073/pnas.2317668121.

    2. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      1. General Statements

      We thank the editor for handling our manuscript and the reviewers for their constructive critiques. We are deeply convinced that the reviewers’ suggestions have substantially raised the quality and possible impact of our manuscript. We also like to thank the reviewers for their judgements that the subject of our manuscript is biologically and clinically significant and of high importance, and that our manuscript might help to increase focus and visibility for affected individuals.

      New text passages in the manuscript are colored in red. Below is a point-by-point response to the reviewers’ comments.

      2. Point-by-point description of the revisions

      Response to reviewer 1 comments

      Major comments


      Point 1-1

      The authors performed qRT-PCR validation for markers of differentiation and hypoxia, with a major absence of VEGF and HIF1a. The paper would be strengthened by mention of these factors, especially by qRT-PCR or Western blot.

      We thank the reviewer for the suggestion to include the bona fide hypoxia markers Vegfa and Hif1-alpha. We followed the suggestion and performed qRT-PCR on Vegfa transcripts at each tested condition (Figs. 1A,2A,3A,4A,5A,5D,5I,5N). As Hif1α is rather regulated on protein than on transcript level, we followed the advice to perform Western blots. We analyzed Hif1α protein levels on proliferating cells and quantified by normalization to actin (Figs. 1B,C and 5 B,C).

      Point 1-2

      Please provide justification of selection 0.5% as their hypoxic condition or perhaps repeat experiments in a less extreme environment to see if their conclusions still hold true.

      We admit that our approach to use 0.5% hypoxia was a drastic challenge for the cells. It should be noted, however, that physiologic oxygen levels during pregnancy at times drop to lower than 1% (Hansen et al, 2020; Ng et al, 2017). In the first place, we had used oxygen levels lower than this, because we had wanted to ensure that we can detect responses by bulk RNA-seq with a limited number of samples. As we had many conditions to compare, we did not want to use more than 3-4 samples per condition. The fact that the cells showed normal proliferation underscores the fact that 0.5% O2 per se was not so low that it would be overly stressful to the cells.

      Nevertheless, we are very grateful to the reviewer for the suggestion to include a milder hypoxic condition. We chose 2% O2, because this equals the physiological oxygen concentration shortly before the onset of cranial neural crest cell (CNCC) differentiation. We could recapitulate the phenomenon of impaired differentiation to chondrocytes, osteoblasts and smooth muscle cells at these mild hypoxic conditions, as shown by qRT-PCR and immunofluorescence of typical markers (Figs. 5D-R). Moreover, the differentiation-specific induction of the two central hypoxia-attenuated risk genes associated with orofacial clefts that we had identified by our bioinformatic analyses at 0.5% O2 (Boc and Cdo1), was still observable at 2% O2 (Figs. EV6C,D). Interestingly, in some rare cases, the attenuation of induction was lost or not as drastic as in 0.5% O2.

      We are convinced that the experiments at 2% O2 strongly increased the relevance of our manuscript, because we thus detected that oxygen levels prevailing shortly before the onset of CNCC differentiation still can influence their differentiation. This leads to the conclusion that only slight decreases of intra-uterine oxygen levels indeed might interfere with correct differentiation of CNCC.

      Point 1-3

      Standard immunohistochemistry or histology of differentiated cells would strengthen the authors' claims of reduced differentiation under hypoxic conditions, e.g., Alcian blue, alk-phos or Alizarin red, and smooth muscle actin or other indicator.

      We are grateful to the reviewer for the suggestion to include stainings of cells, as these stainings visualized the drastic effects of hypoxia on the cells. We performed immunofluorescent stainings against at least one marker protein for each differentiation paradigm. At 0.5% O2, each protein signals were nearly completely absent and cell morphology was disrupted (Figs. 2E,F, 3E, 4E). At 2% O2, we detected some more protein deposition than at 0.5%. Importantly, cells had retained their normal shape at mild hypoxia (Figs. 5H,M,R, EV5A).

      Point 1-4

      The authors identify a few genes that appear down-regulated in all three differentiation conditions. If it is within the scope of the study, it would strengthen the claim of these genes' function to show the effect of knock-down or knock-out for validation.

      We thank the reviewer for the suggestion of gene knock-down or knock-out in order to prove functional relevance of our findings. As this would have been too much effort and beyond the scope of our study, we rather followed the suggestion of reviewer 2 (cf. points 2-6, and 2-8) that headed to the same direction: we mined publicly available sequence data on orofacial development for gene expression or marks of active enhancers. We found robust expression of the two central hypoxia-attenuated OFC risk genes Boc and Cdo1 during human craniofacial development (Fig. 7A) and we identified enhancers that are active in embryonic craniofacial mouse tissue (Fig. 7B). Moreover, we detected expression of both genes during murine craniofacial development in undifferentiated mesenchymal cells, osteoblasts, chondrocytes and smooth muscle cells with the help of a single cell RNA-seq dataset (Figs. 7C-E, EV6B).

      Thus, we found evidence for the in vivo relevance of Boc and Cdo1 and could rule out a possible important role of Actg2, the third gene we had identified. We therefore are grateful for the suggestion to circumvent gene knockouts by reviewer 2, as we think these data strongly emphasized the importance of our findings.

      Point 1-5

      Another major critique lies in the initial claim that proliferation of O9-1 cells is not significantly impacted by hypoxia. In figures 1E-H, photograms of the cells cultured 24 -72 hours and quantifications of live vs dead cells are shown as evidence for this argument. However, the increased density of cells in normoxic conditions may be a confounding variable in this assay. It would be interesting for the researchers to assess the percent of dead vs alive cells between normoxic and hypoxic conditions when the plates reach equivalent densities.

      We apologize for the use of image sections from photographs with different cell densities. Of course, as demonstrated by our quantification, cell densities between 0.5% and 21% O2 in total were equal (cf. Figs. 1D,E). We therefore replaced the formerly used sections with new image sections with equal cell numbers.

      We thank the reviewer for the suggestion to examine if cell numbers influence cell death rates. We followed this advice by several approaches: first, we seeded cells at different densities, incubated them for 72 h (the same time span where a minimal difference had been detected) and performed live/dead stainings (Fig. EV1B). The seeding density did not affect percentages of dead cells and the values were in the same range as in our initial experiment (Fig. 1J). Moreover, we performed TUNEL stainings of apoptotic cells at different time points to have an additional readout of cell death (Figs. 1K,L). As expected, the percentages of TUNEL-positive cells were identical between hypoxic and normoxic cells at all analyzed time points.

      We therefore concluded that hypoxia does not influence the rate of cell death of proliferating CNCC and accordingly specified our wording in the results section.

      Point 1-6

      At end of Fig 1 section authors attempt to tie phenotypes observed in a cell line in vitro to the complex biological processes. They are not comparable and in vivo models would be better suited for these types of comparisons.

      We apologize for the overconfident wording in our manuscript. Of course, our in vitro experiments cannot fully simulate the complex developmental processes taking place in vivo. We therefore changed the text to a more careful formulation. Moreover, we kept the wording in the discussion section that we cannot exclude that in the in vivo situation proliferation of CNCC is also affected by low oxygen levels because nutrients might not be available in such excess as they are in cell culture.


      Point 1-7

      Fig 2: if qRT-PCR did not show statistically different results between experimental and control groups why move on to bulk RNA seq?

      We apologize that the sentence about statistical significance was misleading. What we wanted to express is that there was only a little difference (if any at all) between differentiated cells at 0.5% O2 and proliferating cells at 0.5% O2 or 21% O2. For the sake of clarity and readability, we deleted this misleading sentence.

      Point 1-8

      Fig 5: hypoxia this intense is going to affect broad range of biological processes and genes. Finding a few genes that are affected in extreme hypoxia that are also risk genes is highly unlikely. How can the authors be assured that these overlaps are actually significant and not just by chance?

      We thank the reviewer for the suggestion to test for statistical significance. We tested significance of the overlap of respective gene sets (nsOFC vs. hyp-a; OFC vs. hyp-a) by Fisher’s exact test. We included Venn diagrams depicting the overlap and present the exact p-values (Figs. EV5C,D). In each case where overlap of genes occurred, p-values indicated significance.

      Point 1-9

      Would appreciate discussion on how examination of neural crest is relevant for OFC, as most animal models of OFC demonstrate the pathogenesis in embryonic epithelium or periderm, not in the neural crest. Defects in neural crest are associated with other congenital craniofacial anomalies such as craniosynostosis or complex (Tessier) clefts, not the typical orofacial cleft. Please revise rationale of study, interpretation of data and Discussion to specifically state how neural crest cells are involved in the pathogenesis of orofacial cleft.

      We apologize for not pointing out enough the role of epithelial cells in the emergence of orofacial clefts. We revised our introduction, results and discussion sections in this regard and emphasized the role of epithelial cells. Importantly, we addressed the possible influence of the results gained in CNCC on epithelial cells by analyzing scRNA-seq data with the algorithm CellChat, as suggested by reviewer 2 (cf. point 2-8). We detected several cell communication pathways from CNCC to epithelial cells which contain components that are misexpressed upon hypoxia in our dataset (Figs. 7F-I). Therefore, during hypoxia, these pathways might influence epithelial cells and therefore indirectly cause orofacial clefts. We outlined this possible interplay in the discussion and briefly mentioned it in the abstract.

      We have not discussed more strongly the role of CNCC in the emergence of OFC in the revised manuscript, because we did not want to put even more emphasis on this matter. Numerous studies have proven the contribution of cranial neural crest tissue to the emergence of orofacial clefts. This fact is also pointed out in several review articles about orofacial clefts. In most cases, this knowledge was achieved by mouse models, because tissue-specific conditional knockouts are feasible (in contrast to genetic studies on patients), usually via deletion with the Wnt1-Cre driver. Funato et al. give an excellent (but quite old) overview of mouse models in which the neural crest-specific knockout of a gene leads to emergence of OFC and lists 17 genes for which this is the case (Funato et al, 2015). Moreover, several recent studies also report on the emergence of orofacial clefts upon neural crest-specific deletion (Forman et al, 2024; Li et al, 2025). These include genes responsible for DNA methylation (Ulschmid et al, 2024), and a study on subunits of chromatin remodeling complexes that are necessary for correct transcription of their target genes, which was conducted by our group (Gehlen-Breitbach et al, 2023).

      Minor comments

      __Point 1-10 __

      The author should replace "Final proof" in the introduction with "further evidence supporting."

      We apologize for the incorrect wording. Of course, it is highly questionable if there is such a thing as final proof in life sciences. We re-phrased the text according to the reviewer’s suggestion.

      Point 1-11

      Authors are inconsistent when referring to Figures- sometimes they capitalize (i.e. 1J) and other times they leave lower case (i.e. 1i). Needs to be consistent throughout. Figures are not numbered.

      We apologize for the inconsistency. We corrected the references to figures. Moreover, we apologize for the missing figure numbers. We also corrected this and included figure numbers.

      Point 1-12

      In figures authors would sometimes list 21% O2 first then 0.5% O2 or vice versa. (i.e. Fig on page 21 panels I, J, K). Needs to be consistent.

      We again apologize for being inconsistent. We corrected the inconsistency in Fig. 1D. Now, 21% O2 is presented before/above 0.5% O2.

      Point 1-13

      Figures on pages 28, 29, 30 panel J and page 31 panel F: there is no legend on what the scale/measurement is for the difference in expression level other than it ranges from -1 to +3.

      We thank the reviewer for the hint. We are aware that from the heatmaps we used one cannot infer relative expression rates of different genes or similar. If we would have considered expression strength of single genes, many of the gene-specific differing expression rates under the different conditions would have been hard to detect, as presentation would have been dominated by the differences in expression rates between genes. We therefore plotted gene-wise scaled expression.

      We included an explanation of the procedure in the materials and methods section.

      Point 1-14

      Will the authors please comment on the one normoxic sample in Figure 1I that did not cluster with the others? Did this meet the standards to merit exclusion as an outlier?

      We regret that the default scale of our plot of the principal component analysis is a bit misleading. This is the case because x-axis accounts for 80.3% of variance and y-axis only accounts for 6.1%. Therefore, the sample that might seem as an outlier actually met our standards. Nevertheless, we decided to keep the default scaling as is, in order not to embellish the graph (Fig. 1M).

      Point 1-15

      The authors refer to DEG as deregulated genes; while not strictly incorrect, the more standard usage is "differentially expressed genes." Please address.

      We apologize for the incorrect explanation of the acronym. Of course, this was corrected in the revised manuscript.

      Significance

      This work on neural crest cells and hypoxia are biologically and clinically significant.

      We are deeply grateful to the reviewer for considering our manuscript significant for both biologists and clinicians. We are convinced that the additional data we gathered in the course of the revision has significantly increased the importance of our work. Therefore, we once again express our gratitude to the reviewer for the valuable suggestions.

      Response to reviewer 2 comments

      Major comments


      Point 2-1

      The conclusions drawn from the experimental data are carefully formulated for the most part. One of the main concerns is that the cells were subjected to extreme hypoxic conditions, while it may be more biologically relevant to include a condition representing more mild hypoxia (e.g. 10%).

      Please refer to the response to point 1-2.

      Point 2-2

      One of the opening claims regarding severe hypoxia only mildly affecting cell proliferation is not shown clearly, since no mitotic markers have been analyzed (i.e. KI67 or PCNA staining or a simple EdU incorporation assay). Thus, the claim that they assessed cell proliferation is not very convincing, even though cell death was analyzed.

      We appreciate the reviewer’s suggestion to include a more thorough analysis of proliferation rates. We followed the advice and performed immunofluorescent stainings against Ki67 (accounting for cells in proliferative state) and phospho-histone H3 (accounting for cells undergoing mitosis). We performed this assay at different time points of culture in order to address the question if cell density might influence proliferation rates (Figs. 1F-H). Neither for Ki67 nor for pHH3 a difference was detected between 21% and 0.5% O2.

      We are convinced that these analyses strengthened our initial findings and provide strong evidence that hypoxia does not influence proliferation rates of CNCC.

      Point 2-3

      Additionally, cellular morphology of the cells could be assessed (brightfield images), since previous studies observed that hypoxia can be an inducive factor in cranial neural crest and driving EMT (Scully et al. 2016; Barriga et al. 2013).


      We thank the reviewer’s hint and followed the advice. We analyzed cellular morphology by the parameters cell length, total number of pseudopodia, number of filopodia and number of lobopodia (Figs. EV1C-F). As outlined in the results section, we did not detect a difference in these parameters between 21% and 0.5% O2.

      We included the second reference mentioned by the reviewer (Barriga et al, 2013) additionally to Scully et al. 2016 that had already been cited.

      Point 2-4

      Furthermore, in the RNA seq analysis of chondrogenic fate biased cells the authors draw a conclusion based on the proximity of the samples on the PCA plot, which is not very convincing. More careful analysis of the bulk RNA seq data sets they have generated for key marker genes will be more convincing (for example, a heatmap with selected genes would be a helpful representation).

      We apologize for the rash and inaccurate conclusion based on proximity on PCA plots. We are grateful to the reviewer for the suggestion to include heatmaps with selected marker genes. Following this advice, we generated heatmaps on our bulk RNA-seq data with the GO terms specific for each differentiation paradigm (Figs. EV2F, EV3F, EV4F).

      We are convinced that these maps are perfect additions to the heatmaps of the 200 top differentially-expressed genes that already had been included in the manuscript (Figs. 2K, 3J, 4J) and helped to strengthen our findings. For chondrocytes and smooth muscle cells, the new, GO-specific heatmaps perfectly recapitulated the phenomenon of hypoxia-attenuated induction. Interestingly, for osteoblasts, about half of the induced genes were hypoxia-attenuated, while the other half was induced stronger than under normoxia. This pointed to gene-specific mechanisms of hypoxia-dependent attenuation of transcription. Moreover, it shed light on a hypoxia-evoked complete dysregulation of transcriptional induction in osteoblasts, as nearly none of the genes was induced similar to normoxia.

      __ __


      Point 2-5

      As mentioned above, a straight-forward and not time consuming experiment (given that it was assessed for a maximum of 72 hrs) would be to repeat the culture of NCCs and stain for mitotic markers, and quantify the number of positively stained cells over total cell numbers. Furthermore, it is not that demanding to add an experimental condition of less severe hypoxia in this assay.

      We thank the reviewer for the suggestion and followed the advice (cf. point 2-2). The conducted experiments straightened our results, because the initially detected slight tendency to lower cell numbers at 0.5% O2 could thus be falsified: We did not detect any difference for Ki67 and pHH3 between 0.5% and 21% O2 at any analyzed time point (Figs. 1F-H). Moreover, percentages of dead or apoptotic cells at 0.5% O2 did not vary from 21% (Figs. 1I-L, EV1B). As we could not detect any difference in proliferation between 21% and 0.5% O2, we skipped the analysis of proliferating cells at 2% O2.

      Point 2-6

      Without underestimating how time consuming this would be, a major lack of experimental validation of the key genes they identify as important across all conditions may be the limitation of the study (this would be the difference between correlation and a probable underlying mechanism). This can be circumvented by more extensive reference to in situ data sets from mouse or existing data sets of single cell and spatial transcriptomics. A suggested targeted knock-down (for example with siRNA, shRNA or CRISPR) to validate a few of the key genes revealed as important could take a few months, with an estimated cost up to 5,000 euros per targeted gene and replicate.

      We thank the reviewer for the notion that targeted knockdowns are beyond the scope of our manuscript. We are deeply grateful for the reviewer’s constructive criticism and for the suggestion to analyze publicly available data sets in order to gather data depicting in vivo relevance of our identified central hypoxia-attenuated OFC risk genes Boc, Cdo1 and Actg2 (cf. point 1-4). We detected robust expression of Boc and Cdo1 during human craniofacial development (Fig. 7A) and we identified enhancers that are active in embryonic craniofacial mouse tissue (Fig. 7B). Moreover, we detected expression of both genes during murine craniofacial development in undifferentiated mesenchymal cells, osteoblasts, chondrocytes and smooth muscle cells by reanalysis of a scRNA-seq dataset (Figs. 7C-E, EV6B). This data comprised scRNA-seq of mouse embryonic maxillary prominence at stages E11.5 and E14.5 (Sun et al, 2023).

      Thus, we found evidence for the in vivo relevance of Boc and Cdo1 and could rule out a possible important role of Actg2, the third gene we had identified. We therefore are deeply grateful for the suggestion, as we think these data strongly emphasize the importance of our findings.

      Point 2-7

      On methods, replicates and statistics: The experimental methods and approach are described efficiently and seem reproducible. All biological and technical replicates are of a minimum of N=3 from independent experiments and statistical tests have been run in all cases.


      We thank the reviewer for the appreciation of our methodology, descriptions and statistical analyses.

      Minor points

      Point 2-8

      One of the key implications of NCCs in palate formation is interaction with orofacial epithelial cells, which the authors also mention. It may be interesting to check if any signaling pathways involved in this crosstalk are affected under hypoxic conditions in their existing data sets of bulk RNA SEQ. This can be done by using available algorithms such as CellChat (Jin et al. 2021; Jin, Plikus, and Nie 2023), which has been reported to work also in bulk RNA seq data analysis (according to GitHub). The authors could mine the literature for existing RNA sequencing data that include osteoblasts, chondrocytes and epithelial cells (Ozekin, O'Rourke, and Bates 2023; Piña et al. 2023).

      We are very grateful to the reviewer for this suggestion. Moreover, we like to thank the reviewer for mentioning exemplary references. We followed the advice by the methodology lined out in results and materials and methods sections: we applied the CellChat algorithm on a scRNA-seq dataset (Pina et al, 2023; Sun et al., 2023) to identify pathways containing components that are hypoxia-attenuated (and associated with a risk for OFC) in our bulk RNA-seq dataset (Figs. 7F-I). We did not use the datasets the reviewer had suggested, because the data were not available for us or the file format was not well-suited for the analysis with CellChat. Importantly, the dataset from Sun et al. has the following advantages over the suggested references: the complete maxillary prominence was used (instead of palatal shelves only), and different time points were included. Thus, we were able to follow the expression of genes of interest at different developmental stages before the onset of differentiation and after (Figs. 7C-E and EV6B). By our approach, we identified several OFC-related pathways that contain hypoxia-attenuated components such as BMP and FGF signaling and deposition of collagen and fibronectin (Figs. 7F-I). Importantly, the named pathways (and others) send outgoing communication patterns to epithelial cells. Therefore, hypoxia-attenuated gene induction in CNCC could influence epithelial cells via these pathways.

      We believe that the use of the CellChat algorithm has brought a deeper understanding of how hypoxia can have indirect consequences on the important topic of epithelial cells and thus could also evoke OFC. We therefore once again like to express our gratitude to the reviewer.

      Point 2-9

      Additionally, another process that may be affected is EMT (epithelial-to-mesenchymal-transition) and is possible to assess by re-analysis of bulk RNA-seq data while focusing on key genes implicated in this process (i.e. E-cadherin, vimentin, EpCAM, Snail, Twist, PRRX1).

      We thank the reviewer for the advice. We followed the advice and analyzed cellular morphology by the parameters cell length, total number of pseudopodia, number of filopodia and number of lobopodia (Figs. EV1C-F) (cf. point 2-3). As we did not detect any differences between 21% and 0.5% O2, and because the cells we used for our analyses represent mesenchymal cells, i.e. cells that had already undergone EMT, we did not re-analyze our dataset with the focus on EMT.

      Point 2-10

      Lastly, when the authors report on the significantly up- or down-regulated genes, it may be interesting to categorize them by ligands, receptors, intracellular molecules and transcription factors (and use separate plots to visualize them). While a big focus of the manuscript are down-regulated genes, less emphasis was given in upregulated genes (other than the response to hypoxia gene module).

      We thank the reviewer for the advice. Following this advice, we categorized genes according to Panther protein classes "intercellular signal molecule" (PC00207), "transmembrane signal receptor" (PC00197) and "gene-specific transcriptional regulator" (PC00264) and depicted the results with violin plots (Fig. EV5B). We could not analyze intracellular molecules, because this protein class does not exist in the Panther database. We had not focused on the genes with stronger induction in hypoxic condition, because the number of genes was low in each differentiation paradigm (7 in chondrocytes, less than 30 in osteoblasts, none in smooth muscle cells) and the transcriptional changes were mostly not as drastic as for the attenuated genes. In order to achieve a broader overview of deregulated processes, we now included GO term analyses of genes downregulated during the differentiation regimes both at 21% and 0.5% O2 (Figs. EV2D,E, EV3D,E, EV4D,E).

      Point 2-11

      The authors are referencing extensively and accurately existing studies in the field and the manuscript is exceptionally well-written, with only a few points of limited clarity or increased complexity. Such an example is when the authors refer to OFC risk genes, because it is not clearly stated how the referenced studies reached their conclusions (for example, are they mouse studies, do they involve mutants, are any of these studies based on GWAS on human cohorts). This matter would significantly improve the flow of the text and highlight the importance of the study and their findings.

      We would like to thank the reviewer very much for the appreciation of our scientific writing. We apologize for not explaining exactly how our OFC risk gene lists had been curated. We included this information for both non-syndromic and other OFC risk genes at the respective sites in the results section. Moreover, we included the Human Phenotype Ontology terms that had been used in the search in the materials and methods section.

      We thank the reviewer for this suggestion, as we agree that this information significantly highlights the importance of our findings.

      Point 2-12

      The figures could be redesigned to be more intuitive to interpret. For example, using violin plots and heatmaps, as discussed, and including references or re-analysis/re-use of existing spatial transcriptomics and in situs for marker genes.

      In all cases where there is a comparison of gene expression levels, violin plots would be a better representation of up- and down-regulated genes (i.e. selected genes from Fig1K, comparison of gene expression between normoxic and hypoxic NCCs, Fig 2G when analyzing chondrogenesis and the respective analysis for osteoblasts and smooth muscle cells, as well as when comparing the three fate-biasing conditions to identify common genes that are misregulated).

      We thank the reviewer for the advice and for the appreciation of the usage of heatmaps (Figs. 2K, 3J, 4J, 6F). Unfortunately, as the number of biological replicates is only three to four, the visualization of gene expression data from our bulk RNA-seq data with violin plots was not intuitive. We therefore retained the heatmaps rather than choosing bar graphs, because they are much clearer when presenting expression data of several to many genes. We included violin plots whenever possible due to high numbers of data points (Figs. EV1C, EV1D, EV1E, EV1F, EV5B). Moreover, we added additional heatmaps to depict transcriptional changes of genes associated with GO terms with the various differentiation regimes (Figs. EV2F, EV3F, EV4F). Unfortunately, we did not detect the three central hypoxia-attenuated genes in spatial transcriptomics data on craniofacial development. But we used scRNA-seq data of different stages of orofacial mouse tissue where we could identify expression of Boc and Cdo1 (cf. points 1-4 and 2-6). These data helped, together with other in vivo data to gain evidence for the in vivo function of Boc and Cdo1 during CNCC differentiation and helped to dismiss Actg2 as another central player.

      Significance

      Several pieces of evidence have pointed to hypoxia as an environmental factor contributing to congenital orofacial clefts, ranging from studies in mouse to observations in human. The authors are doing an excellent job in putting this information together and the question they are trying to answer is of high importance, given the prevalence of such congenital syndromes.

      We are deeply grateful to the reviewer for the appreciation of our work and for classifying our research topic as highly important.

      In terms of the methods and model employed, there are some limitations, related to the choice of a mouse cell line over one from human, the severe hypoxia induced (over a more mild), and the conditions of directed differentiation not allowing for simultaneous examination of more complex lineage transitions. The methods as a whole are not that up-to-date, given the single cell and multiplexed transcriptomic advances the last couple of decades, advanced bioinformatics that could be used in combination with in vitro lineage tracing methods.

      We thank the reviewer for the honest evaluation of our methods, especially for the constructive suggestions that were given to address our hypotheses with more up-to-date methods and at milder hypoxic conditions. As outlined above, we followed the advice and re-analyzed existing scRNA-seq datasets (cf. points 2-6 and 2-8) and checked our central hypotheses at milder hypoxic conditions (cf. response to point 1-3).

      We are deeply convinced that both significantly increased the biological relevance of our results, because we thus (1) gathered evidence for the in vivo function of Boc and Cdo1 and (2) were able to show that the phenomenon of hypoxia-attenuated gene induction still holds true at biologically relevant hypoxic conditions.

      The audience this work will reach are neural crest experts, developmental biologists, and potentially clinical doctors. The general public outreach of such a paper is also diverse, as more focus and visibility is required for the individuals affected by those syndromes and their families.

      We thank the reviewer for the judgement that our manuscript will not only reach neural crest experts, but also developmental biologists in general and potentially also clinicians. We are very much pleased that the reviewer shares our opinion that affected individuals should be more in the focus of public attention. We like to express our gratitude for the judgement that our manuscript might help to increase focus and visibility for them.

      References


      Barriga EH, Maxwell PH, Reyes AE, Mayor R (2013) The hypoxia factor Hif-1α controls neural crest chemotaxis and epithelial to mesenchymal transition. The Journal of cell biology 201: 759-776, 10.1083/jcb.201212100.

      Forman TE, Sajek MP, Larson ED, Mukherjee N, Fantauzzo KA (2024) PDGFRα signaling regulates Srsf3 transcript binding to affect PI3K signaling and endosomal trafficking. Elife 13, 10.7554/eLife.98531.

      Funato N, Nakamura M, Yanagisawa H (2015) Molecular basis of cleft palates in mice. World journal of biological chemistry 6: 121-138, 10.4331/wjbc.v6.i3.121.

      Gehlen-Breitbach S, Schmid T, Fröb F, Rodrian G, Weider M, Wegner M, Gölz L (2023) The Tip60/Ep400 chromatin remodeling complex impacts basic cellular functions in cranial neural crest-derived tissue during early orofacial development. International Journal of Oral Science 15: 16, 10.1038/s41368-023-00222-7.

      Hansen JM, Jones DP, Harris C (2020) The Redox Theory of Development. Antioxid Redox Signal 32: 715-740, 10.1089/ars.2019.7976.

      Li D, Tian Y, Vona B, Yu X, Lin J, Ma L, Lou S, Li X, Zhu G, Wang Y et al (2025) A TAF11 variant contributes to non-syndromic cleft lip only through modulating neural crest cell migration. Hum Mol Genet 34: 392-401, 10.1093/hmg/ddae188.

      Ng KYB, Mingels R, Morgan H, Macklon N, Cheong Y (2017) In vivo oxygen, temperature and pH dynamics in the female reproductive tract and their importance in human conception: a systematic review. Human Reproduction Update 24: 15-34, 10.1093/humupd/dmx028.

      Pina JO, Raju R, Roth DM, Winchester EW, Chattaraj P, Kidwai F, Faucz FR, Iben J, Mitra A, Campbell K et al (2023) Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis. Nature communications 14: 5687, 10.1038/s41467-023-41349-9.

      Sun J, Lin Y, Ha N, Zhang J, Wang W, Wang X, Bian Q (2023) Single-cell RNA-Seq reveals transcriptional regulatory networks directing the development of mouse maxillary prominence. J Genet Genomics 50: 676-687, 10.1016/j.jgg.2023.02.008.

      Ulschmid CM, Sun MR, Jabbarpour CR, Steward AC, Rivera-González KS, Cao J, Martin AA, Barnes M, Wicklund L, Madrid A et al (2024) Disruption of DNA methylation-mediated cranial neural crest proliferation and differentiation causes orofacial clefts in mice. Proc Natl Acad Sci U S A 121: e2317668121, 10.1073/pnas.2317668121.

    1. Author response:

      The following is the authors’ response to the current reviews.

      We are disappointed that the reviewers do not acknowledge that our data constitute a major step forward for the field. We will prepare a revised version that takes care of the remaining small issues concerning the technical descriptions and a detailed response to the current round of comments. We will also add a summary of the major new findings of our study.


      The following is the authors’ response to the original reviews.

      We appreciate the time of the reviewers and their detailed comments, which have helped to improve the manuscript.

      Our study presents the largest systematic dataset so far on the evolution of sex-biased gene expression in animals. It is also the first that explores the patterns of individual variation in sex-biased gene expression and the SBI is an entirely new procedure to directly visulize these variance patterns in an intuitive way.

      Also, we should like to point out that our study contradicts recent conclusions that had suggested that a substantial set of sex-biased genes has conserved functions between humans and mice and that mice can therefore be informative for gender-specific medicine studies. Our data suggest that only a very small set of genes are conserved in their sex-biased expression between mice and humans in more than one organ.

      In the revised version we have made the following major updates:

      - added a rate comparison of gene regulation turnover between sex-biased and non-sex-biased genes

      - added additional statistics to the variance comparisons and selection tests

      - added a regulatory module analysis that shows that much of the gene turnover happens within modules

      - added a mosaic pattern analysis that shows the individual complexity of sex-biased patterns

      - extended introduction and discussion

      Reviewer #1 (Public Review):<br /> The authors describe a comprehensive analysis of sex-biased expression across multiple tissues and species of mouse. Their results are broadly consistent with previous work, and their methods are robust, as the large volume of work in this area has converged toward a standardized approach.

      I have a few quibbles with the findings, and the main novelty here is the rapid evolution of sex-biased expression over shorter evolutionary intervals than previously documented, although this is not statistically supported. The other main findings, detailed below, are somewhat overstated.

      (1) In the introduction, the authors conflate gametic sex, which is indeed largely binary (with small sperm, large eggs, no intermediate gametic form, and no overlap in size) with somatic sexual dimorphism, which can be bimodal (though sometimes is even more complicated), with a large variance in either sex and generally with a great deal of overlap between males and females. A good appraisal of this distinction is at . This distinction in gene expression has been recognized for at least 20 years, with observations that sex-biased expression in the soma is far less than in the gonad.

      For example, the authors frame their work with the following statement:

      "The different organs show a large individual variation in sex-biased gene expression, making it impossible to classify individuals in simple binary terms. Hence, the seemingly strong conservation of binary sex-states does not find an equivalent underpinning when one looks at the gene-expression makeup of the sexes"

      The authors use this conflation to set up a straw man argument, perhaps in part due to recent political discussions on this topic. They seem to be implying one of two things. a) That previous studies of sex-biased expression of the soma claim a binary classification. I know of no such claim, and many have clearly shown quite the opposite, particularly studies of intra-sexual variation, which are common - see https://doi.org/10.1093/molbev/msx293, https://doi.org/10.1371/journal.pgen.1003697, https://doi.org/10.1111/mec.14408, https://doi.org/10.1111/mec.13919, https://doi.org/10.1111/j.1558-5646.2010.01106.x for just a few examples. Or b) They are the first to observe this non-binary pattern for the soma, but again, many have observed this. For example, many have noted that reproductive or gonad transcriptome data cluster first by sex, but somatic tissue clusters first by species or tissue, then by sex (https://doi.org/10.1073/pnas.1501339112, https://doi.org/10.7554/eLife.67485)

      Figure 4 illustrates the conceptual difference between bimodal and binary sexual conceptions. This figure makes it clear that males and females have different means, but in all cases the distributions are bimodal.

      I would suggest that the authors heavily revise the paper with this more nuanced understanding of the literature and sex differences in their paper, and place their findings in the context of previous work.

      We are sorry that our introduction seems to have been too short to make our points sufficiently clear. Of course, overlapping somatic variation has been shown for morphological characters, but we were aiming to assess this at the sex-biased transcriptome level. Previous studies looking at sex-biased genes were usually limited by the techniques that were available at their times, resulting in a focus on gonads in most studies and almost all have too few individuals included to study within-group variation. We detail this below for the papers that are mentioned by the referee. In view of this, we cite them now as examples for the prevalent focus on gonadal comparisons in most studies. Only Scharmann et al. 2021 on plant leaf dimorphism is indeed relevant for our study with respect to its general findings and we make now extensive reference to it. In addition, we have generally modified the introduction and substantially extended the discussion to make our points clear.

      Snell-Rood 2010: the paper focuses on sex-specific morphological structures in beetles. It samples six somatic tissues for four individuals each of each class. Analysis is done via microarray hybridizations. While categorial differences were traced, variability between individuals was not discussed. By today´s standards, microarrays have anyway too much technical variability to even consider such a discussion.

      Pointer et al. 2013: this paper studies three sexual phenotypes in a bird species, females, dominant males and subordinate males. Tissues include telencephalon, spleen and left gonad. The focus of the analysis is on the gonads, since only few sex-biased genes were found in spleen and brain (according to suppl. Table S1, 0 for the spleen and 2 for the brain). No inferences could be made on somatic variation.

      Harrison 2015: this paper focuses on gonads plus spleen in six bird species with between 2-6 individuals for each sex collected. In the spleen, only one female biased gene and no male biased gene was detected. Hence, the data do not allow to infer patterns of somatic variation.

      Dean et al. 2016: this paper compares four categories of fish caught around nests, with four to seven individuals per category. Only gonads were analyzed, hence no inferences could be made about somatic variability between individuals.

      Cardoso et al. 2017: this paper test categories of fish with alternative reproductive tactics based on brain transcriptomes. While it uses 9-10 individuals per category, it uses pools for sequencing with two pools per category. This does not allow to make any inference on individual variation.

      Todd et al 2017: this paper focuses on three categories of a fish species, females and dominant and sneaker males. It uses brain and gonads as samples with five individuals each for each category. For the brain, more different genes were found between the two types of males, rather than between females and males (3 and 9 respectively). The paper focuses on individual gene descriptions and does not mention somatic variation.

      Scharmann 2021: the paper focuses on 10 species of plants with sexually dimorphic leafs. 5-6 individuals were sampled per sex. The major finding is that sex-biased gene expression does not correlate with the degree of sexual dimorphism of the leafes. The study shows also a fast evolution of sex-biased expression and states that signatures of adaptive evolution are weak. But it does not discuss variance patterns within populations.

      (2) The authors also claim that "sexual conflict is one of the major drivers of evolutionary divergence already at the early species divergence level." However, making the connection between sex-biased genes and sexual conflict remains fraught. Although it is tempting to use sex-biased gene expression (or any form of phenotypic dimorphism) as an indicator of sexual conflict, resolved or not, as many have pointed out, one needs measures of sex-specific selection, ideally fitness, to make this case (https://doi.org/10.1086/595841, 10.1101/cshperspect.a017632). In many cases, sexual dimorphism can arise in one sex only without conflict (e.g. 10.1098/rspb.2010.2220). As such, sex-biased genes alone are not sufficient to discriminate between ongoing and resolved conflict.

      We imply sexual conflict as a driver of genomic divergence patterns in a similar way as it has been done by many authors before (e.g. Mank 2017a, Price et al. 2023, Tosto et al. 2023). While we fully appreciate the point of the referee, we do not really see where we deviate from the standard wording that is used in the context of genomic data. In such data, it is of course usually assumed that they represent solved conflicts (Figure 1D in Cox and Calsbeek) where selection differentials would not be measurable anyway. (Please note also that the phylogenetic approach used in Oliver and Monteiro 2010 becomes rather problematic in view of introgressive hybridization patterns in butterflies), We have extended the discussion to address this.

      (3) To make the case that sex-biased genes are under selection, the authors report alpha values in Figure 3B. Alpha value comparisons like this over large numbers of genes often have high variance. Are any of the values for male- female- and un-biased genes significantly different from one another? This is needed to make the claim of positive selection.

      Sorry, we had accidentally not included the statistics in the final version of the figure. We have added this now in the supplementary table but have also generally changed the statistical approach and the design of the figure.

      Reviewer #2 (Public Review):

      The manuscript by Xie and colleagues presents transcriptomic experiments that measure gene expression in eight different tissues taken from adult female and male mice from four species. These data are used to make inferences regarding the evolution of sex-biased gene expression across these taxa. The experimental methods and data analysis are appropriate; however, most of the conclusions drawn in the manuscript have either been previously reported in the literature or are not fully supported by the data.

      We are not aware of any study that has analyzed somatic sex-biased expression in such a large and taxonomically well resolved closely related taxa of animals. Only the study by Scharman et al. 2021 on plant leaves comes close to it, but even this did not specifically analyze the intragroup variation aspects. Of course, some of our results confirm previous conclusions, but we should still like to point out that they go far beyond them.

      There are two ways the manuscript could be modified to better strengthen the conclusions.

      First, some of the observed differences in gene expression have very little to no effect on other phenotypes, and are not relevant to medicine or fitness. Selectively neutral gene expression differences have been inferred in previous studies, and consistent with that work, sex-biased and between-species expression differences in this study may also be enriched for selectively neutral expression differences. This idea is supported by the analysis of expression variance, which indicates that genes that show sex-biased expression also tend to show more inter-individual variation. This perspective is also supported by the MK analysis of molecular evolution, which suggests that positive selection is more prevalent among genes that are sex-biased in both mus and dom, and genes that switch sex-biased expression are under less selection at the level of both protein-coding sequence and gene expression.

      We have now revisited these points by additional statistical analysis of the variance patterns and an extended discussion under the heading "Neutral or adaptive?". 

      As an aside, I was confused by (line 176): "implying that the enhanced positive selection pressure is triggered by their status of being sex-biased in either taxon." - don't the MK values suggest an excess of positive selection on genes that are sex-biased in both taxa?

      There are different sets of genes that are sex-biased in these two taxa - hence this observation is actually a strong argument for selection on these genes. We have changed the correspondiung text to make this clearer.

      Without an estimate of the proportion of differentially expressed genes that might be relevant for broader physiological or organismal phenotypes, it is difficult to assess the accuracy and relevance of the manuscript's conclusions. One (crude) approach would be to analyze subsets of genes stratified by the magnitude of expression differences; while there is a weak relationship between expression differences and fitness effects, on average large gene expression differences are more likely to affect additional phenotypes than small expression differences.

      We agree that it remains a challenge to show functional effects for the sex-biased genes. The argument that they should have a function is laid out above (and stated in many reviews on the topic). To use the expression level as a proxy of function does not seem justified, given the current literature. For example, genes that are highly conected in modules are not necessrily highly expressed (e.g. transcription factors). Also, genes may be highly expressed in a rare cell type of an organ and have an important funtion there, but this would not show up across the RNA of the whole organ. The most direct functional relationship between sex-biased expression and phenotype comes from the human data in Naqvi et al. 2019 - which we had cited.

      Another perspective would be to compare the within-species variance to the between-species variance to identify genes with an excess of the latter relative to the former (similar logic to an MK test of amino acid substitutions).

      Such an analysis was actually our intial motivation for this study. However, the new (and surprising!) result is that the status of being sex-biased shows such a high turnover that not many genes are left per organ where one could even try to make such a test. However, we have extended the variance analysis with reciprocal gene sets (as we had done it for the MK test) and extended the discussion on the topic, including citation of our prior work on these questions.

      Second, the analysis could be more informative if it distinguished between genes that are expressed across multiple tissues in both sexes that may show greater expression in one sex than the other, versus genes with specialized function expressed solely in (usually) reproductive tissues of one sex (e.g. ovary-specific genes). One approach to quantify this distinction would be metrics like those used defined by [Yanai I, et al. 2005. Genome-wide midrange transcription profiles reveal expression-level relationships in human tissue specification. Bioinformatics 21:650-659.] These approaches can be used to separate out groups of genes by the extent to which they are expressed in both sexes versus genes that are primarily expressed in sex-specific tissue such as testes or ovaries. This more fine-grained analysis would also potentially inform the section describing the evolution/conservation of sex-biased expression: I expect there must be genes with conserved expression specifically in ovaries or testes (these are ancient animal structures!) but these may have been excluded by the requirement that genes be sex-biased and expressed in at least two organs.

      Given that our study focuses on somatic sex-biased genes, we refrain from a comparative analysis of genes that are only expressed in the sex-organs in this paper. With respect to sharing of sex-biased gene expresssion between the somatic tissues, we show in Figure 8 that there are only very few of them (8 female-biased and 3 male-biased). A separate statistical treatment is not possible for this small set of genes.

      There are at least three examples of statements in the discussion that at the moment misinterpret the experimental results.

      The discussion frames the results in the context of sexual selection and sexually antagonistic selection, but these concepts are not synonymous. Sexual selection can shape phenotypes that are specific to one sex, causing no antagonism; and fitness differences between males and females resulting from sexually antagonistic variation in somatic phenotypes may not be acted on by sexual selection. Furthermore, the conditions promoting and consequence of both kinds of selection can be different, so they should be treated separately for the purposes of this discussion.

      We cannot make such a distinction for gene expression patterns - and we are not aware that this was done before in the literature (except gene expression was directly linked to a morphological structure). We have updated this discussion accordingly.

      The discussion claims that "Our data show that sex-biased gene expression evolves extremely fast" but a comparison or expectation for the rate of evolution is not provided. Many other studies have used comparative transcriptomics to estimate rates of gene expression evolution between species, including mice; are the results here substantially and significantly different from those previous studies? Furthermore, the experimental design does not distinguish between those gene expression phenotypes that are fixed between species as compared to those that are polymorphic within one or more species which prevents straightforward interpretation of differences in gene expression as interspecific differences.

      Our statement was in relation to the comparison between somatic and gondadal gene turnover, as well as the comparison to humans. We have now included an additional analysis for a direct comparison with non-sex-biased genes in the same populations (Figure 2B). Note that gene expression variances cannot get fixed anyway, they can only become different in average and magnitude.

      The conclusion that "Our results show that most of the genetic underpinnings of sex differences show no long-term evolutionary stability, which is in strong contrast to the perceived evolutionary stability of two sexes" - seems beyond the scope of this study. This manuscript does not address the genetic underpinnings of sex differences (this would involve eQTL or the like), rather it looks at sex differences in gene expression phenotypes.

      This comes back to the points discussed above about the validity to infer function from sex-biased expression. We have updated the text to clarify this.

      Simply addressing the question of phenotypic evolutionary stability would be more informative if genes expressed specifically in reproductive tissues were separated from somatic sex-biased genes to determine if they show similar patterns of expression evolution.

      Our study is generally focused on somatic gene expression. The comparison with reproductive tissues serves merely as a reference. Since they are of course very different tissues, they should not be compared with each other in the same way. We have now specifically addressed this point in the discussion.

      Reviewer #3 (Public Review):

      This manuscript reports some interesting and important patterns. The results on sex-bias in different tissues and across four taxa would benefit from alternative (or additional) presentation styles. In my view, the most important results are with respect to alpha (fraction of beneficial amino acid changes) in relation to sex-bias (though the authors have made this as a somewhat minor point in this version).

      The part that the authors emphasize I don't find very interesting (i.e., the sexes have overlapping expression profiles in many nongonadal tissues), nor do I believe they have the appropriate data necessary to convincingly demonstrate this (which would require multiple measures from the same individual).

      This is the first study that reports such overlaps and we show that this is not always the case (e.g. liver and kidney data in mice). We are not aware of any preditions of how such patterns would look like and how they would evolve - why should such a new finding not be interesting? Concerning the appropriateness of the data we do not agree with the point the referee makes - see response below.

      This study reports several interesting patterns with respect to sex differences in gene expression across organs of four mice taxa. An alternative presentation of the data would yield a clearer and more convincing case that the patterns the authors claim are legitimate.

      I recommend that the authors clarify what qualifies as "sex-bias".

      This is defined by the statistical criteria that we have applied, following the general standard of papers on this topic.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) "However, already Darwin has pointed out that the phenotypes of the sexes should evolve fast". I think the authors mean that Darwin was quick to point out that sex-specific phenotypes evolve quickly".

      We have modified this text part.

      (2) Non-gonadal is more often referred to as somatic. I would encourage the authors to use this more common term for accessibility.

      We have adopted this term

      (3) Figure 5 is interesting, however, it is difficult to know whether the decreased bimodality in humans compared to mice is biological or technical due to the differences in the underlying data. For example, the mouse samples tightly controlled age and environmental conditions within each species. It is not possible to do that with human samples, and there are very good reasons to think that these factors will affect variance in both sexes.

      Yes, this is certainly true and we know this also from other comparative data between mice and humans. Still, this is human reality vs mouse artificialness. We pick this now up in the discussion.

      (4) Line 273. The large numbers of cells needed for single-cell analysis require that most studies pool multiple samples, however these pools are helpful in themselves. This approach was used by https://doi.org/10.1093/evlett/qrad013 to quantify the degree of sex-bias within cell types across multiple tissues and to compare how bulk and single-cell sex-bias measures compare. Sex-bias in some somatic cell types was very high, even when bulk sex-bias in those tissues was not. This suggests that the bulk data the authors use in this study may in fact obscure the pattern of sex-bias.

      Yes, we agree, and this is exactly how we did the analysis and interpretation, based on the cited paper.

      (5)- Line 379 "Total RNAs were" should be "Total RNA was"

      Corrected

      References cited in this review and which should be included in the manuscript :

      Sam L Sharpe, Andrew P Anderson, Idelle Cooper, Timothy Y James, Alexandra E Kralick, Hans Lindahl, Sara E Lipshutz, J F McLaughlin, Banu Subramaniam, Alicia Roth Weigel, A Kelsey Lewis, Sex and Biology: Broader Impacts Beyond the Binary, Integrative, and Comparative Biology, Volume 63, Issue 4, October 2023, Pages 960-967.

      Included

      Masculinization of Gene Expression Is Associated with Exaggeration of Male Sexual Dimorphism Pointer MA, Harrison PW, Wright AE, Mank JE (2013) Masculinization of Gene Expression Is Associated with Exaggeration of Male Sexual Dimorphism. PLOS Genetics 9(8): e1003697.

      Included

      Erica V Todd, Hui Liu, Melissa S Lamm, Jodi T Thomas, Kim Rutherford, Kelly C Thompson, John R Godwin, Neil J Gemmell, Female Mimicry by Sneaker Males Has a Transcriptomic Signature in Both the Brain and the Gonad in a Sex-Changing Fish, Molecular Biology and Evolution, Volume 35, Issue 1, January 2018, Pages 225-241.

      Included

      Cardoso SD, Gonçalves D, Goesmann A, Canário AVM, Oliveira RF. Temporal variation in brain transcriptome is associated with the expression of female mimicry as a sequential male alternative reproductive tactic in fish. Mol Ecol. 2018; 27: 789-803.

      Included

      Dean, R., Wright, A.E., Marsh-Rollo, S.E., Nugent, B.M., Alonzo, S.H. and Mank, J.E. (2017), Sperm competition shapes gene expression and sequence evolution in the ocellated wrasse. Mol Ecol, 26: 505-518.

      Included

      Emilie C. Snell‐Rood, Amy Cash, Mira V. Han, Teiya Kijimoto, Justen Andrews, Armin P. Moczek, DEVELOPMENTAL DECOUPLING OF ALTERNATIVE PHENOTYPES: INSIGHTS FROM THE TRANSCRIPTOMES OF HORN‐POLYPHENIC BEETLES, Evolution, Volume 65, Issue 1, 1 January 2011.

      Not included, since its technical approach is not really comparable

      Harrison PW, Wright AE, Zimmer F, Dean R, Montgomery SH, Pointer MA, Mank JE (2015) Sexual selection drives evolution and rapid turnover of male gene expression. Proceedings of the National Academy of Sciences, USA 112: 4393-4398.

      Included

      Mathias Scharmann, Anthony G Rebelo, John R Pannell (2021) High rates of evolution preceded shifts to sex-biased gene expression in Leucadendron, the most sexually dimorphic angiosperms eLife 10:e67485.

      Included

      Sexually Antagonistic Selection, Sexual Dimorphism, and the Resolution of Intralocus Sexual Conflict. Robert M. Cox and Ryan Calsbeek , The American Naturalist 2009 173:2, 176-187.

      Included

      Ingleby FC, Flis I, Morrow EH. Sex-biased gene expression and sexual conflict throughout development. Cold Spring Harb Perspect Biol. 2014 Nov 6;7(1):a017632.

      Included

      Oliver JC, Monteiro A 2011. On the origins of sexual dimorphism in butterflies. Proc Biol Sci 278: 1981-1988.

      Included

      Iulia Darolti, Judith E Mank, Sex-biased gene expression at single-cell resolution: cause and consequence of sexual dimorphism, Evolution Letters, Volume 7, Issue 3, June 2023, Pages 148-156.

      Included

      Reviewer #2 (Recommendations For The Authors):

      I am concerned the smoothed density plots in Figure 4 may be providing a misleading sense of the distributions since each distribution is inferred from only 9 values. A boxplot might better represent the data to the reader.

      Boxplots with 9 values are much more difficult to interpret for a reader, this is the very reason why one tends to smoothen them. In this way, they also become similar to the standard plots that are used for showing morphological variation between the sexes. Note that the original data are availble for the individual values, if these are of special interest in some cases. In addition, our new “mosaic” analysis (Figure 6) provides another presentation for readers.

      Line 235: "the overall numbers are lower" I assume this is the number of genes included in the analyses, but this should be explicitly stated.

      Clarified in the text

      The analysis of gene expression from different brain regions in control individuals from the Alzheimer's study (line 273) suffers from low power and it is not clear to me how much taking samples from different brain regions eliminates the issue of different cell types within a sample (the stated motivation for this analysis). While I support publishing negative results, this section does not feel like it adds much to the manuscript and could be cut in my opinion.

      This is actually a study on single cell types, differentiating each of them. We are sorry that the text was apparently unclear about this. Given that there are studies that show the importance of looking at single cell data, we still think that is a suitable analysis. We have updated the text to make it clearer.

      It might be useful to separate out X-linked genes from autosomal genes to see if they show consistent patterns with regard to sex-bias.

      We have added this information in suppl. Table S2 and include some description in the text.

      Reviewer #3 (Recommendations For The Authors):

      Comments follow the order of the Results section:

      (1) The latter half of this line in the Methods is too vague to be helpful: "We have explored a range of cutoffs and found that a sex-bias ratio of 1.25-fold difference of MEDIAN expression values combined with a Wilcoxon rank sum test and Benjamini-Hochberg FDR correction (using FDR <0.1 as cutoff) (Benjamini & Hochberg, 1995) yields the best compromise between sensitivity and specificity". What precisely is meant by "the best compromise between sensitivity and specificity"?

      We explain now that this was based on pre-tests with comparing randomized with actual data. However, we agree that this is in the end a subjective decision, but there is no single standard used in the literature, especially when somatic organs are included. We consider our criteria as rather stringent.

      (2) The 1.25 number for sex bias is, ultimately, an arbitrary cut-off. It is common in this literature to choose some arbitrary level and, in this sense, the authors are following common practice. The choice of 1.25 should be stated in the main text as it is a lower (but not reasonable) value than has been used in many other papers.

      It is not only the cutoff, but also the Wilcoxon test and FDR correction that defines the threshold. See also comment above.

      (3) In truth, dimorphism is continuous rather than discrete (i.e, greater or less than 1.25 fold different). Thus, where possible it would be useful to present results in a fashion that allows readers to see the continuous range of ratios rather than having to worry about whether the patterns are due to the rather arbitrary choices of how genes were binned into sex-bias categories.

      It is necessary to work with cutoffs in such cases - and this is the usual practice for any such paper. But we provide now in Figure 1 Figure supplement 1 plots with the female/male ratio distributions.

      a) Number of genes that are female- / male-biased. I would like to be able to see a version of Figure 1 showing the full distribution of TPM ratios rather than bar graphs of the numbers of (arbitrarily defined) female- and male-biased genes. This will be, of course, a larger figure (a full distribution rather than 2 bars for each species for each organ) and so could be relegated to Supplementary Material (assuming the message of that figure is the same as the current Figure 1).

      This is a very unusual request, given that no other paper has done this either. It would indeed result in a non-managable figure size, or many separate figures that would be difficult to scrutinize. Note that there would be one plot of two (female and male) TPM distributions for each sex-biased gene in each organ and each taxon, leading to hundreds of thousands of plots. We think that by providing the general distributions as plots (see above), and the original data as supplements is sufficient.

      b) Turnover of genes with sex bias. This important issue is addressed in Figure 2. First, it is not precisely clear what "percentages of sums of shared genes for any pairwise comparison" in Figure 2 legend means and no further detail is given in the Methods; this must be made clearer or the info in Figure 2 is meaningless. Regardless, this approach again relies heavily on the arbitrary criterion of defining sex-bias. Thus, I would like to see correlation plots of the log(TPM ratio) between taxa as done in the classic multispecies fly paper of Zhang et al. 2007. In Figure 2 it is quite clear that male-biased genes evolve with respect to sex bias more rapidly than female-biased genes.

      We have provided a better explanation of this analysis. Note that the Zhang et al. 2007 paper was not focussing on somatic expression and covers a much broader evolutionary spectrum. Hence, the results are not comparable. Also, we doubt that it would be so helpful to generate a huge figure with all these plots.

      (4) Is there a simpler explanation for the results in the "Variance patterns" section? The total variance for any variable can be decomposed into the variance within and among "groups". If we use "sex" as the group, then there are genes - labelled sex-biased genes - that were identified as such, in essence, because they have high among-group variance. Given that we then know a priori at the start of this section of sex-biased genes have high among-group variance, is it at all surprising that they have higher total variance than the unbiased genes (which we know a priori have low among-group variance)? Perhaps I misunderstood the point of this section. Maybe it would be more meaningful to examine the WITHIN-SEX variance (averaged across the two sexes) instead.

      We did calculate IQR/median (“normalized variance”) with the nine mice for each gene and each sex in each organ, hence sex is not a variance factor in this calculation. The algorithm steps are outlined in suppl. Table S17. We have now also added a variance calculation for reciprocal gene sets and added an extended discussion of these results.

      (5) Analysis of alpha for sex-biased genes. This was the most interesting part of this manuscript to me.

      (a) More information about what SNVs were used is required.

      i. Were only sites where SPR was fixed used? (If not, how was polarization done?)

      ii. Were sites only considered diverged if they were fixed for different bases in DOM and MUS? (If not, what was the criteria?)

      iii. Using, say, DOM as the focal species, a site must be polymorphic in DOM. But did its status (polymorphic/fixed) in MUS matter?

      We have added a more detailed description on this in the Methods section. For the direct answers of the three questions: (i) yes; (ii) yes; (iii) no, considering that DOM and MUS are two subspecies of Mus musculus separating recently, a variant might occur before separating and there might be gene flow between them.

      (b) A particularly interesting part of the analysis is the investigation of alpha for genes that are NOT sex-biased in one taxa but are sex-biased in the other. At the moment (as I understand it), alpha is only calculated for these genes in the taxa where they are NOT sex-biased (and this alpha value can be compared to the alpha of sex-biased genes and of unbiased genes in that taxa). I would like to see both sets of genes (set 1: those sex-biased in MUS and not in DOM; set 2: those sex-biased DOM and not in MUS) analyzed in each of the 2 species, with results presented in a 2x2 table.

      By definition of these categories, these genes are sex-biased in the respective other taxon, hence the values are already in the table. They are named as “reciprocal”.

      (c) No confidence intervals are given for the alpha values, despite the legend of Figure 3 referring to them.

      These were accidentally omitted - we now included the full table in suppl. Table S6; Figure 3 was modified to show violin plots of the bootstrap distributions

      The author's creation and use of a "sex-bias index" (SBI). My greatest skepticism of this manuscript is with respect to the value of their manufactured index, SBI. Of course, it is possible to create such an index but does this literature really need this index or does this just add to the "clutter" in the literature for this field? Is it helping to illuminate important patterns? This index is presumably some attempt to quantify how "male-like" or "female-like" overall expression is for a given individual (for a given organ). It is calculated as SBI = (MEDIAN of all female-biased tpm) - (MEDIAN of all male-biased tpm).

      (6) A main result that comes from this is that the sexes tend to overlap for these values for most nongonad tissues but are clearly distinct for gonadal tissues. I do not think this result would come as a surprise to almost anyone and I'm far from convinced that this metric is a good way to quantify that point. Let's consider testes vs. ovaries. Compared to non-gonadal tissues, I am reasonably certain that not only are there many more genes that are classified as "sex-biased" in gonads but also the magnitude of sex-bias among these genes is typically much greater than it is for the so-called sex-biased genes in nongonadal tissue (density plots requested in #3a would make this clear). In other words, males and females are, on average, very different with respect to expression in gonads so even allowing for variation within each sex will still result in a clear separation of all individuals of the two sexes. In contrast, males and females are, on average, much less different in, say, heart so when we consider the variation within each sex, there is overlap. One could imagine a variety of different metrics which could be used to make this point. The merits of "SBI" are unclear. It is a novel metric and its properties are poorly understood. (A simple alternative would be looking at individual scores along the axis separating mean/median males and females; almost certainly, for gonads, this would be very similar to PC scores for PC1.)

      As throughout the text, we use gonadal comparisons only as general reference, not as the main result. The main result that we are stressing is the fast turnover of these patterns, including from binary to overlapping for kidney and liver in mouse. We consider this as a new finding. If it comes "not to a surprise to anyone", isn´t it great that one does not have to guess anymore but has finally real data on this?

      We have now also added a mosaic analysis to show that the SBI can be used as summary measure in different presentations.

      The use of a single PC axis is no good alternative, since it throws away the information from the other axis.

      We have now included an explicit discussion on the usefulness of the SBI.

      (7) For simplicity, let's assume all males are identical and all females are identical. Let's imagine that heart and kidney have the exact same set of sex-biased genes. There are 20 female-biased genes; they all happen to be identical in expression level (within tissue) and look like this:

      Female TPM Male TPM TPM ratio (F:M)

      Heart 4 2 2

      Kidney 40 20 2

      And there are 20 male-biased genes that look like this:

      Female TPM Male TPM TPM ratio (F:M)

      Heart 1 3 1/3

      Kidney 10 30 1/3

      Most people would describe these two tissues as equally sex-biased.

      However, the SBIs would be:

      Female SBI Male SBI Sex difference (F - M)

      Heart 4-1 = 3 2 - 3 = -1 4

      Kidney 40-10 =30 20-30 = -10 40

      Is it a desirable property that by this metric these two tissues have wildly different SBI values for each sex as well as for the difference between sexes? (At the very least, shouldn't you make readers aware of these strange properties of SBI so they can decide how much value they put into them?)

      Actually, in this example the simple ratio between the expression levels has a strange property, since it does not reflect a much higher expression of the relevant genes in the kidney. The SBI is actually more suitable for making such cases clear. Of course, this is under the assumption that expression level has a meaning for the phenotype, but this is the general assumption for all RNA-Seq experiment comparisons.

      (8) With respect to Figure 4, why do females often have mean SBI values close to zero or even negative (e.g., kidney, mammary glands)? Is this simply because the female-biased genes tend to have lower TPM than the male-biased genes? It seems that the value zero for this metric is really not very biologically meaningful because this metric is a difference of two things that are not necessarily expected to be equal.

      This is the extra information about the expression levels that is gained via the SBI values (see comment above). However, we noticed that people can get confused about this. We have now added a re-scaling step to focus completely on the variance information in these plots.

      (9) Interpreting variances. A substantial fraction of the latter half of the manuscript focuses on interpreting variances among individual samples. This is problematic because there is no replication within individuals (i.e.., "repeatability"), thus it is impossible to infer the extent of observed variance among individuals of a given group (e.g., among females) is due to true biological differences among individuals or is simply due to noise (i.e., "measurement error" in the broad sense). Is the larger variance for mammary glands than liver or gonads just due to measurement error? What is the evidence?

      This point was of course a major issue during the times where microarrays were used for transcriptome studies. However, the first systematic RNA-Seq studies showed already that the technical replicability is so high, that technical replicates are not required. In fact, practically all RNA-Seq studies are done without technical replicates for this reason.

      (10) Because I have little confidence in the SBI metric (#7-8) and in interpreting within sex variances (#9), I found little value in the human results and how SBI distributions (and degree of overlap between sexes) compare between humans and mice.

      We disagree - the current published status is that there are thousands of sex-biased gene in humans and this has implications for gender-specific medicine (Oliva et al. 2020). Our results show a much more nuanced picture in this respect.

      (11) I found even less value in the single-cell data. It too suffers from the issues above. Further, as the authors more or less state, the data are too limited to say much of value here. It is impossible to tell to what extent the results are simply due to data limitations.

      We have pointed out that it is still valuable to have them. They are good enough to exclude the possibility that only a small set of cells drives the overall pattern across an organ. We have further clarified this in the text.

      (12) The code for data analysis should be posted on GitHub or some other repository.

      The code for the sex-biased gene detection and analysis has been posted on GitHub (see Code availability in the manuscript).

    1. Author response:

      The following is the authors’ response to the original reviews

      Public reviews:

      Reviewer #1:

      Weaknesses:

      As this paper only uses anatomical analyses, no functional interpretations of cell function are tested.

      The aim of this paper was to describe the ultrastructural organization of compound eyes in the extremely small wasp Megaphragma viggianii. The authors successfully achieved this aim and provided an incredibly detailed description of all cell types with respect to their location, volume, and dimensions. As this is the first of its kind, the results cannot easily be compared with previous work. The findings are likely to be an important reference for future work that uses similar techniques to reconstruct the eyes of other insect species. The FIB-SEM method used is being used increasingly often in structural studies of insect sensory organs and brains and this work demonstrates the utility of this method.

      We thank you for your high assessment of our work. Unfortunately, it is hard to test our functional interpretations and check them with electrophysiological methods due to the extremely small size of the animal. Studies on three-dimensional ultrastructural datasets obtained using vEM have just started to appear, and we hope that a lot of data will become available for comparison in the nearest future.

      Reviewer #2:

      Thank you for your work and for your high assessment of our manuscript.

      Reviewer #3:

      Weaknesses:

      The claim that the large dorsal part of the eye is the dorsal rim area (DRA), supported by anatomical data on rhabdomere geometry and connectomics in authors' earlier work, would eventually greatly benefit from additional evidence, obtained by immunocytochemical staining, that could also reveal a putative substrate for colour vision. The cell nuclei that are located in the optical path in the DRA crystalline cone have only a putative optical function, which may be either similar to pore canals in hymenopteran DRA cornea (scattering) or to photoreceptor nuclei in camera-type eyes (focussing), both explanations being mutually exclusive.

      We thank the Reviewer for high assessment of our study and for detailed analysis of our manuscript. Your comments and recommendations are very valued and helped us to improve the text. We understand that immunocytochemical methods could improve our findings and supply additional evidence, but there is no technical possibility for this in present. Megaphragma is a very complicated model organism for such methods. We are currently working on the optimization of the protocol for staining, which is needed because of the high level of autoluminescence and because of insufficient penetration of dyes into the samples.

      Recommendations for the authors:

      Reviewer #1:

      I do not have any major concerns about the content of the paper.

      There are some minor spelling and grammatical errors throughout the text but these can be identified most readily using a spelling/grammar check.

      We have revised the text, checked the spelling, and fixed the grammatical errors throughout the text.

      I suggest consistency when referring to the capitalization of the term 'non-DRA' as it is sometimes 'Non-DRA' in the text.

      We have fixed the term “non-DRA” throughout the text. Thank you.

      Also, check carefully the spelling of headings in the tables as there are a few mistakes in Table 1 and 5 in particular.

      The grammar errors have been fixed.

      Figure 7 legend: an explanation of the abbreviation RPC should be added.

      We have done so.

      Reviewer #2:

      (1) The paper presents the data in great detail, however, since this is the first time the technique has been applied to get whole insect eyes, even if on a small insect, it would be worth outlining in the methods section what innovations in the staining/ scanning or sample preparation allowed these improvements and a roadmap for extending this method to larger insects if possible.

      The whole method, including sample preparation, staining, and scanning, was described in our previous paper (Polilov et al., 2021), where it was presented in every detail. Due to the complicated methodology we suppose that it is not necessary to include all the stages of the technique in the present paper, and thus described it more briefly.

      (2) The optical modelling needs a statement in the discussion providing a disclaimer on parameters like sensitivity, anatomical measurements can provide limits and some measure, but the inherent optics are also key and it is worth qualifying these as only estimates and measurements that give a sense of the variation in morphology, only coupled with optical and potentially neural measurements could one confirm the true sensitivity and acceptance angle.

      In the absence of experimental data or precise computational models of Megaphragma vision, we try to discuss rather carefully the functions of structures based on their morphology, ultrastructure, first-order visual connectome, and analogies with other species. This is reflected in the methods and those sections of our paper that contain functional interpretations.

      Reviewer #3

      (1) The finding that the CNS neurons are enucleated, while the compound eye contains cell nuclei, deserves another word. I would confidentially say that the optical demands of a miniaturized compound eye (the minimal size of the optics due to diffraction, the rhabdomere size, and the minimal thickness of optically insulating granules) are such that further cellular miniaturization is not possible, and the minimal sizes even render the cells that build the eye sufficiently large to accommodate cell nuclei. This is in my opinion a parsimonious explanation, yet speculative and I leave it up to you to embrace it or not.

      We agree with the Reviewer and understand the limiting factors and the optical demands of a miniaturized compound eye. According to our data, nuclei occupy a considerable volume in the eye (in the cells of compound eye there are more nuclei than in the whole brain), and on average the cell volume is larger than in Trichogramma, which is minute, but larger than Megaphragma. But as the Reviewer rightly assumed, it is speculative; therefore, we would like to avoid it.

      (2) Our current understanding of DRA optics and function is limited and I claim that your interpretation of the cell nuclei in the DRA dioptrical apparatuses is inappropriate. Please consider a few articles on hymenopteran DRA, starting with the one below and the citing literature:

      Meyer, E.P., Labhart, T. Pore canals in the cornea of a functionally specialized area of the honey bee's compound eye. Cell Tissue Res. 216, 491-501 (1981). https://doi.org/10.1007/BF00238646

      Honebyee DRA has a milky appearance under a stereomicroscope and can be discerned from the outside. This is due to pore canals in the cornea. I happen to be studying this exact structure and its function right now. I found that the result of those canals is not so much the extended receptor acceptance angles, but rather a minimized light gain. This is counterintuitive, but think of the following. The DRA photoreceptors must encode the limited range of polarization contrasts with a maximal working dynamic range (= voltage) of the photoreceptors, which results in a very steep stimulus-response curve.

      Physiologically such a curve is due to very high transduction gain and a high cell input resistance. In most of the retina, small contrasts are transcoded by LMC neurons, but DRA receptors are long visual fibres and must do the job themselves. The skylight intensity (especially antisolar, where the polarized pattern is maximal) varies little during the day. Hence, the DRA receptors work almost at a fixed intensity range. In order to prevent receptor saturation and keep steep contrast coding, the corneal lenses in DRA have a built-in diffusor ring, which diminishes the light influx. Unfortunately, I have yet to publish this and I may be wrong, of course. But if I look into your data, I see consistently smaller corneal lenses and crystalline cones in the DRA, plus the cell nuclei obstructing the incident light. I think this is similar to the optics of honeybee DRA.

      You do not support your claim that the nuclei additionally focus light by optical calculations, but cite literature on camera-type eyes, which is not OK.

      In any case, I think it is fair to limit the discussion by saying that the nuclei may have an optical role. Further evidence from hymenopteran and vertebrate literature is controversial. “so that the nuclei act as extra collecting lenses, as was reported for rod cells of nocturnal vertebrates (Solovei et al., 2009; Błaszczak et al., 2014)” - please consider omitting this.

      We thank the Reviewer for this piece of advice. And we have rewritten the text, to omit the comparison with vertebrates, but left the citation as an illustration of the fact that nuclei could perform the optical role.

      “Since the nuclei in DRA and non-DRA ommatidia are arranged differently in cone cells, we suggest that the nuclei of the cone cells of DRA ommatidia in M. viggianii perform some optical role, facilitating the specialization of this group of ommatidia. The optical function for nuclei was described for rod cells of nocturnal vertebrates, where chromatin inside the cell nucleus has a direct effect on light propagation (Solovei et al., 2009; Błaszczak et al., 2014; Feodorova et al., 2020).”

      (3) Please consider comparing the structure and function of ectopic receptors with the eyelet in Drosophila (i.e. https://doi.org/10.1523/JNEUROSCI.22-21-09255.2002 )

      We thank the Reviewer for this advice and have included the comparison fragment into the text:

      “The position of ePR, their morphology and synaptic targets look similar to the eyelet (extraretinal photoreceptor cluster) discovered in Drosophila (Helfrich-Förster et al., 2002). Eyelets are remnants of the larval photoreceptors, Bolwig’s organs in Drosophila (Hofbauer, Buchner, 1989). Unlike Drosophila, Trichogrammatidae are egg parasitoids and their central nervous system differentiation is shifted to the late larva and even early pupa (Makarova et al., 2022). According to the available data on the embryonic development of Trichogrammatidae, no photoreceptors cells were found during the larval stages (Ivanova-Kazas, 1954, 1961).”

      According to this, the analogy question remains open.

      (4) Minor remarks:

      “but also to trace the pathways that connect the analyzer with the brain.” - I find the word analyzer a bit stretched here; sure, the DRA is polarization analyzer, but if the main retina was monochromatic, it would only be a detector, not an analyzer.

      The sentence was changed according to the Reviewer’s advice.

      Table I: thikness -> thickness, wigth -> width

      We have fixed these misprints.

      “The cross-section of Non-DRA ommatidia has a strongly spherical shape” - perhaps circular, not spherical. And not necessary to say “strongly”

      The spelling was changed according to the Reviewer’s advice.

      “which can be rarely visualized in the cell's projections not far from the basement membrane.” - I'd suggest saying “which are nearly absent in retinula axons”

      The spelling was changed according to the Reviewer’s advice.

      “The pigment granules of the retinula cells have an elongated nearly oval shape” - please consider replacing 'elongated nearly oval' with 'prolate' (try googling for “prolate” or “oblate spheroids”; the adjective describes precisely what you wanted to say)

      We thank the Reviewer for this piece of advice but prefer to leave our original phrasing, because it is more readily understandable.

      “The results of our morphological analysis of all ommatidia in Megaphragma are consistent with the light-polarization related features in Hymenoptera and other insects” - please add citations, see my comment on the DRA above.

      We have added the citations according to the Reviewer’s advice.

      “The group of short PRs (R1-R6)” - please consider renaming into “short visual fibre photoreceptors” (as opposed to “long visual fibre PRs”; hence SVFs and LVFs). This naming is quite common.

      The naming was changed according to the Reviewer’s advice.

      “The total rhabdom shortening in M. viggianii ommatidia probably favors polarization and absolute sensitivity,” - please see comments on DRA. Wide rhabdom means also a wider acceptance angle.

      Shortening of DRA rhabdoms does not result in their widening compared to other rhabdoms, so it is difficult to say how this may be related to sensitivity. The comments on DRA given earlier have been taken into account.

      “Ommatidia located across the diagonal area of the eye are more sensitive to light” - I don't understand what is diagonal area.

      We have deleted the sentence.

      “Estimated optical sensitivity of the eyes very close to those reported for diurnal hymenopterans with apposition eyes (Greiner et al., 2004; Gutiérrez et al., 2024) and possess around 0.19 {plus minus} 0.04 μm2 sr. M. viggianii have reasonably huge values of acceptance angle Δρ, and thus should result in a low spatial resolution” - please correct English here. “eyes IS very close”, “should result in a low”

      The grammatical errors were fixed.

      Table 6 legend: “SPC - secondary pigment cells.” -> “SPC – secondary pigment cells.”

      Citation “(Makarova et al., 2025).” - probably 2015

      The typos were fixed.

      Methods, FIB-SEM: I can't understand the sentence “The volumetric data of lenses and cones, some linear measurements (lens thickness, cone length, cone width, curvature radius) and to visualize the complete 3D-model of eye we use (measure or reconstruct) the elements from another eye (left).”

      The sentence is a continuation of the previous one. We have rewritten it as follows to clarify the meaning and move it to the 3D reconstruction section:

      “The right eye, on which the reconstruction was performed, has several damaged regions from milling (see Appendix 1С), which hinder the complete reconstructions of lenses and cones on a few ommatidia. According to this, for the volumetric data on lenses and cones, some linear measurements (lens thickness, cone length, cone width, curvature radius), we use (measure or reconstruct) the corresponding elements from the other (left) eye.”

      “The cells of single interfacet bristles were not reconstructed, because of damaging on right eye and worst quality of section on the left.” - please change to “The cells of the single interfacet bristle were not reconstructed, because of damage to the right eye and inferior quality of the sections of the left eye.”

      The text has been changed as follows:

      “The cells of single interfacet bristles were not reconstructed, because of the damage present in the right eye and because of the generally lower quality of this region on the left eye.”

      “Morphometry. Each ommatidia was” -> “Morphometry. Each ommatidium was”

      The grammatical error has been fixed.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Major comments

      Unfortunately the major conclusions of the article are not well supported by the provided data. Including:

      1. That interhemispheric remodelling occurs in non-mammalian amniotes. It would not surprise me that this may be the case, however, the major evidence for this is a series of horizontal insets that do not evidence this point well. There are broad morphological changes during development that can change the proportions and regionalisation of tissue, and therefore the IHF becoming apparently smaller as development progresses (qualitatively, in single sectioning planes, and without clear n numbers) may easily be explained by sutble differences in sectioning planes, or, for example, more caudal territories of the brain expanding at faster rates than the rostral territories. Quantification of the ratio between the IHF and total midline length across ages and between species may go some way to helping to clarify the degree of potential midline remodelling. Very high quality live imaging of the process would be the definitive way to evidence the claim, although I appreciate this is highly technically difficult and may not be possible. A key opportunity seems to be missed in the Satb2 knockout geckoes, where midline remodelling is purported to not occur. This is shown only qualitatively in a single plane of sectioning and again is not convincing. If the IHF length in these animals was quantified to be longer than wildtype at a comparable age, this would help to evidence the claim that remodelling occurs in these species.

      Our responses

      We take seriously the critique that the series of horizontal section images in the figures do not sufficiently substantiate our claim that interhemispheric remodeling occurs in non-mammalian amniotes. To address this, we plan to create a simplified atlas composed of adjacent sections of various wild-type amniotes as well as Satb2-knockout geckos.

      Additionally, in response to the suggestion that the IHF (interhemispheric fissure) should be quantified relative to the total midline length across developmental stages and species, we note that Figure 1 already presents such an analysis. Specifically, we have quantified changes in the midline collagen content using Principal Component Analysis (PCA) in Satb2 Crispants in geckos(FigureS4). However, if necessary, we also plan to perform a similar analysis on wild-type soft-shelled turtles at developmental stages before and after interhemispheric remodeling.

      That similar cell types contribute to remodelling in non-mammalian amniotes as mice/eutherian mammals. The microphotographs presented are not of very high quality, and it is often difficult to be convinced that the data is showing the strong claims made in the paper. For instance the "MZG-like cells" may in fact be astrocytes or another cell type as it is hard to visualise morphology, and the "intercalation of GFP-positive radial glial fibres" is very unclear from the photos. The colocalization of MMPsense with laminin positive cells is very hard to appreciate from the figure, and again not quantified. Similarly, there is a claim that there was degeneration of laminin-positive leptomeninges during astroglial intercalation, which is an active process that is difficult to infer from a single microphotograph. From the data, I can appreciate that some of the similar broad categories of cell types that exist at the mouse midline (glia, radial glia) are also present in non-mammalian amniote midlines, but it is difficult to be convinced of much more than this from the data presented.

      Our responses

      We take seriously the critique that the degeneration of Laminin-positive leptomeninges close to astroglial components is not accepted and that the evidence for glial fiber intercalation is insufficient.

      Verifying the degeneration of Laminin-positive leptomeninges is highly challenging. However, we have recently developed a method to visualize collagen in the pia mater using μCT and a CHP probe (3Helix Inc.). Preliminary experiments have already revealed pan-collagen deposition in the midline of the telencephalon (with lower amounts in the fusion region) and degeneration of the collagen composing the pia mater. We plan to incorporate these findings into the revised manuscript.


      That the gecko RPC and CPC connect distinct parts of the brain (rostral and caudal). These tracer injections lacked visualisation of the deposition site to confirm specificity, as well as appropriate quantification. Importantly, the absence of axons in the CPC following the rostral dye deposition (and vice versa) was not shown, which is essential to make the claim that these commissures carry axons from specific parts of the brain. The alternative hypothesis is that all axons are intermixed and traverse both commissures, independent of brain area of origin, which is not at all tested or disproved by the data presented.

      Our responses

      Thank you for the valuable critique suggestion. To support our claim that the pallial commissure in geckos consists of axons derived from specific brain regions, we should carefully eliminate the possibility that all axons are intermixed and cross both RPC and CPC regardless of brain region.

      To address this, we are planning additional experiments and will include a schematic diagram clearly indicating the labeling sites.


      Overall, the major conclusions of the study are not well supported by the data. A major effort to quantify phenomena and/or dramatically soften conclusions would be needed in order to make the conclusions well supported.

      Our responses

      We will thoroughly reconsider our conclusions and make significant efforts to revise the manuscript.

      Minor comments

      1. The n numbers are not always clearly reported

      Our responses

      We plan to address the clarification of quantitative data and the exact number of replicates.

      At times important points reference reviews or articles that do not support the statements as well as the most important primary articles might.

      Our responses

      We plan to carefully review the manuscript and, in addition to citing the most important primary papers, revise any descriptions that are not sufficiently supported by the cited reviews or articles, as per the suggestions.

      Figures showing the entire section that insets were taken from would help to convince that sectioning planes were equivalent, and also show the deposition site of neurovue experiments.

      Our response

      We will add a schematic showing the locations labeled in NeuroVue and additional experiments as a similar point made in Major comment 3.

      The fibre direction of GFAP+ fibres in figure 6 is confusing - It seems from the labelling on the figures as if red is used for the WT condition in mouse, but for the Satb2del condition in Gecko? If this is the case, then it would appear that the fibres are more specifically oriented in the del condition in mice, but in the WT condition of geckoes? There are several instances of this where clearer description and labelling would help the reader to interpret the results.

      Our response

      We plan to add clarification and indication of the direction of GFAP+ fibers in Figure 6 to make it easier to understand.

      Reviewer #1 (Significance (Required)):

      This study attempts to address a highly significant, novel and important question, that, if well achieved, would be publishable at a high degree of interest and impact to the basic research fields of brain development and evolution. Unfortunately the major conclusions made by the study are stronger than the data provided is able to evidence, and I remain unconvinced by many of them.

      Our responses

      We take seriously the suggestion that the major claims made by this study are excessive and so strong that they cannot be proven with the data provided. We will revise the manuscript as necessary.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary

      The authors provide a comparative analysis of interhemispheric (IHF) remodeling and its potential role in the generation of commissural axons. Based on histological material from mice, chickens, turtles, and geckos, the IHF remodeling of the midline is divided in two events: caudal and rostral. It is suggested that the rostral event is a preliminary step to the crossing of commissural axons, as it is characteristic of eutherian mammals with a corpus callosum (CC). However, the authors describe similar histologic features in other amniotes during development, particularly reptiles. This is in contrast with the case of the chick, which does not show signs of IHF remodeling nor a rostral pallial commissure. Additionally, deficient transgenic mice and geckos illustrate a potential role of Satb2 in rostral IHF remodeling and subsequent commissural formation. Whereas the topic and the conclusions of the analysis are interesting and provide new knowledge to the evo-devo field, several issues should be addressed prior to publication, such as data precision and presentation to support the main statements in the manuscript.

      Major comments:

      ____-A central point of this article is the splitting of the IHF into rostral and caudal events. The authors suggest that each one can be regulated differentially, and they attribute the rostral remodeling as a step prior to corpus callosum (CC) formation, in contrast to the caudal remodeling. In my opinion, these two events are not sufficiently characterized either in the figures or the manuscript. It is necessary to better describe these two processes that the authors mention. For instance, the authors could add or re-organize information in Figures 1-3 to include wide-field images showing the whole septum from rostral to caudal, and representative dorsoventral sections at important stages (with insets pointing at specific features). Otherwise, a table summarizing the rostral and caudal events would also be helpful to the reader.

      Our responses

      We take the suggestion seriously that the distinction between rostral and caudal remodeling may not be clear, especially regarding rostral remodeling, which is prior to the stage of corpus callosum (CC) formation, in contrast to caudal remodeling. Specifically, we plan to add or restructure the information from Figures 1 to 3 by including wide-field images that show the entire septum from rostral to caudal, as well as representative sagittal sections along the dorsal-ventral axis at key stages, with insets highlighting specific features. These will be added to the Supplementary data. Additionally, a table summarizing the events in both the rostral and caudal regions will also be created and included in the revised manuscript.

      When the authors refer to the reptilian rostral pallial commissure (RPC) and caudal pallial commissure (CPC), are these the same structures as the pallial commissure and anterior commissure described by Lanuza and Halpern (1997), Butler and Hodos (2005) and Puelles et al. (2019)? It is necessary to clarify the nomenclature, given that they are providing data from several species. Also, structures with the same names among species may not be truly homologous. A simple atlas with some horizontal and transverse planes highlighting anatomical landmarks and important structures (commissural tracts in this case) of the non-mammalian species would be extremely useful for the reader.

      Our responses

      As suggested by the reviewer, we are considering to provide a more detailed definition of the nomenclature of the pallial commissure in the revised manuscript, specifically in the introduction. Additionally, as mentioned earlier, we plan to create a simplified atlas with several horizontal and transverse sections, emphasizing anatomical landmarks and important structures (in this case, the commissural pathways) in species other than mammals.

      ____I wonder if the authors tested Fgf8 as marker on any of their sauropsidian tissue samples, as this gene has a known role in murine MZG development, which is required for IHF remodeling (Gobius et al. 2016, already cited in the manuscript). It would be beneficial to test this marker for the study, and if positive, it would open the possibility of designing loss-of-function experiments in avian or reptilian development models to identify mechanisms common to eutherians and support the statements of this work.

      Our responses

      We plan to verify the gene expression necessary for mouse MZG development and IHF remodeling, including Fgf8, DCC, and MMP2, through immunohistochemical staining as suggested.

      It would be really interesting to provide a more elaborate discussion on whether authors consider the sauropsidian IHF as a homologous process to eutherian IHF, and the reptilian RPC as an homologous of the CC.

      Our responses

      Since 3 out of the 4 reviewers consider IHF remodeling in sauropods to be homologous to that in placental mammals, we plan to further emphasize this claim in the revised manuscript. Additionally, we will expand on the discussion regarding whether the process of RPC formation in reptiles is considered homologous to that of the corpus callosum, and I will approach this from the context of character identity mechanisms claimed by Dr. Günter Wagner.

      Data and methods are presented in such a way that, in principle, they could be reproduced. Authors should indicate the number of animals/replicates of each species used in each experiment.


      Our responses

      As suggested, we plan to provide more detailed descriptions of the methods to ensure reproducibility. This will include adding the number of samples and trial repetitions for each animal species used in the experiments, including those for the additional experiments, in the revised manuscript.


      Minor comments:

      In the results section, paragraph 2, line 3: "We detected the accumulation of GFAP-positive cells and phosphorylated vimentin (Ser55) -positive mitotic radial glia in the IHF and telencephalic hinge in developing turtles, geckoes and chicks (Figure 2A)". Figure 2A shows sections from the four analyzed species labeled with radial glia markers at the end of the IHF remodeling. It would be beneficial to have analogous sections at several time points (perhaps before or after the process) to compare and show more clearly the accumulation of glial cells at that location.

      Our responses

      We have prepared serial sections before and after the developmental stages when interhemispheric remodeling occurs, in order to compare and more clearly show the accumulation of glial cells at their respective locations in mice, geckos, and soft-shelled turtles. I plan to add these results to Figure 2A in the revised manuscript.

      The article will improve its quality by adding more comparative information in the introduction about the analyzed sauropsidian structures (rostral pallial commissure and caudal pallial commissure), their relations with the pallial and anterior commissures, the structures/cells connected by them, and homologies previously proposed.

      Our responses

      We will add comparative information regarding the brain structures in sauropod, including the rostral and caudal pallial commissures and their relationship to the pallial commissure and anterior commissure, and the structures they connect, such as pyramidal cells, along with previously proposed homologies. This information will be included in the introduction and summarized in a table.


      In Figure 1 panels A-D, there is a lot of disparity in brain sizes and scales both between sections of the same species and between species. Placing the insets next to their source images is very necessary for clarity.


      Our responses

      As mentioned earlier, I will create a simplified atlas using adjacent sections and continuous μCT tomography images. Additionally, I will adjust the placement of the inset images in the revised manuscript to more visually accessible positions, improving their visibility.

      In the results section, paragraph 2, line 11: "In addition, it was suggested that astroglial intercalation occurs in conjunction with the aforementioned regression of the IHF from st.21 to st.26 in the developing turtle (Figure 2C)." In Figure 2C, all images are at different scales,

      which makes it very hard to properly compare between stages.

      Our responses

      By creating inset images based on the low-magnification images in the upper panel, we will enhance the visibility of GFAP intercalation. Additionally, we will improve the visibility in the revised manuscript by adding scale bars, referencing the simplified atlas in the figure legends, and standardizing the tissue specimen scale. we also plan to correct any typographical errors in the figures.

      In Figure 2D, the authors show the presence of MMP around the leptomeninges, suggesting MMP-mediated degradation. In the images, MMP labeling is revealed in dark blue, which is largely invisible against the black background. Colors should be used properly to allow visualization of this MMP labeling.

      Our responses

      In Figure 2D, we will reconsider the selection of pseudo-colors and use cyan to represent MMPsense.

      In Figure 4, it would really help if the authors provided wide-field images and DAPI counterstaining of the anterograde and retrograde tracings, to provide anatomical landmarks that help readers to identify the midline and understand the orientation of images.


      Our responses

      In addition to the previously mentioned schematic diagram of the gecko's pallial commissure and the additional experiments, we plan to include wide-field images along with forward and retrograde tracing using Hoechst counterstaining.

      In Figure 5B, I understand that the images in the red and blue squares correspond to brain areas in the squares in A. However, some confusion remains, especially with the image in B, which does not seem to be at the same angle as in the diagram representation. This makes it difficult to understand the results.

      Our responses

      According to the comment, we will revise the design of the Figure 5B to be more easily understand, and modify the scheme to match the angle of sections with actual figures.

      In Figure 6D, to better visualize defects in the RPC formation, the asterisk in the middle of the deficient structure needs to be replaced with a more lateral arrow pointing to the malformation.


      Our responses

      To better visualize the absence of RPC formation in Figure 6D, we will replace the asterisk in the center of the missing structure with a horizontal arrow indicating the malformation.

      In Figure S5, violin plots in panel C do not correspond with data in A and B. This needs correction or clarification.

      Our responses

      In Figure S5, the inconsistency between the violin plot in panel C and the data in panels A and B is a clear error, and we will correct this in the revised manuscript.

      In the article, a section appears solely to explain spatial transcriptomics results in a chick coronal section. The conclusion of this experiment is that three markers associated with midline remodeling are present in chick, suggesting that interhemispheric remodeling is conserved between mouse and chick. As these are complementary results and are not deeply analyzed in this manuscript, I think it would be better to summarize these findings in a dedicated paragraph and transfer some of the key images from Figure S2 to one of the main figures. Other problems with Figure S2: color contrast between clusters in the tSNE projection in B is very poor, should be enhanced; color intensity in FeaturePlots of panels D-F is too weak, and it seems that there is not really much expression at all in any cluster for any of these genes.

      Our responses

      In the revised manuscript, we will move some of the key images from Figure S2 to Main Figure 3 to demonstrate that the three markers related to midline remodeling are also present in chickens, showing that interhemispheric remodeling is conserved between mice and chickens. Additionally, we will enhance the contrast between clusters in the tSNE projection of the FeaturePlots in S2B and D-F by increasing the pseudo-color intensity or adjusting the intensity levels to emphasize the color contrast, and incorporate this updated figure into the revised manuscript.

      Reviewer #2 (Significance (Required)):

      The authors identify in the developing brain of sauropsids an event similar to IHF remodeling in eutherians, and suggest a causal relation between the rostral IHF remodeling and the formation of the pallial commissure in reptilian brains. This implies a potential homology between the pallial commissure and the corpus callosum of placental mammals. If this is the intention of the authors, this conclusion should be addressed explicitly and at length in the Discussion section. Whereas the results and conclusions described in the manuscript will be valuable in the field, the data presented in the manuscript needs quite some improvement, particularly for some of the images in the previously mentioned figures. Otherwise, the original data cannot be properly judged and may set reasonable doubt to readers.

      Advance: The findings described in this report are new to my knowledge. The description of the IHF remodeling event prior to corpus callosum development in mice has been published (Gobius et al. 2016, Cell Reports), but not in other mammalian branches or non-mammalian vertebrates. For this reason, the data in this report should be very convincing and better presented.

      Audience: This research will be interesting for a specialized and basic research audience, particularly for researchers in the evo-devo fields.

      My expertise: neuroanatomy, development, evolution, brain, cerebral cortex

      Our responses

      Thank you for your positive feedback on the novelty and high evaluation of identifying phenomena in reptilian development that resemble interhemispheric fissure (IHF) remodeling in placental mammals and demonstrating a causal relationship between rostral IHF remodeling and the formation of the reptilian pallial commissure. we will incorporate the concept of the potential homology between the corpus callosum in placental mammals and the brain commissures in reptiles into the revised manuscript, reflecting this in the context of character Identity mechanisms claimed by Dr. Günter Wagner. This will be clearly and thoroughly discussed in the discussion section. Additionally, we sincerely appreciate the constructive comment about the room for significant improvement, particularly in some of the figures, and we will address these points in the revised manuscript.


      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Conserved interhemispheric morphogenesis in amniotes preceded the evolution

      of the corpus callosum. Noji Kaneko et al., 2025

      The CC is formed exclusively in placental mammals. In other amniotes species, the communication of the two hemispheres is mediated by other structures such as the anterior commissure or the hippocampal commissure. The authors perform anatomical comparisons between species to conclude that interhemispheric fissure remodeling, a prior developmental step for CC formation, is highly conserved in non-mammalian amniotes, such as reptiles and birds. They suggest that might have contributed to the evolution of eutherian-specific CC formation. In an attempt to test their hypothesis, the authors investigate the role of Satb2 in interhemispheric fissure remodeling. They show IH fissure defects in both mice and geckoes. This is a nice manuscript that bridges a gap in the current understanding of CC formation. The study is mostly anatomical and directed at a specialized community.

      Our response

      We appreciate for positive comments on the manuscript.

      I suggest some changes that might contribute to improving the manuscript.

      Main

      1. Much of the most important conclusions are extracted from the anatomical observation of the dynamics of IHF closure and the emergence of the Hinge. It is very clear that the researchers are specialists in the field but for a broader audience, the images they provide are not always easy to interpret. It takes a lot of effort to visualize the anatomical data they use for their conclusions. As an example, perhaps the authors can find ways to explain how to identify the hinge specifically. It is very clear what the hinge is in the schemes (drawings)but forms one picture to the other at different developmental stages neither in the same animal species nor from different species. In Figure 1, it is difficult to see how the hinge in the mouse is similar (i.e. the same structure) to the hinge in the Gecko and chick. Moreover, in panels C , chick brain sections are shown at much greater magnification than the gecko, and thus is very difficult. In addition, in the manuscript text, the authors refer to sequential sectioning, but only one image for each stage is shown. They can show more images in supplementary Figures, otr they can just explain that they show the relevant images of the sectioning. As another example, in Fig2A, in the text, the authors explain that they detect the same specific glial components, but the images show very different co-localizations and distributions. In Figures 1 and 3, there are lines indicating Dorsal to ventral. This refers to the sectioning but in reality, what the sections are illustrating is the anterior-to-posterior differences in the IHF. maybe they can clarify it, because at quick sight it can be confusing.

      Our responses

      We sincerely appreciate the feedback regarding the interpretation of images that show the dynamics of interhemispheric remodeling and the emergence of the hinge, which is central to the most important conclusions of this study, as it may not always be easy to interpret. In the revised manuscript, we plan to address this by making the following revisions. For example, to clarify how the hinge corresponds across different species, we will create a simplified atlas to explain that the sections from the main figure are at the same level within the continuous slices.

      The authors have to revise the manuscript text to be more precise. For example, In the result section quote "To address whether the interhemispheric remodeling in non-mammalian amniotes is dependent on midline glial activities, we next examined the expression of several glial markers in the reptilian and avian midline regions". the anatomical comparison does not address the role of glial.

      Our responses

      Thank you for your feedback. I will correct the expression "midline glial activities" to "midline glial components" and incorporate this more accurate terminology into the revised manuscript.


      As an option to increase the relevance of their work, the authors might want to consider to describe in more detail and moving the results of the RNAseq and the analysis of the Stab2 mutants to the main figures.


      Our responses

      Thank you for your feedback. we will move the RNAseq results and the analysis of Satb2 mutants to the main figures and will describe them in more detail to enhance the relevance of the study. Specifically, we plan to separate Figure 6A-C as independent figures and add Supplementary Figure 5, corresponding to mice and geckos, to the main figures in the revised manuscript.


      Minor:

      Please indicate the length of the scale bars in the figure legends, and not only in the figure panels Fig5. Indicate the animal model in the panel Perhaps they can draw a model of the different mechanisms of caudal and anterior remodeling.


      Our responses

      Thank you for your feedback. I plan to revise the figure legend for Figure 5 by clearly indicating the scale bar length and increasing the font size, as well as including the information in each panel. Additionally, I will add a graphical abstract that illustrates the different mechanisms of caudal and rostral remodeling to enhance visual comprehension.


      Reviewer #3 (Significance (Required)):

      The study addresses a gap in knowledge from an evolutionary perspective. It provides novel hypotheses and an innovative framework for the understanding of cortical development and evolution. however, most of the conclusions are inferred from anatomical observations and the experimental testing of the hypothesis (Mutants and RNAseq analysis) are minor part of the study that could be further developed. The study is interesting for investigators with expertise in brain development and evolution but requires familiarity with comparative anatomy and even then it is difficult to go through the work.

      Reviewer #4 (Evidence, reproducibility and clarity (Required)):

      Overall, this is a well-written manuscript focusing on the evolution of mid-line interhemispheric fusion related to corpus callosum development and evolution from amniotes to eutherian species. The authors also demonstrated that Satb2 plays a critical role in interhemispheric remodeling, which is essential for corpus callosum development. This is a nicely organized and interesting study and the data are compelling. The following are suggestions for improvement, mostly for clarity:

      Minor comments:

      1. Figure 1A: While the E14 and E17 horizontal sections are informative, the addition of the E12 horizontal section does not provide further information. It would be better to place the inset and the whole image side by side, rather than having them far apart across the whole figure. For Figures 1C-D, is it possible to include horizontal sections for chick at

      E14 and Gecko at 45 dpo, as shown in the subsequent images?

      Our responses

      In Figure 1A, we will replace the current figure with a new one that visually enhances the comparison by placing the inset and the full image side by side. we will also add new horizontal sections of the whole image for chicken E14 and gecko 45 dpo, obtained from μCT tomography images and HE staining, to improve visibility between the images.


      When comparing across species it is sometimes helpful to use a standard staging system so that different developmentally staged tissue can be compared. A timeline of how embryonic day or dpo equates to stage might be helpful.

      Our response

      To clarify the developmental stages, I plan to incorporate a time scale into the graphical abstract in the revised manuscript.


      Figure 2B: It is difficult to discern the perspective without a full, lower power section of Gecko at 45 dpo. Adding a full image with an inset would be helpful. In Figure 1C, it would be helpful to define the magnified area by placing a box on the low magnification image.

      Our responses

      We plan to add a low-magnification μCT tomography image or HE-stained whole image of the gecko at 45 dpo in the revised manuscript. As for Figure 1C, it has already been included in the preprint.


      Figures 3B-E: Include the staining methods used for these sections.

      Our response

      We plan to add a note specifying that the image is stained with HE.


      Figure 4B: Add a low magnification image with an inset. The current image is a bit confusing as it is unclear what is being shown.

      Our responses

      We plan to add a low-magnification image showing the entire section and use an inset to indicate the positional relationship of the section's plane in a schematic diagram.

      Figures 6A-E: It would be helpful to denote the genotype as Satb2+/- or heterozygous, rather than Satb2 WT/del, which can be confusing. Ensure consistency in genotyping notation throughout all figures. It is noted that some are CRISPR knockdown and could be denoted as such.

      Our responses

      For CRISPR knockdown, I will adopt the term "CRISPANT" in the revised manuscript. This terminology will be used consistently throughout all figures to avoid confusion in genotype notation.


      Reviewer #4 (Significance (Required)):

      The corpus callosum evolved only in eutherian mammals and its development relies critically on an earlier developmental process known as interhemispheric remodeling. Nomura and colleagues investigate the evolution of these processes and identify that interhemispheric remodeling occurs in reptiles and birds and was therefore already present in the common ancestor of amniotes. This highly conserved developmental process likley evolved early and provided a substrates for major commissures to form throughout evolution.

      3.____Description of the revisions that have already been incorporated in the transferred manuscript.

      Currently we do not incorporate the revision in the transferred manuscript.


      __ Description of analyses that authors prefer not to carry out__

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Major

      That similar cell types contribute to remodelling in non-mammalian amniotes as mice/eutherian mammals. The microphotographs presented are not of very high quality, and it is often difficult to be convinced that the data is showing the strong claims made in the paper. For instance the "MZG-like cells" may in fact be astrocytes or another cell type as it is hard to visualise morphology, and the "intercalation of GFP-positive radial glial fibres" is very unclear from the photos. The colocalization of MMPsense with laminin positive cells is very hard to appreciate from the figure, and again not quantified. Similarly, there is a claim that there was degeneration of laminin-positive leptomeninges during astroglial intercalation, which is an active process that is difficult to infer from a single microphotograph. From the data, I can appreciate that some of the similar broad categories of cell types that exist at the mouse midline____ ____(glia, radial glia) are also present in non-mammalian amniote midlines, but it is difficult to be convinced of much more than this from the data presented.

      Our responses

      We are confident that this paper provides sufficient evidence that cell types similar to those in non-mammalian amniotes, mice, and placental mammals contribute to interhemispheric remodeling and that glial fiber intercalation occurs. This point is also supported by other reviewers.

      In the present study, we have not conducted the MMPsense experiments with the aim of showing the co-localization of MMPsense and laminin-positive cells or pia mater. Contrary to the reviewer's claim, it is important that the non-continuous regions of MMPsense and laminin-positive areas (pia mater), which are extracellular components, are adjacent to each other. Unfortunately, establishing a quantification system using MMPsense is practically impossible.

      Major

      The implication that Satb2 expression at the midline is necessary for appropriate interhemispheric remodeling. Alternative hypotheses for an inappropriately remodeled midline upon whole-brain Satb2 knockout is that it is not dependent on expression at the midline region. Rather, it could be that, for example, the appropriately timed interaction between ingrowing callosal axons and the midline territory is needed for the timely differentiation and/or behavior of midline cells. Other alternatives include that the lack of axonal midline crossing changes the morphology of the midline territory, including potentially "unfusing" the midline. Given the high prevalence of midline remodelling defects concomitant with callosal agenesis referred to be the authors in the literature, it seems like these alternatives would be worth considering. Indeed, the only article the authors reference in their statement that "several studies implicated that agenesis of CC in Satb2-deficient mice is also associated with defects in midline fusion" is an article where Satb2 was knocked out specifically in the cortex and hippocampus. This result is difficult to interpret, as some Emx1 promotors do label some of the midline territory, however the point stands that it is difficult to interpret solely that Satb2 action at the midline is responsible for the effects. I understand that this is a hard question to investigate, so I would suggest allusion to the alternative hypotheses/interpretations as the main priority when interpreting the data.

      Our responses

      This study does not aim to demonstrate the detailed molecular function of Satb2 in the developmental processes of the corpus callosum or pallial commissure. We plan to clearly state this point in the revised manuscript and focus on the findings obtained as a result. Based on the histological relationships, we will classify interhemispheric remodeling and consider adding a section in the Discussion to identify the common character identity mechanisms underlying the development of the pallial commissure and corpus callosum. This addition will help provide a more detailed understanding of the remodeling mechanisms. As is well known, discussions of homology are complex, and we understand that providing concrete evidence is even more challenging. When discussing homology, we will emphasize that it must be handled cautiously, and that discussions on molecular features and homology will rely heavily on future research. As an alternative, we plan to position the results of Satb2 Crispants in mice and geckos as evidence of the disruption of character identity mechanisms. By incorporating this perspective into the revised manuscript, we believe it will deepen our understanding of the role of Satb2 and its molecular mechanisms.

      Reviewer4

      Minor comment 7. There is very valuable data in the supplementary figures. As suggestion is to incorporate Supp. figures S1, S2 and S5 in the main figures.

      Our responses

      Due to space constraints, we plan to move only Supplementary Figure S5 to the supplementary section, and Figures S1 and S2 will not be included in the main figures of the revised manuscript.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The authors provide a comparative analysis of interhemispheric (IHF) remodeling and its potential role in the generation of commissural axons. Based on histological material from mice, chickens, turtles, and geckos, the IHF remodeling of the midline is divided in two events: caudal and rostral. It is suggested that the rostral event is a preliminary step to the crossing of commissural axons, as it is characteristic of eutherian mammals with a corpus callosum (CC). However, the authors describe similar histologic features in other amniotes during development, particularly reptiles. This is in contrast with the case of the chick, which does not show signs of IHF remodeling nor a rostral pallial commissure. Additionally, deficient transgenic mice and geckos illustrate a potential role of Satb2 in rostral IHF remodeling and subsequent commissural formation. Whereas the topic and the conclusions of the analysis are interesting and provide new knowledge to the evo-devo field, several issues should be addressed prior to publication, such as data precision and presentation to support the main statements in the manuscript.

      Major comments:

      • A central point of this article is the splitting of the IHF into rostral and caudal events. The authors suggest that each one can be regulated differentially, and they attribute the rostral remodeling as a step prior to corpus callosum (CC) formation, in contrast to the caudal remodeling. In my opinion, these two events are not sufficiently characterized either in the figures or the manuscript. It is necessary to better describe these two processes that the authors mention. For instance, the authors could add or re-organize information in Figures 1-3 to include wide-field images showing the whole septum from rostral to caudal, and representative dorsoventral sections at important stages (with insets pointing at specific features). Otherwise, a table summarizing the rostral and caudal events would also be helpful to the reader.
      • When the authors refer to the reptilian rostral pallial commissure (RPC) and caudal pallial commissure (CPC), are these the same structures as the pallial commissure and anterior commissure described by Lanuza and Halpern (1997), Butler and Hodos (2005) and Puelles et al. (2019)? It is necessary to clarify the nomenclature, given that they are providing data from several species. Also, structures with the same names among species may not be truly homologous. A simple atlas with some horizontal and transverse planes highlighting anatomical landmarks and important structures (commissural tracts in this case) of the non-mammalian species would be extremely useful for the reader.
      • I wonder if the authors tested Fgf8 as marker on any of their sauropsidian tissue samples, as this gene has a known role in murine MZG development, which is required for IHF remodeling (Gobius et al. 2016, already cited in the manuscript). It would be beneficial to test this marker for the study, and if positive, it would open the possibility of designing loss-of-function experiments in avian or reptilian development models to identify mechanisms common to eutherians and support the statements of this work
      • It would be really interesting to provide a more elaborate discussion on whether authors consider the sauropsidian IHF as a homologous process to eutherian IHF, and the reptilian RPC as an homologous of the CC.
      • Data and methods are presented in such a way that, in principle, they could be reproduced. Authors should indicate the number of animals/replicates of each species used in each experiment.

      Minor comments:

      • In the results section, paragraph 2, line 3: "We detected the accumulation of GFAP-positive cells and phosphorylated vimentin (Ser55) -positive mitotic radial glia in the IHF and telencephalic hinge in developing turtles, geckoes and chicks (Figure 2A)". Figure 2A shows sections from the four analyzed species labeled with radial glia markers at the end of the IHF remodeling. It would be beneficial to have analogous sections at several time points (perhaps before or after the process) to compare and show more clearly the accumulation of glial cells at that location.
      • The article will improve its quality by adding more comparative information in the introduction about the analyzed sauropsidian structures (rostral pallial commissure and caudal pallial commissure), their relations with the pallial and anterior commissures, the structures/cells connected by them, and homologies previously proposed.
      • In Figure 1 panels A-D, there is a lot of disparity in brain sizes and scales both between sections of the same species and between species. Placing the insets next to their source images is very necessary for clarity.
      • In the results section, paragraph 2, line 11: "In addition, it was suggested that astroglial intercalation occurs in conjunction with the aforementioned regression of the IHF from st.21 to st.26 in the developing turtle (Figure 2C)." In Figure 2C, all images are at different scales, which makes it very hard to properly compare between stages.
      • In Figure 2D, the authors show the presence of MMP around the leptomeninges, suggesting MMP-mediated degradation. In the images, MMP labeling is revealed in dark blue, which is largely invisible against the black background. Colors should be used properly to allow visualization of this MMP labeling.
      • In Figure 4, it would really help if the authors provided wide-field images and DAPI counterstaining of the anterograde and retrograde tracings, to provide anatomical landmarks that help readers to identify the midline and understand the orientation of images.
      • In Figure 5B, I understand that the images in the red and blue squares correspond to brain areas in the squares in A. However, some confusion remains, especially with the image in B, which does not seem to be at the same angle as in the diagram representation. This makes it difficult to understand the results.
      • In Figure 6D, to better visualize defects in the RPC formation, the asterisk in the middle of the deficient structure needs to be replaced with a more lateral arrow pointing to the malformation.
      • In Figure S5, violin plots in panel C do not correspond with data in A and B. This needs correction or clarification.
      • In the article, a section appears solely to explain spatial transcriptomics results in a chick coronal section. The conclusion of this experiment is that three markers associated with midline remodeling are present in chick, suggesting that interhemispheric remodeling is conserved between mouse and chick. As these are complementary results and are not deeply analyzed in this manuscript, I think it would be better to summarize these findings in a dedicated paragraph and transfer some of the key images from Figure S2 to one of the main figures. Other problems with Figure S2: color contrast between clusters in the tSNE projection in B is very poor, should be enhanced; color intensity in FeaturePlots of panels D-F is too weak, and it seems that there is not really much expression at all in any cluster for any of these genes.

      Significance

      The authors identify in the developing brain of sauropsids an event similar to IHF remodeling in eutherians, and suggest a causal relation between the rostral IHF remodeling and the formation of the pallial commissure in reptilian brains. This implies a potential homology between the pallial commissure and the corpus callosum of placental mammals. If this is the intention of the authors, this conclusion should be addressed explicitly and at length in the Discussion section. Whereas the results and conclusions described in the manuscript will be valuable in the field, the data presented in the manuscript needs quite some improvement, particularly for some of the images in the previously mentioned figures. Otherwise, the original data cannot be properly judged and may set reasonable doubt to readers.

      Advance: The findings described in this report are new to my knowledge. The description of the IHF remodeling event prior to corpus callosum development in mice has been published (Gobius et al. 2016, Cell Reports), but not in other mammalian branches or non-mammalian vertebrates. For this reason, the data in this report should be very convincing and better presented.

      Audience: This research will be interesting for a specialized and basic research audience, particularly for researchers in the evo-devo fields.

      My expertise: neuroanatomy, development, evolution, brain, cerebral cortex

    1. I may have gotten stuck in a bias and gotten used to not passing to you. I am opento passing to you more often. Let’s work on passing more often during practice andexhibition games, so we can build more skills and trust with each other. I think in doingthat, we’ll get a better sense of how we can work together come game time

      Patrick has some self realization and compromise for the conversation. He proposes a possible solution with positive words such as build, trust, and work together (Let's Rumble).

    Annotators

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:  

      Reviewer #1 (Public Review):

      Strengths:

      The manuscript utilizes a previously reported misfolding-prone reporter to assess its behaviour in ER in different cell line models. They make two interesting observations:

      (1) Upon prolonged incubation, the reporter accumulates in nuclear aggregates.

      (2) The aggregates are cleared during mitosis. They further provide some insight into the role of chaperones and ER stressors in aggregate clearance. These observations provide a starting point for addressing the role of mitosis in aggregate clearance. Needless to say, going ahead understanding the impact of aggregate clearance on cell division will be equally important.

      Weaknesses:

      The study almost entirely relies on an imaging approach to address the issue of aggregate clearance. A complementary biochemical approach would be more insightful. The intriguing observations pertaining to aggregates in the nucleus and their clearance during mitosis lack mechanistic understanding. The issue pertaining to the functional relevance of aggregation clearance or its lack thereof has not been addressed. Experiments addressing these issues would be a terrific addition to this manuscript.

      We have performed protein blotting and proteomics to characterize ER-FlucDM-eGFP expressing cells. We have also provided evidence to support the role of ER reorganization in regulating aggregate clearance. Our proteomic analysis provided a global view of the cellular state of cells expressing ER-FlucDM-eGFP, which potentially revealed functional relevance of ER-FlucDM-eGFP. Details are explained in the following comments. 

      Reviewer #2 (Public Review):

      Summary:

      The authors provide an interesting observation that ER-targeted excess misfolded proteins localize to the nucleus within membrane-entrapped vesicles for further quality control during cell division. This is useful information indicating transient nuclear compartmentalization as a quality control strategy for misfolded ER proteins in mitotic cells, although endogenous substrates of this pathway are yet to be identified.

      Strengths:

      This microscopy-based study reports unique membrane-based compartments of ERtargeted misfolded proteins within the nucleus. Quarantining aggregating proteins in membrane-less compartments is a widely accepted protein quality control mechanism. This work highlights the importance of membrane-bound quarantining strategies for aggregating proteins. These observations open up multiple questions on proteostasis biology. How do these membrane-bound bodies enter the nucleus? How are the singlelayer membranes formed? How exactly are these membrane-bound aggregates degraded? Are similar membrane-bound nuclear deposits present in post-mitotic cells that are relevant in age-related proteostasis diseases? Etc. Thus, the observations reported here are potentially interesting.

      Weaknesses:

      This study, like many other studies, used a set of model misfolding-prone proteins to uncover the interesting nuclear-compartment-based quality control of ER proteins. The endogenous ER-proteins that reach a similar stage of overdose of misfolding during ER stress remain unknown.

      We have included a previous study that showed accumulation of BiP aggregates in the nucleus upon overexpression of BiP (Morris et al., 1997; DOI: 10.1074/jbc.272.7.4327) in the discussion (Line 299).

      The mechanism of disaggregation of membrane-trapped misfolded proteins is unclear. Do these come out of the membrane traps? The authors report a few vesicles in living cells. This may suggest that membrane-untrapped proteins are disaggregated while trapped proteins remain aggregates within membranes.

      We initially made mStayGold-Sec61β to image the ER structures and ER-FlucDM-eGFP aggregates. However, we could not obtain convincing time-lapse images to show the release of ER-FlucDM-eGFP aggregates from the ER membrane as there are abundant ER structures present close to the aggregates during mitosis, preventing the differentiation of the membrane encapsulating aggregates from the ER structures. 

      The authors figure out the involvement of proteasome and Hsp70 during the disaggregation process. However, the detailed mechanisms including the ubiquitin ligases are not identified. Also, is the protein ubiquitinated at this stage?

      We performed cycloheximide chase experiments in cells released from the G2/M and found that ER-FlucDM-eGFP protein level did not fluctuate significantly when cells progressed through mitosis and cytokinesis. Thus, we did not consider protein ubiquitination and degradation of ER-FlucDM-eGFP as a major mechanism for its clearance. We have included this observation in the results (Figure S7A; Line 266) and in the discussion (Line 324) of the revised manuscript.

      This paper suffers from a lack of cellular biochemistry. Western blots confirming the solubility and insolubility of the misfolded proteins are required. This will also help to calculate the specific activity of luciferase more accurately than estimating the fluorescence intensities of soluble and aggregated/compartmentalized proteins. 

      We performed solubility test in cells expressing ER-FlucDM-eGFP and detected insoluble ERFlucDM-eGFP after heat stress (Figure S1E; Line 102). We have also performed protein blotting to detect ER-FlucDM-eGFP to normalize the luciferase activity (Line 609). We have updated the method section for luciferase measurement (Line 494).   

      Microscopy suggested the dissolution of the membrane-based compartments and probably disaggregation of the protein. This data should be substantiated using Western blots. Degradation can only be confirmed by Western blots. The authors should try time course experiments to correlate with microscopy data. Cycloheximide chase experiments will be useful.

      We performed cycloheximide chase experiments in cells released from the G2/M and found that ER-FlucDM-eGFP protein level did not fluctuate significantly when cells progressed through mitosis and cytokinesis (Figure S7A to S7C). Also, live-cell imaging of cells released from the G2/M indicated no significant change of total fluorescence intensity of ER-FlucDMeGFP (Figure S7D). Thus, we do not think that protein degradation of ER-FlucDM-eGFP is the major mechanism for its clearance. 

      The cell models express the ER-targeted misfolded proteins constitutively that may already reprogram the proteostasis. The authors may try one experiment with inducible overexpression.

      We have re-transduced fresh MCF10A cells with lentiviral particles to induce expression of ER-FlucDM-eGFP. The aggregates started to form after 24 h post-transduction. We made similar observations as described in the manuscript (e.g. aggregate clearance) two days after re-transduction.

      It is clear that a saturating dose of ER-targeted misfolded proteins activates the pathway.

      The authors performed a few RT-PCR experiments to indicate the proteostasis-sensitivity.

      Proteome-based experiments will be better to substantiate proteostasis saturation.

      We have performed proteomic analysis in cells expressing ER-FlucDM-eGFP and observed up-regulation of multiple proteins involved in the ER stress response, indicating that cells expressing ER-FlucDM-eGFP experience proteostatic stress (Figure S4A; Line 179).  

      The authors should immunostain the nuclear compartments for other ER-membrane resident proteins that span either the bilayer or a single layer. The data may be discussed.

      We have co-expressed ER-FlucDM-mCherry and mStayGold-Sec61β and detected mStayGold- Sec61β around ER-FlucDM-mCherry aggregates (Figure 1B).  

      All microscopy figures should include control cells with similarly aggregating proteins or without aggregates as appropriate. For example, is the nuclear-targeted FlucDM-EGFP similarly entrapped? A control experiment will be interesting. Expression of control proteins should be estimated by western blots.

      We targeted FlucDM-eGFP to the nucleus by expressing NLS-FlucDM-eGFP (Figure S1A). We found that the nuclear FlucDM-eGFP did not co-localize with the ER-FlucDM-mCherry aggregates (Figure S1B; Line 96). We have also determined the expression levels of NLSFlucDM-eGFP and ER-FlucDM-mCherry (Figure S1C and S1D).

      There are few more points that may be out of the scope of the manuscript. For example, how do these compartments enter the nucleus? Whether similar entry mechanisms/events are ever reported? What do the authors speculate? Also, the bilayer membrane becomes a single layer. This is potentially interesting and should be discussed with probable mechanisms. Also, do these nuclear compartments interfere with transcription and thereby deregulate cell division? What about post-mitotic cells? Similar deposits may be potentially toxic in the absence of cell division. All these may be discussed.

      Thank you for interesting suggestions for our study. We speculated that ER-FlucDM-eGFP aggregates may derive from the invagination of the inner nuclear membrane given that the aggregates are in close proximity to the inner nuclear membrane in interpase cells (Line 299). We have included a previous study that reported a similar aggregate upon BiP overexpression (Morris et al., 1997; DOI: 10.1074/jbc.272.7.4327; Line 300). Our proteomic analysis showed that cells expressing ER-FlucDM-eGFP have several up-regulated proteins related to cell cycle regulation (Figure S4A; Line 346).  

      Reviewer #3 (Public Review):

      Summary:

      This paper describes a new mechanism of clearance of protein aggregates occurring during mitosis.

      The authors have observed that animal cells can clear misfolded aggregated proteins at the end of mitosis. The images and data gathered are solid, convincing, and statistically significant. However, there is a lack of insight into the underlying mechanism. They show the involvement of the ER, ATPase-dependent, BiP chaperone, and the requirement of Cdk1 inactivation (a hallmark of mitotic exit) in the process. They also show that the mechanism seems to be independent of the APC/C complex (anaphase-promoting complex). Several points need to be clarified regarding the mechanism that clears the aggregates during mitosis:

      • What happens in the cell substructure during mitosis to explain the recruitment of BiP towards the aggregates, which seem to be relocated to the cytoplasm surrounded by the ER membrane.

      We have included images to show that BiP co-localizes with ER-FlucDM-eGFP aggregates in interphase cells (Figure S5C). We think that BiP participates in the formation of ER-FlucDMeGFP during interphase instead of getting recruited to the aggregates during mitosis.  

      • How the changes in the cell substructure during mitosis explain the relocation of protein aggregates during mitosis.

      We provided evidence to show that clearance of ER-FlucDM-eGFP aggregates involves the ER remodeling process. We depleted ER membrane fusion proteins ATL2 and ATL3 to perturb the distribution of ER sheets or tubules and found that cells were defective in clearing the aggregates (Figure 7A and B; Line 278). 

      • Why BiP seems to be the main player of this mechanism and not the cyto Hsp70 first described to be involved in protein disaggregation.

      In our proteomic analysis, we found that BiP (HSPA5) but not other Hsp70 family members were up-regulated in cells expressing ER-FlucDM-eGFP (Line 352; Figure S4A). This explains why BiP is the main player of the ER-FlucDM-eGFP aggregate clearance.  

      Strengths:

      Experimental data showing clearance of protein aggregates during mitosis is solid, statistically significant, and very interesting.

      Weaknesses:

      Weak mechanistic insight to explain the process of protein disaggregation, particularly the interconnection between what happens in the cell substructure during mitosis to trigger and drive clearance of protein aggregates.

      In our revised manuscript, we now provided evidence to show that ER-FlucDM-eGFP aggregate clearance involved remodeling of the ER structures during mitotic exit. This is added as a new Figure 7 in the revised manuscript and is described in the result section (Line 278) and in the discussion section (Line 323). We believe that this addition has provided mechanistic insights into ER-FlucDM-eGFP aggregate clearance.

      Recommendations for the authors:

      Reviewing Editor comments:

      I have read these reviews in detail and would like to recommend that the authors perform the experiments according to the reviewers' suggestions, as well as provide the appropriate controls raised by the reviewers.

      I think there are not that many requests and they all seem very reasonable and easily doable. I would recommend that the authors carry out the suggested experiments to develop a stronger story where the evidence transitions from being incomplete presently to a "more complete" standard.

      We have addressed questions raised by three reviewers and updated our manuscript (labeled in red in the main text).

      Reviewer #1 (Recommendations For The Authors):

      The manuscript makes exciting observations about the accumulation of reporter protein aggregates in the nucleus and its clearance during mitosis. It also provides some insight into the role of chaperons in aggregate clearance. These observations provide a good platform to perform in-depth analysis of the underlying mechanism and its functional relevance which perhaps the authors will plan over the long term. However, the below suggestions will help improve the current version of the manuscript:

      (1) Although it is assumed that the aggregates are cleared by the protein degradation mechanism, clear evidence supporting this assumption in the author's experiments is lacking and needs to be provided. Is it possible that mitosis induces disassembly of these aggregates instead of degradation?

      We performed two experiments to verify whether ER-FlucDM-eGFP aggregates are cleared by the protein degradation mechanism. In the first experiment, we treated cells expressing ER-FlucDM-eGFP released from the G2/M boundary with cycloheximide (CHX) and found that ER-FlucDM-eGFP did not decrease in protein abundance in cells progressing through mitosis (Figure S7A to S7C). In the second experiment, we measured the intensity of ERFlucDM-eGFP in early dividing cells and late dividing cells after release from the G2/M boundary and found that there was no significant difference between early and late dividing cells (Figure S7D). Thus, we concluded that protein degradation of ER-FlucDM-eGFP is not the primary mechanism of its clearance during cell division (Line 324). Furthermore, we included new data to show that the ER-FlucDM-eGFP aggregate clearance depends on ER reorganization during cell division, so mitotic exit induces disassembly of the aggregates instead of protein degradation.

      (2) It is intriguing that the aggregates are nuclear. Is the nuclear localization mediated by localization to ER? A time course analysis would reveal this and would provide credence to the idea that the reporter was originally expressed in the ER. It is currently unclear if the reporter ever gets expressed in ER.

      We showed that in interphase cells, ER-FlucDM-eGFP co-localizes with mStayGold-Sec61β, which labels the ER structures (Figure 1B). So, ER-FlucDM-eGFP is expressed and present in the ER network and invaginates into the inner nuclear membrane as aggregates. We attempted to image ER-FlucDM-eGFP for its formation; however it was technically challenging as the aggregates appeared very small and not too visible after clearance under our microscopy system.  

      (3) It would be expected that the persistence of these aggregates would impact cell division and cellular health. An experiment addressing this hypothesis would be very useful in establishing the functional relevance of this observation in the context of the current study.

      We have performed proteomic analysis on cell expressing ER-FlucDM-eGFP and found that multiple proteins involved in the ER stress response were up-regulated (Figure S4A). Additionally, proteins related to cell cycle regulation were up-regulated upon expression of ER-FlucDM-eGFP (Figure S4A). The increase of these proteins may indicate a perturbed cellular health (Line 344). 

      (4) A recent report (PMID: 34467852) identified the role of ER tubules in controlling the size of certain misfolded condensates. Would specific ER substructures affect the nuclear localization and/or clearance of the FlucDM aggregates? This is tied to point#2 and would provide insights into the connection between ER and the nuclear aggregates.

      Thank you for your suggestions. We perturbed the ER remodeling process by knocking down ATL2 and ATL3, which are ER membrane fusion proteins, and found that clearance of ER-FlucDM-eGFP aggregates was affected (Figure 7A and B). Hence, perturbation of the distribution of ER tubules and ER sheets affects ER-FlucDM-eGFP aggregate clearance. We have also added the recent paper about ER tubule size in regulating the sizes of misfolded condensates in the discussion (Line 321)

      Reviewer #2 (Recommendations For The Authors):

      I expect that the images indicate z-sections. Should be indicated in legends as applicable.

      We have indicated whether the images are Z-stack or single Z-slices in the figure legends.  

      Small point: the control region (outside inclusion) that was bleached in 2c may be clearly indicated. 

      We have added the explanation in the figure legend of Figure 2C.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      This study by Zimyanin et al. examines the role of the C. elegans chromokinesin KLP-19 in the formation and architecture of the anaphase central spindle in C. elegans zygotes. Through a combination of electron and light microscopy, along with RNAi-mediated perturbations, the authors propose that KLP-19 influences central spindle stiffness by regulating microtubule dynamics.

      In Figure 5, the effect of KLP-19 depletion on central spindle microtubules appears unconvincing. The FRAP results show no significant difference with or without KLP-19, and overall microtubule density does not consistently respond to its depletion. Additionally, the double klp-19; gpr-1/2 (RNAi) condition does not exhibit a strong increase in microtubule density, though a statistical test is missing for this condition. Furthermore, the spd-1; gpr-1/2 double depletion produces a similar increase in microtubule density to most klp-19 depletion conditions, suggesting that the effect cannot be solely attributed to the absence of KLP-19.

      Figure 5A shows that depletion of KLP-19 leads to an increase in tubulin signal in the spindle midzone. The reviewer is correct, that there are differences in the microtubule density between KLP-19 depletion alone and KLP-19 + GPR-1/2 depletion. While depletion of KLP-19 alone leads to a significant increase, co-depletion of GPR-1/2 and KLP-19 leads to a slight, but not significant increase. Along this line, we have added Supplementary Table 1 that contains all p-Values for the different conditions for Figure 5A. However, depletion of GPR-1/2 alone does not affect the microtubule density in the midzone, arguing that changes in pulling forces do not affect the microtubule density in the midzone. It is possible, that the double RNAi leads to a decrease in efficiency and thus a reduced effect on microtubule intensity. We will demonstrate the RNAi efficiency by western blot. Another possibility is that there are some feedback mechanisms that responds to presence/ absence of pulling forces and some of our data (not from this manuscript) hints in this direction, but we have not yet worked out the details of this. We are planning to publish this in a follow up publication.

      • *

      In response to the spd-1 + gpr-1/2 (RNAi), the reviewer is correct, that the microtubule density in the midzone is not significantly different from klp-19 (RNAi) conditions and we think it is interesting to note that spd-1 + gpr-1/2 (RNAi) leads to an increased microtubule density in the midzone. This could be, as above mentioned caused by some feedback mechanisms that responds to pulling forces, or also due to some functions of SPD-1 that affects microtubules in the midzone. Interestingly, our data also shows that metaphase spindles are significantly shorter in the absence of SPD-1 in comparison to spindles in control embryos, suggesting that SPD-1 plays a role in regulating microtubules or force transmission. We are currently working on understanding SPD-1's role in this process.

      • *

      We also agree that there is no significant effect on the microtubule turn-over as shown in Figure 5B and we have stated this in the text. Our data does show a trend to a decreased turn-over, but the difference is not significant. This could be due to the low sample number.

      • *

      Overall, we think our data, the light microscopy and even more so the EM data does show a clear effect on midzone microtubules.

      • *

      The use of hcp-6 depletion to argue that KLP-19 depletion affects central spindle elongation independently of stretched chromatin is problematic. hcp-6 encodes a component of the Condensin II complex in C. elegans, and its depletion leads to chromatin decompaction rather than the stretched, dense chromatin observed in the midzone during anaphase in klp-19 (RNAi) embryos. These conditions are not equivalent and do not effectively rule out the possibility that chromatin stretching contributes to the observed phenotype.

      We agree with the reviewer that the HCP-6 experiments do not entirely rule out effects from lagging chromosomes. Proving that the reduced spindle and chromosome separation is not due to lagging chromosomes is challenging. Most of the depletions that lead to lagging chromosomes are based on defective kinetochore microtubule connections, such as depletion of KNL-1, NDC-80 or CLS-2 (CLASP). In C. elegans, this leads to the mass of Chromosomes staying behind in anaphase and increased spindle pole separation, which is not comparable to KLP-19 depletion. Perturbations that do not affect kinetochore microtubules but still lead to lagging chromosomes are often targeting cohesin or condensin. Ultimately none of these conditions are directly comparable.

      A probably better way to test this would be to deplete KLP-19 only after metaphase to prevent its effect on chromosome alignment. However, this is currently not possible as the time window is about 1 minute or less. We currently do not have the tools to conduct this type of experiment. As other reviewers also criticized this experiment and its significance for the paper, we have removed this entirely and have added the following part to the discussion about the potential effect of lagging chromosomes:

      " *We can not unambiguously rule out that failure to properly align chromosomes and the resulting lagging chromosomal material could also lead to some of the observed effects on spindle dynamics, such as slow chromosome segregation and pole separation rates as well as preventing spindle rupture in absence of SPD-1. However, several observations argue in favor of KLP-19 actively changing the midzone cytoskeleton network and thus affecting spindle dynamics. *

      Most of the protein depletions in C. elegans that lead to lagging chromosomes are based on defective kinetochore microtubule connections, such as depletion of CeCENP-A, CeCENP-C, KNL-1 or NDC-80 (70-72). This mostly leads to the Chromosome mass staying behind in anaphase and increased spindle pole separation (70-72), which is not comparable to KLP-19 depletion. Perturbations that do not affect kinetochore microtubules but still lead to lagging chromosomes are often targeting cohesin or condensin, which depletion leads to chromatin decompaction (73-74) rather than the stretched, dense chromatin as observed in the midzone during anaphase in klp-19 (RNAi) embryos. Ultimately none of these conditions are directly comparable, making it difficult to completely rule out an effect of lagging chromosomes. A better way to test this would be to deplete KLP-19 only after metaphase to prevent its effect on chromosome alignment. However, this is currently not possible as the time window is about 1 minute or less and we do not have the tools to conduct this type of experiment.

      *Based on our results we hypothesize that the observed spindle dynamics in absence of KLP-19 are not only caused by lagging chromosomes. Instead, KLP-19 RNAi results in a global rearrangement of the spindle and leads to a significant reduction of the spindle size, microtubule overlap, growth rate, and stability. Furthermore, the increase of microtubule interactions after klp-19 (RNAi) could also contribute to lagging of chromosomes and exacerbation of fragmented extrachromosomal material." *

      Additionally, the authors report that KLP-19 influences astral microtubule dynamics (Figure 5E), yet in Figure 3E, they show that KLP-19 localizes exclusively to kinetochores and spindle microtubules, excluding astral microtubules and spindle poles. How do they reconcile this apparent contradiction?

      We think that KLP-19 localizes also to astral Microtubules. Our KLP-19 GFP CRISPR line is very dim and this makes it hard to see. We are proposing to use a TIRF approach to image KLP-19 GFP on the C. elegans cortex, which we will include in the revised version. In addition, in support of our hypothesis of KLP-19 binding to astral Microtubules as well we would like to note that there is a PhD thesis available from Jack Martin in Josana Rodriguez Sanchez's Lab in Newcastle (LINK, will lead to a download of the thesis! ) that has reported KLP-19s localization to cortical Microtubules in C. elegans. In this thesis the author also reports an effect on astral microtubule growth.

      Figure legends lack consistency and do not adhere to standard C. elegans nomenclature conventions (e.g., protein names should not be capitalized, and genetic perturbations should be italicized). Standardizing these elements would improve clarity and readability.

      We have checked our figure legend and to our best knowledge the legends adhere to the C. elegans nomenclature. All RNAi conditions are lower case italicized and Protein names are capitalized as it is standard in other C. elegans publications. We have however noticed some variation in our Figures, i.e. EB-2 instead of EBP-2 and have corrected this in all figures.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Zimyanin et al, Chromokinesin Klp-19 regulates microtubule overlap and dynamics during anaphase in C. elegans.

      The authors used a myriad of techniques, including confocal live-cell imaging, 2-photon microscopy, second harmonic generation imaging, FRAP, microfluidic-coupled TIRF, EM-tomography, to study spindle midzone assembly dynamics in C. elegans one-cell stage embryos. In particular, they illuminated the role of kinesin-4 KLP-19 in maintaining proper midzone length and organization. Inhibition of KLP-19 results in longer more stable midzones, implying KLP-19 functions in depolymerizing microtubules.

      Indeed, much of the results in the current study are consistent with previously published results elsewhere. Nevertheless, the current work represents a tour-de-force showcase of diverse and state-of-the-art technology application to address spindle assembly dynamics. How KLP-19 functions to define microtubule length at the midzone is still not known. But the current work, with diverse and solid data, serves to highlight where future work should focus.

      Minor comments:

      Fig 3E / There is an unusual diagonal line bisecting the embryo. Visually this does not affect viewing of the His::GFP and KLP-19::GFP signals. However, when these signals are quantified and normalized (as in Fig 3F), the diagonal bisect displaying different background signal may impact the measurements.

      We are very sorry about this line in the images. The line is due to a defect in the camera chip of the spinning disc. We will acquire new images for this Figure using our new spinning disc microscope.

      Fig 4B / While the kymographs clearly show KLP-19::GFP motility on microtubules, they also show that the majority of KLP(-::GFP do not move. Perhaps some quantification and discussion of this result is appropriate?

      The reviewer is correct that only a small fraction small fraction of molecules, maybe ~10%, moves. We will add this quantification to the paper and discussion. This could be due to several reasons: Many of the non-moving particles are not visibly colocalized with microtubules, which could mean they are sticking non-specifically to the surface (or sticking to small tubulin aggregates that aren't long enough to support movement). In addition, as this experiment is done in a lysate it is hard to interpret if the immobile KLP-19 is not moving because other proteins are bound along the microtubule blocking its way or if the KLP-19 requires some activation (i.e. phosphorylations) to become mobiles. We think this could be very interesting and will follow up on this in the future.

      • *

      Reviewer #2 (Significance (Required)):

      Indeed, much of the results in the current study are consistent with previously published results elsewhere. Nevertheless, the current work represents a tour-de-force showcase of diverse and state-of-the-art technology application to address spindle assembly dynamics. How KLP-19 functions to define microtubule length at the midzone is still not known. But the current work, with diverse and solid data, serves to highlight where future work should focus.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary:

      The anaphase spindle midzone is an essential structure for cell division. It consists of antiparallel overlapping microtubules organized by the antiparallel microtubule bundler PRC1, molecular motors and other regulatory proteins. This manuscript investigates the role of KLP-19 (C. elegans ortholog of human kinesin-4 KIF4A) and SPD-1 (C. elegans ortholog of PRC1) for spindle midzone organization in the C. elegans embryo and its relevance for proper spindle function. Advanced fluorescence microscopy, 3D electron tomography, and a fluorescence microscopy-based single molecule assay in embryo lysate are used in a unique combination. The authors confirm several aspects of PRC1 and KIF4A function in anaphase, as reported in previous work, mostly in human cells and Drosophila embryos and also in C. elegans embryos. Measurements are mostly very quantitative and to a high quality standard. The main difference to previous conclusions is that here, the authors propose that KLP-19 does not interact with SPD-1, in contrast to what has been established for other animal kinesin-4s and PRC1, and instead localizes to the spindle midzone independently of PRC1 by a mechanism that remains unknown. The authors provide evidence that KLP-19 nevertheless controls microtubule overlap length as in other species and that it produces outward forces sliding midzone microtubules apart a movement that SPD-1 counteracts (presumably by friction). The manuscript presents a rich resource of carefully measured quantitative structural and dynamic C. elegans anaphase spindle data.

      Major comments:

      Key conclusions convincing?

      (1) The key conclusions that the length of the central anaphase spindle microtubule overlap remains constant as the C.elegans spindle elongates (Fig. 1), that PRC1 indeed localizes quite precisely to these overlaps as previously assumed based on its in vitro (purified protein) behavior (Fig. 3B) and that the kinesin-4 KLP-19 controls overlap length as in other species (Fig. 3B) are all convincingly shown. What's missing are quantitative KLP-19 together with microtubule polarity profiles in the presence and absence of SPD-1, leaving it unclear to which extent this kinesin localizes to microtubule overlaps in the two situations. Such data seem crucial, given the authors' claim that KLP-19 localizes to the midzone and that this localization of KLP-19 is mostly unaffected by the absence of SPD-1.

      If we understand this correctly the reviewer is asking for second harmonic imaging (SHG) together with imaging of KLP-19 GFP. This is currently not possible due to the way this imaging must be done (2-photon of GFP-Tubulin followed by the SHG). The only thing we can do is provide KLP-19 GFP profiles for control and SPD-1 depleted embryos. We can also use the line co-expressing SPD-1 Halo-tag and KLP-19 GFP to plot their respective localizations in control conditions. We are happy to provide such plots. Generally, we see KLP-19 going to the midzone in absence of SPD-1 and the SHG data does show that the overlap is increased. If KLP-19 specifically localizes to microtubule overlap (rather to i.e. microtubule ends) can currently not be distinguished in the spindle midzone. In vitro data from other labs and our in vitro assay suggests that KLP-19 does not specifically bind to antiparallel overlaps but rather microtubules in general.

      (2) 'Normalized KLP-19 intensities' are used to demonstrate that the total amount of this kinesin localizing to the spindle midzone does not depend on the presence of SPD-1 (Fig. 3F). Given that this claim represents a major novelty of the study, the efficiency of the SPD-1 knock-down should be documented, ideally by western blot and fluorescence microscopy.

      We agree with the reviewer and will provide western blots.

      (3) The authors show convincingly that the kinesin KLP-19 contributes to outward microtubule sliding (and can contribute to spindle rupture in the absence of SPD-1) (Fig. 2), which is interesting and in line with the author's main claim.

      (4) The interaction between KIF4a and PRC1 is well established in other species and has been clearly demonstrated both in cells and in vitro (with purified proteins). The authors claim that this concept does not apply to the C. elegans orthologs. To show 'in vitro' (outside of the spindle) that the C. elegans homologs KLP-19 and SPD-1 do not interact, the authors use a novel microfluidic fluorescence-based single-molecule assay in lysate (Fig. 4). Although very original, these experiments do not reach the biochemical standard of previous experiments with purified proteins without appropriate controls. Given that the lysate setup is fairly novel, it's advisable to present at least one positive control demonstrating that interactions between soluble proteins can indeed be detected using this assay. It would also be useful to show the absence of interaction between KLP-19 and SPD-1 by a more conventional method like co-IP, again with a positive control, to support the authors' claim. Eventually, experiments with purified proteins will have to unequivocally demonstrate whether KLP-19 and SPD-1 indeed do not interact - something which appears, however, to be beyond the scope of this study. Without additional experimental proof, the authors may want to indicate that these results are of more preliminary nature.

      *We agree with the reviewer, and we will conduct co-IPs of SPD-1 and KLP-19. We will also add CYK-4 as a positive control as previous publications have shown the interaction of CYK-4 with SPD-1. We are now generating lines co-expressing CYK-4 GFP and SPD-1 Halo-tag for the co-IP experiments. *

      (5) Unfortunately, the authors do not propose an alternative mechanism for KLP-19 localization to the midzone in SPD-1 depleted embryos, limiting the conceptual advance. Does KLP-19 bind directly to antiparallel microtubules, for example in the assay presented in Fig. 4 (where signs of microtubule crosslinking are shown for SPD-1)? If not, how would it accumulate in the midzone (if it does) in the C. elegans embryo anaphase spindle? The authors do also not propose a mechanism explaining why central antiparallel microtubule overlap length does not change as the spindle elongates in anaphase. Moreover, there is no discussion regarding the potential mechanism leading to KLP-19 controlling microtubule dynamics globally instead of locally where the motor accumulates, indicating limitations in mechanistic insight.

      *We agree with the reviewer and will add these points to the discussion. *

      *To address some of the points: *

      *How does KLP-19 end up in the midzone? : Our data shows that localization of KLP-19 does depend on AIR-2 and BUB-1 as previously reported. However, those proteins primarily affect the formation of the midzone. The in vitro assay does not suggest that KLP-19 has a preference for overlaps, unlike SPD-1, but rather binds microtubules in general. One possible mechanism of midzone localization could be microtubule end-tagging, as has been suggested for PRC1 (SPD-1 homolog). This could lead to an accumulation of KLP-19 in the midzone. *

      Why does the central overlap stay constant? : This can be explained by constant microtubule growth at the plus-ends why maintaining the overlap length. Alternatively, this could be explained by some (sophisticated) rearrangements of microtubules that ensure the overlap length stays the same. Generally, this is a very interesting question, as each of this scenario still requires that the overlap length is tightly regulated. Our data suggests that this is correlated with microtubule length in the midzone, as KLP-19 depletion leads to longer microtubules and overlap. This suggests that the regulation of microtubule dynamics might be an important factor in this process. We will add this to the discussion.

      • *

      Potential mechanism leading to KLP-19 controlling microtubule dynamics globally: We think that KLP-19 localizes to spindle and astral microtubules and regulates the dynamics on all of those, leading to a global regulation. By increasing it's concentration locally, microtubule dynamics can be regulated in the midzone. We will add data showing the localization of KLP-19 to astral microtubules.

      Claims justified/preliminary and clearly presented?

      The observation that the spindle length remains constant throughout anaphase in C. elegans is based on elegant, but unconventional fluorescence microscopy data (Fig. 1A & B). It would be helpful to add images of SHG and two-photon microscopy to help the reader understand the graphs. Measurements are presented based on distances between the poles. It is unclear why the distances between 15-20 µm were chosen and how they translate to anaphase progression. Can measurements be carried out across the entire duration of cell division to demonstrate that the overlap's 'constant length' property is unique to anaphase? (This could demonstrate already in Fig. 1 that the method in principle is capable of measuring different overlap lengths.)

      We agree with the reviewer and have moved the SHG images from supplementary Fig. 6 to the main Figure 1A for better visibility. In addition, we have added a plot as an inset in (now) Figure 1B and C explanation of how the used spindle pole distances related to the progression through anaphase. Unfortunately, we can only acquire a single timepoint and not a live movie during the SHG.

      Even though the manuscript contains an impressive amount of data, it appears to be lengthy, the motivation for several experiments is not clearly described, and the order of data presentation can probably be improved. For example, it is unclear why SPD-1 profiles are presented late and why KLP-19 profiles are missing - one would expect to see them early on as an essential characterization of the system under study. The motivation of the paragraph investigating the relation of KLP-19 and SPD-1 to HCP-6 is especially unclear (more than 1 page of text describing supplementary material).

      We will go through our text again and will revise the order of presented experiments. As stated above, we have removed the HCP-6 data.

      The absence of interaction between KLP-19 and SPD-1 is not demonstrated to the same quality standard as the presence of interaction between the orthologs in the literature, which should at least be mentioned.

      Additional experiments essential to support the claims of the paper?

      KLP-19 profiles in the presence and absence of SPD-1 seem to be essential.

      We agree with the reviewer and will add this.

      A co-IP of KLP-19 and SPD-1 (including positive control) to prove that the proteins are not interacting would help to support the claim.

      We agree with the reviewer and will add this

      Data and methods presented so that they can be reproduced? Yes.

      Experiments adequately replicated and statistical analysis adequate? Yes.

      Minor comments:

      Generating cellular electron tomography data is very laborious. It is a pity that no raw data is provided; for example, a slice of a reconstructed tomogram or a video of whole volumes without segmentation would be an informative addition and allow assessment of the data quality.

      We agree with the reviewer and will add movies of the raw electron microscopy data.

      The clear evidence for direct interaction between PRC1 and kinesin-4 in other species should be correctly acknowledged throughout the text.

      We agree with the reviewer and have corrected this

      The average (mean or median?) values and STDs reported in the text do not appear to match those in Fig. 1D.

      *We thank the reviewer for pointing this out and have corrected the figure. The violin lot showed the mean and percentiles, we have now changed the plot to show mean and STD. *

      • *

      The kymograph of spd-1 RNAi in Fig. 2A seems stretched, and the size based on the scale bar does not fit the values stated in the text.

      We thank the reviewer for pointing this out and have corrected the figure.

      The figure numbering, as stated in the text, does not seem to agree with those in Supplementary Figure 8.

      *We thank the reviewer for pointing this out and have corrected the text. *

      Page numbers and/or line numbers and figure numbers on the figures would help the reader to navigate the manuscript more easily.

      We agree with the reviewer and have added this.

      Reviewer #3 (Significance (Required)):

      The manuscript is a rich resource of quantitative measurements of C.elegans' structural and dynamic spindle properties, using advanced light microscopy and 3D electron microscopy imaging. In large parts, the work confirms previous conclusions of the function of PRC1 and kinesin-4 in the anaphase spindle, but also reports some interesting differences, namely that the C.elegans proteins differ from their orthologs in that they do not interact with each other, raising the question of how the kinesin-4 KLP-19 localizes to the central spindle in this organism. This work is of interest for researchers studying cell division, and specifically spindle architecture, dynamics, and function.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Overall I found the approach taken by the authors to be clear and convincing. It is striking that the conclusions are similar to those obtained in a recent study using a different computational approach (finite state controllers), and lends confidence to the conclusions about the existence of an optimal memory duration. There are a few questions that could be expanded on in future studies:

      (1) Spatial encoding requirements

      The manuscript contrasts the approach taken here (reinforcement learning in a gridworld) with strategies that involve a "spatial map" such as infotaxis. However, the gridworld navigation algorithm has an implicit allocentric representation, since movement can be in one of four allocentric directions (up, down, left, right), and wind direction is defined in these coordinates. Future studies might ask if an agent can learn the strategy without a known wind direction if it can only go left/right/forward/back/turn (in egocentric coordinates). In discussing possible algorithms, and the features of this one, it might be helpful to distinguish (1) those that rely only on egocentric computations (run and tumble), (2) those that rely on a single direction cue such as wind direction, (3) those that rely on allocentric representations of direction, and (4) those that rely on a full spatial map of the environment.

      We agree that the question of what orientation skills are needed to implement an algorithm is interesting. We remark that our agents do not use allocentric directions in the sense of north, east, west and east relative to e.g. fixed landmarks in the environment. Instead, directions are defined relative to the mean wind, which is assumed fixed and known. (In our first answer to reviewers we used “north east south west relative to mean wind”, which may have caused confusion – but in the manuscript we only use upwind downwind and crosswind).

      (2) Recovery strategy on losing the plume

      The authors explore several recovery strategies upon losing the plume, including backtracking, circling, and learned strategies, finding that a learned strategy is optimal. As insects show a variety of recovery strategies that can depend on the model of locomotion, it would be interesting in the future to explore under which conditions various recovery strategies are optimal and whether they can predict the strategies of real animals in different environments.

      Agreed, it will be interesting to study systematically the emergence of distinct recovery strategies and compare to living organisms.

      (3) Is there a minimal representation of odor for efficient navigation?

      The authors suggest that the number of olfactory states could potentially be reduced to reduce computational cost. They show that reducing the number of olfactory states to 1 dramatically reduces performance. In the future it would be interesting to identify optimal internal representations of odor for navigation and to compare these to those found in real olfactory systems. Does the optimal number of odor and void states depend on the spatial structure of the turbulence as explored in Figure 5?

      We agree that minimal odor representations are an intriguing question. While tabular Q learning cannot derive optimal odor representations systematically, one could expand on the approach we have taken here and provide more comparisons. It will be interesting to follow this approach in a future study.

      Reviewer #2 (Public review):

      Summary:

      The authors investigate the problem of olfactory search in turbulent environments using artificial agents trained using tabular Q-learning, a simple and interpretable reinforcement learning (RL) algorithm. The agents are trained solely on odor stimuli, without access to spatial information or prior knowledge about the odor plume's shape. This approach makes the emergent control strategy more biologically plausible for animals navigating exclusively using olfactory signals. The learned strategies show parallels to observed animal behaviors, such as upwind surging and crosswind casting. The approach generalizes well to different environments and effectively handles the intermittency of turbulent odors.

      Strengths:

      * The use of numerical simulations to generate realistic turbulent fluid dynamics sets this paper apart from studies that rely on idealized or static plumes.

      * A key innovation is the introduction of a small set of interpretable olfactory states based on moving averages of odor intensity and sparsity, coupled with an adaptive temporal memory.

      * The paper provides a thorough analysis of different recovery strategies when an agent loses the odor trail, offering insights into the trade-offs between various approaches.

      * The authors provide a comprehensive performance analysis of their algorithm across a range of environments and recovery strategies, demonstrating the versatility of the approach.

      * Finally, the authors list an interesting set of real-world experiments based on their findings, that might invite interest from experimentalists across multiple species.

      Weaknesses:

      * Using tabular Q-learning is both a strength and a limitation. It's simple and interpretable, making it easier to analyze the learned strategies, but the discrete action space seems somewhat unnatural. In real-world biological systems, actions (like movement) are continuous rather than discrete. Additionally, the ground-frame actions may not map naturally to how animals navigate odor plumes (e.g. insects often navigate based on their own egocentric frame).

      We agree with the reviewer, and will look forward to study this problem further to make it suitable for meaningful comparisons with animal behavior.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The authors have addressed my major concerns and I support publication of this interesting manuscript. A couple of small suggestions:

      (1) In discussing performance in different environments (line 328-362) it might be easier to read if you referred to the environments by descriptive names rather than numbers.

      Thank you for the suggestion, which we implemented

      (2) Line 371: measurements of flow speed depend on antennae in insects. Insects can measure local speed and direct of flow using antennae, e.g. Bell and Kramer, 1979, Suver et al. 2019. Okubo et al. 2020,

      Thank you for the references

      (3) line 448: "Similarly, an odor detection elicits upwind surges that can last several seconds" maybe "Similarly, an odor detection elicits upwind surges that can outlast the odor by several seconds"?

      Thank you for the suggestion

      Reviewer #2 (Recommendations for the authors):

      I commend the authors for their revisions in response to reviewer feedback.

      While I appreciate that the manuscript is now accompanied by code and data, I must note that the accompanying code-repository lacks proper instructions for use and is likely incomplete (e.g. where is the main function one should run to run your simulations? How should one train? How should one recreate the results? Which data files go where?).

      For examples of high-quality code-release, please see the documentation for these RL-for-neuroscience code repositories (from previously published papers):

      https://github.com/ryzhang1/Inductive_bias

      https://github.com/BruntonUWBio/plumetracknets

      The accompanying data does provide snapshots from their turbulent plume simulations, which should be valuable for future research.

      Thank you for the suggestions for how to improve clarity of the code. The way we designed the repository is to serve both the purpose of developing the code as well as sharing. This is because we are going to build up on this work to proceed further. Nothing is missing in the repository (we know it because it is what we actually use).

      We do plan to create a more user-friendly version of the code, hopefully this will be ready in the next few months, but it wont be immediate as we are aiming to also integrate other aspects of the work we are currently doing in the Lab. The Brunton repository is very well organized, thanks for the pointer.


      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Overall I found the approach taken by the authors to be clear and convincing. It is striking that the conclusions are similar to those obtained in a recent study using a different computational approach (finite state controllers), and lend confidence to the conclusions about the existence of an optimal memory duration. There are a few points or questions that could be addressed in greater detail in a revision:

      (1) Discussion of spatial encoding

      The manuscript contrasts the approach taken here (reinforcement learning in a grid world) with strategies that involve a "spatial map" such as infotaxis. The authors note that their algorithm contains "no spatial information." However, I wonder if further degrees of spatial encoding might be delineated to better facilitate comparisons with biological navigation algorithms. For example, the gridworld navigation algorithm seems to have an implicit allocentric representation, since movement can be in one of four allocentric directions (up, down, left, right). I assume this is how the agent learns to move upwind in the absence of an explicit wind direction signal. However, not all biological organisms likely have this allocentric representation. Can the agent learn the strategy without wind direction if it can only go left/right/forward/back/turn (in egocentric coordinates)? In discussing possible algorithms, and the features of this one, it might be helpful to distinguish<br /> (1) those that rely only on egocentric computations (run and tumble),<br /> (2) those that rely on a single direction cue such as wind direction,<br /> (3) those that rely on allocentric representations of direction, and<br /> (4) those that rely on a full spatial map of the environment.

      As Referee 1 points out, even if the algorithm does not require a map of space, the agent is still required to tell apart directions relative to the wind direction which is assumed known. Indeed, although in the manuscript we labeled actions allocentrically as “ up down left and right”, the source is always placed in the same location, hence “left” corresponds to upwind; “right” to downwind and “up” and “down” to crosswind right and left. Thus in fact directions are relative to the mean wind, which is therefore assumed known. We have better clarified the spatial encoding required to implement these strategies, and re-labeled the directions as upwind, downwind, crosswind-right and crosswind-left.

      In reality, animals cannot measure the mean flow, but rather the local flow speed e.g. with antennas for insects, with whiskers for rodents and with the lateral line for marine organisms. Further work is needed to address how local flow measures enable navigation using Q learning.

      (2) Recovery strategy on losing the plume

      While the approach to encoding odor dynamics seems highly principled and reaches appealingly intuitive conclusions, the approach to modeling the recovery strategy seems to be more ad hoc. Early in the paper, the recovery strategy is defined to be path integration back to the point at which odor was lost, while later in the paper, the authors explore Brownian motion and a learned recovery based on multiple "void" states. Since the learned strategy works best, why not first consider learned strategies, and explore how lack of odor must be encoded or whether there is an optimal division of void states that leads to the best recovery strategies? Also, although the authors state that the learned recovery strategies resemble casting, only minimal data are shown to support this. A deeper statistical analysis of the learned recovery strategies would facilitate comparison to those observed in biology.

      We thank Referee 1 for their remarks and suggestion to give the learned recovery a more prominent role and better characterize it. We agree that what is done in the void state is definitely key to turbulent navigation. In the revised manuscript, we have further substantiated the statistics of the learned recovery by repeating training 20 times and comparing the trajectories in the void (Figure 3 figure supplement 3, new Table 1). We believe however that starting with the heuristic recovery is clearer because it allows to introduce the concept of recovery more clearly. Indeed, the learned “recovery” is so flexible that it ends up mixing recovery (crosswind motion) to aspects of exploitation (surge): we defer a more in-depth analysis that disentangles these two aspects elsewhere. Also, we added a whole new comparison with other biologically inspired recoveries both in the native environment and for generalization (Figure 3 and 5).

      (3) Is there a minimal representation of odor for efficient navigation?

      The authors suggest (line 280) that the number of olfactory states could potentially be reduced to reduce computational cost. This raises the question of whether there is a maximally efficient representation of odors and blanks sufficient for effective navigation. The authors choose to represent odor by 15 states that allow the agent to discriminate different spatial regimes of the stimulus, and later introduce additional void states that allow the agent to learn a recovery strategy. Can the number of states be reduced or does this lead to loss of performance? Does the optimal number of odor and void states depend on the spatial structure of the turbulence as explored in Figure 5?

      We thank the referee for their comment. Q learning defines the olfactory states prior to training and does not allow a systematic optimization of odor representation for the task. We can however compare different definitions of the olfactory states, for example based on the same features but different discretizations. We added a comparison with a drastically reduced number of non-empty olfactory states to just 1, i.e. if the odor is above threshold at any time within the memory, the agent is in the non-void olfactory state, otherwise it is in the void state. This drastic reduction in the number of olfactory states results in less positional information and degrades performance (Figure 5 figure supplement 5).

      The number of void states is already minimal: we chose 50 void states because this matches the time agents typically remain in the void (less than 50 void states results in no convergence and more than 50 introduces states that are rarely visited).

      One may instead resort to deep Q-learning or to recurrent neural networks, which however do not provide answers as for what are the features or olfactory states that drive behavior (see discussion in manuscript and questions below).

      Reviewer #2 (Public review):

      Summary:

      The authors investigate the problem of olfactory search in turbulent environments using artificial agents trained using tabular Q-learning, a simple and interpretable reinforcement learning (RL) algorithm. The agents are trained solely on odor stimuli, without access to spatial information or prior knowledge about the odor plume's shape. This approach makes the emergent control strategy more biologically plausible for animals navigating exclusively using olfactory signals. The learned strategies show parallels to observed animal behaviors, such as upwind surging and crosswind casting. The approach generalizes well to different environments and effectively handles the intermittency of turbulent odors.

      Strengths:

      (1) The use of numerical simulations to generate realistic turbulent fluid dynamics sets this paper apart from studies that rely on idealized or static plumes.

      (2) A key innovation is the introduction of a small set of interpretable olfactory states based on moving averages of odor intensity and sparsity, coupled with an adaptive temporal memory.

      (3) The paper provides a thorough analysis of different recovery strategies when an agent loses the odor trail, offering insights into the trade-offs between various approaches.

      (4) The authors provide a comprehensive performance analysis of their algorithm across a range of environments and recovery strategies, demonstrating the versatility of the approach.

      (5) Finally, the authors list an interesting set of real-world experiments based on their findings, that might invite interest from experimentalists across multiple species.

      Weaknesses:

      (1) The inclusion of Brownian motion as a recovery strategy, seems odd since it doesn't closely match natural animal behavior, where circling (e.g. flies) or zigzagging (ants' "sector search") could have been more realistic.

      We agree that Brownian motion may not be biologically plausible -- we used it as a simple benchmark. We clarified this point, and re-trained our algorithm with adaptive memory using circling and zigzaging (cast and surge) recoveries. The learned recovery outperforms all heuristic recoveries (Figure 3D, metrics G). Circling ranks second, and achieves these good results by further decreasing the probability of failure and paying slightly in speed. When tested in the non-native environments 2 to 6, the learned recovery performs best in environments 2, 5 and 6 i.e. from long range more relevant to flying insects; whereas circling generalizes best in odor rich environments 3 and 4, representative of closer range and close to the substrate (Figure 5B, metrics G). In the new environments, similar to the native environment, circling favors convergence (Figure 5B, metrics f<sup>+</sup>) over speed (Figure 5B, metrics g<sup>+</sup> and τ<sub>min</sub>/τ), which is particularly deleterious at large distance.

      (2) Using tabular Q-learning is both a strength and a limitation. It's simple and interpretable, making it easier to analyze the learned strategies, but the discrete action space seems somewhat unnatural. In real-world biological systems, actions (like movement) are continuous rather than discrete. Additionally, the ground-frame actions may not map naturally to how animals navigate odor plumes (e.g. insects often navigate based on their own egocentric frame).

      We agree with the reviewer that animal locomotion does not look like a series of discrete displacements on a checkerboard. However, to overcome this limitation, one has to first focus on a specific system to define actions in a way that best adheres to a species’ motor controls. Moreover, these actions are likely continuous, which makes reinforcement learning notoriously more complex. While we agree that more realistic models are definitely needed for a comparison with real systems, this remains outside the scope of the current work. We have added a remark to clarify this limitation.

      (3) The lack of accompanying code is a major drawback since nowadays open access to data and code is becoming a standard in computational research. Given that the turbulent fluid simulation is a key element that differentiates this paper, the absence of simulation and analysis code limits the study's reproducibility.

      We have published the code and the datasets at

      - code: https://github.com/Akatsuki96/qNav

      - datasets: https://zenodo.org/records/14655992

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Line 59-69: In comparing the results here to other approaches (especially the Verano and Singh papers), it would also be helpful to clarify which of these include an explicit representation of the wind direction. My understanding is that both the Singh and Verano approaches include an explicit representation of wind direction. In Singh wind direction is one of the observations that inputs to the agent, while in Verano, the actions are defined relative to the wind direction. In the current paper, my understanding is that there is no explicitly defined wind direction, but because movement directions are encoded allocentrically, the agent is able to learn the upwind direction from the structure of the plume- is this correct? I think this information would be helpful to spell out and also to address whether an agent without any allocentric direction sense can learn the task.

      Thank you for the comment. In our algorithm the directions are defined relative to the mean wind, which is assumed known, as in Verano et al. As far as we understand, Singh et al provide the instantaneous, egocentric wind velocities as part of the input.

      (1) Line 105: "several properties of odor stimuli depend on the distance from the source" might cite Boie...Victor 2018, Ackles...Schaefer, 2021, Nag...van Breugel 2024.

      Thank you for the suggestions - we have added these references

      (2) Line 130: "we first define a finite set of olfactory states" might be helpful to the reader to state what you chose in this paragraph rather than further down.

      We have slightly modified the incipit of the paragraph. We first declare we are setting out to craft the olfactory states, then define the challenges, finally we define the olfactory states.

      (3) Line 267: "Note that the learned recovery strategy resembles casting behavior observed in flying insects" Might note that insects seem to deploy a range of recovery strategies depending on locomotor mode and environment. For example, flying flies circle and sink when odor is lost in windless environments (Stupski and van Breugel 2024).

      Thank you for your comment. We have included the reference and we now added comparisons to results using circling and cast & surge recovery strategies.

      (4) Line 289: "from positions beyond the source, the learned strategy is unable to recover the plume as it mostly casts sideways, with little to no downwind action" This is curious as many insects show a downwind bias in the absence of odor that helps them locate the plumes in the first place (e.g. Wolf and Wehner, 2000, Alvarez-Salvado et al. 2018). Is it possible that the agent could learn a downwind bias in the absence of odor if given larger environments or a longer time to learn?

      The reviewer is absolutely correct – Downwind motion is not observed in the recovery simply because the agent rarely overshoots the source. Hence overall optimization for that condition is washed out by the statistics. We believe downwind motion will emerge if an agent needs to avoid overshooting the source – we do not have conclusive results yet but are planning to introduce such flexibility in a further work. We added this remark and refs.

      (5) Line 377-391: testing these ideas in living systems. Interestingly, Kathman..Nagel 2024 (bioRxiv) shows exactly the property predicted here and in Verano in fruit flies- an odor memory that outlasts the stimulus by a duration of several seconds, appropriate for filling in "blanks." Relatedly, Alvarez-Salvado et al. 2018 showed that fly upwind running reflected a temporal integration of odor information over ~10s, sufficient to avoid responding to blanks as loss of odor.

      Indeed, we believe this is the most direct connection between algorithms and experiments. We are excited to discuss with our colleagues and pursue a more direct comparison with animal behavior. We were aware of the references and forgot to cite them, thank you for your careful reading of our work !

      Reviewer #2 (Recommendations for the authors):

      Suggestions

      (1) The paper does not clearly specify which type of animals (e.g., flying insects, terrestrial mammals) the model is meant to approximate or not approximate. The authors should consider clarifying how these simulations are suited to be a general model across varied olfactory navigators. Further, it isn't clear how low/high the intermittency studied in this model is compared to what different animals actually encounter. (Minor: The Figure 4 occupancy circles visualization could be simplified).

      Environment 1 represents the lower layers of a moderately turbulent boundary layer. Search occurs on a horizontal plane ~half meter from the ground. The agent is trained at distances of about 10 meters and also tested on longer distances  ~ 17 meters (environment 6), lower heights ~1cm from the ground (environments 3-4), lower Reynolds number (environment 5) and higher threshold of detection (environment 2 and 4). Thus Environments 1,2,5 and 6 are representative of conditions encountered by flying organisms (or pelagic in water), and Environments 3 and 4 of searches near the substrate, potentially involved in terrestrial navigation (benthic in water). Even near the substrate, we use odor dispersed in the fluid, and not odor attached to the substrate (relevant to trail tracking).

      Also note that we pick Schmidt number Sc = 1 and this is appropriate for odors in air but not in water. However, we expect a weak dependence on the Schmidt number as the Batchelor and Kolmogorov scales are below the size of the source and we are interested in the large scale statistics Falkovich et al., 2001; Celani et al., 2014; Duplat et al., 2010.

      Intermittency contours are shown in Fig 1C, they are highest along the centerline, and decay away from the centerline, so that even within the plume detecting odor is relatively rare. Only a thin region near the centerline has intermittency larger than 66%; the outer and most critical bin of the plume has intermittency under 33%; in the furthest point on the centerline intermittency is <10%. For reference, experimental values in the atmospheric boundary layer report intermittency 25% to 20% at 2 to 15m from the source along the centerline (Murlis and Jones, 1981).

      We have more clearly labeled the contours in Fig 1C and added these remarks.

      We included these remarks and added a whole table with matching to real conditions within the different environments.

      (2) Could some biological examples and references be added to support that backtracking is a biologically plausible mechanism?

      Backtracking was observed e.g. in ants displaced in unfamiliar environments (Wystrach et al, P Roy Soc B, 280,  2013), in tsetse flies executing reverse turns uncorrelated to wind, which bring them back towards the location where they last detected odor (Torr, Phys Entom, 13, 1988, Gibson & Brady Phys Entom 10, 1985) and in coackroaches upon loss of contact with the plume (Willis et al, J. Exp. Biol. 211, 2008). It is also used in computational models of olfactory navigation (Park et al, Plos Comput Biol, 12:e1004682, 2016).

      (3) Hand-crafted features can be both a strength and a limitation. On the one hand, they offer interpretability, which is crucial when trying to model biological systems. On the other hand, they may limit the generality of the model. A more thorough discussion of this paper's limitations should address this.

      (4) The authors mention the possibility of feature engineering or using recurrent neural networks, but a more concrete discussion of these alternatives and their potential advantages/disadvantages would be beneficial. It should be noted that the hand-engineered features in this manuscript are quite similar to what the model of Singh et al suggests emerges in their trained RNNs.

      Merged answer to points 3 and 4.

      We agree with the reviewer that hand-crafted features are both a strength and a limitation in terms of performance and generality. This was a deliberate choice aimed at stripping the algorithm bare of implicit components, both in terms of features and in terms of memory. Even with these simple features, our model performs well in navigating across different signals, consistent with our previous results showing that these features are a “good” surrogate for positional information.

      To search for the most effective temporal features, one may consider a more systematic hand crafting, scaling up our approach. In this case one would first define many features of the odor trace; rank groups of features for their accuracy in regression against distance; train Q learning with the most promising group of features and rank again. Note however that this approach will be cumbersome because multiple factors will have to be systematically varied: the regression algorithm; the discretization of the features and the memory.

      Alternatively, to eliminate hand crafting altogether and seek better performance or generalization, one may consider replacing these hand-crafted features and the tabular Q-learning approach with recurrent neural networks or with finite state controllers. On the flip side, neither of these algorithms will directly provide the most effective features or the best memory, because these properties are hidden within the parameters that are optimized for. So extra work is needed to interrogate the algorithms and extract these information. For example, in Singh et al, the principal components of the hidden states in trained agents correlate with head direction, odor concentration and time since last odor encounter. More work is needed to move beyond correlations and establish more systematically what are the features that drive behavior in the RNN.

      We have added these points to the discussion.

      (5) Minor: the title of the paper doesn't immediately signal its focus on recovery strategies and their interplay with memory in the context of olfactory navigation. Given the many other papers using a similar RL approach, this might help the authors position this paper better.

      We agree with the referee and have modified the title to reflect this.

      (6) Minor: L 331: "because turbulent odor plumes constantly switch on and off" -- the signal received rather than the plume itself is switching on and off.

      Thank you for the suggestion, we implemented it.

    1. Author response:

      Reviewer #1:

      The manuscript Xu et al. explores the regulation of the microtubule minus end protein CAMSAP2 localization to the Golgi by the Serine/threonine-protein kinase MARK2 (PAR1, PAR1B). The authors utilize immunofluorescence and biochemical approaches to demonstrate that MARK2 is localized at the Golgi apparatus via its spacer domain. They show that depletion of this protein alters Golgi morphology and diminishes CAMSAP2 localization to the Golgi apparatus. The authors combine mass spectroscopy and immunoprecipitation to show that CAMSAP2 is phosphorylated at S835 by MARK2, and that this phosphorylation regulates localization of CAMSAP2 at Golgi membranes. Further, the authors identify USO1 (p115) as the Golgi resident protein mediating CAMSAP2 recruitment to the Golgi apparatus following S835 phosphorylation. The authors would need to address the following queries to support their conclusions.

      We sincerely thank the reviewer for their valuable time and effort in evaluating our manuscript. We deeply appreciate the constructive feedback and insightful suggestions, which have been instrumental in improving the quality and clarity of our study. We have carefully considered all the comments and have made the necessary revisions to address the concerns raised.

      Major Comments 

      (1) Dynamic localization of CAMSAP2 during Golgi reorientation

      - The authors use fixed wound edges assays and co-localization analysis to describe changes in CAMSAP2 positioning during Golgi reorientation in response to polarizing cues (a free wound edge in this case). In Figure 1C, they present a graphical representation of quantified immunofluorescence images, using color coding to to describe the three states of Golgi reorientation in response to a wound (green, blue, red indicating non-polarised, partial and complete Golgi reorientation, respectively). They then use these 'colour coded' classifications to quantitate CAMSAP2/GM130 co-localization.It is unclear why the authors have not just used representative immunofluorescence images in the main figures. Transparent, color overlays could be placed over the cells in the representative images to indicate which of the three described states each cell is currently exhibiting. However, for clarity, I would recommend changing the color coded 'states' to a descriptor rather than a color. i.e. Figure 1D x axis labels should be 'complete' and 'partial', instead of 'red' and 'blue'. 

      Thank you for this insightful suggestion. We have added representative immunofluorescence images with transparent color overlay to indicate the three Golgi orientation states. These images are included in Supplementary Figure 2B-C, providing a clear visual reference for the quantitative data. Additionally, we have revised the x-axis labels in Figure 1E from "Red" and "Blue" to "Complete" and "Partial" to ensure clarity and consistency with the descriptive terminology in the text. These changes are described in the Results section (page 7, lines 15-19) and the figure legend (page 29, lines 27-29).

      We believe these updates improve the clarity and accessibility of our figures and hope they address the reviewer’s concerns.

      - note- figure 2 F-G, is semi quantitative, why did the authors not just measure Golgi angle using the nucleus and Golgi distribution?

      We appreciate the reviewer’s comment on this point. Following the recommendation, we have performed an additional analysis measuring Golgi orientation angles based on the nucleus-Golgi distribution. This quantitative approach complements our initial semi-quantitative analysis and provides a more precise assessment of Golgi orientation during cell migration.

      The new data have been incorporated into Supplementary Figure 1F-H. These results clearly demonstrate the consistency between the quantitative and semi-quantitative methods, further validating our findings and highlighting the dynamic changes in Golgi orientation during cell migration. These changes are described in the Results section (page 6, lines 24-31).

      - While it is established that the Golgi is dispersed during reorientation in wound edge migration, the Golgi apparatus also becomes dispersed/less condensed prior to cell division. As the authors have used fixed images - how are they sure that the Golgi morphology or CAMSAP2 localization in 'blue cells' are indicative of Golgi reorientation and not division? Live imaging of cells expressing CAMSAP2, and an additional Golgi marker could be used to demonstrate that the described changes in Golgi morphology and CAMSAP2 localization are occurring during the rear-to-front transition of the Golgi.

      Thank you for raising this important question. To address this concern, we carefully examined the nuclear morphology of dispersed Golgi cells and found no evidence of mitotic features, indicating that these cells are not undergoing division (Figure 1A, Supplemental Figure 2A). Furthermore, during the scratch wound assay, we use 2% serum to culture the cells, which helps minimize the impact of cell division. This analysis has been added to the Results section (page7, lines 19-22 in the revised manuscript).

      Additionally, we conducted live-cell imaging, as suggested, using cells expressing a Golgi marker. This approach confirmed that Golgi dispersion occurs transiently during reorientation in cell migration. The new live-cell imaging data have been incorporated into Supplementary Figure 2A, and the corresponding description has been updated in the Results section (page 7, lines 2-5).

      Finally, considering that overexpression of CAMSAP2 can lead to artifactually condensed Golgi structures, we used endogenous staining to observe CAMSAP2 localization at different stages of migration. These observations provide a clearer understanding of CAMSAP2 dynamics during Golgi reorientation and are now presented in revised Figure 1A-B. This information has been described in the Results section (page 7, lines 5-10).

      We hope these additions and clarifications address the reviewer’s concerns. Once again, we are deeply grateful for this constructive feedback, which has greatly improved the robustness of our study.

      (2) MARK2 localization to the Golgi apparatus

      - The authors investigated the positioning of endogenous MARK2 via immunofluorescence staining, and exogenous flag-tagged MARK2 in a KO background. The description of the protocol required to visualize Golgi localization of MARK2 is inconsistent between the results and methods text. The results text reads as through the 2% serum incubation occurs as a blocking step following fixation. Conversely, the methods section describes the 2% serum incubation as occurring just prior to fixation as a form of serum starvation. The authors need to clarify which of these protocols is correct. Further, whilst I can appreciate that the mechanistic understanding of why serum starvation is required for MARK2 Golgi localization is beyond the scope of the current work, the authors should at a minimum speculate in the discussion as to why they think it might occur.

      We sincerely thank the reviewer for the constructive feedback on the localization of MARK2 at the Golgi. Due to the complexity and variability of this phenomenon, we decided to remove the related data from the current manuscript to maintain the rigor of our study. However, we have included a discussion of this phenomenon in the Discussion section (page 13, lines 31-39 and page 14, 1-6in the revised manuscript) and plan to further investigate it in future studies.

      The localization of MARK2 at the Golgi was initially observed in experiments following serum starvation, where cells were fixed and stained (The data is not displayed). This observation was supported by the loss of Golgi localization in MARK2 knockdown cells, indicating the specificity of the antibody (The data is not displayed). However, this phenomenon was not consistently observed across all cells, likely due to its transient nature.We speculate that the localization of MARK2 to the Golgi depends on its activity and post-translational modifications. For example, phosphorylation at T595 has been reported to regulate the translocation of MARK2 from the plasma membrane to the cytoplasm (Hurov et al., 2004). Serum starvation might induce modifications or conformational changes in MARK2, leading to its temporary Golgi localization. Additionally, we hypothesize that this localization may coincide with specific Golgi dynamics, such as the transition from dispersed to ribbon-like structures during cell migration.

      We also acknowledge the inconsistency in the Results and Methods sections regarding serum starvation. We confirm that serum starvation was performed prior to fixation as an experimental condition, rather than as a blocking step in immunostaining. This clarification has been incorporated into the revised Methods section (page 24, lines 11-12).

      We hope this clarification, along with our planned future studies, adequately addresses the reviewer’s concerns. Once again, we deeply appreciate the reviewer’s valuable comments, which have provided important insights for our ongoing work. References:

      Hurov, J.B., Watkins, J.L., and Piwnica-Worms, H. (2004). Atypical PKC phosphorylates PAR-1 kinases to regulate localization and activity. Curr Biol 14 (8): 736-741.

      - The authors should strengthen their findings by using validated tools/methods consistent with previous publications. i.e. Waterman lab has published two MARK2 constructs- Apple and eGFP tagged versions (doi.org/10.1016/j.cub.2022.04.088), and the localization of MARK2 in U2Os cells (using the same antibody (Anti- MARK2 C-terminal, ABCAM Cat# ab136872). The authors should (1) image the cells live using eGFP-tagged MARK2 during serum starvation to show the dynamics of this localization, (2) image U2Os cells using the abcam ab136872 antibody +/- 2% serum starve. Two MARK2 antibodies are listed in Table 2. Does abcam (ab133724) show a similar localisation?

      - The Golgi localization of MARK2 occurs in the absence of the T structural domain, but not when full length MARK2 is expressed. The authors conclude the T- domain is likely inhibitory. When combined with the requirement for serum starvation for this interaction to occur, the authors should clarify the physiological relevance of these observations.

      We sincerely thank the reviewer for their valuable suggestions regarding the use of tools and methods and the physiological relevance of MARK2 localization to the Golgi. Regarding the question of how MARK2 itself localizes to the Golgi, we are currently unable to fully elucidate the underlying mechanism. Therefore, we have removed the discussion of MARK2’s Golgi localization from the manuscript to ensure scientific accuracy. However, Below, we provide our detailed response as soon as possible:

      First, regarding the suggestion to use tools and methods developed by the Waterman lab to strengthen our findings, we have carefully evaluated their applicability. In our live-cell imaging experiments, we found that full-length MARK2 does not stably localize to the Golgi, even under serum starvation conditions. However, truncated MARK2 mutants lacking the Tail (T) domain exhibit robust Golgi localization. Furthermore, our immunofluorescence staining results indicate that the Spacer domain is the minimal region required for MARK2 localization at the Golgi. Based on these findings, we believe that live-cell imaging of EGFP-tagged full-length MARK2 may not effectively reveal the dynamics of its Golgi localization. However, we plan to focus on the truncated constructs in future studies to better explore the mechanisms underlying MARK2's dynamic behavior. 

      Regarding the use of the ab136872 antibody to stain U2OS cells with and without serum starvation, we note that the protocol described by the Waterman lab involves pre-fixation and permeabilization steps, which are not compatible with live-cell imaging. Additionally, we observed that MARK2 Golgi localization appears to be condition-dependent and may coincide with specific Golgi dynamics, such as transitions from dispersed stacks to intact ribbon structures. These events are likely brief and challenging to capture consistently. Nevertheless, we recognize the value of this experimental design and plan to adapt the staining conditions in future work to validate our results further. As for the ab133724 antibody listed in Table 2, we clarify that it has only been validated for Western blotting in our study and does not yield reliable results in immunofluorescence experiments. For this reason, all immunofluorescence staining in this study relied exclusively on ab136872. This distinction has been clarified in the revised Table 2 .

      Regarding the hypothesis that the Tail domain of MARK2 is inhibitory, our observations showed that truncated MARK2 mutants lacking the T domain stably localized to the Golgi, whereas fulllength MARK2 did not. Literature evidence supports this hypothesis, as studies on the yeast homolog Kin2 indicate that the C-terminal region (including the Tail domain) binds to the Nterminal catalytic domain to inhibit kinase activity (Elbert et al., 2005). We speculate that serum starvation disrupts this intramolecular interaction, relieving the inhibition by the T domain, activating MARK2, and promoting its localization to the Golgi. Moreover, we hypothesize that the transient nature of MARK2 localization to the Golgi may be related to specific Golgi remodeling processes, such as the transition from dispersed stacks to intact ribbon structures during cell migration or polarity establishment. 

      References:

      Elbert, M., Rossi, G., and Brennwald, P. (2005). The yeast par-1 homologs kin1 and kin2 show genetic and physical interactions with components of the exocytic machinery. Mol Biol Cell 16 (2): 532-549.

      (3) Phosphorylation of CAMSAP2 by MARK2

      - The authors examined the effects of MARK2 phosphorylation of CAMSAP2 on Golgi architecture through expression of WT-CAMSAP2 and two CAMSAP2 S835 mutants in CAMSAP2 KO cells. They find that CAMSAP2 S835A (non-phosphorylatable) was less capable of rescuing Golgi morphology than CAMSAP2 S835D (phosphomimetic). Golgi area has been measured to demonstrate this phenomenon. Representative immunofluorescence images in Fig. 4D appear to indicate that this is the case. However, quantification in Fig. 4E does not show significance between HA-CAMSAP2 and HA-CAMSAP2A that would support the initial claim. The authors could analyze other aspects of Golgi morphology (e.g. number of Golgi fragments, degree of dispersal around the nucleus) to capture the clear structural defects demonstrated in HACAMSAP2A cells.

      We sincerely thank the reviewer for their valuable feedback and for pointing out potential areas of improvement in our analysis of Golgi morphology. We apologize for any misunderstanding caused by our description of the results in Figure 4E.

      The quantification indeed shows a significant difference between HA-CAMSAP2 and HACAMSAP2A in terms of Golgi area, as indicated in the figure by the statistical annotations (pvalue provided in the legend). To ensure clarity, we have revised the figure legend (page 32, lines 19-23 in the revised manuscript) to explicitly describe the statistical significance, and the method used for quantification.

      Because the quantification indeed shows a significant difference between HA-CAMSAP2 and HA-CAMSAP2A in terms of Golgi area, and to maintain consistency throughout the manuscript, we did not further analyze other aspects of Golgi morphology.

      We hope this clarification, along with the additional analyses, will address the reviewer’s concerns. Once again, we are deeply grateful for these constructive comments, which have helped us improve the quality and robustness of our study.

      - Wound edge assays are used to capture the difference in Golgi reorientation towards the leading edge between CAMSAP2 S835A and CAMSAP2 S835D. However, these studies lack comparison to WT-CAMSAP2 that would support the role of phosphorylated CAMSAP2 in reorienting the Golgi in this context.

      We sincerely thank the reviewer for their insightful suggestion. In response, we have added a comparison between CAMSAP2 S835A/D and WT-CAMSAP2, in addition to HT1080 and MARK2 KO cells, to better evaluate the role of phosphorylated CAMSAP2 in Golgi reorientation.

      The results, now shown in Figure 5A-C, indicate that in the absence of MARK2, there is no significant difference in Golgi reorientation between WT-CAMSAP2 and CAMSAP2 S835A. This observation supports the conclusion that MARK2-mediated phosphorylation of CAMSAP2 at S835 is essential for effective Golgi reorientation.

      To enhance clarity, we have updated the corresponding Results section (page 9, lines 37-40 and page 10, line 1 in the revised manuscript) to describe this additional comparison. We believe this analysis strengthens our findings and provides a clearer understanding of the role of phosphorylated CAMSAP2 in Golgi dynamics.

      We hope this additional data addresses the reviewer’s concerns. Once again, we are grateful for the constructive feedback, which has helped improve the clarity and robustness of our study.

      (4) Identification of CAMSAP2 interaction partners

      - Quantification of interaction ability between CAMSAP2 and CG-NAP, CLASP2, or USO1 in Fig. 5D, 5F and 5J respectively, lack WT-CAMSAP2 comparisons.

      We sincerely thank the reviewer for their valuable suggestion. In response, we have included WT-CAMSAP2 data in the quantification of interaction ability between CAMSAP2 and CG-NAP, CLASP2, and USO1. These results, now shown in revised Figures 5 D-G and Figures 6 C-D, provide a direct comparison that further validates the differential interaction abilities of CAMSAP2 mutants.

      The inclusion of WT-CAMSAP2 allows us to better contextualize the effects of specific mutations on CAMSAP2 interactions and strengthens our conclusions regarding the role of these interactions in Golgi dynamics.

      We hope this addition addresses the reviewer’s concerns and enhances the clarity and robustness of our study. We deeply appreciate the constructive feedback, which has been instrumental in improving our manuscript.

      - The CG-NAP immunoblot presented in Fig. 5C shows that the protein is 310 kDa, which is the incorrect molecular weight. CG-NAP (AKAP450) should appear at around 450 kDa. Further, no CG-NAP antibody is included in Table 2 - Information of Antibodies. The authors need to explain this discrepancy.

      We sincerely apologize for the lack of clarity in our annotation and description, which may have caused confusion regarding the CG-NAP immunoblot presented in Figure 5C (Figure 5D in the revised manuscript). To clarify, CG-NAP (AKAP450) is indeed a 450 kDa protein, and the marker at 310 kDa represents the molecular weight marker’s upper limit, above which CG-NAP is observed. This has been clarified in the figure legend (page 33, lines 21-23 in the revised manuscript).

      Regarding the CG-NAP antibody, it was custom-made and purified in our laboratory. Polyclonal antisera against CG-NAP, designated as αEE, were generated by immunizing rabbits with GSTfused fragments of CG-NAP (aa 423–542). This antibody has been validated extensively in our previous research, demonstrating its specificity and reliability (Wang et al., 2017). The details of the antibody preparation are included in the footnote of Table 2 for reference.

      We hope this clarification, along with the additional context regarding the antibody validation, resolves the reviewer’s concerns. We are deeply grateful for the reviewer’s attention to detail, which has helped us improve the clarity and rigor of our manuscript.

      References:

      Wang, J., Xu, H., Jiang, Y., Takahashi, M., Takeichi, M., and Meng, W. (2017). CAMSAP3dependent microtubule dynamics regulates Golgi assembly in epithelial cells. Journal of genetics and genomics = Yi chuan xue bao 44 (1): 39-49.

      Minor Comments

      - Authors should change immunofluorescence images to colorblind friendly colors. The current presentation of merged overlays makes it really difficult to interpret- I would strongly encourage inverted or at a minimum greyscale individual images of key proteins of interest.

      We sincerely thank the reviewer for their valuable suggestion regarding the presentation of immunofluorescence images. In response, we have converted the images in Figure 1C to greyscale individual images for each key protein of interest. This adjustment ensures that the figures are more accessible and interpretable, including for readers with color vision deficiencies.

      We hope this modification addresses the reviewer’s concern and improves the clarity of our data presentation. We are grateful for the constructive feedback, which has helped us enhance the overall quality of our figures.

      - On p. 8 text should be amended to 'Previous literature has documented MARK2's localization to the microtubules, microtubule-organizing center (MTOC), focal adhesions..'

      We sincerely thank the reviewer for their comment regarding the text on page 8. Considering the reasoning provided in response to question 2, where we clarified that MARK2's Golgi localization is not fully understood, we have decided to remove this section from the manuscript to maintain the accuracy and rigor of our study.

      We appreciate the reviewer’s attention to detail and constructive feedback, which has helped us improve the clarity and focus of our manuscript. 

      - In Fig.1A scale bars are not shown on individual channel images of CAMSAP or GM130

      We sincerely thank the reviewer for pointing out the omission of scale bars in the individual channel images of CAMSAP and GM130 in Figure 1A (Figure 1C in the revised manuscript). In response, we have added a scale bar (5 μm) to the CAMSAP2 channel, as shown in the revised Figure 1C. These updates have been described in the figure legend (page 29, line 21).

      We hope this modification addresses the reviewer’s concern and improves the accuracy and clarity of our figure presentation. We greatly appreciate the reviewer’s constructive feedback, which has helped enhance the quality of our manuscript.

      - In Fig. 1B the title should be amended to 'Colocalization of CAMSAP2/GM130'

      We sincerely thank the reviewer for their suggestion to amend the title in Figure 1B (Figure 1D in the revised manuscript). In response, we have updated the title to "Colocalization of CAMSAP2/GM130," as shown in the revised Figure 1D.

      We hope this modification addresses the reviewer’s concern and improves the clarity and accuracy of the figure. We greatly appreciate the reviewer’s valuable feedback, which has helped us refine the presentation of our results.

      - In Fig. 2F, 5A, and Sup Fig 3C scale bars have been presented vertically

      We sincerely thank the reviewer for pointing out the issue with the vertical orientation of scale bars in Figures 2F (Figure 2D in the revised manuscript), 5A, and Supplementary Figure 3C. In response, we have modified the scale bars in revised Figures 2D and 5A to a horizontal orientation for improved consistency and clarity. Additionally, Supplementary Figure 3C has been removed from the revised manuscript.

      We hope these adjustments address the reviewer’s concerns and enhance the overall presentation quality of the figures. We greatly appreciate the reviewer’s constructive feedback, which has helped us refine our manuscript.

      - Panels are not correctly aligned, and images are not evenly spaced or sized in multiple figures - Fig. 2F, 4D, Sup Fig. 1F, Sup Fig. 2C, Sup Fig. 3E, Sup Fig. 4C

      We sincerely thank the reviewer for pointing out the misalignment and uneven spacing or sizing of panels in multiple figures, including Figures 2F, 4D, Supplementary Figures 1F, 2C, 3E, and 4C (Figure 2D, 4D, Supplementary Figures 1F, 2C, and 3H in the revised manuscript.

      Supplementary Figure 3E was removed from our manuscript). In response, we have standardized the spacing and sizing of all panels throughout the manuscript to ensure consistency and improve visual clarity.

      We hope this modification addresses the reviewer’s concerns and enhances the overall presentation quality of our figures. We greatly appreciate the reviewer’s constructive feedback, which has helped us improve the organization and professionalism of our manuscript.

      - An uncolored additional data point is present in Fig. 3F

      We sincerely thank the reviewer for pointing out the presence of an uncolored additional data point in Figure 3F. In response, we have removed this data point from the revised figure to ensure accuracy and clarity.

      We hope this adjustment resolves the reviewer’s concern and improves the overall quality of the figure. We greatly appreciate the reviewer’s careful review and constructive feedback, which have helped us refine our manuscript.

      - In Fig. 3A 'GAMSAP2/GM130' in the vertical axis label should be amended to 'CAMSAP2/GM130'

      We sincerely thank the reviewer for pointing out the error in the vertical axis label of Figure 3A. In response, we have corrected "GAMSAP2/GM130" to "CAMSAP2/GM130," as shown in the revised Figure 3I.

      We hope this correction resolves the reviewer’s concern and improves the accuracy of our figure. We greatly appreciate the reviewer’s careful review and constructive feedback, which have helped us refine our manuscript.

      - In Fig 5A the green label should be amended to 'GFP-CAMSAP2' instead of 'GFP'

      We sincerely apologize for the confusion caused by our labeling in Figure 5A. To clarify, the green label “GFP” refers to the antibody used, while “GFP-CAMSAP2” is indicated at the top of the figure to specify the construct being analyzed.

      We hope this explanation resolves the misunderstanding and provides clarity regarding the labeling in Figure 5A. We greatly appreciate the reviewer’s feedback, which has allowed us to address this issue and improve the precision of our figure annotations.

      - The repeated use of contractions throughout the manuscript was distracting, I would strongly encourage removing these.

      We sincerely thank the reviewer for pointing out the distracting use of contractions in the manuscript. In response, we have removed and replaced all contractions with their full forms to improve the clarity and formal tone of the text.

      We hope this modification addresses the reviewer’s concern and enhances the readability and professionalism of our manuscript. We greatly appreciate the reviewer’s constructive feedback, which has helped us refine the quality of our writing.

      Reviewer #2: 

      Summary  

      This work by the Meng lab investigates the role of the proteins MARK2 and CAMSAP2 in the Golgi reorientation during cell polarisation and migration. They identified that both proteins interact together and that MARK2 phosphorylates CAMSAP2 on the residue S835. They show that the phosphorylation affects the localisation of CAMSAP2 at the Golgi apparatus and in turn influences the Golgi structure itself. Using the TurboID experimental approach, the author identified the USO1 protein as a protein that binds differentially to CAMSAP2 when it is itself phosphorylated at residue 835. Dissecting the molecular mechanisms controlling Golgi polarisation during cell migration is a highly complex but fundamental issue in cell biology and the author may have identified one important key step in this process. However, although the authors have made a genuine iconographic effort to help the reader understand their point of view, the data presented in this study appear sometimes fragile, lacking rigour in the analysis or over-interpreted. Additional analyses need to be conducted to strengthen this study and elevate it to the level it deserves.

      We sincerely thank the reviewer for their thoughtful evaluation and recognition of our study's significance in understanding Golgi reorientation during cell migration. We appreciate the constructive feedback regarding data robustness, clarity, and interpretation. In response, we have conducted additional analyses, revised data presentation, and ensured cautious interpretation throughout the manuscript. These changes aim to address the reviewer’s concerns comprehensively and strengthen the scientific rigor of our study.

      Major comments

      In order to conclude as they do about the putative role of USO1, the authors need to perform a siRNA/CRISPR of USO1 to validate its role in anchoring CAMSAP2 to the Golgi apparatus in a MARK2 phosphorylation-dependent manner. In other words, does depletion of USO1 affect the recruitment of CAMSAP2 to the Golgi apparatus?

      We sincerely thank the reviewer for their insightful suggestion regarding the role of USO1 in anchoring CAMSAP2 to the Golgi apparatus. In response, we performed USO1 knockdown using siRNA and quantified the Pearson correlation coefficient of CAMSAP2 and GM130 colocalization in control and USO1-knockdown cells.

      The results show that CAMSAP2 localization to the Golgi is significantly reduced in USO1knockdown cells, confirming that USO1 plays a critical role in recruiting CAMSAP2 to the Golgi apparatus. These results are now presented in Figures 6 E–G, and corresponding updates have been incorporated into the Results section (page 10, lines 36-37 in the revised manuscript).

      We hope this additional experiment addresses the reviewer’s concern and strengthens our conclusions regarding the role of USO1. We are grateful for the reviewer’s constructive feedback, which has greatly improved the robustness of our study.  

      It is not clear from this study exactly when and where MARK2 phosphorylates CAMSAP2. What is the result of overexpression of the two proteins in their respective localisation to the Golgi apparatus? As binding between CAMSAP2 and MARK2 appears robust in the immunoprecipitation assay, this should be readily investigated. 

      We sincerely thank the reviewer for their insightful comments and questions. To address the role of MARK2 in regulating CAMSAP2 localization to the Golgi apparatus, we overexpressed GFPMARK2 in cells and compared its effects on CAMSAP2 localization to the Golgi with control cells overexpressing GFP alone. Our results show that CAMSAP2 localization to the Golgi is significantly increased in GFP-MARK2-overexpressing cells, as shown in Supplementary Figures 3C and 3E. Corresponding updates have been incorporated into the Results section (page 8, lines 25-27 in the revised manuscript).

      Regarding the question of how MARK2 itself localizes to the Golgi, we are currently unable to fully elucidate the underlying mechanism. Therefore, we have removed the discussion of MARK2’s Golgi localization from the manuscript to ensure scientific accuracy. Consequently, we have not conducted experiments to assess the effects of CAMSAP2 overexpression on MARK2’s localization to the Golgi.

      We hope this explanation clarifies the reviewer’s concerns. We are grateful for the reviewer’s constructive feedback, which has guided us in improving the clarity and focus of our study.

      To strengthen their results, can the author map the interaction domains between CAMSAP2 and MARK2? The authors have at their disposal all the constructs necessary for this dissection.

      We sincerely thank the reviewer for their insightful suggestion to map the interaction domains between CAMSAP2 and MARK2. In response, we performed immunoprecipitation experiments using truncated constructs of CAMSAP2. Our results reveal that MARK2 interacts specifically with the C-terminus (1149F) of CAMSAP2, as shown in Supplementary Figures 3A and 3B. Corresponding updates have been incorporated into the Results section (page 7, lines 41-42 and page 8, line 1 in the revised manuscript).

      We hope this additional analysis addresses the reviewer’s suggestion and further strengthens our conclusions. We greatly appreciate the reviewer’s constructive feedback, which has helped improve the depth of our study.

      Minor comments

      Sup-fig1  

      H: It is not clear if the polarisation experiment has been repeated three times (as it should) and pooled or is just the result of one experiment?

      We sincerely apologize for the lack of clarity regarding the experimental details for Supplementary Figure 1H. To clarify, the polarization experiment was repeated three times, and the results were pooled to generate the data presented. We have updated the figure legend for Supplementary Figure 1H to explicitly state this information (page 35, lines 27-29 in the revised manuscript).

      We hope this clarification resolves the reviewer’s concern. We greatly appreciate the reviewer’s careful review and constructive feedback, which have helped us improve the accuracy and transparency of our manuscript.

      Sup-fig2  

      C: "Immunofluorescence staining plots" formula used in the legend is not clear. Which condition is presented in the panel, parental HT1080 or CAMSAP2 KO cells?  

      We thank the reviewer for pointing out the lack of clarity regarding the conditions presented in Supplementary Figure 2C. To clarify, the immunofluorescence staining plots shown in this panel are from parental HT1080 cells. We have updated the figure legend to include this information (page 36, line 14 in the revised manuscript).

      We hope this clarification resolves the reviewer’s concern and improves the transparency of our data presentation. We greatly appreciate the reviewer’s feedback, which has helped us refine the manuscript.

      Figure 1  

      D: In the plot, the colour of the points for the "red cells" are red but the one for the "blue cells" are green, this is confusing.

      E: Once again, the colour choice is confusing as blue cells (t=0.5h) are quantified using red dots and red cells (t=2h) quantified using green dots. The t=0h condition should be quantified as well and added to the graph.  

      F: Representative CAMSAP2 immunofluorescence pictures for the three time points should be provided in addition to the drawings.  

      We thank the reviewer for their valuable comments regarding Figure 1D (revised Figure 1E), Figure 1E (revised Figure 1B), and Figure 1F (revised Supplementary Figure 2C).

      - Figure 1D (revised Figure 1E): we have modified the x-axis labels and adjusted the color scheme of the data points to ensure consistency and avoid confusion.

      - Figure 1E (revised Figure 1B): we have updated the x-axis and included the quantification of the t=0h condition, which has been added to the graph.

      - Figure 1F (revised Supplementary Figure 2C): we have provided representative immunofluorescence images of CAMSAP2 for the three-time points to complement the schematic drawings.

      We hope these revisions address the reviewer’s concerns and improve the clarity and completeness of our data presentation. We greatly appreciate the reviewer’s constructive feedback, which has significantly contributed to enhancing our manuscript.

      Figure 2  

      A: No methodology in the material and methods is provided for this analysis.  

      B: Can the authors be more precise regarding the source of the CAMSAP2 interactants? Can the author provide the citation of the publication describing the CAMSAP2-MARK2 interaction?  

      D: Genotyping for the MARK2 KO cell line should be provided the same way it was provided for the CAMSAP2 cell line in Sup-fig1. "MARK2 was enriched around the Golgi apparatus in a  significant proportion of HT1080 cells": which proportion of the cells?  

      F: The time point of fixation is missing  

      G: It is not clear if the polarisation experiment has been repeated three times (as it should) and pooled or is just the result of one experiment?  

      We thank the reviewer for their detailed comments and suggestions regarding Figure 2. Below, we provide clarifications and outline the modifications made:

      - Figure 2A: The methodology for this analysis has been added to section 5.14 (Data statistics). Specifically, we have stated: “GO analysis of proteins was plotted using https://www.bioinformatics.com.cn, an online platform for data analysis and visualization” (page 26 lines 5-6 in the revised manuscript).

      - Figure 2B: The CAMSAP2 interactants were derived from the study by Wu et al., 2016, which provides the source of these interactants. The interaction between CAMSAP2 and MARK2 is referenced from Zhou et al., 2020. These citations have been added to the relevant sections of the manuscript (page 30, lines 10-11 and 13-14).

      - Figure 2D (removed in the revised manuscript): Genotyping for the MARK2 KO cell line has been provided in the same format as for the CAMSAP2 KO cell line in Figure 2G. Additionally, as the MARK2 Golgi localization discussion cannot yet be fully elucidated, we have removed this portion from the manuscript.

      - Figure 2F (revised Figure 2D): The time point of fixation, which occurred 2 hours after the scratch wound assay, has been added to the figure legend (page 30, lines 15-16).

      - Figure 2G (revised Figure 2E-F): The polarization experiment was repeated three times, and the results were pooled. This information has been included in the figure legend (page 30, lines 26 and 29).

      We hope these updates address the reviewer’s concerns and improve the clarity and completeness of the manuscript. We are grateful for the reviewer’s constructive feedback, which has greatly enhanced the rigor of our study. References:

      Wu, J., de Heus, C., Liu, Q., Bouchet, B.P., Noordstra, I., Jiang, K., Hua, S., Martin, M., Yang, C., Grigoriev, I., et al. (2016). Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Dev Cell 39 (1): 44-60.

      Sup-fig3  

      E: Although colocalisation between CAMSAP2 and MARK2 is clear in your serum conditions in HT1080 and RPE1 cells, the deletion domain analysis appears weak and insufficient to implicate the role of the spacer domain. This part should be deleted or strengthened, but the data do not satisfactorily support your conclusion as it stands.  

      We sincerely thank the reviewer for their critical comments regarding the deletion domain analysis of MARK2 and its role in colocalization with CAMSAP2. As the current data do not satisfactorily support our conclusions, we have removed all related content on MARK2 and the deletion domain analysis from the manuscript to maintain scientific rigor.

      We appreciate the reviewer’s valuable feedback, which has helped us refine and improve the quality and focus of our study.

      Figure 3  

      A: Can the reduced CAMSAP2 Golgi localisation phenotype be rescued by the overexpression of MARK2 cDNA in the MARK2 KO cells?  

      F: Presence of a white dot on the HT1080 plot  

      G: The composition of the homogenization buffer is not indicated in the material and methods  

      We thank the reviewer for their valuable comments and suggestions regarding Figure 3. Below, we detail the modifications made:

      - Figure 3A: To address whether the reduced CAMSAP2 Golgi localization phenotype can be rescued, we overexpressed MARK2 cDNA in MARK2 KO cells. Our results show that overexpression of MARK2 successfully rescues the reduced CAMSAP2 localization to the Golgi, as demonstrated in Supplementary Figures 3C and 3E (page 8, lines 5-7).

      - Figure 3F: We have removed the white dot on the HT1080 plot to ensure clarity and accuracy.

      - Figure 3G: The composition of the homogenization buffer used in the experiment has been added to the Materials and Methods section for completeness (page 24, lines 34-41 and page 25, lines 1-10).

      We hope these revisions address the reviewer’s concerns and enhance the clarity and rigor of our study. We are grateful for the reviewer’s constructive feedback, which has significantly improved the quality of our manuscript.

      Figure 4  

      B: Quantification of the effect of the S835A mutation should be provided  

      D: Top left panel: Why Ha antibody stains Golgi structure in absence of Ha-CAMSAP2 transfection ? IF the Ha antibody has unspecific affinity towards the Golgi apparatus, may be it is not the good tag to use in this assay?  

      E: The number of cells studied should be standardized. 119 cells were analyzed in the CAMSAP KO vs only 35 cells in the CAMSAP2 KO (HA-CAMSAP2-S835D) conditions. This could introduce strong bias to the analysis. Furthermore the CAMSAP2 S835A seems to provide a certain level of rescue. It would be interesting to see what is the result of the T test between the HT1080 and HA-CAMSAP S835A conditions.  

      We thank the reviewer for their thoughtful comments and suggestions regarding Figure 4. Below, we detail the revisions and clarifications made:

      - Figure 4B: The S835A mutation renders CAMSAP2 non-phosphorylatable by MARK2. This conclusion is based on our experimental observations and previously reported mechanisms.

      - Figure 4D: The HA antibody does not exhibit non-specific affinity toward the Golgi apparatus. The observed labeling in the top left panel was due to an error in our annotation. We have corrected the label, replacing "HA" with "CAMSAP2" to accurately reflect the experimental conditions.

      - Figure 4E: To standardize the number of cells analyzed across conditions, we reduced the number of CAMSAP2 KO cells analyzed to 50 and balanced the sample sizes for comparison. Additionally, we performed a t-test between the HT1080 and HACAMSAP2 S835A conditions. The results support that CAMSAP2 S835A provides partial rescue, as reflected in the updated analysis (page 32, lines 19-23).

      We hope these revisions address the reviewer’s concerns and improve the accuracy and reliability of our results. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the quality of our study.

      Figure 6  

      6A: The wound position should be indicated on the picture.  

      6B: Given that microtubule labelling is present on the vast majority of the cell surface, this type of quantification provides very little information using conventional light microscopy and should not be used to conclude any change in the microtubule network using Pearson's coefficient.  The text describing the figure 6A and 6B needs re written as I do not understand what the author want to say. "In cells located before the wound edge..." : I do not understand how a cell could be located before the wound edge. Which figure corresponds to the trailing edge of the wounding?

      We thank the reviewer for their valuable comments on Figure 6A (revised Supplementary Figure 6E) and Figure 6B (revised Supplementary Figure 6F). Below, we detail the modifications made:

      - Figure 6A (revised Supplementary Figure 6E), we have added arrows to indicate the wound position, providing clearer guidance for interpreting the image.

      - Figure 6B (revised Supplementary Figure 6F), we revised our quantification method based on the approach used in literature (Wu et al., 2016). Specifically, we analyzed the relationship between microtubules and the Golgi apparatus in cells at the leading edge of the wound. The x-axis represents the distance from the Golgi center, while the y-axis shows the normalized radial fluorescence intensity of microtubules and the Golgi apparatus.

      Additionally, we revised the accompanying text for clarity and accuracy. The original description:

      “In cells located before the wound edge, the Golgi apparatus maintained a ribbon-like shape, with a higher density of microtubules. In contrast, at the trailing edge of the wounding, the Golgi apparatus appeared more as stacks around the nucleus, with fewer microtubules”  was replaced with:

      “Finally, to comprehensively understand the dynamics between non-centrosomal microtubules and the Golgi apparatus during Golgi reorientation, we conducted cell wound-healing experiments (Supplementary Figure 6 E-F). Our observations revealed notable changes in the Golgi apparatus and microtubule network distribution in relation to the wounding. These findings corroborate our earlier results and suggest a highly dynamic interaction between the Golgi apparatus and microtubules during Golgi reorientation” (Revised manuscript page 11 lines 3-10).

      We hope these changes address the reviewer’s concerns and improve the clarity and robustness of our study. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the presentation and interpretation of our data. References:

      Wu, J., de Heus, C., Liu, Q., Bouchet, B.P., Noordstra, I., Jiang, K., Hua, S., Martin, M., Yang, C., Grigoriev, I., et al. (2016). Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Dev Cell 39 (1): 44-60.

      Reviewer #3:  

      Summary  

      In this study, Xu et al. analyzed the wound healing process of HT1080 cells to elucidate the molecular mechanisms by which the Golgi apparatus exhibits transient dispersion before reorienting to the wound edge in the compact assembly structure. They focused on the role of the microtubule minus-end binding protein CAMSAP2, which mediates the linkage between microtubules and the Golgi membrane. At first, they noticed that CAMSAP2 transiently lost Golgi colocalization during the initial phase of the wound healing process. They further found that the cell polarity-regulating kinase MARK2 binds and phosphorylates S835 of CAMSAP2, thereby enhancing the interaction between CAMSAP2 and the Golgi protein Uso1. Together with the phenotypes of CAMSAP2, MARK2, and Uso1 KO cells, these authors argue that the MARK2dependent phosphorylation of CAMSAP2 plays an important role in the reassembly and reorientation of the Golgi apparatus after a transient dispersion observed during the wound healing process.

      We sincerely thank the reviewer for their thoughtful summary of our study and constructive feedback. Your comments have been invaluable in refining our research and enhancing the clarity and impact of our manuscript.

      Major comments

      (1) The premise of this study was that during the wound healing process, the Golgi apparatus exhibits transient dispersion before reorientation to the front of the nucleus.  

      In the first place, this claim has not been well established in previous studies or this paper. Therefore, the authors should present a proof of this claim in a clearer manner.  

      To introduce this cellular event, the authors cite several papers in the introduction (page 4) and the results (page 6) sections. However, many papers cited are review articles, and some of them do not describe this change in the Golgi assembly structure before reorientation. Only two original articles discussed this phenomenon (Bisel et al. 2008 and Wu et al. 2016), and direct evidence was provided by only one paper (Wu et al. 2016) in which changes in the Golgi apparatus in wound-healing RPE1 cells were recorded by live imaging (Fig.7A in Wu et al. 2016).

      Furthermore, it should be noted that this previous paper demonstrated that depletion of CAMSAP2 inhibits Golgi dispersion. Obviously, this conclusion is inconsistent with their statement to introduce this study (page4) that ‟This emphasizes CAMSAP2's role in sustaining Golgi integrity during critical cellular events like migration." In addition, it also contradicts the authors' model of the present paper (Fig. 6E), which argued that disruption of the Golgi association of CAMSAP2 facilitates the Golgi dispersion.  

      We sincerely thank the reviewer for their detailed comments and for providing us with the opportunity to clarify the premise and conclusions of our study. Below, we address the main concerns raised:

      First, to provide direct evidence of Golgi apparatus changes during the wound-healing process, we conducted live-cell imaging experiments. Our observations, presented in revised Supplementary Figure 2A, clearly demonstrate that the Golgi apparatus exhibits a transient dispersion state before reorienting toward the leading edge of the nucleus during migration.

      Regarding the interpretation of previous studies, we acknowledge the reviewer’s concerns about the citation of review articles. To address this, we have revisited the literature and clarified that the phenomenon of Golgi dispersion during reorientation has been directly demonstrated in Wu et al (Wu et al., 2016), where live imaging of wound-healing RPE1 cells showed this dynamic behavior. Furthermore, we note that in Wu et al paper explicitly demonstrates that CAMSAP2 depletion promotes Golgi dispersion, contrary to the reviewer’s interpretation that "depletion of CAMSAP2 inhibits Golgi dispersion."

      Our model focuses on the role of CAMSAP2 in restoring the Golgi from a transiently dispersed structure back to an intact ribbon-like structure during reorientation. Specifically, we propose that during this process, the disruption of CAMSAP2’s association with the Golgi affects this restoration, rather than directly promoting Golgi dispersion as suggested by the reviewer. We believe this distinction aligns with our data and the existing literature.

      To strengthen the background of our study, we have revised the introduction and results sections (page 6, lines 6-13 and page 7, lines 1-17) to minimize reliance on review articles and have provided more explicit citations to original research papers. We hope this addresses the reviewer’s concern about the sufficiency of the cited literature.

      We trust these clarifications and revisions resolve the reviewer’s concerns and enhance the robustness of our study. Once again, we are grateful for the reviewer’s constructive feedback, which has greatly helped refine our manuscript. References:

      Wu, J., de Heus, C., Liu, Q., Bouchet, B.P., Noordstra, I., Jiang, K., Hua, S., Martin, M., Yang, C., Grigoriev, I., et al. (2016). Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Dev Cell 39 (1): 44-60.

      The authors did not provide experimental data for this temporal change in the Golgi assembly structures during the wound-healing process of HT1080 that they analyzed. They only provide an illustration of wound-healing cells (Fig.1F), in which cells are qualitatively discriminated and colored based on the Golgi states, without indicating the experimental basis of the discrimination.

      According to their ambiguous descriptions in the text (page7), the reader can speculate that Fig. 1F is illustrated based on the images in Supplementary Fig. 2C. However, because of the low quality and presentation style of these data, it is impossible to recognize the assembly structures of the Golgi apparatus in wound-edge cells.  

      If the authors hope to establish this premise claim for the present paper, they should provide their own data corresponding to the present Supplementary Fig. 2C in more clarity and present qualitative data verifying this claim, as Wu et al. did in Fig. 7A in their paper.

      We sincerely thank the reviewer for their constructive feedback and the opportunity to address the concern regarding the lack of experimental data supporting the temporal changes in Golgi assembly during the wound-healing process.

      To establish this premise, we conducted live-cell imaging experiments to observe the dynamic changes in the Golgi apparatus during directed cell migration. Our data, now presented in Supplementary Figure 2A, clearly demonstrate that the Golgi apparatus undergoes a transient dispersed state before reorganizing into an intact structure. These findings provide direct experimental evidence supporting our claim.

      In addition, we have revised the data originally presented in Supplementary Figure 2C and enhanced its quality and presentation style. This supplementary figure now includes clearer images and annotations to better illustrate the Golgi assembly structures in wound-edge cells. The improved data presentation aligns with the standards set by Wu et al reported (Wu et al., 2016) and provides qualitative support for our observations.

      We hope these additions and revisions address the reviewer’s concerns and strengthen the scientific rigor and clarity of our manuscript. We are grateful for the reviewer’s valuable suggestions, which have significantly improved the quality of our study. References:

      Wu, J., de Heus, C., Liu, Q., Bouchet, B.P., Noordstra, I., Jiang, K., Hua, S., Martin, M., Yang, C., Grigoriev, I., et al. (2016). Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Dev Cell 39 (1): 44-60.

      (2) In Fig.1A-D, the authors claim that CAMSAP2 dissociates from the Golgi apparatus in cells "that have not yet completed Golgi reorientation and exhibit a transitional Golgi structure, characterized by relative dispersion and loss of polarity (page7)." However, I these analyses, they do not analyze the initial stage (0.5h after wound addition) of cells facing the wound edge, as they do in Supplementary Fig. 2C. Instead, they analyze cells separated from the wound edge at 2 h after wound addition when the wound-edge cells complete their polarization. These data are highly misleading because there is no evidence that the cells separated from the wound edge are really in the transitional state before polarization.  

      In this regard, Fig. 1E shows the analysis of the wound-edge cells at 0.5 and 2 h after the addition of wound, which provides suitable data to verify the authors' claim. However, the corresponding legend indicates that these statistical data are based on the illustration in Fig. 1F, which is probably based on highly ambiguous data in Supplementary Fig. 2C (see above).  

      Taken together, I strongly recommend the authors to remove Fig.1A-D. Instead, they should include the improved figure corresponding to the present Supplementary Fig.2C and present its statistical analysis similar to the present Fig.1E for this claim.

      We sincerely thank the reviewer for their constructive feedback and recommendations. Below, we address the concerns raised regarding Figure 1A-D and Supplementary Figure 2C.

      To provide stronger evidence for the transitional state of the Golgi apparatus during reorientation and the dynamic regulation of CAMSAP2 localization, we conducted live-cell imaging experiments. These results, now presented in Supplementary Figure 2A, clearly demonstrate that the Golgi apparatus undergoes a transitional state characterized by dispersion before reorienting toward the leading edge.

      Additionally, we analyzed fixed wound-edge cells at different time points during directed migration to observe CAMSAP2’s colocalization with the Golgi apparatus. The results, shown in Figures 1A and 1B, reveal dynamic changes in CAMSAP2 localization, confirm its regulation during Golgi reorientation, and include a corresponding statistical analysis (page 7, lines 1-17).

      These updates ensure that our claims are supported by robust and unambiguous data.

      We hope these revisions address the reviewer’s concerns and provide clear and reliable evidence for the transitional state of the Golgi apparatus and CAMSAP2’s dynamic regulation. We are grateful for the reviewer’s constructive suggestions, which have greatly improved the quality and focus of our manuscript.

      (3) In Supplementary Fig. 5 and Fig. 4, the authors claim that MARK2 phosphorylates S835 of CAMSAP2.  

      There are many issues to be addressed. Otherwise, the above claim cannot be assumed to be reliable.  

      First, the descriptions (in the text and method sections) and figures (Supplementary Fig.5) concerning the in vitro kinase assay and subsequent phosphoproteomic analysis are too immature and contain many errors.  

      Legend to Supplementary Fig. 5 is too immature for comprehension. It should be completely rewritten in a more comprehensive manner. The figure in Supplementary Fig. 5C is also too immature for understanding. They simply paste raw mass spectrometric data without any modification for presentation.  

      We sincerely apologize for the lack of clarity and inaccuracies in the original descriptions and figure legends for the in vitro kinase assay and phosphoproteomic analysis. We greatly appreciate the reviewer’s detailed comments, which have allowed us to address these issues comprehensively.

      To improve clarity and accuracy, we have rewritten the figure legend for the original Supplementary Figure 5 (now Supplementary Figure 4) as follows:

      (A): CBB staining of a gel with GFP-CAMSAP2, GST, and GST-MARK2. GFP-CAMSAP2 was expressed in Sf9 cells and purified. GST and GST-MARK2 were expressed in E. coli and purified.

      (B): Western blot analysis of an in vitro kinase assay. GST or GST-MARK2 was incubated with GFP-CAMSAP2 in kinase buffer (50 mM Tris-HCl pH 7.5, 12.5 mM MgCl2, 1 mM DTT, 400 μM ATP) at 30°C for 30 minutes. Reactions were stopped by boiling in the loading buffer.

      (C): Detection of phosphorylation at S835 in CAMSAP2 by mass spectrometry. The observed mass increases in b4, b5, b6, b7, b8, b10, b11, and b12 fragments indicate phosphorylation at Ser835.

      (D): Kinase assay samples analyzed using Phos-tag SDS-PAGE. HEK293 cells were cotransfected with the indicated plasmids. Band shifts of CAMSAP2 mutants were examined via western blot. Phos-tag was used in SDS-PAGE, and arrowheads indicate the shifted bands caused by phosphorylation.

      To address the reviewer’s concern about Supplementary Figure 5C, we have reformatted the mass spectrometry data to improve readability and presentation quality. The revised figure includes clearer annotations and graphical representations of the mass spectrometric evidence for phosphorylation at S835.

      We believe these updates enhance the comprehensibility and reliability of our data, providing robust support for our claim that MARK2 phosphorylates CAMSAP2 at S835. We hope these

      revisions address the reviewer’s concerns and demonstrate our commitment to improving the quality of our manuscript.

      The readers cannot understand how the authors purified GFP-CAMSAP2 for the kinase assay.

      The method section incorrectly states that the product was purified using Ni-resin.  

      We thank the reviewer for their comment regarding the purification of GFP-CAMSAP2 for the kinase assay. We would like to clarify that GFP-CAMSAP2 carries a His-tag, which allows for purification using Ni-resin, as described in the Methods section (page 23, Lines 32-40). Therefore, the description in the Methods section is correct.

      To avoid any potential misunderstanding, we have revised the Methods section to provide more detailed and precise descriptions of the purification process. Specifically, GFP-CAMSAP2 was cloned into the pOCC6_pOEM1-N-HIS6-EGFP vector, which includes a His-tag, and was expressed in Sf9 cells. The His-GFP-CAMSAP2 protein was purified using Ni-resin chromatography. Relevant details have been added to the Methods section (page 21, Lines 34-36:

      “CAMSAP2 was cloned into the pOCC6_pOEM1-N-HIS6-EGFP vector expressed in Sf9, purified as His-GFP-CAMSAP2.”; page 23, Lines 32-33: “His-GFP-CAMSAP2 was cotransfected with bacmids into Sf9 cells to generate the passage 1 (P1) virus.”).

      We hope these clarifications and revisions address the reviewer’s concern and improve the comprehensibility of our experimental details. We appreciate the reviewer’s feedback, which has helped us refine the manuscript.

      In this relation, GST and GST-MARK2 are described as having been purified from Sf9 insect cells in the text section (page9) and legend to Supplementary Fig. 5, but from E. coli in the method section. Which is correct?  

      We thank the reviewer for pointing out the inconsistencies in the descriptions regarding the source of GST and GST-MARK2. To clarify, both GST and GST-MARK2 were purified from E. coli, as stated in the Methods section (page 23, Lines 26-31). We have corrected the erroneous descriptions in the main text (page 8, Lines 35-36) and the legend to Supplementary Figure 4 to ensure consistency.

      Additionally, we have updated the legend for Supplementary Figure 4A to state the sources of each protein explicitly:

      “GFP-CAMSAP2 were expressed in Sf9 cells and purified. GST and GST-MARK2 were expressed in E. coli and purified.” (page 38, Lines 2-3)

      These revisions ensure that the experimental details are accurate and consistent across the manuscript, eliminating any potential confusion. We appreciate the reviewer’s careful review and constructive feedback, which have helped us improve the clarity and reliability of our study.

      Because the phosphoproteomic data (Supplementary Fig. 5C) are not provided clearly, the experimental data for Fig.4A, in which possible CAMSAP2 phosphorylation sites are illustrated, are completely unknown. For me, it is highly strange that only the serine residues are listed in Fig. 4A.

      We sincerely thank the reviewer for raising this important point regarding Figure 4A and the phosphoproteomic data in Supplementary Figure 5C.

      - Phosphorylation Sites in Figure 4A

      The phosphorylation sites illustrated in Figure 4A are derived from our analysis of the original mass spectrometry data. These sites were included based on their high confidence scores and data reliability. Importantly, only serine residues met the stringent criteria for inclusion, as no threonine or tyrosine residues had sufficient evidence for phosphorylation. To clarify this, we have updated the figure legend for Figure 4A (page 32, Lines3-7).

      - Improvements to Supplementary Figure 5C (Supplementary Figure 4D in the revised manuscript)

      To enhance transparency and clarity, we have reformatted Supplementary Figure 4D to include clearer annotations. The revised figure highlights the phosphopeptides used to identify the phosphorylation sites and provides a more comprehensive presentation of the mass spectrometry data. To clarify this, we have updated the figure legend for Supplementary Figure 4D (page 38, Lines 11-13).

      - Data Availability

      We will follow the journal’s guidelines by uploading the raw mass spectrometry data to the required public database upon manuscript acceptance. This ensures that the data are accessible and reproducible in compliance with journal standards.

      We hope these clarifications and updates address the reviewer’s concerns and improve the reliability and comprehensibility of our data presentation. We greatly appreciate the reviewer’s constructive feedback, which has helped us enhance the rigor and clarity of our manuscript.

      Considering the crude nature of the GST-MARK2 sample used for the in vitro kinase assay (Supplementary Fig. 5A), it is unclear whether MARK2 is responsible for all phosphorylation sites on CAMSAP2 detected in the phosphoproteomic analysis. Furthermore, if GFP-CAMSAP2 was purified from Sf9 insect cells, these sites might have been phosphorylated before incubation for the in vitro kinase assay. The authors should address these issues by including a negative control using the kinase-dead mutant of MARK2 in their in vitro kinase assay.

      We sincerely thank the reviewer for raising these important points regarding the potential prephosphorylation of GFP-CAMSAP2 and the role of MARK2 in the phosphorylation sites detected in our analysis.

      To address the possibility that GFP-CAMSAP2 may have been pre-phosphorylated during its expression in Sf9 insect cells, we conducted an in vitro comparison. Specifically, we compared the band shifts observed in GST-MARK2 + GFP-CAMSAP2 versus GST + GFP-CAMSAP2 under identical conditions. As shown in Supplementary Figure 4B, the GST-MARK2 + GFP-CAMSAP2 group exhibited a clear upward band shift compared to the GST + GFP-CAMSAP2 group, indicating additional phosphorylation events induced by MARK2.

      Regarding the inclusion of a kinase-dead MARK2 mutant as a negative control, we acknowledge this as a valuable suggestion for further confirming the specificity of MARK2 in phosphorylating CAMSAP2. While this experiment is not currently included, we plan to conduct it in our future studies to strengthen our findings.

      We hope this clarification and the provided evidence address the reviewer’s concerns. We are grateful for this constructive feedback, which has helped us critically evaluate and refine our experimental approach.

      (4) In Supplementary Fig.6A-C and Fig.5A-B, the authors claim that the phosphorylation of CAMSAP2 S835 is required for restoring the reduced reorientation of the Golgi in wound-healing cells and the delay in wound closure observed in MARK2 KO cells.  

      If the aforementioned claim is adequately supported by experimental data, it indicates that the defects in Golgi repolarization and wound closure in MARK2 KO cells can be mainly attributed to the reduced phosphorylation of S835 of CAMSAP2 in HT1080. Considering the presence of many well-known substrates of MARK2 for regulating cell polarity, this claim is highly striking.  

      However, to strongly support this conclusion, the authors should first perform a rescue experiment using MARK2 KO cells exogenously expressing MARK2. This step is essential for determining whether the defects observed in MARK2 KO cells are caused by the loss of MARK2 expression, but not by other artificial effects that were accidentally raised during the generation of the present MARK2 KO clone.  

      We sincerely thank the reviewer for their insightful suggestion regarding the rescue experiment to confirm that the defects observed in MARK2 KO cells are specifically caused by the loss of MARK2 expression.

      To address this, we performed a rescue experiment in MARK2 KO HT1080 cells by exogenously expressing GFP-MARK2. Our results, presented in Supplementary Figures 3C-E, demonstrate that GFP-MARK2 expression successfully restores the localization of CAMSAP2 on the Golgi apparatus in MARK2 KO cells.

      These findings strongly support the conclusion that the defects in Golgi architecture and CAMSAP2 Golgi localization are directly attributable to the loss of MARK2 expression, rather than any artificial effects potentially introduced during the generation of the MARK2 KO clone.

      We hope these additional experimental results address the reviewer’s concerns and provide robust evidence for the role of MARK2 in regulating Golgi reorientation and wound closure. We are grateful for the reviewer’s constructive feedback, which has significantly improved the rigor and clarity of our study.

      In addition, to evaluate the impact of the rescue effect of CAMSAP2, the authors should include the data of wild-type HT1080 and MARK2 KO cells in Fig. 5B to reliably demonstrate the aforementioned claim.  

      We thank the reviewer for their valuable suggestion to include data from wild-type HT1080 and MARK2 KO cells in Figure 5A-C to better evaluate the rescue effects of CAMSAP2.

      In response, we have incorporated data from wild-type HT1080 and MARK2 KO cells into Figure 5A-C. These additions provide a comprehensive comparison and further demonstrate the impact of CAMSAP2-S835A and CAMSAP2-S835D on Golgi reorientation relative to the wild-type and MARK2 KO conditions.

      These changes are reflected in Figures 5A-C.

      We hope these updates address the reviewer’s concerns and strengthen the reliability of our conclusions. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the robustness of our study.

      Principally, before checking the rescue effects in MARK2 KO cells, the authors should examine the rescue activity of the CAMSAP2 S835 mutants in restoring the reduced reorientation of the Golgi in wound-healing cells and the delay in wound closure observed in CAMSAP2 KO cells (Supplementary Fig.1F-H and Supplementary Fig.2A, B). These experiments are more essential experiments to substantiate the authors' claim.

      We thank the reviewer for their insightful suggestion to examine the rescue activity of CAMSAP2 S835 mutants in CAMSAP2 KO cells to further substantiate our claims.

      In Figure 4D-F, we observed significant differences between CAMSAP2 S835 mutants in their ability to restore Golgi structure and localization, indicating functional differences between these mutants. To better reflect the regulatory role of MARK2-mediated phosphorylation of CAMSAP2, we performed scratch wound-healing experiments in MARK2 KO cells by establishing stable cell lines expressing CAMSAP2 S835 mutants. These experiments allowed us to assess Golgi reorientation during wound healing and are presented in Figure 5A-C.

      We also attempted to generate stable cell lines expressing GFP-CAMSAP2 and its mutants in CAMSAP2 KO cells. Unfortunately, these cells consistently failed to survive, preventing successful construction of the cell lines.

      We hope these experiments and explanations address the reviewer’s concerns. We are grateful for the reviewer’s constructive feedback, which has helped us refine and improve our study.

      (5) The data presented in Fig. 6A and B are not sufficient to support the authors' notion that "our observation revealed notable changes in the Golgi apparatus and microtubule network distribution in relation to the wounding. (page 11)"  

      Fig. 6A, which includes only a single-cell image in each panel, does not demonstrate the general state of microtubules and the Golgi in the wound-edge cells. The reader cannot even know the migration direction of each cell.  

      Fig.6 B are not suitable to quantitatively support the authors' claim. The authors should find a way to quantitatively estimate the microtubule density around the Golgi and the shape and compactness of the Golgi in each cell facing the wound, not estimating the colocalization of microtubules and the Golgi, as in the present Fig. 6B.  

      We sincerely apologize for the confusion caused by our unclear descriptions and presentation.

      Here, we clarify the purpose and improvements made to address the reviewer’s concerns. In this study, we primarily aimed to observe the relationship between microtubules and the Golgi apparatus in cells at the leading edge of the wound during directed migration. In Figure 6A (now Supplementary Figure 6E), the images represent cells located at the wound edge at different time points. To improve clarity, we have added arrows indicating the migration direction and updated the figure legend to describe these details (page 40 lines 13-14).

      To better quantify the relationship between microtubules and the Golgi apparatus, we revised our analysis by referring to the quantitative method used in Figure 3F of the paper Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Specifically, we performed a radial analysis of fluorescence intensity in cells at the wound edge, measuring the distance from the Golgi center (x-axis) and the normalized radial fluorescence intensity of microtubules and the Golgi (y-axis). These results are now presented in Supplementary Figure 6E and 6F.

      We hope these improvements address the reviewer’s concerns and provide stronger evidence for the changes in the Golgi apparatus and microtubule network distribution in relation to wound healing. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the clarity and rigor of our study.

      The legends to Fig. 6A and B indicate that they compared immunofluorescent staining of cells at the edge of the wound after 0.5h and 2 h of migration. However, the authors state in the text that they compared "the cells located before the wound" and "the cells at the trailing edge of the wounding (page 11)."Although this description is highly ambiguous and misleading, if they compared the wound-edge cells and the cells separated from the wound edge at 2 h after cell migration here, they should improve the experimental design as I pointed out in the 2nd major comment.  

      We thank the reviewer for their detailed feedback regarding the experimental design and the need to clarify our descriptions. We have addressed these concerns as follows:

      - Clarification of descriptions:

      We recognize that the previous description in the text regarding "the cells located before the wound" and "the cells at the trailing edge of the wounding" was ambiguous and potentially misleading. We have revised this text to accurately describe the experimental design. Specifically, we compared cells at the leading edge of the wound at different time points (0.5h and 2h post-migration). These corrections are reflected in figure legends (Supplementary Figure 6E and 6F ) and the Results section (page 11,lines 3-8).

      - Improved experimental design:

      To better support our conclusions, we performed live-cell imaging to observe the dynamic changes in the Golgi apparatus during directed migration. As shown in Supplementary Figure 2A, our results confirm that the Golgi apparatus undergoes a transient dispersed state before reorganizing into an intact structure.

      Additionally, we performed fixed-cell staining at different time points to analyze the colocalization of CAMSAP2 with the Golgi apparatus in cells at the leading edge of the wound. The colocalization analysis, presented in Figures 1A-C, further demonstrates the dynamic regulation of CAMSAP2 during Golgi reorientation.

      We hope these updates address the reviewer’s concerns and provide a clearer and more robust foundation for our conclusions. We are grateful for the reviewer’s constructive feedback, which has greatly enhanced the clarity and rigor of our study.

      Minor comments  

      (1) In Fig. 2 and Supplementary Fig. 3, the authors claim that MARK2 is enriched around the Golgi. However, this claim was based on immunofluorescent images of single cells and single-line scans.  

      It is better to present the statistical data for Pearson's coefficient as shown in Figs. 1D and E. To demonstrateMARK2 enrichment around Golgi, but not localization in Golgi, the authors should find a way to quantify the specific enrichment of MARK2 signals in the Golgi region.  

      We thank the reviewer for raising this important point regarding the enrichment of MARK2 around the Golgi apparatus. Upon further consideration, we acknowledge that our current data do not provide sufficient evidence to fully elucidate the mechanism of MARK2 localization to the Golgi.

      To maintain the scientific rigor of our study, we have removed this claim and the corresponding content from the manuscript, including original Figures 2 and Supplementary Figure 3 that specifically discuss MARK2 enrichment. These changes do not affect the primary conclusions of the study, which focus on the role of MARK2-mediated phosphorylation of CAMSAP2.

      We hope this clarification addresses the reviewer’s concerns. In the future, we plan to investigate the precise mechanism of MARK2 localization using additional experimental approaches. We are grateful for the reviewer’s constructive feedback, which has helped us refine the scope and focus of our manuscript.

      (2) In Fig. 3 and Supplementary Fig. 4, the authors report that CAMSAP2 localization on the Golgi is reduced in cells lacking MARK2.  

      Essentially, the present results support this claim. However, the authors should analyze the Golgi localization of CAMASP2 with the same quantification parameter because they used Pearson's coefficient in Fig. 1D, E and Supplementary Fig.4D but Mander's coefficient in Fig. 3C and Fig.4F.  

      We thank the reviewer for their insightful comment regarding the consistency of quantification parameters used in our analysis of CAMSAP2 localization on the Golgi apparatus.

      To address this concern, we have revised Figure 3C to use Pearson’s coefficient for consistency with Figure 1D, 1E (Figure 1B and 1E in the revised manuscript), and Supplementary Figure 4D (Supplementary Figure 3I in the revised manuscript). This ensures uniformity in the quantification parameters across these analyses.

      For Figure 4F, we have retained Mander’s coefficient, as it accounts for variability in expression levels due to overexpression in individual cells. We believe this approach provides a more accurate reflection of CAMSAP2 localization under the experimental conditions shown in Figure 4F.

      We hope these adjustments clarify our analysis and address the reviewer’s concerns. We greatly appreciate the reviewer’s constructive feedback, which has helped improve the consistency and accuracy of our study.

      (3) In Fig.4D-F, the authors claim that S835 phosphorylation of CAMSAP2 is essential for its localization to the Golgi apparatus and for restoring the Golgi dispersion induced by CAMASAP2 depletion.  

      Fig.4E indicates that the S835A mutant of CAMSAP2 significantly restores the compact assembly of the Golgi apparatus, and the differences in the rescue activities of the wild type, S835A, and S835D are rather small. These data contradict the authors' conclusions regarding the pivotal role of MARK2-mediated phosphorylation at the S835 site of CAMSAP2 in maintaining the Golgi architecture (page 9). The authors should remove the phrase "MARK2-mediated" from the sentence unless addressing the aforementioned issues (see 3rd major comment) and describe the role of S835 phosphorylation in more subdued tone.  

      We thank the reviewer for their constructive feedback regarding the conclusions drawn about the role of MARK2-mediated phosphorylation of CAMSAP2 at S835.

      In response, we have revised the relevant sentence to reflect a more nuanced interpretation of the data. Specifically, the original statement:

      “These observations indicate that the phosphorylation of serine 835 in CAMSAP2 is essential for its proper localization to the Golgi apparatus.”

      has been updated to:

      “These observations indicate that MARK2 phosphorylation of serine at position 835 of CAMSAP2 affects the localization of CAMSAP2 on the Golgi and regulates Golgi structure” (page 9, Lines 27-29).

      We hope this modification addresses the reviewer’s concerns. We are grateful for the feedback, which has helped us refine our conclusions and enhance the clarity of our manuscript.

      (4) In Figs. 5I, J and Supplementary Fig.7A-E, the authors claim that the S835 phosphorylationdependent interaction of CAMSAP2 with Uso1 is essential for its localization to the Golgi apparatus.  

      This claim was made based on immunofluorescent images of single cells and single-line scans, and was not sufficiently verified (Supplementary Fig.7B, C). Because this is a crucial claim for the present paper, the authors should present statistical data for Pearson's coefficient, as shown in Fig. 1D and E, to quantitatively estimate the Golgi localization of CAMSAP2.  

      We thank the reviewer for their suggestion to present statistical data using Pearson's coefficient for a more robust quantification of the Golgi localization of CAMSAP2.

      In response, we have revised the statistical analysis for Supplementary Figures 7B-C (Revised Figures 6F and 6G) to use Pearson's coefficient. This change ensures consistency with the quantification methods used in Figures 1D and 1E (Revised Figures 1B and 1E), allowing for a more standardized evaluation of CAMSAP2’s localization to the Golgi apparatus.

      We hope this modification addresses the reviewer’s concerns and strengthens the quantitative support for our claims. We are grateful for the reviewer’s constructive feedback, which has helped improve the rigor of our study.

      (5) The signal intensities of the immunofluorescent data in Fig. 4D, Fig. 5A, Sup-Fig. 3C and E, and Sup-Fig. 7S are very weak for readers to clearly estimate the authors' claims. They should be improved appropriately.  

      We thank the reviewer for highlighting the need to improve the clarity of the immunofluorescent data presented in several figures.

      In response, we have enhanced the signal intensities in Figures 4D, 5A, and Supplementary Figure 7D (Revised Supplementary Figure 6A) to make the signals clearer for readers, while ensuring that the adjustments do not alter the integrity of the original data. Supplementary Figures 3C and 3E was remove from our manuscript.

      Additionally, to improve consistency and readability across the manuscript, we have standardized the quantification methods for similar analyses:

      For CAMSAP2 localization to the Golgi, Pearson's coefficient has been used throughout the manuscript. Figure 3C has been updated to use Pearson's coefficient for consistency.

      For Golgi state analysis in wound-edge cells, we have used the Golgi position relative to the nucleus as a uniform metric. This has been applied to Supplementary Figures 1F and 1G, Figures 2D and 2E, and Figures 5A and 5B.

      We hope these adjustments address the reviewer’s concerns and improve the clarity and consistency of our study. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the quality of our manuscript.

      (6) As indicated above, the authors frequently change the parameters or methods for quantifying the same phenomena (for example, the localization of CAMSAP on the Golgi and Golgi state in wound edge cells) in each figure. This is highly confusing. They should unify them.  

      We thank the reviewer for their valuable feedback regarding the inconsistency in quantification methods across the manuscript.

      To address this concern, we have carefully reviewed the entire manuscript and standardized the methods used for quantifying similar phenomena:

      - CAMSAP2 localization on the Golgi: 

      Pearson's coefficient is now consistently used throughout the manuscript. For example, Figure 3C has been updated to use Pearson's coefficient to align with other figures, such as Figures 1B and 1E.

      - Golgi state in wound-edge cells: 

      The Golgi state is now uniformly measured based on the position of the Golgi relative to the nucleus. This method has been applied to Supplementary Figures 1F and 1G, Figures 2D and 2E, and Figures 5A and 5B.

      We believe these changes significantly improve the clarity and consistency of the manuscript, ensuring that readers can easily interpret the data. We are grateful for the reviewer’s constructive feedback, which has greatly helped us enhance the quality and rigor of our study.

      (7) The legends frequently fail to clearly indicate the number of independent experiments on which each statistical analysis was based.  

      We thank the reviewer for highlighting the need to clearly indicate the number of independent experiments for each statistical analysis.

      In response, we have carefully reviewed the entire manuscript and updated the figure legends to include the number of independent experiments for every statistical analysis. This ensures transparency and allows readers to better evaluate the reliability of the data.

      We hope these updates address the reviewer’s concerns and improve the clarity and rigor of the manuscript. We appreciate the reviewer’s constructive feedback, which has helped us enhance the quality of our work.

      (8) Supplemental Figs. 4E and 4F are not cited in the text.  

      We thank the reviewer for pointing out that Supplemental Figures 4E and 4F were not cited in the text.

      To address this, we have updated the manuscript to cite these figures (Revised Figures 2H and 2I) in the appropriate section (page 8, lines 1-5).

      “the absence of MARK2 can also influence the orientation of the Golgi apparatus during cell wound healing and cause a delay in wound closure (Figure 2 D-I and Figure 3 D).”

      We hope this revision resolves the reviewer’s concern and improves the clarity and completeness of the manuscript. We appreciate the reviewer’s feedback, which has helped us refine our work.

      (9) The data in Fig. 3 analyzed MARK2 knockout cells (not knockdown cells). The caption should be corrected.  

      We thank the reviewer for pointing out the incorrect use of "knockdown" in the caption of Figure 3.

      To address this, we have revised the title of Figure 3 from:

      “MARK2 knockdown reduces CAMSAP2 localization on the Golgi apparatus.”

      to:

      “MARK2 affects CAMSAP2 localization on the Golgi apparatus.”

      This updated caption reflects the inclusion of both MARK2 knockout and knockdown cell lines analyzed in Figure 3.

      We hope this correction resolves the reviewer’s concern and ensures the accuracy of our manuscript. We greatly appreciate the reviewer’s attention to detail, which has helped us improve the clarity and consistency of our work.

      (10) The present caption in Fig. 6 disagrees with the content of the figure.  

      We thank the reviewer for pointing out the inconsistency between the caption and the content of Figure 6.

      To address this issue, we have revised the content of Figure 6 to ensure it aligns accurately with the caption. The updated figure now reflects the description provided in the caption, eliminating any discrepancies and improving clarity for the readers.

      We appreciate the reviewer’s constructive feedback, which has helped us enhance the accuracy and presentation of our manuscript.

      (11) What do "CS" indicate in Fig. 4B and Supplementary Fig. 5D? The style used to indicate point mutants of CAMSAP2 should be unified. 835A or S835A?  

      We thank the reviewer for pointing out the inconsistency in the naming of CAMSAP2 mutants.

      To address this, we have revised all relevant figures and text to use the consistent format "S835A" and "S589A" for CAMSAP2 mutants. Specifically, in Figure 4B and Supplementary Figure 5D (now Supplementary Figure 4C), we have replaced the abbreviation "CS2" with "CAMSAP2" and updated the mutant names from "835A" and "589A" to "S835A" and "S589A," respectively. We hope these updates resolve the reviewer’s concerns and ensure clarity and consistency throughout the manuscript. We are grateful for the reviewer’s attention to detail, which has helped us improve the quality of our work.

      (12) Uso1 is not a Golgi matrix protein.  

      We thank the reviewer for pointing out the incorrect description of Uso1 as a Golgi matrix protein.

      In response, we have revised the manuscript to replace all references to “USO1 as a Golgi matrix protein” with “USO1 as a Golgi-associated protein.” This correction ensures that the terminology used in the manuscript is accurate and consistent with current scientific understanding.

      We appreciate the reviewer’s attention to detail, which has helped us improve the accuracy and quality of our manuscript.

    1. Reviewer #1 (Public review):

      Summary:

      There has been intense controversy over the generality of Hamilton's inclusive fitness rule for how evolution works on social behaviors. All generally agree that relatedness can be a game changer, for example allowing for otherwise unselectable altruistic behaviors when c < rb, where c is the fitness cost to the altruism, b is the fitness benefit to another, and r their relatedness. Many complications have been successfully incorporated into the theory, including different reproductive values and viscous population structures.

      The controversy has centered on another dimension; Hamilton's original model was for additive fitness, but how does his result hold when fitnesses are non-additive? One approach has been not to worry about a general result but just find results for particular cases. A consistent finding is that the results depend on the frequency of the social allele - non-additivity causes frequency dependence that was absent in Hamilton's approach. Two other approaches derive from Queller via the Price equation. Queller 1 is to find forms like Hamilton's rule, but with additional terms that deal with non-additive interaction, each with an r-like population structure variable multiplied by a b-like fitness effect (Queller 1985). Queller 2 redefines the fitness effects c and b as partial regressions of the actor's and recipient's genes on fitness. This leaves Hamilton's rule intact, just with new definitions of c and b that depend on frequency.

      Queller 2 is the version that has been most adopted by the inclusive fitness community along with assertions that Hamilton's rule in completely general. In this paper, van Veelen argues that Queller 1 is the correct approach. He derives a general form that Queller only hinted at. He does so within a more rigorous framework that puts both Price's equation and Hamilton's rule on firmer statistical ground. Within that framework, the Queller 2 approach is seen to be a statistical misspecification - it employs a model without interaction in cases that actually do have interaction. If we accept that this is a fatal flaw, the original version of Hamilton's rule is limited to linear fitness models, which might not be common.

      Strengths:

      While the approach is not entirely new, this paper provides a more rigorous approach and a more general result. It shows that both Queller 1 and Queller 2 are identities and give accurate results, because both are derived from the Price equation, which is an identity. So why prefer Queller 1? It identifies the misspecification issue with the Queller 2 approach and points out its consequences. For example, it will not give the minimum squared differences between the model and data. It does not separate the behavioral effects of the individuals from the population state (b and c become dependent on r and the population frequency).

      The paper also shows how the same problems can apply to non-social traits. Epistasis is the non-additivity of effects of two genes within the individual. (So one wonders why have we not had a similarly fierce controversy over how we should treat epistasis?)

      The paper is clearly written. Though somewhat repetitive, particularly in the long supplement, most of that repetition has the purpose of underscoring how the same points apply equally to a variety of different models.<br /> Finally, this may be a big step towards reconciliation in the inclusive fitness wars. Van Veelen has been one of the harshest critics of inclusive fitness, and now he is proposing a version of it.

      Weaknesses:

      van Veelen argues that the field essentially abandoned the Queller 1 approach after its publication. I think this is putting it too strongly - there have been a number of theoretical studies that incorporate extra terms with higher-order relatednesses. It is probably accurate to say that there has been relative neglect. But perhaps this is partly due to a perception that this approach is difficult to apply.

      The model in this paper is quite elegant and helps clarify conceptual issues, but I wonder how practical it will turn out to be. In terms of modeling complicated cases, I suspect most practitioners will continue doing what they have been doing, for example using population genetics or adaptive dynamics, without worrying about neatly separating out a series of terms multiplying fitness coefficients and population structure coefficients.

      For empirical studies, it is going to be hard to even try to estimate all those additional parameters. In reality, even the standard Hamilton's rule is rarely tested by trying to estimate all its parameters. Instead, it is commonly tested more indirectly, for example by comparative tests of the importance of relatedness. That of course would not distinguish between additive and non-additive models that both depend on relatedness, but it does test the core idea of kin selection. It will be interesting to see if van Veelen's approach stimulates new ways of exploring the real world.

    2. Reviewer #2 (Public review):

      Summary:

      This manuscript reconsiders the "general form" of Hamilton's rule, in which "benefit" and "cost" are defined as regression coefficients. It points out that there is no reason to insist on Hamilton's rule of the form -c+br>0, and that, in fact, arbitrarily many terms (i.e. higher-order regression coefficients) can be added to Hamilton's rule to reflect nonlinear interactions. Furthermore, it argues that insisting on a rule of the form -c+br>0 can result in conditions that are true but meaningless and that statistical considerations should be employed to determine which form of Hamilton's rule is meaningful for a given dataset or model.

      Strengths:

      The point is an important one. While it is not entirely novel-the idea of adding extra terms to Hamilton's rule has arisen sporadically (Queller 1985, 2011; Fletcher & Zwick 2006; van Veelen et al. 2017)--it is very useful to have a systematic treatment of this point. I think the manuscript can make an important contribution by helping to clarify a number of debates in the literature. I particularly appreciate the heterozygote advantage example in the SI.

      Weaknesses:

      Although the mathematical analysis is rigorously done and I largely agree with the conclusions, I feel there are some issues regarding terminology, some regarding the state of the field, and the practice of statistics that need to be clarified if the manuscript is truly to resolve the outstanding issues of the field. Otherwise, I worry that it will in some ways add to the confusion.

      (1) The "generalized" Price equation: I agree that the equations labeled (PE.C) and (GPE.C) are different in a subtle yet meaningful way. But I do not see any way in which (GPE.C) is more general than (PE.C). That is, I cannot envision any circumstance in which (GPE.C) applies but (PE.C) does not. A term other than "generalized" should be used.

      (2) Regression vs covariance forms of the Price equation

      I think the author uses "generalized" in reference to what Price called the "regression form" of his equation. But to almost everyone in the field, the "Price Equation" refers to the covariance form. For this reason, it is very confusing when the manuscript refers to the regression form as simply "the Price Equation".

      As an example, in the box on p. 15, the manuscript states "The Price equation can be generalized, in the sense that one can write a variety of Price-like equations for a variety of possible true models, that may have generated the data." But it is not the Price equation (covariance form) that is being generalized here. It is only the regression that Price used that is being generalized.

      To be consistent with the field, I suggest the term "Price Equation" be used only to refer to the covariance form unless it is otherwise specified as in "regression form of the Price equation".

      (3) Sample covariance: The author refers to the covariance in the Price equation as "sample covariance". This is not correct, since sample covariance has a denominator of N-1 rather than N (Bessel's correction). The correct term, when summing over an entire population, is "population covariance". Price (1972) was clear about this: "In this paper we will be concerned with population functions and make no use of sample functions". This point is elaborated on by Frank (2012), in the subsection "Interpretation of Covariance".

      Of course, the difference is negligible when the population is large. However, the author applies the covariance formula to populations as small as N=2, for which the correction factor is significant.

      The author objects to using the term "population covariance" (SI, pp. 8-9) on the grounds that it might be misleading if the covariance, regression coefficients, etc. are used for inference because in this case, what is being inferred is not a population statistic but an underlying relationship. However, I am not convinced that statistical inference is or should be the primary use of the Price equation (see next point). At any rate, avoiding potential confusion is not a sufficient reason to use incorrect terminology.

      Relatedly, I suggest avoiding using E for the second term in the Price equation, since (as the ms points out), it is not the expectation of any random variable. It is a population mean. There is no reason not to use something like Avg or bar notation to indicate population mean. Price (1972) uses "ave" for average.

      I should add, however, that the distinction between population statistics vs sample statistics goes away for regression coefficients (e.g. b, c, and r in Hamilton's rule) since in this case, Bessel's correction cancels out.

      (4) Descriptive vs. inferential statistics

      When discussing the statistical quantities in the Price Equation, the author appears to treat them all as inferential statistics. That is, he takes the position that the population data are all generated by some probabilistic model and that the goal of computing the statistical quantities in the Price Equation is to correctly infer this model.

      It is worth pointing out that those who argue in favor of the Price Equation do not see it this way: "it is a mistake to assume that it must be the evolutionary theorist, writing out covariances, who is performing the equivalent of a statistical analysis." (Gardner, West, and Wild, 2011); "Neither data nor inferences are considered here" (Rousset 2015). From what I can tell, to the supporters of the Price equation and the regression form of Hamilton's rule, the statistical quantities involved are either population-level *descriptive* statistics (in an empirical context), or else are statistics of random variables (in a stochastic modeling context).

      In short, the manuscript seems to argue that Price equation users are performing statistical inference incorrectly, whereas the users insist that they are not doing statistical inference at all.

      The problem (and here I think the author would agree with me) arises when users of the Price equation go on to make predictive or causal claims that would require the kind of statistical analysis they claim not to be doing. Claims of the form "Hamilton's rule predicts.." or use of terms like "benefit" and "cost" suggest that one has inferred a predictive or causal relationship in the given data, while somehow bypassing the entire theory of statistical inference.

      There is also a third way to use the Price equation which is entirely unobjectionable: as a way to express the relationship between individual-level fitness and population-level gene frequency change in a form that is convenient for further algebraic manipulation. I suspect that this is actually the most common use of the Price equation in practice.

      For a paper that aims to clarify these thorny concepts in the literature, I think it is worth pointing out these different interpretations of statistical quantities in the Price equation (descriptive statistics vs inferential statistics vs algebraic manipulation). One can then critique the conclusions that are inappropriately drawn from the Price equation, which would require rigorous statistical inference to draw. Without these clarifications, supporters of the Price equation will again argue that this manuscript has misunderstood the purpose of the equation and that they never claimed to do inference in the first place.

      (5) "True" models

      Even if one accepts that the statistical quantities in the Price equation are inferential in nature, the author appears to go a step further by asserting that, even in empirical populations, there is a specific "true" model which it is our goal to infer. This assumption manifests at many points in the SI when the author refers to the "true model" or "true, underlying population structure" in the context of an empirical population.

      I do not think it is necessary or appropriate, in empirical contexts, to posit the existence of a Platonic "true" model that is generating the data. Real populations are not governed by mathematical models. Moreover, the goal of statistical inference is not to determine the "true model" for given data but to say whether a given statistical model is justified based on this data. Fitting a linear model, for example, does not rule out the possibility there may be higher-order interactions - it just means we do not have a statistical basis to infer these higher-order interactions from the data (say, because their p-scores are insignificant), and so we leave them out.

      What we can say is that if we apply the statistical model to data generated by a probabilistic model, and if these models match, then as the number of observations grows to infinity, the estimators in the statistical model converge to the parameters of the data-generating one. But this is a mathematical statement, not a statement about real-world populations.

      A resolution I suggest to points 3, 4, and 5 above is:<br /> *A priori, the statistical quantities in the Price Equation are descriptive statistics, pertaining only to the specific population data given.<br /> *If one wishes to impute any predictive power, generalizability, or causal meaning to these statistics, all the standard considerations of inferential statistics apply. In particular, one must choose a statistical model that is justified based on the given data. In this case, one is not guaranteed to obtain the standard (linear) Hamilton's rule and may obtain any of an infinite family of rules.<br /> *If one uses a model that is not justified based on the given data, the results will still be correct for the given population data but will lack any meaning or generalizability beyond that.<br /> *In particular, if one considers data generated by a probabilistic model, and applies a statistical model that does not match the data-generating one, the results will be misleading, and will not generalize beyond the randomly generated realization one uses.

      Of course, the author may propose a different resolution to points 3-5, but they should be resolved somehow. Otherwise, the terminology in the manuscript will be incorrect and the ms will not resolve confusion in the field.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Revision Plan (Response to Reviewers)

      1. General Statements [optional]

      Response: We are pleased the reviewers appreciate the power of this novel proteomics methodology that allowed us to uncover new depths on the complexity of the ribosome ubiquitination code in response to stress. We also appreciate that the reviewers think that this is a “very timely” study and “interesting to a broad audience” that can change the models of translation control currently adopted in the field. Characterizing complex cellular processes is critical to advance scientific knowledge and our work is the first of its kind using targeted proteomics methods to unveil the integrated complexity of ribosome ubiquitin signals in eukaryotic systems. We also appreciate the fairness of the comments received and below we offer a comprehensive revision plan substantially addressing the main points raised by the reviewers. According to the reviewers’ suggestions, we will also expand our studies to two additional E3 ligases (Mag2 and Not4) known to ubiquitinate ribosomes, which will create an even more complete perspective of ubiquitin roles in translation regulation.

      2. Description of the planned revisions

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

      However, the study's focus shifts in a manner that introduces several limitations. Following the rigorous PRM-based analyses, the reliance on Western blotting without replication or quantification (e.g., single-experiment data in Figs. 3-5) significantly weakens the evidence. Experimental design becomes inconsistent, with variable combinations of stressors (H₂O₂, MMS, 4-NQO) and genetic backgrounds (WT, hel2Δ, rad6Δ) that preclude systematic comparisons. For instance, Fig. 3C/E and Fig. 4 omit critical controls (e.g., MMS in Fig. 4, rad6Δ in Fig. 3E), while Fig. 5 conflates distinct variables by comparing H₂O₂-treated rad6Δ with MMS-treated hel2Δ-a design that obscures causal relationships. Furthermore, Fig. 3F highlights that 4-NQO and MMS elicit divergent responses in hel2Δ, undermining the rationale for using these stressors interchangeably. These inconsistencies culminate in a fragmented narrative; attempts to link ISR activation or ribosome stalling to RP ubiquitylation become impossible, leaving the primary takeaway as "stress responses are complex" rather than advancing mechanistic insight.

              __Response: __We appreciate the evaluation of our work and that the power of our proteomics method established a good foundation for the study. We also understand the reviewer’s concerns and we will detail below a plan to enhance quantification and increase systematic comparisons. The experiments presented here were conducted with biological replicates, but in several instances, we focused on presence and absence of bands, or their pattern (mono vs poly-ub) because of the semi-quantitative nature of immunoblots. We will revise the figures and present their quantification and statistical analyses. In additional, we did not intend to use these stressors interchangeably, but instead, to use select conditions to highlight the complexity the stress response. In particular, we followed up with H2O2 *versus* 4-NQO because both chemicals are considered sources of oxidative stress. Even though it is unfeasible to compare every single stress condition in every strain background, in the revised version, we will include additional controls to increase the cohesion of the narrative, and expand the comparison between MMS, H2O2, and 4-NQO, as suggested. Details below.
      

      To strengthen the work, the following revisions are essential:

      R1.1. Repeat and quantify immunoblots: All Western blotting data require biological replicates and statistical analysis to support claims.

              __Response: __As requested, we will display quantification and statistical analysis of the suggested and new immunoblots that will be conducted during the revision period.
      

      R1.3. Remove non-parallel comparisons: The mRNA expression analysis in Fig. 5, which compares dissimilar conditions (e.g., rad6Δ + H₂O₂ vs. hel2Δ + MMS), should be omitted or redesigned to enable direct, strain- and stressor-matched contrasts.

              __Response: __We will follow the reviewers’ suggestion and redesign the analysis to increase consistency and prioritize data under identical conditions. To increase confidence in the mRNA data analysis, we intend to perform follow up experiments and analyze protein abundance of *ARG proteins* and *CTT1 *under different conditions. The remaining data using non-parallel comparisons will be moved to supplemental material and de-emphasized in the final version of the manuscript.
      

      R1.4. Standardize experimental variables: Restructure the study to maintain identical genetic backgrounds and stressors across all figures, enabling systematic interrogation of enzyme- or stress-specific effects on the ubiquitin code.

              __Response: __To ensure a better comparison across strains and conditions, we will re-run several experiments and focus on our main stress conditions. Specifically:
      
      • 3D: We plan to re-run this experiment and include MMS

      • 3E: We plan to perform the same panel of experiments in rad6D ,and display WT data as main figure.

      • 4A-B: We plan to perform translation output (HPG incorporation) experiments with MMS as suggested

      • 4C: We plan to re-run blots for p-eIF2a under MMS for improved comparison.

      Reviewer #1 (Significance (Required)):

      The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In this manuscript the authors use a new target proteomics approach to quantify site-specific ubiquitin modification across the ribosome before and after oxidative stress. Then they validate their findings following in particular ubiquitination of Rps20 and Rps3 and extend their analysis to different forms of oxidative stress. Finally they question the relevance of two known actors of ribosome ubiquitination, Hel2 and Rad6. It is not easy to summarize the observations because in fact the major finding is that the patterns of ribosome ubiquitination occur in a stresser and enyzme specific manner (even when considering only oxidative stress). However, the complexity revealed by this study is very relevant for the field, because it underlies that the ubiquitination code of ribosomes is not easy to interpret with regard to translation dynamics and responses to stress or players involved. It suggests that some of the models that have generally been adopted probably need to be amended or completed. I am not a proteomics expert, so I cannot comment on the validity of the new proteomics approach, of whether the methods are appropriately described to reproduce the experiments. However, for the follow up experiments, the results following Rps20 and Rps3 ubiquitination are well performed, nicely controlled and are appropriately interpreted.

      Maybe what one can regret is that the authors have limited their analysis to the study of Hel2 and Rad6, and not included other enyzmes that have already been associated with regulation of ribosome ubiquitination, to get a more complete picture. It may not take that much time to test more mutants, but of course there is the risk that rather than enable to make a working model it might make things even more complex.

              __Response: __We value the positive evaluation of our work. We also appreciate the notion that it meaningfully expands the knowledge on the complexity of the ribosome ubiquitination code, challenges the current models of translation control, and conducted well-performed, and nicely controlled experiments. To address the main concern of the reviewer, we will expand our work by studying two additional enzymes involved in ribosome ubiquitination (Mag2 and Not4) and provide a more comprehensive picture of this integrated system. Specifically, we will generate yeast strains deleted for *MAG2* and *NOT4*, and evaluate their impact in ribosome ubiquitination under our main conditions of stress. We will investigate the role of these additional E3s in translation output (HPG incorporation), and in inducing the integrated stress response via phosphorylated eIF2α and Gcn4 expression. Additional follow up experiments will be performed according to our initial results.
      

      Reviewer #2 (Significance (Required)):

      In recent years, regulation of translation elongation dynamics has emerged as a much more relevant site of control of gene expression that previously envisonned. The ribosome has emerged as a hub for control of stress responses. Therefore this study is certainly very timely and interesting for a broad audience. However, it does fall short of giving any simple picture, and maybe the only point one can question is whether it is interesting to publish a manuscript that concludes that regulation is complicated, without really being able to provide any kind of suggestive model.

      My feeling is nevertheless that it will impact how scientists in the field design their experiments and what they will conclude. It will certainly also drive new experiments and approaches, and lead to investigations on how all the different players in regulation of ribosome modification talk to each other and signal to signaling pathways.

              __Response: __We appreciate the comments and the balanced view that studies like ours will still be impactful and contribute to a number of fields in multiple and meaningful ways. With the new experiments proposed here, and used of additional mutants and strains, we intend to propose and provide a more unified model that explain this complex and dynamic relationship.
      

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Recent studies have shown that the ubiquitination of uS3 (Rps3) is crucial for the quality control of nonfunctional rRNA, specifically in the process known as 18S noncoding RNA degradation (NRD). Additionally, the ubiquitination of uS10 (Rps20) plays a significant role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress are not yet fully understood.

      In this study, the authors developed a targeted proteomics method to quantify the dynamics of ribosome ubiquitination in response to oxidative stress, both relatively and stoichiometrically. They identified 11 ribosomal sites that exhibited increased ubiquitin modification after exposure to hydrogen peroxide (H2O2). This included two known targets: uS10 and uS3 (of Hel2), which recognize collided ribosomes and initiate the processes of 18S NRD and translation quality control (RQC). Using isotope-labeled peptides, the researchers demonstrated that these modifications are non-stoichiometric and display significant variability among different peptides.

      Furthermore, the authors explored how specific enzymes in the ubiquitin system affect these modifications and their impact on global translation regulation. They found that uS3 (Rps3) and uS10 (Rps20) were modified differently by various stressors, which in turn influenced the Integrated Stress Response (ISR). The authors suggest that different types of stressors alter the pattern of ubiquitinated ribosomes, with Rad6 and Hel2 potentially competing for specific subpopulations of ribosomes.

      Overall, this study emphasizes the complexity of the ubiquitin ribosomal code. However, further experiments are necessary to validate these findings before publication.

      Major Comments:

      I consider the additional experiments essential to support the claims of the paper.

      R3.1. To understand the roles of ribosome ubiquitination at the specific sites, the authors must perform stressor-specific suppression of global translation, as demonstrated in Figures 4 and 5. This should include the uS10-K6R/K8R and uS3-K212R mutants.

              __Response: __We understand the importance of the suggested experiment. We have already requested and kindly received strains expressing these mutations, which will reduce the time required to successfully address this point. We will perform our translation and ISR assays such as the one referred by the reviewer in Figs. 4A-C and 5E, and results will determine the role of individual ribosome ubiquitination sites in translation control.
      

      R3.2. It is crucial to ensure that experiments are adequately replicated and that statistical analysis is thorough, with precise quantification. For a more accurate comparison between wild-type (WT) and Hel2 deletion mutants regarding ribosome ubiquitination, the authors should quantify the ubiquitinated ribosomes in both WT and Hel2 mutants under stress conditions. This quantification should be conducted on the same blot, using diluted control samples. Similarly, in Figures 3F and 4C, for an accurate comparison between WT and Hel2 or Rad6 deletion mutants, the authors should quantify the ubiquitinated ribosomes across these conditions. Again, this quantification should be performed on the same blot with the dilution of control samples.

              __Response: __As was also requested by reviewer 1 and discussed above (point R1.1), we will conduct quantification and display statistical analyses for our immunoblots. In addition, we will re-run the aforementioned experiments to improve quantification following the reviewers’ request (same gel & diluted control samples).
      

      Reviewer #3 (Significance (Required)):

      • General assessment:

      Recent studies reveal that the ubiquitination of uS3 (Rps3) is essential for the quality control of nonfunctional rRNA (18S NRD), while the ubiquitination of uS10 (Rps20) plays a crucial role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress remain unclear.

      • Advance:

      In this study, the authors developed a targeted proteomics method to quantify ribosome ubiquitination dynamics in response to oxidative stress, both relatively and stoichiometrically. By utilizing isotope-labeled peptides, they demonstrated that these modifications are non-stoichiometric and exhibit significant variability across different peptides. They identified 11 ribosomal sites that showed increased ubiquitin modification following H2O2 exposure, including two known targets of Hel2, which recognize collided ribosomes and induce translation quality control (RQC).

      • Audience: This information will be of interest to a specialized audience in the fields of translation, ribosome function, quality control, ubiquitination, and proteostasis.

      • The field: Translation, ribosome function, quality control, ubiquitination, and proteostasis.

      __ Response:__ We appreciate that our work will be valuable to a number of fields in protein dynamics and that our method advances the field by measuring ribosome ubiquitination relatively and stoichiometrically in response to stress.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Response: All requested changes require experiments and data analyses, and a complete revision plan is delineated above in section #2.

      • *

      4. Description of analyses that authors prefer not to carry out

      • *

      R1.2. Leverage the PRM platform: Apply the established quantitative proteomics approach to validate or extend findings in Fig. 3 (e.g., RAD6-dependent ubiquitylation), ensuring methodological consistency.

              __Response: __Although we understand the interest on the proposed result for consistency, this is the only requested experiment that we do not intend to conduct. Because of the lack of overall ubiquitination of ribosomal proteins in *rad6**D* in response to H2O2 (e.g., Silva et al., 2015, Simoes et al., 2022), we believe that this PRM experiment in unlikely to produce meaningful insight on the ubiquitination code. In this context, we expected that sites regulated by Hel2 will be the ones largely modified in rad6*D *and we followed up on them via immunoblot. Moreover, this experiment would not be time or cost-effective, and resources and efforts could be used to strengthen other important areas of the manuscript, such as including the E3’s Mag2 and Not4 into our work.
      
    1. Reviewer #1 (Public review):

      Summary:

      The authors present results and analysis of an experiment studying the genetic architecture of phenology in two geographically and genetically distinct populations of switchgrass when grown in 8 common gardens spanning a wide range of latitudes. They focused primarily on two measures of phenology - the green-up date in the spring, and the date of flowering. They observed generally positive correlations of flowering date across the latitudinal gradient, but negative correlations between northern and southern (i.e. Texas) green-up dates. They use GWAS and multivariate meta-analysis methods to identify and study candidate genetic loci controlling these traits and how their effect sizes vary across these gardens. They conclude that much of the genetic architecture is garden-specific, but find some evidence for photoperiod and rainfall effects on the locus effect sizes.

      Strengths:

      The strengths of the study are in the large scale and quality of the field trials, the observation of negative correlations among genotypes across the latitudinal gradient, and the importance of the central questions: Can we predict how genetic architecture will change when populations are moved to new environments? Can we breed for more/less sensitivity to environmental cues?

      Weaknesses:

      I have tried hard to understand the concept of the GxWeather analysis presented here, but still do not see how it tests for interactions between weather and genetic effects on phenology. I may just not understand it correctly, but if so, then I think more clarity in the logical model would help - maybe a figure explaining how this approach can detect genotype-weather interactions. Also, since this is a proposal for a new approach to detecting gene-environment effects, simulations would be useful to show power and false positive rates, or other ways of validating the results. The QTL validation provided is not very convincing because the same trials and the same ways of calculating weather values are used again, so it's not really independent validation, plus the QTL intervals are so large overlap between QTL and GWAS is not very strong evidence.

      The term "GxWeather" is never directly defined, but based on its pairing with "GxE" on page 5, I assumed it means an interaction between genotypes (either plant lines or genotypes at SNPs) and weather variables, such that different genotypes alter phenology differently as a response to a specific change in weather. For example, some genotypes might initiate green-up once daylengths reach 12 hours, but others require 14 hours. Alternatively (equivalently), an SNP might have an effect on greenup at 12 hours (among plants that are otherwise physiologically ready to trigger greenup on March 21, only those with a genotype trigger), while no effect on greenup with daylengths of 14 hours (e.g., if plants aren't physiologically ready to greenup until June when daylengths are beyond 14 hours, both aa and AA genotypes will greenup at the same time, assuming this locus doesn't affect physiological maturity).

      Either way, GxE and (I assume) GxWeather are typically tested in one of two ways. Either genotype effects are compared among environments (which differ in their mean value for weather variables) and GxWeather would be inferred if environments with similar weather have similar genotype effects. Or a model is fit with an environmental (maybe weather?) variable as a covariate and the genotype:environment interaction is measured as a change of slope between genotypes. Basically, the former uses effect size estimates across environments that differ in mean for weather, while the latter uses variation in weather within an experiment to find GxWeather effects.

      However, the analytical approach here seems to combine these in a non-intuitive way and I don't think it can discover the desired patterns. As I understand from the methods, weather-related variables are first extracted for each genotype in each trial based on their green-up or flowering date, so within each trial each genotype "sees" a different value for this weather variable. For example, "daylength 14 days before green-up" is used as a weather variable. The correlation between these extracted genotype-specific weather variables across the 8 trials is then measured and used as a candidate mixture component for the among-trial covariance in mash. The weight assigned to these weather-related covariance matrices is then interpreted as evidence of genotype-by-weather interactions. However, the correlation among genotypes between these weather variables does not measure the similarity in the weather itself across trials. Daylengths at green-up are very different in MO than SD, but the correlation in this variable among genotypes is high. Basically, the correlation/covariance statistic is mean-centered in each trial, so it loses information about the mean differences among trials. Instead, the covariance statistic focuses on the within-trial variation in weather. But the SNP effects are not estimated using this within-trial variation, they're main effects of the SNP averaged over the within-trial weather variation. Thus it is not clear to me that the interpretation of these mash weights is valid. I could see mash used to compare GxWeather effects modeled in each trial (using the 2nd GxE approach above), but that would be a different analysis. As is, mash is used to compare SNP main effects across trials, so it seems to me this comparison should be based on the average weather differences among trials.

      A further issue with this analysis is that the weather variables don't take into account the sequence of weather events. If one genotype flowers after the 1st rain event and the second flowers after the 2nd rain event, they can get the same value for the cumulative rainfall 7d variable, but the lack of response after the 1st rain event is the key diagnostic for GxWeather. There's also the issue of circularity. Since weather values are defined based on observed phenology dates, they're effectively caused by the phenology dates. So then asking if they are associated with phenology is a bit circular. Also, it takes a couple of weeks after flowering is triggered developmentally before flowers open, so the < 2-week lags don't really make developmental sense.

      Thus, I don't think this sentence in the abstract is a valid interpretation of the analysis: "in the Gulf subpopulation, 65% of genetic effects on the timing of vegetative growth covary with day length 14 days prior to green-up date, and 33% of genetic effects on the timing of flowering covary with cumulative rainfall in the week prior to flowering". There's nothing in this analysis that compares the genetic effects under 12h days to genetic effects under 14h days (as an example), or genetic effects with no rainfall prior to flowering to genetic effects with high rainfall prior to flowering. I think the only valid conclusion is: "65% of SNPs for green-up have a GxE pattern that mirrors the similarity in relationships between green-up and day length among trials." However I don't know how to interpret that statement in terms of the overall goals of the paper.

      Next, I am confused about the framing in the abstract and the introduction of the GxE within and between subpopulations. The statement: "the key expectation that different genetic subpopulations, and even different genomic regions, have likely evolved distinct patterns of GxE" needs justification or clarification. The response to an environmental factor (ie plasticity) is a trait that can evolve between populations. This happens through the changing frequencies of alleles that cause different responses. But this doesn't necessarily mean that patterns of GxE are changing. GxE is the variance in plasticity. When traits are polygenic, population means can change a lot with little change in variance within each population. Most local adaptation literature is focused on changes in mean trait values or mean plasticities between populations, not changes in the variance of trait values or plasticities within populations. Focusing on the goal of this paper, differences in environmental or weather responses between the populations are interesting (Figure 1). However the comparisons of GxE between populations and with the combined population are hard to interpret. GxE within a population means that that population is not fixed for this component of plasticity, meaning that it likely hasn't been strongly locally selected. Doesn't this mean that in the context of comparing the two populations, loci with GxE within populations are less interesting than loci fixed for different values between populations? Also, if there is GxE in the Gulf population, by definition it is also present in the "Both" population. Not finding it there is just a power issue. If individuals in the two subpopulations never cross, the variance across the "Both" population isn't relevant in nature, it's an artificial construct of this experimental design. I wonder if there is confusion about the term "genetic" in GxE and as used in the first paragraph of the intro ("Genetic responses" and "Genetic sensitivity"). These sentences would be most clear if the "genetic" term referred to the mechanistic actions of gene products. But the rest of the paper is about genetic variation, ie the different effects of different alleles at a locus. I don't think this latter definition is what these first uses intend, which is confusing.

      Note that the cited paper (26) is not relevant to this discussion about GxE patterns. This paper discusses the precision of estimating sub-group-specific genetic effects. With respect to the current paper, reference 26 shows that you might get more accurate measures of the SNP effects in the Gulf population using the full "Both" population dataset because i) the sample size is larger, and ii) as long as the true effects are not that different between populations. That paper is not focused on whether effect size variation is caused by evolution but on the technical question of whether GxG or GxE impacts the precision of within-group effect size estimates. The implication of paper 26 is that comparing SNP effects estimated in the "Both" population among gardens might be more powerful for detecting GxE than using only Gulf samples, even if there is some difference in SNP effects among populations. But if there magnitudes (or directions) of SNP effects change a lot among populations (ie not just changes in allele frequency), then modeling the populations separately will be more accurate.

    1. Overview There are three related problems at the intersection of philosophy and science that are fundamental to our understanding of our relationship to the natural world: the mind–body problem, the free will problem, and the nature–nurture problem. It seems that most people, even those without much knowledge of science or philosophy, have opinions about the answers to these questions that come simply from observing the world we live in. Our feelings about our relationship with the physical and biological world often seem incomplete. We are in control of our actions in some ways, but at the mercy of our bodies in others; it feels obvious that our consciousness is some kind of creation of our physical brains, at the same time we sense that our awareness must go beyond just the physical. This incomplete knowledge of our relationship with nature leaves us fascinated and a little obsessed, like a cat that climbs into a paper bag and then out again, over and over, mystified every time by a relationship between inner and outer that it can see but can’t quite understand. It may seem obvious that we are born with certain characteristics while others are acquired, and yet of the three great questions about humans’ relationship with the natural world, only nature–nurture gets referred to as a “debate.” In the history of psychology, no other question has caused so much controversy and offense: We are so concerned with nature–nurture because our very sense of moral character seems to depend on it. The problem is, most human characteristics aren’t usually as clear-cut as, for example height or instrument-mastery, affirming our nature–nurture expectations strongly one way or the other. In fact, even the great violinist might have some inborn qualities—perfect pitch, or long, nimble fingers—that support and reward his hard work. And the basketball player might have eaten a diet while growing up that promoted her genetic tendency for being tall. When we think about our own qualities, they seem under our control in some respects, yet beyond our control in others. And often the traits that don’t seem to have an obvious cause are the ones that concern us the most and are far more personally significant. What about how much we drink or worry? What about our honesty, or religiosity, or sexual orientation? They all come from that uncertain zone, neither fixed by nature nor totally under our own control.

      The nature-nurture problem highlets just how compelx traaits liek personality habits don't fit neatly into either category.

    1. Note: This response was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Dear Review Commons editorial team,

      Thank you for coordinating the thorough and careful review of our manuscript. We are especially grateful to the four anonymous reviewers for recognizing the value of our work and for their constructive suggestions on how to improve it.

      We are encouraged by the positive reception of our main conclusions on the robustness of adaptation to DNA replication stress and its relevance to multiple fields. All reviewers provided insightful comments, with reviewers #2 and #4 emphasizing that further experimental validation of the hypothesized role of reduced dNTPs in alleviating fitness during constitutive DNA replication stress would strengthen the paper. While the precise molecular mechanisms underlying this suppression are not the primary focus of this manuscript, we are eager to perform additional experiments based on the reviewers’ suggestions.

      Below, we present a detailed revision plan in the form of a point-by-point response to their comments.

      Reviewer #1 (Evidence, reproducibility and clarity):

      This study investigates the compensatory evolutionary response of Saccharomyces cerevisiae to DNA replication stress, focusing on the influence of genotype-environment interactions (GXE). The authors used a range of experimental conditions with varying nutrient levels to assess evolutionary outcomes under replication stress. Their genomic analysis reveals that while glucose levels affect initial adaptation rates, the genetics of adaptation remain robust across all nutritional environments. The research offers new insights into the adaptability of S. cerevisiae, emphasizing the role of the nutritional environment in evolutionary processes related to DNA replication stress. It identifies recurrent advantageous mutations under different macronutrient availabilities and uncovers a novel role for the RNA polymerase II mediator complex in adaptation to replication stress. Overall, this well-designed study adds to the growing recognition of the complexity and robustness of evolutionary responses to environmental stressors. It provides strong evidence that compensatory evolution to replication stress is robust across varying nutritional conditions. It both challenges and reinforces previous findings regarding the resilience of the yeast genetic interaction network to environmental perturbations. The detailed analysis of specific compensatory mutations and their fitness impacts across different conditions offers valuable insights into adaptive dynamics over 1000 generations, contributing a clear empirical framework for understanding how replication-associated stress shapes evolutionary outcomes in diverse environments.

      Based on the analysis:

      1) The conclusions are generally well-supported by the presented data. The evolution experiments and genomic analyses are robust and provide convincing evidence for the study's main claims. The authors took steps to eliminate bias, such as maintaining an adequate Ne, which, if not done, could have compromised their conclusions by affecting genetic drift and limiting the population's access to beneficial mutations.

      2) The figures are well-designed and easy to understand.

      3) The methodology is well-described and appears reproducible. The authors provide sufficient details on experimental procedures. Experimental replication is adequate, with multiple evolutionary lines.

      4) They also made efforts to validate their observations, such as the validation of mutations, the prediction of interactions in the Med14 structure, and its potential implication in gene regulation, as well as the analysis of the cumulative fitness benefit and the reconstruction of the quadruple mutant.

      There are, however, a few results that would benefit from further clarification:

      1) The experimental design is strong, offering a diverse range of conditions. However, the high glucose condition (8%) stands out as significantly different from the neutral 2% condition, both in range and margin, compared to the low glucose conditions (0.25-0.5%). While this mainly affects growth profiles and evolvability in the early generations, a brief explanation in the discussion would strengthen the conclusions. Specifically, addressing:

      1. a) The rationale behind selecting these particular glucose concentrations.

      2. b) How other glucose concentrations might influence the outcomes. Providing this additional context would enhance the reader's understanding of the experimental setup and its potential implications, while also offering insights into the broader applicability of the findings and possible directions for future research.

      We thank the reviewer for pointing out the need to clarify the rationale behind the glucose concentrations used in our study, an aspect we agree should have been better explained. In response, we have added the following text detailing the chosen conditions and their established effects on cellular metabolism.

      Line 67: “Glucose is the most abundant monosaccharide in nature, and represents the preferred source of energy for most cells.”

      Line 110: “...we grew WT and ctf4Δ cells in varying glucose concentrations to induce distinct physiological states. Low glucose levels (0.25% and 0.5%) induce caloric restriction and ultimately glucose starvation (Lin et al 2000, Smith et al. 2009). These conditions elicit increased respiration (Lin et al., 2002), sirtuins expression (Guarente, 2013), autophagy (Bagherniya et al. 2018), DNA repair (Heydari et al., 2007), and reduced recombination at the ribosomal DNA locus (Riesen and Morgan, 2009) ultimately extending lifespan in several organisms (Kapahi et al., 2016). In contrast, standard laboratory conditions typically use 2% glucose, promoting a rapid proliferation environment to which strains have been adapted since laboratory domestication (Lindergren, 1949). Finally, elevated glucose concentrations (such as 8%) result in higher ethanol production (Lin et al., 2012) and reactive oxygen species (ROS) levels (Maslanka et al., 2017).

      2) In the discussion section, a more explicit comparison with similar studies in other model organisms would help contextualize the findings within the broader field of evolutionary biology. While the results appear robust, it would be beneficial to explore how they align with or contrast to previous studies on DNA damage, particularly in bacteria or highly complex eukaryotes.

      We appreciate this suggestion to better contextualize our findings within the broader literature, as it provides an opportunity to highlight the unique aspects of our work. While many studies have explored how environmental factors shape fitness landscapes and influence evolutionary strategies, to our knowledge, only a few have addressed this in the context of compensatory evolution, where cells must recover fitness lost due to intracellular perturbations. To address this point, we have added a discussion of additional examples involving other model organisms, highlighting their difference with the question asked in this work.

      Line 34: “Genotype-by-environment (GxE) interactions are well-documented. For example, several studies on E. coli have demonstrated how different environments influence fitness and epistatic interactions among adaptive mutations in the Lenski Long-Term Evolution Experiment (Ostrowski et al., 2005, 2008; Flynn et al., 2012; Hall et al., 2019). Adaptive mutations in viral genomes similarly exhibit variable fitness effects across different hosts (Lalic and Elena, 2012; Cervera, 2016). Furthermore, interactions between mutations in the Plasmodium falciparum dihydrofolate reductase gene have been shown to predict distinct patterns of resistance to antimalarial drugs (Ogbunugafor et al., 2016). However, the role of environmental factors in shaping evolution within the context of compensatory adaptation, when fitness defects primarily arise from intracellular perturbations, remains much less explored.”

      However, if the reviewer have particular additional studies in mind, we welcome further suggestions to include in the final manuscript.

      Minor comments:

      1) The presentation of data in the figures is clear and informative. However, some figure legends could benefit from more detailed explanations. For example, although the statistical tests used are mentioned in the methods section, it would be helpful to also include them in the figure legends, such as in legend 1acde, as well as in all other figures.

      We are now reporting the statistical test used for each comparison also in figure legends.

      2) In terms of broader conclusions, here are a few suggestions, though they are, of course, optional:

      a) The study could benefit from exploring the potential trade-offs of adaptive mutations in the hypothetical return to environments without replication stress, at least theoretically. This would provide a more comprehensive understanding of the evolutionary constraints.

      We thank the reviewer for the suggestion, we had performed the measurements but did not comment on them explicitly. We are now commenting on them as follows:

      Line 310: “In the WT background, all mutations were nearly neutral, with only minimal deleterious or advantageous effects on fitness depending on glucose concentrations (Fig S4A).”

      Line 468: “The nearly neutral effects on fitness of the core adaptive mutations in WT suggest that they are likely to persist even after the initial replication stress is resolved.”

      b) A brief discussion of the potential limitations of using lab strains versus wild isolates of S. cerevisiae would offer valuable context for the generalizability of the findings.

      This is an excellent point. While addressing it fully would warrant a separate manuscript, we provide our comments here, along with similar observations raised by this and other reviewers, as follows:

      Line 450: “How generalizable are our conclusions about the reproducibility of evolutionary repair to DNA replication stress across other organisms, species, or replication challenges? While dedicated future studies are needed to fully address these important questions, several lines of evidence are encouraging. A recent report demonstrated that the identity of suppressor mutations of lethal alleles was conserved when introduced into highly divergent wild yeast isolates (Paltenghi and van Leeuwen, 2024). Similarly, earlier work showed that even ploidy, which significantly alters the target size for loss- and gain-of-function mutations, affected only the identity of the genes targeted by selection, while the broader cellular modules involved remained consistent (Fumasoni and Murray, 2021). Moreover, divergent organisms experiencing different types of DNA replication stress exhibit some of the adaptive responses described here. For example, the yeast genus Hanseniaspora, which lacks the Pol32 subunit of the replisome, has also been reported to have lost the DNA damage checkpoint (Steenwyk et al., 2019). Human Ewing sarcoma cells carrying the fusion oncogene EWS-FLI1 frequently exhibit adaptive amplification of the cohesin subunit RAD21 (Su et al., 2021). Together, these findings suggest that while the specific details of DNA replication perturbations and the genomic features of organisms may shape the precise targets of compensatory evolution, the overarching principles and cellular modules affected are broadly conserved.”

      Furthermore, we plan to search a recently published database of variants found in natural isolates of S. cerevisiae to assess whether similar evolutionary processes to those described in this study may have occurred in wild strains.

      c) It would be valuable to present the differences in ploidy in the context of other studies, such as the nutrient-limitation hypothesis (e.g., 'The Evolutionary Advantage of Haploid Versus Diploid Microbes in Nutrient-Poor Environments' by Bessho, 2015), since, as previously demonstrated by the authors of this article that is being reviewed, ploidy may influence the evolutionary trajectories of DNA repair.

      d) Interrelating these three terms: nutrient-limitation, ploidy, and DNA repair could be an interesting avenue to explore in the discussion.

      In response to comments c and d, we have now commented on the intersection between ploidy and other types of DNA perturbation in the paragraph starting in line 491 (see response above)

      3) Specific details:

      a) Line 116: To improve clarity, it would be beneficial to refer to the figure right after the statement: 'However, their relative fitness improved compared to the WT reference as the initial glucose levels (Figure X).'

      b) Line 404: The statement about antibiotics and cancer progression is somewhat brief here; it might be helpful to provide more context on why this mechanism influences these processes (here or before).

      c) Line 418: "were re-suspended in water containing zymolyase (Zymo Research, Irvine, CA, US, 0.025 μ/μL), incubated at". Something is missing in the units.

      d) Line 459: "and G2 phases for each genotype was estimated by deriving the the relative cell distribution". The article "the" is repeated.

      e) 1a: The x-axis ticks appear misaligned, which makes it difficult to interpret the boxplots. For example, at 0.25, the tick is closer to the orange boxplot than to the black one. In contrast, at 2%, the tick seems well-centered."

      f) Figure 3 could benefit from a general legend at the top regarding the colors, as finding it in 2c was not intuitively easy.

      The typos and suggestions raised in points 3a-f have now been corrected in the manuscript.

      g) I didn't review the code on GitHub.

      Reviewer #1 (Significance):

      The main strength of the study is that it shows robustness of compensatory evolution across varying nutrient conditions. The study adds to the growing body of literature on DNA replication stress and evolutionary adaptation by showing that compensatory evolution can occur regardless of nutrient availability. This fundamental finding challenges prior assumptions that nutrient conditions significantly alter evolutionary outcomes, contributing to a more nuanced understanding of how cells respond to stress. Furthermore, the discovery of the RNA polymerase II mediator complex's role in this process is particularly novel and opens new lines of investigation.

      Advance in the field: The results advance our understanding of evolutionary biology, particularly in the context of DNA replication stress and compensatory evolution. The study demonstrates that evolutionary repair mechanisms are predictable, even under variable environmental conditions, which has key implications for evolutionary biology and therapeutic applications.

      Audience:

      This paper will be of interest to a specialized audience in evolutionary biology, genomics, and cell biology, particularly those interested in DNA replication stress and adaptive evolution. Researchers studying stress responses in model organisms, such as S. cerevisiae, will find the findings valuable, as will those working in applied fields where stress adaptation is a critical factor (e.g., industrial yeast fermentation, drug development, disease resistance, cancer research, or aging studies).

      Expertise:

      Evolutionary biology, genomic analysis, and cellular stress responses, with a particular focus on experimental evolution under DNA damage stress in Saccharomyces cerevisiae. Recently graduated and beginner reviewer.

      Reviewer #2 (Evidence, reproducibility and clarity):

      The paper addresses the effect of sugar availability in shaping compensatory evolution. The first observation of the paper is that cell physiology changes by modulating glucose availability also in strains that come with defective DNA replication (ctf4-null previously studied by the authors). An intriguing result is that ctf4-null grows comparatively better in low concentrations of glucose. This is hypothesized to be a consequence of both the decrease in dNTPs in low glucose, which causes slow down of fork progression, and/or reduced fork collapse at rDNA locus. Hence, wild types and ctf4-null show an opposite trend: in the mutant, the lowest concentration of glucose is the least affected by the mutation; in wild type, the highest concentration is the least affected. Adaptation rate is inversely related with the initial fitness. The effect on physiology and adaptation rate is a starting point for asking the key question: are evolutionary trajectories influnced by the growth conditions? The answer is negative: evolution experiments show the very same core of genetic changes at all sugar concentrations. The result is apparently at odds with previous publications, and the authors conclude that, in this particular setting, availability of carbon sources plays a minor role compared to impaired DNA replication. The different rates of adaptation in WT and mutant is rather explained by the initial fitness at the different glucose concentrations, which, as mentioned, is opposite in WT and ctf4-null mutants. The paper also reports a new mutation in MED14, component of the transcription mediator complex, which rescues the lack of Ctf4 activity. The study is interesting and asks a relevant question. The experiments are well executed and convincing, but the paper can be strengthened by testing some of the hypotheses which are put forward.

      Main points

      1- The raw data for evolutionary dynamics (Figure S2C) are fitted with the power law suggested by Wiser and Lenski, and return different values of the parameter 'b'. The authors say that the result depends greatly on the initial conditions ("due to the varying initial fitness of ctf4Δ cells across different glucose environments, they display an opposite trend to WT"). Around the initial values, however, the curves are non-monotonic, especially for low glucose availability. Both for WT and ctf4-null there is an initial drop in fitness, after which fitness increases. If one would neglect this initial dynamics, the value of the parameter 'b' would likely be different.

      The non-monotonic trend in fitness highlighted by the reviewer is likely due to technical factors: Fitness at Generation 0 was measured with high precision in a low-throughput manner early in the project. In contrast, fitness from Generation 100 to 1000 was measured later in the study in a high-throughput fashion, necessitated by the large number of competitions conducted (96 wells × 4 time points × 6 replicates = 2304 assays). This difference in methodologies may have introduced a slight offset when the datasets were combined at Generation 100. Following the reviewer’s suggestion, we have excluded the data point at Generation 100 responsible for this non-monotonic behavior and re-fitted the curves. While this adjustment has caused minor changes in the parameter ‘b’, the qualitative trends, particularly the opposing trends between WT and ctf4Δ as glucose increases, remain consistent (Figure_rev_only 1). To ensure transparency, we have retained all recorded fitness values in the original figure for reference.

      In general, one can question whether curves with this shape are best fitted by the power law proposed by Wiser and Lenski. For example, for the WT 0.25% glucose the linear fit gives a better R2 (why do the authors show the linear fit anyway?). This impression is further reinforced by the observation that Wiser and Lenski fit dynamics that last 50.000 generation, here the curves last 1/50th of it. In conclusion, I would question whether the parameter 'b' is a solid measurement of 'rate of adaptation'. Also, normalizations makes it difficult to appreciate the result shown in Figure 2B. I think the authors should look for a different way to show the different trend in adaptation dynamics for different glucose concentrations between wild types and mutants. For example, they could move Figure S2C in the main text to stress the result shown in Figure 2C, which already shows the difference between WT and mutant. This is especially true if what Figure 2C shows is (evo-anc)/evo. This is not fully clear to me: in the legend it refers to the delta, in the label of the y-axis I read that this is a percentage.

      We thank the reviewer for prompting us to clarify our methods for reporting fitness changes over time. The fitness values are reported, throughout the paper, as a percentage change relative to the reference WT strain. The gain in fitness during evolution (reported as Δ) represents the difference between the evolved strain (evo%) and the ancestral strain (anc%), calculated as Δ = evo% - anc%. This represents the absolute gain, rather than the relative gain. This value is still reported as a percentage as it’s the same scale and unit as the two values being subtracted. We have included additional details to clarify this aspect in the figure legend.

      “(C) Absolute fitness gains (Δ) at generation 1000 for evolved WT (upper panel, black) and ctf4Δ (lower panel, orange) populations. Box plots show median, IQR, and whiskers extending to 1.5×IQR, with individual data points beyond whiskers considered outliers. Absolute fitness gains were calculated by subtracting the ancestral relative fitness from the relative fitness of the evolved (Δ = evo% - anc%), both calculated as percentages relative to the same reference strain in the same glucose concentration.”

      To conclude: the data show a different trend between wild types and mutants, which is interesting. Fitting it with the power law seems to be neither required nor appropriate. I suggest the authors to show the WT vs mutant pattern differently.

      We followed the reviewer’s suggestion and moved Figure S2C, which depicts the detailed fitness trajectories over time, into the main manuscript as Figure 2D. We agree that presenting these trajectories alongside the absolute fitness gains (now in Figure S2C) provides a more intuitive and effective depiction of the evolutionary dynamics of WT and ctf4Δ strains without relying solely on the power-law fit. Additionally, we quantified the mean adaptation rate, calculated as the absolute fitness gain (Δ) divided by the total number of generations (now Figure 2B). While no individual method definitively captures the adaptation rates across the experiment, these complementary analyses consistently highlight the same trends noted by the reviewer. We have re-written the main text as follows:

      Line 171: “By generation 1000, both WT and ctf4Δ evolved lines achieved, on average, slightly higher fitness in low glucose compared to high glucose conditions (Fig S2B). However, due to the varying initial fitness of ctf4Δ cells across different glucose environments, they recovered the same extent of the original defect (Fig S2C). ctf4Δ lines displayed an opposite trend to WT, with increasing absolute fitness throughout the experiment as glucose concentration rose (Fig S2B vs S2D). The differint absolute fitness gains over the same number of generations highlight distinct mean adaptation rates (Fig 2B). These differences are evident when examining the evolutionary dynamics of the evolved lines over time (Fig 2C). Additionally, we approximated the fitness trajectories using the power law function (Fig 2C, dashed purple lines), previously proposed to describe long-term evolutionary dynamics in constant environments (Wiser et al., 2013). The parameter b in this formula determines the curve's steepness, and can be used to quantify the global adaptation rate over generations (Fig S2E). Collectively, these analyses demonstrate that, unlike WT cells, ctf4Δ lines adapt faster in the presence of high glucose. This evidence aligns with the declining adaptability observed in other studies (Moore et al., 2000; Kryazhimskiy et al., 2014; Couce & Tenaillon, 2015), where low-fitness strains consistently adapt faster than their more fit counterparts (Fig S2F).”

      Overall, these results demonstrate that cells can recover from fitness defects caused by constitutive DNA replication stress regardless of the glucose environment. However, adaptation rates under DNA replication stress exhibit opposing trends compared to WT cells, with faster adaptation yielding greater fitness gains in higher glucose conditions.”

      2- In Figure S2C, the individual trajectories for WT at 2% glucose are strangely variable. In this case, plotting the average does not make too much sense. This result is strange, since this is the default condition, where cells are grown without any change of sugar concentration. Can the authors give any rationale? Are there other available results to replace those published in Figure S2C?

      We agree with the reviewer that the individual trajectories for WT at 2% glucose are intriguing. However, we do not find these results necessarily “strange” as they could be explained by the following rationale: WT cells have been cultivated in 2% glucose since the 1950s, likely fixing most beneficial mutations for this condition. When many isogenic strains are evolved in parallel, (a) some lines show no improvement due to the scarcity of available beneficial mutations, (b) others exhibit slight decreases in fitness due to genetic drift fixing deleterious mutations, and (c) a few lines discover rare beneficial mutations, leading to fitness increases. In contrast, other conditions represent “newer” environments with larger mutational target sizes, resulting in more consistent outcomes.

      Prompted by the reviewer’s comment, we look for other studies reporting detailed fitness measurements of evolved WT strains in standard laboratory media. We downloaded and plotted the fitness data from Johnson et al. 2021, where authors studied the evolution of WT strains over 10.000 generations. Interestingly, we see that in the early phase of the evolution (generations 500-1400) evolved lines show similar levels of variability in fitness as the one reported in our study (Figure_rev_only 2). Of note is that in Johnson et al. 2021 most of the adaptive mutations alleviate the toxicity of the ade2-1 allele. In our WT strain the gene was preemptively restored, furter reducing the target size for adaptation in YPD.

      We believe it is important to report these measurements and decided to leave the original data, with the appropriate quantifications of variability, in Figure 2.

      3- The molecular explanation given for the rescue of ctf4-null proposes a very relevant role for dNTPs downregulation. Particularly, both for Irx1 and med14-H919P, the authors propose that this happens via Rnr1 downregulation. At this stage, this is only a hypothesis. The molecular verification of the central role of Rnr1 downregulation would make the conclusion much stronger. For example, a preliminary test would imply that duplicating RNR1 in ctf4-null irx1-null and/or ctf4-null med14-H919P would revert the rescue. Any other experiment addressing this point would be useful to improve the paper.

      We agree that the experiment suggested by the reviewer, or similar tests, would substantiate our hypotheses and strengthen the paper. Specifically, we plan to perturb dNTP production in both ctf4Δ ixr1Δ and ctf4Δ med14-H919P mutants through genetic manipulation of known factors involved in dNTP synthesis. We will then compare the resulting fitness to the expectations based on our hypotheses: reduced fitness benefits of the double mutants upon increasing dNTP levels and/or increased fitness in ctf4Δ mutants by decreasing dNTP levels through alternative mechanisms.

      4- The authors propose from Figure S4B that the rescue of ixr1-null is less evident at low sugar concentration since both conditions trigger a reduction of dNTPs. I think this is interesting, since it would provide a link between glucose concentration and evolutionary trajectories to adaptation, which is what the authors wanted to study. In particular, one would predict that 0.25% glucose would see less ixr1-null than the other glucose conditions. I could not (was not able to) confute this hypothesis from the data shown in the paper. Likewise, for med14-H919P. If the authors have not tested it, it would be worth trying.

      We had reported the appearance and frequency of all ‘core adaptive mutations’ (Figure S6C) but did not explicitly test the likelihood of their appearance under different glucose conditions. Following the reviewer’s suggestion, we have now performed χ2 tests (on the presence or absence of mutations) and ANOVA tests (on their mean frequency) to determine whether any mutation is particularly enriched or depleted in a given glucose environment. At first glance, the results do not support the hypothesis proposed by the reviewer. However, we note that although ixr1 mutants are less beneficial in low glucose than in high glucose, they still confer an 8% fitness advantage, which is likely sufficient to drive clones to fixation. We believe the reviewer’s reasoning is correct but is potentially masked by the still elevated fitness advantage of ixr1 in low glucose.

      To better convey the results of this analysis, we have included a visual representation of the presence and frequency of the mutations in Figure 6A, and the results of the χ2 and ANOVA tests in Supplementary File 5. We also comment on the analysis as follows:

      Line 314: “Similarly, we did not detect differences in the frequency of occurrence (χ2 tests) or average fractions (ANOVA test) achieved by the mutations in the populations evolved under different glucose environments (Fig 6A, Fig S4C and Supplementary File 5. The presence of all mutations in the final evolved lines correlated with their fitness benefits, suggesting how their selection in all glucose conditions was mostly dictated by their relative fitness benefits, rather than the environment (Fig 6A).”

      5- The combination of the four genetic adaptation (Fig 6B) would benefit from an experimental verification to show that the different solutions are not mutually exclusive. This is not obvious: if more than one solution acts by reducing dNTPs, maybe their combined effect is less strong than what measured theoretically. The authors could derive some clones at the end of the experiment and Sanger sequencing some of the four genes, to confirm the co-presence of some of them in the same cell.

      The co-occurrence of nearly every combination of the four core adaptive mutations we identified can be inferred from their relative frequencies, as revealed by deep whole-genome sequencing of the evolved populations (Fig. S4C). In these data, we observe populations carrying each pairwise combination of mutations at frequencies exceeding 50%, implying their coexistence. Moreover, many combinations of mutations approach or reach fixation. A particularly striking example is ctf4Δ Population 11, evolved in 8% glucose, where all core adaptive mutations are present at 100% frequency. These findings provide robust evidence that the different adaptive solutions are not mutually exclusive and can coexist within the same genetic background.

      Nevertheless, we agree that experimentally verifying the compatibility and fitness of the four genetic adaptations described in Figure 6B (now Fig 6C) would further strengthen our conclusions. To this end, we plan to reconstruct all combinations of mutations observed at high frequency in the final evolved populations. We will then measure their fitness and compare it to that of the evolved populations, as well as to the theoretical expectations based on additivity currently presented in Figure 6C.

      Minor points

      Figures

      • S4B: in the legend it should be explained that it is compared to ctf4D

      We now report how the values were obtained in the figure legend:

      (D = |anc%|-|reconstraucted%|)

      -2A: the color code is not fully clear to me: what does green and blue indicate? higher and lower than 2%?

      We apogise for not having included an explicit description of the color code in Figure 2A. Throughout the paper blue refers to glucose starvation (light blue for 0,25%, dark blue for 0,5%), while green refers to glucose abundance (light blue for 2%, dark blue for 8%). We now include a detailed description of the color code when it first appears (Fig 1B) and make sure is properly reported in all figure legends.

      • S3A: the authors should show the statistical difference between WT and ctf4-null, which is mentioned as non-existent in p.6

      The p value is now represented in Fig S3A

      Text

      • RNR1 is not really the gene with the highest score in Figure 5D, not even close: can you give a rationale for pin-pointing it (see also main point 3)?

      The reviewer is correct. Perturbations of the mediator complex, which regulate the expression of most of RNA PolII transcripts, is expected to result in changes in the expression of a large set of genes. However, our focus on dNTPs and RNR1 is based on the following rationale:

      1. Gene Ontology Enrichment Analysis: The downregulated genes in our dataset are enriched for the 'nucleotide metabolism' term, which includes pathways critical for dNTP production and directly linked to DNA replication and repair.

      2. Role of RNR1: Among the downregulated genes, RNR1 stands out as it encodes the major subunit of ribonucleotide reductase, the rate-limiting enzyme in dNTP synthesis. This enzyme is essential for DNA replication, and cells experiencing constitutive DNA replication stress, as in our system, are particularly sensitive to changes in dNTP levels.

      To make this rationale more explicit to the reader, we are adding the following sentence in the discussion:

      Line 404: “Nucleotide metabolism, particularly ribonucleotide reductase, is essential for dNTP production. Given the role of dNTPs in regulating DNA replication and repair, the advantage of med14-H919P mutants in the ctf4Δ background may stem from reduced dNTP levels caused by the perturbed TID domain."

      In addition, following the reviewers’ suggestions, we are conducting additional experiments to investigate the role of med14-H919P mutants in enhancing fitness under conditions of constitutive DNA replication stress (See response to reviewer #4). We anticipate that the final revised manuscript will offer further insights into the role of dNTPs or present alternative explanations for the observed phenomena.

      • The med14-H919P mutation is observed in 22/48 wells. I guess the authors checked already: are some of these wells close to each other in the plate?

      Correct. We took significant precautions in our experimental design to prevent cross-contamination, as outlined in the Materials and Methods section. Specifically, rows of ctf4Δ samples were alternated with rows of WT samples. Daily dilutions were then performed row by row using a 12 channels pipette. This approach ensured that any potential carry-over of cells would result in them being placed in wells containing a different genotype, where they would be eliminated by the consistent use of genotype-specific drugs.

      As a result of these measures, we do not observe any distinct pattern of core genetic adaptation corresponding to the plate layout (Figure_rev_only 3). The only exception are mutations in IXR1, which appear in all ctf4Δ strains (albeit with different alleles, see supplementary File 3). Moreover, we reasoned that if a highly fit strain had invaded other wells, all the pre-existing mutations from its lineage would have been detected in those wells. However, apart from the recurrent ixr1 and rad9 mutations, which are also strongly adaptive, we find no evidence of shared mutations in wells carrying the med14-H919P allele (Figure_rev_only 4).

      • Compensatory evolution of ctf4-null in 2% glucose is the experiment published by Fumasoni and Murray in eLife. In that paper, there is no trace of mutations in MED14. I think the authors should comment on this (different method for detecting putative compensatory mutations?).

      We also noticed the absence of MED14 mutations in the eLife study by Fumasoni and Murray and find this discrepancy intriguing. One possible explanation lies in methodological differences. Our current study employed an improved version of the mutational analysis pipeline. However, we have not yet reanalyzed the original data from the previous study to determine whether MED14 mutations were present but undetected.

      Interestingly, in the current study, we observed that in 2% glucose, MED14 mutations arose in only 3 out of 12 populations, a frequency lower than in other glucose conditions (Figure S6C). Assuming a similar frequency occurred in the 8 populations evolved in 2% glucose by Fumasoni and Murray (2020), one would expect only 2 populations to carry the mutation. This number falls below the threshold required for our algorithm to detect statistically significant parallelism.

      Additionally, two significant experimental differences may also contribute to the observed discrepancy. First, the culture volumes and vessels differed: 10 mL cultures in tubes were used previously, whereas 1.5 mL cultures in 96-well plates were used in the current study.

      • I may be mistaken, but Szamecz et al do not actually investigate whether different conditions result in different evolutionary trajectories (i.e., different genetics), and so their results may not be at odds with those presented here.

      The reviewer is correct that Szamecz et al. do not explicitly test whether different conditions result in different evolutionary trajectories. However, in the section titled “Compensatory Evolution Generates Diverse Growth Phenotypes across Environments,” they examine how lines evolved in 2% YPD perform across various environments. They report how in roughly 50% of the cases tested, evolved lines showed either no improvement or even some lower fitness than the ancestor (Figure 5A).

      While this could be explained by the accumulation of detrimental non-adaptive mutations in specific contexts, it likely implies that the adaptive strategies compensating for the original mutation in one environment do not confer similar benefits in other environments. This observation contrasts with our findings in Figure 6D, where we demonstrate that the main adaptive strategies provide a consistent benefit across diverse environments, including those with glucose, nitrogen, or phosphate abundance or starvation.

      We have now modified the introduction, results and discussion to avoid misleading interpretations:

      Line 42: “Szamecz and colleagues examined the evolutionary trajectories of 180 haploid yeast gene deletions over 400 generations (Szamecz et al., 2014). They found that, while fitness recovery occurred in the environment where evolution took place, the evolved lines often showed no improvement over their ancestors in other environments. This suggests that compensatory mutations beneficial in one environment often fail to restore fitness in others.”

      Line 327: “A previous study in yeast showed how evolved lines which compensate for detrimental defects of gene deletions in standard laboratory conditions often failed to show fitness benefits compared to their ancestor when tested in other environments (Szamecz et al., 2014). We thus investigated the extent to which the core genetic adaptation to DNA replication stress was beneficial under alternative nutrient conditions.”

      Line 422: “What could explain the discrepancies between our results, and previous studies on evolutionary repair highlighting the role of the environment in shaping evolutionary trajectories (Filteau et al., 2015), and the heterogeneous behavior of evolved lines in various environments (Szamecz et al., 2014)?”

      typos

      p.18, line 564 preformed -> performed

      1. 6 line 189 with a strongly skew -> with a strong skew ?

      Typos are now corrected in the main text

      Reviewer #2 (Significance):

      This is a well-done paper that could be of interest for the community of evolutionary biologists, scientists working on metabolism and cell division. It addresses an interesting problem, how metabolism affects compensatory evolution. Among the strengths: experiments are well done, the results are novel, the cross-talk between metabolism and evolutionary repair is intriguing. Among the weaknesses, the fact that the molecular explanations for the observations are only hypothesized and not tested experimentally. This is where the authors could improve the manuscript.

      Reviewer #3 (Evidence, reproducibility and clarity):

      This paper combines phenotypic and genomic data from an experimental evolution study in yeast to assess how repeatable evolution is in response to DNA replication stress. Importantly, the authors ask whether genotype by environment interactions influence repeatability of their evolved lines. To this end, the authors have constructed an elegant highly-replicated experiment in which two yeast genotypes (WT and CTF4 KO) were evolved under a variety of glucose levels for 1,000 generations. Recurrent mutations are found across many replicates, suggesting that repeatability is robust to GxE interactions. Of course, the authors correctly identify that these results are dependent on many particulars, as is always the case in biology, but provide a comprehensive discussion to accompany their results. I do not have any major comments to give, but simply some suggestions and points of clarification.

      Major comments: N/A

      Minor comments:

      L19: I found the definition for compensatory evolution/mutations to be somewhat vague in the introduction (and subsequently throughout the text). It's clear that this was written for a more medical/physiological audience, but without a more explicit explanation of compensatory evolution/mutations, it became difficult to properly weigh some claims/discussions made by the authors later on. Do you define compensatory mutations as those which completely recover WT function/fitness, or are simply of opposite effect to the altered genotype? Others define "compensatory evolution" as simply any epistastically interacting amino acid substitutions (Ivankov et al, 2014). It would be nice to see more explicitly defined.

      We thank the reviewer for highlighting the need for a precise definition of compensatory evolution and compensatory mutations. We recognize that the literature encompasses multiple definitions, including the one cited by the reviewer, which emphasizes compensatory mutations within the context of structural biology. This particular definition, prevalent in molecular evolution, was introduced by Kimura (Kimura, 1985) and is frequently used to explain the co-occurrence of amino acid mutations within a protein. These mutations offset each other’s defects, restoring or maintaining protein function. Here, however, we are using an older and broader definition of compensatory mutation, first introduced by Wright (Wright, 1964, 1977, 1982) and frequently used in evolutionary genomics (e.g., Moore et al., 2000; Szamecz et al., 2014; Rajon and Mazel, 2013; Eckartt et al., 2024). This definition includes any mutation in the rest of the genome that compensates (fully or partially) for another mutation's detrimental effects on fitness.

      We have now included this definition in the introduction:

      Line 19: “Compensatory evolution is a process by which cells mitigate the negative fitness effects of persistent perturbations in cellular processes across generations. This adaptation occurs through spontaneously arising compensatory mutations anywhere in the genome (Wright, 1964, 1977, 1982) that partially or fully alleviate the negative fitness effects of perturbations (Moore et al., 2000). The successive accumulation of compensatory mutations over evolutionary timescales progressively repair the cellular defects, ultimately restoring fitness.”

      Line 361: “Our findings demonstrate that while glucose availability significantly affects the physiology and adaptation speed of cells under replication stress, it does not alter the fundamental genome-wide compensatory mutations that drive fitness recovery and evolutionary repair.”

      Along these lines, I would have liked to see a more direct comparison/discussion of the degree to which deletion lines recovered. I can see from Fig 2E and Fig S2B that fitness increased quite a bit; would it not be possible to include a figure on the degree of compensation (basically relative fitness of evolved deletion lines - relative fitness of ancestral deletion lines)?

      If the reviewer is suggesting calculating the difference between the evolved and ancestor fitness, the data is already in Figure S2B and S2D, defined as ‘Absolute fitness gains Δ’ and calculated as Δ = evo% - anc%.

      If instead is suggesting to plot the fitness of evolved deletion lines (Y axis) against the relative fitness of ancestral deletion lines (X axis), we have now produced the plot is Figure S2F.

      To better understand the extent of the fitness recovery in Ctf4 strains, we have also calculated and plotted the ‘relative fitness gain’ calculated as |evo%| / |anc%| *100 (Figure S2C)

      We are now commenting on these comparisons in the following paragraph:

      Line 171: “By generation 1000, both WT and ctf4Δ evolved lines achieved, on average, slightly higher fitness in low glucose compared to high glucose conditions (Fig S2B). However, due to the varying initial fitness of ctf4Δ cells across different glucose environments, they recovered the same extenct of the original defect (Fig S2C), displaying an opposite trend to WT, with increasing absolute fitness throughout the experiment as glucose concentration rose (Fig S2B vs S2D). The differint absolute fitness gains over the same number of generations highlight distinct mean adaptation rates (Fig 2B). These differences are evident when examining the evolutionary dynamics of the evolved lines over time (Fig 2C). Additionally, we approximated the fitness trajectories using the power law function (Fig 2C, dashed purple lines), previously proposed to describe long-term evolutionary dynamics in constant environments (Wiser et al., 2013). The parameter b in this formula determines the curve's steepness, and can be used to quantify the global fitness change over generations (Fig S2E). Collectively, these analyses demonstrate that, unlike WT cells, ctf4Δ lines adapt faster in the presence of high glucose. This evidence aligns with the declining adaptability observed in other studies (Moore et al., 2000; Kryazhimskiy et al., 2014; Couce & Tenaillon, 2015), where low-fitness strains consistently adapt faster than their more fit counterparts (Fig S2F).”

      L57: Another minor nitpick that just comes down to semantics. When discussing "96 parallel populations", it invokes a higher sense of replication than is actually present in the study. I would rephrase this to something along the lines of "12 replicate populations across 8 treatments under conditions of [...]".

      We changed the sentence as follows:

      Line 66: “We evolved 96 parallel populations of budding yeast, organized into 12 replicate lines, across four conditions of glucose availability (from starvation to abundance) with or without replication stress.”

      L185-187: The wording here needs to be clarified. Be explicit in that are examine the ratio (or count) of synonymous to non-synonymous mutations here, otherwise the interpretations appears to be direct contradiction to the (as written) results. Only after viewing the supplemental figure was I able to figure out what exactly was meant here.

      We changed the sentence as follows:

      Line 212: “We found no significant differences in the numbers of synonymous mutations detected in evolved populations in WT and ctf4∆ populations (Fig. S3A). These results support the hypothesis that replication stress in ctf4∆ lines favors the retention of beneficial mutations, rather than simply increasing the overall mutation rate.”

      L349-350: The authors observe higher rates of adaptation in deletion lines than WT lines, and discuss this in adequate detail. Although not explicitly mentioned, this is consistent with a diminishing returns epistasis model (that could be beneficial to discuss, but is not necessary), which has been implicated in modulating the degree of repeatability observed along evolutionary trajectories (Wünsche et al. 2017). Although definitely not required for this already very nice manuscript, I think it would be very rewarding if the authors were to eventually analyze fine-scale dynamics of phenotypic and genomic adaptation to mine for these putative interactions and their influence on repeatability.

      We agree with the reviewer on how our results align with a model of diminishing returns epistasis. This pattern is apparent not only between ctf4Δ and WT lines but also among ctf4Δ lines evolved in different glucose conditions. This phenomenon likely arises from the interaction of various adaptive mutations, which we aim to explore further in a dedicated manuscript. However, until we do so, we prefer to refer generally to a pattern of declining adaptability. To explicit this trend we have now included Fig S2F and commented on it in the manuscript:

      Line 181: “This evidence aligns with the declining adaptability observed in other studies (Moore et al., 2000; Kryazhimskiy et al., 2014; Couce & Tenaillon, 2015), where low-fitness strains consistently adapt faster than their more fit counterparts (Fig S2F).”

      Line 388: "Our results are consistent with declining adaptability, as evidenced by the reduced rates of adaptation observed both between ctf4Δ and WT lines and among ctf4Δ lines evolved in different glucose conditions (Fig S2F)"

      Reviewer #3 (Significance):

      It is clear to me that a great deal of time and care has been put into this study and the preparation of this manuscript. The science and analyses are appropriate to answer the questions at hand, and it bodes well that whenever I had a question pop up while reading, they were typically answered immediately after. I think that this manuscript will be broadly relevant to both biologists both evolutionary and clinical, and was written in a way to be accessible to both.

      As someone with an expertise in repeatable evolution, I felt most excited by the observation of so many parallel substitutions at a single amino acid across deletion lines. As the authors rightfully point out in the results and discussion, it's likely that this degree of robustness is highly dependent on the particular mechanism of disruption that cells experience. The authors then go above and beyond to functionally validate the putative molecular mechanisms of (repeatable) adaptation in this system. While it may not always be possible to accomplish in non-model organisms, such multi-modal approaches will be crucial to advance the field of repeatable evolution.

      Reviewer #4 (Evidence, reproducibility and clarity):

      The authors investigated the effects of DNA replication stress on adaptation in different nutrient availabilities by passaging wild-type and ctf4Δ Saccharomyces cerevisiae in media with varying levels of glucose over ~1000 generations. The ctf4Δ strain experiences increased DNA replication stress due to the deletion of a non-essential replication fork protein. The authors found differences in evolution between wild-type and ctf4Δ yeast, which held across different growth media. This study identified a compensatory single amino acid variant in Med14, a protein in the mediator complex of RNA polymerase II, that was specifically selected in ctf4Δ strains. The authors conclude that while environmental nutrient availability has implications for cell fitness and physiology, adaptation is largely independent and instead dependent on genetic background. The data provide excellent support for the key aspects of the models, although some details are (to me) overstated.

      Major comments:

      • A ctf4Δ mutant strain was used to investigate the effects of replication stress. Why was this mutant chosen instead of other deletions that cause different types of replication stress?

      We appreciate the opportunity to clarify our rationale for choosing the ctf4Δ mutant. The following are the main reasons why we believe ctf4Δ strains represent an ideal tool to study a global perturbation of the DNA replication program over evolutionary timescales:

      1. General replication stress: The absence of Ctf4 perturbs replication fork progression, leading to a spectrum of replication stress-related phenotypes, including DNA damage sensitivity, single-stranded DNA gaps, reversed forks (Abe et al., 2018; Fumasoni et al., 2015), checkpoint activation (Poli et al., 2012), cell cycle delays (Miles and Formosa, 1992), increased recombination (Alvaro et al., 2007), and chromosome instability (Kouprina et al., 1992). This broad disruption makes it an excellent model for observing global perturbations in replication processes. In contrast, other mutants typically affect specific enzymatic (e.g., POL32 and RRM3) or signaling (e.g., MRC1) functions, making them better suited to address specific questions.
      2. Constitutive stress: Unlike drug-induced stress (e.g., Hydroxyurea; Krakoff et al., 1968) or conditional depletion systems (e.g., GAL1-POLε; Zhang et al., 2022), which cells can easily circumvent through single mutations, ctf4Δ enforces persistent replication stress. Its deletion cannot be complemented by a single mutation, ensuring a robust and consistent stress environment for evolutionary studies.

      We have now modified the main text to convey these advantages in a concise form:

      Line 91: “In the absence of Ctf4, cells exhibit multiple defects commonly associated with DNA replication stress, such as single-stranded DNA gaps and altered replication forks (Fumasoni et al., 2015), leading to basal cell cycle checkpoint activation (Poli et al., 2012). These defects result in severe and persistent growth impairments, cell cycle delays, elevated nucleotides pools and chromosome instability (Miles and Formosa, 1992; Kouprina et al., 1992; Poli at al., 2012), making ctf4Δ mutants an ideal model for studying the cellular consequences of general and constitutive replication stress over evolutionary time.”

      It's not clear from the study that the effects are generalizable to other forms of replication stress.

      As with any method to induce DNA replication stress (including commonly used drugs like HU) each approach inevitably affects replication in a specific manner. Testing the broader applicability of our conclusions would require evolving additional strains with different replisome perturbations. For instance, mutations in ELG1 and CTF18 (affecting the alternative Replication Factor C), POL30 (affecting the sliding clamp PCNA), POL32 (affecting Polε), RRM3 (protective helicase) and (MRC1 (coordinating leading strand activities and signalling to the checkpoint) would have to be taken into account. Furthermore, specific mutant alleles of Ctf4 that disrupt interactions with particular binding partners (Such as ctf4–4E and ctf4–3E, perturbing the interaction with the CMG helicase and accessory factors respectively) will be highly informative on which specific aspects of the replication stress generated by the lack of Ctf4 each adaptive mutation alleviate.

      However, accommodating such extensive variability would inflate the sample size to an extent that will become unfeasible within the experimental design focused on capturing parallel evolution over a nutrient gradient (the primary focus of this study). We agree that this is an important question and intend to address it comprehensively in a dedicated future study.

      • The authors could be clearer that a (the?) cause of the ctf4∆ fitness defect is spurious upregulation of RNR1. I don't think it is mentioned until the Discussion, but it is highly relevant to Fig 4, and to the adaptations one would expect from ctf4∆.

      We thank the reviewer for the opportunity to clarify this aspect. We do not think that the fitness defects of ctf4∆ cells stem solely from the spurious upregulation of RNR1. However, we believe that a major aspect of the evolutionary adaptation is aimed at decreasing dNTP levels, potentially through different mechanisms. We are now mentionig increased dNTPs as major phenotype of ctf4∆ and commenting on the hypothesis more clearly in the discussion.

      Line 93: “These defects result in severe and persistent growth impairments, cell cycle delays, elevated nucleotides pools and chromosome instability (Miles and Formosa, 1992; Kouprina et al., 1992; Poli at al., 2012)”

      Line 409: “This condition will, in turn, be detrimental when proliferation rates are high (as in WT in high glucose) but beneficial under constitutive DNA replication stress (ctf4Δ), where cells experience spurious upregulation of dNTP production (Poli et al., 2012; Davidson et al., 2012).

      • In Figure 1E, there is a very large spread in the relative fitness at 2% and 8% glucose, but this was not commented on. Is this heteroscedasticity expected?

      The observed heteroscedasticity is expected. Our competition assays tend to exhibit increased variability when a strain approaches very low fitness levels. Specifically, as one strain nears extinction by the third day of competition, its abundance is estimated based on a much smaller number of events in the flow cytometer. Furthermore, we noticed a small number of reference cells carrying pACT1-yCerulean not showing strong fluorescence in 8% glucose. The nature of this effect is uncertain, and possibly linked to metabolism-linked changes in the cytoplasm. The combination of these two phenomena amplifies the impact of noise inherent to the methodology, leading to increased variability across replicates.

      Nontheless, the overall decreasing fitness trend across glucose conditions, combined with the statistical significance observed between high and low glucose levels, collectively convey a roboust phenotype

      • The med14-H919P mutant was highly selected in ctf4Δ strains, independent of glucose availability. Is this variant found in any natural yeast strains (i.e., are there environments that select for this variant)? Also, if this variant is found in natural strains, does it co-occur with other mutations that could affect DNA replication?

      We agree that this is an intriguing question. To address it, we plan to explore existing databases of variants identified in S. cerevisiae natural isolates. Specifically, we will investigate whether the med14-H919P mutation is present in these strains, identify any potential environmental factors that may select for it, and assess whether it co-occurs with other mutations that could influence DNA replication processes.

      • The statement on lines 271-273 is not particularly well-supported. The analysis of the Warfield data suggest that reduced expression of RNR1 could be causal, but the data don't go as far as showing how the med14 mutation is advantageous in ctf4∆. Further experimentation would be necessary to support the possibilities that the authors discuss.

      The sentence the reviewer refers to is: “Overall, these results show how an amino acid substitution in the Med14 subunit of the mediator complex, putatively affecting transcription, is strongly selected, and advantageous, in the presence of constitutive DNA replication stress.” We are unsure which aspect of the statement is seen as unsupported. The mutation's strong selection in ctf4∆ is demonstrated in Figures 5A, 6A, and S4C, while its advantageous nature is supported by Figures 5B and S4B. Regarding the mechanism, we have been cautious with our phrasing, describing its effect on transcription as "putative" (Line 272) and suggesting that our observations “are compatible with” reduced dNTP availability in med14-H919P cells due to RNR1 downregulation (Line 361).

      The main focus of this study is to explore how nutrient availability influences evolutionary dynamics and compensatory adaptation in cells lacking Ctf4. We believe the identification of a novel selected allele (Fig. 5A) and confirmation of its benefit across glucose conditions (Fig. 5B) serves as an excellent complement to the primary conclusions (present in the title). We invite the reviewer to consider that the molecular basis of such a phenotype is not mentioned in our abstract, as we believe that its precise characterization would require a dedicated study on Med14.

      Nonetheless, we are encouraged by the reviewer’s interest in this newly identified compensatory mutant (also noted by Reviewer #2), and we are eager to perform further experiments to better understand the biological processes affected by this mutation. We plan to extend our work as follows:

      Based on known phenotypes associated with perturbations of Med14, we propose the following novel hypotheses regarding the mechanism by which med14-H919P alleviates ctf4Δ defects:

      1. Decreased replication-transcription conflicts: Conflicts between the transcription machinery and replication forks are known to cause fragile sites, leading to increased chromosome breaks and genomic instability (Garcia-Muse and Aguilera, 2016). A general reduction in PolII transcription during replication, resulting from perturbations of the mediator complex, could reduce these conflicts and mitigate the fitness defects observed in ctf4Δ cells.
      2. Increased cohesin loading: We have demonstrated that amplification of the cohesin loader SCC2 is beneficial in the absence of Ctf4. Recent findings (Mattingly et al., 2022) indicate that the mediator complex recruits SCC2 to PolII-transcribed genes. The med14-H919P mutation may enhance the fitness of ctf4Δ cells by facilitating cohesin loading during DNA replication.
      3. Decreased dNTP levels: As discussed in the manuscript, perturbations of Med14 subunits in the mediator complex reduce the expression of genes, including those associated with nucleotide metabolism. Notably, these include RNR1, the major subunit of ribonucleotide reductase. The med14-H919P mutation could benefit the ctf4Δ background by counteracting the reported spurious increase in dNTPs, which affects replication fork speed (Poli et al., 2012).

      We plan to distinguish between these hypotheses using the following approaches. First, the proposed mechanisms underlying Hypotheses 1 and 3 suggest that med14-H919P is a loss-of-function mutation, while Hypothesis 2 implies a gain-of-function effect. Testing the impact of a heterozygous med14-H919P allele in a homozygous ctf4Δ strain will allow us to differentiate between these two categories of mechanisms. Additionally, we aim to investigate the molecular process affected by the med14-H919P allele by analyzing its genetic interactions with genes involved in replication-transcription conflicts, cohesin loading, and dNTP production (See also response to reviewer #2).

      We believe that the results of these experiments will provide further insights on the mechanism of suppression exerted by med14-H919P in the presence of constitutive DNA replication stress, without diverting the reader from the main message of the paper.

      • The authors comment that the med14-H919P mutant could have implications for the stability of Med14, based on computational modelling. Verifying the stability of the med14-H919P in vivo would strengthen this discussion.

      We believe that in vivo and in vitro structural studies investigating the effect of this mutation on the stability and function of the Mediator complex are beyond the scope of this manuscript. These investigations would be more appropriately addressed in future, dedicated studies focused on these specific aspects.

      • In the discussion, the authors propose that the context of the perturbation may influence the robustness of adaptation. A more detailed explanation of this point (including a discussion of the findings of other similar studies investigating different conditions) would be helpful to further bolster this section.

      We are now supporting this concept more explicitly by commenting on other studies as follows:

      Line 429: “Third, the environment’s influence on compensatory evolution may depend on the specific cellular module perturbed and its genetic interactions with other modules that are significantly influenced by environmental conditions. For example, the actin cytoskeleton, which must rapidly respond to extracellular stimuli, is likely to be more directly influenced by environmental factors (Filateau et al., 2015) compared to the DNA replication machinery, which operates within the nucleus and is relatively insulated from such changes. Supporting this idea, a study examining mutants’ fitness across diverse environments found that conditions such as different carbon sources or TOR inhibition, similar to those used in this study, primarily affected genes involved in vesicle trafficking, transcription, protein metabolism, and cell polarity. In contrast, genes associated with genome maintenance, as well as their epistatic interactions, were largely unaffected (Costanzo et al., 2021)”.

      In addition, to further substantiate this hypothesis, we plan to re-analyze published datasets on fitness and epistatic interactions among genes in various environments, testing whether specific cellular modules are more prone to changes following shifts in nutrient conditions.

      Minor comments: - Competitions were performed between ctf4Δ strains and a constructed strain with yCerulean integrated at ACT1. Is the fitness of the fluorescent strain comparable to the ancestral wild-type strain (i.e., in a competition between the ancestral WT and the fluorescent strain, does either have an advantage)?

      We noticed a slight disadvantage of the reference strain compare to WT, likely due to the costs of the extra fluorescence reporter. However, the disadvantage is minimal, ranging from -0.5 to -2.5 depending on the glucose environment (raw measurments are reported supplementary file 1, sheet 5). To take this into account, all fitness reported in figures are normalized for the WT value measured in the same environment line 613: “Relative fitness of the ancestral WT strain was used to normalize fitness across conditions.​​”

      • In Figure 3, the legends for panels B and C appear to be swapped. Discussion of Figure 3 on pages 6 and 7 appear to reference the wrong panels.

      We are unsure about this typo. Main text and figure legend seem to refer to the appropriate panels, 3B for mutation fractions and 3C for mutation counts. Perhaps the organization of the panels with B being under A instead of on its right confounds the reader?

      • In Figure 4A and B, having the same colour scale between both heatmaps is misleading, as the scales are different. Consider having the same scale across both heatmaps so that enrichments are visually comparable.

      Following the reviewer’s suggestion we have have chosen a uniform heatmap to visually represent GO terms enrichment in WT and ctf4∆ genetic backgrounds.

      • In Figure 4C, having a legend in the figure for node size would be helpful to understand the actual number of populations with mutations in each gene.

      A legend for node size has now being added next to Figure 4C.

      Reviewer #4 (Significance):

      In this study, a high-throughput evolution experiment uncovered the effects of genetic background on the development of adaptive mutations. The authors were able to identify a single amino acid variant of Med14 (med14-H919P) that was positively selected in ctf4Δ. Furthermore, they demonstrated the causality of med14-H919P in conferring a fitness advantage in ctf4Δ. The novelty of this mechanistic finding opens future avenues of investigation regarding the interaction network of the mediator complex in conditions of DNA replication stress. A limitation of the study is that only one mechanism of replication stress was assessed (ctf4Δ). Other gene mutations that cause replication stress would be interesting to assess and would provide a more thorough investigation of the effects of DNA replication factors on evolvability. This work will be of interest to researchers in the population genetics and genotype-by-environment fields, as it suggests the robustness of evolvability to environmental factors in the specific condition of DNA replication stress. As discussed by the authors, this finding differs from other works that have linked environmental conditions to adaptive evolution to different conditions, and is concordant with work that indicates the robustness of genetic interactions to environmental stresses. Furthermore, the identification of the highly-selected med14-H919P variant will be of interest to the DNA replication field. There is the potential for future work investigating the role of Med14 in mediating the response to DNA replication stress in both yeast and mammalian cell contexts, since the authors note that there are links between altered mediator complex regulation and cancers. Although I suspect that the very different regulation of RNR in mammalian cells makes it unlikely that the kind of upregulation of dNTP pools seen in ctf4∆ would be induced by replication stress in mammalian cells.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary: 

      The authors investigated causal inference in the visual domain through a set of carefully designed experiments, and sound statistical analysis. They suggest the early visual system has a crucial contribution to computations supporting causal inference. 

      Strengths: 

      I believe the authors target an important problem (causal inference) with carefully chosen tools and methods. Their analysis rightly implies the specialization of visual routines for causal inference and the crucial contribution of early visual systems to perform this computation. I believe this is a novel contribution and their data and analysis are in the right direction. 

      Weaknesses: 

      In my humble opinion, a few aspects deserve more attention: 

      (1) Causal inference (or causal detection) in the brain should be quite fundamental and quite important for human cognition/perception. Thus, the underlying computation and neural substrate might not be limited to the visual system (I don't mean the authors did claim that). In fact, to the best of my knowledge, multisensory integration is one of the best-studied perceptual phenomena that has been conceptualized as a causal inference problem.

      Assuming the causal inference in those studies (Shams 2012; Shams and Beierholm 2022;

      Kording et al. 2007; Aller and Noppeney 2018; Cao et al. 2019) (and many more e.g., by Shams and colleagues), and the current study might share some attributes, one expects some findings in those domains are transferable (at least to some degree) here as well. Most importantly, underlying neural correlates that have been suggested based on animal studies and invasive recording that has been already studied, might be relevant here as well.

      Perhaps the most relevant one is the recent work from the Harris group on mice (Coen et al. 2021). I should emphasize, that I don't claim they are necessarily relevant, but they can be relevant given their common roots in the problem of causal inference in the brain. This is a critical topic that the authors may want to discuss in their manuscript. 

      We thank the reviewer. We addressed this point of the public review in our reply to the reviewer’s suggestions (and add it here again for convenience). The literature on the role of occipital, parietal and frontal brain areas in causal inference is also addressed in the response to point 3 of the public review.

      “We used visual adaptation to carve out a bottom-up visual routine for detecting causal interactions in form of launching events. However, we know that more complex behaviors of perceiving causal relations can result from integrating information across space (e.g., in causal capture; Scholl & Nakayama, 2002), across time (postdictive influence; Choi & Scholl, 2006), and across sensory modalities (Sekuler, Sekuler, & Lau, 1997). Bayesian causal inference has been particularly successful as a normative framework to account for multisensory integration (Körding et al., 2007; Shams & Beierholm, 2022). In that framework, the evidence for a common-cause hypothesis is competing with the evidence for an independent-causes hypothesis (Shams & Beierholm, 2022). The task in our experiments could be similarly formulated as two competing hypotheses for the second disc’s movement (i.e., the movement was caused by the first disc vs. the movement occurred autonomously). This framework also emphasizes the distributed nature of the neural implementation for solving such inferences, showing the contributions of parietal and frontal areas in addition to sensory processing (for review see Shams & Beierholm, 2022). Moreover, even visual adaptation to contrast in mouse primary visual cortex is influenced by top-down factors such as behavioral relevance— suggesting a complex implementation of the observed adaptation results (Keller et al. 2017). The present experiments, however, presented purely visual events that do not require an integration across processing domains. Thus, the outcome of our suggested visual routine can provide initial evidence from within the visual system for a causal relation in the environment that may then be integrated with signals from other domains (e.g., auditory signals). Determining exactly how the perception of causality relates to mechanisms of causal inference and the neural implementation thereof is an exciting avenue for future research. Note, however, that perceived causality can be distinguished from judged causality: Even when participants are aware that a third variable (e.g., a color change) is the best predictor of the movement of the second disc in launching events, they still perceive the first disc as causing the movement of the second disc (Schlottmann & Shanks, 1992).”

      (2) If I understood correctly, the authors are arguing pro a mere bottom-up contribution of early sensory areas for causal inference (for instance, when they wrote "the specialization of visual routines for the perception of causality at the level of individual motion directions raises the possibility that this function is located surprisingly early in the visual system *as opposed to a higher-level visual computation*."). Certainly, as the authors suggested, early sensory areas have a crucial contribution, however, it may not be limited to that. Recent studies progressively suggest perception as an active process that also weighs in strongly, the topdown cognitive contributions. For instance, the most simple cases of perception have been conceptualized along this line (Martin, Solms, and Sterzer 2021) and even some visual illusion (Safavi and Dayan 2022), and other extensions (Kay et al. 2023). Thus, I believe it would be helpful to extend the discussion on the top-down and cognitive contributions of causal inference (of course that can also be hinted at, based on recent developments). Even adaptation, which is central in this study can be influenced by top-down factors (Keller et al. 2017). I believe, based on other work of Rolfs and colleagues, this is also aligned with their overall perspective on vision.  

      Indeed, we assessed bottom-up contributions to the perception of a causal relation. We agree with the reviewer that in more complex situations, for instance, in the presence of contextual influences or additional auditory signals, the perception of a causal relation may not be limited to bottom-up vision. While we had acknowledged this in the original manuscript (see excerpts below), we now make it even more explicit:

      “[…] we know that more complex behaviors of perceiving causal relations can result from integrating information across space (e.g., in causal capture; Scholl & Nakayama, 2002), across time (postdictive influence; Choi & Scholl, 2006), and across sensory modalities (Sekuler, Sekuler, & Lau, 1997).”

      “[…] Neurophysiological studies support the view of distributed neural processing underlying sensory causal interactions with the visual system playing a major role.”

      “[…] Interestingly, single cell recordings in area F5 of the primate brain revealed that motor areas are contributing to the perception of causality (Caggiano et al., 2016; Rolfs, 2016), emphasizing the distributed nature of the computations underlying causal interactions. This finding also stresses that the detection, and the prediction, of causality is essential for processes outside sensory systems (e.g., for understanding other’s actions, for navigating, and for avoiding collisions). The neurophysiology subserving causal inference further extend the candidate cortical areas that might contibute to the detection of causal relations, emphasizing the role of the frontal cortex for the flexible integration of multisensory representations (Cao et al., 2019; Coen et al., 2023).”

      However, there is also ample evidence that the perception of a simple causal relation—as we studied it in our experiments—escapes top-down cognitive influences. The perception of causality in launching events is described as automatic and irresistible, meaning that participants have the spontaneous impression of a causal relation, and participants typically do not voluntarily switch between a causal and a noncausal percept. This irresistibility has led several authors to discuss a modular organization underlying the detection of such events (Michotte, 1963; Scholl & Tremoulet, 2000). This view is further supported by a study that experimentally manipulated the contingencies between the movement of the two discs (Schlottmann & Shanks, 1992). In one condition the authors created a launching event where the second disc’s movement was perfectly correlated with a color change, but only sometimes coincided with the first disc’s movement offset. Nevertheless, participants reported seeing that the first disc caused the movement of second disc (regardless of the stronger statistical relationship with the color change). However, when asked to make conscious causal judgments, participants were aware of the color change as the true cause of the second disc’s motion—therefore recognizing its more reliable correlation. This study strongly suggests that perceived and judged causality (i.e., cognitive causal inference) can be dissociated (Schlottmann & Shanks, 1992). We have added this reference in the revised manuscript. Overall, we argue that our study focused on a visual routine that could be implemented in a simple bottom-up fashion, but we acknowledge throughout the manuscript, that in a more complex situation (e.g., integrating information from other sensory domains) the implementation could be realized in a more distributed fashion including top-down influences as in multisensory integration. However, it is important to stress that these potential top-down influences would be automatic and should not be confused with voluntary cognitive influences.

      “Note, however, that perceived causality can be distinguished from judged causality (Schlottmann & Shanks, 1992). Even when participants are aware that a third variable (e.g., a color change) is the best predictor of the movement of the second disc in launching events, they still perceive the first disc as causing the movement of the second disc (Schlottmann & Shanks, 1992).”

      (3) The authors rightly implicate the neural substrate of causal inference in the early sensory system. Given their study is pure psychophysics, a more elaborate discussion based on other studies that used brain measurements is needed (in my opinion) to put into perspective this conclusion. In particular, as I mentioned in the first point, the authors mainly discuss the potential neural substrate of early vision, however much has been done about the role of higher-tier cortical areas in causal inference e.g., see (Cao et al. 2019; Coen et al. 2021). 

      In the revised manuscript, we addressed the limitations of a purely psychophysical approach and acknowledged alternative implementations in the Discussion section.

      “Note that, while the present findings demonstrate direction-selectivity, it remains unclear where exactly that visual routine is located. As pointed out, it is also possible that the visual routine is located higher up in the visual system (or distributed across multiple levels) and is only using a directional-selective population response as input.”

      Moreover, we cite also the two suggested papers when referring to the role of cortical areas in causal inference (Cao et al, 2019; Coen et al., 2023):

      “Neurophysiological studies support the view of distributed neural processing underlying sensory causal interactions with the visual system playing a major role. Imaging studies in particular revealed a network for the perception of causality that is also involved in action observation (Blakemore et al., 2003; Fonlupt, 2003; Fugelsang et al., 2005; Roser et al., 2005). The fact that visual adaptation of causality occurs in a retinotopic reference frame emphazises the role of retinotopically organized areas within that network (e.g., V5 and the superior temporal sulcus). Interestingly, single cell recordings in area F5 of the primate brain revealed that motor areas are contributing to the perception of causality (Caggiano et al., 2016; Rolfs, 2016), emphasizing the distributed nature of the computations underlying causal interactions, and also stressing that the detection, and the prediction, of causality is essential for processes outside purely sensory systems (e.g., for understanding other’s actions, for navigating, and for avoiding collisions). The neurophysiological underpinnings in causal inference further extend the candidate cortical areas that might contibute to the detection of causal relations, emphasizing the role of the frontal cortex for the flexible integration of multisensory representations (Cao et al., 2019; Coen et al., 2023).”

      There were many areas in this manuscript that I liked: clever questions, experimental design, and statistical analysis.

      Thank you so much.

      Reviewer #1 (Recommendations for the authors):

      I congratulate the authors again on their manuscript and hope they will find my review helpful. Most of my notes are suggestions to the authors, and I hope will help them to improve the manuscript. None are intended to devalue their (interesting) work. 

      We would like to thank the reviewer for their thoughtful and encouraging comments.

      In the following, I use pX-lY template to refer to a particular page number, say page number X (pX), and line number, say line number Y (lY). 

      Major concerns and suggestions 

      - I would suggest simplifying the abstract and significance statement or putting more background in it. It's hard (at least for me) to understand if one is not familiar with the task used in this study. 

      We followed the reviewer’s suggestion and added more background in the beginning of the abstract. 

      We made the following changes:

      “Detecting causal relations structures our perception of events in the world. Here, we determined for visual interactions whether generalized (i.e., feature-invariant) or specialized (i.e., feature-selective) visual routines underlie the perception of causality. To this end, we applied a visual adaptation protocol to assess the adaptability of specific features in classical launching events of simple geometric shapes. We asked observers to report whether they observed a launch or a pass in ambiguous test events (i.e., the overlap between two discs varied from trial to trial). After prolonged exposure to causal launch events (the adaptor) defined by a particular set of features (i.e., a particular motion direction, motion speed, or feature conjunction), observers were less likely to see causal launches in subsequent ambiguous test events than before adaptation. Crucially, adaptation was contingent on the causal impression in launches as demonstrated by a lack of adaptation in non-causal control events. We assessed whether this negative aftereffect transfers to test events with a new set of feature values that were not presented during adaptation. Processing in specialized (as opposed to generalized) visual routines predicts that the transfer of visual adaptation depends on the feature-similarity of the adaptor and the test event. We show that negative aftereffects do not transfer to unadapted launch directions but do transfer to launch events of different speed. Finally, we used colored discs to assign distinct feature-based identities to the launching and the launched stimulus. We found that the adaptation transferred across colors if the test event had the same motion direction as the adaptor. In summary, visual adaptation allowed us to carve out a visual feature space underlying the perception of causality and revealed specialized visual routines that are tuned to a launch’s motion direction.”

      - The authors highlight the importance of studying causal inference and understanding the underlying mechanisms by probing adaptation, however, their introduction justifying that is, in my humble opinion, quite short. Perhaps in the cited paper, this is discussed extensively, but I'd suggest providing some elaboration in the manuscript. Otherwise, the study would be very specific to certain visual phenomena, rather than general mechanisms.  

      We have carefully considered the reviewer’s set of comments and concerns (e.g., the role of top-down influences, the contributions of the frontal cortex, and illustration of the computational level). They all appear to share the theme that the reviewer looks at our study from the perspective of Bayesian inference. We conducted the current study in the tradition of classical phenomena in the field of the perception of causality (in the tradition of Michotte, 1963 and as reviewed in Scholl & Tremoulet, 2000) which aims to uncover the relevant visual parameters and rules for detecting causal relations in the visual domain. Indeed, we think that a causal inference perspective promises a lot of new insights into the mechanisms underlying the classical phenomena described for the perception of causality. In the revised manuscript, we discuss therefore causal inference and how it relates to the current study. We now emphasize that in our study, a) we used visual adaptation to reveal the bottom-up processes that allow for the detection of a causal interaction in the visual domain, b) that the perception of causality also integrates signals from other domains (which we do not study here), and c) that the neural substrates underlying the perception of causality might be best described by a distributed network. By discussing Bayesian causal inference, we point out promising avenues for future research that may bridge the fields of the perception of causality and Bayesian causal inference. However, we also emphasize that perceived causality and judged causality can be dissociated (Schlottmann & Shanks, 1992).

      We added the following discussion:

      “We used visual adaptation to carve out a bottom-up visual routine for detecting causal interactions in form of launching events. However, we know that more complex behaviors of perceiving causal relations can result from integrating information across space (e.g., in causal capture; Scholl & Nakayama, 2002), across time (postdictive influence; Choi & Scholl, 2006), and across sensory modalities (Sekuler, Sekuler, & Lau, 1997). Bayesian causal inference has been particularly successful as a normative framework to account for multisensory integration (Körding et al., 2007; Shams & Beierholm, 2022). In that framework, the evidence for a common-cause hypothesis is competing with the evidence for an independent-causes hypothesis (Shams & Beierholm, 2022). The task in our experiments could be similarly formulated as two competing hypotheses for the second disc’s movement (i.e., the movement was caused by the first disc vs. the second disc did not move). This framework also emphasizes the distributed nature of the neural implementation for solving such inferences, showing the contributions of parietal and frontal areas in addition to sensory processing (for review see Shams & Beierholm, 2022). Moreover, even visual adaptation to contrast in mouse primary visual cortex is influenced by top-down factors such as behavioral relevance— suggesting a complex implementation of the observed adaptation results (Keller et al. 2017). The present experiments, however, presented purely visual events that do not require an integration across processing domains. Thus, the outcome of our suggested visual routine can provide initial evidence from within the visual system for a causal relation in the environment that may then be integrated with signals from other domains (e.g., auditory signals). Determining exactly how the perception of causality relates to mechanisms of causal inference and the neural implementation thereof is an exciting avenue for future research. Note, however, that perceived causality can be distinguished from judged causality: Even when participants are aware that a third variable (e.g., a color change) is the best predictor of the movement of the second disc in launching events, they still perceive the first disc as causing the movement of the second disc (Schlottmann & Shanks, 1992).”

      - I'd suggest, at the outset, already set the context, that your study of causal inference in the brain is specifically targeting the visual domain, if you like, in the discussion connect it  better to general ideas about causal inference in the brain (like the works by Ladan Shams and colleagues). 

      We would like to thank the reviewer for this comment. We followed the reviewer’s suggestion and made clear from the beginning that this paper is about the detection of causal relations in the visual domain. In the revised manuscript we write:

      “Here, we will study the mechanisms underlying the computations of causal interactions in the visual domain by capitalizing on visual adaptation of causality (Kominsky & Scholl, 2020; Rolfs et al., 2013). Adaptation is a powerful behavioral tool for discovering and dissecting a visual mechanism (Kohn, 2007; Webster, 2015) that provides an intriguing testing ground for the perceptual roots of causality.”

      As described in our reply to the previous comment, we now also discussed the ideas about causal inference.

      - To better illustrate the implication of your study on the computational level, I'd suggest putting it in the context of recent approaches to perception (point 2 of my public review). I think this is also aligned with the comment of Reviewer#3 on your line 32 (recommendation for authors).  

      In the revised manuscript, we now discuss the role of top-down influences in causal inference when addressing point 2 of the reviewer’s public review.

      Minor concerns and suggestions 

      - On p2-l3, I'd suggest providing a few examples for generalized and or specialized visual routines (given the importance of the abstract). I only got it halfway through the introduction. 

      We thank the reviewer for highlighting the need to better introduce the concept of a visual routine. We have chosen the term visual routine to emphasize that we locate the part of the mechanism that is affected by the adaptation in our experiments in the visual system. At the same time, the concept leaves space with respect to the extent to which the mechanism further involves mid- and higher-level processes. In the revised manuscript, we now refer to Ullman (1987) who introduced the concept of a visual routine—the idea of a modular operation that sequentially processes spatial and feature information. Moreover, we refer to the concept of attentional sprites (Cavanagh, Labianca, & Thornton, 2001)—attention-based visual routines that allow the visual system to semi-independently handle complex visual tasks (e.g., identifying biological motion).

      We add the following footnote to the introduction:

      “We use the term visual routine here to highlight that our adaptation experiments can reveal a causality detection mechanism that resides in the visual system. At the same time, calling it a routine emphasizes similarities with a local, semi-independent operation (e.g., the recognition of familiar motion patterns; see also Ullman, 1987; Cavanagh, Labianca, & Thornton, 2001) that can engage mid- and higher-level processes (e.g., during causal capture, Scholl & Nakayama, 2002; or multisensory integration, Körding et al., 2007).”

      In the abstract we now write:

      “Here, we determined for visual interactions whether generalized (i.e., feature-invariant) or specialized (i.e., feature-selective) visual routines underlie the perception of causality.”

      - On p4-l31, I'd suggest mentioning the Matlab version. I have experienced differences across different versions of Matlab (minor but still ...). 

      We added the Matlab Version.

      - On p6-l46 OSF-link is missing (that contains data and code). 

      Thank you. We made the OSF repository public and added the link to the revised manuscript.

      We added the following information to the revised manuscript.

      “The data analysis code has been deposited at the Open Science Framework and is publicly available https://osf.io/x947m/.”

      Reviewer #2 (Public Review):

      This paper seeks to determine whether the human visual system's sensitivity to causal interactions is tuned to specific parameters of a causal launching event, using visual adaptation methods. The three parameters the authors investigate in this paper are the direction of motion in the event, the speed of the objects in the event, and the surface features or identity of the objects in the event (in particular, having two objects of different colors). The key method, visual adaptation to causal launching, has now been demonstrated by at least three separate groups and seems to be a robust phenomenon. Adaptation is a strong indicator of a visual process that is tuned to a specific feature of the environment, in this case launching interactions. Whereas other studies have focused on retinotopically specific adaptation (i.e., whether the adaptation effect is restricted to the same test location on the retina as the adaptation stream was presented to), this one focuses on feature specificity. 

      The first experiment replicates the adaptation effect for launching events as well as the lack of adaptation event for a minimally different non-causal 'slip' event. However, it also finds that the adaptation effect does not work for launching events that do not have a direction of motion more than 30 degrees from the direction of the test event. The interpretation is that the system that is being adapted is sensitive to the direction of this event, which is an interesting and somewhat puzzling result given the methods used in previous studies, which have used random directions of motion for both adaptation and test events. 

      The obvious interpretation would be that past studies have simply adapted to launching in every direction, but that in itself says something about the nature of this direction-specificity: it is not working through opposed detectors. For example, in something like the waterfall illusion adaptation effect, where extended exposure to downward motion leads to illusory upward motion on neutral-motion stimuli, the effect simply doesn't work if motion in two opposed directions is shown (i.e., you don't see illusory motion in both directions, you just see nothing). The fact that adaptation to launching in multiple directions doesn't seem to cancel out the adaptation effect in past work raises interesting questions about how directionality is being coded in the underlying process. 

      We would like to thank the reviewer for that thoughtful comment. We added the described implication to the manuscript:

      “While the present study demonstrates direction-selectivity for the detection of launches, previous adaptation protocols demonstrated successful adaptation using adaptors with random motion direction (Rolfs et al., 2013; Kominsky & Scholl, 2020). These results therefore suggest independent direction-specific routines, in which adaptation to launches in one direction does not counteract an adaptation to launches in the opposite direction (as for example in opponent color coding).”

      In addition, one limitation of the current method is that it's not clear whether the motion direction-specificity is also itself retinotopically-specific, that is, if one retinotopic location were adapted to launching in one direction and a different retinotopic location adapted to launching in the opposite direction, would each test location show the adaptation effect only for events in the direction presented at that location? 

      This is an interesting idea! Because previous adaptation studies consistently showed retinotopic adaptation of causality, we would not expect to find transfer of directional tuning for launches to other locations. We agree that the suggested experiment on testing the reference frame of directional specificity constitutes an interesting future test of our findings.

      The second experiment tests whether the adaptation effect is similarly sensitive to differences in speed. The short answer is no; adaptation events at one speed affect test events at another. Furthermore, this is not surprising given that Kominsky & Scholl (2020) showed adaptation transfer between events with differences in speeds of the individual objects in the event (whereas all events in this experiment used symmetrical speeds). This experiment is still novel and it establishes that the speed-insensitivity of these adaptation effects is fairly general, but I would certainly have been surprised if it had turned out any other way. 

      We thank the reviewer for highlighting the link to an experiment reported in Kominsky & Scholl (2020). We report the finding of that experiment now in the revised manuscript.

      We added the following paragraph in the discussion:

      “For instance, we demonstrated a transfer of adaptation across speed for symmetrical speed ratios. This result complements a previous finding that reported that the adaptation to triggering events (with an asymmetric speed ratio of 1:3) resulted in significant retinotopic adaptation of ambiguous (launching) test events of different speed ratios (i.e., test events with a speed ratio of 1:1 and of 1:3; Kominsky & Scholl, 2020).”

      The third experiment tests color (as a marker of object identity), and pits it against motion direction. The results demonstrate that adaptation to red-launching-green generates an adaptation effect for green-launching-red, provided they are moving in roughly the same direction, which provides a nice internal replication of Experiment 1 in addition to showing that the adaptation effect is not sensitive to object identity. This result forms an interesting contrast with the infant causal perception literature. Multiple papers (starting with Leslie & Keeble, 1987) have found that 6-8-month-old infants are sensitive to reversals in causal roles exactly like the ones used in this experiment. The success of adaptation transfer suggests, very clearly, that this sensitivity is not based only on perceptual processing, or at least not on the same processing that we access with this adaptation procedure. It implies that infants may be going beyond the underlying perceptual processes and inferring genuine causal content. This is also not the first time the adaptation paradigm has diverged from infant findings: Kominsky & Scholl (2020) found a divergence with the object speed differences as well, as infants categorize these events based on whether the speed ratio (agent:patient) is physically plausible (Kominsky et al., 2017), while the adaptation effect transfers from physically implausible events to physically plausible ones. This only goes to show that these adaptation effects don't exhaustively capture the mechanisms of early-emerging causal event representation. 

      We would like to thank the reviewer for highlighting the similarities (and differences) to the seminal study by Leslie and Keeble (1987). We included a discussion with respect to that paper in the revised manuscript. Indeed, that study showed a recovery from habituation to launches after reversal of the launching events. In their study, the reversal condition resulted in a change of two aspects, 1) motion direction and 2) a change of what color is linked to either cause (i.e., agent) or effect (i.e, patient). Our study, based on visual adaptation in adults, suggests that switching the two colors is not necessary for a recovery from the habituation, provided the motion direction is reversed. Importantly, the reversal of the motion direction only affected the perception of causality after adapting to launches (but not to slip events), which is consistent with Leslie and Keeble’s (1987) finding that the effect of a reversal is contingent on habituation/adaptation to a causal relationship (and is not observed for non-causal delayed launches). Based on our findings, we predict that switching colors without changing the event’s motion direction would not result in a recovery from habituation. Obviously, for infants, color may play a more important role for establishing an object identity than it does for adults, which could explain potential differences. We also agree with the reviewer’s point that the adaptation protocol might tap into different mechanisms than revealed by habituation studies in infants (e.g, Kominsky et al., 2017 vs. Kominsky & Scholl, 2020). 

      We revised the manuscript accordingly when discussing the role of direction selectivity in our study:

      “Habituation studies in six-months-old infants also demonstrated that the reversal of a launch resulted in a recovery from habituation to launches (while a non-causal control condition of delayed-launches did not; Leslie & Keeble, 1987). In their study, the reversal of motion direction was accompanied by a reversal of the color assignment to the cause-effectrelationship. In contrast, our findings suggest, that in adults color does not play a major role in the detection of a launch. Future studies should further delineate similarities and differences obtained from adaptation studies in adults and habituation studies in children (e.g., Kominsky et al., 2017; Kominsky & Scholl, 2020).”

      One overarching point about the analyses to take into consideration: The authors use a Bayesian psychometric curve-fitting approach to estimate a point of subjective equality (PSE) in different blocks for each individual participant based on a model with strong priors about the shape of the function and its asymptotic endpoints, and this PSE is the primary DV across all of the studies. As discussed in Kominsky & Scholl (2020), this approach has certain limitations, notably that it can generate nonsensical PSEs when confronted with relatively extreme response patterns. The authors mentioned that this happened once in Experiment 3 and that a participant had to be replaced. An alternate approach is simply to measure the proportion of 'pass' reports overall to determine if there is an adaptation effect. I don't think this alternate analysis strategy would greatly change the results of this particular experiment, but it is robust against this kind of self-selection for effects that fit in the bounds specified by the model, and may therefore be worth including in a supplemental section or as part of the repository to better capture the individual variability in this effect. 

      We largely agree with these points. Indeed, we adopted the non-parametric analysis for a recent series of experiments in which the psychometric curves were more variable (Ohl & Rolfs, Vision Sciences Society Meeting 2024). In the present study, however, the model fits were very convincing. In Figures S1, S2 and S3 we show the model fits for each individual observer and condition on top of the mean proportion of launch reports. The inferential statistics based on the points of subjective equality, therefore, allowed us to report our findings very concisely.

      In general, this paper adds further evidence for something like a 'launching' detector in the visual system, but beyond that, it specifies some interesting questions for future work about how exactly such a detector might function. 

      We thank the reviewer for this positive overall assessment.

      Reviewer #2 (Recommendations for the authors):

      Generally, the paper is great. The questions I raised in the public review don't need to be answered at this time, but they're exciting directions for future work. 

      We would like to thank the reviewer for the encouraging comments and thoughtful ideas on how to improve the manuscript.

      I would have liked to see a little more description of the model parameters in the text of the paper itself just so readers know what assumptions are going into the PSE estimation. 

      We followed the reviewer’s suggestion and added more information regarding the parameter space (i.e., ranges of possible parameters of the logistic model) that we used for obtaining the model fits. 

      Specifically, we added the following information in the manuscript:

      “For model fitting, we constrained the range of possible estimates for each parameter of the logistic model. The lower asymptote for the proportion of reported launches was constrained to be in the range 0–0.75, and the upper asymptote in the range 0.25–1. The intercept of the logistic model was constrained to be in the range 1–15, and the slope was constrained to be in the range –20 to –1.”

      The models provided very good fits as can be appreciated by the fits per individual and experimental condition which we provide in response to the public comments. Please note, that all data and analysis scripts are available at the Open Science Framework (https://osf.io/x947m/).

      I also have a recommendation about Figure 1b: Color-code "Feature A", "Feature B", and "Feature C" and match those colors with the object identity/speed/direction text. I get what the figure is trying to convey but to a naive reader there's a lot going on and it's hard to interpret. 

      We followed the reviewer’s suggestion and revised the visualization accordingly.

      If you have space, figures showing the adaptation and corresponding test events for each experimental manipulation would also be great, particularly since the naming scheme of the conditions is (necessarily) not entirely consistent across experiments. It would be a lot of little figures, I know, but to people who haven't spent as long staring at these displays as we have, they're hard to envision based on description alone. 

      We followed the reviewer’s recommendation and added a visualization of the adaptor and the test events for the different experiments in Figure 2.

      Reviewer #3 (Public Review):

      We thank the reviewer for their thoughtful comments, which we carefully addressed to improve the revised manuscript. 

      Summary: 

      This paper presents evidence from three behavioral experiments that causal impressions of "launching events", in which one object is perceived to cause another object to move, depending on motion direction-selective processing. Specifically, the work uses an adaptation paradigm (Rolfs et al., 2013), presenting repetitive patterns of events matching certain features to a single retinal location, then measuring subsequent perceptual reports of a test display in which the degree of overlap between two discs was varied, and participants could respond "launch" or "pass". The three experiments report results of adapting to motion direction, motion speed, and "object identity", and examine how the psychometric curves for causal reports shift in these conditions depending on the similarity of the adapter and test. While causality reports in the test display were selective for motion direction (Experiment 1), they were not selective for adapter-test speed differences (Experiment 2) nor for changes in object identity induced via color swap (Experiment 3). These results support the notion that causal perception is computed (in part) at relatively early stages of sensory processing, possibly even independently of or prior to computations of object identity. 

      Strengths: 

      The setup of the research question and hypotheses is exceptional. The experiments are carefully performed (appropriate equipment, and careful control of eye movements). The slip adaptor is a really nice control condition and effectively mitigates the need to control motion direction with a drifting grating or similar. Participants were measured with sufficient precision, and a power curve analysis was conducted to determine the sample size. Data analysis and statistical quantification are appropriate. Data and analysis code are shared on publication, in keeping with open science principles. The paper is concise and well-written. 

      Weaknesses: 

      The biggest uncertainty I have in interpreting the results is the relationship between the task and the assumption that the results tell us about causality impressions. The experimental logic assumes that "pass" reports are always non-causal impressions and "launch" reports are always causal impressions. This logic is inherited from Rolfs et al (2013) and Kominsky & Scholl (2020), who assert rather than measure this. However, other evidence suggests that this assumption might not be solid (Bechlivanidis et al., 2019). Specifically, "[our experiments] reveal strong causal impressions upon first encounter with collision-like sequences that the literature typically labels "non-causal"" (Bechlivanidis et al., 2019) -- including a condition that is similar to the current "pass". It is therefore possible that participants' "pass" reports could also involve causal experiences. 

      We agree with the reviewer that our study assumes that the launch-pass dichotomy can be mapped onto a dimension of causal to non-causal impressions. Please note that the choice for this launch-pass task format was intentional. We consider it an advantage that subjects do not have to report causal vs non-causal impressions directly, as it allows us to avoid the oftencriticized decision biases that come with asking participants about their causal impression (Joynson, 1971; for a discussion see Choi & Scholl, 2006). This comes obviously at the cost that participants did not directly report their causal impression in our experiments. There is however evidence that increasing overlap between the discs monotonically decreases the causal impression when directly asking participants to report their causal impression (Scholl & Nakayama, 2004). We believe, therefore, that the assumption of mapping between launchesto-passes and causal-to-noncausal is well-justified. At the same time, the expressed concern emphasizes the need to develop further, possibly implicit measure for causal impressions (see Völter & Huber, 2021).

      However, as pointed out by the reviewer, a recent paper demonstrated that on first encounter participants can have impressions in response to a pass event that are different from clearly non-causal impressions (Bechlivanidis et al., 2019). As demonstrated in the same paper, displaying a canonical launch decreased the impression of causality when seeing pass events in subsequent trials. In our study, participants completed an entire training session before running the main experiments. It is therefore reasonable to expect that participants observed passes as non-causal events given the presence of clear causal references. Nevertheless, we now acknowledge this concern directly in the revised manuscript.

      We added the following paragraph to the discussion:

      “In our study, we assessed causal perception by asking observers to report whether they observed a launch or a pass in events of varying ambiguity. This method assumes that launches and passes can be mapped onto a dimension that ranges from causal to non-causal impressions. It has been questioned whether pass events are a natural representative of noncausal events: Observers often report high impressions of causality upon first exposure to pass events, which then decreased after seeing a canonical launch (Bechlivanidis, Schlottmann, & Lagnado, 2019). In our study, therefore, participants completed a separate session that included canonical launches before starting the main experiment.”

      Furthermore, since the only report options are "launch" or "pass", it is also possible that "launch" reports are not indications of "I experienced a causal event" but rather "I did not experience a pass event". It seems possible to me that different adaptation transfer effects (e.g. selectivity to motion direction, speed, or color-swapping) change the way that participants interpret the task, or the uncertainty of their impression. For example, it could be that adaptation increases the likelihood of experiencing a "pass" event in a direction-selective manner, without changing causal impressions. Increases of "pass" impressions (or at least, uncertainty around what was experienced) would produce a leftward shift in the PSE as reported in Experiment 1, but this does not necessarily mean that experiences of causal events changed. Thus, changes in the PSEs between the conditions in the different experiments may not directly reflect changes in causal impressions. I would like the authors to clarify the extent to which these concerns call their conclusions into question. 

      Indeed, PSE shifts are subject to cognitive influences and can even be voluntarily shifted (Morgan et al., 2012). We believe that decision biases (e.g., reporting the presence of launch before adaptation vs. reporting the absence of a pass after the adaptation) are unlikely to explain the high specificity of aftereffects observed in the current study. While such aftereffects are very typical of visual processing (Webster, 2015), it is unclear how a mechanism that increase the likelihood of perceiving a pass could account for the retinotopy of adaptation to launches (Rolfs et al., 2013) or the recently reported selective transfer of adaptation for only some causal categories (Kominsky et al., 2020). The latter authors revealed a transfer of adaptation from triggering to launching, but not from entraining events to launching. Based on these arguments, we decided to not include this point in the revised manuscript.

      Leaving these concerns aside, I am also left wondering about the functional significance of these specialised mechanisms. Why would direction matter but speed and object identity not? Surely object identity, in particular, should be relevant to real-world interpretations and inputs of these visual routines? Is color simply too weak an identity? 

      We agree that it would be beneficial to have mechanisms in place that are specific for certain object identities. Overall, our results fit very well to established claims that only spatiotemporal parameters mediate the perception of causality (Michotte, 1963; Leslie, 1984; Scholl & Tremoulet, 2000). We have now explicitly listed these references again in the revised manuscript. It is important to note, that an understanding of a causal relation could suffice to track identity information based purely on spatiotemporal contingencies, neglecting distinguishing surface features.

      We revised the manuscript and state:

      “Our findings therefore provide additional support for the claim that an event’s spatiotemporal parameters mediate the perception of causality (Michotte, 1963; Leslie, 1984; Scholl & Tremoulet, 2000).”

      Moreover, we think our findings of directional selectivity have functional relevance. First, direction-selective detection of collisions allows for an adaptation that occurs separately for each direction. That means that the visual system can calibrate these visual routines for detecting causal interactions in response to real-world statistics that reflect differences in directions. For instance, due to gravity, objects will simply fall to the ground. Causal relation such as launches are likely to be more frequent in horizontal directions, along a stable ground. Second, we think that causal visual events are action-relevant, that is, acting on (potentially) causal events promises an advantage (e.g., avoiding a collision, or quickly catching an object that has been pushed away). The faster we can detect such causal interactions, the faster we can react to them. Direction-selective motion signals are available in the first stages of visual processing. Visual routines that are based on these direction-selective motion signals promise to enable such fast computations. Please note, however, that while our present findings demonstrate direction-selectivity, they do not pinpoint where exactly that visual routine is located. It is quite possible that the visual routine is located higher up in the visual system, relying on a direction-selective population response as input.

      We added these points to the discussion of the functional relevance: 

      “We suggest that at least two functional benefits result from a specialized visual routine for detecting causality. First, a direction-selective detection of launches allows adaptation to occur separately for each direction. That means that the visual system can automatically calibrate the sensitivity of these visual routines in response to real-world statistics. For instance, while falling objects drop vertically towards the ground, causal relations such as launches are common in horizontal directions moving along a stable ground. Second, we think that causal visual events are action-relevant, and the faster we can detect such causal interactions, the faster we can react to them. Direction-selective motion signals are available very early on in the visual system. Visual routines that are based on these direction-selective motion signals may enable faster detection. While our present findings demonstrate direction-selectivity, they do not pinpoint where exactly that visual routine is located. It is possible that the visual routine is located higher up in the visual system (or distributed across multiple levels), relying on a direction-selective population response as input.”

      Reviewer #3 (Recommendations for the authors):

      - The concept of "visual routines" is used without introduction; for a general-interest audience it might be good to include a definition and reference(s) (e.g. Ullman.). 

      Thank you very much for highlighting that point. We have chosen the term visual routine to emphasize that we locate the part of the mechanism that is affected by the adaptation in our experiments in the visual system, but at the same time it leaves space regarding the extent to which the mechanism further involves mid- and higher-level processes. The term thus has a clear reference to a visual routine by Ullman (1987). We have now addressed what we mean by visual routine, and we also included the reference in the revised manuscript.

      We add the following footnote to the introduction:

      “We use the term visual routine here to highlight that our adaptation experiments can reveal a causality detection mechanism that resides in the visual system. At the same time, calling it a routine emphasizes similarities with a local, semi-independent operation (e.g., the recognition of familiar motion patterns; see also Ullman, 1987; Cavanagh, Labianca, & Thornton, 2001) that can engage mid- and higher-level processes (e.g., during causal capture, Scholl & Nakayama, 2002; or multisensory integration, Körding et al., 2007).”

      - I would appreciate slightly more description of the phenomenology of the WW adaptors: is this Michotte's "entraining" event? Does it look like one disc shunts the other?  

      The stimulus differs from Michotte's entrainment event in both spatiotemporal parameters and phenomenology. We added videos for the launch, pass and slip events as Supplementary Material.

      Moreover, we described the slip event in the methods section:

      “In two additional sessions, we presented slip events as adaptors to control that the adaptation was specific for the impression of causality in the launching events. Slip events are designed to match the launching events in as many physical properties as possible while producing a very different, non-causal phenomenology. In slip events, the first peripheral disc also moves towards a stationary disc. In contrast to launching events, however, the first disc passes the stationary disc and stops only when it is adjacent to the opposite edge of the stationary disc. While slip events do not elicit a causal impression, they have the same number of objects and motion onsets, the same motion direction and speed, as well as the same spatial area of the event as launches.”

      In the revised manuscript, we added also more information on the slip event in the beginning of the results section. Importantly, the stimulus typically produces the impression of two independent movements and thus serves as a non-causal control condition in our study. Only anecdotally, some observers (not involved in this study) who saw the stimulus spontaneously described their phenomenology of seeing a slip event as a double step or a discus throw.

      We added the following description to the results section:

      “Moreover, we compared the visual adaptation to launches to a (non-causal) control condition in which we presented slip events as adaptor. In a slip event, the initially moving disc passes completely over the stationary disc, stops immediately on the other side, and then the initially stationary disc begins to move in the same direction without delay. Thus, the two movements are presented consecutively without a temporal gap. This stimulus typically produces the impression of two independent (non-causal) movements.”

      - In general more illustrations of the different conditions (similar to Figure 1c but for the different experimental conditions and adaptors) might be helpful for skim readers.  

      We followed the reviewer’s recommendation and added a visualization of the adaptor and the test events for the different experiments in Figure 2.

      - Were the luminances of the red and green balls in experiment 3 matched? Were participants checked for color anomalous vision?  

      Yes, we checked for color anomalous vision using the color test Tafeln zur Prüfung des Farbensinnes/Farbensehens (Kuchenbecker & Broschmann, 2016). We added that information to the manuscript. The red and green discs were not matched for luminance. We measured the luminance after the experiment (21 cd/m<sup>2</sup> for the green disc and 6 cd/m<sup>2</sup> for the red disc). Please note, that the differences in luminance should not pose a problem for the interpretation of the results, as we see a transfer of the adaptation across the two different colors.

      We added the following information to the manuscript:

      “The red and green discs were not matched for luminance. Measurements obtained after the experiments yielded a luminance of 21 cd/m<sup>2</sup> for the green disc and 6 cd/m<sup>2</sup> for the red disc.”

      “All observers had normal or corrected-to-normal vision and color vision as assessed using the color test Tafeln zur Prüfung des Farbensinnes/Farbensehens (Kuchenbecker & Broschmann, 2016).”

      - Relationship of this work to the paper by Arnold et al., (2015). That paper suggested that some effects of adaptation of launching events could be explained by an adaptation of object shape, not by causality per se. It is superficially difficult to see how one could explain the present results from the perspective of object "squishiness" -- why would this be direction selective? In other words, the present results taken at face value call the "squishiness" explanation into question. The authors could consider an explanation to reconcile these findings in their discussion. 

      Indeed, the paper by Arnold and colleagues (2014) suggested that a contact-launch adaptor could lead to a squishiness aftereffect—arguing that the object elasticity changed in response to the adaptation.  Importantly, the same study found an object-centered adaptation effect rather than a retinotopic adaptation effect. However, the retinotopic nature of the negative aftereffect as used in our study has been repeatedly replicated (for instance Kominsky & Scholl, 2020). Thus, the divergent results of Arnold and colleagues may have resulted from differences in the task (i.e., observers had to judge whether they perceived a soft vs. hard bounce), or the stimuli (i.e., bounces of a disc and a wedge, and the discs moving on a circular trajectory). It would be important to replicate these results first and then determine whether their squishiness effect would be direction-selective as well. We now acknowledge the study by Arnold and colleagues in the discussion:

      “The adaptation of causality is spatially specific to the retinotopic coordinates of the adapting stimulus (Kominsky & Scholl, 2020; Rolfs et al., 2013; for an object-centered elasiticity aftereffect using a related stimulus on a circular motion path, see Arnold et al., 2015), suggesting that the detection of causal interactions is implemented locally in visual space.”

      - Line 32: "showing that a specialized visual routine for launching events exists even within separate motion direction channels". This doesn't necessarily mean the routine is within each separate direction channel, only that the output of the mechanism depends on the population response over motion direction. The critical motion computation could be quite high level -- e.g. global pattern motion in MST. Please clarify the claim. 

      We agree with the reviewer, that it is also possible that critical parts of the visual routine could simply use the aggregated population response over motion direction at higher-levels of processing. We acknowledge this possibility in the discussion of the functional relevance of the proposed mechanism and when suggesting that a distributed brain network may contribute to the perception of causality.

      We would like to highlight the following two revised paragraphs.

      “[…] Second, we think that causal visual events are action-relevant, and the faster we can detect such causal interactions, the faster we can react to them. Direction-selective motion signals are available very early on in the visual system. Visual routines that are based on these direction-selective motion signals may enable faster detection. While our present findings demonstrate direction-selectivity, they do not pinpoint where exactly that visual routine is located. It is possible that the visual routine is located higher up in the visual system (or distributed across multiple levels), relying on a direction-selective population response as input.”

      Moreover, when discussing the neurophysiological literature we write:

      “Interestingly, single cell recordings in area F5 of the primate brain revealed that motor areas are contributing to the perception of causality (Caggiano et al., 2016; Rolfs, 2016), emphasizing the distributed nature of the computations underlying causal interactions. This finding also stresses that the detection, and the prediction, of causality is essential for processes outside purely sensory systems (e.g., for understanding other’s actions, for navigating, and for avoiding collisions).”

      -  p. 10 line 30: typo "particual".  

      Done.

      -  p. 10 line 37: "This findings rules out (...)" should be singular "This finding rules out (...)". 

      Done.

      -  Spelling error throughout: "underly" should be "underlie". 

      Done.

      -  p.11 line 29: "emerges fast and automatic" should be "automatically". 

      Done.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The manuscript focuses on the olfactory system of Pieris brassicae larvae and the importance of olfactory information in their interactions with the host plant Brassica oleracea and the major parasitic wasp Cotesia glomerata. The authors used CRISPR/Cas9 to knockout odorant receptor co-receptors (Orco), and conducted a comparative study on the behavior and olfactory system of the mutant and wild-type larvae. The study found that Orco-expressing olfactory sensory neurons in antennae and maxillary palps of Orco knockout (KO) larvae disappeared, and the number of glomeruli in the brain decreased, which impairs the olfactory detection and primary processing in the brain. Orco KO caterpillars show weight loss and loss of preference for optimal food plants; KO larvae also lost weight when attacked by parasitoids with the ovipositor removed, and mortality increased when attacked by untreated parasitoids. On this basis, the authors further studied the responses of caterpillars to volatiles from plants attacked by the larvae of the same species and volatiles from plants on which the caterpillars were themselves attacked by parasitic wasps. Lack of OR-mediated olfactory inputs prevents caterpillars from finding suitable food sources and from choosing spaces free of enemies.

      Strengths:

      The findings help to understand the important role of olfaction in caterpillar feeding and predator avoidance, highlighting the importance of odorant receptor genes in shaping ecological interactions.

      Weaknesses:

      There are the following major concerns:

      (1) Possible non-targeted effects of Orco knockout using CRISPR/Cas9 should be analyzed and evaluated in Materials and Methods and Results.

      Thank you for your suggestion. In the Materials and Methods, we mention how we selected the target region and evaluated potential off-target sites by Exonerate and CHOPCHOP. Neither of these methods found potential off-target sites with a more-than-17-nt alignment identity. Therefore, we assumed no off-target effect in our Orco KO. Furthermore, we did not find any developmental differences between WT and KO caterpillars when these were reared on leaf discs in Petri dishes (Fig S4). We will further highlight this information on the off-target evaluation in the Results section of our revised manuscript.

      (2) Figure 1E: Only one olfactory receptor neuron was marked in WT. There are at least three olfactory sensilla at the top of the maxillary palp. Therefore, to explain the loss of Orco-expressing neurons in the mutant (Figure 1F), a more rigorous explanation of the photo is required.

      Thank you for pointing this out. The figure shows only a qualitative comparison between WT and KO and we did not aim to determine the total number of Orco positive neurons in the maxillary palps or antennae of WT and KO caterpillars, but please see our previous work for the neuron numbers in the caterpillar antennae (Wang et al., 2023). We did indeed find more than one neuron in the maxillary palps, but as these were in very different image planes it was not possible to visualize them together. However, we will add a few sentences in the Results and Discussion section to explain the results of the maxillary palp Orco staining.

      (3) In Figure 1G, H, the four glomeruli are circled by dotted lines: their corresponding relationship between the two figures needs to be further clarified.

      Thank you for pointing this out. The four glomeruli in Figure 1G and 1H are not strictly corresponding. We circled these glomeruli to highlight them, as they are the best visualized and clearly shown in this view. In this study, we only counted the number of glomeruli in both WT and KO, however, we did not clarify which glomeruli are missing in the KO caterpillar brain. We will further explain this in the figure legend.

      (4) Line 130: Since the main topic in this study is the olfactory system of larvae, the experimental results of this part are all about antennal electrophysiological responses, mating frequency, and egg production of female and male adults of wild type and Orco KO mutant, it may be considered to include this part in the supplementary files. It is better to include some data about the olfactory responses of larvae.

      Thank you for your suggestion. We do agree with your suggestion, and we will consider moving this part to the supplementary information. Regarding larval olfactory response, we unfortunately failed to record any spikes using single sensillum recordings due to the difficult nature of the preparation; however, we do believe that this would be an interesting avenue for further research.

      (5) Line 166: The sentences in the text are about the choice test between " healthy plant vs. infested plant", while in Fig 3C, it is "infested plant vs. no plant". The content in the text does not match the figure.

      Thank you for pointing this out. The sentence is “We compared the behaviors of both WT and Orco KO caterpillars in response to clean air, a healthy plant and a caterpillar-infested plant”. We tested these three stimuli in two comparisons: healthy plant vs no plant, infested plant vs no plant. The two comparisons are shown in Figure 3C separately. We will aim to describe this more clearly in the revised version of the manuscript.

      (6) Lines 174-178: Figure 3A showed that the body weight of Orco KO larvae in the absence of parasitic wasps also decreased compared with that of WT. Therefore, in the experiments of Figure 3A and E, the difference in the body weight of Orco KO larvae in the presence or absence of parasitic wasps without ovipositors should also be compared. The current data cannot determine the reduced weight of KO mutant is due to the Orco knockout or the presence of parasitic wasps.

      Thank you for pointing this out. We did not make a comparison between the data of Figures 3A and 3E since the two experiments were not conducted at the same time due to the limited space in our BioSafety Ⅲ greenhouse. We do agree that the weight decrease in Figure 3E is partly due to the reduced caterpillar growth shown in Figure 3A. However, we are confident that the additional decrease in caterpillar weight shown in Figure 3E is mainly driven by the presence of disarmed parasitoids. To be specific, the average weight in Figure 3A is 0.4544 g for WT and 0.4230 g for KO, KO weight is 93.1% of WT caterpillars. While in Figure 3E, the average weight is 0.4273 g for WT and 0.3637 g for KO, KO weight is 85.1% of WT caterpillars. We will discuss this interaction between caterpillar growth and the effect of the parasitoid attacks more extensively in the revised version of the manuscript.

      (7) Lines 179-181: Figure 3F shows that the survival rate of larvae of Orco KO mutant decreased in the presence of parasitic wasps, and the difference in survival rate of larvae of WT and Orco KO mutant in the absence of parasitic wasps should also be compared. The current data cannot determine whether the reduced survival of the KO mutant is due to the Orco knockout or the presence of parasitic wasps.

      We are happy that you highlight this point. When conducting these experiments, we selected groups of caterpillars and carefully placed them on a leaf with minimal disturbance of the caterpillars, which minimized hurting and mortality. We did test the survival of caterpillars in the absence of parasitoid wasps from the experiment presented in Figure 3A, although this was missing from the manuscript. There is no significant difference in the survival rate of caterpillars between the two genotypes in the absence of wasps (average mortality WT = 8.8 %, average mortality KO = 2.9 %; P = 0.088, Wilcoxon test), so the decreased survival rate is most likely due to the attack of the wasps. We will add this information to the revised version of the manuscript.

      (8) In Figure 4B, why do the compounds tested have no volatiles derived from plants? Cruciferous plants have the well-known mustard bomb. In the behavioral experiments, the larvae responses to ITC compounds were not included, which is suggested to be explained in the discussion section.

      Thank you for the suggestion. We assume you mean Figure 4D/4E instead of Figure 4B. In Figure 4B, many of the identified chemical compounds are essentially plant volatiles, especially those from caterpillar frass and caterpillar spit. In Figure 4D/4E, most of the tested chemicals are derived from plants. We did include several ITCs in the butterfly EAG tests shown in figure 2A/B, however because the butterfly antennae did not respond strongly to ITCs, we did not include ITCs in the subsequent larval behavioural tests. Instead, the tested chemicals in Figure 4D/4E either elicit high EAG responses of butterflies or have been identified as significant by VIP scores in the chemical analyses. We will add this explanation to the revised version of our manuscript.

      (9) The custom-made setup and the relevant behavioral experiments in Figure 4C need to be described in detail (Line 545).

      We will add more detailed descriptions for the setup and method in the Materials and Methods.

      (10) Materials and Methods Line 448: 10 μL paraffin oil should be used for negative control.

      Thank you for pointing this out. We used both clean filter paper and clean filter paper with 10 μL paraffin oil as negative controls, but we did not find a significant difference between the two controls. Therefore, in the EAG results of Figure 2A/2B, we presented paraffin oil as one of the tested chemicals. We will re-run our statistical tests with paraffin oil as negative control, although we do not expect any major differences to the previous tests.

      Reviewer #2 (Public review):

      Summary:

      This manuscript investigated the effect of olfactory cues on caterpillar performance and parasitoid avoidance in Pieris brassicae. The authors knocked out Orco to produce caterpillars with significantly reduced olfactory perception. These caterpillars showed reduced performance and increased susceptibility to a parasitoid wasp.

      Strengths:

      This is an impressive piece of work and a well-written manuscript. The authors have used multiple techniques to investigate not only the effect of the loss of olfactory cues on host-parasitoid interactions, but also the mechanisms underlying this.

      Weaknesses:

      (1) I do have one major query regarding this manuscript - I agree that the results of the caterpillar choice tests in a y-maze give weight to the idea that olfactory cues may help them avoid areas with higher numbers of parasitoids. However, the experiments with parasitoids were carried out on a single plant. Given that caterpillars in these experiments were very limited in their potential movement and source of food - how likely is it that avoidance played a role in the results seen from these experiments, as opposed to simply the slower growth of the KO caterpillars extending their period of susceptibility? While the two mechanisms may well both take place in nature - only one suggests a direct role of olfaction in enemy avoidance at this life stage, while the other is an indirect effect, hence the distinction is important.

      We do agree with your comment that both mechanisms may be at work in nature, and we do address this in the Discussion section. In our study, we did find that wildtype caterpillars were more efficient in locating their food source and did grow faster on full plants than knockout caterpillars. This faster growth will enable wildtype caterpillars to more quickly outgrow the life-stages most vulnerable to the parasitoids (L1 and L2). The olfactory system therefore supports the escape from parasitoids indirectly by enhancing feeding efficiency directly.

      In addition, we show in our Y-tube experiments that WT caterpillars were able to avoid plant where conspecifics are under the attack by parasitiods (Figure 3D). Therefore, we speculate that WT caterpillars make use of volatiles from the plant or from conspecifics via their spit or faeces to avoid plants or leaves potentially attracting natural enemies. Knockout caterpillars are unable to use these volatile danger cues and therefore do not avoid plants or leaves that are most attractive to their natural enemies, making KO caterpillars more susceptible and leading to more natural enemy harassment. Through this, olfaction also directly impacts the ability of a caterpillar to find an enemy-free feeding site.

      We think that olfaction supports the enemy avoidance of caterpillars via both these mechanisms, although at different time scales. Unfortunately, our analysis was not detailed enough to discern the relative importance of the two mechanisms we found. However, we feel that this would be an interesting avenue for further research. Moreover, we will sharpen our discussion on the potential importance of the two different mechanisms in the revised version of this manuscript.

      (2) My other issue was determining sample sizes used from the text was sometimes a bit confusing. (This was much clearer from the figures).

      We will revise the sample size in the text to make it clearer.

      (3) I also couldn't find the test statistics for any of the statistical methods in the main text, or in the supplementary materials.

      Thank you for pointing this out. We will provide more detailed test statistics in the main text and in the supplementary materials of the revised version of the manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      Summary:

      This paper describes molecular dynamics simulations (MDS) of the dynamics of two T-cell receptors (TCRs) bound to the same major histocompatibility complex molecule loaded with the same peptide (pMHC). The two TCRs (A6 and B7) bind to the pMHC with similar affinity and kinetics, but employ different residue contacts. The main purpose of the study is to quantify via MDS the differences in the inter- and intra-molecular motions of these complexes, with a specific focus on what the authors describe as catch-bond behavior between the TCRs and pMHC, which could explain how T-cells can discriminate between different peptides in the presence of weak separating force.

      Strengths:

      The authors present extensive simulation data that indicates that, in both complexes, the number of high-occupancy interdomain contacts initially increases with applied load, which is generally consistent with the authors’ conclusion that both complexes exhibit catch-bond behavior, although to different extents. In this way, the paper somewhat expands our understanding of peptide discrimination by T-cells.

      a. The reviewer makes thoughtful assessment of our manuscript. While our manuscript is meant to be a “short” contribution, our significant new finding is that even for TCRs targeting the same pMHC, having similar structures, and leading to similar functional outcomes in conventional assays, their response to applied load can be different. This supports out recent experimental work where TCRs targeting the same pMHC differed in their catch bond characteristics, and importantly, in their response to limiting copy numbers of pMHCs on the antigen-presenting cell (Akitsu et al., Sci. Adv., 2024).

      Weaknesses:

      While generally well supported by data, the conclusions would nevertheless benefit from a more concise presentation of information in the figures, as well as from suggesting experimentally testable predictions.

      b. We have updated all figures for clear and streamlined presentation. We have also created four figure supplements to cover more details.

      Regarding testable predictions, an important prediction is that B7 TCR would exhibit a weaker catch bond behavior than A6 (line 297–298). This is a nontrivial prediction because the two TCRs targeting the same pMHC have similar structures and are functionally similar in conventional assays. This prediction can be tested by singlemolecule optical tweezers experiments. Based on our recent experiments Akitsu et al., Sci. Adv. (2024), we also predict that A6 and B7 TCRs will differ in their ability to respond to cases when the number of pMHC molecules presented are limited. Details of how they would differ require further investigation, which is beyond the scope of the present work (line 314-319).

      Another testable prediction for the conservation of the basic allostery mechanism is to test the Cβ FG-loop deletion mutant located at the hinge region of the β chain, where the deletion severely impairs the catch bond formation (line 261–264).

      Reviewer 2:

      In this work, Chang-Gonzalez and coworkers follow up on an earlier study on the force-dependence of peptide recognition by a T-cell receptor using all-atom molecular dynamics simulations. In this study, they compare the results of pulling on a TCR-pMHC complex between two different TCRs with the same peptide. A goal of the paper is to determine whether the newly studied B7 TCR has the same load-dependent behavior mechanism shown in the earlier study for A6 TCR. The primary result is that while the unloaded interaction strength is similar, A6 exhibits more force stabilization.

      This is a detailed study, and establishing the difference between these two systems with and without applied force may establish them as a good reference setup for others who want to study mechanobiological processes if the data were made available, and could give additional molecular details for T-Cell-specialists. As written, the paper contains an overwhelming amount of details and it is difficult (for me) to ascertain which parts to focus on and which results point to the overall take-away messages they wish to convey.

      R2-a. As mentioned above and as the reviewer correctly pointed out, the condensed appearance of this manuscript arose largely because we intended it to be a Research Advances article as a short follow up study of our previous paper on A6 TCR published in eLife. Most of the analysis scripts for the A6 TCR study are already available on Github. For the present manuscript, we have created a separate Github repository containing sample simulation systems and scripts for the B7 TCR.

      Regarding the focus issue, it is in part due to the complex nature of the problem, which required simulations under different conditions and multi-faceted analyses. We believe the extensive updates to the figures and texts make clearer and improved presentation. But we note that even in the earlier version, the reviewer pointed out the main take-away message well: “The primary result is that while the unloaded interaction strength is similar, A6 exhibits more force stabilization.

      Detailed comments:

      (1) In Table 1 - are the values of the extension column the deviation from the average length at zero force (that is what I would term extension) or is it the distance between anchor points (which is what I would assume based on the large values. If the latter, I suggest changing the heading, and then also reporting the average extension with an asterisk indicating no extensional restraints were applied for B7-0, or just listing 0 load in the load column. Standard deviation in this value can also be reported. If it is an extension as I would define it, then I think B7-0 should indicate extension = 0+/- something. The distance between anchor points could also be labeled in Figure 1A.

      R2-b. “Extension” is the distance between anchor points that the reviewer is referring to (blue spheres at the ends of the added strands in Figure 1A). While its meaning should be clear in the section “Laddered extensions” in “MD simulation protocol” (line 357–390), in a strict sense, we agree that using it for the end-to-end distance can be confusing. However, since we have already used it in our previous two papers (Hwang et al., PNAS 2020 and Chang-Gonzalez et al., eLife, 2024), we prefer to keep it for consistency. Instead, in the caption of Table 1, we explained its meaning, and also explicitly labeled it in Figure 1A, as the reviewer suggested.

      Please also note that the no-load case B7<sup>0</sup> was performed by separately building a TCR-pMHC complex without added linkers (line 352), and holding the distal part of pMHC (the α3 domain) with weak harmonic restraints (line 406–408). Thus, no extension can be assigned to B7<sup>0</sup>. We added a brief explanation about holding the MHC α3 domain for B7<sup>0</sup> in line 83–85.

      (2) As in the previous paper, the authors apply ”constant force” by scanning to find a particular bond distance at which a desired force is selected, rather than simply applying a constant force. I find this approach less desirable unless there is experimental evidence suggesting the pMHC and TCR were forced to be a particular distance apart when forces are applied. It is relatively trivial to apply constant forces, so in general, I would suggest this would have been a reasonable comparison. Line 243-245 speculates that there is a difference in catch bonding behavior that could be inferred because lower force occurs at larger extensions, but I do not believe this hypothesis can be fully justified and could be due to other differences in the complex.

      R2-c. There is indeed experimental evidence that the TCR-pMHC complex operates under constant separation. The spacing between a T-cell and an antigen-presenting cell is maintained by adhesion molecules such as the CD2CD58 pair, as explained in our paper on the A6 TCR Chang-Gonzalez et al., eLife, 2024 and also in our previous review paper Reinherz et al., PNAS, 2023. In in vitro single-molecule experiments, pulling to a fixed separation and holding is also commonly done. We added an explanation about this in line 79–83 of the manuscript. On the other hand, force between a T cell and and antigen-presenting cell is also controlled by the actin cytoskeleton, which make the applied load not a simple function of the separation between the two cells. An explanation about this was added in line 300–303. Detailed comparison between constant extension vs. constant force simulations is definitely a subject of our future study.

      Regarding line 243–245 of the original submission (line 297–298 of the revised manuscript), we agree with the reviewer that without further tests, lower forces at larger extensions per se cannot be an indicator that B7 forms a weaker catch bond. But with additional information, one can see it does have relevance to the catch bond strength. In addition to fewer TCR-pMHC contacts (Figure 1C of our manuscript), the intra-TCR contacts are also reduced compared to those of A6 (bottom panel of Figure 1D vs. Chang-Gonzalez et al., eLife, 2024, Figure 8A,B, first column). Based on these data, we calculated the average total intra-TCR contact occupancies in the 500–1000-ns interval, which was 30.4±0.49 (average±std) for B7 and 38.7±0.87 for A6. This result shows that the B7 TCR forms a looser complex with pMHC compared to A6. Also, B7<sup>low</sup> and B7<sup>high</sup> differ in extension by 16.3 ˚A while A6<sup>low</sup> and A6<sup>high</sup> differ by 5.1 ˚A, for similar ∼5-pN difference between low- and high-load cases. With the higher compliance of B7, it would be more difficult to achieve load-induced stabilization of the TCR-pMHC interface, hence a weaker catch bond. We explained this in line 129–132 and line 292–297.

      (3) On a related note, the authors do not refer to or consider other works using MD to study force-stabilized interactions (e.g. for catch bonding systems), e.g. these cases where constant force is applied and enhanced sampling techniques are used to assess the impact of that applied force: https://www.cell.com/biophysj/fulltext/S0006-3495(23)00341-7, https://www.biorxiv.org/content/10.1101/2024.10.10.617580v1. I was also surprised not to see this paper on catch bonding in pMHC-TCR referred to, which also includes some MD simulations: https://www.nature.com/articles/s41467-023-38267-1

      R2-d. We thank the reviewer for bringing the three papers to our attention, which are:

      (1) Languin-Catto¨en, Sterpone, and Stirnemann, Biophys. J. 122:2744 (2023): About bacterial adhesion protein FimH.

      (2) Pen˜a Ccoa, et al., bioRxiv (2024): About actin binding protein vinculin.

      (3) Choi et al., Nat. Comm. 14:2616 (2023): About a mathematical model of the TCR catch bond.

      Catch bond mechanisms of FimH and vinculin are different from that of TCR in that FimH and vinculin have relatively well-defined weak- and strong-binding states where there are corresponding crystal structures. Availability of the end-state structures permits simulation approaches such as enhanced sampling of individual states and studying the transition between the two states. In contrast, TCR does not have any structurally well-defined weak- or strong-binding states, which requires a different approach. As demonstrated in our current manuscript as well as in our previous two papers (Hwang et al., PNAS 2020 and Chang-Gonzalez et al., eLife, 2024), our microsecond-long simulations of the complex under realistic pN-level loads and a combination of analysis methods are effective for elucidating the catch bond mechanism of TCR. These are explained in line 227–238 of the manuscript.

      The third paper (Choi, et al., 2023) proposes a mathematical model to analyze extensive sets of data, and also perform new experiments and additional simulations. Of note, their model assumptions are based mainly on the steered MD (SMD) simulation in their previous paper (Wu, et al., Mol. Cell. 73:1015, 2019). In their model, formation of a catch bond (called catch-slip bond in Choi’s paper) requires partial unfolding of MHC and tilting of the TCR-pMHC interface. Our mechanism does not conflict with their assumptions since the complex in the fully folded state should first bear load in a ligand-dependent manner in order to allow any larger-scale changes. This is explained in line 239–243.

      For the revised text mentioned above (line 227–243), in addition to the 3 papers that the reviewer pointed out, we cited the following papers:

      • Thomas, et al., Annu. Rev. Biophys. 2008: Catch bond mechanisms in general.

      • Bakolitsa et al., Cell 1999, Le Trong et al., Cell 2010, Sauer et al., Nat. Comm. 2016, Mei et al., eLife 2020:

      Crystal structures of FimH and vinculin in different states.

      • Wu, et al., Mol. Cell. 73:1015, 2019: The SMD simulation paper mentioned above.

      (4) The authors should make at least the input files for their system available in a public place (github, zenodo) so that the systems are a more useful reference system as mentioned above. The authors do not have a data availability statement, which I believe is required.

      R2-d. As mentioned in R2-a above, we have added a Github repository containing sample simulation systems and scripts for the B7 TCR.

      Reviewer 3:

      Summary:

      The paper by Chang-Gonzalez et al. is a molecular dynamics (MD) simulation study of the dynamic recognition (load-induced catch bond) by the T cell receptor (TCR) of the complex of peptide antigen (p) and the major histocompatibility complex (pMHC) protein. The methods and simulation protocols are essentially identical to those employed in a previous study by the same group (Chang-Gonzalez et al., eLife 2024). In the current manuscript, the authors compare the binding of the same pMHC to two different TCRs, B7 and A6 which was investigated in the previous paper. While the binding is more stable for both TCRs under load (of about 10-15 pN) than in the absence of load, the main difference is that, with the current MD sampling, B7 shows a smaller amount of stable contacts with the pMHC than A6.

      Strengths:

      The topic is interesting because of the (potential) relevance of mechanosensing in biological processes including cellular immunology.

      Weaknesses:

      The study is incomplete because the claims are based on a single 1000-ns simulation at each value of the load and thus some of the results might be marred by insufficient sampling, i.e., statistical error. After the first 600 ns, the higher load of B7<sup>high</sup> than B7<sup>low</sup> is due mainly to the simulation segment from about 900 ns to 1000 ns (Figure 1D). Thus, the difference in the average value of the load is within their standard deviation (9 +/- 4 pN for B7<sup>low</sup> and 14.5 +/- 7.2 for B7<sup>high</sup>, Table 1). Even more strikingly, Figure 3E shows a lack of convergence in the time series of the distance between the V-module and pMHC, particularly for B7<sup>0</sup> (left panel, yellow) and B7<sup>low</sup> (right panel, orange). More and longer simulations are required to obtain a statistically relevant sampling of the relative position and orientation of the V-module and pMHC.

      R3-a. The reviewer uses data points during the last 100 ns to raise an issue with sampling. But since we are using realistic pN range forces, force fluctuates more slowly. In fact, in our simulation of B7<sup>high</sup>, while the force peaks near 35 pN at 500 ns (Figure 1D of our manuscript), the interfacial contacts show no noticeable changes around 500 ns (Figure 2B and Figure 2–figure supplement 1C of our manuscript). Similarly slow fluctuation of force was also observed for A6 TCR (Figure 8 of Chang-Gonzalez et al., eLife (2024)). Thus, a wider time window must be considered rather than focusing on forces in the last 100-ns interval.

      To compare fluctuation in forces, we added Figure 1–figure supplement 2, which is based on Appendix 3–Figure 1 of our A6 paper. It shows the standard deviation in force versus the average force during 500–1000 ns interval for various simulations in both A6 (open black circles) and B7 (red squares) systems. Except for Y8A<sup>low</sup> and dFG<sup>low</sup> of A6 (explained below), the data points lie on nearly a straight line.

      Thermodynamically, the force and position of the restraint (blue spheres in Figure 1A of our manuscript) form a pair of generalized force and the corresponding spatial variable in equilibrium at temperature 300 K, which is akin to the pressure P and volume V of an ideal gas. If V is fixed, P fluctuates. Denoting the average and std of pressure as ⟨P⟩ and ∆P, respectively, Burgess showed that ∆P/P⟩ is a constant (Eq. 5 of Burgess, Phys. Lett. A, 44:37; 1973). In the case of the TCRαβ-pMHC system, although individual atoms are not ideal gases, since their motion leads to the fluctuation in force on the restraints, the situation is analogous to the case where pressure arises from individual ideal gas molecules hitting the confining wall as the restraint. Thus, the near-linear behavior in the figure above is a consequence of the system being many-bodied and at constant temperature. The linearity is also an indicator that sampling of force was reasonable in the 500–1000-ns interval. The fact that A6 and B7 data show a common linear profile further demonstrates the consistency in our force measurement. About the two outliers of A6, Y8A<sup>low</sup> is for an antagonist peptide and dFG<sup>low</sup> is the Cβ FG-loop deletion mutant. Both cases had reduced numbers of contacts with pMHC, which likely caused a wider conformational motion, hence greater fluctuation in force.

      Upon suggestion by the reviewer, we extended the simulations of B7<sup>0</sup>, B7<sup>low</sup> and B7<sup>high</sup> to about 1500 ns (Table 1). While B7<sup>0</sup> and B7<sup>low</sup> behaved similarly, B7<sup>high</sup> started to lose contacts at around 1300 ns (top panel of Figure 1D and Figure 2B). A closer inspection revealed that destabilization occurred when the complex reached low-force states. Even before 1300 ns, at about 750 ns, the force on B7<sup>high</sup> drops below 5 pN, and another drop in force occurred at around 1250 ns, though to a lesser extent (Figure 1D). These changes are followed by increase in the Hamming distance (Figure 2B). Thus, in B7<sup>high</sup>, destabilization is caused not by a high force, but by a lack of force, which is consistent with the overarching theme of our work, the load-induced stabilization of the TCRαβ-pMHC complex.

      The destabilization of B7<sup>high</sup> during our simulation is a combined effect of its overall weaker interface compared to A6 (despite having comparable number of contacts in crystal structures; line 265–269), and its high compliance (explained in the second paragraph of our response R2-c above). Under a fixed extension, the higher compliance of the complex can reach a low-force state where breakage of contacts can happen. In reality, with an approximately constant spacing between a T cell and an antigen-presenting cell, force is also regulated by the actin cytoskeleton (explained in the first paragraph of R2-c above). While detailed comparison between constant-extension and constant-force simulation is the subject of a future study, for this manuscript, we used the 500–1000-ns interval for calculating time-averaged quantities, for consistency across different simulations. For time-dependent behaviors, we showed the full simulation trajectories, which are Figure 1D, Figure 2B, Figure 2–figure supplement 1 (except for panel E), and Figure 4–figure supplement 1B.

      Thus, rather than performing replicate simulations, we perform multiple simulations under different conditions and analyze them from different angles to obtain a consistent picture. If one were interested in quantitative details under a given condition, e.g., dynamics of contacts for a given extension or the time when destabilization occurs at a given force, replicate simulations would be necessary. However, our main conclusions such as load-induced stabilization of the interface through the asymmetric motion, and B7 forming a weaker complex compared to A6, can be drawn from our extensive analysis across multiple simulations. Please also note that reviewer 1 mentioned that our conclusions are “generally well supported by data.”

      A similar argument applies to Figure 2–figure supplement 1F (old Figure 3B that the reviewer pointed out). If precise values of the V-module to pMHC distance were needed, replicate simulations would be necessary, however, the figure demonstrates that B7<sup>high</sup> maintains more stable interface before the disruption at 1300 ns compared to B7<sup>low</sup>, which is consistent with all other measures of interfacial stability we used. The above points are explained throughout our updated manuscript, including

      • Line 106–110, 125–132, 156–158, 298–303.

      • Figures showing time-dependent behaviors have been updated and Figure 1–figure supplement 2 has been added, as explained above.

      It is not clear why ”a 10 A distance restraint between alphaT218 and betaA259 was applied” (section MD simulation protocol, page 9).

      R3-b. αT218 and β_A259 are the residues attached to a leucine-zipper handle in _in vitro optical trap experiments (Das, et al., PNAS 2015). In T cells, those residues also connect to transmembrane helices. Our newly added Figure 1–figure supplement 1 shows a model of N15 TCR used in experiments in Das’ paper, constructed based on PDB 1NFD. Blue spheres represent C<sub>α</sub> atoms corresponding to αT218 and βA259 of B7 TCR. Their distance is 6.7 ˚A. The 10-˚A distance restraint in simulation was applied to mimic the presence of the leucine zipper that prevents excessive separation of the added strands. The distance restraint is a flatbottom harmonic potential which is activated only when the distance between the two atoms exceeds 10 ˚A, which we did not clarify in our original manuscript. It is now explained in line 371–373. The same restraint was used in our previous studies on JM22 and A6 TCRs.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Clarify the reason for including arguably non-physiological simulations, in which the C domain is missing. Is the overall point that it is essential for proper peptide discrimination?

      R1-c. This is somewhat a philosophical question. Rather than recapitulating experiment, we believe the goal of simulation is to gain insight. Hence, a model should be justified by its utility rather than its direct physiological relevance. The system lacking the C-module is useful since it informs about the allosteric role of the C-module by comparing its behavior with that of the full TCRαβ-pMHC complex. The increased interfacial stability of Vαβ-pMHC is also consistent with our discovery that the C-module likely undergoes a partial unfolding to an extended state, where the bond lifetime increases (Das, et al., PNAS 2015; Akitsu et al., Sci. Adv., 2024). In this sense, Vαβ-pMHC has a more direct physiological relevance. Furthermore, considering single-chain versions of an antibody lacking the C-module (scFv) are in widespread use (Ahmad et al., J. Immunol. Res., 2012) including CAR T cells, a better understanding of a TCR lacking the C-module may help with developing a novel TCR-based immunotherapy. These explanations have been added in line 253–261.

      (2) Suggest changing Vαβ-pMHC to B7<sup>0</sup>∆C to emphasize that the constant domain is deleted.

      R1-d. While we appreciate the reviewer’s suggestion, the notation Vαβ-pMHC was used in our previous two papers (Hwang, PNAS 2020, Chang-Gonzalez, eLife 2024). We thus prefer to keep the existing notation.

      (3) Suggest adding A6 data to table 1 for comparison, making it clear if it is from a previous paper.

      R1-e. Table 1 of the present manuscript and Table 1 of the A6 paper differ in items displayed. Instead of merging, we added the extension and force for A6 corresponding to B7<sup>low</sup> and B7<sup>high</sup> in the caption of Table 1.

      (4) Suggest discussing the catch-bond behavior in terms of departure from equilibrium, e.g. is it possible to distinguish between different (catch vs slip) bond behaviors on the basis of work of separation histograms? If the difference does not show up in equilibrium work, the exponential work averages would be similar, but work histograms could be very different.

      R1-f. Although energetics of the catch versus slip bond will provide additional insight, it is beyond the scope of the present simulations that do not involve dissociation events nor simulations of slip-bond receptors. We instead briefly mention the energetic aspect in terms of T-cell activation in line 316–319.

      (5) Have the simulations in Figure 1 reached steady state? The force and occupancy increase almost linearly up until 500ns, then seem to decrease rather dramatically by 750ns. It might be worthwhile to extend one simulation to check.

      R1-g. We did extend the simulation to about 1500 ns. The large and slow fluctuation in force is an inherent property of the system, as explained in R3-a above.

      (6) Is the loss of contacts for B7<sup>0</sup> due to thermalization and relaxation away from the X-ray structure?

      R1-h. The initial thermalization at 300 K is not responsible for the loss of contacts for B7<sup>0</sup> since we applied distance restraints to the initial contacts to keep them from breaking during the preparatory runs (line 358–370). While ‘relaxation away from the X-ray structure’ gives an impression that the complex approaches an equilibrium conformation in the absence of the crystallographic confinement, our simulation indicates that the stability of the complex depends on the applied load. We made the distinction between relaxation and the load-dependent stability clearer in line 233–238.

      (7) Figure 4 contains a very large amount of data. Could it be simplified and partly moved to SI? For example, panel G is somewhat hard to read at this scale, and seems non-essential to the general reader.

      R1-i. Upon the reviewer’s suggestion, we simplified Figure 4 by moving some of the panels to Figure 4–figure supplement 1. Panels have also been made larger for better readability.

      (8) If the coupling between C and V domains is necessary for catch-bond behavior, can one propose mutations that would disrupt the interface to test by experiment? This would be interesting in light of the authors’ own comment on p. 8 that ’a logical evolutionary pressure would be for the C domains to maximize discriminatory power by adding instability to the TCR chassis,’ which might lead to a verifiable hypothesis.

      R1-j. This has already been computationally and experimentally tested for other TCRs by the Cβ FG-loop deletion mutants that diminish the catch bond (Das, et al., PNAS 2015; Hwang et al., PNAS 2020; ChangGonzalez et al., eLife, 2024). Furthermore, the Vγδ-Cαβ chimera where the C-module of TCRγδ is replaced by that of TCR_αβ_ that strengthens the V-C coupling achieved a gain-of-function catch bond character while the wild-type TCRγδ is a slip-bond receptor (Mallis, et al., PNAS 2021; Bettencourt et al., Biophys. J. 2024). We added our prediction that the FG-loop deletion mutants of B7 TCR will behave similarly in line 261–264.

      (9) Regarding extending TCR and MHC termini using native sequences, as described in the methods, what would be the disadvantage of using the same sequence, which could be made much more rigid, e.g. a poly-Pro sequence? After all, the point seems to be applying a roughly constant force, but flexible/disordered linkers seem likely to increase force fluctuation.

      R1-k. The purpose of adding linkers was to allow a certain degree of longitudinal and transverse motion as would occur in vivo. While it will be worthwhile to explore the effects of linker flexibility on the conformational dynamics of the complex, for the present study, we used the actual sequence for the linkers for those proteins (line 341–344).

      Reviewer #2 (Recommendations for the authors):

      (1) Figure 2 is almost illegible, especially Figure 2A-D. I do not think that these contacts vs time would be useful to anyone except for someone interested in this particular pMHC interaction, so I would suggest moving it to a supporting figure and making it much larger.

      R2-e. Thanks for the suggestion. We created Figure 2–figure supplement 1 and made panels larger for clearer presentation.

      (2) Figure 4 is overwhelming, and does not convey any particular message.

      R2-f. This is the same comment as reviewer 1’s comment (7) above. Please see our response R1-i.

      Reviewer #3 (Recommendations for the authors):

      (1) The label ”beta2m” in Figure 1A should be moved closer to the beta2 microglobulin domain. A label TCR should be added to Figure 1A.

      R3-c. Thanks for pointing out about β2m. We have corrected it. About putting the label ‘TCR,’ to avoid cluttering, we explained that Vα, Vβ, Cα, and Cβ are the 4 subdomains of TCR in the caption of Figure 1A.

      (2) Hydrogen atoms should be removed from the peptide in Figure 1B.

      R3-d. We have removed the hydrogen atoms.

      (3) The authors should consider moving Figures 1 A-D to the SI and show a simpler description of the contact occupancy than the heat maps. The legend of Figure 2A-D is too small.

      R3-e. By ‘Figures 1 A-D’ we believe the reviewer meant Figure 2A–D. This is the same comment as reviewer 2’s comment (1). Please see our response R2-e above.

      (4) Vertical (dashed) lines should be added to Figure 3E at 500 ns to emphasize the segment of the time series used for the histograms.

      R3-f. We added vertical lines in figures showing time-dependent behaviors, which are Figure 1D, Figure 2B, Figure 2–figure supplement 1F, and Figure 4–figure supplement 1B.

    1. Author response:

      Reviewer #1 (Public Review):

      We are grateful to this reviewer for her/his constructive comments, which have greatly improved our work. Individual responses are provided below.

      The authors recorded from multiple mossy cells (MCs) of the dentate gyrus in slices or in vivo using anesthesia. They recorded MC spontaneous activity during spontaneous sharp waves (SWs) detected in area CA3 (in vitro) or in CA1 ( in vivo). They find variability of the depolarization of MCs in response to a SW. They then used deep learning to parse out more information. They conclude that CA3 sends different "information" to different MCs. However, this is not surprising because different CA3 neurons project to different MCs and it was not determined if every SW reflected the same or different subsets of CA3 activity.

      Thank you for your valuable comments. We agree that our finding that different MCs receive different information is unsurprising. These data are, in fact, to be expected from the anatomical knowledge of the circuit structure. However, as a physiological finding, there is a certain value in proving this fact; please note that it was not clear whether the neural activity of individual MCs received heterogeneous/variable information at the physiological level. It was therefore necessary to investigate this by recording neural activity. We believe this study is important because it quantitatively demonstrates this fact.

      The strengths include recording up to 5 MCs at a time. The major concerns are in the finding that there is variability. This seems logical, not surprising. Also it is not clear how deep learning could lead to the conclusion that CA3 sends different "information" to different MCs. It seems already known from the anatomy because CA3 neurons have diverse axons so they do not converge on only one or a few MCs. Instead they project to different MCs. Even if they would, there are different numbers of boutons and different placement of boutons on the MC dendrites, leading to different effects on MCs. There also is a complex circuitry that is not taken into account in the discussion or in the model used for deep learning. CA3 does not only project to MCs. It also projects to hilar and other dentate gyrus GABAergic neurons which have complex connections to each other, MCs, and CA3. Furthermore, MCs project to MCs, the GABAergic neurons, and CA3. Therefore at any one time that a SW occurs, a very complex circuitry is affected and this could have very different effects on MCs so they would vary in response to the SW. This is further complicated by use of slices where different parts of the circuit are transected from slice to slice.

      The first half of this paragraph is closely related to the previous paragraph. We propose that the variation in membrane potential of the simultaneously recorded MCs allows for the expression of diverse information. We also believe that this is highly novel in that no previous work has described the extent to which SWR is encoded in MCs. Our study proposes a new quantitative method that relates two variables (LFP and membrane potential) that are inherently incomparable. Specifically, we used machine learning (please note that it is a neural network, but not "deep learning") to achieve this quantification, and we believe this innovation is noteworthy.

      In the latter part of this article, you raise another important point. First, we would like to point out that this comment contains a slight misunderstanding. Our goal is not to reproduce the circuit structure of the hippocampus in silico but to propose a "function (or mapping/transformation)" that connects the two different modalities, i.e., LFP and Vm. This function should be as simple as possible, which is desirable from an explanatory point of view. In this respect, our machine learning model is a 'perceptron'-like 3-layer neural network. One of the simplest classical neural network models can predict the LFP waveform from Vm, which is quite surprising and an achievement we did not even imagine before. The fact that our model does not consider dendrites or inhibitory neurons is not a drawback but an important advantage. On the other hand, the fact that the data we used for our predictions were primarily obtained using slice experiments may be a drawback of this study, and we agree with your comments. However, we can argue that the new quantitative method we propose here is versatile since we showed that the same machine learning can be used to predict in vivo single-cell data.

      It is also not discussed if SWs have a uniform frequency during the recording session. If they cluster, or if MC action potentials occur just before a SW, or other neurons discharge before, it will affect the response of the MC to the SW. If MC membrane potential varies, this will also effect the depolarization in response to the SW.

      Thank you for raising an important point. We have done some additional analyses in response to your comment. First, we plotted how the SWR parameter fluctuated during our recording time (especially for data recorded for long periods of more than 5 minutes). As shown in the new Figure 1 - figure supplement 4, we can see that the frequency of SWRs was kept uniform during the recording time. These data ensure the rationale for pooling data over time.

      We also calculated the average membrane potentials of MCs before and after SWRs and found that MCs did not show depolarization or hyperpolarization before SWs, unlike Vm of CA1 neurons. These data indicate that the surrounding circuitry was not particularly active before SW, eliminating any concern that such unexpected preceding activity might affect our analysis. These data are shown in Figure 1 - figure supplement 2.

      In vivo, the SWs may be quite different than in vivo but this is not discussed. The circuitry is quite different from in vitro. The effects of urethane could have many confounding influences. Furthermore, how much the in vitro and in vivo SWs tell us about SWs in awake behaving mice is unclear.

      We agree with this point. Ideally, recording in vitro and in vivo under conditions as similar as possible would be optimal. However, as you know, patch-clamp recording from mossy cells in vivo is technically challenging, and currently, there is no alternative to conducting experiments under anesthesia. We believe that science advances not merely through theoretical discourse, but by contributing empirical data collected under existing conditions. However, as we mentioned in the paper, we believe that in vivo and in vitro SWR share some properties and a common principle of occurrence. We also observed that there are similar characteristics in the membrane potential response of MC to SWR. However, as you have pointed out, data derived from these limitations require careful interpretation, and we have explicitly stated in the paper that not only are there such problems, but that there are also common properties in the data obtained in vivo and in vitro (Page 12, Line 357).

      Also, methods and figures are hard to understand as described below.

      Thank you for all your comments. We have carefully considered the reviewers' comments and improved the text and legend. We hope you will take the time to review them.

      Reviewer #2 (Public Review):

      Thank you for the positive evaluations, which have encouraged us to resubmit this manuscript. We have revised our manuscript in accordance with your comments. Our point-by-point responses are as follows:

      • A summary of what the authors were trying to achieve

      Drawing from theoretical insights on the pivotal role of mossy cells (MCs) in pattern separation - a key process in distinguishing between similar memories or inputs - the authors investigated how MCs in the dentate gyrus of the hippocampus encode and process complex neural information. By recording from up to five MCs simultaneously, they focused on membrane potential dynamics linked to sharp wave-ripple complexes (SWRs) originating from the CA3 area. Indeed, using a machine learning approach, they were able to demonstrate that even a single MC's synaptic input can predict a significant portion (approximately 9%) of SWRs, and extrapolation suggested that synaptic input obtained from 27 MCs could account for 90% of the SWR patterns observed. The study further illuminates how individual MCs contribute to a distributed but highly specific encoding system. It demonstrates that SWR clusters associated with one MC seldom overlap with those of another, illustrating a precise and distributed encoding strategy across the MC network.

      We appreciate that this reviewer found scientific value in our manuscript. Thanks to the comments, we were pleased to be able to revise and improve the manuscript. Individual responses are listed below:

      • An account of the major strengths and weaknesses of the methods and results

      Strengths:

      (1) This study is remarkable because it establishes a critical link between the subthreshold activities of individual neurons and the collective dynamics of neuronal populations.

      (2) The authors utilize machine learning to bridge these levels of neuronal activity. They skillfully demonstrate the predictive power of membrane potential fluctuations for neuronal events at the population level and offer new insights into neuronal information processing.

      (3) To investigate sharp wave/ripple-related synaptic activity in mossy cells (MCs), the authors performed challenging experiments using whole-cell current-clamp recordings. These recordings were obtained from up to five neurons in vitro and from single mossy cells in live mice. The latter recordings are particularly valuable as they add to the limited published data on synaptic input to MCs during in vivo ripples.

      We appreciate the reviewer’s critical evaluations, which have encouraged us to revise and resubmit this manuscript. We have revised our manuscript in line with the reviewer’s comments. Our point-by-point responses are provided below:

      Weaknesses:

      (1) The model description could significantly benefit from additional details regarding its architecture, training, and evaluation processes. Providing these details would enhance the paper's transparency, facilitate replication, and strengthen the overall scientific contribution. For further details, please see below.

      Thank you for the suggestions. We have responded with model details based on the following comments.

      (2) The study recognizes the concept of pattern separation, a central process in hippocampal physiology for discriminating between similar inputs to form distinct memories. The authors refer to a theoretical paper by Myers and Scharfman (2011) that links pattern separation with activity backpropagating from CA3 to mossy cells. Despite this initial citation, the concept is not discussed again in the context of the new findings. Given the significant role of MCs in the dentate gyrus, where pattern separation is thought to occur, it would be valuable to understand the authors' perspective on how their findings might relate to or contribute to existing theories of pattern separation. Could the observed functions of MCs elucidated in this study provide new insights into their contribution to processes underlying pattern separation?

      Thank you for your valuable comment. The role of MCs in pattern separation is described in the discussion as follows:

      “It has been shown through theoretical models that MCs are a contributor to pattern separation (Myers and Scharfman, 2011). In general, the pathway of neural information is diverged from the entorhinal cortex through the larger granule cell layer and then compressed into the smaller CA3 cell layer. In this case, there is a high possibility of information loss during the transmission process. Thus, a backprojection mechanism via MCs has been proposed as a device to prevent information loss. Indeed, in theoretical models, such backprojection improves pattern separation and memory capacity, and the results are closer to experimental data than models without built-in backprojection. However, it was unclear what information individual MCs receive during backprojection. Our results show that CA3 SWR is distributed and encoded in the MC population, and that even though the number of MCs is smaller than in other regions, it is possible to reproduce about 30% of the SWR in CA3 from the membrane potential of only five MCs. Based on these results, it is believed that MCs not only play a role in preventing information loss, but also play a role in receiving some kind of newly encoded memory information in the CA3 region, and it is highly likely that the information contained in the backprojections is different from the neural information transmitted through conventional transmission pathways. Indeed, the fact that the information replayed in CA3 is reflected as SWR and propagated to each brain region suggests that the newly encoded memory information in CA3 is propagated to MC. If  backprojection simply returned the information transmitted from DG to CA3, and to MC, this would be unrealistic and extremely inefficient. However, it is still unclear what kind of memory information is actually backprojected and distributed to the MC, and how it differs from the memory information transmitted in the forward direction. These are open questions that need to be addressed in future experiments in awake animals.” (Page 11, Line 333)

      (3) Previous work concluded that sharp waves are associated with mossy cell inhibition, as evidenced by a consistent ripple function-related hyperpolarization of the membrane potential in these neurons when recorded at resting membrane potential (Henze & Buzsáki, 2007). In contrast, the present study reveals an SWR-induced depolarization of the membrane potential. Can the authors explain the observed modulation of the membrane potential during CA1 ripples in more detail? What was the proportion of cases of depolarization or hyperpolarization? What were the respective amplitude distributions? Were there cases of activation of the MCs, i.e., spiking associated with the ripple? This more comprehensive information would add significance to the study as it is not currently available in the literature.

      Sorry for confusing the conclusion. First, we did not mention in the paper that in vivo MC depolarized during SWR. The following sentences have added to result:

      “Previous research has shown that the hyperpolarization of MC membrane potential associated with SWR indicates that SWR is related to the inhibition of mossy cells (Henze and Buzsáki, 2007). However, our data showed that the proportion of cases of depolarization or hyperpolarization was about the same, with a slight excess of depolarization. However, it should be noted that MCs are highly active and fluctuating cells, and the determination of whether they are depolarized or hyperpolarized is highly dependent on the method of analysis. Moreover, the firing rate of MCs that we recorded was 1.07 ± 0.93 Hz (mean ± SD from 6 cells, 6 mice), and 6.68 ± 4.79% (mean ± SD from 6 cells, 6 mice, n = 757 SWR events) of all SWRs recruited MC firing (calculated as firing within 50 ms after the SWR peak). ” (Page 5, Line 143)

      (4) In the study, the observation that mossy cells (MCs) in the lower (infrapyramidal) blade of the dentate gyrus (DG) show higher predictability in SWR patterns is both intriguing and notable. This finding, however, appears to be mentioned without subsequent in-depth exploration or discussion. One wonders if this observed predictability might be influenced by potential disruptions or severed connections inherent to the brain slice preparation method used. Furthermore, it prompts the question of whether similar observations or trends have been noted in MCs recorded in vivo, which could either corroborate or challenge this intriguing in vitro finding.

      As you pointed out, one cannot rule out the possibility that this predictability may be influenced by potential disruptions or disconnections inherent in the methods used to prepare the acute slices. And the number of cells is limited to six with respect to the anatomical location of the MC recorded in vivo, making SWR and MC patch clamp recording very difficult even under anesthesia. Therefore, it is difficult to find statistical significance in the current data. We have added following text in Discussion:

      “In addition, the finding that SWR is more predictive when the recorded location of the MC is near the lower blade of the DG is unexpected, so the possibility that this result is influenced by potential disruptions or severed connections during the preparation of the acute slice cannot be ruled out.” (Page 14, Line 405)

      (5) The study's comparison of SWR predictability by mossy cells (MCs) is complicated by using different recording sites: CA3 for in vitro and CA1 for in vivo experiments, as shown in Fig. 2. Since CA1-SWRs can also arise from regions other than CA3 (see e.g. Oliva et al., 2016, Yamamoto and Tonegawa, 2017), it is difficult to reconcile in vitro and in vivo results. Addressing this difference and its implications for MC predictability in the results discussion would strengthen the study.

      Thank you for your comment. We have added the following discussion to your comment:

      “In this study, we performed MC patch-clamp recording both in vivo and in vitro, and clarified that SWR can be predicted from V_m of MC in both cases. However, there are three caveats to the interpretation of these data. First, the _in vivo SWR cannot be said to be exactly the same as the in vitro SWR: note that in vitro SWR has some similarities to in vivo SWR, such as spatial and spectral profiles and neural activity patterns (Maier et al., 2009; Hájos et al., 2013; Pangalos et al., 2013). The same concern applies to MC synaptic inputs. The in vivo V_m data may contain more information compared to the _in vitro single MC data, because the entire projections that target MCs are intact, resulting in a complete set of synaptic inputs related to SWR activity, as opposed to slices where connections are severed. While we recognize these differences, it is also very likely that there are common ways of expressing information. Second, since the in vivo LFP recordings were obtained from the CA1 region, it is possible that the CA1-SWR receives input from the CA2 region (Oliva et al., 2016) and the entorhinal cortex (Yamamoto and Tonegawa, 2017). In addition, urethane anesthesia has been observed to reduce subthreshold activity, spike synchronization, and SWR (Yagishita et al., 2020), making it difficult to achieve complete agreement with in vitro SWR recorded from the CA3 region. Finally, although we were able to record MC V_m during _in vivo SWR in this study, the in vivo data set consisted of recordings from a single MC, in contrast to the in vitro dataset. To perform the same analysis as in the in vitro experiment, it would be desirable to record LFPs from the CA3 region and collect data from multiple MCs simultaneously, but this is technically very difficult. In this study, it was difficult to directly clarify the consistency between CA3 network activity and in vivo MC synaptic input, but the fact that the SWR waveform can be predicted from in vivo MC V_m in CA1-SWR may be the result of some CA3 network activity being reflected in CA1-SWR. It is undeniable that more accurate predictions would have been possible if it had been possible to record LFP from the CA3 regions _in vivo. ” (Page 12, Line 357)

      • An appraisal of whether the authors achieved their aims, and whether the results support their conclusions

      As outlined in the abstract and introduction, the primary aim is to investigate the role of MCs in encoding neuronal information during sharp wave ripple complexes, a crucial neuronal process involved in memory consolidation and information transmission in the hippocampus. It is clear from the comprehensive details in this study that the authors have meticulously pursued their goals by providing extensive experimental evidence and utilizing innovative machine learning techniques to investigate the encoding of information in the hippocampus by mossy cells (MCs). Together, this study provides a compelling account supported by rigorous experimental and analytical methods. Linking subthreshold membrane potentials and population activity by machine learning provides a comprehensive new analytic approach and sheds new light on the role of MCs in information processing in the hippocampus. The study not only achieves the stated goals, but also provides novel methodology, and valuable insights into the dynamics of neural coding and information flow in the hippocampus.

      We appreciate the reviewer’s critical evaluations, which have encouraged us to revise and resubmit this manuscript. We have revised our manuscript in line with the reviewer’s comments.

      • A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community

      Impact: Both the novel methodology and the provided biological insights will be of great interest to the community.

      Utility of methods/data: The applied deep learning approach will be of particular interest if the authors provide more details to improve its reproducibility (see related suggestions below).

      We appreciate that this reviewer found scientific value in our manuscript. Thanks to the comments.

      Reviewer #3 (Public Review):

      We appreciate that this reviewer raised several important issues. We are pleased to have been able to revise the paper into a better manuscript based on these comments. Individual responses are listed below:

      Compared to the pyramidal cells of the CA1 and CA3 regions of the hippocampus, and the granule cells of the dentate gyrus (DG), the computational role(s) of mossy cells of the DG have received much less attention over the years and are consequently not well understood. Mossy cells receive feedforward input from granule cells and feedback from CA3 cells. One significant factor is the compression of the large number of CA3 cells that input onto a much smaller population of mossy cells, which then send feedback connections to the granule cell layer. The present paper seeks to understand this compression in terms of neural coding, and asks whether the subthreshold activity of a small number of mossy cells can predict above chance levels the shapes of individual SWs produced by the CA3 cells. Using elegant multielectrode intracellular recordings of mossy cells, the authors use deep learning networks to show that they can train the network to "predict" the shape of a SW that preceded the intracellular activity of the mossy cells. Putatively, a single mossy cell can predict the shape of SWs above chance. These results are interesting, but there are some conceptual issues and questions about the statistical tests that must be addressed before the results can be considered convincing.

      We appreciate that this reviewer found scientific value in our manuscript. Thanks to the comments, we were pleased to be able to revise and improve the manuscript. Individual responses are listed below:

      Strengths

      (1) The paper uses technically challenging techniques to record from multiple mossy cells at the same time, while also recording SWs from the LFP of the CA3 layer. The data appear to be collected carefully and analyzed thoughtfully.

      (2) The question of how mossy cells process feedback input from CA3 is important to understand the role of this feedback pathway in hippocampal processing.

      3) Given the concerns expressed below about proper statistical testing are resolved, the data appear supportive of the main conclusions of the authors and suggest that, to some degree, the much smaller population of mossy cells can conserve the information present in the larger population of CA3 cells, presumably by using a more compressed, dense population code.

      We appreciate the reviewer’s critical evaluations, which have encouraged us to revise and resubmit this manuscript. We have revised our manuscript in line with the reviewer’s comments. Our point-by-point responses are provided below:

      Weaknesses

      4) Some of the statistical tests appear inappropriate because they treat each CA3 SW and associated Vm from a mossy cell as independent samples. This violates the assumptions of statistical tests such as the Kolmogorov-Smirnov tests of Figure 3C and Fig 3E. Although there is large variability among the SWs recorded and among the Vm's, they cannot be considered independent measurements if they derive from the same cell and same recording site of an individual animal. This becomes especially problematic when the number of dependent samples adds up to the tens of thousands, providing highly inflated numbers of samples that artificially reduce the p values. Techniques such as mixed-effects models are being increasingly used to factor out the effects of within cell and within animal correlations in the data. The authors need to do something similar to factor out these contributions in order to perform statistical tests, throughout the manuscript when this problem occurs.

      Thank you for the insightful comment. As for the correlation between the animals, since they were brought in at the same age and kept in the same environment, we do not think it is necessary to account for the differences due to environmental factors. As the reviewer pointed out, we cannot completely rule out the possibility that within cell or within animal correlation might influence the results, so we plotted the differences in prediction accuracy between cells, slices, and animals (Figure 3 - figure supplement 7). The results showed that prediction accuracy of the real data was better than that of the shuffled data in 66 of the 87 MCs (75.9%). In response to the comment that measurements from the same animal do not constitute independent samples, we have indicated that the average ΔRMSE for each mouse were calculated and these values were significantly different from 0 (n = 14, *p = 0.0041, Student’s t-test). In other words, even if each animal is considered an independent sample, it is possible to obtain statistically significant differences.

      5) A separate statistical problem occurs when comparing real data against a shuffled, surrogate data set. From the methods, I gather that Figure 3C combined data from 100 surrogate shuffles to compare to the real data. It is inappropriate to do a classic statistical test of data against such shuffles, because the number of points in the pooled surrogate data sets are not true samples from a population. It is a mathematical certainty that one can eventually drive a p value to < 0.05 just by increasing the number of shuffles sufficiently. Thus, the p value is determined by the number of computer shuffles allowed by the time and processing power of a computer, rather than by sampling real data from the population. Figures such as 4C and 5A are examples that test data against shuffle appropriately, as a single value is determined to be within or outside the 95% confidence interval of the shuffle, and this determination is not directly affected by the number of shuffles performed.

      Thank you for raising a very good point. We understand the reviewer's comments, but we cannot fully agree with the part that says "It is mathematical certainty that one can eventually drive a p value to < 0.05 just by increasing the number of shuffles sufficiently". This is because when comparing data with no difference at all, no amount of shuffling will produce a significant difference. In this regard, we agree that increasing the number of shuffles will lower the p-value when comparing data with even a small difference. Based on the reviewer's comments, we used a paired t-test to test whether the difference between RMSEreal and RMSEsurrogate was significantly different from 0, and showed it was significantly different (Figure 3 - figure supplement 5). Even when a paired t-test was used for the test, as in Figure 3E, a significant difference in the prediction error of the real and shuffled data was observed for all MC number inputs and also for the in vivo data.

      6) The last line of the Discussion states that this study provides "important insights into the information processing of neural circuits at the bottleneck layer," but it is not clear what these insights are. If the statistical problems are addressed appropriately, then the results do demonstrate that the information that is reflected in SWs can be reconstructed by cells in the MC bottleneck, but it is not certain what conceptual insights the authors have in mind. They should discuss more how these results further our understanding of the function of the feedback connection from CA3 to the mossy cells, discuss any limitations on their interpretation from recording LFPs rather than the single-unit ensemble activity (where the information is really encoded).

      Thank you for your insightful comment. We have added the following text to the discussion:

      “Given that different SWRs may encode information that correlates with different experiences, it is also possible that the activity of individual MCs may play a role in encoding different experiences via SWRs. Indeed, several in vivo studies have confirmed that MC activity is involved in the space encoding (Bui et al., 2018; Huang et al., 2024). However, the relationship with SWRs has not been investigated. The significance of the fact that the SWR recorded from CA3 is reflected in the MC as synaptic input is that it not only shows the transmission pathway from CA3 to MC, but also reveals the information below the threshold that leads to firing, and in a broad sense, it approaches the mechanism by which information processing by neuronal firing. And the expression of synaptic input to the MC is not uniform, but varies in a variety of ways according to the pattern of SWR. Based on previous research showing that diversity is important for information representation (Padmanabhan and Urban, 2010; Tripathy et al., 2013), it is possible that this heterogeneity in membrane potential levels, rather than the all-or-none output of neuronal firing activity, is the key to encoding more precise information. In this respect, our research, which focuses on information encoding at the subthreshold level, may be able to extract even more information than information encoded by firing activity. ” (Page 14, Line 419)

      7) In Figure 1C, the maximum of the MC response on the first inset precedes the SW, and the onset of the Vm response may be simultaneous with SW. This would suggest that the SW did not drive the mossy cell, but this was a coincident event. How many SW-mossy cell recordings are like this? Do the authors have a technical reason to believe that these are events in which the mossy cell is driven by the CA3 cells active during the SW?

      Thank you for your insightful comment. Based on your comment, we have aligned all the MC EPSPs for each SWR onset and found that the EPSPs rise after the SWR onset (Figure 1 - figure supplement 2). This leads us to believe that the EPSP of the MC is most likely driven by the SWR.

    1. Of course, we don’t just communicate verbally—we have various options, or channels for communication. Encoded messages are sent through a channel, or a sensory route on which a message travels, to the receiver for decoding. While communication can be sent and received using any sensory route (sight, smell, touch, taste, or sound), most communication occurs through visual (sight) and/or auditory (sound) channels. If your roommate has headphones on and is engrossed in a video game, you may need to get his attention by waving your hands before you can ask him about dinner.

      This is especially interesting to me now, as we saw the rise of smartphones people started to talk to each other in person less and less, especially after 2020. And I just think it's interesting to see how peoples interactions with each other changed after that. There is simply a lot more communication that is only text based now, I'd argue more now than there has ever been before. And I know from experience how easy it can be to misinterpret a text that someone sent, because you can't tell what tone they said it in through text and you can't see if they make a hand or arm motion to show its a joke, or a million other things could happen and cause someone to misjudge the situation that could never happen in person for a million different reasons.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, Wu et al. introduce a novel approach to reactivate the Muller glia cell cycle in the mouse retina by simultaneously reducing p27Kip1 and increasing cyclin D1 using a single AAV vector. The approach effectively promotes Muller glia proliferation and reprograming without disrupting retinal structure or function. Interestingly, reactivation of the Muller glia cell cycle downregulates IFN pathway, which may contribute to the induced retinal regeneration. The results presented in this manuscript may offer a promising approach for developing Müller glia cell-mediated regenerative therapies for retinal diseases.

      Strengths:

      The data are convincing and supported by appropriate, validated methodology. These results are both technically and scientifically exciting and are likely to appeal to retinal specialists and neuroscientists in general.

      Weaknesses:

      There are some data gaps that need to be addressed.

      (1) Please label the time points of AAV injection, EdU labeling, and harvest in Figure 1B.

      We thank the reviewer for highlighting the lack of clarity in our experimental design. We have labeled all experiment timelines in the figures where appropriate in the revised version.

      (2) What fraction of Müller cells were transduced by AAV under the experimental conditions?

      We apologize for not clearly explaining the AAV transduction effeciency. AAV transduction efficiency was not uniform across the retinas. The retinal region adjacent to the optic nerve exhibits a transduction efficiency of nearly 100%. In contrast, the peripheral retina shows a lower transduction efficiency compared to the central region. The representative retinal sections with typical infection pattern are shown in Supplementary figure 4. The quantification of Edu+ MG or other markers was conducted in a 250 µm region with the highest efficiency. For scRNA-seq experiment, retinal regions with high AAV transduction efficiency were dissected with the aid of a control GFP virus.   

      (3) It seems unusually rapid for MG proliferation to begin as early as the third day after CCA injection. Can the authors provide evidence for cyclin D1 overexpression and p27 Kip1 knockdown three days after CCA injection?

      We included the data that GFP expression is evident at 3 days post AAV-GFP-GFP injection (Supplementary Fig. 1B). Additionally, we performed immunostaining and confirmed cyclin D1 overexpression at 3 days post CCA injection (Fig. 2E) as well as qPCR analysis to confirm cyclin D1 overexpression and p27kip1 knockdown at the same time point (Supplementary Fig. 5).

      (4) The authors reported that MG proliferation largely ceased two weeks after CCA treatment. While this is an interesting finding, the explanation that it might be due to the dilution of AAV episomal genome copies in the dividing cells seems far-fetched.

      We agree with the reviewer that dilution of AAV episomal genomes is unlikely to be the sole reason for the stop of MG proliferation. By staining cyclin D1 at various days post CCA injection, we found that cyclin D1 is immediately downregulated in the mitotic MG undergoing interkinetic nuclear migration to the outer nuclear layer (Fig. 2G-I). In contrast, the effect of p27<sup>kip1</sup> knockdown by CCA lasted longer (Supplementary Figure 9-10). It is possible that other anti-proliferative genes are involved in the immediate downregulation of Cyclin D1.

      Reviewer #2 (Public Review):

      This manuscript by Wu, Liao et al. reports that simultaneous knockdown of P27Kip1 with overexpression of Cyclin D can stimulate Muller glia to re-enter the cell cycle in the mouse retina. There is intense interest in reprogramming mammalian muller glia into a source for neurogenic progenitors, in the hopes that these cells could be a source for neuronal replacement in neurodegenerative diseases. Previous work in the field has shown ways in which mouse Muller glia can be neurogenically reprogrammed and these studies have shown cell cycle re-entry prior to neurogenesis. In other works, typically, the extent of glial proliferation is limited, and the authors of this study highlight the importance of stimulating large numbers of Muller glia to re-enter the cell cycle with the hopes they will differentiate into neurons. While the evidence for stimulating proliferation in this study is convincing, the evidence for neurogenesis in this study is not convincing or robust, suggesting that stimulating cell cycle-reentry may not be associated with increasing regeneration without another proneural stimulus.

      Below are concerns and suggestions.

      Intro:

      (1) The authors cite past studies showing "direct conversion" of MG into neurons. However, these studies (PMID: 34686336; 36417510) show EdU+ MG-derived neurons suggesting cell cycle re-entry does occur in these strategies of proneural TF overexpression.

      We thank the reviewer for pointing this out. We have revised the statement to "MG reprogramming".

      (2) Multiple citations are incorrectly listed, using the authors first name only (i.e. Yumi, et al; Levi, et al;). Studies are also incompletely referenced in the references.

      We apologize for the mistakes in reference. We have corrected the reference mistakes in the revised version.

      Figure 1:

      (3) When are these experiments ending? On Figure 1B it says "analysis" on the end of the paradigm without an actual day associated with this. This is the case for many later figures too. The authors should update the paradigms to accurately reflect experimental end points.

      We thank the reviewer for highlighting the lack of clarity in our experimental design. We have labeled all experiment timelines in the figures where appropriate in the revised version.

      (4) Are there better representative pictures between P27kd and CyclinD OE, the EdU+ counts say there is a 3 fold increase between Figure 1D&E, however the pictures do not reflect this. In fact, most of the Edu+ cells in Figure 1E don't seem to be Sox9+ MG but rather horizontally oriented nuclei in the OPL that are likely microglia.

      Thanks to the reviewer for pointing this out. We have replaced the image of cyclin D1 OE retina which a more representative image.

      (5) Is the infection efficacy of these viruses different between different combinations (i.e. CyclinD OE vs. P27kd vs. control vs. CCA combo)? As the counts are shown in Figure 1G only Sox9+/Edu+ cells are shown not divided by virus efficacy. If these are absolute counts blind to where the virus is and how many cells the virus hits, if the virus efficacy varies in efficiency this could drive absolute differences that aren't actually biological.

      Rule out the possibility that the differences in MG proliferation across groups are due to variations in viral efficacy, we have examined the p27<sup>kip1</sup> knockdown and cyclin D1 overexpression efficiencies for all four groups by qPCR analysis. The result showed that cyclin D1 overexpression efficiency by AAV-GFAP-Cyclin D1 virus alone or P27 knockdown efficiency by AAV-GFAP-mCherry-p27kip1 shRNA1 is comparable to, if not even higher than, those by CCA virus (Supplementary Fig 5). Therefore, the virus efficacy cannot explain the drastic increase in MG proliferation by CCA. 

      As the central retina usually had 100% infection efficacy (Supplementary Fig. 4), we quantified the Edu+Sox9+ cell number in the 250µm regions next to the optic nerve.

      (6) According to the Jax laboratories, mice aren't considered aged until they are over 18months old. While it is interesting that CCA treatment does not seem to lose efficacy over maturation I would rephrase the findings as the experiment does not test this virus in aged retinas.

      Thank you to the reviewer for bringing this to our attention. We have changed to “older adult mice” in our revised manuscript.

      (7) Supplemental Figure 2c-d. These viruses do not hit 100% of MG, however 100% of the P27Kip staining is gone in the P27sh1 treatment, even the P27+ cell in the GCL that is likely an astrocyte has no staining in the shRNA 1 picture. Why is this?

      We have replaced the images in Supplementary Fig. 2B-D.

      Figure 2

      (8) Would you expect cells to go through two rounds of cell cycle in such a short time? The treatment of giving Edu then BrdU 24 hours later would have to catch a cell going through two rounds of division in a very short amount of time. Again the end point should be added graphically to this figure.

      We thank the reviewer for the comment. We repeated the Edu/BrdU colabelling experiment with extended periods of Edu/BrdU injections. Based on the result of the MG proliferation time course study (Fig. 2A), we injected 5 times of Edu from D1 to D5 and 5 times of BrdU from D6 to D10 post-CCA injection, which covered the major phase of MG proliferation (Fig. 2B-C). Consistent with the previous findings, we did not observe any BrdU&EdU double positive MG cells.

      Additionally, we showed that cyclin D1 overexpression immediately ceased in migrating mitotic MG (Fig. 2G-I), which may explain why CCA-treated MG do not progress to the second round of cell division.

      Figure 3

      (9) I am confused by the mixing of ratios of viruses to indicate infection success. I know mixtures of viruses containing CCA or control GFP or a control LacZ was injected. Was the idea to probe for GFP or LacZ in the single cell data to see which cells were infected but not treated? This is not shown anywhere?

      The virus infection was not uniform across the entire retina (Supplementary Fig. 4). To mark the infection hotspots, we added 10% GFP virus to the mixture. Regions of the retina with low infection efficiency were removed by dissection and excluded from the scRNA-seq analysis. Therefore, we assumed that the vast majority of MG were infected by CCA. We apologize for not clearly explaining this methodological detail in the original text. We have added the experimental design to Fig. 3A and revised the result part (line 191-196) accordingly.

      (10) The majority of glia sorted from TdTomato are probably not infected with virus. Can you subset cells that were infected only for analysis? Otherwise it makes it very hard to make population judgements like Figure 3E-H if a large portion are basically WT glia.

      This question is related to the last one. Since the regions with high virus infection efficiency were selectively dissected and isolated for analysis, the CCA-infected MG should constitute the vast majority of MG in the scRNA-seq data.

      (11) Figure 3C you can see Rho is expressed everywhere which is common in studies like this because the ambient RNA is so high. This makes it very hard to talk about "Rod-like" MG as this is probably an artifact from the technique. Most all scRNA-seq studies from MG-reprogramming have shown clusters of "rods" with MG hybrid gene expression and these had in the past just been considered an artifact.

      We agree with the reviewer that the high rod gene expression in the rod-MG cluster is an artifact. We have performed multiple rounds of RNA in situ hybridization on isolated MG nuclei. The counts of Gnat1 and Rho mRNA signal are largely overlapped between the two samples with and without CCA treatment (Supplementary Fig 14). Some MG in the control retinas without CCA treatment had up to 7 or 8 dots per cell, suggesting contamination of attached rod cell debris during retina dissociation (Supplementary Fig 14). Therefore, the result did not support that rod-MG is a reprogrammed MG population with rod gene upregulation.

      (12) It is mentioned the "glial" signature is downregulated in response to CCA treatment. Where is this shown convincingly? Figure H has a feature plot of Glul, which is not clear it is changed between treatments. Otherwise MG genes are shown as a function of cluster not treatment.

      We have added box plots of several MG-specific genes to illustrate the downregulation of the glial signature in the relevant cell cluster in the revised manuscript (Supplementary Fig. 15).

      Figure 4

      (13) The authors should be commended for being very careful in their interpretations. They employ the proper controls (Er-Cre lineage tracing/EdU-pulse chasing/scRNA-seq omics) and were very careful to attempt to see MG-derived rods. This makes the conclusion from the FISH perplexing. The few puncta dots of Rho and GNAT in MG are not convincing to this reviewer, Rho and GNAT dots are dense everywhere throughout the ONL and if you drew any random circle in the ONL it would be full of dots. The rigor of these counts also comes into question because some dots are picked up in MG in the INL even in the control case. This is confusing because baseline healthy MG do not express RNA-transcripts of these Rod genes so what is this picking up? Taken together, the conclusion that there are Rod-like MG are based off scRNA-seq data (which is likely ambient contamination) and these FISH images. I don't think this data warrants the conclusion that MG upregulate Rod genes in response to CCA.

      Given the results of RNA in situ hybridization on isolated MG, we revisited the result of the RNA in situ hybridization on retinal sections as well. We performed RNA in situ in the retinal section at 1 week post CCA treatment, expecting to see lower Gnat1 and Rho signals in the ONL-localizing MG compared to 3 weeks and 4 months post CCA treatment. However, we observed similar levels across all three time points (data not shown). The lack of dynamic changes in rod gene expression levels also suggests contamination from tightly surrounding neighboring rods. Consequently, we have reinterpreted the scRNA-seq and RNA FISH data and withdrawn the conclusion that MG upregulated rod genes after CCA treatment. We thank the reviewer for pointing out this potential issue and helping us avoid an incorrect conclusion.

      Figure 5

      (14) Similar point to above but this Glul probe seems odd, why is it throughout the ONL but completely dark through the IPL, this should also be in astrocytes can you see it in the GCL? These retinas look cropped at the INL where below is completely black. The whole retinal section should be shown. Antibodies exist to GS that work in mouse along with many other MG genes, IHC or western blots could be done to better serve this point.

      We have replaced the images in Figure 4 in the revised manuscript. Additionally, we have performed the Sox9 antibody staining to demonstrate partial MG dedifferentiation following CCA treatment (Figure 5).

      Figure 6

      (15) Figure 6D is not a co-labeled OTX2+/ TdTomato+ cell, Otx2 will fill out the whole nucleus as can be seen with examples from other MG-reprogramming papers in the field (Hoang, et al. 2020; Todd, et al. 2020; Palazzo, et al. 2022). You can clearly see in the example in Figure 6D the nucleus extending way beyond Otx2 expression as it is probably overlapping in space. Other examples should be shown, however, considering less than 1% of cells were putatively Otx2+, the safer interpretation is that these cells are not differentiating into neurons. At least 99.5% are not.

      We have replaced the image of Otx2+ Tdt+ Edu+ cell, which shows the whole nucleus filled with strong Otx2 staining.  

      (16) Same as above Figure 6I is not convincingly co-labeled HuC/D is an RNA-binding protein and unfortunately is not always the clearest stain but this looks like background haze in the INL overlapping. Other amacrine markers could be tested, but again due to the very low numbers, I think no neurogenesis is occurring.

      Since we didn’t find HuC/D+Tdt+EdU+ cells at 3 weeks post CCA treatment, we believe that the weak HuC/D+ staining in the MG daughter cells at 4 months is not background, but rather reflects an incomplete neurogenic switch. This suggests that the process of neurogenesis may be ongoing but not fully realized within the observed timeframe without additional stimuli.

      (17) In the text the authors are accidently referring to Figure 6 as Figure 7.

      We thank the reviewer for pointing out the mistake. We will correct the mistake in the revised manuscript.

      Figure 7

      (18) I like this figure and the concept that you can have additional MG proliferating without destroying the retina or compromising vision. This is reminiscent of the chick MG reprogramming studies in which MG proliferate in large numbers and often do not differentiate into neurons yet still persist de-laminated for long time points.

      General:

      (19) The title should be changed, as I don't believe there is any convincing evidence of regeneration of neurons. Understanding the barriers to MG cell-cycle re-entry are important and I believe the authors did a good job in that respect, however it is an oversell to report regeneration of neurons from this data.

      We thank the reviewer for the suggestion. We have changed the title to “Simultaneous cyclin D1 overexpression and p27kip1 knockdown enable robust Müller glia cell cycle reactivation in uninjured mouse retina” in the revised manuscript.

      (20) This paper uses multiple mouse lines and it is often confusing when the text and figures switch between models. I think it would be helpful to readers if the mouse strain was added to graphical paradigms in each figure when a different mouse line is employed.

      We have labeled the mouse lines used in each experiment in the figures where appropriate.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Mehmet Mahsum Kaplan et al. demonstrate that Meis2 expression in neural crest-derived mesenchymal cells is crucial for whisker follicle (WF) development, as WF fails to develop in wnt1-Cre;Meis2 cKO mice. Advanced imaging techniques effectively support the idea that Meis2 is essential for proper WF development and that nerves, while affected in Meis2 cKO, are dispensable for WF development and not the primary cause of WF developmental failure. The study also reveals that although Meis2 significantly downregulates Foxd1 in the mesenchyme, this is not the main reason for WF development failure. The paper presents valuable data on the role of mesenchymal Meis2 in WF development. However, further quantification and analysis of the WF developmental phenotype would be beneficial in strengthening the claim that Meis2 controls early WF development rather than causing a delay or arrest in development. A deeper sequencing data analysis could also help link Meis2 to its downstream targets that directly impact the epithelial compartment.

      Strengths:

      (1) The authors describe a novel molecular mechanism involving Mesenchymal Meis2 expression, which plays a crucial role in early WF development.

      (2) They employ multiple advanced imaging techniques to illustrate their findings beautifully.

      (3) The study clearly shows that nerves are not essential for WF development.

      We thank the reviewer for valuable comments that will help improve our study.

      Weaknesses:

      (1) The authors claim that Meis2 acts very early during development, as evidenced by a significant reduction in EDAR expression, one of the earliest markers of placode development. While EDAR is indeed absent from the lower panel in Figure 3C of the Meis2 cKO, multiple placodes still express EDAR in the upper two panels of the Meis2 cKO. The authors also present subsequent analysis at E13.3, showing one escaped follicle positive for SHH and Sox9 in Figures 1 and 3. Does this suggest that follicles are specified but fail to develop? Alternatively, could there be a delay in follicle formation? The increase in Foxd1 expression between E12.5 and E13.5 might also indicate delayed follicle development, or as the authors suggest, follicles that have escaped the phenotype. The paper would significantly benefit from robust quantification to accompany their visual data, specifically quantifying EDAR, Sox9, and Foxd1 at different developmental stages. Additionally, analyzing later developmental stages could help distinguish between a delay or arrest in WF development and a complete failure to specify placodes.

      The earliest DC (FOXD1) and placodal (EDAR, LEF1) markers tested in this study were observed only in the escaped WFs whereas these markers were missing in expected WF sites in mutants. This was also reflected in the loss of typical placodal morphology in the mutant’s epithelium. On the other hand, escaped WFs developed normally as shown by the analysis in Supp Fig 1A-B showing their normal size. These data suggest that development of escaped WFs is not delayed because they would appear smaller in size. To strengthen this conclusion, we assessed whisker development at E18.5 in Meis2 cKO mice by EDAR staining and results are shown in newly added Supplementary Figure 2. This experiment revealed that whisker phenotype persisted until E18.5 therefore this phenotype cannot be explained by a developmental delay.

      As far as quantification is concerned, we have already quantified the number of whiskers in controls and mutants at E12.5 and E13.5 in all whole mount experiments we did, i.e. Shh ISH and SOX9 or EDAR whole mount IFC. We pooled all these numbers together and calculated the whisker number reduction to 5.7+/-2.0% at E12.5 and 17.1+/-5.9 at E13.5. Line:132-134.

      (2) The authors show that single-cell sequencing reveals a reduction in the pre-DC population, reduced proliferation, and changes in cell adhesion and ECM. However, these changes appear to affect most mesenchymal cells, not just pre-DCs. Moreover, since E12.5 already contains WFs at different stages of development, as well as pre-DCs and DCs, it becomes challenging to connect these mesenchymal changes directly to WF development. Did the authors attempt to re-cluster only Cluster 2 to determine if a specific subpopulation is missing in Meis2 cKO? Alternatively, focusing on additional secreted molecules whose expression is disrupted across different clusters in Meis2 cKO could provide insights, especially since mesenchymal-epithelial communication is often mediated through secreted molecules. Did the authors include epithelial cells in the single-cell sequencing, can they look for changes in mesenchyme-epithelial cell interactions (Cell Chat) to indicate a possible mechanism?

      We agree with the reviewer that the effect of Meis2 on cell proliferation and expression of cell adhesion and ECM markers are more general because they take place in the whole underlying mesenchyme. Our genetic tools did not allow specific targeting of DC or pre-DCs. Nonetheless, we trust that our data show that mesenchymal Meis2 is required for the initial steps of WF development including Pc formation. As far as bioinformatics data are concerned, this data set was taken from the large dataset GSE262468 covering the whole craniofacial region which led to very limited cell numbers in the cluster 2 (DC): WT_E12_5 --> 28, WT_E13_5 --> 131, MUT_E12_5 --> 19, MUT_E13_5 --> 28. Unfortunately, such small cell numbers did not allow further sub-clustering, efficient normalization, integration and conclusions from their transcriptional profiles. Although a number of interesting differentially expressed genes were identified (see supplementary datasets), none of them convincingly pointed at reasonable secreted molecule candidate. 

      We agree with the reviewer that cellchat analysis could provide robust indication of the mesenchymal-epithelial communication, however our datasets included only mesenchymal cell population (Wnt1-Cre2progeny) and epithelial cells were excluded by FACS prior to sc RNA-seq. (Hudacova et al. https://doi.org/10.1016/j.bone.2024.117297)

      (3) The authors aim to link Meis2 expression in the mesenchyme with epithelial Wnt signaling by analyzing Lef1, bat-gal, Axin1, and Wnt10b expression. However, the changes described in the figures are unclear, and the phenotype appears highly variable, making it difficult to establish a connection between Meis2 and Wnt signaling. For instance, some follicles and pre-condensates are Lef1 positive in Meis2 cKO. Including quantification or providing a clearer explanation could help clarify the relationship between mesenchymal Meis2 and Wnt signaling in both epidermal and mesenchymal cells. Did the authors include epithelial cells in the sequencing? Could they use single-cell analysis to demonstrate changes in Wnt signaling?

      We have now analyzed changes in LEF1 staining intensity in the epithelium and in the upper dermis. According to these quantifications, we observed a considerable decline in the number of LEF1+ placodes in the epithelium which corresponds to the lower number of placodes. On the other hand, LEF1 intensity in the ‘escaped’ placodes were similar between controls and mutants. LEF1 signal in the upper dermis is very strong overall and its quantification did not reveal any changes in the DC and non-DC region of the upper dermis. These data corroborate with our conclusion that Meis2 in the mesenchyme is not crucial for the dermal WNT signaling but is required for induction of LEF1 expression in the epithelium. However, once ‘escaper’ placodes appear, they display normal wnt signaling in Pc, DC and subsequent development. These quantitative data have been added to the revised manuscript. Line247-260.

      (4) Existing literature, including studies on Neurog KO and NGF KO, as well as the references cited by the authors, suggest that nerves are unlikely to mediate WF development. While the authors conduct a thorough analysis of WF development in Neurog KO, further supporting this notion, this point may not be central to the current work. Additionally, the claim that Meis2 influences trigeminal nerve patterning requires further analysis and quantification for validation.

      We agree with the reviewer that analysis of the Neurogenin1 knockout mice should not be central to this report. Nonetheless, a thorough analysis of WF development in Neurog1 KO was needed to distinguish between two possible mechanisms: whisker phenotype in Meis2 cKO results from 1. impaired nerve branching 2. Function of Meis2 in the mesenchyme. We will modify the text accordingly to make this clearer to readers. We also agree that nerve branching was not extensively analyzed in the current study but two samples from mutant mice were provided (Fig1 and Supp Videos), reflecting the consistency of the phenotype (see also Machon et al. 2015). This section was not central to this report either but led us to focus fully on the mesenchyme. We think that Meis2 function in cranial nerve development is very interesting and deserves a separate study.

      We have edited the introduction to reflect the literature better. Line70-79.

      (5) Meis2 expression seems reduced but has not entirely disappeared from the mesenchyme. Can the authors provide quantification?

      We have attempted to quantify MEIS2 staining in the snout dermis. However, the background fluorescence made it challenging to reliable quantify. Additionally, since at the point, dermal region where MEIS2 expression is relevant to induce WF formation is not known, we were unable to determine the regions to analyze. Instead, we now added three additional images from multiple regions of the snout sections stained with MEIS2 antibody in Supplementary Figure 1C. We believe newly added images will make our conclusion that MEIS2 is efficiently deleted in the mutants more convincing.

      Reviewer #2 (Public review):

      Summary:

      In this manuscript, Kaplan et al. study mesenchymal Meis2 in whisker formation and the links between whisker formation and sensory innervation. To this end, they used conditional deletion of Meis2 using the Wnt1 driver. Whisker development was arrested at the placode induction stage in Meis2 conditional knockouts leading to the absence of expression of placodal genes such as Edar, Lef1, and Shh. The authors also show that branching of trigeminal nerves innervating whisker follicles was severely affected but that whiskers did form in the complete absence of trigeminal nerves.

      Strengths:

      The analysis of Meis2 conditional knockouts convincingly shows a lack of whisker formation and all epithelial whisker/hair placode markers were analyzed. Using Neurog1 knockout mice, the authors show equally convincingly that whiskers and teeth develop in the complete absence of trigeminal nerves.

      We thank the reviewer for valuable comments that will help improve our study.

      Weaknesses:

      The manuscript does not provide much mechanistic insight as to why mesenchymal Meis2 leads to the absence of whisker placodes. Using a previously generated scRNA-seq dataset they show that two early markers of dermal condensates, Foxd1 and Sox2, are downregulated in Meis2 mutants. However, given that placodes and dermal condensates do not form in the mutants, this is not surprising and their absence in the mutants does not provide any direct link between Meis2 and Foxd1 or Sox2. (The absence of a structure evidently leads to the absence of its markers.)

      We apologize for unclear explanation of our data. We meant that Meis2 is functionally upstream of Foxd1 because Foxd1 is reduced upon Meis2 deletion. This means that during WF formation, Meis2 operates before Foxd1 induction and does not mean necessarily that Meis2 directly controls expression of Foxd1. Yes, we agree with reviewer’s note that Foxd1 and Sox2, as known DC markers, decline because the number of WF declines. We wanted to convince readers that Meis2 operates very early in the GRN hierarchy during WF development. We also admit that we provide poor mechanistic insights into Meis2 function as a transcription factor. We think that this weak point does not lower the value of the report showing indispensable role of Meis2 in WFs.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The text could benefit from editing.

      We have proofread the text.

      Some information is missing from the materials and methods section - a description of sequenced cells, the ISH protocol used, etc.

      Methodological section has been updated and single-cell experiments were performed and described in detail by Hudacova et al. 2025  (https://doi.org/10.1016/j.bone.2024.117297). We have utilized these datasets for scRNA analysis which has been described sufficiently in the referred paper. Reference for standard in site protocol has been added.

      Reviewer #2 (Recommendations for the authors):

      In the Introduction of the paper, the authors raise the question on the role of innervation in whisker follicle induction "It has been speculated that early innervation plays a role in initiating WF formation (ref. 1)"...and..."this revives the previous speculations that axonal network may be involved in WF positioning". However, the authors forget to mention that Wrenn & Wessless, 1984 (reference 1 in the manuscript) made exactly the opposite conclusion and stated e.g. "Nerve trunks and branches are present in the maxillary process well before any sign of vibrissa formation. Because innervation is so widespread there appears to be no immediate temporal correlation between the outgrowth of a nerve branch to a site and the generation of a vibrissa there. Furthermore, at the time just prior to the formation of the first follicle rudiment, there is little or no nerve branching to the presumptive site of that first follicle while branches are found more dorsally where vibrissae will not form until later." Therefore, I find that referring to the paper by Wrenn & Wessells is somewhat misleading. Given that the whisker follicles develop in ex vivo cultured whisker pads further hints that innervation is unlikely to play a role in whisker follicle induction.

      The Introduction also hints at the role of innervation in tooth induction but forgets to refer to the literature that shows exactly the opposite. Based on the evidence it rather appears that the developing tooth regulates the establishment of its own nerve supply, not that the nerves would regulate induction of tooth development.

      in my opinion, the Introduction should be partially rewritten to better reflect the literature.

      The introduction has been revised to better reflect the literature on the role of innervation on WF and tooth development. Line70-87.

      The authors conclude that Meis2 is upstream of Foxd1, but the evidence is based on the lack of Foxd1 expression in Meis2 mutants. However, as whiskers do not form, evidently all markers are also absent. More direct evidence of Meis2 being upstream of Foxd1 (or Sox2) should be presented to consolidate the conclusions.

      We have already reacted to this point above in the section Weaknesses. The text is now modified so that the interpretation is correct. Line: 407-409.

      Other comments:

      Author contributions state that XX performed experiments but the author list does not include anyone with such initials.

      This error has been corrected in revision.

    1. “You may haveheard that women don’t do as well as men on difficult standardized math tests,but that’s not true for the particular standardized math test; on this particulartest, women always do as well as men.”

      REACT: In my perspective, this text challenges the stereotype that women perform worse than men on math tests by pointing out that, in this specific case, women perform just as well. It highlights the idea that gender differences in performance might not be as clear-cut as we think and suggests that external factors, not ability, could influence test results.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank the editor and reviewers for their supportive comments about our modeling approach and conclusions, and for raising several valid concerns; we address them briefly below. In addition, a detailed, point-by-point response to the reviewers’ comments are below, along with additions and edits we have made to the revised manuscript. 

      Concerns about model’s biological realism and impact on interpretations

      The goal of this paper was to use an interpretable and modular model to investigate the impact of varying sensorimotor delays. Aspects of the model (e.g. layered architecture, modularity) are inspired by biology; at the same time, necessary abstractions and simplifications (e.g. using an optimal controller) are made for interpretability and generalizability, and they reflect common approaches from past work. The hypothesized effects of certain simplifying assumptions are discussed in detail in Section 3.5. Furthermore, the modularity of our model allows us to readily incorporate additional biological realism (e.g. biomechanics, connectomics, and neural dynamics) in future work. In the revision, we have added citations and edits to the text to clarify these points.

      Concerns that the model is overly complex

      To investigate the impact of sensorimotor delays on locomotion, we built a closed-loop model that recapitulates the complex joint trajectories of fly walking. We agree that locomotion models face a tradeoff between simplicity/interpretability and realism — therefore, we developed a model that was as simple and interpretable as possible, while still reasonably recapitulating joint trajectories and generalizing to novel simulation scenarios. Along these lines, we also did not select a model that primarily recreates empirical data, as this would hinder generalizability and add unnecessary complexity to the model. We do not think these design choices are significant weaknesses of this model; in fact, few comparable models account for all joints involved in locomotion, and fewer explicitly compare model kinematics with kinematics from data. We have add citations and edits to the text to clarify these points in the revision. 

      Concerns about the validity of the Kinematic Similarity (KS) metric to evaluate walking

      We chose to incorporate only the first two PCA modes dimensions in the KS metric because the kernel density estimator performs poorly for high dimensional data. Our primary use of this metric was to indicate whether the simulated fly continues walking in the presence of perturbations. For technical reasons, it is not feasible to perform equivalent experiments on real walking flies, which is one of the reasons we explore this phenomenon with the model. We note the dramatic shift from walking to nonwalking as delay increases (Figure 5). To be thorough, in the revision, we have investigated the effect of incorporating additional PCA modes, and whether this affects the interpretation of our results. We have additionally added to the discussion and presentation of the KS metric to clarify its purpose in this study. We agree with the reviewers that the KS metric is too coarse to reflect fine details of joint kinematics; indeed, in the unperturbed case, we evaluate our model’s performance using other metrics based on comparisons with empirical data (Figures 2, 7, 8). 

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this work, the authors present a novel, multi-layer computational model of motor control to produce realistic walking behaviour of a Drosophila model in the presence of external perturbations and under sensory and motor delays. The novelty of their model of motor control is that it is modular, with divisions inspired by the fly nervous system, with one component based on deep learning while the rest are based on control theory. They show that their model can produce realistic walking trajectories. Given the mostly reasonable assumptions of their model, they convincingly show that the sensory and motor delays present in the fly nervous system are the maximum allowable for robustness to unexpected perturbations.

      Their fly model outputs torque at each joint in the leg, and their dynamics model translates these into movements, resulting in time-series trajectories of joint angles. Inspired by the anatomy of the fly nervous system, their fly model is a modular architecture that separates motor control at three levels of abstraction:

      (1) oscillator-based model of coupling of phase angles between legs,

      (2) generation of future joint-angle trajectories based on the current state and inputs for each leg (the trajectory generator), and

      (3) closed-loop control of the joint-angles using torques applied at every joint in the model (control and dynamics).

      These three levels of abstraction ensure coordination between the legs, future predictions of desired joint angles, and corrections to deviations from desired joint-angle trajectories. The parameters of the model are tuned in the absence of external perturbations using experimental data of joint angles of a tethered fly. A notable disconnect from reality is that the dynamics model used does not model the movement of the body and ground contacts as is the case in natural walking, nor the movement of a ball for a tethered fly, but instead something like legs moving in the air for a tethered fly.

      n order to validate the realism of the generated simulated walking trajectories, the authors compare various attributes of simulated to real tethered fly trajectories and show qualitative and quantitative similarities, including using a novel metric coined as Kinematic Similarity (KS). The KS score of a trajectory is a measure of the likelihood that the trajectory belongs to the distribution of real trajectories estimated from the experimental data. While such a metric is a useful tool to validate the quality of simulated data, there is some room for improvement in the actual computation of this score. For instance, the KS score is computed for any given time-window of walking simulation using a fraction of information from the joint-angle trajectories. It is unclear if the remaining information in joint-angle trajectories that are not used in the computation of the KS score can be ignored in the context of validating the realism of simulated walking trajectories.

      The authors validate simulated walking trajectories generated by the trained model under a range of sensorimotor delays and external perturbations. The trained model is shown to generate realistic jointangle trajectories in the presence of external perturbations as long as the sensorimotor delays are constrained within a certain range. This range of sensorimotor delays is shown to be comparable to experimental measurements of sensorimotor delays, leading to the conclusion that the fly nervous system is just fast enough to be robust to perturbations.

      Strengths:

      This work presents a novel framework to simulate Drosophila walking in the presence of external perturbations and sensorimotor delay. Although the model makes some simplifying assumptions, it has sufficient complexity to generate new, testable hypotheses regarding motor control in Drosophila. The authors provide evidence for realistic simulated walking trajectories by comparing simulated trajectories generated by their trained model with experimental data using a novel metric proposed by the authors. The model proposes a crucial role in future predictions to ensure robust walking trajectories against external perturbations and motor delay. Realistic simulations under a range of prediction intervals, perturbations, and motor delays generating realistic walking trajectories support this claim. The modular architecture of the framework provides opportunities to make testable predictions regarding motor control in Drosophila. The work can be of interest to the Drosophila community interested in digitally simulating realistic models of Drosophila locomotion behaviors, as well as to experimentalists in generating testable hypotheses for novel discoveries regarding neural control of locomotion in Drosophila. Moreover, the work can be of broad interest to neuroethologists, serving as a benchmark in modelling animal locomotion in general.

      We thank the reviewer for their positive comments.

      Weaknesses:

      As the authors acknowledge in their work, the control and dynamics model makes some simplifying assumptions about Drosophila physics/physiology in the context of walking. For instance, the model does not incorporate ground contact forces and inertial effects of the fly's body. It is not clear how these simplifying assumptions would affect some of the quantitative results derived by the authors. The range of tolerable values of sensorimotor delays that generate realistic walking trajectories is shown to be comparable with sensorimotor delays inferred from physiological measurements. It is unclear if this comparison is meaningful in the context of the model's simplifying assumptions.

      We now discuss how some of these assumptions affect the quantitative results in the section “Towards biomechanical and neural realism”. We reproduce the relevant sentences below:

      “The inclusion of explicit leg-ground contact interactions would also make it harder for the model to recover when perturbed, because perturbations during walking often occur upon contact with the ground (e.g. the ground is slippery or bumpy).”

      “We anticipate that the increased sensory resolution from more detailed proprioceptor models and the stability from mechanical compliance of limbs in a more detailed biomechanical model would make the system easier to control and increase the allowable range of delay parameters. Conversely, we expect that modeling the nonlinearity and noise inherent to biological sensors and actuators may decrease the allowable range of delay parameters.”

      The authors propose a novel metric coined as Kinematic Similarity (KS) to distinguish realistic walking trajectories from unrealistic walking trajectories. Defining such an objective metric to evaluate the model's predictions is a useful exercise, and could potentially be applied to benchmark other computational animal models that are proposed in the future. However, the KS score proposed in this work is calculated using only the first two PCA modes that cumulatively account for less than 50% of the variance in the joint angles. It is not obvious that the information in the remaining PCA modes may not change the log-likelihood that occurs in the real walking data.

      The primary reason we designed the KS metric was to determine whether the simulated fly continues walking in the presence of perturbations. We initially limited the analysis of the KS to the first 2 principal components. For completeness, we now investigate the additional principal components in Appendix 9 and the effect of evaluating KS with different numbers of components in Appendix 10. 

      Overall, the results look similar when including additional components for impulse perturbations. For stochastic perturbations, the range of similar walking decreases as we increase the number of components used to evaluate walking kinematics. Comparing this with Appendix 9, which shows that higher components represent higher frequencies of the walking cycle, we conclude that at the edge of stability for delays (where sum of sensory and actuation delays are about 40ms), flies can continue walking but with impaired higher frequencies (relative to no perturbations) during and after perturbation. 

      We added the following text in the methods:

      “We chose 2 dimensions for PCA for two key reasons. First, these 2 dimensions alone accounted for a large portion of the variance in the data (52.7% total, with 42.1% for first component and 10.6% for second component). There was a big drop in variance explained from the first to the second component, but no sudden drop in the next 10 components (see Appendix 9). Second, the KDE procedure only works effectively in low-dimensional spaces, and the minimal number of dimensions needed to obtain circular dynamics for walking is 2. We investigate the effect of varying the number of dimensions of PCA in Appendix 10.”

      (Note that we have corrected the percentage of variance accounted for by the principal components, as these numbers were from an older analysis prior to the first draft.)

      We also reference Appendix 10 in the results:

      “We observed that robust walking was not contingent on the specific values of motor and sensory delay, but rather the sum of these two values (Fig. 5E). Furthermore, as delay increases, higher frequencies of walking are impacted first before walking collapses entirely (Appendix 10).”

      Reviewer #2 (Public Review):

      Summary:

      In this study, Karashchuk et al. develop a hierarchical control system to control the legs of a dynamic model of the fly. They intend to demonstrate that temporal delays in sensorimotor processing can destabilize walking and that the fly's nervous system may be operating with as long of delays as could possibly be corrected for.

      Strengths:

      Overall, the approach the authors take is impressive. Their model is trained using a huge dataset of animal data, which is a strength. Their model was not trained to reproduce animal responses to perturbations, but it successfully rejects small perturbations and continues to operate stably. Their results are consistent with the literature, that sensorimotor delays destabilize movements.

      Weaknesses:

      The model is sophisticated and interesting, but the reviewer has great concerns regarding this manuscript's contributions, as laid out in the abstract:

      (1) Much simpler models can be used to show that delays in sensorimotor systems destabilize behavior (e.g., Bingham, Choi, and Ting 2011; Ashtiani, Sarvestani, and Badri-Sproewitz 2021), so why create this extremely complex system to test this idea? The complexity of the system obscures the results and leaves the reviewer wondering if the instability is due to the many, many moving parts within the model. The reviewer understands (and appreciates) that the authors tested the impact of the delay in a controlled way, which supports their conclusion. However, the reviewer thinks the authors did not use the most parsimonious model possible, and as such, leave many possible sources for other causes of instability.

      We thank the reviewer for this observation — we agree that we did not make the goal of the work quite clear. The goal of this paper was to build an interpretable and generalizable model of fly walking, which was then used to investigate varying sensorimotor delays in the context of locomotion. To this end, we used a modular model to recreate walking kinematics, and then investigated the effect of delays on locomotion. Locomotion in itself is a complex phenomenon — thus, we have chosen a model that is complex enough to reasonably recapitulate joint trajectories, while remaining interpretable.

      We have clarified this in the text near the end of the introduction:

      “Here, we develop a new, interpretable, and generalizable model of fly walking, which we use to investigate the impact of varying sensorimotor delays in Drosophila locomotion.”

      We also emphasize the investigation of sensorimotor delays in the context of locomotion in the beginning of the “Effect of sensory and motor delays on walking” section:

      “... we used our model to investigate how changing sensory and motor delays affects locomotor robustness.”

      We also remark that while they are very relevant papers for our work, neither of the prior papers focus on locomotion: the first involves a 2D balance model of a biped, and the second involves drop landings of quadrupeds.

      Lastly, we note that the investigation of delay is not the only use for this model —  in the future, this model can also be used to study other aspects of locomotion such as the role of proprioceptive feedback (see “Role of proprioceptive feedback in fly walking” section). The layered framework of the model can also be extended to other animals and locomotor strategies (see “Layered model produces robust walking and facilitates local control” section”).

      (2) In a related way, the reviewer is not sure that the elements the authors introduced reflect the structure or function of the fly's nervous system. For example, optimal control is an active field of research and is behind the success of many-legged robots, but the reviewer is not sure what evidence exists that suggests the fly ventral nerve cord functions as an optimal controller. If this were bolstered with additional references, the reviewer would be less concerned.

      We thank the reviewer for the comment — we have now further clarified how our model elements reflect the fly’s nervous system. The elements we introduce are plausible but only loosely analogous to the fly’s nervous system. While we draw parallels from these elements to anatomy (e.g. in Fig 1A-B, and in the first paragraph of the Results section), we do not mean to suggest that these functional elements directly correspond to specific structures in the fly’s nervous system. A substantial portion of the suggested future work (see “Towards biomechanical and neural realism”) aims to bridge the gap between these functional elements and fly physiology, which is beyond the scope of this work. 

      We have added clarifying text to the Results section:

      “While the model is inspired by neuroanatomy, its components do not strictly correspond to components of the nervous system --- the construction of a neuroanatomically accurate model is deferred to future work (see Discussion).”

      In the specific case of optimal control — optimal control is a theoretical model that predicts various aspects of motor control in humans, there is evidence that optimal control is implemented by the human nervous system (Todorov and Jordan, 2002; Scott, 2004; Berret et al., 2011). Based on this, we make the assumption that optimal control is a reasonable model for motor control in flies implemented by the fly nervous system as well. Fly movement makes use of proprioceptive feedback signals (Mendes et al., 2013; Pratt et al., 2024; Berendes et al., 2016), and optimal control is a plausible mechanism that incorporates feedback signals into movement.

      We have added the following clarifying text in the Results section: 

      “The optimal controller layer maintains walking kinematics in the presence of sensori motor delays and helps compensate for external perturbations. This design was inspired by optimal control-based models of movements in humans (Todorov and Jordan, 2002; Scott, 2004; Berret et al., 2011)”

      (3) "The model generates realistic simulated walking that matches real fly walking kinematics...". The reviewer appreciates the difficulty in conducting this type of work, but the reviewer cannot conclude that the kinematics "match real fly walking kinematics". The range of motion of several joints is 30% too small compared to the animal (Figure 2B) and the reviewer finds the video comparisons unpersuasive. The reviewer would understand if there were additional constraints, e.g., the authors had designed a robot that physically could not complete the prescribed motions. However the reviewer cannot think of a reason why this simulation could not replicate the animal kinematics with arbitrary precision, if that is the goal.

      We agree with the reviewer that the model-generated kinematics are not perfectly indistinguishable from real walking kinematics, and now clarify this in the text. We also agree with the reviewer that one could build a model that precisely replicates real kinematics, but as they intuit, that was not our goal. Our goal was to build a model that both replicates animal kinematics, and is interpretable and generalizable (which allows us to investigate what happens when perturbations and varying sensorimotor delays are introduced). There is a trade-off between realism and generalizability — a simulation that fully recreates empirical data would require a model that is completely fit to data, which is likely to be more complex (in terms of parameters required) and less generalizable to novel scenarios. We have made design choices that result in a model that balances these trade-offs. We do not consider this to be a weakness of the model; in fact, few comparable models account for all joints involved in locomotion, and fewer explicitly compare model kinematics with kinematics from data.

      We have tempered the language in the abstract:

      “The model generates realistic simulated walking that resembles real fly walking kinematics”

      The tempered statement, we believe, is a fair characterization of the walking — it resembles but does not perfectly match real kinematics.

      We have also introduced clarifying text in the introduction:

      “Overall, existing walking models focus on either kinematic or physiological accuracy, but few achieve both, and none consider the effect of varying sensorimotor delays. Here, we develop a new, interpretable, and generalizable model of fly walking, which we use to investigate the impact of varying sensorimotor delays in Drosophila locomotion.”

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Potential typo on page 5:

      2.1.2 Joint kinematics trajectory generator

      Paragraph 4, last line: Original text - ".....it also estimates the current phase". Suggested correction - "...it also estimates the current phase velocity"

      Done

      Potential typo on page 8:

      2.3 Model maintains walking under unpredictable external perturbations.

      Paragraph 3, line 2: Original text - "...brief, unexpected force (e.g. legs slipping on an unstable surface)".

      Consider replacing force with motion, or providing an example of a force as opposed to displacement (slipping).

      Done

      Potential typo on page 8:

      2.3 Model maintains walking under unpredictable external perturbations.

      Paragraph 3, line 4: Original text - "The magnitude of this velocity is drawn from a normal distribution...".

      Is this really magnitude? If so, please discuss how the sign (+/-) is assigned to velocity, and how the normal distribution is centred so as to sample only positive values representing magnitude.

      Indeed the magnitude of the velocity is drawn from a normal distribution. A positive or negative sign is then assigned with equal odds. We have added text to clarify this:

      “The sign of the velocity was drawn separately so that there is equal likelihood for negative or positive perturbation velocities.”

      Page 8:

      2.3 Model maintains walking under unpredictable external perturbations.

      In Paragraph 5: Why is the data reduced to only 2 dimensions? Could higher order PCA modes (cumulatively accounting for more than 50% variance in the data) not have distinguishing information between realistic and unrealistic walking trajectories?

      We provide a longer response for this in the public review above.

      Page 11:

      Why wouldn't a system trained in the presence of external perturbations perform better? What is the motivation to remove external perturbations during training?

      We agree that a system trained in the presence of external perturbations would probably perform better — however, we do not have data that contains walking with external perturbations. Nothing was removed — all the data used in this study involve a fly walking without perturbations.

      We have added a clarification:

      “our model maintains realistic walking in the presence of external dynamic perturbations, despite being trained only on data of walking without perturbations (no perturbation data was available).”

      Page 16:

      4.1 Tracking joint angles of D. melanogaster walking in 3D.

      Paragraph 1: Readers who wish to collect similar data might benefit from specifying the exposure time, animal size in pixels (or camera sensor format and field of view), in addition to the frame rate. Alternatively, consider mentioning the camera and lens part numbers provided by the manufacturer.

      This is a good point. We have updated the text to include these specifications:

      “We obtained fruit fly D. melanogaster walking kinematics data following the procedure previously described in (Karashchuk et al, 2021). Briefly, a fly was tethered to a tungsten wire and positioned on a frictionless spherical treadmill ball suspended on compressed air. Six cameras (Basler acA800-510um with Computar zoom lens MLM3X-MP) captured the movement of all of the fly's legs at 300 Hz. The fly size in pixels ranges from about 300x300 up to 700x500 pixels across the 6 cameras. Using Anipose, we tracked 30 keypoints on the fly, which are the following 5 points on each of the 6 legs: body-coxa, coxa-femur, femur-tibia, and tibia-tarsus joints, as well as the tip of the tarsus.”

      Potential typos on page 18:

      4.3.3 Training procedure

      Paragraph 2, line 1: Original text - "..(, p)"

      Do the authors mean "...(, )"

      Paragraph 2, line 2: Original text - "... (,, v, p)" Do the authors mean "... (,, v, )"?

      Paragraph 3, line 3: Original text - "... (,, v, p)" Do the authors mean "... (,, v, )"?

      Thank you for pointing out this issue. We have now fixed the phase p to be \phi to be consistent with the rest of the text.

      Paragraph 3, line 3: Original text - "...()"

      Do the authors mean "(d)"? If not, please discuss the difference between and d.

      Thank you for pointing this out. \hat \theta and \theta_d were used interchangeably which is confusing. We have standardized our reference to the desired trajectory as \theta_d throughout the text.

      Page 19:

      Typo after eqn. (6):

      Original text: "where x := q - q, ... A and B are Jacobians with respect to...."

      Correction: "where x := q - q, ... Ac and Bc are Jacobians with respect to...."

      Similar corrections in eqn. 7 and eqn. 8: A and B should be replaced with Ac and Bc. Done

      Page 19, eqn. (10b):

      Should the last term be qd(t+T) as opposed to qd(t+1)?

      No: in fact (10a) contains the typo: it should be y(t+1) as opposed to y(t+T). This has been fixed.

      Page 19

      The authors' detailed description of the initial steps leading up to the dynamics model, involving the construction of the ODE, linearizing the system about the fixed point makes the text broadly accessible to the general reader. Similarly, adding some more description of the predictive model (eqn. 11 - 15) could improve the text's accessibility and the reader's appreciation for the model. This is especially relevant since the effects of sensorimotor delay and external perturbations, which are incorporated in the control and dynamics model, form a major contribution to this work. What do the matrices F, G, L, H, and K look like for the Drosophila model? Are there any differences between the model in Stenberg et al. (referenced in the paper) and the authors' model for predictive control? Are there any differences in the assumptions made in Stenberg et al. compared to the model presented in this work? The readers would likely also benefit from a figure showing the information flow in the model, and describing all the variables used in the predictive control model in eqn. 11 through eqn. 15 (analogous to Figure 1 in Stenberg et al. (2022)). Such a detailed description of the control and dynamics model would help the reader easily appreciate the assumptions made in modelling the effects of sensorimotor delay and external perturbations.

      Done

      Page 20:

      Eqn. 12: Should z(t+1) be z(t+T) instead?

      Similar comment for eqn. 14

      No: we made a mistake in (10a); there should be no (t+T) terms; all terms should be (t+1) terms to reflect a standard discrete-time difference equation.

      Eqn. 13: r(t) can be defined explicitly

      Done

      4.5 Generate joint trajectories of the complete model with perturbations Paragraph 2, line 2: Please read the previous comment

      \hat \theta and \theta_d were previously used interchangeably which is confusing. We have standardized our reference to the desired trajectory as \theta_d throughout the text.

      Original text - "Every 8 timesteps, we set :=...."

      Does this mean dis set to? If so, the motivation for this is not clear.

      We mean that \theta_d is set to be equal to \theta. We have replaced “:=” with “=” for clarity.

      General comments for the authors:

      Could the authors discuss the assumptions regarding Drosophila physiology implied in the control model?

      The control model is primarily included as a plausible functional element of the fly’s nervous system, and as such implies minimal assumptions on physiology itself. The main assumption, which is evident from the description of the model components, is that the fly uses proprioceptive feedback information to inform future movements.

      We have added clarifying text to the Results section:

      “While the model is inspired by neuroanatomy, its components do not strictly correspond to components of the nervous system --- the construction of a neuroanatomically accurate model is deferred to future work (see Discussion).”

      The authors acknowledge the absence of ground contact forces in the model. It is probably worth discussing how this simplification may affect inferences regarding the acceptable range of sensorimotor delay in generating realistic walking trajectories.

      We agree, and discuss how some of these assumptions affect the quantitative results in the section “Towards biomechanical and neural realism”. We replicate the relevant sentences below:

      “The inclusion of explicit leg-ground contact interactions would also make it harder for the model to recover when perturbed, because perturbations during walking often occur upon contact with the ground (e.g. the ground is slippery or bumpy).”

      The effects of other simplifications are also mentioned in the same section.

      Can the authors provide an insight into why the use of a second derivative of joint angles as the output of the trajectory generator () leads to more realistic trajectories (4.3.1 Model formulation, paragraph 1)?

      Does the use of a second-order derivative of joint angles lead to drift error because of integration?

      Could the distribution of θd produced be out of the domain due to drift errors? Could this affect the performance of the neural network model approximating the trajectory generator?

      We are not sure why the second derivative works better than the first derivative. It is possible that modeling the system as a second order differential equation gives the network more ability to produce complex dynamics. 

      As can be seen in the example time series in Figures 2 and 3 and supplemental videos, there is no drift error from integration, so it is unlikely to affect the performance of the neural network.

      What does the model's failure (quantified by a low KS score) look like in the context of fly dynamics? What do the joint angles look like for low values of KS score? Does the fly fall down, for example?

      Since the model primarily considers kinematics, a low KS score means that kinematics are unrealistic, e.g. the legs attain unnatural angles or configurations. Examples of this can be seen in videos 4-7 (linked from Appendix 1 of the paper), as well as in the bottom row of Fig. 5, panel A. Here, at 40ms of motor delay, L2 femur rotation is seen to attain values that far exceed the normal ranges. 

      We have added a small clarification in the caption of Fig.5 panel A:

      “low KS indicates that the perturbed walking deviates from data and results in unnatural angles

      (as seen at 40ms motor delay)” 

      We remark that since our simulations do not incorporate contact forces (as the reviewer remarks above, we simulate something like legs moving in the air for a tethered fly), the fly cannot “fall down” per se. However, if forces were incorporated then yes, these unrealistic kinematics would correspond to a fly that falls down or is no longer walking.

      Reviewer #2 (Recommendations For The Authors):

      L49: "Computational models of locomotion do not typically include delay as a tunable parameter, and most existing models of walking cannot sustain locomotion in the presence of delays and external perturbations". This remark confuses the reviewer.

      (1) If models do not "typically" include delay as a tunable parameter, this suggests that atypical models do. Which models do? Please provide references.

      Our initial phrasing was confusing. We meant to say that most models do not include delay, and some models do include delay as a fixed value (rather than a tunable value). We clarify in the updated text, which is replicated below:

      “Computational models of locomotion typically have not included delays as a tunable parameter, although some models have included them as fixed values (Geyer and Herr, 2010; Geijtenbeek et al., 2013).”

      (2) Has the statement that most existing models cannot sustain locomotion with delays been tested? If so, provide references. If not, please remove this statement or temper the language.

      Since most models don’t include delays, they cannot be run in scenarios with delays. We clarify in the updated text, which is replicated below:

      “Computational models of locomotion have not typically included delays. Some have included delay as a fixed value rather than a tunable parameter (Geyer and Herr, 2010; Geijtenbeek et al., 2013). However, in general, the impact of sensorimotor delays on locomotor control and robustness remains an underexplored topic in computational neuroscience.”

      L57: "two of six legs lift off the ground at a time" - Two legs are off the ground at any time, but they do not "lift off" simultaneously in the fruit fly. To lift off simultaneously, contralateral leg pairs would need to be 33% out of phase with one another, but they are almost always 50% out of phase.

      Thank you for pointing out this oversight. We have updated the text accordingly:

      “Flies walk rhythmically with a continuum of stepping patterns that range from tetrapod (where two of six legs are off the ground at a time) to tripod (where three of six legs are off the ground at a time)"

      L88: "a new model of fly walking" - The intention of the authors is to produce a model from which to learn about walking in the fly, is that correct? The reviewer has read the paper several times now and wants to be sure that this is the authors' goal, not to engineer a control system for an animation or a robot.

      Indeed, this is our goal. We were previously unclear about this, and have made text edits to clarify this — we provide a longer response for this in the public review above (see (1)).

      L126: "These desired phases are synchronized across pairs of legs to maintain a tripod coordination pattern, even when subject to unpredictable perturbations." - Does the animal maintain tripod coordination even when perturbed? In the reviewer's experience, flies vary their interleg coordination all the time. The reviewer would also expect that if perturbed strongly (as the supplemental videos show), the animal would adapt its interleg coordination in response. The author finds this assumption to be a weak point in the paper for the use of this disturbance exploring animal locomotion.

      We do not know exactly how flies may react to our mechanical perturbations. However, we may hypothesize based on past papers. 

      Couzin-Fuchs et al (2015) apply a mechanical perturbation to walking cockroaches. They find that that tripod is temporarily broken immediately after the perturbation but the cockroach recovers to a full tripod within one step cycle. 

      DeAngelis et al (2019) apply optogenetic perturbations to fly moonwalker neurons that drive backward walking. Flies slow down following perturbation, but then recover after 200ms (about 2-3 steps) to their original speed (on average). 

      Thus, we think it is reasonable to model a fly’s internal phase coupling to maintain tripod and for its intended speed to remain the same even after a perturbation. 

      We do agree with the reviewer that it is plausible a fly might also slow down or even stop after a perturbation and we do not model such cases. We have added some text to the discussion on future work:

      “Future work may also model how higher-level planning of fly behavior interacts with the lowerlevel coordination of joint angles and legs. Walking flies continuously change their direction and speed as they navigate the environment (Katsov et al, 2017; Iwasaki et al 2024). Past work shows that flies tend to recover and walk at similar speeds following perturbations (DeAngelis et al, 2019), but individual flies might still change walking speed, phase coupling, or even transition to other behaviors, such as grooming. Modeling these higher-level changes in behavior would involve combining our sensorimotor model with models for navigation (Fisher 2022) or behavioral transitions (Berman et al, 2016).”

      L136: "...to output joint torques to the physical model of each leg" - Is this the ultimate output of the nervous system? Muscles are certainly not idealized torque generators. There are dynamics related to activation and mechanics. The reviewer is skeptical that this is a model of neural control in the animal, because the computation of the nervous system would be tuned to account for all these additional dynamics.

      We agree with the reviewer that joint torques are not the ultimate output of the nervous system. We use a torque controller because it is parsimonious, and serves our purpose of creating an interpretable and modular locomotion model.

      We also agree that muscles are an important consideration — we make mention of them later on in the paper under the section “Toward biomechanical and neural realism”, where we state “Another step toward biological realism is the incorporation of explicit dynamical models of proprioceptors, muscles, tendons, and other biomechanical aspects of the exoskeleton.”

      Our goal is not to directly model neural control of the animal. We have introduced text clarifications to emphasize this — we provide a longer response for this in the public review above (see (2)).

      L143: "To train the network from data, we used joint kinematics of flies walking on a spherical treadmill..." This is an impressive approach, but then the reviewer is confused about why the kinematics of the model are so different from those of the animal. The animal takes longer strides at a lower frequency than the model. If the model were trained with data, why aren't they identical? This kind of mismatch makes the reviewer think the approach in this paper is too complicated to address the main problem.

      The design of our trajectory generator model is one of the simplest for reproducing the output of a dynamical system. It consists of a multilayer perceptron model that models the phase velocity and joint angle accelerations at each timestep. All of its inputs are observable and interpretable: the current joint angles, joint angle derivatives, desired walking speed, and phase angle. 

      We chose this model for ease of interpretability, integration with the optimal controller, and to allow for generalization across perturbations. Given all of these constraints, this is the best model of desired kinematics we could obtain. We note that the simulated kinematics do match real fly kinematics qualitatively (Figure 2A and supplemental videos) and are close quantitatively (Figure 2B and C). We speculate that matching the animals’ strides at all walking frequencies may require explicitly modeling differences across individual flies. We leave the design and training of more accurate (but more complex) walking models for future work.

      We add some further discussion about fitting kinematics in the discussion:

      “Although we believe our model matches the fly walking sufficiently for this investigation, we do note that our model still underfits the joint angle oscillations in the walking cycle of the fly (see Figure 2 and Appendix 3). More precise fitting of the joint angle kinematics may come from increasing the complexity of the neural network architecture, improving the training procedure based on advances in imitation learning (Hussein et al., 2018), or explicitly accounting for individual differences in kinematics across flies (Deangelis et al., 2019; Pratt et al., 2024).”

      Figure 2: The reviewer thinks the violin plots in Figure 2C are misleading. Joint angles could be greater or less than 0, correct? If so, why not keep the sign (pos/neg) in the data? Taking the absolute value of the errors and "folding over" the distribution results in some strange statistics. Furthermore, the absolute value would shroud any systematic bias in the model, e.g., joint angles are always too small. The reviewer suggests the authors plot the un-rectified data and simply include 2 dashed lines, one at 5.56 degrees and one at -5.56 degrees.

      These violin plots are averages of errors over all phases within each speed. We chose to do this to summarize the errors across all phase angle plots, which are shown in detail in Appendix 3 and 4.

      For the reviewer, we have added a plot of the raw errors across all phase angle plots in Appendix 5, E.

      L156: Should "\phi\dot" be "\phi"?

      We originally had a typo: we said “phase” when we meant “phase velocity”. This has been fixed. \phi\dot is correct.

      L160: "This control is possible because the controller operates at a higher temporal frequency than the trajectory generator...". This statement concerns the reviewer. To the reviewer, this sounds like the higher-level control system communicates with the "muscles" at a higher frequency than the low-level control system, which conflicts with the hierarchical timescales at which the nervous system operates. Or do the authors mean that the optimal controller can perform many iterations in between updates from the trajectory generator level? If so, please clarify.

      We mean that the optimal controller can perform many iterations in between updates from the trajectory generator level. The text has been clarified:

      “This control is possible because the controller operates at a higher temporal frequency than the trajectory generator in the model. The controller can perform many iterations (and reject disturbances) in between updates to and from the trajectory generator.”

      L225: "We considered two types of perturbations: impulse and persistent stochastic". Are these realistic perturbations? Realistic perturbations such as a single leg slipping, or the body movement being altered would produce highly correlated joint velocities.

      These perturbations are not quite realistic — nonetheless, we illustrate their analogousness to real perturbations in the subsequent text in the paper, and restrict our simulations to ranges that would be biologically plausible (see Appendix 7). We agree that realistic perturbations would produce highly correlated joint accelerations and velocities, whereas our perturbations produce random joint accelerations. 

      L265: "...but they are difficult to manipulate experimentally..." This is true, but it can and has been done. The authors should cite:

      Bässler, U. (1993). The femur-tibia control system of stick insects-A model system for the study of the neural basis of joint control. Brain Research Reviews, 18(2), 207-226. 

      Thank you for the suggestion, we have incorporated it into the text at the end of the referenced sentence.

      L274: "...since the controller can effectively compensate for large delays by using predictions of joint angles in the future". But can the nervous system do this? Or, is there a reason to think that the nervous system can? The reviewer thinks the authors need stronger justification from the literature for their optimal control layer.

      To clarify, this sentence describes a feature of the model’s behavior when no external perturbations are present. This is not directly relevant to the nervous system, since organisms do not typically exist in an environment free of perturbations — we are not suggesting that the nervous system does this.

      In response to the question of whether the nervous system can compensate for delays using predictions: we know that delays are present in the nervous system, perturbations exist in the environment, and that flies manage to walk in spite of them. Thus, some type of compensation must exist to offset the effects of delays (the reviewer themself has provided some excellent citations that study the effects of delays). In our model, we use prediction as the compensation mechanism — this is one of our central hypotheses. We further discuss this in the section “Predictive control is critical for responding to perturbations due to motor delay”.

      L319: "The formulation of a modular, multi-layered model for locomotor control makes new experimentally-testable hypotheses about fly motor control...". What testable hypotheses are these? The authors should explicitly state them. They are not clear to the reviewer, especially given the nonphysiological nature of the control system and the mechanics.

      A number of testable hypotheses are mentioned throughout the Discussion section:

      “Our model predicts that at the same perturbation magnitude, walking robustness decreases as delays increase. This could be experimentally tested by altering conduction velocities in the fly, for example by increasing or decreasing the ambient temperature (Banerjee et al, 2021).  If a warmer ambient temperature decreases delays in the fly, but fly walking robustness remains the same in response to a fixed perturbation, this would indicate a stronger role for central control in walking than our modeling results suggest.”

      “In our model, robust locomotion was constrained by the cumulative sensorimotor delay. This result could be experimentally validated by comparing how animals with different ratios of sensory to motor delays respond to perturbations. Alternatively, it may be possible to manipulate sensory vs. motor delays in a single animal, perhaps by altering the development of specific neurons or ensheathing glia (Kottmeier et al., 2020). If sensory and motor delays have significantly different effects on walking quality, then additional compensatory mechanisms for delays could play a larger role than we expect, such as prediction through sensory integration, mechanical feedback, or compensation through central control.”

      “we hypothesize that removing proprioceptive feedback would impair an insect's ability to sustain locomotion following external perturbations.”

      “We propose that fly motor circuits may encode predictions of future joint positions, so the fly may generate motor commands that account for motor neuron and muscle delays.”

      L323: "...and biomechanical interactions between the limb and the environment". In the reviewer's experience, the primary determinant of delay tolerance is the mechanical parameters of the limb: inertia, damping, and parallel elasticity. For example, in Ashtiani et al. 2021, equation 5 shows exactly how this comes about: the delay changes the roots and poles of the control system. This is why the reviewer is confused by the complexity of the model in this submission; a simpler model would explain why delays cannot be tolerated in certain circumstances.

      We were previously unclear about the goal of the model, and have made text edits to clarify this — we provide a longer response for this in the public review above (see (1)).

      L362: Another highly relevant reference here would be Sutton et al. 2023.

      Done

      L366: Szczecinski et al. 2018 is hardly a "model"; it is mostly a description of experimental data. How about Goldsmith, Szczecinski, and Quinn 2020 in B&B? Their model of fly walking has patterngenerating elements that are coordinated through sensory feedback. In their model, motor activation is also altered by sensory feedback. The reviewer thinks the statement "Models of fly walking have ignored the role of feedback" is inaccurate and their description of these references should be refined.

      Thank you for the suggestion; we have tempered the language and revised this section to include more references, including the suggested one — text is replicated below. 

      “Many models of fly walking ignore the role of feedback, relying instead on central pattern generators (Lobato-Rios et al., 2022; Szczecinski et al., 2018; Aminzare et al., 2018) or metachondral waves (Deangelis et al., 2019) to model kinematics. Some models incorporate proprioceptive feedback, primarily as a mechanism that alters timing of movements in inter-leg coordination (Goldsmith et al., 2020; Wang-Chen et al., 2023).”

      We remark that Szczecinski et al does include a model that replicates data without using sensory feedback, so we think it is fair to include.  

      L371: "...highly dependent on proprioceptive feedback for leg coordination during walking." What about Berendes et al. 2016, which showed that eliminating CS feedback from one leg greatly diminished its ability to coordinate with the other legs? This suggests that even flies depend on sensory feedback for proper coordination, at least in some sense.

      Interesting suggestion – we have integrated it into the text a little further down, where it better fits:

      “Silencing mechanosensory chordotonal neurons alters step kinematics in walking Drosophila (Mendes et al., 2013; Pratt et al., 2024). Additionally, removing proprioceptive signals via amputation interferes with inter-leg coordination in flies at low walking speeds (Berendes et al., 2016)”

      L426: "The layered model approach also has potential applications for bio-mimetic robotic locomotion.". How fast can this model be computed? Can it run faster than real-time? This would be an important prerequisite for use as a robot control system.

      The model should be able to be run quite fast, as it involves only

      (1) Addition, subtraction, matrix multiplication, and sinusoidal computation on scalars (for the phase coordinator and optimal controller)

      (2) Neural network inference with a relatively small network (for the trajectory generator) Whether this can run in real-time depends on the hardware capabilities of the specific robot and the frequency requirements — it is possible to run this on a desktop or smaller embedded device.

      We do note that the model needs to first be set up and trained before it can be run, which takes some time (see panel D of Figure 1).

      L432: "...which is a popular technique in robotics.". Please cite references supporting this statement.

      We have added citations: the text and relevant citations are reproduced below:

      “... which is a popular technique in robotics (Hua et al., 2021; Johns, 2021)

      Hua J, Zeng L, Li G, Ju Z. Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning. Sensors. 2021; 21(4):1278

      Johns E. Coarse-to-fine imitation learning: Robot manipulation from a single demonstration. In:

      2021 IEEE international conference on robotics and automation (ICRA) IEEE; 2021. p. 4613–4619

      L509: "We find that the phase offset across legs is not modulated across walking speeds in our dataset". This is a surprising result to the reviewer. Looking at Figure 6C, the reviewer understands that there are no drastic changes in coordinate with speed, but there are certainly some changes, e.g., L1-R3, L3-R1. In the reviewer's experience, even very small changes in interleg phasing can change the visual classification of walking from "tripod" to "tetrapod" or "metachronal". Furthermore, several leg pairs do not reside exactly at 0 or \pi radians apart, e.g., L1-L3, L2-L3, R1-R3, R2-R3. In conclusion, the reviewer thinks that setting the interleg coordination to tripod in all cases is a large assumption that requires stronger justification (or, should be eliminated altogether).

      We made a simplifying assumption of a tripod coordination across all speeds. The change in relative phase coordination across speeds is indeed relatively small and additionally we see little change in our results across forward speeds (see Figures 4B, 5C and 5D). 

      We have added text to clarify this assumption and what could be changed for future studies in the methods:

      “We estimate $\bar \phi_{ij}$ from the walking data by taking the circular mean over phase differences of pairs the legs during walking bouts. We find that the phase offset across legs is not strongly modulated across walking speeds in our dataset (see Appendix 2) so we model $\bar \phi_{ij}$ as a single constant independent of speed. In future studies, this could be a function of forward and rotation speeds to account for fine phase modulation differences.”

      L581: "of dimension...". Should the asterisk be replaced by \times? The asterisk makes the reviewer think of convolution. This change should be made throughout this paragraph.

      Good point, done.

      Figure 6: Rotational velocities in all 3 sections are reported in mm/s, but these units do not make sense. Rotational velocities must be reported in rad/s or deg/s.

      The rotation velocity of mm/s corresponded to the tangential velocity of the ball the fly walked on. We agree that this does not easily generalize across setups, so we have updated the figure rotation velocities in rad/s. 

      L619: The reviewer is unconvinced by using only 2 principal components of the data to compare the model and animal kinematics. The authors state on line 626 that the 2 principal components do not capture 56.9% of the variation in the data, which seems like a lot to the reviewer. This is even more extreme considering that the model has 20 joints, and the authors are reducing this to 2 variables; the reviewer can't see how any of the original waveforms, aside from the most fundamental frequencies, could possibly be represented in the PCA dataset. If the walking fly models looked similar to each other, the reviewer could accept that this method works. But the fact that this method says the kinematics are similar, but the motion is clearly different, leads the reviewer to suspect this method was used so the authors could state that the data was a good match.

      Our primary use of the KS metric was to indicate whether the simulated fly continues walking in the presence of perturbations, hence we limited the analysis of the KS to the first 2 principal components. 

      For completeness, we investigate the principal components in Appendix 9 and the effect of evaluating KS with different numbers of components in Appendix 10. 

      The results look similar across components for impulse perturbations. For stochastic perturbations, the range of similar walking decreases as we increase the number of components used to evaluate walking kinematics. Comparing this with Appendix 9 showing that higher components represent higher frequencies of the walking cycle, we conclude that at the edge of stability for delays (where sum of sensory and actuation delays are about 40ms), flies can continue walking but with impaired higher frequencies (relative to no perturbations) during and after perturbation. 

      We add text in the methods:

      “We chose 2 dimensions for PCA for two key reasons. First, these 2 dimensions alone accounted for a large portion of the variance in the data (52.7% total, with 42.1% for first component and 10.6% for second component)). There was a big drop in variance explained from the first to the second component, but no sudden drop in the next 10 components (see Appendix 9). Second, the KDE procedure only works effectively in low-dimensional spaces, and the minimal number of dimensions needed to obtain circular dynamics for walking is 2. We investigate the effect of varying the number of dimensions of PCA in Appendix 10.”

      (Note that we have corrected the percentage of variance accounted for by the principal components, as these numbers were from an older analysis prior to the first draft.)

      We also reference Appendix 10 in the results:

      “We observed that robust walking was not contingent on the specific values of motor and sensory delay, but rather the sum of these two values (Fig. 5E). Furthermore, as delay increases, higher frequencies of walking are impacted first before walking collapses entirely (Appendix 10).”

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      The paper nicely shows that PP2A antagonizes Crb-dependent and Crb-independent phosphorylation and degradation of Expanded (Ex), in cell culture and in wing discs. The authors focus on the Mts catalytic subunit of PP2A, but also demonstrate the involvement of the Wrd and Tws B regulatory subunits. They also show via use of transcriptional reporters that PP2A directly affects Hpo signaling in vivo. Finally, they show a potential role for Merlin and Kibra in regulating Ex levels, and that Kib binds to Mts and Wrd. The experiments are on the whole well executed and quantified.

      Major comments:- (1) I am not convinced that the authors can entirely rule out a role for the STRIPAK complex. Mutation of MtsR268A reduces binding of Wrd by 60% and abrogates the effect of Mts on Ex. However mutation of MtsL186A reduces binding of Cka by less than 50% and doesn't disrupt Mts regulation of Ex. Perhaps Cka is more abundant than Wrd, and 50% of Mts/Cka complex is more than sufficient for it to carry out its enzymatic function.

      To further investigate whether PP2A can indeed stabilise Ex independently of the STRIPAK complex we will conduct the following experiments in response to the comments from Reviewers 1 and 3:

      • Test whether knocking down other components of the STRIPAK complex such as FGOP2 and Mob4 affects the ability of Mts to stabilise Ex degradation in the presence or absence of Crbintra in vitro using S2 cells. If we do observe any effect, we will also test whether knocking these components in the posterior compartment of the wing disc also has an effect on the Ex stability reporter levels.
      • The reviewers raised the point that the MtsL186A mutant results in 50% reduction in binding with Cka and that a 50% reduction in the Mts/Cka complex may still be sufficient to stabilise Ex levels. To address this, we will knock down either Wrd or Cka and test whether this affects the ability of MtsL186A to stabilise Ex both in the presence/absence of Crbintra. This will test whether the stabilisation of Ex by MtsL186A can be attributed to the function of the MtsL186A::Cka holoenzyme or the MtsL186A::Wrd holoenzyme. We will test this both in vitro and in vivo.

        I also note that in Fig 1H, Ex levels in Crb/Mts+Cka RNAi appear to be intermediate between those in Crb and Crb/Mts. Ideally this would be quantified. Similarly in 4J, mtsL186A (while not significant) appears intermediate between mtsH118N and mts-WT. What is the actual P value for the comparison to Mts-WT? In any case I would suggest the authors tone down these conclusions.

      We have now provided quantification for the blot in Fig. 1H (now Fig. 1I) in Fig. 1J. We will tone down our conclusions regarding the role of STRIPAK based on our results from the experiments detailed above.

      (2) I also found it rather confusing that the authors discuss the Cka B subunit in the context of the STRIPAK complex in Figure 1, then don't look at the other B subunits until Figures 3/4. In my opinion, it would be easier to follow the flow of the manuscript if the authors discussed Crb-dependent and independent regulation of Ex, then the roles of Gish/CKI, then the role of the B subunits including Cka. In this context, it would also be interesting to see if there was any redundancy between Cka and Wrd - have the authors tried any double knockdown experiments (with appropriate controls for RNAi dosage)?

      We thank the reviewer for their suggestion to potentially alter the order by which some of the results of the paper are presented. At the moment, we believe the current description of the results fits well with the observations and their significance, but we will assess this after the revisions are completed and, if required, we will change the order of the results to improve the clarity of the manuscript. To test for any redundancy between Cka and Wrd, we will undertake knock down both Cka and Wrd using S2 cells.

      (3) The authors examine Crb-independent Ex regulation in the wing disc, which appears to be wing discs that do not overexpress Crb. I would expect that wing discs do express Crb - or is this not the case? Please clarify whether this is in the absence of Crb, or the absence of overexpressed Crb.

      This is now clarified in the text Line 358.

      (4) I was confused by the section 'CKIs and Slmb regulate Ex proteostasis via the 452-457 Slmb consensus sequence'. The authors conclude that 'these results show that the machinery that facilitates Crb-mediated Ex phosphorylation and degradation is also partly involved in the Crb-independent regulation of Ex protein stability.' However, I had concluded the opposite, as it appeared that Slimb and gish RNAi only affected Ex1-468, and similarly Slmb only affected Ex1-468, but not Ex1-450 (which in the previous section was shown to be regulated by Mts independent of Crb). Please could the authors explain/clarify this.

      We have previously shown that, in the presence of Crbintra, Gish/Ck1α/Slmb act on Ex via the Ex452-457 aa sequence, which corresponds to a b-TrCP/Slmb consensus sequence (Fulford et al., 2019). In the absence of Crbintra, we observed that Gish/Ck1α/Slmb require the 452-457 site to be present to be able to phosphorylate and degrade Ex (i.e. the Ex1-450 truncation that lacks this site is refractory to the regulation by Gish/Ck1α/Slmb). This suggests that Gish/Ck1α/Slmb regulate Ex via the 452-457 site, both in absence and presence of Crbintra. We have now clarified this in the text: Lines 387-388 and Lines 405-406.

      (5) The regulation of Ex by Merlin and Kibra is potentially interesting, but a bit preliminary. This part of the manuscript could be strengthened by showing for example if Mts or Wrd knockdown affects the stabilization of Ex by Kib.

      As suggested by the reviewer we will further characterise the interaction between Kib and Mts in stabilising Ex. We will test whether Kib can stabilise Ex when either mts or wrd is knocked down. We will also test whether Kib can stabilise Ex in the absence of ectopic Crb expression in vivo and whether this is indeed dependent on the Wrd subunit.

      Minor comments: (1) The Introduction gives a quite comprehensive review of known interactions between STRIPAK, Expanded and Hippo pathway components. However, it is hard to keep track of all the components and interactions if you are not deeply into the field. To improve accessibility, I would suggest a summary diagram of the key interactions (currently the manuscript has no introductory figures at all!) and if possible the authors might consider whether there are details they could leave out or which could just be mentioned as necessary in the results sections.

      We have now added an introductory figure, Fig.1A, detailing the key elements of Hpo regulation that is pertinent for this study.

      (2) Could the authors show a shorter exposure of the Ex blot in Figure 1A, in order to better visualize the loss of band shift?

      A shorter exposure of the Ex blot has now been added to the Fig. 1B (previously Fig. 1A).

      (3) Line 307 '(Fig. 1B,D,G,I)' the call-out to Fig.1I appears to be in strike-through font, presumably because 1I shouldn't be cited here? It also looks like Fig.1I is wrongly cited on line 342 as that sentence only describes action of L168A in wing discs. I think a sentence describing the experiment in Fig.1I is missing?

      The Figures have now been cited appropriately. Fig. 1J (previously Fig. 1I) is now referred to in Line 336.

      (4) Line 355 ambiguous, should this read low expression of Crb in S2 cells?

      This has now been changed from extremely low expression to low expression.

      (5) Line 369 reads 'PP2A was able to stabilize full-length Ex', Mts-WT would be more precise.

      This has now been changed to MtsWT was able to stabilise full-length Ex.

      (6) The blot in panel 2O is mislabeled Ex1-468, I think this should be Ex1-450.

      The blot in panel 2O is now correctly labelled as Ex1-450.* *

      (7) The nomenclature of 'Mts-WT' for their own transgene and 'Mts-BL' for the Bloomington transgene. is confusing, as both are, I believe, wild type. Maybe leave this detail for the M&M, at least if the authors believe there is no difference in behavior.

      We are happy to change this if required.

      (8) Figure S6 appears to be missing from the uploaded version.

      We thank the reviewer for noticing this. Fig. S6 is now included in the supplementary figure file.

      (9) Lines 480-481: 'Using co-IP analyses, we observed that Mts interacts with Ex, both in the presence and absence of Crbintra.' No figure call-out is given for this statement, and I can't see the data anywhere, but from the figure legends it seems to be in the missing Fig.S6? And everything that follows in this paragraph should have call-outs for Fig.4K?

      Fig. S6 has now been appended and the call-outs to Fig. 4K have been added to in the paragraph Line 475-490.

      (10) Lines 503-504: 'we found that Kib associated with Mts (Fig. 5C)' - Fig.5B?

      This has now been changed.

      (11) Lines 504-505: 'no interaction was observed between Mts and Mer (Fig.5B)' - Fig.5C?

      This has now been changed.

      (12) In Figure 6G, authors note that 'the mean diap1GFP4.3 levels of MtsWT+Crb-Intra were lower than those of Crb-Intra, this difference was not statistically significant when all genotypes were included in the comparisons, but only when the Control, crbintra and mtsWT+crbintra conditions were considered.' It might be useful to have a table showing the actual P values of all the comparisons (or maybe better still just put actual P values on the graphs?). Sometimes an arbitrary cut-off of 0.05 for significant can be misleading.

      We have now added the actual p-values for those >0.05 to the graph.

      Reviewer #1 (Significance (Required)):

      The Hippo signaling pathway is a conserved regulator of tissue growth, and understanding how this pathway is activated and modulated is of great importance. Levels of the upstream activator Expanded are known to be regulated by phosphorylation/degradation, but whether dephosphorylation of Ex is important for growth control has not been widely investigated. This paper utilizes cell culture and the fruit fly model organism to provide clear evidence for a role for PP2A in regulation of Ex levels, independent of its known role in regulating phosphorylation of Hpo. It will therefore be of interest to biologists working in the fields of growth control and tissue homeostasis.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: The authors show that the protein phosphatase PP2A antagonizes Crb-mediated phosphorylation and subsequent degradation of Expanded in vivo. Using Drosophila imaginal wing discs and the GAL4-UAS system, the authors provide evidence that the PP2A holoenzyme dephosphorylates Ex, stabilizing its protein levels, in a manner independent of the STRIPAK complex and identifies Wrd as a key regulatory subunit of PP2A in this process. Importantly, the study also shows that PP2A stabilizes Ex protein levels independent of Crb-driven phosphorylation and that, via this stabilization, PP2A activates Hpo pathway signaling to repress transcriptional targets of Yki.

      Major comments: Overall, the study is strong, and the conclusions are supported by the data. The data does largely lean on overexpression models in the wing disc and it would strengthen the biological relevance to include genomic alleles (i.e., do Ex-GFP levels go down in PP2A/mts mutant clones?). Materials and methods are thoroughly presented, and statistical analyses are adequate. OPTIONAL: While not necessarily required for publication, note that full in vivo confirmation would require altering the PP2A target sites in Ex by generating phospho-deficient and phospho-mimetic versions and seeing if they match the model. This would push the conclusions to the highest degree of confidence and rigor.

      We agree with the reviewer and indeed have tried to undertake MARCM experiments with mts null mutant clones. However, since mts is an essential gene, even when MtsWT was expressed in the presence of mts mutant, we were only able to obtain few single cell clones, which was difficult to analyse. Hence, clonal analysis using mts mutant clones will not be feasible in this case. (see also revision plan for figure illustrating the data referred to here).

      Minor comments: Text and figures are clear and accurate. It may be helpful to include a modified version of the Mts mutants table in SF1 in a main figure for easier reference but is not necessary.

      If required, we can move the table to one of the main figures based on whether additional data will be presented in the revised manuscript.

      Reviewer #2 (Significance (Required)):

      The studies strengths include biochemical and in vivo validation of the effect of PP2A and its various regulatory subunits on Ex phosphorylation and stabilization. The study very methodically parses out the context in which PP2A is stabilizing Ex (i.e., both in the context of Crb stimuli and independently, and it does so independently of the STRIPAK complex). As noted previously, recapitulating the major results in clones using genomic alleles would strengthen the biological relevance. The study advances our understanding of mechanisms tightly controlling downstream transcriptional outputs of the Hpo pathway via regulating Ex protein stability/turnover. Though the primary audience may be those well-versed in the Hpo field and Drosophila genetics, the implications for the research are broad.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      The authors hypothesized that Crb mediated Ex phosphorylation and degradation, that they previously established, should be countered and set on to identify the phosphatase involved. Surprisingly, they find that Mts, the catalytic subunit of PP2A, counters the effect of ectopically expressed intracellular domain of Crb on Ex stability. This was surprising because PP2A and the STRIPAK complex was shown to counter Hippo activity previously, suggesting that PP2A would inject both positive and negative inputs into Hpo activity. The title reflects this finding.

      Overall, the experiments are well controlled and are of high quality. I especially appreciate the effort to show results of parallel experiments both in S2 cells and in vivo in wing discs.

      The manuscript convincingly demonstrates that Mts expression stabilizes Ex1-468::GFP in the presence or absence of ectopic Crb-intra. This effect is mainly mediated by the Wrd adaptor subunit, and requires the catalytic activity of Mts. However, results shown in Fig4K highlights the Tws adaptor as the main one that binds to and stabilizes Ex in S2 cells, in the presence or absence of Crb-intra expression. This is slightly at odds with Wrd-RNAi experiments nicely reversing the effects of Crb-intra expression.

      We would like to highlight that results shown in Fig. 4K were obtained upon the transfection of HA-tagged Wrd/Tws and, hence, they are not necessarily indicative of the levels of binding between the endogenous Ex and the regulatory subunits. Additionally, we would argue that the Ex:Tws interaction is merely indicative of the steady state regulation of Ex, which occurs both in the presence and absence of Crbintra, thereby explaining why we can detect the interaction in both settings. As for Wrd, given that we have shown that it is involved in the regulation of Ex only in the presence of Crbintra and antagonises its effect on Ex protein stability, it is only interacting with Ex in conditions where Crbintra is affecting Ex protein levels.

      The manuscript is not easy to read given the vast amount of data using many different constructs, but there is little the authors can do about it as the story is complex and layered.

      The argument that the effects of Mts are independent of the STRIPAK complex is less convincing. This conclusion is based on Mts-L186A mutant which should not bind Cka which is the PP2A adaptor subunit found in the STRIPAK complex. Fig S3F and G show that Cka binding to Mts is reduced by half when Mts-L186A mutant is expressed in lieu wt Mts. Consistent with this in Fig1F rescue of Ex degradation by Mts-L186A is half as effective as the rescue seen in 1F by the wt Mts.

      We will conduct the experiments mentioned in the reply to Major comments 1 of Reviewer 1 to address this.

      Towards the same argument, data shown on S3A-D is deemed inconclusive based on quantification in S3E which does not reflect the clear reduction in Ex that is seen in S3B. Hence FigS3 is in favour of Cka4 being involved in the rescue effect.

      In Fig. S3 we show that expression of either Crbintra or MtsWT+Crbintra does not cause any changes in the levels of the Ex reporter when the crosses were raised at 18°C. Hence, we believe that in this setting, we are unable to fully study PP2A-mediated stabilisation of Ex in the presence of Crbintra. Cka RNAi causes dramatic effects on tissue growth at 18°C (where Crbintra cannot modulate Ex protein levels), and lethality prior to the late L3 stage (where Crbintra modulates Ex protein levels), and this precludes us from testing the role of Cka. However, the results shown with the Mts mutant that has reduced binding to the STRIPAK complex strongly suggest that Cka is not essential for the role of PP2A in regulating Ex protein levels.

      In Figures 5A and 3A, Crb-intra expression does not destabilize Ex1-468::GFP, why is that?

      This is due to the expression levels of Crbintra in this particular biological repeat of the experiment. We will repeat this experiment to obtain a more representative image of the effect of Crbintra.

      The authors connect effects on Ex stability to the influence on Hippo pathway activity in Fig 6, which is a very nice touch.

      Finally, I wonder whether the dual effect of PP2A on Hippo activity (inhibiting Hippo and stabilizing Ex) could be a single effect. I am guessing the Ex1-468::GFP construct, having its own regulatory elements, would act independently of the transcriptional activity of Hippo. However, I was not able to find this demonstrated in the literature. Can the authors show that? For example, make hpo or wts mutant clones in the presence of the Ex1-468::GFP construct. Otherwise, an alternative explanation could be that PP2A, with its various adaptor subunits, counters Hippo activity which translates into higher levels of expanded transcription and Ex protein production.

      Since the reporter is under the control of the ubiquitin 63E promoter as opposed to the endogenous promoter, we do not envisage that its transcription is regulated by Yki. Indeed, a similar method of decoupling potential transcriptional and post-translational effects of Hpo signalling has been successfully used in studies that have focused on other Hpo pathway components, such as Kibra (Tokamov et al., 2021) and Salvador (Aerne et al., 2015). The reviewer suggests that we should assess the effect of hpo or wts mutant clones and determine of these affect the levels of the ubi-Ex1-468::GFP reporter. However, we believe this may lead to results that will be difficult to interpret. Although hpo or wts clones are expected to result in higher Yki activity, they will also remove Hpo or Wts function, and these proteins may be involved in the molecular mechanisms that regulate Ex protein stability. Therefore, as an alternative approach to assess the impact of Hpo signalling on the Ex reporter, we will perform RT-PCR experiments to monitor the transcriptional regulation of the transgenic reporter in the presence or absence of Yki overexpression.

      It was also demonstrated that there are higher levels of Crb in hippo mutants likely due to the expansion of the apical domain. This would be consistent with the stabilized Crb-intra seen in Figures 1A&3A upon Mts expression. Stabilization of Crb upon Mts expression (not commented on in the manuscript) is very interesting as extra Crb should further push the balance towards Ex degradation but Mts seems to be able to reverse the effect. I agree that this alternative explanation may be far-fetched, yet it is also easily tested, and would greatly simplify the model put forward.

      The reviewer suggests that Mts may potentially be involved in regulating Crbintra levels. To test this, we will test whether overexpression of various doses of either MtsWT or MtsH118N affects the stability of Crbintra using S2 cells.

      Finally, if indeed various PP2A complexes, depending on the adaptor subunits they contain, have a range of effects on Ex stability and Hippo pathway activity, this brings in the question of what regulates the availability of various adaptor subunits and the PP2A complexes they form? The question is outside the scope of the manuscript but it is worth discussing.

      We agree with the reviewer that this is a crucial question. However, tackling this experimentally would be challenging at this stage and we believe this is beyond the scope of the current manuscript. However, we will address this point in the discussion of the revised manuscript.

      Reviewer #3 (Significance (Required)):

      A vast amount of data is presented in both in vivo and in vitro settings. The study uses biochemical and genetic approaches and combines them aptly.

      I think the findings showing multiple and various effects on PP2A on the same pathway would be of higher interest to the PP2A enthusiasts than the Hippo researchers.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      In this manuscript, the authors study the effects of synaptic activity on the process of eye-specific segregation, focusing on the role of caspase 3, classically associated with apoptosis. The method for synaptic silencing is elegant and requires intrauterine injection of a tetanus toxin light chain into the eye. The authors report that this silencing leads to increased caspase 3 in the contralateral eye (Figure 1) and demonstrate evidence of punctate caspase 3 that does not overlap neuronal markers like map2. However, the quantifications showing increased caspase 3 in the silenced eye (done at P5) are complicated by overlap with the signal from entire dying cells in the thalamus. The authors also show that global caspase 3 deficiency impairs the process of eye-specific segregation and circuit refinement (Figures 3-4).

      The reviewer states: “this silencing leads to increased caspase 3 in the contralateral eye”. We observed increased caspase-3 activity, not protein levels, in the contralateral dLGN, not eye.

      The reviewer states: “and demonstrate evidence of punctate caspase 3 that does not overlap neuronal markers like map2”. We do not believe that this statement is accurate, as we show that the punctate active caspase-3 signals overlap with the dendritic marker MAP2 (Figure S4A).

      The reviewer also states: “, the quantifications showing increased caspase 3 [activity] in the silenced [dLGN] (done at P5) are complicated by overlap with the signal from entire dying cells in the thalamus”. We do not believe that this statement is accurate. The apoptotic neurons we observed are relay neurons (confirmed by their morphology and positive staining of NeuN – Figure S4B-C) located in the dLGN (the dLGN is clearly labeled by expression of fluorescent proteins in RGCs, and only caspase-3 activity in the dLGN area is analyzed), not “cells” of unknown lineage (as suggested by the reviewer) in the general “thalamus” area (as suggested by the reviewer). If the dying cells were non-neuronal cells, that would indeed confound our quantification and conclusions, but that is not the case.

      We argue that whole-cell caspase-3 activation in dLGN relay neurons is a bona fide response to synaptic silencing by TeTxLC and therefore should be included in the quantification. We have two sets of controls: one is between the strongly inactivated dLGN and the weakly inactivated dLGN in the same TeTxLC-injected animal; and the second is between the dLGN of TeTxLC-injected animals and mock-injected animals. In both controls, only the dLGNs receiving strong synapse inactivation have more apoptotic dLGN relay neurons, demonstrating that these cells occur because of synapse inactivation. It is also unlikely that our perturbation is causing cell death through a non-synaptic mechanism. Since mock injections do not cause apoptosis in dLGN neurons, this phenomenon is not related to surgical damage. TeTxLC is injected into the eyes and only expressed in presynaptic RGCs, not in postsynaptic relay neurons, so this phenomenon is also unlikely to be caused by TeTxLC-related toxicity. Furthermore, if apoptosis of dLGN relay neurons is not related to synapse inactivation, then when TeTxLC is injected into both eyes, one would expect to see either the same amount or more apoptotic relay neurons, but we instead observed a reduction in dLGN neuron apoptosis, suggesting that synapse-related mechanisms are responsible. Considering the above, occasional whole-cell caspase-3 activation in relay neurons in TeTxLC-inactivated dLGN is causally linked to synapse inactivation and should be included in the quantification.

      We also revised the manuscript to better explain the possible mechanistic connection between localized caspase-3 activity and whole-cell caspase-3 activity. We propose that whole-cell caspase-3 activation occurs because of uncontrolled accumulation of localized caspase-3 activation. Please see line 127-140 and line 403-413 for details.

      Additionally, we would like to clarify that we are not claiming that synapse inactivation leads to only localized caspase-3 activation or only whole-cell caspase-3 activation, as is suggested by the editors and reviewers in the eLife assessment. We have clearly stated in the manuscript that both types of signals were observed. However, we reasoned that, because whole-cell caspase-3 activation in unperturbed dLGNs – which undergo normal synapse elimination – is infrequently observed, whole-cell caspase-3 activation may not be a significant driver of synapse elimination during normal development. In this revision, we included a new experiment to corroborate this hypothesis. If whole-cell caspase-3 activation in dLGN relay neurons is a prevalent phenomenon during normal development, such caspase-3 activity would lead to significant death of dLGN relay neurons during normal development. Consequently, if we block caspase-3 activation by deleting caspase-3, the number of relay neurons in the dLGN should increase. However, in support of our hypothesis, we observed comparable numbers of relay neurons in Casp3<sup>+/+</sup> and Casp3<sup>-/-</sup> mice. Please see Figure S7 for details.

      The authors also report that "synapse weakening-induced caspase-3 activation determines the specificity of synapse elimination mediated by microglia but not astrocytes" (abstract). They report that microglia engulf fewer RGC axon terminals in caspase 3 deficient animals (Figure 5), and that this preferentially occurs in silenced terminals, but this preferential effect is lost in caspase 3 knockouts. Based on this, the authors conclude that caspase 3 directs microglia to eliminate weaker synapses. However, a much simpler and critical experiment that the authors did not perform is to eliminate microglia and show that the caspase 3 dependent effects go away. Without this experiment, there is no reason to assume that microglia are directing synaptic elimination.

      The reviewer states: “microglia engulf fewer RGC axon terminals in caspase 3 deficient animals (Figure 5), and that this preferentially occurs in silenced terminals, but this preferential effect is lost in caspase 3 knockouts”. We are not sure what the reviewer means by “this preferentially occurs in silenced terminals”. Our results show that microglia preferentially engulf silenced terminals, and such preference is lost in caspase-3 deficient mice (Figure 6).

      We do not understand the experiment where the reviewer suggested to: “eliminate microglia and show that the caspase 3 dependent effects go away”. To quantify caspase-3 dependent engulfment of synaptic material by microglia or preferential engulfment of silenced terminals by microglia, microglia must be present in the tissue sample. If we eliminate microglia, neither of these measurements can be made. What could be measured if microglia are eliminated is the refinement of retinogeniculate pathway. This experiment would test whether microglia are required for caspase-3 dependent phenotypes. This is not a claim made in the manuscript. Instead, we claimed caspase-3 is required for microglia to engulf weak synapses, as supported by the evidence presented in Figure 6.

      We did not claim that “microglia are directing synaptic elimination”. Our claim is that synapse inactivation induces caspase-3 activity, and caspase-3 activation in turn leads to engulfment of weak synapses by microglia. Based on this model, it is the neuronal activity that fundamentally directs synapse elimination. Synapse engulfment by microglia is only a readout we used to measure the outcome of activity-dependent synapse elimination. We have revised all sections in the manuscript that are related to synapse engulfment by microglia to emphasize the logic of this model.

      We have also revised the abstract and title of the paper to better align it with our main claims, removed the reference to astrocytes, and clarified that microglia engulfment measurements are used as readouts of synapse elimination.

      Finally, the authors also report that caspase 3 deficiency alters synapse loss in 6-month-old female APP/PS1 mice, but this is not really related to the rest of the paper.

      We respectfully disagree that Figure 7 is not related to the rest of the paper. Many genes involved in postnatal synapse elimination, such as C1q and C3, have been implicated in neurodegeneration. It is therefore natural and important to ask whether the function of caspase-3 in regulating synaptic homeostasis extends to neurodegenerative diseases in adult animals. The answer to this question may have broad therapeutic impacts.

      Reviewer #2 (Public Review):

      Summary:

      This manuscript by Yu et al. demonstrates that activation of caspase-3 is essential for synapse elimination by microglia, but not by astrocytes. This study also reveals that caspase 3 activation-mediated synapse elimination is required for retinogeniculate circuit refinement and eye-specific territories segregation in dLGN in an activity-dependent manner. Inhibition of synaptic activity increases caspase-3 activation and microglial phagocytosis, while caspase-3 deficiency blocks microglia-mediated synapse elimination and circuit refinement in the dLGN. The authors further demonstrate that caspase-3 activation mediates synapse loss in AD, loss of caspase-3 prevented synapse loss in AD mice. Overall, this study reveals that caspase-3 activation is an important mechanism underlying the selectivity of microglia-mediated synapse elimination during brain development and in neurodegenerative diseases.

      Strengths:

      A previous study (Gyorffy B. et al., PNSA 2018) has shown that caspase-3 signal correlates with C1q tagging of synapses (mostly using in vitro approaches), which suggests that caspase-3 would be an underlying mechanism of microglial selection of synapses for removal. The current study provides direct in vivo evidence demonstrating that caspase-3 activation is essential for microglial elimination of synapses in both brain development and neurodegeneration.

      The paper is well-organized and easy to read. The schematic drawings are helpful for understanding the experimental designs and purposes.

      Weaknesses:

      It seems that astrocytes contain large amounts of engulfed materials from ipsilateral and contralateral axon terminals (Figure S11B) and that caspase-3 deficiency also decreased the volume of engulfed materials by astrocytes (Figures S11C, D). So the possibility that astrocyte-mediated synapse elimination contributes to circuit refinement in dLGN cannot be excluded.

      We would like to clarify that we do not claim that astrocytes are unimportant for synapse elimination or circuit refinement. We acknowledge that the claim made in the original submitted manuscript that caspase-3 does not regulate synapse elimination by astrocytes lacks strong supporting evidence. We have removed this claim and revised the section related to synapse engulfment by astrocytes to provide a more rigorous interpretation of our data. We also removed the section in discussion regarding distinct substrate preferences of microglia and astrocytes.

      Does blocking single or dual inactivation of synapse activity (using TeTxLC) increase microglial or astrocytic engulfment of synaptic materials (of one or both sides) in dLGN?

      We assume that by “blocking single or dual inactivation of synapse activity”, the reviewer refers to inactivating retinogeniculate synapses from one or both eyes.

      We showed that inactivating retinogeniculate synapses from one eye (single inactivation) increases engulfment of inactive synapses by microglia (Figure 6). We did not measure synapse engulfment by microglia while inactivating retinogeniculate synapses from both eyes (dual inactivation). However, based on the total active caspase-3 signal (Figure 2) in the dual inactivation scenario, we do not expect to see an increase in engulfment of synaptic material by microglia.

      We did not measure astrocyte-mediated engulfment with single or dual inactivation, as we did not see a robust caspase-3 dependent phenotype in synapse engulfment by astrocytes.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the Authors):

      (1) Figure 1 - It is not clear from this figure whether the authors are measuring caspase 3 in dendritic compartments or in dying relay neurons in the thalamus. The authors state that "either" whole cell death (1B) or smaller punctate signals (1F) were observed. When quantifying "photons" in Figure 1E, it appears most of the signal captured will be of dying relay neurons. What determined which signal was observed, and what is being quantified in Figure 1E? This also applies to the quantifications being reported in Figure 2.

      The quantification includes both types of signals – it is sum of all active caspase-3 signal within the dLGN boundary. We note that there is a significant amount of punctate signal in the TeTxLC-inactivated dLGN. Unfortunately, due to file compression, these signals are not clearly visible in the submitted manuscript file. We have provided high resolution figures in this revision.

      As argued above in the response to the public review, apoptotic relay neurons in TeTxLC-inactivated dLGN (not the general thalamus area) occur as a direct consequence of synapse inactivation. Therefore, active caspase-3 signals in these relay neurons should be included in the quantification.

      We believe it is the extent of synapse inactivation (i.e., the number of synapses that are inactivated) that determines whether dLGN relay neuron apoptosis occurs or not. Such apoptosis is expected considering the nature of the apoptosis signaling cascade. In the intrinsic apoptosis pathway, release of cytochrome-c from mitochondria induces cleavage of the initiator caspase, caspase-9, and caspase-9 in turn cleaves the executioner caspases, caspase-3/7, which causes apoptosis. Caspase-3 can cleave upstream factors in the apoptosis pathway, leading to explosive amplification of caspase-3 activity (McComb et al., DOI: 10.1126/sciadv.aau9433). When a relay neuron receives a few inactivated synapses, caspase-3 activation in the postsynaptic dendrite can remain local (as we observed in Figure 1), constrained by mechanisms such as proteasomal degradation of cleaved caspase-3 (Erturk et al., DOI: 10.1523/JNEUROSCI.3121-13.2014). However, when a relay neuron receives many inactivated synapses, the cumulative caspase-3 activity induced in the dendrite can overwhelm negative regulation and lead to significantly higher levels of caspase-3 activity in entire dendrites (Figure S4B) through positive feedback amplification, eventually leading to caspase-3 activation in entire relay neurons. Please see line 127-140 and line 403-413 for our discussion in the main text.

      (2) Figure 5 - Figures 5c-d and Fig 6 are confounded by pseudoreplication, whereby performing statistics on 50-60 microglia inflates statistical significance. Could the authors show all these data per mouse?

      If we understand the reviewer correctly, the reviewer is suggesting that reporting measurements from multiple microglia in one animal constitutes pseudo-replication. This is correct in a strict sense, as microglia in the same animal are more likely to be similar than microglia from different animals. In the revised version, we have plotted the data by animal in Figure S11 and S13. The observations remain valid. However, we would like to point out that averaging measurements from all microglia in each animal and report by mouse is very conservative, as measurements from microglia in the same animal still vary greatly due to cell-to-cell differences.

      (3) Although the authors are not the only ones to use this strategy, it is worth noting that performing all microglial experiments in Cx3cr1 heterozygotes could lead to alterations in microglial function that may not be reflective of their homeostatic roles.

      We acknowledge that Cx3cr1 heterozygosity could cause alterations in microglial physiology.

      While Cx3cr1 heterozygosity may impact microglia physiology, we note that the engulfment assay in Figure 5 is comparing microglia in Cx3cr1<sup>+/-</sup>; Casp3<sup>+/-</sup> and Cx3cr1<sup>+/-</sup>; Casp3<sup>-/-</sup> animals. Therefore, the impact of Cx3cr1 heterozygosity is controlled for in our experiment, and the observed difference in engulfed synaptic material in microglia is an effect specific to caspase-3 deficiency. However, we acknowledge that this difference could be quantitatively affected by Cx3cr1 heterozygosity.

      It is important to note that we did not perform all microglia engulfment analyses using Cx3cr1<sup>+/-</sup> mice. We have edited the manuscript to make this more clear. In the activity-dependent microglia engulfment analysis performed in Figure 6, we used Casp3<sup>+/+</sup> and Casp3<sup>-/-</sup> animals and detected microglia with anti-Iba1 immunostaining. Therefore, the impact of Cx3cr1 heterozygosity is not a problem for this experiment.

      Minor:

      (1) Figures are presented out of order, which makes the manuscript difficult to follow.

      We have revised text regarding the segregation analysis to align with the order of figures.

      (2) Figure S3 is very confusing- the terms "left" and "right" are used in three or four partly overlapping contexts (which eye, which injection, which panel or subpanel of the figure is being referred to). Would this not be more appropriately analyzed with a repeated measures ANOVA (multiple comparisons not necessary) rather than multiple separate T-tests?

      We have revised Figure S3 and S5 with better annotation and legends.

      Yes, it is possible to use repeated measure two-way ANOVA. The analysis reports significant effect from genotypes, with a dF of 1, SoS and MoS of 0.0001081, F(1,13) = 7.595, and p = 0.0164. We used multiple separate t-tests because we wanted to show how genotype effects change with increasing thresholds, whereas two-way ANOVA only provides one overall p-value.

      (3) Could the authors clarify why the percentage overlap (in the controls) is so different between Figure 3C and Figure S3C, and why different thresholds are applied?

      This difference is primary due to difference in age. Figure 3 and Figure S5 are acquired at age of P10, while Figure S3 is acquired at P8. While the segregation process is largely complete by P8, the segregation continues from P8 to P10. Therefore, overlap measured at P10 will be lower than that measured at P8. If we compare overlap at the same threshold (e.g., 10%) and at the same age in Figure 3 and S5, the overlap is very similar.

      The choice of threshold is related to the methods of labeling. In Figure 3, RGC terminals are labeled with AlexaFlour conjugated cholera toxin subunit-beta (CTB). In Figure S3 and S5, RGC axons are labeled by expression of fluorescent proteins. Labeling with CTB only labels membrane surfaces but yields stronger and slightly different signals at fine scales than labeling with fluorescent protein which are cell fillers. For Figure S3 and S5 (which use fluorescent protein labeling), higher thresholds such as those used in Figure 3 (which use CTB labeling) can be applied and the same trend still holds, but the data will be noisier. Regardless of the small difference in thresholds used, the important observation is that the defects in TeTxLC-injected or caspase-3 deficient animals are clear across multiple thresholds.

      (4) Many describe the eye-specific segregation process as being complete "between P8-10". Other studies have quantified ESS at P10 (Stevens 2007). The authors state they did all quantifications at P8 (l. 82) and refer to Figure 3, but Figure 3 shows images from P10, whereas Figure S3 shows data from P8.

      We did not say we performed all quantification at P8. In line 85, we said “To validate the efficacy of our synapse inactivation method, we injected AAV-hSyn-TeTxLC into the right eye of wildtype E15 embryos and analyzed the segregation of eye-specific territories at postnatal day 8 (P8), when the segregation process is largely complete”. The age of postnatal day 8 in this context is specifically referring to the experiment shown in Figure S3. For the segregation analysis in Figure 3, we specifically stated that the experiment was conducted at P10 (line 277).

      Although the experiment in Figure S3 is conducted at P8, and Figure S5 and Figure 3 show results at P10, each dataset always included appropriate age-matched controls.  P8 is generally considered an age where segregation is mostly complete and sufficient for us to assess the potency of TeTxLC-delivered AAV on eye segregation.  We don’t think performing the experiment shown in Figure S3 at P8 impacts the interpretation of the data.

      (5) Is Figure 6 also using Cx3cr1 GFP to label microglia? This is not clarified.

      We apologize for this oversight. In Figure 6 microglia are labeled by anti-Iba1 immunostaining. We have clarified this in figure legends and text.

      Reviewer #2 (Recommendations for the Authors):

      (1) The authors quantified the caspase-3 activity using immunostaining and confocal microscopy (Figures 1B-E). They may need to verify the result (increased level of activated caspase-3 upon synapse inactivation) using alternative methods, such as western blotting.

      Both western blot and immunostaining are based on antibody-antigen interaction. These two methods are not likely sufficiently independent. Additionally, to perform a western blot, we would need to surgically collect the TeTxLC-inactivated dLGN to avoid sample contamination from other brain regions. Such collection at the age we are interested in (P5) is very challenging. We have tested the anti-cleaved caspase-3 antibody using caspase-3 deficient mice and we can confirm it is a highly specific antibody that doesn’t generate signal in the caspase-3 deficient tissue samples.

      (2) Does caspase-3 deficiency alter the density of microglia or astrocytes in dLGN?

      No. Neither the density of microglia nor astrocytes changed with caspase-3 deficiency. In the case of microglia, we find that the mean density of microglia per unit area of dLGN is virtually the same in wild type and caspase-3 deficient mice (two-tailed t test P = 0.8556, 6 wild type and 5 Casp3<sup>-/-</sup> mice). Some overviews showing microglia in dLGNs of wildtype and caspase-3 deficient mice can be found in Figure S10.  Similarly for astrocytes, we did not observe overt changes in astrocytes dLGN density linked to caspase-3 deficiency.

      (3) During dLGN eye-specific segregation in normal developing animals, did the authors observe different levels of activated caspase-3 in different regions (territories)?

      For normal developing animals, the activated caspase-3 signal is generally sparse, and it is difficult to distinguish whether the signal is related to synapse elimination. For animals receiving TeTxLC-injection, we did notice that in the dLGN contralateral to the injection, where most inactivated synapses are located, the punctate caspase-3 signal tends to concentrate on the ventral-medial side of the dLGN (Figure 1B), which is the region preferentially innervated by the contralateral eye.

      (4) Recording of NMDAR-mediated synaptic currents may not be necessary for demonstrating that caspase 3 is essential for dLGN circuit refinement. In addition, the PPR may not necessarily reflect the number of innervations that a dLGN neuron receives. Instead, showing the changes in the frequency of mEPSCs (or synapse/spine density) may be more supportive.

      Thank you for the comment. We have performed the suggested mEPSC measurements and reported the results in revised Figure 4D-F.

      (5) Why is caspase 3 activation enhanced (compared to control) only at 4 months of age, when A-beta deposition has not formed yet, but not at later time points in AD mice (Figure S17)?

      A prevailing hypothesis in the field is that the form of A-beta that is most neurotoxic is the soluble oligomeric form, not the fibril form that leads to plaque deposition. As the oligomeric form appears before plaque deposition, the enhanced caspase-3 activation we observed at 4-month may reflect an increase in oligomeric A-beta, which occurs before any visible A-beta plaque formation.

      (6) The manuscript can be made more concise, and the figures more organized.

      We removed superfluous details and corrected text-figure mismatches in the revised manuscript to improve readability.

    1. Author response:

      We would like to express our gratitude to all three reviewers for their time and valuable feedback on the manuscript. Below, we provide our point-by-point responses to their comments. Additionally, we summarize here the experiments we plan to conduct in accordance with the reviewers' suggestions:

      Revision plan 1. To include live imaging of Dl/Notch trafficking in normal and GlcT mutant ISCs.

      We agree that the effect of GlcT mutation on Dl trafficking was not convincingly demonstrated in our previous work. Although we attempted live imaging of the intestine using GFP tagged at the C-terminal of Dl, the fluorescent signal was regrettably too weak for reliable capture. In this revision, we will optimize the imaging conditions to determine if this issue can be resolved. Alternatively, we will transiently express GFP/RFP-tagged Dl in both normal and mutant ISCs to investigate the trafficking dynamics through live imaging.

      Revision plan 2. To update and improve the presentation of the data regarding the features of early/late/recycling endosomes in GlcT mutant ISCs.

      Our analysis of Rab5 and Rab7 endosomes in both normal and GlcT mutant ISCs revealed that Dl tends to accumulate in Rab5 endosomes in GlcT mutant ISCs. To strengthen our findings, we will include additional quantitative data and conduct further analysis on recycling endosomes labeled with Rab11-GFP. We acknowledge that this portion of the data is not entirely convincing, and in accordance with the reviewers' suggestions, we will revise our conclusions to present a more tempered interpretation.

      Revision plan 3. To include western blot analysis of Dl in normal and GlcT mutant ISCs.

      While we propose that MacCer may function as a component of lipid rafts, facilitating the anchorage of Dl on the membrane and its proper endocytosis, it is also possible that it acts as a substrate for the modification of Dl, which is essential for its functionality. To investigate this further, we will conduct Western blot analysis to determine whether the depletion of GlcT alters the protein size of Dl.

      Please find our detailed point-by-point responses below.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      From a forward genetic mosaic mutant screen using EMS, the authors identify mutations in glucosylceramide synthase (GlcT), a rate-limiting enzyme for glycosphingolipid (GSL) production, that result in EE tumors. Multiple genetic experiments strongly support the model that the mutant phenotype caused by GlcT loss is due to by failure of conversion of ceramide into glucosylceramide. Further genetic evidence suggests that Notch signaling is comprised in the ISC lineage and may affect the endocytosis of Delta. Loss of GlcT does not affect wing development or oogenesis, suggesting tissue-specific roles for GlcT. Finally, an increase in goblet cells in UGCG knockout mice, not previously reported, suggests a conserved role for GlcT in Notch signaling in intestinal cell lineage specification.

      Strengths:

      Overall, this is a well-written paper with multiple well-designed and executed genetic experiments that support a role for GlcT in Notch signaling in the fly and mammalian intestine. I do, however, have a few comments below.

      Weaknesses:

      (1) The authors bring up the intriguing idea that GlcT could be a way to link diet to cell fate choice. Unfortunately, there are no experiments to test this hypothesis.

      We indeed attempted to establish an assay to investigate the impact of various diets (such as high-fat, high-sugar, or high-protein diets) on the fate choice of ISCs. Subsequently, we intended to examine the potential involvement of GlcT in this process. However, we observed that the number or percentage of EEs varies significantly among individuals, even among flies with identical phenotypes subjected to the same nutritional regimen. We suspect that the proliferative status of ISCs and the turnover rate of EEs may significantly influence the number of EEs present in the intestinal epithelium, complicating the interpretation of our results. Consequently, we are unable to conduct this experiment at this time. The hypothesis suggesting that GlcT may link diet to cell fate choice remains an avenue for future experimental exploration.

      (2) Why do the authors think that UCCG knockout results in goblet cell excess and not in the other secretory cell types?

      This is indeed an interesting point. In the mouse intestine, it is well-documented that the knockout of Notch receptors or Delta-like ligands results in a classic phenotype characterized by goblet cell hyperplasia, with little impact on the other secretory cell types. This finding aligns very well with our experimental results, as we noted that the numbers of Paneth cells and enteroendocrine cells appear to be largely normal in UGCG knockout mice. By contrast, increases in other secretory cell types are typically observed under conditions of pharmacological inhibition of the Notch pathway.

      (3) The authors should cite other EMS mutagenesis screens done in the fly intestine.

      To our knowledge, the EMS screen on 2L chromosome conducted in Allison Bardin’s lab is the only one prior to this work, which leads to two publications (Perdigoto et al., 2011; Gervais, et al., 2019). We will include citations for both papers in the revised manuscript.

      (4) The absence of a phenotype using NRE-Gal4 is not convincing. This is because the delay in its expression could be after the requirement for the affected gene in the process being studied. In other words, sufficient knockdown of GlcT by RNA would not be achieved until after the relevant signaling between the EB and the ISC occurred. Dl-Gal4 is problematic as an ISC driver because Dl is expressed in the EEP.

      We agree that the lack of an observable phenotype using NRE-Gal4 might be attributed to a delay in its expression, which could result in missing the critical window necessary for effective GlcT knockdown. Consequently, we cannot rule out the possibility that GlcT may also play a role in early EBs or EEPs. We will revise our manuscript to present a more cautious conclusion on this issue.

      (5) The difference in Rab5 between control and GlcT-IR was not that significant. Furthermore, any changes could be secondary to increases in proliferation.

      We agree that it is possible that the observed increase in proliferation could influence the number of Rab5+ endosomes, and we will temper our conclusions on this aspect accordingly. However, it is important to note that, although the difference in Rab5+ endosomes between the control and GlcT-IR conditions appeared mild, it was statistically significant and reproducible. As we have indicated earlier, we plan to further analyze Rab11+ endosomes, as this additional analysis may provide further support for our previous conclusions.

      Reviewer #2 (Public review):

      Summary:

      This study genetically identifies two key enzymes involved in the biosynthesis of glycosphingolipids, GlcT and Egh, which act as tumor suppressors in the adult fly gut. Detailed genetic analysis indicates that a deficiency in Mactosyl-ceramide (Mac-Cer) is causing tumor formation. Analysis of a Notch transcriptional reporter further indicates that the lack of Mac-Ser is associated with reduced Notch activity in the gut, but not in other tissues.

      Addressing how a change in the lipid composition of the membranes might lead to defective Notch receptor activation, the authors studied the endocytic trafficking of Delta and claimed that internalized Delta appeared to accumulate faster into endosomes in the absence of Mac-Cer. Further analysis of Delta steady-state accumulation in fixed samples suggested a delay in the endosomal trafficking of Delta from Rab5+ to Rab7+ endosomes, which was interpreted to suggest that the inefficient, or delayed, recycling of Delta might cause a loss in Notch receptor activation.

      Finally, the histological analysis of mouse guts following the conditional knock-out of the GlcT gene suggested that Mac-Cer might also be important for proper Notch signaling activity in that context.

      Strengths:

      The genetic analysis is of high quality. The finding that a Mac-Cer deficiency results in reduced Notch activity in the fly gut is important and fully convincing.

      The mouse data, although preliminary, raised the possibility that the role of this specific lipid may be conserved across species.

      Weaknesses:

      This study is not, however, without caveats and several specific conclusions are not fully convincing.

      First, the conclusion that GlcT is specifically required in Intestinal Stem Cells (ISCs) is not fully convincing for technical reasons: NRE-Gal4 may be less active in GlcT mutant cells, and the knock-down of GlcT using Dl-Gal4ts may not be restricted to ISCs given the perdurance of Gal4 and of its downstream RNAi.

      As previously mentioned, we acknowledge that a role for GlcT in early EBs or EEPs cannot be completely ruled out. We will revise our manuscript to present a more cautious conclusion and explicitly describe this possibility in the updated version.

      Second, the results from the antibody uptake assays are not clear.: i) the levels of internalized Delta were not quantified in these experiments; ii) additionally, live guts were incubated with anti-Delta for 3hr. This long period of incubation indicated that the observed results may not necessarily reflect the dynamics of endocytosis of antibody-bound Delta, but might also inform about the distribution of intracellular Delta following the internalization of unbound anti-Delta. It would thus be interesting to examine the level of internalized Delta in experiments with shorter incubation time.

      We thank the reviewer for these excellent questions. In our antibody uptake experiments, we noted that Dl reached its peak accumulation after a 3-hour incubation period. We recognize that quantifying internalized Dl would enhance our analysis, and we will include the corresponding statistical graphs in the revised version of the manuscript. In addition, we agree that during the 3-hour incubation, the potential internalization of unbound anti-Dl cannot be ruled out, as it may influence the observed distribution of intracellular Dl. To address this concern, we plan to supplement our findings with live imaging experiments to capture the dynamics of Dl endocytosis in GlcT mutant ISCs.

      Overall, the proposed working model needs to be solidified as important questions remain open, including: is the endo-lysosomal system, i.e. steady-state distribution of endo-lysosomal markers, affected by the Mac-Cer deficiency? Is the trafficking of Notch also affected by the Mac-Cer deficiency? is the rate of Delta endocytosis also affected by the Mac-Cer deficiency? are the levels of cell-surface Delta reduced upon the loss of Mac-Cer?

      Regarding the impact on the endo-lysosomal system, this is indeed an important aspect to explore. While we did not conduct experiments specifically designed to evaluate the steady-state distribution of endo-lysosomal markers, our analyses utilizing Rab5-GFP overexpression and Rab7 staining did not indicate any significant differences in endosome distribution in MacCer deficient conditions. Moreover, we still observed high expression of the NRE-LacZ reporter specifically at the boundaries of clones in GlcT mutant cells (Fig. 4A), indicating that GlcT mutant EBs remain responsive to Dl produced by normal ISCs located right at the clone boundary. Therefore, we propose that MacCer deficiency may specifically affect Dl trafficking without impacting Notch trafficking.

      In our 3-hour antibody uptake experiments, we observed a notable decrease in cell-surface Dl, which was accompanied by an increase in intracellular accumulation. These findings collectively suggest that Dl may be unstable on the cell surface, leading to its accumulation in early endosomes.

      Third, while the mouse results are potentially interesting, they seem to be relatively preliminary, and future studies are needed to test whether the level of Notch receptor activation is reduced in this model.

      In the mouse small intestine, olfm4 is a well-established target gene of the Notch signaling pathway, and its staining provides a reliable indication of Notch pathway activation. While we attempted to evaluate Notch activation using additional markers, such as Hes1 and NICD, we encountered difficulties, as the corresponding antibody reagents did not perform well in our hands. Despite these challenges, we believe that our findings with Olfm4 provide an important start point for further investigation in the future.

      Reviewer #3 (Public review):

      Summary:

      In this paper, Tang et al report the discovery of a Glycoslyceramide synthase gene, GlcT, which they found in a genetic screen for mutations that generate tumorous growth of stem cells in the gut of Drosophila. The screen was expertly done using a classic mutagenesis/mosaic method. Their initial characterization of the GlcT alleles, which generate endocrine tumors much like mutations in the Notch signaling pathway, is also very nice. Tang et al checked other enzymes in the glycosylceramide pathway and found that the loss of one gene just downstream of GlcT (Egh) gives similar phenotypes to GlcT, whereas three genes further downstream do not replicate the phenotype. Remarkably, dietary supplementation with a predicted GlcT/Egh product, Lactosyl-ceramide, was able to substantially rescue the GlcT mutant phenotype. Based on the phenotypic similarity of the GlcT and Notch phenotypes, the authors show that activated Notch is epistatic to GlcT mutations, suppressing the endocrine tumor phenotype and that GlcT mutant clones have reduced Notch signaling activity. Up to this point, the results are all clear, interesting, and significant. Tang et al then go on to investigate how GlcT mutations might affect Notch signaling, and present results suggesting that GlcT mutation might impair the normal endocytic trafficking of Delta, the Notch ligand. These results (Fig X-XX), unfortunately, are less than convincing; either more conclusive data should be brought to support the Delta trafficking model, or the authors should limit their conclusions regarding how GlcT loss impairs Notch signaling. Given the results shown, it's clear that GlcT affects EE cell differentiation, but whether this is via directly altering Dl/N signaling is not so clear, and other mechanisms could be involved. Overall the paper is an interesting, novel study, but it lacks somewhat in providing mechanistic insight. With conscientious revisions, this could be addressed. We list below specific points that Tang et al should consider as they revise their paper.

      Strengths:

      The genetic screen is excellent.

      The basic characterization of GlcT phenotypes is excellent, as is the downstream pathway analysis.

      Weaknesses:

      (1) Lines 147-149, Figure 2E: here, the study would benefit from quantitations of the effects of loss of brn, B4GalNAcTA, and a4GT1, even though they appear negative.

      We will incorporate the quantifications for the effects of the loss of brn, B4GalNAcTA, and a4GT1 in the updated Figure 2.

      (2) In Figure 3, it would be useful to quantify the effects of LacCer on proliferation. The suppression result is very nice, but only effects on Pros+ cell numbers are shown.

      We will add quantifications of the number of EEs per clone to the updated Figure 3.

      (3) In Figure 4A/B we see less NRE-LacZ in GlcT mutant clones. Are the data points in Figure 4B per cell or per clone? Please note. Also, there are clearly a few NRE-LacZ+ cells in the mutant clone. How does this happen if GlcT is required for Dl/N signaling?

      In Figure 4B, the data points represent the fluorescence intensity per single cell within each clone. It is true that a few NRE-LacZ+ cells can still be observed within the mutant clone; however, this does not contradict our conclusion. As noted, high expression of the NRE-LacZ reporter was specifically observed around the clone boundaries in MacCer deficient cells (Fig. 4A), indicating that the mutant EBs can normally receive Dl signal from the normal ISCs located at the clone boundary and activate the Notch signaling pathway. Therefore, we believe that, although affecting Dl trafficking, MacCer deficiency does not significantly affect Notch trafficking.

      (4) Lines 222-225, Figure 5AB: The authors use the NRE-Gal4ts driver to show that GlcT depletion in EBs has no effect. However, this driver is not activated until well into the process of EB commitment, and RNAi's take several days to work, and so the author's conclusion is "specifically required in ISCs" and not at all in EBs may be erroneous.

      As previously mentioned, we acknowledge that a role for GlcT in early EBs or EEPs cannot be completely ruled out. We will revise our manuscript to present a more cautious conclusion and describe this possibility in the updated version.

      (5) Figure 5C-F: These results relating to Delta endocytosis are not convincing. The data in Fig 5C are not clear and not quantitated, and the data in Figure 5F are so widely scattered that it seems these co-localizations are difficult to measure. The authors should either remove these data, improve them, or soften the conclusions taken from them. Moreover, it is unclear how the experiments tracing Delta internalization (Fig 5C) could actually work. This is because for this method to work, the anti-Dl antibody would have to pass through the visceral muscle before binding Dl on the ISC cell surface. To my knowledge, antibody transcytosis is not a common phenomenon.

      We thank the reviewer for these insightful comments and suggestions. In our in vivo experiments, we observed increased co-localization of Rab5 and Dl in GlcT mutant ISCs, indicating that Dl trafficking is delayed at the transition to Rab7⁺ late endosomes, a finding that is further supported by our antibody uptake experiments. We acknowledge that the data presented in Fig. 5C are not fully quantified and that the co-localization data in Fig. 5F may appear somewhat scattered; therefore, we will include additional quantification and enhance the data presentation in the revised manuscript.

      Regarding the concern about antibody internalization, we appreciate this point. We currently do not know if the antibody reaches the cell surface of ISCs by passing through the visceral muscle or via other routes. Given that the experiment was conducted with fragmented gut, it is possible that the antibody may penetrate into the tissue through mechanisms independent of transcytosis.

      As mentioned earlier, we plan to supplement our findings with live imaging experiments to investigate the dynamics of Dl/Notch endocytosis in both normal and GlcT mutant ISCs. Anyway, due to technical challenges and potential pitfalls associated with the assays, we agree that this part of data is not fully convincing and we will provide a more cautious conclusion in the revised manuscript.

      (6) It is unclear whether MacCer regulates Dl-Notch signaling by modifying Dl directly or by influencing the general endocytic recycling pathway. The authors say they observe increased Dl accumulation in Rab5+ early endosomes but not in Rab7+ late endosomes upon GlcT depletion, suggesting that the recycling endosome pathway, which retrieves Dl back to the cell surface, may be impaired by GlcT loss. To test this, the authors could examine whether recycling endosomes (marked by Rab4 and Rab11) are disrupted in GlcT mutants. Rab11 has been shown to be essential for recycling endosome function in fly ISCs.

      We agree that assessing the state of recycling endosomes, especially by using markers such as Rab11, would be valuable in determining whether MacCer regulates Dl-Notch signaling by directly modifying Dl or by influencing the broader endocytic recycling pathway. We will incorporate these experiments into our future experimental plans to further characterize Dl trafficking in GlcT mutant ISCs.

      (7) It remains unclear whether Dl undergoes post-translational modification by MacCer in the fly gut. At a minimum, the authors should provide biochemical evidence (e.g., Western blot) to determine whether GlcT depletion alters the protein size of Dl.

      While we propose that MacCer may function as a component of lipid rafts, facilitating Dl membrane anchorage and endocytosis, we also acknowledge the possibility that MacCer could serve as a substrate for protein modifications of Dl necessary for its proper function. Conducting biochemical analyses to investigate potential post-translational modifications of Dl by MacCer would indeed provide valuable insights. To address this, we will incorporate Western blot analysis into our experimental plan to determine whether GlcT depletion affects the protein size of Dl.

      (8) It is unfortunate that GlcT doesn't affect Notch signaling in other organs on the fly. This brings into question the Delta trafficking model and the authors should note this. Also, the clonal marker in Figure 6C is not clear.

      In the revised working model, we will explicitly specify that the events occur in intestinal stem cells. Regarding Figure 6C, we will delineate the clone with a white dashed line to enhance its clarity and visual comprehension.

      (9) The authors state that loss of UGCG in the mouse small intestine results in a reduced ISC count. However, in Supplementary Figure C3, Ki67, a marker of ISC proliferation, is significantly increased in UGCG-CKO mice. This contradiction should be clarified. The authors might repeat this experiment using an alternative ISC marker, such as Lgr5.

      Previous studies have indicated that dysregulation of the Notch signaling pathway can result in a reduction in the number of ISCs. While we did not perform a direct quantification of ISC numbers in our experiments, our olfm4 staining—which serves as a reliable marker for ISCs—demonstrates a clear reduction in the number of positive cells in UGCG-CKO mice.

      The increased Ki67 signal we observed reflects enhanced proliferation in the transit-amplifying region, and it does not directly indicate an increase in ISC number. Therefore, in UGCG-CKO mice, we observe a decrease in the number of ISCs, while there is an increase in transit-amplifying (TA) cells (progenitor cells). This increase in TA cells is probably a secondary consequence of the loss of barrier function associated with the UGCG knockout.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      The authors propose a transformer-based model for the prediction of condition - or tissue-specific alternative splicing and demonstrate its utility in the design of RNAs with desired splicing outcomes, which is a novel application. The model is compared to relevant existing approaches (Pangolin and SpliceAI) and the authors clearly demonstrate its advantage. Overall, a compelling method that is well thought out and evaluated.

      Strengths:

      (1) The model is well thought out: rather than modeling a cassette exon using a single generic deep learning model as has been done e.g. in SpliceAI and related work, the authors propose a modular architecture that focuses on different regions around a potential exon skipping event, which enables the model to learn representations that are specific to those regions. Because each component in the model focuses on a fixed length short sequence segment, the model can learn position-specific features. Another difference compared to Pangolin and SpliceAI which are focused on modeling individual splice junctions is the focus on modeling a complete alternative splicing event.

      (2) The model is evaluated in a rigorous way - it is compared to the most relevant state-of-the-art models, uses machine learning best practices, and an ablation study demonstrates the contribution of each component of the architecture.

      (3) Experimental work supports the computational predictions.    

      (4) The authors use their model for sequence design to optimize splicing outcomes, which is a novel application.

      We wholeheartedly thank Reviewer #1 for these positive comments regarding the modeling approach we took to this task and the evaluations we performed. We have put a lot of work and thought into this and it is gratifying to see the results of that work acknowledged like this.

      Weaknesses:

      No weaknesses were identified by this reviewer, but I have the following comments:

      (1) I would be curious to see evidence that the model is learning position-specific representations.

      This is an excellent suggestion to further assess what the model is learning. We have several ideas on how to test this which we will plan to report in the revised version. 

      (2) The transformer encoders in TrASPr model sequences with a rather limited sequence size of 200 bp; therefore, for long introns, the model will not have good coverage of the intronic sequence. This is not expected to be an issue for exons.

      Yes we can divide predictions by intron length, that’s a good suggestion. We will report on that in the revision.

      (3) In the context of sequence design, creating a desired tissue- or condition-specific effect would likely require disrupting or creating motifs for splicing regulatory proteins. In your experiments for neuronal-specific Daam1 exon 16, have you seen evidence for that? Most of the edits are close to splice junctions, but a few are further away.

      That is another good question and suggestion. In the original paper describing the mutation locations some motif similarities were noted to PTB (CU) and CUG/Mbnl-like elements (Barash et al Nature 2010). We could revisit this now with an RBP motif D.B. such as http://rbpdb.ccbr.utoronto.ca/. We note the ENCODE uses human cell lines and cannot be used for this but we will also look for mouse CLIP and KD data supporting such regulatory findings. 

      (4) For sequence design, of tissue- or condition-specific effect in neuronal-specific Daam1 exon 16 the upstream exonic splice junction had the most sequence edits. Is that a general observation? How about the relative importance of the four transformer regions in TrASPr prediction performance?

      This is another excellent question that we plan to follow up with matching analysis in the revision.

      (5) The idea of lightweight transformer models is compelling, and is widely applicable. It has been used elsewhere. One paper that came to mind in the protein realm:

      Singh, Rohit, et al. "Learning the language of antibody hypervariability." Proceedings of the National Academy of Sciences 122.1 (2025): e2418918121.

      Yes, we are for sure not the only/first to advocate for such an approach. We will be sure to make that point clear in the revision and thank the reviewer for the example from a different domain.  

      Reviewer #2 (Public review):

      Summary:

      The authors present a transformer-based model, TrASPr, for the task of tissue-specific splicing prediction (with experiments primarily focused on the case of cassette exon inclusion) as well as an optimization framework (BOS) for the task of designing RNA sequences for desired splicing outcomes.

      For the first task, the main methodological contribution is to train four transformer-based models on the 400bp regions surrounding each splice site, the rationale being that this is where most splicing regulatory information is. In contrast, previous work trained one model on a long genomic region. This new design should help the model capture more easily interactions between splice sites. It should also help in cases of very long introns, which are relatively common in the human genome.

      TrASPr's performance is evaluated in comparison to previous models (SpliceAI, Pangolin, and SpliceTransformer) on numerous tasks including splicing predictions on GTEx tissues, ENCODE cell lines, RBP KD data, and mutagenesis data. The scope of these evaluations is ambitious; however, significant details on most of the analyses are missing, making it difficult to evaluate the strength of the evidence. Additionally, state-of-the-art models (SpliceAI and Pangolin) are reported to perform extremely poorly in some tasks, which is surprising in light of previous reports of their overall good prediction accuracy; the reasoning for this lack of performance compared to TrASPr is not explored.

      In the second task, the authors combine Latent Space Bayesian Optimization (LSBO) with a Transformer-based variational autoencoder to optimize RNA sequences for a given splicing-related objective function. This method (BOS) appears to be a novel application of LSBO, with promising results on several computational evaluations and the potential to be impactful on sequence design for both splicing-related objectives and other tasks.

      We thank Reviewer #2 for this detailed summary and positive view of our work. It seems the main issue raised in this summary regards the evaluations: The reviewer finds details of the evaluations missing and the fact that SpliceAI and Pangolin perform poorly on some of the tasks to be surprising. In general, we made a concise effort to include the required details, including code and data tables, but will be sure to include more details based on the specific questions/comments listed below. As for the perceived performance issues for Pangolin/SpliceAI we believe this may be the result of not making it clear what tasks they perform well on vs those in which they do not work well. We give more details below. 

      Strengths:

      (1) A novel machine learning model for an important problem in RNA biology with excellent prediction accuracy.

      (2) Instead of being based on a generic design as in previous work, the proposed model incorporates biological domain knowledge (that regulatory information is concentrated around splice sites). This way of using inductive bias can be important to future work on other sequence-based prediction tasks.

      Weaknesses:

      (1) Most of the analyses presented in the manuscript are described in broad strokes and are often confusing. As a result, it is difficult to assess the significance of the contribution.

      We made an effort to make the tasks be specific and detailed,  including making the code and data of those available. Still, it is evident from the above comment Reviewer #2 found this to be lacking. We will review the description and make an effort to improve that given the clarifications we include below. 

      (2) As more and more models are being proposed for splicing prediction (SpliceAI, Pangolin, SpliceTransformer, TrASPr), there is a need for establishing standard benchmarks, similar to those in computer vision (ImageNet). Without such benchmarks, it is exceedingly difficult to compare models. For instance, Pangolin was apparently trained on a different dataset (Cardoso-Moreira et al. 2019), and using a different processing pipeline (based on SpliSER) than the ones used in this submission. As a result, the inferior performance of Pangolin reported here could potentially be due to subtle distribution shifts. The authors should add a discussion of the differences in the training set, and whether they affect your comparisons (e.g., in Figure 2). They should also consider adding a table summarizing the various datasets used in their previous work for training and testing. Publishing their training and testing datasets in an easy-to-use format would be a fantastic contribution to the community, establishing a common benchmark to be used by others.

      There are several good points to unpack here. First, we agree that a standard benchmark will be useful to include. We will work to create and include one for the revision. That said, we note that unlike the example given by Reviewer #2 (ImageNet) there are no standards for the splicing prediction tasks. There are actually different task definitions with different input/outputs as we tried to cover briefly in the introduction section. 

      Second, regarding the usage of different data and distribution shifts as potential reasons for Pangolin performance differences. We originally evaluated Pangolin after retraining it with MAJIQ based quantifications and found no significant changes. We will include a more detailed analysis of Pangolin retrained like this in the revision. We also note that Pangolin original training involved significantly more data as it was trained on four species with four tissues each, and we only evaluated it on three of those tissues (for human), in exons the authors deemed as test data. That said, we very much agree that retraining Pangolin as mentioned above is warranted, as well as clearly listing what data was used for training as suggested by the reviewer.

      (3) Related to the previous point, as discussed in the manuscript, SpliceAI, and Pangolin are not designed to predict PSI of cassette exons. Instead, they assign a "splice site probability" to each nucleotide. Converting this to a PSI prediction is not obvious, and the method chosen by the authors (averaging the two probabilities (?)) is likely not optimal. It would interesting to see what happens if an MLP is used on top of the four predictions (or the outputs of the top layers) from SpliceAI/Pangolin. This could also indicate where the improvement in TrASPr comes from: is it because TrASPr combines information from all four splice sites? Also, consider fine-tuning Pangolin on cassette exons only (as you do for your model).

      As mentioned above, we originally did try to retrain Pangolin with MAJIQ PSI values without observing much differences, but we will repeat this and include the results in the revision. Trying to combine 4 different SpliceAI models as proposed by the Reviewer seems to be a different kind of a new model, one that takes 4 large ResNets and combines those with annotation. Related to that, we did try to replace the transformers in our ablation study. The reviewer’s suggestion seems like another interesting architecture to try but since this is a non existing model that would likely require some adjustments. Given that, we view adding such a new model architecture as beyond the scope of this work.

      (4) L141, "TrASPr can handle cassette exons spanning a wide range of window sizes from 181 to 329,227 bases - thanks to its multi-transformer architecture." This is reported to be one of the primary advantages compared to existing models. Additional analysis should be included on how TrASPr performs across varying exon and intron sizes, with comparison to SpliceAI, etc.

      Yes, that is a good suggestion, similar to one made by Reviewer #1 as well. We plan to include such analysis in the revision. 

      (5) L171, "training it on cassette exons". This seems like an important point: previous models were trained mostly on constitutive exons, whereas here the model is trained specifically on cassette exons. This should be discussed in more detail.

      Previous models were not trained exclusively on constitutive exons and Pangolin specifically was trained with their version of junction usage across tissues. That said, the reviewer’s point is valid (and similar to ones made above) about a need to have a matched training/testing. As noted above we plan to include Pangolin training on our PSI values for comparison.

      (6) L214, ablations of individual features are missing.

      OK

      (7) L230, "ENCODE cell lines", it is not clear why other tissues from GTEx were not included.

      The task here was to assess predictions in very different conditions, hence we tested on completely different data of human cell lines rather than similar tissue samples. Yes, we can also assess on unseen GTEX tissues as well.

      (8) L239, it is surprising that SpliceAI performs so badly, and might suggest a mistake in the analysis. Additional analysis and possible explanations should be provided to support these claims. Similarly, the complete failure of SpliceAI and Pangolin is shown in Figure 4d.

      Line 239 refers to predicting relative inclusion levels between competing 3’ and 5’ splice sites. We admit we too expected this to be better for SpliceAI and Pangolin and will be sure to recheck for bugs, but to be fair we are not aware of a similar assessment being done for either of those algorithms (i.e. relative inclusion for 3’ and 5’ alternative splice site events).

      One issue we ran into, reflected in Reviewer #2 comments, is the mix between tasks that SpliceAI and Pangolin excel at and other tasks where they should not necessarily be expected to excel. Both algorithms focus on cryptic splice site creation/disruption. This has been the focus of those papers and subsequent applications.  While Pangolin added tissue specificity to SpliceAI training, the authors themselves admit “...predicting differential splicing across tissues from sequence alone is possible but remains a considerable challenge and requires further investigation”. The actual performance on this task is not included in Pangolin’s main text, but we refer Reviewer #2 to supplementary figure S4 in that manuscript to get a sense of Pangolin’s reported performance on this task. Similar to that, Figure 4d is for predicting *tissue specific* regulators. We do not think it is surprising that SpliceAI (tissue agnostic) and Pangolin (slight improvement compared to SpliceAI in tissue specific predictions) do not perform well on this task.  Similarly, we do not find the results in Figure 4C surprising either. These are for mutations that slightly alter inclusion level of an exon, not something SpliceAI was trained on, as it was simply trained on splice sites yes/no predictions. As noted and we will stress in the revision as well, training Pangolin on this dataset like TrASPr gives similar performance. That is to be expected as well - Pangolin is constructed to capture changes in PSI, those changes are not even tissue specific for CD19 data and the model has no problem/lack of capacity to generalize from the training set just like TrASPr does. In fact, if you only use combination of known mutations seen during training a simple regression model gives correlation of ~92-95% (Cortés-López et al 2022). In summary, we believe that better understanding of what one can realistically expect from models such as SpliceAI, Pangolin, and TrASPr will go a long way to have them better understood and used effectively. We will try to improve on that in the revision.

      (9) BOS seems like a separate contribution that belongs in a separate publication. Instead, consider providing more details on TrASPr.

      We thank the reviewer for the suggestion. We agree those are two distinct contributions and we indeed considered having them as two separate papers. However, there is strong coupling between the design algorithm (BOS) and the predictor that enables it (TrASPr). This coupling is both conceptual (TrASPr as a “teacher”) and practical in terms of evaluations. While we use experimental data (experiments done involving Daam1 exon 16, CD19 exon 2) we still rely heavily on evaluations by TrASPr itself. A completely independent evaluation would have required a high-throughput experimental system to assess designs, which is beyond the scope of the current paper. For those reasons we eventually decided to make it into what we hope is a more compelling combined story about generative models for prediction and design of RNA splicing. 

      (10) The authors should consider evaluating BOS using Pangolin or SpliceTransformer as the oracle, in order to measure the contribution to the sequence generation task provided by BOS vs TrASPr.

      We can definitely see the logic behind trying BOS with different predictors. That said, as we note above most of BOS evaluations are based on the “teacher”. As such, it is unclear what value replacing the teacher would bring. We also note that given this limitation we focus mostly on evaluations in comparison to existing approaches (genetic algorithm or random mutations as a strawman).

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      Reply to the reviewers

      Manuscript number: RC-2024-0284z

      Corresponding author(s): Bérénice, Benayoun A

      1. General Statements [optional]

      This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

      2. Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      This paper by McGill and colleagues explores sex differences in murine macrophages from different niches. They use a combination of publicly available, and newly developed datasets, and combine these using meta-analysis approaches. They explore DEGs between sexes - both common across niches, and specific to certain niches - and use enrichment analyses to identify pathways linked to these genes. Their overall conclusions are that gene expression changes in females are more consistent across niches, than for males, and are enriched in extracellular matrix-related genes. The paper is easy to follow and very well written.

      Major Comments:

      1. I would suggest Figure 1 be moved to a supplemental figure. We agree that the Xist and Ddx3y is QC and can be removed. However, we believe that the separation of macrophage transcriptomes based on sex in the Multidimensional Scaling plot is an important result. Thus, we have revised Figure 1 to only include the MDS plots and have moved the Xist/Ddx3y plots to the supplement (new Supplemental Figure S1) in line with the reviewer’s suggestion.

      Line 106 - It should be clarified why 50 DEGs was selected as the cut off for exclusion.

      We apologize that our cut off criteria was not explained clearly enough. Because these are publicly available datasets, every lab used different numbers of biological replicates, methods, and sequencing depths, impacting the power of the assay to detect differences in gene expression robustly. Since we were interested in functions that were sex-dimorphic, and that requires running functional enrichment analysis, we needed to have a minimum gene set size to be able to run these analyses, which, in the field, is usually accepted to be 50 genes for robustness. Thus, we used 50 DEGs and have updated the methods to explain our reasoning: “Applying a cutoff for the number of differentially expressed genes (DEGs) helps ensure data consistency and comparability across datasets with varying methodologies and sequencing depths. This prevents datasets with excessively low DEG counts from disproportionately influencing downstream analyses. A cutoff also reduces noise from spurious findings, prioritizing datasets with robust transcriptional changes that are more likely to be biologically meaningful. The excluded microglia dataset contained only 11 DEGs (whereas all other microglia datasets had hundreds of DEGs), the pleural macrophage dataset had 37 (whereas all other lung-related macrophage datasets had above 50), and the spleen macrophage dataset had only 30.” (page 12, lines 381-388).

      Optional - would suggest sex chromosome-linked genes are excluded and the analysis redone to see if there are other autosomal genes that are statistically shadowed by the X and Y linked genes.

      We thank the reviewer for this great suggestion, and we now added this point to the discussion (page 9, lines 260-268). However, we think that genes on the X and Y chromosomes will impact overall function of the macrophages and that they are necessary to understand how macrophages from males and females may support differences in immune function throughout life. We now add this in the discussion as a potential future direction: “We find that a majority of genes similarly differential across sexes among the macrophage niches are sex chromosome linked. X-linked genes like Tlr7, Cxcr3, and Kdm6a enhance immune responses in female macrophages, potentially increasing inflammation with age (Feng et al., 2024). Meanwhile, Y-linked genes such as Uty and Sry influence transcriptional regulation and inflammatory signaling in male macrophages, which may contribute to chronic low-grade inflammation (Lusis, 2019). These genetic differences affect macrophage activity, tissue-specific immune responses, and susceptibility to age-related diseases, highlighting the importance of sex-specific factors in immune research. Future research should also explore how non-sex chromosome-linked genes interact with these sex-specific mechanisms to further shape macrophage and immune function.” (page 9, lines 260-268).

      More metadata about the included studies should be included eg mouse ages, strains, experimental manipulations etc. I can't seem to access all of the Supplemental tables so this may already be included in Table S1.

      We agree that this information is important to take into consideration and have now included this information in Supplemental Table S1A, along with the accession numbers to each dataset. All mice were aged between 2 to 24 weeks and all on variations of the C57BL/6 background.

      How relevant the findings in mice are for humans should be explained further in the discussion.

      We agree that our discussion needs to better explain broader implications. Our findings are relevant for human health because macrophages play key roles in immunity, inflammation, and tissue homeostasis, and their functions are known to differ between sexes. Understanding these sex-specific transcriptional differences in mice can provide insights into how male and female immune systems respond differently to infections, autoimmune diseases, and aging in humans. Since macrophage phenotypes are influenced by both systemic factors (e.g., hormones) and tissue-specific environments, studying multiple macrophage subtypes from different organs helps identify conserved and context-dependent sex differences. Indeed, our findings suggest the ECM may be a potential mechanism underlying sex-biased diseases, such as higher autoimmune prevalence in females or increased susceptibility to certain infections in males. We have added this detail to the discussion (page 10, lines 269-275).

      Minor Comments:

      1. Lines 63-66 - need references here. This mirrors Reviewer 2’s major point #2. We agree with the reviewer that references are needed and now cite PMID: 31541153, PMID: 29533975, PMID: 37863894, PMID: 33415105, and PMID: 37491279 (page 4 line 68-69).

      Line 61 and 69 - repeated.

      We thank the reviewer for catching this oversight and have deleted the first instance of the sentence.

      Reviewer #1 (Significance (Required)):

      Although this study is primarily descriptive, it adds to the current knowledge about sex differences in macrophages, an important and relatively understudied area. Those interested in sex differences and in the innate immune system generally, plus those who study macrophages in any context, should be interested in this work.

      We thank the reviewer for their interest in our work and their helpful suggestions.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: The study investigates sex-specific differences in macrophage gene expression across various tissue niches by analyzing both newly generated and publicly available datasets of varying quality. The key finding is the identification of three consistently differentially expressed genes (DEGs) across all macrophage niches: the Y-chromosome-encoded genes Ddx3y and Eif2s3y, and the X-chromosome-specific gene Xist. However, the number of sex-dimorphic DEGs varied significantly between macrophage niches, with female-biased genes showing more consistency across datasets. To further explore these sex-specific differences, the authors performed an overrepresentation analysis of the DEGs across datasets. They found enriched gene sets associated with specific biological terms in female-biased macrophages from peritoneal macrophages, bone marrow-derived macrophages (BMDMs), and osteoclast progenitors (OCPs), while male-biased enrichment was observed in microglia, exudate macrophages, OCPs, and BMDMs. Notably, extracellular matrix (ECM)-related genes were specifically enriched in female peritoneal macrophages and OCPs, whereas the term "nucleic acid binding" was more prominent in male samples from microglia, BMDMs, and OCPs, driven by the Y-chromosome genes Uty and Kdm5d. A gene set enrichment analysis (GSEA) using Gene Ontology (GO) and Reactome databases further confirmed the enrichment of sex-biased pathways. Based on these findings, the authors conclude that three sex chromosome-associated genes are consistently differentially expressed across all datasets and that female-associated gene expression appears to be more stable, particularly in relation to ECM-associated processes.

      Major Comments:

      Are the key conclusions convincing?

      1. The study provides valuable insights into sex-dimorphic gene expression in macrophages across different niches. However, some conclusions appear overinterpreted due to the limited number of differentially expressed genes (DEGs) driving specific terms in the overrepresentation analysis. The reliance on only a few recurring genes (e.g., Kdm5d, Eif2s3y, Uty, and Ddx3y) raises concerns about the biological significance of some enriched terms. A clearer discussion on the limitations of such findings is necessary. We apologize for the confusion. Although the Venn Diagram may give the impression that our comparisons are limited to those few genes, we only highlight them with bold text because they are a good quality control mechanism for our analyses.

      Importantly, methods like gene set enrichment analysis [GSEA] use whole-transcriptome ranking, which means the results we obtain are driven by the entire transcriptome and not just a few genes (GSEA results are reported in Figure 5). We agree that further explanation of these methodologies would improve interpretation of our findings for readers unfamiliar with these analytical techniques. To address this, we have now added the following to the methods: “GSEA relies on whole-transcriptome ranking, ensuring that the results reflect global transcriptomic patterns rather than being influenced by only a few genes.” (page 13, lines 415-417).

      Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?Some claims, particularly those regarding the role of macrophages in diseases such as AD, histiocytosis, and osteoporosis, lack relevant references.

      This mirrors minor point #1 from Reviewer #1. We apologize for not originally including references for this statement and have now updated the introduction and discussion with appropriate references: “Excessive macrophage activation is associated with numerous conditions, including neurodegeneration, atherosclerosis, osteoporosis, and cancer, many of which exhibit sex-biased tendencies (Chen et al., 2020; Hou et al., 2023; Li et al., 2023; Mammana et al., 2018)” (page 4, lines 67-69) and “Thus, investigating female and male-biased processes in macrophages, including the contribution of the ECM, will be an important step in developing treatments for diseases including, but not limited to, AD, histiocytosis, and osteoporosis(Chen et al., 2020; Cox et al., 2021; Hou et al., 2023; Li et al., 2023; Mammana et al., 2018)” (page 10, lines 285-288).

      Would additional experiments be essential to support the claims of the paper? While additional wet-lab experiments are not strictly necessary, a deconvolution analysis of the datasets could be highly beneficial. This would allow the identification of enriched macrophage subtypes and help assess whether differences between datasets are driven by specific macrophage populations rather than global sex differences. Since peritoneal macrophage origin is influenced by age and inflammation status, deconvolution could also clarify dataset comparability.

      The reviewer makes an interesting point. We apologize for the confusion regarding the purity and origin of these datasets. All the datasets we curated from public repositories for our analysis are from purified populations of macrophages. To clarify this, we now include a column with the purification method used for each of the datasets based on the original manuscript in revised Supplemental Table S1A.

      Since all the used datasets were derived from pure macrophage populations, deconvolution (which is used to identify cellular proportions in heterogeneous contexts) would not accomplish much, predicting that all the cells in the data are macrophages. While some people have argued that deconvolution may be used to identify different cell states, this is very controversial, especially since the “pure” reference and the heterogeneous query are subject to batch effects (i.e. either from differences in bench processing, sex of provenance for target/query datasets, transcriptional impact of sorting methods, differences in transcriptomic quantification methods, etc.) which overshadow most differences beyond cell types. Thus, due to the known batch sensitivity of deconvolution methods and the fact that we only selected pure macrophage transcriptomic profiling datasets, using deconvolution to identify macrophage subtypes would not be informative/feasible. Importantly, we focused our analyses on datasets derived only from young, healthy, naïve animals (2 to 24 weeks), without any interference from age-related inflammation.

      To make this caveat clearer, we have added sentences to the results section indicating the age range of the animals (page 6, lines 100-101), as well as in the discussion to discuss how inflammation states and age may change some of our findings (page 10, lines 295-299).

      Are the suggested experiments realistic in terms of time and resources? Performing cell-type deconvolution using established computational tools (e.g., CIBERSORT, BisqueRNA, or single-cell deconvolution methods) would be a realistic approach within a few weeks and would significantly strengthen the study. This analysis would not require additional experimental work but could refine the interpretation of the dataset. Additionally, a PCA of all datasets could help identify potential similarities among macrophages from different niches and between sexes.

      As explained in our response to point #4, the use of only datasets from purified macrophages from young animals (before any influence of age or disease) makes deconvolution analysis meaningless, especially due to batching concerns. Specifically, it would require us to generate paired single-cell and bulk datasets on all macrophage subtypes in house to remove batch-inducing experimental biases, which we believe is outside of the scope of this small bioinformatics study.

      To the second point, doing a PCA of all the datasets together would not provide much new information beyond cell type of origin due to batching concerns that could not be corrected, which are a known problem in transcriptomics analyses (PMID:20838408, PMID:28351613). Since datasets come from different labs, using different isolation methods, RNA capture choices, library construction kits and sequencing platforms, the main separating effects overall will be batch/dataset, not biology (PMID:20838408, PMID:28351613). Indeed, this is what we observe (Reviewer Figure 1), with broad separation of datasets by tissue of origin, then dataset of origin. Additionally, the top 10 loadings for PC1 and PC2 are primarily associated to autosomal genes (i.e. not on the sex chromosomes; Reviewer Table 1).

      Reviewer Figure 1. (A) PCA of all samples across datasets. Read counts were processed together through R package sva v.3.46.0 for surrogate variable estimation, and surrogate variables were removed using the removeBatchEffect function from ‘limma’ v.3.54.2. DESeq2 normalized counts were used to make the PCA. (B) Zoomed in PCA excluding three outlier sample to enable easier visual discrimination of samples.

      Principal Component – Gene

      Loading

      Chromosome

      PC1- Srcin1

      0.013601

      11

      PC1- Cacna1c

      0.013593

      6

      PC1- Pclo

      0.01357

      5

      PC1- Tro

      0.013547

      X

      PC1- Ppp4r4

      0.013541

      12

      PC1- Ppp1r1a

      0.01354

      15

      PC1- Homer2

      0.013538

      7

      PC1- Caskin1

      0.013535

      17

      PC1- Arhgef9

      0.013527

      X

      PC1- Slc4a3

      0.013499

      1

      PC2- Gm15446

      0.017978

      5

      PC2- 1810034E14Rik

      0.017897

      13

      PC2- Gm19557

      0.017871

      19

      PC2- Pkd1l2

      0.017792

      8

      PC2- H60b

      0.017274

      10

      PC2- Appbp2os

      0.01723

      11

      PC2- Mir7050

      0.017221

      7

      PC2- Nkapl

      0.017166

      13

      PC2- Tmem51os1

      0.017083

      4

      PC2- Dpep3

      0.016962

      8

      Reviewer Table 1. Top 10 loadings for principal component 1 and principal component 2 with their respective chromosomal location.

      Thus, since batch effects can only be accounted for rigorously when they are not confounded by biology (and in our case since each dataset only looks at one type of macrophage), this cannot be corrected in a rigorous manner to yield the desired results.

      We have added a sentence to the discussion to highlight how future work where macrophages from diverse niches would be profiled in parallel may give greater insights into niche-specific sex-dimorphic effects (page 10, line 295-296).

      Are the data and the methods presented in such a way that they can be reproduced? Some methodological details are missing, particularly regarding:

      The isolation of mouse peritoneal macrophages (details on injection and harvesting procedure needed). Quality control of isolated macrophages (How were contaminating cells excluded? Was additional validation performed beyond using the kit?)

      The age of mice used for bone marrow-derived macrophages (BMDMs) is not provided, which is important given that immune responses can be age-dependent.

      We appreciate the reviewer’s request for additional methodological details. We apologize for not being clear with our details and have updated the methods to be clearer (page 11, lines 320-346), as well as added this information in revised Supplemental Table S1A (e.g. age of animals and purification method as described in the original papers). For all our in house datasets, mice were 4-months old, and the text is now updated to reflect this: “Long bones (tibia and femur) of young (4-months-old) from both sexes were collected and bone marrow was flushed into 1.5mL Eppendorf tubes via centrifugation (30 seconds, 10,000g) (Amend et al., 2016)” (page 11, lines 334-336).

      While we couldn’t check the purity post hoc for published datasets we identified for meta-analysis, we performed a purity check on our isolated peritoneal macrophages using Cd11b-F4/80 staining by flow cytometry and have now included this data (including gating strategy) in Supplemental Figure S4. For BMDMs, no purity check was performed, as there is extensive literature on the efficiency of this differentiation protocol which consistently yields > 90% of macrophages. This has been added to the methods: “We used a protocol that is expected to yield ~90% Cd11b+ F4/80+ cells (Mendoza et al., 2022; Toda et al., 2021)” (page 11, lines 336-337).

      Are the experiments adequately replicated and statistical analysis adequate? The statistical analysis appears generally appropriate, but there are concerns about dataset inconsistencies that should be addressed. Some datasets were not used across all analyses, which is not clearly indicated in figures or text. This should be explicitly mentioned to avoid misleading interpretations.

      We appreciate the reviewer’s careful evaluation of our statistical analysis and the concern regarding dataset inconsistencies.

      We believe that the reviewer is referring to the omission of the exudate dataset from the Venn Diagram analysis (Figure 2C), as this is the only time that we did not report the results from all datasets. We originally chose not to include the exudate dataset in the shared differentially expressed gene (DEG) analysis, because it contained over 1,300 DEGs, whereas all other datasets had between 4–30 DEGs, resulting in an unreadable figure.

      However, we agree that it is important to include for the readers, and while we have decided to still exclude the exudate dataset from Figure 1C for readability purposes, we now include the overlap analyses for all datasets in Supplemental Figure S2 using an upset plot (an alternative visualization method) showing all 6 niches, as well as a table panel that lists the shared genes across niches “Three genes were found to be differentially expressed across all six niches: Xist, Ddx3y, and Eif2s3y (Figure 2C, Supplemental Figure 2A,B)” (page 6, lines 124-126). We thank the reviewer for drawing our attention to this and making our analysis clearer for future readers.

      Minor Comments

      1. Figures are included twice in the manuscript. We apologize for this, and figures are now only included once.

      The use of stereotypic colors in figures (e.g., blue for male, pink for female) could be reconsidered for better readability and to avoid reinforcing gender stereotypes.

      While we understand that this color choice might feel gender normative, we respectfully disagree with the reviewer, as we believe that for the expediency of scientific communication it is important to choose a color palette that is easily understandable without confusion without even needing to consult a legend.

      Importantly, we have been using the same color palette in all publications from the lab on sex-differences for consistency (Lu et al, Nat aging 2021 PMID: 34514433; McGill et al, PLoS ONE, 2023 PMID: 38032907; Kang et al, J Neuroinflammation, 2024 PMID: 38840206; McGill et al, STAR Protocols, 2021 PMID: 34820637), which is crucial for scientific rigor and communication consistency.

      Results - Section 1

      Line 92: The word 'identified' may not be the most appropriate choice here, as it implies discovery rather than selection. Consider rephrasing to 'compiled' or 'gathered' to more accurately reflect the process of assembling the datasets. Additionally, the sentence structure could be refined for clarity, such as specifying that the datasets include both newly generated and publicly available data.

      We have changed two instances of using the word identified to “collected” and “gathered” (page 4, line 83 and page 6, line 98). We also adjusted the sentence to say, “Although we initially collected 21 datasets, both newly generated and publicly available, for our study, only 18 datasets were retained after various quality filtering steps for downstream analysis” (page 4, lines 83-85).

      Line 95: Specify the source of exudate-derived macrophage data.

      We have updated Supplemental Table S1A to make sure it was comprehensively describing the datasets we used in our analysis and double checked that it was complete (including for the exudate data). We have updated the text to reflect this: “All accession numbers and corresponding manuscripts are found in Supplemental Table S1A” (page 6, lines 103-104).

      Figure 1/2A: The scheme overview lacks clarity-its purpose is unclear. The two identical boxes are redundant and do not provide additional insight. Consider illustrating the origins of different macrophage subtypes instead. The cutoff of >50 DEGs should be included in the schematic to improve clarity. Overrepresentation and GSEA analysis should not be illustrated multiple times across different figures-it is redundant.

      In Figure 1A, we included the identical boxes to indicate that no datasets were excluded for incorrect labeling of males/females. However, we agree that this is unnecessary and have removed the second box as suggested.

      In Figure 2A, we agree the identical boxes are unneeded as the Xist/Ddx3y quality control step was listed in Figure 1A, and we have modified the figure accordingly.

      We also agree that including the DEG cutoff and removing the GSEA mention will streamline the figures and have updated them accordingly as well.

      Line 100: The mention of R software should be moved to the Methods section instead of appearing in the Results section.

      We have now updated the text to say, “Expression levels of male-specific Ddx3y and female-specific Xist genes across all samples were examined to ensure proper sex labeling of samples (Supplemental Figure 1A-U)” (page 6, lines 111-112).

      Figure 1B-V: The current figure layout is visually cluttered. Consider plotting male and female datasets together in a single graph with different point shapes instead of separate panels for each specific niche.

      This seems to echo the above request for a global PCA in Reviewer 2’s Major Point #4, which unfortunately cannot be included due to the disproportionate impact of batch effects that has been well documented in the literature (Reviewer Figure 1; PMID:20838408, PMID:28351613). However, to make the figure clearer and less cluttered, and to address related Reviewer 1’s Major Point #1, we have moved the Xist/Ddx3y plots to Supplemental Figure S1 and only include the Multidimensional Scaling plots in Figure 1 to showcase the sex separation in each dataset.

      Text-Figure alignment: The text describes male/female-specific gene expression levels first, while the figure starts with MDS analysis. The order should be consistent.

      We agree and have adjusted the text accordingly (lines 109-112).

      Figure 2C: Exudate data is missing-explain why.

      This point echoes major point #6. As explained above, we have clarified this and included new data panels for clarity (New Supplemental Figure S2).

      Results - Section 2

      Line 151: Use consistent terminology-either "DEGs" or "DE genes", not both.

      We replaced all instances of “DE genes” with DEGs (lines 132, 137, 141, 147, 149, 163, and 397).

      Figure 3A: The text suggests not all datasets were included in this analysis-this should be explicitly indicated in the figure.

      We apologize for the confusion. All datasets were included in this analysis; however, some niches did not have any GO terms passing the FDR

      Show the number of DEGs used for analysis.

      We apologize for the confusion. For the ORA analyses (Figures 3 and 4), we indicate the number of DEGs used for analysis in the panel header. For the GSEA analysis (Figure 5, Supplemental Figure S3), all expressed genes are ranked based on effect size without any prior filter (see response to major point #1), so DEGs are irrelevant for these analyses.

      Figure 3B: Smaller pale dots in the bubble plot are difficult to distinguish-consider using a darker outline.

      We have now added outlines to all the bubbles in the plots to help improve visibility.

      Line 158: The term "phagocytosis" appears inconsistent with the figure, where it is labeled "phagocytosis, recognition".

      We have updated the text accordingly (page 7, line 170).

      Figure 4B, D, E: The overrepresentation analysis is based on very few genes (often only 1-2 genes per term), which may lead to overinterpretation.

      We apologize for the lack of clarity of our previous manuscript. The number of genes used for DEG analysis is in the panel titles of Figure 3 and 4. While the overlap is small, this is unlikely to be spurious since all of the pathways we discuss show significant enrichment with FDR

      Consider explicitly naming these genes and discussing their biological role instead of assigning terms based on minimal evidence.

      We now discuss these genes in the results: “Male-biased GO terms for microglia, OCPs, and BMDMs derived from four genes: Kdm5d, Uty, Ddx3y, and Eif2s3y. All of these are Y-linked genes and play crucial roles in regulating innate and adaptive immune responses (Meester et al., 2020). Kdm5d and Uty influence adaptive immunity through chromatin remodeling and histone modification, while Ddx3y and Eif2s3y shape innate immune responses by modulating macrophage activation and cytokine production via translation initiation and RNA processing (Bloomer et al., 2013; Hamlin et al., 2024; Meester et al., 2020) “(page 8, lines 195-200).

      Figures S3G and S3H seem to be switched.

      We are puzzled by this comment, as our original manuscript did not include a Supplemental Figure S3. Out of an abundance of caution, however, we checked that Supplemental Table S3G and H were correctly labelled, and independently confirmed that they are not switched.

      Results - Section 3

      Figure 5A does not add significant new insights. Consider refining its content to highlight key findings more effectively.

      We respectfully disagree and believe that schematic overviews help readers understand what is accomplished in any specific figure and have thus decided to keep it.

      Number of genes included in the analysis is not provided-this is important to assess significance and should be stated in methods and figure legends.

      We apologize for the lack of clarity. As explained above, GSEA uses all the genes in rank order (PMID: 16199517), we now explain GSEA more explicitly in the text “GSEA relies on whole-transcriptome ranking, ensuring that the results reflect global transcriptomic patterns rather than being influenced by only a few genes” (page 13, lines 415-417).

      Discussion 20. Line 201-203: Missing reference.

      We have now updated the text with the proper reference: “Tissue-resident macrophages are crucial to proper immune system function (Guilliams et al., 2020). While all macrophages share the responsibility of clearing cellular debris and foreign bodies, tissue-resident macrophages also have unique responsibilities that facilitate homeostasis throughout the body (Guilliams et al., 2020; Varol et al., 2015)” (page 9, lines 227-230).

      Reference 23 (1999) is outdated. Newer literature should be cited to reflect modern insights into sex differences in macrophages.

      We have now updated the text with an updated reference for two outdated references: (i) “Sex differences have previously been reported in macrophages, with female macrophages having higher phagocytic activity than males (Scotland et al., 2011)” (page 9, lines 232-233) and (ii) “Dysfunctional OCPs are associated with development of osteoporosis, a disease that is four times more prevalent in women (Alswat, 2017)” (page 10, lines 284-285).

      Peritoneal macrophages and OCPs originate from monocytes. Would deconvolution help identify enriched subtypes and assess dataset comparability?

      As noted in Reviewer 2’s Major Points #3 and #4, deconvolution analysis is not meaningful for subtype analysis without paired isolated/bulk datasets, which are outside of the scope of this study to generate.

      The 'more consistent' pathways found for female datasets are not discussed.

      We now discuss pathways found among the female datasets: “In addition, GSEA analysis of REACTOME gene sets showed male-biased expression for cell cycle related pathways (average set size 499), and female-biased expression for G protein-coupled receptor (GPCR) signaling (average set size 122) and extracellular matrix organization (average set size 127) (Figure 5C, Supplemental Table S4S-AJ; consistent with our ECM observation, Supplemental Figure S3A). Macrophages express a wide variety of GPCRs that allow them to respond to different stimuli. The expression of specific GPCRs influences macrophage polarization toward either a pro-inflammatory or anti-inflammatory state (Wang et al., 2019). A manual review of the genes contributing to this GPCR enrichment reveals the presence of several chemokine-related genes (such as Ccl4, Ccr4, Cxcl1, and others) (Supplemental Table S4). This suggests that females may have an increased abundance of chemokine GPCRs, potentially contributing to heightened autoimmune activity, among other factors.” (page 8, lines 212-222).

      Methods - Peritoneal macrophage isolation:

      Details on injection and harvesting are missing.

      We apologize for not being clear with our details and have modified the methods to be clearer (page 11, lines 320-331).

      How was contamination from other cell types assessed? F4/80 selection may not be fully macrophage-specific, and contamination could occur due to insufficient washing or the presence of non-macrophage F4/80+ cells.

      For the peritoneal macrophage datasets we generated, the macrophages were checked for purity through flow cytometry using Cd11b and F4/80 antibodies. We considered double positive Cd11b+ F4/80+ cells to be macrophages, which represents >95% of cells using our methodology (Supplemental Figure S4), without a difference between sexes.

      For the BMDMs, we utilize a protocol that is expected to yield ~90% Cd11b+ F4/80+ cells (PMID: 35212988 and PMID: 33458708).

      Finally, we now include the purification method for all publicly available datasets according to their original manuscript in Supplemental Table S1A and explicitly discuss the information for our in-house datasets in the methods (page 11, lines 321-346).

      • Bone marrow macrophages:

      Mouse age is not provided in the results part.

      We now provide this information in the methods (page 11, line 334). All ages for all datasets are now included in Supplemental Table S1A.

      Figure Legends

      Figure 2: Peritoneal macrophages are abbreviated as PeriMac-consider using this abbreviation consistently in the text.

      We respectfully disagree with the reviewer and choose to keep Peritoneal Macrophages spelled out in the text for clarity. We use the shorthand “PeriMac” in Figure 2 and Figure 5 solely for spacing purposes, but these are explained in the figure legend.

      Reviewer #2 (Significance (Required)):

      The study's strengths include the integration of multiple datasets, the use of both overrepresentation and GSEA, and the exploration of tissue-specific macrophage niches. These findings have relevance for diverse communities, including immunologists, sex-difference researchers, and those studying macrophage-driven diseases such as osteoporosis, neurodegeneration, and chronic inflammation. The work provides a foundation for further studies on sex-specific macrophage biology and may have implications for sex-specific therapeutic strategies. However, the study has limitations. The conclusions regarding enriched pathways rely heavily on a small number of DEGs, raising concerns about overinterpretation. Additionally, dataset variability and missing data for some analyses (e.g., exudate macrophages) could affect the robustness of the results.

      Despite these limitations, the study makes a meaningful but incremental advance by highlighting stable sex-dimorphic patterns in macrophage biology. It provides insights for both fundamental and translational research, particularly for audiences focused on immune regulation, sex-specific gene expression, and tissue-specific macrophage function.

      We thank the reviewer for understanding the importance of our work.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary: McGill et al. explore sex-based differences in macrophage gene expression across various tissues. Using a meta-analysis of publicly available and newly generated datasets, they identify conserved and divergent sex-dimorphic genes and pathways between tissues. Overall, the report is easy to follow and guides the reader through the analysis. The authors highlight the relevance of the report by noting sex differences in immune responses to infection, autoimmunity, and chronic diseases. The inclusion of 17 independent transcriptomic datasets provides a robust and extensive analysis of sex-based transcriptional differences. The authors explore potential biological implications of sex-based transcriptional differences using pathway analysis. Despite the overall strengths, there are some points for which further clarification and analysis would improve the manuscript. Detailed comments are listed below.

      Major comments:

      1. A comparison of the overall transcriptomic profiles of macrophages regardless of sex would be additive. Knowing the degree of similarities and differences among macrophages from different niches would help the reader determine what genetic programs vary by compartment. If macrophages are very different by niche, it is not surprising that they share few sex-dimorphic patterns. This mirrors Reviewer 2’s Major Point #4. While this approach may seem valuable, it would only be feasible if all datasets were generated simultaneously by the same lab using identical sequencing and library preparation protocols to avoid batch effects. In this case, biology and batch effects are confounded, making any global analysis misleading. Although the reviewer may find the limited overlap unsurprising, given that macrophages are generally considered to be the same cell type, our goal was to explore the extent of shared versus distinct features across datasets, which we believe to be an invaluable question for the field.

      Although it would not be possible to do this rigorously with the data we curated, the question of niche specific gene regulation of macrophages has been studied, showing extensive niche-specific regulation: “While the question of niche-specific gene regulation has been studied, showing extensive niche-specific regulation (Gosselin et al., 2014; Lavin et al., 2014), a comprehensive and systematic study of sex-differences across macrophage subtypes has not yet been performed” (page 4, lines 78-81).

      It is unclear what age and strain the mice were and the number of samples that were included (n) for each dataset. This information should be included in S1A. If different ages or strains were used, how might this impact findings?

      This mirrors Reviewer 1’s Major Point #4. We agree that this information is important to take into consideration and have now included this information in Supplemental Table 1A, along with the accession numbers to each dataset. Because there is no aging effect (all mice are aged between 2 to 24 weeks) and all mice are on a variation of the C57BL/6 background, we don’t expect this to be a major problem impacting our findings.

      The authors used a Jaccard index to examine similarities in sex-based differences across tissue compartments. They claim that there are more similarities in females. However, the male are female graphs (Fig. 1E,D) do not look that different. Is there a better way to display this?

      We apologize for the lack of clarity. We clustered the Jaccard matrices using hierarchical clustering to determine patterns of sharing. Thus, in these figures, the samples cluster based on the degree of similarity in sex-biased genes. In the females, there is clear separation by macrophage origin (yolk sac or circulating monocytes); whereas males have some separation but also have some mixing (e.g. Peritoneal Macrophage 2 clustering with the yolk-sac derived macrophage datasets). Additionally, four microglia datasets are together in the females with only one separate, whereas in the males they are split into three. We included colored bars by the dataset names to help highlight clear separation by niche of origin.

      We have added this detail to the text to better explain the similarities: “Our results indicate that female-biased genes were more consistent among the cell types compared to male-biased genes (Figures 2D,E). In females, there is clear separation by macrophage origin (yolk sac or circulating monocytes), with all the peritoneal macrophages clustering together, followed by bone-related macrophages, then microglia and lung macrophages. In the males, the five microglia datasets are split into three groups, and Peritoneal Macrophage 2 clusters with the yolk-sac derived macrophage datasets” (page 7, lines 155-160).

      In the Gene Ontology analysis, it is unclear what type of GO pathways were included (biological process, cellular component, molecular function). Also, some of the GO analyses were done with very few genes (as little as 4).

      This echoes Reviewer #2’s Major Comment #1. For the Overrepresentation analysis (ORA) using Gene Ontology, we use the “ALL” option to include biological process, cellular component, and molecular function terms. We used ORA to look at shared DEGs across datasets of the same niche which is why some have very low input. For this reason, we also performed Gene Set Enrichment Analysis that uses all genes, not just those differentially expressed at FDR 5%, to examine gene changes at a broader level. In the methods we have added this information: “The differentially expressed genes shared within each niche were divided into up and down-regulated based on the sign of the DEseq2 log2 fold change. These gene lists were used as the shared genes and all expressed genes across datasets in that specific niche were used as the universe for the clusterProfiler function ‘enrichGO’, using the “ALL” option to include biological process, cellular component, and molecular function terms” (page 13, lines 405-410) and “GSEA relies on whole-transcriptome ranking, ensuring that the results reflect global transcriptomic patterns rather than being influenced by only a few genes.” (page 13, lines 415-417)”.

      Is it possible to combine datasets by tissue to remove potential batch effects before downstream analyses? At the very least, PCA on combined data may help determine if some biological (e.g., age, strain) or technical (batch) differences are contributing to identifying few common sex differences.

      This mirrors Reviewer #2’s Major Point #4. Unfortunately, since every dataset only examined a single niche, biology and batches are confounded, and thus performing a PCA on all datasets together will be driven by technical rather than biological drivers. Batch effects are a well-documented issue in genomics (PMID:20838408, PMID:28351613) Indeed, this is largely observed when we attempt this analysis, with datasets clustering by batch (Reviewer Figure 1). Due to the issue of uncorrectable batch effects, we do not believe this analysis meets the rigor required to be included in the revised manuscript and have chosen to not include it.

      Validation of key results would further strengthen the manuscript.

      We agree that future validation is important but is beyond the scope of this purely bioinformatic analysis. We have included text in the revision to highlight the importance of future validation studies: “Thus, investigating female- and male-biased processes in macrophages, including the contribution of the ECM, will be an important step in developing treatments for diseases including, but not limited to, AD, histiocytosis, and osteoporosis, and future research will be essential to validate these findings and further refine therapeutic strategies (Chen et al., 2020; Cox et al., 2021; Hou et al., 2023; Li et al., 2023; Mammana et al., 2018)” (page 10, lines 285-289).

      Further contextualization of key results would enhance the discussion. For example, ECM-related differences in female macrophages could have broader roles in wound healing, fibrosis, and migration.

      We agree with the reviewers and have added this detail to the discussion: “ECM components are emerging as key regulators of innate immune responses (García-García & Martin, 2019). Macrophages contribute to ECM remodeling by producing and degrading collagens (Sutherland et al., 2023), and ECM-related differences in female macrophages may impact wound healing, fibrosis, and migration. In lung and kidney tissues, macrophages recruit and activate fibroblasts, influencing fibrosis through direct interactions and ECM-degrading enzymes (Nikolic-Paterson et al., 2014). The balance between ECM deposition and degradation is crucial for tissue homeostasis, as excessive fibrosis leads to pathology (Nikolic-Paterson et al., 2014; Ran et al., 2025). Mechanical properties of the ECM, such as stiffness and collagen crosslinking, enhance macrophage adhesion, migration, and inflammatory activation (Hsieh et al., 2019). These ECM cues direct macrophage behavior during injury response, influencing their ability to reach inflammation sites and promote repair. Thus, female-biased expression of ECM-related genes may contribute to phenotypes such as enhanced wound healing or even fibrosis(Balakrishnan et al., 2021; Harness-Brumley et al., 2014; Rønø et al., 2013) “ (page 9, lines 248-259).

      Minor comments:

      1. Line 51: In the introduction, the authors state that macrophages produce chemokines. There are other signaling molecules produced by macrophages (e.g., cytokines) that also contribute to immune responses. We apologize for this and have updated the text to say: “Macrophages are a key component of the mammalian immune system and are responsible for producing a diverse array of signaling molecules including (but not limited to) cytokines, chemokines, and interferons that activate the rest of the immune system to combat infection (Shapouri-Moghaddam et al., 2018)” (page 4, lines 49-52).

      Line 53: The authors state that after birth the primary source of new macrophages come from differentiation of monocytes. However, some tissue resident macrophages are self-renewing.

      We apologize for this oversight and have adjusted the text to say: “After birth, the primary source of new macrophages comes from the differentiation of monocytes, which can be recruited to tissues throughout life. However, some tissue resident macrophages can self-renew, including those from the pleural and peritoneal cavities (Röszer, 2018)” (page 4, lines 53-56).

      Line 123: "spermatogenial" should be "spermatogonial"

      We have updated the text accordingly (page 6, line 130).

      Reviewer #3 (Significance (Required)):

      Significance: • General assessment: The study provides a novel and comprehensive analysis of sex-dimorphic gene expression in macrophages, with key findings that emphasize the importance of ECM remodeling in female macrophages. The strengths include the broad dataset inclusion, rigorous quality control, and methodological rigor. However, consideration of potential confounding variables (e.g., age, strain) should be included and validation of key results would strengthen the manuscript. • Advance: This study advances knowledge by analyzing sex differences across multiple macrophage niches rather than focusing on a single tissue type. It extends findings from previous immune studies. • Audience: This report would be of interest to immunologists and researchers studying sex differences. Expertise: Immunology, sex differences in disease, macrophage biology, transcriptomics, and inflammation research.

      We thank the reviewer for their positive comments on the impact of our work and for their useful feedback.

      __ __


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      Hamlin, R. E., Pienkos, S. M., Chan, L., Stabile, M. A., Pinedo, K., Rao, M., Grant, P., Bonilla, H., Holubar, M., Singh, U., Jacobson, K. B., Jagannathan, P., Maldonado, Y., Holmes, S. P., Subramanian, A., & Blish, C. A. (2024). Sex differences and immune correlates of Long Covid development, symptom persistence, and resolution. Sci Transl Med, 16(773), eadr1032. https://doi.org/10.1126/scitranslmed.adr1032

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    1. Author response:

      The following is the authors’ response to the original reviews.

      General responses:

      The authors sincerely thank all the reviewers for their valuable and constructive comments. We also apologize for the long delay in providing this rebuttal due to logistical and funding challenges. In this revision, we modified the bipolar gradients from one single direction to all three directions. Additionally, in response to the concerns regarding data reliability, we conducted a thorough examination of each step in our data processing pipeline. In the original processing workflow, the projection-onto-convex-set (POCS) method was used for partial Fourier reconstruction. Upon examination, we found that applying the POCS method after parallel image reconstruction significantly altered the signal and resulted in considerable loss of functional feature. Futhermore, the original scan protocol employed a TE of 46 ms, which is notably longer than the typical TE of 33 ms. A prolonged TE can increase the ratio of extravascular to intravascular contributions. Importantly, the impact of TE on the efficacy of phase regression remains unclear, introducing potential confounding effects. To address these issues, we revised the protocol by shortening the TE from 46 ms to 39 ms. This adjustment was achieved by modifying the SMS factor to 3 and the in-plane acceleration rate to 3, thereby minimizing the confounding effects associated with an extended TE.

      Following these changes, we recollected task-based fMRI data (N=4) and resting-state fMRI data (N=14) under the updated protocol. Using the revised dataset, we validated layer-specific functional connectivity (FC) through seed-based analyses. These analyses revealed distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with statistically significant inter-layer differences. Furthermore, additional analyses with a seed in the primary sensory cortex (S1) corroborated the robustness and reliability of the revised methodology. We also changed the ‘directed’ functional connectivity in the title to ‘layer-specific’ functional connectivity, as drawing conclusions about directionality requires auxiliary evidence beyond the scope of this study.

      We provide detailed responses to the reviewers’ comments below.

      Reviewer #1 (Public Review):

      Summary:

      (1)   This study aims to provide imaging methods for users of the field of human layer-fMRI. This is an emerging field with 240 papers published so far. Different than implied in the manuscript, 3T is well represented among those papers. E.g. see the papers below that are not cited in the manuscript. Thus, the claim on the impact of developing 3T methodology for wider dissemination is not justified. Specifically, because some of the previous papers perform whole brain layer-fMRI (also at 3T) in more efficient, and more established procedures.

      3T layer-fMRI papers that are not cited:

      Taso, M., Munsch, F., Zhao, L., Alsop, D.C., 2021. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL). Journal of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678X20982382

      Wu, P.Y., Chu, Y.H., Lin, J.F.L., Kuo, W.J., Lin, F.H., 2018. Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex. Scientific Reports 8, 1-14. https://doi.org/10.1038/s41598-018-31292-x

      Lifshits, S., Tomer, O., Shamir, I., Barazany, D., Tsarfaty, G., Rosset, S., Assaf, Y., 2018. Resolution considerations in imaging of the cortical layers. NeuroImage 164, 112-120. https://doi.org/10.1016/j.neuroimage.2017.02.086

      Puckett, A.M., Aquino, K.M., Robinson, P.A., Breakspear, M., Schira, M.M., 2016. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage 139, 240-248. https://doi.org/10.1016/j.neuroimage.2016.06.019

      Olman, C.A., Inati, S., Heeger, D.J., 2007. The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring. NeuroImage 34, 1126-1135. https://doi.org/10.1016/j.neuroimage.2006.08.045

      Ress, D., Glover, G.H., Liu, J., Wandell, B., 2007. Laminar profiles of functional activity in the human brain. NeuroImage 34, 74-84. https://doi.org/10.1016/j.neuroimage.2006.08.020

      Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stocker, T., Fernández-Cabello, S., Poser, B.A., Kronbichler, M., 2023. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 3. https://doi.org/10.52294/001c.85117

      Scheeringa, R., Bonnefond, M., van Mourik, T., Jensen, O., Norris, D.G., Koopmans, P.J., 2022. Relating neural oscillations to laminar fMRI connectivity in visual cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhac154

      We thank the reviewer for listing out 8 papers related to 3T layer-fMRI papers. The primary goal of our work is to develop a methodology for brain-wide, layer-dependent resting-state functional connectivity at 3T. Upon review of the cited papers, we found that:

      (1) One study (Lifshits et al.) was not an fMRI study.

      (2) One study (Olman et al.) was conducted at 7T, not 3T.

      (3) Two studies (Taso et al. and Wu et al.) employed relatively large voxel sizes (1.6 × 2.3 × 5 mm³ and 1.5 mm isotropic, respectively), which limits layer specificity.

      (4) Only one of the listed studies (Huber et al., Aperture Neuro 2023) provides coverage of more than half of the brain.

      While each of these studies offers valuable insights, the VASO study by Huber et al. is the most relevant to our work, given its brain-wide coverage. However, the VASO method employs a relatively long TR (14.137 s), which may not be optimal for resting-state functional connectivity analyses.

      To address these limitations, our proposed method achieves submillimeter resolution, layer specificity, brain-wide coverage, and a significantly shorter TR (<5 s) altogether. We believe this advancement provides a meaningful contribution to the field, enabling broader applicability of layer-fMRI at 3T.

      (2) The authors implemented a sequence with lots of nice features. Including their own SMS EPI, diffusion bipolar pulses, eye-saturation bands, and they built their own reconstruction around it. This is not trivial. Only a few labs around the world have this level of engineering expertise. I applaud this technical achievement. However, I doubt that any of this is the right tool for layer-fMRI, nor does it represent an advancement for the field. In the thermal noise dominated regime of sub-millimeter fMRI (especially at 3T), it is established to use 3D readouts over 2D (SMS) readouts. While it is not trivial to implement SMS, the vendor implementations (as well as the CMRR and MGH implementations) are most widely applied across the majority of current fMRI studies already. The author's work on this does not serve any previous shortcomings in the field.

      We would like to thank the reviewer for their comments and the recognition of the technical efforts in implementing our sequence. We would like to address the points raised:

      (1) We completely agree that in-house implementation of existing techniques does not constitute an advancement for the field. We did not claim otherwise in the manuscript. Our focus was on the development of a method for brain-wide, layer-dependent resting-state functional connectivity at 3T, as mentioned in the response above.

      (2) The reviewer stated that "it is established to use 3D readouts over 2D (SMS) readouts". This is a strong claim, and we believe it requires robust evidence to support it. While it is true that 3D readouts can achieve higher tSNR in certain regions, such as the central brain, as shown in the study by Vizioli et al. (ISMRM 2020 abstract; https://cds.ismrm.org/protected/20MProceedings/PDFfiles/3825.html?utm_source=chatgpt.com ), higher tSNR does not necessarily equate to improved detection power in fMRI studies. For instance, Le Ster et al. (PLOS ONE, 2019; https://doi.org/10.1371/journal.pone.0225286 ). demonstrated that while 3D EPI had higher tSNR in the central brain, SMS EPI produced higher t-scores in activation maps.

      (3) When choosing between SMS EPI and 3D EPI, multiple factors should be taken into account, not just tSNR. For example, SMS EPI and 3D EPI differ in their sensitivity to motion and the complexity of motion correction. The choice between them depends on the specific research goals and practical constraints.

      (4) We are open to different readout strategies, provided they can be demonstrated suitable to the research goals. In this study, we opted for 2D SMS primarily due to logistical considerations. This choice does not preclude the potential use of 3D readouts in the future if they are deemed more appropriate for the project objectives.

      The mechanism to use bi-polar gradients to increase the localization specificity is doubtful to me. In my understanding, killing the intra-vascular BOLD should make it less specific. Also, the empirical data do not suggest a higher localization specificity to me.

      We will elaborate the mechanism and reasoning in the later responses.

      Embedding this work in the literature of previous methods is incomplete. Recent trends of vessel signal manipulation with ABC or VAPER are not mentioned. Comparisons with VASO are outdated and incorrect.

      The reproducibility of the methods and the result is doubtful (see below).

      In this revision, we updated the scan protocol and recollected the imaging data. Detailed explanations and revised results are provided in the later responses.

      I don't think that this manuscript is in the top 50% of the 240 layer-fmri papers out there.

      We respect the reviewer’s personal opinion. However, we can only address scientific comments or critiques.

      Strengths:

      See above. The authors developed their own SMS sequence with many features. This is important to the field. And does not leave sequence development work to view isolated monopoly labs. This work democratises SMS.

      The questions addressed here are of high relevance to the field: getting tools with good sensitivity, user-friendly applicability, and locally specific brain activity mapping is an important topic in the field of layer-fMRI.

      Weaknesses:

      (1) I feel the authors need to justify why flow-crushing helps localization specificity. There is an entire family of recent papers that aim to achieve higher localization specificity by doing the exact opposite. Namely, MT or ABC fRMRI aims to increase the localization specificity by highlighting the intravascular BOLD by means of suppressing non-flowing tissue. To name a few:

      Priovoulos, N., de Oliveira, I.A.F., Poser, B.A., Norris, D.G., van der Zwaag, W., 2023. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Human Brain Mapping hbm.26227. https://doi.org/10.1002/hbm.26227.

      Pfaffenrot, V., Koopmans, P.J., 2022. Magnetization Transfer weighted laminar fMRI with multi-echo FLASH. NeuroImage 119725. https://doi.org/10.1016/j.neuroimage.2022.119725

      Schulz, J., Fazal, Z., Metere, R., Marques, J.P., Norris, D.G., 2020. Arterial blood contrast ( ABC ) enabled by magnetization transfer ( MT ): a novel MRI technique for enhancing the measurement of brain activation changes. bioRxiv. https://doi.org/10.1101/2020.05.20.106666

      Based on this literature, it seems that the proposed method will make the vein problem worse, not better. The authors could make it clearer how they reason that making GE-BOLD signals more extra-vascular weighted should help to reduce large vein effects.

      The proposed VN fMRI method employs VN gradients to selectively suppress signals from fast-flowing blood in large vessels. Although this approach may initially appear to diverge from the principles of CBV-based techniques (Chai et al., 2020; Huber et al., 2017a; Pfaffenrot and Koopmans, 2022; Priovoulos et al., 2023), which enhance sensitivity to vascular changes in arterioles, capillaries, and venules while attenuating signals from static tissue and large veins, it aligns with the fundamental objective of all layer-specific fMRI methods. Specifically, these approaches aim to maximize spatial specificity by preserving signals proximal to neural activation sites and minimizing contributions from distal sources, irrespective of whether the signals are intra- or extra-vascular in origin. In the context of intravascular signals, CBV-based methods preferentially enhance sensitivity to functional changes in small vessels (proximal components) while demonstrating reduced sensitivity to functional changes in large vessels (distal components). For extravascular signals, functional changes are a mixture of proximal and distal influences. While tissue oxygenation near neural activation sites represents a proximal contribution, extravascular signal contamination from large pial veins reflects distal effects that are spatially remote from the site of neuronal activity. CBV-based techniques mitigate this challenge by unselectively suppressing signals from static tissues, thereby highlighting contributions from small vessels. In contrast, the VN fMRI method employs a targeted suppression strategy, selectively attenuating signals from large vessels (distal components) while preserving those from small vessels (proximal components). Furthermore, the use of a 3T scanner and the inclusion of phase regression in the VN approach mitigates contamination from large pial veins (distal components) while preserving signals reflecting local tissue oxygenation (proximal components). By integrating these mechanisms, VN fMRI improves spatial specificity, minimizing both intravascular and extravascular contributions that are distal to neuronal activation sites. We have incorporated the responses into Discussion section.

      The empirical evidence for the claim that flow crushing helps with the localization specificity should be made clearer. The response magnitude with and without flow crushing looks pretty much identical to me (see Fig, 6d).

      In the new results in Figure 4, the application of VN gradients attenuated the bias towards pial surface. Consistent with the results in Figure 4, Figure 5 also demonstrated the suppression of macrovascular signal by VN gradients.

      It's unclear to me what to look for in Fig. 5. I cannot discern any layer patterns in these maps. It's too noisy. The two maps of TE=43ms look like identical copies from each other. Maybe an editorial error?

      In this revision, the original Figure 5 has been removed. However, we would like to clarify that the two maps with TE = 43 ms in the original Figure 5 were not identical. This can be observed in the difference map provided in the right panel of the figure.

      The authors discuss bipolar crushing with respect to SE-BOLD where it has been previously applied. For SE-BOLD at UHF, a substantial portion of the vein signal comes from the intravascular compartment. So I agree that for SE-BOLD, it makes sense to crush the intravascular signal. For GE-BOLD however, this reasoning does not hold. For GE-BOLD (even at 3T), most of the vein signal comes from extravascular dephasing around large unspecific veins, and the bipolar crushing is not expected to help with this.

      The reviewer’s statement that "most of the vein signal comes from extravascular dephasing around large unspecific veins" may hold true for 7T. However, at 3T, the susceptibility-induced Larmor frequency shift is reduced by 57%, and the extravascular contribution decreases by more than 35%, as shown by Uludağ et al. 2009 ( DOI: 10.1016/j.neuroimage.2009.05.051 ).

      Additionally, according to the biophysical models (Ogawa et al., 1993; doi: 10.1016/S0006-3495(93)81441-3 ), the extravascular contamination from the pial surface is inversely proportional to the square of the distance from vessel. For a vessel diameter of 0.3 mm and an isotropic voxel size of 0.9 mm, the induced frequency shift is reduced by at least 36-fold at the next voxel. Notably, a vessel diameter of 0.3 mm is larger than most pial vessels. Theoretically, the extravascular effect contributes minimally to inter-layer dependency, particularly at 3T compared to 7T due to weaker susceptibility-related effects at lower field strengths. Empirically, as shown in Figure 7c, the results at M1 demonstrated that layer specificity can be achieved statistically with the application of VN gradients. We have incorporated this explanation into the Introduction and Discussion sections of the manuscript.

      (2) The bipolar crushing is limited to one single direction of flow. This introduces a lot of artificial variance across the cortical folding pattern. This is not mentioned in the manuscript. There is an entire family of papers that perform layer-fmri with black-blood imaging that solves this with a 3D contrast preparation (VAPER) that is applied across a longer time period, thus killing the blood signal while it flows across all directions of the vascular tree. Here, the signal cruising is happening with a 2D readout as a "snap-shot" crushing. This does not allow the blood to flow in multiple directions.

      VAPER also accounts for BOLD contaminations of larger draining veins by means of a tag-control sampling. The proposed approach here does not account for this contamination.

      Chai, Y., Li, L., Huber, L., Poser, B.A., Bandettini, P.A., 2020. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage 207, 116358. https://doi.org/10.1016/j.neuroimage.2019.116358

      Chai, Y., Liu, T.T., Marrett, S., Li, L., Khojandi, A., Handwerker, D.A., Alink, A., Muckli, L., Bandettini, P.A., 2021. Topographical and laminar distribution of audiovisual processing within human planum temporale. Progress in Neurobiology 102121. https://doi.org/10.1016/j.pneurobio.2021.102121

      If I would recommend anyone to perform layer-fMRI with blood crushing, it seems that VAPER is the superior approach. The authors could make it clearer why users might want to use the unidirectional crushing instead.

      We understand the reviewer’s concern regarding the directional limitation of bipolar crushing. As noted in the responses above, we have updated the bipolar gradient to include three orthogonal directions instead of a single direction. Furthermore, flow-related signal suppression does not necessarily require a longer time period. Bipolar diffusion gradients have been effectively used to nullify signals from fast-flowing blood, as demonstrated by Boxerman et al. (1995; DOI: 10.1002/mrm.1910340103). Their study showed that vessels with flow velocities producing phase changes greater than p radians due to bipolar gradients experience significant signal attenuation. The critical velocity for such attenuation can be calculated using the formula: 1/(2gGDd) where g is the gyromagnetic ratio, G is the gradient strength, d is the gradient pulse width and D is the time between the two bipolar gradient pulses. In the framework of Boxerman et al. at 1.5T, the critical velocity for b value of 10 s/mm<sup>2</sup> is ~8 mm/s, resulting in a ~30% reduction in functional signal. In our 3T study, b values of 6, 7, and 8 s/mm<sup>2</sup> correspond to critical velocities of 16.8, 15.2, and 13.9 mm/s, respectively. The flow velocities in capillaries and most venules remain well below these thresholds. Notably, in our VN fMRI sequences, bipolar gradients were applied in all three orthogonal directions, whereas in Boxerman et al.'s study, the gradients were applied only in the z-direction. Given the voxel dimensions of 3 × 3 × 7 mm<sup>3</sup> in the 1.5T study, vessels within a large voxel are likely oriented in multiple directions, meaning that only a subset of fast-flowing signals would be attenuated. Therefore, our approach is expected to induce greater signal reduction, even at the same b values as those used in Boxerman et al.'s study. We have incorporated this text into the Discussion section of the manuscript.

      (3) The comparison with VASO is misleading.

      The authors claim that previous VASO approaches were limited by TRs of 8.2s. The authors might be advised to check the latest literature of the last years.

      Koiso et al. performed whole brain layer-fMRI VASO at 0.8mm at 3.9 seconds (with reliable activation), 2.7 seconds (with unconvincing activation pattern, though), and 2.3 (without activation).

      Also, whole brain layer-fMRI BOLD at 0.5mm and 0.7mm has been previously performed by the Juelich group at TRs of 3.5s (their TR definition is 'fishy' though).

      Koiso, K., Müller, A.K., Akamatsu, K., Dresbach, S., Gulban, O.F., Goebel, R., Miyawaki, Y., Poser, B.A., Huber, L., 2023. Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset. Aperture Neuro 34. https://doi.org/10.1101/2022.08.19.504502

      Yun, S.D., Pais‐Roldán, P., Palomero‐Gallagher, N., Shah, N.J., 2022. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Human Brain Mapping. https://doi.org/10.1002/hbm.25855

      Pais-Roldan, P., Yun, S.D., Palomero-Gallagher, N., Shah, N.J., 2023. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front. Neurosci. 17, 1151544. https://doi.org/10.3389/fnins.2023.1151544

      We thank the reviewer for providing these references. While the protocol with a TR of 3.9 seconds in Koiso’s work demonstrated reasonable activation patterns, it was not tested for layer specificity. Given that higher acceleration factors (AF) can cause spatial blurring, a protocol should only be eligible for comparison if layer specificity is demonstrated.

      Secondly, the TRs reported in Koiso’s study pertain only to either the VASO or BOLD acquisition, not the combined CBV-based contrast. To generate CBV-based images, both VASO and BOLD data are required, effectively doubling the TR. For instance, if the protocol with a TR of 3.9 seconds is used, the effective TR becomes approximately 8 seconds. The stable protocol used by Koiso et al. to acquire whole-brain data (94.08 mm along the z-axis) required 5.2 seconds for VASO and 5.1 seconds for BOLD, resulting in an effective TR of 10.3 seconds. The spatial resolution achieved was 0.84 mm isotropic.

      Unfortunately, we could not find the Juelich paper mentioned by the reviewer.

      To have a more comprehensive comparison, we collated relevant literature on brain-wide layer-specific fMRI. We defined brain-wide acquisition as imaging protocols that cover more than half of the human brain, specifically exceeding 55 mm along the superior-inferior axis. We identified five studies and summarized their scan parameters, including effective TR, coverage, and spatial resolution, in Table 1.

      The authors are correct that VASO is not advised as a turn-key method for lower brain areas, incl. Hippocampus and subcortex. However, the authors use this word of caution that is intended for inexperienced "users" as a statement that this cannot be performed. This statement is taken out of context. This statement is not from the academic literature. It's advice for the 40+ user base that wants to perform layer-fMRI as a plug-and-play routine tool in neuroscience usage. In fact, sub-millimeter VASO is routinely being performed by MRI-physicists across all brain areas (including deep brain structures, hippocampus etc). E.g. see Koiso et al. and an overview lecture from a layer-fMRI workshop that I had recently attended: https://youtu.be/kzh-nWXd54s?si=hoIJjLLIxFUJ4g20&t=2401

      In this revision, we decided to focus on cortico-cortical functional connectivity and have removed the LGN-related content. Consequently, the text mentioned by the reviewer was also removed. Nevertheless, we apologize if our original description gave the impression that functional mapping of deep brain regions using VASO is not feasible. The word of caution we used is based on the layer-fMRI blog ( https://layerfmri.com/2021/02/22/vaso_ve/ ) and reflects the challenges associated with this technique, as outlined by experts like Dr. Huber and Dr. Strinberg.

      According to the information provided, including the video, functional mapping of the hippocampus and amygdala using VASO is indeed possible but remains technically challenging. The short arterial arrival times in these deep brain regions can complicate the acquisition, requiring RF inversion pulses to cover a wider area at the base of the brain. For example, as of 2023, four or more research groups were attempting to implement layer-fMRI VASO in the hippocampus. One such study at 3T required multiple inversion times to account for inflow effects, highlighting the technical complexity of these applications. This is the context in which we used the word of caution. We are not sure whether recent advancements like MAGEC VASO have improved its applicability. As of 2024, we have not identified any published VASO studies specifically targeting deep brain structures such as the hippocampus or amygdala. Therefore, it is difficult to conclude that “sub-millimeter VASO is routinely being performed by MRI physicists on deep brain structures such as the hippocampus.”

      Thus, the authors could embed this phrasing into the context of their own method that they are proposing in the manuscript. E.g. the authors could state whether they think that their sequence has the potential to be disseminated across sites, considering that it requires slow offline reconstruction in Matlab?

      We are enthusiastic about sharing our imaging sequence, provided its usefulness is conclusively established. However, it's important to note that without an online reconstruction capability, such as the ICE, the practical utility of the sequence may be limited. Unfortunately, we currently don’t have the manpower to implement the online reconstruction. Nevertheless, we are more than willing to share the offline reconstruction codes upon request.

      Do the authors think that the results shown in Fig. 6c are suggesting turn-key acquisition of a routine mapping tool? In my humble opinion, it looks like random noise, with most of the activation outside the ROI (in white matter).

      As we mentioned in the ‘general response’ in the beginning of the rebuttal, the POCS method for partial Fourier reconstruction caused the loss of functional feature, potentially accounting for the activation in white matter. In this revision, we have modified the pulse sequence, scan protocol and processing pipelines.

      According to the results in Figure 4, stable activation in M1 was observed at the single-subject level across most scan protocols. Yet, the layer-dependent activation profiles in M1 were spatially unstable, irrespective of the application of VN gradients. This spatial instability is not entirely unexpected, as T2*-based contrast is inherently sensitive to various factors that perturb the magnetic field, such as eye movements, respiration, and macrovascular signal fluctuations. Furthermore, ICA-based artifact removal was intentionally omitted in Figure 4 to ensure fair comparisons between protocols, leaving residual artifacts unaddressed. Inconsistency in performing the button-pressing task across sessions may also have contributed to the observed variability. These results suggest that submillimeter-resolution fMRI may not yet be suitable for reliable individual-level layer-dependent functional mapping, unless group-level statistics are incorporated to enhance robustness. We have incorporated this text into the Limitation section of the manuscript.

      (4) The repeatability of the results is questionable.

      The authors perform experiments about the robustness of the method (line 620). The corresponding results are not suggesting any robustness to me. In fact, the layer profiles in Fig. 4c vs. Fig 4d are completely opposite. The location of peaks turns into locations of dips and vice versa.

      The methods are not described in enough detail to reproduce these results.

      The authors mention that their image reconstruction is done "using in-house MATLAB code" (line 634). They do not post a link to github, nor do they say if they share this code.

      We thank the reviewer for the comments regarding reproducibility and data sharing. In response, we have revised the Methods section and elaborated on the technical details to improve clarity and reproducibility.

      Regarding code sharing, we acknowledge that the current in-house MATLAB reconstruction code requires further refinement to improve its readability and usability. Due to limited manpower, we have not yet been able to complete this task. However, we are committed to making the code publicly available and will upload it to GitHub as soon as the necessary resources are available.

      For data sharing, we face logistical challenges due to the large size of the dataset, which spans tens of terabytes. Platforms like OpenNeuro, for example, typically support datasets up to 10TB, making it difficult to share the data in its entirety. Despite this limitation, we are more than willing to share offline reconstruction codes and raw data upon request to facilitate reproducibility.

      Regarding data robustness, we kindly refer the reviewer to our response to the previous comment, where we addressed these concerns in greater detail.

      It is not trivial to get good phase data for fMRI. The authors do not mention how they perform the respective coil-combination.

      No data are shared for reproduction of the analysis.

      Obtaining phase data is relatively straightforward when the images are retrieved directly from raw data. For coil combination, we employed the adaptive coil combination approach described by (Walsh et al.; DOI: 10.1002/(sici)1522-2594(200005)43:5<682::aid-mrm10>3.0.co;2-g ) The MATLAB code for this implementation was developed by Dr. Diego Hernando and is publicly available at https://github.com/welton0411/matlab .

      (5) The application of NODRIC is not validated.

      Previous applications of NORDIC at 3T layer-fMRI have resulted in mixed success. When not adjusted for the right SNR regime it can result in artifactual reductions of beta scores, depending on the SNR across layers. The authors could validate their application of NORDIC and confirm that the average layer-profiles are unaffected by the application of NORDIC. Also, the NORDIC version should be explicitly mentioned in the manuscript.

      Akbari, A., Gati, J.S., Zeman, P., Liem, B., Menon, R.S., 2023. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD (preprint). Neuroscience. https://doi.org/10.1101/2023.04.06.535924

      Knudsen, L., Guo, F., Huang, J., Blicher, J.U., Lund, T.E., Zhou, Y., Zhang, P., Yang, Y., 2023. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv. https://doi.org/10.1101/2023.10.29.564658

      We appreciate the reviewer’s suggestion. To validate the application of NORDIC denoising in our study, we compared the BOLD activation maps before and after denoising in the visual and motor cortices, as well as the depth-dependent activation profiles in M1. These results are presented in Figure 3. The activation patterns in the denoised maps were consistent with those in the non-denoised maps but exhibited higher statistical significance. Notably, BOLD activation within M1 was only observed after NORDIC denoising, underscoring the necessity of this approach. Figure 3c shows the depth-dependent activation profiles in M1, highlighted by the green contours in Figure 3b. Both denoised and non-denoised profiles followed similar trends; however, as expected, the non-denoised profile exhibited larger confidence intervals compared to the NORDIC-denoised profile. These results confirm that NORDIC denoising enhances sensitivity without introducing distortions in the functional signal. The corresponding text has been incorporated into the Results section.

      Regarding the implementation details of NORDIC denoising, the reconstructed images were denoised using a g-factor map (function name: NIFTI_NORDIC). The g-factor map was estimated from the image time series, and the input images were complex-valued. The width of the smoothing filter for the phase was set to 10, while all other hyperparameters were retained at their default values. This information has been integrated into the Methods section for clarity and reproducibility.

      Reviewer #2 (Public Review):

      This study developed a setup for laminar fMRI at 3T that aimed to get the best from all worlds in terms of brain coverage, temporal resolution, sensitivity to detect functional responses, and spatial specificity. They used a gradient-echo EPI readout to facilitate sensitivity, brain coverage and temporal resolution. The former was additionally boosted by NORDIC denoising and the latter two were further supported by parallel-imaging acceleration both in-plane and across slices. The authors evaluated whether the implementation of velocity-nulling (VN) gradients could mitigate macrovascular bias, known to hamper the laminar specificity of gradient-echo BOLD.

      The setup allows for 0.9 mm isotropic acquisitions with large coverage at a reasonable TR (at least for block designs) and the fMRI results presented here were acquired within practical scan-times of 12-18 minutes. Also, in terms of the availability of the method, it is favorable that it benefits from lower field strength (additional time for VN-gradient implementation, afforded by longer gray matter T2*).

      The well-known double peak feature in M1 during finger tapping was used as a test-bed to evaluate the spatial specificity. They were indeed able to demonstrate two distinct peaks in group-level laminar profiles extracted from M1 during finger tapping, which was largely free from superficial bias. This is rather intriguing as, even at 7T, clear peaks are usually only seen with spatially specific non-BOLD sequences. This is in line with their simple simulations, which nicely illustrated that, in theory, intravascular macrovascular signals should be suppressible with only minimal suppression of microvasculature when small b-values of the VN gradients are employed. However, the authors do not state how ROIs were defined making the validity of this finding unclear; were they defined from independent criteria or were they selected based on the region mostly expressing the double peak, which would clearly be circular? In any case, results are based on a very small sub-region of M1 in a single slice - it would be useful to see the generalizability of superficial-bias-free BOLD responses across a larger portion of M1.

      We appreciate and understand the reviewer’s concerns. Given the small size of the hand knob region within M1 and its intersubject variability in location, defining this region automatically remains challenging. However, we applied specific criteria to minimize bias during the delineation of M1: 1) the hand knob region was required to be anatomically located in the precentral sulcus or gyrus; 2) it needed to exhibit consistent BOLD activation across the majority of testing conditions; and 3) the region was expected to show BOLD activation in the deep cortical layers under the condition of b = 0 and TE = 30 ms. Once the boundaries across cortical depth were defined, the gray matter boundaries of hand knob region were delineated based on the T1-weighted anatomical image and the cortical ribbon mask but excluded the BOLD activation map to minimize potential bias in manual delineation. Based on the new criteria, the resulting depth-dependent profiles, as shown in Figure 4, are no longer superficial-bias-free.

      As repeatedly mentioned by the authors, a laminar fMRI setup must demonstrate adequate functional sensitivity to detect (in this case) BOLD responses. The sensitivity evaluation is unfortunately quite weak. It is mainly based on the argument that significant activation was found in a challenging sub-cortical region (LGN). However, it was a single participant, the activation map was not very convincing, and the demonstration of significant activation after considerable voxel-averaging is inadequate evidence to claim sufficient BOLD sensitivity. How well sensitivity is retained in the presence of VN gradients, high acceleration factors, etc., is therefore unclear. The ability of the setup to obtain meaningful functional connectivity results is reassuring, yet, more elaborate comparison with e.g., the conventional BOLD setup (no VN gradients) is warranted, for example by comparison of tSNR, quantification and comparison of CNR, illustration of unmasked-full-slice activation maps to compare noise-levels, comparison of the across-trial variance in each subject, etc. Furthermore, as NORDIC appears to be a cornerstone to enable submillimeter resolution in this setup at 3T, it is critical to evaluate its impact on the data through comparison with non-denoised data, which is currently lacking.

      We appreciate the reviewer’s comments and acknowledge that the LGN results from a single participant were not sufficiently convincing. In this revision, we have removed the LGN-related results and focused on cortico-cortical FC. To evaluate data quality, we opted to present BOLD activation maps rather than tSNR, as high tSNR does not necessarily translate to high functional significance. In Figure 3, we illustrate the effect of NORDIC denoising, including activation maps and depth-dependent profiles. Figure 4 presents activation maps acquired under different TE and b values, demonstrating that VN gradients effectively reduce the bias toward the pial surface without altering the overall activation patterns. The results in Figure 4 and Figure 5 provide evidence that VN gradients retain sensitivity while reducing superficial bias. The ability of the setup to obtain meaningful FC results was validated through seed-based analyses, identifying distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with significant inter-layer differences (see Figure 7). Further analyses with a seed in the primary sensory cortex (S1) demonstrated the reliability of the method (see Figure 8). For further details on the results, including the impact of VN gradients and NORDIC denoising, please refer to Figures 3 to 8 in the Results section.

      Additionally, we acknowledge the limitations of our current protocol for submillimeter-resolution fMRI at the individual level. We found that robust layer-dependent functional mapping often requires group-level statistics to enhance reliability. This issue has been discussed in detail in the Limitations section.

      The proposed setup might potentially be valuable to the field, which is continuously searching for techniques to achieve laminar specificity in gradient echo EPI acquisitions. Nonetheless, the above considerations need to be tackled to make a convincing case.

      Reviewer #3 (Public Review):

      Summary:

      The authors are looking for a spatially specific functional brain response to visualise non-invasively with 3T (clinical field strength) MRI. They propose a velocity-nulled weighting to remove the signal from draining veins in a submillimeter multiband acquisition.

      Strengths:

      - This manuscript addresses a real need in the cognitive neuroscience community interested in imaging responses in cortical layers in-vivo in humans.

      - An additional benefit is the proposed implementation at 3T, a widely available field strength.

      Weaknesses:

      - Although the VASO acquisition is discussed in the introduction section, the VN-sequence seems closer to diffusion-weighted functional MRI. The authors should make it more clear to the reader what the differences are, and how results are expected to differ. Generally, it is not so clear why the introduction is so focused on the VASO acquisition (which, curiously, lacks a reference to Lu et al 2013). There are many more alternatives to BOLD-weighted imaging for fMRI. CBF-weighted ASL and GRASE have been around for a while, ABC and double-SE have been proposed more recently.

      The major distinction between diffusion-weighted fMRI (DW-fMRI) and our methodology lies in the b-value employed. DW-fMRI typically measures cellular swelling using b-values greater than 1000 s/mm<sup>2</sup> (e.g., 1800 s/mm(sup>2</sup>). In contrast, our VN-fMRI approach measures hemodynamic responses by employing smaller b-values specifically designed to suppress signals from fast-flowing draining veins rather than detecting microstructural changes.

      Regarding other functional contrasts, we agree that more layer-dependent fMRI approaches should be mentioned. In this revision, we have expanded the Introduction section to include discussions of the double spin-echo approach and CBV-based methods, such as MT-weighted fMRI, VAPER, ABC, and CBF-based method ASL. Additionally, the reference to Lu et al. (2013) has been cited in the revised manuscript. The corresponding text has been incorporated into the Introduction section to provide a more comprehensive overview of alternative functional imaging techniques.

      - The comparison in Figure 2 for different b-values shows % signal changes. However, as the baseline signal changes dramatically with added diffusion weighting, this is rather uninformative. A plot of t-values against cortical depth would be much more insightful.

      - Surprisingly, the %-signal change for a b-value of 0 is not significantly different from 0 in the gray matter. This raises some doubts about the task or ROI definition. A finger-tapping task should reliably engage the primary motor cortex, even at 3T, and even in a single participant.

      - The BOLD weighted images in Figure 3 show a very clear double-peak pattern. This contradicts the results in Figure 2 and is unexpected given the existing literature on BOLD responses as a function of cortical depth.

      - Given that data from Figures 2, 3, and 4 are derived from a single participant each, order and attention affects might have dramatically affected the observed patterns. Especially for Figure 4, neither BOLD nor VN profiles are really different from 0, and without statistical values or inter-subject averaging, these cannot be used to draw conclusions from.

      We appreciate the reviewer’s suggestions. In this revision, we have made significant updates to the participant recruitment, scan protocol, data processing, and M1 delineation. Please refer to the "General Responses" at the beginning of the rebuttal and the first response to Reviewer #2 for more details.

      Previously, the variation in depth-dependent profiles was calculated across upscaled voxels within a specific layer. However, due to the small size of the hand knob region, the number of within-layer voxels was limited, resulting in inaccurate estimations of signal variation. In the revised manuscript, the signal was averaged within each layer before performing the GLM analysis, and signal variation was calculated using the temporal residuals. The technical details of these changes are described in the "Materials and Methods" section. Furthermore, while the initial submission used percentage signal change for the profiles of M1, the dramatic baseline fluctuations observed previously are no longer an issue after the modifications. For this reason, we retained the use of percentage signal change to present the depth-dependent profiles. After these adjustments, the profiles exhibited a bias toward the pial surface, particularly in the absence of VN gradients.

      - In Figure 5, a phase regression is added to the data presented in Figure 4. However, for a phase regression to work, there has to be a (macrovascular) response to start with. As none of the responses in Figure 4 are significant for the single participant dataset, phase regression should probably not have been undertaken. In this case, the functional 'responses' appear to increase with phase regression, which is contra-intuitive and deserves an explanation.

      We agreed with reviewer’s argument. In the revised results, the issues mentioned by the reviewer are largely diminished. The updated analyses demonstrate that phase regression effectively reduces superficial bias, as shown in Figures 4 and 5.

      - Consistency of responses is indeed expected to increase by a removal of the more variable vascular component. However, the microvascular component is always expected to be smaller than the combination of microvascular + macrovascular responses. Note that the use of %signal changes may obscure this effect somewhat because of the modified baseline. Another expected feature of BOLD profiles containing both micro- and microvasculature is the draining towards the cortical surface. In the profiles shown in Figure 7, this is completely absent. In the group data, no significant responses to the task are shown anywhere in the cortical ribbon.

      We agreed with reviewer’s comments. In the revised manuscript, the results have been substantially updated to addressing the concerns raised. The original Figure 7 is no longer relevant and has been removed.

      - Although I'd like to applaud the authors for their ambition with the connectivity analysis, I feel that acquisitions that are so SNR starved as to fail to show a significant response to a motor task should not be used for brain wide directed connectivity analysis.

      We appreciate the reviewer’s comments and share the concern about SNR limitations. In the updated results presented in Figure 5, the activation patterns in the visual cortex were consistent across TEs and b values. At the motor cortex, stable activation in M1 was observed at the single-subject level across most scan protocols. However, the layer-dependent activation profiles in M1 exhibited spatial instability, irrespective of the application of VN gradients. This spatial instability is not entirely unexpected, as T2*-based contrast is inherently sensitive to factors that perturb the magnetic field, such as eye movements, respiration, and macrovascular signal fluctuations. Additionally, ICA-based artifact removal was intentionally omitted in Figure 4 to ensure fair comparisons across protocols, leaving some residual artifacts unaddressed. Variability in task performance during button-pressing sessions may have further contributed to the observed inconsistencies.

      Although these findings suggest that submillimeter-resolution fMRI may not yet be reliable for individual-level layer-dependent functional mapping, the group-level FC analyses can still yield robust results. In Figure 7, group-level statistics revealed distinct functional connectivity (FC) patterns associated with superficial and deep layers in M1. These FC maps exhibited significant differences between layers, demonstrating that VN fMRI enhances inter-layer independence. Additional FC analyses with a seed placed in S1 further validated these findings (see Figure 8).

      The claim of specificity is supported by the observation of the double-peak pattern in the motor cortex, previously shown in multiple non-BOLD studies. However, this same pattern is shown in some of the BOLD weighted data, which seems to suggest that the double-peak pattern is not solely due to the added velocity nulling gradients. In addition, the well-known draining towards the cortical surface is not replicated for the BOLD-weighted data in Figures 3, 4, or 7. This puts some doubt about the data actually having the SNR to draw conclusions about the observed patterns.

      We appreciate the reviewer’s comments. In the updated results, the efficacy of the VN gradients is evident near the pial surface, as shown in Figures 4 and 5. In Figure 4, comparing the second and third columns (b = 0 and b = 6 s/mm<sup>2</sup>, respectively, at TE = 38 ms), the percentage signal change in the superficial layers is generally lower with b = 6 s/mm<sup>2</sup> than with b = 0. This indicates that VN gradient-induced signal suppression is more pronounced in the superficial layers. Additionally, in Figure 5, the VN gradients effectively suppressed macrovascular signals as highlighted by the blue circles. These observations support the role of VN gradients in enhancing specificity by reducing superficial bias and macrovascular contamination. Furthermore, bias towards cortical surface was observed in the updated results in Figure 4.

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      (1) L141: "depth dependent" is slightly misleading here. It could be misunderstood to suggest that the authors are assessing how spatial specificity varies as a function of depth. Rather, they are assessing spatial specificity based on depth-dependent responses (double peak feature). Perhaps "layer-dependent spatial specificity" could be substituted with laminar specificity?

      We thank the reviewer for the suggestion. The term “depth dependent” has been replaced by “layer dependent” in the revised manuscript.

      (2) L146-149: these do not validate spatial specificity.

      The original text is removed.

      (3) L180: Maybe helpful to describe what the b-value is to assist unfamiliar readers.

      We have clarified the b-value as “the strength of the bipolar diffusion gradients” where it is first mentioned in the manuscript.

      (4) Figure 1B: I think it would be appropriate with a sentence of how the authors define micro/macrovasculature. Figure 1B seems to suggest that large ascending veins are considered microvascular which I believe is a bit unconventional. Nevertheless, as long as it is clearly stated, it should be fine.

      In our context, macrovasculature refers to vessels that are distal to neural activation sites and contribute to extravascular contamination. These vessels are typically larger in size (e.g., > 0.1 mm in diameter) and exhibit faster flow rates (e.g., > 10 mm/s).

      (5) I think the authors could be more upfront with the point about non-suppressed extravascular effects from macrovasculature, which was briefly mentioned in the discussion. It could already be highlighted in the introduction or theory section.

      We thank the reviewer’s suggestions. We have expanded the discussion of extravascular effects from macrovasculature in both the Introduction (5th paragraph) and Discussion (3rd paragraph) sections.

      (6) The phase regression figure feels a bit misplaced to me. If the authors agree: rather than showing the TE-dependency of the effect of phase regression, it may be more relevant for the present study to compare the conventional setup with phase regression, with the VN setup without phase regression. I.e., to show how the proposed setup compares to existing 3T laminar fMRI studies.

      In this revision, both the TE-dependent and VN-dependent effects of phase regression were investigated. The results in Figure 4 and Figure 5 demonstrated that phase regression effectively suppresses macrovascular contributions primarily near the gray matter/CSF boundary, irrespective of TE or the presence of VN gradients.

      (7) L520: It might be beneficial to also cite the large body of other laminar studies showing the double peak feature to underscore that it is highly robust, which increases its relevance as a test-bed to assess spatial specificity.

      We agreed. More literatures have been cited (Chai et al., 2020; Huber et al., 2017a; Knudsen et al., 2023; Priovoulos et al., 2023).

      (8) L557: The argument that only one participant was assessed to reduce inter-subject variability is hard to buy. If significant variability exists across subjects, this would be highly relevant to the authors and something they would want to capture.

      We thank the reviewer for the suggestions. In this revision, we have increased the number of participants to 4 for protocol development and 14 for resting-state functional connectivity analysis, allowing us to better assess and account for inter-subject variability.

      (9) L637: add download link and version number.

      The download link has been added as requested. The version number is not applicable.

      (10) L638: How was the phase data coil-combined?

      The reconstructed multi-channel data, which were of complex values, were combined using the adaptive combination method (Walsh et al.; DOI: 10.1002/(sici)1522-2594(200005)43:5<682::aid-mrm10>3.0.co;2-g). The MATLAB code for this implementation was developed by Dr. Diego Hernando and is publicly available at https://github.com/welton0411/matlab . The phase data were then extracted using the MATLAB function ‘angle’.

      (11) L639: Why was the smoothing filter parameter changed (other parameters were default)?

      The smoothing filter parameter was set based on the suggestion provided in the help comments of the NIFTI_NORDIC function:

      function  NIFTI_NORDIC(fn_magn_in,fn_phase_in,fn_out,ARG)

      % fMRI

      %

      %  ARG.phase_filter_width=10;

      In other words, we simply followed the recommendation outlined in the NIFTI_NORDIC function’s documentation.

      (12) I assume the phase data was motion corrected after transforming to real and imaginary components and using parameters estimated from magnitude data? Maybe add a few sentences about this.

      Prior to phase regression, the time series of real and imaginary components were subjected to motion correction, followed by phase unwrapping. The phase regression was incorporated early in the data processing pipeline to minimize the discrepancy in data processing between magnitude and phase images (Stanley et al., 2021).

      (13) Was phase regression applied with e.g., a deming model, which accounts for noise on both the x and y variable? In my experience, this makes a huge difference compared with regular OLS.

      We appreciate the reviewer’s insightful comment. We are aware that the noise present in both magnitude and phase data therefore linear Deming regression would be a good fit to phase regression (Stanley et al., 2021). To perform Deming regression, however, the ratio of magnitude error variance to phase error variance must be predefined. In our initial tests, we found that the regression results were sensitive to this ratio. To avoid potential confounding, we opted to use OLS regression for the current analysis. However, we agreed Deming model could enhance the efficacy of phase regression if the ratio could be determined objectively and properly.

      (14) Figure 2: What is error bar reflecting? I don't think the across-voxel error, as also used in Figure 4, is super meaningful as it assumes the same response of all voxels within a layer (might be alright for such a small ROI). Would it be better to e.g. estimate single-trial response magnitude (percent signal change) and assess variability across? Also, it is not obvious to me why b=30 was chosen. The authors argue that larger values may kill signal, but based on this Figure in isolation, b=48 did not have smaller response magnitudes (larger if anything).

      We agreed with the reviewer’s opinion on the across-voxel error. In the revised manuscript, the signal was averaged within each layer before performing the GLM analysis, and signal variation was calculated using the temporal residuals. The technical details of these changes are described in the "Materials and Methods" section.

      Additionally, the bipolar diffusion gradients were modified from a single direction to three orthogonal directions. As a result, the questions and results related to b=30 or b=48 are no longer applicable.

      (15) Figure 5: would be informative to quantify the effect of phase regression over a large ROI and evaluate reduction in macrovascular influence from superficial bias in laminar profiles.

      We appreciate the reviewer’s suggestion. In the revised manuscript, the reduction in macrovascular influence from superficial bias across a large ROI is displayed in Figure 5. Additionally, the impact on laminar profiles is demonstrated in Figure 4.

      (16) L406-408: What kind of robustness?

      We acknowledge that describing the protocol as “robust” was an overstatement. The updated results indicate that the current protocol for submillimeter fMRI may not yet be suitable for reliable individual-level layer-dependent functional mapping. However, group-level functional connectivity (FC) analyses demonstrated clear layer-specific distinctions with VN fMRI, which were not evident in conventional fMRI. These findings highlight the enhanced layer specificity achievable with VN fMRI.

      (17) Figure 8: I think C) needs pointers to superficial, middle, and deep layers? Why is it not in the same format as in Figure 9C? The discussion of the FC results could benefit from more references supporting that these observations are in line with the literature.

      In the revised results, the layer pooling shown in Figure 9c has been removed, making the question regarding format alignment no longer applicable. Additionally, references supporting the FC results have been added to the revised Discussion section (7th paragraph).

      (18) L456-457: But correlation coefficients may also be biased by different CNR across layers.

      That is correct. In the updated FC results in Figure 7 to 9, we used group-level statistics rather than correlation coefficients.

      Reviewer #3 (Recommendations For The Authors):

      The results in Figure 2-6 should be repeated over, or averaged over, a (small) group of participants. N=6 is usual in this field. I would seriously reconsider the multiband acceleration - the acquisition seemingly cannot support the SNR hit.

      A few more specific points are given below:

      (1) Abstract: The sentence about LGN in the abstract came for me out of the blue - why would LGN be important here, it's not even a motor network node? Perhaps the aims of the study should be made more clear - if it's about networks as suggested earlier then a network analysis result would be expected too. Expanding the directed FC findings would improve the logical flow of the abstract. Given the many concerns, removing the connectivity analysis altogether would also be an option.

      We thank the reviewer for the suggestions. The LGN-related results indeed diluted the focus of this study and have been completely removed in this revision.

      (2) Line 105: in addition to the VASO method, ..

      The corresponding text has been revised, and as a result, the reviewer’s suggestion is no longer applicable.

      (3) If out of the set MB 4 / 5 / 6 MB4 was best, why did the authors not continue with a comparison including MB3 and MB2? It seems to me unlikely that the MB4 acquisition is actually optimal.

      Results: We appreciate the reviewer’s suggestions. In this revision, we decreased the MB factor to 3, as it allowed us to increase the in-plane acceleration rate to 3, thereby shortening the TE. The resulting sensitivity for both individual and group-level results is detailed in earlier responses, such as the response to Q16 for Reviewer #2.

      (4) The formatting of the references is occasionally flawed, including first names and/or initials. Please consider using a reliable reference manager.

      We used Zotero as our reference manager in this revision to ensure consistency and accuracy. The references have been formatted according to the APA style.

      (5) In the caption of Figure 5, corrected and uncorrected p values are identical. What multiple comparisons correction was made here? A multiple comparisions over voxels (as is standard) would usually lead to a cut-off ~z=3.2. That would remove most of the 'responses' shown in figure 5.

      We appreciate the reviewer’s comment. The original results presented in Figure 5 have been removed in the revised manuscript, making this comment no longer applicable.

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      Referee #3

      Evidence, reproducibility and clarity

      The manuscript from Craig et al., (2023) leverages a previously reported atoh1a reporter to drive expression of lifeact-egfp in Merkel cells (MC) to assess MC morphology during both scale development and regeneration, in the optically tractable zebrafish. Using a combination of live-imaging approaches and genetic perturbations, the authors show that MCs arise from a more immature population of dendritic Merkel cells (dMC) and that dMCs themselves derive from basal keratinocytes. The authors show that following injury, dMCs are the major cell type to infiltrate the regenerating scale region, with MCs becoming the predominant cell type at later stages of regeneration (presumably as the dMCs mature). The authors present evidence suggesting that dMCs are molecularly similar to both keratinocytes and MCs and argue that dMCs may represent an intermediate cell type. Data in the manuscript suggests MC and dMC protrusions are differently polarized, and that MC and dMC dynamics are also different. The authors provide direct evidence that dMCs mature into MCs morphologically and suggest that the reverse may also occur. Finally, the authors show that MC microvilli morphology is impaired in eda-/- mutants, suggesting a role for eda in the normal morphology of MCs, more specifically in the trunk.

      Major comments:

      1. The discovery and characterization of dMCs in this study relies entirely on their labeling by an atoh1a-lifeact transgenic reporter. Given the striking similarity of dMCs to melanocytes, it is important to confirm the atoh1a reporter labels dMCs and MCs specifically, and not melanocytes. For example, it would be useful to see confirmation of cell type by double labelling of dMCs, e.g. with atoh1a:lifeact-egfp together with an antibody for atoh1a or preferably, another MC/dMC marker. dMCs look morphologically similar to melanocytes, which also display many of the behaviors noted in this manuscript. According to RNA-seq data (see https://hair-gel.net/), atoh1 is expressed in melanocytes in embryonic mouse skin and hair follicle stem cell precursors in post-natal skin. We recommend that the authors mine a similar dataset for zebrafish to ascertain whether atho1a is also expressed in pigment cells (e.g. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE190115). We would also recommend that the authors run a stain for a melanocyte marker such as Mitf/Tyr/Dct to show this is not expressed in dMCs.
      2. A major conclusion of the paper is that dMCs display molecular properties that overlap with both MCs and basal keratinocytes based on expression of three markers. I feel this conclusion is a little strong given the evidence presented; global transcriptomic analysis of these cells (RNA-seq) would better define where along a differentiation trajectory dMCs lie.
      3. More data regarding the function of the dMC intermediate cell type would greatly strengthen the significance of the study. The characterization of dMCs forms the core of the report, yet little is shown/discussed regarding the function of this cell population. For example, why is this intermediary even required? Presumably this is to facilitate the migration of MCs from the basal layer into the upper strata and their dispersion upon arrival. In this case, one could argue that the morphology of the dMC is directly related to its migratory function, as the authors suggest dMCs arise from basal keratinocytes, then migrate upwards towards the more superficial strata, where mature MCs are located. However, very little evidence in support of this upward migration is presented - most of the migratory data are related to lateral movement. Experiments to alter the migratory properties of dMCs, for example using inhibitors of Arp2/3, would address whether migration is the key function of dMCs. Finally, there is insufficient evidence to suggest contact-inhibition is occurring, and in the cell division movie 5, it doesn't appear to happen (or the movie isn't long enough to show it). More examples are required or this observation should be reworded accordingly.
      4. Eda is shown to be important for MC morphology, especially in MCs located in the trunk. More discussion of how eda may function would be helpful to the reader. For example, in what cells are Eda and Edar expressed? Do the authors think Edar signaling is cell autonomous within the MCs? Or does the loss of Eda indirectly affect MC morphology as a result of impaired scale formation? Additionally, the authors state that corneal MCs in both WT and eda-/- have similar microvilli morphologies. The figure, however, shows that corneal MCs from these genotypes do look different, with eda-/- corneal MCs having a more evenly distributed microvilli than the polarized microvilli of their WT counterparts. The metric '% of MCs with microvilli' does not capture this aspect of their morphology.
      5. In several places, the number of biological replicates is unclear. A major concern is that data presented as 'number of cells' may only have been collated from n=1 animal. The authors should specify the number of both biological and technical replicates per experiment and consider displaying the data in superplots. Where stats are undertaken, particularly on percentages, it should be made clear whether the stats test was perfomed on raw numbers or the % (particularly true for Chi square). Examples of this issue can be found in figures 3C-H, 4F-H, 5B-C and supplemental.

      Minor comments:

      • Line 124. Why did the authors choose developmental stages 11mm and 28mm for the quantification? The images in Figure 1 show 8, 10 and 12mm but not 11mm.
      • Line 126. It is unclear what the difference is between circularity and roundness.
      • Line 645 and Fig 1I. 'Cells manually classified as MC or dMC'. Please provide further clarification on this categorization (e.g. number of protrusions/roundness value etc.)
      • Line 141 and Fig 1O. The authors comment on the mosaic nature of DsRed expression, but it seems particularly sparse in the image. Similarly, there are numerous GFP+ cells that do not express DsRed, and the ones that do are found at a distance from the DsRed+ basal keratinocytes. Further explanation is required here. For example, if MCs ultimately arise from dMCs, why are so few of them labelled? It would be useful to know the % of cre-recombination that is actually occurring (i.e. how efficient the cre driver is in keratinocytes by DsRed+/total number) to put these data in context.
      • Line 170 and 179. The authors do not comment on the possibility of de/trans-differentiation of mature MCs as an explanation of how dMCs and 'new' MCs arise on regenerating scales.
      • Line 176. Can the authors comment on how quickly the nls-Eos protein turns over? This is pertinent given it is possible that by 7 dpp all the red nls-Eos could potentially have been replaced by green nls-Eos in an 'existing' atoh1a+ cell.
      • Figure 2M-P. Both channels (green and magenta) should be shown here. Cells will express both and it is unclear from the image panel what this looks like.
      • Line 186, 200 and 206. 'regenerating dMCs' this is confusing. Perhaps reword to 'dMCs associated with regenerating scales'.
      • Line 186. Why did the authors focus on 5dpp, particularly given that at 3 dpp the proportion of dMCs:MCs is more evenly spread?
      • Figure 3A-B. An additional panel with DAPI is needed here to enable Tp63 negative nuclei to be visualized. Also, what is the cell in the top right of 3B? It has a red nucleus but is not marked by an asterisk.
      • Figure 3D-E. This data panel also needs to show a dMC that is negative for SV2.
      • Figure 4D-E and line 235. It is intuitive that dMCs will not have basal facing processes if they are already in the basal layer of keratinocytes - there simply isn't the physical space (unless they penetrate the scales/basement membrane which presumably they don't). Also, the authors need to comment on, and quantify dMC protrusions in relation to the directionality of dMC migration in the main text. This is referred to in line 762 as part of the figure legend (Fig 5) and Movie 3 legend (line 809), but this is not quantified anywhere.
      • Line 258. How do these unipolar protrusions correlate with directionality?
      • Line 287 and Figure 5G. There is insufficient evidence to conclude that MCs can revert back to dMCs, particularly given that MCs are considered post-mitotic. N=2 (cells/fish?) is not sufficient without further evidence, and the MC depicted in Figure 5G doesn't resemble a bona fide MC at the start of imaging. Suggest removing this conclusion and data or increasing n and providing further evidence.
      • Line 394. 'These protrusions extended from lateral-facing membranes and interdigitated between basal and suprabasal keratinocytes'. Did the authors specifically show this? It is not clear from the data.
      • Line 430. The reference to Merkel Cell carcinoma needs more commentary with regards to the relevance of the authors' findings.
      • Line 491. Denoise.ai was used on images as stated. Can the authors confirm that any image quantification was done on raw images prior to using the Denoise.ai function?
      • Line 528. Include details of the tp63 antibody here.

      Significance

      Overall, the data are novel and of interest to researchers in several fields, including development, skin biology and MC carcinoma. This work provides an important step forward in our understanding of how basal keratinocytes give rise to MCs in zebrafish - via a dMC intermediary cell type. The imaging presented therein is of a high quality, and the movies are beautiful; capturing the cellular behaviors very clearly. This paper does not however, comment on the molecular mechanisms regulating this transition, nor on the cellular mechanisms resulting in the altered morphology and migration of dMCs and maturation into MCs. Inclusion of data as described above in the major comments section would increase the significance and impact of this work. Notwithstanding, the observations made in this work describe, for the first time to my knowledge, a morphologically distinct cell type in zebrafish (dMCs) similar to that having been described in other vertebrates and provide the ground work for future investigation.

      Reviewer expertise: skin biology, live-imaging, zebrafish, mouse, developmental biology.

    1. In particular, it is important to help students see each culture through the eyes of its own people rather than through outsiders’ stereotypes, to emphasize cultural universals and similarities in purposes and motives more than differences, and to show that what at first may seem exotic or bizarre upon closer inspection usually can be seen as sensible adaptation to the time and place or as parallel to certain features of our own culture.

      I think a lot of focus on culture is in the eyes of the individual who is shaped by that culture. I think it is important to stress seeing culture through others’ eyes. Some expect others to understand their culture without understanding others, but it is a two way street that we can walk down with our students.

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      Reply to the reviewers

      General Statement:

      We appreciate the reviewers for acknowledging the impact of our work to the field of neurodegeneration and motor neuron diseases as well as for the understanding of the biology and function of VAPB itself; “the idea of assaying the function of ALS-causing VAPB mutants in iPSC-derived neurons is great and would be a great asset to the field” (Reviewer 1) “The new iPSC-derived system to study VAPB mutations in human motor neurons is significant and has led the authors to discover new functions for VAPB (i.e., mitochondria-ER contacts).” (Reviewer 2). The main concern raised by both reviewers is that the doxycycline inducible VAPB iPSC lines may not fully recapitulate the physiological environment found in patients, as patients are heterozygous for the VAPB P56S mutation, and our lines had VAPB under the control of an exogenous doxycycline inducible promoter. While we maintain that the doxycycline inducible lines do provide their own substantial benefits to the interrogation of the ALS pathogenesis, namely the opportunity to identify mutant VAPB interactors compared to wild type VAPB interactors through proteomics, as well as to identify pathogenesis associated to mutant VAPB without the confounding effects of wild type VAPB, we do agree with both reviewers that the inclusion of heterozygous patient iPSC lines would increase the significance of our study. Thus, in this revised manuscript we have included iPSC patient lines harboring the VAPB P56S mutation which we reprogrammed in our lab and to uphold the highest standards in the stem cell field we also performed CRISPR mediated genomic editing to generate the isogenic corrected pair. In our assessment of the ALS patient iPSC-derived motor neurons, we have already observed the same mitochondria and translation dysfunction previously described in our work with the doxycycline-inducible VAPB P56S mutant iPSC lines. Most importantly, these phenotypes were also similarly rescued by the integrated stress response inhibitor (ISRIB). Collectively, these findings suggest that the proposed mechanism initially identified in doxycycline-inducible iPSC-derived motor neurons is preserved in ALS patient iPSC-derived motor neurons.

      Reviewer #1 Major Point 1. The method of knocking out and selecting an inducible line in problematic. VAPB is an essential gene-patients with P56S are always heterozygotes, since nonfunctional VAPB is embryonic lethal. Selecting a knockout cell line is already choosing a parent that is very far from physiological, and the reexpression of P56S VAPB as the sole form also is not a good a model for understanding the contributions of P56S to disease. This approach is unusual, as it seems to overlook the advantages of working with iPSCs and patient-derived neurons. Unfortunately, the value of this amazing and rare system is diminished by the design of the selection method.

      *Reviewer #2 Major Point 1. Why did the authors decide to make VAPB knockouts and then introduce the WT or P56S VAPB constructs on a lentivirus instead of generating the point mutations (or correcting them) directly in the endogenous locus? Data in Extended Fig. 1c and Extended Fig. 2a indicate significant differences in either the kinetics of WT vs. P56S VAPB expression (1c) or levels (2a). It seems important to be able to compare comparable levels of WT and mutant proteins, especially for the interpretation of the subsequent IP-MS experiments to identify PTP151. The authors may wish to consider generating (or obtaining) isogenic lines harboring the mutations at the endogenous locus so that equal levels of expression of WT and mutant VAPB can be assessed. *

      Carried Out Revisions

      The development of the inducible system for VAPB was specifically designed to enable a systematic investigation of the effects of mutant VAPB (VAPB P56S) on cellular homeostasis while minimizing confounding influences from the wild-type (WT) protein. Additionally, this system allowed us to assess VAPB P56S binding partners and compare them to those of VAPB WT, which would not have been feasible in the context of heterozygous ALS8 patient cells.

      In response to Reviewer 2’s concern regarding differences in VAPB WT and VAPB P56S expression levels, we utilized ALS8 patient cells and familial controls to calibrate the doxycycline dose response. This approach allowed us to precisely adjust VAPB protein levels in the inducible system to match those observed in ALS8 patient and familial control iPSCs. As a result, the inducible VAPB P56S iPSCs recapitulate the VAPB expression levels found in ALS8 patient iPSCs, whereas the inducible VAPB WT iPSCs mimic the levels present in familial control iPSCs. Furthermore, the differential expression of VAPB between ALS8 patient and control cells is well documented in the literature (Mitne-Neto, et al., 2011)

      Nonetheless, we acknowledge the significance of studying ALS patient-derived iPSCs. To address this, we obtained fibroblasts from an ALS8 patient carrying the heterozygous VAPB P56S mutation, originating from a genetic background distinct from the cells used in our inducible system. These fibroblasts were reprogrammed into iPSCs in our laboratory, followed by CRISPR/Cas9-mediated genome editing to generate isogenic corrected iPSCs as controls.

      The resulting iPSC isogenic pair was differentiated into motor neurons following the protocol described in our manuscript. Notably, ALS8 patient iPSC-derived motor neurons exhibited reduced mRNA translation, as assessed by the SUnSET assay (Fig. 6A), along with a decrease in mitochondrial membrane potential, as determined using the JC-1 assay (Fig. 6B). These findings confirm that the hypotranslation and mitochondrial dysfunction initially identified in VAPB P56S doxycycline-inducible iPSC-derived motor neurons were successfully recapitulated in ALS8 patient iPSC-derived motor neurons. Furthermore, ISRIB treatment effectively rescued these phenotypic defects.

      Overall, these results demonstrate that the molecular and cellular abnormalities identified in the original inducible system can be reliably reproduced in an ALS patient-derived model with a different genetic background, thereby reinforcing the significance and broader applicability of our findings.

      Currently, we are investigating the electrophysiological properties of ALS8 patient iPSC-derived motor neurons compared to the isogenic control using the multi-electrode array (MEA) system. If a reduction in electrophysiological activity is observed, consistent with our initial findings in doxycycline-inducible VAPB P56S iPSC-derived motor neurons, we plan to treat the heterozygous patient-derived cultures with ISRIB on day 45 of differentiation. This will allow us to determine whether neuronal firing deficits in the heterozygous patient-derived motor neurons can be rescued.

      All other concerns have been addressed in this revision.

      Citation:

      1. Mitne-Neto M, Machado-Costa M, Marchetto MC, Bengtson MH, Joazeiro CA, Tsuda H, Bellen HJ, Silva HC, Oliveira AS, Lazar M et al (2011) Downregulation of VAPB expression in motor neurons derived from induced pluripotent stem cells of ALS8 patients. Hum Mol Genet 20: 3642-3652 Reviewer #1 Major Point 2. The interactome analysis is not controlled properly to interpret. It is not the total amount of VAPB that needs to be used as the normalization control, since it is already known 90+% of the P56S VAPB is in cytoplasmic aggregates. The authors need to normalize to the amount of VAPB that made it to the contact sites-a much smaller amount in the cells expressing the diseased form. For example, the fact that the authors can still pull down detectable PTPIP51 in Fig. 2e actually argues for the opposite conclusion than what the authors have stated-if the authors have actually expressed just P56S in a true knock out condition, this means that P56S CAN still bind to PTPIP51 (and possibly even better than WT, as several previous papers have suggested-since there is ~100-fold less available for binding). Without an appropriate normalization, the authors cannot make any conclusion about how to interpret the results of this part of the paper.

      Carried Out Revisions

      We sincerely thank Reviewer 1 for highlighting this critical point. Previous studies have demonstrated that the VAPB P56S mutation increases its binding affinity for PTPIP51; however, it has been proposed that the overall reduction in VAPB levels in cells harboring the P56S mutation leads to a decrease in ER-mitochondrial contacts despite the enhanced affinity (De Vos et al., 2012).

      To address this, we have repeated the co-immunoprecipitation experiment and normalized the data to VAPB levels. Consistent with Reviewer 1’s hypothesis, when normalized to soluble VAPB, we observe an increased affinity of VAPB P56S for PTPIP51. However, the total amount of PTPIP51 co-immunoprecipitated with VAPB remains significantly lower in the mutant compared to WT, likely due to the overall reduced levels of soluble VAPB P56S. This finding aligns with both Reviewer 1’s comment and the previous observations reported by De Vos et al. (2012).

      Figure 2E has been updated to reflect the normalized co-immunoprecipitation data.

      Citation:

      1. De Vos, K. J. et al. VAPB interacts with the mitochondrial protein PTPIP51 to regulate calcium homeostasis. Hum Mol Genet 21, 1299-1311, doi:10.1093/hmg/ddr559 (2012). *Reviewer #1 Major Point 3. The electron microscopy data is not interpretable in this form. The authors have provided no data at all on how analysis was performed, how contact sites were defined, how samples were collected and ensured to be representative, blinding that was performed, how sources of bias were accounted for, etc. It is clear even from what little is shown that the authors are not focused on what matters to address their own questions. For example, apart from the P56S Day 35 example, none of the "contact sites" selected for the figure are even possible to be mediated by VAPB, since the distance between the ER and the mitochondria is too far for the maximum tethering distance of VAPB-PTPIP51. Since the authors have neglected to include scale bars in their zooms, the reader cannot be sure of the distance, but it is clearly in excess of 50 nm since there are obviously visible ribosomes between the two organelles. Additionally, the authors provide no information on what "% mitochondria in contact with ER" means (By organelle? By unit surface area? Is the data grouped by cell or all comes from a single cell? How do you account for contact sites vs. proximity by crowding? Etc.). *

      2. *

      Carried Out Revisions

      We thank Reviewer 1 for their insightful comments on the analysis of the electron microscopy (EM) data and recognize the need for greater clarity in describing our quantification approach. To address this, we have revised the Electron Microscopy section of the Methods to explicitly detail our methodology for quantifying ER-mitochondria-associated membranes (ER-MAMs), as follows:

      "A series of images at various magnifications were provided, and data were collected from unique images at the highest magnification for each condition: D35 WT (13 unique images), D35 P56S (21 unique images), D60 WT (13 unique images), and D60 P56S (18 unique images). All images for a given condition originated from a single well of a 12 mm Snapwell™ Insert with 0.4 µm Pore Polyester Membranes (Corning). No information on cell grouping or sampling strategy was supplied with the images; therefore, we treated the dataset as a random sampling of the culture. Images were blinded, and quantification was performed using FIJI. Mitochondria were identified based on the presence of cristae and a double membrane. The mitochondrial perimeter was traced using the free-draw tool, and the length of ER membranes within 50 nm of this perimeter was quantified. The final measurement represents the percentage of each mitochondrion’s perimeter in contact with the ER, aggregating all visually distinct ER-MAMs, as continuity beyond the imaging plane cannot be determined (Cosson et al., 2012; Csordás et al., 2010; Stoica et al., 2014). Each data point on the graph corresponds to a single mitochondrion, with data collected from multiple cells across the unique images provided by the Core, originating from a single biological replicate."

      Regarding the quantification of ER-MAM distances, VAPB has not been definitively localized exclusively to either the rough or smooth ER. To ensure comprehensive analysis, we quantified ER-MAMs without restricting our assessment to a specific ER subdomain. We adopted a 50 nm threshold as the maximum distance for defining ER-MAMs, a well-established criterion that Reviewer 1 also referenced.

      Furthermore, we disagree with Reviewer 1’s assertion that the presence of ribosomes should justify extending the ER-MAM threshold beyond 50 nm. Ribosomes in human cells have a well-documented diameter of 20–30 nm (Anger et al., 2013), which does not support the claim that an observed ribosome within the contact site necessitates a redefinition of the ER-MAM boundary.

      We stand by our methodological approach and have updated the manuscript to ensure precision and clarity in our EM data analysis.

      Citations:

      1. Cosson, P., Marchetti, A., Ravazzola, M. & Orci, L. Mitofusin-2 independent juxtaposition of endoplasmic reticulum and mitochondria: an ultrastructural study. PLoS One 7, e46293 (2012).
      2. Csordás, G. et al. Imaging interorganelle contacts and local calcium dynamics at the ER-mitochondrial interface. Mol Cell 39, 121-132 (2010).
      3. Stoica, R. et al. ER–mitochondria associations are regulated by the VAPB–PTPIP51 interaction and are disrupted by ALS/FTD-associated TDP-43. Nat Commun 5, 3996 (2014).
      4. Anger AM, Armache JP, Berninghausen O, Habeck M, Subklewe M, Wilson DN, Beckmann R. Structures of the human and Drosophila 80S ribosome. Nature. 2013 May 2;497(7447):80-5. doi: 10.1038/nature12104. PMID: 23636399. We would like to thank the Editor of Review Commons for clarifying Reviewer #1’s Major Point 4 and will be responding to the Editor’s interpretations as detailed in the Editorial Note.

      Reviewer #1 Major Point 4. The strange pooling of data without explanation, unusual sample sizes, and lack of clarity about statistical testing, false hypothesis testing, and really any clear rigor in statistics of any kind make it impossible for a reader to have any confidence in the results presented here. The fact that every experiment in the paper has just enough n to trigger statistical significance as determined by the authors raises some concerns, suggesting potential biases. The reliability of these conclusions is questionable, especially if the authors were blinded to the identity of their own samples. This is particularly relevant for the EM data, where the determination of contact sites appears to have been made subjectively.

      Reviewer #1: "The strange pooling of data without explanation"

      • *

      - When looking into the figures and their captions in more detail, we could also not understand the nature of the replicates and how the data was aggregated or “pooled”. In Figure 1, the stated number of replicates is "N=8 separate wells”. It is unclear whether these are 8 wells from a single dissociation/replating procedure (the procedure is described in Materials & Methods as follows: "Motor neurons were dissociated on day 25 of differentiation and re-plated onto 48-well MEA plate") or whether the eight are sampled across multiple plates across cultures obtained from independent dissociations procedures.

      • We apologize for the lack of clarity and specificity. We have updated the Multi-Electrode Array Recordings portion of the Methods Section with the following: “iPSC-derived MNs from a single well of a 6-well plate thawed as day 15 MNP were dissociated and plated across 8 wells of the MEA plate. Each point on the graph is an average of the weighted mean firing rate of those 8 wells, normalized for cell count across genotypes, obtained after all firings were recorded by dissociating 2 wells per line, counting and averaging the cell numbers, and then normalizing all firings by the ratio of cell number between WT and P56S. Wells with no firing detected were excluded from quantification.”

      - In Figure 3, the number of replicates is "N=13-21 images”. Here, it is unclear whether these images come from the same or independent samples, how many quantifications were performed per image, and how many images per sample were used.

      • We have updated the Electron Microscopy Methods Section with the following: “We were provided with a series of images and magnifications and were able to gather data from unique images at the highest magnification level for each of the following categories: D35 WT: 13 unique images, D35 P56S: 21 unique images, D60 WT 13 unique images, D60 P56S: 18 unique images. All images for a given line come from a single well of a 12 mm Snapwell™ Insert with 0.4 µm Pore Polyester Membranes (Corning). No indication of cell grouping or sampling techniques was provided with the images, therefore the images were quantified as a random sampling of the culture. *Images were then blinded, and FIJI was used to quantify.” *

      We are happy to make all images publicly available.

      *- We also note that replicates are not mentioned in the proteomics analysis. *

      • We apologize for missing this and thank the editor for mentioning it. The Proteomics portion of the methods section has been updated with the following: “The identification of VAPB binding partners via mass spectrometry was performed with one biological sample, while the validation of VAPB-PTPIP51 binding via co-immunoprecipitation and Western Blot was performed with three separate biological replicates.”

      Reviewer #1: “unusual sample sizes”:

      • *

      - The wording is indeed not very explicit, but we believe it is reasonable to assume that this point refers to "N=13-21 images” and that it is not clear how the data were pooled. The reviewer makes the related point: "Is the data grouped by cell or all comes from a single cell?", which provides further context to this point.

      • We thank the editor for this clarification, our response to Reviewer #1 Major Point 3 details the updates to Electron Microscopy section of the Methods and covers this. All images were provided to us by the Case Western Reserve University Electron Microscopy Core based on the number of quality images their team were able to obtain from our samples. Reviewer #1: “lack of clarity about statistical testing”:

      • *

      - We agree that without a clear description of the nature of the replicates, the statistical analysis is unclear.

      • We hope with the updated clarity on the description of the nature of the replicates as detailed above, the nature of the statistical analysis is clearer. In addition, we have added a Statistical Analysis subsection in the Methods Section. Reviewer #1: "The reliability of these conclusions is questionable, especially if the authors were blinded to the identity of their own samples.”:

      • *

      - This is a typo; the word “not” is missing. It should read: "if the authors were NOT blinded to the identity…” and refers to concerns raised by the reviewers about evaluating the EM images.

      • We apologize for this omission, each unique image was blinded after we received them from the core, and then quantification was performed on the blinded images. The Electron Microscopy portion of the methods section has been updated to include: “We were provided with a series of images and magnifications and were able to gather data from unique images at the highest magnification level for each of the following categories: D35 WT: 13 unique images, D35 P56S: 21 unique images, D60 WT 13 unique images, D60 P56S: 18 unique images. All images for a given line come from a single well of a 12 mm Snapwell™ Insert with 0.4 µm Pore Polyester Membranes (Corning). No indication of cell grouping or sampling techniques was provided with the images, therefore the images were quantified as a random sampling of the culture. Images were then blinded, and FIJI was used to quantify.”

      Reviewer #1: “The figures suggest a lack of appropriate blinding, with cherry-picking evident even in the ‘representative’ images'”

      • *

      - We agree the wording is somewhat problematic. However, we also feel that there is a discrepancy between the differences highlighted between the EM images shown in Fig 3A and a rather modest change of the median by only a few percent, as shown in the respective violin plots. We agree with the reviewer that the images of Fig 3A might, therefore, not be “representative” of the quantified changes.

      • We appreciate the editor's clarification and have selected images that more accurately represent the subtle changes in ER-MAMs observed in our quantification. These images have been included in Figure EV6 and referenced accordingly in the manuscript to ensure a balanced depiction of our findings. Additionally, we are prepared to make all images publicly available. We would like to again thank the editor for their clarification on Reviewer #1’s Major Point 4 as well as their agreement on the inappropriate nature of some of Reviewer #1’s comments.

      *Reviewer#1 Minor points: 1. It is not accurate to describe Day 60 neurons as "aged" in the context of P56S-induced disease or imply they are a model for human aging. I could be mistaking, as I am not an iPSC expert, but I believe the field uses these terms in the context of iPSC-derived neurons to mean something more akin to "mature". The authors try to invoke this to argue for the relevance of their results to patient disease, unless the authors know this is somehow actually representative of neurons from older patients, I think this is misleading. *

      Carried Out Revisions

      We apologize for any confusion. Our use of the term "aged" was intended solely as a relative descriptor, indicating that day 60 motor neurons had been maintained in culture for a longer duration than day 35 motor neurons. It was not meant to suggest that these neurons represent a specific age or disease state, but rather that they had been cultured for an extended period.

      Furthermore, we use the term "mature" specifically in the context of motor neuron differentiation to indicate the expression of motor neuron-specific markers, which occurs by day 25 of differentiation. To avoid ambiguity, we have revised the manuscript to use the term "culture time" instead, ensuring clarity in our terminology.

      *Reviewer #1 Minor Point 2. The JC-1 experiment is not being appropriately controlled. These results are predicted by increased cell or mitochondrial death even if the membrane potentials are identical. The authors need to control for apoptotic signaling if they want to make this conclusion. There is an accepted standard in the mitochondrial field for assaying mitochondrial membrane potential (generally using TMRE or TMRM, but JC-1 can be used with proper controls), but it requires lots of careful controls not performed here (normalization to oligomycin- and FCCP-treated cells as a bare minimum. *

      Carried Out Revisions

      We would like to thank Reviewer 1 for this comment. We apologize for the omission, and we did treat the cells with CCCP provided in the JC-1 kit as a positive control. The JC-1 subsection of the methods has been updated to reflect this with the following: “A separate aliquot of cell suspension was also incubated with 1 uL of the supplied 50mM CCCP for 15 min prior to JC-1 dye addition, to act as a positive control and ensure the JC-1 dye was correctly detecting low MMP populations.”

      • The flow cytometry experiments are problematic in general since the authors state that part of their incentive for studying mitochondria in this model is due to effects at synapses, and the sample preparation for the cytometer involved dissociating the cells (i.e.-removing all of the processes where synapses mostly reside). *

      Carried Out Revisions

      We thank Reviewer #1 for this comment. Our citation of the study by Gómez-Suaga et al. (2019) was not intended to suggest that our investigation focuses exclusively on mitochondria at synapses but rather to provide context on the current understanding of the field. To clarify this point, we have revised the manuscript to include the following statement: "It has also been shown that this interaction can occur at synapses, and disruptions to it may impact synaptic activity (Gómez-Suaga et al., 2019)."

      Citation:

      Gómez-Suaga, P. et al. The VAPB-PTPIP51 endoplasmic reticulum-mitochondria tethering proteins are present in neuronal synapses and regulate synaptic activity. Acta Neuropathologica Communications 7, 35, doi:10.1186/s40478-019-0688-4 (2019).

      • The normalization for VAPB in the inducible lines is unclear-how is normalization performed simultaneously to two genes at once? The authors do not provide enough information for us to understand what they have actually done, and I wonder if the data presented in the supplement on this is actually sufficiently different from random noise to be interpretable, since no statistics of any kind are given.*

      In response, we have added a qPCR section to the Methods, detailing our experimental approach as follows:

      "Quantitative PCR: RNA was extracted using TRIzol Reagent (Thermo Fisher), and the procedure was performed according to their provided protocol. cDNA was generated using SuperScript™ IV VILO™ Master Mix (Thermo Fisher), following the manufacturer’s instructions. qPCR was conducted using PowerTrack™ SYBR Green Master Mix for qPCR (Thermo Fisher), following the provided protocol, on a BioRad CFX96 thermocycler. Samples were run in triplicate. Quantification was performed using CFX Maestro software (BioRad). VAPB expression was normalized to Neomycin and RPL3 using the software, and the resultant expression values were graphed along with the provided SEM, per standards in the field (Livak & Schmittgen, 2001; Wong & Medrano, 2005)."

      Additionally, we have modified the graph to more clearly illustrate the comparison between VAPB WT and P56S, emphasizing that there is no significant difference in mRNA expression.

      Citations

      1. Wong, M. L. & Medrano, J. F. Real-time PCR for mRNA quantitation. Biotechniques 39, 75-85 (2005).
      2. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402-408 (2001).

      3. I don't think the tunicamycin experiments make sense in this context. The authors start with premise that I do not understand: "if the decrease in MERC was underlying the decrease in MMP seen later in differentiation, inducing cell stress early in differentiation could mimic the decreased MMP." Most cell stress pathways enhance ER-mito contact, not decrease it, so I am not sure why they expected this to work this way. They then continue: "We selected tunicamycin, an ER stressor, as VAPB is an ER protein, and if the decreased MMP could be caused, at least partially, by loss of MERCs, ER stress would likely exacerbate it." I don't understand this either- Tunicamycin is not a general ER-stressing agent-it is a specific inhibitor of some N-linked glycosylation-maturation pathways in the ER lumen, which causes ER stress by dysregulation of misfolded protein pathways. Since VAPB has no luminal domains to speak of, is not known to interact with the protein folding and maturation machinery at all, and Tunicamycin has no obvious connection I'm aware of to MERCs, I am not able to follow the authors' intentions or conclusions here. I suspect this needs a major rewrite to explain what the goals were and how the authors controlled for their findings. *

      Carried Out Revisions

      We thank Reviewer 1 for this insightful comment. To provide greater clarity on this point, we have revised the manuscript to include the following statement:

      "MAMs are known to be a hot spot for the transfer of stress signals from the ER to mitochondria (van Vliet & Agostinis, 2018). Consequently, to test whether we could induce mitochondrial dysfunction by exposing iPSC-derived motor neurons to stressors, we selected tunicamycin (TM), an ER stressor, as VAPB is an ER protein, and if the decreased MMP could be caused, at least partially, by loss of ER-MAM, ER stress would likely exacerbate it."

      This revision aims to more clearly articulate the rationale behind our approach and the selection of tunicamycin as an ER stressor.

      Citations

      1. van Vliet AR, Agostinis P (2018) Mitochondria-Associated Membranes and ER Stress. Curr Top Microbiol Immunol 414: 73-102 Minor Adjustments Not in Response to Reviewer Comments

      Several minor adjustments have been made in response to internal reviews and feedback, independent of any specific Reviewer comment. The only modification affecting the presented data resulted from a comment noting a minor discrepancy in the gating of green-fluorescing cells between VAPB WT and VAPB P56S on Day 30 (Figure 3C). To ensure consistency, the gating was redrawn and applied uniformly to both plots, leading to a slight change in values. However, the overall difference remains non-significant, and our interpretation of the data remains unchanged. Additionally, to facilitate visual comparison, the Y-axes of the quantification graphs in Figures 3C and 3D have been standardized, though the data in Figure 3D itself was not modified—only the Y-axis scaling was adjusted.

      Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      We have responded to both of Reviewer #2’s Major Points 2 and 3 together, as the answer applies to both questions and the points raised in each idea.

      • *

      *Reviewer #2 Major Point 2. The authors highlight PTP151 binding to VAPB as a way to promote mitochondria ER contacts (MERC). They provide evidence that this association is diminished by the P56S VAPB mutation. This raises an important question. How does PTPIP51 binding connect with other phenotypes, such as the neuronal firing and ER stress sensitivity? Can the authors consider experiments to test this directly? For example, is there a way to drive PTP151 : VAPB interactions even in the face of mutant VAPB and see if this rescues the MERC defects and other phenotypes? *

      Reviewer #2 Major Point 3. The authors propose that the detachment of the mitochondria from the ER most likely be the cause for why their mutant motor neurons are more sensitive to ER stressors. Along the lines of the above, is there a way to test this hypothesis directly? Can they use other means to promote ER mitochondria association even in the face of VAPB mutation and test if this rescues phenotypes?

      Analyses We Prefer Not or Are Unable to Carry Out

      We thank Reviewer 2 for these insightful suggestions and fully agree that enhancing PTPIP51:VAPB interactions in the presence of mutant VAPB would be an effective approach to directly demonstrate that the loss of this interaction is the causative event underlying the observed phenotypes or to drive increased ER-mitochondria attachment.

      However, we have not identified a method to achieve this without introducing substantial alterations to the model system, which would likely compromise the interpretability of the results. The most promising approach we considered was the use of rapamycin-inducible linkers, as described by Csordás et al. (2010), which facilitate ER-mitochondria tethering upon rapamycin addition. Unfortunately, rapamycin directly affects translational regulation via mTOR (mammalian target of rapamycin) and given that translation dysregulation is a key phenotype in our study, its addition could influence multiple pathways, making it difficult to attribute any observed effects specifically to the intended manipulation.

      If the reviewers or editors have suggestions for alternative approaches, we would greatly appreciate their input. However, based on the current state of the field, we do not believe there is a method to selectively drive ER-mitochondria attachment or specifically enhance VAPB-PTPIP51 interactions without introducing confounding factors that would obscure whether the resulting effects are due to VAPB P56S pathophysiology or the intervention itself.

      Citation:

      1. Csordás G, Várnai P, Golenár T, Roy S, Purkins G, Schneider TG, Balla T, Hajnóczky G. Imaging interorganelle contacts and local calcium dynamics at the ER-mitochondrial interface. Mol Cell. 2010 Jul 9;39(1):121-32. doi: 10.1016/j.molcel.2010.06.029. PMID: 20603080; PMCID: PMC3178184.
    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      This study demonstrates the significant role of secretory leukocyte protease inhibitor (SLPI) in regulating B. burgdorferi-induced periarticular inflammation in mice. They found that SLPI-deficient mice showed significantly higher B. burgdorferi infection burden in ankle joints compared to wild-type controls. This increased infection was accompanied by infiltration of neutrophils and macrophages in periarticular tissues, suggesting SLPI's role in immune regulation. The authors strengthened their findings by demonstrating a direct interaction between SLPI and B. burgdorferi through BASEHIT library screening and FACS analysis. Further investigation of SLPI as a target could lead to valuable clinical applications.

      The conclusions of this paper are mostly well supported by data, but two aspects need attention:

      (1) Cytokine Analysis:

      The serum cytokine/chemokine profile analysis appears without TNF-alpha data. Given TNF-alpha's established role in inflammatory responses, comparing its levels between wild-type and infected B. burgdorferi conditions would provide valuable insight into the inflammatory mechanism.

      (2) Sample Size Concerns:

      While the authors note limitations in obtaining Lyme disease patient samples, the control group is notably smaller than the patient group. This imbalance should either be addressed by including additional healthy controls or explicitly justified in the methodology section.

      We thank the reviewer for the careful review and positive comments.

      (1) We did look into the level of TNF-alpha in both WT and SLPI-/- mice with and without B. burgdorferi infection. At serum level, using ELISA, we did not observe any significant difference between all four groups. At gene expression level, using RT-qPCR on the tibiotarsal tissue, we also did not observe any significant differences. Our RT-qPCR result is consistent with the previous microarray study using the whole murine joint tissue (DOI: 10.4049/jimmunol.177.11.7930). The microarray study did not show significant changes in TNF-alpha level in C57BL/6 mice following B. burgdorferi infection. A brief discussion has been added, and the above data is provided as Supplemental figure 4 in the revised manuscript, line 334-339, and 756-763.

      (2) We agree with the reviewer that the control group is smaller than the patient group. Among the archived samples that are available, the number of adult healthy controls are limited. It has been shown that the serum level of SLPI in healthy volunteers is in average about 40 ng/ml  (DOI: 10.3389/fimmu.2019.00664 and 10.1097/00003246-200005000-00003). The median level in the healthy control in our data was 38.92 ng/ml, which is comparable to the previous results. A brief discussion has been added in the revised manuscript, line 364-369.

      Reviewer #2 (Public review):

      Summary:

      This manuscript by Yu and coworkers investigates the potential role of Secretory leukocyte protease inhibitor (SLPI) in Lyme arthritis. They show that, after needle inoculation of the Lyme disease (LD) agent, B. burgdorferi, compared to wild type mice, a SLPI-deficient mouse suffers elevated bacterial burden, joint swelling and inflammation, pro-inflammatory cytokines in the joint, and levels of serum neutrophil elastase (NE). They suggest that SLPI levels of Lyme disease patients are diminished relative to healthy controls. Finally, they find that SLPI may interact directly the B. burgdorferi.

      Strengths:

      Many of these observations are interesting and the use of SLPI-deficient mice is useful (and has not previously been done).

      We appreciate the reviewer’s careful reading and positive comments.

      Weaknesses:

      (a) The known role of SLPI in dampening inflammation and inflammatory damage by inhibition of NE makes the enhanced inflammation in the joint of B. burgdorferi-infected mice a predicted result;

      We agree that the observation of the elevated NE level and the enhanced inflammation is theoretically likely. Indeed, that was the hypothesis that we explored, and often what is theoretically possible does not turn out to occur. In addition, despite the known contribution of neutrophils to the severity of murine Lyme arthritis, the importance of the neutrophil serine proteases and anti-protease has not been specifically studied, and neutrophils secrete many factors. Therefore, our data fill an important gap in the knowledge of murine Lyme arthritis development – and set the stage for the further exploration of this hypothesis in the genesis of human Lyme arthritis.

      (b) The potential contribution of the greater bacterial burden to the enhanced inflammation is not addressed;

      We agree with the reviewer’s viewpoint that the increased infection burden in the tibiotarsal tissue of the infected SLPI-/- mice could contribute to the enhanced inflammation. A brief discussion of this possibility has been added in the revised manuscript, line 287-288.

      (c) The relationship of SLPI binding by B. burgdorferi to the enhanced disease of SLPI-deficient mice is not clear; and

      We agree with the reviewer that we have not shown the importance of the SLPI-B. burgdorferi binding in the development of periarticular inflammation. It is an ongoing project in our lab to identify the SLPI binding partner in B. burgdorferi. Our hypothesis is that SLPI could bind and inhibit an unknown B. burgdorferi virulence factor that contributes to murine Lyme arthritis. A brief discussion has been added in the revised manuscript, line 401-407.

      (d) Several methodological aspects of the study are unclear.

      We appreciate the critique. We have modified the methods section in greater detail in the revised manuscript.

      Reviewer #3 (Public review):

      Summary:

      The authors investigated the role of secretory leukocyte protease inhibitors (SLPI) in developing Lyme disease in mice infected with Borrelia burgdorferi. Using a combination of histological, gene expression, and flow cytometry analyses, they demonstrated significantly higher bacterial burden and elevated neutrophil and macrophage infiltration in SLPI-deficient mouse ankle joints. Furthermore, they also showed direct interaction of SLPI with B. burgdorferi, which likely depletes the local environment of SLPI and causes excessive protease activity. These results overall suggest ankle tissue inflammation in B. burgdorferi-infected mice is driven by unchecked protease activity.

      Strengths:

      Utilizing a comprehensive suite of techniques, this is the first study showing the importance of anti-protease-protease balance in the development of periarticular joint inflammation in Lyme disease.

      We greatly appreciate the reviewer’s careful reading and positive comments.

      Weaknesses:

      Due to the limited sample availability, the authors investigated the serum level of SLPI in both in Lyme arthritis patients and patients with earlier disease manifestations.

      We agree with the reviewer that it would be ideal to have more samples from Lyme arthritis patients. However, among the available archived samples, samples from Lyme arthritis patients are limited. For the samples from patients with single EM, the symptom persisted into 3-4 month after diagnosis, the same timeframe when acute arthritis is developed. A brief discussion has been added in the revised manuscript, line 364-369.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) In Figure 2, for histological scoring, do they have similar n numbers?

      In panel B, 20 infected WT mice and 19 infected SLPI-/- mice were examined. In panel D, 13 infected WT and SLPI-/- mice were examined. Without infection, WT and SLPI-/- mice do not develop spontaneous arthritis. Due to the slow breeding of the SLPI-/- mice, a small number of uninfected control animals were used. All the supporting data values are provided in the supplemental excel.

      (2) In Figure 3, for macrophage population analysis, maybe consider implementing Ly6G-negative gating strategy to prevent neutrophil contamination in macrophage population?

      We appreciate reviewer’s suggestion. We have analyzed the data using the Ly6G-negative gating strategy and provided the result in the Supplemental figure 1. The two gating strategies showed consistent result, significantly higher percentage of infiltrating macrophages in the tibiotarsal tissue from infected SLPI-/- mice, line 154-158, line 726-729.

      Reviewer #2 (Recommendations for the authors):

      (1) The investigators should address the possibility that much of the enhanced inflammatory features of infected SLPI-deficient mice are simply due to the higher bacterial load in the joint.

      We agree with the reviewer’s viewpoint that the increased infection burden in the tibiotarsal tissue of the infected SLPI-/- mice could contribute to the enhanced inflammation. A brief discussion of this possibility has been added in the revised manuscript, line 287-288.

      (2) Fig. 1. (A) There is no statistically significant difference in the bacterial load in the heart or skin, in contrast to the tibiotarsal joint. It would be of interest to know whether other tissues that are routinely sampled to assess the bacterial load, such as injection site, knee, and bladder, also harbored increased bacterial load in SLPI-deficient mice. (B) Heart and joint burden were measured at "21-28" days. The two time points should be analyzed separately rather than pooled.

      (A) We appreciate the reviewer’s suggestion. We agree that looking into the infection load in other tissues is helpful. However, studies into murine Lyme arthritis have been predominantly focused on tibiotarsal tissue, which displays the most consistent and prominent swelling that’s easy to observe and measure. Thus, we focused on the tibiotarsal joint in our study. (B) We collected the heart and joint tissue approximately 3-week post infection within a 3-day window based on the feasibility and logistics of the laboratory. Using “21-28 d”, we meant to describe between 21 to 24 days post infection. We apologize for the mislabeling and it has been corrected it in the revised manuscript. In the methods, we defined the timeframe as “Mice were euthanized approximately 3-week post infection within a 3-day window (between 21 to 24 dpi) based on the feasibility and logistics of the laboratory”, line 464-466. In the results and figure legend, we corrected it as “between 21 to 24 dpi”.

      (3) Fig. 2. (A) The same ambiguity as to the days post-infection as cited above in Point 2B exists in this figure. (B) Panel B: Caliper measurements to assess joint swelling should be utilized rather than visual scoring. (In addition, the legend should make clear that the black circles represent mock-infected mice.)

      (A) The histology scoring, and histopathology examination were performed at the same time as heart and joint tissue collection, approximately 3 weeks post infection within a 3-day window based on the feasibility and logistics of the laboratory. We apologize for the mislabeling and it has been corrected in the revised manuscript. (B) We appreciate the reviewer’s suggestion. However, our extensive experience is that caliper measurement can alter the assessment of swelling by placing pressure on the joints and did not produce consistent results. Double blinded scoring was thus performed. Histopathology examination was performed by an independent pathologist and confirmed the histology score and provided additional measurements.

      (4) Fig. 3. (A) See Point 2B. (B) For Panels C-E, uninfected controls are lacking.

      We apologize for this omission. Uninfected controls have been provided in Figure 3 in the revised manuscript.

      (5) Fig. 4. Fig. 4. Some LD subjects were sampled multiple times (5 samples from 3 subjects with Lyme arthritis; 13 samples from 4 subjects with EM), and samples from same individuals apparently are treated as biological replicates in the statistical analysis. In contrast, the 5 healthy controls were each sampled only once.

      We agree with the reviewer that the control group is smaller than the patient group. Among the archived samples that are available, the number of adult healthy controls are limited, and sampled once. We used these samples to establish the baseline level of SLPI in the serum. It has been shown that the serum level of SLPI in healthy volunteers is in average about 40 ng/ml  (DOI: 10.3389/fimmu.2019.00664 and 10.1097/00003246-200005000-00003). The median level in the healthy control in our data was 38.92 ng/ml, which is comparable to the previous results. A brief discussion has been added in the revised manuscript, line 364-369.

      (6) Fig. 5. (A) Panel A: does binding occur when intact bacteria are used? (B) Panels B, C: Were bacteria probed with PI to indicate binding likely to occur to surface? How many biological replicates were performed for each panel? Is "antibody control" a no SLPI control? What is the blue line?

      Actively growing B. burgdorferi were collected and used for binding assays. We do not permeabilize the bacteria for flow cytometry. Thus, all the binding detected occurs to the bacterial surface. Three biological replicates were performed for each panel. The antibody control is no SLPI control. For panel D, the bacteria were stained with Hoechst, which shows the morphology of bacteria. We apologize for the missing information. A complete and detailed description of Figure 5 has been provided in both methods and figure legend in the revised manuscript. 

      (7) Sup Fig. 1. (A) Panel A: Was this experiment performed multiple times? I.e., how many biological replicates? (B) Panel B: Strain should be specified.

      The binding assay to B. burgdorferi B31A was performed two times. In panel B, B. burgdorferi B31A3 was used. We apologize for the missing information. A complete and detailed description has been provided in the figure legend in the revised manuscript. 

      (8) Fig. S2. It is not clear that the condition (20% serum) has any bactericidal activity, so the potential protective activity of SLPI cannot be determined. (Typical serum killing assays in the absence of specific antibody utilized 40% serum.)

      In Fig. S2, panel B, the first two bars (without SLPI, with 20% WT anti serum) showed around 40% viability. It indicates that the 20% WT anti serum has bactericidal activity. Serum was collected from B. burgdorferi-infected WT mice at 21 dpi, which should contain polyclonal antibody against B. burgdorferi.

      Reviewer #3 (Recommendations for the authors):

      It was a pleasure to review! I congratulate the authors on this elegant study. I think the manuscript is very well-written and clearly conveys the research outcomes. I only have minor suggestions to improve the readability of the text.

      We greatly appreciate the reviewer’s recognition of our work.

      Line 92: Please briefly summarize the key results of the study at the end of the introduction section.

      We appreciate the reviewer’s suggestion. A brief summary has been added in the revised manuscript, line 93-103.

      Line 108: Why is the inflammation significantly occurred only in ankle joints of SLPI-I mice? Could you please provide a brief explanation?

      The inflammation may also happen in other joints the B. burgdorferi infected SLPI-/- mice, which has not been studied. The study into murine Lyme arthritis has been predominantly done in the tibiotarsal tissue, which displays the most prominent swelling that’s easy to observe and measure. Thus, we focused on the tibiotarsal joint in our study.

      Line 136: Please also include the gene names in Figure 3.

      We apologize for the omission. Gene names has been included in figure legend in the revised manuscript.

      Line 181: Please briefly introduce BASEHIT. Why did you use this tool? What are the benefits?

      We appreciate the reviewer’s suggestion. We have provided a brief introduction on BASEHIT in the revised manuscript, line 216-218.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      As our understanding of the immune system increases it becomes clear that murine models of immunity cannot always prove an accurate model system for human immunity. However, mechanistic studies in humans are necessarily limited. To bridge this gap many groups have worked on developing humanised mouse models in which human immune cells are introduced into mice allowing their fine manipulation. However, since human immune cells will attack murine tissues, it has proven complex to establish a human-like immune system in mice. To help address this, Vecchione et al have previously developed several models using human cell transfer into mice with or without human thymic fragments that allow negative selection of autoreactive cells. In this report they focus on the examination of the function of the B-helper CD4 T-cell subsets T-follicular helper (Tfh) and T-peripheral helper (Tph) cells. They demonstrate that these cells are able to drive both autoantibody production and can also induce B-cell independent autoimmunity.

      Strengths:

      A strength of this paper is that currently there is no well-established model for Tfh or Tph in HIS mice and that currently there is no clear murine Tph equivalent making new models for the study of this cell type of value. Equally, since many HIS mice struggle to maintain effective follicular structures Tfh models in HIS mice are not well established giving additional value to this model.

      Weaknesses:

      A weakness of the paper is that the models seem to lack a clear ability to generate germinal centres. For Tfh it is unclear how we can interpret their function without the structure where they have the greatest influence. In some cases, the definition of Tph does not seem to differentiate well between Tph and highly activated CD4 T-cells in general.

      The limited ability of HIS mice to generate well-defined lymphoid tissue structures is well noted. While the emergence of T cells in HIS mice increases the size of lymphoid tissues, the structure remains suboptimal and vaccination responses are limited. We believe this is mainly due to the common gamma chain knockout, which results in a lack of murine lymphoid tissue inducer (LTi) cells, which require IL-7 signaling to interact with murine mesenchymal cells for normal lymphoid tissue development. Ongoing efforts by our group and others aim to address this challenge by providing the necessary signals. Despite this challenge, these mice do develop Tfh cells, allowing us to study this cell subset.

      We agree with the reviewer that the distinction between Tph and highly activated CD4 T cells is incomplete.

      However, we have provided several distinctions in our manuscript that support the presence of Tph in HIS mice: 1) Tph cells exhibit very high levels of PD-1 expression, whereas other activated CD4 cells have varying levels of PD-1 expression. 2) Tph cells express IL-21. 3) Tph cells promote B cell differentiation and antibody production. 

      Reviewer #2 (Public Review):

      Summary:

      Humanized mice, developed by transplanting human cells into immunodeficient NSG mice to recapitulate the human immune system, are utilized in basic life science research and preclinical trials of pharmaceuticals in fields such as oncology, immunology, and regenerative medicine. However, there are limitations to using humanized mice for mechanistic analysis as models of autoimmune diseases due to the unnatural T cell selection, antigen presentation/recognition process, and immune system disruption due to xenogeneic GVHD onset.

      In the present study, Vecchione et al. detailed the mechanisms of autoimmune disease-like pathologies observed in a humanized mouse (Human immune system; HIS mouse) model, demonstrating the importance of CD4+ Tfh and Tph cells for the disease onset. They clarified the conditions under which these T cells become reactive using techniques involving the human thymus engraftment and mouse thymectomy, showing their ability to trigger B cell responses, although this was not a major factor in the mouse pathology. These valuable findings provide an essential basis for interpreting past and future autoimmune disease research conducted using HIS mice.

      Strengths:

      (1) Mice transplanted with human thymus and HSCs were repeatedly executed with sufficient reproducibility, with each experiment sometimes taking over 30 weeks and requiring desperate efforts. While the interpretation of the results is still debatable, these description is valuable knowledge for this field of research.

      (2) Mechanistic analysis of T-B interaction in humanized mice, which has not been extensively addressed before, suggests part of the activation mechanism of autoreactive B cells. Additionally, the differences in pathogenicity due to T cell selection by either the mouse or human thymus are emphasized, which encompasses the essential mechanisms of immune tolerance and activation in both central and peripheral systems.

      Weaknesses:

      (1) In this manuscript, for example in Figure 2, the proportion of suppressive cells like regulatory T cells is not clarified, making it unclear to what extent the percentages of Tph or Tfh cells reflect immune activation. It would have been preferable to distinguish follicular regulatory T cells, at least. While Figure 3 shows Tregs are gated out using CD25- cells, it is unclear how the presence of Treg cells affects the overall cell population immunogenic functionally.

      We analyzed the % FOXP3+ cells and the % of ICOS+ cells within the Tfh and Tph cells in the spleen of Hu/Hu and Mu/Hu mice at 20 weeks post-transplantation. Importantly, we see no difference in FOXP3 expression between Tfh of Mu/Hu and Hu/Hu mice. The results have been added to panels J and K of Figure 2. 

      (2) The definition of "Disease" discussed after Figure 6 should be explicitly described in the Methods section. It seems to follow Khosravi-Maharlooei et al. 2021. If the disease onset determination aligns with GVHD scoring, generally an indicator of T cell response, it is unsurprising that B cell contribution is negligible. The accelerated disease onset by B cell depletion likely results from lymphopenia-induced T cell activation. However, this result does not prove that these mice avoid organ-specific autoimmune diseases mediated by auto-antibodies and the current conclusion by the authors may overlook significant changes. For instance, would defining Disease Onset by the appearance of circulating autoantibodies alter the result of Disease-Free curve? Are there possibly histological findings at the endpoint of the experiment suggesting tissue damage by autoantibodies?

      We have added a definition of disease to the Methods section as requested. Regarding the possibility of antibody-mediated disease that may be missed by this definition, we acknowledge this point in the Discussion section. However, we also discuss the point that the deficient complement pathway in NSG mice is likely to have protected the HIS mice from autoantibody-mediated organ damage.

      (3) Helper functions, such as differentiating B cells into CXCR5+, were demonstrated for both Hu/Hu and Mu/Huderived T cells. This function seemed higher in Hu/Hu than in Mu/Hu. From the results in Figure 7-8, Hu/Hu Tph/Tfh cells have a stronger T cell identity and higher activation capacity in vivo on a per-cell basis than Mu/Hu's ones. However, Hu/Hu-T cells lacked an ability to induce class-switching in contrast to Mu/Hu's. The mechanisms causing these functional differences were not fully discussed. Discussions touching on possible changes in TCR repertoire diversity between Mu/Hu- and Hu/Hu- T cells would have been beneficial. 

      Consistent with the reviewer’s suggestion, we have previously shown that the TCR repertoire in Mu/Hu mice is less diverse than that in Hu/Hu mice (Khosravi-Maharlooei M, et al., J Autoimmun., 2021). We believe that the narrowed TCR repertoire in the periphery of Mu/Hu mice, combined with the inadequate negative selection in the murine thymus reported in the paper cited above, results in selective peripheral expansion primarily of the few T cell clones that are cross-reactive with HLA/murine self peptide complexes presented by human APCs in the periphery.  We have discussed the reasons why these cells, when transferred to secondary recipients containing the same APCs, might not be as active as the more diverse, HLA-selected T cell repertoire transferred from Hu/Hu mice.  These possible reasons include exhaustion of the T cells in Mu/Hu mice, limited expression of the few targeted HLA-peptide complexes recognized by the narrow cross-reactive TCR repertoire of Mu/Hu T cells and the consequent relatively impaired T-B cell collaboration in these mice.   

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors):

      The authors note that they removed an outlier result from Figures 1 B & C. With only 4 mice it seems difficult to see exactly how they determined the result was an outlier. Presumably, it was quite different from the others but in such a small dataset removing data without a very clear statistical rationale seems likely to strongly influence the results.

      We have revised Fig 1 to include the previously-deleted outlier mouse.   

      Figure 4. The authors describe the follicular area. Were they able to observe any GC-like structures in their data?

      From the examples, I can see that the PNA staining is sometimes diffuse but even if the authors felt they could not observe a distinct GC this should be stated and discussed in the text.

      We now describe the three colors IF staining in more detail in accordance with this comment. We characterized 4 Hu/Hu and 3 Mu/Hu spleens earlier than 20 weeks post-transplant. In all of these mice, distinct B cell areas (CD20+) were obvious and PNA+ cells were more concentrated in the B cell zones. We stained 4 Hu/Hu and 3 Mu/Hu spleens from mice between 20-30 weeks post-transplant and found that B cell areas were smaller in all these spleens compared to those taken before 20-weeks post-transplant. PNA+ areas are also more diffusely distributed and are not enriched in the B cell areas. Only 2 Mu/Hu mice showed clear B cell zones with some enriched PNA+ areas in the B cell zones. Additionally, we stained 2 Hu/Hu and 2 Mu/Hu mice later than week 30 post-transplant. No distinct B cell areas were observed in any of the spleens of these mice and PNA+ cells were diffusely distributed.  

      In Figure 3E the authors sort CD25-CXCR5-CD45RA- CD4 T-cells as Tph. This does seem a very loose definition including essentially all non-naïve CD4 cells that are not Tregs or Tfh.

      We agree with the reviewer that the distinction between Tph and highly activated CD4 T cells is incomplete.

      However, we have provided several distinctions in our manuscript that support the presence of Tph in HIS mice: 1) Tph cells exhibit very high levels of PD-1, whereas other activated CD4 cells have varying levels of PD-1 expression. 2) Tph cells express IL-21. 3) Tph cells promote B cell differentiation and antibody production. 

      Tph is sometimes a hard cell type to separate from more general highly activated CD4 T-cells. The broad CXCR5PD1+ phenotype they have used is common in the literature and the authors have confirmed some enrichment of IL21 production by these cells. However, they should consider if there are ways of further confirming this by examination of other markers such as CCR2 and CCR5 or elimination of other effector identities such as Th1 and Th17 or PD1+ exhaustion phenotypes.

      For this study, we chose to follow the commonly used definitions in the literature for Tph and Tfh cells. For this reason, we are careful to refer to “Tph-like” cells rather than Tph cells in this manuscript. Distinguishing Tph cells from other subsets of activated CD4 cells would require further studies such as single cell RNA seq, which we hope to be able to perform in the future with additional funding.  

      Figure 8. The authors perform some analysis of B-cell phenotypes looking at markers such as CD27, IgD in 8B, and CD11c in 8C. Why is CD11c considered in isolation? The level of expression of the other markers would change how this data would be interpreted e.g. IgD-CD27-CD11c+ = DN2/Atypical cells, IgD-CD27+CD11c+ = Activated or ageassociated, etc.

      In response to this comment, we reanalyzed the splenic samples of the donor Mu/Hu and Hu/Hu mice and their adoptive recipients. Interestingly, in the T cell donors, the Mu/Hu B cells included greater proportions of activated/age-associated B cells (IgD-CD27+CD11c+) and atypical cells (IgD-CD27-CD11c+), compared to the Hu/Hu B cells. This is consistent with the increased disease, increased Tph/Tfh and increased IgG antibody findings in the primary Mu/Hu compared to Hu/Hu mice. These results have been added to Figure 5G. We performed a similar analysis in the blood (week 9) and spleen of adoptive recipient mice. These studies showed that activated/ageassociated B cells (IgD-CD27+CD11c+) and atypical cells (IgD-CD27-CD11c+) were significantly increased in the adoptive recipients of Hu/Hu Tph and Tfh cells compared to the adoptive recipients of Mu/Hu Tph and Tfh cells (Fig. 8C). These results are consistent with the disease, T cell expansion and antibody results in the adoptive recipients. 

      Data not shown occurs often in this manuscript. In some cases what is not shown is potentially important. The authors note in the text relating to Figure 7 that the "purity of the cell populations as assessed by FCM ranged from 56-60% (data not shown)". Those numbers are a little alarming. They are referring to the purity of the FCS sorted Tfh and Tph prior to transfer? Currently, some of the discussion of this paper is about the possibility of plasticity, with Tfh switching into a Tph phenotype. If the transferred cell populations are 56-60% pure I don't think it is possible to make any interpretation of plasticity.

      We looked into this further and realized that the purity figure cited in the original manuscript was erroneous due to a misunderstanding on the part of the first author of a question from the senior author. Unfortunately, data on the purity of the FACS-sorted population was not saved. However, we have added panel B to Figure 7 to show the sorting strategy for Tfh and Tph cells.   We agree that any discussion of plasticity between these cell types is speculative, as outgrowth of a minor population is possible even from well-purified sorted cells.  

      Minor points:

      Some graphs have issues with presentation; Figures 5D and 5E, split scale clips data points. 5F the color representing time would be better replaced with direct labels. 6C and 6C some distortion of text clipping other elements.

      We changed 5D and 5E y axis scales to avoid cutting the data points. Also, we changed 5F labels. Distortion of text clipping and other elements in Fig 6E and 6A have been corrected.  

      The abbreviation LIP is used in the abstract without a clear definition until later in the text.

      This abbreviation has been defined again in the text.

      Generally, the discussion section is quite long.

      We agree that the discussion is quite long, but the results are quite complex and require considerable discussion.  We have attempted to be as concise as possible.

      Reviewer #2 (Recommendations For The Authors):

      Suggestion

      Can Supplementary Figures be merged into the mains for the convenience of readers? There is enough extra margin.

      We prefer to keep the order of main and supplementary figures as they are. 

      There are some confusing results which I would recommend to make the additional explanation for readers. For example, about 10% of Hu/Hu CD3+ T cells reacted to Auto-DC in Figure 1B, but neither CD4+ nor CD8+ cells did in Figure 1C.

      We have re-analyzed the data in Fig 1 and included the previously-deleted outlier mouse. 

      Minor

      Figure 3C

      The figure legend does not explain the figure. Hu/Mu or Mu/Mu?

      Both groups were combined in the figure, as the results were similar for both.  The N per group is given in the figure legend.  The same applies to figure 3D.

      Figure 4B, 4C

      Why were Hu/Hu and Mu/Hu data merged only in 4B? They should be discussed in the context of parallel comparison. Both y-axis labels are the same between B and C despite the legend saying differently.

      We switched the order of Figure 4B and 4C, each of which serves a different purpose. Figure 4B aims to demonstrate the similarity between the two groups at each timepoint.  Figure 4C combines the two groups in order to provide sufficient animal numbers to demonstrate the statistically significant changes over time. 

      Figure 5D

      The axis label was missing and the uncertain bar emerged. The authors should replace it with the corrected one.

      The axis and the bar in 5D have been corrected.

      Figure 5F

      The legend does not explain the figure. What are these numbers? Also, it is better if the authors add a detailed explanation to the manuscript about the reason why the sum of antibody titer represents the poly-reactivity of IgM in these mice.

      The numbers in the previous version of the figure were eartag numbers, which we have now renumbered as animal 1,2,3, etc in each group. Please refer to the final paragraph of the "Autoreactivity of IgM and IgG in HIS Mice" section in the Results section for an explanation of IgM polyreactivity.

      Fig. 7D-E etc.

      The definition of Asterisk is insufficient. Between what to what in the multiple comparisons?

      The green asterisks show significant differences between the Tph in Hu/Hu vs Mu/Hu mice, while the orange asterisks show significant differences between the Tfh in Hu/Hu vs Mu/Hu mice. This has been added to the figure legend.

      Figure 7 ~ Figure 8

      The legends on the figure are confusing due to the different order of figures. The scales are inappropriate in some figures. The readers cannot interpret the data from the unfairly compressed plots.

      We made the plots bigger to make them readable and changed the order.

      Methods

      In the description of B cell depletion Experiments, the authors should directly mention the figure number instead of "In the second Experiment ..."

      We have corrected this in the Methods section.

      There is no definition of how to define the "disease" onset.

      This definition has been added to the Methods section.

      Several undefined abbreviations: "LIP", "BLT" ...

      We defined these in the text.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public reviews):

      Summary:

      In this study, Fakhar et al. use a game-theoretical framework to model interregional communication in the brain. They perform virtual lesioning using MSA to obtain a representation of the influence each node exerts on every other node, and then compare the optimal influence profiles of nodes across different communication models. Their results indicate that cortical regions within the brain's "rich club" are most influential.

      Strengths:

      Overall, the manuscript is well-written. Illustrative examples help to give the reader intuition for the approach and its implementation in this context. The analyses appear to be rigorously performed and appropriate null models are included.

      Thank you.

      Weaknesses:

      The use of game theory to model brain dynamics relies on the assumption that brain regions are similar to agents optimizing their influence, and implies competition between regions. The model can be neatly formalized, but is there biological evidence that the brain optimizes signaling in this way? This could be explored further. Specifically, it would be beneficial if the authors could clarify what the agents (brain regions) are optimizing for at the level of neurobiology - is there evidence for a relationship between regional influence and metabolic demands? Identifying a neurobiological correlate at the same scale at which the authors are modeling neural dynamics would be most compelling.

      This is a fundamental point, and we put together a new project to address it. The current work focuses on, firstly, rigorously formalizing a prevailing assumption that brain regions optimize communication, and then uncovering what are the characteristics of communication if this optimization is indeed taking place. Based on our findings, we suspect the mechanism of an optimal communication to be through broadcasting (compared to other modes explored in our work, e.g., the shortest-path signalling or diffusion). However, we recognize that our game-theoretical framework does not directly address “how” this mechanism is implemented. Thus, in our follow-up work, we are analyzing available datasets of signal propagation in the brain to see if communication dynamics there match the predictions of the game-theoretical setup. However, following your question, we extended our discussion to cover this point, cited five other works on this topic, and what, we think, could be the neurobiological mechanism of optimal signalling.  

      It is not entirely clear what Figure 6 is meant to contribute to the paper's main findings on communication. The transition to describing this Figure in line 317 is rather abrupt. The authors could more explicitly link these results to earlier analyses to make the rationale for this figure clearer. What motivated the authors' investigation into the persistence of the signal influence across steps?

      Great question. Figure 6 in part follows Figure 5, which summarizes a key aspect of our work: Signals subside at every step but not exponentially (Figure 5), and they nearly fall apart after around 6 steps (Figure 6 A and B). Subplots A and B together suggest that although measures like communicability account for all possible pathways, the network uses a handful instead, presumably to balance signalling robustness versus the energetic cost of signalling. Subplot C, one of our main findings, then shows how one simple model is all needed to predict a large portion of optimal influence compared to other models and variables. In sum, Figure 5 focused on the decay dynamics while Figure 6 focused on the extent, in terms of steps, given that the decay is monotonic. Together, our motivation for this figure was to show how the right assumption about decay rate and dynamics can outperform other measures in predicting optimal communication. 

      The authors used resting-state fMRI data to generate functional connectivity matrices, which they used to inform their model of neural dynamics. If I understand correctly, their functional connectivity matrices represent correlations in neural activity across an entire fMRI scan computed for each individual and then averaged across individuals. This approach seems limited in its ability to capture neural dynamics across time. Modeling time series data or using a sliding window FC approach to capture changes across time might make more sense as a means of informing neural dynamics.

      We agree with you on the fact that static fMRI is limited in capturing neural dynamics. However, we opted not to perform dynamic functional connectivity fitting just yet for a practical reason: Other communication models used here do not fit to any empirical data and provide a static view of the dynamics, comparable to the static functional connectivity. Since one of our goals was to compare different communication regimes, and the fact that fitting dynamics does not seem to substantially change the outcome if the end result is static (Figure 7), we decided to go with the poorer representation of neural data for this work. However, part of our follow-up project involves looking into the dynamics of influence over time and for that, we will fit our models to represent more realistic dynamics.

      The authors evaluated their model using three different structural connectomes: one inferred from diffusion spectrum imaging in humans, one inferred from anterograde tract tracing in mice, and one inferred from retrograde tract-tracing in macaque. While the human connectome is presumably an undirected network, the mouse and macaque connectomes are directed. What bearing does experimentally inferred knowledge of directionality have on the derivation of optimal influence and its interpretation?

      In terms of if directionality changes the interpretation of optimal influence, we think it sets limits for how much we can compare communication dynamics of these two types of networks. We think interpreting optimal communication in directed graphs needs to disentangle incoming influence from outgoing influence, e.g., analyzing “projector hubs/coordinators” and “receiver hubs/integrators” instead of putting both into a common class of hubs. Also, here we showed the extent of which a signal travels before it significantly degrades, having done so in an undirected graph. One of its implications for a directed graph is the possibility that some nodes can be unreachable from others, given the more restricted navigation. A possibility that we did not observe in the human connectome as all nodes could reach others, although with limited influence (see Figure 2. C). We did not explore these differences, as we used mice and macaque connectomes primarily to control for modality-specific confounds of DSI. However, our relatively poorer fit for directed networks (Supplementary Figure 2) motivated us to analyze how reciprocal connections shape dynamics and what impact do they have on networks’ function. Using the same connectomes as the current work, we addressed this question in a separate publication (Hadaeghi et al., 2024) and plan to extend both works by analyzing the signalling properties of directed networks.

      It would be useful if the authors could assess the performance of the model for other datasets. Does the model reflect changes during task engagement or in disease states in which relative nodal influence would be expected to change? The model assumes optimality, but this assumption might be violated in disease states.

      This is a wonderful idea that we initially had in mind for this work as well, but decided to dedicate a separate work on deviations in different tasks states, as well as disease states (mainly neurodegenerative disorders). We noticed the practical challenges of fitting large-scale models to task dynamics and harmonizing neuroimaging datasets of neurodegenerative disorders is beyond the scope of the current work. Unfortunately, this effort, although exciting and promising, is still pending as the corresponding author does not yet have the required expertise of neuroimaging processing pipelines.

      The MSA approach is highly computationally intensive, which the authors touch on in the Discussion section. Would it be feasible to extend this approach to task or disease conditions, which might necessitate modeling multiple states or time points, or could adaptations be made that would make this possible?

      Continuing our response from the previous point, yes, we think, in theory, the framework is applicable to both settings. Currently, our main point of concern is not the computational cost of the framework but the harmonization of the data, to ensure differences in results are not due to differences in preprocessing steps. However, assuming that all is taken care of, we believe a reasonable compute cluster should suffice by parallelizing the analytical pipeline over subjects. We acknowledge that the process would still be time-consuming, but besides the fitting process, we expect a modern high-performance CPU with about 32–64 threads to take up to 3 days analyzing one subject, given 100 brain regions or fewer. This performance then scales with the number of cluster nodes that can each work on one subject. We note that the analytical estimators such as SAR could be used instead, as it largely predicts the results from MSA. The limitations are then the lack of dynamics over time and potential estimation errors.

      Reviewer #2 (Public review):

      Summary:

      The authors provide a compelling method for characterizing communication within brain networks. The study engages important, biologically pertinent, concerns related to the balance of dynamics and structure in assessing the focal points of brain communication. The methods are clear and seem broadly applicable, however further clarity on this front is required.

      Strengths:

      The study is well-developed, providing an overall clear exposition of relevant methods, as well as in-depth validation of the key network structural and dynamical assumptions. The questions and concerns raised in reading the text were always answered in time, with straightforward figures and supplemental materials.

      Thank you.

      Weaknesses:

      The narrative structure of the work at times conflicts with the interpretability. Specifically, in the current draft, the model details are discussed and validated in succession, leading to confusion. Introducing a "base model" and "core datasets" needed for this type of analysis would greatly benefit the interpretability of the manuscript, as well as its impact.

      Following your suggestion, we modified the introduction to emphasize on the human connectome and the linear model as the main toolkit. We also added a paragraph explaining the datasets that can be used instead.

      Recommendations for the authors:

      Essential Revisions (for the authors):

      (1) The method presents an important and well-validated method for linking structural and functional networks, but it was not clear precisely what the necessary data inputs were and what assumptions about the data mattered. To improve the clarity of the presentation for the reader, it would be beneficial to have an early and explicit description of the flow of the method - what exact kinds of datasets are needed and what decisions need to be made to perform the analysis. In addition, there were questions about how the use or interpretation of the method might change with different methods of measuring structure or function, which could be answered via an explicit discussion of the issue. For example, how do undirected fMRI correlation networks compare to directed tracer injection projection networks? Similarly, could this approach apply in cases like EM connectomics with linked functional imaging that do not have full observability in both modalities?

      This is an important point that we missed addressing in detail in the original manuscript. Now we did so, by first adding a paragraph (lines 292-305, page 10) explaining the pipeline and how our framework handles different modeling choices, and then further discussing it in the Discussion (lines 733-748, page 28). Moreover, we adjusted Figure 1, by delineating two main steps of the pipeline. Briefly, we clarified that MSA is model-agnostic, meaning that, in principle, any model of neural dynamics can be used with it, from the most abstract to the most biologically detailed. Moreover, the approach extends to networks built on EM connectomics, tract-tracing, DTI, and other measures of anatomical connectivity. However, we realized that a key detail was not explicitly discussed (pointed to by Reviewer #2), that is, the fact that these models naturally need to be fitted to the empirical dataset, even though this fitting step appears not to be critical, as shown in Figure 7.

      Lines 292-305:

      “The MSA begins by defining a ‘game.’ To derive OSP, this game is formulated as a model of dynamics, such as a network of interacting nodes. These can range from abstract epidemic and excitable models (Garcia et al., 2012; Messé et al., 2015a) to detailed spiking neural networks (Pronold et al., 2023) and to mean-field models of the whole brain dynamics, as chosen here (see below). The model should ideally be fitted to reflect real data dynamics, after which MSA systematically lesions all nodes to derive the OSP. Put together, the framework is general and model-agnostic in the sense that it accommodates a wide range of network models built on different empirical datasets, from human neuroimaging and electrophysiology to invertebrate calcium imaging, and anything in between. In essence, the framework is not bound to specific modelling paradigms, allowing direct comparison among different models (e.g., see section Global Network Topology is More Influential Than Local Node Dynamics).”

      Lines 733-740:

      “As noted in the introduction, OI is model-agnostic, here, we leveraged this liberty to compare signaling under different models of local dynamics, primarily built upon undirected human connectome data. We also considered different modalities, e.g., tract tracing in Macaque (see Structural and Functional Connectomes under Materials and Methods) to confirm that the influence of weak connections is not inflated due to imaging limitations (Supplementary Figure 5. A). The game theoretical formulation of signaling allows for systematic comparison among many combinations of modeling choices and data sources.”

      We then continued with addressing the issue of full observability. We clarified that in this work, full observability was assumed. However, the mathematical foundations of our method capture unobserved contributors/influencers as an extra term, similar to the additive error term of a linear regression model. To keep the paper as non-technical as possible, we omitted expanding the axioms and the proof of how this is achieved, and instead referred to previous papers introducing the framework. 

      Lines 740-748:

      “Nonetheless, in this work, we assumed full observability, i.e., complete empirical knowledge of brain structure and function that is not necessarily practically given. Although a detailed investigation of this issue is needed, mathematical principles behind the method suggest that the framework can isolate the unobserved influences. In these cases, activity of the target node is decomposed such that the influence from the observed sources is precisely mapped, while the unobserved influences form an extra term, capturing anything that is left unaccounted for, see (Algaba et al., 2019b; Fakhar et al., 2024) for more technical details.”

      (2) The value of the normative game theoretic approach was clear, but the neurobiological interpretation was less so. To better interpret the model and understand its range of applicability, it would be useful to have a discussion of the potential neurobiological correlates that were at the same level of resolution as the modeling itself. Would such an optimization still make sense in disease states that might also be of interest?

      This is a brilliant question, which we decided to explore further in separate studies. Specifically, the link between optimal communication and brain disorders is a natural next step that we are pursuing. Here, we expanded our discussion with a few lines first explaining the roots of our main assumption, which is that neurons optimize information flow, among other goals. We then hypothesized that the biological mechanisms by which this goal is achieved include (based on our findings) adopting a broadcasting regime of signaling. We suspect that this mode of communication, operationalized on complex network topologies, is a trade-off between robust signaling and energy efficiency. Currently, we are planning practical steps to test this hypothesis.

      Lines 943-962:

      “Nonetheless, our framework is grounded in game theory where its fundamental assumption is that nodes aim at maximizing their influence over each other, given the existing constraints. This assumption is well explored using various theoretical frameworks (Buehlmann and Deco, 2010; Bullmore and Sporns, 2012; Chklovskii et al., 2002; Laughlin and Sejnowski, 2003; O’Byrne and Jerbi, 2022) and remains open to further empirical investigation. Here, we used game theory to mathematically formalize a theoretical optimum for communication in brain networks. Our findings then provide a possible mechanism for achieving this optimality through broadcasting. Based on our results, we speculate that, there exists an optimal broadcasting strength that balances robustness of the signal with its metabolic cost. This hypothesis is reminiscent of the concept of brain criticality, which suggests the brain to be positioned in a state in which the information propagates maximally and efficiently (O’Byrne and Jerbi, 2022; Safavi et al., 2024). Together, we suggest broadcasting to be the possible mechanism with which communication is optimized in brain networks, however, further research directions include investigating whether signaling within brain networks indeed aligns with a game-theoretic definition of optimality. Additionally, if it does, subsequent studies could then examine how deviations from optimal communication contribute to or result from various brain states or neurological and psychiatric disorders.”

      Reviewer #1 (Recommendations for the authors):

      I would recommend that the authors consider the following point in a revision, as well as the major weaknesses of the public review. Some aspects of Figure 1 could be clearer. What is being illustrated by the looping arrow to MSA? What is being represented in the matrices (labeling "source" and "target" on the matrix might enhance clarity)? Is R2 the metric used to assess the degree of similarity between communication models? These could be addressed by making small additions to the figure legend or to the figure itself.

      Thank you for your constructive comment on Figure 1, which is arguably the most important figure in the manuscript. We adjusted the figure and its caption (see above) based on your suggestions. After doing so, we think the figure is now clearer regarding the pipeline used in this work.

      Reviewer #2 (Recommendations for the authors):

      Overall, as stated in the public review and the short assessment, the manuscript is in a clearly mature state and brings an important method to link the fields of structural and functional brain networks.

      Nevertheless, the paper would benefit from an early, and clear, discussion of the:

      (1) components of the model, and assumptions of each, should be stated at the end of the introduction, or early in results. (2) datasets necessary to run the analysis.

      The confusion arises from lines 130-131, stating "In the present work (summarized in Figure 1), we used the human connectome, large-131 scale models of dynamics, and a game-theoretical perspective of signaling." This, to me, indicated that a structural connectivity map may be the only dataset required, as the dynamics model and game theory component are solely simulated. However, later, lines 214-216 state that the empirical functional connectivity is estimated from the structural connectivity, indicating that the method is only applied to cases where we have both.

      Finally, Supplemental Figure 5 validates a number of metrics on different solely structural networks (which is a very necessary and well-done control). Similarly, while the dynamical model is discussed in depth, and beautifully shown that the specific choice of dynamical model does not directly impact the results, it would be helpful to clarify the dynamical model utilized in the early figures.

      Thank you for pointing out a critical detail that we missed elaborating sufficiently early in the paper: the modelling step. Following your suggestions, we added a paragraph from line 292 to 305 (page 10) expanding on the modelling framework. We also explicitly divided the modelling step in Figure 1 and briefly clarified our modelling choices in the caption. Together, we emphasized the fact that our framework is generally model agnostic, which allows different models of dynamics to be plugged into various anatomical networks. We then clarified that, like in any modelling effort, one needs to first fit/optimize the model parameters to reproduce empirical data. In other words, we emphasized the fact that our framework relies on a computational model as its ‘game’ to infer how regions interact, and we fine-tuned our models to reproduce the empirical FC.

      Again, this is not a critique of the methods, which are excellent, but the presentation. It would help readers, and even me, to have a clear indication of the model earlier. Further, it would help to discuss, both in the introduction and discussion, the datasets required for applying these methods more broadly. For instance, 2-photon recordings are discussed - would it be possible to apply this method then to EM connectomes with functional data recorded for them? In theory, it seems like yes, although the current datasets have 100% observability, whereas 2-photon imaging, or other local methods, will not have perfect overlap between structural and functional connectomes. Discussions like this, related to the assumptions of the model, the necessary datasets, and broader application directions beyond DSI, fMRI, and BOLD cases where the method was validated, would increase the impact and interpretability for a broad readership.

      This is a valid point that we should have been more explicit about. The revised manuscript now contains a paragraph (lines 740-748) clarifying the fact that, throughout this work, we assumed full observability. We then briefly discuss, based on the mathematical principles of the framework, what we expect to happen in cases with partial observability. We then point at two references in which the details of a framework with partial observability are laid out, one containing mathematical proofs and the other using numerical simulations.

      References:

      Hadaeghi, F., Fakhar, K., & Hilgetag, C. C. (2024). Controlling Reciprocity in Binary and Weighted Networks: A Novel Density-Conserving Approach (p. 2024.11.24.625064). bioRxiv. https://doi.org/10.1101/2024.11.24.625064

    1. Author response:

      The following is the authors’ response to the original reviews.

      Alternate explanations for major conclusions.

      The major conclusions are (a) surface motility of W3110 requires pili which is not novel, (b) pili synthesis and pili-dependent surface motility require putrescine — 1 mM is optimal, and 4 mM is inhibitory, and (c) the existence of a putrescine homeostatic network that maintains intracellular putrescine that involves compensatory mechanisms for low putrescine, including diversion of energy generation toward putrescine synthesis.

      Conclusion a: Reviewer 3 suggests that the mutant may have lost surface motility because of outer surface structures that actually mediate motility but are co-regulated with or depend on pili synthesis. The reviewer explicitly suggests flagella as the alternate appendage, although flagella and pili are reciprocally regulated. Most experiments were performed in a Δ_fliC_ background, which lacks the major flagella subunit, in order to prevent the generation of fast-moving flagella-dependent variants. Furthermore, no other surface structure that could mediate surface motility is apparent in the electron microscope images. This observation does not definitively rule out this possibility, especially because of the large transcriptomic changes with low putrescine. Our explanation is the simplest.

      Conclusion b, first comment: Reviewer 1 states that “it is not possible to conclude that the effects of gene deletions to biosynthetic, transport or catabolic genes on pili-dependent surface motility are due to changes in putrescine levels unless one takes it on faith that there must be changes to putrescine levels.” The comment ignores both the nutritional supplementation and the transcript changes that strongly suggest compensatory mechanisms for low putrescine. Why compensate if the putrescine concentration does not change? The reviewer then implicitly acknowledges changes in putrescine content: “it is important to know how much putrescine must be depleted in order to exert a physiological effect”.

      Conclusion b, second comment: Reviewer 1 proposes that agmatine accumulation can account for some of the observed properties, but which property is not specified. With respect to motility, agmatine accumulation cannot account for motility defects because motility is impaired in (a) a speA mutant which cannot make agmatine and (b) a speC speF double mutant which should not accumulate agmatine. With respect to the transcriptomic results, even if high agmatine is the reason for some transcript changes, the results still suggest a putrescine homeostasis network.

      Conclusion c: the reviewers made no comments on the RNAseq analysis or the interpretation of the existence of a homeostatic network.

      Additional experiments proposed.

      Complementation. Reviewers 1 and 3 suggested complementation experiments, but the latter states that nutritional supplementation strengthens our arguments. The most relevant complementation is with speB.  We tried complementation and found that our control plasmid inhibited motility by increasing the lag time before movement commenced. A plasmid with speB did stimulate motility relative to the control plasmid, but movement with the speB plasmid took 4 days, while wild-type movement took 1.5 days. We think that interpretation of this result is ambiguous. We did not systematically search for plasmids that had no effect on motility.

      The purpose of complementation is to determine whether a second-site mutation is the actual cause of the motility defect. In this case, the artifact is that an alteration in polyamine metabolism is not the cause of the defect. However, external putrescine reverses the effects on motility and pili synthesis in the speB mutant. This result is inconsistent with a second-site mutation. Still, we agree that complementation is important, and because of our difficulties, we tested numerous mutants with defects in polyamine metabolism. The results present an interpretable and coherent pattern. For example, if putrescine is not the regulator, then mutants in putrescine transport and catabolism should have had no effect. Every single mutant is consistent with a role in movement and pili synthesis. The simplest explanation is that putrescine affects movement and pili synthesis.

      Phase variation. Reviewer 2 noted that we did not discuss phase variation. The comment came from the observation that the speB mutant had fewer fimB transcripts which could explain the loss of motility. The reviewer also suggested a simple experiment, which we performed and found that putrescine does not control phase variation. We present those results in the supplemental material. Our discussion of this topic includes a major qualification.

      Testing of additional strains. Published results from another lab showed that surface motility of MG1655 requires spermidine instead of putrescine (PMID 19493013 and 21266585). MG1655 and the W3110 that we used in our study are E. coli K-12 derivatives and phylogenetic group A. Any number of changes in enzymes that affect intracellular putrescine concentration could result in different responses to putrescine. We are currently studying pili synthesis and motility in other strains. While that study is incomplete, loss of speB in a strain of phylogenetic group D eliminates no surface motility. This work was intended as our initial analysis and the focus was on a single strain.

      Measuring intracellular polyamines. We felt that we had provided sufficient evidence to conclude that putrescine controls pili synthesis and putrescine concentrations are lower in the speB mutant: the nutritional supplementation, the lower levels of transcripts for putrescine catabolic enzymes which require putrescine for their expression strongly suggest lower putrescine in a mutant lacking a putrescine biosynthesis gene, and a transcriptomic analysis that found the speB mutant had transcript changes to compensate for low putrescine. We understand the importance of measuring intracellular polyamines. We are currently examining the quantitative relationship between intracellular polyamines and pili synthesis in multiple strains which respond differently to loss of speB.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The authors should measure putrescine, agmatine, cadaverine, and spermidine levels in their gene deletion strains.

      Polyamine concentration measurements will be part of a separate study on polyamine control of pili synthesis of a uropathogenic strain. A comparison is essential, and the results from W3110 will be part of that study.

      Reviewer #2 (Recommendations for the authors):

      (1) Line 28. Your statements about urinary tract infections are pure speculation. They are fine for the discussion, but should not be in the abstract.

      The abstract from line 27 on has been reworked. The comment of the reviewer is fair.

      (2) Line 65. Do we need this discussion about the various strains? If you keep it, you should point out that they were all W3110 strains. But you could just say that you confirmed that your background strain can do PDSM (since you are also not showing any data for the other isolates). Discussing the various strains implies that you are not confident in your strain and raises the question of why you didn't use a sequenced wt MG1655, or something like that.

      This section has been reworked. Our strain of W3110 has an insertion in fimB which is relevant for movement but does not affect our results. The insertion limits our conclusions about phase variation. We want to point out that strains variations are large. We also sequenced our strain of W3110.

      (3) Related. You occasionally use "W3110-LR" to designate the wild type. You use this or not, but be consistent throughout the text.

      Fixed

      (4) Line 99. Does eLife allow "data not shown"?  

      (5) Line 119. As you note, the phenotype of the puuA patA double mutant is exactly the opposite of what one would expect. Although you provide additional evidence that high levels also inhibit motility, complementing the double mutant would provide confidence that the strain is correct.

      We rapidly ran into issues with complementation which are discussed in public responses to reviewer comments.

      (6) Figure 6C. Either you need to quantify these data or you need a better picture.

      The files were corrupted. It was repeated several time, but we lost the other data.

      (7) Figure 7. Label panels A and B to indicate that these strains are speB. Also, you need to switch panels C and D to match the order of discussion in the manuscript.

      Done

      (8) Line 134. Is there a statistically significant difference in the ELISA between 1 and 4 mM? You need to say one way or the other.

      No statistical significance and this has been added to the paper

      (9) Figure 10C. You need to quantify these data.

      Quantification added as an extra panel.

      (10) Line 164. You include H-NS in the group of "positive effectors that control fim operon expression" and you reference Ecocyc, rather than any primary reference. Nowhere in the manuscript do you mention phase variation. In the speB mutant, you see decreased fimB, increased fimE, and decreased hns expression. My interpretation of the literature suggests that this would drive the fim switch to the off-state. This could certainly explain some of the results. It is also easily measurable with PCR. This might require testing cells scraped directly from the plates.

      The experiments were performed. There is no need to scrap cells from plates because the fimB result from RNAseq was from a liquid culture, and the prediction would be that the phase-locking should be evident in these cells.

      (11) Figure 10. Likewise, do you know that your hns mutant is not locked in the off-state? Granted, the original hns mutants (pilG) showed increased rates of switching, but growth conditions might matter.

      We also did phase variation for the hns mutant and the hns mutant was not phase locked. This result is shown. In addition to growth conditions, the strain probably matters.

      (12) Line 342. You describe the total genome sequencing of W3110, yet this is not mentioned anywhere else in the manuscript.

      It is now

      Minor points:

      (13) Line 192. "One of the most differentially expressed genes...".

      (14) Line 202. "...implicates extracellular putrescine in putrescine homeostasis."

      (15) Line 209. "...potential pili regulators...".

      (16) You are using a variety of fonts on the figures. Pick one.

      (17) Figure 9A. It took me a few minutes to figure out the labeling for this figure and I was more confused after reading the legend. It would be simpler to independently label red triangles, blue triangles, red circles, and blue circles.

      (18) Figure 9B and 10. The reader can likely figure out what W3110_1.0_3 means, but more straightforward labeling would be better, or you need to define these labels.

      All points were addressed and fixed.

      Reviewer #3 (Recommendations for the authors):

      Other comments:

      (1) Please go through the figures and the reference to figures in the text, as they often do not refer to the right panel (ex: figures 2 and 7 for instance). In the text, please homogenize the reference to figures (Figure 2C vs Figure 3). To help compare motility experiments between figures, please use the same scale in all figures.

      This has been fixed.

      (2) Lines 65-70: I am not sure I get the reason behind choosing the W3110 strain from your lab stock. In what background were the initial mutants constructed (from l.64-65)? Were the nine strains tested, all variations of W3110? If so, is the phenotype described in the manuscript robust in all strains?

      We have provided more explanation. W3110 was the most stable: insertions that allowed flagella synthesis in the presence of glucose were frequent. We deleted the major flagella subunit for most experiments. Before introduction of the fliC deletion, we needed to perform experiments 10 times so that fast-moving variants, which had mutationally altered flagella synthesis, did not complicate results.

      (3) Line 82-84: As stated in the public review, I think more controls are needed before making this conclusion, especially as type I fimbriae are usually involved in sessile phenotypes.

      Response provided in the public response.

      (4) In Figure 3: Changing the order of the image to follow the text would make the figure easier to follow.

      Fixed as requested

      (5) Lines 100-101: simultaneous - the results presented here do not support this conclusion. In Figure 4b, the addition of putrescine to speB mutants is actually not different from WT. From the results, it seems like one of biosynthesis or transport is needed, but it's not clear if both are needed simultaneously. For this, a mutant with no biosynthesis and no transport is needed and/or completely non-motile mutants would be needed to compare.

      We disagree. If there are two pathways of putrescine synthesis and both are needed, then our conclusion follows.

      (6) Lines 104-105: '... because E. coli secretes putrescine.' - not sure why this statement is there, as most transporters tested after are importers of putrescine? It is also not clear to me if putrescine is supplemented in the media in these experiments. If not, is there putrescine in the GT media?

      Good points, and this section has been reworded to clarify these issues. Some of the material was moved to the discussion.

      (7) Line 109: 'We note that potE and plaP are more highly expressed than potE and puuP...' - first potE should be potF?

      This has been corrected.

      (8) Figure 8: What is the difference between the TEM images in Figure 1 and here? The WT in Figure 1 does show pili without the supplementation unless I'm missing something here. Please specify.

      The reviewer means Figure 2 and not Figure 1. Figure 2 shows a wild-type strain which has both putrescine anabolic pathways while Figure 8 is the ΔspeB strain which lacks one pathway.

      (9) Line160-162: Transcripts for the putrescine-responsive puuAP and puuDRCBE operons, which specify genes of the major putrescine catabolic pathway, were reduced from 1.6- to 14- fold (FDR {less than or equal to} 0.02) in the speB mutant (Supplemental Table 1), which implies lower intracellular putrescine. I might not get exactly the point here. If the catabolic pathways are repressed in the speB mutant, then there will be less degradation which means more putrescine!?

      Expression of these genes is a function of intracellular putrescine: higher expression means more putrescine. Any discussion of steady putrescine must include the anabolic pathways: the catabolic pathways do not determine the intracellular putrescine, they are a reflection of intracellular putrescine.

      (10) Lines 162-163: Deletion of speB reduced transcripts for genes of the fimA operon and fimE, but not of fimB. It seems that the results suggest the opposite a reduction of fimB but not fimE!?

      The reviewer is correct, and it is our mistake, and the text now states what is in the figure..

    1. Reviewer #1 (Public review):

      Summary:

      In this interesting and original paper, the authors examine the effect that heat stress can have on the ability of bacterial cells to evade infection by lytic bacteriophages. Briefly, the authors show that heat stress increases the tolerance of Klebsiella pneumoniae to infection by the lytic phage Kp11. They also argue that this increased tolerance facilitates the evolution of genetically encoded resistance to the phage. In addition, they show that heat can reduce the efficacy of phage therapy. Moreover, they define a likely mechanistic reason for both tolerance and genetically encoded resistance. Both lead to a reorganization of the bacterial cell envelope, which reduces the likelihood that phage can successfully inject their DNA.

      Strengths:

      I found large parts of this paper well-written and clearly presented. I also found many of the experiments simple yet compelling. For example, the experiments described in Figure 3 clearly show that prior heat exposure can affect the efficacy of phage therapy. In addition, the experiments shown in Figures 4 and 6 clearly demonstrate the likely mechanistic cause of this effect. The conceptual Figure 7 is clear and illustrates the main ideas well. I think this paper would work even without its central claim, namely that tolerance facilitates the evolution of resistance. The reason is that the effect of environmental stressors on stress tolerance has to my knowledge so far only been shown for drug tolerance, not for tolerance to an antagonistic species.

      Weaknesses:

      I did not detect any weaknesses that would require a major reorganization of the paper, or that may require crucial new experiments. However, the paper needs some work in clarifying specific and central conclusions that the authors draw. More specifically, it needs to improve the connection between what is shown in some figures, how these figures are described in the caption, and how they are discussed in the main text. This is especially glaring with respect to the central claim of the paper from the title, namely that tolerance facilitates the evolution of resistance. I am sympathetic to that claim, especially because this has been shown elsewhere, not for phage resistance but for antibiotic resistance. However, in the description of the results, this is perhaps the weakest aspect of the paper, so I'm a bit mystified as to why the authors focus on this claim. As I mentioned above, the paper could stand on its own even without this claim.

      More specific examples where clarification is needed:

      (1) A key figure of the paper seems to be Figure 2D, yet it was one of the most confusing figures. This results from a mismatch between the accompanying text starting on line 92 and the figure itself. The first thing that the reader notices in the figure itself is the huge discrepancy between the number of viable colonies in the absence of phage infection at the two-hour time point. Yet this observation is not even mentioned in the main text. The exclusive focus of the main text seems to be on the right-hand side of the figure, labeled "+Phage". It is from this right-hand panel that the authors seem to conclude that heat stress facilitates the evolution of resistance. I find this confusing, because there is no difference between the heat-treated and non-treated cells in survivorship, and it is not clear from this data that survivorship is caused by resistance, not by tolerance/persistence. (The difference between tolerance and resistance has only been shown in the independent experiments of Figure 1B.) Figure 2F supports the resistance claim, but it is not one of the strongest experiments of the paper, because the author simply only used "turbidity" as an indicator of resistance. In addition, the authors performed the experiments described therein at small population sizes to avoid the presence of resistance mutations. But how do we know that the turbidity they describe does not result from persisters?

      I see three possibilities to address these issues. First, perhaps this is all a matter of explaining and motivating this particular experiment better. Second, the central claim of the paper may require additional experiments. For example, is it possible to block heat induced tolerance through specific mutations, and show that phage resistance does not evolve as rapidly if tolerance is blocked? A third possibility is to tone down the claim of the paper, and make it about heat tolerance rather than the evolution of heat resistance.

      A minor but general point here is that in Figure 2D and in other figures, the labels "-phage" and "+phage" do not facilitate understanding, because they suggest that cells in the "-phage" treatment have not been exposed to phage at all, but that is not the case. They have survived previous phage treatment and are then replated on media lacking phage.

      (2) Another figure with a mismatch between text and visual materials is Figure 5, specifically Figures 5B-F. The figure is about two different mutants, and it is not even mentioned in the text how these mutants were identified, for example in different or the same replicate populations. What is more, the two mutants are not discussed at all in the main text. That is, the text, starting on line 221 discusses these experiments as if there was only one mutant. This is especially striking as the two mutants behave very differently, as, for example, in Figure 5C. Implicitly, the text talks about the mutant ending in "...C2", and not the one ending in "...C1". To add to the confusion, the text states that the (C2) mutant shows a change in the pspA gene, but in Figure 5f, it is the other (undiscussed) mutant that has a mutation in this gene. Only pspA is discussed further, so what about the other mutants? More generally, it is hard to believe that these were the only mutants that occurred in the genome during experimental evolution. It would be useful to give the reader a 2-3 sentence summary of the genetic diversity that experimental evolution generated.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      This manuscript presents an interesting exploration of the potential activation mechanisms of DLK following axonal injury. While the experiments are beautifully conducted and the data are solid, I feel that there is insufficient evidence to fully support the conclusions made by the authors.

      In this manuscript, the authors exclusively use the puc-lacZ reporter to determine the activation of DLK. This reporter has been shown to be induced when DLK is activated.

      However, there is insufficient evidence to confirm that the absence of reporter activation necessarily indicates that DLK is inactive. As with many MAP kinase pathways, the DLK pathway can be locally or globally activated in neurons, and the level of DLK activation may depend on the strength of the stimulation. This reporter might only reflect strong DLK activation and may not be turned on if DLK is weakly activated. The results presented in this manuscript support this interpretation. Strong stimulation, such as axotomy of all synaptic branches, caused robust DLK activation, as indicated by puc-lacZ expression. In contrast, weak stimulation, such as axotomy of some synaptic branches, resulted in weaker DLK activation, which did not induce the puc-lacZ reporter. This suggests that the strength of DLK activation depends on the severity of the injury rather than the presence of intact synapses. Given that this is a central conclusion of the study, it may be worthwhile to confirm this further. Alternatively, the authors may consider refining their conclusion to better align with the evidence presented.

      In Figure 1E we have replotted the puc-lacZ data to show comparisons between different injuries that leave different numbers of spared (or lost) boutons and branches.  We observed no differences between injuries that remove only a small fraction of boutons (injury location (a)) and injuries that remove nearly all of them (injury locations (b) and (c)) and uninjured neurons (Figure 1E). These observations argue against the interpretation that the strength of DLK activation (at least within the cell body) depends on the severity of injury. Rather, puc-lacZ induction appears to be bimodal. It is either induced (in various injuries that remove all synaptic boutons), or not induced, including in injuries that spared only a small fraction of the total boutons. We therefore think that the presence of a remaining synaptic connection rather than the extent of the injury per se is a major determinant of whether the cell body component of Wnd signaling can be activated. 

      The reviewer (and others) fairly point out that our current study focuses on puc-lacZ as a reporter of Wnd signaling in the cell body. We consider this to be a downstream integration of events in axons that are more challenging to detect. It is striking that this integration appears strongly sensitized to the presence of spared synaptic boutons. Examination of Wnd’s activation in axons and synapses is a goal for our future work.

      As noted by the authors, DLK has been implicated in both axon regeneration and degeneration. Following axotomy, DLK activation can lead to the degeneration of distal axons, where synapses are located. This raises an important question: how is DLK activated in distal axons? The authors might consider discussing the significance of this "synapse connection-dependent" DLK activation in the broader context of DLK function and activation mechanisms.

      While it has been noted that inhibition of DLK can mildly delay Wallerian degeneration (Miller et al., 2009), this does not appear to be the case for retinal ganglion cell axons following optic nerve crush (Fernandes et al., 2014). It is also not the case for Drosophila motoneurons and NMJ terminals following peripheral nerve injury (Xiong et al., 2012; Xiong and Collins, 2012). Instead, overexpression of Wnd or activation of Wnd by a conditioning injury leads to an opposite phenotype - an increase in resiliency to Wallerian degeneration for axons that have been previously injured (Xiong et al., 2012; Xiong and Collins, 2012). The downstream outcome of Wnd activation is highly dependent on the context; it may be an integration of the outcomes of local Wnd/DLK activation in axons with downstream consequences of nuclear/cell body signaling.  The current study suggests some rules for the cell body signaling, however, how Wnd is regulated at synapses and why it promotes degeneration in some circumstances but not others are important future questions.

      For the reviewer’s suggestion, it is interesting to consider DLK’s potential contributions to the loss of NMJ synapses in a mouse model of ALS (Le Pichon et al., 2017; Wlaschin et al., 2023). Our findings suggest that the synaptic terminal is an important locus of DLK regulation, while dysfunction of NMJ terminals is an important feature of the ‘dying back’ hypothesis of disease etiology (Dadon-Nachum et al., 2011; Verma et al., 2022). We propose that the regulation of DLK at synaptic terminals is an important area for future study, and may reveal how DLK might be modulated to curtail disease progression. Of note, DLK inhibitors are in clinical trials (Katz et al., 2022; Le et al., 2023; Siu et al., 2018), but at least some have been paused due to safety concerns (Katz et al., 2022). Further understanding of the mechanisms that regulate DLK are needed to understand whether and how DLK and its downstream signaling can be tuned for therapeutic benefit.

      Reviewer #2 (Public review):

      Summary:

      The authors study a panel of sparsely labeled neuronal lines in Drosophila that each form multiple synapses. Critically, each axonal branch can be injured without affecting the others, allowing the authors to differentiate between injuries that affect all axonal branches versus those that do not, creating spared branches. Axonal injuries are known to cause Wnd (mammalian DLK)-dependent retrograde signals to the cell body, culminating in a transcriptional response. This work identifies a fascinating new phenomenon that this injury response is not all-or-none. If even a single branch remains uninjured, the injury signal is not activated in the cell body. The authors rule out that this could be due to changes in the abundance of Wnd (perhaps if incrementally activated at each injured branch) by Wnd, Hiw's known negative regulator. Thus there is both a yet-undiscovered mechanism to regulate Wnd signaling, and more broadly a mechanism by which the neuron can integrate the degree of injury it has sustained. It will now be important to tease apart the mechanism(s) of this fascinating phenomenon. But even absent a clear mechanism, this is a new biology that will inform the interpretation of injury signaling studies across species.

      Strengths:

      (1) A conceptually beautiful series of experiments that reveal a fascinating new phenomenon is described, with clear implications (as the authors discuss in their Discussion) for injury signaling in mammals.

      (2) Suggests a new mode of Wnd regulation, independent of Hiw.

      Weaknesses:

      (1) The use of a somatic transcriptional reporter for Wnd activity is powerful, however, the reporter indicates whether the transcriptional response was activated, not whether the injury signal was received. It remains possible that Wnd is still activated in the case of a spared branch, but that this activation is either local within the axons (impossible to determine in the absence of a local reporter) or that the retrograde signal was indeed generated but it was somehow insufficient to activate transcription when it entered the cell body. This is more of a mechanistic detail and should not detract from the overall importance of the study

      We agree. The puc-lacZ reporter tells us about signaling in the cell body, but whether and how Wnd is regulated in axons and synaptic branches, which we think occurs upstream of the cell body response, remains to be addressed in future studies.

      (2) That the protective effect of a spared branch is independent of Hiw, the known negative regulator of Wnd, is fascinating. But this leaves open a key question: what is the signal?

      This is indeed an important future question, and would still be a question even if Hiw were part of the protective mechanism by the spared synaptic branch. Our current hypothesis (outlined in Figure 4) is that regulation of Wnd is tied to the retrograde trafficking of a signaling organelle in axons. The Hiw-independent regulation complements other observations in the literature that multiple pathways regulate Wnd/DLK (Collins et al., 2006; Feoktistov and Herman, 2016; Klinedinst et al., 2013; Li et al., 2017; Russo and DiAntonio, 2019; Valakh et al., 2013). It is logical for this critical stress response pathway to have multiple modes of regulation that may act in parallel to tune and restrain its activation. 

      Reviewer #3 (Public review):

      Summary:

      This manuscript seeks to understand how nerve injury-induced signaling to the nucleus is influenced, and it establishes a new location where these principles can be studied. By identifying and mapping specific bifurcated neuronal innervations in the Drosophila larvae, and using laser axotomy to localize the injury, the authors find that sparing a branch of a complex muscular innervation is enough to impair Wallenda-puc (analogous to DLK-JNKcJun) signaling that is known to promote regeneration. It is only when all connections to the target are disconnected that cJun-transcriptional activation occurs.

      Overall, this is a thorough and well-performed investigation of the mechanism of sparedbranch influence on axon injury signaling. The findings on control of wnd are important because this is a very widely used injury signaling pathway across species and injury models. The authors present detailed and carefully executed experiments to support their conclusions. Their effort to identify the control mechanism is admirable and will be of aid to the field as they continue to try to understand how to promote better regeneration of axons.

      Strengths:

      The paper does a very comprehensive job of investigating this phenomenon at multiple locations and through both pinpoint laser injury as well as larger crush models. They identify a non-hiw based restraint mechanism of the wnd-puc signaling axis that presumably originates from the spared terminal. They also present a large list of tests they performed to identify the actual restraint mechanism from the spared branch, which has ruled out many of the most likely explanations. This is an extremely important set of information to report, to guide future investigators in this and other model organisms on mechanisms by which regeneration signaling is controlled (or not).

      Weaknesses:

      The weakest data presented by this manuscript is the study of the actual amounts of Wallenda protein in the axon. The authors argue that increased Wnd protein is being anterogradely delivered from the soma, but no support for this is given. Whether this change is due to transcription/translation, protein stability, transport, or other means is not investigated in this work. However, because this point is not central to the arguments in the paper, it is only a minor critique.

      We agree and are glad that the reviewer considers this a minor critique; this is an area for future study. In Supplemental Figure 1 we present differences in the levels of an ectopically expressed GFP-Wnd-kinase-dead transgene, which is strikingly increased in axons that have received a full but not partial axotomy. We suspect this accumulation occurs downstream of the cell body response because of the timing. We observed the accumulations after 24 hours (Figure S1F) but not at early (1-4 hour) time points following axotomy (data not shown). Further study of the local regulation of Wnd protein and its kinase activity in axons is an important future direction.

      As far as the scope of impact: because the conclusions of the paper are focused on a single (albeit well-validated) reporter in different types of motor neurons, it is hard to determine whether the mechanism of spared branch inhibition of regeneration requires wnd-puc (DLK/cJun) signaling in all contexts (for example, sensory axons or interneurons). Is the nerve-muscle connection the rule or the exception in terms of regeneration program activation?

      DLK signaling is strongly activated in DRG sensory neurons following peripheral nerve injury (Shin et al., 2012), despite the fact that sensory neurons have bifurcated axons and their projections in the dorsal spinal cord are not directly damaged by injuries to the peripheral nerve. Therefore it is unlikely that protection by a spared synapse is a universal rule for all neuron types. However the molecular mechanisms that underlie this regulation may indeed be shared across different types of neurons but utilized in different ways. For instance, nerve growth factor withdrawal can lead to activation of DLK (Ghosh et al., 2011), however neurotrophins and their receptors are regulated and implemented differently in different cell types. We suspect that the restraint of Wnd signaling by the spared synaptic branch shares a common underlying mechanism with the restraint of DLK signaling by neurotrophin signaling. Further elucidation of the molecular mechanism is an important next step towards addressing this question. 

      Because changes in puc-lacZ intensity are the major readout, it would be helpful to better explain the significance of the amount of puc-lacZ in the nucleus with respect to the activation of regeneration. Is it known that scaling up the amount of puc-lacZ transcription scales functional responses (regeneration or others)? The alternative would be that only a small amount of puc-lacZ is sufficient to efficiently induce relevant pathways (threshold response).

      While induction of puc-lacZ expression correlates with Wnd-mediated phenotypes, including sprouting of injured axons (Xiong et al., 2010), protection from Wallerian degeneration (Xiong et al., 2012; Xiong and Collins, 2012) and synaptic overgrowth (Collins et al., 2006), we have not observed any correlation between the degree of puc-lacZ induction (eg modest, medium or high) and the phenotypic outcomes (sprouting, overgrowth, etc). Rather, there appears to be a striking all-or-none difference in whether puc-lacZ is induced or not induced. There may indeed be a threshold that can be restrained through multiple mechanisms. We posit in figure 4 that restraint may take place in the cell body, where it can be influenced by the spared bifurcation. 

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      This is a beautiful study. Naturally, you're searching now for the underlying mechanism.

      A few questions:

      (1) At present you can not determine if the Wnd signal is never initiated (when a spared branch is present) or if it gets to the cell body but is incapable of activating the puckered reporter. Is there any optical reporter (JNK activation?) that could differentiate this?

      The reviewer is correct that a tool to detect local activity of JNK kinase in axons would be ideal for probing the mechanisms that underlie our observations. A FRET reporter for JNK kinase activity has been developed and utilized in cultured cells (Fosbrink et al. 2010). It would be interesting to implement this reporter in Drosophila; it would need to be sensitive enough to visualize  in single Drosophila axons. We have previously noted Wnd-dependent phosphorylated JNK in the cell body of injured motoneurons following nerve crush (Xiong et al., 2010). However anti-pJNK antibodies detect what appears to be a constitutive signal in uninjured axons that does not appear to be influenced by activation or inhibition of Wnd (Xiong et al., 2010).

      (2) What happens when you injure the axon in a dSarm KO? This is more of a curiosity, not a necessity, but is it the axon dying or the detection of the injury itself?

      We have tested whether overexpression of Nmnat or the WldS transgene, which inhibit Wallerian degeneration of injured axons, affect the induction of puc-lacZ following nerve injury. This manipulation has no effect on puc-lacZ expression in uninjured animals, and also has no effect on the induction of puc-lacZ following peripheral nerve crush (TJ Waller, personal communication).

      (3) Are Wnd rescue experiments possible in this context? Would be an interesting place to do Wnd structure-function and compare it to the synaptic work.

      This is not possible with current reagents. Expression of wild type wnd cDNA under the Gal4/UAS promoter leads to strong induction of puc-lacZ in uninjured animals, even when weak Gal4 driver lines are used (Xiong et al., 2012, 2010). Similar observations of constitutively active signaling have been observed for expression studies of DLK in mammalian cells ((Hao et al., 2016; Huntwork-Rodriguez et al., 2013; Nihalani et al., 2000), and data not shown). These and other observations suggest that the levels of Wnd/DLK protein are tightly controlled by posttranscriptional mechanisms. Delineation of sequences within Wnd/DLK that are required for its regulation would be helpful for addressing this question.

      This will be required reading in my lab.

      That is an honor. We look forward to help from the field to understand how and why this pathway is restrained at synapses. Your students may bring new ideas to the table.

      Reviewer #3 (Recommendations for the authors):

      Piezo is spelled incorrectly in the supplemental table in multiple places.

      Thank you for pointing this out! We have made the correction.

      References cited (in rebuttal)

      Collins CA, Wairkar YP, Johnson SL, DiAntonio A. 2006. Highwire restrains synaptic growth by attenuating a MAP kinase signal. Neuron 51:57–69.

      Dadon-Nachum M, Melamed E, Offen D. 2011. The “dying-back” phenomenon of motor neurons in ALS. J Mol Neurosci 43:470–477.

      Feoktistov AI, Herman TG. 2016. Wallenda/DLK protein levels are temporally downregulated by Tramtrack69 to allow R7 growth cones to become stationary boutons. Development 143:2983–2993.

      Fernandes KA, Harder JM, John SW, Shrager P, Libby RT. 2014. DLK-dependent signaling is important for somal but not axonal degeneration of retinal ganglion cells following axonal injury. Neurobiol Dis 69:108–116.

      Ghosh AS, Wang B, Pozniak CD, Chen M, Watts RJ, Lewcock JW. 2011. DLK induces developmental neuronal degeneration via selective regulation of proapoptotic JNK activity. J Cell Biol 194:751–764.

      Hao Y, Frey E, Yoon C, Wong H, Nestorovski D, Holzman LB, Giger RJ, DiAntonio A, Collins C. 2016. An evolutionarily conserved mechanism for cAMP elicited axonal regeneration involves direct activation of the dual leucine zipper kinase DLK. Elife 5. doi:10.7554/eLife.14048

      Huntwork-Rodriguez S, Wang B, Watkins T, Ghosh AS, Pozniak CD, Bustos D, Newton K, Kirkpatrick DS, Lewcock JW. 2013. JNK-mediated phosphorylation of DLK suppresses its ubiquitination to promote neuronal apoptosis. J Cell Biol 202:747–763.

      Katz JS, Rothstein JD, Cudkowicz ME, Genge A, Oskarsson B, Hains AB, Chen C, Galanter J, Burgess BL, Cho W, Kerchner GA, Yeh FL, Ghosh AS, Cheeti S, Brooks L, Honigberg L, Couch JA, Rothenberg ME, Brunstein F, Sharma KR, van den Berg L, Berry JD, Glass JD. 2022. A Phase 1 study of GDC-0134, a dual leucine zipper kinase inhibitor, in ALS. Ann Clin Transl Neurol 9:50–66.

      Klinedinst S, Wang X, Xiong X, Haenfler JM, Collins CA. 2013. Independent pathways downstream of the Wnd/DLK MAPKKK regulate synaptic structure, axonal transport, and injury signaling. J Neurosci 33:12764–12778.

      Le K, Soth MJ, Cross JB, Liu G, Ray WJ, Ma J, Goodwani SG, Acton PJ, Buggia-Prevot V, Akkermans O, Barker J, Conner ML, Jiang Y, Liu Z, McEwan P, Warner-Schmidt J, Xu A, Zebisch M, Heijnen CJ, Abrahams B, Jones P. 2023. Discovery of IACS-52825, a potent and selective DLK inhibitor for treatment of chemotherapy-induced peripheral neuropathy. J Med Chem 66:9954–9971.

      Le Pichon CE, Meilandt WJ, Dominguez S, Solanoy H, Lin H, Ngu H, Gogineni A, Sengupta Ghosh A, Jiang Z, Lee S-H, Maloney J, Gandham VD, Pozniak CD, Wang B, Lee S, Siu M, Patel S, Modrusan Z, Liu X, Rudhard Y, Baca M, Gustafson A, Kaminker J, Carano RAD, Huang EJ, Foreman O, Weimer R, Scearce-Levie K, Lewcock JW. 2017. Loss of dual leucine zipper kinase signaling is protective in animal models of neurodegenerative disease. Sci Transl Med 9. doi:10.1126/scitranslmed.aag0394

      Li J, Zhang YV, Asghari Adib E, Stanchev DT, Xiong X, Klinedinst S, Soppina P, Jahn TR, Hume RI, Rasse TM, Collins CA. 2017. Restraint of presynaptic protein levels by Wnd/DLK signaling mediates synaptic defects associated with the kinesin-3 motor Unc-104. Elife 6. doi:10.7554/eLife.24271

      Miller BR, Press C, Daniels RW, Sasaki Y, Milbrandt J, DiAntonio A. 2009. A dual leucine kinase-dependent axon self-destruction program promotes Wallerian degeneration. Nat Neurosci 12:387–389.

      Nihalani D, Merritt S, Holzman LB. 2000. Identification of structural and functional domains in mixed lineage kinase dual leucine zipper-bearing kinase required for complex formation and stress-activated protein kinase activation. J Biol Chem 275:7273–7279.

      Russo A, DiAntonio A. 2019. Wnd/DLK is a critical target of FMRP responsible for neurodevelopmental and behavior defects in the Drosophila model of fragile X syndrome. Cell Rep 28:2581–2593.e5.

      Shin JE, Cho Y, Beirowski B, Milbrandt J, Cavalli V, DiAntonio A. 2012. Dual leucine zipper kinase is required for retrograde injury signaling and axonal regeneration. Neuron 74:1015– 1022.

      Siu M, Sengupta Ghosh A, Lewcock JW. 2018. Dual Leucine Zipper Kinase Inhibitors for the Treatment of Neurodegeneration. J Med Chem 61:8078–8087.

      Valakh V, Walker LJ, Skeath JB, DiAntonio A. 2013. Loss of the spectraplakin short stop activates the DLK injury response pathway in Drosophila. J Neurosci 33:17863–17873.

      Verma S, Khurana S, Vats A, Sahu B, Ganguly NK, Chakraborti P, Gourie-Devi M, Taneja V. 2022. Neuromuscular junction dysfunction in amyotrophic lateral sclerosis. Mol Neurobiol 59:1502–1527.

      Wlaschin JJ, Donahue C, Gluski J, Osborne JF, Ramos LM, Silberberg H, Le Pichon CE. 2023. Promoting regeneration while blocking cell death preserves motor neuron function in a model of ALS. Brain 146:2016–2028.

      Xiong X, Collins CA. 2012. A conditioning lesion protects axons from degeneration via the Wallenda/DLK MAP kinase signaling cascade. J Neurosci 32:610–615.

      Xiong X, Hao Y, Sun K, Li J, Li X, Mishra B, Soppina P, Wu C, Hume RI, Collins CA. 2012. The Highwire ubiquitin ligase promotes axonal degeneration by tuning levels of Nmnat protein. PLoS Biol 10:e1001440.

      Xiong X, Wang X, Ewanek R, Bhat P, Diantonio A, Collins CA. 2010. Protein turnover of the Wallenda/DLK kinase regulates a retrograde response to axonal injury. J Cell Biol 191:211– 223.

    1. Credibility can be established through many means: using appropriate professional language, citing highly respected sources, providing reliable evidence, and using sound logic

      I think understanding our ability to establish ethical skills in order to create credibility is an interesting topic. I'd be interested in the impact that non electronic writing communication has on our credibility. In terms of technical writing I don't think it has much impact. But in an environment of handwritten documentation s necessary, many individuals may be lost. Electronic communication has had a major influence on our development as student and writers, but have we lost a element of good handwriting, spelling, and meaning behind paper written communication?

    1. Nonhuman Animal Subject Research One area of controversy regarding research techniques is the use of nonhuman animal subjects. One of the keys to behaving in an ethical manner is to ensure that one has given informed consent to be a subject in a study. Obviously, animals are unable to give consent. For this reason and others related to animal welfare, there are some who believe that researchers should not use nonhuman animal subjects in any case. There are others that advocate for using nonhuman animal subjects because nonhuman animal subjects many times will have distinct advantages over human subjects. Their nervous systems are frequently less complex than human systems, which facilitates the research. It is much easier to learn from a system with thousands of neurons compared to one with billions of neurons like humans. Also, nonhuman animals may have other desirable characteristics such as shorter life cycles, larger neurons, and translucent embryos. However, it is widely recognized that this research must proceed with explicit guidelines ensuring the safe treatment of the animals. For example, any research institution that will be conducting research using nonhuman animal subjects must have an Institutional Animal Care and Use Committee (IACUC). IACUCs review the proposed experiments to ensure an appropriate rationale for using nonhuman animals as subjects and ensure ethical treatment of those subjects. Furthermore, many researchers who work with nonhuman animal subjects adhere to the Three R's: Replacement, Reduction, and Refinement (Russell & Burch, 1959). Replacement suggests that researchers should seek to use inanimate systems as a replacement for nonhuman animal subjects whenever possible. Furthermore, replacement is also suggested to replace higher level organisms with lower level organisms whenever possible. The idea is that instead of choosing a primate to conduct the study, researchers are encouraged to use a lower level animal such as an invertebrate (a sea slug, for example) to conduct the study. Reduction refers to reducing the number of nonhuman animal subjects that will be used in the particular study. The idea here is that if a study can learn sufficient information from one nonhuman animal, then they should only use one. Finally, refinement is about how the nonhuman animals are cared for. The goal is to minimize discomfort that the subject experiences and to enhance the conditions that the subject experiences throughout their life. For a full discussion of the Three R's, see Tannenbaum and Bennett (2015). In conclusion, many researchers argue that what we have learned from nonhuman animal subjects has been invaluable. These studies have led to drug therapies for treating pain and other disorders; for instance, most drugs are studied using animals first, to ensure they are safe for humans. Animal nervous systems are used as models for the human nervous systems in many areas. Sea slugs (Aplysia californica) have been used to learn about neural networks involved in learning and memory. Cats have been studied to learn about how our brain's visual system is organized. Owls have been used to learn about sound localization in the auditory system. Indeed, research using nonhuman animal subjects has led to many important discoveries.

      Interesting how the Three R’s Replacement, Reduction, and Refinement help balance the need for research with animal welfare. Makes me think about how many major discoveries wouldn’t be possible without animal models.

    2. Ethics in Neuroscience Research Research has a very complicated history with respect to ethics. This is true when discussing our treatment of nonhuman animal subjects and our treatment of human subjects as well. Let’s start by discussing the ethical considerations for nonhuman animal subject research. Nonhuman Animal Subject Research One area of controversy regarding research techniques is the use of nonhuman animal subjects. One of the keys to behaving in an ethical manner is to ensure that one has given informed consent to be a subject in a study. Obviously, animals are unable to give consent. For this reason and others related to animal welfare, there are some who believe that researchers should not use nonhuman animal subjects in any case. There are others that advocate for using nonhuman animal subjects because nonhuman animal subjects many times will have distinct advantages over human subjects. Their nervous systems are frequently less complex than human systems, which facilitates the research. It is much easier to learn from a system with thousands of neurons compared to one with billions of neurons like humans. Also, nonhuman animals may have other desirable characteristics such as shorter life cycles, larger neurons, and translucent embryos. However, it is widely recognized that this research must proceed with explicit guidelines ensuring the safe treatment of the animals. For example, any research institution that will be conducting research using nonhuman animal subjects must have an Institutional Animal Care and Use Committee (IACUC). IACUCs review the proposed experiments to ensure an appropriate rationale for using nonhuman animals as subjects and ensure ethical treatment of those subjects. Furthermore, many researchers who work with nonhuman animal subjects adhere to the Three R's: Replacement, Reduction, and Refinement (Russell & Burch, 1959). Replacement suggests that researchers should seek to use inanimate systems as a replacement for nonhuman animal subjects whenever possible. Furthermore, replacement is also suggested to replace higher level organisms with lower level organisms whenever possible. The idea is that instead of choosing a primate to conduct the study, researchers are encouraged to use a lower level animal such as an invertebrate (a sea slug, for example) to conduct the study. Reduction refers to reducing the number of nonhuman animal subjects that will be used in the particular study. The idea here is that if a study can learn sufficient information from one nonhuman animal, then they should only use one. Finally, refinement is about how the nonhuman animals are cared for. The goal is to minimize discomfort that the subject experiences and to enhance the conditions that the subject experiences throughout their life. For a full discussion of the Three R's, see Tannenbaum and Bennett (2015). In conclusion, many researchers argue that what we have learned from nonhuman animal subjects has been invaluable. These studies have led to drug therapies for treating pain and other disorders; for instance, most drugs are studied using animals first, to ensure they are safe for humans. Animal nervous systems are used as models for the human nervous systems in many areas. Sea slugs (Aplysia californica) have been used to learn about neural networks involved in learning and memory. Cats have been studied to learn about how our brain's visual system is organized. Owls have been used to learn about sound localization in the auditory system. Indeed, research using nonhuman animal subjects has led to many important discoveries.

      Do you think the benefits of animal research outweigh the ethical concerns, even with guidelines like the Three R’s in place?

    1. One additional way to study the contributions of each hemisphere separately is through a procedure known as a Wada. In a Wada procedure, a barbiturate (a depressant drug used for various purposes including sedation) is used to put one half of the brain “to sleep” and then the contributions of the other hemisphere can be studied. Wada procedures are typically used for similar purposes as are cortical mapping techniques such as direct cortical stimulation. But, instead of mapping specific functions to specific areas (as with direct cortical stimulation), the Wada procedure maps functions to hemispheres. Usually, the Wada is used to identify which hemisphere is responsible for language processing and memory tasks. Although scientists know that language functions are usually in the left hemisphere, it is not always the case (particularly in left-handed individuals), so the Wada will help determine which hemisphere is dominant for language functions. For memory functions, both hemispheres play a significant role, but during the Wada, doctors are able to determine which hemisphere has stronger memory function. One Major Concern With Lesion/Surgery Studies One thing to remember about all studies of lesion or surgical patients is that the ability to generalize to the population during these studies may be questionable. It is important to keep in mind that that the reason these patients are studied is because they had some sort of issue with their brain. It is reasonable to wonder whether their brains are representative of “normal subjects,” that is, subjects who do not have lesions or other issues. For example, perhaps someone with epilepsy, after having years of seizures, has a different brain organization than someone without epilepsy. In that circumstance, what we learn from them in a split brain study may not be applicable to a non-epileptic population.

      Do you think the Wada procedure's ability to test each hemisphere separately outweighs the risks involved in using a barbiturate?

    1. Using Indirect Functional Imaging Techniques to Study a Disorder: Autism Spectrum Disorder PET and fMRI studies of ASD have found different levels of neuronal activity in the amygdala and the hippocampus compared to subjects without ASD. These areas are notable because they are a part of the “social brain.” These studies have largely focused on patients with ASD when they are viewing faces. As the viewing of faces is a large part of socializing (for example, reading expressions and making eye contact) and socializing is one area where many autistic patients have issues, these studies help provide further information for doctors and researchers to use. (See Philip et al. (2012) for a review of the fMRI studies of ASD.) Transcranial Magnetic Stimulation Another technique that is worth mentioning is transcranial magnetic stimulation (TMS). TMS is a noninvasive method that causes depolarization or hyperpolarization in neurons near the scalp. Depolarizations are increases in the electrical state of the neuron, while hyperpolarizations are decreases. In TMS, a coil of wire is placed just above the participant’s scalp (as shown in Figure 2.4.42.4.4\PageIndex{4}). When electricity flows through the coil, it produces a magnetic field. This magnetic field travels through the skull and scalp and affects neurons near the surface of the brain. When the magnetic field is rapidly turned on and off, a current is induced in the neurons, leading to depolarization or hyperpolarization, depending on the number of magnetic field pulses. Single- or paired-pulse TMS depolarizes site-specific neurons in the cortex, causing them to fire. If this method is used over certain brain areas involved with motor control, it can produce or block muscle activity, such as inducing a finger twitch or preventing someone from pressing a button. If used over brain areas involved with visual perception, it can produce sensations of flashes of light or impair visual processes. This has proved to be a valuable tool in studying the function and timing of specific processes such as the recognition of visual stimuli. Repetitive TMS produces effects that last longer than the initial stimulation. Depending on the intensity, coil orientation, and frequency, neural activity in the stimulated area may be either attenuated or amplified. Used in this manner, TMS is able to explore neural plasticity, which is the ability of connections between neurons to change. This has implications for treating psychological disorders, such as depression, as well as understanding long-term changes in neuronal excitability. Note that TMS is different from the previous techniques in that we are not taking images of what the brain is doing. TMS disrupts or stimulates the brain and actively changes what the brain is doing.

      Since TMS can stimulate or block brain activity, do you think it’s more valuable for research or as a treatment tool (like for depression)?

    1. Functional Imaging Many researchers are also interested in how the brain works. Some studies begin with the scientific question of “what does this part do?” Or more commonly, “Where in the brain does this happen?” Functional imaging techniques allow researchers to learn about the brain activity during various tasks by creating images based on the electrical activity or the absorption of various substances that occurs while a subject is engaging in a task. Such techniques can be used, for example, to visualize the parts of the brain that respond when we're exposed to stimuli that upset us or make us happy. Temporal Versus Spatial Resolution Within functional imaging techniques, researchers are frequently focused on one of two questions. They may ask “When does this activity occur?” Or “Where does this activity occur?” Some techniques are better for answering one of these questions, whereas other techniques are better for answering the other question. We describe how well a technique can determine when the activity has occurred as temporal resolution. For example, was the brain region activity occurring sometime in the last hour, the last minute, the last second, or within milliseconds? While some techniques are excellent at determining precisely when the activity occurred and other techniques are quite terrible at it. Additionally, we can describe how well a technique can determine where the activity has occurred as spatial resolution. For example, did the activity occur in the temporal lobe somewhere or can we narrow that down to a specific gyrus (ridge) or sulcus (groove) of the cerebral cortex? If it occurred on a particular gyrus can we narrow it down to a particular portion of that gyrus? As with temporal resolution, some techniques are excellent at determining precisely where the activity occurred whereas other techniques are less accurate.

      Quick question you guys, which do you think matters more in brian studies: When the activity happens or where it happens? and Why?

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer #1:

      Major Comments:

      1. The data in the paper strongly suggests that the new copper shuttles are selective for copper and have faster binding kinetics (Fig 1) than the previous one. However, the data regarding the copper shuttling from the copper(Aβ) peptides is not very convincing. It appears to be due to the Cu effect alone (Fig.3), as the reduction in viability with Cu(II)+ AscH- is almost the same as the Cu(II)(Aβ)+AscH-. To convincingly show that the peptide shuttle can strip copper from (Aβ) peptides, the authors need to show that the copper is bound to the (Aβ) peptide before it is used in the experiment. Rightfully so, the effect of the toxicity of Cu(II)+ AscH- is similar to that of Cu(II)(Aβ16)+AscH-. This is due to the fact that Aβ16 is not toxic to the cells, so therefore there is no compounded effect of Cu and Aβ16 as seen for Cu(II)(Aβ40). As for the toxicity of Cu(II)+ AscH-, it is be similar to Cu(II)(Aβ)+AscH- because Cu(II) will be bound to a weaker ligand in the medium and such loosely bound Cu is also able to produce ROS with AscH- with similar rates as Cu-Ab.

      Data from our lab and others have shown that in HEPES solution at pH 7.4, Aβ forms a complex with Cu. The present work is also in line with Cu-binding to Ab, as in Figure 1C (GSH), the rate of Cu withdrawal by the shuttle can only be explained by Cu bound to Ab, as Cu in the buffer binds to the shuttle much faster. Also, the AscH- consumption rate measured in Fig S5D-E are congruent of Cu bound to Ab, unbound Cu has a much faster rate of AscH- consumption (Santoro et al. 2018, doi.org/10.1039/C8CC06040A).

      The concentrations of Aβ and Cu used in our experimental condition were determined with a UV-Vis spectrophotometer.

      Minor comments:

      1. The paper does not cite Figure 1A and some supplementary figures, especially Supp. Fig. 1-2. All the figures and supplementary figures should be cited. This has been rectified for all the concerned figures.

      The data presentation in Figures 3B and S8 is confusing."-" signs indicate no addition or the blank box means no addition. Also, the AKH-αR5W4 has no "-" sign in the first bar. For clarity, please indicate the -, +, or no sign means in the figure legends. Also, what does "Batch A" refer to in Figure 3B?

      The figures have been modified as suggested by the reviewer.

      Page 7, correct (Error! Referencesource not found.Figure 1C).

      This has been rectified.

      The Giantin staining in Figure 2B is making it hard to visualize ATP7A trafficking. If the Giantin image overlay is removed, it may be easier to see the movement of ATP7A from the perinuclear region to the vesicles.

      The images have been modified to better appreciate the ATP7A change in distribution upon the increase in intracellular Cu level. We have reduced the number of conditions for which images are provided and provided individual staining for clarity. Zoomed images are also provided. The remainder of the conditions are in Figure S7B

      In the introduction, the authors mention, "These molecules have, however, a major pitfall as is seen for Elesclemol, a candidate for Menkes disease treatments 32. The authors cite reference " Tsvetkov, P. et al. Copper induces cell death by targeting lipoylated TCA cycle proteins." The paper showing elesclomol as a candidate for Menkes disease treatments is Guthrie L et al., Elesclomol alleviates Menkes pathology and mortality by escorting Cu to cuproenzymes in mice. Science. 2020.

      We thank the reviewer for pointing this out, which was apparently not clearly explained. Our intention here was to show that a major pitfall of shuttles like Elesclomol, as seen in the study by Tsvetkov, P. et al. Science (2022), is cuprotoxicity. The sentence has been clarified and the work of Guthrie L et al is cited for Elesclomol as a candidate for Menkes disease.

      Reviewer #2 :

      Major issues:

      1. This reviewer is not convinced that the authors' experimental system is well suited for studies of glia activation and protective effects. With the exception of a couple of panels it is very hard to see differences. The authors should significantly improve the quality of images in Figure 5 to make this set of data convincing. We thank the reviewer for his/her detailed evaluation and for bringing to light the quality of the image in Figure 5. We have therefore improved the quality of the images by improving the signal to noise ratio to better show the differences between conditions.

      Similarly, the quality of giantin staining is low and needs to be improved and more experimental details are needed (see details below).

      As stated in our answer to reviewer 1, the images have been modified to better appreciate ATP7A redistribution upon increase of intracellular Cu levels. We have reduced the number of conditions for which images are provided and provided individual staining for clarity. Zoomed images are also provided. The remainder of the conditions are in Figure S7B.

      Given that shuttles are found within vesicles, the authors should discuss the mechanism through which Cu is released into the cytosol to trigger ATP7B trafficking.

      The mechanism of Cu escape from endosomes remains poorly understood. However, supported by our recent observations that Cu quickly (within 10 min) dissociates from the Cu-shuttle AKH-αR5W4NBD in endosomes (Okafor et al., 2024, /doi.org/10.3389/fmolb.2024.1355963), we discuss the potential involvement CTR1/2 and DMT1 (page 16).

      There are numerous small writing issues that make paper difficult to read. The authors are encouraged to carefully edit their manuscript.

      We thank the reviewer for pointing this out and several errors have been corrected whereas various sentences have been clarified.

      Minor issues

      * „A solution of monomerized Aβ complex in 10% DMEM (diluted with DMEM salt solution) was prepared in microcentrifuge tubes" - here and further the description of media composition is confusing What is the rest 90%?

      This has been rectified. The composition of the salt solution that makes up the 90% has been provided (page 4).

      * „Afterwards, AscH- was added to the tubes and vortexed, the mixture was then added to PC12 cells" - concentration of ascorbate is mentioned only once (later in the figure legend) where it can be barely found, also without explaining the choice of concentration. Additionally, ascorbate's product code is not listed. Please, correct.

      These points have been rectified.

      * Description of the cell (PC12 line) handling conditions is absent (growth medium, passage number used etc) and should be included.

      This information is now provided.

      * ATP7A delocalization assay. Details for the secondary antibodies are absent (full name (e.g. AlexaFluor 488), manufacturer, code) and should be added.

      Missing information has been added.

      * page 6: „Next, we investigated the capacity of the shuttles to withdraw Cu(II) from cell culture media, DMEM 10% and DMEM/F12 1:1 (D/F)." Here and further explanation is needed why the mixture of DMEM/F12 is needed (F12 is also not listed in the materials list).

      DMEM/F12 is a media that is commercially available used for some cell types, and it has been added to the materials list (page 4).

      * Page 7. Legend to the figure 1B: „Conditions: Cu(II)=AKH-αR5W4NBD=DapHH-αR5W4NBD=HDapH-αR5W4NBD= 5 μM, DMEM 10%, D/F 100%, 25{degree sign}C, n=3." - „DMEM/F12" ratio equals to „100%" is confusing, please clarify

      This has been clarified.

      * Page 8-9. Legend to the Figure 2A. „Similar observations were obtained with 5 different cell cultures." Same remark goes to the legend to supplementary figure 7 ("Similar observations were obtained with at least 3 different cell cultures"). Do the authors mean independent experiments or different cell lines? Please clarify. If different cell lines, consider including these data into the supplement.

      Indeed we meant independent experimentations. This has been clarified.

      * Page 8-9, figure 2B. Giantin is a cis-golgi marker, which should localize perinuclearly. In the cells shown the signal is diffuse and appears non-specific. Please improve the quality.

      We have reduced the number of conditions for which images are provides and are providing individual staining for clarity. Zoomed images are also provided allowing visualization of the typical cis-Golgi distribution of Giantin.

      * Page 8-9, figure 2B. ATP7A is shown in green. The authors did not specify the secondary antibody has been used for it. If the secondary antibody used for labeling of ATP7A has green fluorescence then how does one distinguish between the transporter signal and signal of the green fluorescent shuttle? Please provide more details.

      We thank the reviewer for pointing this point as we missed to mention this technical issue in the original manuscript. The Cu-shuttles labeled with NBD indeed emit in the green signal, but they are not fixable under our conditions and are washed out during ICC procedure. Accordingly, they do generate any background signal and do not interfere with the ICC as shown by the controls and test conditions (Figure S7B and Figure 2B). This is now mentioned (page 11).

      * Page 9 and Figure 2B. Why did authors use Cu(II)EDTA for the experiment? What was the concentration? Please, add this information as well as Cu(II)GTSM treatment conditions to the experiment description in materials and methods.

      EDTA is a strong chelator of Cu(II), however due to its negative charge it cannot penetrate the plasma membrane thus importing Cu. It is therefore used as a negative control, to eliminate the speculation of Cu non-specifically crossing the plasma membrane or through a channel.

      * Figure 2 and supplementary figure 7. It would be beneficial to have higher magnification images. Please, add them, if possible.

      These higher magnification images have been provided.

      * Page 11. „In conclusion, the novel Cu(II)-selective peptide shuttles .... capable of instantly preventing ... toxicity on PC12 cells, whereas ... instantly rescue Cu(II)Aβ1-42 toxicity". Authors should be more careful with terminology. According to the materials and methods, the survival assay was carried out after 24h of cells' treatment with the reagents. Effect visible after 24h and „instant rescue" is not the same, Please clarify or modify the wording

      In principle, the peptides cannot reverse the production of ROS, however they prevent ROS production. Therefore, for the peptides to have an effect, they have to instantly halt ROS production. This is justified by the novel shuttles being more effective than AKH-αR5W4NBD in preventing toxicity, given we modified just the Cu binding sequence. We have however restricted the use of the term instantly to ROS production.

      * Page 13, figure 5, panels C and D. In both quantitations Cu(II) was used as one of the control conditions. Why in panel D the percentage of activated microglial cells (second graphs from right) is several fold higher (appr. 150% vs >500%)?

      This variability was observed throughout our set of experiments and could be linked to the quality of the hippocampal slices used. Slight variations in the age of the animals or in the traces of metals in the mediums are likely explanations. However, the different groups that are compared represent experiments performed simultaneously.

      * Supplementary Figure S3B. The lowest solid line does not correspond to any color in the legend (please, check and correct). However, by the method of exclusion, one may conclude that it refers to Cu(II)+HDapH-shuttle. What could be a potential explanation for stronger quenching of this shuttle by binding Cu(II) directly from the spiked media comparing to when it is pre-complexed with copper (also supported by the panel D)?

      The stronger quenching of this shuttle by binding Cu(II) directly from the spiked media comparing to when it is pre-complexed with copper is not significant.

      * In discussion the authors mention that the designed shuttles are prone to degradation in 48 hours. In the viability assays, they treat cells for 24 hours, in the fluorescent and confocal microscopy experiments for one hour or less. What is the lifetime of these shuttle peptides in the cells?

      The lifetime of the shuttle peptide in the cells is currently unknown. However, after 24h incubation of PC12 cells with the AKH-αR5W4NBD, DapHH-αR5W4NBD and HDapH-αR5W4NBD, the Cu shuttles lose their punctate distribution and appear diffuse inside the cells. We have recently shown that AKH-αR5W4NBD cycles through different endosomal compartments and eventually reaches the lysosomes where it could be degraded (Okafor et al., 2024, /doi.org/10.3389/fmolb.2024.1355963). Therefore, the diffuse distribution of the fluorescence signal could suggest degradation of the Cu-shuttles.

      * From the microscopy observations, the mechanism of entry of apo-shuttles (with no Cu(II) in the complex) and in complex with Cu(II) looks quite different. Namely, in figure S7 the fluorescent signal is very strong in the plasma membrane with significantly less vesicular pattern when compared to figure 2A. It is especially apparent for DapHH shuttle at 15 minutes of incubation. Can authors hypothesize/discuss the reason for these differences?

      The difference of the shuttle’s signal in the presence or absence of Cu binding, is due to fluorescence quenching by Cu bound and was at the heart of the design of these shuttles. Hence a strong signal at the plasma membrane is seen in the absence of Cu as these CPP-based shuttles interact strongly with the plasma membrane. However in presence of Cu, they become less visible due to quenching by Cu. Interestingly however, is that when Cu dissociates from the shuttle inside the cells (likely in acid endosomes), this quenching is suppressed and the fluorescence reappears. This is now better explained (page 10).

      * Please, show the figures in the supplementary file in the same order as you refer to them.

      This has been rectified.

      * Introduction. Description of the shuttle peptides: „(3) a cell penetrating peptide (CPP), αR5W4, with sequence RRWWRRRWWR, for cell entry35" - one R is the middle is extra.

      This has been rectified.

      *Kd units are missing (pages 2, 3 and 15) and should be added.

      This has been added.

      * Figure 1A is either not referred at all or mislabeled.

      * Page 7, Figure 1B: x axis on the second panel (+Mn+) misses a label.

      * Page 8. „Upon addition of DapHH-αR5W4NBD or HDapH-αR5W4NBD, an immediate slow-down in ROS production was observed (Figure 1D and S1E), ..." - mislabeled supplementary figure, please, correct.

      * Page 11. „...but not in the presence of AKH-αR5W4NBD which required pre-incubation to prevent toxicity (Figure 3AFigure)." Please, correct the reference to the figure.

      * Page 11. „This is in line with the faster retrieval ... previously demonstrated in vitro (Figure 1)" - please, specify the panel.

      * Supplementary materials and methods, subsection „Retrieval of Cu by peptide shuttles from Aβ", page 2: „The same was done for 10 μM Cu(II)...to give the estimated 100% saturated emission level." - check the spelling of the shuttle species.

      * Supplementary Figure S4. By the behavior of AKH-shuttle in the presence of copper and other metals, it looks that panels are shuffled, i.e. panel C looks corresponding to the panel B with DMEM/F12 conditions, whish is also supported by the values in the Table S1. Please, check and correct, if needed.

      * Supplementary figure S9, panel A. Apparently, mislabeled images with Abeta1-42 and Cu(II)Abeta1-42. Please, correct.

      We apologize for the different issues in referencing figures. This has been rectified.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Minor Concerns

      I think that authors can add some concepts of general interest on AD, as follows

      evidence showed that AD top-line disease-modifying drugs employing monoclonal antibodies (donanemab, lecanemab, and aducanumab) that tag Aβ, based on the 'Amyloid cascade hypothesis', are able to rid the brain of Aβ plaques, but the drug benefits consist in a reduction of 35% of cognitive decline. The remaining disease burden (more than 65%) has no disease-modifying therapeutic options, at the moment. Furthermore, monoclonal antibodies against Aβ have strong side- events (ARIA). On this basis, it could be suggested that removing Aβ plaque might not be sufficient to slow the 100% percentage of clinical decline in AD. This is why the Cu(II) shuttle invention presented by the candidate may represent a valid and concrete means to fight AD, since also meta-analyses demonstrate that Cu and more specifically non-Cp Cu is increased in AD (PMID: 34219710). The authors can add some of these clinical considerations in the Discussion.

      There is only a very brief description of the scenario of evidence of the involvement of copper in Alzheimer's, especially from a clinical point of view, I mean the scenario resulting from clinical studies carried out on AD patients. This would have highlighted the unmet medical need to which these new compounds (the Cu shuttles) can provide an answer. At least for a subpopulation of Alzheimer's patients, and we know that there are different subtypes of Alzheimer's disease (for example 10.1016/j.neurobiolaging.2004.04.001, but authors can find others), these Cu(II) selective shuttles could provide beneficial effects. Literature reports about a percentage of AD patients with increased levels of Cu (some papers on this topic e can be easily retrieved,), who may primarily benefit from these compounds. These can be easily identified as it is also characterized by a different biochemical, cognitive, and genetic profile. The current study is timely since AD patients with high Cu can be easily identified since they are characterized by a different biochemical, cognitive, and genetic profile as per recent findings (PMID: 37047347). This information can improve the quality of the manuscript by providing information about the unmet clinical need that this study can answer

      We thank the reviewer for his very positive evaluation and for his suggestion that gives more perspective to our work. Accordingly, we have added these parts to the introduction and discussion sections.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: The paper addresses the design and synthesis of novel copper (Cu)-selective peptide transporters to prevent Cu(Aβ)-induced toxicity and microglial activation in organotypic hippocampal slices.This is a very interesting study. I would define the study as pioneering and I hope that it is a seminal study, as it could be a study that opens the doors to future studies in the sector but above all applications in the clinical field. The methods are very complex and demonstrate an excellent knowledge of the biochemistry of beta-amyloid and copper. Methods are also clear and provide information for reproducibility

      Minor Concerns

      I think that authors can add some concepts of general interest on AD, as follows evidence showed that AD top-line disease-modifying drugs employing monoclonal antibodies (donanemab, lecanemab, and aducanumab) that tag Aβ, based on the 'Amyloid cascade hypothesis', are able to rid the brain of Aβ plaques, but the drug benefits consist in a reduction of 35% of cognitive decline. The remaining disease burden (more than 65%) has no disease-modifying therapeutic options, at the moment. Furthermore, monoclonal antibodies against Aβ have strong side- events (ARIA). On this basis, it could be suggested that removing Aβ plaque might not be sufficient to slow the 100% percentage of clinical decline in AD. This is why the Cu(II) shuttle invention presented by the candidate may represent a valid and concrete means to fight AD, since also meta-analyses demonstrate that Cu and more specifically non-Cp Cu is increased in AD (PMID: 34219710). The authors can add some of these clinical considerations in the Discussion

      there is only a very brief description of the scenario of evidence of the involvement of copper in Alzheimer's, especially from a clinical point of view, I mean the scenario resulting from clinical studies carried out on AD patients. This would have highlighted the unmet medical need to which these new compounds (the Cu shuttles) can provide an answer. At least for a subpopulation of Alzheimer's patients, and we know that there are different subtypes of Alzheimer's disease (for example 10.1016/j.neurobiolaging.2004.04.001, but authors can find others), these Cu(II) selective shuttles could provide beneficial effects. Literature reports about a percentage of AD patients with increased levels of Cu (some papers on this topic e can be easily retrieved,), who may primarily benefit from these compounds. These can be easily identified as it is also characterized by a different biochemical, cognitive, and genetic profile. The current study is timely since AD patients with high Cu can be easily identified since they are characterized by a different biochemical, cognitive, and genetic profile as per recent findings (PMID: 37047347). This information can improve the quality of the manuscript by providing information about the unmet clinical need that this study can answer

      Significance

      The significance of the study relies on that the Cu(II) selective shuttles can import Cu into cells and also release Cu once inside the cells, which was shown to be bioavailable, as revealed by the delocalization of ATP7A from the TGN to the sub-plasmalemma zone in PC12 cells. The novelty is well expressed by the implementation of Cu(II) selective shuttles that can release Cu inside the cells. Thus, they can restore Cu physiological levels in conditions of brain Cu deficiency that typify the neuronal cells in AD. Furthermore, this Cu trafficking can be finely tuned, thus preventing potential adverse drug reactions when transferred into human clinical phase I and II studies. This application may represent a step forward concerning previous copper attenuating compounds/Cu(II) ionophores such as Clioquinol and GTSM which mediated non-physiological Cu import into the cells that have likely contributed to neurotoxicity processes in previous unsuccessful phase II clinical trials.

      Furthermore, the originality of the current study relies on the fact that these shuttles can be tracked in real-time, once in the cell, since they employ a fluorophore moiety sensitive to Cu binding. Furthermore, DapHH-αR5W4NBD and HDapH-αR5W4NBD, can import bioavailable Cu(II) and can prevent ROS production by Cu(II)Aβ instantly, due to the much faster Cu-binding. Importantly, DapHH-αR5W4NBD and HDapH-αR5W4NBD shuttles have been also capable of preventing OHSC slices from Cu-induced neurotoxicity, microglial proliferation, and activation. Another important feature of the Cu shuttles is that they can be designed to control their site of cell delivery. In fact, previous ionophores had the tendency to accumulate in the mitochondria, and, in doing so, excess Cu in the mitochondria might have competed with other transitional metals (mainly Fe) and triggered mitochondrial dysfunction as well as cuproptosis. These are the main strengths of the study.

      The audience of this article is currently that of expert biochemists, but by adding aspects regarding the unmet clinical need relating to the large population of AD patients with high copper in the introduction and discussion, the article can capture the attention of clinicians.

      I am a neuroscientist working on biochemical pathways and metals in Alzheimer's disease.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      (1) The questions after reading this manuscript are what novel insights have been gained that significantly go beyond what was already known about the interaction of these receptors and, more importantly, what are the physiological implications of these findings? The proposed significance of the results in the last paragraph of the Discussion section is speculative since none of the receptor interactions have been investigated in TNBC cell lines. Moreover, no physiological experiments were conducted using the PRLR and GH knockout T47D cells to provide biological relevance for the receptor heteromers. The proposed role of JAK2 in the cell surface distribution and association of both receptors as stated in the title was only derived from the analysis of box 1 domain receptor mutants. A knockout of JAK2 was not conducted to assess heteromers formation.

      We thank the reviewer for these comments. The novel insight is that two different cytokine receptors can interact in an asymmetric, ligand-dependent manner, such that one receptor regulates the other receptor’s surface availability, mediated by JAK2. To our knowledge this has not been reported before. Beyond our observations, there is the question if this could be a much more common regulatory mechanism and if it has therapeutic relevance. However, answering these questions is beyond the scope of this work.

      Along the same line, the question regarding the biological relevance of our receptor heteromers and JAK2’s role in cell surface distribution is undoubtfully very important. Studying GHR-PRLR cell surface distributions in JAK2 knockout cells and certain TNBC cell lines as proposed by the reviewer could perhaps be insightful. However, most TNBCs down-regulate PRLR [1], so we would first have to identify TNBC cell lines that actually express PRLR at sufficiently high levels. Moreover, knocking out JAK2 is known to significantly reduce GHR surface availability [2,3], such that the proposed experiment would probably provide only limited insights.

      Unfortunately, our team is currently not in the position to perform any experiments (due to lack of funding and shortage of personnel). However, to address the reviewer’s comment as much as possible, we have revised the respective paragraph of the discussion section to emphasize the speculative nature of our statement and have added another paragraph discussing shortcoming and future experiments (see revised manuscript, pages 23-24).

      (1) López-Ozuna, V., Hachim, I., Hachim, M. et al. Prolactin Pro-Differentiation Pathway in Triple Negative Breast Cancer: Impact on Prognosis and Potential Therapy. Sci Rep 6, 30934 (2016). https://www.nature.com/articles/srep30934

      (2) He, K., Wang, X., Jiang, J., Guan, R., Bernstein, K.E., Sayeski, P.P., Frank, S.J. Janus kinase 2 determinants for growth hormone receptor association, surface assembly, and signaling. Mol Endocrinol. 2003;17(11):2211-27. doi: 10.1210/me.2003-0256. PMID: 12920237.

      (3) He, K., Loesch, K., Cowan, J.W., Li, X., Deng, L., Wang, X., Jiang, J., Frank, S.J. Janus Kinase 2 Enhances the Stability of the Mature Growth Hormone Receptor, Endocrinology, Volume 146, Issue 11, 2005, Pages 4755–4765,https://doi.org/10.1210/en.2005-0514

      (2) Except for some investigation of γ2A-JAK2 cells, most of the experiments in this study were conducted on a single breast cancer cell line. In terms of rigor and reproducibility, this is somewhat borderline. The CRISPR/Cas9 mutant T47D cells were not used for rescue experiments with the corresponding full-length receptors and the box1 mutants. A missed opportunity is the lack of an investigation correlating the number of receptors with physiological changes upon ligand stimulation (e.g., cellular clustering, proliferation, downstream signaling strength).

      We appreciate the reviewer’s comments. While we are confident in the reproducibility of our findings, including those obtained in the T47D cell line, we acknowledge that testing in additional cell lines would have strengthened the generalizability of our results. We also recognize that performing a rescue experiment using our T47D hPRLR or hGHR KO cells would have been valuable. Furthermore, examining physiological changes, such as proliferation rates and downstream signaling responses, would have provided additional insights. Unfortunately, these experiments were not conducted at the time, and we currently lack the resources to carry them out.

      (3) An obvious shortcoming of the study that was not discussed seems to be that the main methodology used in this study (super-resolution microscopy) does not distinguish the presence of various isoforms of the PRLR on the cell surface. Is it possible that the ligand stimulation changes the ratio between different isoforms? Which isoforms besides the long form may be involved in heteromers formation, presumably all that can bind JAK2?

      This is a very good point. We fully agree with the reviewer that a discussion of the results in the light of different PRLR isoforms is appropriate. We have added information on PRLR isoforms to the Introduction (see revised manuscript, page 2) and Discussion sections (see revised manuscript, pages 23-24).

      (4) Changes in the ligand-inducible activation of JAK2 and STAT5 were not investigated in the T47D knockout models for the PRL and GHR. It is also a missed opportunity to use super-resolution microscopy as a validation tool for the knockouts on the single cell level and how it might affect the distribution of the corresponding other receptor that is still expressed.

      We thank the reviewer for his comment. We fully agree that such additional experiments could be very valuable. We are sorry but, as already mentioned above, this is not something we are able to address at this stage due to lack of personnel and funding. However, we do hope to address these and other proposed experiments in the future.

      (5) Why does the binding of PRL not cause a similar decrease (internalization and downregulation) of the PRLR, and instead, an increase in cell surface localization? This seems to be contrary to previous observations in MCF-7 cells (J Biol Chem. 2005 October 7; 280(40): 33909-33916).

      It has been recently reported for GHR that not only JAK2 but also LYN binds to the box1-box2 region, creating competition that results in divergent signaling cascades and affects GHR nanoclustering [1]. So, it is reasonable to assume that similar mechanisms may be at work that regulate PRLR cell surface availability. Differences in cells’ expression of such kinases could perhaps play a role in the perceived inconsistency. Also, Lu et al. [2] studied the downregulation of the long PRLR isoform in response to PRL. All other PRLR isoforms were not detectable in MCF-7 cells. So, differences between MCF-7 and T47D may lead to this perceived contradiction.

      At this stage, we can only speculate about the actual reasons for these seemingly contradictory results. However, for full transparency, we are now mentioning this apparent contradiction in the Discussion section (see page 23) and have added the references below.

      (1) Chhabra, Y., Seiffert, P., Gormal, R.S., et al. Tyrosine kinases compete for growth hormone receptor binding and regulate receptor mobility and degradation. Cell Rep. 2023;42(5):112490. doi: 10.1016/j.celrep.2023.112490. PMID: 37163374.

      https://www.cell.com/cell-reports/pdf/S2211-1247(23)00501-6.pdf

      (2) Lu, J.C., Piazza, T.M., Schuler, L.A. Proteasomes mediate prolactin-induced receptor down-regulation and fragment generation in breast cancer cells. J Biol Chem. 2005 Oct 7;280(40):33909-16. doi: 10.1074/jbc.M508118200. PMID: 16103113; PMCID: PMC1976473.

      (6) Some figures and illustrations are of poor quality and were put together without paying attention to detail. For example, in Fig 5A, the GHR was cut off, possibly to omit other nonspecific bands, the WB images look 'washed out'. 5B, 5D: the labels are not in one line over the bars, and what is the point of showing all individual data points when the bar graphs with all annotations and SD lines are disappearing? As done for the y2A cells, the illustrations in 5B-5E should indicate what cell lines were used. No loading controls in Fig 5F, is there any protein in the first lane? No loading controls in Fig 6B and 6H.

      We thank the reviewer for pointing this out. We have amended Fig. 5A to now show larger crops of the two GHR and PRLR Western Blot images and thus a greater range of proteins present in the extracts. Please note that the bands in the WBs other than what is identified as GHR and PRLR are non-specific and reflect roughly equivalent loading of protein in each lane.

      We also made some changes to Figures 5B-5E.

      (7) The proximity ligation method was not described in the M&M section of the manuscript.

      We thank the reviewer for pointing this out. We have added a description of the PL method to the Methods section.

      Reviewer #1 (Recommendations for the Authors):

      A final suggestion for future investigations: Instead of focusing on the heteromer formation of the GHR/PRLR which both signal all through the same downstream effectors (JAK2, STAT5), it would have been more cancer-relevant, and perhaps even more interesting, to look for heteromers between the PRLR and receptors of the IL-6 family since it had been shown that PRL can stimulate STAT3, which is a unique feature of cancer cells. If that is the case, this would require a different modality of the interaction between different JAK kinases.

      We highly appreciate the reviewer’s recommendation and hope to follow up on it in the near future.

      Reviewer #2 (Public Review):

      (1) I could not fully evaluate some of the data, mainly because several details on acquisition and analysis are lacking. It would be useful to know what the background signal was in dSTORM and how the authors distinguished the specific signal from unspecific background fluorescence, which can be quite prominent in these experiments. Typically, one would evaluate the signal coming from antibodies randomly bound to a substrate around the cells to determine the switching properties of the dyes in their buffer and the average number of localisations representing one antibody. This would help evaluate if GHR or PRLR appeared as monomers or multimers in the plasma membrane before stimulation, which is currently a matter of debate. It would also provide better support for the model proposed in Figure 8.

      We are grateful for the reviewer’s comment. In our experience, the background signal is more relevant in dSTORM when imaging proteins that are located at deeper depths (> 3 μm) above the coverslip surface. In our experiments, cells are attached to the coverslip surface and the proteins being imaged are on the cell membrane. In addition, we employed dSTORM’s TIRF (total internal reflection fluorescence) microscopy mode to image membrane receptor proteins. TIRFM exploits the unique properties of an induced evanescent field in a limited specimen region immediately adjacent to the interface between two media having different refractive indices. It thereby dramatically reduces background by rejecting fluorescence from out-of-focus areas in the detection path and illuminating only the area right near the surface.

      Having said that, a few other sources such as auto-fluorescence, scattering, and non-bleached fluorescent molecules close to and distant from the focal plane can contribute to the background signal. We tried to reduce auto-fluorescence by ensuring that cells are grown in phenol-red-free media, imaging is performed in STORM buffer which reduces autofluorescence, and our immunostaining protocol includes a quenching step aside from using blocking buffer with different serum, in addition to BSA. Moreover, we employed extensive washing steps following antibody incubations to eliminate non-specifically bound antibodies. Ensuring that the TIRF illumination field is uniform helps reduce scatter. Additionally, an extended bleach step prior to the acquisition of frames to determine localizations helped further reduce the probability of non-bleached fluorescent molecules.

      In short, due to the experimental design we do not expect much background. However, in the future, we will address this concern and estimate background in a subtype dependent manner. To this end we will distinguish two types of background noise: (A) background with a small change between subsequent frames, which mainly consists of auto-fluorescence and non-bleached out-of-focus fluorescent molecules; and (B) background that changes every imaging frame, which is mainly from non-bleached fluorescent molecules near the focal plane. For type (A) background, temporal filters must be used for background estimation [1]; for type (B) background, low-pass filters (e.g., wavelet transform) should be used for background estimation [2].

      (1) Hoogendoorn, Crosby, Leyton-Puig, Breedijk, Jalink, Gadella, and Postma (2014). The fidelity of stochastic single-molecule super-resolution reconstructions critically depends upon robust background estimation. Scientific reports, 4, 3854. https://doi.org/10.1038/srep03854

      (2) Patel, Williamson, Owen, and Cohen (2021). Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM). Bioinformatics, Volume 37, Issue 17, September 2021, Pages 2730–2737, https://doi.org/10.1093/bioinformatics/btab136

      (2) Since many of the findings in this work come from the evaluation of localisation clusters, an image showing actual localisations would help support the main conclusions. I believe that the dSTORM images in Figures 1 and 2 are density maps, although this was not explicitly stated. Alexa 568 and Alexa 647 typically give a very different number of localisations, and this is also dependent on the concentration of BME. Did the authors take that into account when interpreting the results and creating the model in Figures 2 and 8?

      I believe that including this information is important as findings in this paper heavily rely on the number of localisations detected under different conditions.

      Including information on proximity labelling and CRISPR/Cas9 in the methods section would help with the reproducibility of these findings by other groups.

      Figures 1 and 2 show Gaussian interpolations of actual localizations, not density maps. Imaging captured the fluorophores’ blinking events and localizations were counted as true localizations, when at least 5 consecutive blinking events had been observed. Nikon software was used for Gaussian fitting. In other words, we show reconstructed images based on identifying true localizations using gaussian fitting and some strict parameters to identify true fluorophore blinking. This allowed us to identify true localizations with high confidence and generate a high-resolution image for membrane receptors.

      Indeed, Alexa 568 and 647 give different numbers of localization. This is dependent on the intrinsic photo-physics of the fluorophores. Specifically, each fluorophore has a different duty cycle, switching cycle, and survival fraction. However, we note that we focused on capturing the relative changes in receptor numbers over time, before and after stimulation by ligands, not the absolute numbers of surface GHR and PRLR. We are not comparing the absolute numbers of localizations or drawing comparisons for localization numbers between 568 and 647. For all these different conditions/times, the photo-physics for a particular fluorophore remains the same. This allows us to make relative comparisons.

      As far as the effect of BME is concerned, the concentration of mercaptoethanol needs to be carefully optimized, as too high a concentration can potentially quench the fluorescence or affect the overall stability of the sample. However, we are using an optimized concentration which has been previously validated across multiple STORM experiments. This makes the concerns relating to the concentration of BME irrelevant to the current experimental design. Besides, the concentration of BME is maintained across all experimental conditions.

      We have added information regarding PL and CRISPR/Cas9 for generating hGHR KO and hPRLR KO cells in two new subsections to the Methods section.

      Reviewer #2 (Recommendations for the authors):

      In the methods please include:<br /> (1) A section with details on proximity ligation assays.

      We have added a description of the PL method to the Methods section.

      (2) A section on CRISPR/Cas9 technology.

      We have added two new sections on “Generating hGHR knockout and hPRLR knockout T47D cells” and “Design of sgRNAs for hGHR  or hPRLR knockout” to the Methods section.

      (3) List the precise composition of the buffer or cite the paper that you followed.

      We used the buffer recipe described in this protocol [1] and have added the components with concentrations as well as the following reference to the manuscript.

      (1) Beggs, R.R., Dean, W.F., Mattheyses, A.L. (2020). dSTORM Imaging and Analysis of Desmosome Architecture. In: Turksen, K. (eds) Permeability Barrier. Methods in Molecular Biology, vol 2367. Humana, New York, NY. https://doi.org/10.1007/7651_2020_325

      (4) Exposure time used for image acquisition to put 40 000 frames in the context of total imaging time and clarify why you decided to take 40 000 images per channel.

      Our Nikon Ti2 N-STORM microscope is equipped with an iXon DU-897 Ultra EMCCD camera from Andor (Oxford Instruments). According to the camera’s manufacturer, this camera platform uses a back-illuminated 512 x 512 frame transfer sensor and overclocks readout to 17 MHz, pushing speed performance to 56 fps (in full frame mode). We note that we always tried to acquire STORM images at the maximal frame rate. As for the exposure time, according to the manufacturer it can be as short as 17.8 ms. We would like to emphasize that we did not specify/alter the exposure time.

      See also: https://andor.oxinst.com/assets/uploads/products/andor/documents/andor-ixon-ultra-emccd-specifications.pdf

      The decision to take 40,000 images per frame was based on our intention to identify the true population of the molecules of interest that are localized and accurately represented in the final reconstruction image. The total number of frames depends on the sample complexity, density of sample labeling and desired resolution. We tested a range of frames between 20,000 and 60,000 and found for our experimental design and output requirements that 40,000 frames provided the best balance between achieving maximal resolution and desired localizations to make consistent and accurate localization estimates across different stimulation conditions compared to basal controls.

      (5) The lasers used to switch Alexa 568 and Alexa 647. Were you alternating between the lasers for switching and imaging of dyes? Intermittent and continuous illumination will produce very different unspecific background fluorescence.

      Yes, we used an alternating approach for the lasers exciting Alexa 647 and Alexa 568, for both switching and imaging of the dyes.

      (6) A paragraph with a detailed description of methods used to differentiate the background fluorescence from the signal.

      We have addressed the background fluorescence under Point 1 (Public Review). We have added a paragraph in the Methods section on this issue.

      (7) Minor corrections to the text:

      It appears as though there is a large difference in the expression level of GHR and PRLR in basal conditions in Figure 1. This can be due to the switching properties of the dyes, which is related to the amount of BME in the buffer, or it can be because there is indeed more PRL. Would the authors be able to comment on this?

      We thank the reviewer for this suggestions. According to expression data available online there is indeed more PRLR than GHR in T47D cells. According to CellMiner [1], T47D cells have an RNA-Seq gene expression level log2(FPKM + 1) of 6.814 for PRLR, and 3.587 for GHR, strongly suggesting that there is more PRLR than GHR in basal conditions, matching the reviewer’s interpretation of our images in Fig. 1 (basal). However, we would advise against using STORM images for direct comparisons of receptor expression. First, with TIRF images, we are only looking at the membrane fraction (~150 nm close to the coverslip membrane interface) that is attached to the coverslip. Secondly, as discussed above, our data represent relative cell surface receptor levels that allow for comparison of different conditions (basal vs. stimulation) and does not represent absolute quantifications. Everything is relative and in comparison to controls.

      Also, BME is not going to change the level of expression. The differences in growth factor expression as estimated by relative comparison can be attributed to the actual changes in growth factors and is not an artifact of the amount of BME in the buffer or the properties of dyes. These factors are maintained across all experimental conditions and do not influence the final outcome.

      (1) https://discover.nci.nih.gov/cellminer/

      (8) I would encourage the authors to use unspecific binding to characterize the signal coming from single antibodies bound to the substrate. This would provide a mean number of localizations that a single antibody generates. With this information, one can evaluate how many receptors there are per cluster, which would strengthen the findings and potentially provide additional support for the model presented in Figure 8. It would also explain why the distributions of localisations per cluster in Fig. 3B look very different for hGHR and hPRLR. As the authors point out in the discussion, the results on predimerization of these receptors in basal conditions are conflicting and therefore it is important to shed more light on this topic.

      We thank the reviewer for this suggestions. While we are unable to perform this experiment at this stage, we will keep it in mind for future experiments.

      (9) Minor corrections to the figures:

      Figure 1:

      In the legend, please say what representation was used. Are these density maps or another representation? Please provide examples of actual localisations (either as dots or crosses representing the peaks of the Gaussians). Most findings of this work rely on the characterisation of the clusters of localisations and therefore it is of essence to show what the clusters look like. This could potentially go to the supplemental info to minimise additional work. It's very hard to see the puncta in this figure.

      If the authors created zoomed regions in each of the images (as in Figure 3), it would be much easier to evaluate the expression level and the extent of colocalisation. Halfway through GHR 3 min green pixels become grey, but this may be the issue with the document that was created. Please check. Either increase the font on the scale bars in this figure or delete it.

      As described above, Figure 1 does not show density maps. Imaging captured the fluorophores’ blinking events and localizations were counted as true localizations, when at least 5 consecutive blinking events had been observed. Nikon software was used for Gaussian fitting and smoothing.

      We have generated zoomed regions. In our files (original as well as pdf) we do not see pixels become grey. We increased the font size above one of the scale bars and removed all others.

      Figure 3:

      In A, the GHR clusters are colour coded but PRLR are not. Are both DBSCN images? Explain the meaning of colour coding or show it as black and white. Was brightness also increased in the PRLR image? The font on the scale bars is too small. In B, right panels, the font on the axes is too small. In the figure legend explain the meaning of 33.3 and 16.7

      In our document, both GHR and PRLR are color coded but the hGHR clusters are certainly bigger and therefore appear brighter than the hPRLR clusters. Both are DBSCAN images. The color coding allows to distinguish different clusters (there is no other meaning). We have kept the color-coding but have added a sentence to the caption addressing this. Brightness was increased in both images of Panel B equally. 33.3 and 16.7 are the median cluster sizes. We have added a sentence to the caption explaining this. We have increased the font on the axes in B (right panels).

      Figure 4:

      I struggled to see any colocalization in the 2nd and the 3rd image. Please show zoomed-in sections. In the panels B and C, the data are presented as fractions. Is this per cell? My interpretation is that ~80% of PRL clusters also contain GHR.

      Is this in agreement with Figures 1 and 2? In Figure 1, PRL 3 min, Merge, colocalization seems much smaller. Could the authors give the total numbers of GHR and PRLR from which the fractions were calculated at least in basal conditions?

      We have provided zoom-in views. As for panels B and C, fractions are number of clusters containing both receptors divided by the total number of clusters. We used the same strategy that we had used for calculating the localization changes: We randomly selected 4 ROIs (regions of interest) per cell to calculate fractions and then calculated the average of three different cells from independently repeated experiments. We did not calculate total numbers of GHR/PRLR. The numbers are fractions of cluster numbers.

      Moreover, the reviewer interprets results in panels B and C that ~80% of PRLR clusters also contain GHR. We assume the reviewer refers to Basal state. Now, the reviewer’s interpretation is not correct for the following reason: ~80% of clusters have both receptors. How many of the remaining (~20%) clusters have only PRLR or only GHR is not revealed in the panels. Only if 100% of clusters have PRLR, we can conclude that 80% of PRLR clusters also contain GHR.

      Also, while Figures 1 and 2 show localization based on dSTORM images, Figure 3 indicates and quantifies co-localization based on proximity ligation assays following DBSCAN analysis using Clus-DoC. We do not think that the results are directly comparable.

      Reviewer #3 (Public Review):

      (1) The manuscript suffers from a lack of detail, which in places makes it difficult to evaluate the data and would make it very difficult for the results to be replicated by others. In addition, the manuscript would very much benefit from a full discussion of the limitations of the study. For example, the manuscript is written as if there is only one form of the PRLR while the anti-PRLR antibody used for dSTORM would also recognize the intermediate form and short forms 1a and 1b on the T47D cells. Given the very different roles of these other PRLR forms in breast cancer (Dufau, Vonderhaar, Clevenger, Walker and other labs), this limitation should at the very least be discussed. Similarly, the manuscript is written as if Jak2 essentially only signals through STAT5 but Jak2 is involved in multiple other signaling pathways from the multiple PRLRs, including the long form. Also, while there are papers suggesting that PRL can be protective in breast cancer, the majority of publications in this area find that PRL promotes breast cancer. How then would the authors interpret the effect of PRL on GHR in light of all those non-protective results? [Check papers by Hallgeir Rui]

      We thank the reviewer for such thoughtful comments. We have added a paragraph in the Discussion section on the limitations of our study, including sole focus on T47D and γ2A-JAK2 cells and lack of PRLR isoform-specific data. Also, we are now mentioning that these isoforms play different roles in breast cancer, citing papers by Dufau, Vonderhaar, Clevenger, and Walker labs.

      We did not mean to imply that JAK2 signals only via STAT5 or by only binding the long form. We have made this point clear in the Introduction as well as in our revised Discussion section. Moreover, we have added information and references on JAK2 signaling and PRLR isoform specific signaling.

      In our Discussions section we are also mentioning the findings that PRL is promoting breast cancer. We would like to point out that it is well perceivable that PRL is protective in BC by reducing surface hGHR availability but that this effect may depend on JAK2 levels as well as on expression levels of other kinases that competitively bind Box1 and/or Box2 [1]. Besides, could it not be that PRL’s effect is BC stage dependent? In any case, we have emphasized the speculative nature of our statement.

      (1) Chhabra, Y., Seiffert, P., Gormal, R.S., et al. Tyrosine kinases compete for growth hormone receptor binding and regulate receptor mobility and degradation. Cell Rep. 2023;42(5):112490. doi: 10.1016/j.celrep.2023.112490. PMID: 37163374.

      Reviewer #3 (Recommendations for the authors):

      Points for improvement of the manuscript:

      (1) Method details -

      a) "we utilized CRISPR/Cas9 to generate hPRLR knockout T47D cells ......" Exactly how? Nothing is said under methods. Can we be sure that you knocked out the whole gene?

      We have addressed this point by adding two new sections on “Generating hGHR knockout and hPRLR knockout T47D cells” and “Design of sgRNAs for hGHR or hPRLR knockout” to the Methods section.

      b) Some of the Western blots are missing mol wt markers. How specific are the various antibodies used for Westerns? For example, the previous publications are quoted as providing characterization of the antibodies also seem to use just band cutouts and do not show the full molecular weight range of whole cell extracts blotted. Anti-PRLR antibodies are notoriously bad and so this is important.

      There is an antibody referred to in Figure 5 that is not listed under "antibodies" in the methods.

      We have modified Figure 5a, showing the entire gel as well as molecular weight markers. As for specificity of our antibodies, we used monoclonal antibodies Anti-GHR-ext-mAB 74.3 and Anti-PRLR-ext-mAB 1.48, which have been previously tested and used. In addition, we did our own control experiments to ensure specificity. We have added some of our many control results as Supplementary Figures S2 and S3.

      We thank the reviewer for noticing the missing antibody in the Methods section. We have now added information about this antibody.

      c) There is no description of the proximity ligation assay.

      We have addressed this by adding a paragraph on PLA in the Methods section.

      d) What is the level of expression of GHR, PRLR, and Jak2 in the gamma2A-JAK2 cells compared to the T47D cells? Artifacts of overexpression are always a worry.

      γ2A-JAK2 cell series are over-expressing the receptors. That’s the reason we did not only rely on the observation in γ2A-JAK2 cell lines but also did the experiment in T47D cell lines.

      e) There are no concentrations given for components of the dSTORM imaging buffer. On line 380, I think the authors mean alternating lasers not alternatively.

      Thank you. Indeed, we meant alternating lasers. We are referring to [1] (the protocol we followed) for information on the imaging buffer.

      (1) Beggs, R.R., Dean, W.F., Mattheyses, A.L. (2020). dSTORM Imaging and Analysis of Desmosome Architecture. In: Turksen, K. (eds) Permeability Barrier. Methods in Molecular Biology, vol 2367. Humana, New York, NY. https://doi.org/10.1007/7651_2020_325

      f) In general, a read-through to determine whether there is enough detail for others to replicate is required. 4% PFA in what? Do you mean PBS or should it be Dulbecco's PBS etc., etc.?

      We prepared a 4% PFA in PBS solution. We mean Dulbecco's PBS.

      (2) There are no controls shown or described for the dSTORM. For example, non-specific primary antibody and second antibodies alone for non-specific sticking. Do the second antibodies cross-react with the other primary antibody? Is there only one band when blotting whole cell extracts with the GHR antibody so we can be sure of specificity?

      We used monoclonal antibodies Anti-GHR-ext-mAB 74.3 and Anti-PRLR-ext-mAB 1.48 (but also tested several other antibodies). While these antibodies have been previously tested and used, we performed additional control experiments to ensure specificity of our primary antibodies and absence of non-specific binding of our secondary antibodies. We have added some of our many control results as Supplementary Figures S2 and S3.

      (3) Writing/figures-

      a) As discussed in the public review regarding different forms of the PRLR and the presence of other Jak2-dependent signaling

      We have added paragraphs on PRLR isoforms and other JAK2-dependent signaling pathways to the Introduction. Also, we have added a paragraph on PRLR isoforms (in the context of our findings) to the Discussion section.

      b) What are the units for figure 3c and d?

      The figures show numbers of localizations (obtained from fluorophore blinking events). In the figure caption to 3C and 3D, we have specified the unit (i.e. counts).

      c) The wheat germ agglutinin stains more than the plasma membrane and so this sentence needs some adjustment.

      We thank the reviewer for this comment. We have rephrased this sentence (see caption to Fig. 4).

      d) It might be better not to use the term "downregulation" since this is usually associated with expression and not internalization.

      While we understand the reviewer’s discomfort with the use of the word “downregulation”, we still think that it best describes the observed effect. Moreover, we would like to note that in the field of receptorology “downregulation” is a specific term for trafficking of cell surface receptors in response to ligands. That said, to address the reviewer’s comment, we are now using the terms “cell surface downregulation” or “downregulation of cell surface [..] receptor” throughout the manuscript in order to explicitly distinguish it from gene downregulation.

      e) Line 420 talks about "previous work", a term that usually indicates work from the same lab. My apologies if I am wrong, but the reference doesn't seem to be associated with the authors.

      At the end of the sentence containing the phrase “previous work”, we are referring to reference [57], which has Dr. Stuart Frank as senior and corresponding author. Dr. Frank is also a co-corresponding author on this manuscript. While in our opinion, “previous work” does not imply some sort of ownership, we are happy to confirm that one of us was responsible for the work we are referencing.

      Reviewing Editor's recommendations:

      The reviewers have all provided a very constructive assessment of the work and offered many useful suggestions to improve the manuscript. I'd advise thinking carefully about how many of these can be reasonably addressed. Most will not require further experiments. I consider it essential to improve the methods to ensure others could repeat the work. This includes adding methods for the PLA and including detail about the controls for the dSTORM. The reviewers have offered suggestions about types of controls to include if these have not already been done.

      We thank the editor for their recommendations. We have revised the methods section, which now includes a paragraph on PLA as well as on CRISPR/Cas9-based generation of mutant cell lines. We have also added information on the dSTORM buffer to the manuscript. Data of controls indicating antibody specificity (using confocal microscopy) have been added to the manuscript’s supplementary material (see Fig. S2 and S3).

      I agree with the reviewers that the different isoforms of the prolactin receptor need to be considered. I think this could be done as an acknowledgment and point of discussion.

      We have revised the discussions section and have added a paragraph on the different PRLR isoforms, among others.

      For Figure 2E, make it clear in the figure (or at least in legend) that the middle line is the basal condition.

      We thank the editor for their comment. We have made changes to Fig 2E and have added a sentence to the legend making it clear that the middle depicts the basal condition.

      My biggest concern overall was the fact that this is all largely conducted in a single cell line. This was echoed by at least one of the reviewers. I wonder if you have replicated this in other breast cancer cell lines or mammary epithelial cells? I don't think this is necessary for the current manuscript but would increase confidence if available.

      We thank the editor for their comment and fully agree with their assessment. Unfortunately, we have not replicated these experiments in other BC cell lines nor mammary epithelial cells but would certainly want to do so in the near future.

    1. Reviewer #2 (Public review):

      Summary:

      In this manuscript, the authors examined the representational geometry of orientation representations during visual perception and working memory along the visual hierarchy. Using representational similarity analysis, they found that similarity was relatively evenly distributed among all orientations during perception, while higher around oblique orientations during WM. There were some noticeable differences along the visual hierarchy. IPS showed the most pronounced oblique orientation preferences during WM but no clear patterns during perception, likely due to the different task demands for the WM orientation task and the perception contrast discrimination task. The authors proposed two models to capture the differences. The veridical model estimated the representational geometry in perception by assuming an efficient coding framework, while the categorical model estimated the pattern in WM using psychological distances to measure the differences among orientations (including estimates from a separate psychophysical study performed outside the scanner). Therefore, I think this work is valuable and advances our understanding of the transition from perception to memory.

      Strengths:

      The use of RSA to identify representational biases goes beyond simply relying on response patterns and helps identify how representational formats change from perception to WM. The study nicely leverages ideas about efficient coding to explain perceptual representations that are more veridical, while leaning on ideas about abstractions of percepts that are more categorical-psychological in nature (but see (1) below). Moreover, the match between memory biases of orientation and the patterns estimated with RSA were compelling (but see (2) below). I found the analyses showing how RSA and decoding (eg, cross-generalization) are associated and how/why they may differ to be particularly interesting.

      Weaknesses:

      (1) The idea that later visual maps (ie, IPS0) encode perceptions of orientation in a veridical form and then in a categorical form during WM is an attractive idea. However, the support is somewhat weakened by a few issues. The RSA plots in Figure 1C for IPS0 appear to show a similar pattern, but just of lower amplitude during perception. But in the model fits either for orientation statistics or estimated from the psychophysics task, the Veridical model fits best for perception and the Categorical model fits best for memory in IPS0. By my eye, the modeled RSMs in Figures 2 & 3 do not look like the observed ones in Figure 1C. Those modeled RSMs look way more categorical than the observed IPS0. They look like something in between.

      (2) My biggest concern is the omission of the in-scanner behavioral data. Yes, on the one hand, they used the N=17 outside the scanner psychophysics dataset for the analyses in Figure 3. On the other hand, they do not even mention the behavioral data collected in the scanner along with the BOLD data. Those data had clear oblique effects if I recall correctly. Why use the data from the psychophysics experiment? Also, perhaps a missed opportunity; I wonder if the Veridical/Categorical models fit a single subject's RSA data matches that subject's behavioral biases. That would really be compelling if found.

      The data were collected (reanalysis of published study) without consideration for the aims of the current study, and are therefore not optimized to test their goals. The biggest issue is that "The distractors are really distracting me." I'm somewhat concerned about how the distractors may have impacted the results. I honestly did not notice that the authors were using delay periods that had 11s of distractor stimuli until way into the paper. On the one hand, the "patterns" of the model fits across the ROIs appear to be qualitatively similar. That's good if you want to pool data like the authors did. But, while the authors state on line 350 "..we also confirmed that the presence of distractors during the delay did not impact the pattern of results in the memory task (Supplementary Figure 5)." When looking at Supplementary Figure 5, I noticed that there are a couple of exceptions to this. In the Gratings distractor data, V1 shows a better fit to the Veridical model, while V4 and IPS0 shows no better fit to either model. And in the Noise distractor data, neither model fits better for any ROI. At first glance, I was concerned, but then looking at the No distractor data, the pattern is identical to that of the combined data. Thus, this can be seen as a glass half full/empty issue as almost all of the ROIs show a similar pattern, but still it would concern me if I were leading this study. This gets me to my key question, why even use the distractor trials at all, where the interpretation can get dicey? For instance, the authors have shown in this exact data that the impact of distraction affects the fidelity of representations differently along the visual hierarchy (Rademaker, 2019), consistent with several other studies (eg., Bettencourt & Xu, 2016; Lorenc, 2018; Hallenbeck et al., 2022) and with one of the author's preprints (Rademaker & Serences, 2024). My guess is that without the full dataset, some of the RSA analyses are underpowered. If that is the case, I'm fine with it, but it might be nice to state that.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      *Reviewer #1 (Evidence, reproducibility and clarity (Required)): *

      • The authors investigate in this study the function of LIN-42 for the process of precise molting timing in C. elegans. To achieve this, they compare LIN-42 with its mammalian ortholog, Period. They found that similar to Period, LIN-42 interacted with the kinase KIN-20, a mammalian Casein kinase 1 (CK1) ortholog. Hence, two different proteins involved in rhythmic processes, LIN-42 and Period function in a conserved manner. *
      • First, they used mutants with specific deletions to untangle various phenotypes during C. elegans development. From this analysis they identify a specific region, corresponding to a CK1-binding region in mammals, to be mainly involved in the rhythmic molting phenotype. Next, they identify KIN-20, the CK1 ortholog as interaction partner of LIN-42. They even were able to demonstrate an interaction of CK1 with the region of LIN-42. Using CK1, they identified potential phosphorylation sites within LIN-42 and compared those with immunoprecipitated protein in vivo. There was a substantial overlap. While the C-terminal tail of LIN-42 was heavily phosphorylated, deletion of the C-terminal part resulted only in a minor phenotype for rhythmic molting. Last but not least, they demonstrated that point mutations that inactivate the catalytic function of KIN-20 produced a rhythmic molting phenotype. The interaction of LIN-42 with KIN-20 affected the localization of the kinase, similar to what was found to Period and CK1. *
      • Overall, the experiments are well done, well controlled and well described even for non-specialists. I guess it was not easy to kind of sort out the many overlapping phenotypes. It was certainly helpful just to focus on the clear rhythmic molting phenotype. *

      • I have no major or minor comments. *

      • Reviewer #1 (Significance (Required)): *

      • The manuscript is well written and can be followed by non-specialists of the field. The experiments are well performed. Even if some experiments did not yield the expected phenotype, e.g. deletion of the C-terminal tail of LIN-42 had only a minor phenotype inspire of heavy phosphorylation, these experiments are anyhow included and explained. *

      • Overall, the study is interesting for people in the C. elegans field and by similarity mammalian chronobiology. I would expect that most of the progress based on this study will be on the further elucidation of the molting phenotype and how the other phenotypes related to this. Then this could emerge as a blueprint for molting phenomena in other species as well. *
      • I am a mammalian chronobiologist working on Period proteins. *

      We thank the reviewer for their positive evaluation of our work.

      *Reviewer #2 (Evidence, reproducibility and clarity (Required)): *

      • This study represents pioneering work on LIN-42, the C. elegans ortholog of PER, uncovering its role in molting rhythms and heterochronic timing. A key strength of this work lies in its integrative approach, combining genetic and developmental analyses in C. elegans with biochemical characterization of LIN-42 protein. *

      • At the organismal level, the authors take advantage of the power of C. elegans as a model system, employing precise genetic manipulations and high-resolution developmental assays to dissect the contributions of LIN-42 and its interaction partner KIN-20, the C. elegans ortholog of CK1, to molting rhythms. Their findings provide in vivo evidence that binding of LIN-42 with KIN-20 promotes the nuclear accumulation of KIN-20 and is crucial for molting rhythms, while its PAS domain appears dispensable for this function. This detailed phenotypic analysis of multiple LIN-42 and KIN-20 mutants represents a significant contribution to our understanding of the developmental clock. *

      • At the biochemical level, the study provides a detailed analysis of the mechanism underlying LIN-42's interaction with CK1, demonstrating that LIN-42 contains a functionally conserved CK1-binding domain (CK1BD). Through their in vitro kinase assays and structural insights, the authors identified distinct roles for CK1BD-A and CK1BD-B: the former in kinase inhibition and the latter in stable CK1 binding and phosphorylation. Importantly, their data align well with previous findings on PER-CK1 regulation in mammalian and Drosophila systems, reinforcing the evolutionary conservation of key clock components. *

      • Overall, this work stands out for its deep and important insights into how CK1-mediated regulation extends beyond the circadian clock to regulate the developmental clock. The combination of genetic approaches with biochemical analyses makes this an outstanding contribution to both chronobiology and nematode developmental biology. *

      We thank the reviewer for the strong endorsement for publication of our work

      *Major comment 1: * * In Figure 2D, I could not find a crucial control if the authors claim that KIN-20 binds to LIN-42. For example, a single mutant of LIN-42-3xFLAG could be used as a control for the double mutant. *

      We will do an appropriate control experiment.

      *Major comment 2: * * The sizes of the KIN20 bands were very diverged (~40 kDa and ~60 kDa), but the authors provide no explanation for this. *

      The worm produces several KIN-20 isoforms. We will state this in the revised manuscript.

      *Major comment 3: * * Regarding the MS study, the raw data are available, but the detailed supplemental Excel files would be more informative for readers. For example, are other interactors such as REV-ERB/NHR-85 detected in Figure 2A? Regarding Figure 4F, the list of phosphorylation sites and MS scores is also informative. *

      We apologize for our omission in stating clearly in the figure legend that the significantly enriched proteins were labeled with a red dot. These were only LIN-42 itself and KIN-20. NHR-85 was not enriched. We will state this explicitly in a revised version and provide all relevant information.

      *Major comment 4: * * It is an important finding that the PAS domain of LIN-42 is not essential for the molting rhythms. Is the PAS domain also dispensable for binding with KIN-20? *

      Although we have currently no reason to assume that the PAS domain would be required for KIN-20 binding, we will perform an in vitro experiment to test for binding.

      *Major comment 5 (Optional): * * In this study, the authors carefully performed in vitro kinase assays, and I strongly suggest that they investigate whether the CKI-mediated phosphorylation of LIN-42 is temperature-compensated and whether the CKI-BD-AB regions affect it. *

      Although this is an interesting question, addressing it appears outside the scope of the manuscript and a revision; please see section 4 below.

      *Major comment 6 (Optional): * * In Figure 6, the authors argue that the CKI-BD of LIN-42 is important for CK1 nuclear translocation. It would be better to show the effect of the nuclear accumulation of CKI on nuclear proteins, like the mammalian CKI-PER2-CLOCK story. Does CKI localization affect phosphorylation status of other clock-related proteins including REV-ERB/NHR-85? * * Phospho-proteome analysis would identify nuclear substrates of CK1. In addition, is phosphorylation of LIN-42 dispensable for the CK1 nuclear translocation? *

      This is another interesting question yet currently nothing is known about other CK1/KIN-20 targets, and we have no evidence for NHR-85 being one. Please see our detailed comments in the section 4 below.

      To address whether LIN-42 phosphorylation affects CK1/KIN-20 nuclear accumulation, we will seek to examine KIN-20 localization in LIN-42∆Tail animals.

      *Major comment 7 (Optional): * * LIN-42 rhythmic expression could drive rhythmic nuclear accumulation of KIN-20. It would be better to examine this possibility using kin-20::GFP in lin-42 mutants. *

      We agree that the mutant analysis is important for this and Fig. 6C shows reduced KIN-20 nuclear accumulation in LIN-42∆CK1BD.

      Minor 1: * * I could not find the full gel images of the Western blot analyses as supplemental materials.

      This data will be added.

      Minor 2: * * The authors discussed a conserved module in two different clocks. A statement regarding a recently published paper (Hiroki and Yoshitane, Commun Biol, 2024) would be informative for readers.

      We will add such a statement.

      ***Referee cross-commenting** *

      • I basically agree with reviewer 1 and hope that this paper will be published soon as it is very valuable for our field. I have constructively pointed out some parts that could be improved, but depending on the editor's judgement, I believe that even if not all of these are revised, it will be sufficient for publication. *

      • Reviewer #2 (Significance (Required)): *

      • This work stands out for its deep and important insights into how CK1-mediated regulation extends beyond the circadian clock to regulate the developmental clock. The combination of genetic approaches with biochemical analyses makes this an outstanding contribution to both chronobiology and nematode developmental biology. *

      • I strongly suggest editors to accept this study with minor modifications according to the following comments.*

      We thank the reviewer for their strong support and the clear indication of required vs. optional revisions.

      *Reviewer #3 (Evidence, reproducibility and clarity (Required)): *

      • In their manuscript "A conserved chronobiological complex times C. elegans development", Spangler, Braun, Ashley et al. investigate the mechanisms through which the PERIOD orthologue, lin-42, regulates rhythmic molting in C. elegans. Through precise genetic manipulations, the authors identify a particular region of lin-42, the 'CK1BD', which regulates molting timing, with less effect on other lin-42 phenotypes (e.g. heterochrony). They show that LIN-42 and the casein kinase 1 (CK1) homologue KIN-20 interact in vivo, and identify phosphorylation sites of LIN-42. Using biochemical assays, they find that the CK1BD of LIN-42 is sufficient for interaction with the human homologue of KIN-20, CK1, in vitro. The LIN-42 CK1BD is also required for the proper nuclear accumulation of KIN-20 in vivo. Furthermore, a point mutation that should disrupt the catalytic activity of KIN-20 also shows an irregular molting phenotype, similar to the lin-42 CK1BD mutant. The manuscript is very well-written and the data and methods are well-presented and detailed. Overall this work makes a convincing case that the C. elegans lin-42:Kin-20 and mammalian period:Ck1 interactions have functionally conserved roles in the oscillatory developmental programs of each organism (molting timing and circadian rhythms, respectively), with a few caveats below that can be addressed.*

      We thank the reviewer for their positive evaluation of our work.

      *Major comments: *

        1. The authors have shown that LIN-42 is phosphorylated in vivo, but the dependence of this phosphorylation on KIN-20 is not fully addressed. In the discussion (lines 417-420), the authors mention that the unhealthy phenotype of the kin-20 mutant animals prevented them from assessing LIN-42 phosphorylation in this genetic background. To bolster their model and to circumvent this issue, it should be feasible to generate a kin-20 degron allele and to perform the LIN-42 phospho-proteomics upon inducible degradation. Alternatively, perhaps a phos-tag western blot for LIN-42 could be used to compare the kin-20 wild-type to kin-20 mutants.*

      We agree, and acknowledged in the discussion, that phoshorylation of LIN-42 by KIN-20 in vivo has not been demonstrated by us. However, as discussed in the section 4 below, we find that this costly, challenging and time-consuming experiment is not warranted by the expected gain.

      For technical reasons, the in vitro biochemistry was done using human CK1 protein. There are a few places (e.g. results, line 248 and discussion line 437), where the language, in my opinion, is extrapolating the CK1 results too strongly to KIN-20. The authors mention that feedback inhibition is a known property of human CK1. It is indeed quite striking that the LIN-42 CK1BD region interacts with and is phosphorylated by the human counterpart of KIN-20, and that feedback inhibition is also seen! However, the language about KIN-20 itself should be softened, since there does not appear to be clear evidence that KIN-20 exhibits the same properties as human CK1 (unless perhaps human CK1 can functionally replace KIN-20 in worms, or the proteins were extremely similar?)

      We will follow the reviewer’s advice and carefully examine the text for instances where we extrapolated too much and tone these down. (We note that this does not apply to the example of line 248 where we wrote “Collectively, our data establish that the LIN-42

      CK1BD is functionally conserved and mediates stable binding to the CK1 kinase domain.”, i.e., there was no mentioning of KIN-20.)

      The role of the three LIN-42 isoforms should be further clarified. Minimally, it should be explained why the alleles where both b and c isoforms should be flag-tagged seem to only produce detectable b isoform (e.g. Fig. 2C).

      We will clarify that the individual roles of the isoforms are largely unknown and that we can only speculate that the c-isoform may exhibit either generally low expression or expression in only few cells or tissues.

      4. Related to points 2 and 3 above, the authors have shown that the CKIBD mediates association with human CK1 in vitro, and is required for nuclear accumulation of KIN-20 in vivo, but not that the complex formation between LIN-42 and KIN-20 depends on the CK1BD. Given the reciprocal co-IP findings, it should be feasible to create tagged versions of lin-42(deltaCK1BD) and to determine the effect on LIN-42-KIN-20 complex formation. While there is already a b-isoform tag, an a-isoform tag would also help to address whether both the b and a isoforms interact with KIN-20 in a CK1BD-dependent manner in vivo. These strains would also allow the authors to determine how the CK1BD deletion affects overall levels/stability/rhythmic accumulation of LIN-42(a or b), which would potentially serve to strengthen their conclusions about the role of the lin-42 CK1BD.

      We will attempt to generate a FLAG-tagged LIN-42∆CK1BD to perform IP and check for binding of KIN-20.

      As detailed in section 4, we cannot tag LIN-42a individually due to the structure of the genomic locus, and its level appear very low to begin with.

      In the molting timing assay, there is an unexpected result where the delta-C-terminal-tail lin-42 allele resembles the n1089 (N-terminal deletion) (line 315). Could the authors more clearly explain this finding?

      As we point out in the manuscript, n1089 is a partial deletion with a breakpoint in a noncoding (intronic) region of lin-42. Accordingly, it is currently unknown, what mature transcripts and proteins are made in the mutant animals. This prevents us from making educated guesses as to why there is a phenotypic resemblance between these and lin-42∆tail mutant animals. We will clarify in the manuscript that this is an interesting, but currently unexplained observation.

      *Minor comments: *

        1. The correspondence between the LIN-42 "SYQ" and "LT" motifs and the motifs referred to as "A" and "B" should be clarified, and consistent names/labels used. Are these interchangeable names? If it is necessary to use both names, the differences between SYQ/LT and A/B should be made more clear.*

      We agree that the situation is not completely satisfactory but feel that we need to use both names since they have both been used in the literature. We will work to revise the text to reflect more clearly the correspondence.

      For data presented as "% of animals", please indicate the number of animals scored (e.g. egl, alae assays - ~ how many animals per replicate (dot)?).

      We will provide these numbers.

      Line 145-148 - Mentioning the relevant phenotype(s) of the lin-42 null allele from the cited paper would provide a good point of comparison here.

      We will mention the previously described phenotypes.

      Line 201 - the phrase "This is also true for the proteins:" is unclear, as the previous sentence states that both lin-42 and kin-20 mRNAs oscillate, while the next sentence says that only LIN-42 protein oscillates.

      We apologize for the confusion and will correct the text.

      Line 231 - please explain the significance of the 'lower response signal' in the BLI assay for the CKIBD(no tail).

      We will clarify that the lower response signal observed for the CK1BD compared to the CK1BD+Tail (residues 402-589; same construct used in Fig. 3B) reflects its smaller molecular weight, which reduces the overall mass contribution to the BLI sensor.

      Fig. 2 - C/D - the genotype lane labels should I think indicate an N-terminal rather

      We will fix this mistake.

      7. Fig. 6, line 367 - lin-42 is variably described as promoting increased KIN-20 'nuclear accumulation' or 'localization'. I think that 'accumulation' is more accurate, as it doesn't imply a specific mechanism for the difference (transport vs stabilization, etc.)

      We will revise the manuscript accordingly.

      *8. Fig 6B - an overlay of the panels or another way of quantifying the colocalization would make this result more clear. *

      We will supply the requested overlay.

      *Reviewer #3 (Significance (Required)): *

      • This work presents a major mechanistic and conceptual advance in our understanding of the role of lin-42/Period, a conserved key regulator of C. elegans development. Previously, it was not clear if the heterochronic and circadian functions of lin-42 were genetically separable, nor was it known how LIN-42 physically interacted with the CK1 homologue. This work addresses these questions using precise genome engineering and detailed phenotypic and biochemical approaches. The work also reveals the conservation of bi-directional/reciprocal regulation between lin-42 and kin-20. The main limitations of the study, which can potentially be addressed as outlined in the 'major points' above, are that evidence should be provided that lin-42 phosphorylation depends on kin-20 in vivo, and that the CK1BD mediates the interaction in vivo (since the in vitro work is with human CK1). As the authors indicate, this is the first 'conserved clock module' of this type, and this work will therefore be of significant interest to both the C. elegans developmental biology and the more general biological timing fields. *

      • Field of expertise of the reviewer- C. elegans genetics and development.*

      Description of the studies that the authors prefer not to carry out

      *Major comment 5 (Optional): * * In this study, the authors carefully performed in vitro kinase assays, and I strongly suggest that they investigate whether the CKI-mediated phosphorylation of LIN-42 is temperature-compensated and whether the CKI-BD-AB regions affect it. *

      Temperature compensation is of course one of the most striking features of circadian clocks, and CK1-mediated phosphorylation of PER appears a critical component. We agree that it would be interesting to examine whether or not this feature exists in an animal whose development is not or only partially temperature-compensated. However, these studies are not straightforward – we would first have to set up an assay and demonstrate temperature compensation for the mammalian PER – CK1 pair as a positive control. We were not able to purify KIN-20 so could only test whether the LIN-42 substrate promoted temperature compensation. Moreover, either result for LIN-42 – CK1 would immediately raise new questions that would deserve extensive follow-up: if there is temperature compensation, why is worm development not compensated? If there is none, where/how do the interactions between CK1 and LIN-42 differ from those between CK1 and PER? Hence, we propose that these studies are outside the scope of the current study.

      *Major comment 6 (Optional): * * In Figure 6, the authors argue that the CKI-BD of LIN-42 is important for CK1 nuclear translocation. It would be better to show the effect of the nuclear accumulation of CKI on nuclear proteins, like the mammalian CKI-PER2-CLOCK story. Does CKI localization affect phosphorylation status of other clock-related proteins including REV-ERB/NHR-85? * * Phospho-proteome analysis would identify nuclear substrates of CK1. In addition, is phosphorylation of LIN-42 dispensable for the CK1 nuclear translocation? *

      We agree that it will be important to identify relevant targets of KIN-20 in future work. Unfortunately, at this point, none are known, and we especially do not have any knowledge of the phosphorylation status of NHR-85. Indeed, in unrelated (and unpublished) work we have done a phosphoproteomics time course of wild-type animals. We have not detected any NHR-85-derived phosphopeptides in our analysis. Thus, this would establish a completely new line of research, incompatible with the timelines of a revision.

      @Ref. 3:

      1. *The authors have shown that LIN-42 is phosphorylated in vivo, but the dependence of this phosphorylation on KIN-20 is not fully addressed. In the discussion (lines 417-420), the authors mention that the unhealthy phenotype of the kin-20 mutant animals prevented them from assessing LIN-42 phosphorylation in this genetic background. To bolster their model and to circumvent this issue, it should be feasible to generate a kin-20 degron allele and to perform the LIN-42 phospho-proteomics upon inducible degradation. Alternatively, perhaps a phos-tag western blot for LIN-42 could be used to compare the kin-20 wild-type to kin-20 mutants. * We agree, and acknowledged in the discussion, that phoshorylation of LIN-42 by KIN-20 in vivo has not been demonstrated by us. However, since our data from the LIN-42∆Tail mutant also suggest that LIN-42 phosphorylation be functionally largely dispensable for KIN-20’s function in rhythmic molting, we consider further elucidation of this point a lower priority, especially considering the challenges involved. As we have seen for our unpublished work on wild-type animals, a phosphoproteomics experiments would be costly and time-consuming, with a non-trivial analysis (due to the underlying dynamics of protein level changes). A phos-tag gel would be subject to multiple confounders given the abundance of the phosphosites that we detected on immunoprecipitated LIN-42 – unlikely to stem only from KIN-20 activity – and an increase in total LIN-42 levels that we observe upon KIN-20 depletion, and thus appears unsuited to providing a meaningful answer.

      *Related to points 2 and 3 above, the authors have shown that the CKIBD mediates association with human CK1 in vitro, and is required for nuclear accumulation of KIN-20 in vivo, but not that the complex formation between LIN-42 and KIN-20 depends on the CK1BD. Given the reciprocal co-IP findings, it should be feasible to create tagged versions of lin-42(deltaCK1BD) and to determine the effect on LIN-42-KIN-20 complex formation. While there is already a b-isoform tag, an a-isoform tag would also help to address whether both the b and a isoforms interact with KIN-20 in a CK1BD-dependent manner in vivo. These strains would also allow the authors to determine how the CK1BD deletion affects overall levels/stability/rhythmic accumulation of LIN-42(a or b), which would potentially serve to strengthen their conclusions about the role of the lin-42 CK1BD. *

      As detailed in section 2, we will address the point concerning LIN-42∆CK1BD. However, due to the overlapping exons, we are unable to tag the a-isoform independently of the b-isoform. Moreover, in a western blot of a line where both a- and b-isoforms are tagged, we have observed only little or no LIN-42a signal, suggesting that, like the c-isoform, its expression may be more limited, making biochemical characterization difficult. Hence, these experiments are not feasible.

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      Reply to the reviewers

      We thank the reviewers for their comments and have included substantial new data to strengthen the work by specifically addressing questions regarding the molecular mechanisms driving the proteomic and phenotypic changes observed in these disease models. We have generated a new ganglioside disease model (GM1 gangliosidosis) and demonstrated that the lysosomal exocytosis mechanism identified for GM2 gangliosidosis is a conserved mechanism that alters the PM proteome (see new Figure 5).

      We have also carried out substantial additional experimental work to address the question of whether specific protein-lipid interactions drive some of these changes. We have preliminary data supporting this (included below) but we are not confident that these data are robust enough for inclusion in this manuscript. This work required substantial in vitro experiments including the expression and purification of several proteins for use in liposome binding assays. Although these data are promising, they have been challenging to reproduce and we would prefer to develop this work further for inclusion in a subsequent paper.

      Although not requested by any reviewers we have also included substantial additional multielectrode array (MEA) data in Figure 4 to further support the phenotypic changes to electrical signalling seen in the Tay Sachs disease model.

      We would like to note that even without these new data the reviewers highlighted that the “high-quality data presented significantly advance the field” and that the work “exposes key conceptual novelties” using “new insight” and “new tools” that shed “light on the complex pathophysiology that links lipid accumulation to neuronal dysfunction”. And that this highlights “an underappreciated dimension of these diseases” allowing them to be “understood better thanks to this study”. More generally the reviewers state that the work is of interest to both “clinicians and basic researchers” and is relevant to “broader fields in cellular and neurodegenerative biology”.

      Point-by-point description of the revisions

      • *

      Reviewer 1

      Confirmation of Neuronal Differentiation: To confirm neuronal differentiation in their i3N cell model, the authors show qPCR results indicating the expression of mature neuronal markers and the downregulation of stem cell markers by day 14. However, single-cell RNA sequencing (scRNA-seq) could provide a more detailed evaluation of the differentiation process, addressing the fine-grained cell-type composition within the cell population. Depending on the results, the authors might more precisely interpret functional data and assess the possible influence of increased GM2 levels on cell fate decisions.

      The accumulation of GM2 may not be identical across all neurons and so it is possible that, although the neuronal populations as a whole display mature differentiation, individual cells may respond differently to the amount of lipid debris. However, there are several technical reasons why obtaining samples for scRNAseq is extremely challenging. By 14 dpi the separation of individual neurons from each other is very difficult as they are in a densely grown and highly attached and interconnected network. Furthermore, the individual neurons have a highly polarized differentiated morphology with long delicate axonal and dendritic projections, that are readily cleaved and lysed in the process of harvesting and dissociation to obtain single cell suspensions for FACS sorting. In neurons, mRNAs are also abundantly localised along the length of their neuritic projections [1], thus these damaged preparations would provide unreliably meaningful data. Alternatively, sufficiently isolated individual neurons show poor survival and do not mature. If these technical difficulties could be overcome, in order to monitor altered differentiation, it would be necessary to determine which timepoint was most relevant to capture differences between day 0 stem cells and day 28 when they are synchronously firing glutamatergic neuron cultures. For this analysis to be robust it would require sample preparation and analysis of multiple stages of the differentiation process. For all the reasons above we cannot address this reviewer’s request.

      Mechanistic Links Between Lipid Accumulation and Proteomic Changes: The authors report specific proteome changes upon HEXA/B KO. What are the mechanistic links between lipid accumulation and proteomic changes? Is the overall degradative performance of lysosomes compromised? The authors note that certain proteins, such as TSPANs, can bind directly to GSL headgroups. Clarifying whether the observed proteomic changes result from specific, direct lipid-protein interactions versus indirect effects could strengthen the argument for targeted lipid-mediated proteomic shifts.

      In response to these questions, we have carried out substantial additional experimental work testing the lipid interactions of some of the proteins that are most altered in their abundance at the PM. We focussed on the top non-lysosomal proteins as we are proposing that the lysosomal ones are primarily changed due to lysosomal exocytosis, suggesting the non-lysosomal are the best candidates for direct GSL-binding. To robustly identify specific lipid-protein interactions is highly challenging but something we have demonstrated previously [2].

      In vitro lipid-binding assays require expression and purification of the proteins of interest to then be used in liposome pulldown experiments using liposomes of defined composition. As we are most interested in the specificity of the headgroup interaction we focussed on producing the extracellular portions of these proteins that would be predicted to bind these headgroups (again this is a strategy we have successfully used previously [2]). We expressed and purified the extracellular domains of three top non-lysosomal hits: CNTNAP4, CNTN5 and NTRK2 (Fig. R1A, provided in attached response document). These purified proteins were used in liposome-binding assays using liposomes composed of different sphingolipids and gangliosides (Fig. R1B). These data demonstrate that the GPI-anchored protein CNTN5 and its potential binding partner CNTNAP4 bind promiscuously to different headgroups. This may be consistent with their being incorporated into GSL-rich membrane microdomains via the GPI-anchor. Interestingly, in this assay NTRK2 demonstrates specific and substantial binding to GM2, with some weaker binding to GD3.

      These data support that the increased abundance of NTRK2 at the PM could be driven by direct interactions with the same lipid that is accumulating at the PM. As exciting and compelling as these data are, we have subsequently been unable to repeat this observation for NTRK2. We are unsure why and have tried several different strategies to test this interaction, but at this stage with only an N=1 for this observation we do not feel confident to include these data in the manuscript.

      We intend to pursue this further using a range of alternative techniques and protein constructs but this will take substantial additional time and effort that we feel go beyond the scope of this current manuscript.

      Additionally, does this phenomenon extend to other sphingolipidoses (e.g., Gaucher disease)? Comparing the proteomes of i3N cells across different sphingolipidoses could reveal whether the accumulation of distinct GSLs produces unique or shared proteomic profiles, highlighting similarities or specificities across lysosomal storage disorders.

      We agree with the reviewer that this is an interesting and important question and had intended to do this as follow-up work in a future publication. However, in the interests of addressing this point here, we are including additional data we have generated from a new i3N model of GM1 gangliosidosis. As for the GM2 gangliosidosis models, we used CRISPRi to knockdown GLB1 and have confirmed this KD by q-PCR. We have also profiled the GSL composition and quantified the increased GM1 abundance. We have followed this up with both whole-cell and PM proteomics. We have presented comparative proteomics of the two models and demonstrated that they both result in significant accumulation of lysosomal proteins both in cells and at the PM. This shared proteomic profile is consistent with lysosomal exocytosis being a conserved mechanism driving altered PM composition in these diseases. We have included this work as an additional results section and an additional figure (Figure 5) as well as expanding the discussion. For this analysis we collected mass spec data at 28 dpi based on our observations in the paper that electrical signalling was synchronised at this point (Fig 4). In the text we discuss additional changes in these new WCP data such as the appearance of other trafficking molecules such as Arl8a that further support a lysosomal exocytosis mechanism.

      In terms of the unique proteomic profiles of these diseases, the read depth of the PMP data in this case was not sufficient to confidently identify differences between the two gangliosidosis models and therefore we intend to pursue this work with additional LSDs in future studies to be included in a follow-up paper.

      In terms of mechanistic links between lipid accumulation and proteome changes, we feel these new data provide substantial additional support that the appearance of lysosomal proteins at the PM is driven by lysosomal exocytosis and have preliminary data supporting that some non-lysosomal protein changes may be driven by altered protein-lipid interactions.

      Impact of Increased PM GM2 Levels on Endocytic Pathways: Along similar lines, the authors show differences in the PM proteome and in the representation of specific PM lipid domain-associated proteins. As some of these proteins are turned over by mechanisms involving lipid domain-dependent endocytosis, the authors might want to examine the effect of increased PM GM2 levels on various endocytic pathways.

      We thank the reviewer for this suggestion and have attempted assays monitoring endocytosis using several approaches including the uptake of fluorescently labelled bovine serum albumin (DQ-BSA) [3–5]. These endocytosis assays are well established in standard cell lines such as HeLa cells. Despite several attempts by us to get this working in neurons using multiple alternative readouts (microscopy and plate-based fluorescence) we have been unable to measure changes in endocytosis. Exploration of alternative methods to probe Clathrin-independent/dynamin-independent endocytosis (CLIC/GEEC) suggests these pathways are difficult to observe by fluorescence microscopy as there is minimal concentration of cargo proteins during the formation of carriers before endocytosis [6]. As an alternative strategy to probe changes in lipid-domain dependent endocytosis we have analysed the proteomics data for changes in galectins but no changes were identified in the data. We also explored available tools for modulating lysosomal exocytosis and monitoring lysosomal movement including activating TRPML1 to trigger exocytosis and activating ABCA3 to drive more lipid accumulation [7–10]. Similarly to the endocytosis assays above, these were not translatable to neurons in our hands due to a range of challenges including increased toxicity of these drugs on this cell type. We have made a substantial effort to try and address these questions and have conferred with colleagues who have also reported difficulties in establishing these assays in neurons. We are keen to continue to pursue this question but due to the technical challenges we feel this work lies beyond the scope of the current manuscript.

      Multifaceted Nature of Gangliosidoses as PM Disorders: The manuscript presents an important perspective by reframing gangliosidoses as multifaceted PM disorders that disrupt neuronal function and membrane composition. By further elaborating on the connection between membrane lipid alterations, neuronal excitability, and synaptic composition, and by exploring the interplay with lysosomal dysfunction, the authors could provide a richer understanding of gangliosidoses and GSL function in general.

      We appreciate that the reviewer agrees with us that reframing gangliosidoses as more complex multifaceted diseases is important. We are not sure if there is a request here for more elaboration in the text but based on the new data included in the paper, we have expanded some of the discussion around these points. We are very enthusiastic to continue to probe the connections and interplay as described by the reviewer and this is the focus of our ongoing studies.

      Reviewer 2

      1. T-tests and one-way ANOVAs were used, but it is not clear if datasets were tested for normality and equal standard deviations. Please add these details. If data are not normal or standard deviations are unequal, other tests will have to be used.

      All graphs were checked for normality and variance in standard deviation and for figure 1F, where the data was not normally distributed, a Kruskal-Wallace test was used in place of a one-way ANOVA. All significantly different results are now labelled on graphs and the relevant tests described in the figure legends. This has also all been updated in the Supplementary data.

      1. It needs to be clearly explained how many data points were used for statistical analyses and what the data points were. E.g., N=3 independent experiments on 3 different days, each done in n=3 different wells, total n=9. Each well can be considered a biological replicate, but it's of lesser value than the "big Ns" done on different days. The authors can choose different ways of defining their N/n numbers, but it has to be transparent. The bar graphs would ideally display the data points.

      All figure legends now clearly explain N and n numbers used in experiments. Individual data points are displayed on qPCR graphs where N and n are mixed, with shapes denoting the biological repeat (N). In addition to clarification in figure legends, N and n numbers are described in the methods sections where appropriate.

      For completeness we also include here details of these N/n numbers.

      • For the q-PCR experiments, technical triplicates (repeats on the same day, n=3) were carried out for 3 separate biological replicates on different days (N=3). We have changed how these data are plotted to clarify this.
      • For the activity assays, N=3 biological replicates were carried out on cell lysates from cultures grown on different days.
      • For the microscopy analysis, coverslips from N=3 biological replicates on different days were used. n=2 coverslips per N were used to generate 15 images per N.
      • For the glycan analysis, N=3 independent cell pellets were prepared on different days.
      • For the proteomics experiments, these were done as N=3 independent cell cultures grown and prepared on different days. Specifically, one of each cell line SCRM, HEXA-1, HEXA-2, HEXB-1 and HEXB-2 were grown and harvested or biotinylated at a time (for WCP or PMP), with repeats on different days. These N=3 were then combined for the ΔHEX-A/B lines to provide N=12 biological repeats for disease cell lines to be compared to N=3 biological repeats for “SCRM” control cell lines.
      • For calcium imaging, n=4 wells for each of SCRM, ΔHEXA-1 and ΔHEXB1 were averaged and the mean from each was used to provide n=3 data points across two biological repeats of this experiment, N=2.
      • For the MEA data, we now include substantially more data than in the original manuscript (see comments at the top of this document). This is now N=3 biological replicates across n=52 wells over a time period from 38-45 dpi.
      • The N/n values and statistical tests have also all been updated in the Supplementary data.
        1. There should be a comment on how statistical power was calculated upfront and if not: how N/n numbers were chosen ("based on similar expts in the past").

      N/n numbers, as detailed above, were chosen based on previous experiments by ourselves and others, as well as recommended practice [2,11–15]. Typically, these papers do not describe the statistical power upfront. We have added statements to this effect and relevant references to the methods section of the manuscript.

      1. "This suggests that some of the proteins that are accumulating in these diseases are specifically products of lipid accumulation rather than a product of general lysosomal dysfunction. In further support of this, several lysosomal proteins including V-type ATPases (ATP6 family), mannose-6-phosphate receptor (M6PR) and biogenesis of lysosomal organelle complex subunits (BLOC1) are quantified in the WCP but are not increased in abundance." This part is confusing. It seems like the authors observe an accumulation of endolysosomes in general (page 6), but then only certain endolysosomal proteins accumulate - and the authors speculate that this is due to decreased degradation or enhanced translation (mRNA levels are unaffected). This question should be addressed better, ideally experimentally: are endolysosomes accumulating in general or not? And what defines the endolysosomal proteins that accumulate vs. those that don't? How is that regulated?

      Recently published work has identified that late endosomes/lysosomes do not possess one composition; they are dynamically remodelled and there is substantial heterogeneity in the composition of different lysosomes [16,17]. While some components, such as LAMP1 and Cathepsin D, are common across all lysosomal compartments there is considerable heterogeneity in the composition of these organelles. These studies also demonstrate that in disease-relevant conditions or upon drug treatment, lysosomes change their protein composition. For example, in a LIPL-4 KO mouse model they observe an increased abundance of Ragulator complex components, similarly to the increase in LAMTOR3 seen in our new 28 dpi WCP data for GM1 and GM2 gangliosidoses. Interestingly, in this study they demonstrate that lysosomal lipolysis leads to bigger changes in lysosomal protein composition than other pro-longevity mechanisms [17]. Another recent paper looking at a different lysosomal storage disease in microglia with accumulating GSLs and cholesterol has also identified abundance changes in a subset of lysosomal proteins including several we observe here including TTYH3, NPC1, PSAP and TSPAN7 [18]. Beyond proteomic analyses, the experimental tools for identifying these different populations are currently very limited, but these published studies support that it is possible to have accumulation of what we define as lysosomes by IF (using LAMP1 or lysotracker) but for the proteomic analysis to identify increased abundance of only a subset of lysosomal proteins.

      These papers do not identify or speculate on how these differences are regulated. Analysis of the changes in our WCP as well as the new data for GM1 gangliosidoses support that the proteins that are most changed in response to GSL accumulation are membrane proteins involved in lipid and cholesterol binding and transport (New Fig 2D and 5E and see response below). This specific enrichment suggests that the changes are directly linked to the lipid changes, thus our suggestion that these accumulate due to a need for the cell to process these lipids but also that they may get “trapped” in the membrane whorls such that they are not efficiently degraded.

      We have included the references above and a more detailed description of lysosomal heterogeneity into the main text to help address the reviewer’s questions.

      1. Fig. 1D: The GO terms are confusing. Why are there more proteins in the category lysosomal membrane than lysosome as a whole? Other categories seem to be overlapping as well.

      We apologize for the confusion; this graph does not display protein counts it is the adjusted P values for the enrichment of the term. To make this clearer, the DAVID analysis graphs are now presented in a new format. We present in this new graph the false discovery rate (FDR) (adjusted P value) which is a measure of the significance of whether that GO term is specifically enriched in the dataset. We have also expanded the GO term analysis to include molecular function and biological process descriptors in addition to the cellular component originally described. For full clarity, to the right of each term we include the number of significant hits that have this term, that being the number of proteins that are contributing to this GO term enrichment.

      1. Fig. 2C/3A: It'd be good to also show the hits that don't match the expectation/pathways of interest.

      We provide a full list in the Supplementary Information of all hits that are considered significant allowing the reader to access this information without having to download the datasets from PRIDE. We did not label all hits in these panels to avoid cluttering the image. In the main text we have focused on those that clearly fall within related categories or pathways as we feel that several “hits” in the same area represents a more compelling and confident assessment of the data. Several of the additional hits not mentioned in the main text do still match the expectations/pathways. For example, one of the top hits not labelled in the WCP is GPR155 (a cholesterol binding protein at the lysosomal membrane) and one of the top unlabelled hits in the PMP data is OPCML (a GPI-anchored protein that clusters in GSL-rich microdomains). There are some, such as KITLG (up in the PMP data), that we don’t currently have a hypothesis for why/how they change, but we are reluctant to describe and speculate upon additional isolated/orphan hits in the main text when these have not been further validated.

      1. Fig. 3: It is not intuitive that synaptic proteins in particular would accumulate at the plasma membrane due to the lipid storage defect. Are they mis-trafficked or are they at synaptic membranes? That could, e.g, be addressed by isolating synaptosomes. And why this selectivity for synaptic proteins? Neurons should have more plasma membrane that is not synaptic. And, e.g, the release of lysosomal material should not happen at synapses (and lysosomes should not deliver synaptic proteins to the PM, unless there is a failure to degrade them).

      We agree that synapses represent a relatively small proportion of the entire PM of neurons, but synapses are particularly enriched with glycosphingolipids where they affect synaptogenesis and synaptic transmission [19–22]. For these reasons we think that some synaptic proteins are particularly sensitive to these lipid changes as they are localised in GSL-rich membrane microdomains. We have now clarified this point in the text. We have also further clarified that we were not proposing that lysosomal proteins are present at the synapses. We observed that lysosomal proteins are enriched at the PM and this may be more generally across the whole PM, while the changes to synaptic proteins may or may not be localised at the synapse. We apologise for the confusion and have modified the text at the end of the PM proteomics results section to make this clearer.

      To try and address experimentally the question of whether these proteins are at synapses, we have attempted synaptosome enrichment. However, lysosomal compartments co-sedimented with synaptosomes during the preparation – LAMP1 staining was enriched in the synaptosome preparations of all samples including SCRM controls. Therefore, we cannot distinguish these compartments which is particularly problematic in this disease model.

      (7. Continued) Or is there an effect on synaptic vesicles? Are there more? Do they deliver their cargo more readily? Or is there a failure to do endocytosis of synaptic proteins, and that's why the accumulate? What is the connection between SVs and endolysosomes? More clarity would be good here.

      We do think that there is an effect on synaptic vesicles particularly as the SV proteins SYT1 and SV2b are significantly increased in abundance at the PM suggesting they are not being internalized normally. Furthermore, the new WCP data going out to 28 dpi for both GM1 and GM2 gangliosidoses have identified a significant increase in Arl8a which plays a shared role in lysosomal and SV anterograde trafficking [23,24]. Whilst previously thought of as discrete pathways, evidence now suggests that endolysosomal and SV recycling pathways form a continuum with several shared proteins involved in the fusion, trafficking and sorting in both pathways [25]. Arl8a provides a good example of an adaptor protein that functions in both pathways and also when overexpressed results in enhanced neurotransmission consistent with our studies [26]. We have adjusted the discussion text to include a description of the links between SVs and endolysosomal trafficking and the potential shared role Arl8a may be playing in both pathways.

      Regarding the question of whether there are more SVs or not, this is hard to determine directly as they are particularly small (~50 nm) and difficult to visualise or specifically stain for using microscopy. Not all SV-associated proteins are increased in the PMP data, for example SNAP25 and several other synaptotagmins are not changed in the 28 dpi data for both gangliosidosis models. We hope in the future to address SV changes more directly with higher resolution imaging such as electron microscopy or cryo-tomography but cannot currently confidently answer these specific questions.

      1. Fig. 4: The assumption that there is more synaptic activity because there are more synaptic proteins at the membrane seems to be plausible, but also speculative at this point.

      We have modified the text at the end of this results section to highlight that this is a speculative link.

      1. The possible contribution of glial cells should at least be discussed.

      We mention potential deleterious effects on bystander cells including other neurons, astrocytes and microglia in the second last paragraph of the discussion. In response to this request we have expanded and modified this text.

      Minor: there are some typos etc.

      Although no specific examples were listed, we have endeavored to find and correct typos, we have also checked for English spelling (not American) throughout.

      Reviewer 3

      1. Results section, 1st paragraph- to develop disease models- -- Please add cellular models as we already have KO mouse models.

      This has been added to the text.

      1. It was not clear what was the percentage of mutation success with their CRISPR technique.

      The CRISPR method employed here was CRISPRi so there is no mutation of the genome. Instead, inactive/dead-Cas9 is targeted to the promotor/early exon of the HEXA or HEXB gene to inhibit mRNA production. We have included qPCR data to demonstrate the extent of the KD for two different guides to each of these genes in Fig 1.

      1. Will the anti-GM2 antibody be available for other researchers? The researcher details needs to be clarified.

      The anti-GM2 antibody is not commercial available and was generated by one of the co-authors. We invite scientists with an interest in this antibody to contact the corresponding author for details.

      1. Hex activity assay was shown in 1C, but it was not clear that it is MUG or MUGS.

      We apologise for this and have relabelled these activity assay graphs and expanded the legend text to clarify how these two substrates were used to distinguish the two different KD lines. We also corrected a small mistake in the methods section.

      1. Is there a significance in Figure 2 B, 4A, 4B,4C and 4E?

      Based on additional requests from reviewer 2 we have added significance indicators and details of significance tests for several panels in Figures 1-5 including 2B and 4B. For 4A we do not state a significant difference, we use these data to select a timepoint (28 dpi) where all cell lines have synchronous (correlated) signal. The data in Figure 4C and D have been substantially updated and expanded. Analysis of the data in 4C is plotted in 4D where we show significance. For 4E we are stating that the applied stimulation (white triangles) stimulates the HEXA cells every time but the SCRM do not respond to each stimulation. It is not clear how we would quantify this difference and there is no precedent for doing this in the MEA literature or by the Axion company who provided the instrument. We have also included additional references for best practice when analysing MEA data.

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    1. Reviewer #1 (Public review):

      The IBL here presents an important paper that aims to assess potential reproducibility issues in rodent electrophysiological recordings across labs and suggests solutions to these. The authors carried out a series of analyses on data collected across 10 laboratories while mice performed the same decision-making task, and provided convincing evidence that basic electrophysiology features, single-neuron functional properties, and population-level decoding were fairly reproducible across labs with proper preprocessing. This well-motivated large-scale collaboration allowed systematic assessment of lab-to-lab reproducibility of electrophysiological data, and the suggestions outlined in the paper for streamlining preprocessing pipelines and quality metrics will provide general guidance for the field, especially with continued effort to benchmark against standard practices (such as manual curation).

      The authors have carefully incorporated our suggestions. As a result, the paper now better reflects where reproducibility is affected when using common, simple, and more complex analyses and preprocessing methods, and it is more informative-and more reflective of the field overall. We thank the reviewers for this thorough revision. We have 2 remaining suggestions on text clarification:

      (1) Regarding benchmarking the automated metrics to manual curation of units: although we appreciate that a proper comparison may require a lot of effort potentially beyond the scope of the current paper; we do think that explicit discussion regarding this point is needed in the text, to remind the readers (and indeed future generations of electrophysiologists) the pros and cons of different approaches.

      In addition to what the authors have currently stated (line 469-470):<br /> "Another significant limitation of the analysis presented here is that we have not been able to assess the extent to which other choices of quality metrics and inclusion criteria might have led to greater or lesser reproducibility."

      Maybe also add:<br /> "In particular, a thorough comparison of automated metrics against a careful, large, manually-curated dataset, is an important benchmarking step for future studies.

      (2) The authors now include in Figure 3-Figure Supplement 1 that highlight how much probe depth is adjusted by using electrophysiological features such as LFP power to estimate probe and channel depth. This plot is immensely informative for the field, as it implies that there can be substantial variability-sometimes up to 1 mm discrepancy between insertions-in depth estimation based on anatomical DiI track tips alone. Using electrophysiological features in this way for probe depth estimation is currently not standard in the field and has only been made possible with Neuropixels, which span several millimeters. These figures highlight that this should be a critical step in preprocessing pipelines, and the paper provides solid evidence for this.

      Currently, this part of the figure is only subtly referenced to in the text. We think it would be helpful to explicitly reference this particular panel with discussions of its implication in the text.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews: 

      Reviewer #1 (Public review): 

      The manuscript consists of two separate but interlinked investigations: genomic epidemiology and virulence assessment of Salmonella Dublin. ST10 dominates the epidemiological landscape of S. Dublin, while ST74 was uncommonly isolated. Detailed genomic epidemiology of ST10 unfolded the evolutionary history of this common genotype, highlighting clonal expansions linked to each distinct geography. Notably, North American ST10 was associated with more antimicrobial resistance compared to others. The authors also performed long-read sequencing on a subset of isolates (ST10 and ST74) and uncovered a novel recombinant virulence plasmid in ST10 (IncX1/IncFII/IncN). Separately, the authors performed cell invasion and cytotoxicity assays on the two S. Dublin genotypes, showing differential responses between the two STs. ST74 replicates better intracellularly in macrophages compared to ST10, but both STs induced comparable cytotoxicity levels.

      Comparative genomic analyses between the two genotypes showed certain genetic content unique to each genotype, but no further analyses were conducted to investigate which genetic factors were likely associated with the observed differences. The study provides a comprehensive and novel understanding of the evolution and adaptation of two S. Dublin genotypes, which can inform public health measures. 

      The methodology included in both approaches was sound and written in sufficient detail, and data analysis was performed with rigour. Source data were fully presented and accessible to readers. Certain aspects of the manuscript could be clarified and extended to improve the manuscript. 

      (1) For epidemiology purposes, it is not clear which human diseases were associated with the genomes included in this manuscript. This is important since S. Dublin can cause invasive bloodstream infections in humans. While such information may be unavailable for public sequences, this should be detailed for the 53 isolates sequenced for this study, especially for isolates selected to perform experiments in vitro.

      Thank you for the suggestion. We have added the sample type for the 53 isolates sequenced for this study. These additional details have been added to Supplementary Tables 1, 4, 9 and 10.

      (2) The major AMR plasmid in described S. Dublin was the IncC associated with clonal expansion in North America. While this plasmid is not found in the Australian isolates sequenced in this study, the reviewer finds that it is still important to include its characterization, since it carries blaCMY-2 and was sustainedly inherited in ST10 clade 5. If the plasmid structure is already published, the authors should include the accession number in the Main Results.

      We have provided accessions and context for two of the IncC hybrid plasmids that have been previously reported in the literature in the Introduction. The text now reads:

      “These MDR S. Dublin isolates all type as sequence type 10 (ST10), and the AMR determinants have been demonstrated to be carried on an IncC plasmid that has recombined with a virulence plasmid encoding the spvRABCD operon (12,16,18,19).  This has resulted in hybrid virulence and AMR plasmids circulating in North America including a 329kb megaplasmid with IncX1, IncFIA, IncFIB, and IncFII replicons (isolate CVM22429, NCBI accession CP032397.1) (12,16) and a smaller hybrid plasmid 172,265 bases in size with an IncX1 replicon (isolate N13-01125, NCBI accession KX815983.1) (19).”

      Further characterisation of the IncA/C plasmid circulating in North America was beyond the scope of this study.

      (a) The reviewer is concerned that the multiple annotations missing in  plasmid structures in Supplementary Figures 5 & 6, and  genetic content unique to ST10 and ST74 was due to insufficient annotation by Prokka. I would recommend the authors use another annotation tool, such as Bakta (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8743544/) for plasmid annotation, and reconstruction of the pangenome described in Supplementary Figure 10. Since the recombinant virulence plasmid in ST10 is a novel one, I would recommend putting Supplementary Figure 5 as a main figure, with better annotations to show the virulence region, plasmid maintenance/replication, and possible conjugation cluster.

      In the supplementary figures of the plasmids, we sought to highlight key traits on interest on the plasmids, namely plasmid replicons, antimicrobial resistance and heavy metal resistance (Supplementary Figure 5) and virulence genes (Supplementary Figure 6). The inclusion of the accessions of publicly available isolates provide for characterised plasmids such as the S. Dublin virulence plasmid (NCBI accession: CP001143). 

      For the potentially hybrid plasmid with IncN/IncX1/IncFII reported in Supplementary Figure 6, we have undertaken additional analyses of the two Australian isolates to reannotate these isolates with Bakta which provides for more detailed annotations. 

      We have added new text to the methods which reads as: 

      “The final genome assemblies were confirmed as S. Dublin using SISTR and annotated using both Prokka v1.14.6 (69) for consistency with the draft genome assemblies and  Bakta v1.10.1 (93) which provides for more detailed annotations (Supplementary Table 13). Both Prokka and Bakta annotations were in agreement for AMR, HMR and virulence genes, with Bakta annotating between 3-7 additional CDS which were largely ‘hypothetical protein’.”

      For the pangenome analysis of the seven ST74 and ten ST10 isolates, we have continued to use the Prokka annotated draft genome assemblies for input to Panaroo. 

      (4) The authors are lauded for the use of multiple strains of ST10 and ST74 in the in vitro experiment. While results for ST74 were more consistent, readouts from ST10 were more heterogenous (Figure 5, 6). This is interesting as the tested ST10 were mostly clade 1, so ST10 was, as expected, of lower genetic diversity compared to tested ST74 (partly shown in Figure 1D. Could the authors confirm this by constructing an SNP table separately for tested ST10 and ST74? Additionally, the tested ST10 did not represent the phylogenetic diversity of the global epidemiology, and this limitation should be reflected in the Discussion.

      In response to the reviewer’s comments, we have provided a detailed SNP table (Supplementary Table 12) to further clarify the genetic diversity within the tested ST10 and ST74 strains. 

      Additionally, we have expanded on the limitation regarding the phylogenetic diversity of the ST10 isolates in the Discussion, highlighting how the strains used in the in vitro experiments may not fully represent the global epidemiological diversity of S. Dublin ST10. The new text now reads:

      “This study has limitations, including a focus on ST10 isolates from clade 1, which do not represent global phylogenetic diversity. Nonetheless, our pangenome analysis identified >900 uncharacterised genes unique to ST74, offering potential targets for future research. Another limitation is the geographic bias in available genomes, with underrepresentation from Asia and South America. This reflects broader disparities in genomic research resources but may improve as public health genomics capacity expands globally.”

      (5) The comparative genomics between ST10 and ST74 can be further improved to allow more interpretation of the experiments. Why were only SPI-1, 2, 6, and 19 included in the search for virulome, how about other SPIs? ST74 lacks SPI-19 and has truncated SPI-6, so what would explain the larger genome size of ST74? Have the authors screened for other SPIs using more well-annotated databases or references (S. Typhi CT18 or S. Typhimurium ST313)? The mismatching between in silico prediction of invasiveness and phenotypes also warrants a brief discussion, perhaps linked to bigger ST74 genome size (as intracellular lifestyle is usually linked with genome degradation).

      Systematic screening for SPIs with detailed reporting on individual genes and known effectors is still an area of development in Salmonella comparative genomics. In our characterisation of the virulome in this S. Dublin dataset we decided to focus on SPI1, SPI-2, SPI-6 and SPI-19 as these had been identified in previous studies and were considered to be most likely linked to the invasive phenotype of S. Dublin. We thought the truncation of SPI-6 and lack of SPI-19 in ST74 compared to the ST10 isolates would provide a basis to explore genomic differences in the two genotypes, with the screening for individual genes on each SPIs reported in Supplementary Figure 7 and Supplementary Table 9.  

      We have expanded upon the mismatching of the in silico prediction of invasiveness and phenotypes in the Discussion. We now explore the increased genome size and intracellular replication of the ST74 population. We hypothesise that invasiveness has not been studied as thoroughly in zoonotic iNTS as much as human adapted iNTS and S. Typhi, and the increased genome content may be required for survival in different host species. The new text now reads:

      “Our phenotypic data demonstrated a striking difference in replication dynamics between ST10 and ST74 populations in human macrophages. ST74 isolates replicated significantly over 24 hours, whereas ST10 isolates were rapidly cleared after 9 hours of infection. ST74 induced significantly less host cell death during the early-mid stage of macrophage infection, supported by limited processing and release of IL-1ß at 9 hpi. While NTS are generally potent inflammasome activators (60), most supporting data come from laboratory-adapted S. Typhimurium strains. Our findings suggest that ST74 isolates may employ immune evasion mechanisms to avoid host recognition and activation of cell death signaling in early infection stages. Similar trends have been observed with S. Typhimurium ST313, which induces less inflammasome activation than ST19 during murine macrophage infection (61). This could facilitate increased replication and dissemination at later stages of infection. Consistent with this, we observed comparable cytotoxicity between ST10 and ST74 isolates at 24 hpi, suggesting ST74 induces cell death via alternative mechanisms once intracellular bacterial numbers are unsustainable. Further research is needed to identify genomic factors underpinning these observations.”

      (6) On the epidemiology scale, ST10 is more successful, perhaps due to its ongoing adaptation to replication inside GI epithelial cells, favouring shedding. ST74 may tend to cause more invasive disease and less transmission via fecal shedding. The presence of T6SS in ST10 also can benefit its competition with other gut commensals, overcoming gut colonization resistance. The reviewer thinks that these details should be more clearly rephrased in the Discussion, as the results highly suggested different adaptations of two genotypes of the same serovar, leading to different epidemiological success.

      We thank the reviewer for highlighting that we could rephrase this important point. We have added additional text in the Discussion to better interpret the differences in the two genotypes of S. Dublin and how this relates to difference epidemiological success. The new text now reads:

      “While machine learning predicted lower invasiveness for ST74 compared to ST10, the increased genomic content of ST74 may support higher replication in macrophages. We speculate that increased intracellular replication could enhance systemic dissemination, though this requires in vivo validation. Invasiveness of S. enterica is often linked to genome degradation (4,62–64). However, this is mostly based on studies of human-adapted iNTS (ST313) and S. Typhi, leaving open the possibility that the additional genomic content of ST74 supports survival in diverse host species. An uncharacterised virulence factor may underlie this replication advantage. Collectively, these findings highlight phenotypic differences between S. Dublin populations ST10 and ST74. Enhanced intra-macrophage survival of ST74 could promote invasive disease, whereas the prevalence of ST10 may relate to better intestinal adaptation and enhanced faecal shedding. In vivo models are needed to test this hypothesis. Interestingly, the absence of SPI-19 in ST74, which encodes a T6SS, may reflect adaptation to enhanced replication in macrophages. SPI-19 has been linked to intestinal colonisation in poultry (23,56) and mucosal virulence in mice (56). It’s possible that the efficient replication of ST74 in macrophages might compensate for the absence of SPI-19, relying instead on phagocyte uptake via M cells or dendritic cells. The larger pangenome of ST74 compared to ST10 could further enhance survival within hosts. These findings highlight important knowledge gaps in zoonotic NTS host-pathogen interactions and drivers of emerging invasive NTS lineages with broad host ranges.”

      Reviewer #2 (Public review): 

      This is a comprehensive analysis of Salmonella Dublin genomes that offers insights into the global spread of this pathogen and region-specific traits that are important to understanding its evolution. The phenotyping of isolates of ST10 and ST74 also offers insights into the variability that can be seen in S. Dublin, which is also seen in other Salmonella serovars, and reminds the field that it is important to look beyond lab-adapted strains to truly understand these pathogens. This is a valuable contribution to the field. The only limitation, which the authors also acknowledge, is the bias towards S. Dublin genomes from high-income settings. However, there is no selection bias; this is simply a consequence of publically available sequences.

      Reviewer #1 (Recommendations for the authors): 

      (1) The Abstract did not summarize the main findings of the study. The authors should rewrite to highlight the key findings in genomic epidemiology (low AMR generally, novel plasmid of which Inc type, etc.) and the in vitro experiments. The findings clearly illustrate the differing adaptations of the two genotypes. Suggest to omit 'economic burden' and 'livestock' as this study did not specifically address them.

      We agree with the Reviewer and have re-written the abstract to directly reflect the major outcomes of the research. We have also deleted wording such as ‘livestock’, ‘economic burden’ and ‘One Health’ as we did not specifically address these issues as highlighted by the Reviewer. 

      (2) Figure 2: The MCC tree should include posterior support in major internal nodes. The current colour scheme is also confusing to readers (columns 1, 2). Suggest to revise and include additional key information as columns: major AMR genes (blaCMY-2, strAB, floR) and mer locus, so this info can be visualized in the main figure. 

      Thank you for your valuable feedback. We have revised Figure 2 with the MCC tree to include posterior support on the internal nodes. We have also amended the figure legend to explain the additional coloured internal nodes. We have also amended the heatmap in Figure 2 to include additional white space between the columns to make it easier for the readers to distinguish. We didn’t change the colours in this figure as we have used the same colours throughout for the different traits reported in this study. Further, we chose to keep the AMR profiles reported in Figure 2 at the susceptible, resistant or MDR. This was done to convey the overview of the AMR profiles, and we provide detail in the AMR and HMR determinants in the Supplementary Figures and Tables. 

      (3) The manuscript title is not informative, as it did not study the 'dynamics' of the two genotypes. Suggest to revise the study title along the lines of main results.

      Thank you for the feedback on the title. We have amended this to better reflect the main findings of the study, and it now reads as “Distinct adaptation and epidemiological success of different genotypes within Salmonella enterica serovar Dublin”

      (4) The co-occurrence of AMR and heavy metal resistance genes (like mer) are quite common in Salmonella and E. coli. This is not a novel finding. The reviewer would suggest shortening the details related to heavy metal resistance in Results and Discussion, to make the writing more streamlined. 

      In line with the Reviewer comments, we have shortened the details in the Results and Discussion on the co-occurrence of AMR and HMR.  

      (5) L185: missing info after n=82. 

      This has been revised to now read as “n=82 from Canada”. 

      (6) I think Vi refers to the capsular antigen, not flagelle. Please double-check this.

      Thank you for highlighting this mistake. We have revised all instances.

      (7) L252-253: which statistic was used to state 'no association'. Also, there is no evidence presented to support 'no fitness cost associated with resistance and virulence."

      We have removed this sentence.

      (8) 320: Figure 6F is a scatterplot, not PCA. Please confirm. 

      The reviewer is correct, this is in fact a scatterplot. We have amended the figure legend and text.

      (9) For Discussion, it would be helpful to compare the phenotype findings with that of other invasive Salmonella like Typhi or Typhimurium ST313.

      Thank you for noting this, we had alluded to findings from ST313 but have now expanded include some further comparisons to S. Typhimurium ST313 and added references for these within the Discussion. The additional text now reads:

      “Similar trends have been observed with S. Typhimurium ST313, which induces less inflammasome activation than ST19 during murine macrophage infection (61). This could facilitate increased replication and dissemination at later stages of infection.”

      "Invasiveness of S. enterica is often linked to genome degradation (4,62–64).

      However, this is mostly based on studies of human-adapted iNTS (ST313) and S. Typhi, leaving open the possibility that the additional genomic content of ST74 supports survival in diverse host species. An uncharacterised virulence factor may underlie this replication advantage.”

      (10) L440: no evidence for "successful colonization" of ST74. Actually, the findings suggested otherwise.

      Thank you for picking this up, we have amended the sentence to better reflect the findings. The amended text now reads as:

      “It’s possible that the efficient replication of ST74 in macrophages might compensate for the absence of SPI-19, relying instead on phagocyte uptake via M cells or dendritic cells. The larger pangenome of ST74 compared to ST10 could further enhance survival within hosts.”

      (11) L460-461: The data did not show an increasing trend of iNTS related to S. Dublin.

      Thank you for identifying this. This sentence has been revised accordingly and now reads as:

      “While the data did not indicate an increasing trend of iNTS associated with S. Dublin, the potential public health risk of this pathogen suggests it may still warrant considering it a notifiable disease, similar to typhoid and paratyphoid fever.”

      (12) L465: Data were not analyzed explicitly in the context of animal vs. human. Suggest omitting 'One Health' from the conclusion.

      Thank you for the suggestion. We have omitted “One Health” from the conclusion

      (13) L500: Was the alignment not checked for recombination using Gubbins? The approach here is inconsistent with the method described in the subtree selected for BEAST analysis (L546).

      We have now applied Gubbins to the phylogenetic tree constructed using IQTREE, and the methods and results have been updated accordingly.

      (14) What was the output of Tempest? Correlation or R2 value? 

      We have now included the R2 value from Tempest and reported this in the manuscript. 

      (15) L556: marginal likelihood to allow evaluation of the best-fit model. Please rephrase to state this clearly.

      We have rephrased this in the manuscript to state this clearly.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Turi, Teng and the team used state-of-the-art techniques to provide convincing evidence on the infraslow oscillation of DG cells during NREM sleep, and how serotonergic innervation modulates hippocampal activity pattern during sleep and memory. First, they showed that the glutamatergic DG cells become activated following an infraslow rhythm during NREM sleep. In addition, the infraslow oscillation in the DG is correlated with rhythmic serotonin release during sleep. Finally, they found that specific knockdown of 5-HT receptors in the DG impairs the infraslow rhythm and memory, suggesting that serotonergic signaling is crucial for regulating DG activity during sleep. Given that the functional role of infraslow rhythm still remains to be studied, their findings deepen our understanding on the role of DG cells and serotonergic signaling in regulating infraslow rhythm, sleep microarchitecture and memory.

      Reviewer #2 (Public review):

      Summary:

      The authors investigated DG neuronal activity at the population and single cell level across sleep/wake periods. They found an infraslow oscillation (0.01-0.03 Hz) in both granule cells (GC) and mossy cells (MC) during NREM sleep. The important findings are 1) the antiparallel temporal dynamics of DG neuron activities and serotonin neuron activities/extracellular serotonin levels during NREM sleep, and 2) the GC Htr1a-mediated GC infraslow oscillation.

      Strengths:

      (1) The combination of polysomnography, Ca-fiber photometry, two-photon microscopy and gene depletion is technically sound. The coincidence of microarousals and dips in DG population activity is convincing. The dip in activity in upregulated cells is responsible for the dip at the population level.

      (2) DG GCs express excitatory Htr4 and Htr7 in addition to inhibitory Htr1a, but deletion of Htr1a is sufficient to disrupt DG GC infraslow oscillation, supporting the importance of Htr1a in DG activity during NREM sleep.

      Weaknesses:

      (1) The current data set and analysis are insufficient to interpret the observation correctly.<br /> a. In Fig 1A, during NREM, the peaks and troughs of GC population activities seem to gradually decrease over time. Please address this point.

      b. In Fig 1F, about 30% of Ca dips coincided with MA (EMG increase) and 60% of Ca dips did not coincide with EMG increase. If this is true, the readers can find 8 Ca dips which are not associated with MAs from Fig 1E. If MAs were clustered, please describe this properly.<br /> c. In Fig 1F, the legend stated the percentage during NREM. If the authors want to include the percentage of wake and REM, please show the traces with Ca dips during wake and REM. This concern applies to all pie charts provided by the authors.

      d. In Fig 1C, please provide line plots connecting the same session. This request applies to all related figures.

      e. In Fig 2C, the significant increase during REM and the same level during NREM are not convincing. In Fig 2A, the several EMG increasing bouts do not appear to be MA, but rather wakefulness, because the duration of the EMG increase is greater than 15 seconds. Therefore, it is possible that the wake bouts were mixed with NREM bouts, leading to the decrease of Ca activity during NREM. In fact, In Fig 2E, the 4th MA bout seems to be the wake bout because the EMG increase lasts more than 15 seconds.

      f. Fig 5D REM data are interesting because the DRN activity is stably silenced during REM. The varied correlation means the varied DG activity during REM. The authors need to address it.

      g. In Fig 6, the authors should show the impact of DG Htr1a knockdown on sleep/wake structure including the frequency of MAs. I agree with the impact of Htr1a on DG ISO, but possible changes in sleep bout may induce the DG ISO disturbance.

      (2) It is acceptable that DG Htr1a KO induces the reduced freezing in the CFC test (Fig. 6E, F), but it is too much of a stretch that the disruption of DG ISO causes impaired fear memory. There should be a correlation.

      (3) It is necessary to describe the extent of AAV-Cre infection. The authors injected AAV into the dorsal DG (AP -1.9 mm), but the histology shows the ventral DG (Supplementary Fig. 4), which reduces the reliability of this study.

      Responses to weaknesses mentioned above have been addressed in the first revision.

      Comments on revisions:

      In the first revision, I pointed out the inappropriate analysis of the EEG/EMG/photometry data and gave examples. The authors responded only to the points raised and did not seem to see the need to improve the overall analysis and description. In this second revision, I would like to ask the authors to improve them. The biggest problem is that the detection criteria and the quantification of the specific event are not described at all in Methods and it is extremely difficult to follow the statement. All interpretations are made by the inappropriate data analysis; therefore, I have to say that the statement is not supported by the data.

      Please read my following concerns carefully and improve them.

      (1) The definition of the event is critical to the detection of the event and the subsequent analysis. In particular, the authors explicitly describe the definition of MA (microarousal), the trough and peak of the population level of intracellular Ca concentrations, or the onset of the decline and surge of Ca levels.

      (1-1) The authors categorized wake bouts of <15 seconds with high EMG activity as MA (in Methods). What degree of high EMG is relevant to MA and what is the lower limit of high EMG? In Fig 1E, there are some EMG spikes, but it was unclear which spike/wave (amplitude/duration) was detected as MA-relevant spike and which spike was not detected. In Fig 2E, the 3rd MA coincides with the EMG spike, but other EMG spikes have comparable amplitude to the 3rd MA-relevant EMG spike. Correct counting of MA events is critical in Fig 1F, 2F, 4C.

      We have added more information about the MA definition in Methods, including EMG amplitude. Furthermore, we have re-analyzed MA and MA-related calcium signals in Fig1 and Fig2. Fig-S1 shows the traces of EMG aptitude for all MA events show in Fig1G and Fig2G.

      (1-2) Please describe the definition of Ca trough in your experiments. In Fig 1G, the averaged trough time is clear (~2.5 s), so I can acknowledge that MA is followed by Ca trough. However, the authors state on page 4 that "30% of the calcium troughs during NREM sleep were followed by an MA epoch". This discrepancy should be corrected.

      We apologize for the misleading statement. We meant 30% of ISO events during NERM sleep. We have corrected this. To detect the calcium trough of ISO, we first calculated a moving baseline (blue line in Fig-S2 below) by smoothing the calcium signals over 60 s, then set a threshold (0.2 standard deviation from the moving baseline) for events of calcium decrease, and finally detected the minimum point (red dots in Fig-S2) in each event as the calcium trough. We have added these in Methods.

      (1-3) Relating comment 1-2, I agree that the latency is between MA and Ca through in page 4, as the authors explain in the methods, but, in Fig 1G, t (latency) is labeled at incorrect position. Please correct this.

      We are sorry for the mistake in describing the latency in the Methods. The latency was defined as the time difference between the onset of calcium decline (see details below in 1-4) and the onset of the MA. We have corrected this in the revised manuscript. Thus, the labeling in Fig1G was correct.

      (1-4) The authors may want to determine the onset of the decline in population Ca activity and the latency between onset and trough (Fig 1G, latency t). If so, please describe how the onset of the decline is determined. In Fig 1G, 2G, S6, I can find the horizontal dashed line and infer that the intersection of the horizontal line and the Ca curve is considered the onset. However, I have to say that the placement of this horizontal line is super arbitrary. The results (t and Drop) are highly dependent on the position of horizontal line, so the authors need to describe how to set the horizontal line.

      Indeed, we used the onset of calcium decline to calculate the latency as mentioned above. First, we defined the baseline (dashed line in Fig1G) by calculating the average of calcium signals in the10s window before the MA (from -15s to -5s in Fig1G). The onset of calcium decline is defined as the timepoint where calcium decrease was larger than 0.05 SD from this baseline. We have added these in Methods.

      (1-5) In order to follow Fig 1F correctly, the authors need to indicate the detection criteria of "Ca dip (in legend)". Please indicate "each Ca dip" in Fig 1E. As a reader, I would like to agree with the Ca dip detection of this Ca curve based on the criteria. Please also indicate "each Ca dip" in Fig 2E and 2F. In the case of the 2nd and 3rd MAs, do they follow a single Ca dip or does each MA follow each Ca dip? This chart is highly dependent on the detection criteria of Ca dip.

      We have indicated each ca dip in Fig 1 and Fig 2.

      As I mentioned above, most of the quantifications are not based on the clear detection criteria. The authors need to re-analyze the data and fix the quantification. Please interpret data and discuss the cellular mechanism of ISO based on the re-analyzed quantification.

      As suggested, we have re-analyzed the MA and MA-related photometry signals. Accordingly, parts of Fig1 and Fig2 have been revised. Although there are some small changes, the main results and conclusions remain unchanged.

      Reviewer #3 (Public review):

      Summary:

      The authors employ a series of well-conceived and well-executed experiments involving photometric imaging of the dentate gyrus and raphe nucleus, as well as cell-type specific genetic manipulations of serotonergic receptors that together serve to directly implicate serotonergic regulation of dentate gyrus (DG) granule (GC) and mossy cell (MC) activity in association with an infra slow oscillation (ISO) of neural activity has been previously linked to general cortical regulation during NREM sleep and microarousals.

      Strengths:

      There are a number of novel and important results, including the modulation of dentage granule cell activity by the infraslow oscillation during NREM sleep, the selective association of different subpopulations of granule cells to microarousals (MA), the anticorrelation of raphe activity with infraslow dentate activity.

      The discussion includes a general survey of ISOs and recent work relating to their expression in other brain areas and other potential neuromodulatory system involvement, as well as possible connections with infraslow oscillations, micro arousals, and sensory sensitivity.

      Weaknesses:

      - The behavioral results showing contextual memory impairment resulting from 5-HT1a knockdown are fine, but are over-interpreted. The term memory consolidation is used several times, as well as references to sleep-dependence. This is not what was tested. The receptor was knocked down, and then 2 weeks later animals were found to have fear conditioning deficits. They can certainly describe this result as indicating a connection between 5-HT1a receptor function and memory performance, but the connection to sleep and consolidation would just be speculation. The fact that 5-HT1a knockdown also impacted DG ISOs does not establish dependency. Some examples of this are:

      – The final conclusion asserts "Together, our study highlights the role of neuromodulation in organizing neuronal activity during sleep and sleep-dependent brain functions, such as memory.", but the reported memory effects (impairment of fear conditioning) were not shown to be explicitly sleep-dependent.

      – Earlier in the discussion it mentions "Finally, we showed that local genetic ablation of 5-HT1a receptors in GCs impaired the ISO and memory consolidation". The effect shown was on general memory performance - consolidation was not specifically implicated.

      – The assertion on page 9 that the results demonstrate "that the 5-HT is directly acting in the DG to gate the oscillations" is a bit strong given the magnitude of effect shown in Fig. 6D, and the absence of demonstration of negative effect on cortical areas that also show ISO activity and could impact DG activity (see requested cortical sigma power analysis).

      – Recent work has shown that abnormal DG GC activity can result from the use of the specific Ca indicator being used (GCaMP6s). (Teng, S., Wang, W., Wen, J.J.J. et al. Expression of GCaMP6s in the dentate gyrus induces tonic-clonic seizures. Sci Rep 14, 8104 (2024). https://doi.org/10.1038/s41598-024-58819-9). The authors of that study found that the effect seemed to be specific to GCaMP6s and that GCaMP6f did not lead to abnormal excitability. Note this is of particular concern given similar infraslow variation of cortical excitability in epilepsy (cf Vanhatalo et al. PNAS 2004). While I don't think that the experiments need to be repeated with a different indicator to address this concern, you should be able to use the 2p GCaMP7 experiments that have already been done to provide additional validation by repeating the analyses done for the GCaMP6s photometry experiments. This should be done anyway to allow appropriate comparison of the 2p and photometry results.

      – While the discussion mentions previous work that has linked ISOs during sleep with regulation of cortical oscillations in the sigma band, oddly no such analysis is performed in the current work even though it is presumably available and would be highly relevant to the interpretation of a number of primary results including the relationship between the ISOs and MAs observed in the DG and similar results reported in other areas, as well as the selective impact of DG 5-HT1a knockdown on DG ISOs. For example, in the initial results describing the cross correlation of calcium activity and EMG/EEG with MA episodes (paragraph 1, page 4), similar results relating brief arousals to the infraslow fluctuation in sleep spindles (sigma band) have been reported also at .02 Hz associated with variation in sensory arousability (cf. Cardis et al., "Cortico-autonomic local arousals and heightened somatosensory arousability during NREMS of mice in neuropathic pain", eLife 2021). It would be important to know whether the current results show similar cortical sigma band correlations. Also, in the results on ISO attenuation following 5-HT1 knockdown on page 7 (fig. 6), how is cortical EEG affected? is ISO still seen in EEG but attenuated in DG?

      – The illustrations of the effect of 5-HT1a knockdown shown in Figure 6 are somewhat misleading. The examples in panels B and C show an effect that is much more dramatic than the overall effect shown in panel D. Panels B and C do not appear to be representative examples. Which of the sample points in panel D are illustrated in panels B, C? it is not appropriate to arbitrarily select two points from different animals for comparison, or worse, to take points from the extremes of the distributions. If the intent is to illustrate what the effect shown in D looks like in the raw data, then you need to select examples that reflect the means shown in panel D. It is also important to show the effect on cortical EEG, particularly in sigma band to see if the effects are restricted to the DG ISOs. It would also be helpful to show that MAs and their correlations as shown in Fig 1 or G as well as broader sleep architecture are not affected.

      – On page 9 of the results it states that GCs and MCs are upregulated during NREM and their activity is abruptly terminated by MAs through a 5-HT mediated mechanism. I didn't see anything showing the 5-HT dependence of the MA activity correlation. The results indicate a reduction in ISO modulation of GC activity but not the MA correlated activity. I would like to see the equivalent of Fig 1,2 G panels with the 5-HT1a manipulation.

      Responses to Revewer#3 have been addressed in the first revision. 

      Reviewer #1 (Recommendations for the authors):

      Minor comment: Several recent publications from different laboratories have shown rhythmic release of norepinephrine (NE) (~0.03 Hz) in the medial prefrontal cortex, the thalamus, and in the locus coeruleus (LC) of the mouse during sleep-wake cycles-> Please add "preoptic area" here

      We have added the citation.

      Reviewer #2 (Recommendations for the authors):

      Minor

      (1) (abstract, page 2 line 9) what kind of "increased activity" did the authors find?

      Increased activity compared to that during wakefulness. We have added this.

      (2) (result, page 4) please define first, early, and late stage of NREM sleep in the methods.

      We have added these in the Methods.

      (3) (result, page 6) please define "the risetime of the phasic increase".

      It refers to the latency between the increase of 5-HT and the MA onset. We have clarified this in the text.

      (4) (supplement Fig 3 legend) please reword "5-HT events" and "5-HT signals" because these are ambiguous.

      We have defined the events in the legend.

      (5) (Fig 5A) please replace the picture without bubbles.

      We have replaced the image in Fig5A.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank the reviewers for their efforts. They have pointed out several shortcomings and made very helpful suggestions. Based on their feedback, we have substantially revised the manuscript and feel the paper has been much improved because of it.

      Notable changes are:

      (1) As our model does not contain feed-back connections, the focus of the study is now more clearly communicated to be on feed-forward processes only, with appropriate justifications for this choice added to the Introduction and Discussion sections. Accordingly, the title has been changed to include the term “feed-forward”.

      (2) The old Figure 5 has been removed in favor of reporting correlation scores to the right of the response profiles in other figures.

      (3) We now discuss changes to the network architecture (new Figure 5) and fine-tuning of the hyperparameters (new Figure 6) in the main text instead of only the Supplementary Information.

      (4) The discussion on qualitative versus quantitative analysis has been extended and given its own subsection entitled “On the importance of experimental contrasts and qualitative analysis of the model”.

      Below, we address each point that the reviewers brought up in detail and outline what improvements we have made in the revision to address them.

      Reviewer #1 (Public Review):

      Summary:

      This study trained a CNN for visual word classification and supported a model that can explain key functional effects of the evoked MEG response during visual word recognition, providing an explicit computational account from detection and segmentation of letter shapes to final word-form identification.

      Strengths:

      This paper not only bridges an important gap in modeling visual word recognition, by establishing a direct link between computational processes and key findings in experimental neuroimaging studies, but also provides some conditions to enhance biological realism.

      Weaknesses:

      The interpretation of CNN results, especially the number of layers in the final model and its relationship with the processing of visual words in the human brain, needs to be further strengthened.

      We have experimented with the number of layers and the number of units in each layer. In the previous version of the manuscript, these results could be found in the supplementary information. For the revised version, we have brought some of these results into the main text and discuss them more thoroughly.

      We have added a figure (Figure 5 in the revised manuscript) showing the impact of the number of convolution and fully-connected layers on the response profiles of the layers, as well as the correlation with the three MEG components.

      We discuss the figure in the Results section as follows:

      “Various variations in model architecture and training procedure were evaluated. We found that the number of layers had a large impact on the response patterns produced by the model (Figure 5). The original VGG-11 architecture defines 5 convolution layers and 3 fully connected layers (including the output layer). Removing a convolution layer (Figure 5, top row), or removing one of the fully connected layers (Figure 5, second row), resulted in a model that did exhibit an enlarged response to noisy stimuli in the early layers that mimics the Type-I response. However, such models failed to show a sufficiently diminished response to noisy stimuli in the later layers, hence failing to produce responses that mimic the Type-II or N400m, a failure which also showed as low correlation scores.

      Adding an additional convolution layer (Figure 5, third row) resulted in a model where none of the layer response profiles mimics that of the Type-II response. The Type-II response is characterized by a reduced response to both noise and symbols, but an equally large response to consonant strings, real and pseudo words. However, in the model with an additional convolution layer, the consonant strings evoked a reduced response already in the first fully connected layer, which is a feature of the N400m rather than the Type-II. These kind of subtleties in the response pattern, which are important for the qualitative analysis, generally did not show quantitatively in the correlation scores, as the fully connected layers in this model correlate as well with the Type-II response as models that did show a response pattern that mimics the Type-II.

      Adding an additional fully connected layer (Figure 5, fourth row) resulted in a model with similar response profiles and correlation with the MEG components as the original VGG-11 architecture (Figure 5, bottom row) The N400m-like response profile is now observed in the third fully connected layer rather than the output layer. However, the decrease in response to consonant strings versus real and pseudo words, which is typical of the N400m, is less distinct than in the original VGG-11 architecture.”

      And in the Discussion section:

      “In the model, convolution units are followed by pooling units, which serve the purpose of stratifying the response across changes in position, size and rotation within the receptive field of the pooling unit. Hence, the effect of small differences in letter shape, such as the usage of different fonts, was only present in the early convolution layers, in line with findings in the EEG literature (Chauncey et al., 2008; Grainger & Holcomb, 2009; Hauk & Pulvermüller, 2004). However, the ability of pooling units to stratify such differences depends on the size of their receptive field, which is determined by the number of convolution-and-pooling layers. As a consequence, the response profiles of the subsequent fully connected layers was also very sensitive to the number of convolution-and-pooling layers. The optimal number of such layers is likely dependent on the input size and pooling strategy. Given the VGG-11 design of doubling the receptive field after each layer, combined with an input size of 225×225 pixels, the optimal number of convolution-andpooling layers for our model was five, or the model would struggle to produce response profiles mimicking those of the Type-II component in the subsequent fully connected layers (Figure 5).”

      Reviewer #1 (Recommendations For The Authors):

      (1) The similarity between CNNs and human MEG responses, including type-I (100ms), type-II (150ms), and N400 (400ms) components, looks like separately, lacking the sequential properties among these three components. Is the recurrent neural network (RNN), which can be trained to process and convert a sequential data input into a specific sequential data output, a better choice?

      When modeling sequential effects, meaning that the processing of the current word is influenced by the word that came before it, such as priming and top-down modulations, we agree that such a model would indeed require recurrency in its architecture. However, we feel that the focus of modeling efforts in reading has been overwhelmingly on the N400 and such priming effects, usually skipping over the pixel-to-letter process. So, for this paper, we were keen on exploring more basic effects such as noise and symbols versus letters on the type-I and type-II responses. And for these effects, a feed-forward model turns out to be sufficient, so we can keep the focus of this particular paper on bottom-up processes during single word reading, on which there is already a lot to say.

      To clarify our focus on feed-forward process, we have modified the title of the paper to be:

      “Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition” furthermore, we have revised the Introduction to highlight this choice, noting:

      “Another limitation is that these models have primarily focused on feed-back lexicosemantic effects while oversimplifying the initial feed-forward processing of the visual input.

      […]

      For this study, we chose to focus on modeling the early feed-forward processing occurring during visual word recognition, as the experimental setup in Vartiainen et al. (2011) was designed to demonstrate.

      […]

      By doing so, we restrict ourselves to an investigation of how well the three evoked components can be explained by a feed-forward CNN in an experimental setting designed to demonstrate feed-forward effects. As such, the goal is not to present a complete model of all aspects of reading, which should include feed-back effects, but rather to demonstrate the effectiveness of using a model that has a realistic form of input when the aim is to align the model with the evoked responses observed during visual word recognition.”

      And in the Discussion section:

      “In this paper we have restricted our simulations to feed-forward processes. Now, the way is open to incorporate convolution-and-pooling principles in models of reading that simulate feed-back processes as well, which should allow the model to capture more nuance in the Type-II and N400m components, as well as extend the simulation to encompass a realistic semantic representation.”

      (2) There is no clear relationship between the layers that signal needs to traverse in the model and the relative duration of the three components in the brain.

      While some models offer a tentative mapping between layers and locations in the brain, none of the models we are aware of actually simulate time accurately and our model is no exception.

      While we provide some evidence that the three MEG components are best modeled with different types of layers, and the type-I becomes somewhere before type-II and N400m is last in our model, the lack of timing information is a weakness of our model we have not been able to address. In our previous version, this already was the main topic of our “Limitations of the model” section, but since this weakness was pointed out by all reviewers, we have decided to widen our discussion of it:

      “One important limitation of the current model is the lack of an explicit mapping from the units inside its layers to specific locations in the brain at specific times. The temporal ordering of the components is simulated correctly, with the response profile matching that of the type-I occurring the layers before those matching the type-II, followed by the N400m. Furthermore, every component is best modeled by a different type of layer, with the type-I best described by convolution-and-pooling, the type-II by fully-connected linear layers and the N400m by a one-hot encoded layer. However, there is no clear relationship between the number of layers the signal needs to traverse in the model to the processing time in the brain. Even if one considers that the operations performed by the initial two convolution layers happen in the retina rather than the brain, the signal needs to propagate through three more convolution layers to reach the point where it matches the type-II component at 140-200 ms, but only through one more additional layer to reach the point where it starts to match the N400m component at 300-500 ms. Still, cutting down on the number of times convolution is performed in the model seems to make it unable to achieve the desired suppression of noise (Figure 5). It also raises the question what the brain is doing during the time between the type-II and N400m component that seems to take so long. It is possible that the timings of the MEG components are not indicative solely of when the feed-forward signal first reaches a certain location, but are rather dictated by the resolution of feed-forward and feedback signals (Nour Eddine et al., 2024).”

      See also our response to the next comment of the Reviewer, in which we dive more into the effect of the number of layers, which could be seen as a manipulation of time.

      (3) I am impressed by the CNN that authors modified to match the human brain pattern for the visual word recognition process, by the increase and decrease of the number of layers. The result of this part was a little different from the author’s expectation; however, the author didn’t explain or address this issue.

      We are glad to hear that the reviewer found these results interesting. Accordingly, we now discuss these results more thoroughly in the main text.

      We have moved the figure from the supplementary information to the main text (Figure 5 in the revised manuscript). And describe the results in the Results section:

      “Various variations in model architecture and training procedure were evaluated. We found that the number of layers had a large impact on the response patterns produced by the model (Figure 5). The original VGG-11 architecture defines 5 convolution layers and 3 fully connected layers (including the output layer). Removing a convolution layer (Figure 5, top row), or removing one of the fully connected layers (Figure 5, second row), resulted in a model that did exhibit an enlarged response to noisy stimuli in the early layers that mimics the Type-I response. However, such models failed to show a sufficiently diminished response to noisy stimuli in the later layers, hence failing to produce responses that mimic the Type-II or N400m, a failure which also showed as low correlation scores.

      Adding an additional convolution layer (Figure 5, third row) resulted in a model where none of the layer response profiles mimics that of the Type-II response. The Type-II response is characterized by a reduced response to both noise and symbols, but an equally large response to consonant strings, real and pseudo words. However, in the model with an additional convolution layer, the consonant strings evoked a reduced response already in the first fully connected layer, which is a feature of the N400m rather than the Type-II. These kind of subtleties in the response pattern, which are important for the qualitative analysis, generally did not show quantitatively in the correlation scores, as the fully connected layers in this model correlate as well with the Type-II response as models that did show a response pattern that mimics the Type-II.

      Adding an additional fully connected layer (Figure 5, fourth row) resulted in a model with similar response profiles and correlation with the MEG components as the original VGG-11 architecture (Figure 5, bottom row) The N400m-like response profile is now observed in the third fully connected layer rather than the output layer. However, the decrease in response to consonant strings versus real and pseudo words, which is typical of the N400m, is less distinct than in the original VGG-11 architecture.”

      We also incorporated these results in the Discussion:

      “However, the ability of pooling units to stratify such differences depends on the size of their receptive field, which is determined by the number of convolution-andpooling layers. This might also explain why, in later layers, we observed a decreased response to stimuli where text was rendered with a font size exceeding the receptive field of the pooling units (Figure 8). Hence, the response profiles of the subsequent fully connected layers was very sensitive to the number of convolution-and-pooling layers. This number is probably dependent on the input size and pooling strategy. Given the VGG11 design of doubling the receptive field after each layer, combined with an input size of 225x225 pixels, the optimal number of convolution-and-pooling layers for our model was five, or the model would struggle to produce response profiles mimicking those of the type-II component in the subsequent fully connected layers (Figure 5).

      […]

      A minimum of two fully connected layers was needed to achieve this in our case, and adding more fully connected layers would make them behave more like the component (Figure 5).”

      (4) Can the author explain why the number of layers in the final model is optimal by benchmarking the brain hierarchy?

      We have incorporated the figure describing the correlation between each model and the MEG components (previously Figure 5) with the figures describing the response profiles (Figures 4 and 5 in the revised manuscript and Supplementary Figures 2-6). This way, we (and the reader) can now benchmark every model qualitatively and quantitatively.

      As we stated in our response to the previous comment, we have added a more thorough discussion on the number of layers, which includes the justification for our choice for the final model. The benchmark we used was primarily whether the model shows the same response patterns as the Type I, Type II and N400 responses, which disqualifies all models with fewer than 5 convolution and 3 fully connected layers. Models with more layers also show the proper response patterns, however we see that there is actually very little difference in the correlation scores between different models. Hence, our justification for sticking with the original VGG11 architecture is that it produces the qualitative best response profiles, while having roughly the same (decently high) correlation with the MEG components. Furthermore, by sticking to the standard architecture, we make it slightly easier to replicate our results as one can use readily available pre-trained ImageNet weights.

      As well as always discussing the correlation scores in tandem with the qualitative analysis, we have added the following statement to the Results:

      “Based on our qualitative and quantitative analysis, the model variant that performed best overall was the model that had the original VGG11 architecture and was preinitialized from earlier training on ImageNet, as depicted in the bottom rows of Figure 4 and Figure 5.”

      Reviewer #2 (Public Review):

      As has been shown over many decades, many potential computational algorithms, with varied model architectures, can perform the task of text recognition from an image. However, there is no evidence presented here that this particular algorithm has comparable performance to human behavior (i.e. similar accuracy with a comparable pattern of mistakes). This is a fundamental prerequisite before attempting to meaningfully correlate these layer activations to human neural activations. Therefore, it is unlikely that correlating these derived layer weights to neural activity provides meaningful novel insights into neural computation beyond what is seen using traditional experimental methods.

      We very much agree with the reviewer that a qualitative analysis of whether the model can explain experimental effects needs to happen before a quantitative analysis, such as evaluating model-brain correlation scores. In fact, this is one of the intended key points we wished to make.

      As we discuss at length in the Introduction, “traditional” models of reading (those that do not rely on deep learning) are not able to recognize a word regardless of exact letter shape, size, and (up to a point) rotation. In this study, our focus is on these low-level visual tasks rather than high-level tasks concerning semantics. As the Reviewer correctly states, there are many potential computational algorithms able to perform these visual task at a human level and so we need to evaluate the model not only on its ability to mimic human accuracy but also on generating a comparable pattern of mistakes. In our case, we need a pattern of behavior that is indicative of the visual processes at the beginning of the reading pipeline. Hence, rather than relying on behavioral responses that are produced at the very end, we chose the evaluate the model based on three MEG components that provide “snapshots” of the reading process at various stages. These components are known to manifest a distinct pattern of “behavior” in the way they respond to different experimental conditions (Figure 2), akin to what to Reviewer refers to as a “pattern of mistakes”. The model was first evaluated on its ability to replicate the behavior of the MEG components in a qualitative manner (Figure 4). Only then do we move on to a quantitative correlation analysis. In this manner, we feel we are in agreement with the approach advocated by the Reviewer.

      In the Introduction, we now clarify:

      “Another limitation is that these models have primarily focused on feed-back lexicosemantic effects while oversimplifying the initial feed-forward processing of the visual input.

      […]

      We sought to construct a model that is able to recognize words regardless of length, size, typeface and rotation, as well as humans can, so essentially perfectly, whilst producing activity that mimics the type-I, type-II, and N400m components which serve as snapshots of this process unfolding in the brain.

      […]

      These variations were first evaluated on their ability to replicate the experimental effects in that study, namely that the type-I response is larger for noise embedded words than all other stimuli, the type-II response is larger for all letter strings than symbols, and that the N400m is larger for real and pseudowords than consonant strings. Once a variation was found that could reproduce these effects satisfactorily, it was further evaluated based on the correlation between the amount of activation of the units in the model and MEG response amplitude.”

      To make this prerequisite more clear, we have removed what was previously Figure 5, which showed the correlation between the various models the MEG components out of the context of their response patterns. Instead, these correlation values are now always presented next to the response patterns (Figures 4 and 5, and Supplementary Figures 2-6 in the revised manuscript). This invites the reader to always consider these metrics in relation to one another.

      One example of a substantial discrepancy between this model and neural activations is that, while incorporating frequency weighting into the training data is shown to slightly increase neural correlation with the model, Figure 7 shows that no layer of the model appears directly sensitive to word frequency. This is in stark contrast to the strong neural sensitivity to word frequency seen in EEG (e.g. Dambacher et al 2006 Brain Research), fMRI (e.g. Kronbichler et al 2004 NeuroImage), MEG (e.g. Huizeling et al 2021 Neurobio. Lang.), and intracranial (e.g. Woolnough et al 2022 J. Neurosci.) recordings. Figure 7 also demonstrates that the late stages of the model show a strong negative correlation with font size, whereas later stages of neural visual word processing are typically insensitive to differences in visual features, instead showing sensitivity to lexical factors.

      We are glad the reviewer brought up the topic of frequency balancing, as it is a good example of the importance of the qualitative analysis. Frequency balancing during training only had a moderate impact on correlation scores and from that point of view does not seem impactful. However, when we look at the qualitative evaluation, we see that with a large vocabulary, a model without frequency balancing fails to properly distinguish between consonant strings and (pseudo)words (Figure 4, 5th row). Hence, from the point of view of being able to reproduce experimental effects, frequency balancing had a large impact. We now discuss this more explicitly in the revised Discussion section:

      “Overall, we found that a qualitative evaluation of the response profiles was more helpful than correlation scores. Often, a deficit in the response profile of a layer that would cause a decrease in correlation on one condition would be masked by an increased correlation in another condition. A notable example is the necessity for frequency-balancing the training data when building models with a vocabulary of 10 000. Going by correlation score alone, there does not seem to be much difference between the model trained with and without frequency balancing (Figure 4A, fifth row versus bottom row). However, without frequency balancing, we found that the model did not show a response profile where consonant strings were distinguished from words and pseudowords (Figure 4A, fifth row), which is an important behavioral trait that sets the N400m component apart from the Type-II component (Figure 2D). This underlines the importance of the qualitative evaluation in this study, which was only possible because of a straightforward link between the activity simulated within a model to measurements obtained from the brain, combined with the presence of clear experimental conditions.”

      It is true that the model, even with frequency balancing, only captures letter- and bigramfrequency effects and not the word-frequency effects that we know the N400m is sensitive to. Since our model is restricted to feed-forward processes, this finding adds to the evidence that frequency-modulated effects are driven by feed-back effects as modeled by Nour Eddine et al. (2024, doi:10.1016/j.cognition.2024.105755). See also our response to the next comment by the Reviewer where we discuss feed-back connections. We have added the following to the section about model limitations in the revised Discussion:

      “The fact that the model failed to simulate the effects of word-frequency on the N400m (Figure 8), even after frequency-balancing of the training data, is additional evidence that this effect may be driven by feed-back activity, as for example modeled by Nour Eddine et al. (2024).”

      Like the Reviewer, we initially thought that later stages of neural visual word processing would be insensitive to differences in font size. When diving into the literature to find support for this claim, we found only a few works directly studying the effect of font size on evoked responses, but, surprisingly, what we did find seemed to align with our model. We have added the following to our revised Discussion:

      “The fully connected linear layers in the model show a negative correlation with font size. While the N400 has been shown to be unaffected by font size during repetition priming (Chauncey et al., 2008), it has been shown that in the absence of priming, larger font sizes decrease the evoked activity in the 300–500 ms window (Bayer et al., 2012; Schindler et al., 2018). Those studies refer to the activity within this time window, which seems to encompass the N400, as early posterior negativity (EPN). What possibly happens in the model is that an increase in font size causes an initial stronger activation in the first layers, due to more convolution units receiving input. This leads to a better signal-to-noise ratio (SNR) later on, as the noise added to the activation of the units remains constant whilst the amplitude of the input signal increases. A better SNR translates ultimately in less co-activation of units corresponding to orthographic neighbours in the final layers, hence to a decrease in overall layer activity.”

      Another example of the mismatch between this model and the visual cortex is the lack of feedback connections in the model. Within the visual cortex, there are extensive feedback connections, with later processing stages providing recursive feedback to earlier stages. This is especially evident in reading, where feedback from lexical-level processes feeds back to letter-level processes (e.g. Heilbron et al 2020 Nature Comms.). This feedback is especially relevant for the reading of words in noisy conditions, as tested in the current manuscript, as lexical knowledge enhances letter representation in the visual cortex (the word superiority effect). This results in neural activity in multiple cortical areas varying over time, changing selectivity within a region at different measured time points (e.g. Woolnough et al 2021 Nature Human Behav.), which in the current study is simplified down to three discrete time windows, each attributed to different spatial locations.

      We agree with the Reviewer that a full model of reading in the brain must include feed-back connections and share their sentiment that these feed-back processes play an important role and are a fascinating topic to study. The intent for the model presented in our study is very much to be a stepping stone towards extending the capabilities of models that do include such connections.

      However, there is a problem of scale that cannot be ignored.

      Current models of reading that do include feedback connections fall into the category we refer to in the paper as “traditional models” and all only a few layers deep and operate on very simplified inputs, such as pre-defined line segments, a few pixels, or even a list of prerecognized letters. The Heilbron et al. 2020 study that the Reviewer refers to is a good example of such a model. (This excellent and relevant work was somehow overlooked in our literature discussion in the Introduction. We thank the Reviewer for pointing it out to us.) Models incorporating realistic feed-back activity need these simplifications, because they have a tendency to no longer converge when there are too many layers and units. However, in order for models of reading to be able to simulate cognitive behavior such as resolving variations in font size or typeface, or distinguish text from non-text, they need to operate on something close to the pixel-level data, which means they need many layers and units.

      Hence, as a stepping stone, it is reasonable to evaluate a model that has the necessary scale, but lacks the feed-back connections that would be problematic at this scale, to see what it can and cannot do in terms of explaining experimental effects in neuroimaging studies. This was the intended scope of our study. For the revision, we have attempted to make this more clear.

      We have changed the title to be:

      “Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition” and added the following to the Introduction:

      “The simulated environments in these models are extremely simplified, partly due to computational limitations and partly due to the complex interaction of feed-forward and feed-back connectivity that causes problems with convergence when the model grows too large. Consequently, these models have primarily focused on feed-back lexico-semantic effects while oversimplifying the initial feed-forward processing of the visual input. 

      […]

      This rather high level of visual representation sidesteps having to deal with issues such as visual noise, letters with different scales, rotations and fonts, segmentation of the individual letters, and so on. More importantly, it makes it impossible to create the visual noise and symbol string conditions used in the MEG study to modulate the type-I and type-II components. In order to model the process of visual word recognition to the extent where one may reproduce neuroimaging studies such as Vartiainen et al. (2011), we need to start with a model of vision that is able to directly operate on the pixels of a stimulus. We sought to construct a model that is able to recognize words regardless of length, size, typeface and rotation with very high accuracy, whilst producing activity that mimics the type-I, type-II, and N400m components which serve as snapshots of this process unfolding in the brain. For this model, we chose to focus on the early feed-forward processing occurring during visual word recognition, as the experimental setup in the MEG study was designed to demonstrate, rather than feed-back effects

      […]

      By doing so, we restrict ourselves to an investigation of how well the three evoked components can be explained by a feed-forward CNN in an experimental setting designed to demonstrate feed-forward effects. > As such, the goal is not to present a complete model of all aspects of reading, which should include feed-back effects, but rather to demonstrate the effectiveness of using a model that has a realistic form of input when the aim is to align the model with the evoked responses observed during visual word recognition.”

      And we have added the following to the Discussion section:

      “In this paper we have restricted our simulations to feed-forward processes. Now, the way is open to incorporate convolution-and-pooling principles in models of reading that simulate feed-back processes as well, which should allow the model to capture more nuance in the Type-II and N400m components, as well as extend the simulation to encompass a realistic semantic representation. A promising way forward may be to use a network architecture like CORNet (Kubilius et al., 2019), that performs convolution multiple times in a recurrent fashion, yet simultaneously propagates activity forward after each pass. The introduction of recursion into the model will furthermore align it better with traditional-style models, since it can cause a model to exhibit attractor behavior (McLeod et al., 2000), which will be especially important when extending the model into the semantic domain.

      Furthermore, convolution-and-pooling has recently been explored in the domain of predictive coding models (Ororbia & Mali, 2023), a type of model that seems particularly well suited to model feed-back processes during reading (Gagl et al., 2020; Heilbron et al., 2020; Nour Eddine et al., 2024).”

      We also would like to point out to the Reviewer that we did in fact perform a correlation between the model and the MNE-dSPM source estimate of all cortical locations and timepoints (Figure 7B). Such a brain-wide correlation map confirms that the three dipole groups are excellent summaries of when and where interesting effects occur within this dataset.

      The presented model needs substantial further development to be able to replicate, both behaviorally and neurally, many of the well-characterized phenomena seen in human behavior and neural recordings that are fundamental hallmarks of human visual word processing. Until that point, it is unclear what novel contributions can be gleaned from correlating low-dimensional model weights from these computational models with human neural data.

      We hope that our revisions have clarified the goals and scope of this study. The CNN model we present in this study is a small but, we feel, essential piece in a bigger effort to employ deep learning techniques to further enhance already existing models of reading. In our revision, we have extended our discussion where to go from here and outline our vision on how these techniques could help us better model the phenomena the reviewer speaks of. We agree with the reviewer that there is a long way to go, and we are excited to be a part of it.

      In addition to the changes described above, we now end the Discussion section as follows: 

      “Despite its limitations, our model is an important milestone for computational models of reading that leverages deep learning techniques to encompass the entire computational process starting from raw pixels values to representations of wordforms in the mental lexicon. The overall goal is to work towards models that can reproduce the dynamics observed in brain activity observed during the large number of neuroimaging experiments performed with human volunteers that have been performed over the last few decades. To achieve this, models need to be able to operate on more realistic inputs than a collection of predefined lines or letter banks (for example: Coltheart et al., 2001; Heilbron et al., 2020; Laszlo & Armstrong, 2014; McClelland & Rumelhart, 1981; Nour Eddine et al., 2024). We have shown that even without feed-back connections, a CNN can simulate the behavior of three important MEG evoked components across a range of experimental conditions, but only if unit activations are noisy and the frequency of occurrence of words in the training dataset mimics their frequency of use in actual language.”

      Reviewer #3 (Public Review):

      The paper is rather qualitative in nature. In particular, the authors show that some resemblance exists between the behavior of some layers and some parts of the brain, but it is hard to quantitively understand how strong the resemblances are in each layer, and the exact impact of experimental settings such as the frequency balancing (which seems to only have a very moderate effect according to Figure 5).

      The large focus on a qualitative evaluation of the model is intentional. The ability of the model to reproduce experimental effects (Figure 4) is a pre-requisite for any subsequent quantitative metrics (such as correlation) to be valid. The introduction of frequency balancing is a good example of this. As the reviewer points out, frequency balancing during training has only a moderate impact on correlation scores and from that point of view does not seem impactful. However, when we look at the qualitative evaluation, we see that with a large vocabulary, a model without frequency balancing fails to properly distinguish between consonant strings and (pseudo)words (Figure 4, 5th row). Hence, from the point of view of being able to reproduce experimental effects, frequency balancing has a large impact.

      That said, the reviewer is right to highlight the value of quantitative analysis. An important limitation of the “traditional” models of reading that do not employ deep learning is that they operate in unrealistically simplified environments (e.g. input as predefined line segments, words of a fixed length), which makes a quantitative comparison with brain data problematic. The main benefit that deep learning brings may very well be the increase in scale that makes more direct comparisons with brain data possible. In our revision we attempt to capitalize on this benefit more. The reviewer has provided some helpful suggestions for doing so in their recommendations, which we discuss in detail below.

      We have added the following discussion on the topic of qualitative versus quantitative analysis to the Introduction:

      “We sought to construct a model that is able to recognize words regardless of length, size, typeface and rotation, as well as humans can, so essentially perfectly, whilst producing activity that mimics the type-I, type-II, and N400m components which serve as snapshots of this process unfolding in the brain.

      […]

      These variations were first evaluated on their ability to replicate the experimental effects in that study, namely that the type-I response is larger for noise embedded words than all other stimuli, the type-II response is larger for all letter strings than symbols, and that the N400m is larger for real and pseudowords than consonant strings. Once a variation was found that could reproduce these effects satisfactorily, it was further evaluated based on the correlation between the amount of activation of the units in the model and MEG response amplitude.”

      And follow this up in the Discussion with a new sub-section entitled “On the importance of experimental contrasts and qualitative analysis of the model”

      The experiments only consider a rather outdated vision model (VGG).

      VGG was designed to use a minimal number of operations (convolution-and-pooling, fullyconnected linear steps, ReLU activations, and batch normalization) and rely mostly on scale to solve the classification task. This makes VGG a good place to start our explorations and see how far a basic CNN can take us in terms of explaining experimental MEG effects in visual word recognition. However, we agree with the reviewer that it is easy to envision more advanced models that could potentially explain more. In our revision, we expand on the question of where to go from here and outline our vision on what types of models would be worth investigating and how one may go about doing that in a way that provides insights beyond higher correlation values.

      We have included the following in our Discussion sub-sections on “Limitations of the current model and the path forward”:

      “The VGG-11 architecture was originally designed to achieve high image classification accuracy on the ImageNet challenge (Simonyan & Zisserman, 2015). Although we have introduced some modifications that make the model more biologically plausible, the final model is still incomplete in many ways as a complete model of brain function during reading.

      […]

      In this paper we have restricted our simulations to feed-forward processes. Now, the way is open to incorporate convolution-and-pooling principles in models of reading that simulate feed-back processes as well, which should allow the model to capture more nuance in the Type-II and N400m components, as well as extend the simulation to encompass a realistic semantic representation. A promising way forward may be to use a network architecture like CORNet (Kubilius et al., 2019), that performs convolution multiple times in a recurrent fashion, yet simultaneously propagates activity forward after each pass. The introduction of recursion into the model will furthermore align it better with traditional-style models, since it can cause a model to exhibit attractor behavior (McLeod et al., 2000), which will be especially important when extending the model into the semantic domain. Furthermore, convolution-and-pooling has recently been explored in the domain of predictive coding models (Ororbia & Mali, 2023), a type of model that seems particularly well suited to model feed-back processes during reading (Gagl et al., 2020; Heilbron et al., 2020; Nour Eddine et al., 2024).”

      Reviewer #3 (Recommendations For The Authors):

      (1) The method used to select the experimental conditions under which the behavior of the CNN is the most brain-like is rather qualitative (Figure 4). It would have been nice to have a plot where the noisyness of the activations, the vocab size and the amount of frequency balancing are varied continuously, and show how these three parameters impact the correlation of the model layers with the MEG responses.

      We now include this analysis (Figure 6 in the revised manuscript, Supplementary Figures 47) and discuss these factors in the revised Results section:

      “Various other aspects of the model architecture were evaluated which ultimately did not lead to any improvements of the model. The response profiles can be found in the supplementary information (Supplementary Figures 4–7) and the correlations between the models and the MEG components are presented in Figure 6. The vocabulary of the final model (10 000) exceeds the number of units in its fullyconnected layers, which means that a bottleneck is created in which a sub-lexical representation is formed. The number of units in the fully-connected layers, i.e. the width of the bottleneck, has some effect on the correlation between model and brain (Figure 6A), and the amount of noise added to the unit activations less so (Figure 6B). We already saw that the size of the vocabulary, i.e. the number of wordforms in the training data and number of units in the output layer of the model, had a large effect on the response profiles (Figure 4). Having a large vocabulary is of course desirable from a functional point of view, but also modestly improves correlation between model and brain (Figure 6C). For large vocabularies, we found it beneficial to apply frequency-balancing of the training data, meaning that the number of times a word-form appears in the training data is scaled according to its frequency in a large text corpus. However, this cannot be a one-to-one scaling, since the most frequent words occur so much more often than other words that the training data would consist of mostly the top-ten most common words, with less common words only occurring once or not at all. Therefore, we decided to scale not by the frequency 𝑓 directly, but by 𝑓𝑠, where 0 < 𝑠 < 1, opting for 𝑠 = 0.2 for the final model (Figure 6D).”

      (2) It is not clear which layers exactly correspond to which of the three response components. For this to be clearer, it would have been nice to have a plot with all the layers of VGG on the x-axis and three curves corresponding to the correlation of each layer with each of the three response components.

      This is a great suggestion that we were happy to incorporate in the revised version of the manuscript. Every figure comparing the response patterns of the model and brain now includes a panel depicting the correlation between each layer of the model and each of the three MEG components (Figures 4 & 5, Supplementary Figures 2-5). This has given us (and now also the reader) the ability to better benchmark the different models quantitatively, adding to our discussion on qualitative to quantitative analysis.

      (3) It is not clear to me why the authors report the correlation of all layers with the MEG responses in Figure 5: why not only report the correlation of the final layers for N400, and that of the first layers for type-I?

      We agree with the reviewer that it would have been better to compare the correlation scores for those layers which response profile matches the MEG component. While the old Figure 5 has been merged with Figure 4, and now provides the correlations between all the layers and all MEG components, we have taken the Reviewer’s advice and marked the layers which qualitatively best correspond to each MEG component, so the reader can take that into account when interpreting the correlation scores.

      (4) The authors mention that the reason that they did not reproduce the protocol with more advanced vision models is that they needed the minimal setup capable of yielding the desired experiment effect. I am not fully convinced by this and think the paper could be significantly strengthened by reporting results for a vision transformer, in particular to study the role of attention layers which are expected to play an important role in processing higher-level features.

      We appreciate and share the Reviewer’s enthusiasm in seeing how other model architectures would fare when it comes to modeling MEG components. However, we regard modifying the core model architecture (i.e., a series of convolution-and-pooling followed by fully-connected layers) to be out of scope for the current paper.

      One of the key points of our study is to create a model that reproduces the experimental effects of an existing MEG study, which necessitates modeling the initial feed-forward processing from pixel to word-form. For this purpose, a convolution-and-pooling model was the obvious choice, because these operations play a big role in cognitive models of vision in general. In order to properly capture all experimental contrasts in the MEG study, many variations of the CNN were trained and evaluated. This iterative design process concluded when all experimental contrasts could be faithfully reproduced.

      If we were to explore different model architectures, such as a transformer architecture, reproducing the experimental contrasts of the MEG study would no longer be the end goal, and it would be unclear what the end goal should be. Maximizing correlation scores has no end, and there are a nearly endless number of model architectures one could try. We could bring in a second MEG study with experimental contrasts that the CNN cannot explain and a transformer architecture potentially could and set the end goal to explain all experimental effects in both MEG studies. But even if we had access to such a dataset, this would almost double the length of the paper, which is already too long.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Insects and their relatives are commonly infected with microbes that are transmitted from mothers to their offspring. A number of these microbes have independently evolved the ability to kill the sons of infected females very early in their development; this male killing strategy has evolved because males are transmission dead-ends for the microbe. A major question in the field has been to identify the genes that cause male killing and to understand how they work. This has been especially challenging because most male-killing microbes cannot be genetically manipulated. This study focuses on a male-killing bacterium called Wolbachia. Different Wolbachia strains kill male embryos in beetles, flies, moths, and other arthropods. This is remarkable because how sex is determined differs widely in these hosts. Two Wolbachia genes have been previously implicated in male-killing by Wolbachia: oscar (in moth male-killing) and wmk (in fly male-killing). The genomes of some male-killing Wolbachia contain both of these genes, so it is a challenge to disentangle the two.

      This paper provides strong evidence that oscar is responsible for male-killing in moths. Here, the authors study a strain of Wolbachia that kills males in a pest of tea, Homona magnanima. Overexpressing oscar, but not wmk, kills male moth embryos. This is because oscar interferes with masculinizer, the master gene that controls sex determination in moths and butterflies. Interfering with the masculinizer gene in this way leads the (male) embryo down a path of female development, which causes problems in regulating the expression of genes that are found on the sex chromosomes.

      We would like to thank you for evaluating our manuscript.

      Strengths:

      The authors use a broad number of approaches to implicate oscar, and to dissect its mechanism of male lethality. These approaches include: a) overexpressing oscar (and wmk) by injecting RNA into moth eggs, b) determining the sex of embryos by staining female sex chromosomes, c) determining the consequences of oscar expression by assaying sex-specific splice variants of doublesex, a key sex determination gene, and by quantifying gene expression and dosage of sex chromosomes, using RNASeq, and d) expressing oscar along with masculinizer from various moth and butterfly species, in a silkmoth cell line. This extends recently published studies implicating oscar in male-killing by Wolbachia in Ostrinia corn borer moths, although the Homona and Ostrinia oscar proteins are quite divergent. Combined with other studies, there is now broad support for oscar as the male-killing gene in moths and butterflies (i.e. order Lepidoptera). So an outstanding question is to understand the role of wmk. Is it the master male-killing gene in insects other than Lepidoptera and if so, how does it operate?

      We would like to thank you for evaluating our manuscript. Our data demonstrated that Oscar homologs play important roles in male-killing phenotypes in moths and butterflies; however, the functional relevance of wmk remains uncertain. As you noted, whether wmk acts as a male-killing gene in insects such as flies and beetles—or even in certain lepidopteran species—requires further investigation using diverse insect models, which we are eager to explore in future research.

      Weaknesses:

      I found the transfection assays of oscar and masculinizer in the silkworm cell line (Figure 4) to be difficult to follow. There are also places in the text where more explanation would be helpful for non-experts.

      Thank you for your suggestion. We have revised the section on the cell-based experiment. Further, we revised the manuscript to make it accessible to a broader audience. We believe these revisions have significantly improved the clarity and comprehensiveness of our manuscript.

      Reviewer #2 (Public review):

      Summary:

      Wolbachia are maternally transmitted bacteria that can manipulate host reproduction in various ways. Some Wolbachia induce male killing (MK), where the sons of infected mothers are killed during development. Several MK-associated genes have been identified in Homona magnanima, including Hm-oscar and wmk-1-4, but the mechanistic links between these Wolbachia genes and MK in the native host are still unclear.

      In this manuscript, Arai et al. show that Hm-oscar is the gene responsible for Wolbachia-induced MK in Homona magnanima. They provide evidence that Hm-Oscar functions through interactions with the sex determination system. They also found that Hm-Oscar disrupts sex determination in male embryos by inducing female-type dsx splicing and impairing dosage compensation. Additionally, Hm-Oscar suppresses the function of Masc. The manuscript is well-written and presents intriguing findings. The results support their conclusions regarding the diversity and commonality of MK mechanisms, contributing to our understanding of the mechanisms and evolutionary aspects of Wolbachia-induced MK.

      We would like to thank you for evaluating our manuscript.

      Comments on revisions:

      The authors have already addressed the reviewer's concerns.

      We would like to thank you for evaluating our manuscript.

      Reviewer #3 (Public review):

      Summary:

      Overall, this is a clearly written manuscript with nice hypothesis testing in a non-model organism that addresses the mechanism of Wolbachia-mediated male killing. The authors aim to determine how five previously identified male-killing genes (encoded in the prophage region of the wHm Wolbachia strain) impact the native host, Homona magnanima moths. This work builds on the authors' previous studies in which

      (1) they tested the impact of these same wHm genes via heterologous expression in Drosophila melanogaster

      (2) also examined the activity of other male-killing genes (e.g., from the wFur Wolbachia strain in its native host: Ostrinia furnacalis moths).

      Advances here include identifying which wHm gene most strongly recapitulates the male-killing phenotype in the native host (rather than in Drosophila), and the finding that the Hm-Oscar protein has the potential for male-killing in a diverse set of lepidopterans, as inferred by the cell-culture assays.

      We would like to thank you for evaluating our manuscript.

      Strengths:

      Strengths of the manuscript include the reverse genetics approaches to dissect the impact of specific male-killing loci, and use of a "masculinization" assay in Lepidopteran cell lines to determine the impact of interactions between specific masc and oscar homologs.

      We would like to thank you for evaluating our manuscript.

      Weaknesses:

      It is clear from Figure 1 that the combinations of wmk homologs do not cause male killing on their own here. While I largely agree with the author's conclusions that oscar is the primary MK factor in this system, I don't think we can yet rule out that wmk(s) may work synergistically or interactively with oscar in vivo. This might be worth a small note in the discussion. (eg at line 294 'indicating that wmk likely targets factors other than masc." - this could be downstream of the impacts of oscar; perhaps dependent on oscar-mediated impacts on masc first).

      We sincerely appreciate your suggestion. Whilst wmk genes themselves did not exhibit apparent lethal effects on the native host, as you noted, we cannot entirely rule out the possibility that wmk may be involved in male-killing actions, either directly or indirectly assisting the function of Hb-oscar. Following your suggestion, we have added a brief note in the discussion section regarding the interpretation of wmk functions.

      “In addition, Katsuma et al. (2022) reported that the wmk homologs encoded by wFur did not affect the masculinizing function of masc in vitro, indicating that wmk likely targets factors other than masc. Whilst we cannot rule out the possibility that wmk may work synergistically or interactively with oscar in vivo—potentially acting downstream of oscar’s impact—our results strongly suggested that Wolbachia strains have acquired multiple MK genes through evolution.” (lines 287-292)

      Regarding the perceived male-bias in Figure 2a: I think readers might be interpreting "unhatched" as "total before hatching". You could eliminate ambiguity by perhaps splitting the bars into male and female, and then within a bar, coloring by hatched versus unhatched. But this is a minor point, and I think the updated text helps clarify this.

      Thank you for your suggestion. We have accordingly revised the figure 2a. In addition, we have included more detailed information in the first sentence of the section Males are killed mainly at the embryonic stage.

      “The sex of hatched larvae (neonates) and the remaining unhatched embryos was determined by the presence or absence of W chromatin, a condensed structure of the female-specific W chromosome observed during interphase.” (lines 171-173)

      The new Figure 4b looks to be largely redundant with the oscar information in Figure 1a.

      Thank you for your suggestion. We have removed Figure 4b due to its overlap with Figure 1a and have incorporated relevant figure legends into the Figure 1a legend.

      Updated statistical comparisons for the RNA-seq analysis are helpful. However these analyses are based on single libraries (albeit each a pool of many individuals), so this is still a weaker aspect of the manuscript.

      Thank you for your suggestion. As you noted, the use of single libraries (due to the limited number of available individuals, though each includes approximately 50 males and females) may be a potential limitation of this study. However, as demonstrated in the qPCR assay for the Z-linked gene provided in the previous revision, we believe that our data and conclusion—that Wolbachia/ Hb-oscar disrupts dosage compensation by causing the overexpression of Z-linked genes—are well-supported and robust.

      The new information on masc similarity is useful (Fig 4d) - if the authors could please include a heatmap legend for the colors, that would be helpful. Also, please avoid green and red in the same figure when key for interpretation.

      Thank you for your suggestion. We have accordingly included a heatmap legend and revised the colors.

      Figure 1A "helix-turn-helix" is misspelled. ("tern").

      We have revised.

      Recommendations for the authors:

      Comments from the reviewing editor: I would suggest you address the comments of the reviewer on the revised version.

      We have further revised the manuscript to address all the questions, comments and suggestions provided by the reviewers. We believe that the resulting revisions have significantly enhanced the quality and comprehensiveness of our manuscript.

      Reviewer #1 (Recommendations for the authors):

      Thank you for revising this manuscript. I have a few last recommendations:

      - Line 214: re: 'Statistical data are available in the supplementary data file', it would be more helpful to add a few words here that actually summarize the statistical results

      We would like to thank you for your suggestion. We have revised the sentence to describe the overview of the statistical results.

      “RNA-seq analysis revealed that, in Hm-oscar-injected embryos, Z-linked genes (homologs on the B. mori chromosomes 1 and 15) were more expressed in males than in females (Fig. 3a), which was not observed in the GFP-injected group (Fig. 3b). Similarly, as previously reported by Arai et al. (2023a), high levels of Z-linked gene expression were also observed in wHm-t-infected males, but not in NSR males (Fig. 3c,d). The high (i.e., doubled) Z-linked gene expression in both Hm-oscar-expressed and wHm-t-infected males was further confirmed by quantification of the Z-linked Hmtpi gene (Fig. 3e). These trends were statistically supported, with all data available in the supplementary data file.” (lines 205-213)

      - Figure 1 legend: do you mean 'bridged' instead of 'brigged'?

      We have accordingly revise, thank you for the suggestion.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Experiments in model organisms have revealed that the effects of genes on heritable traits are often mediated by environmental factors---so-called gene-by-environment (or GxE) interactions. In human genetics, however, where indirect statistical approaches must be taken to detect GxE, limited evidence has been found for pervasive GxE interactions. The present manuscript argues that the failure of statistical methods to detect GxE may be due to how GxE is modelled (or not modelled) by these methods.

      The authors show, via re-analysis of an existing dataset in Drosophila, that a polygenic ‘amplification’ model can parsimoniously explain patterns of differential genetic effects across environments. (Work from the same lab had previously shown that the amplification model is consistent with differential genetic effects across the sexes for several traits in humans.) The parsimony of the amplification model allows for powerful detection of GxE in scenarios in which it pertains, as the authors show via simulation.

      Before the authors consider polygenic models of GxE, however, they present a very clear analysis of a related question around GxE: When one wants to estimate the effect of an individual allele in a particular environment, when is it better to stratify one’s sample by environment (reducing sample size, and therefore increasing the variance of the estimator) versus using the entire sample (including individuals not in the environment of interest, and therefore biasing the estimator away from the true effect specific to the environment of interest)? Intuitively, the sample-size cost of stratification is worth paying if true allelic effects differ substantially between the environment of interest and other environments (i.e., GxE interactions are large), but not worth paying if effects are similar across environments. The authors quantify this trade-off in a way that is both mathematically precise and conveys the above intuition very clearly. They argue on its basis that, when allelic effects are small (as in highly polygenic traits), single-locus tests for GxE may be substantially underpowered.

      The paper is an important further demonstration of the plausibility of the amplification model of GxE, which, given its parsimony, holds substantial promise for the detection and characterization of GxE in genomic datasets. However, the empirical and simulation examples considered in the paper (and previous work from the same lab) are somewhat “best-case” scenarios for the amplification model, with only two environments, and with these environments amplifying equally the effects of only a single set of genes. It would be an important step forward to demonstrate the possibility of detecting amplification in more complex scenarios, with multiple environments each differentially modulating the effects of multiple sets of genes. This could be achieved via simulations similar to those presented in the current manuscript.

      Reviewer #2 (Public Review):

      Summary:

      Wine et al. describe a framework to view the estimation of gene-context interaction analysis through the lens of bias-variance tradeoff. They show that, depending on trait variance and context-specific effect sizes, effect estimates may be estimated more accurately in context-combined analysis rather than in context-specific analysis. They proceed by investigating, primarily via simulations, implications for the study or utilization of gene-context interaction, for testing and prediction, in traits with polygenic architecture. First, the authors describe an assessment of the identification of context-specificity (or context differences) focusing on “top hits” from association analyses. Next, they describe an assessment of polygenic scores (PGSs) that account for context-specific effect sizes, showing, in simulations, that often the PGSs that do not attempt to estimate context-specific effect sizes have superior prediction performance. An exception is a PGS approach that utilizes information across contexts. Strengths:

      The bias-variance tradeoff framing of GxE is useful, interesting, and rigorous. The PGS analysis under pervasive amplification is also interesting and demonstrates the bias-variance tradeoff.

      Weaknesses:

      The weakness of this paper is that the first part -- the bias-variance tradeoff analysis -- is not tightly connected to, i.e. not sufficiently informing, the later parts, that focus on polygenic architecture. For example, the analysis of “top hits” focuses on the question of testing, rather than estimation, and testing was not discussed within the bias-variance tradeoff framework. Similarly, while the PGS analysis does demonstrate (well) the bias-variance tradeoff, the reader is left to wonder whether a bias-variance deviation rule (discussed in the first part of the manuscript) should or could be utilized for PGS construction.

      We thank the editors and the reviewers for their thoughtful critique and helpful suggestions throughout. In our revision, we focused on tightening the relationship between the analytical single variant bias-variance tradeoff derivation and the various empirical analyses that follow.

      We improved discussion of our scope and what is beyond our scope. For example, our language was insufficiently clear if it suggested to the editor and reviewers that we are developing a method to characterize polygenic GxE. Developing a new method that does so (let alone evaluating performance across various scenarios) is beyond the scope of this manuscript.

      Similarly, we clarify that we use amplification only as an example of a mode of GxE that is not adequately characterized by current approaches. We do not wish to argue it is an omnibus explanation for all GxE in complex traits. In many cases, a mixture of polygenic GxE relationships seems most fitting (as observed, for example, in Zhu et al., 2023, for GxSex in human physiology).

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      MAJOR COMMENT

      The amplification model is based on an understanding of gene networks in which environmental variables concertedly alter the effects of clusters of genes, or modules, in the network (e.g., if an environmental variable alters the effect of some gene, it indirectly and proportionately alters the effects of genes downstream of that gene in the network---or upstream if the gene acts as a bottleneck in some pathway). It is clear in this model that (i) multiple environmental variables could amplify distinct modules, and (ii) a single environmental variable could itself amplify multiple separate modules, with a separate amplification factor for each module.

      However, perhaps inspired by their previous work on GxSex interactions in humans, the authors’ focus in the present manuscript is on cases where there are only two environments (“control” and “high-sugar diet” in the Drosophila dataset that they reanalyze, and “A” and “B” in their simulations [and single-locus mathematical analysis]), and they consider models where these environments amplify only a single set of genes, i.e., with a single amplification factor. While it is of course interesting that a single-amplification-factor model can generate data that resemble those in the Drosophila dataset that the authors re-analyze, most scenarios of amplification GxE will presumably be more complex. It seems that detecting amplification in these more complex scenarios using methods such as the authors do in their final section will be correspondingly more difficult. Indeed, in the limit of sufficiently many environmental variables amplifying sufficiently many modules, the scenario would resemble one of idiosyncratic single-locus GxE which, as the authors argue, is very difficult to detect. That more complex scenarios of amplification, with multiple environments separately amplifying multiple modules each, might be difficult to detect statistically is potentially an important limitation to the authors’ approach, and should be tested in their simulations.

      We agree that characterizing GxE when there is a mixture of drivers of context-dependency is difficult. Developing a method that does so across multiple (and perhaps not pre-defined) contexts is of high interest to us but beyond the scope of the current manuscript

      We note that for GxSex, modeling this mixture does generally improve phenotypic prediction, and more so in traits where we infer amplification as a major mode of GxE.

      MINOR COMMENTS

      Lines 88-90: “This estimation model is equivalent to a linear model with a term for the interaction between context and reference allele count, in the sense that context-specific allelic effect estimators have the same distributions in the two models.”

      Does this equivalence require the model with the interaction term also to have an interaction term for the intercept, i.e., the slope on a binary variable for context (since the generative model in Eq. 1 allows for context-specific intercepts)?

      It does require an interaction term for the intercept. This is e_i (and its effect beta_E) in Eq. S2 (line 70 of the supplement).

      Lines 94-96: Perhaps just a language thing, but in what sense does the estimation model described in lines 92-94 “assume” a particular distribution of trait values in the combined sample? It’s just an OLS regression, and one can analyze its expected coefficients with reference to the generative model in Eq. 1, or any other model. To say that it “assumes” something presupposes its purpose, which is not clear from its description in lines 92-94.

      We corrected “assume” to “posit”.

      Lines 115-116: It should perhaps be noted that the weights wA and wB need not sum to 1.

      Indeed; it is now explicitly stated.

      Lines 154-160: I think the role of r could be made even clearer by also discussing why, when VA>>VB, it is better to use the whole-sample estimate of betaA than the sample-A-specific estimate (since this is a more counterintuitive case than the case of VA<<VB discussed by the authors).

      This is addressed in lines 153-154, stating: “Typically, this (VA<<VB) will also imply that the additive estimator is greatly preferable for estimating β_B , as β_B will be extremely noisy”

      Line 243 and Figure 4 caption: The text states that the simulated effects in the high-sugar environment are 1.1x greater than those in the control environment, while the caption states that they are 1.4x greater.

      We have corrected the text to be consistent with our simulations.

      TYPOS/WORDING

      Line 14: “harder to interpret” --> “harder-to-interpret”

      Line 22: We --> we

      Line 40: “as average effect” -> “as the average effect”?

      Line 57: “context specific” --> “context-specific”

      Line 139: “re-parmaterization” --> “re-parameterization”

      Lines 140, 158, 412: “signal to noise” --> “signal-to-noise”

      Figure 3C,D: “pule rate” --> “pulse rate”

      The caption of Figure 3: “conutinous” --> “continuous”

      Line 227: “a variant may fall” --> “a variant may fall into”

      Line 295: “conferring to more GxE” --> “conferring more GxE” or “corresponding to more GxE”? This is very pedantic, but I think “bias-variance” should be “bias--variance” throughout, i.e., with an en-dash rather than a hyphen.

      We have corrected all of the above typos.

      Reviewer #2 (Recommendations For The Authors):

      (This section repeats some of what I wrote earlier).

      - First polygenic architecture part: the manuscript focuses on “top hits” in trying to identify sets of variants that are context-specific. This “top hits” approach seems somewhat esoteric and, as written, not connected tightly enough to the bias-variance tradeoff issue. The first section of the paper which focuses on bias-variance trade-off mostly deals with estimation. The “top hits” section deals with testing, which introduces additional issues that are due to thresholding. Perhaps the authors can think of ways to make the connection stronger between the bias-variance tradeoff part to the “top hits” part, e.g., by introducing testing earlier on and/or discussion estimation in addition to testing in the “top hits” part of the manuscript. The second polygenic architecture part: polygenic scores that account for interaction terms. Here the authors focused (well, also here) on pervasive amplification in simulations. This part combines estimation and testing (both the choice of variants and their estimated effects are important). In pervasive amplification the idea is that causal variants are shared, the results may be different than in a model with context-specific effects and variant selection may have a large impact. Still, I think that these simulations demonstrate the idea developed in the bias-variance tradeoff part of the paper, though the reader is left to wonder whether a bias-variance decision rule should or could be utilized for PGS construction.

      In both of these sections we discuss how the consideration of polygenic GxE patterns alters the conclusions based on the single-variant tradeoff. In the “top hits” section, we show that single-variant classification itself, based on a series of marginal hypothesis tests alone, can be misleading. The PGS prediction accuracy analysis shows that both approaches are beaten by the polygenic GxE estimation approach. Intuitively, this is because the consideration of polygenic GxE can mitigate both the bias and variance, as it leverages signals from many variants.

      We agree that the links between these sections of the paper were not sufficiently clear, and have added signposting to help clarify them (lines 176-180; lines 275-277; lines 316-321).

      - Simulation of GxDiet effects on longevity: the methods of the simulation are strange, or communicated unclearly. The authors’ report (page 17) poses a joint distribution of genetic effects (line 439), but then, they simulated effect estimates standard errors by sampling from summary statistics (line 445) rather than simulated data and then estimating effect and effect SE. Why pose a true underlying multivariate distribution if it isn’t used?

      We rewrote the Methods section “Simulation of GxDiet effects on longevity in Drosophila to make our simulation approach clearer (lines 427-449). We are indeed simulating the true effects from the joint distribution proposed. However, in order to mimic the noisiness of the experiment in our simulations, we sample estimated effects from the true simulated effects, with estimation noise conferring to that estimated in the Pallares et al. dataset (i.e., sampling estimation variances from the squares of empirical SEs).

      - How were the “most significantly associated variants” selected into the PGS in the polygenic prediction part? Based on a context-specific test? A combined-context test of effect size estimates?

      For the “Additive” and “Additive ascertainment, GxE estimation” models (red and orange in Fig. 5, respectively), we ascertain the combined-context set. For the “GxE” and “polygenic GxE” (green and blue in Fig. 5, respectively) models, we ascertain in a context-specific test. We now state this explicitly in lines 280-288 and lines 507-526.

      - As stated, I find the conclusion statement not specific enough in light of the rest of the manuscript. “the consideration of polygenic GxE trends is key” - this is very vague. What does it mean “to consider polygenic GxE trends” in the context of this paper? I can’t tell. “The notion that complex trait analyses should combine observations at top associated loci” - I don’t think the authors really refer to combining “observations”, rather perhaps combine information from top associated loci. But this does not represent the “top hits” approach that merely counts loci by their testing patterns. “It may be a similarly important missing piece...” What does “it” refer to? The top loci? What makes it an important missing piece?

      We rewrote the conclusion paragraph to address these concerns (lines 316-321).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      […] Overall, this is an important paper that demonstrates that one model for transgenerational inheritance in C. elegans is not reproducible. This is important because it is not clear how many of the reported models of transgenerational inheritance reported in C. elegans are reproducible. The authors do demonstrate a memory for F1 embryos that could be a maternal effect, and the authors confirm that this is mediated by a systemic small RNA response. There are several points in the manuscript where a more positive tone might be helpful.

      We would like to correct the statement made in the second to last sentence. The demonstration of an F1 response to PA14 was first reported by Moore et al., (2019) and then by Pereira et al., (2020) using a different behavioral assay. We merely confirmed these results in our hands, and confirmed the observation, first reported by Kaletsky et al., (2020), that sid-1 and sid-2 are required for this F1 response; although we did find that sid-1 and sid-2 are not required for the PA14-induced increase in daf-7p::gfp expression in ASI neurons in the F1 progeny of trained adults, which had not been addressed in the published work.

      Yes, the intergenerational F1 response could be a maternal effect, but the in utero F1 embryos and their precursor germ cells were directly exposed to PA14 metabolites and toxins (non-maternal effect) as well as any parental response, whether mediated by small RNAs, prions, hormones, or other unknown information carriers. While the F1 aversion response does require sid-1 and sid-2, we would not presume that the substrate is therefore an RNA molecule, particularly because the systemic RNAi response supported by sid-1 and sid-2 is via long double-stranded RNA. To date, no evidence suggests that either protein transports small RNAs, particularly single-stranded RNAs.

      Strengths:

      The authors note that the high copy number daf-7::GFP transgene used by the Murphy group displayed variable expression and evidence for somatic silencing or transgene breakdown in the Hunter lab, as confirmed by the Murphy group. The authors nicely use single copy daf-7::GFP to show that neuronal daf-7::GFP is elevated in F1 but not F2 progeny with regards to the memory of PA14 avoidance, speaking to an intergenerational phenotype.

      The authors nicely confirm that sid-1 and sid-2 are generally required for intergenerational avoidance of F1 embryos of moms exposed to PA14. However, these small RNA proteins did not affect daf-7::GFP elevation in the F1 progeny. This result is unexpected given previous reports that single copy daf-7::GFP is not elevated in F1 progeny of sid mutants. Because the Murphy group reported that daf-7 mutation abolishes avoidance for F1 progeny, this means that the sid genes function downstream of daf-7 or in parallel, rather than upstream as previously suggested.

      The published report (Moore et al., 2019) shows only multicopy daf-7p::gfp results and does not address the daf-7p::gfp response in sid-1 or sid-2 mutants. Thus, our discovery that systemic RNAi, exogenous RNAi, and heritable RNAi mutants don’t disrupt elevated daf-7p::gfp in ASI neurons in the F1 progeny of PA14 trained P0’s is only unexpected with respect to the published models (Moore et al., 2019, Kaletsky et al., 2020).

      The authors studied antisense small RNAs that change in Murphy data sets, identifying 116 mRNAs that might be regulated by sRNAs in response to PA14. Importantly, the authors show that the maco-1 gene, putatively targeted by piRNAs according to the Kaletsky 2020 paper, displays few siRNAs that change in response to PA14. The authors conclude that the P11 ncRNA of PA14, which was proposed to promote interkingdom RNA communication by the Murphy group, is unlikely to affect maco-1 expression by generating sRNAs that target maco-1 in C. elegans. The authors define 8 genes based on their analysis of sRNAs and mRNAs that might promote resistance to PA14, but they do not further characterize these genes' role in pathogen avoidance. The Murphy group might wish to consider following up on these genes and their possible relationship with P11.

      Weaknesses:

      This very thorough and interesting manuscript is at times pugnacious.

      We reiterate that we never claimed that Moore et al., (2019) did not obtain their reported results. We simply stated that we could not replicate their results using the published methods and then failed in our search to identify variable(s) that might account for our results. In revising the manuscript, we have striven to make clear, unmuddied statements of facts and state that future investigations may provide independent evidence that supports the original claims and explains our divergent results.

      Please explain more clearly what is High Growth media for E. coli in the text and methods, conveying why it was used by the Murphy lab, and if Normal Growth or High Growth is better for intergenerational heritability assays.

      We added the standard recipes and the following explanations in the methods section to the revised text.

      “NG plates minimally support OP50 growth, resulting in a thin lawn that facilitates visualization of larvae and embryos. HG plates (8X more peptone) support much higher OP50 growth, resulting in a thick bacterial lawn that supports larger worm populations.”

      We have also included the following text in our presentation and discussion of the effects of growth conditions on worm choice in PA14 vs OP50 choice assays.

      “Furthermore, because OP50 pathogenicity is enhanced by increased E. coli nutritive conditions (Garsin et al., 2003, Shi et al., 2006), the growth of F1-F4 progeny on High Growth (HG) plates (Moore et al., 2019; 2021b), which contain 8X more peptone than NG plates and therefore support much higher OP50 growth levels, immediately prior to the F1-F4 choice assays may further contribute to OP50 aversion among the control animals.”

      We don’t know enough to claim that HG or NG media is better than the other for intergenerational assays, but they are different. Thus, switching between the two in a multigenerational experiment likely introduces unknown variability.

      Reviewer #2 (Public Review):

      This paper examines the reproducibility of results reported by the Murphy lab regarding transgenerational inheritance of a learned avoidance behavior in C. elegans. It has been well established by multiple labs that worms can learn to avoid the pathogen pseudomonas aeruginosa (PA14) after a single exposure. The Murphy lab has reported that learned avoidance is transmittable to 4 generations and dependent on a small RNA expressed by PA14 that elicits the transgenerational silencing of a gene in C. elegans. The Hunter lab now reports that although they can reproduce inheritance of the learned behavior by the first generation (F1), they cannot reproduce inheritance in subsequent generations.

      This is an important study that will be useful for the community. Although they fail to identify a "smoking gun", the study examines several possible sources for the discrepancy, and their findings will be useful to others interested in using these assays. The preference assay appears to work in their hands in as much as they are able to detect the learned behavior in the P0 and F1 generations, suggesting that the failure to reproduce the transgenerational effect is not due to trivial mistakes in the protocol. An obvious reason, however, to account for the differing results is that the culture conditions used by the authors are not permissive for the expression of the small RNA by PA14 that the MUrphy lab identified as required for transgenerational inheritance. It would seem prudent for the authors to determine whether this small RNA is present in their cultures, or at least acknowledge this possibility.

      We thank the reviewer for raising this issue and have added the following statement to this effect in the revised manuscript.

      “We note that previous bacterial RNA sequence analysis identified a small non-coding RNA called P11 whose expression correlates with bacterial growth conditions that induce heritable avoidance (Kaletsky et al., 2020). Critically, C. elegans trained on a PA14 ΔP11 strain (which lacks this small RNA) still learn to avoid PA14, but their F1 and F2-F4 progeny fail to show an intergenerational or transgenerational response (Figure 3L in Kaletsky et al., 2020). The fact that we observed an intergenerational (F1) avoidance response is evidence that our PA14 growth conditions induce P11 expression.”

      We believe that this addresses the concern raised here.

      The authors should also note that their protocol was significantly different from the Murphy protocol (see comments below) and therefore it remains possible that protocol differences cumulatively account for the different results.

      As suggested below, we have added to the supplemental documents the protocol we followed for the aversion assay. In our view, this document shows that our adjustments to the core protocol were minor. Furthermore, where possible, these adjustments were explicitly tested in side-by-side experiments for both the aversion assay and the daf-7p::gfp expression assay and presented in the manuscript.

      To discover the source(s) of discrepancy between our results and the published results we subsequently introduced variations to this core protocol to exclude likely variables (worm and bacteria growth temperatures, assay conditions, worm handling methods, bacterial culture and storage conditions, and some minor developmental timing issues). Again, where possible, the effect of variations was tested in side-by-side experiments for both the aversion assay and the daf-7p::gfp expression assay and were presented in or have now been added to the manuscript.

      It remains possible that we misunderstood the published Murphy lab protocols, but we were highly motivated to replicate the results so we could use these assays to investigate the reported RNAi-pathway dependent steps, thus we read every published version with extreme care.

      Reviewer #3 (Public Review):

      […] Strengths:

      (1) The authors provide a thorough description of their methods, and a marked-up version of a published protocol that describes how they adapted the protocol to their lab conditions. It should be easy to replicate the experiments.

      As noted above in response to a suggestion by reviewer #2, we have replaced the annotated published protocol with the protocol that we followed. This will aid other groups' attempts to replicate our experimental conditions.

      (2) The authors test the source of bacteria, growth temperature (of both C. elegans and bacteria), and light/dark husbandry conditions. They also supply all their raw data, so that the sample size for each testing plate can be easily seen (in the supplementary data). None of these variations appears to have a measurable effect on pathogen avoidance in the F2 generation, with all but one of the experiments failing to exhibit learned pathogen avoidance.

      We note that the parallel analysis of daf-7p::gfp expression in ASI neurons was also tested for several of these conditions and also failed to replicate the published findings.

      (3) The small RNA seq and mRNA seq analysis is well performed and extends the results shown in the original paper. The original paper did not give many details of the small RNA analysis, which was an oversight. Although not a major focus of this paper, it is a worthwhile extension of the previous work.

      (4) It is rare that negative results such as these are accessible. Although the authors were unable to determine the reason that their results differ from those previously published, it is important to document these attempts in detail, as has been done here. Behavioral assays are notoriously difficult to perform and public discourse around these attempts may give clarity to the difficulties faced by a controversial field.

      Thank you for your support. Choosing to pursue publication of these negative results was not an easy decision, and we thank members of the community for their support and encouragement.

      Weaknesses:

      (1) Although the "standard" conditions have been tested over multiple biological replicates, many of the potential confounders that may have altered the results have been tested only once or twice. For example, changing the incubation temperature to 25{degree sign}C was tested in only two biological replicates (Exp 5.1 and 5.2) - and one of these experiments actually resulted in apparent pathogen avoidance inheritance in the F2 generation (but not in the F1). An alternative pathogen source was tested in only one biological replicate (Exp 3). Given the variability observed in the F2 generation, increasing biological replicates would have added to the strengths of the report.

      We agree that our study was not exhaustive in our exploration of variables that might be interfering with our ability to detect F2 avoidance. We also note that some of these variables also failed (with many more independent experiments) to induce elevated daf-7p::gfp expression in ASI neurons in F2 progeny. Our goal was not to show that variation in some growth or assay condition would generate reproducible negative results, but the exploration was designed to tweak conditions to enable detection of a robust F2 response. Given the strength of the data presented in Moore et al., (2019) we expected that adjustment of the problematic variable would produce positive results apparent in a single replicate, which could then be followed up. If we had succeeded, then we would have documented the conditions that enabled robust F2 inheritance and would have explored molecular mechanisms that support this important but mysterious process.

      (2) A key difference between the methods used here and those published previously, is an increase in the age of the animals used for training - from mostly L4 to mostly young adults. I was unable to find a clear example of an experiment when these two conditions were compared, although the authors state that it made no difference to their results.

      We can state firmly that the apparent time delay did not affect P0 learned avoidance (new Figure S1) or, as documented in Table S1, daf-7p::gfp expression in ASI neurons. In our experience, training mostly L4’s on PA14 frequently failed to produce sufficient F1 embryos for both F1 avoidance assays or daf-7p::gfp measurements in ASI neurons and collection of F2 progeny. Indeed, in early attempts to detect heritable PA14 aversion, trained P0 and F1 progeny were not assayed in order to obtain sufficient F2’s for a choice assay. These animals failed to display aversion, but without evidence of successful P0 training or an F1 intergenerational response this was deemed a non-fruitful trouble-shooting approach. We have added supplemental Figure S1 which presents P0 choice assay results from experiments using younger trained animals that failed to produce sufficient F1’s to continue the inheritance experiments.

      The different timing at the start of training between the two protocols may reflect the age of the recovered bleached P0 embryos. It is reasonable to assume that bleaching day 1 adults vs day 2 or 3 adults from the P-1 population could shift the average age of recovered P0 embryos by several hours. The Murphy protocol only states that P0 embryos were obtained by bleaching healthy adults. Regardless, if the hypothesis entertained here is true, that a several hour difference in larval/adult age during 24 hours of training affects F2 inheritance of learned aversion but does not affect P0 learned avoidance, then we would argue that this paradigm for heritable learned avoidance, as described in Moore et al., (2019, 2021), is not sufficiently robust for mechanistic investigations.

      (3) The original paper reports a transgenerational avoidance effect up to the F5 generation. Although in this work the authors failed to see avoidance in the F2 generation, it would have been prudent to extend their tests for more generations in at least a couple of their experiments to ensure that the F2 generation was not an aberration (although this reviewer acknowledges that this seems unlikely to be the case).

      We would point out that we also failed to robustly replicate the F2 response in the daf-7p::gfp expression assays. An F2-specific aberration that affects two different assays seems quite unlikely, and it remains unclear how we would interpret a positive result in F3 and F4 generations without a positive result in the F2 generation. Were we to further extend these investigations, we believe that exploration of additional culture conditions would warrant higher priority than extension of our results to the F3 and F4 generations.

      Reviewing Editor Comments:

      The reviewers' suggestions for improving the manuscript were mostly minor, to change the wording in some places and to add some more explanation regarding the methods.

      What should be highlighted in the section on OP50 growth conditions is that the initial preference for PA14 in the Murphy lab has also been observed by multiple other labs (Bargmann, Kim, Zhang, Abbalay). The fact that this preference was not observed by the Hunter lab is one of several indicators of subtle differences in the environment that might add up to explain the differences in results.

      We agree that subtle known and unknown differences in OP50 and PA14 culture conditions can have measurable effects on the detection of PA14 attraction/aversion relative to OP50 attraction/aversion that could obscure or create the appearance of heritable effects between generations. We have added (see below) to the text a fuller description of the variability in the initial or naive preference observed in different laboratories using similar or variant 2-choice assays and culture conditions. It is worth emphasizing that direct comparison of the OP50 growth conditions specified in Moore et al., (2021) frequently revealed a much larger effect on the naïve choice index than is reported between labs (Figure 4).  

      “Naïve (OP50 grown) worms often show a bias towards PA14 in choice assays (Zhang et al., 2005; Ha et al., 2010; Moore et al., 2019; Pereira et al., 2020; Lalsiamthara and Aballay, 2022). This response, rather than representing an innate attraction to PA14, likely reflects the context of the worm's recent growth on OP50, a mild C. elegans pathogen (Garigan et al., 2002; Garsin et al., 2003; Shi et al., 2006). Thus, the naïve worms presented with a choice between a recently experienced mild pathogen (OP50) and a novel food choice (PA14) initially choose the novel food instead of the known mild pathogen (OP50 aversion).

      In line with our results, some other groups have also reported higher naïve choice index scores (Lee et al., 2017). This variability in naïve choice may reflect differences in growth conditions of either the OP50 or PA14 bacteria. In addition, we note that among the studies that show naïve worm attraction to Pseudomonas (OP50 aversion) there are extensive methodological differences from the methods in Moore et al., (2019; 2021b), including differences in bacterial growth temperature, incubation time, whether the bacteria is diluted or concentrated prior to placement on the choice plates, the concentration of peptone in the choice plates, the length of the choice assay, and the inclusion of sodium azide in the choice assays (Zhang et al., 2005; Ha et al., 2010; Moore et al., 2019; Pereira et al 2020; Lalsiamthara and Aballay, 2022). Thus, the cause of the variability across published reports is not clear.”

      Overall, an emphasis on the absence of robustness of the reported results, rather than failure to reproduce them (which can always have many reasons), is appropriate.

      We agree that an emphasis on robustness is appropriate and have modified the text throughout the manuscript to shift the emphasis to absence of robustness. This includes a change to the manuscript title, which is now, “Reported transgenerational responses to Pseudomonas aeruginosa in C. elegans are not robust”

      A significant experimental addition would be some attempts to determine whether the bacterial PA14 pathogen in the authors' lab produces the P11 small RNA, which has been proposed to have a causal role in initiating the previously reported transgenerational inheritance.

      We acknowledge in the revised manuscript that a subsequent publication (Kaletsky et al., 2020) identified a correlation between PA14 training conditions that induced transgenerational memory and the expression of P11, a P. aeruginosa small non-coding RNA (see our response above to Reviewer #2’s similar query). While testing for the presence of P11 in Harvard culture conditions would be an important assay in any study whose purpose was to investigate the proposed P11-mediated mechanism underlying the transgenerational responses reported by the Murphy Lab, our goal was rather to replicate the robust transgenerational (F2) responses to PA14 training and then to investigate in more detail how sid-1 and sid-2 contribute to transgenerational epigenetic inheritance. Neither sid-1 nor sid-2 are predicted to transport small RNAs or single-stranded RNAs, thus testing for the presence of P11 is less relevant to our goals. Regardless, we note that Figure 3L in Kaletsky et al., (2020) showed that PA14 ΔP11 bacteria failed to induce an F1 avoidance response. Thus, the fact that we observed F1 avoidance implies that our culture conditions successfully induced P11 expression.

      Reviewer #1 (Recommendations For The Authors):

      The abstract could be more positive by concluding that 'We conclude that this example of transgenerational inheritance lacks robustness but instead reflects an example of small RNA-mediated intergenerational inheritance.'

      As recommended, we have added additional clarifying information to the abstract and moderated the conclusion sentence.

      “We did confirm that the dsRNA transport proteins SID-1 and SID-2 are required for the intergenerational (F1) inheritance of pathogen avoidance, but not for the F1 inheritance of elevated daf-7 expression. Furthermore, our reanalysis of RNA seq data provides additional evidence that this intergenerational inherited PA14 response may be mediated by small RNAs.”

      “We conclude that this example of transgenerational inheritance lacks robustness, confirm that the intergenerational avoidance response, but not the elevated daf-7p::gfp expression in F1 progeny, requires sid-1 and sid-2, and identify candidate siRNAs and target genes that may mediate this intergenerational response.”

      Differential expression of sRNAs or mRNAs might be better understood quantitatively by presenting data in scatterplots (Reed and Montgomery 2020) rather than in volcano plots.

      We agree and have modified Figure 6A and 6B.

      This statement in the main text might be unnecessary, as it affects the tenor of the conclusion of this significant manuscript. 'We note that none of the raw data for the published figures and unpublished replicate experiments . . . this hampered our ability to fully compare'.

      We have rewritten this paragraph to focus on our goal: to identify the source of the discrepancy between our results and the published results. We considered discarding this statement but ultimately decided that our inability to directly compare our data to that of previously published work is a shortcoming of our study that deserves to be acknowledged and explained.

      “Ideally, we would have compared our results with the published results (Moore et al., 2019), to possibly identify additional experimental parameters for further investigation; for example, a quantitative comparison of naïve choice in the P0 and F1 generations could help to determine the role of bacterial growth in the choice assay response. However, none of the raw data for the published figures and unpublished replicate experiments (Moore et al., 2019) were available on the publisher’s website or provided upon request to the corresponding author. In the absence of a quantitative comparison, it remains possible that an explanation for the discrepancies between our results and those of Moore et al., (2019) has been overlooked.”

      The final sentence of the Discussion could be tempered and more positive by stating 'Thus independent reproducibility is of paramount concern, and we have tried to be completely transparent as a model for how heritability research should be conducted within the C. elegans community'.

      Thank you. The suggested sentence nicely captures our intention. We now use it, almost verbatim, as our final sentence.

      “Thus, independent reproducibility is of paramount concern, and we have tried to be completely transparent as a model for how heritability research should be presented within the C. elegans community.”

      Reviewer #2 (Recommendations For The Authors):

      Specific comments:

      (1) Protocol: It is difficult to assess from the Methods the exact protocol used by the authors to assay food preference. The annotated Murphy protocol is not sufficient. The authors should provide their own protocol - a detailed lab-ready protocol where every step is outlined, and any steps that deviate from the Murphy lab protocol are called out.

      Thank you for this excellent suggestion. We now include a protocol that documents the precise steps, timings, and controls that we followed (S1_aversion_protocol). We also include footnotes to both explain the reasons behind particular steps and to document known differences to the published protocol. Given the thoroughness of this suggested approach, we have thus removed the annotated version of Moore et al., (2021) from the revised submission.

      (2) The authors imply in the methods that, unlike the Murphy lab, they did NOT use azide in the assay, and instead used 4oC to "freeze" the worms in place - It is not clear whether this method was used throughout all their assays and whether this could be a source of the difference. This change is NOT indicated in the annotated Murphy lab STAR Protocol they provide in the supplement.

      We apologize for the lack of clarity. Concerned that azide may be interfering with our ability to detect heritable silencing we tested and then used cold-induced rigor to preserve worm choice in some choice assay results. This was not a change to the core protocol, but a variation used in some assays to determine whether azide could reduce our ability to detect heritable behavioral responses to PA14 exposure. As Moore et al., (2021) show, too much azide can affect measurement of worm choice. Too little or ineffective azide also can affect measurement of worm choice. Azide also affects bacteria (both OP50 and PA14), which could affect the production of molecules that attract or repel worms, much like performing the assay in light vs dark conditions can influence the measured choice index.

      In our hands, cold-induced rigor worked well and within biological replicates was indistinguishable from azide (Figure S10). Thus, we include those results in our analysis and now indicate in Tables 2 and S2 and in Figures 1 and 3 which experiments used which method. As suggested, we now provide a detailed protocol that includes a note describing our precise method for cold-induced rigor.

      Also, the number of worms used in each assay needs to be specified (same or different from Murphy protocol?), and whether any worms were "censored" as in the Murphy protocol, and if so on what basis.

      While we published the exact number of worms scored in each assay (on each plate) it is unknown how this might compare to the results published in Moore et al., (2019), as the number of animals in the presented choice assays (either per plate or per choice) were not reported. Details on censoring, when to exclude data, and additional criteria to abandon an in-progress experiment are now detailed in the protocol (S1_aversion_protocol)

      (3) Several instances in the text cite changes in the protocol as producing "no meaningful differences" without referring to a specific experiment that supports that statement (for example, line 399 regarding azide).

      We now include data and methods comparing azide and cold-induced rigor (Supplemental document S1_aversion_protocol, Supplemental Figure S10), and data showing the P0 choice index for 48-52 hour post-bleach L4/young adults (Supplemental Figure S1), in addition to the previously noted absence of effects due to differences in embryo bleaching protocols (Figures 2, 3 and Tables 1, 2, S1, and S2).

      (4) If the authors want to claim the irreproducibility of the Murphy lab results, they should use the exact protocol used by the Murphy lab in its entirety. It is not sufficient to show that individual changes do not affect the outcome, since the protocol they use appears to include SEVERAL changes which could cumulatively affect the results. If the authors do not want to do this, they should at least acknowledge and summarize in their discussion ALL their protocol changes.

      We acknowledge these minor differences between the protocols we followed and the published methods but disagree that they invalidate our results. We transparently present the effect of known minimal protocol changes. We also present analysis of possible invalidating variations (number of animals in a choice assay). We emphasize that in our hands both measures of TEI, the choice assay and measurement of daf-7p::gfp in ASI neurons, failed to replicate the published transgenerational results.

      If the protocol is sensitive to how animals are counted, whether bleached embryos are mixed gently or vigorously or a few hours difference in age at training, then in our view this TEI paradigm is not robust.

      See also our response to reviewer #3’s public reviews above.

      (5) The authors acknowledge that "non-obvious growth culture differences" could account for the different results. In this respect, the Murphy lab has proposed that the transgenerational effect requires a small RNA expressed in PA14. The authors should check that this RNA is expressed in the cultures they grow in their lab and use for their experiments. This could potentially identify where the two protocols diverge.

      The bacterial culture conditions and worm training procedures described in Moore et al., (2019) successfully produced trained P0 animals that transmitted a PA14 aversion response to their F1 progeny. In a subsequent publication (Kaletsky et al., 2020), the Murphy lab showed a correlation between the culture conditions that induce heritable avoidance and the expression of P11, a P. aeruginosa small non-coding RNA. As mentioned above in response to Reviewer #2’s public review and the Reviewing Editor’s comments to authors, the Murphy lab showed that PA14 ΔP11 bacteria fail to induce an F1 avoidance response (Figure 3L in Kaletsky et al., (2020)). Thus, the fact that we observed F1 avoidance implies that our culture conditions successfully induced P11 expression. We believe that this addresses the concern raised here. Furthermore, if P11 is not reliably expressed in pathogenic PA14, then the published model is unlikely to be relevant in a natural environment. Again, we thank the reviewer for raising this issue and have added this information to the revised manuscript (see above response to Reviewer #2’s Public Reviews).

      (6) Legend to Figure 1: please clarify which experiments were done with which PA14 isolates especially for A-C. What is the origin of the N2 strain used here?

      These details from Tables 2 and S2 have been added to Figure 1 panels A-C and Figure 3. Bristol N2, obtained from the CGC (reference 257), was used for aversion experiments.

      (7) Growth conditions: "These young adults produced comparable P0 and F1 results (Figure 1, Figure 2, and Figure 3)." It is not clear from the text what specific figure panels need to be compared to examine the effect of the variables described in the text. Please indicate which figure panels should be compared (lines 70-95).

      The information for the daf-7p::gfp expression experiments displayed in Figure 1 and Figure 2 is presented in Table 1 and Table S1. The data for P0 aversion training using younger animals is now presented in Figure S1.

      Reviewer #3 (Recommendations For The Authors):

      While overall I found this easy to follow and well-written, I think the clarity of the figures could be improved by incorporating some of the information from S2 into Figure 3. Besides the figure label listing the experiment (Exp1, Exp2, etc) it would be helpful to add pertinent information about the experiment. For example Exp 1.1 (light, 20{degree sign}C), Exp1.2 (dark, 20{degree sign}C), Exp 5 (25{degree sign}C, light), etc.

      Thank you for the suggestion. These details from Tables 2 and S2 have been added to Figures 1 A-C, and 3.

      Citations

      • Moore, R.S., Kaletsky, R., and Murphy, C.T. (2019). Piwi/PRG-1 Argonaute and TGF-beta Mediate Transgenerational Learned Pathogenic Avoidance. Cell 177, 1827-1841 e1812.

      • Moore, R.S., Kaletsky, R., and Murphy, C.T. (2021). Protocol for transgenerational learned pathogen avoidance behavior assays in Caenorhabditis elegans. STAR Protoc 2, 100384.

      • Kaletsky, R., Moore, R.S., Vrla, G.D., Parsons, L.R., Gitai, Z., and Murphy, C.T. (2020). C. elegans interprets bacterial non-coding RNAs to learn pathogenic avoidance. Nature 586, 445-451.

      • Pereira, A.G., Gracida, X., Kagias, K., and Zhang, Y. (2020). C. elegans aversive olfactory learning generates diverse intergenerational effects. J Neurogenet 34, 378-388.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this detailed study, Cohen and Ben-Shaul characterized the AOB cell responses to various conspecific urine samples in female mice across the estrous cycle. The authors found that AOB cell responses vary with the strains and sexes of the samples. Between estrous and non-estrous females, no clear or consistent difference in responses was found. The cell response patterns, as measured by the distance between pairs of stimuli, are largely stable. When some changes do occur, they are not consistent across strains or male status. The authors concluded that AOB detects the signals without interpreting them. Overall, this study will provide useful information for scientists in the field of olfaction.

      Strengths:

      The study uses electrophysiological recording to characterize the responses of AOB cells to various urines in female mice. AOB recording is not trivial as it requires activation of VNO pump. The team uses a unique preparation to activate the VNO pump with electric stimulation, allowing them to record AOB cell responses to urines in anesthetized animals. The study comprehensively described the AOB cell responses to social stimuli and how the responses vary (or not) with features of the urine source and the reproductive state of the recording females. The dataset could be a valuable resource for scientists in the field of olfaction.

      Weaknesses:

      (1) The figures could be better labeled.

      Figures will be revised to provide more detailed labeling.

      (2) For Figure 2E, please plot the error bar. Are there any statistics performed to compare the mean responses?

      We did not perform statistical comparisons (between the mean rates across the population). We will add this analysis and the corresponding error bars. 

      (3) For Figure 2D, it will be more informative to plot the percentage of responsive units.

      We will do it.

      (4) Could the similarity in response be explained by the similarity in urine composition? The study will be significantly strengthened by understanding the "distance" of chemical composition in different urine.

      We agree. As we wrote in the Discussion: “Ultimately, lacking knowledge of the chemical space associated with each of the stimuli, this and all the other ideas developed here remain speculative.”

      A better understanding of the chemical distance is an important aspect that we aim to include in our future studies. However, this is far from trivial, as it is not chemical distance per se (which in itself is hard to define), but rather the “projection” of chemical space on the vomeronasal receptor neurons array. That is, knowledge of the chemical composition of the stimuli, lacking full knowledge of which molecules are vomeronasal system ligands, will only provide a partial picture. Despite these limitations, this is an important analysis which we would have done had we access to this data.

      (5) If it is not possible for the authors to obtain these data first-hand, published data on MUPs and chemicals found in these urines may provide some clues.

      Measurements about some classes of molecules may be found for some of the stimuli that we used here, but not for all. We are not aware of any single dataset that contains this information for any type of molecules (e.g., MUPs) across the entire stimulus set that we have used. More generally, pooling results from different studies has limited validity because of the biological and technical variability across studies. In order to reliably interpret our current recordings, it would be necessary to measure the urinary content of the very same samples that were used for stimulation. Unfortunately, we are not able to conduct this analysis at this stage.

      (6) It is not very clear to me whether the female overrepresentation is because there are truly more AOB cells that respond to females than males or because there are only two female samples but 9 male samples.

      It is true that the number of neurons fulfilling each of the patterns depends on the number of individual stimuli that define it. However, our measure of “over-representation” aims to overcome this bias, by using bootstrapping to reveal if the observed number of patterns is larger than expected by chance. We also note that more generally, the higher frequency of responses to female, as compared to male stimuli, is obtained in other studies by others and by us, also when the number of male and female stimuli is matched (e.g., Bansal et al BMC Biol 2021, Ben-Shaul et al, PNAS 2010, Hendrickson et al, JNS, 2008).

      (7) If the authors only select two male samples, let's say ICR Naïve and ICR DOM, combine them with responses to two female samples, and do the same analysis as in Figure 3, will the female response still be overrepresented?

      We believe that the answer is positive, but we can, and will perform this analysis to check.

      (8) In Figure 4B and 4C, the pairwise distance during non-estrus is generally higher than that during estrus, although they are highly correlated. Does it mean that the cells respond to different urines more distinctively during diestrus than in estrus?

      This is an important observation. For the Euclidean distance there might be a simple explanation as the distance depends on the number of units (and there are more units recorded in non-estrus females). However, this simple explanation does not hold for the correlation distance. A higher distance implies higher discrimination during the non-estrus stage, but our other analyses of sparseness and the selectivity indices do not support this idea. We note that absolute values of distance measures should generally be interpreted cautiously, as they may depend on multiple factors including sample size. Also, a small number of non-selective units could increase the correlation in responses among stimuli, and thus globally shift the distances. For these reasons, we focus on comparisons, rather than the absolute values of the correlation distances. In the revised manuscript, we will note and discuss this important observation.

      (9) The correlation analysis is not entirely intuitive when just looking at the figures. Some sample heatmaps showing the response differences between estrous states will be helpful.

      If we understand correctly, the idea is to show the correlation matrices from which the values in 4B and 4C are taken. We can and will do this, probably as a supplementary figure.

      Reviewer #2 (Public review):

      Summary:

      Many aspects of the study are carefully done, and in the grand scheme this is a solid contribution. I have no "big-picture" concerns about the approach or methodology. However, in numerous places the manuscript is unnecessarily vague, ambiguous, or confusing. Tightening up the presentation will magnify their impact.

      We will revise the text with the aim of tightening the presentation.

      Strengths:

      (1) The study includes urine donors from males of three strains each with three social states, as well as females in two states. This diversity significantly enhances their ability to interpret their results.

      (2) Several distinct analyses are used to explore the question of whether AOB MCs are biased towards specific states or different between estrus and non-estrus females. The results of these different analyses are self-reinforcing about the main conclusions of the study.

      (3) The presentation maintains a neutral perspective throughout while touching on topics of widespread interest.

      Weaknesses:

      (1) Introduction:

      The discussion of the role of the VNS and preferences for different male stimuli should perhaps include Wysocki and Lepri 1991

      Agreed. we will refer to this work in our discussion.

      (2) Results:

      a) Given the 20s gap between them, the distinction between sample application and sympathetic nerve trunk stimulation needs to be made crystal clear; in many places, "stimulus application" is used in places where this reviewer suspects they actually mean sympathetic nerve trunk stimulation.

      In this study, we have considered both responses that are triggered by sympathetic trunk activation, and those that occur (as happens in some preparations) immediately following stimulus application (and prior to nerve trunk stimulation). An example of the latter Is provided in the second unit shown in Figure 1D (and this is indicated also in the figure legend). In our revision, we will further clarify this confusing point.

      b) There appears to be a mismatch between the discussion of Figure 3 and its contents. Specifically, there is an example of an "adjusted" pattern in 3A, not 3B.

      True. Thanks for catching this error. We will correct this.

      c) The discussion of patterns neglects to mention whether it's possible for a neuron to belong to more than one pattern. For example, it would seem possible for a neuron to simultaneously fit the "ICR pattern" and the "dominant adjusted pattern" if, e.g., all ICR responses are stronger than all others, but if simultaneously within each strain the dominant male causes the largest response.

      This is true. In the legend to Figure 3B, we actually write: “A neuron may fulfill more than one pattern and thus may appear in more than one row.”, but we will discuss this point in the main text as well.

      (3) Discussion:

      a) The discussion of chemical specificity in urine focuses on volatiles and MUPs (citation #47), but many important molecules for the VNS are small, nonvolatile ligands. For such molecules, the corresponding study is Fu et al 2015.

      We fully agree. We will expand our discussion and refer to Fu et al.

      b) "Following our line of reasoning, this scarcity may represent an optimal allocation of resources to separate dominant from naïve males": 1 unit out of 215 is roughly consistent with a single receptor. Surely little would be lost if there could be more computational capacity devoted to this important axis than that? It seems more likely that dominance is computed from multiple neuronal types with mixed encoding.

      We agree, and we are not claiming that dominance, nor any other feature, is derived using dedicated feature selective neurons.  Our discussion of resource allocation is inevitably speculative. Our main point in this context is that a lack of overrepresentation does not imply that a feature is not important. We will revise our discussion to better clarify our view of this issue.

      (4) Methods:

      a) Male status, "were unambiguous in most cases": is it possible to put numerical estimates on this? 55% and 99% are both "most," yet they differ substantially in interpretive uncertainty.

      This sentence is actually misleading and irrelevant. Ambiguous cases were not considered as dominant for urine collection. We only classified mice as dominant if they were “won” in the tube test and exhibited dominant behavior in the subsequent observation period in the cage. We will correct the wording in the revised manuscript.

      b) Surgical procedures and electrode positioning: important details of probes are missing (electrode recording area, spacing, etc).

      True. We will add these details.

      c) Stimulus presentation procedure: Are stimuli manually pipetted or delivered by apparatus with precise timing?

      They are delivered manually. We will clarify this as well.

      d) Data analysis, "we applied more permissive criteria involving response magnitude": it's not clear whether this is what's spelled out in the next paragraph, or whether that's left unspecified. In either case, the next paragraph appears to be about establishing a noise floor on pattern membership, not a "permissive criterion."

      True, the next paragraph is not the explanation for the more permissive criteria. The more permissive criteria involving response magnitude are actually those described in Figure 3A and 3B. The sentence that was quoted above merely states that before applying those criteria, we had also searched for patterns defined by binary designation of neurons as responsive, or not responsive, to each of the stimuli (this is directly related to the next comment below). Using those binary definitions, we obtained a very small number of neurons for each pattern and thus decided to apply the approach actually used and described in the manuscript.

      e) Data analysis, method for assessing significance: there's a lot to like about the use of pooling to estimate the baseline and the use of an ANOVA-like test to assess unit responsiveness.

      But:

      i) for a specific stimulus, at 4 trials (the minimum specified in "Stimulus presentation procedure") kruskalwallis is questionable. They state that most trials use 5, however, and that should be okay.

      The number of cases with 4 trials is truly a minority, and we will provide the exact numbers in our revision.

      ii) the methods statement suggests they are running kruskalwallis individually for each neuron/stimulus, rather than once per neuron across all stimuli. With 11 stimuli, there is a substantial chance of a false-positive if they used p < 0.05 to assess significance. (The actual threshold was unstated.) Were there any multiple comparison corrections performed? Or did they run kruskalwallis on the neuron, and then if significant assess individual stimuli? (Which is a form of multiple-comparisons correction.)

      First, we indeed failed to mention that our criterion was 0.05. We will correct that in our revision. We did not apply any multiple comparison measures. We consider each neuron-stimulus pair as an independent entity, and we are aware that this leads to a higher false positive rate. On the other hand, applying multiple comparisons would be problematic, as we do not always use the same number of stimuli in different studies. Applying multiple comparison corrections would lead to different response criteria across different studies. Notably, most, if not all, of our conclusions involve comparisons across conditions, and for this purpose we think that our procedure is valid. We do not attach any special meaning to the significance threshold, but rather think of it as a basic criterion that allows us to exclude non-responsive neurons, and to compare frequencies of neurons that fulfill this criterion.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      The study by Pinho et al. presents a novel behavioral paradigm for investigating higher-order conditioning in mice. The authors developed a task that creates associations between light and tone sensory cues, driving mediated learning. They observed sex differences in task acquisition, with females demonstrating faster-mediated learning compared to males. Using fiber photometry and chemogenetic tools, the study reveals that the dorsal hippocampus (dHPC) plays a central role in encoding mediated learning. These findings are crucial for understanding how environmental cues, which are not directly linked to positive/negative outcomes, contribute to associative learning. Overall, the study is well-designed, with robust results, and the experimental approach aligns with the study's objectives.

      Strengths:

      (1) The authors develop a robust behavioral paradigm to examine higher-order associative learning in mice.

      (2) They discover a sex-specific component influencing mediated learning, with females exhibiting enhanced learning abilities.

      (3) Using fiber photometry and chemogenetic techniques, the authors identify the dorsal hippocampus but not the ventral hippocampus, which plays a crucial for encoding mediated learning.

      Weaknesses:

      (1) The study would be strengthened by further elaboration on the rationale for investigating specific cell types within the hippocampus.

      We will add more information to better explain the rationale of our experiments and/or manipulations.

      (2) The analysis of photometry data could be improved by distinguishing between early and late responses, as well as enhancing the overall presentation of the data.

      We will provide new photometry analysis to differentiate between early and late responses during stimuli presentations.

      (3) The manuscript would benefit from revisions to improve clarity and readability.

      We will improve the clarity and readability of our manuscript.

      Reviewer #2 (Public review):

      Summary:

      Pinho et al. developed a new auditory-visual sensory preconditioning procedure in mice and examined the contribution of the dorsal and ventral hippocampus to learning in this task. Using photometry they observed activation of the dorsal and ventral hippocampus during sensory preconditioning and conditioning. Finally, the authors combined their sensory preconditioning task with DREADDs to examine the effect of inhibiting specific cell populations (CaMKII and PV) in the DH on the formation and retrieval/expression of mediated learning.

      Strengths:

      The authors provide one of the first demonstrations of auditory-visual sensory preconditioning in male mice. Research on the neurobiology of sensory preconditioning has primarily used rats as subjects. The development of a robust protocol in mice will be beneficial to the field, allowing researchers to take advantage of the many transgenic mouse lines. Indeed, in this study, the authors take advantage of a PV-Cre mouse line to examine the role of hippocampal PV cells in sensory preconditioning.

      Weaknesses:

      (1) The authors report that sensory preconditioning was observed in both male and female mice. However, their data only supports sensory preconditioning in male mice. In female mice, both paired and unpaired presentations of the light and tone in stage 1 led to increased freezing to the tone at test. In this case, fear to the tone could be attributed to factors other than sensory preconditioning, for example, generalization of fear between the auditory and visual stimulus.

      To address the pertinent doubt raised by the reviewer, we will perform new experiments to generate a new unpaired group in female mice through the increase of the temporal interval between light and tone exposure during the preconditioning phase. We believe this new results will bring additional information to better understand the performance of female mice in sensory preconditioning.

      (2) In the photometry experiment, the authors report an increase in neural activity in the hippocampus during both phase 1 (sensory preconditioning) and phase 2 (conditioning). In the subsequent experiment, they inhibit neural activity in the DH during phase 1 (sensory preconditioning) and the probe test, but do not include inhibition during phase 2 (conditioning). It was not clear why they didn't carry forward investigating the role of the hippocampus during phase 2 conditioning. Sensory preconditioning could occur due to the integration of the tone and shock during phase two, or retrieval and chaining of the tone-light-shock memories at test. These two possibilities cannot be differentiated based on the data. Given that we do not know at which stage the mediate learning is occurring, it would have been beneficial to additionally include inhibition of the DH during phase 2.

      We will perform new experiments to generate novel data by inhibiting the CamK-positive neurons of the dorsal hippocampus during the conditioning phase.

      (3) In the final experiment, the authors report that inhibition of the dorsal hippocampus during the sensory preconditioning phase blocked mediated learning. While this may be the case, the failure to observe sensory preconditioning at test appears to be due more to an increase in baseline freezing (during the stimulus off period), rather than a decrease in freezing to the conditioned stimulus. Given the small effect, this study would benefit from an experiment validating that administration of J60 inhibited DH cells. Further, given that the authors did not observe any effect of DREADD inhibition in PV cells, it would also be important to validate successful cellular silencing in this protocol.

      By combining chemogenetic and fiber photometry approaches, we will perform a control experiments to demonstrate that our chemogenetic experiments are decreasing CAMK- or PV-dependent activity in dorsal and ventral hippocampus.

      Reviewer #3 (Public review):

      Summary:

      Pinho et al. investigated the role of the dorsal vs ventral hippocampus and the gender differences in mediated learning. While previous studies already established the engagement of the hippocampus in sensory preconditioning, the authors here took advantage of freely-moving fiber photometry recording and chemogenetics to observe and manipulate sub-regions of the hippocampus (dorsal vs. ventral) in a cell-specific manner. The authors first found sex differences in the preconditioning phase of a sensory preconditioning procedure, where males required more preconditioning training than females for mediating learning to manifest, and where females displayed evidence of mediated learning even when neutral stimuli were never presented together within the session.

      After validation of a sensory preconditioning procedure in mice using light and tone neutral stimuli and a mild foot shock as the unconditioned stimulus, the authors used fiber photometry to record from all neurons vs. parvalbumin_positive_only neurons in the dorsal hippocampus or ventral hippocampus of male mice during both preconditioning and conditioning phases. They found increased activity of all neurons, as well as PV+_only neurons in both sub-regions of the hippocampus during both preconditioning and conditioning phases. Finally, the authors found that chemogenetic inhibition of CaMKII+ neurons in the dorsal, but not ventral, hippocampus specifically prevented the formation of an association between the two neutral stimuli (i.e., light and tone cues), but not the direct association between the light cue and the mild foot shock. This set of data: (1) validates the mediated learning in mice using a sensory preconditioning protocol, and stresses the importance of taking sex effect into account; (2) validates the recruitment of dorsal and ventral hippocampi during preconditioning and conditioning phases; and (3) further establishes the specific role of CaMKII+ neurons in the dorsal but not ventral hippocampus in the formation of an association between two neutral stimuli, but not between a neutral-stimulus and a mild foot shock.

      Strengths:

      The authors developed a sensory preconditioning procedure in mice to investigate mediated learning using light and tone cues as neutral stimuli, and a mild foot shock as the unconditioned stimulus. They provide evidence of a sex effect in the formation of light-cue association. The authors took advantage of fiber-photometry and chemogenetics to target sub-regions of the hippocampus, in a cell-specific manner and investigate their role during different phases of a sensory conditioning procedure.

      Weaknesses:

      The authors went further than previous studies by investigating the role of sub-regions of the hippocampus in mediated learning, however, there are several weaknesses that should be noted:

      (1) This work first validates mediated learning in a sensory preconditioning procedure using light and tone cues as neutral stimuli and a mild foot shock as the unconditioned stimulus, in both males and females. They found interesting sex differences at the behavioral level, but then only focused on male mice when recording and manipulating the hippocampus. The authors do not address sex differences at the neural level.

      As discussed above, we will perform additional experiment to evaluate the presence of a reliable sensory preconditioning in female mice. In addition, although observing sex differences at the neural level can be very interesting, we think that it is out of the scope of the present work. However, we will mention this issue/limitation in the Discussion in the new version of the manuscript.

      (2) As expected in fear conditioning, the range of inter-individual differences is quite high. Mice that didn't develop a strong light-->shock association, as evidenced by a lower percentage of freezing during the Probe Test Light phase, should manifest a low percentage of freezing during the Probe Test Tone phase. It would interesting to test for a correlation between the level of freezing during mediated vs test phases.

      We will provide correlations between the behavioral responses in both probe tests.

      (3) The use of a synapsin promoter to transfect neurons in a non-specific manner does not bring much information. The authors applied a more specific approach to target PV+ neurons only, and it would have been more informative to keep with this cell-specific approach, for example by looking also at somatostatin+ inter-neurons.

      We will better justify the use of specific promoters and the targeting of PV-positive neurons. We will also add discussion on potential interesting future experiments such as the targeting of other GABAergic subtypes.

      (4) The authors observed event-related Ca2+ transients on hippocampal pan-neurons and PV+ inter-neurons using fiber photometry. They then used chemogenetics to inhibit CaMKII+ hippocampal neurons, which does not logically follow. It does not undermine the main finding of CaMKII+ neurons of the dorsal, but not ventral, hippocampus being involved in the preconditioning, but not conditioning, phase. However, observing CaMKII+ neurons (using fiber photometry) in mice running the same task would be more informative, as it would indicate when these neurons are recruited during different phases of sensory preconditioning. Applying then optogenetics to cancel the observed event-related transients (e.g., during the presentation of light and tone cues, or during the foot shock presentation) would be more appropriate.

      We will perform new experiments to analyze the activity of CAMK-positive neurons during light-tone associations during the preconditioning phase in male mice.

      (5) Probe tests always start with the "Probe Test Tone", followed by the "Probe Test Light". "Probe Test Tone" consists of an extinction session, which could affect the freezing response during "Probe Test Light" (e.g., Polack et al. (http://dx.doi.org/10.3758/s13420-013-0119-5)). Preferably, adding a group of mice with a Probe Test Light with no Probe Test Tone could help clarify this potential issue. The authors should at least discuss the possibility that the tone extinction session prior to the "Probe Test Light" could have affected the freezing response to the light cue.

      We will add discussion on this issue raised by the reviewer.

      Reviewer #4 (Public review):

      Summary

      Pinho et al use in vivo calcium imaging and chemogenetic approaches to examine the involvement of hippocampal sub-regions across the different stages of a sensory preconditioning task in mice. They find clear evidence for sensory preconditioning in male but not female mice. They also find that, in the male mice, CaMKII-positive neurons in the dorsal hippocampus: (1) encode the audio-visual association that forms in stage 1 of the task, and (2) retrieve/express sensory preconditioned fear to the auditory stimulus at test. These findings are supported by evidence that ranges from incomplete to convincing. They will be valuable to researchers in the field of learning and memory.

      Abstract

      Please note that sensory preconditioning doesn't require the stage 1 stimuli to be presented repeatedly or simultaneously.

      We will correct this wrong sentence in the abstract.

      "Finally, we combined our sensory preconditioning task with chemogenetic approaches to assess the role of these two hippocampal subregions in mediated learning."

      This implies some form of inhibition of hippocampal neurons in stage 2 of the protocol, as this is the only stage of the protocol that permits one to make statements about mediated learning. However, it is clear from what follows that the authors interrogate the involvement of hippocampal sub-regions in stages 1 and 3 of the protocol - not stage 2. As such, most statements about mediated learning throughout the paper are potentially misleading (see below for a further elaboration of this point). If the authors persist in using the term mediated learning to describe the response to a sensory preconditioned stimulus, they should clarify what they mean by mediated learning at some point in the introduction. Alternatively, they might consider using a different phrase such as "sensory preconditioned responding".

      Through the text, we will avoid the term “mediated learning” and we will replace it with more accurate terms. In addition, we will interrogate the role of dHPC in Stage 2 as commented above.

      Introduction

      "Low-salience" is used to describe stimuli such as tone, light, or odour that do not typically elicit responses that are of interest to experimenters. However, a tone, light, or odour can be very salient even though they don't elicit these particular responses. As such, it would be worth redescribing the "low-salience" stimuli in some other terms.

      We will substitute “low-salience” for “innocuous”.

      "These higher-order conditioning processes, also known as mediated learning, can be captured in laboratory settings through sensory preconditioning procedures2,6-11."

      Higher-order conditioning and mediated learning are not interchangeable terms: e.g., some forms of second-order conditioning are not due to mediated learning. More generally, the use of mediated learning is not necessary for the story that the authors develop in the paper and could be replaced for accuracy and clarity. E.g., "These higher-order conditioning processes can be studied in the laboratory using sensory preconditioning procedures2,6-11."

      Through the text, we will avoid the term “mediated learning” and we will replace it with more accurate terms.

      In reference to Experiment 2, it is stated that: "However, when light and tone were separated on time (Unpaired group), male mice were not able to exhibit mediated learning response (Figure 2B) whereas their response to the light (direct learning) was not affected (Figure 2D). On the other hand, female mice still present a lower but significant mediated learning response (Figure 2C) and normal direct learning (Figure 2E). Finally, in the No-Shock group, both male (Figure 2B and 2D) and female mice (Figure 2C and 2E) did not present either mediated or direct learning, which also confirmed that the exposure to the tone or light during Probe Tests do not elicit any behavioral change by themselves as the presence of the electric footshock is required to obtain a reliable mediated and direct learning responses."<br /> The absence of a difference between the paired and unpaired female mice should not be described as "significant mediated learning" in the latter. It should be taken to indicate that performance in the females is due to generalization between the tone and light. That is, there is no sensory preconditioning in the female mice. The description of performance in the No-shock group really shouldn't be in terms of mediated or direct learning: that is, this group is another control for assessing the presence of sensory preconditioning in the group of interest. As a control, there is no potential for them to exhibit sensory preconditioning, so their performance should not be described in a way that suggests this potential.

      We will re-write the text to clarify the right comments raised by the Reviewer.

      Methods - Behavior

      I appreciate the reasons for testing the animals in a new context. This does, however, raise other issues that complicate the interpretation of any hippocampal engagement: e.g., exposure to a novel context may engage the hippocampus for exploration/encoding of its features - hence, it is engaged for retrieving/expressing sensory preconditioned fear to the tone. This should be noted somewhere in the paper given that one of its aims is to shed light on the broader functioning of the hippocampus in associative processes.

      We will further discuss this aspect on the manuscript.

      This general issue - that the conditions of testing were such as to force engagement of the hippocampus - is amplified by two further features of testing with the tone. The first is the presence of background noise in the training context and its absence in the test context. The second is the fact that the tone was presented for 30 s in stage 1 and then continuously for 180s at test. Both changes could have contributed to the engagement of the hippocampus as they introduce the potential for discrimination between the tone that was trained and tested.

      We will consider the aspect raised by the reviewer on the manuscript.

      Results - Behavior

      The suggestion of sex differences based on differences in the parameters needed to generate sensory preconditioning is interesting. Perhaps it could be supported through some set of formal analyses. That is, the data in supplementary materials may well show that the parameters needed to generate sensory preconditioning in males and females are not the same. However, there needs to be some form of statistical comparison to support this point. As part of this comparison, it would be neat if the authors included body weight as a covariate to determine whether any interactions with sex are moderated by body weight.

      We will add statistical comparisons between male and female mice.

      What is the value of the data shown in Figure 1 given that there are no controls for unpaired presentations of the sound and light? In the absence of these controls, the experiment cannot have shown that "Female and male mice show mediated learning using an auditory-visual sensory preconditioning task" as implied by its title. Minimally, this experiment should be relabelled.

      We will relabel Figure 1.

      "Altogether, this data confirmed that we successfully set up an LTSPC protocol in mice and that this behavioral paradigm can be used to further study the brain circuits involved in higher-order conditioning."

      Please insert the qualifier that LTSPC was successfully established in male mice. There is no evidence of LTSPC in female mice.

      We will generate new experiments to try to demonstrate that SPC can be also observed in female mice.

      Results - Brain

      "Notably, the inhibition of CaMKII-positive neurons in the dHPC (i.e. J60 administration in DREADD-Gi mice) during preconditioning (Figure 4B), but not before the Probe Test 1 (Figure 4B), fully blocked mediated, but not direct learning (Figure 4D)."

      The right panel of Figure 4B indicates no difference between the controls and Group DPC in the percent change in freezing from OFF to ON periods of the tone. How does this fit with the claim that CaMKII-positive neurons in the dorsal hippocampus regulate associative formation during the session of tone-light exposures in stage 1 of sensory preconditioning?

      We will rephrase and add more Discussion regarding this section of the results to stick to what the graphs are showing. We will clarify that the group where dHPC activity is inhibited during preconditioning is the only one where the % of change is not significantly different from 0 (compared to the control or the group where the dHPC activity was modulated during the test).

      Discussion

      "When low salience stimuli were presented separated on time or when the electric footshock was absent, mediated and direct learning were abolished in male mice. In female mice, although light and tone were presented separately during the preconditioning phase, mediated learning was reduced but still present, which implies that female mice are still able to associate the two low-salience stimuli."

      This doesn't quite follow from the results. The failure of the female unpaired mice to withhold their freezing to the tone should not be taken to indicate the formation of a light-tone association across the very long interval that was interpolated between these stimulus presentations. It could and should be taken to indicate that, in female mice, freezing conditioned to the light simply generalized to the tone (i.e., these mice could not discriminate well between the tone and light).

      We will rewrite this part depending on the results observed in female mice.

      "Indeed, our data suggests that when hippocampal activity is modulated by the specific manipulation of hippocampal subregions, this brain region is not involved during retrieval."

      Does this relate to the results that are shown in the right panel of Figure 4B, where there is no significant difference between the different groups? If so, how does it fit with the results shown in the left panel of this figure, where differences between the groups are observed?

      We will re-write it to clearly describe our results and we will also revise all the statistical analysis.

      "In line with this, the inhibition of CaMKII-positive neurons from the dorsal hippocampus, which has been shown to project to the restrosplenial cortex56, blocked the formation of mediated learning."

      Is this a reference to the findings shown in Figure 4B and, if so, which of the panels exactly? That is, one panel appears to support the claim made here while the other doesn't. In general, what should the reader make of data showing the percent change in freezing from stimulus OFF to stimulus ON periods?

      We will rewrite the text to clearly describe our results, and we will also revise all the statistical analysis. In addition, we will better explain the data showing the % of change.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      This work considers the biases introduced into pathogen surveillance due to congregation effects, and also models homophily and variants/clades. The results are primarily quantitative assessments of this bias but some qualitative insights are gained e.g. that initial variant transmission tends to be biased upwards due to this effect, which is closely related to classical founder effects.

      Strengths:

      The model considered involves a simplification of the process of congregation using multinomial sampling that allows for a simpler and more easily interpretable analysis.

      Weaknesses:

      This simplification removes some realism, for example, detailed temporal transmission dynamics of congregations.

      We appreciate Reviewer #1's comments. We hope our framework, like the classic SIR model, can be adapted in the future to build more complex and realistic models.

      Reviewer #2 (Public review):

      Summary:

      In "Founder effects arising from gathering dynamics systematically bias emerging pathogen surveillance" Bradford and Hang present an extension to the SIR model to account for the role of larger than pairwise interactions in infectious disease dynamics. They explore the impact of accounting for group interactions on the progression of infection through the various sub-populations that make up the population as a whole. Further, they explore the extent to which interaction heterogeneity can bias epidemiological inference from surveillance data in the form of IFR and variant growth rate dynamics. This work advances the theoretical formulation of the SIR model and may allow for more realistic modeling of infectious disease outbreaks in the future.

      Strengths:

      (1) This work addresses an important limitation of standard SIR models. While this limitation has been addressed previously in the form of network-based models, those are, as the authors argue, difficult to parameterize to real-world scenarios. Further, this work highlights critical biases that may appear in real-world epidemiological surveillance data. Particularly, over-estimation of variant growth rates shortly after emergence has led to a number of "false alarms" about new variants over the past five years (although also to some true alarms).

      (2) While the results presented here generally confirm my intuitions on this topic, I think it is really useful for the field to have it presented in such a clear manner with a corresponding mathematical framework. This will be a helpful piece of work to point to to temper concerns about rapid increases in the frequency of rare variants.

      (3) The authors provide a succinct derivation of their model that helps the reader understand how they arrived at their formulation starting from the standard SIR model.

      (4) The visualizations throughout are generally easy to interpret and communicate the key points of the authors' work.

      (5) I thank the authors for providing detailed code to reproduce manuscript figures in the associated GitHub repo.

      Weaknesses:

      (1) The authors argue that network-based SIR models are difficult to parameterize (line 66), however, the model presented here also has a key parameter, mainly P_n, or the distribution of risk groups in the population. I think it is important to explore the extent to which this parameter can be inferred from real-world data to assess whether this model is, in practice, any easier to parameterize.

      (2) The authors explore only up to four different risk groups, accounting for only four-wise interactions. But, clearly, in real-world settings, there can be much larger gatherings that promote transmission. What was the justification for setting such a low limit on the maximum group size? I presume it's due to computational efficiency, which is understandable, but it should be discussed as a limitation.

      (3) Another key limitation that isn't addressed by the authors is that there may be population structure beyond just risk heterogeneity. For example, there may be two separate (or, weakly connected) high-risk sub-groups. This will introduce temporal correlation in interactions that are not (and can not easily be) captured in this model. My instinct is that this would dampen the difference between risk groups shown in Figure 2A. While I appreciate the authors's desire to keep their model relatively simple, I think this limitation should be explicitly discussed as it is, in my opinion, relatively significant.

      We appreciate Reviewer 2's thoughtful comments and wish to address some of the weaknesses:

      We agree that inferring P_n from real data will be challenging, but think this is an important direction for future research. Further, we’d like to reframe our claim that our approach is "easier to parameterize" than network models. Rather, P_n has fewer degrees of freedom than analogous network models, just as many different networks can share the same degree distribution. Fewer degrees of freedom mean that we expect our model to suffer from fewer identifiability issues when fitting to data, though non-identifiability is often inescapable in models of this nature (e.g., \beta and \gamma in the SIR model are not uniquely identifiable during exponential growth). Whether this is more or less accurate is another question. Classic bias-variance tradeoffs argue that a model with a moderate complexity trained on one data set can better fit future data than overly simple or overly complex models.

      We chose four risk groups for purposes of illustration, but this can be increased arbitrarily. It should be noted that the simulation bottleneck when increasing the numbers of risk groups is numerical due the stiffness of the ODEs. This arises because the nonlinearity of infection terms scales with the number of risk groups (e.g., ~ \beta * S * I^3 for 4 risk groups). As such, a careful choice of numerical solvers may be required when integrating the ODEs. Meanwhile, this is not an issue for stochastic, individual based implementation (e.g., Gillespie). As for how well this captures super-spreading, we believe choosing smaller risk groups does not hinder modeling disease spread at large gatherings. Consider a statistical interpretation, where individuals at a large gathering engage in a series of smaller interactions over time (e.g., 2/3/4/etc person conversations). The key determinants of the resulting gathering size distribution at any one large gathering are the number of individuals within some shared proximity over time and the infectiousness/dispersal of the pathogen. Of course, whether this interpretation is a sufficient approximation for classic super-spreading events (e.g., funerals during 2014-2015 West Africa Ebola outbreak) is a matter of debate. Our framework is best interpreted at a population level where the effects of any single gathering are washed out by the overall gathering distribution, P_n. As the prior weakness highlighted, establishing P_n is challenging, but we believe empirically measuring proxies of it may provide future insight in how behavior impacts disease spread. For example, prior work has combined contact tracing and co-location data from connection to WiFi networks to estimate the distribution of contacts per individual, and its degree of overdispersion (Petros et al. Med 2022).

      We chose to introduce our framework in a simple SIR context familiar to many readers. This decision does not in any way limit applying it to settings with more population structure. Rather, we believe our framework is easily adaptable and that our presentation (hopefully) makes it clear how to do this. For example, two weakly connected groups could be easily achieved by (for each gathering) first sampling the preferred group and then sampling from the population in a biased manner. The biased sampling could even be a function of gathering sizes, time, etc. The resulting infection terms are still (sums of) multinomials. More generally, the sampling probabilities for an individual of some type need not be its frequency (e.g., S/N, I/N). Indeed, we believe generating models with complex social interactions is both simplified and made more robust by focusing on modeling the generative process of attending gatherings.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      This paper uses single-molecule FRET to investigate the molecular basis for the distinct activation mechanisms between 2 GPCR responding to the chemokine CXCL12 : CXCR4, that couples to G-proteins, and ACKR3, which is G-protein independent and displays a higher basal activity.

      Strengths:

      It nicely combines the state-of-the-art techniques used in the studies of the structural dynamics of GPCR. The receptors are produced from eukaryotic cells, mutated, and labeled with single molecule compatible fluorescent dyes. They are reconstituted in nanodiscs, which maintain an environment as close as possible to the cell membrane, and immobilized through the nanodisc MSP protein, to avoid perturbing the receptor's structural dynamics by the use of an antibody for example.

      The smFRET data are analysed using the HHMI technique, and the number of states to be taken into account is evaluated using a Bayesian Information Criterion, which constitutes the state-of-the-art for this task.

      The data show convincingly that the activation of the CXCR4 and ACKR3 by an agonist leads to a shift from an ensemble of high FRET states to an ensemble of lower FRET states, consistent with an increase in distance between the TM4 and TM6. The two receptors also appear to explore a different conformational space. A wider distribution of states is observed for ACKR3 as compared to CXCR4, and it shifts in the presence of agonists toward the active states, which correlates well with ACKR3's tendency to be constitutively active. This interpretation is confirmed by the use of the mutation of Y254 to leucine (the corresponding residue in CXCR4), which leads to a conformational distribution that resembles the one observed with CXCR4. It is correlated with a decrease in constitutive activity of ACKR3.

      Weaknesses:

      Although the data overall support the claims of the authors, there are however some details in the data analysis and interpretation that should be modified, clarified, or discussed in my opinion

      Concerning the amplitude of the changes in FRET efficiency: the authors do not provide any structural information on the amplitude of the FRET changes that are expected. To me, it looks like a FRET change from ~0.9 to ~0.1 is very important, for a distance change that is expected to be only a few angstroms concerning the movement of the TM6. Can the authors give an explanation for that? How does this FRET change relate to those observed with other GPCRs modified at the same or equivalent positions on TM4 and TM6?

      The large FRET change in our system was initially unexpected. However, the reviewer is mistaken that the expected distance change is only a few angstroms. Crystal structures of the homologous beta2 adrenergic receptor (β<sub>2</sub>AR) in inactive and active conformations reveal that the cytoplasmic end of TM6 moves outwards by 16 angstroms during activation (Rasmussen et al., 2011, ref 47).  Consistent with this, smFRET studies of β<sub>2</sub>AR labeled in TM4 and TM6 (as here) showed that the donor-acceptor (D-A) distance was 14 angstroms longer in the active conformation (Gregorio et al., ref 38).  Surprisingly, the apparent distance change in our system (calculated for our FRET probes, A555/Cy5, using FPbase.com) is almost 30 angstroms. A possible explanation is that the fluorophore attached to TM6 interacts with lipids within the nanodisc when TM6 moves outwards, which could stretch the fluorophore linker and thereby increase the D-A distance (lipids were absent in the β<sub>2</sub>AR study). Such an interaction could also constrain the fluorophore in an unfavorable orientation for energy transfer, also leading to lower than expected FRET efficiencies and inflated distance calculations. Regardless, it is important to emphasize that none of the interpretations or conclusions of our study are based on computed D-A distances. Rather, we resolved different receptor conformations and quantified their relative populations based on the measured FRET efficiency distributions.

      Finally, we note that a recent smFRET study of the glucagon receptor (labeled in TM4 and TM6, as here) also revealed a large difference in apparent FRET efficiencies between inactive (E<sub>app</sub> = 0.83) and active (E<sub>app</sub> = 0.32) conformations (Kumar et al., ref. 39). Thus, the large change in FRET efficiency observed in our study is not unprecedented.

      Concerning the intermediate states: the authors observe several intermediate states.

      (1) First I am surprised, looking at the time traces, by the dwell times of the transitions between the states, which often last several seconds. Is such a long transition time compatible with what is known about the kinetic activation of these receptors?

      We too were surprised by the apparent kinetics of the receptors in our system. However, it was previously noted that purified systems, including nanodiscs, lead to slower activation times for GPCRs compared to cellular membrane systems (Lohse et al, Curr. Opin. Cell Biology, 27, 8792, 2014). Indeed, slow transitions among different FRET states (dwell times in the seconds range) were also observed in recent smFRET studies of the mu opioid receptor (Zhao et al., 2024, ref. 41) and the glucagon receptor (Kumar et al., 2023, ref. 39). These studies are consistent with the observed time scale of the FRET transitions reported here.

      (2) Second is it possible that these “intermediate” states correspond to differences in FRET efficiencies, that arise from different photophysical states of the dyes? Alexa555 and Cy5 are Cyanines, that are known to be very sensitive to their local environment. This could lead to different quantum yields and therefore different FRET efficiencies for a similar distance. In addition, the authors use statistical labeling of two cysteines, and have therefore in their experiment a mixture of receptors where the donor and acceptor are switched, and can therefore experience different environments. The authors do not speculate structurally on what these intermediate states could be, which is appreciated, but I think they should nevertheless discuss the potential issue of fluorophore photophysics effects.

      The reviewer is correct that the intermediate FRET states could, in principle, arise from a conformational change of the receptor that alters the local environment of the donor and/or acceptor fluorophores, rather than a change in donor-acceptor distance. This caveat is now included in the discussion on Pg. 10:

      “In principle, the intermediates in CXCR4 and ACKR3 could represent partial movements of TM6 from the inactive to active conformation or more subtle conformational changes altering the photophysical characteristics of the probes without drastically altering the donor-acceptor distance. Either possibility leads to detectable changes in apparent FRET efficiency and reflect discrete conformational steps on the activation pathway; however, it is not possible to resolve specific structural changes from the data.”

      Regarding the second possibility, it is true that our labeling methodology leads to a statistical mixture of labeled species (D on TM6 and A on TM4, D on TM4 and A on TM6). If the photophysical properties of the fluorophores were markedly different for the two labeling orientations, this would produce two different FRET efficiencies for a given receptor conformation. Assuming two receptor conformations, this scenario would produce four distinct FRET states: E<sub>1</sub> (inactive receptor, labeling configuration 1), E<sub>2</sub> (active receptor, labeling configuration 1), E<sub>3</sub> (inactive receptor, labeling configuration 2) and E<sub>4</sub> (active receptor, labeling configuration 2), with two cross peaks in the TDP plots, corresponding to E<sub>1</sub> ↔ E<sub>2</sub> and E<sub>3</sub> ↔ E<sub>4</sub> transitions. Notably, E<sub>2</sub> ↔ E<sub>3</sub> cross peaks would not be present, since states E<sub>2</sub> and E<sub>3</sub> exist on separate molecules. Instead, we see all states inter-connected sequentially, R ↔ R’ ↔ R* in CXCR4 and R ↔ R’ ↔ R*’ ↔ R* in ACKR3 (Fig. 2), suggesting that the resolved FRET states represent interconnected conformational states.

      We added the following text to the Results section on Pg. 6:

      “Two-dimensional transition density probability (TDP) plots revealed that the three FRET states were connected in a sequential fashion (Figs. 2A & B), indicating that the transitions occurred within the same molecules. Notably, these observations exclude the possibility that the midFRET state arises from different local fluorophore environments (hence FRET efficiencies) for the two possible labeling orientations of the introduced cysteines: assuming two receptor conformations, this model would produce four distinct FRET states, but only two cross peaks in the TDP plot.”

      (3) It would also have been nice to discuss whether these types of intermediate states have been observed in other studies by smFRET on GPCR labeled at similar positions.

      Intermediate states have also been reported in previous smFRET studies of other GPCRs. For example, in the glucagon receptor (also labeled in TM4 and TM6), a third FRET state (E<sub>app</sub> =  0.63) was resolved between the inactive (E<sub>app</sub>  = 0.85) and active (E<sub>app</sub>  = 0.32) states (Kumar et al., Ref. 39).  Discrete intermediate receptor conformations were also observed in the A<sub>2A</sub>R labeled in TM4 and TM6 (Fernandes et al., Ref 40). These examples are now cited in the Discussion.

      On line 239: the authors talk about the R↔R' transitions that are more probable. In fact it is more striking that the R'↔R* transition appears in the plot. This transition is a signature of the behavior observed in the presence of an agonist, although IT1t is supposed to be an inverse agonist. This observation is consistent with the unexpected (for an inverse agonist) shift in the FRET histogram distribution. In fact, it appears that all CXCR4 antagonists or inverse agonists have a similar (although smaller) effect than the agonist. Is this related to the fact that these (antagonist or inverse agonist) ligands lead to a conformation that is similar to the agonists, but cannot interact with the G-protein ?? Maybe a very interesting experiment would be here to repeat these measurements in the presence of purified G-protein. G-protein has been shown to lead to a shift of the conformational space explored by GPCR toward the active state (using smFRET on class A and class C GPCR). It would be interesting to explore its role on CXCR4 in the presence of these various ligands. Although I am aware that this experiment might go beyond the scope of this study, I think this point should be discussed nevertheless.

      We thank the reviewer for this observation and the possible explanation offered.  In response, we have added the following text to the Results section on Pg. 7:

      “The small-molecule ligand IT1t is reported to act as an inverse agonist of CXCR4 (54-56). However, the conformational distribution of CXCR4 showed little change to the overall apparent

      FRET profile, although R’ ↔ R* transitions appeared in the TDP plot (Figs. 3A & B, Fig. S8). This suggests that the small molecule does not suppress CXCR4 basal signaling by changing the conformational equilibrium. Instead IT1t appears to increase transition probabilities which may impair G protein coupling by CXCR4.”

      We have also added the following text to the Results on Pg. 8:

      “Despite the ability of CXCL12<sub>P2G</sub> and CXCL12<sub>LRHQ</sub> to stabilize the active R* conformation of CXCR4, both variants are known to act as antagonists (20). This suggests that the CXCL12 mutants inhibit CXCR4 coupling to G proteins not by suppressing the active receptor population but rather by increasing the dynamics of the receptor state transitions. Our results suggest that the helical movements considered classic signatures of the active state may not be sufficient for CXCR4 to engage productively with G proteins.”

      In addition, we have added the following text to the Discussion on Pg. 11:

      “The chemokine variants CXCL12<sub>P2G</sub> and CXCL12<sub>LRHQ</sub> are reported to act as antagonists of CXCR4 (19, 20), and the small molecule IT1t acts as an inverse agonist (54-56). Surprisingly, none of these ligands inhibit formation of the active R* conformation of CXCR4. In fact, the chemokine variants both stabilize and increase this state to some degree, although less effectively than CXCL12<sub>WT</sub>. Thus, the antagonism and inverse agonism of these ligands does not appear to be linked exclusively to receptor conformation, suggesting that the ligands inhibit coupling of G proteins to CXCR4 or disrupt the ligand-receptor-G protein interaction network required for signaling (Fig. S10) (21, 23).  Interestingly, these ligands also increase the probabilities of state-to-state transitions (Figs. 3B & 4B), suggesting that enhanced conformational exchange prevents the receptor from productively engaging G proteins. Similarly, ACKR3 is naturally dynamic and lacks G protein coupling, suggesting a common mechanism of G protein antagonism.”

      Finally, we also agree that experiments with G proteins could be informative. In fact, we initiated such experiments during the course of this study.  However, it soon became apparent that significant optimization would be required to identify fluorophore labeling positions that report receptor conformation without inhibiting G protein coupling. Accordingly, we decided that G protein experiments would be the subject of future studies.

      However, we added the following text to the Discussion on Pg. 12:

      “Future smFRET studies performed in the presence of G proteins should be informative in this regard”.

      The authors also mentioned in Figure 6 that the energetic landscape of the receptors is relatively flat ... I do not really agree with this statement. For me, a flat conformational landscape would be one where the receptors are able to switch very rapidly between the states (typically in the submillisecond timescale, which is the timescale of protein domain dynamics). Here, the authors observed that the transition between states is in the second timescale, which for me implies that the transition barrier between the states is relatively high to preclude the fast transitions.

      We thank the reviewer for the comment. We have modified the description of the energy landscapes of ACKR3 and CXCR4 in the discussion on Pg. 10 as follows:

      “These observations imply that ACKR3 has a relatively flat energy landscape, with similar energy minima for the different conformations, whereas the energy landscape of CXCR4 is more rugged (Fig. 6). For both receptors, the energy barriers between states are sufficiently high that transitions occur relatively slowly with seconds long dwell times (Figs. 1C and S2).”

      Reviewer #2 (Public Review):

      Summary:

      his manuscript uses single-molecule fluorescence resonance energy transfer (smFRET) to identify differences in the molecular mechanisms of CXCR4 and ACKR3, two 7transmembrane receptors that both respond to the chemokine CXCL12 but otherwise have very different signaling profiles. CXCR4 is highly selective for CXCL12 and activates heterotrimeric G proteins. In contrast, ACKR3 is quite promiscuous and does not couple to G proteins, but like most G protein-coupled receptors (GPCRs), it is phosphorylated by GPCR kinases and recruits arrestins. By monitoring FRET between two positions on the intracellular face of the receptor (which highlights the movement of transmembrane helix 6 [TM6], a key hallmark of GPCR activation), the authors show that CXCR4 remains mostly in an inactive-like state until CXCL12 binds and stabilizes a single active-like state. ACKR3 rapidly exchanges among four different conformations even in the absence of ligands, and agonists stabilize multiple activated states.

      Strengths:

      The core method employed in this paper, smFRET, can reveal dynamic aspects of these receptors (the breadth of conformations explored and the rate of exchange among them) that are not evident from static structures or many other biophysical methods. smFRET has not been broadly employed in studies of GPCRs. Therefore, this manuscript makes important conceptual advances in our understanding of how related GPCRs can vary in their conformational dynamics.

      Weaknesses:

      (1) The cysteine mutations in ACKR3 required to site-specifically install fluorophores substantially increase its basal and ligand-induced activity. If, as the authors posit, basal activity correlates with conformational heterogeneity, the smFRET data could greatly overestimate the conformational heterogeneity of ACKR3.

      The change in basal ACKR3 activity with the Cys introductions are modest in comparison and insignificantly different as determined by extra-sum-of-squares F test (P=0.14).

      (2) The probes used cannot reveal conformational changes in other positions besides TM6. GPCRs are known to exhibit loose allosteric coupling, so the conformational distribution observed at TM6 may not fully reflect the global conformational distribution of receptors. This could mask important differences that determine the ability of intracellular transducers to couple to specific receptor conformations.

      We agree that the overall conformational landscape of the receptors has not been investigated and we have added this caveat to the discussion on Pg. 12.

      “An important caveat is that our study does not report on the dynamics of the other TM helices and H8, some of which are known to participate in arrestin interactions.”

      (3) While it is clear that CXCR4 and ACKR3 have very different conformational dynamics, the data do not definitively show that this is the main or only mechanism that contributes to their functional differences. There is little discussion of alternative potential mechanisms.

      The main functional difference between CXCR4 and ACRK3 is their effector coupling: CXCR4 couples to G proteins, whereas ACKR3 only couples to arrestins (following phosphorylation of the C-terminal tail by GRKs). As currently noted in the discussion, ACKR3 has many features that may contribute to its lack of G protein coupling, including lack of a well-ordered intracellular pocket due to conformational dynamics, lack of an N-term-ECL3 disulfide, different chemokine binding mode, and the presence of Y257. Steric interference due to different ICL loop structures may also interfere with G protein activation. No one thing has proven to confer ACKR3 with G protein activity including swapping all of the ICLs to those of canonical chemokine receptor, suggesting it is a combination of these different factors. The following has been added to the discussion on Pg. 13 to clearly note that any one feature is unlikely to drive the atypical behavior of ACKR3:

      “The atypical activation of ACKR3 does not appear to be dependent on any singular receptor feature and is likely a combination of several factors.”

      (4) The extent to which conformational heterogeneity is a characteristic feature of ACKRs that contributes to their promiscuity and arrestin bias is unclear. The key residue the authors find promotes ACKR3 conformational heterogeneity is not conserved in most other ACKRs, but alternative mechanisms could generate similar heterogeneity.

      Despite the commonalities in the roles of the ACKRs, they all appear to have evolved independently. Thus, we do not believe that all features observed and described for one ACKR will explain the behavior of another. We have carefully avoided expanding our observations to other ACKRs to avoid suggesting common mechanisms.

      (5) There are no data to confirm that the two receptors retain the same functional profiles observed in cell-based systems following in vitro manipulations (purification, labeling, nanodisc reconstitution).

      We agree this is an important point. All labeled receptors responded to agonist stimulation as expected. As only properly folded receptors are able to make the extensive interactions with ligands necessary for conformational changes (for instance, CXCL12 interacts with all TMs and ECLs), this suggests that the proteins are folded correctly and functional following all manipulations.

      Reviewer #3 (Public Review):

      Summary:

      This is a well-designed and rigorous comparative study of the conformational dynamics of two chemokine receptors, the canonical CXCR4 and the atypical ACKR3, using single-molecule fluorescence spectroscopy. These receptors play a role in cell migration and may be relevant for developing drugs targeting tumor growth in cancers. The authors use single-molecule FRET to obtain distributions of a specific intermolecular distance that changes upon activation of the receptor and track differences between the two receptors in the apo state, and in response to ligands and mutations. The picture emerging is that more dynamic conformations promote more basal activity and more promiscuous coupling of the receptor to effectors.

      Strengths:

      The study is well designed to test the main hypothesis, the sample preparation and the experiments conducted are sound and the data analysis is rigorous. The technique, smFRET, allows for the detection of several substates, even those that are rarely sampled, and it can provide a "connectivity map" by looking at the transition probabilities between states. The receptors are reconstituted in nanodiscs to create a native-like environment. The examples of raw donor/acceptor intensity traces and FRET traces look convincing and the data analysis is reliable to extract the sub-states of the ensemble. The role of specific residues in creating a more flat conformational landscape in ACKR3 (e.g., Y257 and the C34-C287 bridge) is well documented in the paper.

      Weaknesses:

      The kinetics side of the analysis is mentioned, but not described and discussed. I am not sure why since the data contains that information. For instance, it is not clear if greater conformational flexibility is accompanied by faster transitions between states or not.

      The reviewer is correct that kinetic information is available, in principle, from smFRET experiments. However, a detailed kinetic analysis will require a much larger data set than we currently possess, to adequately sample all possible transitions and the dwell times of each FRET state. We intend to perform such an analysis in the future as more data becomes available. The purpose of this initial study was to explore the conformational landscapes of CXCR4 and ACKR3 and to reveal differences between them. To this end, we have documented major differences in conformational preferences and response to ligands of the two receptors that are likely relevant to their different biological behavior. Future kinetic information will add further detail, but is not expected to alter the conclusions drawn here.

      The method to choose the number of states seems reasonable, but the "similarity" of states argument (Figures S4 and S6) is not that clear.

      We thank the reviewer for noting a need for further clarification. We qualitatively compared the positions of the various FRET peaks across treatments to gain insight into the consistency of the conformations and avoid splitting real states by overfitting the data. For instance, fitting the ACKR3 treatments with three states leads to three distinct FRET populations for the R’ intermediate. Adding a fourth state results in two intermediates that are fairly well overlapping. In contrast, the two-intermediate model for CXCR4 appears to split the R* state of the CXCL12 treated sample and causes a general shift in both intermediate states to lower FRET values when CXCL12 is present. As we assume that the conformations are consistent throughout the treatments, we conclude that this represents an overfitting artifact and not a novel CXCL12CXCR4 R*’ state. Additional sentences have been added to the supplemental figure legend to better describe the comparative analysis.

      “(Top) With the 3-state model, the R’ states for apo-CXCR4 and for CXCL12- and IT1t-bound receptor overlapped well with similar apparent FRET values across all of the tested conditions. In the case of the four-state model, the R*’ (Middle) and R’ (Bottom) states were substantially different across the ligand treatments. In particular, the R*’ state with CXCL12 treatment appears to arise from a splitting of the R* conformation, indicating that the model was overfitting the data.”

      Also, the "dynamics" explanation offered for ACKR3's failure to couple and activate G proteins is not very convincing. In other studies, it was shown that activation of GPCRs by agonists leads to an increase in local dynamics around the TM6 labelling site, but that did not prevent G protein coupling and activation.

      We agree with the reviewer that any single explanation for ACKR3 bias, including the dynamics argument presented here, is insufficient to fully characterize the ACKR3 responses. As noted by the reviewer, the TM6 movement and dynamics is generally correlated with G protein coupling, whereas other dynamics studies (Wingler et al. Cell 2019) have noted that arrestinbiased ligands do not lead to the same degree of TM6 movement. We have added the following statement to the discussion on Pg. 13:

      “The atypical activation of ACKR3 does not appear to be dependent on any singular receptor feature and is likely a combination of several factors.” 

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors):

      I would like to raise a technical point about the calculation and reporting of the FRET efficiency. The authors report the FRET efficiency as E=IA/(IA+ID). There is now a strong recommendation from the FRET community (https://doi.org/10.1038/s41592-018-0085-0) to use the term “FRET efficiency” only when a proper correction procedure of all correction factors has been applied, which is not the case here (gamma factor has not been calculated). The authors should therefore use the term “Apparent FRET Efficiency” and  E<sub>app</sub> in all the manuscripts.

      Also, it would be nice to indicate directly on the figures whether a ligand that is used is an agonist, antagonist, inverse agonist, etc...

      We thank the reviewer for suggesting this clarification in terminology. We now refer to apparent FRET efficiency (or E<sub>app</sub>) throughout the manuscript and in the figures. In addition, we have added ligand descriptions to the relevant figures.

      Reviewer #2 (Recommendations For The Authors):

      (1) M159(4.40)C/Q245(6.28)C ACKR3 appears to have higher constitutive activity than ACKR3 Wt (Fig. S1). While the vehicle point itself is likely not significant due to the error in the Wt, the overall trend is clear and arguably even stronger than the effect of Y257(6.40)L (Fig. S9). While this is an inherent limitation of the method used, it should be clearly acknowledged; the comment in lines 162-164 seems to skirt the issue by only saying that arrestin recruitment is retained. It would be helpful and more rigorous to report the curve fit parameters (basal, E<sub>max</sub>, EC50) for the arrestin recruitment experiments and the associated errors/significance (see https://www.graphpad.com/guides/prism/latest/statistics/stat_qa_multiple_comparisons_ after_.htm for a discussion).

      The Emin, E<sub>max</sub>, and EC50 for M159<sup>4</sup>.<sup>40</sup>C/Q245<sup>6</sup>.<sup>28</sup>C ACKR3 were compared against the values for WT ACKR3 from Fig. S1 and only the E<sub>max</sub> was determined to be significantly different by the extra sum of squares F test. A note has been added to the text to reflect these results on Pg. 5.

      “Only the E<sub>max</sub> for arrestin recruitment to CXCL12-stimulated ACKR3 was significantly altered by the mutations, while all other pharmacological parameters were the same as for WT receptors.”

      (2) The methods do not specify the reactive group of the dyes used for labeling (i.e., AlexaFluor 555-maleimide and Cy5-maleimide?).

      We regret the omission and have added the necessary details to the materials and methods.

      (3) Were any of the native Cys residues removed from ACKR3 and CXCR4 in the constructs used for smFRET? ACKR3 appears to have two additional Cys residues in the N-terminus besides the one involved in the second disulfide bridge, and these would presumably be solvent-exposed. If so, please specify in the Methods and clarify whether the constructs tested in functional assays included these. (Also, please specify if the human receptors were used.)

      No additional cysteine residues were mutated in either receptor. All exposed cysteines are predicted to form disulfides. The residues in the N-terminus that the reviewer alludes to, C21 and C26, form a disulfide (Gustavsson et al. Nature Communications 2017) and are thus protected from our probes. Consistent with these expectations, neither WT CXCR4 nor ACKR3 exhibited significant fluorophore labeling (now mentioned in the text on Pg. 5). The species of origin has been added to the material and methods.

      (4) There are a few instances where the data seem to slightly diverge from the proposed models that may be helpful to comment on explicitly in the text:

      - Figure 4E (ACKR3/CXCL12(P2G)): As noted in the legend, despite stabilizing R*/R*', CXCL12(P2G) reduces transitions between these states compared to Apo. This is more similar to the effects of VUF16840 (Figure 3D) than the other ACKR3 agonists. The authors note the difference between CXCL12(LHRQ) and CXCL12(P2G) (but not vs Apo) in this regard. There might be some other information here regarding the relative importance of the conformational equilibrium vs transition rates for receptor activity.

      Although the TDPs for CXCL12<sub>P2G</sub> and VUF16840 are similar, as noted by the reviewer, the overall FRET envelopes are drastically different.

      The differences in transition probabilities for R ↔ R’ and R*’ « R* transitions observed in the presence of CXCL12<sub>P2G</sub> or CXCL12<sub>LRHQ</sub> relative to the apo receptor are now explicitly noted in the Results.

      - The conformational distributions of ACKR3 apo and ACKR3 Y257L CXCL12 are very similar (Figure 5A,D). However, there is a substantial difference in the basal activity of WT vs CXCL12stimulated Y257L (Figure S9).

      The mutation Y257L appears to promote the highest and lowest FRET states at the expense of the intermediates. Although the distribution appears similar between Apo-WT and CXCL12Y257L, the depopulation of the R’ state may lead to the observed activation in cells.

      (5) There are inconsistent statements regarding the compatibility of G protein binding to the "active-like" ACKR3 conformation observed in the authors' previous structures (Yen et al, Sci Adv 2022). In the introduction, the authors seem to be making the case that steric clashes cannot account for its lack of coupling; in the discussion, they seem to consider it a possibility.

      The introduction to previous research on the molecular mechanisms governing the lack of ACKR3-G protein coupling was not intended to be all encompassing, but rather to highlight previous efforts to elucidate this process and justify our study of the role  of dynamics. Due to the positions of the probes, we can only comment on the impact on TM6 movements and not other conformational changes. The steric clash reported in Yen et al. was in ICL2 and not directly tested here, so our observations do not preclude changes occurring in this region. We also do not claim that the active-like state resolved in our previous structures matches any specific state isolated here by smFRET.

      (6) Line 83-85: "Having excluded other mechanisms we therefore surmised that the inability of ACKR3 to activate G proteins may be due to differences in receptor dynamics."

      Line 400-402: "It is possible that the active receptor conformation clashes sterically with the G protein as suggested by docking of G proteins to structures of ACKR3."

      As mentioned above, we suspect the mechanisms governing the inability of  ACKR3 to couple to G proteins may be more complex than one particular feature but instead due to a combination of several factors. Accordingly, we have not completely eliminated a contribution of steric hindrance as we described in Yen et al. Sci Adv 2022 and instead include it as a possibility. Following the line highlighted here, we list several alternatives: 

      “Alternatively, the receptor dynamics and conformational transitions revealed here may prevent formation of productive contacts between ACKR3 and G protein that are required for coupling, even though G proteins appear to constitutively associate with the receptor.”

      And, at the end of the paragraph, we have added the following sentence: 

      “The atypical activation of ACKR3 does not appear to be dependent on any singular receptor feature and is likely a combination of several factors.”

      (7) If the authors believe that the various ligands/mutations are only altering the distribution/dynamics of the same 3/4 conformations of CXCR4/ACKR3, respectively, is there a reason each FRET efficiency histogram is fit independently instead of constraining the individual components to Gaussian components with the same centroids, and/or globally fitting all datasets for the same receptor?

      We performed global analysis across all data sets for each sample and condition. Since the peak positions of the various FRET states recovered in this way were consistent across treatments (Fig. S4,S6), we did not feel it was necessary to perform a further global analysis across all samples for a given receptor.

      Reviewer #3 (Recommendations For The Authors):

      The manuscript is well-written, the arguments are easy to follow and the figures are helpful and clear. Here are a few questions/suggestions that the authors might want to address before the paper will be published:

      (1) Include a table with kinetic rates between states in SI and have a brief discussion in the main text to support the trends observed in transition probabilities.

      As noted above, determining rate constants for each of the state-to-state transitions will require a much larger set of experimental smFRET data than is currently available and will be the subject of future studies.

      (2) The argument of state similarity (Figure S4 and S6)... why are the profiles not Gaussian, like in the fits on Figures S3 and S5, repectively? I would also suggest that once the number of states is chosen to do a global fit, where the FRET values of a certain sub-state across different conditions for one receptor are shared.

      The state distributions presented in Figs. S4 and S6 (as well as throughout the rest of the paper) are derived from HMM fitting of the time traces themselves, and are not constrained to be Gaussian, whereas the GMM analysis in Figs. S3 and S5 are Gaussian fits to the final apparent FRET efficiency histograms.

      Similar to our response to Review 2 above, due to the consistency of the fitted peak positions obtained across different conditions for a given sample, we did not feel that further global analysis was necessary.

      (3) It is shown FRET changes from ~0.85 in the inactive (closed) state to ~0.25 in the active (open) state. How do these values match the expectations based on crystal structure and dye properties?

      As noted in our response to Reviewer 1, translating the apparent FRET values using the assumed Förster distances for A555/Cy5 (per FPbase) suggest a change in D-A distance of ~30 angstroms, whereas the expected change from structures is ~16 Å. We suspect this discrepancy is due to the lipids immediately adjacent to the fluorophores, which may lead to the probes being constrained in an extended position when TM6 moves outwards, thus also reporting the linker length in the distance change. Additionally, such interactions may constrain the donor and acceptor in unfavorable orientations for energy transfer, which would also reduce the FRET efficiency in the active state. Since the calculated D-A distance changes appear too large for GPCR activation, we have opted to not make any structural interpretations. Instead, all of our conclusions are based on resolving individual conformational states and quantifying their relative populations, which is based directly on the measured FRET efficiency distributions, not computed distances.

      (4) The results on the effect of CXCL12-P2G on CXCR4 are confusing...despite being an antagonist, this ligand stabilizes the "active state"...I am not sure if the explanation offered is sufficient that the opening of the intracellular cleft is not sufficient to drive the G protein coupling/activation.

      We agree that the explanation related to the opening of the intracellular cleft being insufficient to drive G protein coupling/activation is speculative and we have removed that text. We now simply propose that the CXCL12 variants inhibit coupling of G proteins to CXCR4 or disrupt interactions necessary for signaling, as stated in the following text to the results on Pg. 8:

      “Despite the ability of CXCL12<sub>P2G</sub> and CXCL12<sub>LRHQ</sub> to stabilize the active R* conformation of CXCR4, both variants are known to act as antagonists (20). This suggests that the CXCL12 mutants inhibit CXCR4 coupling to G proteins not by suppressing the active receptor population but rather by increasing the dynamics of the receptor state-to-state transitions. Our results suggest that the helical movements considered classic signatures of the active state may not be sufficient for CXCR4 to engage productively with G proteins.”

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      General Statements [optional]

      This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

      • We thank the reviewers for their useful suggestions regarding how to improve our manuscript.
      • Reviewer 3 declared that s/he did not find and evaluate the provided Supplementary Materials. As a result, many of her/his criticisms seem invalid: the requested data, validations etc. were already there in the Supplementary Figures and Tables.
      • To avoid confusion, we renamed the transgene that is commonly used as a readout for STAT-activated transcription from 10xStat92E-GFP to 10xStat92E DNA binding site-GFP (please see comments by Reviewer 2 that show how easily one can think that Stat92E protein levels go up because of the misleading name of this transgene).
      • One co-author, Martin Csordós was among the authors by mistake. Although first considered, his contribution was not included in either the original or the current manuscript version, so we removed his name from the revised version with his permission.
      • We prefer to use colour coding for Sections 2., 3. and 4. in our responses to Reviewer comments rather than splitting the responses to queries in separate sections, because many of our answers contain a mixture of planned experiments (labeled as bold), already available data (labeled as underlined), and *explanations why we think that no additional analyses are necessary* (between asterisks). Data already provided in the original submission but missed by Reviewers has white background in our responses. Reviewer comments

      Reviewer 1

      Major comments:

      R1/1. ”Figure 6E seems to indicate that a subset of Su(var)2-10/PIAS isoforms may bind to ATG8 (directly or indirectly). This leads to the straightforward prediction that this subset should be differentially affected by the selective autophagy at the center of the manuscript. That could be tested to strengthen that point. “

      Response:

      The Atg8a-binding subset of Su(var)2-10/PIAS isoforms could indeed be differentially affected by selective autophagy__. To test this, we will analyze in vivo Su(var)2-10 isoform abundance on western blots with an anti- Su(var)2-10 antibody in __Atg8aΔ12and ____Atg8aK48A/Y49A (Atg8aLDS) mutants.

      Minor comments:

      R1/2. “ in Fig S1B,C the colocalization between GFP reporters for STAT92E and AP-1 activity and glia marker does not seem convincing, indicating other cell types may be expressing them as well.”

      *Response: *

      *The overlap between glia labelling and STAT92E and AP-1 transcriptional readout reporter expression is indeed not complete. First of all, epithelial cells in the wing display both STAT92E and AP-1 activity even in uninjured conditions when glial expression of these reporters is not yet observed. Transcriptional reporter activity outside of the wing nerve was previously indicated in figures with arrowheads, now the epithelium is labeled and the regions containing nerve glia are outlined everywhere. *

      The fiber-like reporter expression after injury in the wing nerve could correspond to either glia or axons1–3. Glia in the wing nerve have a filament-like appearance resembling axons in confocal images, even glial nuclei are flat/elongated1. Importantly, STAT92E enhancer-driven GFP also labels the nucleus in expressing cells, as opposed to glially driven mtdTomato that is membrane-tethered (and thus excluded from the nucleus: see Fig. S1B, C). Of note, TRE-GFP and Stat-GFP are not expressed in neurons because the cell bodies and nuclei of wing vein neurons are never GFP-positive, see Fig. 2C, Figs. S1, S4 in Neukomm et al.1 and Figure 1 for Reviewers. We also explain this better now in the revised manuscript (please see the legend of Fig. S1).

      Nonetheless, we plan to analyze colocalization of mtdTomato-labeled neurons and TRE-GFP and Stat-GFP around the neuronal cell bodies to unequivocally show their different identities. Additionally, we will include transverse confocal sections of the genotypes in Fig. S1B, C that may better illustrate the colocalization.

      Fig. 1 for Reviewers. Neuronal (nSyb+) and Stat92E-GFP+ cell morphology in the L1 vein at the anterior wing margin around the neuronal cell bodies which occupy a stereotypical position at the sensilla1. The location and shape of neuronal nuclei (left panel) are different from Stat-GFP+ cell nuclei (right panel, please see also Fig. S1B, C) based on the circumferential GFP signal. Therefore, cells expressing TRE-GFP and Stat-GFP in injured wing nerves are glia and not neurons.

      R1/3. “p.7 Instead of "Su(var)2-10 is mainly nuclear due to its transcriptional repressor and chromatin organizer functions" It may be better to say" .. .consistent with its transcriptional repressor and chromatin organizer functions"”

      Response:

      We have modified the manuscript accordingly.

      R1/4. It is not clear whether the differences in Su(var)2-10/PIAS accumulation between Atg16 and Atg101 RNAi indicate functional differences of blocking autophagy at different stages or simply differences in RNAi efficiency (Atg16) versus the Atg101 mutant.”

      Response:

      We have added glial Atg1 (the catalytic subunit of the autophagy initiation complex that also includes Atg101) knockdown experiments that show the same lack of Su(var)2-10 accumulation in uninjured conditions as seen in the Atg101 null mutant (please see Fig. S6C). Please note that Atg16-Atg5-Atg12 dependent conjugation of LC3/Atg8a is involved in various vesicle trafficking pathways in addition to autophagy4–6, alterations of which may perturb baseline Su(var)2-10 levels in uninjured animals.

      Significance:

      R1/5. “STAT92E-dependent glial upregulation of vir-1, but not Draper, is shown, but consequences for glial functions in nerve injury are not tested.”

      Response:

      We will test antimicrobial peptide (AMP) expression in glia after nerve injury and whether this is affected by STAT92E and vir-1. Certain AMPs such as Attacin C are known to be regulated by both the Stat and NF-____κΒpathways7, and AMPs can be generally upregulated in response to brain injury8,9. This could serve pathogen clearance functions after defence lines such as the epithelium and blood-brain barrier are compromised. In addition, we will test the recruitment of glial processes into the antennal lobe after olfactory nerve injury in animals with glial STAT92E or vir-1 deficiency. Glial invasion is an adaptive response to axon injury and a first step towards debris clearance10.

      R1/6. “experiments indicate a role for Su(var)2-10/PIAS SUMOylation activity in tis autophagic degradation, but it is not clear whether the critical substrata Su(var)2-10/PIAS itself or another protein.”

      “binding of Su(var)2-10/PIAS to ATG8 is indicated, but no in vitro experiment performed to test whether this is direct and perhaps SUMOylation dependent.”

      Response:

      *We aimed to answer this question by using a point mutant form of Su(var)2-10: CTD2, which is unable to properly autoSUMOylate itself11, see Fig. 6D. CTD2 mutant Su(var)2-10 levels increased in S2 cells transfected with the mutant construct relative to the wild-type, similar to lysosome inhibition affecting the wild-type protein level but not the mutant variant. Importantly, wild-type Su(var)2-10 is present in CTD2 mutant Su(var)2-10-transfected cells, which can still SUMOylate other Su(var)2-10 targets. It is thus the intrinsic SUMOylation defect of the CTD2 mutant that results in its impaired degradation. It is firmly established that increased Su(var)2-10/PIAS levels repress STAT92E activity12, mammalian example: Liu et al., 199813, pointing to Su(var)2-10 as the critical substrate for autophagy during STAT92E derepression.*

      We will further address this point and investigate if Su(var)2-10 directly binds to Atg8a by in vitro SUMOylation of GST-Su(var)2-10 and subsequent GST pulldown assay with HA-Atg8a. In vitro SUMOylation reaction with purified GST-Su(var)2-10 and negative controls are available via in-house collaboration11. We will incubate the resulting proteins and non-SUMOylated counterparts with in vitro transcribed /translated HA-Atg8a, and interactions will be tested by anti-HA western blotting with quantitative fluorescent LICOR Odyssey CLX detection.

      Reviewer 2

      Major comments:

      R2/1. The working hypothesis is that upon injury, Su(var)2-10 is degraded by autophagy and, as a consequence, Stat92E induces vir-1 expression.

      Could the authors clarify why do Stat92E levels increase upon injury? Does Stat92E stability increase upon ATG mediated Su(var)2-10 degradation? Or does it expression/nuclear translocation change?“

      Response:

      We did not state that Stat92E levels increase during injury - we only used the 10xStat92E DNA binding site-GFP reporter (we have renamed it as such in our revised manuscript to avoid confusion) that is commonly referred to as 10xStat92E-GFP in the literature14, as a readout for Stat92E-dependent transcription.

      To address these questions, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene (https://flybase.org/reports/FBti0147707.htm) to test if STAT92E stability/expression or translocation is altered during injury or upon disruption of selective autophagy. We have already tested this reporter and it is detected in the wing nerve nuclei after injury (Figure 2 for Reviewers, panel A).

      As the Atg8aLDS mutation specifically impairs selective autophagy, we will use this mutant and wild-type controls to assess STAT92E::GFP::FLAG abundance on western blots from fly lysates with anti-GFP antibody. To assess STAT92E::GFP::FLAG nuclear translocation as well as stability/expression, we will use independently Atg8aLDS and Su(var)2-10 RNAi in glia to perturb STAT92E -dependent transactivation and visualize glia cell membrane by membrane-tethered tdTomato, glial nuclei by DAPI/anti-Repo and STAT92E with the STAT92E::GFP::FLAG fusion transgene in dissected brains. We can also evaluate STAT92E nuclear translocation with the same genotypes in the injured wing nerve glia. Of note, studies in mammals failed to identify an obvious effect of PIAS1 on STAT1 abundance13, please see Figure 2B from this paper as Figure 2 for Reviewers, panel B. Rather, PIAS family proteins bind tyrosine-phosporylated STAT dimers and impair their DNA binding thereby their transcriptional activation function15.

      A.

      Proc. Natl. Acad. Sci. USA Vol. 95, pp. 10626–10631

      https://doi.org/10.1073/pnas.95.18.10626.

      Fig. 2 for Reviewers.

      1. Stat92E::GFP::FLAG expression and nuclear appearance in the wing nerve before and after injury
      2. Increasing PIAS1 (Su(var)2-10 ortholog) levels does not affect STAT1 abundance in mammalian cells R2/2. Also, since Su(var) levels increase upon ATG RNAi, independently of injury, do ATG levels increase upon injury? It does not seem to be the case from Fig 6D, but then, if the ATG levels do not increase, how to explain the injury mediated effects of Su(var)2-10? “

      Response:

      *We have not seen an effect of injury on the rate of autophagic degradation (flux) using the common flux reporter GFP-mCherry -Atg8a in glia after injury (shown in Fig. S2D – not 6D). Also, levels of the typical autophagic cargo p62/Ref(2)P and core autophagy proteins such as Atg12, Atg5, Atg16 do not change after nervous system injury16suggesting no change in general autophagic turnover. *

      *An increase in general autophagy would be one option to promote degradation of a given cargo. Just as for the ubiquitin-proteasome system, in selective autophagy the labelling of the cargo/substrate for degradation is a regulated process. Dynamic ubiquitylation of a cargo often promotes its autophagic degradation17. We hypothesize that SUMO may fulfil a similar role in labelling cargo for elimination and this may be promoted by injury in the case of Su(var)2-10, which warrants future studies. *

      R2/3. “Su(var)2-10 levels in control and injured wings are different between ATG18RNAi and ATG101 mutant (Fig 5). Could the authors explain the rational for using two ATG mutants? and the meaning of this difference? Also, why comparing data using the RNAi approach and a mutation?”

      Response:

      This issue was also raised in R1/4 and we refer the Reviewer/Editor to that section for our new Atg1 knockdown data and explanations.

      *There is a consensus in the autophagy community that mutants for multiple Atg genes should always be used to ensure that it is indeed canonical autophagy that is affected (because Atg proteins can have non-autophagic roles, as is the case for Atg16 in regulation of phagosome maturation - LAP). *

      R2/4. “Fig 6 What is the relevance of the Atg8, Sumo and Su(var)2-10 colocalization at puncta, since there is a lot of colocalization outside the puncta and also lots of Su(var)2-10 or Atg8 labeling that does not colocalize? “

      Response:

      *Su(var)2-10 orthologs PIAS1-4 localize to the nuclear matrix and certain foci in the chromatin and may play roles in heterochromatin formation, DNA repair, and repression of transposable elements in addition to transcriptional repression18–20. SUMO-modified proteins accumulate in response to PIAS activity in phase-separated foci also referred to as SUMO glue21. We show colocalization of Atg8a with similar Su(var)2-10 and SUMO double positive structures in foci. *

      *We do not expect a full overlap between Su(var)2-10 and Atg8a labeling for a number of reasons. First, Su(var)2-10 has many different roles that may not be regulated by autophagy. Second, Atg8a+ autophagosomes in the cytoplasm deliver not only indidivual proteins such as Su(var)2-10 for degradation but also many other cellular components. Third, nuclear Atg8a is implicated in the removal of the Sequoia transcriptional repressor from autophagy genes that is unlikely to involve Su(var)2-1022. Now we include these points in the Discussion section.*

      R2/5. “The statement made in the first sentence of the discussion is very strong: 'we have uncovered an activation mechanism for Stat92E', without sufficient supporting evidence.”

      Response:

      We have rephrased this section as follows:

      Here we have uncovered the autophagy-dependent clearance of a direct repressor of the Stat92E transcription factor. This, synergistically with injury-induced Stat92E phosphorylation, may ensure proper Stat92E-dependent responses in glia after nerve injury to promote glial reactivity.

      R2/6. “Could the authors validate (some) expression data by in situ hybridization experiments?”

      Response:

      *Our gene expression data were derived from wing nerve imaging or wing tissue. Unfortunately, in situ hybridization is not feasible in this organ because probes do not penetrate the thick chitin-based cuticule and wax cover of the wing (and the same is true for wing immunostaining).* We do provide independent evidence for vir-1 upregulation in the wing after injury via quantitative PCR (qPCR) in Fig. S5C. To corroborate reporter-based data, we will also analyze drpr in qPCR using wing material after injury at the same time points.

      R2/7. “Could the authors validate the RNAi lines molecularly (or refer to published data on these lines?”

      Response:

      *Almost all RNAi lines have already been validated by qPCR, western blot, or immunostaining in Szabo et al., 202316 and other publications23–25. The only exception is Su(var)2-10JF03384 and we show that it is indistinguishable from the validated Su(var)2-10HMS00750 RNAi line (which causes 95% transcript reduction): it also strongly derepresses STAT activity. These reagents have also been widely used in the community (e.g. https://flybase.org/reports/FBal0242556.htm, https://flybase.org/reports/FBal0233496.htm).*

      R2/8. „Clarifying the role of Su(var)2-10 on Stat92E would benefit to the presented work. Does Atg8-Su(var)2-10 binding affect Stat92E accumulation, expression, translocation to the nucleus? Some of these experiments could be obtained in S2 cell transfection assays, if too complex in vivo.”

      Response:

      As explained in R2/1, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene to test if STAT92E stability/expression or translocation is altered upon disruption of selectiveautophagy (in Atg8aLDS mutant flies).

      R2/9. „Also, what happens to the axons in the mutant conditions described in the manuscript? This would higher the impact of the work, but would require in vivo work with fly stocks containing several transgenes.”

      Response:

      We have already published in our previous paper, Szabo et al., 202316 that the mutants used in the current study display normal axon morphology__. There are only two mutants that we did not test in that paper: Atg8aLDS and our new Atg8anull and we will examine these remaining two during the revision, __but we already published in the above paper that axons appear normal in Atg8aΔ4, a widely used Atg8a mutant allele.

      R2/10. „It has been published that Draper is involved in the response to injury in the adult wing nerve. See for example Neukomm et al (2014). The authors should discuss how this fits with their hypothesis and data. In this respect, Fig S4B, which should support the hypothesis, should be improved. It is rather hard to interpret it.”

      Response:

      Fig. S3 (draper protein trap-Gal4 driven GFP-RFP reporter expression) and S4B (intronic STAT92E binding site of the draper gene driven GFP-RFP reporter expression) show similar results: drpr is already expressed in wing nerve glia before injury, which is in line with Draper’s crucial role in the injury response because Draper-mediated glial signaling triggers glial reactivity. This has been added to the Discussion.

      Minor comments:

      R2/11. „Rubicon is also a negative regulator of autophagy (doi:10.1038/s41598-023-44203-6). in (Fig2 B, D) we have a higher GFP intensity in both uninjured and injured, and the difference between Injured/uninjured is less significant compared to control. It is possible that Rubicon KD causes more autophagy leading to a higher activation of Stat92E even in control. I wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”

      Response:

      Loss of Rubicon could indeed potentially remove more Su(var)2-10 via increased autophagy, leading to higher Stat92E activity. However, there is no statistically significant difference between injured and uninjured controls and injured and uninjured Rubicon knockdown, respectively, in Fig2 B, D (p=0.6975 and >0.9999 for each comparison). We are puzzled by the statement that the reviewer „wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”. We analyzed Rubicon as a factor critical for LAP and its deficiency does not prevent Stat transcriptional activity following injury unlike the loss of Atg8a, Atg16, Atg13 and Atg5. We will further support this result with a mutant of Atg16 with part of the WD40 domain deleted, because this region is critical for LAP but not for autophagy.16,26,27

      R2/12. „The rationale for using both repoGal4 and repoGS is unclear. If, as mentioned, the goal is to avoid developmental defects, repoGS should be consistently used. Especially I don't understand how both were utilized to knock down the same genes, such as Atg16”

      Response:

      *We had to use repoGS (a drug-inducible Gal4 active in glia) because knocking down Su(var)2-10 with repoGal4 resulted in no viable adult progeny. Su(var)2-10 is an essential gene as opposed to most autophagy genes and its absence results in embryonic lethality24. Thus all Su(var)2-10 silencing experiments were done with repoGS. Similarly, Stat92E is involved in various developmental processes and its loss is embryonic lethal. repoGal4 was used for genes generally not having an adverse effect when absent during development16 in the first two figures. In Fig. 4D, we silenced Atg16 by repoGS because it is one of the controls for testing a genetic epistasis between Su(var)2-10 and Atg16. Please note that we see exactly the same phenotype in case of Atg16 knockdown when using either Gal4 version.* This has been explained in the revised methods section.

      R2/13. „In the third paragraph of the introduction, I am confused whether Stat92E regulates drpr of the reverse”

      Response:

      Upon antennal injury, Drpr receptor binding to phagocytic cargo initiates a positive feedback loop in glial cells to promote its own transcription28. Drpr receptor in the plasma membrane regulates Stat92E and AP-1 activity via signal transduction. Stat92E and AP-1, in turn, increases drpr transcription10,28–30 that will result in more plasma membrane Drpr protein expression. We have explained this more clearly in the revised Introduction.

      R2/14. „I cannot find the evidence for vir-1 being expressed in glia and target of Gcm in the refences that have been cited.”

      Response:

      We apologize for not explaining this better: vir-1 is called CG5453 in Freeman et al., 200331. It is listed in Table 1 as a Gcm target since there is no detectable CG5453 expression in a Gcm null mutant, please see below. We have updated the manuscript with this gene name.

      .....

      .....

      Part of Table 1 from Freeman et al., 200331.

      R2/15. „The presence of a Stat92E binding site on the vir-1 promoter has already bene described in the paper from Imler and collaborators, Nature immunology 2005. Actually, if this site is present in their transgenic line, it would help the authors strengthen the argument that Stat92E has a direct role on vir1 (for which they make a very strong statement in the discussion, with no direct evidence).”

      Response:

      *The evidence that Stat92E may have a direct role in vir-1 transcription in glia comes exactly from the same reporter transgene described by Imler and collaborators in the mentioned paper32. We received this transgenic line from the Imler group and monitored its expression after injury upon depletion of Stat92E (Fig. 3B). It thus contains the studied Stat binding site. This was referenced in the Methods and in all relevant sections of the main text, and we now explicitly state this in the revised text.*

      R2/16. In the Fig S2D, I do not see a lot of GFP+ (Glia) cells. I see more Atg8a in injured 3 dpi regardless of colocalization with glia”

      Response:

      Fig S2D uses one of the standard assays for autophagic turnover, which we now explain in more detail in the Results section. Basically, the dual tagged GFP::mCherry::Atg8a transgene is expressed in glia, and GFP is quenched in lysosomes after delivery by autophagy while mCherry remains fluorescent. So, in addition to double positive dots (autophagosomes), there are mCherry dots lacking GFP (autolysosomes) if autophagy is functional. All of these dots are in glia but the cell boudaries are not visible.

      The images shown are single optical slices. The number of mCherry+ puncta are around 7-8 per field in both uninjured and injured (3 dpi) conditions, but puncta brightness is always variable. Since most mCherry+ puncta were rather bright in the original 3 dpi image, we changed it to a more representative image.

      R2/17. „The quantification of the signals is made in a specific region of the wing, I guess throughout the nerve thickness. This could be represented more carefully in a schematic and It would also help defining colocalization in the first figure, by using a transverse section.”

      Response:

      The quantification method is described in Materials and Methods and we have added that quantification was done on single optical slices. The imaged region is depicted in Fig. S1A, where we indicated the rectangular region used in Fiji for image quantification. We will add transverse sections of wings as suggested.

      R2/18. „A number of ATG genes are considered in the manuscript, but the rational for using them is not always clear. Showing a schematic would help clarify this. „

      Response:

      We have added a table showing the different steps of autophagy where the studied Atg genes/proteins function (now Supplementary Table 1). We also added whether the gene is considered specific for autophagy or can play a role in another process, e.g. LAP. We studied different autophagy genes in line with the assumption that disabling distinct autophagic complexes should produce the same phenotype if this process is indeed autophagy (and not LC3-associated phagocytosis for example).

      R2/19. „Fig 7 is not cited and its legend is very short.”

      Response:

      We have now cited Fig 7 and expanded its legend.

      R2/20. „Clarify the color coding in Fig S1E”

      Response:

      We added that red is injured, black is uninjured.

      R2/21. „What is the tandem tagged autophagic fly reporter in fig S2D?”

      Response:

      This is one of the most common tools to study autophagy, please see the updated explanation above at your first question regarding Fig. S2D.

      R2/22. „Add a schematic on the vir-1 isoforms.”

      Response:

      We have added a a schematic showing the vir-1 isoforms in Fig. S5B.

      R2/23. „Fig S6B and Fig 5 relate on the levels of Su(var)2-10 upon Atg16 RNAi, but the scale is not the same, why?”

      Response:

      *The scales are different because these two images measure different things. Fig. 5 indeed displays quantification of Su(var)2-10 levels in brain glia. However, Fig S6B shows quantification of Stat92E-induced GFP reporter levels (as a proxy of Stat92E transcriptional activity) in the wing nerve upon Atg16 knockdown. *

      Reviewer 3

      R3/1. „The claim that the negative regulator of Stat92E signaling is removed by selective autophagy, involving selective autophagy receptors different from/in addition to Ref(2)P/p62 is not convincingly shown. This claim probably needs to be softened.”

      Response:

      *We have rephrased this sentence as follows: *

      „These data suggest that selective autophagy is involved in Stat92E-dependent transcriptional activation in glia.”

      R3/2. „The reporter that was used (10xSTAT92E-eGFP) is not a dynamic reporter of STAT92E activity. It accumulates in glia and is highly stable. The appropriate reporter to look at dynamic changes would be 10XSTAT92E-dGFP, which has a degradable (unstable) GFP that is required to see dynamic changes even in the CNS. All of the claims about STAT92E regulation use this reporter, so they are questionable.”

      Response:

      10XSTAT92E-dGFP featuring destabilized GFP could be a more appropriate tool for monitoring dynamic changes in transcription when short term- e.g. few hours - changes are investigated. However, we did not see any expression of 10XSTAT92E-dGFP (we tried 2 different transgenic insertions) in the wing nerve, please see Figure 3 for Reviewers. In the brain, dGFP expression with this reporter is also several times lower than stable GFP, please compare Fig. 4A and B in Doherty et al28.

      The use of 10xSTAT92E-eGFP to follow dynamic expression changes is justified by many lines of evidence. First, there is no 10xSTAT92E-EGFP expression in uninjured wing nerves (Fig. S1D,E). Injury induces EGFP expression in the wing nerve with a sustained activation from 1 to 3 dpi (days post injury), and the EGFP expression returns to the baseline by 5 dpi (Fig. S1D, E). Second, the initial Stat-dependent upregulation of drpr and the 10XSTAT92E-dGFP signal in the brain both occur in the first 24 hours after injury and are sustained for 72 hours28 similar to our results with 10xSTAT92E-EGFP ((Fig. S1D,E). These results indicate that the dynamics of 10xSTAT92E-EGFP expression allows monitoring changes in Stat-dependent transcription occurring over days.

      Figure 3 for Reviewers. Lack of 10XSTAT92E-dGFP signal in the wing nerve from two independent insertions of the same transgene at the indicated time points after wing injury.

      R3/3. „The claim that glial drpr is not upregulated by wing injury and drpr accumulation is not apparently a prerequisite for efficient debris processing within the wing is weak. First, they did not stain for Draper using antibodies, rather they used expression constructs. Dee7 is a promoter that was found to be injury activated in the CNS (were they able to replicate that result? I did not receive the supplemental data), but it might not be the crucial regulator in the periphery. The MIMIC line that was converted is better, but might not represent the full spectrum of regulatory events at the draper locus. Finally, they never actually test for endogenous RNA changes, or use the antibody on westerns. Their lack of evidence is not as compelling as it could be.”

      Response:

      The__ original Supplemental Material already provides answers for this and subsequent questions of Reviewer 3__. We deposited the Supplemental Material to bioRxiv at the time of the first Review Commons submission and it was/is available at https://www.biorxiv.org/content/10.1101/2024.08.28.610109v2.supplementary-material.

      Figs. S3 and S4 show in the wing and the brain (using two different drpr reporters for its transcriptional regulation) that drpr expression does not change much in the wing after nerve injury, as opposed to the brain.

      *We did indeed replicate that dee7-Gal4 expression is induced in the brain after antennal injury using UAS- TransTimer (Fig. S4A). In contrast, wing cell nuclei already show expression of both fluorescent proteins in uninjured conditions, and RFP+ nucleus numbers do no change after wing injury (Fig. S4B, C). drpr-Gal4 was generated by conversion of a MiMIC gene trap element into a Gal4 that traps all transcripts. drprMI07659 is in an intron that is common in all drpr isoforms so it should capture the regulation of all transcript isoforms. *

      We will further analyze drpr expression via independent methods during the revision: qPCR amplification of a common region of drpr transcripts, and western blot with anti-Drpr antibody to compare injured and uninjured wing material. Of note, we see no upregulation of drpr 2 days after wing injury in our (unpublished) RNAseq results either.

      *Unfortunately, immunostaining of the adult wing is not feasible because antibodies do not penetrate the thick chitin-based cuticle and wax cover of the wing.*

      R3/4. „The authors claim autophagy contributes to glial reactive states in part by acting on JAK-STAT pathway via regulation of Stat92E. They did not investigate other potential STAT92E targets. Does Atg16 knockdown alter STAT92E expression? Apparently Vir1 is still upregulated in the absence of Atg16 following injury, but they don’t show STAT92E changes.”

      Response:

      We did investigate other potential STAT92E targets besides vir-1. This is referred to in the text as „*immunity-related gene reporters” and it again can be found in the Supplemental Material (____Supplementary Table 2). None of these genes showed glia-specific upregulation following injury. *

      We will investigate STAT92E expression with the STAT92E::GFP::FLAG protein-protein fusion transgene after disrupting autophagy as also suggested by Reviewer 2. Please see our detailed answer to the first comment of Reviewer 2.

      *We do not agree with the comment that „Vir1 is still upregulated in the absence of Atg16 following injury” because Fig. 3F,G show that lack of Atg16 abolishes the upregulation of the vir-1 reporter: the change from uninjured to injured becomes statistically not significant and the mean GFP intensities are practically identical. *

      R3/5. „The authors claim Su(var)2-10 is an autophagic cargo. They should better characterize Su(var)2-10 degradation and its regulation, and image quality needs to be improved (better images, merged examples, and clearer indication of what they are highlighting. There are many arrows in figures that I don't know what they are pointing to. Much of the labeling in Fig 1 (and others) looks like axons. Could TRE-GFP be turned on in neurons? How did they discriminate?”

      Response:

      As also explained to Reviewer 1’s last comment, we will carry out experiments to address whether SUMOylated Su(var)2-10 binds Atg8a, which can provide evidence for a direct SUMO-dependent autophagic elimination of Su(var)2-10. Please see our detailed response there.

      We will further improve image quality for brain images and we already incorporated new images in Fig. S6. *Merged images were missing only in Fig 5, which we have included in the current version. Arrows and arrowheads were used as described in Figure legends, but instead of those, we now clearly label the epithelium and we outlined the region of wing nerve glia in all images. *

      Please see our response to the first minor comment of Reviewer 1 regarding the expression of reporters in wing tissues.

      R3/6. „The authors claim interaction of Su(var)2-10 with Atg8a in the nucleus and cytoplasm can trigger autophagic breakdown, involving Su(var)2-10 SUMOylation. The paper would benefit from showing direct SUMOylation of Su(var)2-10 after injury. Is there any way to examine this in vivo?”

      Response:

      We will test direct SUMOylation of Su(var)2-10 using a recently described method by Andreev et al., 202233. FLAG-GFP-Smt3 (SUMO)____ is expressed under SUMO transcriptional regulation and we will immunoprecipitate FLAG-GFP-SUMO and GFP alone as negative control with GFPTrap beads from lysates of heads subjected to traumatic brain injury that results in glial reactivity16____, and also from uninjured head lysates. We will use anti-____Su(var)2-10 ____western blotting to visualize SUMOylated Su(var)2-10 and whether its levels are modulated by brain injury.

      R3/7. „The authors state in discussion "we find that draper is highly expressed in wing nerve glia already in uninjured conditions and it is not further induced by wing transection - indicating high phagocytic capacity in wing glia ... axon debris clearance takes substantially longer in the wing nerve than in antennal lobe glomeruli, thus draper levels may not readily predict actual phagocytic activity in glia". However, they never actually assess this in their experiments. All the conclusions about Draper are made from promoter fusions of integrated reporters, which are imperfect. This conclusion cannot be made.”

      Response:

      As described in our response to R3/3, we will further test drpr expression changes after wing injury using two independent methods: qPCR and western blot .

      We deleted this part from the Discussion that were criticized by the reviewer because these are not important for the main message of our manuscript.

      R3/8. „Both STAT92E and Jun are activated by a stress response. Could this be a stress response to disrupting autophagy that is somehow enhance by injury?”

      Response:

      *Stress responses are indeed relayed by AP-1 and Stat signaling, and impaired autophagy could be a source of stress. We would like to emphasize, though, that the main finding of our manuscript is that disrupting autophagy suppresses Stat-dependent transcription. Autophagy inhibition does not increase Stat signaling in uninjured wing nerves and while control flies upregulate Stat activity upon injury, autophagy-deficient animals fail to do so (Fig. 1). Thus, Stat signaling is not activated by loss of autophagy – it is activated by injury (that is the stress) and Stat activation requires autophagy in this setting.*

      R3/9. „Minor:

      I don't think that "glially" is a word.”

      Response:

      Online dictionaries such as Wiktionary list glially as a word, and many scientific articles use it: https://doi.org/10.1016/j.conb.2022.102653, https://doi.org/10.1016/j.yexcr.2013.08.016,https://doi.org/10.1016/j.jpain.2006.04.001*, to give some examples. *

      We nonetheless refrain from using it in the updated text.

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      Referee #3

      Evidence, reproducibility and clarity

      In this study the authors explore a potential role for STAT92E and Su(var)2-10 in glial responses to injury in the adult Drosophila wing. The major claims are that canonical autophagy and not LAP sustains STAT92E signaling after in jury. The negative regulator STAT92E is removed by selective autophagy, but this is not ref(2)p/p62 (perhaps). Glial draper expression is not upregulated and Draper accumulation is not apparently a prerequisite for efficient debris clearance in the wing. Su(var)2-10 is an autophagic cargo, mediator of STAT92E-dependennt transcription; and interacts with Atg8a, perhaps sumoylating targets. In general, the model is reasonable, but the data do not support the conclusions, and the quality of the data needs improvement before firm conclusions can be reached. Concerns include:

      1. The claim that the negative regulator of Stat92E signaling is removed by selective autophagy, involving selective autophagy receptors different from/in addition to Ref(2)P/p62 is not convincingly shown. This claim probably needs to be softened.
      2. The reporter that was used (10xSTAT92E-eGFP) is not a dynamic reporter of STAT92E activity. It accumulates in glia and is highly stable. The appropriate reporter to look at dynamic changes would be 10XSTAT92E-dGFP, which has a degradable (unstable) GFP that is required to see dynamic changes even in the CNS. All of the claims about STAT92E regulation use this reporter, so they are questionable.
      3. The claim that glial drpr is not upregulated by wing injury and drpr accumulation is not apparently a prerequisite for efficient debris processing within the wing is weak. First, they did not stain for Draper using antibodies, rather they used expression constructs. Dee7 is a promoter that was found to be injury activated in the CNS (were they able to replicate that result? I did not receive the supplemental data), but it might not be the crucial regulator in the periphery. The MIMIC line that was converted is better, but might not represent the full spectrum of regulatory events at the draper locus. Finally, they never actually test for endogenous RNA changes, or use the antibody on westerns. Their lack of evidence is not as compelling as it could be.
      4. The authors claim autophagy contributes to glial reactive states in part by acting on JAK-STAT pathway via regulation of Stat92E. They did not investigate other potential STAT92E targets. Does Atg16 knockdown alter STAT92E expression? Apparently Vir1 is still upregulated in the absence of Atg16 following injury, but they don't show STAT92E changes.
      5. The authors claim Su(var)2-10 is an autophagic cargo. They should better characterize Su(var)2-10 degradation and its regulation, and image quality needs to be improved (better images, merged examples, and clearer indication of what they are highlighting. There are many arrows in figures that I don't know what they are pointing to. Much of the labeling in Fig 1 (and others) looks like axons. Could TRE-GFP be turned on in neurons? How did they discriminate?
      6. The authors claim interaction of Su(var)2-10 with Atg8a in the nucleus and cytoplasm can trigger autophagic breakdown, involving Su(var)2-10 SUMOylation. The paper would benefit from showing direct SUMOylation of Su(var)2-10 after injury. Is there any way to examine this in vivo? The authors state in discussion "we find that draper is highly expressed in wing nerve glia already in uninjured conditions and it is not further induced by wing transection - indicating high phagocytic capacity in wing glia ... axon debris clearance takes substantially longer in the wing nerve than in antennal lobe glomeruli, thus draper levels may not readily predict actual phagocytic activity in glia". However, they never actually assess this in their experiments. All the conclusions about Draper are made from promoter fusions of integrated reporters, which are imperfect. This conclusion cannot be made. Both STAT92E and Jun are activated by a stress response. Could this be a stress response to disrupting autophagy that is somehow enhance by injury?

      Minor:

      I don't think that "glially" is a word.

      Significance

      Based on the quality of the data, it is hard to consider this manuscript having made a major step forward. A significant amount of work needs to be done to firm up the conclusions. In its present form, the major contributions are the identification vir-1 as upregualted (maybe) and a potential role for autophagy.

    1. Author response:

      Reviewer #1 (Evidence, reproducibility and clarity):

      Summary:

      In this manuscript, Hammond et al. study robustness of the vertebrate segmentation clock against morphogenetic processes such as cell ingression, cell movement and cell division to ask whether the segmentation clock and morphogenesis are modular or not. The modularity of these two would be important for evolvability of the segmenting system. The authors adopt a previously proposed 3D model of the presomitic mesoderm (Uriu et al. 2021 eLife) and include new elements; different types of cell ingression, tissue compaction and cell cycles. Based on the results of numerical simulations that synchrony of the segmentation clock is robust, the authors conclude that there is a modularity in the segmentation clock and morphogenetic processes. The presented results support the conclusion. The manuscript is clearly written. I have several comments that could help the authors further strengthen their arguments.

      Major comment: 

      [Optional] In both the current model and Uriu et al. 2021, coupling delay in phase oscillator model is not considered. Given that several previous studies (e.g. Lewis 2003, Herrgen et al. 2010, Yoshioka-Kobayashi et al. 2020) suggested the presence of coupling delays in DeltaNotch signaling, could the authors analyze the effect of coupling delay on robustness of the segmentation clock against morphogenetic processes?

      We thank the reviewer for the suggestion. Owing to the computational demands of including such a delay in the model, we cannot feasibly repeat every simulation analysed here in the presence of delay, and would like to note that the increased computational demand that delays put on the simulations is also the reason why Uriu et al 2021 did not include it, as stated in their published exchange with reviewers. However, analogous to our analysis in figure 7, we can analyse how varying the position of progenitor cell ingression affects synchrony in the presence of the coupling delay measured in zebrafish by Herrgen et al. (2010). We show this analysis in a new figure 8 (8B, specifically), on page 21, and discuss its implications in the text on pages 2022. Our analysis reveals that the model cannot recover synchrony using the default parameters used by Uriu et al. (2021) and reveal a much stronger dependence on the rate of cell mixing (vs) than shown in the instantaneous coupling case (cf. figure 7). However, by systematically varying the value of the delay we find that a relatively minor increase in the delay is sufficient to recover synchrony using the parameter set of Uriu et al. (see figure 8C). Repeating this across the three scenarios of cell ingression we see that the combination of coupling strength and delay determine the robustness of synchrony to varying position of cell ingression. This suggests that the combination of these two parameters constrain the evolution of morphogenesis.

      Minor comments: 

      -  PSM radius and oscillation synchrony are both denoted by the same alphabet r. The authors should use different alphabets for these two to avoid confusion.

      We thank the reviewer for spotting this. This has now been changed throughout to rT, as shorthand for ‘radius of tissue’.

      -  page 5 Figure 1 caption: (x-x_a/L) should be (x-x_a)/L.

      We thank the reviewer for spotting this. This has now been corrected.

      -  Figure 3C: Description of black crosses in the panels is required in the figure legend.

      Thank you for spotting this. The legend has now been corrected.

      -  Figure 3C another comment: In this panel, synchrony r at the anterior PSM is shown. It is true that synchrony at anterior PSM is most relevant for normal segment formation. However, in this case, the mobility profile is changed, so it may be appropriate to show how synchrony at mid and posterior PSM would depend on changes in mobility profile. Is synchrony improved by cell mobility at the region where cell ingression happens?

      We thank the reviewer for the suggestion. We have now plotted the synchrony along the AP axis for varying motility profiles, and this can be seen in figure 3 supplement 1, and is briefly discussed in the text on page 11. We show that while the synchrony varies with x-position (as already expected, see figure 2), there is no trend associated with the shape of the motility profile.

      -  In page 12, the authors state that "the results for the DP and DP+LV cases are exactly equal for L = 185 um, as .... and the two ingression methods are numerically equivalent in the model". I understood that in this case two ingression methods are equivalent, but I do not understand why the results are "exactly" equal, given the presence of stochasticity in the model.

      These results can be exactly equal despite the simulations being stochastic because they were both initialised using the same ‘seed’ in the source code. However, we now see that this might be confusing to the reader, and we have re-generated this figure but this time initialising the simulations for each ingression scenario using a different seed value. This is now reflected in the text on page 12 and in figure 4.

      -  The authors analyze the effect of cell density on oscillation synchrony in Fig. 4 and they mention that higher density increases robustness of the clock by increasing the average number of interacting neighbours. I think it would be helpful to plot the average number of neighbouring cells in simulations as a function of density to quantitatively support the claim.

      We thank the reviewer for their suggestion. Distributions of neighbour numbers for exemplar simulations with varying density can now be found in  figure 4 supplementary figure 1 and are referred to in the text on page 11.

      -  The authors analyze the effect of PSM length on synchrony in Fig. 4. I think kymographs of synchrony r as shown in Fig. 2D would also be helpful to show that indeed cells get synchronized while advecting through a longer PSM.

      We thank the reviewer for their suggestion and agree that visualising the data in this way is an excellent idea. We have generated the suggested kymographs and added them to figure 4 as supplements 2 and 4, and discussed these results in the text on page 12.

      -  I understand that cells in M phase can interact with neighboring cells with the same coupling strength kappa in the model, although their clocks are arrested. If so, this aspect should be also mentioned in the main text in page 16, as this coupling can be another noise source for synchrony.

      We agree this is an important clarification. We explicitly state this, and briefly justify our choice, in the text on page 16.

      -  Figure 5-figure supplement 2: panel labels A, B, C are missing. 

      Thank you for bringing this to our attention. These have now been added.

      – Figure 5-figure supplement 3: panel labels A, B, C are missing.

      Thank you for bringing this to our attention. These have now been added.

      Reviewer #1 (Significance):

      Synchronization of the segmentation clock has been studied by mathematical modeling, but most previous studies considered cells in a static tissue without morphogenesis. In the previous study by Uriu et al. 2021, morphogenetic processes such as cell advection due to tissue elongation, tissue shortening, and cell mobility were considered in synchronization. The current manuscript provides methodological advances in this aspect by newly including cell ingression, tissue compaction and cell cycle. In addition, the authors bring a concept of modularity and evolvability to the field of the vertebrate segmentation clock, which is new. On the other hand, the manuscript confirms that the synchronization of the segmentation clock is robust by careful simulations, but it does not propose or reveal new mechanisms for making it robust or modular. The main targets of the manuscript will be researchers working on somitogenesis and evolutionary biologists who are interested in evolution of developmental systems. The manuscript will also be interested by broader audiences, like developmental biologists, biophysicists, and physicists and computer scientists who are working on dynamical systems.

      We thank the reviewer for their interest in our manuscript and for acknowledging us as one of the first to address the modularity and evolvability of somitogenesis. We hope that this work will encourage others to think about these concepts in this system too.  

      In the original submission, we identified a high enough coupling strength as the main mechanism underlying the identified modularity in somitogenesis. Since, we have included an analysis of the coupling delay and find that it is the interplay between coupling strength and coupling delay that mediate the identified modularity, allowing PSM morphogenesis and the segmentation clock to evolve independently in regions of parameter space that are constrained and determined by the interplay between these two parameters. We have now added an extra figure (figure 8) where we explore this interplay and have discussed it at length in the last section of the results and in the discussion. We again thank the reviewer for encouraging us to include delays in our analysis.

      Reviewer #2 (Evidence, reproducibility and clarity):

      SUMMARY 

      The manuscript from Hammond et al., investigates the modularity of the segmentation clock and morphogenesis in early vertebrate development, focusing on how these processes might independently evolve to influence the diversity of segment numbers across vertebrates.

      Methodology: The study uses a previously published computational model, parameterized for zebrafish, to simulate and analyse the interactions between the segmentation clock and the morphogenesis of the pre-somitic mesoderm (PSM). Their model integrates cell advection, motility, compaction, cell division, and the synchronization of the embryo clock. Three alternative scenarios of PSM morphogenesis were modeled to examine how these changes affect the segmentation clock.

      Model System: The computational model system combines a representation of cell movements and the phase oscillator dynamics of the segmentation clock within a three-dimensional horseshoe-shaped domain mimicking the geometry of the vertebrate embryo PSM. The parameters used for the mathematical model are mostly estimated from previously published experimental findings.

      Key Findings and Conclusions: (1) The segmentation clock was found to be broadly robust against variations in morphogenetic processes such as cell ingression and motility; (2) Changes in the length of the PSM and the strength of phase coupling within the clock significantly influenced the system's robustness; (3) The authors conclude that the segmentation clock and PSM morphogenesis exhibited developmental modularity (i.e. relative independence), allowing these two phenomena to evolve independently, and therefore possibly contributing to the diverse segment numbers observed in vertebrates.

      MAJOR COMMENTS

      (1) The key conclusion drawn by the authors (that there is robustness, and therefore modularity, between the morphogenetic cellular processes modeled and the embryo clock synchronization) stems directly from the modeling results appropriately presented and discussed in the manuscript. The model comprises some strong assumptions, however all have been clearly explained and the parameterization choices are supported by experimental findings, providing biological meaning to the model. Estimated parameters are well explained and seem reasonable assumptions (from the embryology perspective).

      We thank the reviewer for their positive comments about our work

      (2) This study, as is, achieves its proposed goal of evaluating the potential robustness of the embryo clock to changes in (some) morphogenetic processes. The authors do not claim that the model used is complete, and they properly identify some limitations, including the lack of cellcell interactions. Given the recognized importance of cellular physical interactions for successful embryo development, including them in the model would be a significant addition in future studies.

      We would like to clarify that the model does include cell-cell interactions as cells interact with their neighbours’ clock phase to synchronise and to avoid occupying the same physical space. 

      (3) The authors have deposited all the code used for analysis in a public GitHub repository that is updated and available for the research community.

      We support open source coding practices.

      (4) In page 6, the authors justify their choice of clock parameters for cells ingressing the PSM: "As ingressing cells do not appear to express segmentation clock genes (Mara et al. (2007)), the position at which cells ingress into the PSM can create challenges for clock patterning, as only in the 'off' phase of the clock will ingressing cells be in-phase with their neighbours."  However, there are several lines of evidence (in chick and mouse), that some oscillatory clock genes are already being expressed as early as in the gastrulation phase (so prior to PSM ingression) (Feitas et al, 2001 [10.1242/dev.128.24.5139]; Jouve et al, 2002 [10.1242/dev.129.5.1107]; Maia-Fernandes at al, 2024 [10.1371/journal.pone.0297853]) Question: Is this also true in zebrafish? (I.e. is there any recent experimental evidence that the clock genes are not expressed at ingression, since the paper cited to support this assumption is from 2007). If they are expressed in zebrafish (as they are in mouse and chick), then the cell addition should have random clock gene periods when they enter the PSM and not start all with a constant initial phase of zero. Probably this will not impact the results since the cells will also be out of phase with their neighbours when they "ingress", however, it will model more closely the biological scenario (and avoid such criticism).

      We thank the reviewer for their comments. While it is known that in zebrafish the clock begins oscillating during epiboly and before the onset of segmentation (Riedel-Kruse et al., 2007), to our knowledge no-one has examined whether posteriorly or laterally ingressing progenitor cells express clock genes prior to their ingression into the PSM, which occurs later in development than the first oscillations which give rise to the first somites. We have not found any published evidence of her/hes gene expression in the dorsal donor tissues or lateral tissues surrounding the PSM, however we acknowledge that this has not been actively studied before and our assumption relies on an absence of evidence, rather than evidence of absence. 

      However, we agree with the reviewer that one should include such an analysis for completeness, and we have now generated additional simulations where progenitor cells ingress with a random clock phase. This data is presented in figure 2 supplement 1 and mentioned in the main text on page 9.

      MINOR COMMENTS 

      (1) The citations are appropriate and cover the major labs that have published work related to this study (although with some overrepresentation of the lab that published the model used).

      We have cited the vast literature on somitogenesis to the best of our ability and do recognise that the work of the Oates lab appears prominently, but this is probably because their experimental data were originally used to parametrise the model in Uriu et al. 2021.

      (2) The text is clear, carefully written, and both the methods and the reasoning behind them are clearly explained and supported by proper citations.

      We are very glad to see that the reviewer found that the manuscript was clearly presented.

      (3) The figures are comprehensive, properly annotated, with explanatory self-contained legends. I have no comments regarding the presentation of the results.

      Thank you

      (4) Minor suggestions: 

      a. Page 26: In the Cell addition sub-section of the Methods section, correct all instances where the word domain is used, but subdomain should be used (for clarity and coherence with the description of the model, stated as having a single domain comprising 3 subdomains).

      We thank the reviewer for raising this, this is a good point. We have now corrected to ‘subdomain’ where appropriate.

      b. Page 32: Table 1. Parameter values used in our work, unless otherwise stated -> Suggestion: Add a column with the individual citations used for each parameter (to facilitate the confirmation of each corresponding reference).

      Thank you for the suggstion, we have now done this (see table 1 page 36).

      Reviewer #2 (Significance):

      GENERAL ASSESSMENT 

      This study uses a previously published model to simulate alternative scenarios of morphogenetic parameters to infer the potential independence (termed here modularity) between the segmentation clock and a set of morphogenetic processes, arguing that such modularity could allow the evolution of more flexible body plans, therefore partially explaining the variability in the number of segments observed in the vertebrates. This question is fundamental and relevant, yet still poorly researched. This work provides a comprehensive simulation with a model that tries to simplify the many morphogenetic processes described in the literature, reducing it to a few core fundamental processes that allow drawing the conclusions seeked. It provides theoretical insight to support a conceptual advance in the field of evolutionary vertebrate embryology.

      ADVANCE

      This study builds on a model recently published by Uriu et al. (eLife, 2021) that incorporates quantitative experimental data within a modeling framework including cell and tissue-level parameters, allowing the study of multiscale phenomena active during zebrafish embryo segmentation. Uriu's publication reports many relevant and often non-intuitive insights uncovered by the model, most notably the description of phase vortices formed by the synchronizing genetic oscillators interfering with the traveling-wave front pattern.  However, this model can be further explored to ask additional questions beyond those described in the original paper. A good example is the present study, which uses this mathematical framework to investigate the potential independence between two of the modeled processes, thereby extracting extra knowledge from it. Accordingly, the present study represents a step forward in the direction of using relevant theoretical frameworks to quantitatively explore the landscape of complex molecular hypotheses in silico, and with it shed some light on fundamental open questions or inform the design of future experiments in the lab.

      The study incorporates a wide range of existing literature on the developmental biology of vertebrates. It comprehensively cites prior work, such as the foundational studies by Cooke and Zeeman on the segmentation clock and the role of FGF signaling in PSM development as discussed by Gomez et al. The literature properly covers the breadth of knowledge in this field.

      AUDIENCE

      Target audience | This study is relevant for fundamental research in developmental biology, specifically targeting researchers who focus on early embryo development and morphogenesis from both experimental and theoretical perspectives. It is also relevant for evolutionary biologists investigating the genetic factors that influence vertebrate evolution, as well as to computational biologists and bioinformatics researchers studying developmental processes and embryology.

      Developmental researchers studying the segmentation clock in other vertebrate model organisms (namely mouse and chick), will find this publication especially valuable since it provides insights that can help them formulate new hypotheses to elucidate the molecular mechanisms of the clock (for example finding a set of evolutionarily divergent genes that might interfere with PSM length). Additionally, this study provides a set of cellular parameters that have yet to be measured in mouse and chick, therefore guiding the design of future experiments to measure them, allowing the simulation of the same model with sets of parameters from different vertebrate model organisms, therefore testing the robustness of the findings reported for zebrafish.

      Reviewer #3 (Evidence, reproducibility and clarity): 

      In this manuscript, Verd and colleagues explored how various biologically relevant factors influence the robustness of clock dynamics synchronization among neighboring cells within the context of somatogenesis, adapting a mathematical model presented by Urio et. al in 2021 in a similar context. Specifically they show that clock dynamics is robust to different biological mechanisms such as cell infusion, cellular motility, compaction-extension and cell-division. On the other hand , the length of Presomitic Mesoderm (PSM) and density of cells in it has a significant role in the robustness of clock dynamics. While the manuscript is well-written and provides clear descriptions of methods and technical details, it tends to be somewhat lengthy.

      Below are the comments I would like the authors to address:

      (1) The authors mention that "...the model is three dimensional and so can quantitatively recapture the rates of cell mixing that we observe in the PSM". I am not convinced with this justification of using a 3D model. None of the effects the authors explore in this manuscript requires a three dimensional model or full physical description of the cellular mechanics such as excluded volume interaction etc. A one-dimensional model characterized by cell position along the arclength of PSM and somatic region and segmentation clock phase θ can incorporate all the physics authors described in this manuscript as well as significantly computationally cheap allowing the authors to explore the effect of different parameters in greater detail.

      One of the main objectives of the work we present in this manuscript is to assess how the evolution of PSM morphogenesis affects, or does not affect, segment patterning. The PSM is a three-dimensional tissue with differing cell rearrangement dynamics along its anterior-posterior axis. In addition, PSM dimension, density, the rearrangement rate, and patterns of cell ingression all vary across vertebrate species, and they are functional, especially cell mixing as it promotes synchronisation and drives elongation. In order to answer questions on the modularity of somitogenesis we therefore consider it absolutely necessary to include a three-dimensional representation of the PSM that captures single cells and their movements. In addition, this will allow us, as Reviewer #2 also pointed out, to reparametrize our model using species-specific data as it becomes available. 

      While the reviewer is right in that lower dimensional representations would be computationally more efficient, and are generally more tractable, it would not be possible to represent cell mixing in one dimension, as this happens in three dimensions. One could perhaps encode the synchrony-promoting effect of cell mixing via some coupling function κ(x) that increases towards the posterior, however it is unclear what existing biological data one could use to parameterise this function or determine its form. Cell mixing can be modelled in a two-dimensional framework, however this cannot quantitatively recapture the rate of cell mixing observed in vivo, which is an advantage of this model. 

      Furthermore, it is unclear how one would simulate processes such as compactionextension using a one-dimensional model. The two different scenarios of cell ingression which we consider can also not be replicated in a one-dimensional model, as having a population of cells re-acquiring synchrony on the dorsal surface of the tissue while new material is added to the ventral side, creating asynchrony, is qualitatively different than a one-dimensional scenario where cells are introduced continuously along the spatial axis.

      (2) I am not sure about the justification for limiting the quantification of phase synchrony in a very limited (one cell diameter wide) region at one end of the somatic part (Page 33 below Fig. 9). From my understanding of the manuscript, the segments appear in significant length anterior to this region. Wouldn't an ensemble average of multiple such one cell diameter wide regions in the somatic region be a more accurate metric for quantifying synchrony?

      Indeed, such a metric (e.g. as that used by Uriu et al. to quantify synchrony along the xaxis) would be more accurate for determining synchrony within the PSM. However, as per the clock and wavefront model of somitogenesis, only synchrony at the very anterior of the PSM (or at the wavefront, equivalently) is functional for somitogenesis and thus evolution. Therefore, we restrict our analysis to the anterior-most region of the PSM. We now further justify this in the main text on page 9.

      (3) While studying the effect of cellular ingression, the authors study three discrete modes- random, DP and DP+LV and show that in the DP+LV mode the clock synchrony becomes affected. I would like the authors to explore this in a continuous fashion from a pure DP ingression to Pure LV ingression and intermediates.

      We thank the reviewer for this suggestion; this is a very interesting question. We are currently working on a related computational and experimental project to address the question of how PSM morphogenesis can change over evolutionary time to evolve the different modes that we see across species. As part of this work, we are running precisely the simulations suggested by the reviewer to find regions of parameter space in which all the relevant morphogenetic processes can freely evolve.  While interesting, this work is however outside the scope of the current manuscript.

      (4) While studying the effect of length and density of cells in PSM on cellular synchrony, the authors restrict to 3 values of density and 6 values of PSM length keeping the other parameter constant. I would be interested to see a phase diagram similar to Fig. 7 in the two-dimensional parameter space of L and ρ0. I am curious if a scaling relation exists for the parameter values that partition the parameter space with and without synchrony.

      We thank the reviewer for their suggestion and agree that this would constitute an interesting addition to the manuscript. We have now generated these data, which are shown in figure 4 supplement 5 and mentioned on page 13. We see no clear relationship between these two variables when co-varying in the presence of random ingression. 

      (5) Both in the abstract and introduction, the authors discuss at a great length about the variability in the number of segments. I am curious how the number and width of the segments observed depend on different parameters related to cellular mechanics and the segmentation clock ?

      We thank the reviewer for this question. It was not clear to us if this was something the reviewer wants us to address in the study’s background and introduction, or an analysis we should include in the results. Therefore, we have responded to both comprehensively below:

      The prevailing conceptual framework for understanding this is the clock and wavefront model (Cooke and Zeeman, 1976), which posits that the somite length is inversely proportional to the frequency of the clock relative to the speed of the wavefront, and that the total number of segments is the relative frequency multiplied by the total duration of somitogenesis.

      Experimentally we know that the frequency is determined in part by the coupling strength (Liao, Jorg, and Oates, 2016), and from comparative embryological studies (Gomez et al., 2008; Steventon et al., 2016) we know that changes in the elongation dynamics of the PSM correlate with changes in somite number, presumably by altering the total duration of somitogenesis (Gomez et al., 2009). These changes in elongation are thought to be driven by the changes in cell and tissue mechanics we test in our manuscript. 

      Within our model, we cannot in general predict how the number of segments responds to changes in either clock parameters or cell mechanical parameters, as we lack understanding of what causes somitogenesis to cease; this is thus not encoded in our model and segmentation can in principle proceed indefinitely. Therefore, we have not performed this analysis.

      Similarly, we have not included an analysis of somite length. This is for two reasons: 1) as per the clock and wavefront model, the frequency at the PSM anterior (which we analyse) is equivalent to this measurement, as we assume (in general) the wavefront ($x = x_{a}$) is inertial. 2) the length of the nascent somite is not thought to be of much relevance to the adult phenotype, and by extension evolution. Somites undergo cell division and growth soon after their patterning by the segmentation clock, therefore their final size does not majorly depend on the dynamics of the segmentation clock. Rather, the main function of the clock is to control their number (and polarity).

      (6) The authors assume that the phase dynamics of the chemical network may be described by an oscillator with constant frequency. For the completeness of the manuscript, the author should discuss in detail, for which chemical networks this is a good assumption.

      We thank the reviewer for their suggestion and now justify this assumption in the methods on page 31. 

      Such an assumption is appropriate for the segmentation clock, as the clock in the posterior of the PSM is thought to oscillate with a constant frequency, at least for the majority of somitogenesis although the frequency of somite formation slows towards the end of this process in zebrafish (Giudicelli et al., 2007, PLoS Biol.). In addition, PSM cells isolated and cultured in the presence of FGF (thus replicating the signalling environment of the posterior PSM) will continue to exhibit her1 oscillations with an apparently constant frequency (Webb et al., 2016). 

      We note that such formulations are widely used within the segmentation clock literature (e.g. Riedel-Kruse et al., 2007, Morelli et al., 2009).

      (7) Figure 3 and the associated text shows no effect of the cellular motility profile in the synchrony of the segmentation clock. This may be moved to the supplementary considering the length of this manuscript.

      Thank you for the suggestion. However, we would argue that the lack of effect is a crucial result when discussing modularity. Reviewer #2 agrees with this assessment.

      Reviewer #3 (Significance): 

      The manuscript answers some important questions in the synchrony of segmentation clock in the vertebrates utilizing a model published earlier. However, the presented result is incomplete in some aspects (points 2 to 5 of section A) and that could be overcome by a more detailed analysis using a simpler one dimensional (point 1 of section A). I believe this manuscript could be of interest to an intersecting audience of developmental biologists, systems biologists, and physicists/engineers interested in dynamical systems.

    1. Reviewer #2 (Public review):

      Summary:

      The manuscript reports an fMRI study looking at whether there is animacy organization in a non-primate, mammal, the domestic dog, that is similar to that observed in humans and non-human primates (NHPs). A simple experiment was carried out with four kinds of stimulus videos (dogs, humans, cats, and cars), and univariate contrasts and RSA searchlight analysis was performed. Previous studies have looked at this question or closely associated questions (e.g. whether there is face selectivity in dogs). The import of the present study is that it looks at multiple types of animate objects, dogs, humans, and cats, and tests whether there was overlapping/similar topography (or magnitude) of responses when these stimuli were compared to the inanimate reference class of cars. The main finding was of some selectivity for animacy though this was primarily driven by the dog stimuli, which did overlap with the other animate stimulus types, but far less so than in humans.

      Strengths:

      I believe that this is an interesting study in so far as it builds on other recent work looking at category-selectivity in the domestic dog. Given the limited number of such studies, I think it is a natural step to consider a number of different animate stimuli and look at their overlap. While some of the results were not wholly surprising (e.g. dog brains respond more selectively for dogs than humans or cats), that does not take away from their novelty, such as it is. The findings of this study are useful as a point of comparison with other recent work on the organization of high-level visual function in the brain of the domestic dog.

      Weaknesses:

      (1) One challenge for all studies like this is a lack of clarity when we say there is organization for "animacy" in the human and NHP brains. The challenge is by no means unique to the present study, but I do think it brings up two more specific topics.

      First, one property associated with animate things is "capable of self-movement". While cognitively we know that cars require a driver, and are otherwise inanimate, can we really assume that dogs think of cars in the same way? After all, just think of some dogs that chase cars. If dogs represent moving cars as another kind of self-moving thing, then it is not clear we can say from this study that we have a contrast between animate vs inanimate. This would not mean that there are no real differences in neural organization being found. It was unclear whether all or some of the car videos showed them moving. But if many/most do, then I think this is a concern.

      Second, there is quite a lot of potential complexity in the human case that is worth considering when interpreting the results of this study. In the human case, some evidence suggests that animacy may be more of a continuum (Sha et al. 2015), which may reflect taxonomy (Connolly et al. 2012, 2016). However moving videos seem to be dominated more by signals relevant to threat or predation relative to taxonomy (Nastase et al. 2017). Some evidence suggests that this purported taxonomic organization might be driven by gradation in representing faces and bodies of animals based on their relative similarity to humans (Ritchie et al. 2021). Also, it may be that animacy organization reflects a number of (partially correlated) dimensions (Thorat et al. 2019, Jozwik et al. 2022). One may wonder whether the regions of (partial) overlap in animate responses in the dog brain might have some of these properties as well (or not).

      (2) It is stated that previous studies provide evidence that the dog brain shows selectivity to "certain aspects of animacy". One of these already looked at selectivity for dog and human faces and bodies and identified similar regions of activity (Boch et al. 2023). An earlier study by Dilks et al. (2015), not cited in the present work (as far as I can tell), also used dynamic stimuli and did not suffer from the above limitations in choosing inanimate stimuli (e.g. using toy and scene objects for inanimate stimuli). But it only included human faces as the dynamic animate stimulus. So, as far as stimulus design, it seems the import of the present study is that it included a *third* animate stimulus (cats) and that the stimuli were dynamic.

      (3) I am concerned that the univariate results, especially those depicted in Figure 3B, include double dipping (Kriegesorte et al. 2009). The analysis uses the response peak for the A > iA contrast to then look at the magnitude of the D, H, C vs iA contrasts. This means the same data is being used for feature selection and then to estimate the responses. So, the estimates are going to be inflated. For example, the high magnitudes for the three animate stimuli above the inanimate stimuli are going to inherently be inflated by this analysis and cannot be taken at face value. I have the same concern with the selectivity preference results in Figure 3E.

      I think the authors have two options here. Either they drop these analyses entirely (so that the total set of analyses really mirrors those in Figure 4), or they modify them to address this concern. I think this could be done in one of two ways. One would be to do a within-subject standard split-half analysis and use one-half of the data for feature selection and the other for magnitude estimation. The other would be to do a between-subject design of some kind, like using one subject for magnitude estimation based on an ROI defined using the data for the other subjects.

      (4) There are two concerns with how the overlap analyses were carried out. First, as typically carried out to look at overlap in humans, the proportion is of overlapping results of the contrasts of interest, e.g, for face and body selectivity overlap (Schwarlose et al. 2006), hand and tool overlap (Bracci et al. 2012), or more recently, tool and food overlap (Ritchie et al. 2024). There are a number of ways of then calculating the overlap, with their own strengths and weaknesses (see Tarr et al. 2007). Of these, I think the Jaccard index is the most intuitive, which is just the intersection of two sets as a proportion of their union. So, for example, the N of overlapping D > iA and H > iA active voxels is divided by the total number of unique active voxels for the two contrasts. Such an overlap analysis is more standard and interpretable relative to previous findings. I would strongly encourage the authors to carry out such an analysis or use a similar metric of overlap, in place of what they have currently performed (to the extent the analysis makes sense to me).

      Second, the results summarized in Figure 3A suggest multiple distinct regions of animacy selectivity. Other studies have also identified similar networks of regions (e.g. Boch et al. 2023). These regions may serve different functions, but the overlap analysis does not tell us whether there is overlap in some of these portions of the cortex and not in others. The overlap is only looked at in a very general sense. There may be more overlap locally in some portions of the cortex and not in others.

      (5) Two comments about the RSA analyses. First, I am not quite sure why the authors used HMAX rather than layers of a standardly trained ImageNet deep convolutional neural network. This strikes me also as a missed opportunity since many labs have looked at whether later layers of DNNs trained on object categorization show similar dissimilarity structures as category-selective regions in humans and NHPs. In so far as cross-species comparisons are the motivation here, it would be genuinely interesting to see what would happen if one did a correlation searchlight with the dog brain and layers of a DNN, a la Cichy et al. (2016).

      Second, from the text is hard to tell what the models for the class- and category-boundary effects were. Are there RDMs that can be depicted here? I am very familiar with RSA searchlight and I found the description of the methods to be rather opaque. The same point about overlap earlier regarding the univariate results also applies to the RSA results. Also, this is again a reason to potentially compare DNN RDMs to both the categorical models and the brains of both species.

      (6) There has been emphasis of late on the role of face and body selective regions and social cognition (Pitcher and Ungerleider, 2021, Puce, 2024), and also on whether these regions are more specialized for representing whole bodies/persons (Hu et al. 2020, Taubert, et al. 2022). It may be that the supposed animacy organization is more about how we socialize and interact with other organisms than anything about animacy as such (see again the earlier comments about animacy, taxonomy, and threat/predation). The result, of a great deal of selectivity for dogs, some for humans, and little for cats, seems to readily make sense if we assume it is driven by the social value of the three animate objects that are presented. This might be something worth reflecting on in relation to the present findings.

    2. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary

      Farkas and colleagues conducted a comparative neuroimaging study with domestic dogs and humans to explore whether social perception in both species is underpinned by an analogous distinction between animate and inanimate entities an established functional organizing principle in the primate and human brain. Presenting domestic dogs and humans with clips of three animate classes (dogs, humans, cats) and one inanimate control (cars), the authors also set out to compare how dogs and humans perceive their own vs other species. Both research questions have been previously studied in dogs, but the authors used novel dynamic stimuli and added animate and inanimate classes, which have not been investigated before (i.e., cats and cars). Combining univariate and multivariate analysis approaches, they identified functionally analogous areas in the dog and human occipitotemporal cortex involved in the perception of animate entities, largely replicating previous observations. This further emphasizes a potentially shared functional organizing principle of social perception in the two species. The authors also describe between- species divergencies in the perception of the different animate classes, arguing for a less generalized perception of animate entities in dogs, but this conclusion is not convincingly supported by the applied analyses and reported findings.

      Strengths

      Domestic dogs represent a compelling model species to study the neural bases of social perception and potentially shared functional organizing principles with humans and primates. The field of comparative neuroimaging with dogs is still young, with a growing but still small number of studies, and the present study exemplifies the reproducibility of previous research. Using dynamic instead of static stimuli and adding new stimuli classes, Farkas and colleagues successfully replicated and expanded previous findings, adding to the growing body of evidence that social perception is underpinned by a shared functional organizing principle in the dog and human occipito-temporal cortex.

      Weaknesses

      The study design is imbalanced, with only one category of inanimate objects vs. three animate entities. Moreover, based on the example videos, it appears that the animate stimuli also differed in the complexity of the content from the car stimuli, with often multiple agents interacting or performing goal-directed actions. Moreover, while dogs are familiar with cars, they are definitely of lower relevance and interest to them than the animate stimuli. Thus, to a certain extent, the results might also reflect differences in attention towards/salience of the stimuli.

      We agree with the Reviewer and were aware that using only one class of inanimate objects but three classes of animate entities, along with the differences in complexity and relevance between the animate and the inanimate stimuli potentially elicited more attention to the inanimate condition and may have thus introduced a confound. We are revising the related limitation in the discussion to acknowledge this and to emphasize why we believe these differences do not compromise our main findings.

      The methods section and rationale behind the chosen approaches were often difficult to follow and lacked a lot of information, which makes it difficult to judge the evidence and the drawn conclusions, and it weakens the potential for reproducibility of this work. For example, for many preprocessing and analysis steps, parameters were missing or descriptions of the tools used, no information on anatomical masks and atlas used in humans was provided, and it is often not clear if the authors are referring to the univariate or multivariate analysis.

      We acknowledge the concerns regarding the clarity and completeness of the methods section and are significantly revising the descriptions of the methods. Of note, in humans, the Harvard-Oxford Cortical Structural Atlas (Frazier et al., 2005; Makris et al., 2006; Desikan et al., 2006; Goldstein et al., 2007), implemented within the FSL software package, was used for anatomical masks, while the Automated Anatomical Labeling atlas (Tzourio-Mazoyer et al., 2002) was used for assigning labels.

      In regard to the chosen approaches and rationale, the authors generally binarize a lot of rich information. Instead of directly testing potential differences in the neural representations of the different animate entities, they binarize dissimilarity maps for, e.g. animate entity > inanimate cars and then calculate the overlap between the maps.

      We thank the Reviewer for these comments and ideas. We also appreciate the second Reviewer for their related concerns and suggestions about the overlap calculation. Since the neural processing of different animate entities in the dog brain is largely unexplored, in some of our analyses we aimed to provide a straightforward and directly comparable characterization of animacy perception in the two species. We believe that a measure of how overlapping the neural representations of different animate classes are in the dog vs. the human visual cortex is a simple but meaningful and insightful characterization of how animacy perception is structured in the two species, despite the lack of spatial detail. Our decision to use binarization was based on these considerations. In response to this Reviewer’s request for providing richer information, in our revised manuscript, we will present more details and additional non-binarized calculations. Specifically, we are going to use nonbinarized data to present the response profiles of a broad, anatomically defined set of regions that have been related in other works to visual functions, to thus show where there is significant difference and overlap between the neural responses for the three animate classes in each species.

      The comparison of the overlap of these three maps between species is also problematic, considering that the human RSA was constricted to the occipital and temporal cortex (there is now information on how they defined it) vs. whole-brain in dogs.

      We thank this Reviewer for raising yet another relevant point about overlap calculation. We note that the overlap calculation for univariate results used the visually responsive cortex in both dogs and humans. The decision to restrict the multivariate analysis to the occipital and temporal lobes in humans, where the visual areas are, was to reduce computational load. Since RSA in dogs yielded significant voxels almost exclusively in the occipital and temporal cortices, we believe this decision did not introduce major bias in our results. This concern will also be discussed in our revised submission.

      Of note, in the category- and class-boundary test, as for the other multivariate tests, the occipital and temporal cortex of humans was delineated based on the MNI atlas.

      Considering that the stimuli do differ based on low-level visual properties (just not significantly within a run), the RSA would also allow the authors to directly test if some of the (dis)similarities might be driven by low-level visual features like they, e.g. did with the early visual cortex model. I do think RSA is generally an excellent choice to investigate the neural representation of animate (and inanimate) stimuli, but the authors should apply it more appropriately and use its full potential.

      We thank the Reviewer for this suggestion. While this study did not aim to investigate the correlation between low-level visual features and animacy, the data is available, and the suggested analysis can be conducted in the future. This issue will also be discussed in our revised submission.

      The authors localized some of the "animate areas" also with the early visual cortex model (e.g. ectomarginal gyrus, mid suprasylvian); in humans, it only included the known early visual cortex - what does this mean for the animate areas in dogs?

      We thank the Reviewer for raising this point. Although the labels are the same, both EMG and mSSG are relatively large gyri, and the clusters revealed by each of the two analyses hardly overlap, with peak coordinates more than 12 mm apart for R EMG, and in different hemispheres for mSSG (but more than 11 mm apart even if projected on the same hemisphere). We will detail the differences and the overlaps in the revised submission.

      The results section also lacks information and statistical evidence; for example, for the univariate region-of-interest (ROI) analysis (called response profiles) comparing activation strength towards each stimulus type, it is not reported if comparisons were significant or not, but the authors state they conducted t-tests. The authors describe that they created spheres on all peaks reported for the contrast animate > inanimate, but they only report results for the mid suprasylvian and occipital gyrus (e.g. caudal suprasylvian gyrus is missing).

      We thank this Reviewer for catching these errors. The missing statistics will be provided in the revised manuscript. Also, we mistakenly named the peak in caudal suprasylvian gyrus occipital gyrus on the figure depicting the response profiles. This will also be corrected.

      Furthermore, considering that the ROIs were chosen based on the contrast animate > inanimate stimuli, activation strength should only be compared between animate entities (i.e., dogs, humans, cats), while cars should not be reported (as this would be double dipping, after selecting voxels showing lower activation for that category).

      We thank both Reviewers for raising this relevant point about potential double dipping. The aim of this analysis was to describe the relationship between the neural response elicited by the three animate stimulus classes, to show that the animacy-sensitive peaks are not the results of the standalone greater response to a single animate class. We conducted t-tests only to assess significant difference between these three animate conditions and no stats were performed or reported for any animate class vs. inanimate comparisons in these ROIs. In addition to providing the missing t-tests (comparing animate classes), we will present response profiles and corresponding statistics for a broad set of additional, independent ROIs, defined either anatomically or functionally by other studies in the revised version.

      The descriptive data in Figure 3B (pending statistical evidence) suggests there were no strong differences in activation for the three species in dog and human animate areas. Thus, the ROI analysis appears to contradict findings from the binary analysis approach to investigate species preference, but the authors only discuss the results of the latter in support of their narrative for conspecific preference in dogs and do not discuss research from other labs investigating own-species preference.

      Studying conspecific-preference was not the primary aim of this study. We only used our data to characterize the animate-sensitive regions from this aspect. The species-preference test provides an overall characterization of the entire animate-sensitive region, revealing a higher number of voxels with a maximal response to conspecific than other stimuli in dogs (and a similar tendency in humans), confirming previous evidence on neural conspecific preference in visual areas in both species. The response profiles presented so far describe only the ROIs around the main animate-sensitive peaks and, as the Reviewer points out, in most cases reveal no significant conspecific bias. We believe there is no contradiction here: the entire animate-sensitive region may weakly but still be conspecific-preferring, whereas the main animate-sensitive peaks are not; the centers of conspecific preference may be located elsewhere in the visual cortex and may be supported by mechanisms other than animacy-sensitivity. In the revised manuscript, we will elaborate more on this. Additionally, in response to other comments, and for a better and more coherent characterization of species preference (and animacy sensitivity) across the visual cortex, we will present response profiles for other, independently defined regions and explore conspecific-sensitivity in those additional regions as well. Furthermore, we will discuss related own-species preference literature in greater detail.

      The authors also unnecessarily exaggerate novelty claims. Animate vs inanimate and own vs other species perceptions have both been investigated before in dogs (and humans), so any claims in that direction seem unsubstantiated - and also not needed, as novelty itself is not a sign of quality; what is novel, and a sign of theoretical advance besides the novelty, are as said the conceptual extension and replication of previous work.

      We agree with this Reviewer regarding novelty claims in general, and we confirm that we had no intention to overstate the uniqueness of our results. We also did not mean to imply that this work would be the first one on animacy perception in dogs, which it obviously is not. But we understand that we could have been more explicit presenting our work as a conceptual extension and replication of previous works, and we are revising the wording of the discussion from this aspect.

      Overall, more analyses and appropriate tests are needed to support the conclusions drawn by the authors, as well as a more comprehensive discussion of all findings.

      We are thankful for all comments. We will revise the methods section to provide sufficient detail and ensure replicability; conduct additional analyses as detailed above; and provide a more comprehensive discussion of all findings.

      Reviewer #2 (Public review):

      Summary:

      The manuscript reports an fMRI study looking at whether there is animacy organization in a non-primate, mammal, the domestic dog, that is similar to that observed in humans and non-human primates (NHPs). A simple experiment was carried out with four kinds of stimulus videos (dogs, humans, cats, and cars), and univariate contrasts and RSA searchlight analysis was performed. Previous studies have looked at this question or closely associated questions (e.g. whether there is face selectivity in dogs). The import of the present study is that it looks at multiple types of animate objects, dogs, humans, and cats, and tests whether there was overlapping/similar topography (or magnitude) of responses when these stimuli were compared to the inanimate reference class of cars. The main finding was of some selectivity for animacy though this was primarily driven by the dog stimuli, which did overlap with the other animate stimulus types, but far less so than in humans.

      Strengths:

      I believe that this is an interesting study in so far as it builds on other recent work looking at category-selectivity in the domestic dog. Given the limited number of such studies, I think it is a natural step to consider a number of different animate stimuli and look at their overlap. While some of the results were not wholly surprising (e.g. dog brains respond more selectively for dogs than humans or cats), that does not take away from their novelty, such as it is. The findings of this study are useful as a point of comparison with other recent work on the organization of high-level visual function in the brain of the domestic dog.

      Weaknesses:

      (1) One challenge for all studies like this is a lack of clarity when we say there is organization for "animacy" in the human and NHP brains. The challenge is by no means unique to the present study, but I do think it brings up two more specific topics.

      First, one property associated with animate things is "capable of self-movement". While cognitively we know that cars require a driver, and are otherwise inanimate, can we really assume that dogs think of cars in the same way? After all, just think of some dogs that chase cars. If dogs represent moving cars as another kind of selfmoving thing, then it is not clear we can say from this study that we have a contrast between animate vs inanimate. This would not mean that there are no real differences in neural organization being found.

      It was unclear whether all or some of the car videos showed them moving. But if many/most do, then I think this is a concern.

      We thank this Reviewer for raising this relevant point about the potential animacy of cars for dogs and its implication for our results. Of note, two-thirds of our car stimuli showed a car moving (slow, accelerating, or fast). We acknowledge that these stimuli contained motionbased animacy cues, and in this regard, there was no clear difference between our animate and inanimate conditions, and possibly between some of the representations they elicited. However, our animate and inanimate stimuli differed in other key factors accounting for animacy organization, such as visual features including the presence of faces, bodies, body parts, postures, and certain aspects of biological motion. So we believe that this limitation does not compromise our main conclusions. We will elaborate on this point further in the revised discussion, also considering how dogs’ differential behavioral responses to cars and animate entities may provide additional insights in this regard.

      Second, there is quite a lot of potential complexity in the human case that is worth considering when interpreting the results of this study. In the human case, some evidence suggests that animacy may be more of a continuum (Sha et al. 2015), which may reflect taxonomy (Connolly et al. 2012, 2016). However moving videos seem to be dominated more by signals relevant to threat or predation relative to taxonomy (Nastase et al. 2017). Some evidence suggests that this purported taxonomic organization might be driven by gradation in representing faces and bodies of animals based on their relative similarity to humans (Ritchie et al. 2021). Also, it may be that animacy organization reflects a number of (partially correlated) dimensions (Thorat et al. 2019, Jozwik et al. 2022). One may wonder whether the regions of (partial) overlap in animate responses in the dog brain might have some of these properties as well (or not).

      We agree that it would be interesting to dissect which animacy-related factor(s) contribute to the observed animacy sensitivity in different regions, and although this was not the original aim of the study, we agree that we could have made better use of the variation in our stimuli to discuss this aspect. Specifically, some animacy features are shared by all three animate stimulus classes, namely the presence of biological motions, faces, and bodies. In contrast, animate classes differed in some other aspects, for example in how dogs perceived dogs, humans, and cats as social agents and in their potential behavioral goals towards them. It can therefore be argued that regions with two- and especially three-way overlapping activations are more probably involved in processing biological motion, face and body aspects, and non-overlapping ones the social agency- and behavioural goal-related aspects. In line with this, the shared animacy features are indeed ones that have been reported to be central in human animacy representation and that may have made the overlaps in human brain responses greater. We will provide a more detailed discussion of the results from this viewpoint in the revised manuscript.

      (2) It is stated that previous studies provide evidence that the dog brain shows selectivity to "certain aspects of animacy". One of these already looked at selectivity for dog and human faces and bodies and identified similar regions of activity (Boch et al. 2023). An earlier study by Dilks et al. (2015), not cited in the present work (as far as I can tell), also used dynamic stimuli and did not suffer from the above limitations in choosing inanimate stimuli (e.g. using toy and scene objects for inanimate stimuli). But it only included human faces as the dynamic animate stimulus. So, as far as stimulus design, it seems the import of the present study is that it included a *third* animate stimulus (cats) and that the stimuli were dynamic.

      We agree with this Reviewer that the findings of Dilks et al. (2015) are relevant to our study and have therefore cited them. However, the citation itself was imprecise and will be corrected in the revised manuscript.

      (3) I am concerned that the univariate results, especially those depicted in Figure 3B, include double dipping (Kriegesorte et al. 2009). The analysis uses the response peak for the A > iA contrast to then look at the magnitude of the D, H, C vs iA contrasts. This means the same data is being used for feature selection and then to estimate the responses. So, the estimates are going to be inflated. For example, the high magnitudes for the three animate stimuli above the inanimate stimuli are going to inherently be inflated by this analysis and cannot be taken at face value. I have the same concern with the selectivity preference results in Figure 3E.

      I think the authors have two options here. Either they drop these analyses entirely (so that the total set of analyses really mirrors those in Figure 4), or they modify them to address this concern. I think this could be done in one of two ways. One would be to do a within- subject standard split-half analysis and use one-half of the data for feature selection and the other for magnitude estimation. The other would be to do a between-subject design of some kind, like using one subject for magnitude estimation based on an ROI defined using the data for the other subjects.

      We thank both Reviewers again for raising this important point about potential double dipping. We also thank this Reviewer for specific suggestions for split-half analyses – we agree that, had our original analyses involved double dipping, such a modification would be necessary. But, as we explained in our response above, this was not the case. Indeed, whereas we do visualize all four conditions in Fig. 3B, we only conducted t-tests to assess differences between the three animate conditions (the corresponding stats have been missing from the original manuscript but will be added during revision). So, importantly, we did not evaluate the magnitude of the D, H, C vs iA contrasts in any of the ROIs defined by animate-sensitive peaks; therefore, we believe that these analyses do not involve double dipping. This holds for the species preference results in Fig. 3E as well. We will clarify this in the revised manuscript. Of note, in response to a request by the other reviewer and to provide richer information about the univariate results, we will also provide response profiles and corresponding stats for a broad set of additional ROIs, defined either anatomically or functionally by other studies (e.g., Boch et al., 2023).

      (4) There are two concerns with how the overlap analyses were carried out. First, as typically carried out to look at overlap in humans, the proportion is of overlapping results of the contrasts of interest, e.g, for face and body selectivity overlap (Schwarlose et al. 2006), hand and tool overlap (Bracci et al. 2012), or more recently, tool and food overlap (Ritchie et al. 2024). There are a number of ways of then calculating the overlap, with their own strengths and weaknesses (see Tarr et al. 2007). Of these, I think the Jaccard index is the most intuitive, which is just the intersection of two sets as a proportion of their union. So, for example, the N of overlapping D > iA and H > iA active voxels is divided by the total number of unique active voxels for the two contrasts. Such an overlap analysis is more standard and interpretable relative to previous findings. I would strongly encourage the authors to carry out such an analysis or use a similar metric of overlap, in place of what they have currently performed (to the extent the analysis makes sense to me).

      We agree with this Reviewer that the Jaccard index is an intuitive and straightforward overlap measure. Importantly, for our overlap calculations we already use this measure (and a very similar one) – but we acknowledge that this was not clear from the original description. Specifically, for the multivariate overlap test, we used the Jaccard index exactly as described by this Reviewer. For the univariate overlap test, we use a very similar measure, with the only difference that there, to reference the search space, the intersection of specific animate-inanimate contrasts was divided by the total voxel number of animate-sensitive areas (which is highly similar to the union of the specific animate-inanimate contrasts). In the revised submission we will provide a more detailed explanation of the overlap calculations, making it explicit that we used the Jaccard index (and a variant of it).

      Second, the results summarized in Figure 3A suggest multiple distinct regions of animacy selectivity. Other studies have also identified similar networks of regions (e.g. Boch et al. 2023). These regions may serve different functions, but the overlap analysis does not tell us whether there is overlap in some of these portions of the cortex and not in others. The overlap is only looked at in a very general sense. There may be more overlap locally in some portions of the cortex and not in others.

      We thank this Reviewer for this comment, we agree that adding spatial specificity to these results will improve the manuscript. Therefore, during revision, we will assess the anatomical distribution of the overlap results, making use of a broad set of ROIs potentially relevant for animacy perception, defined either anatomically or functionally by other studies (e.g., Boch et al., 2023 for dogs).

      (5) Two comments about the RSA analyses. First, I am not quite sure why the authors used HMAX rather than layers of a standardly trained ImageNet deep convolutional neural network. This strikes me also as a missed opportunity since many labs have looked at whether later layers of DNNs trained on object categorization show similar dissimilarity structures as category-selective regions in humans and NHPs. In so far as cross-species comparisons are the motivation here, it would be genuinely interesting to see what would happen if one did a correlation searchlight with the dog brain and layers of a DNN, a la Cichy et al. (2016).

      We thank the Reviewer for this comment and suggestion. At the start of the project, HMAX was the most feasible model to implement given our time and expertise constrains. Additionally, the biologically motivated HMAX was also an appropriate choice, as it simulates the selective tuning of neurons in the primary visual cortex (V1) of primates, which is considered homologous with V1 in carnivores (Boch et al., 2024).

      Although we agree that using DNNs have recently been extensively and successfully used to explore object representations and could provide valuable additional insights for dogs’ visual perception as well, we believe that adding a large set of additional analyses would stretch the frames of this manuscript, disproportionately shifting its focus from our original research question. Also, our experiment, designed with a different, more specific aim in mind, did not provide a large enough stimulus variety of animate stimuli for a general comparison of the cortical hierarchy underlying object representations in dog and human brains and thus our data are not an optimal starting point for such extensive explorations. Having said that, we are thankful for this Reviewer for the idea and will consider using a DNN to uncover dog’ visual cortical hierarchy in future studies with a better suited stimulus set. Furthermore, in accordance with eLife’s data-sharing policies, we will make the current dataset publicly available so further hypothesis and models can be tested.

      Second, from the text is hard to tell what the models for the class- and categoryboundary effects were. Are there RDMs that can be depicted here? I am very familiar with RSA searchlight and I found the description of the methods to be rather opaque. The same point about overlap earlier regarding the univariate results also applies to the RSA results. Also, this is again a reason to potentially compare DNN RDMs to both the categorical models and the brains of both species.

      In the revised manuscript we will provide a more detailed explanation of the methods used to determine class- and category-boundary effects. In short, the analysis we performed here followed Kriegeskorte et al. (2008), and the searchlight test looked for regions in which between-class/category differences were greater than within-class/category differences. We will also include RDMs. Additionally, we will provide anatomical details for the overlap results for RSA, just as for the univariate results, using the same independently defined broad set of ROIs, defined either anatomically or functionally by other studies (e.g., Boch et al., 2023 for dogs).

      (6) There has been emphasis of late on the role of face and body selective regions and social cognition (Pitcher and Ungerleider, 2021, Puce, 2024), and also on whether these regions are more specialized for representing whole bodies/persons (Hu et al. 2020, Taubert, et al. 2022). It may be that the supposed animacy organization is more about how we socialize and interact with other organisms than anything about animacy as such (see again the earlier comments about animacy, taxonomy, and threat/predation). The result, of a great deal of selectivity for dogs, some for humans, and little for cats, seems to readily make sense if we assume it is driven by the social value of the three animate objects that are presented. This might be something worth reflecting on in relation to the present findings.

      We thank the Reviewer for this suggestion. The original manuscript already discussed how motion-related animacy cues involved in social cognition may explain that animacysensitive regions reported in our study extend beyond those reported previously and also the role of biological motion in the observed across-species differences. This discussion of the role of visual diagnostic features and features that involved in perceiving social agents will be extended in the revised discussion, also in response to the first comment of this Reviewer, to reflect on how social cognition-related animacy cues may have affected our results in dogs.

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      Reply to the reviewers

      *Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary: This manuscript authored by Kakui and colleagues aims to understand on how mitotic chromosomes get their characteristic, condensed X shape, which is functionally important to ensure faithful chromosome segregation and genome inheritance to both daughter cells. The authors focus on the condensin complex, a central player in chromosome condensation. They ask whether it condenses chromosomes through a now broadly popular "loop-extrusion" mechanism, in which a chromatin-bound condensin complex reels chromatin into loops until it dissociates or encounters a roadblock on the polymer (another condensin or some other protein complex), or through an alternative, "diffusion-capture" mechanism, in which a chromatin-bound condensin complex forms loops by encountering another chromatin-bound condensin until they dissociate from DNA (or from each other.) The authors measured the progressive changes in the shape of mitotic chromosomes by taking samples at given time points from synchronized and mitotically arrested cells and found that while all chromosomes become more condensed and shorter, their width correlated with the length of the chromosome arms. They also observed that chromosome compaction/shortening evolves on a time scale much longer than the interval between the onset of chromosome condensation and the start of chromosome segregation, suggesting that chromatin condensation does not reach its steady-state during an unperturbed mitosis. The observed width-length correlation could be described by a power law with an exponent that increases with the time (i.e. chromosome condensation). The authors also performed polymer simulations of the diffusion-capture mechanism and found that the simulations semi-quantitatively recapitulate their experimental observations. Major Comments My most substantial comments focus on somewhat technical details of the image analysis approaches taken and the polymer models employed. However, as all reported data are derived from those details, I feel it is crucial to address them. *

      We thank the reviewer for their suggestions on how to improve our image analysis and polymer modelling experiments. We are keen to develop both aspects of our manuscript with additional experiments as detailed below.

      1. * Definition/measurement of chromatin arms width and length. The approach taken to manually threshold an "arm" object and then fitting it with a same-area ellipse is not an ideal approach to gauge length and width of the arm, for the following reasons: (1) An ellipse appears to do a poor job approximating many of the objects that we see in the zoom-in insets of Fig.1. Importantly, for somewhat bent shapes we see in the insets it likely strongly underestimates the length of the arms; this approach also presents potential problems for measuring width as well (see 2 and 3 here). (2) One concern is that, due to the diffraction limit, a cylindrical fluorescent object could appear somewhat wider at the mid-length than the real underlying cylinder or the poles; this effect could become more pronounced as the object gets brighter and shorter. (3) Forcing the fit to an ellipse to objects that are not truly rod-shaped can drive an overestimation of the width of the object, and I suspect that this effect also might correlate with the length and brightness of the object. (4) Given 1-3 above, I think the approach the authors used for the first two time points, while not perfect, is better suited and likely more robust while avoiding these caveats. Moreover, why the authors cannot use this same approach (but just for each arm separately) for the later (30+ min) time points as they used for first two is unclear. This point is underscored by the observation that there is a drastic difference in the results between the first two and all subsequent points. When the authors compared the two approaches at the 30 min time point (where width-length dependence is still weak) in different cell lines they did indeed see different results (Fig. S2), although they concluded that the difference was acceptable. * While the manuscript was under review, we have developed an improved pipeline to measure chromosome widths. As suggested by the reviewer, this approach is based on the method used for the first two time points. An additional improvement allows us to take automated measurements along the entire chromosome arm length, instead of being restricted to straight segments. We propose to use the improved algorithm to repeat the measurements at later time points.

      * Along these lines, the difference between short and long arms for the chromosome in the insets of Fig.1 are quite subtle, except maybe at 180 and 240 min. On a related note, it might be informative to compare data for the two sister chromatid arms (as the underlying polymer has the same length) long vs long and short vs short and long vs short to help establish the robustness of the approach. *

      The chromosome arm width differences are clear and measurable. We will select insets that illustrate the arm width differences in a more representative way, and we will furthermore conduct the suggested analyses on subsets of chromosome arms to test the robustness of our approach.

      * Regarding the power-law distribution, it is hard to judge based on the presented data whether it is a really good description of the data or not. In Fig.1c, the points for a given time can barely be distinguished, while in Fig.1b the authors plot individual time points in the panels, but the fits and points are overlapping so much that it is challenging to the main trends described by the clouds. The most informative approach for the reader would be to provide confidence intervals of the best fit parameters for all parameters that were varied in the fit. As the authors make some conclusions based on the power-law exponent values they observed, it would be helpful to know how confident we are in those values. *

      Confidence intervals of the power law exponents will be provided.

      * The conclusion that short arms equilibrate faster based on Fig.3a is not fully convincing. For example, in a scenario where ~1.5 microns is the equilibrium length for all arms, and that the longest arms equilibrate the fastest - you would see the same qualitative pattern for quantiles, not much change in low percentiles, while you would observe a decrease in the values for the high percentiles. The authors might be right, but Fig. 3A does not unambiguously demonstrate that it is so based on this evidence alone. *

      Our reasoning is based on the observation that the shortest percentiles do not change or do not change rapidly after 30 minutes, while the longest percentiles are clearly still relaxing towards a steady state. We will repeat this analysis with the new measurements, obtained in response to point 1.

      * As for chromosome roundness, typically in image analysis, roundness is defined through the ratio of (perimeter)2/area; it might be better to use "aspect ratio" for the metrics used by the authors. And, perhaps, one should expect that shorter (measured, not necessarily by polymer contour length) arms should have a higher width/length ratio? If one selects for more round objects, there should be no surprise that the width and length get almost proportional. Given all of this, I am not sure whether width/aspect ratio serves as a good proxy for the chromatin condensation progression, which is how the authors are employing this data in the manuscript as written. *

      We thank the reviewer for alerting us to an alternatively used definition of ‘roundness’. We will consider this concern, with one solution being to use ‘width-length ratio’ in its place.

      * For the diffusion-capture model simulations, I think the results of the simulation would strongly depend on the assumptions of the probability to associate and the time scale of dissociation of the beads representing the condensin complex. For example, for a very strong association one might expect that all condensin will end up in one big condensate, even in the case of a long polymer. This is not explored/discussed at all. Did the authors optimize their model in any way? If not, how have they estimated the values they used? Moreover, perhaps this is an opportunity to learn/predict something about condensin properties, but the authors do not take advantage of this opportunity. *

      We in fact explored the consequences of altering diffusion capture on and off rates when we initially developed the loop capture simulations, and we will report on the robustness of our model to the probability of dissociation as part of our revisions.

      * In addition, the authors did some checks to show that the steady-state results of the simulations do not depend on the initial conditions. However, as some of the results reported concern the polymer evolution to the steady state (Fig.6b-c), they also need to examine whether these results depend on the chosen initial conditions (or not), and if they do, what is the rationale for the choices the authors have made? *

      The current manuscript contains a comparison of steady states reached after simulations were started from elongated or random walk initial states (see Supplementary Figure 4). We will provide better justification for the choice of a 4x elongated initial state, which approximates the initial state observed in vivo.

      * A more thorough discussion of other possible models, beyond diffusion-capture model considered here, would be beneficial to the reader. First, the authors practically discard the possibility of the loop-extrusion model to explain their observations (although they never explicitly state this in the abstract or discussion). However, they neither leveraged simulations to rigorously compare models nor included some other substantiated arguments to explain why they prefer their model. This is important, as one of the major findings here is that the chromatin never reaches steady state for condensation, making it challenging to intuit what one should expect in this very dynamic state. Second, the authors, while briefly mentioning that there might be some other mechanisms contributing to the mitotic chromosome reshaping, do not really discuss those possibilities in a scholarly way. For example, work by the Kleckner group has suggested an involvement of bridges between sister chromatids into their shortening dynamics (Chu et al. Mol Cell 2020). Third, the authors do not discuss how they envision the interplay between the different SMC complexes - cohesin, condensin I and condensin II - as they act on the same chromatin polymer, or at least acknowledge a possible role that this interplay might contribute to the observed time dependencies. The reviewer raises important points, which we are keen to explore by performing loop extrusion simulations, as well as in an expanded discussion section.

      Reviewer #1 (Significance (Required)):

      Significance: The question the authors are trying to address is fundamental and important. While loop extrusion-driven mitotic chromosome organization is a popular model, considering alternative models is always crucial, especially when one can find experimental observations that allow us to discriminate between possible models. The main limitations are: 1) the performance of the approach the authors take to measure chromosome shape is in question and 2) the main competitive model (loop extrusion) is not modeled. If all shortcomings are addressed this work may provide strong evidence for the diffusion-capture model and thus advance our mechanistic understanding of mitotic processes, which will be of broad interest to the fields of genome and chromosome biology. We are happy to hear that the reviewer agrees that our work ‘may provide strong evidence for the diffusion-capture model and thus advance our mechanistic understanding of mitotic processes’. See above for how we propose to address the two main limitations.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      SUMMARY The authors tracked the progression of mitotic chromosome compaction over time by imaging chromatin spreads from HeLa cells that were released from G2/M arrest. By measuring the mitotic chromosome arms' width and length at different times post-release, the authors demonstrated that the speed at which the chromosome arms reach an equilibrium state is dependent on their length. The authors were able to recapitulate this observation using polymer simulations that they previously developed, supporting the model of loop capture as the mechanism for mitotic chromosome compaction.

      MAIN COMMENTS This is a straightforward paper that supports an alternative mechanism (relative to the highly popular loop-extrusion) model for chromosome compaction. My comments are meant to help the manuscript reach a wider audience.

      I suggest that "equilibrium" be replaced with "equilibrium length" since it is the only equilibrium parameter of concern. *

      The reviewer is correct, and we will implement this change, also taking into account the reasoning of reviewer 3 that ‘steady state’ is a better term to describe a final shape that is maintained by an active process.*

      In the results, it may help to describe how loop capture and loop extrusion are incorporated into the simulations, using terminology that non-experts can understand. Such a description should be accompanied by figures that can be related to the other figures (color scheme, nomenclature if possible). *

      Following from the reviewer’s suggestion, we will provide schematics of the loop capture and loop extrusion mechanisms.*

      OTHER COMMENTS P5: Is it possible the chromosome-spread processing may distort the structures of the chromosomes? *

      We will compare chromosome dimension in live cells with those following spreading to investigate this possibility.*

      Please clarify whether mitosis can complete after drug removal at the various treatment intervals. *

      Drug treatment and removal is often used as an experimental tool. We will perform a control experiment to explore whether mitosis can indeed complete after drug removal under our experimental conditions.*

      P6: "Our records are not, therefore, meant as an accurate absolute measure of individual arms. Rather, fitting allows us to sample all chromosome arms and deduce overall trends of chromosome shape changes over time" It would be better to state this sentence earlier in this paragraph, or earlier in the section so that readers' expectations are curbed when they're reading the detailed analysis plan. *

      Note that we will employ an additional image analysis method, in response to comments from reviewer 1, which should lead to more reliable width measurements.*

      P6: "As soon as individual chromosome arms become discernible (30 minutes), longer chromosome arms were wider, a trend that became more pronounced as time progressed." Implies that at early time points, when the lengths of the arms were unknown, the longer arms were equal or narrower than the short arms. I think it's more accurate to say that as soon as the arms were resolved, the longer arms appeared wider. *

      We will adopt the reviewers’ more accurate wording.*

      P7: Is there a functional consequence to the long arms not equilibrating before anaphase onset? *

      The reviewer raises an interesting question, which we will explore in our revised discussion. One consequence of not reaching ‘steady state’ is that ‘time in mitosis’ becomes a key parameter that defines compaction at anaphase onset.*

      P13: "In a loop capture scenario, we can envision how condensin II sets up a coarse rosette architecture, with condensin I inserting a layer of finer-grained rosettes." This should be illustrated in a figure. *

      We will consider such a figure, though the roles of two condensin complexes is peripheral to our current study. Investigating the consequences of two distinct condensins for chromosome formation will provide fertile ground for future investigations. *

      FIGURES Fig. 1: "...while insets show chromosomes at increasing magnification over time" sounds like the microscope magnification is changing over time. Please change "magnification" to "enlargement". Alternatively, if the goal of the figure is to illustrate the shape/dimensions change of the chromosomes over time, wouldn't it be better to keep all the enlargements at the same scale? *

      During the revisions, we will explore whether to show the insets at the same magnification, or to adjust the wording as suggested by the reviewer.*

      Fig. 2a plot: Does the distribution of normalized intensities really justify a Gaussian fit? I see a double Gaussian. *

      The chosen example indeed resembles a double Gaussian. We will explore whether this is due to noise in the measurement and a poor choice of an example, or whether a double Gaussian fit is indeed merited.*

      Please label the structures that resemble "rosettes". Good idea, which we will implement.

      Lu Gan

      Reviewer #2 (Significance (Required)):

      General - This is a simulation-centric study of mammalian chromosome compaction that supports the loop-capture mechanism. It may be viewed as provocative by some readers because loop-extrusion has dominated the chromosome-compaction literature in the past decade. The only limitation, which is best addressed by future studies, is the absence of more direct molecular evidence of loop capture in situ. Though this same limitation applies to studies of the loop-extrusion mechanism.

      Advance - It is valuable for the field to consider alternative mechanisms. In my opinion, the dominant one has been studied to death by indirect methods without a direct molecular-resolution readout in situ. While the field awaits better experimental tools, more mechanisms should be explored.

      Audience - The chromosome-biology community (both bacterial and eukaryotic) will be interested.

      Expertise - My lab uses cryo-ET to study chromatin in situ.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Kakui et al. measured the length/width relationships of mitotic chromosomes in human cells that had entered mitosis for different durations. This simple measurement revealed very interesting behaviors of mitotic chromosomes. They found that the longer chromosome arms were wider than shorter ones. Mitotic chromosoms became progressively wider over time, with shorter ones reached the final state faster than the longer ones. They then built a loop-capture polymer model, which explained the time-dependent increase of width/length ration rather well, but did not quite explain the final roundness of chromosomes.

      I suggest the following points for the authors to consider.

      Major points (1) There is no experimental evidence that the loop capture mechanism is condensin-depdendent. Can the authors deplete condensin I or II or both and measure chromosome length and width in similar assays? This will link their models to molecular players. *

      Such analyses have been conducted by others, and we will provide a brief survey with relevant references to the literature in our revised introduction.*

      (2) It seems rather intuitive to me that if one defines the spacing the condensin-binding sites, then the loop sizes will be the same between shorter and longer chromosomes. It then follows that shorter chromosomes are rounder. Is it that simple? If not, can the authors provide a better explanation. *

      The reviewer makes an interesting point that roundness (width-length ratio), is greater for shorter chromosome arms, even if chromosome width is constant. We will make this clear in the revised manuscript.*

      (3) If the loop sizes are the same between shorter and longer chromosomes, why can't loop extrusion model explain this phenomenon? If one assumes that condensin is stopped by the same barrier element and has the same distrution at the loop base, this should produce the same outcome as loop capture. *

      The key feature of loop extrusion is the formation of a linear condensin backbone, resulting in a bottle brush-shaped chromosome. This arrangement prevents further equilibration of loops into a wider structure, as occurs in the loop capture mechanism by rosette rearrangements. These differences will be better explained, using a schematic, in the revised manuscript.*

      Minor points (1) "We are aware that this approximation underestimates the length of the longest chromosome arms and overestimates the length of the shortest arms." should be "We are aware that this approximation underestimates the length of the longer chromosome (q) arms and overestimates the length of the shorter (p) arms.". Right? *

      In fact, this comparison applies to all longer and shorter arms, not only pairs of p and q arms, which we will clarify.*

      (2) Some scientists argue that the final chromosome conformation might be kinetically driven. Even if the short chromosomes have reached the final roundness, this doesn't necessarily mean that they have reached equilibrium in cells. "Steady state" might be a better term to describe the chromosomes in vivo, as there are clearly energy-burning processes. *

      The reviewer is right that the term ‘equilibrium’ can be seen as misleading, which we will replace with ‘steady state’.*

      Reviewer #3 (Significance (Required)):

      I find the paper intellectually stimulating and a pleasure to read. It suggests a plausible explanation for mitotic chromosome formation. As such, it will be of great interest to scientists in the chromatin field.

      Reviewer #4 (Evidence, reproducibility and clarity (Required)):

      The take home message of this study is that chromosome structure can be attained through mechanisms of looping that do not require an explicit loop extrusion function. As the authors states, alternative models of loop capture have been proposed, dating from 2015-2016. THese models show DNA chains through simply Brownian diffusion can adopt a loop structure (citation 27, 28 and similarly Entropy gives rise to topologically associating domains Vasquez et al 2016 DOI: 10.1093/nar/gkw510).*

      The reviewer makes an excellent point in that entropy considerations, e.g. depletion attraction, likely contribute to the efficiency of loop capture. We will refer to this principle, including a citation to the Vasquez et al. study, in the revised manuscript.

      * In this study, the authors go through careful and well-documented chromosome length measurements through prophase and metaphase. The modeling studies clearly show that loop capture provides a tenable mechanism that accounts for the biological results. The results are clearly written and propose an important alternative narrative for the foundation of chromosome organization.

      Reviewer #4 (Significance (Required)):

      The study is important because it takes a reductionist approach using just Brownian motion and loop capture to ask how well the fundamental processes will recapitulate the biological outcome. The fact that loop capture can account for the arm length to width relationships on biological time scales is important to report to the community. The work is extremely well done and the analysis of chromosome features is thorough and well-documented.*

      • *
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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The authors tracked the progression of mitotic chromosome compaction over time by imaging chromatin spreads from HeLa cells that were released from G2/M arrest. By measuring the mitotic chromosome arms' width and length at different times post-release, the authors demonstrated that the speed at which the chromosome arms reach an equilibrium state is dependent on their length. The authors were able to recapitulate this observation using polymer simulations that they previously developed, supporting the model of loop capture as the mechanism for mitotic chromosome compaction.

      Main Comments

      This is a straightforward paper that supports an alternative mechanism (relative to the highly popular loop-extrusion) model for chromosome compaction. My comments are meant to help the manuscript reach a wider audience.

      I suggest that "equilibrium" be replaced with "equilibrium length" since it is the only equilibrium parameter of concern.

      In the results, it may help to describe how loop capture and loop extrusion are incorporated into the simulations, using terminology that non-experts can understand. Such a description should be accompanied by figures that can be related to the other figures (color scheme, nomenclature if possible).

      Other comments

      P5: Is it possible the chromosome-spread processing may distort the structures of the chromosomes?

      Please clarify whether mitosis can complete after drug removal at the various treatment intervals.

      P6: "Our records are not, therefore, meant as an accurate absolute measure of individual arms. Rather, fitting allows us to sample all chromosome arms and deduce overall trends of chromosome shape changes over time" It would be better to state this sentence earlier in this paragraph, or earlier in the section so that readers' expectations are curbed when they're reading the detailed analysis plan.

      P6: "As soon as individual chromosome arms become discernible (30 minutes), longer chromosome arms were wider, a trend that became more pronounced as time progressed." Implies that at early time points, when the lengths of the arms were unknown, the longer arms were equal or narrower than the short arms. I think it's more accurate to say that as soon as the arms were resolved, the longer arms appeared wider.

      P7: Is there a functional consequence to the long arms not equilibrating before anaphase onset?

      P13: "In a loop capture scenario, we can envision how condensin II sets up a coarse rosette architecture, with condensin I inserting a layer of finer-grained rosettes." This should be illustrated in a figure.

      Figures

      Fig. 1: "...while insets show chromosomes at increasing magnification over time" sounds like the microscope magnification is changing over time. Please change "magnification" to "enlargement". Alternatively, if the goal of the figure is to illustrate the shape/dimensions change of the chromosomes over time, wouldn't it be better to keep all the enlargements at the same scale?

      Fig. 2a plot: Does the distribution of normalized intensities really justify a Gaussian fit? I see a double Gaussian.

      Please label the structures that resemble "rosettes".

      Lu Gan

      Significance

      General This is a simulation-centric study of mammalian chromosome compaction that supports the loop-capture mechanism. It may be viewed as provocative by some readers because loop-extrusion has dominated the chromosome-compaction literature in the past decade. The only limitation, which is best addressed by future studies, is the absence of more direct molecular evidence of loop capture in situ. Though this same limitation applies to studies of the loop-extrusion mechanism.

      Advance It is valuable for the field to consider alternative mechanisms. In my opinion, the dominant one has been studied to death by indirect methods without a direct molecular-resolution readout in situ. While the field awaits better experimental tools, more mechanisms should be explored.

      Audience The chromosome-biology community (both bacterial and eukaryotic) will be interested.

      Expertise My lab uses cryo-ET to study chromatin in situ.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: This manuscript authored by Kakui and colleagues aims to understand on how mitotic chromosomes get their characteristic, condensed X shape, which is functionally important to ensure faithful chromosome segregation and genome inheritance to both daughter cells. The authors focus on the condensin complex, a central player in chromosome condensation. They ask whether it condenses chromosomes through a now broadly popular "loop-extrusion" mechanism, in which a chromatin-bound condensin complex reels chromatin into loops until it dissociates or encounters a roadblock on the polymer (another condensin or some other protein complex), or through an alternative, "diffusion-capture" mechanism, in which a chromatin-bound condensin complex forms loops by encountering another chromatin-bound condensin until they dissociate from DNA (or from each other.)

      The authors measured the progressive changes in the shape of mitotic chromosomes by taking samples at given time points from synchronized and mitotically arrested cells and found that while all chromosomes become more condensed and shorter, their width correlated with the length of the chromosome arms. They also observed that chromosome compaction/shortening evolves on a time scale much longer than the interval between the onset of chromosome condensation and the start of chromosome segregation, suggesting that chromatin condensation does not reach its steady-state during an unperturbed mitosis. The observed width-length correlation could be described by a power law with an exponent that increases with the time (i.e. chromosome condensation). The authors also performed polymer simulations of the diffusion-capture mechanism and found that the simulations semi-quantitatively recapitulate their experimental observations.

      Major Comments

      My most substantial comments focus on somewhat technical details of the image analysis approaches taken and the polymer models employed. However, as all reported data are derived from those details, I feel it is crucial to address them. 1. Definition/measurement of chromatin arms width and length. The approach taken to manually threshold an "arm" object and then fitting it with a same-area ellipse is not an ideal approach to gauge length and width of the arm, for the following reasons: (1) An ellipse appears to do a poor job approximating many of the objects that we see in the zoom-in insets of Fig.1. Importantly, for somewhat bent shapes we see in the insets it likely strongly underestimates the length of the arms; this approach also presents potential problems for measuring width as well (see 2 and 3 here). (2) One concern is that, due to the diffraction limit, a cylindrical fluorescent object could appear somewhat wider at the mid-length than the real underlying cylinder or the poles; this effect could become more pronounced as the object gets brighter and shorter. (3) Forcing the fit to an ellipse to objects that are not truly rod-shaped can drive an overestimation of the width of the object, and I suspect that this effect also might correlate with the length and brightness of the object. (4) Given 1-3 above, I think the approach the authors used for the first two time points, while not perfect, is better suited and likely more robust while avoiding these caveats. Moreover, why the authors cannot use this same approach (but just for each arm separately) for the later (30+ min) time points as they used for first two is unclear. This point is underscored by the observation that there is a drastic difference in the results between the first two and all subsequent points. When the authors compared the two approaches at the 30 min time point (where width-length dependence is still weak) in different cell lines they did indeed see different results (Fig. S2), although they concluded that the difference was acceptable. Along these lines, the difference between short and long arms for the chromosome in the insets of Fig.1 are quite subtle, except maybe at 180 and 240 min. On a related note, it might be informative to compare data for the two sister chromatid arms (as the underlying polymer has the same length) long vs long and short vs short and long vs short to help establish the robustness of the approach. 2. Regarding the power-law distribution, it is hard to judge based on the presented data whether it is a really good description of the data or not. In Fig.1c, the points for a given time can barely be distinguished, while in Fig.1b the authors plot individual time points in the panels, but the fits and points are overlapping so much that it is challenging to the main trends described by the clouds. The most informative approach for the reader would be to provide confidence intervals of the best fit parameters for all parameters that were varied in the fit. As the authors make some conclusions based on the power-law exponent values they observed, it would be helpful to know how confident we are in those values. 3. The conclusion that short arms equilibrate faster based on Fig.3a is not fully convincing. For example, in a scenario where ~1.5 microns is the equilibrium length for all arms, and that the longest arms equilibrate the fastest - you would see the same qualitative pattern for quantiles, not much change in low percentiles, while you would observe a decrease in the values for the high percentiles. The authors might be right, but Fig. 3A does not unambiguously demonstrate that it is so based on this evidence alone. 4. As for chromosome roundness, typically in image analysis, roundness is defined through the ratio of (perimeter)2/area; it might be better to use "aspect ratio" for the metrics used by the authors. And, perhaps, one should expect that shorter (measured, not necessarily by polymer contour length) arms should have a higher width/length ratio? If one selects for more round objects, there should be no surprise that the width and length get almost proportional. Given all of this, I am not sure whether width/aspect ratio serves as a good proxy for the chromatin condensation progression, which is how the authors are employing this data in the manuscript as written. 5. For the diffusion-capture model simulations, I think the results of the simulation would strongly depend on the assumptions of the probability to associate and the time scale of dissociation of the beads representing the condensin complex. For example, for a very strong association one might expect that all condensin will end up in one big condensate, even in the case of a long polymer. This is not explored/discussed at all. Did the authors optimize their model in any way? If not, how have they estimated the values they used? Moreover, perhaps this is an opportunity to learn/predict something about condensin properties, but the authors do not take advantage of this opportunity. In addition, the authors did some checks to show that the steady-state results of the simulations do not depend on the initial conditions. However, as some of the results reported concern the polymer evolution to the steady state (Fig.6b-c), they also need to examine whether these results depend on the chosen initial conditions (or not), and if they do, what is the rationale for the choices the authors have made? 6. A more thorough discussion of other possible models, beyond diffusion-capture model considered here, would be beneficial to the reader. First, the authors practically discard the possibility of the loop-extrusion model to explain their observations (although they never explicitly state this in the abstract or discussion). However, they neither leveraged simulations to rigorously compare models nor included some other substantiated arguments to explain why they prefer their model. This is important, as one of the major findings here is that the chromatin never reaches steady state for condensation, making it challenging to intuit what one should expect in this very dynamic state. Second, the authors, while briefly mentioning that there might be some other mechanisms contributing to the mitotic chromosome reshaping, do not really discuss those possibilities in a scholarly way. For example, work by the Kleckner group has suggested an involvement of bridges between sister chromatids into their shortening dynamics (Chu et al. Mol Cell 2020). Third, the authors do not discuss how they envision the interplay between the different SMC complexes - cohesin, condensin I and condensin II - as they act on the same chromatin polymer, or at least acknowledge a possible role that this interplay might contribute to the observed time dependencies.

      Significance

      The question the authors are trying to address is fundamental and important. While loop extrusion-driven mitotic chromosome organization is a popular model, considering alternative models is always crucial, especially when one can find experimental observations that allow us to discriminate between possible models. The main limitations are: 1) the performance of the approach the authors take to measure chromosome shape is in question and 2) the main competitive model (loop extrusion) is not modeled. If all shortcomings are addressed this work may provide strong evidence for the diffusion-capture model and thus advance our mechanistic understanding of mitotic processes, which will be of broad interest to the fields of genome and chromosome biology.

    1. Here we are introduced to the “normal world.” Now, the normal world may exist in a far future on an interstellar starship, or it may be set in a suburban ranch house with a swing set in the back yard, but the audience will give us great latitude as we establish the definition of “normal.”

      Reading this part just made me think about how authors can write a more dull or normal exposition if they want to have a bigger effect on their tension. Like if the world or introduction they have is very mundane, but not enough to have the reader lose interest, they can make their tension seem more intense than it really is. Shift their perspective if that makes sense.

    1. Editor’s Note: As Director of the Office of Scientific Research and Development, Dr. Vannevar Bush has coordinated the activities of some six thousand leading American scientists in the application of science to warfare. In this significant article he holds up an incentive for scientists when the fighting has ceased. He urges that men of science should then turn to the massive task of making more accessible our bewildering store of knowledge. For years inventions have extended man's physical powers rather than the powers of his mind. Trip hammers that multiply the fists, microscopes that sharpen the eye, and engines of destruction and detection are new results, but not the end results, of modern science. Now, says Dr. Bush, instruments are at hand which, if properly developed, will give man access to and command over the inherited knowledge of the ages. The perfection of these pacific instruments should be the first objective of our scientists as they emerge from their war work. Like Emerson's famous address of 1837 on "The American Scholar," this paper by Dr. Bush calls for a new relationship between thinking man and the sum of our knowledge.

      Hay que poner atención a que no haya saturación de informración y eso pueda llegar a abrumar al lector

    2. Trip hammers that multiply the fists, microscopes that sharpen the eye, and engines of destruction and detection are new results, but not the end results, of modern science.

      It overlooks the ethical and social implications of these developments, which shape their true impact beyond mere scientific achievement.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      1. General Statements

      We thank the reviewers for their thorough evaluation of this manuscript. We are pleased that overall, they found our work and results valuable for the scientific community. Based on their feedback, we performed additional experiments and made several changes to strengthen the manuscript and expand the target audience.

      *All three reviewers pointed out that the manuscript lacked demonstration of OneSABER method applicability across sample types (i.e., its claimed versatility) and other whole-mount systems beyond the Macrostomum lignano flatworm. *

      We now include an additional results section with accompanying figures (Figs. 6 and 7) that demonstrate the application of OneSABER in whole-mount samples of another flatworm, the planarian Schmidtea mediterranea (Fig. 6), which is much larger than M. lignano, and in formalin-fixed paraffin-embedded (FFPE) mouse small intestine tissue sections (Fig. 7). We believe that these additional experiments on different sample types demonstrate the versatility of the OneSABER approach.

      Please note that two more authors, Jan Freark de Boer and Folkert Kuipers, have been added for their contribution to mouse FFPE sections.

      Furthermore, two reviewers asked for an additional main figure with a comparison of the signal strengths between the different OneSABER methods.

      We have addressed this comment by including an additional results section and its adjacent figure (Fig. 5), where we provide a comparison of fluorescent signals from the same probes and gene but different OneSABER development methods.

      Additionally, to implement the revisions, we modified Fig. 1 and Supplementary Fig. 6 and broadened Supplementary Tables S1-S2, S4-S6.

      2. Point-by-point description of the revisions

      Reviewer #1

      1) “Fig.1 seems to suggest that the protocol for in vitro swapping of 3' concatemers happens in two consecutive PCR steps. I recommend indicating in the figure that the switching can be conducted in a single in vitro reaction.”

      We have changed Fig. 1 to make this clearer.

      2) “Is it possible to multiplex the switching in one single reaction? For example, perform p27 to p28 and p29 to p30 simultaneously? This will be crucial for the split-probe methodology.”

      We did not test it. This should be possible if there is no overlap between the 3’ initiator sequences. However, it seems counterproductive as the elongation efficiencies of switching reactions from the 3’ initiator sequences to another concatemer may vary (Supplementary Fig. S6). Running independent extension/switch reactions and performing equimolar mixing of purified extended probes could be a better solution.

      3) “Did the authors encounter any switching hairpins sequence that does not work? If not, can they postulate, what are the requirements for the design of switching sequences.”

      The design criteria followed the requirements postulated in the original SABER article and its Supplementary Materials (Kishi et al 2019). All switching hairpins we tested in the pairs of the 3 used 3’ initiator sequences (p27, p28 and p30) worked, but elongation efficiencies varied (see an example in Supplementary Fig. S6).

      4) “Is there cross hybridization between the switched and original hairpins? For example, can the authors show that the signals from p27 and p30 do not overlaps?”

      The in situ hybridization results with swapped primary probes are shown in Fig. 6B (multiplexed HCR in S. mediterranea). All probes were originally designed using a p27 PER initiator. We swapped Smed-vit-1 with p30 and Smedwi-1 with p28. We also updated Fig. S6, by adding the second section (B) showing the in vitro results after concatemer swapping, as well as hybridization specificity of the secondary imager probes.

      5) “Can the authors quantify results from the direct, AP, TSA, and HCR? What do you mean by 'narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible'?”

      *“I agree with reviewer #2 regarding the lack of comparison to standard SABER.” *

      A comparison of fluorescent signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5.

      We have rephrased the sentence for clarity. From “As a result, despite higher intracellular resolution, some narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible for the human eye after SABER HCR (Figs. 3, 4).” to “As a result, despite higher intracellular resolution, some fine anatomical structures like neural chords (syt11) or muscles (tnnt2) are less resolved by widefield fluorescence microscopy after SABER HCR FISH compared to SABER TSA FISH”

      Reviewer #2

      1) “This work is building on standard SABER (a set of PER-extended primary probes that serve as landing pads for secondary fluorescently-labeled readout oligos) and pSABER (the readout oligo carries HRP instead of a dye for downstream TSA). The novelty of the work presented here is introducing additional variations of signal amplification, i.e. by using an hapten-labeled oligo to recruit a tertiary readout probe (antibodies conjugated with HRP or AP) or using SABER in combination with HCR. Since SABER can be seen as the underlying platform and pSABER was (arguably) also already introduced as a new platform by Attar et al. 2023, it seems difficult to introduce OneSABER as yet another new platform, of which standard SABER and pSABER are a part of. The reviewer encourages the authors to overthink the conceptual introduction, which in view of its certainly distinct novel features might allow a clearer distinction to previous work.”

      We agree with the reviewer’s comments. We have added additional information in the Introduction section to clarify the novelty and key distinct features of OneSABER that justify its separation from other SABER protocols.

      2) “Although the authors take care in tributing prior work, some of the studies are only mentioned in the results section, one of such cases is pSABER by Attar et al. 2023. The close relation between pSABER and SABER TSA (HRP on readout oligo vs. hapten on readout oligo + HRP-conjugated antibody) needs to be better positioned in the introduction, clearly framing earlier work, inspirations drawn etc.. This is in line with my previous point.”

      The pSABER preprint article by Attar et al. 2023 (now published in a peer-reviewed journal as Attar et al. 2025) is now mentioned in the Introduction, and its inspirational impact on our research is clearly stated.

      3) “Fig. 1 lists the individual modules of the OneSABER platform: i) standard SABER, ii) AP SABER, iii) SABER TSA, iv) pSABER (TSA FISH) (would recommend leaving it with original name when introducing it and include additional explanation in parentheses) and iv) SABER HCR. The main figures feature only AP SABER, SABER TSA and SABER HCR, for standard SABER and pSABER one must look up the SI. Since the authors describe the limited performance of standard SABER for one of their targets of interest (syt11) and since they have tested this target for all five conditions, it would be valuable to include a comparative view of all five platform modules in a single figure for syt11 or even also piwi, which also seems to have been tested for all five. Comparing the signal strength would be useful for the community, at least of each SABER variation compared to standard SABER.”

      We agree with the reviewer’s comments. Except for pSABER, a comparison of fluorescence signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5. To make the comparison as objective as possible, all FISH developments were re-done using available “far red” fluorophores, except for pSABER. Unfortunately, our directly labeled HRP oligonucleotides for pSABER lost their activity after a year of storage at +4oC. These conjugated oligonucleotides are very expensive and, given their limited shelf life, we cannot justify ordering a new batch for this experiment. Therefore, we only have the data for pSABER syt11 with FITC green tyramide, which is not comparable to “far red” fluorophore signals. This issue has also been discussed in the main text.

      In addition, we have modified Fig. 1, as suggested.

      4) “The description of how the authors designed their probes is very detailed and they also provide a nice step-by-step protocol for their individual commands using Oligominer and BLAT software. This reviewer is wondering how the authors chose their PER sequences that they appended to their mined set of homologous in situ hybridization probes (p27,p28,p30). This is a general problem of multiplexed ISH approaches with single-stranded overhang, could the author's comment on potential self-interaction of the appended sequence with the homologous part, which might limit the PER efficiency, or elaborate on their choice?”

      As being ourselves novice to SABER when we started our work, we based our selection of the p27, p28, and p30 PER sequences on their multiple co-occurrences in previous publications (Amamoto et al. 2019, doi: 10.7554/eLife.51452; Saka et al. 2019, doi: 10.1038/s41587-019-0207-y; Wang et al. 2020, doi: 10.1016/j.omtm.2020.10.003; Salinas-Saavedra et al. 2023, doi: 10.1016/j.celrep.2023.112687; and Attar et al. 2023, doi: 10.1101/2023.01.30.526264). We did not consider the potential interference between PER concatemers and homologous primary probe-binding sequences. However, as all PER concatemers were specifically designed to lack G nucleotides to keep them from self-annealing (Kishi et al. 2019, doi: 10.1038/s41592-019-0404-0), we assumed that it would also reduce potential annealing to the homologous part of the probe.

      5) “Fig.1 and l. 125 describe straightforward in vitro switching of the concatemer sequence for an existing set of primary probes as a central feature of the OneSABER platform. However, the authors to my knowledge do not show such experiments themselves and only cite the original SABER paper by Kishi et al. 2019. This reviewer would be grateful to be pointed toward where in Kishi et al. 2019 this was demonstrated, however in view of this central part of the swopping scheme in the OneSABER platform an experiment showing this swopping is missing.”

      In the article by Kishi et al. 2019, concatemer switching/swapping is termed as “primer remapping”. We found this term confusing because it does not describe the essence of the reaction. The in situ hybridization results with swapped primary probes are shown in Fig. 6B (multiplexed HCR in S. mediterranea). All probes were originally designed using a p27 PER initiator. We swapped Smed-vit-1 with p30 and Smewi-1 with p28. We also updated Fig. S6, by adding the second section (B) showing the in vitro results after concatemer swapping, as well as hybridization specificity of the secondary imager probes.

      6) “the description of Table S6 could use additional information in the legend such that the reader does not have to scroll down to Section S1 to retrieve the information (PER reaction, gel conditions, ladder is dsDNA, what are the individual bands)”

      Probably, the reviewer meant Fig. S6. We now wrote a more detailed caption for the figure and extended it with a second panel (B) to illustrate the results of 3’ concatemer swapping.

      7) “the manuscript features an extensive set of resources in main body, supplementary materials and protocols. It is important and usually not merited sufficiently making the effort to compare orthogonal approaches for a given aim. This reviewer particularly appreciates the detailed strengths & weaknesses discussion in Table S6.”

      We thank the reviewer for the appreciation of our work.

      8) “Minor comments:

      -Definitions should be consistent, in Fig. 1 all approaches are defined with FISH added, but this definition is not followed consistently in the main text.”

      These definitions are now made consistent throughout the text.

      9) “Optional:

      -The authors describe several newly developed optimization steps during sample preparation for M. lignano ISH experiments compared to established ones. If the data exists, they include a supplementary figure showing improvements of optimized protocol steps”

      As almost every step and the buffer recipes were different from the original ISH protocol by Pfister et al. (2007) because of the use of liquid-exchange columns, different probes, and development chemistry, we believe that a comparison would be excessive. We think that the key difference points are already substantially highlighted in the results section.

      Reviewer #3

      1) “Despite including a whole figure (Figure 1) featuring the operation scheme of the OneSABER platform, the figure as well as the associated text fall short with respect to clearly stating the advantage of the different aspects of the platform. Consider a clearer and more thorough explanation of the different aspects of the platfrom.”

      Details on the advantages and disadvantages of using different OneSABER methods in terms of their experimental application and cost efficiency are described in Supplementary Tables S4-S6 of the submitted manuscript. However, we agree that the description in Fig. 1 was too concise and also did not refer to these tables. We have expanded the description in Fig. 1.

      2) “Related to the first comment: A more detailed description of the similarities and/or differences of this platform relative to similar applications such as the study by Hall et al, 2024”

      The mere point of mentioning the preprint of Hall et al. 2024 (now peer-reviewed, https://doi.org/10.1016/j.celrep.2024.114892) was to acknowledge that in M. lignano the HCR technology has been previously applied (although only once), while all other previously published works on M. lignano utilized canonical antisense RNA probes colorimetric in situ hybridization. We have extensively mentioned the HCR approach and its working principles throughout the submitted manuscript.

      3) “The authors describe the probes used as short, synthetic DNA probes targeting short RNA transcripts. Are these probes Oligopaints (Beliveau et al, 2015)? Why is that not more clearly stated in the text?”

      Oligopaints use oligo libraries as a renewable source of FISH probes, and these libraries are amplified with fluorophore-conjugated PCR primers. We used synthetic DNA probes directly. In this sense, our probe sets are not oligopaints. However, we used the OligoMiner pipeline of Oligopaints for the design of the probes, and thus used the same tiling strategy as oligopaints. We believe that this has been explained in the manuscript. Please refer to comment 4 of Reviewer 2.

      4) “Line 105, p5: The authors state that the number of probes depends on the target RNA length and its expression strength. This data should be in the main text and described in detail since it is a major aspect of the platform design.”

      We believe that this statement is common sense, as one cannot design more than 5x 30-50 bp probes for 200 nt transcripts, while for a 2000 bp mRNA, the theoretical limit is ~50 probes. Similarly, weakly expressed genes (regardless of their length) would require either more probes to reach the detection threshold or stronger amplification through choice of concatemer length and/or signal developing techniques. We have rephrased this sentence in the main text to reflect this.

      5) “Figure 2 showcases one of the most compelling data supporting the versatility of the platform. Can the signals in each panel be quantified and compared to 1. Published Ab staining? Is there a clear correlation in the intensity of the signals? 2. Between Vector Blue and NBT? 3. Chemical staining and FISH signals?”

      Since M. lignano is a relatively new model, there are no published antibody stainings for M. lignano genes used in this study. Furthermore, colorimetric precipitate methods are not quantitative but rather qualitative, because their signal strength is proportional to both the target RNA level and the development time; thus, signals from weakly expressed transcripts can be “boosted” simply by longer development. Therefore, a correct quantitative comparison with colorimetric methods, as requested by the reviewer, was not possible. However, with some corrections on fluorophore differences and animal-to-animal variability, it is possible to roughly compare peak saturation intensities for FISH methods if the experiments are designed for this aim. We performed these experiments, and a comparison of fluorescent signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5.

      Minor comments:

      6) “The whole mount images and signals are often diffuse, can they be visualized using a DIC where the morphology of the organism is clearer?”

      We are unsure which images appear to be diffused to the reviewer. The other reviewers have not pointed out similar issues. Perhaps the question resolves once full-resolution uncompressed images are uploaded.

      7) “In order to support the claim that this is a universal approach for whole-mount staining, can the authors show an example of applicability to C. elegans?”

      This is now addressed. We included two additional results sections with two accompanying figures (Figs. 6 and 7) that demonstrate OneSABER’s application in whole-mount samples of a much larger than M. lignano model flatworm, the planarian Schmidtea mediterranea (Fig. 6), as well as in formalin-fixed paraffin-embedded (FFPE) small intestine tissue sections of a mouse model (Fig. 7).

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Recommendations for the authors:

      Reviewer #1:

      The authors have thoroughly changed the manuscript and addressed most of my concerns. I appreciate adding the activity assays of the C115/120S mutants, however, I suggest that the authors embed and also discuss these data more clearly. It also escaped my attention earlier that the positioning of the disulfide bond is 117-122 in the deposited PDBs instead of 115-120. The authors should carefully check which positioning is correct here.

      We thank reviewer #1 for his or her careful assessment of our revised manuscript. As suggested, we detailed the results section “CrSBPase enzymatic activity” with additional numerical values, and discussed more clearly the comparisons of results for activity assays of mutants C115S and C120S in the section “Oligomeric states of CrSBPase”. Residues numbering was carefully proof-checked throughout the manuscript for correctness and homogeneity. C115 and C120 are numbered according to best databases consensus, ie. GenBank and Uniprot, and may differ from one database to another (including PDB) due to varying numbering rules. We clarified the chosen nomenclature in methods section “Cloning and mutagenesis of CrSBPase expression plasmids”.

      Line 246-250: I think it is evident that the two SBPase structures superpose well given the sequence identity of more than 70%. However, it would be great to include a superposition of the two structures in Figure 1, especially with regard to the region harboring C115 and C120.

      We added a panel showing superimposition of CrSBPase 7b2o and PpSBPase 5iz3 and made a close-up view around the region C115-C120 in supplementary figure 5. Given the density in information of figure 1 we prefer not to add additional images on it. Supplementary figure 5 was initially intended to illustrate sequence conservation/variation among homologs, thus fitting with the objective to compare past and present XRC results.

      Line 255-266: I am again missing a panel in Figure 1 here, e.g. a side-by-side view of Xray vs AF2/3 structure.

      We added another panel in supplementary figure 5 to visually compare side-by-side SBPase crystallographic structure 7b2o and our AF3 model. Again, for the sake of clarity we prefer not to overload figure 1 with additional panels. This will also enable thorough comparison of past XRC of PpSBPase, present XRC of CrSBPase, and various AF models (see below, oligomer comparisons).

      Line 261-266: Did the authors predict dimers and tetramers using AF3? What are the confidence metrics in this case? Do the authors see differences to the monomer prediction in case a multimer is confidently predicted?

      We modeled dimers and tetramers using AF3 and added them on supplementary figure 5 side by side with protomer of XRC model 7b2o and with monomer predicted by AF3. Color code for supplementary figure 5 panels F-H is according to AF standard representation of plDDT. Confidence metrics per residue correspond to very high reliability (navy blue) or, locally, confident prediction (cyan) and overall prediction scores range from pTM=0.85-0.91, a high-quality prediction. Interface prediction score is high for both dimer (ipTM=0.9) and tetramer (ipTM=0.82). We reported these data in supplementary figure 5 and corresponding updated legend. XRC and AF models all align with RMSD<0.5 Å, indicating a globally unchanged structure of the protomer in the various methods and oligomeric states.

      Line 441: How does the oligomeric equilibrium change in C115/120S mutants? This information should be added for the mutants. Besides, the mAU units in Fig. 6 could be normalized to allow an easier comparison between the chromatograms of wt and mutants.

      Change in oligomeric equilibrium is assessed by size-exclusion chromatography of WT and mutants C115S, C120S as reported in figure 6A. We made quantitative estimation of WT, and C115S and C120S mutants equilibrium by comparing maximal peak intensity and added this information in the text. Briefly, the oligomer ratio on a scale of 100 is 9:48:43 for WT, 42:25:33 for mutant C115S, and 29:17:54 for mutant C120S (ratio expressed as tetramer:dimer:monomer). We prefer not to normalize values of absorbance, but rather keep the actual measurement of absorbance at 280 nm on the chromatogram of figure 6, for the sake of consistency with the added text and for a more transparent report of the experiment.

      Line 447: WT activity is 12.15+-2.15 and both mutants have a higher activity. The authors should check if their values (96% and 107%) are correct. Besides, did the authors check if the increase in C120S is statistically significant? My impression is that both mutants have a higher activity than the wildtype, in both correlating with increased fractions of the tetramer. This would also make sense, as the corresponding region is part of the tetramer interface in the crystal packing.

      The reported activity values were checked for correctness. Wild-type SBPase specific activity at 12.5 ±2.15 µmol(NADPH) min<sup>-1</sup> mg(SBPase)<sup>-1</sup> was obtained by pre-incubating the enzyme with 1 µM CrTRXf2 supplemented with 1 mM DTT and 10 mM Mg<sup>2+</sup>, while the results of supplementary figure 14 reporting the comparison of activation of WT and mutants, with a variation of 107 or 96 %, were obtained with a slightly different protocol for pre-incubation of the enzyme with 10 mM DTT and 10 mM Mg<sup>2+</sup>. Please note that whether WT enzyme was assayed in 10 mM DTT 10 mM Mg or in 1 µM TRX 1 mM DTT 10 mM Mg, its specific activity appears equal within experimental error. Both mutants have nearly the same activity than the WT in the assay reported in supplementary figure 14: we fully agree that 107% (and 96%) variation is indeed not significant considering the uncertainty of the measurement (see error bars representing standard deviations of the mean in supplementary figure 14). We added this important information in the text. Even though both mutations stabilize the most active tetramer in untreated recombinant protein, we think that after reducting treatment both WT and mutants all reach the same maximal activity because they all form an equivalent proportion of the active tetramer versus alternative oligomeric states. We furhter interprete this piece of data as a decoupling of reduction and catalysis: in physiological conditions we assume that SBPase would initiate activation upon the reduction of disulfide bridges, including but not limited to C115-C120 that restricts the entry into fully active tetramer, at which point SBPase in reduced form reaches maximal activity until another post-translational signal eventually changes its conformation and oligomerisation.

      We thank again reviewer 1 for his or her assessment and valuable suggestions.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors used a subset of a very large, previously generated 16S dataset to:<br /> (1) Assess age-associated features; and (2) develop a fecal microbiome clock, based on an extensive longitudinal sampling of wild baboons for which near-exact chronological age is known. They further seek to understand deviation from age-expected patterns and uncover if and why some individuals have an older or younger microbiome than expected, and the health and longevity implications of such variation. Overall, the authors compellingly achieved their goals of discovering age-associated microbiome features and developing a fecal microbiome clock. They also showed clear and exciting evidence for sex and rank-associated variation in the pace of gut microbiome aging and impacts of seasonality on microbiome age in females. These data add to a growing understanding of modifiers of the pace of age in primates, and links among different biological indicators of age, with implications for understanding and contextualizing human variation. However, in the current version, there are gaps in the analyses with respect to the social environment, and in comparisons with other biological indicators of age. Despite this, I anticipate this work will be impactful, generate new areas of inquiry, and fuel additional comparative studies.

      Thank you for the supportive comments and constructive reviews.

      Strengths:

      The major strengths of the paper are the size and sampling depth of the study population, including the ability to characterize the social and physical environments, and the application of recent and exciting methods to characterize the microbiome clock. An additional strength was the ability of the authors to compare and contrast the relative age-predictive power of the fecal microbiome clock to other biological methods of age estimation available for the study population (dental wear, blood cell parameters, methylation data). Furthermore, the writing and support materials are clear, informative and visually appealing.

      Weaknesses:

      It seems clear that more could be done in the area of drawing comparisons among the microbiome clock and other metrics of biological age, given the extensive data available for the study population. It was confusing to see this goal (i.e. "(i) to test whether microbiome age is correlated with other hallmarks of biological age in this population"), listed as a future direction, when the authors began this process here and have the data to do more; it would add to the impact of the paper to see this more extensively developed.

      Comparing the microbiome clock to other metrics of biological age in our population is a high priority (these other metrics of biological age are in Table S5 and include epigenetic age measured in blood, the non-invasive physiology and behavior clock (NPB clock), dentine exposure, body mass index, and blood cell counts (Galbany et al. 2011; Altmann et al. 2010; Jayashankar et al. 2003; Weibel et al. 2024; Anderson et al. 2021)). However, we have opted to test these relationships in a separate manuscript. We made this decision because of the complexity of the analytical task: these metrics were not necessarily collected on the same subjects, and when they were, each metric was often measured at a different age for a given animal. Further, two of the metrics (microbiome clock and NPB clock) are measured longitudinally within subjects but on different time scales (the NPB clock is measured annually while microbiome age is measured in individual samples). The other metrics are cross-sectional. Testing the correlations between them will require exploration of how subject inclusion and time scale affect the relationships between metrics.

      We now explain the complexity of this analysis in the discussion in lines 447-450. In addition, we have added the NPB clock (Weibel et al. 2024) to the text in lines 260-262 and to Table S5.

      An additional weakness of the current set of analyses is that the authors did not explore the impact of current social network connectedness on microbiome parameters, despite the landmark finding from members of this authorship studying the same population that "Social networks predict gut microbiome composition in wild baboons" published here in eLife some years ago. While a mother's social connectedness is included as a parameter of early life adversity, overall the authors focus strongly on social dominance rank, without discussion of that parameter's impact on social network size or directly assessing it.

      Thank you for raising this important point, which was not well explained in our manuscript. We find that the signatures of social group membership and social network proximity are only detectable our population for samples collected close in time. All of the samples analyzed in  Tung et al. 2015 (“Social networks predict gut microbiome composition in wild baboons”) were collected within six weeks of each other. By contrast, the data set analyzed here spans 14 years, with very few samples from close social partners collected close in time. Hence, the effects of social group membership and social proximity are weak or undetectable. We described these findings in Grieneisen et al. 2021 and Bjork et al. 2022, and we now explain this logic on line 530, which states, “We did not model individual social network position because prior analyses of this data set find no evidence that close social partners have more similar gut microbiomes, probably because we lack samples from close social partners sampled close in time (Grieneisen et al. 2021; Björk et al. 2022).”

      We do find small effects of social group membership, which is included as a random effect in our models of how each microbiome feature is associated with host age (line 529) and our models predicting microbiome Dage (line 606; Table S6).

      Reviewer #2 (Public review):

      Summary:

      Dasari et al present an interesting study investigating the use of 'microbiota age' as an alternative to other measures of 'biological age'. The study provides several curious insights into biological aging. Although 'microbiota age' holds potential as a proxy of biological age, it comes with limitations considering the gut microbial community can be influenced by various non-age related factors, and various age-related stressors may not manifest in changes in the gut microbiota. The work would benefit from a more comprehensive discussion, that includes the limitations of the study and what these mean to the interpretation of the results.

      We agree and have text to the discussion that expands on the limitations of this study and what those limitations mean for the interpretation of the results. For instance, lines 395-400 read, “Despite the relative accuracy of the baboon microbiome clock compared to similar clocks in humans, our clock has several limitations. First, the clock’s ability to predict  individual age is lower than for age clocks based on patterns of DNA methylation—both for humans and baboons (Horvath 2013; Marioni et al. 2015; Chen et al. 2016; Binder et al. 2018; Anderson et al. 2021). One reason for this difference may be that gut microbiomes can be influenced by several non-age-related factors, including social group membership, seasonal changes in resource use, and fluctuations in microbial communities in the environment”

      In addition, lines 405-411 now reads, “Third, the relationships between potential socio-environmental drivers of biological aging and the resulting biological age predictions were inconsistent. For instance, some sources of early life adversity were linked to old-for-age gut microbiomes (e.g., males born into large social groups), while others were linked to young-for-age microbiomes (e.g., males who experienced maternal social isolation or early life drought), or were unrelated to gut microbiome age (e.g., males who experienced maternal loss; any source of early life adversity in females).”

      Strengths:

      The dataset this study is based on is impressive, and can reveal various insights into biological ageing and beyond. The analysis implemented is extensive and high-level.

      Weaknesses:

      The key weakness is the use of microbiota age instead of e.g., DNA-methylation-based epigenetic age as a proxy of biological ageing, for reasons stated in the summary. DNA methylation levels can be measured from faecal samples, and as such epigenetic clocks too can be non-invasive. I will provide authors a list of minor edits to improve the read, to provide more details on Methods, and to make sure study limitations are discussed comprehensively.

      Thank you for this point. In response, we have deleted the text from the discussion that stated that non-invasive sampling is an advantage of microbiome clocks. In addition, we now propose a non-invasive epigenetic clock from fecal samples as an important future direction for our population (see line 450).

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Abstract - The opening 2 sentences are not especially original or reflective of the potential value/ premise of the study. Members of this team have themselves measured variation in biological age in many different ways, and the implication that measuring a microbiome clock is easy or straightforward is not compelling. This paper is very interesting and provides unique insight, but I think overall there is a missed opportunity in the abstract to emphasize this, given the innovative science presented here. Furthermore, the last 2 sentences of the abstract are especially interesting - but missing a final statement on the broader significance of research outside of baboons.

      We appreciate these comments and have revised the Abstract accordingly. The introductory sentences now read, “Mammalian gut microbiomes are highly dynamic communities that shape and are shaped by host aging, including age-related changes to host immunity, metabolism, and behavior. As such, gut microbial composition may provide valuable information on host biological age.” (lines 31-34). The last two sentences of the abstract now read, “Hence, in our host population, gut microbiome age largely reflects current, as opposed to past, social and environmental conditions, and does not predict the pace of host development or host mortality risk. We add to a growing understanding of how age is reflected in different host phenotypes and what forces modify biological age in primates.” (lines 40-43).

      If possible, it would be highly useful to present some comments on concordance in patterns at different levels. Are all ASVs assessed at both the family and genus levels? Do they follow similar patterns when assessed at different levels? What can we learn about the system by looking at different levels of taxonomic assignment?

      The section on relationships between host age and individual microbiome features is already lengthy, so we have not added an analysis of concordance between different taxonomic levels. However, we added a justification for why we tested for age signatures in different levels of taxa to line 171, which reads, “We tested these different taxonomic levels in order to learn whether the degree to which coarse and fine-grained designations categories were associated with host age.”

      To calculate the delta age - please clarify if this was done at the level of years, as suggested in Figure 3C, or at the level of months or portion months, etc?

      Delta age is measured in years. This is now clarified in lines 294, 295, and 578.

      Spelling mistake in table S12, cell B4 (Octovber)

      Thank you. This typo has been corrected.

      Given the start intro with vertebrates, the second paragraph needs some tweaking to be appropriate. Perhaps, "At least among mammals, one valuable marker of biological aging may lie in the composition and dynamics of the mammalian gut microbiome (7-10)." Or simply remove "mammalian".

      We have updated this sentence based on your suggestions in line 54. It reads, “In mammals, one valuable marker of biological aging may lie in the composition and dynamics of the gut microbiome (Claesson et al. 2012; Heintz and Mair 2014; O’Toole and Jeffery 2015; Sadoughi et al. 2022).”

      A rewrite at the end of the introduction is needed to avoid the almost direct repetition in lines 115-118 and 129-131 (including lit cited). One potentially effective way to approach this is to keep the predictions in the earlier paragraph and then more clearly center the approach and the overarching results statement in the latter paragraph. (I.e., "we find that season and social rank have stronger effects on microbiome age than early life events. Further, microbiome age does not predict host development or mortality.").

      Thank you for pointing this out. We have re-organized the predictions in the introduction based on your suggestion. The alternative “recency effects” model now appears in the paragraph that starts in line 110. The final paragraph then centers on the overall approach and the results statement (lines 128-140)

      Be clear in each case where taxon-level trends are discussed if it's at Family, Genus, or other level. It's there most, but not all, of the time.

      We have gone through the text and clarified what taxa or microbiome feature was the subject of our analyses in any places where this was not clear.

      In the legend for Figure 2, add clarification for how values to right versus left of the centered value should be interpreted with respect to age (e.g. "values to x of the center are more abundant in older individuals").

      We now clarify in Figure 2C and 2D that “Positive values are more abundant in older hosts”.

      Figure 3 - Are Panels A, B, and C all needed - can the value for all individuals not also be overlaid in the panel showing sex differences and the same point showing individuals with "old" and "young" microbiomes be added in the same plot if it was slightly larger?

      We agree and have simplified Figure 3. We reduced the number of panels from three to two, and we added the information about how to calculate delta age to Panel A. We also moved the equation from the top of Panel C to the bottom right of Panel A.

      Reviewer #2 (Recommendations for the authors):

      Dasari et al present an interesting study investigating the use of 'microbiota age' as an alternative to other measures of 'biological age'. The study provides several curious insights which in principle warrant publication. However, I do think the manuscript should be carefully revised. Below I list some minor revisions that should be implemented. Importantly, the authors should discuss in the Discussion the pros and cons of using 'microbiota age' as a proxy of 'biological age'. Further, the authors should provide more information on Methods, to make sure the study can be replicated.

      Thank you for these important points. Based on your comments and those of the first reviewer, we have expanded our discussion of the limitations of using microbiota age as a proxy for biological age (see edits to the paragraph starting in line 395).

      We have also expanded our methods around sample collection, DNA extraction, and sequencing to describe our sampling methods, strategies to mitigate and address possible contamination, and batch effects. See lines 483-490 and our citations to the original papers where these methods are described in detail.

      (1) Lines 85-99: I think this paragraph could be revisited to make the assumptions clearer. For instance, the last sentence is currently a little confusing: are authors expecting males to exhibit old-for-age microbiomes already during the juvenile period?

      This prediction has been clarified. Line 96 now reads, “Hence, we predicted that adult male baboons would exhibit gut microbiomes that are old-for-age, compared to adult females (by contrast, we expected no sex effects on microbiome age in juvenile baboons).”

      (2) Lines 118-121: Could the authors discuss this assumption in relation to what has been observed e.g., in humans in terms of delays in gut microbiome development? Delayed/accelerated gut microbiome development has been studied before, so this assumption would be stronger if related to what we know from previous studies.

      This comment refers to the sentence which originally stated, “However, we also expected that some sources of early life adversity might be linked to young-for-age gut microbiota. For instance, maternal social isolation might delay gut microbiome development due to less frequent microbial exposures from conspecifics.” We have slightly expanded the text here (line 117) to explain our logic. We now include citations for our predictions. We did not include a detailed discussion of prior literature on microbiome development in the interest of keeping the same level of detail across all sections on our predictions.

      (3) As the authors discuss, various adversities can lead to old-for-age but also young-for-age microbiome composition. This should be discussed in the limitations.

      We agree. This is now discussed in the sentence starting at line 371, which reads, “…deviations from microbiome age predictions are explained by socio-environmental conditions experienced by individual hosts, especially recent conditions, although the effect sizes are small and are not always directionally consistent.” In addition, the text starting at line 405 now reads, “Third, the relationships between potential socio-environmental drivers of biological aging and the resulting biological age predictions were inconsistent. For instance, some sources of early life adversity were linked to old-for-age gut microbiomes (e.g., males born into large social groups), while others were linked to young-for-age microbiomes (e.g., males who experienced maternal social isolation or early life drought), or were unrelated to gut microbiome age (e.g., males who experienced maternal loss; any source of early life adversity in females).”

      (4) In various places, e.g., lines 129-131, it is a little unclear at what chronological age authors are expecting microbiota to appear young/old-for-age.

      This sentence was removed while responding to the comments from the first reviewer.

      (5) Lines 132-133: this statement could be backed by stating that this is because the gut microbiota can change rapidly e.g., when diet changes (or whatever the authors think could be behind this).

      We have added an expository sentence at line 123, including new citations. This sentence reads, “Indeed, gut microbiomes are highly dynamic and can change rapidly in response to host diet or other aspects of host physiology, behavior, or environments”.

      We now cite:

      · Hicks, A.L., et al. (2018). Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nature Communications 9, 1786.

      · Kolodny, O., et al. (2019). Coordinated change at the colony level in fruit bat fur microbiomes through time. Nature Ecology & Evolution 3, 116-124.

      · Risely, A., et al. (2021) Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats. Nat Commun 12, 6017.

      (6) Lines 135-137: current or past season and social rank? This paragraph introduces the idea that it could be past rather than current socio-environmental factors that might predict microbiota age, so the authors should clarify this sentence.

      We have clarified the information in this sentence. line 135 now reads, “In general, our results support the idea that a baboon’s current socio-environmental conditions, especially their current social rank and the season of sampling, have stronger effects on microbiome age than early life events—many of which occurred many years prior to sampling.”

      (7) Lines 136-137: this sentence could include some kind of a conclusion of this finding. What might this mean?

      We have added a sentence at line 138, which speculates that, “…the dynamism of the gut microbiome may often overwhelm and erase early life effects on gut microbiome age.”

      (8) Use 'microbiota' or 'microbiome' across the manuscript; currently, the terms are used interchangeably. I don't have a strong opinion on this, although typically 'microbiota' is used when data comes from 16S rRNA.

      We have updated the text to replace any instance of “microbiota” with “microbiome”. We use the term microbiome in the sense of this definition from the National Human Genome Research Institute, which defines a microbiome as “the community of microorganisms (such as fungi, bacteria and viruses) that exists in a particular environment”.

      (9) Figure 1 legend: make sure to unify formatting; e.g., present sample sizes as N= or n=, rather than both, and either include or do not include commas in 4-digit values (sample sizes).

      We have checked the formatting related to sample sizes and the use of commas in 4-digits in the main text and supplement. The formats are now consistent.

      (10) Line 166: relative abundances surely?

      Following Gloor et al. (2017), our analyses use centered log-ratio (CLR) transformations of read counts, which is the recommended approach for compositional data such as 16S rRNA amplicon read counts. CLR transformations are scale-invariant, so the same ratio is obtained in a sample with few read versus many reads. We now cite Gloor et al. (2017) at line 169 and in the methods in line 517, which reads “centered log ratio (CLR) transformed abundances (i.e., read counts) of each microbial phyla (n=30), family (n=290), genus (n=747), and amplicon sequence variance (ASV) detected in >25% of samples (n=358). CLR transformations are a recommended approach for addressing the compositional nature of 16S rRNA amplicon read count data (Gloor et al. 2017).”  

      (11) Lines 167-172: were technical factors, e.g., read depth or sequencing batch, included as random effects?

      Thank you for catching this oversight in the text. We did model sequencing depth and batch effects. The sentence starting at line 173 now reads, “For each of these 1,440 features, we tested its association with host age by running linear mixed effects models that included linear and quadratic effects of host age and four other fixed effects: sequencing depth, the season of sample collection (wet or dry), the average maximum temperature for the month prior to sample collection, and the total rainfall in the month prior to sample collection (Grieneisen et al. 2021; Björk et al. 2022; Tung et al. 2015). Baboon identity, social group membership, hydrological year of sampling, and sequencing plate (as a batch effect) were modeled as random effects.”

      (12) Lines 175-180: When discussing how these alpha diversity results relate to previous findings, the authors should be clear about whether they talk about weighted or non-weighted measures of alpha diversity. - also maybe this should be included in the discussion rather than the results? Please consider this when revisiting the manuscript (see how it reads after edits).

      Richness is the only unweighted metric, which we now clarify in line 181. We opted to retain the interpretation in the text in its original location to maintain the emphasis in the discussion on the microbiome clock results.

      (13) Table S1 is very hard to interpret in the provided PDF format as columns are not presented side-by-side. It is currently hard to check model output for e.g., specific families. This needs to be revisited.

      We agree. We believe that eLife’s submission portal automatically generates a PDF for any supplementary item. However, we also include the supplementary tables as an Excel workbook which has the columns presented side-by-side.

      (14) Line 184: taxa meaning what? Unclear what authors refer to with this sentence, taxa across taxonomic levels, or ASVs, or what does the 51.6% refer to?

      We have edited line 191 to clarify that this sentence refers to taxa at all taxonomic levels (phyla to ASVs).

      (15) Line 191: a punctuation mark missing after ref (81).

      We have added the missing period at the end of this sentence.

      (16) Lines 189-197: this should go into the discussion in my opinion.

      We have opted to retain this interpretation, now at line 183.

      (17) Lines 215-219: Not sure what this means; do the authors mean features were not restricted to age-associated taxa, ie also e.g., diversity and other taxa-independent patterns were included? If so, the rest of the highlighted lines should be revisited to make this clear, currently to me it is very unclear what 'These could include features that are not strongly age-correlated in isolation' means. Currently, that sounds like some features included were only age-associated in combination with other features, but unclear how this relates to taxa-dependency/taxa-independency.

      We agree this was not clear. We have revised line 224 to read, “We included all 9,575 microbiome features in our age predictions, as opposed to just those that were statistically significantly associated with age because removing these non-significant features could exclude features that contribute to age prediction via interactions with other taxa.”

      (18) Line 403-407: There is now a paper showing epigenetic clocks can be built with faecal samples, so this argument is not valid. Please revisit in light of this publication: https://onlinelibrary.wiley.com/doi/epdf/10.1111/mec.17330

      Thank you for bringing this paper to our attention. We deleted the text that describes epigenetic clocks as invasive, and we now cite this paper in line 450, which reads, “We also hope to measure epigenetic age in fecal samples, leveraging methods developed in Hanski et al. 2024.”

      (19) Line 427: a punctuation mark/semicolon missing before However.

      We have corrected this typo.

      (20) Lines 419-428: I don't quite understand this speculation. Why would the priority of access to food lead to an old-looking gut microbiome? This paragraph needs stronger arguments, currently unclear and also not super convincing.

      We agree this was confusing. We have revised this text to clarify the explanation. The text starting at line 424 now reads, “This outcome points towards a shared driver of high social status in shaping gut microbiome age in both males and females. While it is difficult to identify a plausible shared driver, one benefit shared by both high-ranking males and females is priority of access to food. This access may result in fewer foraging disruptions and a higher quality, more stable diet. At the same time, prior research in Amboseli suggests that as animals age, their diets become more canalized and less variable (Grieneisen et al. 2021). Hence aging and priority of access to food might both be associated with dietary stability and old-for-age microbiomes. However, this explanation is speculative and more work is needed to understand the relationship between rank and microbiome age.”

      (21) Line 434: remove 'be'.

      We have corrected this typo.

      (22) Line 478: add information on how samples were collected; e.g., were samples collected from the ground? How was cross-contamination with soil microbiota minimised? Were samples taken from the inner part of depositions? These factors can influence microbiota samples quite drastically so detailed info is needed. Also what does homogenisation mean in this context? How soon were samples freeze-dried after sample collection?

      We have expanded our methods with respect to sample collection. This text starts in line 483 and reads, “Samples were collected from the ground within 15 minutes of defecation. For each sample, approximately 20 g of feces was collected into a paper cup, homogenized by stirring with a wooden tongue depressor, and a 5 g aliquot of the homogenized sample was transferred to a tube containing 95% ethanol. While a small amount of soil was typically present on the outside of the fecal sample, mammalian feces contains 1000 times the number of microbial cells in a typical soil sample (Sender, Fuchs, and Milo 2016; Raynaud and Nunan 2014), which overwhelms the signal of soil bacteria in our analyses (Grieneisen et al. 2021). Samples were transported from the field in Amboseli to a lab in Nairobi, freeze-dried, and then sifted to remove plant matter prior to long term storage at -80°C.”

      (23) Line 480 onwards: were negative controls included in extraction batches? Were samples randomised into extraction batches?

      Yes, we included extraction blanks. These are now described in lines 495-500. This text reads, “We included one extraction blank per batch, which had significantly lower DNA concentrations than sample wells (t-test; t=-50, p < 2.2x10-16; Grieneisen et al. 2021). We also included technical replicates, which were the same fecal sample sequenced across multiple extraction and library preparation batches. Technical replicates from different batches clustered with each other rather than with their batch, indicating that true biological differences between samples are larger than batch effects.”

      (24) Were extraction, library prep, and sequencing negative controls included? Is data available?

      We included extraction blanks (described above) and technical replicates, which were the same sample sequenced across multiple extraction and library preparation batches. Technical replicates from different batches clustered with each other rather than with their batch, indicating that true biological differences between samples are larger than batch effects.

      We have updated the data availability statement to read, “All data for these analyses are available on Dryad at https://doi.org/10.5061/dryad.b2rbnzspv. The 16S rRNA gene sequencing data are deposited on EBI-ENA (project ERP119849) and Qiita (study 12949). Code is available at the following GitHub repository: https://github.com/maunadasari/Dasari_etal-GutMicrobiomeAge”.

      (25) Line 562: how were corrected microbiome delta ages calculated? Currently, the authors state x, y and z factors were corrected for, but it is unclear how this was done.

      The paragraph starting at line 577 describes how microbiome delta age was calculated. We have made only a few changes to this text because we were not sure which aspects of these methods confused the reviewer. However, briefly, we calculated sample-specific microbiome Dage in years as the difference between a sample’s microbial age estimate, age<sub>m</sub> from the microbiome clock, and the host’s chronological age in years at the time of sample collection, age<sub>c</sub>. Higher microbiome Dages indicate old-for-age microbiomes, as age<sub>m</sub> > age<sub>c</sub>, and lower values (which are often negative) indicate a young-for-age microbiome, where age<sub>c</sub> > age<sub>m</sub> (see Figure 3).

      (26) Line 579: typo 'as'.

      We have corrected this typo.

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    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this article, Nedbalova et al. investigate the biochemical pathway that acts in circulating immune cells to generate adenosine, a systemic signal that directs nutrients toward the immune response, and S-adenosylmethionine (SAM), a methyl donor for lipid, DNA, RNA, and protein synthetic reactions. They find that SAM is largely generated through the uptake of extracellular methionine, but that recycling of adenosine to form ATP contributes a small but important quantity of SAM in immune cells during the immune response. The authors propose that adenosine serves as a sensor of cell activity and nutrient supply, with adenosine secretion dominating in response to increased cellular activity. Their findings of impaired immune action but rescued larval developmental delay when the enzyme Ahcy is knocked down in hemocytes are interpreted as due to effects on methylation processes in hemocytes and reduced production of adenosine to regulate systemic metabolism and development, respectively. Overall this is a strong paper that uses sophisticated metabolic techniques to map the biochemical regulation of an important systemic mediator, highlighting the importance of maintaining appropriate metabolite levels in driving immune cell biology.

      Strengths:

      The authors deploy metabolic tracing - no easy feat in Drosophila hemocytes - to assess flux into pools of the SAM cycle. This is complemented by mass spectrometry analysis of total levels of SAM cycle metabolites to provide a clear picture of this metabolic pathway in resting and activated immune cells.

      The experiments show that the recycling of adenosine to ATP, and ultimately SAM, contributes meaningfully to the ability of immune cells to control infection with wasp eggs.

      This is a well-written paper, with very nice figures showing metabolic pathways under investigation. In particular, the italicized annotations, for example, "must be kept low", in Figure 1 illustrate a key point in metabolism - that cells must control levels of various intermediates to keep metabolic pathways moving in a beneficial direction.

      Experiments are conducted and controlled well, reagents are tested, and findings are robust and support most of the authors' claims.

      Weaknesses:

      The authors posit that adenosine acts as a sensor of cellular activity, with increased release indicating active cellular metabolism and insufficient nutrient supply. It is unclear how generalizable they think this may be across different cell types or organs.

      In the final part of the Discussion, we elaborate slightly more on a possible generalization of our results, while being aware of the limited space in this experimental paper and therefore intend to address this in more detail and comprehensively in a subsequent perspective article.

      The authors extrapolate the findings in Figure 3 of decreased extracellular adenosine in ex vivo cultures of hemocytes with knockdown of Ahcy (panel B) to the in vivo findings of a rescue of larval developmental delay in wasp egg-infected larvae with hemocyte-specific Ahcy RNAi (panel C). This conclusion (discussed in lines 545-547) should be somewhat tempered, as a number of additional metabolic abnormalities characterize Ahcy-knockdown hemocytes, and the in vivo situation may not mimic the ex vivo situation. If adenosine (or inosine) measurements were possible in hemolymph, this would help bolster this idea. However, adenosine at least has a very short half-life.

      We agree with the reviewer, and in the 4th paragraph of the Discussion we now discuss more extensively the limitations of our study in relation to ex vivo adenosine measurements and the importance of the SAM pathway on adenosine production.

      Reviewer #2 (Public review):

      Summary:

      In this work, the authors wish to explore the metabolic support mechanisms enabling lamellocyte encapsulation, a critical antiparasitic immune response of insects. They show that S-adenosylmethionine metabolism is specifically important in this process through a combination of measurements of metabolite levels and genetic manipulations of this metabolic process.

      Strengths:

      The metabolite measurements and the functional analyses are generally very strong and clearly show that the metabolic process under study is important in lamellocyte immune function.

      Weaknesses:

      The gene expression data are a potential weakness. Not enough is explained about how the RNAseq experiments in Figures 2 and 4 were done, and the representation of the data is unclear.

      The RNAseq data have already been described in detail in our previous paper (doi.org/10.1371/journal.pbio.3002299), but we agree with the reviewer that we should describe the necessary details again here. The replicate numbers for RNAseq data were added to figure legends, the TPM values for the selected genes shown in figures are in S1_Data and new S4_Data file with complete RNAseq data (TPM and DESeq2) was added to this revised version.

      The paper would also be strengthened by the inclusion of some measure of encapsulation effectiveness: the authors show that manipulation of the S-adenosylmethionine pathway in lamellocytes affects the ability of the host to survive infection, but they do not show direct effects on the ability of the host to encapsulate wasp eggs.

      The reviewer is correct that wasp egg encapsulation and host survival may be different (the host can encapsulate and kill the wasp egg and still not survive) and we should also include encapsulation efficiency. This is now added to Figure 3D, which shows that encapsulation efficiency is reduced upon Ahcy-RNAi, which is consistent with the reduced number of lamellocytes.

      Reviewer #3 (Public review):

      Summary:

      The authors of this study provide evidence that Drosophila immune cells show upregulated SAM transmethylation pathway and adenosine recycling upon wasp infection. Blocking this pathway compromises the lamellocyte formation, developmental delay, and host survival, suggesting its physiological relevance.

      Strengths:

      Snapshot quantification of the metabolite pool does not provide evidence that the metabolic pathway is active or not. The authors use an ex vivo isotope labelling to precisely monitor the SAM and adenosine metabolism. During infection, the methionine metabolism and adenosine recycling are upregulated, which is necessary to support the immune reaction. By combining the genetic experiment, they successfully show that the pathway is activated in immune cells.

      Weaknesses:

      The authors knocked down Ahcy to prove the importance of SAM methylation pathway. However, Ahcy-RNAi produces a massive accumulation of SAH, in addition to blocking adenosine production. To further validate the phenotypic causality, it is necessary to manipulate other enzymes in the pathway, such as Sam-S, Cbs, SamDC, etc.

      We are aware of this weakness and have addressed it in a much more detailed discussion of the limitations of our study in the 6th paragraph of the Discussion.

      The authors do not demonstrate how infection stimulates the metabolic pathway given the gene expression of metabolic enzymes is not upregulated by infection stimulus.

      Although the goal of this work was to test by 13C tracing whether the SAM pathway activity is upregulated, not to analyze how its activity is regulated, we certainly agree with the reviewer that an explanation of possible regulation, especially in the context of the enzyme expressions we show, should be included in our work. Therefore, we have supplemented the data with methyltransferase expressions (Figure 2-figure supplement 3. And S3_Data) and better describe the changes in expression of some SAM pathway genes, which also support stimulation of this pathway by changes in expression. The enzymes of the SAM transmethylation pathway are highly expressed in hemocytes, and it is known that the activity of this pathway is primarily regulated by (1) increased methionine supply to the cell and (2) the actual utilization of SAM by methyltransferases. Therefore, a possible increase in SAM transmethylation pathway in our work can be suggested (1) by increased expression of 4 transporters capable of transporting methionine, (2) by decreased expression of AhcyL2 (dominant-negative regulator of Ahcy) and (3) by increased expression of 43 out of 200 methyltransferases. This was now added to the first section of Results.

      Recommendations for the authors:

      Reviewing Editor Comments:

      In the discussion with the reviewers, two points were underlined as very important:

      (1) Knocking down Ahyc and other enzymes in the SAM methylation pathway may give very distinct phenotypes. Generalising the importance of "SAM methyaltion" only by Ahcy-RNAi is a bit cautious. The authors should be aware of this issue and probably mention it in the Discussion part.

      We are aware of this weakness and have addressed it in a much more detailed discussion of the limitations of our study in the 6th paragraph of the Discussion.

      (2) Sample sizes should be indicated in the Figure Legends. Replicate numbers on the RNAseq are important - were these expression levels/changes seen more than once?

      Sample sizes are shown as scatter plots with individual values wherever possible and all graphs are supplemented with S1_Data table with raw data. The RNAseq data have already been described in detail in our previous paper (doi.org/10.1371/journal.pbio.3002299), but we agree with the reviewers that we should describe the necessary details again here. The replicate numbers for RNAseq data were added to figure legends, the TPM values for the selected genes shown in figures are in S1_Data and new S4_Data file with complete RNAseq data (TPM and DESeq2) was added to this revised version.

      Reviewer #1 (Recommendations for the authors):

      Major points:

      (1) Please provide sample sizes in the legends rather than in a supplementary table.

      Sample sizes are shown either as scatter plots with individual values or added to figure legends now.

      (2) More details in the methods section are needed:

      For hemocyte counting, are sessile and circulating hemocytes measured?

      We counted circulating hemocytes (upon infection, most sessile hemocytes are released into the circulation). While for metabolomics all hemocyte types were included, for hemocyte counting we were mainly interested in lamellocytes. Therefore, we counted them 20 hours after infection, when most of the lamellocytes from the first wave are fully differentiated but still mostly in circulation, as they are just starting to adhere to the wasp egg. This was added to the Methods section.

      How were levels of methionine and adenosine used in ex vivo cultures selected? This is alluded to in lines 158-159, but no references are provided.

      The concentrations are based on measurements of actual hemolymph concentrations in wild-type larvae in the case of methionine, and in the case of adenosine, we used a slightly higher concentration than measured in the adgf-a mutant to have a sufficiently high concentration to allow adenosine to flow into the hemocytes. This is now added to the Methods section.

      Minor points:

      Response to all minor points:  Thank you, errors has now been fixed.

      (1) Line 186 - spell out MTA - 5-methylthioadenosine.

      (2) Lines 196-212 (and elsewhere) - spelling out cystathione rather than using the abbreviation CTH is recommended because the gene cystathione gamma-lyase (Cth) is also discussed in this paragraph. Using the full name of the metabolite will reduce confusion.

      We rather used cystathionine γ-lyase as a full name since it is used only three times while CTH many more times, including figures.

      (3) Figure 2 - supplement 2: please include scale bars.

      (4) Line 303 - spelling error: "trabsmethylation" should be "transmethylation".

      (5) Line 373 - spelling error: "higer" should be "higher".

      Reviewer #2 (Recommendations for the authors):

      For the RNAseq data, it's unclear whether the gene expression data in Figures 2 and 4 include biological replicates, so it's unclear how much weight we should place on them.

      The replicate numbers for RNAseq data were added to figure legends, the TPM values for the selected genes shown in figures are in S1_Data and new S4_Data file with complete RNAseq data (TPM and DESeq2) was added to this revised version.

      The representation of these data is also a weakness: Figure 2 shows measurements of transcripts per million, but we don't know what would be high or low expression on this scale.

      We have added the actual TPM values for each cell in the RNAseq heatmaps in Figure 2, Figure 2-figure supplement 3, and Figure 4 to make them more readable. Although it is debatable what is high or low expression, to at least have something for comparison, we have added the following information to the figure legends that only 20% of the genes in the presented RNAseq data show expression higher than 15 TPM.

      Figure 4 is intended to show expression changes with treatment, but expression changes should be shown on a log scale (so that increases and decreases in expression are shown symmetrically) and should be normalized to some standard level (such as uninfected lamellocytes).

      The bars in Figure 4C,D show the fold change (this is now stated in the y-axis legend) compared to 0 h (=uninfected) Adk3 samples - the reason for this visualization is that we wanted to show (1) the differences in levels between Adk3 and Adk2 and in levels between Ak1 and Ak2, respectively, and at the same time (2) the differences between uninfected and infected Adk3 and Ak1. In our opinion, these fold change differences are also much more visible in normal rather than log scale.

      Reviewer #3 (Recommendations for the authors):

      (1) It might be interesting to test how general this finding would be. How about Bacterial or fungal infection? The authors may also try genetic activation of immune pathways, e.g. Toll, Imd, JAK/STAT.

      Although we would also like to support our results in different systems, we believe that our results are already strong enough to propose the final hypothesis and publish it as soon as possible so that it can be tested by other researchers in different systems and contexts than the Drosophila immune response.

      (2) How does the metabolic pathway get activated? Enzyme activity? Transporters? Please test or at least discuss the possible mechanism.

      The response is already provided above in the Reviewer #3 (Public review) section.

      (3) The authors might test overexpression or genetic activation of the SAM transmethylation pathway.

      Although we agree that this would potentially strengthen our study, it may not be easy to increase the activity of the SAM transmethylation pathway - simply overexpressing the enzymes may not be enough, the regulation is primarily through the utilization of SAM by methyltransferases and there are hundreds of them and they affect numerous processes. 

      (4) Supplementation of adenosine to the Ahcy-RNAi larvae would also support their conclusion.

      Again, this is not an easy experiment, dietary supplementation would not work, direct injection of adenosine into the hemolymph would not last long enough, adenosine would be quickly removed.

      (5) It is interesting to test genetically the requirement of some transporters, especially for gb, which is upregulated upon infection.

      Although this would be an interesting experiment, it is beyond the scope of this study; we did not aim to study the role of the SAM transmethylation pathway itself or its regulation, only its overall activity and its role in adenosine production.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary of what the authors were trying to achieve:

      In this manuscript, the authors investigated the role of β-CTF on synaptic function and memory. They report that β-CTF can trigger the loss of synapses in neurons that were transiently transfected in cultured hippocampal slices and that this synapse loss occurs independently of Aβ. They confirmed previous research (Kim et al, Molecular Psychiatry, 2016) that β-CTF-induced cellular toxicity occurs through a mechanism involving a hexapeptide domain (YENPTY) in β-CTF that induces endosomal dysfunction. Although the current study also explores the role of β-CTF in synaptic and memory function in the brain using mice chronically expressing β-CTF, the studies are inconclusive because potential effects of Aβ generated by γ-secretase cleavage of β-CTF were not considered. Based on their findings, the authors suggest developing therapies to treat Alzheimer's disease by targeting β-CTF, but did not address the lack of clinical improvement in trials of several different BACE1 inhibitors, which target β-CTF by preventing its formation.

      We would like to thank the reviewer for his/her suggestions. We have addressed the specific comments in following sections.

      Major strengths and weaknesses of the methods and results:

      The conclusions of the in vitro experiments using cultured hippocampal slices were well supported by the data, but aspects of the in vivo experiments and proteomic studies need additional clarification.

      (1) In contrast to the in vitro experiments in which a γ-secretase inhibitor was used to exclude possible effects of Aβ, this possibility was not examined in in-vivo experiments assessing synapse loss and function (Figure 3) and cognitive function (Figure 4). The absence of plaque formation (Figure 4B) is not sufficient to exclude the possibility that Aβ is involved. The potential involvement of Aβ is an important consideration given the 4-month duration of protein expression in the in vivo studies.

      We appreciate the reviewer for raising this question. While our current data did not exclude the potential involvement of Aβ-induced toxicity in the synaptic and cognitive dysfunction observed in mice overexpressing β-CTF, addressing this directly remains challenging. Treatment with γ-secretase inhibitors could potentially shed light on this issue. However, treatments with γ-secretase inhibitors are known to lead to brain dysfunction by itself likely due to its blockade of the γ-cleavage of other essential molecules, such as Notch[1, 2]. Therefore, this approach is unlikely to provide a clear answer, which prevents us from pursuing it further experimentally in vivo. We hope the reviewer understands this limitation. We have included additional discussion (page 14 of the revised manuscript) to highlight this question.

      (2) The possibility that the results of the proteomic studies conducted in primary cultured hippocampal neurons depend in part on Aβ was also not taken into consideration.

      We thank the reviewer for raising this question. In the revised manuscript, we examined the protein levels of synaptic proteins after treatment with γ-secretase inhibitors and found that the levels of certain synaptic proteins were further reduced in neurons expressing β-CTF (Supplementary figure 5A-B). These results do not support Aβ as a major contributor of the proteomic changes induced by β-CTF.

      Likely impact of the work on the field, and the utility of the methods and data to the community:

      The authors' use of sparse expression to examine the role of β-CTF on spine loss could be a useful general tool for examining synapses in brain tissue.

      We thank the reviewer for these comments.

      Additional context that might help readers interpret or understand the significance of the work:

      The discovery of BACE1 stimulated an international effort to develop BACE1 inhibitors to treat Alzheimer's disease. BACE1 inhibitors block the formation of β-CTF which, in turn, prevents the formation of Aβ and other fragments. Unfortunately, BACE1 inhibitors not only did not improve cognition in patients with Alzheimer's disease, they appeared to worsen it, suggesting that producing β-CTF actually facilitates learning and memory. Therefore, it seems unlikely that the disruptive effects of β-CTF on endosomes plays a significant role in human disease. Insights from the authors that shed further light on this issue would be welcome.

      Response: We would like to express our gratitude to the reviewer for raising this question. It remains puzzling why BACE1 inhibition has failed to yield benefits in AD patients, while amyloid clearance via Aβ antibodies are able to slow down disease progression. One possible explanation is that pharmacological inhibition of BACE1 may not be as effective as its genetic removal. Indeed, genetic depletion of BACE1 leads to the clearance of existing amyloid plaques[3], whereas its pharmacological inhibition prevents the formation of new plaques but does not deplete the existing ones[4]. We think the negative results of BACE1 inhibitors in clinical trials may not be sufficient to rule out the potential contribution of β-CTF to AD pathogenesis. Given that cognitive function continues to deteriorate rapidly in plaque-free patients after 1.5 years of treatment with Aβ antibodies in phase three clinical studies[5], it is important to consider the potential role of other Aβ-related fragments in AD pathogenesis, such as β-CTF. We included further discussion in the revised manuscript (page 15 of the revised manuscript) to discusss this question.

      Reviewer #2 (Public Review):

      Summary:

      In this study, the authors investigate the potential role of other cleavage products of amyloid precursor protein (APP) in neurodegeneration. They combine in vitro and in vivo experiments, revealing that β-CTF, a product cleaved by BACE1, promotes synaptic loss independently of Aβ. Furthermore, they suggest that β-CTF may interact with Rab5, leading to endosomal dysfunction and contributing to the loss of synaptic proteins.

      We would like to thank the reviewer for his/her suggestions. We have addressed the specific comments in following sections.

      Weaknesses:

      Most experiments were conducted in vitro using overexpressed β-CTF. Additionally, the study does not elucidate the mechanisms by which β-CTF disrupts endosomal function and induces synaptic degeneration.

      We would like to thank the reviewer for this comment. While a significant portion of our experiments were conducted in vitro, the main findings were also confirmed in vivo (Figure 3 and 4). Repeating all the experiments in vivo would be challenging and may not be possible because of technical difficulties. Regarding the use of overexpressed β-CTF, we acknowledge that this represents a common limitation in neurodegenerative disease studies. These diseases progress slowly over decades in patients. To model this progression in cell or mouse models within a time frame feasible for research, overexpression of certain proteins is often inevitable. Since β-CTF levels are elevated in AD patients[6], its overexpression is not a irrelevant approach to investigate its potential effects.

      We did not further investigate the mechanisms by which β-CTF disrupted endosomal function because our preliminary results align with previous findings that could explain its mechanism. Kim et al. demonstrated that β-CTF recruits APPL1 (a Rab5 effector) via the YENPTY motif to Rab5 endosomes, where it stabilizes active GTP-Rab5, leading to pathologically accelerated endocytosis, endosome swelling and selectively impaired transport of Rab5 endosomes[6]. However, this paper did not show whether this Rab5 overactivation-induced endosomal dysfunction leads to any damages in synapses. In our study, we observed that co-expression of Rab5<sub>S34N</sub> with β-CTF effectively mitigated β-CTF-induced spine loss in hippocampal slice cultures (Figures 6L-M), indicating that Rab5 overactivation-induced endosomal dysfunction contributed to β-CTF-induced spine loss. We included further discussion in the revised manuscript to clarify this (page 15 of the revised manuscript).

      Reviewer #3 (Public Review):

      Summary:

      Most previous studies have focused on the contributions of Abeta and amyloid plaques in the neuronal degeneration associated with Alzheimer's disease, especially in the context of impaired synaptic transmission and plasticity which underlies the impaired cognitive functions, a hallmark in AD. But processes independent of Abeta and plaques are much less explored, and to some extent, the contributions of these processes are less well understood. Luo et all addressed this important question with an array of approaches, and their findings generally support the contribution of beta-CTF-dependent but non-Abeta-dependent process to the impaired synaptic properties in the neurons. Interestingly, the above process appears to operate in a cell-autonomous manner. This cell-autonomous effect of beta-CTF as reported here may facilitate our understanding of some potentially important cellular processes related to neurodegeneration. Although these findings are valuable, it is key to understand the probability of this process occurring in a more natural condition, such as when this process occurs in many neurons at the same time. This will put the authors' findings into a context for a better understanding of their contribution to either physiological or pathological processes, such as Alzheimer's. The experiments and results using the cell system are quite solid, but the in vivo results are incomplete and hence less convincing (see below). The mechanistic analysis is interesting but primitive and does not add much more weight to the significance. Hence, further efforts from the authors are required to clarify and solidify their results, in order to provide a complete picture and support for the authors' conclusions.

      We would like to thank the reviewer for the suggestions. We have addressed the specific comments in following sections.

      Strengths:

      (1) The authors have addressed an interesting and potentially important question

      (2) The analysis using the cell system is solid and provides strong support for the authors' major conclusions. This analysis has used various technical approaches to support the authors' conclusions from different aspects and most of these results are consistent with each other.

      We would like to thank the reviewer for these comments.

      Weaknesses:

      (1) The relevance of the authors' major findings to the pathology, especially the Abeta-dependent processes is less clear, and hence the importance of these findings may be limited.

      We would like to thank the reviewer for this question. Phase 3 clinical trial data from Aβ antibodies show that cognitive function continues to decline rapidly, even in plaque-free patients, after 1.5 years of treatment[5]. This suggests that plaque-independent mechanisms may drive AD progression. Therefore, it is crucial to consider the potential contributions of other Aβ species or related fragments, such as alternative forms of Aβ and β-CTF. While it is early to predict how much β-CTF contributes to AD progression, it is notable that β-CTF induced synaptic deficits in mice, which recapitulates a key pathological feature of AD. Ultimately, the contribution of β-CTF in AD pathogenesis can only be tested through clinical studies in the future.

      (2) In vivo analysis is incomplete, with certain caveats in the experimental procedures and some of the results need to be further explored to confirm the findings.

      We would like to thank the reviewer for this suggestion. We have corrected these caveats in the revised manuscript.

      (3) The mechanistic analysis is rather primitive and does not add further significance.

      We would like to thank the reviewer for this comment. We did not delve further into the underlying mechanisms because our analysis indicates that Rab5 overactivation-induced endosomal dysfunction underlies β-CTF-induced synaptic dysfunction, which is consistent with another study and has been addressed in our study[6]. We hope the reviewer could understand that our focus in this paper is on how β-CTF triggers synaptic deficits, which is why we did not investigate the mechanisms of β-CTF-induced endosomal dysfunction further.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Suggestions for improved or additional experiments, data, or analyses:

      (1) In Figures 4H, 4J, 4K and Supplemental Figures 3C, 3E, and 3G, it was unclear whether a repeated measures 2-way ANOVA, rather than a 2-way ANOVA, followed by appropriate post-hoc analyses was used to strengthen the conclusion that there were significant effects in the behavioral tests.

      We appreciate the reviewer for raising this point and apologize for the lack of clear description in the manuscript. In those figures mentioned above, we use a repeated measures 2-way ANOVA to analyze the data by Graphpad Prism. In Figure 4H, fear conditioning tests were conducted. The same cohort of mice were used in the baseline, contextual and cued tests. Firstly, baseline freezing was tested; then these mice underwent tone and foot shock training, followed by contextual test and cued test. So, a repeated measures 2-way ANOVA is more appropriate for the experiment.

      In water T maze tests (Figure 4J and K), the same cohort of mice were trained and tested each day. So, it’s also appropriate to use a repeated measures 2-way ANOVA.

      In Supplementary figure 3C, 3E and 3G, OFT was conducted. In this experiment, the locomotion of the same cohort of mice were recorded. Also, it’s appropriate to use a repeated measures 2-way ANOVA.

      Clearer description for these experiments has been provided in the revised manuscript.

      (2) Including gender analyses would be helpful.

      The mice we used in this study were all males.

      Minor corrections to text and figures:

      (1) Quantitative analyses in Figures 5A-C, 5H, 6G, 6H, and Supplementary Figures 4 and 5C would be helpful.

      We have provided quantitative analysis of these results (Figure 5D, 5J, 6K, Supplementary figure 4D, 5F) mentioned above in the revised manuscript.

      (2) Percent correct (%) in Figures 4J and 4K should be labeled as 0, 50, and 100 instead of 0.0, 0.5, and 1.0.

      We would like to thank the reviewer for pointing out this. We have made corrections in the revised manuscript.

      Reviewer #2 (Recommendations For The Authors):

      In the study conducted by Luo et al, it was observed that the fragment of amyloid precursor protein (APP) cleaved by beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), known as β-CTF, plays a crucial role in synaptic damage. The study found increasing expression of β-CTF in neurons could induce synapse loss both in vitro and in vivo, independent of Aβ. Mechanistically, they explored how β-CTF could interfere with the endosome system by interacting with RAB5. While this study is intriguing, there are several points that warrant further investigation:

      (1) The study involved overexpressing β-CTF in neurons. It would be valuable to know if the levels of β-CTF are similarly increased in Alzheimer's disease (AD) patients or AD mouse models.

      We would like to thank the reviewer for the suggestion. It’s reported β-CTF levels were significantly elevated in the AD cerebral cortex[6]. Most AD mouse models are human APP transgenic mouse models with elevated β-CTF levels[7].

      (2) The study noted that β-CTF in neurons is a membranal fragment, but the overexpressed β-CTF was not located in the membrane. It is important to ascertain whether the membranal β-CTF and cytoplasmic β-CTF lead to synapse loss in a similar manner.

      We apologize for not clearly explaining the localization of β-CTF in the original manuscript. β-CTF is produced from APP through β-cleavage, a process that occurs in organelles such as endo-lysosomes[8]. The overexpressed β-CTF is also primarily localized in the endo-lysosomal systems (Figure 5C and Supplementary figure 4C), similar to those generated by APP cleavage.

      (3) The study found a significant decrease in GluA1, a subunit of AMPA receptors, due to β-CTF. It would be beneficial to investigate whether there are systematic alterations in NMDA receptors, including GluN2A and GluN2B.

      We would like to express our gratitude to the reviewer for bringing up this question. The protein levels of GluN2A and GluN2B are also reduced in neurons expressing β-CTF (Figure 6E-F)

      (4) The study showed a significant decrease in the frequency of miniature excitatory postsynaptic currents (mEPSC), indicating disrupted presynaptic vesicle neurotransmitter release. It would be pertinent to test whether the expression level of the presynaptic SNARE complex, which is required for vesicle release, is altered by β-CTF.

      We would like to express our gratitude to the reviewer for bringing up this question. The protein level of the presynaptic SNARE complex, such as VAMP2, is also reduced in neurons expressing β-CTF (Figure 6E, G).

      (5) Since AMPA receptors are glutamate receptors, it is important to determine whether the ability of glutamate release is altered by β-CTF. In vivo studies using a glutamate sensor should be conducted to examine glutamate release.

      We would like to express our gratitude to the reviewer for this suggestion. It will be interesting to use glutamate sensors to assess the ability of glutamate release in the future.

      (6) The quality of immunostaining associated with Figures 4B and 4C was noted to be suboptimal.

      We apologize for the suboptimal quality of these images. The immunostaining in Figures 4B and 4C were captured using the stitching function of a confocal microscope to display larger areas, including the entire hemisphere and hippocampus. We have reprocessed the images to obtain higher-quality versions.

      (7) It would be insightful to investigate whether treatment with a BACE1 inhibitor in the study could reverse synaptic deficits mediated by β-CTF.

      We would like to thank the reviewer for this sggestion. In Figure 1I-M, we constructed an APP mutant (APP<sub>MV</sub>), which cannot be cleaved by BACE1 to produce β-CTF and Aβ but has no impact on β’-cleavage. When co-expressed with BACE1, APP<sub>MV</sub> failed to induce spine loss, supporting the effect of β-CTF. We think these results domonstrate that β-CTF underlies the synaptic deficits. It would be interesting to test the effects of BACE1 inhibition in the future.

      (8) Considering the potential implications for therapeutics, it is worth exploring whether extremely low levels of β-CTF have beneficial effects in regulating synaptic function or promoting synaptogenesis at a physiological level.

      We would like to thank the reviewer for raising this question. We found that when the plasmid amount was reduced to 1/8 of the original dose, β-CTF no longer induced a decrease in dendritic spine density (Supplementary figure 2E-F). It’s reported APP-Swedish mutation in familial AD increased synapse numbers and synaptic transmission, whereas inhibition of BACE1 lowered synapse numbers, suppressed synaptic transmission in wild type neurons, suggesting that at physiological level, β-CTF might be synaptogenic[9].

      (9) The molecular mechanism through which β-CTF interferes with Rab5 function should be elucidated.

      We would like to thank the reviewer for raising this question. Kim et al have elucidated the mechanism through which β-CTF interferes with Rab5 function. β-CTF recruited APPL1 (a Rab5 effector) via YENPTY motif to Rab5 endosomes, where it stabilizes active GTP-Rab5, leading to pathologically accelerated endocytosis, endosome swelling and selectively impaired transport of Rab5 endosomes[6]. We have included additional discussion for this question in the revised manuscript (page 15 of the revised manuscript).

      (10) The study could compare the role of β-CTF and Aβ in neurodegeneration in AD mouse models.

      We would like to thank the reviewer for raising this point. While it is easier to dissect the role of Aβ and β-CTF in vitro, some of the critical tools are not applicabe in vivo, such as γ-secretase inhibitors, which lead to severe side effects because of their inhibition on other γ substrates[1, 2]. Therefore it will be difficult to deomonstrate their different roles in vivo. There are studies showing that β-CTF accumulation precedes Aβ deposition in model mice and mediates Aβ independent intracellular pathologies[10, 11], consistent with our results.

      (11) Based on the findings, it would be valuable to discuss possible explanations for the failure of most BACE1 inhibitors in recent clinical trials for humans.

      Response: We would like to express our gratitude to the reviewer for raising this recommendation. It is a big puzzle why BACE1 inhibition failed to provide beneficial effects in AD patients whereas clearance of amyloid by Aβ antibodies could slow down the AD progress. One potential answer is that pharmacological inhibition of BACE1 might be not as effective as its genetic removal. Indeed, genetic depletion of BACE1 leads to clearance of existing amyloid plaques[3], whereas pharmacological inhibition of BACE1 could not stop growth of existing plaques, although it prevents formation of new plaques[4]. The negative result of BACE1 inhibitors might not be sufficient to exclude the possibility that β-CTF could also contribute to the AD pathogenesis. We have included additional discussion for this question in the revised manuscript (page 15 of the revised manuscript).

      Reviewer #3 (Recommendations For The Authors):

      Major:

      (1) The cell experiments were performed at DIV 9, do the authors know whether at this age, the neurons are still developing and spine density has not reached a pleated yet? If so, the observed effect may reflect the impact on development and/or maturation, rather than on the mature neurons. The authors should be more specific about this issue.

      We would like to thank the reviewer for pointing out this question. These slice cultures were made from 1-week-old rats. DIV 9 is about two weeks old. These neurons are still developing and spine density has not reached a plateau yet[12]. In addition, we also investigated the effects of β-CTF on the synapses of mature neurons in two-month-old mice (Figure 3). So we think the observed effect reflects the impact on both immature and mature neurons.

      (2) mEPSCs shown in Figure 3D were of small amplitudes, perhaps also indicating that these synapses are not yet mature.

      In Figure 3D, the mEPSC results were obtained from pyramidal neurons in the CA1 region of two-month-old mice. At the age of two months, neurotransmitter levels and synaptic density have reached adult levels[13].

      (3) There was no data on the spine density or mEPSCs in the mice OE b-CTF, hence it is unclear whether a primary impact of this manipulation (b-CTF effect) on the synaptic transmission still occurs in vivo.

      In Figure 3, we examined the density of dendritic spines and mEPSCs from CA1 pyramidal neurons infected with lentivirus expressing β-CTF in mice and showed that those neurons expressing additional amount of β-CTF exhibited lower spine density and less mEPSCs, supporting that β-CTF also damaged synaptic transmission in vivo.

      (4) OE of b-CTF should lead to the production of Abeta, although this may not lead to the formation of significant plaques. How do the authors know whether their findings on behavioral and cognitive impairments were not largely mediated by Abeta, which has been widely reported by previous studies?

      We would like to thank the reviewer for pointing out this question. Indeed, our in vivo data could not exclude the potential involvement of Aβ in the pathology, despite the absence of amyloid plaque formation. It will be difficult to demonstrate this question in vivo because of the severe side effects from γ inhibition.

      (5) Figure 4H, the freezing level in the cued fear conditioning was very high, likely saturated; this may mask a potential reduction in the b-CTF OE mice (there is a hint for that in the results). The authors should repeat the experiments using less strong footshock strength (hence resulting in less freezing, <70%).

      We would like to express our gratitude to the reviewer for bringing up this question. The contextual fear conditioning test assesses hippocampal function, while the cued fear conditioning test assesses amygdala function. We hope the reviewer understands that our primary goal is to assess hippocampus-related functions in this experiment and we did see a significant difference between GFP and β-CTF groups. Therefore, we think the intensity of footshock we used was suitable to serve the primary purpose of this experiment.

      (6) Why was the deficit in the Morris water maze in the b-CTF OE mice only significant in the training phase?

      We would like to thank the reviewer for rasing this question and apologize for not describing the test clearly. This is a water T maze test, not Morris water maze test.

      To make the behavioral paradigm of the water T maze test easier to understand, we have provided a more detailed description of the methods in the new version of the manuscript.

      The acquisition phase of the Water T Maze (WTM) evaluates spatial learning and memory, where mice use spatial cues in the environment to navigate to a hidden platform and escape from water, while the reversal learning measures cognitive flexibility in which mice must learn a new location of the hidden platform[14]. In reversal learning task (Figure 4J-K), the learning curves of the two groups of mice did not show any significant differences, indicating that the expression of β-CTF only damages spatial learning and memory but not cognitive flexibility. This is consistent with a previous report using APP/PS1 mice[15].

      (7) Will the altered Rab5 in the b-CTF OE condition also affect the level of other proteins?

      We would like to express our gratitude to the reviewer for raising this interesting question.  Expression of Rab5<sub>S34N</sub> in β-CTF-expressing neurons did not alter the levels of synapse-related proteins that were reduced in these neurons (Supplementary figure 5G-H), suggesting Rab5 overactivation did not contribute to these protein expression changes induced by β-CTF.

      (8) How do the authors reconcile their findings with the well-established findings that Abeta affects synaptic transmission and spine density? Do they think these two processes may occur simultaneously in the neurons, or, one process may dominate in the other?

      APP, Aβ, and presenilins have been extensively studied in mouse models, providing convincing evidence that high Aβ concentrations are toxic to synapses[16]. Moreover, addition of Aβ to murine cultured neurons or brain slices is toxic to synapses[17]. However, Aβ-induced synaptotoxicity was not observed in our study. A major difference between our study and others is that our study used a isolated expression system that apply Aβ only to individual neurons surrounded by neurons without excessive amount of Aβ, whereas the rest studies generally apply Aβ to all the neurons. Therefore, we predict that Aβ does not lead to synaptic deficits from individual neurons in cell autonomous manners, whereas β-CTF does. Aβ and β-CTF represent two parallel pathways of action. Additional discussion for this question has been included in the revised manuscript (page 14 of the revised manuscript).

      Minor:

      Fig 2F-G, "prevent" rather than "reverse"?

      We would like to thank the reviewer for pointing this out. We have made corrections in the revised manuscript.

      Reference:

      (1) GüNER G, LICHTENTHALER S F. The substrate repertoire of γ-secretase/presenilin [J]. Seminars in cell & developmental biology, 2020, 105: 27-42.

      (2) DOODY R S, RAMAN R, FARLOW M, et al. A phase 3 trial of semagacestat for treatment of Alzheimer's disease [J]. The New England journal of medicine, 2013, 369(4): 341-50.

      (3) HU X, DAS B, HOU H, et al. BACE1 deletion in the adult mouse reverses preformed amyloid deposition and improves cognitive functions [J]. The Journal of experimental medicine, 2018, 215(3): 927-40.

      (4) PETERS F, SALIHOGLU H, RODRIGUES E, et al. BACE1 inhibition more effectively suppresses initiation than progression of β-amyloid pathology [J]. Acta neuropathologica, 2018, 135(5): 695-710.

      (5) SIMS J R, ZIMMER J A, EVANS C D, et al. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial [J]. Jama, 2023, 330(6): 512-27.

      (6) KIM S, SATO Y, MOHAN P S, et al. Evidence that the rab5 effector APPL1 mediates APP-βCTF-induced dysfunction of endosomes in Down syndrome and Alzheimer's disease [J]. Molecular psychiatry, 2016, 21(5): 707-16.

      (7) MONDRAGóN-RODRíGUEZ S, GU N, MANSEAU F, et al. Alzheimer's Transgenic Model Is Characterized by Very Early Brain Network Alterations and β-CTF Fragment Accumulation: Reversal by β-Secretase Inhibition [J]. Frontiers in cellular neuroscience, 2018, 12: 121.

      (8) ZHANG X, SONG W. The role of APP and BACE1 trafficking in APP processing and amyloid-β generation [J]. Alzheimer's research & therapy, 2013, 5(5): 46.

      (9) ZHOU B, LU J G, SIDDU A, et al. Synaptogenic effect of APP-Swedish mutation in familial Alzheimer's disease [J]. Science translational medicine, 2022, 14(667): eabn9380.

      (10) LAURITZEN I, PARDOSSI-PIQUARD R, BAUER C, et al. The β-secretase-derived C-terminal fragment of βAPP, C99, but not Aβ, is a key contributor to early intraneuronal lesions in triple-transgenic mouse hippocampus [J]. The Journal of neuroscience : the official journal of the Society for Neuroscience, 2012, 32(46): 16243-1655a.

      (11) KAUR G, PAWLIK M, GANDY S E, et al. Lysosomal dysfunction in the brain of a mouse model with intraneuronal accumulation of carboxyl terminal fragments of the amyloid precursor protein [J]. Molecular psychiatry, 2017, 22(7): 981-9.

      (12) HARRIS K M, JENSEN F E, TSAO B. Three-dimensional structure of dendritic spines and synapses in rat hippocampus (CA1) at postnatal day 15 and adult ages: implications for the maturation of synaptic physiology and long-term potentiation [J]. The Journal of neuroscience : the official journal of the Society for Neuroscience, 1992, 12(7): 2685-705.

      (13) SEMPLE B D, BLOMGREN K, GIMLIN K, et al. Brain development in rodents and humans: Identifying benchmarks of maturation and vulnerability to injury across species [J]. Progress in neurobiology, 2013, 106-107: 1-16.

      (14) GUARIGLIA S R, CHADMAN K K. Water T-maze: a useful assay for determination of repetitive behaviors in mice [J]. Journal of neuroscience methods, 2013, 220(1): 24-9.

      (15) ZOU C, MIFFLIN L, HU Z, et al. Reduction of mNAT1/hNAT2 Contributes to Cerebral Endothelial Necroptosis and Aβ Accumulation in Alzheimer's Disease [J]. Cell reports, 2020, 33(10): 108447.

      (16) CHAPMAN P F, WHITE G L, JONES M W, et al. Impaired synaptic plasticity and learning in aged amyloid precursor protein transgenic mice [J]. Nature neuroscience, 1999, 2(3): 271-6.

      (17) WANG Z, JACKSON R J, HONG W, et al. Human Brain-Derived Aβ Oligomers Bind to Synapses and Disrupt Synaptic Activity in a Manner That Requires APP [J]. The Journal of neuroscience : the official journal of the Society for Neuroscience, 2017, 37(49): 11947-66.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      A number of modifications/additions have been made to the text which help to clarify the background and details of the study and I feel have improved the study.

      NAD deficiency induced using the dietary/Haao null model showed a window of susceptibility at E7.5-10.5. Further, HAAO enymze activity data has been added at E11.5 and the minimal HAAO activity in the embryo act E11.5 supports the hypothesis that the NAD synthesis pathway from kynurenine is not functional until the liver starts to develop.

      The caveat to this is that absence of expression/activity in embryonic cells at E7.5-10/5 relies on previous scRNA-seq data. Both reviewers commented that analysis of RNA and/or protein expression at these stages (E7.5-10.5) would be necessary to rule this out, and would strongly support the conclusions regarding the necessity for yolk sac activity.

      There are a number of antibodies for HAAO, KNYU etc so it is surprising if none of these are specific for the mouse proteins, while an alternative approach in situ hydridisation would also be possible.

      We have tested 2 anti-HAAO antibodies, 2 anti-KYNU antibodies and 1 anti-QPRT antibody on adult liver and various embryonic tissues.

      Given that all tested antibodies only detected a specific band in tissues with very high expression and abundant target protein levels (adult liver), they were determined to be unsuitable to conclusively prove that these proteins of the NAD _de novo_synthesis pathway are absent in embryos prior to the development of a functional liver. They were also unsuitable for IHC experiments to determine which cell types (if any) have these proteins.

      The antibodies, tested assays and samples, and the results obtained were as follows:

      Anti-HAAO antibody (ab106436, Abcam, UK) 

      • Was tested in western blots of liver, E11.5-E14.5 yolk sac, E14.5 placenta, and E14.5 and E16.5 embryonic liver lysates from wild-type (WT) and Haao-/- mice. The target band (32.5 KD) was visible in the WT liver samples and absent in_Haao_-/- livers, and faintly visible in E11.5-E14.5 WT yolk sac, with intensity gradually increasing in E12.5 and E13.5 WT yolk sac. Multiple strong non-specific bands occurred in all samples, requiring cutting off the >50 KD area of the blots.

      • Was re-tested in western blots comparing WT, Haao-/-, and Kynu-/- E9.5-E11.5 embryo, E9.5 yolk sac, and adult liver tissues. It detected the target band faintly only in WT and Kynu-/- liver lysates. No target band could be resolved in E9.5 yolk sac or embryo lysates. Due to the low sensitivity of the antibody, it is unsuitable to conclusively determine whether HAAO is present or absent in E9.5 yolk sacs and E9.5-E11.5 embryos.

      • Was tested in IHC with DAB and IF, producing non-specific staining on both WT and Haao-/- liver and kidney tissue. 

      Anti-HAAO antibody (NBP1-77361, Novus Biologicals, LLC, CO, USA)

      • Was tested in western blots and detected a very faint target band in WT liver lysate that was absent in Haao-/- lysate, with stronger non-specific bands occurring in both genotypes.

      • Was tested in IHC with DAB, producing non-specific staining on both WT and Haao-/- liver and kidney tissue 

      Anti-L-Kynurenine Hydrolase antibody (11796-1-AP, Proteintech Group, IL, USA)

      • Was tested in western blots and detected a faint target band (52 KD) in E11.5, E12.5 E13.5, and E14.5 yolk sac lysates. Detected a weak band in E14.5 liver, a stronger band in E16.5 liver, but not in E14.5 placenta. The target band was only resolved with normal ECL substrate and extended exposure when the >75 KD part of the blot was cut off. 

      • Was re-tested in western blots comparing WT, Haao-/-, and Kynu-/- E9.5-E11.5 embryo, E9.5 yolk sac, and adult liver tissues. It detected the target band only in WT and Haao-/- liver lysates, requiring Ultra Sensitive Substrate. No target band could be resolved in yolk sac or embryo lysates of any genotype.

      Anti-L-Kynurenine Hydrolase antibody (ab236980, Abcam, UK)

      • Was tested in western blots and detected a very faint target band (52 KD) in WT liver lysates and no band in Kynu-/- liver lysates. Multiple non-specific bands occurred irrespective of the Kynu genotype of the lysate.

      • Was tested in IHC with DAB and IF, producing non-specific staining on both WT and Kynu-/- liver and kidney tissue 

      Anti-QPRT (orb317756, Biorbyt, NC, USA)

      • Was tested in western blots and detected a faint target band (31 KD) with multiple other bands between 25-75 KD and an extremely strong band around 150 KD on WT liver lysates.

      The following is the authors’ response to the original reviews.

      Reviewer 1 Public Review:

      The current dietary study narrows the period when deficiency can cause malformations (analysed at E18.5), and altered metabolite profiles (eg, increased 3HAA, lower NAD) are detected in the yolk sac and embryo at E10.5. However, without analysis of embryos at later stages in this experiment it is not known how long is needed for NAD synthesis to be recovered - and therefore until when the period of exposure to insufficient NAD lasts. This information would inform the understanding of the developmental origin of the observed defects.

      Our previous published work (Cuny et al 2023 https://doi.org/10.1242/dmm.049647) indicates that the timing of NAD de novo synthesis pathway precursor availability and consequently the timing of NAD deficiency during organogenesis drives which organs are affected in their development. Furthermore, experimental data of another project (manuscript submitted) shows that mouse embryos (from mothers on an NAD precursor restricted diet that induces CNDD) were NAD deficient at E9.5 and E11.5, but embryo NAD levels were fully recovered at E14.5 when compared to same-stage embryos from mothers on precursor-sufficient diet. This was observed irrespective of the embryos’ Haao genotype. In the current study, NAD precursor provision was only restricted until E10.5. Thus, we expect that our embryos phenotyped at E18.5 had recovered their NAD levels back to normal by E14.5 at the latest.  More research, beyond the scope of the current manuscript, is required to spatio-temporally link embryonic NAD deficiency to the occurrence of specific defect types and elucidate the mechanistic origin of the defects. To acknowledge this, we updated the respective Discussion paragraph on page 7 and added the following statement: “This observation supports our hypothesis that the timing of NAD deficiency during organogenesis determines which organs/tissues are affected (Cuny et al., 2023), but more research is needed to fully characterise the onset and duration of embryonic NAD deficiency in dietary NAD precursor restriction mouse models.”

      More importantly, there is still a question of whether in addition to the yolk sac, there is HAAO activity within the embryo itself prior to E12.5 (when it has first been assayed in the liver - Figure 1C). The prediction is that within the conceptus (embryo, chorioallantoic placenta, and visceral yok sac) the embryo is unlikely to be the site of NAD synthesis prior to liver development. Reanalysis of scRNA-seq (Fig 1B) shows expression of all the enzymes of the kynurenine pathway from E9.5 onwards. However, the expression of another available dataset at E10.5 (Fig S3) suggested that expression is 'negligible'. While the expression in Figure 1B, Figure S1 is weak this creates a lack of clarity about the possible expression of HAAO in the hepatocyte lineage, or especially elsewhere in the embryo prior to E10.5 (corresponding to the period when the authors have demonstrated that de novo NAD synthesis in the conceptus is needed). Given these questions, a direct analysis of RNA and/or protein expression in the embryos at E7.5-10.5 would be helpful. 

      We now have included additional data showing that whole embryos at E11.5 and embryos with their livers removed at E14.5 have negligible HAAO enzyme activity. The observed lack of HAAO activity in the embryo at E11.5 is consistent with the absence of a functional embryonic liver at that stage. Thus, it confirms that the embryo is dependent of extraembryonic tissues (the yolk sac) for NAD de novo synthesis prior to E12.5. The additional datasets are now included in Supplementary Table S1 and as Supplementary Figure 2. The Results section on page 2 has been updated to refer to these datasets.

      Reviewer #2 (Public Review): 

      Page 4 and Table S4. The descriptors for malformations of organs such as the kidney and vertebrae are quite vague and uninformative. More specific details are required to convey the type and range of anomalies observed as a consequence of NAD deficiency. 

      We now provide more information about the malformation types in the Results on page 4. Also, Table S4 now defines the missing vertebral, sternum, and kidney descriptors.

      Can the authors define whether the role of the NAD pathway in a couple of tissue or organ systems is the same? By this I mean is the molecular or cellular effect of NAD deficiency is the same in the vertebrae and organs such as the kidney. What unifies the effects on these specific tissues and organs and are all tissues and organs affected? If some are not, can the authors explain why they escape the need for the NAD pathway? 

      This is a good comment, highlighting that further research, beyond the scope of this manuscript, is needed to better understand the underlying mechanisms of CNDD causation. We have expanded the Discussion paragraph “NAD deficiency in early organogenesis is sufficient to cause CNDD” to indicate that while the timing of NAD deficiency during embryogenesis explains variability in phenotypes among the CNDD spectrum, it is unknown why other organs/tissues are seemingly not affected by NAD deficiency.

      To answer the reviewer’s questions and elucidate the underlying cellular and molecular processes in individual organs affected by NAD deficiency, a multiomic approach is required. This is because NAD is involved in hundreds of molecular and cellular processes affecting gene expression, protein levels, metabolism, etc. For details of NAD functions that have relevance to embryogenesis, the reviewer may refer to our recent review article (Dunwoodie et al 2023 https://doi.org/10.1089/ars.2023.0349). 

      Page 5 and Figure 6C. The expectation and conclusion for whether specific genes are expressed in particular cell types in scRNA-seq datasets depend on the number of cells sequenced, the technology (methodology) used, the depth of sequencing, and also the resolution of the analysis. It is therefore essential to perform secondary validation of the analysis of scRNA-seq data. At a minimum, the authors should perform in situ hybridization or immunostaining for Tdo2, Afmid, Kmo, Kynu, Haao, Qprt, and Nadsyn1 or some combination thereof at multiple time points during early mouse embryogenesis to truly understand the spatiotemporal dynamics of expression and NAD synthesis. 

      We have tested antibodies against HAAO, KYNU, and QPRT in adult mouse liver samples (the main site of NAD de novo synthesis) but these produced non-specific bands in western blotting experiments. Therefore, immunostaining studies on embryonic tissues were not feasible. 

      However, we agree that histological methods such as in situ hybridisation would provide secondary validation of the exact cell types that express these genes. To acknowledge this, we have updated a sentence on page 5 referring to the data shown in Figure 6C as follows: “While histological methods such as in situ hybridisation would be required to confirm the exact cell types expressing these genes, the available expression data indicates that the genes encoding those enzymes required to convert L-kynurenine to NAD (kynurenine pathway) are exclusively expressed in the yolk sac endoderm lineage from the onset of organogenesis (E8.0-8.5).”

      Absolute functional proof of the yolk sac endoderm as being essential and required for NAD synthesis in the context of CNDD might require conditional deletion of Haao in the yolk sac versus embryo using appropriate Cre driver lines or in the absence of a conditional allele, could be performed by tetraploid embryo-ES cell complementation approaches. But temporal dietary intervention can also approximate the same thing by perturbing NAD synthesis Shen the yolk sac is the primary source versus when the liver becomes the primary source in the embryo. 

      Reviewer 1 has made a similar comment about confirming that indeed NAD de novo synthesis activity is limited to extraembryonic tissues (=yolk sacs) and absent in the embryo prior to development of an embryonic liver. We now have included additional data showing that whole embryos at E11.5 and embryos with their livers removed at E14.5 have negligible HAAO enzyme activity. The observed lack of HAAO activity in the embryo at E11.5 is consistent with the absence of a functional embryonic liver at that stage. We think this provides enough proof that the embryo is dependent of extraembryonic tissues (the yolk sac) for NAD de novo synthesis prior to E12.5. The additional datasets are now included in Supplementary Table S1 and as Supplementary Figure 2. The Results section on page 2 has been updated to refer to these data.

      Reviewer #1 (Recommendations For The Authors): 

      (1) Introduction (page 1) introduces mouse models with defects in the kynurenine pathway "confirming that NAD de novo synthesis is required during embryogenesis ...". This requirement is revealed by the imposition of maternal dietary deficiency and more detail (or a more clear link to the following sentences) here would help the reader who is not familiar with the previous papers using the HAAO mice and dietary modulation.

      We have updated this paragraph in the Introduction to better indicate that the requirement of NAD de novo synthesis for embryogenesis was confirmed in mouse models by modulating the maternal dietary NAD precursor provision during pregnancy.

      (2) Discussion - throughout the introduction and results the authors refer to the NAD de novo synthesis pathway, with the study focussing on the effects of HAAO loss of function. Data implies that the kynurenine pathway is active in the yolk sac but whether de novo synthesis from L-tryptophan occurs has not been addressed. The first sub-heading of the discussion could be more accurate referring to the kynurenine pathway, or synthesis from kynurenine. 

      We agree that our manuscript needed to make better distinction between NAD de novo synthesis starting from kynurenine and starting from tryptophan. We removed “from Ltryptophan” from the sub-heading in the Discussion and clarified in this paragraph which genes are required to convert tryptophan to kynurenine and which genes to convert kynurenine to NAD. We also updated two Results paragraphs (page 2, 2nd paragraph; page 5, 5th paragraph) to improve clarity.

      It is worth noting that our statement in the Discussion “this is the first demonstration of NAD de novo synthesis occurring in a tissue outside of the liver and kidney.” is valid because vascular smooth muscle cells express Tdo2 and in combination with the other requisite genes expressed in endoderm cells, the yolk sac has the capability to synthesise NAD de novo from L-tryptophan.

      (3) Outlook - While this section is designed to be looking ahead to the potential implications of the work, the last section on gene therapy of the yolk sac seems far removed from the paper content and highly speculative. I feel this could detract from the main points of the study and could be removed. 

      We have updated the Outlook paragraph and shortened the final part to “Further research is required to better understand the mechanisms of CNDD causation and of other causes of adverse pregnancy outcomes involving the yolk sac.”

      (4) In Figure 2D it would be useful to label the clusters as the colours in the legend are difficult to match to the heatmap. 

      We now have labelled the clusters with lowercase letters above the heatmap to make it easier to match the clusters in Figure 2D to the colours used for designating tissues and genotypes. These labels are described in the figure’s key and the figure legend.  

      Reviewer #2 (Recommendations For The Authors): 

      Page 4 and Table S4. The descriptors for malformations of organs such as the kidney and vertebrae are quite vague and uninformative. More specific details are required to convey the type and range of anomalies observed as a consequence of NAD deficiency. 

      We now provide more information about the malformation types in the Results on page 4. Also, Table S4 now defines the missing vertebral, sternum, and kidney descriptors.

      Can the authors define whether the role of the NAD pathway in a couple of tissue or organ systems is the same? By this I mean is the molecular or cellular effect of NAD deficiency is the same in the vertebrae and organs such as the kidney. What unifies the effects on these specific tissues and organs and are all tissues and organs affected? If some are not, can the authors explain why they escape the need for the NAD pathway? 

      This is a good comment, highlighting that further research, beyond the scope of this manuscript, is needed to better understand the underlying mechanisms of CNDD causation. We have expanded the Discussion paragraph “NAD deficiency in early organogenesis is sufficient to cause CNDD” to indicate that while the timing of NAD deficiency during embryogenesis explains variability in phenotypes among the CNDD spectrum, it is unknown why other organs/tissues are seemingly not affected by NAD deficiency.

      To answer the reviewer’s questions and elucidate the underlying cellular and molecular processes in individual organs affected by NAD deficiency, a multiomic approach is required. This is because NAD is involved in hundreds of molecular and cellular processes affecting gene expression, protein levels, metabolism, etc. For details of NAD functions that have relevance to embryogenesis, the reviewer may refer to our recent review article (Dunwoodie et al 2023 https://doi.org/10.1089/ars.2023.0349). 

      Page 5 and Figure 6C. The expectation and conclusion for whether specific genes are expressed in particular cell types in scRNA-seq datasets depend on the number of cells sequenced, the technology (methodology) used, the depth of sequencing, and also the resolution of the analysis. It is therefore essential to perform secondary validation of the analysis of scRNA-seq data. At a minimum, the authors should perform in situ hybridization or immunostaining for Tdo2, Afmid, Kmo, Kynu, Haao, Qprt, and Nadsyn1 or some combination thereof at multiple time points during early mouse embryogenesis to truly understand the spatiotemporal dynamics of expression and NAD synthesis. 

      We have tested antibodies against HAAO, KYNU, and QPRT in adult mouse liver samples (the main site of NAD de novo synthesis) but these produced non-specific bands in western blotting experiments. Therefore, immunostaining studies on embryonic tissues were not feasible. 

      However, we agree that histological methods such as in situ hybridisation would provide secondary validation of the exact cell types that express these genes. To acknowledge this, we have updated a sentence on page 5 referring to the data shown in Figure 6C as follows: “While histological methods such as in situ hybridisation would be required to confirm the exact cell types expressing these genes, the available expression data indicates that the genes encoding those enzymes required to convert L-kynurenine to NAD (kynurenine pathway) are exclusively expressed in the yolk sac endoderm lineage from the onset of organogenesis (E8.0-8.5).”

    1. Author response:

      The following is the authors’ response to the original reviews.

      General Response to Public Reviews

      We thank the three reviewers for their positive evaluation of our work, which presents the first molecular characterization of type-II NB lineages in an insect outside the fly Drosophila. They seem convinced of our finding of an additional type-II NB and increased proliferation during embryogenesis in the red flour beetle. The reviewers expressed hesitations on our interpretation that the observed quantitative differences of embryonic lineages can directly be linked to the embryonic development of the central complex in Tribolium. While we still believe that a connection of both observations is a valid and likely hypothesis, we acknowledge that due the lack of functional experiments and lineage tracing a causal link has not directly been shown. We have therefore changed the manuscript to an even more careful wording that on one hand describes the correlation between increased embryonic proliferation with the earlier development of the Cx but on the other hand also stresses the need for additional functional and lineage tracing experiments to test this hypothesis. We have also strengthened the discussion on alternative explanations of the increased lineage size and emphasize the less disputed elements like presence and conservation of type-II NB lineages. 

      While our manuscript could in conclusion not directly show that the reason of the heterochronic shift lies in the progenitor behaviour, we still provide a first approach to answering the question of the developmental basis of this shift and testable hypotheses directly emerge from our work. We agree with reviewer#1 that functional work is best suited to test our hypothesis and we are planning to do so. However, we believe that the presented work is already rich in novel data and significantly advances our understanding on the conservation and divergence of type-II NBs in insects. We would also like to stress that most transgenic tools for which genome-wide collections exist for Drosophila have to be created for Tribolium and doing so can be quite time consuming. Conducting RNAi experiments is certainly possible in Tribolium but observing phenotypes in this defined cellular context will need laborious optimization. We have for example tried knocking down Tc-fez/erm but could not see any embryonic phenotype which might be due to an escaper effect in which only mildly affected or wild type-like embryos survive while the others die in early embryogenesis. Due to pleiotropic functions of the involved genes a cell-specific knockdown might be necessary and we are working towards establishing a system to do that in the red flour beetle. For the stated reasons, we see our work as an important basis to inspire future functional studies that build up on the framework that we introduced. 

      In response to these common points, we have made the following changes to the manuscript

      -        The title has been changed from ‘being associated’ to ‘correlate’

      -        The conclusions part of the abstract has been changed

      -        We deleted the statement ‘…thus providing the material for the early central complex formation…’

      -        Rephrased to saying that the two observations just correlate

      -        The part of the discussion ‘Divergent timing of type-II NB activity and heterochronic development of the central complex’ has been extensively rewritten and now discusses several alternative explanations that were suggested by the reviewers. It also stresses the need for further functional work and lineage tracing (line 859-862 (608-611)).

      In addition, we have made numerous changes to the manuscript to account for more specific comments of the reviewers and to the recommendations for the authors.

      Our responses to the individual comments can be found in the following. 

      Public Reviews: 

      Reviewer #1 (Public Review): 

      Summary: 

      Insects inhabit diverse environments and have neuroanatomical structures appropriate to each habitat. Although the molecular mechanism of insect neural development has been mainly studied in Drosophila, the beetle, Tribolium castaneum has been introduced as another model to understand the differences and similarities in the process of insect neural development. In this manuscript, the authors focused on the origin of the central complex. In Drosophila, type II neuroblasts have been known as the origin of the central complex. Then, the authors tried to identify those cells in the beetle brain. They established a Tribolium fez enhancer trap line to visualize putative type II neuroblasts and successfully identified 9 of those cells. In addition, they also examined expression patterns of several genes that are known to be expressed in the type II neuroblasts or their lineage in Drosophila. They concluded that the putative type II neuroblasts they identified were type II neuroblasts because those cells showed characteristics of type II neuroblasts in terms of genetic codes, cell diameter, and cell lineage. 

      Strengths: 

      The authors established a useful enhancer trap line to visualize type II neuroblasts in Tribolium embryos. Using this tool, they have identified that there are 9 type II neuroblasts in the brain hemisphere during embryonic development. Since the enhancer trap line also visualized the lineage of those cells, the authors found that the lineage size of the type II neuroblasts in the beetle is larger than that in the fly. They also showed that several genetic markers are also expressed in the type II neuroblasts and their lineages as observed in Drosophila. 

      Weaknesses: 

      I recommend the authors reconstruct the manuscript because several parts of the present version are not logical. For example, the author should first examine the expression of dpn, a well-known marker of neuroblast. Without examining the expression of at least one neuroblast marker, no one can say confidently that it is a neuroblast. The purpose of this study is to understand what makes neuroanatomical differences between insects which is appropriate to their habitats. To obtain clues to the question, I think, functional analyses are necessary as well as descriptive analyses. 

      The expression of an exclusive type-II neuroblast marker would indeed have been the most convincing evidence. However, asense is absent from type-II NBs and deadpan is not specific enough as it is expressed in many other cells of the developing protocerebrum. The gene pointed, although also expressed elsewhere, emerged as the the most specific marker. Therefore, we start with pointed and fez/erm to describe the first appearance and developmental progression of the cells and then add further evidence that these cells are indeed type-II neuroblasts. Further evidence is provided in the following chapters.  We have discussed the need for functional work in the general response. 

      Reviewer #2 (Public Review): 

      The authors address the question of differences in the development of the central complex (Cx), a brain structure mainly controlling spatial orientation and locomotion in insects, which can be traced back to the neuroblast lineages that produce the Cx structure. The lineages are called type-II neuroblast (NB) lineages and are assumed to be conserved in insects. While Tribolium castaneum produces a functional larval Cx that only consists of one part of the adult Cx structure, the fan-shaped body, in Drosophila melanogaster a non-functional neuropile primordium is formed by neurons produced by the embryonic type-II NBs which then enter a dormant state and continue development in late larval and pupal stages. 

      The authors present a meticulous study demonstrating that type-II neuroblast (NB) lineages are indeed present in the developing brain of Tribolium castaneum. In contrast to type-I NB lineages, type-II NBs produce additional intermediate progenitors. The authors generate a fluorescent enhancer trap line called fez/earmuff which prominently labels the mushroom bodies but also the intermediate progenitors (INPs) of the type-II NB lineages. This is convincingly demonstrated by high-resolution images that show cellular staining next to large pointed labelled cells, a marker for type-II NBs in Drosophila melanogaster. Using these and other markers (e.g. deadpan, asense), the authors show that the cell type composition and embryonic development of the type-II NB lineages are similar to their counterparts in Drosophila melanogaster. Furthermore, the expression of the Drosophila type-II NB lineage markers six3 and six4 in subsets of the Tribolium type-II NB lineages (anterior 1-4 and 1-6 type-II NB lineages) and the expression of the Cx marker skh in the distal part of most of the lineages provide further evidence that the identified NB lineages are equivalent to the Drosophila lineages that establish the central complex. However, in contrast to Drosophila, there are 9 instead of 8 embryonic type-II NB lineages per brain hemisphere and the lineages contain more progenitor cells compared to the Drosophila lineages. The authors argue that the higher number of dividing progenitor cells supports the earlier development of a functional Cx in Tribolium. 

      While the manuscript clearly shows that type-II NB lineages similar to Drosophila exist in Tribolium, it does not considerably advance our understanding of the heterochronic development of the Cx in these insects. First of all, the contribution of these lineages to a functional larval Cx is not clear. For example, how do the described type-II NB lineages relate to the DM1-4 lineages that produce the columnar neurons of the Cx? What is the evidence that the embryonically produced type-II NB lineage neurons contribute to a functional larval Cx? The formation of functional circuits could rely on larval neurons (like in Drosophila) which would make a comparison of embryonic lineages less informative with respect to understanding the underlying variations of the developmental processes. Furthermore, the higher number of progenitors (and consequently neurons) in Tribolium could simply reflect the demand for a higher number of cells required to build the fan-shaped body compared to Drosophila. In addition, the larger lineages in Tribolium, including the higher number of INPs could be due to a greater number of NBs within the individual clusters, rather than a higher rate of proliferation of individual neuroblasts, as suggested. What is the evidence that there is only one NB per cluster? The presented schemes (Fig. 7/12) and description of the marker gene expression and classification of progenitor cells are inconsistent but indicate that NBs and immature INPs cannot be consistently distinguished. 

      We thank this reviewer for pointing out the inconsistency in our classification of cells within the lineages as one central part of our manuscript. These were due to a confusion in the used terms (young vs. immature). We have corrected this mistake and have changed the naming of the INP subtypes to immature-I and immature-II. We are confident that based on the analysed markers, type-II NBs and immature INPs can actually be distinguished with confidence.

      We agree that a functional link of increased proliferation to heterochronic CX development is not shown although we consider it to be likely. As stated in the general response we have changed the manuscript to saying that the two observations (higher number of progenitors and larger lineages/more INPs) correlate but that a causal link can only be hypothesized for the time being. At the same time, we have strengthened the discussion on alternative explanations.

      We would like to remain with our statement of an increased number of embryonic progeny of Tribolium type-II NBs. We counted the total number of progenitor cells emerging from the anterior median cluster and divided this by the number of type II NBs in that cluster. Hence, the shown increased number of cells represents an average per NB but is not influenced by the increased number of NBs. On the same line, we have never seen indication for the presence of additional NBs within any cluster while one type-II NB is what we regularly found. Hence, we are confident that we know the number of respective NBs. The fact that the fly data included also neurons and was counted at a later stage indicates that the observed differences are actually minimum estimates.

      We have discussed that based on the position and comparison to the grasshopper we believe that Tribolium type-II NB 1-4 contribute to the x, y, z and w tracts. To confirm this, lineage tracing experiments would be necessary, for which tools remain to be developed. 

      We agree that the role of larvally born neurons and the fate of Tribolium neuroblasts through the transition from embryo to larva and pupa need to be further studied.

      Available data suggests that the adult fan shaped body in Tribolium does not hugely differ in size from the Drosophila counterpart, although no data in terms of cell number is available. In the larva, however, no fan shaped body or protocerebral bridge can be distinguished in flies while in beetle larvae, these structures are clearly developed. Hence, we think that it is more likely that differences observed in the embryo reflect differences in the larval central complex. We discuss the need for further investigation of larval stages.

      The main difference between Tribolium and Drosophila Cx development with regards to the larval functionality might be that Drosophila type-II NB lineage-derived neurons undergo quiescence at the end of embryogenesis so that the development of the Cx is halted, while a developmental arrest does not occur in Tribolium. However, this needs to be confirmed (as the authors rightly observe). 

      Indeed, there is evidence that cells contributing to the CX go into quiescence in flies – hence, this certainly is one of the mechanisms. However, based on our data we would suggest that in addition, the balance of embryonic versus larval proliferation of type-II lineages is different between the two insects: The increased embryonic proliferation and development leads to a functional larval CX in beetles while in flies, postembryonic proliferation may be increased in order to catch up.

      Reviewer #3 (Public Review):

      Summary: 

      In this paper, Rethemeier et al capitalize on their previous observation that the beetle central complex develops heterochronically compared to the fly and try to identify the developmental origin of this difference. For this reason, they use a fez enhancer trap line that they generated to study the neuronal stem cells (INPs) that give rise to the central complex. Using this line and staining against Drosophila type-II neuroblast markers, they elegantly dissect the number of developmental progression of the beetle type II neuroblasts. They show that the NBs, INPs, and GMCs have a conserved marker progression by comparing to Drosophila marker genes, although the expression of some of the lineage markers (otd, six3, and six4) is slightly different. Finally, they show that the beetle type II neuroblast lineages are likely longer than the equivalent ones in Drosophila and argue that this might be the underlying reason for the observed heterochrony. 

      Strengths: 

      - A very interesting study system that compares a conserved structure that, however, develops in a heterochronic manner. 

      - Identification of a conserved molecular signature of type-II neuroblasts between beetles and flies. At the same time, identification of transcription factors expression differences in the neuroblasts, as well as identification of an extra neuroblast. 

      - Nice detailed experiments to describe the expression of conserved and divergent marker genes, including some lineaging looking into the co-expression of progenitor (fez) and neuronal (skh) markers. 

      Weaknesses: 

      - Comparing between different species is difficult as one doesn't know what the equivalent developmental stages are. How do the authors know when to compare the sizes of the lineages between Drosophila and Tribolium? Moreover, the fact that the authors recover more INPs and GMCs could also mean that the progenitors divide more slowly and, therefore, there is an accumulation of progenitors who have not undergone their programmed number of divisions. 

      We understand the difficulty of comparing stages between species, but we feel that our analysis is on the save side. At stages comparable with respect to overall embryonic development (retracting or retracted germband), the fly numbers are clearly smaller. To account for potential heterochronic shifts in NB activity, we have selected the stages to compare based on the criteria given: In Drosophila the number of INPs goes down after stage 16, meaning that they reach a peak at the selected stages. In Tribolium the chosen stages also reflect the phase when lineage size is larger than in all previous stages. Therefore, we believe that the conclusion that Tribolium has larger lineages and more INPs is well founded. Lineage size in Tribolium might further increase just before hatching (stage 15) but we were for technical reasons not able to look at this. As lineage size goes down in the last stage of Drosophila embryogenesis the number of INPs goes down and type-II NB enter quiescence, we think it is highly unlikely that the ratio between Tribolium and Drosophila INPs reverses at this stage, but a study of the behaviour of type-II NB in Tribolium and whether there is a stage of quiescence is still needed.

      - The main conclusion that the earlier central complex development in beetles is due to the enhanced activity of the neuroblasts is very handwavy and is not the only possible conclusion from their data. 

      As discussed in the general response we have made several changes to the manuscript to account for this criticism and discuss alternative explanations for the observations.

      - The argument for conserved patterns of gene expression between Tribolium and Drosophila type-II NBs, INPs, and GMCs is a bit circular, as the authors use Drosophila markers to identify the Tribolium cells. 

      We tested the hypothesis that in Tribolium there are type-II NBs with a molecular signature similar to flies. Our results are in line with that hypothesis. If pointed had not clearly marked cells with NB-morphology or fez/erm had not marked dividing cells adjacent to these NBs, we would have concluded that no such cells/lineages exist in the Tribolium embryo, or that central complex producing lineages exist but express different markers. Therefore, we regard this a valid scientific approach and hence find this argument not problematic.  

      An appraisal of whether the authors achieved their aims, and whether the results support their conclusions: Based on the above, I believe that the authors, despite advancing significantly, fall short of identifying the reasons for the divergent timing of central complex development between beetle and fly. 

      We agree that based on the available data, we cannot firmly make that link and we have changed the text accordingly.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors): 

      In addition to these descriptive analyses, functional analyses can be included. RNAi is highly effective in this beetle. 

      We agree that functional analyses of some of the studied genes and possible effects of gene knockdowns on the studied cell lineages and on central complex development could be highly informative. However, when studying specific cell types or organs these experiments are less straight forward than it may seem as knockdowns often lead to pleiotropic effects, sterility or lethality. All the genes involved are expressed in additional cells and may have essential functions there. Given the systemic RNAi of Tribolium, it is challenging to unequivocally assign phenotypes to one of the cell groups. Overcoming these challenges is often possible but needs extensive optimization. Our study, though descriptive is already rich in data and is the first description of NB-II lineages in Tribolium central complex development. We see it as a basis for future studies on central complex development that will include functional experiments.

      (1) Introduction 

      For these reasons the beetle... 

      Could you explain the differences in the habitats between Tribolium and Drosophila? or What is the biggest difference between these two species at the ecological aspect? 

      We have added a short characterisation of the main differences.

      The insect central complex is an anterior... 

      The author should explain why they focus on the structure. 

      Added

      It is however not known how these temporal... 

      If the authors want to get the answer to the question, they need to conduct functional analyses. 

      While we agree with the importance of functional work (see above) we believe that detailed descriptions under the inclusion of molecular markers as presented here is very informative by itself for understanding developmental processes and sets the foundation for the analysis of mutant/RNAi- phenotypes in future studies.

      CX - Central complex? 

      We have opted to not use this abbreviation anymore for clarity.

      “because intermediate cycling progenitors have also been...” 

      Is the sentence correct? 

      We have included ‘INPs’ in the sentence to make clear what the comparison refers to and added a comma

      “However, molecular characterization of such lineage in another...” 

      The authors should explain why molecular characterization is necessary. 

      We have done so

      (2) Results 

      a) Figure 8. Could you delineate the skh/eGFP expression region? 

      We have added brackets to figure 1 panel A to indicate the extent of skh and other gene expressions within the lineages.

      b) This section should be reorganized for better logical flow. 

      There certainly are different ways to organize this part and we have considered different structures of the results part. We eventually subjectively concluded that the chosen one is the best fit for our data (also see comment below on dpn-expression).

      c) For the tables. The authors should mention what statistical analysis they have conducted. 

      The tables themselves are just listing the raw numbers. They are the basis for the graph in figure 9. Statistical tests (t-test) are mentioned in the legend of that figure and now also in the Methods sections.

      “We also found that the large Tc-pnt...” 

      The authors could examine the mitotic index using an anti-pH3 antibody. 

      We have used the anti-pH3 antibody to detect mitoses (figure 3C, table 1 and 3) but as data on mitoses based on this antibody is only a snapshot it would require a lot of image data to reliably determine an index in this specific cells. While mitotic activity over time possibly combined with live imaging might be very interesting in this system also with regards to the timing of development, for this basic study we are satisfied with the statement that the type-II NB are indeed dividing at these stages.

      “Based on their position by the end of embryogenesis...” 

      How can the authors conclude that they are neuroblasts without examining the expression of NB markers? 

      Type-II NB do not express asense as the key marker for type I neuroblasts. To corroborate our argument that the cells are neuroblasts we have used several criteria:

      - We have used the same markers that are used in Drosophila to label type-II NBs (pnt, dpn, six4). We are not aware of any other marker that would be more specific.

      - We have shown that these cells are larger and have larger nuclei than neighbouring cells and they are dividing

      - We have shown that these cells through their INP lineages give rise to central complex neuropile

      We believe that these features taken together leave little doubt that the described cells are indeed neuroblasts. 

      “We found that the cells they had assigned as...” 

      How did the authors distinguish that they are really neuroblasts? 

      We see the difficulty that we first describe the position and development of these cells (e.g. fig 3) and then add further evidence (cell size, additional marker dpn) that these are neuroblasts (also see above). However, without previous knowledge on position (and on pnt expression as the most specific marker) the type-II NB could not have been distinguished from other NBs based on cell size or expression of other markers.

      “Conserved patterns of gene expression...” 

      This must be the first (especially dpn). 

      Dpn is not specific to type-II NB because it is also expressed in type-1 NBs, mature INPs and possibly other neural cells. It is therefore impossible to identify type-II NBs based on this gene alone. We therefore first used the most specific marker, pnt, in addition to adjacent fez expression to identify candidates for type-II lineages. Then we mapped expression of further genes on these lineages to support the interpretation (and show homology to the Drosophila lineages). Although of course the structure of a paper does not necessarily have to reflect the sequence in which experiments were done we would find putting dpn expression first misleading as it would not be clear why exactly a certain part of the expression should belong to type-II NB. Also, our pnt-fez expression data shows the position of the NB-II in the context of the whole head lobe whereas the other gene expressions are higher magnifications focussing on details. We therefore believe that the structure we chose best fits our data and the other reviewers seemed to find it acceptable as well.  

      “As type-II NBs contribute to central...” 

      Before the sentence, the author could explain differences in the central complex structure between Tribolium and Drosophila in terms of cell number and tissue size. 

      We have added references on the comparisons of tissue sizes, but unfortunately there is no Tribolium data that can be directly compared to available Drosophila resources in terms of cell number.  

      “We conclude that the embryonic development of...” 

      How did the authors conclude? They must explain their logic. 

      Actually, before this sentence, I only found the description of the comparison between Tribolium NBs and Drosophila once. 

      We agree that this conclusion is not fully evident from the presented data. We have therefore changed this part to stating that there is a correlation with the earlier central complex development described in Tribolium. See also response to the general reviewer comments.

      “Hence, we wondered...” 

      The authors need to do a functional assessment of the genes they mentioned. 

      We agree that the goals originally stated at the beginning of this paragraph can only be achieved with functional experiments. We have therefore rephrased this part.

      (3) Discussion

      “A beetle enhancer trap line...” 

      This part should be moved elsewhere (it does not seem to be a discussion) 

      In accordance with this comment and reviewer#2’s similar comment we have removed this section. We have added a statement on the importance of testing the expression of an enhancer trap line to the results part and an added the use of CRISPR-Cas9 for line generation to the introduction. 

      “We have identified a total...” 

      The authors emphasized that they discovered 9 type II NBs. The authors should clarify how important this it

      We have added some discussion on the importance of this finding.

      Dpn is a neural marker - Is this correct? 

      According to Bier et al 1992 (now added as reference) dpn is a pan-neural marker. Reviewer#2 also recommended calling dpn a neural marker.

      “Previous work described a heterochronic...” - reference? 

      Reference have been added

      “By contrast, we show that Tribolium...” 

      What about the number of neurons in the central complex in Tribolium and Drosophila? 

      Does the lineage size of type II NBs reflect the number? 

      Unfortunately, we do not have numbers for that.  

      Reviewer #2 (Recommendations For The Authors): 

      I recommend using page and line numbers to make reviewing and revising less timeconsuming. 

      We apologize for this oversight. We include a line numbering system into our resubmission.

      (1) Abstract 

      "These neural stem cells are believed to be conserved among insects, but their molecular characteristics and their role in brain development in other insect neurogenetics models, such as the beetle Tribolium castaneum have so far not been studied." 

      I recommend explaining the importance of studying Tribolium with regard to the evolution of brain centres rather than just stating that data are lacking. 

      We have now emphasized the importance of Tribolium as model for the evolution of brain centres.

      "Intriguingly, we found 9 type-II neuroblast lineages in the Tribolium embryo while Drosophila produces only 8 per brain hemisphere." 

      It should be made clear that the 9 lineages also refer to brain hemispheres. 

      We have added this information

      (2) Introduction 

      I would remove the first paragraph of the introduction; the use of Tribolium as model representative for insects is too general. The authors should focus on the specific question, i.e. the introduction should start with paragraph 2. 

      While we can relate to the preference for short and concise writing, we feel that giving some background on Tribolium might be important as we expect that many of our readers might be primarily Drosophila researchers. Keeping this paragraph also seems in line with a recommendation of reviewer#1 to add some additional information on Tribolium ecology.  

      "Several NBs of the anterior-most part of the neuroectoderm contribute to the CX and compared…”

      The abbreviation has not been introduced. 

      For clarity we have now opted to not use this abbreviation but to always spell out central complex.

      "Several NBs of the anterior-most part of the neuroectoderm contribute to the CX and compared to the ventral ganglia produced by the trunk segments, it is of distinctively greater complexity..." 

      Puzzling statement. Why would you compare a brain center with ventral ganglia? I recommend removing this. 

      We have changed this statement to just emphasizing the complexity of the brain structure.

      "The dramatically increased number of neural cells that are produced by individual type-II lineages, and the fact that one lineage can produce different types of neurons..."  In my opinion, this statement is too vague and unprofessional in style. Instead of "dramatically increased" use numbers. 

      We have removed ‘dramatically increased’ and now give a numeric example.

      "The dramatically increased number of neural cells that are produced by individual type-II lineages, and the fact that one lineage can produce different types of neurons, leads to the generation of increased neural complexity within the anterior insect brain when compared to the ventral nerve cord.." 

      I assume that this statement relates to the comparison of type I and II nb lineages. However, type I NB lineages also produce different types of neurons due to GMC temporal identity, and neuronal hemi-lineage identity. 

      We have rephrased and tried to make clear that the second part of the statement is not specific to type-II NB only. In line with the comment above we have also removed the reference to the ventral nerve cord.

      "In addition, in Drosophila brain tumours have been induced from type-II NBs lineages [34], opening up the possibility of modelling tumorigenesis in an invertebrate brain, thus making these lineages one of the most intriguing stem cell models in invertebrates [35,36]." 

      This statement is misplaced here; it should be mentioned at the start (if at all). 

      We have moved this statement up.

      "However, molecular characterisation of such lineages in another insect but the fly and a thorough comparison of type-II NBs lineages and their sub-cell-types between fly and beetle are still lacking" 

      The background information should include what is known about type-II NB lineages in Tribolium, including marker gene expression, e.g. Farnworth et al. 

      We refer to He et al 2019, Farnworth et al 2020 and Garcia-Perez 2021. All these publications speculate about a contribution of type-II NBs to Tribolium central complex development but do not show evidence of it. As we emphasize throughout the manuscript, the present work is the first description of type-II NB in Tribolium. 

      "The ETS-transcription factor pointed (pnt) marks type-II NBs [40,41], which do not express the type-I NB marker asense (ase) but the pro-neural gene deadpan (dpn)"  Deadpan is considered a pan-neural gene. To avoid confusion, I would remove "proneural" throughout.

      We have done so throughout the manuscript.

      "We further found that, like the type-II NBs itself, the youngest Tc-pnt-positive but fezmm-eGFP-negative INPs neither express Tc-ase (Fig. 5D, pink arrowheads)."  What is the evidence that these are the youngest pnt positive cells? Position? This needs to be explained. 

      We have clarified that ‘youngest pnt-positive cells’ refers to the position of these cells close to the type-II NB.

      "Therefore these neural markers can be used for a classification of type II NBs (Tc-pnt+, Tcase-), young INPs (Tc-pnt+, Tc-fez/erm-, Tc-ase-), immature INPs (Tc-pnt+, Tcfez/erm+, Tcase+), mature INPs (Tc-dpn+, Tc-ase+, Tc-fez/erm+, Tc-pros+), and GMCs (Tc-ase+, Tcfez/ erm+, Tc-pros+, Tc-dpn). This classification is summarized in Fig. 7 A-B." 

      This is not the best classification and not in line with the schemes in Figure 7 - the young INPs are also immature. What is the difference? It needs to be explained what "mature" means (dividing?). 

      Thank you for pointing this out. We have corrected the error in this part that confused the two original groups (young and immature). To take the immaturity of both types of INPs into account we have then also changed our naming of INP subtypes into immature-I and immature-II and throughout the manuscript). Figure 7 and figure 12 were also changed accordingly. While our classification if primarily based on gene expression the available data indicates that both types of immature INPs are not dividing, whereas mature INPs are. We have added a statement on that to this part.

      "In beetles a single-unit functional central complex develops during embryogenesis while in flies the structure is postembryonic." 

      This statement is vague - the authors need to explain what is meant by "single-unit". The phrase "The structure is postembryonic" also needs more explanation. The Drosophila CX neuroblasts lineages originate in the embryo and the neurons form a commissural tract that becomes incorporated into the fan-shaped body of the Cx. 

      We have explained single-unit central complex and have improved our summary of known differences in central complex development between fly and beetle.

      "To assess the size of the embryonic type-II NBs lineages in beetles we counted the Tc- fez/erm positive (fez-mm-eGFP) cells (INPs and GMCs) associated with a Tc-pntexpressing type-II NBs of the anterior medial group (type-II NBs lineages 1-7).  It is not clear what is meant by "with a Tc-pnt-expressing type-II NBs". Is this a typo?" 

      We have removed this bit.

      (3) Discussion 

      I would remove the first paragraph "A beetle enhancer trap lines reflects Tc-fez/earmuff expression". This is a repetition of the methods rather than a discussion. 

      This part has been removed also in line with reviewer#1’s comment.

      (4) Figures 

      Figure 2 

      To which developing structure do the strongly labelled areas in Figure 2D correspond? 

      We believe that these areas from the protocerebrum including central complex, mushroom bodies and optic lobe. We have added this to the text and to the figure legend.

      Figure 7 

      What do A and B represent? Different stages? 

      A and B show the same lineage but map the expression of different additional markers for clarity. We have added an explanation of this. 

      The classification contradicts the description in the section "Conserved patterns of gene expression mark Tribolium type-II NBs, different stages of INPs and GMCs" (last sentence) where young INPs are first in the sequence and described as pnt+, erm-, ase- and immature INPs as pnt+ erm+ and ase+. 

      We have corrected this mistake and changed the names of the subtypes into immatureI and immature-II (see above).

      "We conclude that the evolutionary ancient six3 territory gives rise to the neuropile of the z, y, x and w tracts." 

      Please clarify if six3 is also expressed in the corresponding grasshopper NB lineages or if your conclusion is based on the comparison of Drosophila and Tribolium and you assume that this is the ancestral condition. 

      Six3 expression has not been studied in grasshoppers. Owing to the highly conserved nature of an anterior median six3 domain in arthropods and bilaterian animals in general, we would expect it to be expressed anterior-medially in grasshoppers as well. In Drosophila the gene is expressed in the anterior-medial embryonic region where the type-II NBs are expected to develop, but to our knowledge it has not been specifically studied which type-II NB lineages are located within this domain. We have clarified in our text that we do not claim that the origin of anterior-medial type-II NB 1-4 and the X,Y, Z and W lineages from the six3 territory is highly conserved but only the territory itself. As far as we know our work is the first to analyse the relationship of type-II lineages and the conserved head patterning genes six3 and otd. We have added some clarification of this into this part of the discussion.

      (5) Methods 

      The methods section should include the methods for cell counting, as well as cell and nuclei size measurements including statistics (e.g. how many embryos, how many NB lineages). The comparison of the Tribolium NB lineage cell numbers to published Drosophila data should include a brief description of the method used in Drosophila (in addition to the method used here in Tribolium) so that the reader can understand how the data compare. 

      We have added a separate section on this to the Methods part which also includes the criteria used in Drosophila. We have also included some more information to the results part on the inclusion of neurons in the Drosophila counts that may only be partially included in our numbers. This does however not change the results in terms of larger numbers of progenitor cells in Tribolium.

      (6) Typos and minor errors 

      Abstract 

      “However, little is known on the developmental processes that create this diversity” 

      Change to ... little is known about

      Changed.

      NBs lineages 

      Change to NB lineages throughout. 

      We have used text search to find and replace all position where this was used erroneously,

      Results 

      "Schematic drawing of expression different markers in type-II NB lineages.." 

      Schematic drawing of expression of different markers 

      Corrected

      Discussion 

      "However, the type-II NB 7, which is we assigned to the anterior medial group but which..." 

      .... which we assigned.... 

      corrected

      "......might be the one that does not have a homologue in the fly embryo The identification of more..."  Full stop missing. 

      Added.

      "Adult like x, y, and w tracts as well as protocerebral bridge are...." 

      Change to "The adult like x, y, and w tracts as well as the protocerebral bridge are.... 

      This part has been removed with the rewriting of this paragraph.  

      Reviewer #3 (Recommendations For The Authors): 

      (1) Suggestions for improved or additional experiments, data, or analyses: 

      a) The analysis of nuclear size is wrong. The authors compare the largest cell of a cluster of cells with a number of random cells from the same brain. It is obvious that the largest cell of a cluster will be larger than the average cell of the same brain. A better control would be to compare the largest cell of the pnt+ cluster with the largest cell of a random sample of cells, although this also comes with biases. Personally, I have no doubt that the authors are looking at neuroblasts, based on the markers they are using, so I would recommend completely eliminating Figure 4.

      We agree that we produced a somewhat biased and expected result when we select the largest cell of a cluster for size comparison. However, we found it important to show based on a larger sample that these cells are also statistically larger than the average cell of a brain, which we think our assessment shows. We do not claim that type-II NBs are the largest cells of a brain, or that they are larger than type-I NBs, therefore in a random sample there might be cells that are equally big (see also distribution of the control sample shown in figure 4, and we have added a note on this to the text). We are happy to hear that this reviewer has no doubts we are looking at neural stem cells. However, reviewer#1 did express some hesitations and therefore we think it is important to keep the information on cell size as part of our argument that we are indeed looking at type-II NBs (gene expression, cell size, dividing, part of a neural lineage).

      b) The comparison of NB, INP, and GMC numbers between Drosophila and Trbolium (section "The Tribolium embryonic lineages of type-II NBs are larger and contain more mature INPs than those of Drosophila") compares an experiment that the authors did with published data. I would suggest that the authors repeat the Drosophila stainings and compare themselves to avoid cases of batch effects, inconsistent counting, etc.

      None of the authors is a Drosophila expert or has any experience at working with this model and reassessing the lineage size would require a number of combinatorial staining. Therefore, we feel that using the published data produced by experts and which also includes repeat experiments is for us the more reliable approach.

      c) In Figure 10, there are some otd+ GFP+ cells laterally. What are these? 

      We believe that these cells contribute to the eye anlagen. We have added this information to the legend.

      (2) Minor corrections to the text and figures: 

      a) There are some typos in the text: e.g. "pattering" in the abstract. 

      We have carefully checked the text for typos and hope that we have found everything.

      b) The referencing of figures in the text is inconsistent (eg "Figure 5 panel A" vs "Figure 5D" on page 12). 

      We have checked throughout the manuscript and made sure to always refer to a panel correctly.

      c) In Figure 3C, the white staining (anti-PH3) is not indicated in the Figure. 

      The label has been added in the figure.

      d) Moreover, in Figure 3, green is not very visible in the images. 

      We have improved the colour intensity where possible.

      e) In the figures, it might be better to outline the cells with color-coded dashed circles instead of using arrows. 

      We think that this would obscure some details of the stainings and create a rather artificial representation. We also feel that doing this consistently in all our images is an amount of work not justified by the degree of expected improvement to the figures

      NOTE: We are submitting a revised version of the supplementary material which only contains two minor changes: a headline was added to Table S4 (Antibodies and staining reagents) and a typo was corrected in line one of table S5 (TC to Tc).

    1. Author response:

      The following is the authors’ response to the current reviews.

      Reviewer #1 (Public review):

      When different groups (populations, species) are presented with similar environmental pressures, how similar are the ultimate targets (genes, pathways)? This study sought to illuminate this broader question via experimental evolution in D. simulans and quantifying gene-expression changes, specifically in the context of standing genetic variation (and not de novo mutation). Ultimately, the authors showed pleiotropy and standing-genetic variation play a significant role in the "predictability" of evolution.

      The results of this manuscript look at the interplay between pleiotropy, standing genetic variation and parallelism (i.e. predictability of evolution) in gene expression. Ultimately, their results suggest that (a) pleiotropic genes typically have a smaller range in variation/expression, and (b) adaptation to similar environments tends to favor changes in pleiotropic genes, which leads to parallelism in mechanisms (though not dramatically). However, it is still uncertain how much parallelism is directly due to pleiotropy, instead of a complex interplay between them and ancestral variation.

      Yes, the reviewer is correct that our results for the direct effects of pleiotropy were not consistent for both measures of pleiotropy. We highlight this in the discussion:” Only tissue specificity had a significant direct effect, which was even larger than the indirect effect (Table 2). No significant direct effect was found for network connectivity. The discrepancy between the two measures of pleiotropy is particularly interesting given their significant correlation (Supplementary Figure 1). This suggests that both measures capture aspects of pleiotropy that differ in their biological implications.”

      Reviewer #2 (Public review):

      Summary:

      Lai and collaborators use a previously published RNAseq dataset derived from an experimental evolution set up to compare the pleiotropic properties of genes which expression evolved in response to fluctuating temperature for over 100 generations. The authors correlate gene pleiotropy with the degree of parallelisms in the experimental evolution set up to ask: are genes that evolved in multiple replicates more or less pleiotropic?

      They find that, maybe counter to expectation, highly pleiotropic genes show more replicated evolution. And such effect seems to be driven by direct effects (which the authors can only speculate on) and indirect effect through low variance in pleiotropic genes (which the authors indirectly link to genetic variation underlying gene expression variance).

      Weaknesses:

      The results offer new insights into the evolution of gene expression and into the parameters that constrain such evolution, i.e., pleiotropy. Although the conclusions are supported by the data, I find the interpretation of the results a little bit complicated.

      We are very happy to read that the reviewer finds our conclusions to be supported by the data.

      Major comment:

      The major point I ask the authors to address is whether the connection between polygenic adaptation and parallelism can indeed be used to interpret gene expression parallelism. If the answer is not, please rephrase the introduction and discussion, if the answer is yes, please make it explicit in the text why it is so.

      Yes, we think that gene expression parallelism can be explained by polygenic adaptation.

      The authors argument: parallelism in gene expression is the same as parallelism in SNP allele frequency (AFC) (see L389-383 here they don't mention that this explanation is derived from SNP parallelism and not trait parallelism, and see Fig1 b). In previous publications the authors have explained the low level of AFC parallelism using a polygenic argument. Polygenic traits can reach a new trait optimum via multiple SNPs and therefore although the trait is parallel across replicates, the SNPs are not necessarily so.

      In the current paper, they seem to be exchanging SNP AFC by gene expression, and to me, those are two levels that cannot be interchanged. Gene expression is a trait, not a SNP, and therefore the fact that a gene expression doesn't replicate cannot be explained by polygenic basis, because again the trait is gene expression itself. And, actually the results of the simulations show that high polygenicity = less trait parallelism (Fig4).

      We agree with the reviewer that it is important to consider different hierarchies when talking about the implications of polygenic adaptation. The lowest hierarchical level is SNP variation and the highest level is fitness. In-between these extreme hierarchical levels is gene expression. While gene expression is a trait itself, as correctly pointed out by the reviewer, it is possible that selection is not favoring a specific trait value, because selection targets a trait on a higher hierarchical level. This implies that not only SNPs, but also intermediate traits such as gene expression can exhibit redundancy. Considering a simple example of one selected trait (e.g. body size), which is affected by the expression level of two genes A and B, each regulated by SNP A1, A2 and B1, B2. It is now possible to modulate the focal trait by allele frequency changes of A1, which in turn will only affect gene A. Alternatively, SNP B2 may change, modifying the expression of gene B, leading to the same change in body size. Hence, we could have redundancy both at the SNP level as well as on the gene expression level (although higher redundancy is expected on the SNP level). Most importantly, this redundancy at intermediate hierarchical levels is not pure theory, but it is supported by empirical evidence. We have shown that redundancy exists not only for gene expression (10.1111/mec.16274) but also for metabolite concentrations (10.1093/gbe/evad098).

      Now, if the authors focus on high parallel genes (present in e.g. 7 or more replicates) and they show that the eQTLs for those genes are many (highly polygenic) and the AFC of those eQTL are not parallel, then I would agree with the interpretation. But, given that here they just assess gene expression and not eQTL AFC, I do not think they can use the 'highly polygenic = low parallelism' explanation.

      This is clearly an interesting proposed research project, but we doubt that it would result in the expected outcome. Since most of the adaptive gene expression changes are not having a simple genetic basis (10.1093/gbe/evae077) and most expression variation is determined by trans-regulatory effects (10.1038/s41576-020-00304-w), eQTL mapping will most likely not identify all contributing loci. Large effect loci are more easily identified, but they are also expected to be more parallel.

      The interpretation of the results to me, should be limited to: genes with low variance and high pleiotropy tend to be more parallel, and the explanation might be synergistic pleiotropy.

      We thank the reviewer for the suggestion, but prefer to stick to our interpretation of the data.

      Comments on revisions: The authors didn't really address any of the comments made by any of the reviewers - basically nothing was changed in the main text. Therefore, I leave my original review unchanged.

      We modestly disagree, in our point to point reply, we respond to all reviewers’ comments. Since, we did not identify any major problem in our manuscript, we only modified the wording in some parts where we felt that a clarification could resolve the misunderstanding of the reviewers. In response to the reviewers’ comments, we added a new paragraph in the discussion and generated a new figure.

      Reviewer #3 (Public review):

      The authors aim to understand how gene pleiotropy affects parallel evolutionary changes among independent replicates of adaptation to a new hot environment of a set of experimental lines of Drosophila simulans using experimental evolution. The flies were RNAsequenced after more than 100 generations of lab adaptation and the changes in average gene expression were obtained relative to ancestral expression levels from reconstructed ancestral lines. Parallelism of gene expression change among lines is evaluated as variance in differential gene expression among lines relative to error variance. Similarly, the authors ask how the standing variation in gene expression estimated from a handful of flies from a reconstructed outbred line affects parallelism. The main findings are that parallelism in gene expression responses is positively associated with pleiotropy and negatively associated with expression variation. Those results are in contradiction with theoretical predictions and empirical findings. To explain those seemingly contradictory results the authors invoke the role of synergistic pleiotropy and correlated selection, although they do not attempt to measure either.

      Strengths:

      The study uses highly replicated outbred laboratory lines of Drosophila simulans evolved in the lab under constant hot regime for over 100 generations. This allows for robust comparisons of evolutionary responses among lines.

      The manuscript is well written and the hypotheses are clearly delineated at the onset.

      The authors have run a causal analysis to understand the causal dependencies between pleiotropy and expression variation on parallelism.

      The use of whole-body RNA extraction to study gene expression variation is well justified.

      Weaknesses:

      The accuracy of the estimate of ancestral phenotypic variation in gene expression is likely low because estimated from a small sample of 20 males from a reconstructed outbred line. It might not constitute a robust estimate of the genetic variation of the evolved lines under study.

      We agree with the reviewer that variation estimates based on 20 samples are not very precise. Nevertheless, we demonstrated that the estimated variance in gene expression was highly correlated between two independent samples from the same ancestral population. Furthermore, we identified a significant correlation of expression variance with evolutionary parallelism. In other words, the biological signal has been sufficiently strong despite the variance estimate has been noisy.

      There are no estimates of the standing genetic variation of expression levels of the genes under study, only estimates of their phenotypic variation. I wished the authors had been clear about that limitation and had refrained from equating phenotypic variation in expression level with standing genetic variation.

      The reviewer is right that we did not estimate genetic variation of gene expression, but use expression variation as a proxy for the standing genetic variation. There are two potential problems with this approach. First, a large expression variation could be caused by a single large effect variant segregating at intermediate frequency. Such large effect variants will exhibit a highly parallel selection response-contrary to our empirical results. Since we have shown previously (10.1093/gbe/evae077) that adaptive gene expression changes are mostly polygenic we do not consider this extreme scenario to be very relevant in our study. Rather, we would like to emphasize that neither a SNP analysis of the 5’ region nor an eQTL study will provide an unbiased estimator of genetic variation of gene expression. The second problem arises if gene expression noise differs among genes, hence more noisy genes will appear to have more standing genetic variation than genes with less noise. Since, we average across many different cells and cell types, gene expression noise is expected to be levelled out- this aspect is discussed in detail in the manuscript.

      In other words, despite these two potential limitations, we consider our approach superior to alternative approaches of estimating genetic variation in gene expression.

      Moreover, since the phenotype studied is gene expression, its genetic basis extends beyond expressed sequences. The phenotypic variation of a gene's expression may thus likely misrepresent the genetic variation available for its evolution. The authors do not present evidence that sequence variation correlates with expression variation.

      Gene expression is determined by the joint effects of cis-regulatory and trans-regulatory variation. Hence, recombination can create more extreme phenotypes than the one of the parental lines (in quantitative genetics this is called transgressive segregation). It is unclear to what extent this constitutes a problem for our analyses. Nevertheless, we would like to point out that eQTL mapping will miss many trans-acting variants and therefore we doubt that the requested empirical evidence for correlation between genetic variation (estimated by eQTL mapping) and observed expression variation is as straight forward as suggested by the reviewer.

      Nevertheless, we reference an empirical study, which showed a positive correlation between expression variation and cis-regulatory variation.

      The authors have not attempted to estimate synergistic pleiotropy among genes, nor how selection acts on gene expression modules. It makes their conclusion regarding the role of synergistic pleiotropy rather speculative.

      The reviewer is correct that we did not demonstrate synergistic pleiotropy, but we discuss this as a possible explanation for the observed direct effects of pleiotropy.


      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      The results of this manuscript look at the interplay between pleiotropy, standing genetic variation, and parallelism (i.e. predictability of evolution) in gene expression. Ultimately, their results suggest that (a) pleiotropic genes typically have a smaller range in variation/expression, and (b) adaptation to similar environments tends to favor changes in pleiotropic genes, which leads to parallelism in mechanisms (though not dramatically). However, it is still uncertain how much parallelism is directly due to pleiotropy, instead of a complex interplay between them and ancestral variation.

      I have a few things that I was uncertain about. It may be these things are easily answered but require more discussion or clarity in the manuscript.

      (1) The variation being talked about in this manuscript is expression levels, and not SNPs within coding regions (or elsewhere). The cause of any specific gene having a change in expression can obviously be varied - transcription factors, repressors, promoter region variation, etc. Is this taken into account within the "network connectivity" measurement? I understand the network connectivity is a proxy for pleiotropy - what I'm asking is, conceptually, what can be said about how/why those highly pleiotropic genes have a change (or not) in expression. This might be a question for another project/paper, but it feels like a next step worth mentioning somewhere.

      In current study, we are only able to detect significant and repeatable expression changes but unable to identify the underlying causal variants. An eQTL study in the founder population in combination with genomic resequencing for both evolved and ancestral populations would be required to address this question.

      (2) The authors do have a passing statement in line 361 about cis-regulatory regions. Is the assumption that genetic variation in promoter regions is the ultimate "mechanism" driving any change in expression? In the same vein, the authors bring up a potential confounding factor, though they dismiss it based on a specific citation (lines 476-481; citation 65). I'm of the mindset that in order to more confidently disregard this "issue" based on previous evidence, it requires more than one citation. Especially since the one citation is a plant. That specific point jumps out to me as needing a more careful rebuttal.

      It was not our intention to claim that the expression changes in our experiment are caused by cis-regulatory variation only. We believe that the observed expression variation has both cis- and trans-genetic components, where as some studies tend to estimate much higher cisvariation for gene expression in Drosophila populations (e.g. [1, 2]). We mentioned the positive correlation between cis-regulatory polymorphism and expression variation to (1) highlight the genetic control of gene expression and (2) make the connection between polygenic adaptation and gene expression evolutionary parallelism.

      (3) I feel like there isn't enough exploration of tissue specificity versus network connectivity. Tissue specificity was best explained by a model in which pleiotropy had both direct and indirect effects on parallelism; while network connectivity was best explained (by a small margin) via the model which was mostly pleiotropy having a direct effect on ancestral variation, that then had a direct effect on parallelism. When the strengths of either direct/indirect effects were quantified, tissue specificity showed a stronger direct effect, while network connectivity had none (i.e. not significant). My confusion is with the last point - if network connectivity is explained by a direct effect in the best-supported model, how does this work, since the direct effect isn't significant? Perhaps I am misunderstanding something.

      To clarify, for network connectivity, there’s a significant “indirect” effect on parallelism (i.e. network connectivity affect ancestral gene expression and ancestral gene expression affect parallelism). Hence, in table 2, the direct effect of network connectivity on parallelism is weak and not significant while the indirect effect via ancestral variation is significant.

      Also, network connectivity might favor the most pleiotropic genes being transcription factor hubs (or master regulators for various homeostasis pathways); while the tissue specificity metric perhaps is a kind of a space/time element. I get that a gene having expression across multiple tissues does fit the definition of pleiotropy in the broad sense, but I'm wondering if some important details are getting lost - I'm just thinking about the relative importance of what tissue specificity measurements say versus the network connectivity measurement.

      We examined the statistical relationship between the two measures and found a moderate positive correlation on the basis of which we argued that the two measures may capture different aspects of pleiotropy. We appreciate the reviewer’s suggestions about the biological basis of the two estimates of pleiotropy, but we think that without further experimental insights, an extended discussion of this topic is too premature to provide meaningful insights to the readership.

      Reviewer #2 (Public review):

      Summary:

      Lai and collaborators use a previously published RNAseq dataset derived from an experimental evolution set up to compare the pleiotropic properties of genes whose expression evolved in response to fluctuating temperature for over 100 generations. The authors correlate gene pleiotropy with the degree of parallelisms in the experimental evolution set up to ask: are genes that evolved in multiple replicates more or less pleiotropic?

      They find that, maybe counter to expectation, highly pleiotropic genes show more replicated evolution. Such an effect seems to be driven by direct effects (which the authors can only speculate on) and indirect effects through low variance in pleiotropic genes (which the authors indirectly link to genetic variation underlying gene expression variance).

      Weaknesses:

      The results offer new insights into the evolution of gene expression and into the parameters that constrain such evolution, i.e., pleiotropy. Although the conclusions are supported by the data, I find the interpretation of the results a little bit complicated.

      Major comment:

      The major point I ask the authors to address is whether the connection between polygenic adaptation and parallelism can indeed be used to interpret gene expression parallelism. If the answer is not, please rephrase the introduction and discussion, if the answer is yes, please make it explicit in the text why it is so.

      Our answer is yes, we interpreted gene expression parallelism (high ancestral variance -> less parallelism) using the same framework that links polygenic adaptation and parallelism (high polygenicity = less trait parallelism). We believe that our response covers several of the reviewer’s concerns.

      The authors' argument: parallelism in gene expression is the same as parallelism in SNP allele frequency (AFC) (see L389-383 here they don't mention that this explanation is derived from SNP parallelism and not trait parallelism, and see Figure 1 b). In previous publications, the authors have explained the low level of AFC parallelism using a polygenic argument. Polygenic traits can reach a new trait optimum via multiple SNPs and therefore although the trait is parallel across replicates, the SNPs are not necessarily so.

      Importantly, our rationale is based on the idea that gene expression is rarely the direct target of selection, but rather an intermediate trait [3]. Recently, we have specifically tested this assumption for gene expression and metabolite concentrations and our analysis showed that both traits were are redundant [4], as previously shown for DNA sequences [5]. The important implication for this manuscript is that gene expression is also redundant, so that adaptation can be achieved by distinct changes in gene expression in replicate populations adapting to the same selection pressure. This implies that we can use the same simulation framework for gene expression as for sequencing data. In our case different SNP frequencies correspond to different expression levels (averaged across individuals from a population), which in turn increases fitness by modifying the selected trait. Importantly, the selected trait in our simulations is not gene expression, but a not defined high level phenotype. A key insight from our simulations is that with increasing polygenicity the expression of a gene is more variable in the ancestral population.

      In the current paper, they seem to be exchanging SNP AFC by gene expression, and to me, those are two levels that cannot be interchanged. Gene expression is a trait, not an SNP, and therefore the fact that a gene expression doesn't replicate cannot be explained by a polygenic basis, because again the trait is gene expression itself. And, actually, the results of the simulations show that high polygenicity = less trait parallelism (Figure 4).

      As detailed above, because adaptation can be reached by changes in gene expression at different sets of genes, redundancy is also operating on the expression level not just on the level of SNPs. To clarify, the x-axis of Fig. 4 is the expression variation in the ancestral population.

      Now, if the authors focus on high parallel genes (present in e.g. 7 or more replicates) and they show that the eQTLs for those genes are many (highly polygenic) and the AFC of those eQTLs are not parallel, then I would agree with the interpretation. But, given that here they just assess gene expression and not eQTL AFC, I do not think they can use the 'highly polygenic = low parallelism' explanation.

      The interpretation of the results to me, should be limited to: genes with low variance and high pleiotropy tend to be more parallel, and the explanation might be synergistic pleiotropy.

      While we understand the desire to model the full hierarchy from eQTLs to gene expression and adaptive traits, we raise caution that this would be a very challenging task. eQTLs very often underestimate the contribution of trans-acting factors, hence the understanding of gene expression evolution based on eQTLs is very likely incomplete and cannot explain the redundancy of gene expression during adaptation. Hence, we think that the focus on redundant gene expression is conceptually simpler and thus allows us to address the question of pleiotropy without the incorporation of allele frequency changes.  

      Reviewer #3 (Public review):

      The authors aim to understand how gene pleiotropy affects parallel evolutionary changes among independent replicates of adaptation to a new hot environment of a set of experimental lines of Drosophila simulans using experimental evolution. The flies were RNAsequenced after more than 100 generations of lab adaptation and the changes in average gene expression were obtained relative to ancestral expression levels from reconstructed ancestral lines. Parallelism of gene expression change among lines is evaluated as variance in differential gene expression among lines relative to error variance. Similarly, the authors ask how the standing variation in gene expression estimated from a handful of flies from a reconstructed outbred line affects parallelism. The main findings are that parallelism in gene expression responses is positively associated with pleiotropy and negatively associated with expression variation. Those results are in contradiction with theoretical predictions and empirical findings. To explain those seemingly contradictory results the authors invoke the role of synergistic pleiotropy and correlated selection, although they do not attempt to measure either.

      Strengths:

      (1) The study uses highly replicated outbred laboratory lines of Drosophila simulans evolved in the lab under a constant hot regime for over 100 generations. This allows for robust comparisons of evolutionary responses among lines.

      (2) The manuscript is well written and the hypotheses are clearly delineated at the onset.

      (3) The authors have run a causal analysis to understand the causal dependencies between pleiotropy and expression variation on parallelism.

      (4) The use of whole-body RNA extraction to study gene expression variation is well justified.

      Weaknesses:

      (1) It is unclear how well phenotypic variation in gene expression of the evolved lines has been estimated by the sample of 20 males from a reconstructed outbred line not directly linked to the evolved lines under study. I see this as a general weakness of the experimental design.

      Our intention was not to measure the phenotypic variance of the evolved lines, but rather to estimate the phenotypic variance at the beginning of the experiment. Hence, we measured and investigated the variation of gene expression in the ancestral population since this was the beginning of the replicated experimental evolution. Furthermore, since the ancestral population represents the natural population in Florida, the gene expression variation reflects the history of selection history acting on it.

      (2) There are no estimates of standing genetic variation of expression levels of the genes under study, only phenotypic variation. I wished the authors had been clear about that limitation and had discussed the consequences of the analysis. This also constitutes a weakness of the study.

      The reviewer is correct that we do not aim to estimate the standing genetic variation, which is responsible for differences in gene expression. While we agree that it could be an interesting research question to use eQTL mapping to identify the genetic basis of gene expression, we caution that trans-effects are difficult to estimate and therefore an important component of gene expression evolution will be difficult to estimate. Hence, we consider that our focus on variation in gene expression without explicit information about the genetic basis is simpler and sufficient to address the question about the role of pleiotropy.

      (3) Moreover, since the phenotype studied is gene expression, its genetic basis extends beyond expressed sequences. The phenotypic variation of a gene's expression may thus likely misrepresent the genetic variation available for its evolution. The genetic variation of gene expression phenotypes could be estimated from a cross or pedigree information but since individuals were pool-sequenced (by batches of 50 males), this type of analysis is not possible in this study.

      We agree with the reviewer that gene expression variation may also have a non-genetic basis, we discuss this in depth in the discussion of the manuscript.  

      (4) The authors have not attempted to estimate synergistic pleiotropy among genes, nor how selection acts on gene expression modules. It makes any conclusion regarding the role of synergistic pleiotropy highly speculative.

      We mentioned synergistic pleiotropy as a possible explanation for our results. A positive correlation between the fitness effect of gene expression variation would predict more replicable evolutionary changes. A similar argument has been made by [6]. 

      I don't understand the reason why the analysis would be restricted to significantly differentially expressed genes only. It is then unclear whether pleiotropy, parallelism, and expression variation do play a role in adaptation because the two groups of adaptive and non-adaptive genes have not been compared. I recommend performing those comparisons to help us better understand how "adaptive" genes differentially contribute to adaptation relative to "nonadaptive" genes relative to their difference in population and genetic properties.

      We agree with the reviewer that the comparison between the pleiotropy of adaptive and nonadaptive genes is interesting. We performed the analysis but omitted from the current manuscript for simplicity. Similar to the results in [6], non-adaptive genes are more pleiotropic than the adaptive genes. For adaptive genes we find a positive correlation between the level of pleiotropy and evolutionary parallelism. Thus, high pleiotropy limits the evolvability of a gene, but moderate and potentially synergistic pleiotropy increases the repeatability of adaptive evolution. We included this result in the revised manuscript and discuss it.

      There is a lack of theoretical groundings on the role of so-called synergistic pleiotropy for parallel genetic evolution. The Discussion does not address this particular prediction. It could be removed from the Introduction.

      We modestly disagree with the reviewer, synergistic pleiotropy is covered by theory and empirical results also support the importance of synergistic pleiotropy. 

      References

      (1) Genissel A, McIntyre LM, Wayne ML, Nuzhdin SV. Cis and trans regulatory effects contribute to natural variation in transcriptome of Drosophila melanogaster. Molecular biology and evolution. 2008;25(1):101-10. Epub 20071112. doi: 10.1093/molbev/msm247. PubMed PMID: 17998255.

      (2) Osada N, Miyagi R, Takahashi A. Cis- and Trans-regulatory Effects on Gene Expression in a Natural Population of Drosophila melanogaster. Genetics. 2017;206(4):2139-48. Epub 20170614. doi: 10.1534/genetics.117.201459. PubMed PMID: 28615283; PubMed Central PMCID: PMCPMC5560811.

      (3) Barghi N, Hermisson J, Schlötterer C. Polygenic adaptation: a unifying framework to understand positive selection. Nature reviews Genetics. 2020;21(12):769-81. Epub 2020/07/01. doi: 10.1038/s41576-020-0250-z. PubMed PMID: 32601318.

      (4) Lai WY, Otte KA, Schlötterer C. Evolution of Metabolome and Transcriptome Supports a Hierarchical Organization of Adaptive Traits. Genome biology and evolution. 2023;15(6). Epub 2023/05/26. doi: 10.1093/gbe/evad098. PubMed PMID: 37232360; PubMed Central PMCID: PMCPMC10246829.

      (5) Barghi N, Tobler R, Nolte V, Jaksic AM, Mallard F, Otte KA, et al. Genetic redundancy fuels polygenic adaptation in Drosophila. PLoS biology. 2019;17(2):e3000128. Epub 2019/02/05. doi: 10.1371/journal.pbio.3000128. PubMed PMID: 30716062.

      (6) Rennison DJ, Peichel CL. Pleiotropy facilitates parallel adaptation in sticklebacks. Molecular ecology. 2022;31(5):1476-86. Epub 2022/01/09. doi: 10.1111/mec.16335. PubMed PMID: 34997980; PubMed Central PMCID: PMCPMC9306781.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      Point 1 of public reviews and point 2 of recommendations to authors. 

      Temporal ambiguity in credit assignment: While the current design provides clear task conditions, future studies could explore more ambiguous scenarios to further reflect real-world complexity…. The role of ambiguity is very important for the credit assignment process. However, in the current task design, the instruction of the task design almost eliminates the ambiguity of which the trial's choice should be assigned credit to. The authors claim the realworld complexity of credit assignment in this task design. However, the real-world complexity of this type of temporal credit assignment involves this type of temporal ambiguity of responsibility as causal events. I am curious about the consequence of increasing the complexity of the credit assignment process, which is closer to the complexity in the real world.

      We agree that the structure of causal relationships can be more ambiguous in real-world contexts. However, we also believe that there are multiple ways in which a task might approach “real-world complexity”. One way is by increasing the ambiguity in the relationships between choices and outcomes (as done by Jocham et al., 2016). Another is by adding interim decisions that must be completed between viewing the outcome of a first choice, which mimics task structures such as the cooking tasks described in the introduction. In such tasks, the temporal structure of the actions maybe irrelevant, but the relationship between choice identities and the actions is critical to be effective in the task (e.g., it doesn’t matter whether I add spice before or after the salt, all I need to know that adding spice will result in spicy soup).  While ambiguity about either form of causal relation is clearly an important part of real-world complexity, and would make credit assignment harder, our study focuses on how links between outcomes and specific past choice identities are created at the neural level when they are known to be causal. 

      We consequently felt it necessary to resolve temporal ambiguity for participants. Instructing participants on the structure of the task allowed us to make assumptions about how credit assignment for choice identities should proceed (assign credit to the choice made N trials back) and allowed us make positive predictions about the content of representations in OFC when viewing an outcome. This gave the highest power to detect multivariate information about the causal choice and the highest interpretability of such findings. 

      In contrast, if we had not resolved this ambiguity, it would be difficult to tell if incorrect decoding from the classifier resulted from noise in the neural signal, or if on that trial participants were assigning credit to non-causal choices that they erroneously believed to have caused the outcome due to the perceived temporal structure. We believe this would have ultimately decreased our power to determine whether representations of the causal choice were present at the time of outcome because we would have to make assumptions about what counts as a “true” causal representation. 

      We have commented on this in the discussions (p.13): 

      “While our study was designed to focus on the complexity of assigning credit in tasks with different known causal structures, another important component of real-world credit assignment is temporal ambiguity. To isolate the mechanisms which create associations between specific choices and specific outcomes, we instructed participants on the causal structure of each task, removing temporal ambiguity about the causal choice.  However, our results are largely congruent with previously reported results in tasks that dissolved the typical experimental trial structure, producing temporal ambiguity, and which observed more pronounced spreading of effect, in addition to appropriate credit assignment (Jocham et al, 2016).  Namely, this study found that activation in the lOFC increased only when participants received rewards contingent on a previous action, an effect that was more pronounced in subjects whose behavior reflected more accurate credit assignment. This suggests a shared lOFC mechanism for credit assignment in different types of complex environments. Whether these mechanisms extend to situations where the temporal causal structure is completely unknown remains an important question.”

      Point 2 of public reviews and point 1 of recommendations to authors

      Role of task structure understanding: The difference in task comprehension between human subjects in this study and animal subjects in previous studies offers an interesting point of comparison…. The credit assignment involves the resolution of the ambiguity in which the causal responsibility of an outcome event is assigned to one of the preceding events. In the original study of Walton and his colleagues, the monkey subjects could not be instructed on the task structure defining the causal relationships of the events. Then, the authors of the original study observed the spreading of the credit assignments to the "irrelevant" events, which did not occur in the same trial of the outcome event but to the events (choices) in neighbouring trials. This aberrant pattern of the credit assignment can be due to the malfunctions of the credit assignment per se or the general confusion of the task structure on the part of the monkey subjects. In the current study design, the subjects are humans and they are not confused about the task structure. Consistently, it is well known that human subjects rarely show the same patterns of the "spreading of credit assignment". So the implicit mechanism of the credit assignment process involves the understanding of the task structure. In the current study, there are clearly demarked task conditions that almost resolve the ambiguity inherent in the credit assignment process. Yet, the focus of the current analysis stops short of elucidating the role of understanding the task structure. It would be great if the authors could comment on the general difference in the process between the conditions, whether it is behavioral or neural.

      We would like to thank the reviewer for making this important point. We believe that understanding the structure of the credit-assignment problem above is quite important, at least for the type of credit assignment described here. That is, because participants know that the outcome viewed is caused by the choice they made, 0 or 1 trials into the past, they can flexibly link choice identities to the newly observed outcomes as the probabilities change. Note, however, that this is already very challenging in the 1-back condition because participants need to track the two independently changing probabilities. We believe this is critical to address the questions we aimed to answer with this experiment, as described above. 

      We agree that this might be quite different from previous studies done with non-human primates, which also included many more training trials and lesions to the lOFC. Both of these aspects could manifest as difference in task performance and processing at behavioural and neural levels, respectively. Consistent with this possibility, in our task, we found no differences in credit spreading between conditions, suggesting that humans were quite precise in both, despite causal relationships being harder to track in the “indirect transition condition”. This lack of credit spreading could be because humans better understood the task-structure compared to macaques or be due to differences in functioning of the OFC and other regions. Because all participants were trained to understand, and were cued with explicit knowledge of, the task structure, it is difficult to isolate its role as we would need another condition in which they were not instructed about the task structure. This would also be an interesting study, and we leave it to future research to parse the contributions of task-structure ambiguity to credit assignment. 

      Point 3 of public reviews. 

      The authors used a sophisticated method of multivariate pattern analysis to find the neural correlate of the pending representation of the previous choice, which will be used for the credit assignment process in the later trials. The authors tend to use expressions that these representations are maintained throughout this intervening period. However, the analysis period is specifically at the feedback period, which is irrelevant to the credit assignment of the immediately preceding choice. This task period can interfere with the ongoing credit assignment process. Thus, rather than the passive process of maintaining the information of the previous choice, the activity of this specific period can mean the active process of protecting the information from interfering and irrelevant information. It would be great if the authors could comment on this important interpretational issue.

      We agree that lFPC is likely actively protecting the pending choice representation from interference with the most recent choice for future credit assignment. This interpretation is largely congruent with the idea of “prospective memory” (e.g., Burgess, Gonen-Yaacovi, Volle, 2011), in which the lFPC can be thought of as protecting information that will be needed in the future but is not currently needed for ongoing behavior. That said, from our study alone it is difficult to make claims about whether the information maintained in frontal pole is actively protecting this information because of potentially interfering processes. Our “indirect transition condition” only contains trials where there is incoming, potentially interfering information about new outcomes, but no trials that might avoid interference (e.g., an interim choice made but there is nothing to be learned from it). We comment on this important future direction on page 14:  

      “One interpretation of these results is that the lFPC actively protects information about causal choices when potentially interfering information must be processed. Future studies will be needed to determine if the lFPC’s contributions are specific to these instances of potential interference, and whether this is a passive or active process”

      Point 3 of recommendation to authors 

      A slightly minor, but still important issue is the interpretation of the role of lOFC. The authors compared the observed patterns of the credit assignment to the ideal patterns of credit assignment. Then, the similarity between these two matrices is used to find the associated brain region. In the assumption that lOFC is involved in the optimal credit assignment, the result seems reasonable. But as mentioned above, the current design involves the heavy role of understanding the task structure, it is debatable whether the lOFC is just involved in the credit assignment process or a more general role of representing the task structure.

      We agree that this is an important distinction to make, and it is very likely that multiple regions of the OFC carry information about the task structure, and the extent to which participants understood this structure may be reflected in behavioral estimates of credit assignment or the overall patterns of the matrices (though all participants verbalized the correct structure prior to the task). However, we believe that in our task the lOFC is specifically involved in credit-assignment because of the content of the information we decoded. We demonstrated that the lOFC and HPC carry information about the causal choice during the outcome. These results cannot be explained by differences in understanding of the task structure because that understanding would have been consistent across trials where participants choose either shape identity. Thus, a classifier could not use this to separate these types of trials and would reflect chance decoding.   

      One interpretation of the lOFC’s role in credit assignment is that it is particularly important when a model of the task structure has to be used to assign credit appropriately. Here, we show lOFC the reinstates specific causal representations precisely at the time credit needs to be assigned, which are appropriate to participants’ knowledge of the task structure.  These representations may exist alongside representations of the task structure, in the lOFC and other regions of the brain (Park et al., 2020; Boorman et al., 2021; Seo and Lee, 2010; Schuck et al., 2016). We have added the following sentences to clarify our perspective on this point in the discussion (p. 13):

      “Our results from the “indirect transition” condition show that these patterns are not merely representations of the most recent choice but are representations of the causal choice given the current task structure, and may exist alongside representations of the task structure, in the lOFC and elsewhere (Boorman et al., 2021; Park et al., 2020; Schuck et al., 2016; Seo & Lee, 2010).”

      Point 4 of public reviews and point 4 of recommendation to authors

      Broader neural involvement: While the focus on specific regions of interest (ROIs) provided clear results, future studies could benefit from a whole-brain analysis approach to provide a more comprehensive understanding of the neural networks involved in credit assignment… Also, given the ROI constraint of the analysis, the other neural structure may be involved in representing the task structure but not detected in the current analysis

      Given our strong a priori hypotheses about regions of interest (ROIs) in this study, we focused on these specific areas. This choice was based on theoretical and empirical grounds that guided our investigation. However, we thank the reviewer for pointing this out and agree that there could be other unexplored areas that are critical to credit-assignment which we did not examine. 

      We conducted the same searchlight decoding procedure on a whole brain map and corrected for multiple comparisons using TFCE. We found no significant regions of the brain in the “direct transition condition” but did find other significant regions in our information connectivity analysis of the “indirect transition condition”. In addition to replicating the effects in lOFC and HPC, we also found a region of mOFC which showed a strong correlation with pending choice in lFPC. It’s difficult to say whether this region is involved in credit assignment per se, because we did not see this region in the “direct transition condition” and so we cannot say that it is consistently related to this process. However, the mOFC is thought to be critical to representing the current task state (Schuck et al., 2016), and the task structure (Park et al., 2020). In our task, it could be a critical region for communicating how to assign credit given the more complex task structure of the “indirect transition condition” but more evidence would be needed to support this interpretation. 

      For now, we have added the results of this whole brain analysis to a new supplementary figure S7 (page 41), and all unthresholded maps have been deposited in a Neurovault repository, which is linked in the paper, for interested readers to assess.  

      Minor points:

      There are some missing and confusing details in the Figure reference in the main text. For example, references to Figure 3 are almost missing in the section "Pending item representations in FPl during indirect transitions predict credit assignment in lOFC". For readability, the authors should improve this point in this section and other sections.

      Thank you to the reviewer for pointing this out. We have now added references to Figure 3 on page 8:

      “Our analysis revealed a cluster of voxels specifically within the right lFPC ([x,y,z] = [28, 54, 8], t(19) = 3.74, pTFCE <0.05 ROI-corrected; left hemisphere all pTFCE > 0.1, Fig. 3A)”

      And on page 10: 

      Specifically, we found significant correlations in decoding distance between lFPC and bilateral lOFC ([x,y,z] = [-32,24, -22], t(19) = 3.81, [x,y,z] = [20, 38, -14], t(19) = 3.87, pTFCE <0.05 ROI corrected]) and bilateral HC ([x,y,z] = [-28, -10, -24], t(19) = 3.41, [x,y,z] = [22, -10, -24], t(19) = 4.21, pTFCE <0.05 ROI corrected]), Fig. 3C).

      Task instructions for the two conditions (direct and indirect) play important roles in the study. If possible, please include the following parts in the figures and descriptions in the introduction and/or results sections.

      We have now included a short description of the condition instructions beginning on page 5: 

      “Participants were instructed about which condition they were in with a screen displaying “Your latest choice” in the direct transition condition, and “Your previous choice” in the indirect condition.”

      And have modified Figure 1 to include the instructions in the title of each condition. We thought this to be the most parsimonious solution so that the choice options in the examples were not occluded. 

      The subject sample size might be slightly too small in the current standards. Please give some justifications.

      We originally selected the sample size for this study to be commensurate with previous studies that looked for similar behavioral and neural effects (see Boorman et al., 2016; Howard et al., 2015; Jocham et al., 2016). This has been mentioned in the “methods” section on page 24.  

      However, to be thorough, we performed a power analysis of this sample size using simulations based on an independently collected, unpublished data set. In this data set, 28 participants competed an associative learning task similar to the task in the current manuscript. We trained a classifier to decode causal choice option at the time of feedback, using the same searchlight and cross-validation procedures described in the current manuscript, for the same lateral OFC ROI. We calculated power for various sample sizes by drawing N participants with replacement 1000 times, for values of N ranging from 15 to 25. After sampling the participants, we tested for significant decoding for the causal choice within the subset of data, using smallvolume TFCE correction to correct for multiple comparisons. Finally, we calculated the proportion of these samples that were significant at a level of pTFCE <.05.  

      The results of this procedure show that an N of 20 would result in 84.2% power, which is slightly above the typically acceptable level of 80%. We have added the following sentences to the methods section on page 25: 

      “Using an independent, unpublished data set, we conducted a power analysis for the desire neural effect in lOFC. We found that this number of participants had 84% power to detect this effect (Fig. S8).” 

      We also added the following figure to the supplemental figures page (42):

      Reviewer 2:

      I have several concerns regarding the causality analyses in this study. While Multivariate analyses of information connectivity between regions are interesting and appear rigorous, they make some assumptions about the nature of the input data. It is unclear if fMRI with its poor temporal resolution (in addition to possible region-specific heterogeneity in the readouts), can be coupled with these casual analysis methods to meaningfully study dynamics on a decision task where temporal dynamics is a core component (i.e., delay). It would be helpful to include more information/justification on the methods for inferring relationships across regions from fMRI data. Along this line, discussing the reported findings in light of these limitations would be essential.

      We agree that fMRI is limited for capturing fast neural dynamics, and that it can be difficult to separate events that occur within a few seconds. However, we designed the information connectivity analysis to maximally separate the events in question – the representations of the causal choice being held in a pending state, and the representation of the causal choice during credit assignment. These events were separated by at least 10 seconds and by 15 seconds on average, which is commensurate with recommended intervals for disentangling information in such analysis (Mumford et al., 2012, 2014, also see van Loon et al., 2018, eLife; as example of fluctuations in decodability over time). This feature of our task design may not have been clear because information connectivity analyses are typically performed in the same task period. We clarify this point on page 32:

      “Note that the decoding fidelity metric at each time point represents the decodability of the same choice at different phases of the task. These phases were separated by at least 10 seconds and 15 seconds on average, which can be sufficient for disentangling unique activity (Mumford et al., 2012, 2014).”

      However, we agree with the reviewer that the limitations of fMRI make it difficult to precisely determine how roles of the OFC and lFPC might change over time, and whether other regions may contribute to information transfer at times scales which cannot be detected by fMRI. Further, we do not wish to imply causality between lFPC and lOFC (something we believe we do not claim in the paper), only that information strength in lFPC predicts subsequent strength of the same information in the OFC and HC. We have clarified this limitation on page 14:

      “Although we show evidence that lFPC is involved in maintaining specific content about causal choices during interim choices, the limited temporal resolution of fMRI makes it difficult to tell if other regions may be supporting the learning processes at timescales not detectable in the BOLD response. Thus, it is possible that the network of regions supporting credit assignment in complex tasks may be much larger. Our results provide a critical first stem in discerning the nature of interactions between cognitive subsystems that make different contributions to the learning process in these complex tasks.”

      Reviewer 3:  

      Point 1 of public reviews:

      They do find (not surprisingly) that the one-back task is harder. It would be good to ensure that the reason that they had more trouble detecting direct HC & lOFC effects on the harder task was not because the task is harder and thus that there are more learning failures on the harder oneback task. (I suspect their explanation that it is mediated by FPl is likely to be correct. But it would be nice to do some subsampling of the zero-back task [matched to the success rate of the one-back task] to ensure that they still see the direct HC and lOFC there).

      We would like to thank the reviewer for this comment and agree that the “indirect transition condition” is more difficult than the direct transition condition. However, in this task it is difficult to have an explicit measure of learning failures per se because the “correctness” of a choice is to some extent subjective (i.e., based on the gift card preference and the computational model). We could infer when learning failures occur through the computational model by looking at trials in which participants made choices that the model would consider improbable, (i.e., non-reward maximizing) while accounting for outcome preference. However, there are also a myriad of other possible explanations for these choices, such as exploratory/confirmatory strategies, lapses in attention etc. Thus, we could not guarantee that the two conditions would be uniquely matched in difficulty with specific regard to learning even if we subsampled these trials. We feel it would be better left to future experiments which can specifically compare learning failures to tackle this issue. We have now addressed this point when discussing the model on page 31:  

      “Note that learning failures are not trivial to identify in our paradigm and model, because every choice is based on a participant’s preference between gift card outcomes, and the ability of the computational model to accurately estimate participants’ beliefs in the stimulus-outcome transition probabilities.”

      Point 2 of public reviews:

      The evidence that they present in the main text (Figure 3) that the HC and lOFC are mediated by FPl is a correlation. I found the evidence presented in Supplemental Figure 7 to be much more convincing. As I understand it, what they are showing in SF7 is that when FPl decodes the cue, then (and only then) HC and lOFC decode the cue. If my understanding is correct, then this is a much cleaner explanation for what is going on than the secondary correlation analysis. If my understanding here is incorrect, then they should provide a better explanation of what is going on so as to not confuse the reader.

      SF7 (now Figures 3C and 3D) does show that positive decoding in the HC and lOFC are more likely to occur when there is positive decoding in lFPC. However, the analysis shown in these figures are only meant to be control analysis to further characterise what is being captured, but not necessarily implied, by the information connectivity analysis. For example, in principle the classifier might never correctly decode a choice label in the lOFC or HC while still getting closer to the hyperplane when the lFPC patterns are correctly decoded. This would lead to a positive correlation, but a difficult to interpret result since patterns in lOFC and HPC are incorrect. Figure SF7A (now Fig. 3C) shows that this is not the case. Lateral OFC and HC have higher than chance positive decoding when lFPC has positive decoding. Figure SF7B (now Fig. 3D) shows that we can decode that information even if a new hyperplane is constructed. However, both cases have less information about the relationship between these regions because they do not include the trials where lOFC/HC and lFPC classifiers were incorrect at the same time. The correlation in Figure 3B includes these failures, giving a more wholistic picture of the data. We therefore try to concisely clarify this point on page 10:

      “These signed distances allow us to relate both success in decoding information, as well as failures, between regions.”

      And here on page 10: 

      “Subsequent analyses confirmed that this effect was due to these regions showing a significant increase in positive (correct) decoding in trials where pending information could be positively (correctly) decoded in lFPC, and not simply due to a reduction in incorrect information fidelity (see Fig. 3C & 3D).”

      And have integrated these figures on page 9:

      Point 3 of public reviews:

      I like the idea of "credit spreading" across trials (Figure 1E). I think that credit spreading in each direction (into the past [lower left] and into the future [upper right]) is not equivalent. This can be seen in Figure 1D, where the two tasks show credit spreading differently. I think a lot more could be studied here. Does credit spreading in each of these directions decode in interesting ways in different places in the brain?

      We agree that this an interesting question because each component of the off diagonal (upper and lower triangles) may reflect qualitatively different processes of credit spreading. However, we believe this analysis is difficult to carry out with the current dataset for two reasons. First, we designed this study to ask specifically about the information represented in key credit assignment regions during precise credit assignment, meaning we did not optimize the task to induce credit spreading at any point. Indeed, our efforts to train participants on the task were to ensure they would correctly assign credit as much as possible. Figure 1F shows that the regression coefficients representing credit spreading in each condition are near zero (in the negative direction), with little individual differences compared to the credit assignment coefficients. Thus, any analysis aiming to test for credit spreading would unfortunately be poorly powered. Studies such as Jocham et al. (2016), with more variability in causal structures, or studies with ambiguity about the causal structure by dissolving the typical trial structure would be better suited to address this interesting question. The second reason why such an analysis would be challenging is that due to our design, it is difficult to intuitively determine what kind of information should be coded by neural regions when credit spreads to the upper diagonal, since these cells reflect current outcomes that are being linked to future choices. 

      Replace all the FPl with LFPC (lateral frontal polar cortex)

      We have no replace “FPl” with “LFPC” throughout the text and figures

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Barlow and coauthors utilized the high-parameter imaging platform of CODEX to characterize the cellular composition of immune cells in situ from tissues obtained from organ donors with type 1 diabetes, subjects presented with autoantibodies who are at elevated risk, or non-diabetic organ donor controls. The panels used in this important study were based on prior publications using this technology, as well as a priori and domain-specific knowledge of the field by the investigators. Thus, there was some bias in the markers selected for analysis. The authors acknowledge that these types of experiments may be complemented moving forward with the inclusion of unbiased tissue analysis platforms that are emerging that can conduct a more comprehensive analysis of pathological signatures employing emerging technologies for both high-parameter protein imaging and spatial transcriptomics.

      Strengths:

      In terms of major findings, the authors provide important confirmatory observations regarding a number of autoimmune-associated signatures reported previously. The high parameter staining now increases the resolution for linking these features with specific cellular subsets using machine learning algorithms. These signatures include a robust signature indicative of IFN-driven responses that would be expected to induce a cytotoxic T-cell-mediated immune response within the pancreas. Notable findings include the upregulation of indolamine 2,3-dioxygenase-1 in the islet microvasculature. Furthermore, the authors provide key insights as to the cell:cell interactions within organ donors, again supporting a previously reported interaction between presumably autoreactive T and B cells.

      Weaknesses:

      These studies also highlight a number of molecular pathways that will require additional validation studies to more completely understand whether they are potentially causal for pathology, or rather, epiphenomenon associated with increased innate inflammation within the pancreas of T1D subjects. Given the limitations noted above, the study does present a rich and integrated dataset for analysis of enriched immune markers that can be segmented and annotated within distinct cellular networks. This enabled the authors to analyze distinct cellular subsets and phenotypes in situ, including within islets that peri-islet infiltration and/or intra-islet insulitis.

      Despite the many technical challenges and unique organ donor cohort utilized, the data are still limited in terms of subject numbers - a challenge in a disease characterized by extensive heterogeneity in terms of age of onset and clinical and histopathological presentation. Therefore, these studies cannot adequately account for all of the potential covariates that may drive variability and alterations in the histopathologies observed (such as age of onset, background genetics, and organ donor conditions). In this study, the manuscript and figures could be improved in terms of clarifying how variable the observed signatures were across each individual donor, with the clear notion that non-diabetic donors will present with some similar challenges and variability.

      Thank you to all reviewers and editors for their thoughtful and constructive engagement with our manuscript. We agree that patient heterogeneity and the sample size limited the impact of this study. In the future, more cases with insulitis will become available and spatial technologies will become more scalable.

      Given these constraints, we have made a significant effort to illustrate the individual heterogeneity of the disease by using the same color for each nPOD case ID throughout the manuscript and showing individual donors whenever feasible (e.g. Figures 1D-E, 2C, 2I, 3E, 3G, 4B-C, 5C, and 5F). For figures related to insulitis, we do not typically include non-T1D controls since they did not have any insulitis (Figure 2C). We also explicitly discuss the differences in the two autoantibody-positive, non-T1D cases: one closely resembled the T1D cases with respect to multiple features and the other more closely resembled the non-T1D, autoantibody-negative controls.

      Reviewer #2 (Public review):

      Summary:

      The authors aimed to characterize the cellular phenotype and spatial relationship of cell types infiltrating the islets of Langerhans in human T1D using CODEX, a multiplexed examination of cellular markers

      Strengths:

      Major strengths of this study are the use of pancreas tissue from well-characterized tissue donors, and the use of CODEX, a state-of-the-art detection technique of extensive characterization and spatial characterization of cell types and cellular interactions. The authors have achieved their aims with the identification of the heterogeneity of the CD8+ T cell populations in insulitis, the identification of a vasculature phenotype and other markers that may mark insulitis-prone islets, and the characterization of tertiary lymphoid structures in the acinar tissue of the pancreas. These findings are very likely to have a positive impact on our understanding (conceptual advance) of the cellular factors involved in T1D pathogenesis which the field requires to make progress in therapeutics.

      Weaknesses:

      A major limitation of the study is the cohort size, which the authors directly state. However, this study provides avenues of inquiry for researchers to gain further understanding of the pathological process in human T1D.

      Thank you for your analysis. We point the reader to our above description of our efforts to faithfully report the patient variability despite the small sample size.

      Reviewer #3 (Public review):

      Summary:

      The authors applied an innovative approach (CO-Detection by indEXing - CODEX) together with sophisticated computational analyses to image pancreas tissues from rare organ donors with type 1 diabetes. They aimed to assess key features of inflammation in both islet and extra-islet tissue areas; they reported that the extra-islet space of lobules with extensive islet infiltration differs from the extra-islet space of less infiltrated areas within the same tissue section. The study also identifies four sub-states of inflamed islets characterized by the activation profiles of CD8+T cells enriched in islets relative to the surrounding tissue. Lymphoid structures are identified in the pancreas tissue away from islets, and these were enriched in CD45RA+ T cells - a population also enriched in one of the inflamed islet sub-states. Together, these data help define the coordination between islets and the extra-islet pancreas in the pathogenesis of human T1D.

      Strengths:

      The analysis of tissue from well-characterized organ donors, provided by the Network for the Pancreatic Organ Donor with Diabetes, adds strength to the validity of the findings.

      By using their innovative imaging/computation approaches, key known features of islet autoimmunity were confirmed, providing validation of the methodology.

      The detection of IDO+ vasculature in inflamed islets - but not in normal islets or islets that have lost insulin-expression links this expression to the islet inflammation, and it is a novel observation. IDO expression in the vasculature may be induced by inflammation and may be lost as disease progresses, and it may provide a potential therapeutic avenue.

      The high-dimensional spatial phenotyping of CD8+T cells in T1D islets confirmed that most T cells were antigen-experienced. Some additional subsets were noted: a small population of T cells expressing CD45RA and CD69, possibly naive or TEMRA cells, and cells expressing Lag-3, Granzyme-B, and ICOS.

      While much attention has been devoted to the study of the insulitis lesion in T1D, our current knowledge is quite limited; the description of four sub-clusters characterized by the activation profile of the islet-infiltrating CD8+T cells is novel. Their presence in all T1D donors indicates that the disease process is asynchronous and is not at the same stage across all islets. Although this concept is not novel, this appears to be the most advanced characterization of insulitis stages.

      When examining together both the exocrine and islet areas, which is rarely done, authors report that pancreatic lobules affected by insulitis are characterized by distinct tissue markers. Their data support the concept that disease progression may require crosstalk between cells in the islet and extra-islet compartments. Lobules enriched in β-cell-depleted islets were also enriched in nerves, vasculature, and Granzyme-B+/CD3- cells, which may be natural killer cells.

      Lastly, authors report that immature tertiary lymphoid structures (TLS) exist both near and away from islets, where CD45RA+ CD8+T cells aggregate, and also observed an inflamed islet-subcluster characterized by an abundance of CD45RA+/CD8+ T cells. These TLS may represent a point of entry for T cells and this study further supports their role in islet autoimmunity.

      Weaknesses:

      As the authors themselves acknowledge, the major limitation is that the number of donors examined is limited as those satisfying study criteria are rare. Thus, it is not possible to examine disease heterogeneity and the impact of age at diagnosis. Of 8 T1D donors examined, 4 would be considered newly diagnosed (less than 3 months from onset) and 4 had longer disease durations (2, 2, 5, and 6 years). It was unclear if disease duration impacted the results in this small cohort. In the introduction, the authors discuss that most of the pancreata from nPOD donors with T1D lack insulitis. This is correct, yet it is a function of time from diagnosis. Donors with shorter duration will be more likely to have insulitis. A related point is that the proportion of islets with insulitis is low even near diagnosis, Finally, only one donor was examined that while not diagnosed with T1D, was likely in the preclinical disease stage and had autoantibodies and insulitis. This is a critically important disease stage where the methodology developed by the investigators could be applied in future efforts.

      While this was not the focus of this investigation, it appears that the approach was very much immune-focused and there could be value in examining islet cells in greater depth using the methodology the authors developed.

      Additional comments:

      Overall, the authors were able to study pancreas tissues from T1D donors and perform sophisticated imaging and computational analysis that reproduce and importantly extend our understanding of inflammation in T1D. Despite the limitations associated with the small sample size, the results appear robust, and the claims well-supported.

      The study expands the conceptual framework of inflammation and islet autoimmunity, especially by the definition of different clusters (stages) of insulitis and by the characterization of immune cells in and outside the islets.

      Thank you for your feedback. We agree that it would be very informative to expand on our analysis of autoantibody-positive cases and look at additional non-immune features. 

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Do any of the observed cellular or structural features correlate with age of onset or disease duration? While numbers of subjects are low, considering these as continuous variables may clarify some of the findings.

      Thank you for the suggestion. In Supplemental Figure 5B-C, we plotted the key immune signatures from the manuscript against the diabetes duration and age of onset.

      (2) The IDO is an interesting observation and has prior support in the literature. The authors speculate this may be induced as a feature of IFNg expressed by lymphocytes in the local microenvironment. Can any of these concepts be further validated by staining for transcription factors or surrogate downstream markers associated with Th1 skewing (e.g., Tbet, CXCR3, etc)?

      The only other interferon-stimulated gene in our panel is HLA-ABC. We updated Supplemental Figure 2F to include HLA-ABC expression in IDO- and IDO+ islets (within the “Inflamed” group). Consistent with the hypothesis that IDO is stimulated by interferon, HLA-ABC is also significantly higher in IDO+ islets than IDO- islets. PDL1, another interferon-stimulated gene. was included in the panel but we did not detect any signal. This antibody was very weak during testing in the tonsil, so we couldn’t confidently claim that PDL1 was not expressed.

      (3) The authors discuss the potential that CD45RA may be expressed in Temra populations. This could use additional clarification and a distinction from Tscm if possible.

      Unfortunately, we did not have the appropriate markers to distinguish naïve, TEMRA, or Tscm cells from each other. We updated the text in the discussion to include this consideration (Line 432).

      (4) Supplemental Figure 5 is not informative in the current display.

      Thank you, we replotted these data.

      (5) Supplemental Table 1 could be expanded with additional metadata of interest, including the genetic features of the donors (e.g, class II diplotype and GRS2 values) that are published and available in the nPOD program.

      Some genetic data are only available to nPOD investigators. We think it is more appropriate to request the data directly from them.

      Reviewer #2 (Recommendations for the authors):

      (1) I had only a few specific comments. I think the statement in Lines 317 and 318 is too strong. It implies that each lobe is always homogeneous for having all islets with insulitis or not having insulitis. Some lobes are certainly enriched for islets with insulitis but insulin+ islets without insulitis in some lobes in some T1D donors are seen. Please soften that statement.

      We apologize for our lack of clarity. We have edited the text (line 305-309) to better articulate that organ donors fall on a spectrum. Thank you for raising this point as we think the motivation for our analysis is much clearer after these revisions.

      (2) Please cite and discuss In't Veld Diabetes 20210 PMID: 20413508. While the main point of the paper is that there is beta cell replication after prolonged life support, another observation is that there is a correlation between prolonged life support and CD45+ cells in the pancreas parenchyma. This might indicate that not all immune cells in the parenchyma are T1D associated in donors with T1D.

      Thank you, we have added this citation to our discussion of the importance of duration of stay in the ICU (Line 471).

      (3) Can you rule out that CD46RA+/CD69+ CD8+ T cells in the islets are not TSCM?

      (See above)

      Reviewer #3 (Recommendations for the authors):

      Similar studies in experimental models may afford increased opportunity to evaluate the significance of these findings and model their potential relevance for disease staging and therapeutic targeting.

      We agree that the lack of experimental data limits the ability to interpret and validate the significance of our findings. We hope that our study motivates and helps inform such experiments.

    1. Reviewer #1 (Public review):

      The revision by Ruan et al clarifies several aspects of the original manuscript that were difficult to understand, and I think it presents some useful and interesting ideas. I understand that the authors are distinguishing their model from the standard Wright-Fisher model in that the population size is not imposed externally, but is instead a consequence of the stochastic reproduction scheme. Here, the authors chose a branching process but in principle any Markov chain can probably be used. Within this framework, the authors are particularly interested in cases where the variance in reproductive success changes through time, as explored by the DDH model, for example. They argue with some experimental results that there is a reason to believe that the variance in reproductive success does change over time.

      One of the key aspects of the original manuscript that I want to engage with is the DDH model. As the authors point out, their equations 5 and 6 are assumptions, and not derived from any principles. In essence, the authors are positing that that the variance in reproductive success, given by 6, changes as a function of the current population size. There is nothing "inherent" to a negative binomial branching mechanism that results in this: in fact, the the variance in offspring number could in principle be the same for all time. As relates to models that exist in the literature, I believe that this is the key difference: unlike Cannings models, the authors allow for a changing variance in reproduction through time.

      This is, of course, an interesting thing to consider, and I think that the situation the authors point out, in which drift is lower at small population sizes and larger at large population sizes, is not appreciated in the literature. However, I am not so sure that there is anything that needs to be resolved in Paradox 1. A very strong prediction of that model is that Ne and N could be inversely related, as shown by the blue line in Fig 3b. This suggests that you could see something very strange if you, for example, infer a population size history using a Wright-Fisher framework, because you would infer a population *decline* when there is in fact a population *expansion*. However, as far as I know there are very few "surprising population declines" found in empirical data. An obvious case where we know there is very rapid population growth is human populations; I don't think I've ever seen an inference of recent human demographic history from genetic data that suggests anything other than a massive population expansion. While I appreciate the authors empirical data supporting their claim of Paradox 1 (more on the empirical data later), it's not clear to me that there's a "paradox" in the literature that needs explaining so much as this is a "words of caution about interpreting inferred effective population sizes". To be clear, I think those words of caution are important, and I had never considered that you might be so fundamentally misled as to infer decline when there is growth, but calling it a "paradox" seems to suggest that this is an outstanding problem in the literature, when in fact I think the authors are raising a *new* and important problem. Perhaps an interesting thing for the authors to do to raise the salience of this point would be to perform simulations under this model and then infer effective population sizes using e.g. dadi or psmc and show that you could identify a situation in which the true history is one of growth, but the best fit would be one of decline

      The authors also highlight that their approach reflects a case where the population size is determined by the population dynamics themselves, as opposed to being imposed externally as is typical in Cannings models. I agree with the authors that this aspect of population regulation is understudied. Nonetheless, several manuscripts have dealt with the case of population genetic dynamics in populations of stochastically fluctuating size. For example, Kaj and Krone (2003) show that under pretty general conditions you get something very much like a standard coalescent; for example, combining their theorem 1 with their arguments on page 36 and 37, they find that exchangeable populations with stochastic population dynamics where the variance does not change with time still converge to exactly the coalescent you would expect from Cannings models. This is strongly suggestive that the authors key result isn't about stochastic population dynamics per se, but instead related to arguing that variance in reproductive success could change through time. In fact, I believe that the result of Kaj and Krone (2003) is substantially more general than the models considered in this manuscript. That being said, I believe that the authors of this manuscript do a much better job of making the implications for evolutionary processes clear than Kaj and Krone, which is important---it's very difficult to understand from Kaj and Krone the conditions under which effective population sizes will be substantially impacted by stochastic population dynamics.

      I also find the authors exposition on Paradox 3 to be somewhat strange. First of all, I'm not sure there's a paradox there at all? The authors claim that the lack of dependence of the fixation probability on Ne is a paradox, but this is ultimately not surprising---fixation of a positively selected allele depends mostly on escaping the boundary layer, which doesn't really depend on the population size (see Gillespie's book "The Causes of Molecular Evolution" for great exposition on boundary layer effects). Moreover, the authors *use a Cannings-style argument* to get gain a good approximation of how the fixation probability changes when there is non-Poisson reproduction. So it's not clear that the WFH model is really doing a lot of work here. I suppose they raise the interesting point that the particularly simple form of p(fix) = 2s is due to the assumption that variance in offspring is equal to 1.

      In addition, I raised some concerns about the analysis of empirical results on reproductive variance in my original review, and I don't believe that the authors responded to it at all. I'm not super worried about that analysis, but I think that the authors should probably respond to me.

      Overall, I feel like I now have a better understanding of this manuscript. However, I think it still presents its results too strongly: Paradox 1 contains important words of caution that reflect what I am confident is an under appreciated possibility, and Paradox 3 is, as far as I'm concerned, not a paradox at all. I have not addressed Paradox 2 very much because I think that another reviewer had solid and interesting comments on that front and I am leaving it to them. That being said, I do think Paradox 2 actually presents a deep problem in the literature and that the authors' argument may actually represent a path toward a solution.

      This manuscript can be a useful contribution to the literature, but as it's presented at the moment, I think most of it is worded too strongly and it continues to not engage appropriately with the literature. Theoretical advances are undoubtedly important, and I think the manuscript presents some interesting things to think about, but ultimately needs to be better situated and several of the claims strongly toned down.

      References:<br /> Kaj, I., & Krone, S. M. (2003). The coalescent process in a population with stochastically varying size. Journal of Applied Probability, 40(1), 33-48.

    2. Author response:

      The following is the authors’ response to the previous reviews.

      eLife Assessment (divided into 3 parts)

      This study presents a useful modification of a standard model of genetic drift by incorporating variance in reproductive success, claiming to address several paradoxes in molecular evolution. ……

      It is crucial to emphasize that our model is NOT a modification of the standard model. The Haldane model, which is generalized here for population regulation, is based on the branching process. The Haldane model and the WF model which is based on population sampling are fundamentally different. We referred to our model as the integrated WF-H model because the results obtained from the WF model over the last 90 years are often (but not always) good approximations for the Haldane model. The analogy would be the comparisons between the Diffusion model and the Coalescence model. Obviously, the results from one model are often good approximations for the other.  But it is not right to say that one is a useful modification of the other.

      We realize that it is a mistake to call our model the integrated WFH model, thus causing confusions over two entirely different models. Clearly, the word “integrated” did not help. We have now revised the paper by using the more accurate name for the model – the Generalized Haldane (GH) model. The text explains clerarly that the original Haldane model is a special case of the GH model.

      Furthermore, we present the paradoxes and resolve them by the GH model.  We indeed overreached by claiming that WF models could not resolve them. Whether the WF models have done enough to resolve the paradoxes or at least will be able to resolve them should not be a central point of our study. Here is what we state at the end of this study.:

      “We understand that further modifications of the WF models may account for some or all of these paradoxes. However, such modifications have to be biologically feasible and, if possible, intuitively straightforward. Such possible elaborations of WF models are beyond the scope of this study. We are only suggesting that the Haldane model can be extensively generalized to be an alternative approach to genetic drift. The GH model attempts to integrate population genetics and ecology and, thus, can be applied to genetic systems far more complex than those studied before. The companion study is one such example.”

      ….. However, some of the claimed "paradoxes" seem to be overstatements, as previous literature has pointed out the limitations of the standard model and proposed more advanced models to address those limitations….

      As stated in the last paragraph of the paper, it is outside of the scope of our study to comment on whether the earlier WF models can resolve these paradoxes.  So, all such statements have been removed or at least drastically toned down in the formal presentation.  That said, editors and reviewers may ask whether we are re-inventing the wheels.  The answers are as follows:

      First, two entirely different models reaching the same conclusion are NOT the re-invention of wheels. The coalescence theory does not merely rediscover the results obtained by the diffusion models. The process of obtaining the results is itself a new invention.  This would lead to the next question: is the new process more rigorous and more efficient?  I think the Haldane model is indeed more efficient in comparisons with the very complex modifications of the WF models. 

      Second, we are not sure that the paradoxes have been resolved, or even can be resolved.  Note that these skepticisms have been purged from the formal presentation. Thefore, I am presenting the arguments outside of the paper for a purely intellectual discourse. Below, please allow us to address the assertions that the WF models can resolve the paradoxes. 

      The first paradox is that the drift strength in relation to N is often opposite of the WF model predictions.  Since the WF models (standard or modified) do not generate N from within the model, how can it resolve the paradox?  In contrast, the Generalized Haldane model generates N within the model. It is the regulation of N near the carrying capacity that creates the paradox – When N increases, drift also increases.

      The second paradox that the same locus experiences different drifts in males and females is accepted by the reviewers.  Nevertheless, we would like to point out that this second paradox echoed the first one as newly stated in the Discussion section “The second paradox of sex-dependent drift is about different V(K)’s between sexes (generally Vm > Vf) but the same E(K) between them. In the conventional models of sampling, it is not clear what sort of biological sampling scheme could yield V(K) ≠ E(K), let alone two separate V(K)’s with one single E(K). Mathematically, given separate K distributions for males and females, it is unlikely that E(K) for the whole population could be 1, hence, the population would either explode in size or decline to zero. In short, N regulation has to be built into the genetic drift model as the GH model does to avoid this paradox.”

      The third paradox stems from the fact that drift is operating even for genes under selection. But then the drift strength, 2s/V(K) for an advantage of s, is indepenent of N or Ne. Since the determinant of drift strength in the WF model is ALWAYS Ne, how is Paradox 3 not a paradox for the WF model?

      The 4th paradox about multi-copy gene systems is the subject of the companion paper (Wang et al.). Note that the WF model cannot handle systems of evolution that experience totally different sorts of drift within vs. between hosts (viruses, rDNAs etc).  This paradox can be understood by the GH model and and will be addressed in the next paper.

      While the modified model presented in this paper yields some intriguing theoretical predictions, the analysis and simulations presented are incomplete to support the authors' strong claims, and it is unclear how much the model helps explain empirical observations.

      The objections appear to be that our claims of “paradox resolution” being too strong.  We interpret this objection is based on the view (which we agree) that these paradoxes are intrisicallly difficult to resolve by the WF models. Since our model has been perceived to be a modified WF model, the claim of resolution is clearly too strong.  However, the GH model is conceptually and operationally entirely different from the WF models as we have emphasized above. In case our reading of the editorial comments is incorrect, would it be possible for some clarifications on the nature of “incomplete support”?  We would be grateful for the help.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      This manuscript presents evidence of ’vocal style’ in sperm whale vocal clans. Vocal style was defined as specific patterns in the way that rhythmic codas were produced, providing a fine-scale means of comparing coda variations. Vocal style effectively distinguished clans similar to the way in which vocal repertoires are typically employed. For non-identity codas, vocal style was found to be more similar among clans with more geographic overlap. This suggests the presence of social transmission across sympatric clans while maintaining clan vocal identity.

      Strengths:

      This is a well-executed study that contributes exciting new insights into cultural vocal learning in sperm whales. The methodology is sound and appropriate for the research question, building on previous work and ground-truthing much of their theories. The use of the Dominica dataset to validate their method lends strength to the concept of vocal style and its application more broadly to the Pacific dataset. The results are framed well in the context of previous works and clearly explain what novel insights the results provide to the current understanding of sperm whale vocal clans. The discussion does an overall great job of outlining why horizontal social learning is the best explanation for the results found.

      Weaknesses:

      The primary issues with the manuscript are in the technical nature of the writing and a lack of clarity at times with certain terminology. For example, several tree figures are presented and ’distance’ between trees is key to the results, yet ’distance’ is not clearly defined in a way for someone unfamiliar with Markov chains to understand. However, these are issues that can easily be dealt with through minor revisions with a view towards making the manuscript more accessible to a general audience.

      I also feel that the discussion could focus a bit more on the broader implications - specifically what the developed methods and results might imply about cultural transmission in other species. This is specifically mentioned in the abstract but not really delved into in detail during the discussion.

      We are grateful for the Reviewer’s recognition of the study’s contributions to understanding cultural vocal learning in sperm whales. In response to the concerns regarding clarity and accessibility, we have revised the manuscript to improve the definition of key concepts, such as the notion of “distance” between subcoda trees. This adjustment ensures clarity for readers unfamiliar with the technical details of Markov chains. Additionally, we have expanded the discussion to highlight broader implications of our findings, particularly their relevance to understanding cultural transmission in other species, as suggested.

      Reviewer #2 (Public review):

      Summary:

      The current article presents a new type of analytical approach to the sequential organisation of whale coda units.

      Strengths:

      The detailed description of the internal temporal structure of whale codas is something that has been thus far lacking.

      Weaknesses:

      It is unclear how the insight gained from these analyses differs or adds to the voluminous available literature on how codas varies between whale groups and populations. It provides new details, but what new aspects have been learned, or what features of variation seem to be only revealed by this new approach? The theoretical basis and concepts of the paper are problematical and indeed, hamper potentially the insights into whale communication that the methods could offer. Some aspects of the results are also overstated.

      We appreciate the Reviewer’s acknowledgment of the novelty in describing the internal temporal structure of whale codas. Regarding the concern about the unique contributions of this approach, we have further emphasized in the revised manuscript how our methodology reveals previously uncharacterized dimensions of coda structure. Specifically, our work highlights how non-identity codas, which have received limited attention, play a significant role in inter-clan acoustic interactions. By leveraging Variable Length Markov Chains, we provide a nuanced understanding of coda subunits that complements existing studies and demonstrates the value of this analytical approach.

      Reviewer #3 (Public review):

      Summary:

      The study presented by Leitao et al., represents an important advancement in comprehending the social learning processes of sperm whales across various communicative and socio-cultural contexts. The authors introduce the concept of ”vocal style” as an addition to the previously established notion of ”vocal repertoire,” thereby enhancing our understanding of sperm whale vocal identity.

      Strengths:

      A key finding of this research is the correlation between the similarity of clan vocal styles for non-ID codas and spatial overlap (while no change occurs for ID codas), suggesting that social learning plays a crucial role in shaping symbolic cultural boundaries among sperm whale populations. This work holds great appeal for researchers interested in animal cultures and communication. It is poised to attract a broad audience, including scholars studying animal communication and social learning processes across diverse species, particularly cetaceans.

      Weaknesses:

      In terms of terminology, while the authors use the term ”saying” to describe whale vocalizations, it may be more conservative to employ terms like ”vocalize” or ”whale speech” throughout the manuscript. This approach aligns with the distinction between human speech and other forms of animal communication, as outlined in prior research (Hockett, 1960; Cheney & Seyfarth, 1998; Hauser et al., 2002; Pinker & Jackendoff, 2005; Tomasello, 2010).

      We thank the Reviewer for recognizing the importance of our findings and their appeal to broader audiences interested in animal cultures and communication. In response to the suggestion regarding terminology, we have adopted a more conservative language to align with distinctions between human and non-human communication systems. For example, terms like “vocalize” and “vocal repertoire” are used in place of anthropomorphic terms such as “saying”. This ensures consistency with established conventions while maintaining clarity for a broad readership.

      Reviewer #1 (Recommendations):

      Comment 1

      Lines 11-13: As mentioned above, the implications for comparing communication systems and cultural transmission in other species isn’t really discussed much and I think it’s a really interesting component of the study’s broader implications.

      Thank you for the comment.

      Action - We added a few more sentences to the discussion regarding this.

      Comment 2

      Figure 1: More information on the figure of these trees would help. What do the connecting lines represent? What do the plain black dots and the black dot with the white dot represent? Especially since the ”distance between trees” is a key result, it’s important that someone unfamiliar with Markov chains can understand the basics of how this is calculated and what it represents. It is explained in the methods, but a brief explanation here would make the results and the figure a lot clearer since the methods are the last section of the manuscript.

      These were omitted as we believed that attempting to introduce the mathematical structure and the methodology to compare two instances, in a figure caption, would have caused more ambiguity than necessary.

      Action - Added an informal introduction to these concepts on the figure caption. Also added a pointer to the Supplementary Materials.

      Comment 3

      Table 1: A definition of dICIs should be included here.

      Added the definition of discrete ICI to the table.

      Comment 4

      Figure 2: The placement of the figures is a bit confusing because they are quite far from the text that references them.

      We thank the reviewer for pointing this out, we tried to edit the manuscript to improve this issue, but this part of the editing is more within the journal’s powers than our own.

      Action - Moved images closes to the corresponding text in manuscript.

      Comment 5

      Line 117: Probabilistic distance needs to be briefly explained earlier when you first mention distance (see Lines 11-13 comments).

      Action - Clarifications added in the caption of figure 1. as per comment on Lines 11-13

      Comment 6

      Figure 4: Is order considered in these pairwise comparisons? It looks like there are two dots for each pairwise comparison. Additionally, why is the overlap different in these two comparisons? For example, short:four-plus has an overlap of 0.6, while four-plus:short has an overlap of 0.95.

      The x-axis of the plots in Figure 4 is geographical clan overlap. This is calculated as per (Hersh et al., 2022) and is described in our Methods (see “Measuring clan overlap” section). Given two clans—for example, the Four-Plus and the Short clan—spatial overlap is calculated twice: as the proportion of the Four-Plus clan’s repertoires that were recorded within 1,000 km of at least one of the Short clan’s repertoires, and as the proportion of the Short clan’s repertoires that were recorded within 1,000 km of at least one of the Four-Plus clan’s repertoires.

      Order is important in these pairwise comparisons and generates an asymmetric matrix because the clans have different spatial extents. A clan found in only one small region might overlap completely with a clan that spans the Pacific Ocean, while the opposite is not true. For example, the Short clan spans the Pacific Ocean while the Four-Plus clan has been documented over a smaller area (but that smaller area overlaps extensively with the Short clan range). That is why the value is smaller (0.6) when considering how much of the Short clan’s range is shared with the Four-Plus clan, and larger ( 0.95) when considering how much of the Four-Plus clan’s range is shared with the Short clan.

      Action - We have now added a reference to that section of the Methods in our Figure 4 caption and include the clan spatial overlap matrix as a supplemental table (Table S5).

      Comment 7

      Figure 4: I think the reference should be Hersh et al. [11].

      Thank you for catching this.

      Action - Reference corrected

      Comment 8

      Line 227: What aspect of your analysis looked at how often codas were produced? You mention coda frequency, but it is unclear how this was incorporated into your analysis. If this is included in the methods, the language is a bit too technical to easily parse it out.

      Indeed here we are referencing the results of the paper mentioned in the previous line. We do not look at coda production frequency.

      Action - Added citation to paper that actually performs this analysis.

      Comment 9

      Lines 253-255: I think you could dig into this a little more, as ”there is currently no evidence” is not the most convincing argument that something is not a driver. Perhaps expanding on the latter sentence that clans are recognizable across oceans basins would be helpful. Does this suggest that clans with similar geographic overlap experience diverse environmental conditions across ocean basins? If so, this might better strengthen your argument against environmental drivers.

      Thank you for pointing this out. We feel that the next sentence highlights that clans are recognizable across environmental variation from one side to the other of the ocean basin, which supports the inductive reasoning that codas do not vary systematically with environment. However, we have edited these sentences for clarity.

      Comment 10

      Lines 311-314: It would also be interesting to look at vocal style across non-ID coda types. Are some more similar to each other across clans than others? Perhaps vocal style can further distinguish types of non-ID codas.

      In supplementary Materials 3.4.2 and 3.5 we highlight our results when the codas are separated by coda type summarized in Table S4. We do compare the vocal style across non-ID coda types across clans and within the same clan. The results however are aggregated to highlight the differences in style between the clans and a a coda type-only comparison is not shown.

      Comment 11

      Lines 390-392: I’m assuming this is why pairwise comparisons were directional (i.e., there was both an A:B and a B:A comparison)? Can you speak to why A:B and B:A comparisons can have such different overlap values?

      Given two clans—for example, the Four-Plus and the Short clan—spatial overlap is calculated twice: as the proportion of the Four-Plus clan’s repertoires that were recorded within 1,000 km of at least one of the Short clan’s repertoires, and as the proportion of the Short clan’s repertoires that were recorded within 1,000 km of at least one of the Four-Plus clan’s repertoires.

      Order is important in these pairwise comparisons and generates an asymmetric matrix because the clans have different spatial extents. A clan found in only one small region might overlap completely with a clan that spans the Pacific Ocean, while the opposite is not true. For example, the Short clan spans the Pacific Ocean while the Four-Plus clan has been documented over a smaller area (but that smaller area overlaps extensively with the Short clan range). That is why the value is smaller (0.6) when considering how much of the Short clan’s range is shared with the Four-Plus clan, and larger (0.95) when considering how much of the Four-Plus clan’s range is shared with the Short clan.

      Action - We now include the clan spatial overlap matrix as a supplemental table (Table S5).

      Comment 13

      Line 56: Can you briefly explain what memory means in the context of Markov chains?

      We provide an explanation of the meaning of memory in the Methods section on ”Variable length Markov Chains”. Briefly, the memory in this case means how many states in the past of the Markov chain’s current state are required to predict the next transition of the chain itself. Standard Markov chains “look” back only one time step, while k-th order Markov chains look back k steps. In our case, there was no reason to assume that the memory required to predict different sequences of states (interclick intervals) should be the same across all sequences, and thus we adopted the formalism of variable length Markov chains, that allow for different levels of memory across the system.

      Comment 14

      Supplementary Figure S3: Like in the main manuscript, briefly explain or remind us what the blank nodes and the yellow nodes are.

      Action - Clarified that the orange node represents the root of the tree in the figures.

      Comment 15

      Supplementary Figure S7: Put the letters before the dataset name.

      Action - Done.

      Comment 16

      Supplementary Figure S10: Unclear what ’inner vs outer’ means.

      One specifies comparisons across clans (outer) and the other within the same clan (inner)

      Action - Added clarification on the caption of Figure S10

      Comment 17

      Supplementary Figure S14: Include a-c labels in the figure itself.

      Action - Labels added to figure

      Comment 18

      Supplementary Figure S14: The information about the nodes is what needs to be included earlier and in the main body when discussing the trees.

      Action - Added the explanation earlier in the text and in the main body

      Reviewer #2 (Recommendations):

      Comment 19

      Line 22: ”Symbolic” and ”Arbitrary” are not synonyms. Please see the comment above.

      We agree. Here, we make the point that the evolution of symbolic markers of group identity can be explained from what are initially arbitrary, and meaningless, signals (see [L1, L2]). Our point being that any vocalization, any coda, could have become selected for as an identity coda, and to become symbolic, and evolve to play a key role in cultural group formation and in-group favoritism because they enable a community of individuals to solve the problem of with whom to collaborate. The specific coda itself does not affect collaborative pay offs, but group specific differences in behavior can, as such the coda is arguably symbolic; as it is observable and recognizable, and can serve as a means for social assortment even when the behavioural differences are not. This can explain the means by which the social segregation which is observed among behaviorally distinct clans of sperm whales. However, in this manuscript, we do not extend this discussion of existing literature and have attempted to concisely describe this in a couple of lines, which clearly do a disservice to the large body of literature on the evolution of symbolic markers and human ethnic groups. We have added some citations to this section so that the reader may follow up should they disagree with out brief introductory statements.

      Action - Added citations and pointers to the literature.

      Comment 20

      Line 24: The authors’ terminology around ”markers”, ”arbitrary”, ”symbolic” is unnecessarily confusing and mystifying, giving the impression these terms are interchangeable. They are not. These terms are an integral and long-established part of key definitions in signal theory. Term use should be followed accordingly. The observation that whale vocal signals vary per population does not necessarily mean that they function as a social tag. The word ”dog” varies per population but its use relates to an animal, not the population that utters the word. ”Dog” is not ”symbolic” of England, English-speaking populations or the English language. Furthermore, the function of whale vocal signals is extremely challenging to determine. In the best conditions, researchers can pin the signal’s context, this is distinct from signal’s function and further even for the signal’s meaning. How exactly the authors determine that whale vocal signals are arbitrary is, thus, perplexing given that this would require a detailed description and understanding of who is producing the song, when, towards whom, and how the receivers react, none of which the authors have and without which no claim on the signals’ function can be made. This terminological laxness and the sensu latu in extremis to various terms in an unjustified, unnecessary and unhelpful.

      We use these terms as established in Hersh et al 2022 and the works leading up to it over the last 20 years in the study of sperm whales. These are often derived from definitions by Boyd and Richerson’s work on culture in humans and animals along with evolution of symbolic markers both in theory and in humans. We agree with the reviewer that these are difficult to establish in non-humans, whales or otherwise, but feel strongly that the accumulating evidence provides strong support for the function of these signals as symbolic markers of cultural groups, and that they likely evolved from initially arbitrary calls which were a part of the vocal repertoire (similar to the process and selective environment in Efferson et al. [L1] and McElreath et al. [L2]). We feel that we do not use these terms interchangeably here, and have inherited their use from definitions from anthropology. The work presented here uses terminology built across two decades of work in cetacean, and sperm whale, culture. And do not feel that these terms should be omitted here.

      Comment 21

      Lines 21-27: Overly broad and hazy paragraph.

      We hope the replies above and our changes satisfy this comment and clarify the text.

      Comment 22

      Figure 1 legend: What are ”memory structures”? Unjustified descriptor.

      The phrase was chosen to make draw some intuition on the variation of context length in variable length markov models.

      Action - Re-worded from memory structures to statistical properties

      Comment 23

      Line 30: Omit ”finite”.

      Action - Omitted.

      Comment 24

      Line 31: Please define and distinguish ”rhythm” and ”tempo”. Also see comment above, rhythm and tempo definitions require the use of IOIs.

      We disagree with the reviewer’s claims here. In our research specifically, and for sperm whale research generally, coda inter-click intervals (ICIs) are calculated as the time between the start of the first click and the start of the subsequent click. This makes ICIs identical to inter-onset intervals (IOIs) under all definitions we are aware of. For example, Burchardt and Knornschild [L3] define IOIs as such: “In a sequence of acoustic signals, the time span between the start of an element and the next element, comprising the element duration and the following gap duration”. We now include a sentence making this point.

      Regardless, we disagree on a more fundamental level with the statement that unless researchers quantify inter-onset intervals (IOIs), they cannot make any claims about rhythm. There are many studies that investigate rhythmic aspects of human and animal vocalizations without using IOIs [L4–L7]. If the duration of sound elements of interest is relatively constant (as is the case for sperm whale clicks), then rhythm analyses can still be meaningfully conducted on inter-call intervals (the silent intervals between calls).

      For sperm whales, coda rhythm is defined by the relative ICIs standardized by their total duration. These can be clustered into discrete, defined rhythm types based on characteristic ICI patterns. Coda tempo is relative to the total duration of the coda itself. This can also be clustered into discrete tempo types across all coda durations as well (see [L8]).

      Action - We added a sentence specifying that in this case we can use both ICIs and IOIs because of the standardized length of a single click.

      Comment 25

      Line 36: Are there non-vocalized codas to require the disambiguation here?

      No, we have omitted for clarity.

      Comment 26

      Line 44: ”Higher” than which other social group class?

      Sperm whales live in a multi-level social organization. Clans are a “higher” level of social organization than the social “units” which we define in line 40. Clans are made up of all units which share similar production repertoire of codas.

      Action - We have added ’above social units’ on line 44 to make this clear.

      Comment 27

      Line 47: The use of “symbolic” continues to be enigmatic, even if authors are taking in this classification from other researchers. In signal theory (semiotics), not all biomarkers are necessarily symbols. I advise the authors to avoid the use of the term colloquially and instead adopt the definition used in the research field within which the study falls in.

      There is ample examples of the use of ”symbolic” when referring to markers of in-group membership both in human and non-human cultures.Our choice to use the term “symbolic” is based on a previous study [L9] that found quantitative evidence that sperm whale identity codas function as symbolic markers of cultural identity, at least for Pacific Ocean clans. The full reasoning behind why the authors used the term “symbolic markers” is given in that paper, but briefly, they found evidence that identity coda usage becomes more distinct as clan overlap increases, while non-identity coda usage does not change. This matches theoretical and empirical work on human symbolic markers[L1, L2, L10, L11].

      Action - We retain the use of the term here, as defined in the works cited, and based on its prior usage in the study of both human and non-human cultures.

      Comment 28

      Line 50: This statement is not technically accurate. The use of a signal as a marker by individuals can only be determined by how individuals ”interpret” and react to that signal - e.g., via playback experiments - it cannot be determined by how different populations use and produce the signals.

      We respectfully disagree. While we agree that the optimal situation would be that of playback, the contextual use can provide insight into the functional use of signals; as can expected patterns of use and variation, as was tested in the papers we cite. However, this argument is not the scope nor the synthesis of this paper. These statements are supported by existing published works, as cited, and we encourage the reviewer to take exception with those papers.

      Comment 29

      Line 69: ”Meaningful speech characteristics”??? These terms do not logically or technically follow the previous statement. Why not stay faithful to the results and state that the method used seems to be valid and reliable because it confirms former studies and methods?

      Action - Reworded to better underline the method’s results with previous studies

      Comment 30

      Lines 72-74: This statement doesn’t seem to accurately capture/explain/resume the difference between ID and non-ID codas.

      We are not sure what the reviewer is referring to in this case. The sentence in this case was meant to explain the different relations that ID/non-ID codas have with clan sympatry.

      Comment 31

      Line 75: The information provided in the few previous sentences does not allow the reader to understand why these results support the notion that cultural transmission and social learning occurs between clans.

      We conclude out introduction with a brief summary of our overall findings, which we then use the rest of the manuscript to support these statements.

      Comment 32

      Table 1: So far, the authors refer to their analyses as capturing the ”rhythm” of whale clicks. Consequently, it is not readily clear at this point why the authors rely on ”ICIs” (inter click intervals) instead of the ”universal” measure used across taxa to capture the rhythm of signal sequences - IOIs (inter onset intervals). If ICIs are the same measure as IOIs, why not use the common term, instead of creating a new term name? Alternatively, if ICIs are not equivalent to IOIs, then arguably the analyses do not capture the ”rhythm” of whale clicks, as claimed by the authors. Any rhythmic claim will need to be based on IOI measures. In animal behaviour, stereotyped is primarily used to describe pathological, dysfunctional behaviour. I suggest the use of other adjective, such as ”regular”, ”repetitive”, ”recurring”, ”predictable”. Another deviation from typical terminology: ”usage frequency” -¿ ”production rate”. Why is a clan a ”higher-order” level of social organization? This requires explanation, at least a mention, of what are the ”lower-order” levels. To the non-expert reader, there is a logical circularity/gap here: Clans are said to produce clan-specific codas, and then, it is said that codas are used to delineate clans. Either one deduces, or one infers, but not both. This raises the question, are clans confirmed by any other means than codas?

      We are not creating a “new term name”: inter-click interval (ICI) is the standard terminology used in odontocete (toothed whale) research. We take the reviewer’s point that some readers will not be coming to our paper with that background, however, and now explicitly point out that ICI is synonymous with IOI for sperm whales. Please see our response to your earlier comment for more on this point.

      Comment 33

      Line 92: Unclear term, ”sub-sequence”. Fig. 1B doesn’t seem to readily help disambiguate the meaning of the term.

      In fact reference to Fig. 1B is misplaced as it does not refer to the text. A sub-sequence is simply a contiguous subset of a coda, a subset of it.

      Action - Removed ambiguous reference to Fig. 1B

      Comment 34

      Line 94: How does the use of ”sequence” compare here with ”sub-sequence” above?

      In fact its the same situation although the previous comment highlighted a source of ambiguity.

      Action - Reworded the sentence to be less confusing.

      Comment 35

      Line 95: Signal sequences don’t ”contain” memory, they require memory for processing.

      Action - Rephrased from “sequences contain memory” to “states depend on previous sequences of varying length”.

      Comment 36

      Lines 95-97: The analogy with human language seems forced, combinatorics in any given species are expected to entail different transitions between unit/unit-sequences.

      Thank you for the comment. Indeed, the purpose of the analogy is to illustrate how variable length Markov Chains work (which have been shown to be good at discerning even accents of the same language). We used human language as an analogy to provide the readers’ with a more intuitive understanding of the results.

      Action - Revised paragraph to read: “Despite we do not have direct evidence of unitary blocks in sperm whale communication, on can imagine this effect similarly to what happens with words (e.g., a word beginning with “re” can continue in more ways than one starting with “zy”).”

      Comment 37

      Line 97: Unclear which possibility is this.

      Action - Made the wording clearer.

      Comment 38

      Line 99: Invocation of memory, although common in the use of Markov chains, in inadequate here given that the research did not study how individuals perceived or processed click sequences, only how individual produced click sequences. If the authors are referring to the cognitive load imposed by producing clicks sequences, terms such as ”sequence planning” will be more accurate.

      Here, we use the term “fixed-memory” in relation to the definition of a variable length Markov model. We feel that, in this section of the manuscript, the context is clear that it is a mathematical definition and in no way invokes the biological idea of memory or cognition. It is rather standard to use memory to describe the order of Markov chains. Swapping words in the definition of mathematical objects when the context is clear seems to cause unnecessary ambiguity.

      Action - We clarified this in the manuscript (see comments above).

      Reviewer #3 (Recommendations):

      Comment 39

      Line 16: Add ”broadly defined” as there are many other more restricted definitions (see for example Tomasello 1999; 2009). Tomasello M (1999) The cultural origins of human cognition. Harvard University Press, Cambridge Tomasello M (2009) The question of chimpanzee culture, plus postscript (chimpanzee culture 2009). In: Laland KN, Galef BG (eds) The question of animal culture. Harvard University Press, Cambridge, pp 198-221.

      Thanks for the clarification.

      Action - We added the term “broadly” and added the last reference.

      Comment 40

      Line 22: Is all stable social learned behavior that becomes idiosyncratic and ”distinguishable” considered symbolic markers? If not, consider adding ”potentially.”

      No, but the evolution of cultural groups with differing behavior can reorganize the selective environment in such a way that it can favour an in-group bias that was not initially advantageous to individuals and lead to a preference towards others who share an overt symbolic marker that initially had no meaning and a random frequency in both populations. That is to say, even randomly assigned trivial groups can evolve arbitrary symbolic markers through in-group favouritism once behavioural differences exist even in the absence of any history of rivalry, conflict, or competition between groups. See for example [L1, L2].

      Comment 41

      Table 1: Identity codas are defined as a ”Subset of coda types most frequently used by a sperm whale clan; canonically used to define vocal clans.” Therefore, I infer that an identity coda is not exclusively used by a specific clan and may be utilized by other clans, albeit less frequently. If this is the case, what criteria determine the frequency of usage for a coda to be categorized as an identity or non-identity coda? Does the criteria used to differentiate between ID and non-ID codas reflect the observed differences in micro changes between the two and within clans?

      The methods for this categorization are defined, discussed, and justified in previous work in [L9, L12]. We feel its outside the scope of this paper to review these details here in this manuscript. However, the differences between vocal styles discussed here and the frequency production repertoires which allow for the definition of identity codas are on different scales. The differences between identity and non-identity codas are not the observed differences in vocal style reported here.

      Comment 42

      Table 1: The definition of vocal style states that it ”Encodes the rhythmic variations within codas.” However, if rhythm changes, does the type of coda change as well? Typically, in musical terms, the component that maintains the structure of a rhythm is ”tempo,” not ”rhythm.” How much microvariation is acceptable to maintain the same rhythm, and when do these variations constitute a new rhythm?

      Thank you for raising this important point about the relationship between rhythmic variations and coda categorization. In our definition, ”vocal style” refers to subtle, micro-level variations in the rhythmic structure of codas that do not alter their overarching categorical identity. These microvariations are akin to ”tempo” changes in musical terms, which can modify the expression of a rhythm without fundamentally altering its structure.

      The threshold at which microvariations constitute a new rhythm, and thus a new coda type, remains an open question and is a limitation of current analytical approaches. In our study, we used established classification methods to group codas into types, treating variations within these groups as part of the same rhythm. Future work could refine these thresholds to better distinguish between meaningful rhythmic variation and the emergence of new coda types.

      Comment 43

      Table 1: Change ”say” to ”vocalize” (similarly as used in line 273 for humpback whales ”vocalizations”).

      Thanks.

      Action - Done.

      Comment 44

      Lines 33-35 and Figure 1-C: Can a lay listener discern the microvariations within each coda type by ear? Consider including sound samples of individual rhythmic microvariations for the same coda type pattern (e.g., Four plus, Palindrome, Plus One, Regular) to provide readers/listeners with an impression of their detectability. If authors considered too much or redundant Supplemental material at least give a sound sample for each the 4 subcodas modeled structures examples of 4R2 coda variations depicted in Figure 1-C so the reader can have an acoustic impression of them.

      We do not think that human listeners would be able to all of the variation detected here. However, this does not mean that it is not important variation for the whales. Human observers being able to classify call variation aurally shouldn’t be seen as a bar representing important biological variation for non-human species, given that their hearing and vocal production systems have evolved independently. Importantly, ’Four Plus’,’Palindrome’, etc are names of Clans; sympatric, but socially segregated, communities of whale families, which share a distinct vocal dialect of coda types. These clans each have have distinguishable coda dialects made up of dozens of coda types (and delineated based on identity codas), these are not names/categorical coda types themselves.

      Action - We now provide audio samples of all coda types listed in Figure 1B in the paper’s Github repository.

      Comment 45

      Line 69: As stated above, it may be confusing to refer to it as ”speech.” I suggest adding something like: ”Our method does capture one essential characteristic of human speech: phonology.” Reply 45.—Thank you for drawing our attention to this.

      Action - We removed the word “speech” from the manuscript, using “communication” and/or “vocalization” depending on the context.

      Comment 46

      Line 111-112: Consider adding a sound sample of the variation of the 4R2 coda type that can be vocalized as BCC but also as CBB as supplementary data.

      What the reviewer has correctly observed is that the traditional categorical coda type ’names’ do not capture the variation within a type by rhythm nor by tempo.

      Action - We have added samples of all coda types listed in Figure 1B in the paper’s Github repo.

      Comment 47

      Figure 3: Include a sound sample for each of the 7 coda types in Figure 1B (”specific vocal repertoires”) to illustrate the set of coda types used and their associated usage frequencies, or at least for each of the 7 coda types in Figure 3 and tables S1 and S2.

      Sperm whales in the Eastern Caribbean produce dozens of rhythm types across at least five categorical tempo types [L8, L13]. The coda types represented in Figure 1B do not demonstrate all the variability inherent in the sperm whales’ vocal dialect. Importantly, Figure 3, as well as table S1 and S2, refer to clan-level dialects not specific individual coda types.

      Action - We added sound samples for each coda rhythm type listed in Figure 1B to the Github repository.

      Comment 48

      Lines 184-190: It is unclear what human analogy term is used for ID codas. This needs clarification.

      We are not making an analogy in humans for the role of ID vs non-ID codas, but only providing the example of accents as changes in vocalization (style) without a change in the actual words used (repertoire).

      Action - We tried to make it clearer in the manuscript.

      Comment 49

      Line 190: Change ”whale speech” to ”whale vocalizations.”

      Thanks.

      Action - Done.

      Comment 50

      Figure 4: Correct citation number Hersh ”10” to Hersh ”11.”

      Thanks.

      Action - Fixed the reference.

      Comment 51

      Lines 224-232: Clarify whether the reference to how spatial overlap affects the frequency of ID codas refers to shared ID codas between clans or the production frequency of each coda within the total repertoire of codas.

      The similarity between ID coda repertoires we are referring to there is based on the ID codas of both clans.

      More details on the comparison can be found in [L9].

      Action - We added a sentence explaining the comparison is made using the joint set of ID codas.

      Comment 52

      Lines 240-241: What are non-ID codas vocal cues for?

      Non-ID codas likely serve as flexible, context-dependent signals that facilitate group coordination, convey environmental or social context, and promote social learning, especially in mixed-clan or overlapping habitats. Their variability suggests multifunctional roles shaped by ecological and social pressures.

      Comment 53

      Lines 267-268: It’s unclear whether non-ID coda vocal styles are genetically inherited or not, as argued in lines 257-258.

      We did not intend to argue that non-ID coda vocal styles are genetically inherited. Instead, we aimed to present a hypothetical consideration: if non-ID coda vocal styles were genetically inherited, one would expect a direct correlation between vocal style similarity and genetic relatedness. This hypothetical framework was introduced to strengthen our argument that the observed patterns are unlikely to be explained by genetic inheritance, as such correlations have not been observed. While we acknowledge that we lack definitive proof to rule out genetic influences entirely, the evidence available strongly suggests that social learning, rather than genetic transmission, is the more plausible mechanism.

      Action - Clarified in manuscript.

      Comment 54

      Line 277: Can males mate with females from different clans?

      Yes, genetic evidence shows that males may even switch ocean basins.

      Action - We have clarified that we mean the female members of units from different clans have only rarely been observed to interact at sea between clans.

      Comment 55

      Lines 287-292: Consider discussing the difference between controlled/voluntary and automatic/involuntary imitation and their implications for cultural selection and social learning (see Heyes 2011; 2012). Heyes, C. (2011). Automatic imitation. Psychological bulletin, 137(3), 463. Heyes, C. (2012). What’s social about social learning?. Journal of comparative psychology, 126(2), 193.

      Thank you for your insightful comment regarding this. The distinction between controlled/voluntary and automatic/involuntary imitation, as highlighted by Heyes [L14, L15], provides a potentially valuable framework for interpreting social learning mechanisms in sperm whales. Automatic imitation refers to reflexive, often unconscious mimicry driven by perceptual or motor coupling, while controlled imitation involves deliberate and goal-directed efforts to replicate behaviors. Both forms likely play complementary roles in the cultural transmission observed in sperm whales.

      This dual-process perspective highlights the potential for cultural selection to act at different levels. Automatic imitation may drive convergence in shared environments, promoting acoustic homogeneity and facilitating inter-clan communication. In contrast, controlled imitation ensures the preservation of clan-specific vocal traditions, maintaining cultural diversity. This interplay between automatic and controlled processes could reflect a balancing act between cultural assimilation and differentiation, underscoring the adaptive value of these mechanisms in dynamic social and ecological contexts.

      Action - We have incorporated a short discussion of this distinction and its implications for our findings in the Discussion. Additionally, we have cited [L14, L15] to provide theoretical grounding for this interpretation.

      Comment 56

      Methods: Consider integrating the paragraph from lines 319-321 into lines 28-35 and eliminate redundant information.

      Thanks.

      Action - We implemented the suggestion, removing the first paragraph of the Dataset description and integrating the information when we introduce the concepts of codas and clicks.

      [L1] C. Efferson, R. Lalive, and E. Fehr, Science 321, 1844 (2008).

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      [L10] R. Boyd and P. J. Richerson, Cult Anthropol 2, 65 (1987). [L11] E. Cohen, Curr. Anthropol. 53, 588 (2012).

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    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The manuscript by Cao et al. examines an important but understudied question of how chronic exposure to heat drives changes in affective and social behaviors. It has long been known that temperature can be a potent driver of behaviors and can lead to anxiety and aggression. However, the neural circuitry that mediates these changes is not known. Cao et al. take on this question by integrating optical tools of systems neuroscience to record and manipulate bulk activity in neural circuits, in combination with a creative battery of behavior assays. They demonstrate that chronic daily exposure to heat leads to changes in anxiety, locomotion, social approach, and aggression. They identify a circuit from the preoptic area (POA) to the posterior paraventricular thalamus (pPVT) in mediating these behavior changes. The POA-PVT circuit increases activity during heat exposure. Further, manipulation of this circuit can drive affective and social behavioral phenotypes even in the absence of heat exposure. Moreover, silencing this circuit during heat exposure prevents the development of negative phenotypes. Overall the manuscript makes an important contribution to the understudied area of how ambient temperature shapes motivated behaviors.

      Strengths:

      The use of state-of-the-art systems neuroscience tools (in vivo optogenetics and fiber photometry, slice electrophysiology), chronic temperature-controlled experiments, and a rigorous battery of behavioral assays to determine affective phenotypes. The optogenetic gain of function of affective phenotypes in the absence of heat, and loss of function in the presence of heat are very convincing manipulation data. Overall a significant contribution to the circuit-level instantiation of temperature-induced changes in motivated behavior, and creative experiments.

      Weaknesses:

      (1) There is no quantification of cFos/rabies overlap shown in Figure 2, and no report of whether the POA-PVT circuit has a higher percentage of Fos+ cells than the general POA population. Similarly, there is no quantification of cFos in POA recipient PVT cells for Figure 2 Supplement 2.

      Thanks for the comment. The quantification results of c-Fos signal have been provided in the main text and figures.  

      (2) The authors do not address whether stimulation of POA-PVT also increases core body temperature in Figure 3 or its relevant supplements. This seems like an important phenotype to make note of and could be addressed with a thermal camera or telemetry.

      Thanks for raising this point. We did indeed monitor the core body temperature during stimulation of POA-PVT pathway, but we did not observe any significant changes. We have included this finding in the revised manuscript.

      (3) In Figure 3G: is Day 1 vs Day 22 "pre-heat" significant? The statistics are not shown, but this would be the most conclusive comparison to show that POA-PVT cells develop persistent activity after chronic heat exposure, which is one of the main claims the authors make in the text. This analysis is necessary in order to make the claim of persistent circuit activity after chronic heat exposure.

      Figure 3G does compare the Day 1 preheat to Day22 preheat, and the difference was significant. The wording has been corrected to avoid confusion. Also, we have modified Figure 3D to 3H in our revised manuscript to improve the clarity of these plots.

      (4) In Figure 4, the control virus (AAV1-EYFP) is a different serotype and reporter than the ChR2 virus (AAV9-ChR2-mCherry). This discrepancy could lead to somewhat different baseline behaviors.

      Thanks for bringing out this issue. We acknowledge that using AA1-EGFP (a different serotype and reporter compared to the AAV9-ChR2-mCherry) as our control virus is not ideal. But based on our own prior experiments, we observed no significant differences in baseline behaviors between animals injected with AAV1 and AAV9 EYFP as well as control mice without virus injection. Therefore, we believe that the baseline behaviors of the animals were unaffected.

      (5) In Figure 5G, N for the photometry data: the authors assess the maximum z-score as a measure of the strength of calcium response, however the area under the curve (AUC) is a more robust and useful readout than the maximum z score for this. Maximum z-score can simply identify brief peaks in amplitude, but the overall area under the curve seems quite similar, especially for Figure 5N.

      Thanks for the comment. We agree with the reviewer that the area under the curve (AUC) is an alternative readout for measurement of the strength of calcium response. However, the reason why we chose the maximum z-score is based on the observation that we found POA recipient pPVT neurons after chronic heat treatment exhibited a higher calcium peak corresponding to certain behavioral performances when compared to pre-heat conditions. We thus applied the maximum z-score as a representative way to describe the neuronal activity changes of mice during certain behaviors before and after chronic heat treatment. The other consideration is that we want to reflect that POA recipient pPVT neurons become more sensitive and easier to be activated after chronic heat exposure under the same stressful situations compared to control mice. The maximum z score represented by peak in combination with particular behavioral performances is considered more suitable to highlight our findings in this study.

      (6) For Fig 5V: the authors run the statistics on behavior bouts pooled from many animals, but it is better to do this analysis as an animal average, not by compiling bouts. Compiling bouts over-inflates the power and can yield significant p values that would not exist if the analysis were carried out with each animal as an n of 1.

      Thanks for the comment and suggestion. We had tried both methods and the statistical results were similar. As suggested, we have updated Fig 5V, as well as Fig. 5H and 5O by comparing animal average in our revised manuscript.

      (7) In general this is an excellent analysis of circuit function but leaves out the question of whether there may be other inputs to pPVT that also mediate the same behavioral effect. Future experiments that use activity-dependent Fos-TRAP labeling in combination with rabies can identify other inputs to heat-sensitive pPVT cells, which may have convergent or divergent functions compared to the POA inputs.

      Thanks for the valuable suggestion, which would enhance the conclusion. We will consider adopting this approach in future investigations into this question.

      Reviewer #2 (Public review):

      Summary

      The study by Cao et al. highlights an interesting and important aspect of heat- and thermal biology: the effect of repetitive, long-term heat exposure and its impact on brain function.

      Even though peripheral, sensory temperature sensors and afferent neuronal pathways conveying acute temperature information to the CNS have been well established, it is largely unknown how persistent, long-term temperature stimuli interact with and shape CNS function, and how these thermally-induced CNS alterations modulate efferent pathways to change physiology and behavior. This study is therefore not only novel but, given global warming, also timely.

      The authors provide compelling evidence that neurons of the paraventricular thalamus change plastically over three weeks of episodic heat stimulation and they convincingly show that these changes affect behavioral outputs such as social interactions, and anxiety-related behaviors.

      Strengths

      (1) It is impressive that the assessed behaviors can be (i) recruited by optogenetic fiber activation and (ii) inhibited by optogenetic fiber inhibition when mice are exposed to heat. Technically, when/how long is the fiber inhibition performed? It says in the text "3 min on and 3 min off". Is this only during the 20-minute heat stimulation or also at other times?

      Thanks for pointing out the need for clarification. Our optogenetic inhibition had been conducted for 21 days during the heat exposure period (90 mins) for each mouse. And to avoid the light-induced heating effect, we applied the cyclical mode of 3 minutes’ light on and 3 minutes’ light off only during the process of heat exposure but not other time. The detailed description has been supplemented in the Method part of our revised manuscript.

      (2) It is interesting that the frequency of activity in pPVT neurons, as assessed by fiber photometry, stays increased after long-term heat exposure (day 22) when mice are back at normal room temperature. This appears similar to a previous study that found long-term heat exposure to transform POA neurons plastically to become tonically active (https://www.biorxiv.org/content/10.1101/2024.08.06.606929v1). Interestingly, the POA neurons that become tonically active by persistent heat exposure described in the above study are largely excitatory, and thus these could drive the activity of the pPVT neurons analyzed in this study.

      Thanks for pointing out this study that suggests similar plasticity of POA neurons under long-term heat exposure serving a different purpose. We have included this information in our discussion as well.  

      (3) How can it be reconciled that the majority of the inputs from the POA are found to be largely inhibitory (Fig. 2H)? Is it possible that this result stems from the fact that non-selective POA-to-pPVT projections are labelled by the approach used in this study and not only those pathways activated by heat? These points would be nice to discuss.

      Thanks for raising these important questions. Although it is not our primary focus, we are aware of the substantial inhibitory inputs from POA to pPVT which suggests an important function. However, we do not think that this pathway, which would exert an opposite effect on POA-recipient pPVT neurons compared to the excitatory input, contributes to the long-term effect of chronic heat exposure. This is due to the increased, rather than decreased, excitability of the neurons. There is a possibility that this inhibitory input serves as a short-term inhibitory control for other purpose. Further work is needed to fully address this question.

      (4) It is very interesting that no LTP can be induced after chronic heat exposure (Figures K-M); the authors suggest that "the pathway in these mice were already saturated" (line 375). Could this hypothesis be tested in slices by employing a protocol to extinguish pre-existing (chronic heat exposure-induced) LTP? This would provide further strength to the findings/suggestion that an important synaptic plasticity mechanism is at play that conveys behavioral changes upon chronic heat stimulation.

      We agree with the reviewer that the results of the suggested experiment would further strengthen our hypothesis. We will try to confirm this in future studies.

      (5) It is interesting that long-term heat does not increase parameters associated with depression (Figure 1N-Q), how is it with acute heat stress, are those depression parameters increased acutely? It would be interesting to learn if "depression indicators" increase acutely but then adapt (as a consequence of heat acclimation) or if they are not changed at all and are also low during acute heat exposure.

      Based on our observations, we did not find increased depression parameters after acute heat stress in our experiments (data not shown), which was consistent with other two previous studies (Beas et al., 2018; Zhang et al., 2021). It appears that acute heat stress is more associated with anxiety-like behavior and may not be sufficient to induce depression-like phenotypes in rodents, aligning with our observation during experiments.

      Beas BS, Wright BJ, Skirzewski M, Leng Y, Hyun JH, Koita O, Ringelberg N, Kwon HB, Buonanno A, Penzo MA (2018) The locus coeruleus drives disinhibition in the midline thalamus via a dopaminergic mechanism Nat Neurosci 21:963-973.

      Zhang GW, Shen L, Tao C, Jung AH, Peng B, Li Z, Zhang LI, Whit Tao HZ (2021) Medial preoptic area antagonistically mediates stress-induced anxiety and parental behavior Nat Neurosci 24:516-528.

      Weaknesses/suggestions for improvement.

      (1) The introduction and general tenet of the study is, to us, a bit too one-sided/biased: generally, repetitive heat exposure --heat acclimation-- paradigms are known to not only be detrimental to animals and humans but also convey beneficial effects in allowing the animals and humans to gain heat tolerance (by strengthening the cardiovascular system, reducing energy metabolism and weight, etc.).

      Thanks for the suggestion. We have modified the introduction in our revised manuscript to make it more balanced.

      (2) The point is well taken that these authors here want to correlate their model (90 minutes of heat exposure per day) to heat waves. Nevertheless, and to more fully appreciate the entire biology of repetitive/chronic/persistent heat exposure (heat acclimation), it would be helpful to the general readership if the authors would also include these other aspects in their introduction (and/or discussion) and compare their 90-minute heat exposure paradigm to other heat acclimation paradigms. For example, many past studies (using mice or rats)m have used more subtle temperatures but permanently (and not only for 90 minutes) stimulated them over several days and weeks (for example see PMID: 35413138). This can have several beneficial effects related to cardiovascular fitness, energy metabolism, and other aspects. In this regard: 38{degree sign}C used in this study is a very high temperature for mice, in particular when they are placed there without acclimating slowly to this temperature but are directly placed there from normal ambient temperatures (22{degree sign}C-24{degree sign}C) which is cold/coolish for mice. Since the accuracy of temperature measurement is given as +/- 2{degree sign}C, it could also be 40{degree sign}C -- this temperature, 40{degree sign}C, non-heat acclimated C57bl/6 mice will not survive for long.

      The authors could consider discussing that this very strong, short episodic heat-stress model used here in this study may emphasize detrimental effects of heat, while more subtle long-term persistent exposure may be able to make animals adapt to heat, become more tolerant, and perhaps even prevent the detrimental cognitive effects observed in this study (which would be interesting to assess in a follow-up study).

      Thanks for pointing out the important aspect regarding the different heat exposure paradigms and their potential impacts. We have incorporated these points into both the Introduction and Discussion sections of the revised manuscript.

      (3) Line 140: It would help to be clear in the text that the behaviors are measured 1 day after the acute heat exposure - this is mentioned in the legend to the figure, but we believe it is important to stress this point also in the text. Similarly, this is also relevant for chronic heat stimulation: it needs to be made very clear that the behavior is measured 1 day after the last heat stimulus. If the behaviors had been measured during the heat stimulus, the results would likely be very different.

      Thanks for the suggestion, and we have clarified the procedure in the revised manuscript.

      (4) Figure 2 D and Figure 2- Figure Supplement 1: since there is quite some baseline cFos activity in the pPVT region we believe it is important to include some control (room temperature) mice with anterograde labelling; in our view, it is difficult/not possible to conclude, based on Fig 2 supplement 2C, that nearly 100% of the cfos positive cells are contacted by POA fibre terminals (line 168). By eye there are several green cells that don't have any red label on (or next to) them; additionally, even if there is a little bit of red signal next to a green cell: this is not definitive proof that this is a synaptic contact. It is therefore advisable to revisit the quantification and also revisit the interpretation/wording about synaptic contacts.

      In relation to the above: Figure 2h suggests that all neurons are connected (the majority receiving inhibitory inputs), is this really the case, is there not a single neuron out of the 63 recorded pPVT neurons that does not receive direct synaptic input from the POA?

      Thanks for the comments. For Figure 2-figure supplement 1, the baseline c-Fos activity in pPVT were indeed measured from mouse under room temperature. Observed activity may be attributed to the diverse functions that the pPVT is responsible for. Compared to the heat-exposed group, we observed significant increases in c-Fos signals, suggesting the effect of heat exposure.

      For Figure 2-figure supplement 2, through targeted injection of AAV1-Cre into the POA, we achieved selective expression of Cre-dependent ChR2-mCherry in pPVT neurons receiving POA inputs. Following heat exposure, we observed substantial colocalization between heat-induced c-Fos expression (green signal) and ChR2-mCherry-labeled neurons (red signal) in the pPVT. This extensive overlap indicates that POA-recipient pPVT neurons are predominantly heat-responsive and likely mediate the behavioral alterations induced by chronic heat exposure. We have validated these signals and included updated quantification in our revised manuscript.

      For Fig 2H, we specifically patched those neurons that were surrounded by red fluorescence under the microscope, ensuring that the patched neurons had a high likelihood of being innervated from POA. This is why all 63 recorded pPVT neurons were found to receive direct synaptic input from the POA.

      (5) It would be nice to characterize the POA population that connects to the pPVT, it is possible/likely that not only warm-responsive POA neurons connect to that region but also others. The current POA-to-pPVT optogenetic fibre stimulations (Figure 4) are not selective for preoptic warm responsive neurons; since the POA subserves many different functions, this optogenetic strategy will likely activate other pathways. The referees acknowledge that molecular analysis of the POA population would be a major undertaking. Instead, this could be acknowledged in the discussion, for example in a section like "limitation of this study".

      Thanks for the suggestion. We have supplemented this part in our revised manuscript.

      (6) Figure 3a the strategy to express Gcamp in a Cre-dependent manner: it seems that the Gcamp8f signal would be polluted by EGFP (coming from the Cre virus injected into the POA): The excitation peak for both is close to 490nm and emission spectra/peaks of GCaMP8f (510-520 nm) and EGFP (507-510 nm) are also highly overlapping. We presume that the high background (EGFP) fluorescence signal would preclude sensitive calcium detection via Gcamp8f, how did the authors tackle this problem?

      Thank you for pointing out this issue. We acknowledge that we included AAV1-EGFP when recording the GCaMP8F signal to assist in the post-verification of the accuracy of the injection site. But we also collected recording data from mice with AAV1-Cre without EGFP injected into POA and Cre-dependent GCaMP8F in pPVT, albert in a smaller number. We did not observe any obvious differences in the change in calcium signal between these two virus strategies, suggesting that the sensitivity of the GCaMP signals was not significantly affected by the increased baseline fluorescence due to EGFP.

      (7) How did the authors perform the social interaction test (Figures 1F, G)? Was the intruder mouse male or female? If it was a male mouse would the interaction with the female mouse be a form of mating behavior? If so, the interpretation of the results (Figures 1F, G) could be "episodic heat exposure over the course of 3 weeks reduces mating behavior".

      Thanks for the comment. For this female encounter test, we strictly followed the protocol by Ago Y, et al., (2015). During this test, both the strange male and female mice were placed into a wired cup (which is made up of mental wire entanglement and the size for each hole is 0.5 cm [L] x 0.5 cm [W]), which successfully prevented large body contact and the mating behavior but only innate sex-motivated moving around the cup. We have supplemented the details in the method part of our revised manuscript.

      Ago Y, Hasebe S, Nishiyama S, Oka S, Onaka Y, Hashimoto H, Takuma K, Matsuda T (2015) The Female Encounter Test: A Novel Method for Evaluating Reward-Seeking Behavior or Motivation in Mice Int J Neuropsychopharmacol 18: pyv062.

      Reviewer #3 (Public review):

      In this study, Cao et al. explore the neural mechanisms by which chronic heat exposure induces negative valence and hyperarousal in mice, focusing on the role of the posterior paraventricular nucleus (pPVT) neurons that receive projections from the preoptic area (POA). The authors show that chronic heat exposure leads to heightened activity of the POA projection-receiving pPVT neurons, potentially contributing to behavioral changes such as increased anxiety level and reduced sociability, along with heightened startle responses. In addition, using electrophysiological methods, the authors suggest that increased membrane excitability of pPVT neurons may underlie these behavioral changes. The use of a variety of behavioral assays enhances the robustness of their claim. Moreover, while previous research on thermoregulation has predominantly focused on physiological responses to thermal stress, this study adds a unique and valuable perspective by exploring how thermal stress impacts affective states and behaviors, thereby broadening the field of thermoregulation. However, a few points warrant further consideration to enhance the clarity and impact of the findings.

      (1) The authors claim that behavior changes induced by chronic heat exposure are mediated by the POA-pPVT circuit. However, it remains unclear whether these changes are unique to heat exposure or if this circuit represents a more general response to chronic stress. It would be valuable to include control experiments with other forms of chronic stress, such as chronic pain, social defeat, or restraint stress, to determine if the observed changes in the POA-pPVT circuit are indeed specific to thermal stress or indicative of a more universal stress response mechanism.

      We also share similar considerations as the reviewer and indeed have conducted experiments to explore this possibility. Our findings suggest that the POA-pPVT pathway may also mediate behavioral changes induced by other chronic stress, e.g. chronic restraint stress. Nevertheless, given the well-known prominent role of POA neurons in heat perception, we do believe that the POA-pPVT has a specialized role in mediating chronic heat induced changes. The role of this pathway in other stress-related responses will need a more comprehensive study in the future.

      (2) The authors use the term "negative emotion and hyperarousal" to interpret behavioral changes induced by chronic heat (consistently throughout the manuscript, including the title and lines 33-34). However, the term "emotion" is broad and inherently difficult to quantify, as it encompasses various factors, including both valence and arousal (Tye, 2018; Barrett, L. F. 1999; Schachter, S. 1962). Therefore, the reviewer suggests the authors use a more precise term to describe these behaviors, such as valence. Additionally, in lines 117 and 137-139, replacing "emotion" with "stress responses," a term that aligns more closely with the physiological observations, would provide greater specificity and clarity in interpreting the findings.

      Thanks for the suggestion. We have modified the description of “emotion” to “emotional valence” in various places throughout the revised manuscript.

      (3) Related to the role of POA input to pPVT,

      a) The authors showed increased activity in pPVT neurons that receive projections from the POA (Figure 3), and these neurons are necessary for heat-induced behavioral changes (Figures 4N-W). However, is the POA input to the pPVT circuit truly critical? Since recipient pPVT neurons can receive inputs from various brain regions, the reviewer suggests that experiments directly inhibiting the POA-to-pPVT projection itself are needed to confirm the role of POA input. Alternatively, the authors could show that the increased activity of pPVT neurons due to chronic heat exposure is not observed when the POA is blocked. If these experiments are not feasible, the reviewer suggests that the authors consider toning down the emphasis on the role of the POA throughout the manuscript and discuss this as a limitation.<br /> b) In the electrophysiology experiments shown in Figures 6A-I, the authors conducted in vitro slice recordings on pPVT neurons. However, the interpretation of these results (e.g., "The increase in presynaptic excitability of the POA to pPVT excitatory pathway suggested plastic changes induced by the chronic heat treatment.", lines 349-350) appears to be an overclaim. It is difficult to conclude that the increased excitability of pPVT neurons due to heat exposure is specifically caused by inputs from the POA. To clarify this, the reviewer suggests the authors conduct experiments targeting recipient neurons in the pPVT, with anterograde labeling from the POA to validate the source of excitatory inputs.

      For point (a), we acknowledge that pPVT neurons receiving POA inputs may also receive projections from other brain regions. While these additional inputs warrant investigation, they fall beyond the scope of our current study and represent promising directions for future research. Notably, compared to other well-characterized regions such as the amygdala and ventral hippocampus, the pPVT receives particularly robust projections from hypothalamic nuclei (Beas et al., 2018). Our optogenetic inhibition of POA-recipient pPVT neurons during chronic heat exposure effectively prevented the influence of POA excitatory projections on pPVT neurons. Furthermore, selective optogenetic activation of POA excitatory terminals within the pPVT was sufficient to induce similar behavioral abnormalities in mice, strongly supporting the causal role of POA inputs in mediating chronic heat exposure-induced behavioral alterations.

      Beas BS, Wright BJ, Skirzewski M, Leng Y, Hyun JH, Koita O, Ringelberg N, Kwon HB, Buonanno A, Penzo MA (2018) The locus coeruleus drives disinhibition in the midline thalamus via a dopaminergic mechanism Nat Neurosci 21:963-973.

      Regarding point (b), we acknowledge certain limitations in our in vitro patch-clamp recordings when attributing increased pPVT neuronal excitability to enhanced presynaptic POA inputs. Nevertheless, our brain slice recordings clearly demonstrated heightened excitability of pPVT neurons following chronic heat exposure. This finding was further corroborated by our in vivo fiber photometry recordings specifically targeting POA-recipient pPVT neurons, which confirmed that the increased pPVT neuronal activity was indeed modulated by POA inputs. The causal relationship was strengthened by our observation that optogenetic activation of POA excitatory terminals within the pPVT reproduced behavioral abnormalities similar to those observed in chronic heat-exposed mice. Additionally, our inability to induce circuit-specific LTP in the POA-pPVT pathway suggests that these synapses were already potentiated and saturated, reflecting enhanced excitatory inputs from the POA to pPVT. Collectively, these findings support our conclusion that increased excitatory projections from the POA to pPVT likely represent a key mechanism underlying chronic heat exposure-induced behavioral alterations in mice.

      (4) The authors focus on the excitatory connection between the POA and pPVT (e.g., "Together, our results indicate that most of the pPVT-projecting POA neurons responded to heat treatment, which would then recruit their downstream neurons in the pPVT by exerting a net excitatory influence.", lines 169-171). However, are the POA neurons projecting to the pPVT indeed excitatory? This is surprising, considering i) the electrophysiological data shown in Figures 2E-K that inhibitory current was recorded in 52.4% of pPVT neurons by stimulation of POA terminal, and ii) POA projection neurons involved in modulating thermoregulatory responses to other brain regions are primarily GABAergic (Tan et al., 2016; Morrison and Nakamura, 2019). The reviewer suggests showing whether the heat-responsive POA neurons projecting to the pPVT are indeed excitatory (This could be achieved by retrogradely labeling POA neurons that project to the pPVT and conducting fluorescence in situ hybridization (FISH) assays against Slc32a1, Slc17a6, and Fos to label neurons activated by warmth). Alternatively, demonstrate, at least, that pPVT-projecting POA neurons are a distinct population from the GABAergic POA neurons that project to thermoregulatory regions such as DMH or rRPa. This would clarify how the POA-pPVT circuit integrates with the previously established thermoregulatory pathways.

      Thanks for the comment and suggestion. We acknowledge that there are both excitatory and inhibitory projections from POA to pPVT. Although it is not our primary focus, we are aware of the substantial inhibitory inputs from POA to pPVT which suggests an important function. However, we do not think that this pathway, which would exert an opposite effect on POA-recipient pPVT neurons compared to the excitatory input, contributes to the long-term effect of chronic heat exposure. This is due to the increased, rather than decreased, excitability of the neurons. There is a possibility that this inhibitory input serves as a short-term inhibitory control for other purpose. Further work is needed to fully address this question.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      I have a number of suggested minor edits that would improve the readability and interpretation of figures for the reader. In many figures, there are places where it is unclear what is being tested, and making minor changes would make the manuscript flow more easily for the reader:

      (1) The authors could add additional details about the behavior paradigms in the Figures, especially Figure 1. How long was the chronic heat exposure for? At what temperature? What is the length of time between the end of heat exposure and the start of behaviors? What was the schedule of testing for EPM and social behaviors? Was it all on the same day or on different days? These details will make it easier for the reader to understand the behavior tests.

      We have revised our experimental scheme, especially Figure 1, and added more detailed descriptions in the method section. The modifications have also been applied to the other figures.

      (2) In Figures 1J and 1K, it is a bit unclear what is being shown in the right panel, since there are no axes or labels to interpret what is being plotted.

      We have added body kinetics (purple dot) in the left panel of Figure 1J and 1K to align with the right panels, and we have updated our descriptions in the figure legend.

      (3) In general, Figure 1 would benefit from more headers/labels or schematics to demonstrate what is being tested (for example, it's unclear that forced swim, tail suspension, open field, aggression, sucrose preference, or acoustic startle are being studied unless the reader looks at the figure legend in depth. Simple schematics or titles for each panel would help.

      We have added the abbreviated titles for each panel of Figure 1 to help readers to better understand what was being tested.

      (4) Figure 2A would benefit from edits to the schematic so that it is clear that heat exposure is being done before the animal is sacrificed and cFos is stained.

      We have revised the text to clarify that heat exposure occurred before the animal was sacrificed and c-Fos was stained.

      (5) Figure 2D: would help if the quantification of overlap of cFos and rabies was shown in the figure in addition to reporting it in the text (84%).

      We have added quantification in Figure 2D.

      (6) The supplemental data in Figure 2 - Supplemental Figure 1 showing increased Fos in PVT and POA after heat exposure would actually help if it was in main Figure 2 so that the reader can more clearly see the rationale for choosing the POA-PVT circuit. But this is a matter of preference and up to the author where they want to show this data.

      Thanks for the suggestion. But considering the layout and space, we will prefer to retain this part in Figure 2-supplemental figure 1.

      (7) Figure 3 would benefit from a behavior schematic illustrating the time course of the experiment and what the heat exposure protocol is for each day (how many minutes heat 'on' vs 'off', the temperature of heat, etc). Also, what is different about day 22 that makes it chronic heat vs day 21? Currently, it is a bit hard to understand the protocol.

      We have added the temperature and time of chronic heat exposure in the schematic of Figure 3. The “day 22” represented the time point after chronic heat exposure. And we measured the calcium activity of POA recipient pPVT neurons on day 22 to compare with day 1 to demonstrate that the activity changes of POA recipient pPVT neurons after chronic heat exposure.

      (8) Figure 3D, it is unclear what the difference is between the Day 1 data on the left and Day 1 data on the right. Same with Figure 3H, unclear what the difference is between the left and the right.

      The left panel and right panel reflect different parameters: frequency /min (left) and amplitude (△F/F) for Figure 3D-3H. By doing this, we want to reflect the dynamic activity changes of POA recipient pPVT neurons throughout chronic heat exposure process. Now, all figures in panel 3D to 3H have been revised to make them clearer in meaning.

      (9) Figure 4A would benefit from schematics showing the stimulation protocol for chronic optogenetics (how many days? Frequency? Duration of time? Etc)

      We have added detailed schematics in our Figure 4A.

      Reviewer #2 (Recommendations for the authors)

      (1) It is interesting that social behavior appears to be reduced upon long-term heat exposure but not after acute heat exposure. Interaction of animals, such as huddling, can be used by animals as a form of behavioral thermoregulation in cold environments and heat may drive animals apart to allow for better heat dissipation. The social interaction measured here is not huddling (because, I assume, the animals are separated by a divider?) but is this form of behavior measured here related to huddling/"social thermoregulation"? This could be discussed.

      Our behavioral tests were performed at room temperature. Even though huddling is a type of social behavior, based on our observation, the tested mouse was actively revolving around the mental cap, suggesting this type of behavior is not related to huddling/social thermoregulation type of social behavior.

      (2) Line 113: The statement "Chronic treatment did not change body temperature" should be clarified/rephrased because 90 minutes of 38 degrees centigrade exposure to heat will increase the body temperature of mice. It would be helpful if the authors made clear that they measure body temperature before the heat stimulus (and not during the heat stimulus), which is now only obvious if one digs into the methods section.

      We have revised the text and clarified that body temperature was measured before the heat stimulus in the revised manuscript.

      (3) Figure 1J and K: for the non-experts, these graphs are difficult to interpret, some more explanation is needed (what exactly is measured ?). We believe that the term "arousal" may not be justified in this context because the authors have not measured sleep patterns (EEG and EMG) to show that the mice arouse from a sleep (or sleep-like) stage; the authors may consider changing the terminology, e.g. something along the lines of "agitation" or "activity".

      We have further elaborated the meaning of Figure 1J and K in our revised manuscript. The acoustic startle response is a well-recognized behavioral parameter reflecting arousal levels in rodent model. The more agitation in response to stimulus, the higher the arousal levels in mice. We have used the term “agitation” to describe mice’s performance in the acoustic startle response test.

      Reviewer #3 (Recommendations for the authors):

      (1) The authors suggest in the introduction of the manuscript that the HPA axis and other multifaceted factors may influence emotional changes caused by heat stress (lines 63-78). However, there are no experiments or discussions on how the POA-pPVT circuit interacts with these factors. In line with the study's proposed direction in the introduction section, it would be valuable to explore, or at least discuss, whether and how the POA-pPVT circuit interacts with the HPA axis or other neural circuits known to regulate emotional and stress responses. Alternatively, the reviewer suggests revising the content of the introduction to align with the focus of the study.

      Although POA is known to possibly interact with the HPA axis via its connection with the paraventricular nucleus of the hypothalamus, there is hardly any evidence for the pPVT. Thus, we prefer not to speculate this question, which remains open, in our current manuscript.

      (2) In Figure 5, the authors report that pPVT neurons that receive projections from the POA exhibited increased responses to stressful situations following chronic heat exposure. However, considering the long pre- and post-recording time gap of approximately three weeks, the additional expression of GCaMP protein over time could potentially account for the increased signal. Therefore, the reviewer recommends including a control group without heat exposure to rule out this possibility.

      We have included Figure 3-figure supplement 1 in our manuscript to exclude the effect of expression of GCaMP protein over time on the recording of calcium signal.

      (3) Related to Figure 2, a) Please include quantification data of the overlap between retrogradely labeled and c-Fos-expressing POA neurons, which can be presented as a bar graph in Figure 2. This would be beneficial for readers to estimate how many warm-activated POA neurons connected to the pPVT are actively engaged under these conditions.

      In the revised manuscript, we have included the quantification analysis in Figure 2.

      b) The images in Figure 2 - Figure Supplement 1 seem to degrade in quality when magnified, making it difficult to discern finer details. Higher-resolution images would greatly improve the clarity and help in accurately visualizing the c-Fos expression patterns in the POA and pPVT regions.

      We have changed our images of Figure 2-figure supplement 1 to higher-resolution in the revised manuscript.

      c) The c-Fos images in Figure 2D and Figure 2 - Figure Supplement 2C appear unusual in that the c-Fos signal seems to fill the entire cell, whereas c-Fos protein is localized to the nucleus. Could the authors clarify whether this image accurately represents c-Fos staining or if there might be an issue with the staining or imaging process?

      We are confident that the green signals in both Figure 2D and Figure 2-figure supplement 2C, which did not occupy the whole cell body, have already accurately reflected the c-Fos and that they were nucleus staining. We have updated the amplified picture in Figure 2D.

      d) In Supplemental Figure 2B, the square marking the region of interest should be clearly explained in the figure legend to ensure that readers can fully understand the context and focus of the image.

      We have further modified our figure legend in Figure 2-figure supplement 1 in our revised manuscript.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public Review):  

      Summary:  

      Satoshi Yamashita et al., investigate the physical mechanisms driving tissue bending using the cellular Potts Model, starting from a planar cellular monolayer. They argue that apical length-independent tension control alone cannot explain bending phenomena in the cellular Potts Model, contrasting with previous works, particularly Vertex Models. They conclude that an apical elastic term, with zero rest value (due to endocytosis/exocytosis), is necessary to achieve apical constriction, and that tissue bending can be enhanced by adding a supracellular myosin cable. Additionally, a very high apical elastic constant promotes planar tissue configurations, opposing bending.  

      Strengths:  

      - The finding of the required mechanisms for tissue bending in the cellular Potts Model provides a natural alternative for studying bending processes in situations with highly curved cells. 

      - Despite viewing cellular delamination as an undesired outcome in this particular manuscript, the model's capability to naturally allow T1 events might prove useful for studying cell mechanics during out-of-plane extrusion. 

      We thank the reviewer for the careful comments and suggestions.

      Weaknesses: 

      - The authors claim that the cellular Potts Model (CPM) is unable to achieve the results of the vertex model (VM) simulations due to naturally non-straight cellular junctions in the CPM versus the VM. The lack of a substantial comparison undermines this assertion. None of the references mentioned in the manuscript are from a work using vertex model with straight cellular junctions, simulating apical constriction purely by a enhancing a length-independent apical tension. Sherrard et al and Pérez-González et al. use 2D and 3D Vertex Models, respectively, with a "contractility" force driving apical constriction. However, their models allow cell curvature. Both references suggest that the cell side flexibility of the CPM shouldn't be the main issue of the "contractility model" for apical constriction. 

      We appreciate the comment.

      For the reports by Sherrard et al and Pérez-Gonález et al, lack of the cell rearrangement (T1 transition) might have caused the difference. Other than these, Muñoz et al. (doi:10.1016/j.jbiomech.2006.05.006), Polyakov et al. (doi:10.1016/j.bpj.2014.07.013), Inoue et al.

      (doi:10.1007/s10237-016-0794-1), Sui et al.

      (doi:10.1038/s41467-018-06497-3), and Guo et al. (doi:10.7554/eLife.69082) used simulation models with the straight lateral surface.

      We updated an explanation about the difference between the vertex model and the cellular Potts model in the discussion.

      P12L318 “An edge in the vertex model can be bent by interpolating vertices or can be represented with an arc of circle (Brakke, 1992). Even in cases where vertex models were extended to allow bent lateral surfaces, the model still limited cell rearrangement and neighbor changes (Pérez-González et al., 2021), limiting the cell delamination. Thus the difference in simulation results between the models could be due to whether the cell rearrangement was included or not. However, it is not clear how the absence of the cell rearrangement affected cell behaviors in the simulation, and it shall be studied in future. In contrast to the vertex model, the cellular Potts model included the curved cell surface and the cell rearrangement innately, it elucidated the importance of those factors.”

      - The myosin cable is assumed to encircle the invaginated cells. Therefore, it is not clear why the force acts over the entire system (even when decreasing towards the center), and not locally in the contour of the group of cells under constriction. The specific form of the associated potential is missing. It is unclear how dependent the results of the manuscript are on these not-well-motivated and model-specific rules for the myosin cable.

      A circle radius decreases when the circle perimeter shrinks, and this was simulated with the myosin cable moving toward the midline in the cross section.

      We added an explanation in the introduction and the results.

      P2L74 “In the same way with the contracting circumferential myosin belt in a cell decreasing the cell apical surface, the circular supracellular myosin cable contraction decreases the perimeter, the radius of the circle, and an area inside the circle.”

      P6L197 “In the cross section, the shrinkage of the circular supracellular myosin cable was simulated with a move of adherens junction under the myosin cable toward the midline.”

      - The authors are using different names than the conventional ones for the energy terms. Their current attempt to clarify what is usually done in other works might lead to further confusion. 

      The reviewer is correct. However we named the energy terms differently because the conventional naming would be misleading in our simulation model.

      We added an explanation in the results.

      P4L140 “Note that the naming for the energy terms differs from preceding studies. For example, Farhadifar et al. (2007) named a surface energy term expressed by a proportional function "line tensions" and a term expressed by a quadratic function "contractility of the cell perimeter". In this study, however, calling the quadratic term "contractility" would be misleading since it prevents the contraction when  < _0. Therefore we renamed the terms accordingly.”

      Reviewer #2 (Public Review): 

      Summary: 

      In their work, the Authors study local mechanics in an invaginating epithelial tissue. The work, which is mostly computational, relies on the Cellular Potts model. The main result shows that an increased apical "contractility" is not sufficient to properly drive apical constriction and subsequent tissue invagination. The Authors propose an alternative model, where they consider an alternative driver, namely the "apical surface elasticity". 

      Strengths: 

      It is surprising that despite the fact that apical constriction and tissue invagination are probably most studied processes in tissue morphogenesis, the underlying physical mechanisms are still not entirely understood. This work supports this notion by showing that simply increasing apical tension is perhaps not sufficient to locally constrict and invaginate a tissue. 

      We thank the reviewer for the careful comments.

      Weaknesses: 

      Although the Authors have improved and clarified certain aspects of their results as suggested by the Reviewers, the presentation still mostly relies on showing simulation snapshots. Snapshots can be useful, but when there are too many, the results are hard to read. The manuscript would benefit from more quantitative plots like phase diagrams etc. 

      We agree with the comment.

      However, we could not make the qualitative measurement for the phase diagram since 1) the measurement must be applicable to all simulation results, and 2) measured values must match with the interpretation of the results. To do so, the measurement must distinguish a bent tissue, delaminated cells, a tissue with curved basal surface and flat apical surface, and a tissue with closed invagination. Such measurement is hardly designed.

      Recommendations for the authors: 

      Reviewing Editor (Recommendations For The Authors): 

      I see that the authors have worked on improving their paper in the revision. However, I agree with both reviewer #1 and reviewer #2 that the presentation and discussion of findings could be clearer. 

      Concrete recommendations for improvement: 

      (1) I find the observation by reviewer #1 on cell rearrangement very illuminating: It is indeed another key difference between the Cellular Potts Model that the authors use compared to typical Vertex Models, and could very well explain the different model outcomes. The authors could expand on the discussion of this point. 

      We updated an explanation about the difference between the vertex model and the cellular Potts model in the discussion.

      P12L318 “An edge in the vertex model can be bent by interpolating vertices or can be represented with an arc of circle (Brakke, 1992). Even in cases where vertex models were extended to allow bent lateral surfaces, the model still limited cell rearrangement and neighbor changes (Pérez-González et al., 2021), limiting the cell delamination. Thus the difference in simulation results between the models could be due to whether the cell rearrangement was included or not. However, it is not clear how the absence of the cell rearrangement affected cell behaviors in the simulation, and it shall be studied in future. In contrast to the vertex model, the cellular Potts model included the curved cell surface and the cell rearrangement innately, it elucidated the importance of those factors.”

      (2) In lines 161-164, the authors write "Some preceding studies assumed that the apical myosin generated the contractile force (Sherrard et al, 2010: Conte et al., 2012; Perez-Mockus et al., 2017; Perez-Gonzalez et al., 2021), while others assumed the elastic force (Polyakov et al., 2014; Inoue et al. 2016; Nematbakhsh et al., 2020)." 

      Similarly, in lines 316-319 the authors write "In the preceding studies, the apically localized myosin was assumed to generate either the contractile force (Sherrard et al, 2010: Conte et al., 2012; Perez-Mockus et al., 2017; Perez-Gonzalez et al., 2021), or the elastic force (Polyakov et al., 2014; Inoue et al. 2016; Nematbakhsh et al., 2020)." 

      The phrasing here is poor, as it suggests that the latter three studies (Polyakov et al., 2014; Inoue et al. 2016; Nematbakhsh et al., 2020) do not use the assumption that apical myosin generated contractile forces. This is wrong. All three of these studies do in fact assume apical surface contractility mediated by myosin. In addition, they also include other factors such as elastic restoring forces from the cell membrane (but not mediated by myosin as far as I understand). 

      These statements should be corrected. 

      We named the energy term expressed with the proportional function “contractility” and the energy term expressed with the quadratic function “elasticity”. Here we did not define what biological molecules correspond with the contractility or the elasticity.

      For the three studies, the effect of myosin was expressed by the quadratic function, and Polyakov et al. (2014) named it “springlike elastic properties”, Inoue et al. (2016) named it “Apical circumference elasticity”, and Nematbakhsh et al. (2020) named it “Actomyosin contractility”. To explain that the for generated by myosin was expressed with the quadratic function in these studies, we wrote that they “assumed the elastic force”.

      We assumed the myosin activity to be approximated with the proportional function in later parts and proposed that the membrane might be expressed with the quadratic function and responsible for the apical constriction based on other studies.

      To clarify this, we added it to the results.

      P4L175 “Some preceding studies assumed that the apical myosin generated the contractile force (Sherrard et al., 2010; Conte et al., 2012; Perez-Mockus et al., 2017; Pérez-González et al., 2021), while the others assumed the myosin to generate the elastic force (Polyakov et al., 2014; Inoue et al., 2016; Nematbakhsh et al., 2020).”

      (3) Lines 294-296: The phrasing suggests that the "alternative driving mechanism" consists of apical surface elasticity remodelling alone. This is not true, it's an additional mechanism, not an alternative. The authors' model works by the combined action of increased apical surface contractility and apical surface elasticity remodelling (and the effect can be strengthened by including a supracellular actomyosin cable). 

      We agree with the comment that the surface remodeling is not solely driving the apical constriction but with myosin activity. However, if we wrote it as an additional mechanism, it might look like that both the myosin activity alone and the surface remodeling alone could drive the apical constriction, and they would drive it better when combined together. So we replaced “mechanism” with “model”.

      P12L311 “In this study, we demonstrated that the increased apical surface contractility could not drive the apical constriction, and proposed the alternative driving model with the apical surface elasticity remodeling.”

      (4) In general, the part of the results section encompassing equations 1-5 should more explicitly state which equations were used in all simulations (Eqs1+5), and which ones were used only for certain conditions (Eqs2+3+4). 

      We added it as follows.

      P4L153 “While the terms Equation 1 and Equation 5 were included in all simulations since they were fundamental and designed in the original cellular Potts model (Graner and Glazier, 1992), the other terms Equation 2-Equation 4 were optional and employed only for certain conditions.”

      (5) Lines 150-152: Please state which parameters were examined. I assume Equation 4 was also left out of this initial simulation, as it is the potential energy of the actomyosin cable that was only included in some simulations. 

      We added it as follows.

      P4L163 “The term Equation 4 was not included either. For a cell, its compression was determined by a balance between the pressure and the surface tension, i.e., the heigher surface tension would compress the cell more. The bulk modulus 𝜆 was set 1, the lateral cell-cell junction contractility 𝐽_𝑙 was varied for different cell compressions, and the apical and basal surface contractilities 𝐽_𝑎 and 𝐽_𝑏 were varied proportional to 𝐽_𝑙.”

      (6) Lines 118-122: The sentence is very long and hard to parse. I suggest the following rephrasing: 

      “In this study, we assumed that the cell surface tension consisted of contractility and elasticity. We modelled the contractility as constant to decrease the surface, but not dependent on surface width or strain. We modelled the elasticity as proportional to the surface strain, working to return the surface to its original width." 

      We updated the explanation as follows.

      P3L121 “In this study, we assumed that the cell surface tension consisted of contractility and elasticity. We modeled the contractility as a constant force to decrease the surface, but not dependent on surface width or strain. We modeled the elasticity as a force proportional to the surface strain, working to return the surface to its original width.”

      (7) Lines 270-274: Another long sentence that is difficult to understand.

      Suggested rephrasing: 

      "Note that the supracellular myosin cable alone could not reproduce the apical constriction (Figure 2c), and cell surface elasticity in isolation caused the tissue to stay almost flat. However, combining both the supracellular myosin cable and the cell surface elasticity was sufficient to bend the tissue when a high enough pulling force acted on the adherens junctions." 

      We updated the sentence as follows.

      P9L287 “Note that the supracellular myosin cable alone could not reproduce the apical constriction (Figure 2c), and that with some parameters the modified cell surface elasticity kept the tissue almost flat (Figure 4). However, combining both the supracellular myosin cable and the cell surface elasticity made a sharp bending when the pulling force acting on the adherens junction was sufficiently high.”

      (8) Lines 434-435: Unclear what is meant with sentence starting with "Rest of sites" 

      We update the sentence as follows.

      P17L456 “At the initial configuration and during the simulation, sites adjacent to medium and not marked as apical are marked as basal.”

      (9) Fixing typos and other minor grammar and wording changes would improve readability. Following is a list in order of appearance in the text with suggestions for improvement. 

      We greatly appreciate the careful editing, and corrected the manuscript accordingly.

      Line 14: "a" is not needed in the phrase "increased a pressure" 

      Line 15: "cell into not the wedge shape" --"cell not into the wedge shape"  In fact it might be better to flip the sentence around to say, e.g. "making the cells adopt a drop shape instead of the expected wedge shape". 

      Line 24: "cells decrease its apical surface" --"cells decrease their apical surface" 

      Line 25: instead of "turn into wedge shape", a more natural-sounding expression could be "adopt a wedge shape" 

      Line 28: "which crosslink and contract" --because the subject is the singular "motor protein", the verb tense needs to be changed to "crosslinks and contracts" 

      Line 29: I suggest to use the definite article "the" before "actin filament network" as this is expected to be a known concept to the reader. 

      Line 31: "adherens junction and tight junction" --use the plural, because there are many per cell: "adherens junctions and tight junctions" 

      Line 42: "In vertebrate" --"In vertebrates" 

      Line 46: "Since the interruption to" --"Since the interruption of" 

      Line 56: "the surface tension of the invaginated cells were" --since the subject is "the surface tension", the verb "were" needs to be changed to "was"  Line 63: "extra cellular matrix" --generally written as "extracellular matrix" without the first space 

      Line 66: "many epithelial tissues" --"in many epithelial tissues" 

      Line 70: "This supracellular cables" --"These supracellular cables" 

      Line 72: "encircling salivary gland" --either "encircling the salivary gland" or "encircling salivary glands" 

      Lines 76-77: "investigated a cell physical property required" --"investigated what cell physical properties were required" 

      Line 78: "was another framework" --"is another framework" (it is a generally and currently valid true statement, so use the present tense) 

      Line 79: "simulated an effect of the apically localized myosin" --for clarity, I suggest rephrasing as "simulated the effect of increased apical contractility mediated by apically localized myosin" 

      Similarly, in Line 80: "did not reproduce the apical constriction" --"did not reproduce tissue invagination by apical constriction", as technically the cells in the model do reduce their apical area, but fail to invaginate as a tissue. 

      Line 82: "we found that a force" --"we found that the force" 

      Line 101: "apico-basaly" --"apico-basally" 

      Lines 107-108: "in order to save a computational cost" --"in order to save on computational cost" 

      Line 114: "Therefore an area of the cell" --"Therefore the interior area of the cell" 

      Line 139: "formed along adherens junction" --"formed along adherens junctions" 

      Line 166: "we ignored an effect" --"we ignored the effect" 

      Line 167: "and discussed it later" --"and discuss it later" 

      Lines 167-168: "an experiment with a cell cultured on a micro pattern showed that the myosin activity was well corresponded by the contractility" --"an experiment with cells cultured on a micro pattern showed that the myosin activity corresponded well to the contractility" 

      Line 172: "success of failure" --"success or failure" 

      Figure 1 caption: "none-polar" --"non-polarized"; "reg" --"red" 

      Line 179: "To prevented the surface" --"To prevent the surface" 

      Line 180: "It kept the cells surface" --"It kept the cells' surface" (apostrophe missing) 

      Line 181: "cells were delaminated and resulted in similar shapes" --"cells were delaminated and adopted similar shapes" 

      Line 190: "To investigate what made the difference" --"To investigate the origin of the difference" 

      Line 203: For clarity, I would suggest to add more specific wording. "the pressure, and a difference in the pressure between the cells resulted in" --"the internal pressure due to cell volume conservation, and a difference in the pressure between the contracting and non-contracting cells resulted in" 

      Line 206: "by analyzing the energy with respect to a cell shape" --"by analyzing the energy with respect to cell shape" 

      Line 220: "indicating that cell could shrink" --"indicating that a cell could shrink" 

      Line 224: For clarity, I would suggest more specific wording "lateral surface, while it seems not natural for the epithelial cells" --"lateral surface imposed on the vertex model, a restriction that seems not natural for epithelial cells" 

      Line 244: "succeeded in invaginating" --"succeeding in invaginating" 

      Line 247: "were checked whether the cells" --"were checked to assess whether the cells" 

      Line 250: "cells became the wedge shape" --"cells adopted the wedge shape" 

      Line 286: "there were no obvious change in a distribution pattern" --"there was no obvious change in the distribution pattern" 

      Lines 296-297: "When the cells were assigned the high apical surface contractility, the cells were rounded" --"When the cells were assigned a high apical surface contractility, the cells became rounded" 

      Line 298: "This simulation results" --"These simulation results" 

      Lines 301-302: I suggest to increase clarity by somewhat rephrasing.  "Even when the vertex model allowed the curved lateral surface, the model did not assume the cells to be rearranged and change neighbors" --"Even in cases where vertex models were extended to allow curved lateral surfaces, the model still limited cell rearrangement and neighbor changes" 

      Line 326: "high surface tension tried to keep" --"high surface tension will keep" 

      Line 334: "In many tissue" --"In many tissues" 

      Line 345: "turned back to its original shape" --"turned back to their original shape" (subject is the plural "cells") 

      Lines 348-349: "resembles the result of simulation" --"resembles the result of simulations" 

      Line 352: "how the myosin" --"how do the myosin" 

      Line 356: "it bears the surface tension when extended and its magnitude" What does the last "its" refer to? The surface tension? 

      Line 365: "the endocytosis decrease" --"the endocytosis decreases" 

      Line 371: "activatoin" --"activation" 

      Line 374 "the cells undergoes" --"the cells undergo" 

      Line 378: "entier" --"entire" 

      Line 389: "individual tissue accomplish" --"individual tissues accomplish" 

      Line 423: "is determined" --"are determined" (subject is the plural "labels") 

      Line 430: "phyisical" --"physical" 

      Table 6 caption: "cell-ECN" --cell-ECM 

      Line 557: "do not confused" --"should not be confused" 

      Reviewer #1 (Recommendations For The Authors): 

      - The phrase "In addition, the encircling supracellular myosin cable largely promoted the invagination by the apical constriction, suggesting that too high apical surface tension may keep the epithelium apical surface flat." is not clear to me. It sounds contradictory. 

      This finding was unexpected and surprising for us too. However, it is actually not contradictory since stronger surface tension will make the surface flatter in general. Figure 4 shows the flat apical surface with the wedge shape cells for the too strong apical surface tension. On the other hand, the supracellular myosin cable promoted the cell shape changes without raising the surface tension, and thus it could make a sharp bending (Figure 5).

      We updated the explanation for the effect of the supracellular myosin cable as follows.

      P2L74 “In the same way as the contracting circumferential myosin belt in a cell decreasing the cell apical surface, the circular supracellular myosin cable contraction decreases the perimeter, the radius of the circle, and an area inside the circle.”

      P6L197 “In the cross section, the shrinkage of the circular supracellular myosin cable was simulated with a move of adherens junction under the myosin cable toward the midline.”

      - Even when the authors now avoid to say "in contrast to vertex model simulations" in pg.4, in the next section there is still the intention to compare VM to CPM. Idem in the Discussion section. The conclusion in that section is that the difference between the results arising with VM (achieving the constriction) and the CPM (not achieving the constriction, and leading to cell delamination) are due to the straight lateral surfaces. However, Sherrard et at could achieve the constriction with an enhanced apical surface contractility using a 2D VM that allows curvatures. Therefore, I don't think the main difference is given by the deformability of the lateral surfaces. Instead, it might be due to the facility of the CPM to drive cellular rearrangements, coupled to specific modeling rules such as the permanent lost of the "apical side" once a delamination occurs and the boundary conditions. A clear example is the observation of loss of cell-cell adherence when all the tensions are set the same. Instead, in a VM cells conserve their lateral neighbors in the uniform tension regime (Sherrard et at). Is it noteworthy that the two mentioned works using vertex models to achieve apical constriction (Sherrard et at. (2D) and Pérez-González (3D) et al.) seem to neglect T1 transitions. I specifically think the added discussion on the impact of the T1 events (fundamental for cell delamination) is quite poor. A more detailed description would help justify the differences between model outcomes. 

      We updated an explanation about the difference between the vertex model and the cellular Potts model in the discussion.

      P12L318 “ An edge in the vertex model can be bent by interpolating vertices or can be represented with an arc of circle (Brakke, 1992). Even in cases where vertex models were extended to allow bent lateral surfaces, the model still limited cell rearrangement and neighbor changes (Pérez-González et al., 2021), limiting the cell delamination. Thus the difference in simulation results between the models could be due to whether the cell rearrangement was included or not. However, it is not clear how the absence of the cell rearrangement affected cell behaviors in the simulation, and it shall be studied in future. In contrast to the vertex model, the cellular Potts model included the curved cell surface and the cell rearrangement innately, it elucidated the importance of those factors.”

      - Fig6c: cell boundary colors are quite difficult to see. 

      The images were drawn by custom scripts, and those scripts do not implement a method to draw wide lines.

      - Title Table 1: "epitherila". 

      We corrected the typo.

      Reviewer #2 (Recommendations For The Authors): 

      The Authors have addressed most of my initial comments. In my opinion, the results could be better represented. Overall, the manuscript contains too many snapshots that are hard to read. I am sure the Authors could come up with a parameter that would tell the overall shape of the tissue and distinguish between a proper invagination and delamination. Then they could plot this parameter in a phase diagram using color plots to show how varying values of model parameters affects the shape. Presentation aside, I believe the manuscript will be a valuable piece of work that will be very useful for the community of computational tissue mechanics. 

      We agree with the comment.

      However, we could not make a suitable qualitative measurement method. For the phase diagrams, the measurement must be applicable to simulation results, otherwise each figure introduce a new measurement and a color representation would just redraw the snapshots but no comparison between the figures. So the different measurements would make the figures more difficult to read.

      The single measurement must distinguish the cell delamination by the increased surface contractility from the invagination by the modified surface elasticity and the supracellular contractile ring, even though the center cells were covered by the surrounding cells and lost contact with apical side extracellular medium in both cases.

      With the center of mass, the delaminated cells would return large values because they were moved basally. With the tissue basal surface curvature, it would not measure if the tissue apical surface was also curved or kept flat. If the phase diagram and interpretation of the simulation results do not match with each other, it would be misleading.

      A measurement meeting all these conditions was hardly designed.

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      Reply to the reviewers

      Reviewer #1 Evidence, reproducibility and clarity Summary: Bhatt et al. seek to define factors that influence H3.3 incorporation in the embryo. They test various hypotheses, pinpointing the nuclear/cytoplasmic ratio and Chk1, which affects cell cycle state, as influencers. The authors use a variety of clever Drosophila genetic manipulations in this comprehensive study. The data are presented well and conclusions reasonably drawn and not overblown. I have only minor comments to improve readability and clarity. I suggest two OPTIONAL experiments below. We thank the reviewer for their positive and helpful comments. Major comments: We found this manuscript well written and experimentally thorough, and the data are meticulously presented. We have one modification that we feel is essential to reader understanding and one experimental concern: The authors provide the photobleaching details in the methodology, but given how integral this measurement is to the conclusions of the paper, we feel that this should be addressed in clear prose in the body of the text. The authors explain briefly how nuclear export is assayed, but not import (line 99). Would help tremendously to clarify the methods here. This is especially important as import is again measured in Fig 4. This should also be clarified (also in the main body and not solely in the methods). We have added the following sentences to the main body of the text to clarify how photobleaching and import were assayed. “We note that these differences are not due to photobleaching as our measurements on imaged and unimaged embryos indicate that photobleaching is negligible under our experimental conditions (see methods, Figure S1G-H)” lines 98-101 and “Since nuclear export is effectively zero, we attribute the increase in total H3.3 over time solely to import and therefore the slope of total H3.3 over time corresponds to the import rate.” lines 111-113 Revision Plan In addition we have clarified how import was calculated to figure legends in Figure 5D (formerly 4D) and S1F which now read: “Initial slopes of nuclear import curves (change in total nuclear intensity over time for the first 5 timepoints) …” We also added the following explanation of how nuclear import rates were calculated to the methods section: “Import rates were calculated by using a linear regression for the total nuclear intensity over time for the first 5 timepoints in the nuclear import curves.” lines 471-473, methods If the embryos appeared "reasonably healthy" (line 113) after slbp RNAi, how do the authors know that the RNAi was effective, especially in THESE embryos, given siblings had clear and drastic phenotype? This is especially critical given that the authors find no effect on H3.3 incorporation after slbp RNAi (and presumably H3 reduction), but this result would also be observed if the slbp RNAi was just not effective in these embryos. We apologize for the confusion caused by our word choice. The “healthy” slbp-RNAi embryos had measurable phenotypes consistent with histone depletion that we have reported previously (Chari et al, 2019) including cell cycle elongation and early cell cycle arrest (Figure S4D). However, they did not have the catastrophic mitosis observed in more severely affected embryos. We agree with the reviewer that a concern of this experiment is that the less severely affected embryos likely have more remaining RD histones including H3. To address this we also tested H3.3 incorporation in the embryos that fail to progress to later cell cycles in the cycles that we could measure. Even in these more severely affected embryos we were not able to detect a change in H3.3 incorporation relative to controls (lines 240-243 and Fig S4B). Unfortunately, it is impossible to conduct the ideal experiment, which would be a complete removal of H3 since this is incompatible with oogenesis and embryo survival. To address this confusion we have added supplemental videos of control, moderately affected and severely affected SLBP-RNAi embryos as movies 3-5 and modified the text to read: “All embryos that survive through at least NC12, had elongated cell cycles in NC12 and 60% arrested in NC13 as reported previously indicating the effectiveness of the knockdown (Figure S4C, Movie 3-5)39. In these embryos, H3.3 incorporation is largely unaffected by the reduction in RD H3 (Figure 6B).” lines 236-240 Finally, to characterize the range of SLBP knockdown in the RNAi embryos we propose to do single embryo RT-qPCRs for SLBP mRNA for multiple individual embryos. This will provide a measure of the range knockdown that we observed in our H3.3 movies. Minor comments: Introduction: Revision Plan Consider using "replication dependent" (RD) rather than "replication coupled." Both are used in the field, but RD parallels RI ("replication independent"). We thank the reviewer for this suggestion. We have made the text edits to change "replication coupled" (RC) to "replication dependent" (RD) throughout the manuscript. Would help for clarity if the authors noted that H3 is equivalent to H3.2 in Drosophila. Also it is relevant that there are two H3.3 loci as the authors knock mutations into the H3.3A locus, but leave the H3.3B locus intact. The authors should clarify that there are two H3.3 genes in the Drosophila genome. We have changed the text as follows to increase clarity as suggested: “Similarly, we have previously shown that RD H3.2 (hereafter referred to as H3) is replaced by RI H3.3 during these same cycles, though the cause remains unclear29” lines 52-54 “There are ~100 copies of H3 in the Drosophila genome, but only 2 of H3.3 (H3.3A and H3.3B)26. To determine which factor controls nuclear availability and chromatin incorporation, we genetically engineered flies to express Dendra2-tagged H3/H3.3 chimeras at the endogenous H3.3A locus, keeping the H3.3B locus intact.” lines 127-131 Please add information and citation (line 58): H3.3 is required to complete development when H3.2 copy number is reduced (PMID: 37279945, McPherson et al. 2023) We have added the suggested information. The text now reads “Nonetheless, H3.3 is required to complete development when H3.2 copy number is reduced54.” lines 61-62 Results: Embryo genotype is unclear (line 147): Hira[ssm] haploid embryos inherit the Hira mutation maternally? Are Hira homozygous mothers crossed to homozygous fathers to generate these embryos, or are mothers heterozygous? This detail should be in the main text for clarity. The Hira mutants are maternal effect. We crossed homozygous Hirassm females to their hemizygous Hirassm or FM7C brothers. However, the genotype of the male is irrelevant since the Hira phenotype prevents sperm pronuclear fusion and therefore there is no paternal contribution to the embryonic genotype. We have clarified this point in the text: “We generated embryos lacking functional maternal Hira using Hirassm-185b (hereafter Hirassm) homozygous mothers which have a point mutation in the Hira locus57.” lines 160-162 Revision Plan Line 161: Shkl affects nuclear density, but it also appears from Fig 3 to affect nuclear size? The authors do not address this, but it should at least be mentioned. We thank the reviewer for the astute observation. More dense regions of the Shkl embryos do in fact have smaller nuclei. We believe that this is a direct result of the increased N/C ratio since nuclear size also falls during normal development as the N/C ratio increases. We have added a new figure 1 in which we more carefully describe the events of early embryogenesis in flies including a quantification of nuclear size and number in the pre-ZGA cell cycles (Figure 1C). We also note the correlation of nuclear size with nuclear density in the text: “During the pre-ZGA cycles (NC10-13), the maximum volume that each nucleus attains decreases in response to the doubling number of nuclei with each division (Figure 1C).” lines 86-87 “To test this, we employed mutants in the gene Shackleton (shkl) whose embryos have non-uniform nuclear densities and therefore a gradient of nuclear sizes across the anterior/posterior axis (Figure 3A-B, Movie 1-2)58.” lines 180-183 The authors often describe nuclear H3/H3.3 as chromatin incorporated, but these image-based methods do not distinguish between chromatin-incorporated and nuclear protein. To distinguish between chromatin incorporated and nuclear free histone we have exploited the fact that histones that are not incorporated into DNA freely diffuse away from the chromatin mass during mitosis while those that are bound into nucleosomes remain on chromatin during this time. In our previous study we showed that H3-Dendra2 that is photoconverted during mitosis remains stably associated with the mitotic chromatin through multiple cell cycles (Shindo and Amodeo, 2019) strengthening our use of this metric. To help clarify this point as well as other methodological details we have added a new Figure 1B which documents the time points at which we make various measurements within the lifecycle of the nucleus. We also edited the text to read: “We have previously shown that with each NC, the pool of free H3 in the nucleus is depleted and its levels on chromatin during mitosis decrease (Figure 1D, S1C-D)29. In contrast, H3.3 mitotic chromatin levels increase during the same cycles (Figure 1D, S1C-D)29.” lines 89-92 I very much appreciate how the authors laid out their model in Fig 3 and then used the same figure to explain which part of the model they are testing in Figs 4 and 5. This is not a critique- we can complement too! Thank you! Revision Plan OPTIONAL experimental suggestion: The experiments in Figure 4 and 5 are clever. One would expect that H3 levels might exhaust faster in embryos lacking all H3.2 histone genes (Gunesdogan, 2010, PMID: 20814422), allowing a comparison testing the H3 availability > H3.3 incorporation portion of the hypothesis without manipulating the N/C ratio. This might also result in a more consistent system than slbp RNAi (below). We thank the reviewer for the experimental suggestion. We also considered this experimental manipulation to decrease RD histone H3.2. We chose not to do this experiment because in the Gunesdogan paper they show that the zygotic HisC nulls have normal development until after NC14 (unlike the maternal SLBP-RNAi that we used) suggesting that maternal H3.2 supplies do not become limiting until after the stages under consideration in our paper. Maternal HisC-nulls are, of course, impossible to generate since histones are essential. O'Haren 2024 (PMID: 39661467) did not find increased Pol II at the HLB after zelda RNAi (line 227). Might also want to mention here that zelda RNAi does not result in changes to H3 at the mRNA level (O'Haren 2024), as that would confound the model. We thank the reviewer for the suggestion. We have removed the discussion of Pol II localization and replaced it with the information about histone mRNA : “zelda controls the transcription of the majority of Pol II genes during ZGA but disruption of zelda does not change RD histone mRNA levels67–70”. lines 249-251 Discussion: Should discuss results in context of McPherson et al. 2023 (PMID: 37279945), who showed that decreasing H3.2 gene numbers does not increase H3.3 production at the mRNA or protein levels. We expanded our discussion to include the following: “Given the fact that H3.3 pool size does not respond to H3 copy number in other Drosophila tissues,54 our results suggest that H3.3 incorporation dynamics are likely independent of H3 availability.” lines 278-280 The Shackleton mutation is a clever way to alter N/C ratio, but the authors should point out that it is difficult (impossible?) to directly and cleanly manipulate the N/C ratio. For example, Shkl mutants seem to also have various nuclear sizes. As discussed above, we think that nuclear size is a direct response to the N/C ratio. We have added the following sentence to the discussion as well as a citation to a paper which discusses how the N/C ratio might contribute to nuclear import in early embryos to the discussion: “This may be due to N/C ratio-dependent changes in nuclear import dynamics which may also contribute to the observed changes in nuclear size across the shkl embryo75.” lines 307-309 Revision Plan How is H3.3 expression controlled? Is it possible that H3.3 biosynthesis is affected in Chk1 mutants? To address this question we propose to perform RT-qPCR for H3.3A and H3.3B as well as Hira in the Chk1 mutant. Unfortunately, we do not have antibodies that reliably distinguish between H3 and H3.3 in our hands (despite literature reports), but we will also perform a pan-H3 immunostaining in the Chk1 embryos to measure how the total H3-type histone pool changes as a result of the loss of Chk1. Figures: While I appreciate the statistical summaries in tables, it is still helpful to display standard significance on the figures themselves. We have added statistical comparisons in Figure 3 (formerly Figure 2). We do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. Although we plot H3 on the same graph as the other proteins to allow for ease of comparison of their trends over time it is not appropriate to directly compare their normalized intensities which including statistical tests would encourage. We have added a note to the legend of Figure 1 explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” Fig 1: A: Is it possible to label panels with the nuclear cycle? We have done this. B: Statistics required - caption suggests statistics are in Table S2, but why not put on graph? Please see the explanation above for why we do not feel that it is appropriate to perform this comparison. C/D: Would be helpful if authors could plot H3/H3.3 on same graph because what we really need to compare is NC13 between H3/H3.3 (and statistics between these curves) Please see the explanation above for why we do not feel that it is appropriate to perform this comparison. These curves can be directly compared within a construct and we can evaluate their trends over time, but the normalized values should not be directly compared in the way that would be encouraged by plotting the data as suggested. E: The comparison in the text is between H3.3 and H3, but only H3.3 data is shown. I realize that it is published prior, but the comparison in figure would be helpful. We have added the previously published values to the text. Revision Plan “These changes in nuclear import and incorporation result in a less complete loss of the free nuclear H3.3 pool (~70% free in NC11 to ~30% in NC13) than previously seen for H3 (~55% free in NC11 to ~20% in NC13)” lines 116-119 Fig 2: A: A very helpful figure. Slightly unclear that the H3 that is not Dendra tagged is at the H3.3 locus. Also unclear that the H3.3A-Dendra2 line exists and used as control, as is not shown in figure. Should show H3 and H3.3 controls (Figure S2) We have edited the figure to add Dendra2 to all of the constructs and made clear the location of each construct including adding the landing site for H3-Dendra2. We have also cited Figure S1 in the legend which contains a more detailed diagram of the integration strategy. F/H- As the comparison is between H3 and ASVM, it would help to combine these data onto the same graph. As the color is currently used unnecessarily to represent nuclear cycle, the authors could use their purple/pink color coding to represent H3/ASVM. We have combined these data onto a single graph as requested and changed the colors appropriately. We have not added statistical comparisons to this graph as we again believe that they would be inappropriate. In the legend of Fig 2 the authors write "in the absence of Hira." Technically, there is only a point mutation in Hira. It is not absent. Good catch! We have changed this to “in Hirassm mutants”. Fig 3: G: Please show WT for comparison. Can use data in Fig 3A. We have added the color-coded number of neighbor embryo representations for WT and Shkl embryos underneath the example embryo images in 4A-B (formerly 3A-B,G). Model in H is very helpful (complement)! Thank you. Fig 4: B/C/F/G: The authors use a point size scale to represent the number of nuclei, but the graphs are so overlaid that it is not particularly useful. Is there a better way to display this dimension? We chose to represent the data in this way so that the visual impact of each line is representative of the amount of data (number of nuclei in each bin) that underlies it. This helps to prevent sparsely populated outlier bins at the edges of the distribution from dominating the interpretation of the data. If the reviewer has a suggestion for a better way to visualize this information we would welcome their suggestion, but we cannot think of a better way at this time. D/E/H/I: What does "min volume" mean on the X axis? Since the uneven N/C ratio in the shkl embryos results in a wavy cell cycle pattern there is no single time point where we can calculate the number of neighbors for the whole embryo (since Revision Plan not all nuclei are in the same cell cycle at a given point). Therefore, we had to choose a criterion for when we would calculate the number of neighbors for each nucleus. We chose nuclear size as a proxy for nuclear age since nuclear size increases throughout interphase (see new figure 1B). So, the minimum volume is the newly formed nucleus in a given cell cycle. We also tested other timepoints for the number of neighbors (maximum nuclear volume, just before nuclear envelope breakdown and midway between these two points) and found similar results. We chose to use minimum volume in this paper because this is the time point when the nucleus is growing most quickly and nuclear import is at its highest. We have added the following explanation to the methods: “For shkl embryos, as the nuclear cycles are asynchronous, nuclear divisions start at different timepoints within the same cell cycle and the nuclear density changes as the neighboring nuclei divide. Therefore, the total intensity traces were aligned to match their minimum volumes (as shown in Figure 1B) to T0.” lines 485-488, methods And the following detail to the figure legend: “...plotted by the number of nuclear neighbors at their minimum nuclear volume…” Figure 5 legend We also added a depiction of the lifecycle of the nucleus in which we marked the minimum volume as the new Figure 1B. Fig 5: F: OPTIONAL Experimental request: Here I would like to see H3 as a control. This is a very good suggestion, and we are currently imaging H3-Dendra2 in the Chk1 background. However, our preliminary results suggest that there may be some synthetic early lethality between the tagged H3-Dendra2 and Chk1 since these embryos are much less healthy than H3.3-Dendra2 Chk1 embryos or Chk1 with other reporters. In addition, we have observed a much higher level of background fluorescence in this cross than in the H3-Dendra2 control. We are uncertain if we will be able to obtain usable data from this experiment, but will continue to try to find conditions that allow us to analyze this data. As an orthogonal approach to answer the question, we will perform immunostaining with a pan-H3 antibody in Chk1 mutant embryos to measure total H3 levels under these conditions. Since the majority of H3-type histone is H3.2 and we know how H3.3 changes, this staining will give us insight into the dynamics of H3 in Chk1 mutant embryos. Significance General assessment: Many long-standing mysteries surround zygotic genome activation, and here the authors tackle one: what are the signals to remodel the zygotic chromatin around ZGA? This is a tricky question to answer, as basically all manipulations done to the embryo Revision Plan have widespread effects on gene expression in general, confounding any conclusions. The authors use clever novel techniques to address the question. Using photoconvertible H3 and H3.3, they can compare the nuclear dynamics of these proteins after embryo manipulation. Their model is thorough and they address most aspects of it. The hurdle this study struggles to overcome is the same that all ZGA studies have, which is that manipulation of the embryo causes cascading disasters (for example, one cannot manipulate the nuclear:cytoplasmic ratio without also altering cell cycle timing), so it's challenging to attribute molecular phenotypes to a single cause. This doesn't diminish the utility of the study. Advance: The conceptual advance of this study is that it implicates the nuclear:cytoplasmic ratio and Chk1 in H3.3 incorporation. The authors suggest these factors influence cell cycle closing, which then affects H3.3 incorporation, although directly testing the granularity of this model is beyond the scope of the study. The authors also provide technical advancement in their use of measuring histone dynamics and using changes in the dynamics upon treatment as a useful readout. I envision this strategy (and the dendra transgenes) to be broadly useful in the cell cycle and developmental fields. Audience: The basic research presented in this study will likely attract colleagues from the cell cycle and embryogenesis fields. It has broader implications beyond Drosophila and even zygotic genome activation. This reviewer's expertise: Chromatin, Drosophila, Gene Regulation Reviewer #2 (Evidence, reproducibility and clarity (Required)): This manuscript investigates the regulation of H3.3 incorporation during zygotic genome activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication. We thank the reviewers for their careful reading of this manuscript. We have sought to clarify the concerns regarding clarity through numerous text edits detailed below. We did include ANOVA analysis for all of the relevant statistical comparisons in the supplemental table. However, to increase clarity we have also added some statistical comparisons in the main figures. We note that we do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. Although we plot H3 on the same graph as the other proteins to allow for ease of comparison of their trends over time it is not appropriate to directly compare their normalized intensities which including statistical tests would encourage. We have added a note to the legend of the new Figure 1 Revision Plan explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” Major Concerns The manuscript would benefit from a clearer introduction that explicitly distinguishes between previously known mechanisms of histone regulation during ZGA and the novel contributions of this study. Currently, the introduction lacks sufficient background on early embryonic chromatin regulation, making it difficult for readers unfamiliar with the field to grasp the significance of the findings. The authors should also be more precise when discussing the timing of ZGA. While they state that ZGA occurs after 13 nuclear divisions, it is well established that a minor wave of ZGA begins at nuclear cycle 7-8, whereas the major wave occurs after cycle 13. Clarifying this distinction will improve the manuscript's accessibility to a broader audience. We have added a new figure 1 to make the timing and nuclear behaviors of the embryo during ZGA in Drosophila more clear. We have also added information about how the chromatin changes during Drosophila ZGA in the following sentence: “ In Drosophila, these changes include refinement of nucleosomal positioning, partitioning of euchromatin and heterochromatin and formation of topologically associated domains20–22,24.” lines 39-41 We have clarified the major and minor waves of ZGA in the introduction and results by adding the following sentences to the introduction and results respectively: “In most organisms ZGA happens in multiple waves but the chromatin undergoes extensive remodeling to facilitate bulk transcription during the major wave of ZGA (hereafter referred to as ZGA)18–20,22–25..” lines 36-39 “In Drosophila, ZGA occurs in 2 waves. The minor wave starts as early as the 7th cycle, while major ZGA occurs after 13 rapid syncytial nuclear cycles (NCs) and is accompanied by cell cycle slowing and cellularization (Figure 1A-B).” lines 83-85 We hope that these changes help to reduce confusion and make the paper more accessible. However, we are happy to add additional information if the reviewer can provide specific points which require further attention. One of the primary weaknesses of this study is the lack of adequate control experiments. In Figure 1, the authors suggest that the levels of H3 and H3.3 are influenced by the N/C ratio, but Revision Plan it is unclear whether transcription itself plays a role in these dynamics. To properly test this, RNA-seq or Western blot analyses should be performed at nuclear cycles 10 and 13-14 to compare the levels of newly transcribed H3 or H3.3 against maternally supplied histones. Without such data, the authors cannot rule out transcriptional regulation as a contributing factor. In the pre-ZGA cell cycles the vast majority of protein including histones is maternally loaded. Gunesdogan et al. (2010) showed that the zygotic RD histone cluster nulls survive past NC14 (well past ZGA) with no discernible defects indicating that maternal RD histone supplies are sufficient for normal development during the cell cycles under consideration. Therefore, new transcription of replication coupled histones is not needed for apparently normal development during this period. Moreover, we have done the western blot analysis using a Pan-H3 antibody as suggested by the reviewer in our previously published paper (Shindo and Amodeo, 2019 supplemental figure S3A-B) and found that total H3-type histone proteins only increase moderately during this period of development, nowhere near the rate of the nuclear doublings. We have added the following sentence to clarify this point. “These divisions are driven by maternally provided components and the total amount of H3 type histones do not keep up with the pace of new DNA produced29.” lines 88-89 We have also previously done RNA-seq on wild-type embryos (and those with altered maternal histone levels) (Chari et al 2019). In this RNA-seq (like most RNA-seq in flies) we used poly-A selection and therefore cannot detect the RD histone mRNAs (which have a stem-loop instead of a poly-A tail). We have plotted the mRNA concentrations for both H3.3 variants from that dataset below for the reviewers reference (we have not included this in the revised manuscript). The total H3.3 mRNA levels are nearly constant from egg laying (NC0- these are from unfertilized embryos) until after ZGA (NC14). These data combined with the westerns discussed above give us confidence that what we are observing is the partitioning of large pools of maternally provided histones with only a relatively small contribution of new histone synthesis. Revision Plan In Figure 2, the manuscript introduces chimeric embryos expressing modified histone variants, but their developmental viability is not addressed. It is essential to determine whether these embryos survive and whether they exhibit any phenotypic consequences such as altered hatching rates, defects in nuclear division, or developmental arrest. Tagging histones is often deleterious to organismal health. In Drosophila there are two H3.3 loci (H3.3A and H3.3B). In all of our chimera experiments we have left the H3.3B and one copy of the H3.3A locus unperturbed to provide a supply of untagged H3.3. This allows us to study H3.3 and chimera dynamics without compromising organism health. All of our chimeras are viable and fertile with no obvious morphological defects. We have added the following sentences to the text to clarify this point: “There are ~100 copies of H3 in the Drosophila genome, but only 2 of H3.3 (H3.3A and H3.3B)26. To determine which factor controls nuclear availability and chromatin incorporation, we genetically engineered flies to express Dendra2-tagged H3/H3.3 chimeras at the endogenous H3.3A locus, keeping the H3.3B locus intact….These chimeras were all viable and fertile. ” lines 127-131, 136 In addition we propose performing hatch rate assays for embryos from the chimeric embryos of S31A, SVM and ASVM to assess if there is any decrease in fecundity due to the presence of the chimeras. Moreover, given that H3.3 is associated with actively transcribed genes, an RNA-seq analysis of chimeric embryos should be included to assess transcriptional changes linked to H3.3 incorporation. This is an excellent suggestion and will definitely be a future project for the lab. However, to do this experiment correctly we will need to generate untagged chimeric lines that will (hopefully) allow for the full replacement of H3.3 with the chimeric histones instead of a single copy among 4. This is beyond the scope of this paper. Figures 3 and 4 raise additional concerns about whether histone cluster transcription is altered in shkl mutant embryos. The authors propose that the shkl mutation affects the N/C ratio, yet it remains unclear whether this leads to changes in the transcription of histone clusters. Furthermore, since HIRA is a key chaperone for H3.3, it would be important to assess whether its levels or function are compromised in shkl mutants. To address these gaps, RT-qPCR or RNA-seq should be performed to quantify histone cluster transcription, and Western blot analysis should be used to determine if HIRA protein levels are affected. The changes in the N/C ratio that are observed in the shkl mutant are within SINGLE embryo (differences in nuclear spacing). In these experiments we are comparing nuclei within a common cytoplasm that have different local nuclear densities (N/C ratios). Therefore, if Shkl Revision Plan were somehow affecting the transcription of histones or their chaperones we would expect all of the nuclei within the same mutant embryo to be equally affected since they are genetically identical and share a common cytoplasm. We do not directly compare the behavior of shkl embryos to wildtype except to demonstrate that there is no positional effect on the import of H3 and H3.3 across the length of the embryo in wildtype. To clarify our experimental system for these experiments we have added additional panels to Figure 4A and B that depict the number of neighbors for both control and Shkl embryos. Nonetheless, to address the reviewer’s concern that shkl may change the amount of H3 present in the embryo, we propose to conduct a western blot comparison of wildtype and shkl embryos using a pan-H3 antibody. There are no tools (antibodies or fluorescently tagged proteins) to assess HIRA protein levels in Drosophila. We therefore propose to perform RT-qPCR for HIRA in wildtype and shkl embryos. A similar issue arises in Figure 5, where the authors claim that H3.3 incorporation is dependent on cell cycle state but do not sufficiently test whether this is linked to changes in HIRA levels. Given the importance of HIRA in H3.3 deposition, its levels should be examined in Slbp, Zelda, and Chk1 RNAi embryos to verify whether changes in H3.3 incorporation correlate with HIRA function. Without this, it is difficult to conclude that the observed effects are strictly due to cell cycle regulation rather than histone chaperone dynamics. Since H3.3 incorporation is unaffected in the Slbp and Zelda-RNAi lines there is no reason to suspect a change in HIRA function. There are no available tools (antibodies or fluorescently tagged proteins) to directly measure HIRA protein in Drosophila. To test if changes in HIRA loading might contribute to the decreased H3.3 incorporation in the Chk1 mutant we propose to perform RT-qPCR for HIRA in wildtype and Chk1 embryos. Several figures require additional statistical analyses to support the claims made. In Figure 1B, statistical testing should be included to validate the reported differences. Figure 1C-D states that "H3.3 accumulation reduces more slowly than H3," yet there is no quantitative comparison to substantiate this claim. Similarly, Figure 1E presents the conclusion that "These changes in nuclear import and incorporation result in a less dramatic loss of the free nuclear H3.3 pool than previously seen for H3," despite the fact that H3 data are not included in this figure. The conclusions drawn from these data need to be supported with appropriate statistical comparisons and more precise descriptions of what is being measured. For Figure 1B (now 2B) we do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings and therefore we do not feel that direct statistical tests are appropriate. Rather, we plot the two histones on the same graph normalized to their own NC10 values so that the trend in their decrease over time may be compared. The statistical tests for H3.3 compared to the chimeras which were originally in the supplemental table have been added to Figure 3 (formerly figure 2). Revision Plan It is important to note that in this directly comparable situation the ASVM mutant (whose trends closely mirror H3) is highly statistically distinct from H3.3. We have added a note to the legend of the new Figure 1 explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” For Figure 1C-D (now 2C-D) we have removed this claim from the text. We were referring to the plateau in nuclear import for H3 that is less dramatic in H3.3, but this is more carefully discussed in the next paragraph and its addition at that point generated confusion. The text now reads: “To further assess how nuclear uptake dynamics changed during these cycles, we tracked total nuclear H3 and H3.3 in each cycle (Figure 2C-D). Since nuclear export is effectively zero, we attribute the increase in total H3.3 over time solely to import and therefore the slope of total H3.3 over time corresponds to the import rate. Though the change in initial import rates between NC10 and NC13 are similar between the two histones (Figure S1F), we observed a notable difference in their behavior in NC13. H3 nuclear accumulation plateaus ~5 minutes into NC13, whereas H3.3 nuclear accumulation merely slows (Figure 2C-D).” lines 109-116 For Figure 1E (now 2E), to address the difference between H3 and H3.3 free pools we have added the previously published values to the text and changed the phrasing from “less dramatic” to “less complete”. The sentence now reads: “These changes in nuclear import and incorporation result in a less complete loss of the free nuclear H3.3 pool (~70% free in NC11 to ~30% in NC13) than previously seen for H3 (~55% free in NC11 to ~20% in NC13)” lines 116-119 Figure 2 presents additional concerns regarding data interpretation. The comparisons between H3.3 and H3.3S31A to H3 and H3.3SVM/ASVM lack statistical analysis, making it difficult to determine the significance of the observed differences. As discussed above, it is not appropriate to directly compare H3 to H3.3 and the chimeras at the H3.3A locus since they are expressed from different promoters and imaged with different settings. The ANOVA comparisons between all of the constructs in the H3.3A locus can be found in the supplemental table. We have also added the statistical significance between each chimera and H3.3 within a cell cycle to the figure. Including the full set of comparisons for all genotypes and timepoints makes the figure nearly impossible to interpret, but they remain available in the supplemental table. Revision Plan The disappearance of H3.3 from mitotic chromosomes in Figure 2E is also not explained. If this phenomenon is functionally relevant, the authors should provide a mechanistic interpretation, or at the very least, discuss potential explanations in the text. In Figures 2F-H, the reasoning behind comparing the nuclear intensity of H3.3 to H3 in Hira mutants is unclear. To properly assess the role of HIRA in H3.3 chromatin accumulation, a more appropriate comparison would be between wild-type H3.3 and H3.3 levels in Hira knockdown embryos. As explained in the text and depicted in Figure 3D (formerly 2D), the HIRAssm mutant is a point mutation that prevents observable H3.3 chromatin incorporation, but not nuclear import. This is what is depicted in Figure 3E (formerly 2E). The loss of H3.3 from mitotic chromatin is due to the inability to incorporate H3.3 into chromatin as expected for a HIRA mutant. We have edited the figure 3 legend to make this more clear. It now reads: “Hirassm mutation nearly abolishes the observable H3.3 on mitotic chromatin (E).” In Figure 3F (formerly 2F-H) we ask what happens to H3 chromatin incorporation when there is almost no incorporation of H3.3 due to the HIRA mutation. In this mutant there is so little H3.3 incorporation that we cannot quantify H3.3 levels on mitotic chromatin (see the new Figure 1B for the stage where chromatin levels are quantified). This experiment was done to test if H3.3ASVM (expressed at the H3.3A locus) is incorporated into chromatin in embryos lacking the function of H3.3’s canonical chaperone. We have edited the text to make this more clear: “Since the chromatin incorporation of the H3/H3.3 chimeras appears to depend on their chaperone binding sites, we asked if impairing the canonical H3.3 chaperone, Hira, would affect the incorporation of H3.3ASVMexpressed from the H3.3A locus.”lines 158-160 A broader concern is that the authors only test HIRA as a histone chaperone but do not consider alternative chaperones that could influence H3.3 deposition. Since multiple chaperone systems regulate histone incorporation, it would strengthen the conclusions if additional chaperones were tested. Since HIRAssm reduced H3.3-Dendra2 incorporation to nearly undetectable levels (Figure 3E) we believe that it is the primary H3.3 incorporation pathway during this period of development. Therefore, we believe that removing HIRA function is a sufficient test of the dependance of H3.3ASVM on the major H3.3 chaperone at this time. Although it would be interesting to fully map how all H3 and H3.3 chimera constructs respond to all histone chaperone pathways, we believe that this is beyond the scope of this manuscript. Additionally, the manuscript does not include any validation of the RNAi knockdown efficiencies used throughout the study. This raises concerns about whether the observed phenotypes are truly due to target gene depletion or off-target effects. RT-qPCR or Western blot analyses should be performed to confirm knockdown efficiency. Revision Plan Both the Zelda and slbp-RNAi lines used for knockdowns have been used and validated in the early fly embryo in previously published works ((Yamada et al., 2019), (Duan et al., 2021), (O’Haren et al., 2025), (Chari et al, 2019)) and the phenotypes that we observe in our embryos are consistent with the published data including altered cell cycle durations (Figure S4C) and lack of cellularization/gastrulation. We note that the zelda RNAi phenotypes are also highly consistent with the effects of Zelda germline clones. To validate that slbp-RNAi knocks down histones we included a western blot for Pan-H3 in slbp-RNAi embryos that demonstrates a large effect on total H3 levels (Figure S4A). To further demonstrate the phenotypic effects of the slbp-RNAi we have added supplemental movies (Videos 4 and 5). To fully characterize the RNAi efficiency under our conditions we propose to perform RT-qPCR for slbp in slbp-RNAi and Zelda in Zelda-RNAi compared to control (w) RNAi embryos. Finally, the section discussing "H3.3 incorporation depends on cell cycle state, but not cell cycle duration" is unclear. The term "cell cycle state" is vague and should be explicitly defined. Does this refer to a specific phase of the cell cycle, changes in chromatin accessibility, or another regulatory mechanism? The term cell cycle state is deliberately vague. We know that Chk1 regulates many aspects of cell cycle progression and cannot determine from our data which aspect(s) of cell cycle regulation by Chk1 are important for H3.3 incorporation. Our data indicate that it is not simply interphase duration as we originally hypothesized. We have expanded our discussion section to underscore some aspects of Chk1 regulation that we speculate may be responsible for the change in H3.3 behavior. “Chk1 mutants decrease H3.3 incorporation even before the cell cycle is significantly slowed. Cell cycle slowing has been previously reported to regulate the incorporation of other histone variants in Drosophila15. However, our results indicate that cell cycle state and not duration per se, regulates H3.3 incorporation. In most cell types, the primary role of Chk1 is to stall the cell cycle to protect chromatin in response to DNA damage. Therefore, Chk1 activity directly or indirectly affects the chromatin state in a variety of ways. We speculate that Chk1’s role in regulating origin firing may be particularly important in this context73,74. Late replicating regions and heterochromatin first emerge during ZGA, and Chk1 mutants proceed into mitosis before the chromatin is fully replicated22,23,25,71. Since H3.3 is often associated with heterochromatin, the decreased H3.3 incorporation in Chk1 mutants may be an indirect result of increased origin firing and decreased heterochromatin formation73,74.” lines 287-298 Reviewer #2 (Significance (Required)): This manuscript investigates the regulation of H3.3 incorporation during zygotic genome Revision Plan activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication. Reviewer #3 (Evidence, reproducibility and clarity (Required)): Summary: Based on previous findings of the changing ratios of histone H3 to its variant H3.3, the authors test how H3.3 incorporation into chromatin is regulated for ZGA. They demonstrate here that H3 nuclear availability drops and replacement by H3.3 relies on chaperone binding, though not on its typical chaperone Hira. Furthermore, they show that nuclear-cytoplasmic (N/C) ratios can influence this histone exchange likely by influencing cell cycle state. We thank the reviewer for their thoughtful comments. We note that our data ARE consistent with H3.3 incorporation depending on Hira through its chaperone binding site. Major comments: 1. The claims are largely supported by the data but I think a couple more experiments could help bolster the claims about cell cycle and chk1 regulation. a. Creating a phosphomimetic of the chk1 phosphorylation site on H3.3 to see if it can overcome the defects seen in chk1 mutants b. Assessing heterochromatin of embryos without chk1 (or ASVM mutants) for example, by looking at H3K9me3 levels The first experiments could take several months if the flies haven't already been generated by the authors but the second should be quicker. a. This is an excellent experimental suggestion which is bolstered by the fact that in frogs H3.3 S31A cannot rescue H3.3 morpholino during gastrulation, but H3.3S31D can (Sitbon et al, 2020). However, to correctly conduct this experiment would require generating and validating multiple additional endogenous H3.3 replacement lines, likely without a fluorescent tag as they can interfere with histone rescue constructs in most species. As the reviewer notes, this would take several months of work (we have not generated the critical flies yet) and may not yield a satisfying answer since there are reports that H3.3 may be dispensable in flies aside from as a source of H3-type histone outside of S-phase (Hödl and Bassler, 2012). While we hope to continue experiments along these lines in the future we feel that this is beyond the scope of the current manuscript. Revision Plan b. To address this we propose to stain for H3K9me3 in wildtype and Chk1-/- embryos. Since the ASVM line is not a full replacement of all H3.3 we think that staining for H3K9me3 in this line is unlikely to yield a detectable difference. 2. It would also be interesting to see what the health of the flies with some mutations in this paper are beyond the embryo stage if they are viable (e.g., development to adulthood, fertility etc.) a. the SVM, ASVM mutations b. the hira + ASVM mutations The authors might already have this data but if not they have the flies and it shouldn't take long to get these data. a. To address this concern we propose to conduct hatch rate assays for embryos from the Dendra tagged H3.3, S31A, SVM, ASVM flies. However, we do note that in our experiments only one copy of the H3.3A locus was mutated and tagged with Dendra2 leaving one copy of H3.3A and both copies of H3.3B untouched to ensure normal development as tagging all copies of histone genes can lead to lethality. b. All Hira mutants develop as haploids due to the inability to decondense the sperm chromatin (which is dependent on Hira). This leads to one extra division to restore the N/C ratio prior to cell cycle slowing and ZGA. These embryos go on to gastralate and die late in development after cuticle formation (presumably due to their decreased ploidy) (Loppin et al., 2000). The addition of ASVM into the Hira background does not appear to rescue the ploidy defect as these embryos also undergo the extra division (Figure 3H). We are therefore confident that these embryos will not hatch. We have added the information about the development of Hira mutant to the text as follow: “These embryos develop as haploids and undergo one additional syncytial division before ZGA (NC14). Hirassmembryos develop otherwise phenotypically normally through organogenesis and cuticle formation, but die before hatching57.” lines 164-167 3. In the discussion section, can the authors speculate on how they think H3.3 ASVM is getting incorporated if not through Hira. Are there other known H3 variant chaperones, or can the core histone chaperone substitute? We have expanded our discussion to include the the following: “In the case of the chimeric histone proteins the incorporation behavior was dependent on the chaperone binding site. For example, H3.3ASVM import and incorporation was similar to H3 in control embryos and H3.3ASVM was still incorporated in Hirassm mutants. This is consistent with the chaperone binding site determining the chromatin incorporation pathway and suggests that H3.3ASVM likely interacts with H3 chaperones such as Caf1.” lines 280-285 Revision Plan Minor comments: While the paper is well written, I found the figures very confusing and difficult to interpret. Comments here are meant to make it easier to interpret. 1. Fig 1 and most of the paper would benefit from a schematic of early embryo transitions labelled with time and stages of cell cycle to make interpreting data easier This is an excellent suggestion! We have added a new figure (Figure 1) to explain both the biological system and the way that we measured many properties in this paper. 2. Fig 1- same green color is used for nuclear cycle 12 and for H3.3 making it confusing when reading graphs. Please check other figures where there is a similar use of color for two different things We have changed the colors so that they are more distinct. 3. Fig 1C,D might benefit more from being split up into 3 graphs by cell cycle with H3 and H3.3 plotted on the same graphs rather than the way it is now We do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. These curves can be directly compared within a construct and we can evaluate their trends over time, but the normalized values should not be directly compared in the way that would be encouraged by plotting the data as suggested. 4. Line 130-133: can they also comment on the different between SVM and ASVM. It seems like SVM might be even worse than ASVM (Fig 2C). Is this related to chk1 phosphorylation? We think that this is a property of the mixed chimeras since S31A is also imported less efficiently than H3.3 (though we cannot be sure without further experiments). We have added this explanation to the text: “We speculate that chimeric histone proteins (H3.3S31A and H3.3SVM) are not as efficiently handled by the chaperone machinery as species that are normally found in the organism including H3.3ASVM which is protein-identical to H3.” lines 150-152 5. Fig 2F-G: It is very difficult to compare between histones when they are on different graphs, please consider putting H3, H3.3 and H3.3ASVM in a hirassm background on the same graph. We have done this in the new Figure 3F. Revision Plan 6. Fig 3- move G to become A and then have A and B. We have restructured this figure to include the nuclear density map of control in response to a comment from Reviewer 1. Although not exactly what the reviewer has envisioned, we hope that this adds clarity to the figure. 7. The initial slope graphs in 4D, E, H and I are not easy to understand and would benefit from an explanation in the legend. We have edited the legend of Figure 5D (formerly 4D) and S1F which now read: “Initial slopes of nuclear import curves (change in total nuclear intensity over time for the first 5 timepoints) …” In addition we have updated the methods to include: “Import rates were calculated by using a linear regression for the total nuclear intensity over time for the first 5 timepoints in the nuclear import curves.” lines 471-473, methods Reviewer #3 (Significance (Required)): This paper addresses an important and understudied question- how do histones and their variants mediate chromatin regulation in the early embryo before zygotic genome activation? The authors follow up on some previous findings and provide new insights using clever genetics and cell biology in Drosophila melanogaster. However, the authors do not directly look at chromatin structural changes using existing genomic tools. This may be beyond the scope of this work but would make for a nice addition to strengthen their claims if they can implement these chromatin accessibility techniques in the early embryo. Histones affect a majority of biological processes and understanding their role in the early embryo is key to understanding development. I believe this study applies to a broad audience interested in basic science. However, I do think the authors might benefit from a more broad discussion of their results to attract a broad readership.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This important study proposes a framework to understand and predict generalization in visual perceptual learning in humans based on form invariants. Using behavioral experiments in humans and by training deep networks, the authors offer evidence that the presence of stable invariants in a task leads to faster learning. However, this interpretation is promising but incomplete. It can be strengthened through clearer theoretical justification, additional experiments, and by rejecting alternate explanations.

      We sincerely thank the editors and reviewers for their thoughtful feedback and constructive comments on our study. We have taken significant steps to address the points raised, particularly the concern regarding the incomplete interpretation of our findings.

      In response to Reviewer #1, we have included long-term learning curves from the human experiments to provide a clearer demonstration of the differences in learning rates across invariants, and have incorporated a new experiment to investigate location generalization within each invariant stability level. These new findings have shifted the focus of our interpretation from learning rates to the generalization patterns both within and across invariants, which, alongside the observed weight changes across DNN layers, support our proposed framework based on the Klein hierarchy of geometries and the Reverse Hierarchy Theory (RHT).

      We have also worked to clarify the conceptual foundation of our study and strengthen the theoretical interpretation of our results in light of the concerns raised by Reviewers #1 and #2. We have further expanded the discussion linking our findings to previous work on VPL generalization, and addressed alternative explanations raised by Reviewers #1.

      Reviewer #1 (Public Review):

      Summary:

      Visual Perceptual Learning (VPL) results in varying degrees of generalization to tasks or stimuli not seen during training. The question of which stimulus or task features predict whether learning will transfer to a different perceptual task has long been central in the field of perceptual learning, with numerous theories proposed to address it. This paper introduces a novel framework for understanding generalization in VPL, focusing on the form invariants of the training stimulus. Contrary to a previously proposed theory that task difficulty predicts the extent of generalization - suggesting that more challenging tasks yield less transfer to other tasks or stimuli - this paper offers an alternative perspective. It introduces the concept of task invariants and investigates how the structural stability of these invariants affects VPL and its generalization. The study finds that tasks with high-stability invariants are learned more quickly. However, training with low-stability invariants leads to greater generalization to tasks with higher stability, but not the reverse. This indicates that, at least based on the experiments in this paper, an easier training task results in less generalization, challenging previous theories that focus on task difficulty (or precision). Instead, this paper posits that the structural stability of stimulus or task invariants is the key factor in explaining VPL generalization across different tasks

      Strengths:

      - The paper effectively demonstrates that the difficulty of a perceptual task does not necessarily correlate with its learning generalization to other tasks, challenging previous theories in the field of Visual Perceptual Learning. Instead, it proposes a significant and novel approach, suggesting that the form invariants of training stimuli are more reliable predictors of learning generalization. The results consistently bolster this theory, underlining the role of invariant stability in forecasting the extent of VPL generalization across different tasks.

      - The experiments conducted in the study are thoughtfully designed and provide robust support for the central claim about the significance of form invariants in VPL generalization.

      Weaknesses:

      - The paper assumes a considerable familiarity with the Erlangen program and the definitions of invariants and their structural stability, potentially alienating readers who are not versed in these concepts. This assumption may hinder the understanding of the paper's theoretical rationale and the selection of stimuli for the experiments, particularly for those unfamiliar with the Erlangen program's application in psychophysics. A brief introduction to these key concepts would greatly enhance the paper's accessibility. The justification for the chosen stimuli and the design of the three experiments could be more thoroughly articulated.

      We appreciate your feedback regarding the accessibility of our paper, particularly concerning the Erlangen Program and its associated concepts. We have revised the manuscript to include a more detailed introduction to Klein’s Erlangen Program in the second paragraph of Introduction section. It provides clear descriptions and illustrative examples for the three invariants within the Klein hierarchy of geometries, as well as the nested relationships among them (see revised Figure 1). We believe this addition will enhance the accessibility of the theoretical framework for readers who may not be familiar with these concepts.

      In the revised manuscript, we have also expanded the descriptions of the stimuli and experimental design for psychophysics experiments. These additions aim to clarify the rationale behind our choices, ensuring that readers can fully understand the connection between our theoretical framework and experimental approach.

      - The paper does not clearly articulate how its proposed theory can be integrated with existing observations in the field of VPL. While it acknowledges previous theories on VPL generalization, the paper falls short in explaining how its framework might apply to classical tasks and stimuli that have been widely used in the VPL literature, such as orientation or motion discrimination with Gabors, vernier acuity, etc. It also does not provide insight into the application of this framework to more naturalistic tasks or stimuli. If the stability of invariants is a key factor in predicting a task's generalization potential, the paper should elucidate how to define the stability of new stimuli or tasks. This issue ties back to the earlier mentioned weakness: namely, the absence of a clear explanation of the Erlangen program and its relevant concepts.

      We thank you for highlighting the necessary to integrate our proposed framework with existing observations in VPL research.

      Prior VPL studies have not concurrently examined multiple geometrical invariants with varying stability levels, making direct comparisons challenging. However, we have identified tasks from the literature that align with specific invariants. For example, orientation discrimination with Gabors (e.g., Dosher & Lu, 2005) and texture discrimination task (e.g., Wang et al., 2016) involve Euclidean invariants, and circle versus square discrimination (e.g., Kraft et al., 2010) involves affine invariants. On the other hand, our framework does not apply to studies using stimuli that are unrelated to geometric transformations, such as motion discrimination with Gabors or random dots, depth discrimination, vernier acuity, spatial frequency discrimination, contrast detection or discrimination.

      By focusing on geometrical properties of stimuli, our work addresses a gap in the field and introduces a novel approach to studying VPL through the lens of invariant extraction, echoing Gibson’s ecological approach to perceptual learning.

      In the revised manuscript, we have added a clearer explanation of Klein’s Erlangen Program, including the definition of geometrical invariants and their stability (the second paragraph in Introduction section). Additionally, we have expanded the Discussion section to draw more explicit comparisons between our results and previous studies on VPL generalization, highlighting both similarities and differences, as well as potential shared mechanisms.

      - The paper does not convincingly establish the necessity of its introduced concept of invariant stability for interpreting the presented data. For instance, consider an alternative explanation: performing in the collinearity task requires orientation invariance. Therefore, it's straightforward that learning the collinearity task doesn't aid in performing the other two tasks (parallelism and orientation), which do require orientation estimation. Interestingly, orientation invariance is more characteristic of higher visual areas, which, consistent with the Reverse Hierarchy Theory, are engaged more rapidly in learning compared to lower visual areas. This simpler explanation, grounded in established concepts of VPL and the tuning properties of neurons across the visual cortex, can account for the observed effects, at least in one scenario. This approach has previously been used/proposed to explain VPL generalization, as seen in (Chowdhury and DeAngelis, Neuron, 2008), (Liu and Pack, Neuron, 2017), and (Bakhtiari et al., JoV, 2020). The question then is: how does the concept of invariant stability provide additional insights beyond this simpler explanation?

      We appreciate your thoughtful alternative explanation. While this explanation accounts for why learning the collinearity task does not transfer to the orientation task—which requires orientation estimation—it does not explain why learning the collinearity task fails to transfer to the parallelism task, which requires orientation invariance rather than orientation estimation. Instead, the asymmetric transfer observed in our study could be perfectly explained by incorporating the framework of the Klein hierarchy of geometries.

      According to the Klein hierarchy, invariants with higher stability are more perceptually salient and detectable, and they are nested hierarchically, with higher-stability invariants encompassing lower-stability invariants (as clarified in the revised Introduction). In our invariant discrimination tasks, participants need only extract and utilize the most stable invariant to differentiate stimuli, optimizing their ability to discriminate that invariant while leaving the less stable invariants unoptimized.

      For example:

      • In the collinearity task, participants extract the most stable invariant, collinearity, to perform the task. Although the stimuli also contain differences in parallelism and orientation, these lower-stability invariants are not utilized or optimized during the task.

      • In the parallelism task, participants optimize their sensitivity to parallelism, the highest-stability invariant available in this task, while orientation, a lower-stability invariant, remains irrelevant and unoptimized.

      • In the orientation task, participants can only rely on differences in orientation to complete the task. Thus, the least stable invariant, orientation, is extracted and optimized.

      This hierarchical process explains why training on a higher-stability invariant (e.g., collinearity) does not transfer to tasks involving lower-stability invariants (e.g., parallelism or orientation). Conversely, tasks involving lower-stability invariants (e.g., orientation) can aid in tasks requiring higher-stability invariants, as these higher-stability invariants inherently encompass the lower ones, resulting in a low-to-high-stability transfer effect.

      This unique perspective underscores the importance of invariant stability in understanding generalization in VPL, complementing and extending existing theories such as the Reverse Hierarchy Theory. To help the reader understand our proposed theory, we revised the Introduction and Discussion section.

      - While the paper discusses the transfer of learning between tasks with varying levels of invariant stability, the mechanism of this transfer within each invariant condition remains unclear. A more detailed analysis would involve keeping the invariant's stability constant while altering a feature of the stimulus in the test condition. For example, in the VPL literature, one of the primary methods for testing generalization is examining transfer to a new stimulus location. The paper does not address the expected outcomes of location transfer in relation to the stability of the invariant. Moreover, in the affine and Euclidean conditions one could maintain consistent orientations for the distractors and targets during training, then switch them in the testing phase to assess transfer within the same level of invariant structural stability.

      We thank you for this good suggestion. Using one of the primary methods for test generalization, we performed a new psychophysics experiment to specifically examine how VPL generalizes to a new test location within a single invariant stability level (see Experiment 3 in the revised manuscript). The results show that the collinearity task exhibits greater location generalization compared to the parallelism task. This finding suggests the involvement of higher-order visual areas during high-stability invariant training, aligning with our theoretical framework based on the Reverse Hierarchy Theory (RHT). We attribute the unexpected location generalization observed in the orientation task to an additional requirement for spatial integration in its specific experimental design (as explained in the revised Results section “Location generalization within each invariant”). Moreover, based on previous VPL studies that have reported location specificity in orientation discrimination (Fiorentini and Berardi, 1980; Schoups et al., 1995; Shiu and Pashler, 1992), along with the substantial weight changes observed in lower layers of DNNs trained on the orientation task (Figure 9B, C), we infer that under a more controlled experimental design—such as the two-interval, two-alternative forced choice (2I2AFC) task employed in DNN simulations, where spatial integration is not required for any of the three invariants—the plasticity for orientation tasks would more likely occur in lower-order areas.

      In the revised manuscript, we have discussed how these findings, together with the observed asymmetric transfer across invariants and the distribution of learning across DNN layers, collectively reveal the neural mechanisms underlying VPL of geometrical invariants.

      - In the section detailing the modeling experiment using deep neural networks (DNN), the takeaway was unclear. While it was interesting to observe that the DNN exhibited a generalization pattern across conditions similar to that seen in the human experiments, the claim made in the abstract and introduction that the model provides a 'mechanistic' explanation for the phenomenon seems overstated. The pattern of weight changes across layers, as depicted in Figure 7, does not conclusively explain the observed variability in generalizations. Furthermore, the substantial weight change observed in the first two layers during the orientation discrimination task is somewhat counterintuitive. Given that neurons in early layers typically have smaller receptive fields and narrower tunings, one would expect this to result in less transfer, not more.

      We appreciate your suggestion regarding the clarity of DNN modeling. While the DNN employed in our study recapitulates several known behavioral and physiological VPL effects (Manenti et al., 2023; Wenliang and Seitz, 2018), we acknowledge that the claim in the abstract and introduction suggesting the model provides a ‘mechanistic’ explanation for the phenomenon may have been overstated. The DNN serves primarily as a tool to generate important predictions about the underlying neural substrates and provides a promising testbed for investigating learning-related plasticity in the visual hierarchy.

      In the revised manuscript, we have made significant improvements in explaining the weight change across DNN layers and its implication for understanding “when” and “where” learning occurs in the visual hierarchy. Specifically, in the Results ("Distribution of learning across layers") and Discussion sections, we have provided a more explicit explanation of the weight change across layers, emphasizing its implications for understanding the observed variability in generalizations and the underlying neural mechanisms.

      Regarding the substantial weight change observed in the first two layers during the orientation discrimination task, we interpret this as evidence that VPL of this least stable invariant relies more on the plasticity of lower-level brain areas, which may explain the poorer generalization performance to new locations or features observed in the previous literature (Fiorentini and Berardi, 1980; Schoups et al., 1995; Shiu and Pashler, 1992). However, this does not imply that learning effects of this least stable invariant cannot transfer to more stable invariants. From the perspective of Klein’s Erlangen program, the extraction of more stable invariants is implicitly required when processing less stable ones, which leads to their automatic learning. Additionally, within the framework of the Reverse Hierarchy Theory (RHT), plasticity in lower-level visual areas affects higher-level areas that receive the same low-level input, due to the feedforward anatomical hierarchy of the visual system (Ahissar and Hochstein, 2004, 1997; Markov et al., 2013; McGovern et al., 2012). Therefore, the improved signal from lower-level plasticity resulted from training on less stable invariants can enhance higher-level representations of more stable invariants, facilitating the transfer effect from low- to high-stability invariants.

      Reviewer #2 (Public Review):

      The strengths of this paper are clear: The authors are asking a novel question about geometric representation that would be relevant to a broad audience. Their question has a clear grounding in pre-existing mathematical concepts, that, to my knowledge, have been only minimally explored in cognitive science. Moreover, the data themselves are quite striking, such that my only concern would be that the data seem almost *too* clean. It is hard to know what to make of that, however. From one perspective, this is even more reason the results should be publicly available. Yet I am of the (perhaps unorthodox) opinion that reviewers should voice these gut reactions, even if it does not influence the evaluation otherwise. Below I offer some more concrete comments:

      (1) The justification for the designs is not well explained. The authors simply tell the audience in a single sentence that they test projective, affine, and Euclidean geometry. But despite my familiarity with these terms -- familiarity that many readers may not have -- I still had to pause for a very long time to make sense of how these considerations led to the stimuli that were created. I think the authors must, for a point that is so central to the paper, thoroughly explain exactly why the stimuli were designed the way that they were and how these designs map onto the theoretical constructs being tested.

      We thank you for reminding us to better justify our experimental designs. In response, we have provided a detailed introduction to Klein’s Erlangen Program, describing projective, affine, and Euclidean geometries, their associated invariants, and the hierarchical relationships among them (see revised Introduction and Figure 1).

      All experiments in our study employed stimuli with varying structural stability (collinearity, parallelism, orientation, see revised Figure 2, 4), enabling us to investigate the impact of invariant stability on visual perceptual learning. Experiment 1 was adapted from paradigms studying the "configural superiority effect," commonly used to assess the salience of geometric invariants. This paradigm was chosen to align with and build upon related research, thereby enhancing comparability across studies. To address the limitations of Experiment 1 (as detailed in our Results section), Experiments 2, 3, and 4 employed a 2AFC (two-alternative forced choice)-like paradigm, which is more common in visual perceptual learning research. Additionally, we have expanded descriptions of our stimuli and designs. aiming to ensure clarity and accessibility for all readers.

      (2) I wondered if the design in Experiment 1 was flawed in one small but critical way. The goal of the parallelism stimuli, I gathered, was to have a set of items that is not parallel to the other set of items. But in doing that, isn't the manipulation effectively the same as the manipulation in the orientation stimuli? Both functionally involve just rotating one set by a fixed amount. (Note: This does not seem to be a problem in Experiment 2, in which the conditions are more clearly delineated.)

      We appreciate your insightful observation regarding the design of Experiment 1 and the potential similarity between the manipulations of the parallelism and orientation stimuli.

      The parallelism and orientation stimuli in Experiment 1 were originally introduced by Olson and Attneave (1970) to support line-based models of shape coding and were later adapted by Chen (1986) to measure the relative salience of different geometric properties. In the parallelism stimuli, the odd quadrant differs from the others in line slope, while in the orientation stimuli, the odd quadrant contains identical line segments but differs in the direction pointed by their angles. The faster detection of the odd quadrant in the parallelism stimuli compared to the orientation stimuli has traditionally been interpreted as evidence supporting line-based models of shape coding. However, as Chen (1986, 2005) proposed, the concept of invariants over transformations offers a different interpretation: in the parallelism stimuli, the fact that line segments share the same slope essentially implies that they are parallel, and the discrimination may be actually based on parallelism. This reinterpretation suggests that the superior performance with parallelism stimuli reflects the relative perceptual salience of parallelism (an affine invariant property) compared to the orientation of angles (a Euclidean invariant property).

      In the collinearity and orientation tasks, the odd quadrant and the other quadrants differ in their corresponding geometries, such as being collinear versus non-collinear. However, in the parallelism task, participants could rely either on the non-parallel relationship between the odd quadrant and the other quadrants or on the difference in line slope to complete the task, which can be seen as effectively similar to the manipulation in the orientation stimuli, as you pointed out. Nonetheless, this set of stimuli and the associated paradigm have been used in prior studies to address questions about Klein’s hierarchy of geometries (Chen, 2005; Wang et al., 2007; Meng et al., 2019). Given its historical significance and the importance of ensuring comparability with previous research, we adopted this set of stimuli despite its imperfections. Other limitations of this paradigm are discussed in the Results section (“The paradigm of ‘configural superiority effects’ with reaction time measures”), and optimized experimental designs were implemented in Experiment 2, 3, and 4 to produce more reliable results.

      (3) I wondered if the results would hold up for stimuli that were more diverse. It seems that a determined experimenter could easily design an "adversarial" version of these experiments for which the results would be unlikely to replicate. For instance: In the orientation group in Experiment 1, what if the odd-one-out was rotated 90 degrees instead of 180 degrees? Intuitively, it seems like this trial type would now be much easier, and the pattern observed here would not hold up. If it did hold up, that would provide stronger support for the authors' theory.

      It is not enough, in my opinion, to simply have some confirmatory evidence of this theory. One would have to have thoroughly tested many possible ways that theory could fail. I'm unsure that enough has been done here to convince me that these ideas would hold up across a more diverse set of stimuli.

      Thanks for your nice suggestion to validate our results using more diverse stimuli. However, the limitations of Experiment 1 make it less suitable for rigorous testing of diverse or "adversarial" stimuli. In addition to the limitation discussed in response to (2), another issue is that participants may rely on grouping effects among shapes in the quadrants, rather than solely extracting the geometrical invariants that are the focus of our study. As a result, the reaction times measured in this paradigm may not exclusively reflect the extraction time of geometrical invariants but could also be influenced by these grouping effects.

      Therefore, we have shifted our focus to the improved design used in Experiment 2 to provide stronger evidence for our theory. Building on this more robust design, we have extended our investigations to study location generalization (revised Experiment 3) and long-term learning effects (revised Figure 6—figure supplement 2). These enhancements allow us to provide stronger evidence for our theory while addressing potential confounds present in Experiment 1.

      While we did not explicitly test the 90-degree rotation scenario in Experiment 1, future studies could employ more diverse set of stimuli within the Experiment 2 framework to better understand the limits and applicability of our theoretical predictions. We appreciate this suggestion, as it offers a valuable direction for further research.

      Reviewer #1 (Recommendations For The Authors):

      Major comments:

      - A concise introduction to the Erlangen program, geometric invariants, and their structural stability would greatly enhance the paper. This would not only clarify these concepts for readers unfamiliar with them but also provide a more intuitive explanation for the choice of tasks and stimuli used in the study.

      - I recommend adding a section that discusses how this new framework aligns with previous observations in VPL, especially those involving more classical stimuli like Gabors, random dot kinematograms, etc. This would help in contextualizing the framework within the broader spectrum of VPL research.

      - Exploring how each level of invariant stability transfers within itself would be an intriguing addition. Previous theories often consider transfer within a condition. For instance, in an orientation discrimination task, a challenging training condition might transfer less to a new stimulus test location (e.g., a different visual quadrant). Applying a similar approach to examine how VPL generalizes to a new test location within a single invariant stability level could provide insightful contrasts between the proposed theory and existing ones. This would be particularly relevant in the context of Experiment 2, which could be adapted for such a test.

      - I suggest including some example learning curves from the human experiment for a more clear demonstration of the differences in the learning rates across conditions. Easier conditions are expected to be learned faster (i.e. plateau faster to a higher accuracy level). The learning speed is reported for the DNN but not for the human subjects.

      - In the modeling section, it would be beneficial to focus on offering an explanation for the observed generalization as a function of the stability of the invariants. As it stands, the neural network model primarily demonstrates that DNNs replicate the same generalization pattern observed in human experiments. While this finding is indeed interesting, the model currently falls short of providing deeper insights or explanations. A more detailed analysis of how the DNN model contributes to our understanding of the relationship between invariant stability and generalization would significantly enhance this section of the paper.

      Minor comments:

      - Line 46: "it is remains" --> "it remains"

      - Larger font sizes for the vertical axis in Figure 6B would be helpful.

      We thank your detailed and constructive comments, which have significantly helped us improve the clarity and rigor of our manuscript. Below, we provide a response to each point raised.

      Major Comments

      (1) A concise introduction to the Erlangen program, geometric invariants, and their structural stability:

      We appreciate your suggestion to provide a clearer introduction to these foundational concepts. In the revised manuscript, we have added a dedicated section in the Introduction that offers a concise explanation of Klein’s Erlangen Program, including the concept of geometric invariants and their structural stability. This addition aims to make the theoretical framework more accessible to readers unfamiliar with these concepts and to better justify the choice of tasks and stimuli used in the study.

      (2) Contextualizing the framework within the broader spectrum of VPL research:

      We have expanded the Discussion section to better integrate our framework with previous VPL studies that reported generalization, including those using classical stimuli such as Gabors (Dosher and Lu, 2005; Hung and Seitz, 2014; Jeter et al., 2009; Liu and Pack, 2017; Manenti et al., 2023) and random dot kinematograms (Chang et al., 2013; Chen et al., 2016; Huang et al., 2007; Liu and Pack, 2017). In particular, we now discuss the similarities and differences between our findings and these earlier studies, exploring potential shared mechanisms underlying VPL generalization across different types of stimuli. These additions aim to contextualize our framework within the broader field of VPL research and highlight its relevance to existing literature.

      (3) Exploring transfer within each invariant stability level:

      In response to this insightful suggestion, we have added a new psychophysics experiment in the revised manuscript (Experiment 3) to examine how VPL generalizes to a new test location within the same invariant stability level. This experiment provides an opportunity to further explore the neural substrates underlying VPL of geometrical invariants, offering a contrast to existing theories and strengthening the connection between our framework and location generalization findings in the VPL literature.

      (4) Including example learning curves from the human experiments:

      We appreciate your suggestion to include learning curves for human subjects. In the revised manuscript, we have added learning curves of long-term VPL (see revised Figure 6—figure supplement 2) to track the temporal learning processes across invariant conditions. Interestingly, and in contrast to the results reported in the DNN simulations, these curves show that less stable invariants are learned faster and exhibit greater magnitudes of learning. We interpret this discrepancy as a result of differences in initial performance levels between humans and DNNs, as discussed in the revised Discussion section.

      (5) Offering a deeper explanation of the DNN model's findings:

      We acknowledge your concern that the modeling section primarily demonstrates that DNNs replicate human generalization patterns without offering deeper mechanistic insights. To address this, we have expanded the Results and Discussion sections to more explicitly interpret the weight change patterns observed across DNN layers in relation to invariant stability and generalization. We discuss how the model contributes to understanding the observed generalization within and across invariants with different stability, focusing on the neural network's role in generating predictions about the neural mechanisms underlying these effects.

      Minor Comments

      (1) Line 46: Correction of “it is remains” to “it remains”:

      We have corrected this typo in the revised manuscript.

      (2) Vertical axis font size in Figure 6B:

      We have increased the font size of the vertical axis labels in revised Figure 8B for improved readability.

      Reviewer #2 (Recommendations For The Authors):

      (1) There are many details throughout the paper that are confusing, such as the caption for Figure 4, which does not appear to correspond to what is shown (and is perhaps a copy-paste of the caption for Experiment 1?). Similarly, I wasn't sure about many methodological details, like: How participants made their second response in Experiment 2? It says somewhere that they pressed the corresponding key to indicate which one was the target, but I didn't see anything explaining what that meant. Also, I couldn't tell if the items in the figures were representative of all trials; the stimuli were described minimally in the paper.

      (2) The language in the paper felt slightly off at times, in minor but noticeable ways. Consider the abstract. The word "could" in the first sentence is confusing, and, more generally, that first sentence is actually quite vague (i.e., it just states something that would appear to be true of any perceptual system). In the following sentence, I wasn't sure what was meant by "prior to be perceived in the visual system". Though I was able to discern what the authors were intending to say most times, I was required to "read between the lines" a bit. This is not to fault the authors. But these issues need to be addressed, I think.

      (1) We sincerely apologize for the oversight regarding the caption for (original) Figure 4, and thank you for pointing out this error. In the revised manuscript, we have corrected the caption for Figure 4 (revised Figure 5) and ensured it accurately describes the content of the figure. Additionally, we have strengthened the descriptions of the stimuli and tasks in both the Materials and Methods section and the captions for (revised) Figures 4 and 5 to provide a clearer and more comprehensive explanation of Experiment 2. These revisions aim to help readers fully understand the experimental design and methodology.

      (2) We appreciate your feedback regarding the clarity and precision of the language in the manuscript. We acknowledge that some expressions, particularly in the abstract, were unclear or imprecise. In the revised manuscript, we have rewritten the abstract to improve clarity and ensure that the statements are concise and accurately convey our intended meaning. Additionally, we have thoroughly reviewed the entire manuscript to address any other instances of ambiguous language, aiming to eliminate the need for readers to "read between the lines." We are grateful for your suggestions, which have helped us enhance the overall readability of the paper.

    1. Reviewer #1 (Public review):

      Summary:

      This study examined the changes in ATL GABA levels induced by cTBS and its relationship with BOLD signal changes and performance in a semantic task. The findings suggest that the increase in ATL GABA levels induced by cTBS is associated with a decrease in BOLD signal. The relationship between ATL GABA levels and semantic task performance is nonlinear, and more specifically, the authors propose that the relationship is an inverted U-shaped relationship.

      Strengths:

      The findings of the research regarding the increase of GABA and decrease of BOLD caused by cTBS, as well as the correlation between the two, appear to be reliable. This should be valuable for understanding the biological effects of cTBS.

      Weakness:

      I am pleased to see the authors' feedback on my previous questions and suggestions, and I believe the additional data analysis they have added is helpful. Here are my reserved concerns and newly discovered issues.

      (1) Regarding the Inverted U-Shaped Curve In the revised manuscript, the authors have accepted some of my suggestions and conducted further analysis, which is now presented in Figure 3B. These results provide partial support for the authors' hypothesis. However, I still believe that the data from this study hardly convincingly support an inverted U-shaped distribution relationship.<br /> The authors stated in their response, "it is challenging to determine the optimal level of ATL GABA," but I think this is achievable. From Figures 4C and 4D, the ATL GABA levels corresponding to the peak of the inverted U-shaped curve fall between 85 and 90. In my understanding, this can be considered as the optimal level of ATL GABA estimated based on the existing data and the inverted U-shaped curve relationship. However, in the latter half of the inverted U-shaped curve, there are quite few data points, and such a small number of data points hardly provides reliable support for the quantitative relationship in the latter half of the curve. I suggest that the authors should at least explicitly acknowledge this and be cautious in drawing conclusions. I also suggest that the authors consider fitting the data with more types of non-linear relationships, such as a ceiling effect (a combination of a slope and a horizontal line), or a logarithmic curve.

      (2) In Figure 2F, the authors demonstrated a strong practice effect in this study, which to some extent offsets the decrease in behavioral performance caused by cTBS. Therefore, I recommend that the authors give sufficient consideration to the practice effect in the data analysis.<br /> One issue is the impact of the practice effect on the classification of responders and non-responders. Currently, most participants are classified as non-responders, suggesting that the majority of the population may not respond to the cTBS used in this study. This greatly challenges the generalizability of the experimental conclusions. However, the emergence of so many non-responders is likely due to the prominent practice effect, which offsets part of the experimental effect. If the practice effect is excluded, the number of responders may increase. The authors might estimate the practice effect based on the vertex simulation condition and reclassify participants after excluding the influence of the practice effect.<br /> Another issue is that considering the significant practice effect, the analysis in Figure 4D, which mixes pre- and post-test data, may not be reliable.

      (3) The analysis in Figure 3A has a double dipping issue. Suppose we generate 100 pairs of random numbers as pre- and post-test scores, and then group the data based on whether the scores decrease or increase; the pre-test scores of the group with decreased scores will have a very high probability of being higher than those of the group with increased scores. Therefore, the findings in Figure 3A seem to be meaningless.

      (4) The authors use IE as a behavioral measure in some analyses and use accuracy in others. I recommend that the authors adopt a consistent behavioral measure.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1(Public review):

      Strengths:

      Utilization of both human placental samples and multiple mouse models to explore the mechanisms linking inflammatory macrophages and T cells to preeclampsia (PE).<br /> Incorporation of advanced techniques such as CyTOF, scRNA-seq, bulk RNA-seq, and flow cytometry.

      Identification of specific immune cell populations and their roles in PE, including the IGF1-IGF1R ligand-receptor pair in macrophage-mediated Th17 cell differentiation.<br /> Demonstration of the adverse effects of pro-inflammatory macrophages and T cells on pregnancy outcomes through transfer experiments.

      Weaknesses:

      Comment 1. Inconsistent use of uterine and placental cells, which are distinct tissues with different macrophage populations, potentially confounding results.

      Response1: We thank the reviewers' comments. We have done the green fluorescent protein (GFP) pregnant mice-related animal experiment, which was not shown in this manuscript. The wild-type (WT) female mice were mated with either transgenic male mice, genetically modified to express GFP, or with WT male mice, in order to generate either GFP-expressing pups (GFP-pups) or their genetically unmodified counterparts (WT-pups), respectively. Mice were euthanized on day 18.5 of gestation, and the uteri of the pregnant females and the placentas of the offspring were analyzed using flow cytometry. The majority of macrophages in the uterus and placenta are of maternal origin, which was defined by GFP negative. In contrast, fetal-derived macrophages, distinguished by their expression of GFP, represent a mere fraction of the total macrophage population. We have added the GFP pregnant mice-related data in uterine and placental cells (Line204-212).

      Comment 2. Missing observational data for the initial experiment transferring RUPP-derived macrophages to normal pregnant mice.

      Response 2: We thank the reviewers' comments. We have added the observational data (Figure 4-figure supplement 1D, 1E) and a corresponding description of the data (Line 198-203).

      Comment 3. Unclear mechanisms of anti-macrophage compounds and their effects on placental/fetal macrophages.

      Response 3: We thank the reviewers' comments. PLX3397, the inhibitor of CSF1R, which is needed for macrophage development (Nature. 2023, PMID: 36890231; Cell Mol Immunol. 2022, PMID: 36220994), we have stated that on Line 227-230. However, PLX3397 is a small molecule compound that possesses the potential to cross the placental barrier and affect fetal macrophages. We have discussed the impact of this factor on the experiment in the Discussion section (Line457-459).

      Comment 4. Difficulty in distinguishing donor cells from recipient cells in murine single-cell data complicates interpretation.

      Response 4: We thank the reviewers' comments. Upon analysis, we observed a notable elevation in the frequency of total macrophages within the CD45<sup>+</sup> cell population. Then we subsequently performed macrophage clustering and uncovered a marked increase in the frequency of Cluster 0, implying a potential correlation between Cluster 0 and donor-derived cells. RNA sequencing revealed that the F480<sup>+</sup>CD206<sup>-</sup> pro-inflammatory donor macrophages exhibited a Folr2<sup>+</sup>Ccl7<sup>+</sup>Ccl8<sup>+</sup>C1qa<sup>+</sup>C1qb<sup>+</sup>C1qc<sup>+</sup> phenotype, which is consistent with the phenotype of cluster 0 in macrophages observed in single-cell RNA sequencing (Figure 4D and Figure 5E). Therefore, we believe that the donor cells should be cluster 0 in macrophages.

      Comment 5. Limitation of using the LPS model in the final experiments, as it more closely resembles systemic inflammation seen in endotoxemia rather than the specific pathology of PE.

      Response 5: We thank the reviewers' comments. Firstly, our other animal experiments in this manuscript used the Reduction in Uterine Perfusion Pressure (RUPP) mouse model to simulate the pathology of PE. However, the RUPP model requires ligation of the uterine arteries in pregnant mice on day 12.5 of gestation, which hinders T cells returning from the tail vein from reaching the maternal-fetal interface. In addition, this experiment aims to prove that CD4<sup>+</sup> T cells are differentiated into memory-like Th17 cells through IGF-1R receptor signaling to affect pregnancy by clearing CD4<sup>+</sup> T cells in vivo with an anti-CD4 antibody followed by injecting IGF-1R inhibitor-treated CD4<sup>+</sup> T cells. And we proved that injection of RUPP-derived memory-like CD4<sup>+</sup> T cells into pregnant mice induces PE-like symptoms (Figure 6F-6H). In summary, the application of the LPS model in the final experiments does not affect the conclusions.

      Reviewer #2 (Public review):

      Strengths:

      (1) This study combines human and mouse analyses and allows for some amount of mechanistic insight into the role of pro-inflammatory and anti-inflammatory macrophages in the pathogenesis of pre-eclampsia (PE), and their interaction with Th17 cells.

      (2) Importantly, they do this using matched cohorts across normal pregnancy and common PE comorbidities like gestation diabetes (GDM).

      (3) The authors have developed clear translational opportunities from these "big data" studies by moving to pursue potential IGF1-based interventions.

      Weaknesses:

      (1) Clearly the authors generated vast amounts of multi-omic data using CyTOF and single-cell RNA-seq (scRNA-seq), but their central message becomes muddled very quickly. The reader has to do a lot of work to follow the authors' multiple lines of inquiry rather than smoothly following along with their unified rationale. The title description tells fairly little about the substance of the study. The manuscript is very challenging to follow. The paper would benefit from substantial reorganizations and editing for grammatical and spelling errors. For example, RUPP is introduced in Figure 4 but in the text not defined or even talked about what it is until Figure 6. (The figure comparing pro- and anti-inflammatory macrophages does not add much to the manuscript as this is an expected finding).

      Response 1: We thank the reviewers' comments. According to the reviewer's suggestion, we have made the necessary revisions. Firstly, the title of the article has been modified to be more specific. We also introduce the RUPP mouse model when interpreted Figure 4-figure supplement 1. Thirdly, We have moved the images of Figure 7 to the Figure 6-figure supplement 2 make them easier to follow. Finally, we diligently corrected the grammatical and spelling errors in the article. As for the figure comparing pro- and anti-inflammatory macrophages, the Editor requested a more comprehensive description of the macrophage phenotype during the initial submission. As a result, we conducted the transcriptome RNA-seq of both uterine-derived pro-inflammatory and anti-inflammatory macrophages and conducted a detailed analysis of macrophages in scRNA-seq.

      Comment 2. The methods lack critical detail about how human placenta samples were processed. The maternal-fetal interface is a highly heterogeneous tissue environment and care must be taken to ensure proper focus on maternal or fetal cells of origin. Lacking this detail in the present manuscript, there are many unanswered questions about the nature of the immune cells analyzed. It is impossible to figure out which part of the placental unit is analyzed for the human or mouse data. Is this the decidua, the placental villi, or the fetal membranes? This is of key importance to the central findings of the manuscript as the immune makeup of these compartments is very different. Or is this analyzed as the entirety of the placenta, which would be a mix of these compartments and significantly less exciting?

      Response 2: We thank the reviewers' comments. Placental villi rather than fetal membranes and decidua were used for CyToF in this study. This detail about how human placenta samples were processed have been added to the Materials and Methods section (Line564-576).

      Comment 3. Similarly, methods lack any detail about the analysis of the CyTOF and scRNAseq data, much more detail needs to be added here. How were these clustered, what was the QC for scRNAseq data, etc? The two small paragraphs lack any detail.

      Response 3: We thank the reviewers' comments. The details about the analysis of the CyTOF (Line577-586) and scRNAseq (Line600-615) data have been added in the Materials and Methods section.

      Comment 4. There is also insufficient detail presented about the quantities or proportions of various cell populations. For example, gdT cells represent very small proportions of the CyTOF plots shown in Figures 1B, 1C, & 1E, yet in Figures 2I, 2K, & 2K there are many gdT cells shown in subcluster analysis without a description of how many cells are actually represented, and where they came from. How were biological replicates normalized for fair statistical comparison between groups?

      Response 4: We thank the reviewers' comments. In our study, approximately 8×10^<sup>5</sup> cells were collected per group for analysis using CyTOF. Of these, about 10% (8×10^<sup>4</sup> cells per group) were utilized to generate Figure 1B. As depicted in Figure 1B, gdT cells constitute roughly 1% of each group, with specific percentages as follows: NP group (1.23%), PE group (0.97%), GDM group (0.94%), and GDM&PE group (1.26%), which equates to approximately 800 cells per group. For the subsequent gdT cell analysis presented in Figure 2I, we employed data from all cells within each group to construct the tSNE maps, comprising approximately 8000 cells per group. Consequently, it may initially appear that the number of gdT cells is significantly higher than what is shown in Figure 1B. To clarify this, we have included pertinent explanations in the figure legend. Given the relatively low proportions of gdT cells, we did not pursue further investigations of these cells in subsequent experiments. Following your suggestion, we have relocated this result to the supplementary materials, where it is now presented as Figure 2-figure supplement 1D-E.

      The number of biological replicates (samples) is consistent with Figure 1, and this information has been added to the figure legend.

      Comment 5. The figures themselves are very tricky to follow. The clusters are numbered rather than identified by what the authors think they are, the numbers are so small, that they are challenging to read. The paper would be significantly improved if the clusters were clearly labeled and identified. All the heatmaps and the abundance of clusters should be in separate supplementary figures.

      Response 5: We thank the reviewers' comments. Based on your suggestions, we have labeled and defined the Clusters (Figure 2A, 2F, Figure 3A, Figure 5C and Figure 6A). Additionally, we have moved most of the heatmaps to the supplementary materials.

      Comment 6. The authors should take additional care when constructing figures that their biological replicates (and all replicates) are accurately represented. Figure 2H-2K shows N=10 data points for the normal pregnant (NP) samples when clearly their Table 1 and test denote they only studied N=9 normal subjects.

      Response 6: We thank the reviewers' careful checking. During our verification, we found that one sample in the NP group had pregnancy complications other than PE and GDM. The data in Figure 2H-2K was not updated in a timely manner. We have promptly updated this data and reanalyze it.

      Comment 7. There is little to no evaluation of regulatory T cells (Tregs) which are well known to undergird maternal tolerance of the fetus, and which are well known to have overlapping developmental trajectory with RORgt+ Th17 cells. We recommend the authors evaluate whether the loss of Treg function, quantity, or quality leaves CD4+ effector T cells more unrestrained in their effect on PE phenotypes. References should include, accordingly: PMCID: PMC6448013 / DOI: 10.3389/fimmu.2019.00478; PMC4700932 / DOI: 10.1126/science.aaa9420.

      Response 7: We thank the reviewers' comments. We have done the Treg-related animal experiment, which was not shown in this manuscript. We have added the Treg-related data in Figure 6F-6H. The injection of CD4<sup>+</sup>CD44<sup>+</sup> T cells derived from RUPP mouse, characterized by a reduced frequency of Tregs, could induce PE-like symptoms in pregnant mice (Line297-304). Additionally, we have added a necessary discussion about Tregs and cited the literature you mentioned (Line433-439).

      Comment 8. In discussing gMDSCs in Figure 3, the authors have missed key opportunities to evaluate bona fide Neutrophils. We recommend they conduct FACS or CyTOF staining including CD66b if they have additional tissues or cells available. Please refer to this helpful review article that highlights key points of distinguishing human MDSC from neutrophils: https://doi.org/10.1038/s41577-024-01062-0. This will both help the evaluation of potentially regulatory myeloid cells that may suppress effector T cells as well as aid in understanding at the end of the study if IL-17 produced by CD4+ Th17 cells might recruit neutrophils to the placenta and cause ROS immunopathology and fetal resorption.

      Response 8: We thank the reviewers' comments. Although we do not have additional tissues or cells available to conduct FACS or CyTOF staining, including for CD66b, we have utilized CD15 and CD66b antibodies for immunofluorescence stain of placental tissue, and our findings revealed a pronounced increase in the proportion of neutrophils among PE patients, fostering the hypothesis that IL-17A produced by Th17 cells might orchestrate the migration of neutrophils towards the placental milieu (Figure 6-figure supplement 2F; Line 325-328). We have cited these references and discussed them in the Discussion section (Line 459-465).

      Comment 9. Depletion of macrophages using several different methodologies (PLX3397, or clodronate liposomes) should be accompanied by supplementary data showing the efficiency of depletion, especially within tissue compartments of interest (uterine horns, placenta). The clodronate piece is not at all discussed in the main text. Both should be addressed in much more detail.

      Response 9: We thank the reviewers' comments. We already have the additional data on the efficiency of macrophage depletion involving PLX3397 and clodronate liposomes, which were not present in this manuscript, and we'll add it to the Figure 4-figure supplement 2A,2B. The clodronate piece is mentioned in the main text (Line236-239), but only briefly described, because the results using clodronate we obtained were similar to those using PLX3397.

      Comment 10. There are many heatmaps and tSNE / UMAP plots with unhelpful labels and no statistical tests applied. Many of these plots (e.g. Figure 7) could be moved to supplemental figures or pared down and combined with existing main figures to help the authors streamline and unify their message.

      Response 10: We thank the reviewers' comments. We have moved the images of Figure 7 to the Figure 6-figure supplement 2. We also have moved most of the heatmaps to the supplementary materials.

      Comment 11. There are claims that this study fills a gap that "only one report has provided an overall analysis of immune cells in the human placental villi in the presence and absence of spontaneous labor at term by scRNA-seq (Miller 2022)" (lines 362-364), yet this study itself does not exhaustively study all immune cell subsets...that's a monumental task, even with the two multi-omic methods used in this paper. There are several other datasets that have performed similar analyses and should be referenced.

      Response 11: We thank the reviewers' comments. We have search for more literature and reference additional studies that have conducted similar analyses (Line382-393).

      Comment 12. Inappropriate statistical tests are used in many of the analyses. Figures 1-2 use the Shapiro-Wilk test, which is a test of "goodness of fit", to compare unpaired groups. A Kruskal-Wallis or other nonparametric t-test is much more appropriate. In other instances, there is no mention of statistical tests (Figures 6-7) at all. Appropriate tests should be added throughout.

      Response 12: We thank the reviewers' comments. As stated in the Statistical Analysis section (lines 672-676), the Kruskal-Wallis test was used to compare the results of experiments with multiple groups. Comparisons between the two groups in Figures 5 were conducted using Student's t-test. The aforementioned statistical methods have been included in the figure legends.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Overall, the study has several strengths, including the use of human samples and animal models, as well as the incorporation of multiple cutting-edge techniques. However, there are some significant issues with the murine model experiments that need to be addressed:

      Comment 1. The authors are not consistent in their use of or focus on uterine and placental cells. These are distinct tissues, and numerous prior reports have indicated differences in the macrophage populations of these tissues, due in part to the predominantly maternal origin of macrophages in the uterus and the largely fetal origin of those in the placenta. The rationale for switching between uterine and placental cells in different experiments is not clear, and the inclusion of cells from both (such as in the bulk RNAseq experiments) could be potentially confounding.

      Response 1: We thank the reviewers' comments. We have done the green fluorescent protein (GFP) pregnant mice-related animal experiment, which was not shown in this manuscript. The wild-type (WT) female mice were mated with either transgenic male mice, genetically modified to express GFP, or with WT male mice, in order to generate either GFP-expressing pups (GFP-pups) or their genetically unmodified counterparts (WT-pups), respectively. Mice were euthanized on day 18.5 of gestation, and the uteri of the pregnant females and the placentas of the offspring were analyzed using flow cytometry. The majority of macrophages in the uterus and placenta are of maternal origin, which was defined by GFP negative. In contrast, fetal-derived macrophages, distinguished by their expression of GFP, represent a mere fraction of the total macrophage population, signifying their inconsequential or restricted presence amidst the broader cellular landscape. We have added the GPF pregnant mice-related data in Figure 4-figure supplement 1D-1E to explain the different macrophage populations in the uterine and placental cells.

      Comment 2. The observational data for the initial experiment transferring RUPP-derived macrophages to normal pregnant mice (without any other manipulations) seems to be missing. They do not seem to be presented in Figure 4 where they are expected based on the results text.

      Response 2: We thank the reviewers' comments. We thank the reviewers' comments. We have added the observational data (Figure 4-figure supplement 1D, 1E) and a corresponding description of the data (Line 198-203).

      Comment 3. The action of the anti-macrophage compounds is not well explained, nor are their mechanisms validated as affecting or not affecting the placental/fetal macrophage populations. It is important to clarify whether the macrophages are depleted or merely inhibited by these treatments, and it is absolutely critical to determine whether these treatments are affecting placental/fetal macrophage populations (the latter indicative of placental transfer), given the focus on placental macrophages.

      Response 3: We thank the reviewers' comments. PLX3397, the inhibitor of CSF1R, which is needed for macrophage development (Nature. 2023, PMID: 36890231; Cell Mol Immunol. 2022, PMID: 36220994), we have stated that on Line227-230. However, PLX3397 is a small molecule compound that possesses the potential to cross the placental barrier and affect fetal macrophages. We will discuss the impact of this factor on the experiment in the Discussion section (Line457-459).

      Comment 4. The interpretation of the murine single-cell data is hampered by the lack of means for distinguishing donor cells from recipient cells, which is important when seeking to identify the influence of the donor cells.

      Response 4: We thank the reviewers' comments. Upon analysis, we observed a notable elevation in the frequency of total macrophages within the CD45<sup>+</sup> cell population. Then we subsequently per formed macrophage clustering and uncovered a marked increase in the frequency of Cluster 0, implying a potential correlation between Cluster 0 and donor-derived cells. RNA sequencing revealed that the F480<sup>+</sup>CD206<sup>-</sup> pro-inflammatory donor macrophages exhibited a Folr2<sup>+</sup>Ccl7<sup>+</sup>Ccl8<sup>+</sup>C1qa<sup>+</sup>C1qb<sup>+</sup>C1qc<sup>+</sup> phenotype, which is consistent with the phenotype of cluster 0 in macrophages observed in single-cell RNA sequencing (Figure 4D and Figure 5E). Therefore, the donor cells should be in cluster 0 in macrophages.

      Comment 5. The switch to the LPS model in the final experiments is a limitation, as this model more closely resembles the systemic inflammation seen in endotoxemia rather than the specific pathology of preeclampsia (PE). While this is not an exhaustive list, the number of weaknesses in the experimental design makes it difficult to evaluate the findings comprehensively.

      Response 5: We thank the reviewers' comments. Firstly, our other animal experiments in this manuscript used the RUPP mouse model to simulate the pathology of PE. However, the RUPP model requires ligation of the uterine arteries in pregnant mice on day 12.5 of gestation, which hinders T cells returning from the tail vein from reaching the maternal-fetal interface. In addition, this experiment aims to prove that CD4<sup>+</sup> T cells are differentiated into memory-like Th17 cells through IGF-1R receptor signaling to affect pregnancy by clearing CD4<sup>+</sup> T cells in vivo with an anti-CD4 antibody followed by injecting IGF-1R inhibitor-treated CD4<sup>+</sup> T cells. We proved that injection of RUPP-derived memory-like CD4<sup>+</sup> T cells into pregnant rats induces PE-like symptoms (Figure 6F-6H). In summary, applying the LPS model in the final experiments does not affect the conclusions.

      Minor comments:

      Comment 1. Introduction, Lines 67-74: The phrasing here is unclear as to the roles that each mentioned immune cell subset is playing in preeclampsia. Given the statement "Elevated levels of maternal inflammation...", does this imply that the numbers of all mentioned immune cell subsets are increased in the maternal circulation? If not, please consider rewording this.

      Response 1: We thank the reviewers' comments. We have revised the manuscript as follows: Currently, the pivotal mechanism underpinning the pathogenesis of preeclampsia is widely acknowledged to involve an increased frequency of pro-inflammatory M1-like maternal macrophages, along with an elevation in Granulocytes capable of superoxide generation, CD56<sup>+</sup> CD94<sup>+</sup> natural killer (NK) cells, CD19<sup>+</sup>CD5<sup>+</sup> B1 lymphocytes, and activated γδ T cells. Conversely, this pathological process is accompanied by a notable decrease in the frequency of anti-inflammatory M2-like macrophages and NKp46<sup>+</sup> NK cells (Line67-77).

      Comment 2. Introduction, Lines 67-80: Is the involvement of the described immune cell subsets largely ubiquitous to preeclampsia? Recent multi-omic studies suggest that preeclampsia is a heterogeneous condition with different subsets, some more biased towards systemic immune activation than others. Thus, it is important to clarify whether the involvement of specific immune subsets is generally observed or more specific.

      Response 2: We thank the reviewers' comments. We have added a new paragraph as follows: Moreover, as PE can be subdivided into early- and late-onset PE diagnosed before 34 weeks or from 34 weeks of gestation, respectively. Research has revealed that among the myriad of cellular alterations in PE, pro-inflammatory M1-like macrophages and intrauterine B1 cells display an augmented presence at the maternal-fetal interface of both early-onset and late-onset PE patients. Decidual natural killer (dNK) cells and neutrophils emerge as paramount contributors, playing a more crucial role in the pathogenesis of early-onset PE than late-onset PE (Front Immunol. 2020. PMID: 33013837) (Line83-89).

      Comment 3. Introduction, Lines 81-86: The point of this short paragraph is not clear; the authors mention two very specific cellular interactions without explaining why.

      Response 3: In the previous paragraph, we uncovered a heightened inflammatory response among multiple immune cells in patients with PE, yet the intricate interplay between these individual immune cells has been seldom elucidated in the context of PE patient. This is precisely why we delve into the realm of specific immune cellular interactions in relation to other pregnancy complications in this paragraph (Line91-98).

      Comment 4. Methods: What placental tissues (e.g., villous tree, chorionic plate, extraplacental membranes) were included for CyTOF analysis? Was any decidual tissue (e.g., basal plate) included? Please clarify.

      Response 4: Placental villi rather than chorionic plate and extraplacental membranes were used for CyToF in this study. The relevant content has been incorporated into the "Materials and Methods" section (Line564-576).

      Comment 5. Results, Table 1: The authors should clarify that all PE samples were not full term (i.e., were less than 37 weeks of gestation), which is to be expected. In addition, were the PE cases all late-onset PE?

      Response 5: All PE samples enumerated in Table 1 demonstrate a late-onset preeclampsia, with placental specimens being procured from patients more than 35 weeks of gestation and less than the 38 weeks of pregnancy. The relevant content has been incorporated into the "Materials and Methods" section (Line574-576).

      Comment 6. Results, Figure 1: Are the authors considering the identified Macrophage cluster as being largely fetal (e.g., Hofbauer cells)? This also depends on whether any decidual tissue was included in the placental samples for CyTOF.

      Response 6: Firstly, the specimens subjected to CyToF analysis were devoid of decidual tissue and exclusively comprised placental villi. Secondly, the Macrophage cluster in Figure 1 undeniably encompasses Hofbauer cells, and we considering fetal-derived macrophages likely constituting the substantial proportion of the cellular population. However, a limitation of the CyToF technique lies in its inability to discern between maternal and fetal origins of these cells, thereby precluding a definitive distinction.

      Comment 7. Results, Figure 2C: Did the authors validate other T-cell subset markers (e.g., Th1, Th2, Th9, etc.)?

      Response 7: In this study, we did not validate additional T-cell subset markers presented in Figure 2C, recognizing the potential for deeper insights. As we embark on our subsequent research endeavors, we aim to meticulously explore and characterize the intricate changes in diverse T-cell populations at the maternal-fetal interface, with a particular focus on preeclampsia patients, thereby advancing our understanding of this complex condition.

      Comment 8. Results, Figure 2D: Where were the detected memory-like T cells located in the placenta? Did they cluster in certain areas or were they widely distributed?

      Response 8: Upon a thorough re-evaluation of the immunofluorescence images specific to the placenta, we observed a notable preponderance of memory-like T cells residing within the placental sinusoids (Line135-139).

      Comment 9. Results, Figure 2E: I would suggest separating the two plots so that the Y-axis can be expanded for TIM3, as it is impossible to view the medians currently.

      Response 9: We thank the reviewers' comments. We have made the adjustment to Figure 2E according to the reviewers' suggestions.

      Comment 10. Results, Lines 138-140: Do the authors consider that the altered T-cells are largely resident cells of the placenta or newly invading/recruited cells? The clarification of distribution within the placental tissues as mentioned above would help answer this.

      Response 10: Our analysis revealed the presence of memory-like T cells within the placental sinusoids, as evident from the immunofluorescence examination of placental tissues. Consequently, these T cells may represent recently recruited cellular entities, traversing the placental vasculature and integrating into this unique maternal-fetal microenvironment (Line135-139).

      Comment 11. Results, Figure 3C: Has a reduction of gMDSCs (or MDSCs in general) been previously reported in PE?

      Response 11: Myeloid-derived suppressor cells (MDSCs) constitute a diverse population of myeloid-derived cells that exhibit immunosuppressive functions under various conditions. Previous reports have documented a decrease in the levels of gMDSCs from peripheral blood or umbilical cord blood among patients with preeclampsia (Am J Reprod Immunol. 2020, PMID: 32418253; J Reprod Immunol. 2018, PMID: 29763854; Biol Reprod. 2023, PMID: 36504233). Nevertheless, there was no documented reports thus far on the alterations and specific characteristics in gMDSCs within the placenta of PE patients.

      Comment 12. Results, Figure 3D-E: It is not clear what new information is added by the correlations, as the increase of both cluster 23 in CD11b+ cells and cluster 8 in CD4+ T cells in PE cases was already apparent. Are these simply to confirm what was shown from the quantification data?

      Response 12: Despite the evident increase in both cluster 23 within CD11b<sup>+</sup> cells and cluster 8 within CD4<sup>+</sup> T cells in PE cases, the existence of a potential correlation between these two clusters remains elusive. To gain insight into this question, we conducted a Pearson correlation analysis, which is presented in Figure 3D-E, revealing a positive correlation between the two clusters.

      Comment 13. Results, Figure 4A: Please clarify in the results text that the RNA-seq of macrophages from RUPP mice was performed prior to their injection into normal pregnant mice.

      Response 13: We thank the reviewers' comments. We have updated Figure 4A according to the reviewers' suggestions.

      Comment 14. Results / Methods, Figure 4: For the transfer of macrophages from RUPP mice into normal mice, why were the uterine tissues included to isolate cells? The uterine macrophages will be almost completely maternal, as opposed to the largely fetal placental macrophages, and despite the sorting for specific markers these are likely distinct subsets that have been combined for injection. This could potentially impact the differential gene expression analysis and should be accounted for. In addition, did murine placental samples include decidua? This should be clarified.

      Response 14: We thank the reviewers' comments. For our experimental design involving human samples, we meticulously selected placental tissue as the primary focus. Initially, we aimed for uniformity by contemplating the utilization of mouse placenta. However, a pivotal revelation emerged from the GFP pregnant mice-related data in Figure 4-figure supplement 1D,1E: the uterus and placenta of mice are predominantly populated by maternal macrophages, with fetal macrophages virtually absent, marking a notable divergence from the human scenario. Furthermore, the uterine milieu exhibits a macrophage concentration exceeding 20% of total cellular composition, whereas in the placenta, this proportion dwindles to less than 5%, underscoring a distinct distribution pattern. Given these discrepancies and considerations, we incorporated mouse uterine tissues into our protocol to isolate cells, ensuring a more comprehensive and informative exploration that acknowledges the inherent differences between human and mouse placental biology.

      Comment 15. Results, Lines 186-187: I think the figure citation should be Figure 4D here.

      Response 15: We thank the reviewers' careful checking. We have revised and updated Figure 4 accordingly.

      Comment 16. Results, Figure 4: Where are the results of the injection of anti-inflammatory and pro-inflammatory macrophages into normal mice? This experiment is mentioned in Figure 4A, but the only results shown in Figure 4 are with the PLX3397 depletion.

      Response 16: The aim of this experiment in figure 4 is to conclusively ascertain the influence of pro-inflammatory and anti-inflammatory macrophages on the other immune cells within the maternal-fetal interface, as well as their implications for pregnancy outcomes. To achieve this, we employed a strategic approach involving the administration of PLX3397, a compound capable of eliminating the preexisting macrophages in mice. Subsequently, anti-inflam or pro-inflam macrophages were injected to these mice, thereby eliminating the confounding influence of the native macrophage population. This methodology allows for a more discernible observation of the specific effects these two types of macrophages exert on the immune landscape at the maternal-fetal interface and their ultimate impact on pregnancy outcomes.

      Comment 17. Results, Lines 189-190: Does PLX3397 inhibit macrophage development/signaling/etc. or result in macrophage depletion? This is an important distinction. If depletion is induced, does this affect placental/fetal macrophages or just maternal macrophages?

      Response 17: We thank the reviewers' comments. We have updated the additional data on the efficiency of macrophage depletion involving PLX3397 in Figure 4-figure supplement 2A. PLX3397 is a small molecule compound that possesses the potential to cross the placental barrier and affect fetal macrophages. We have discussed the impact of this factor on the experiment in the Discussion section (Line457-459).

      Comment 18. Results, Lines 197-198: Similarly, does clodronate liposome administration affect only maternal macrophages, or also placental/fetal macrophages?

      Response 18: We thank the reviewers' comments. We have updated the additional data on the efficiency of macrophage depletion involving Clodronate Liposomes in Figure 4-figure supplement 2B. Clodronate Liposomes, which are intricate vesicles encapsulating diverse substances, while only small molecule compounds possess the potential to cross the placental barrier. Consequently, we hold the view that the influence of these liposomes is likely confined to the maternal macrophages (Artif Cells Nanomed Biotechnol. 2023. PMID: 37594208).  

      Comment 19. Results, Line 206: A minor point, but consider continuing to refer to the preeclampsia model mice as RUPP mice rather than PE mice.

      Response 19: We thank the reviewers' comments. We have revised and updated this section accordingly.

      Comment 20. Results / Methods, Figure 5: For these experiments, why did the authors focus on the mouse uterus?

      Response 20: We have previously addressed this query in our Response 14. We incorporated mouse uterine tissues for cell isolation due to the profound differences in placental biology between humans and mice.

      Comment 21. Results, Figure 5: Did the authors have a means of distinguishing the transferred donor cells from the recipient cells for their single-cell analysis? If the goal is to separate the effects of the macrophage transfer on other uterine immune cells, then it would be important to identify and separate the donor cells.

      Response 21: We thank the reviewers' comments. Upon analysis, we observed a notable elevation in the frequency of total macrophages within the CD45<sup>+</sup> cell population. Then we subsequently performed macrophage clustering and uncovered a marked increase in the frequency of Cluster 0, implying a potential correlation between Cluster 0 and donor-derived cells. RNA sequencing revealed that the F480<sup>+</sup>CD206<sup>-</sup> pro-inflammatory donor macrophages exhibited a Folr2<sup>+</sup>Ccl7<sup>+</sup>Ccl8<sup>+</sup>C1qa<sup>+</sup>C1qb<sup>+</sup>C1qc<sup>+</sup> phenotype, which is consistent with the phenotype of cluster 0 in macrophages observed in single-cell RNA sequencing (Figure 4D and Figure 5E). Therefore, the donor cells should be in cluster 0 in macrophages.

      Comment 22. Results, Lines 247-248: While the authors have prudently noted that the observed T-cell phenotypes are merely suggestive of immunosuppression, any claims regarding changes in the immunosuppressive function after macrophage transfer would require functional studies of the T cells.

      Response 22: We thank the reviewers' comments. Upon revisiting and meticulously reviewing the pertinent literature, we have refined our terminology, transitioning from 'immunosuppression' to 'immunomodulation', thereby enhancing the accuracy and precision of our Results (Line285-287).

      Comment 23. Results, Figure 6G: The observation of worsened outcomes and PE-like symptoms after T-cell transfer is interesting, but other models of PE induced by the administration of Th1-like cells have already been reported. Are the authors' findings consistent with these reports? These findings are strengthened by the evaluation of second-pregnancy outcomes following the transfer of T cells in the first pregnancy.

      Response 23: We thank the reviewers' comments. As we verified in Figure 6F-6H, the injection of CD4<sup>+</sup>CD44<sup>+</sup> T cells derived from RUPP mouse, characterized by a reduced frequency of Tregs and an increased frequency of Th17 cells, could induce PE-like symptoms in pregnant mice. In line with other studies, which have implicated Th1-like cells in the manifestation of PE-like symptoms, we posit a novel hypothesis: beyond Th1 cells, Th17 cells also have the potential to induce PE-like symptoms.

      Comment 24. Results, Lines 327-337: The disease model implied by the authors here is not clear. Given that the authors' human findings are in the placental macrophages, are the authors proposing that placental macrophages are induced to an M1 phenotype by placenta-derived EVs? Please elaborate on and clarify the proposed model.

      Response 24 In the article authored by our team, titled "Trophoblast-Derived Extracellular Vesicles Promote Preeclampsia by Regulating Macrophage Polarization" published in Hypertension (Hypertension. 2022, PMID: 35993233), we employed trophoblast-derived extracellular vesicles isolated from PE patients as a means to induce an M1-like macrophage phenotype in macrophages from human peripheral blood in vitro. Consequently, in the present study, we have directly leveraged this established methodology to induce pro-inflammatory macrophages.

      Comment 25. Results / Methods, Figure 8E-H: What is the reasoning for switching to an LPS model in this experiment? LPS is less specific to PE than the RUPP model.

      Response 25: We thank the reviewers' comments. Firstly, our other animal experiments in this manuscript used the RUPP mouse model to simulate the pathology of PE. However, the RUPP model requires ligation of the uterine arteries in pregnant mice on day 12.5 of gestation, which hinders T cells returning from the tail vein from reaching the maternal-fetal interface. In addition, this experiment aims to prove that CD4<sup>+</sup> T cells are differentiated into memory-like Th17 cells through IGF-1R receptor signaling to affect pregnancy by clearing CD4<sup>+</sup> T cells in vivo with an anti-CD4 antibody followed by injecting IGF-1R inhibitor-treated CD4<sup>+</sup> T cells. And we proved that injection of RUPP-derived memory-like CD4<sup>+</sup> T cells into pregnant mice induces PE-like symptoms (Figure 6). In summary, the application of the LPS model in the final experiments does not affect the conclusions.

      Comment 26. Discussion: What do the authors consider to be the origins of the inflammatory cells associated with PE onset? Are these maternal cells invading the placental tissues, or are these placental resident (likely fetal) cells?

      Response 26: We thank the reviewers' comments. Numerous reports have consistently observed the presence of inflammatory cells and factors in the maternal peripheral blood and placenta tissues of PE patients, fostering the prevailing notion that the progression of PE is intricately linked to the maternal immune system's inflammatory response towards the fetus. Nevertheless, intriguing findings from single-cell RNA sequencing, analyzed through bioinformatic methods, have challenged this perspective (Elife. 2019. PMID: 31829938;Proc Natl Acad Sci U S A. 2017.PMID: 28830992). These studies reveal that the placenta harbors not just immune cells of maternal origin but also those of fetal origin, raising questions about whether these are maternal cells infiltrating placental tissues or resident (possibly fetal) placental cells. Further investigation is imperative to elucidate this complex interplay.

      Comment 27. Discussion: Given the observed lack of changes in the GDM or GDM+PE groups, do the authors consider that GDM represents a distinct pathology that can lead to secondary PE, and thus is different from primary PE without GDM?

      Response 27: It's possible. Though previous studies reported GDM is associated with aberrant maternal immune cell adaption the findings remained controversial. It seems that GDM does not induce significant alterations in placental immune cell profile in our study, which made us pay more attention to the immune mechanism in PE. However, it is confusing for the reasons why individuals with GDM&PE were protected from the immune alterations at the maternal fetal interface. Limited placental samples in the GDM&PE group can partly explain it, for it is hard to collect clean samples excluding confounding factors. A study reported that macrophages in human placenta maintained anti-inflammatory properties despite GDM (Front Immunol, 2017, PMID: 28824621).Barke et al. also found that more CD163<sup>+</sup> cells were observed in GDM placentas compared to normal controls (PLoS One, 2014, PMID: 24983948). Thus, GDM is likely to have a protective property in the placental immune environment when the individuals are complicated with PE.

      Reviewer #2 (Recommendations for the authors):

      Comment 1. IF images need to be quantified.

      Response 1: We thank the reviewers' comments. We have quantified and calculated the fluorescence intensity and added it in Figure 2D.

      Comment 2. Cluster 12 in Figure 3 is labeled as granulocytes but listed under macrophages.

      Response 2: We thank the reviewers' careful checking. We have revised and updated Figure 3A.

      Comment 3. Figure 4 labels in the text and figure do not match, no 4G in the figure.

      Response 3: We thank the reviewers' careful checking. The figure labels of Figure 4 have been revised and updated.

    1. Reviewer #3 (Public review):

      A bias in how people infer the amount of control they have over their environment is widely believed to be a key component of several mental illnesses including depression, anxiety, and addiction. Accordingly, this bias has been a major focus in computational models of those disorders. However, all of these models treat control as a unidimensional property, roughly, how strongly outcomes depend on action. This paper proposes---correctly, I think---that the intuitive notion of "control" captures multiple dimensions in the relationship between action and outcome is multi-dimensional. In particular, the authors propose that the degree to which outcome depends on how much *effort* we exert, calling this dimension the "elasticity of control". They additionally propose that this dimension (rather than the more holistic notion of controllability) may be specifically impaired in certain types of psychopathology. This idea thus has the potential to change how we think about mental disorders in a substantial way, and could even help us better understand how healthy people navigate challenging decision-making problems.

      Unfortunately, my view is that neither the theoretical nor empirical aspects of the paper really deliver on that promise. In particular, most (perhaps all) of the interesting claims in the paper have weak empirical support.

      Starting with theory, the elasticity idea does not truly "extend" the standard control model in the way the authors suggest. The reason is that effort is simply one dimension of action. Thus, the proposed model ultimately grounds out in how strongly our outcomes depend on our actions (as in the standard model). Contrary to the authors' claims, the elasticity of control is still a fixed property of the environment. Consistent with this, the computational model proposed here is a learning model of this fixed environmental property. The idea is still valuable, however, because it identifies a key dimension of action (namely, effort) that is particularly relevant to the notion of perceived control. Expressing the elasticity idea in this way might support a more general theoretical formulation of the idea that could be applied in other contexts. See Huys & Dayan (2009), Zorowitz, Momennejad, & Daw (2018), and Gagne & Dayan (2022) for examples of generalizable formulations of perceived control.

      Turning to experiment, the authors make two key claims: (1) people infer the elasticity of control, and (2) individual differences in how people make this inference are importantly related to psychopathology.

      Starting with claim 1, there are three sub-claims here; implicitly, the authors make all three. (1A) People's behavior is sensitive to differences in elasticity, (1B) people actually represent/track something like elasticity, and (1C) people do so naturally as they go about their daily lives. The results clearly support 1A. However, 1B and 1C are not supported.

      Starting with 1B, the experiment cannot support the claim that people represent or track elasticity because the effort is the only dimension over which participants can engage in any meaningful decision-making (the other dimension, selecting which destination to visit, simply amounts to selecting the location where you were just told the treasure lies). Thus, any adaptive behavior will necessarily come out in a sensitivity to how outcomes depend on effort. More concretely, any model that captures the fact that you are more likely to succeed in two attempts than one will produce the observed behavior. The null models do not make this basic assumption and thus do not provide a useful comparison.

      For 1C, the claim that people infer elasticity outside of the experimental task cannot be supported because the authors explicitly tell people about the two notions of control as part of the training phase: "To reinforce participants' understanding of how elasticity and controllability were manifested in each planet, [participants] were informed of the planet type they had visited after every 15 trips." (line 384).

      Finally, I turn to claim 2, that individual differences in how people infer elasticity are importantly related to psychopathology. There is much to say about the decision to treat psychopathology as a unidimensional construct. However, I will keep it concrete and simply note that CCA (by design) obscures the relationship between any two variables. Thus, as suggestive as Figure 6B is, we cannot conclude that there is a strong relationship between Sense of Agency and the elasticity bias---this result is consistent with any possible relationship (even a negative one). The fact that the direct relationship between these two variables is not shown or reported leads me to infer that they do not have a significant or strong relationship in the data.

      There is also a feature of the task that limits our ability to draw strong conclusions about individual differences in elasticity inference. As the authors clearly acknowledge, the task was designed "to be especially sensitive to overestimation of elasticity" (line 287). A straightforward consequence of this is that the resulting *empirical* estimate of estimation bias (i.e., the gamma_elasticity parameter) is itself biased. This immediately undermines any claim that references the directionality of the elasticity bias (e.g. in the abstract). Concretely, an undirected deficit such as slower learning of elasticity would appear as a directed overestimation bias.

      When we further consider that elasticity inference is the only meaningful learning/decision-making problem in the task (argued above), the situation becomes much worse. Many general deficits in learning or decision-making would be captured by the elasticity bias parameter. Thus, a conservative interpretation of the results is simply that psychopathology is associated with impaired learning and decision-making.

      Minor comments:

      Showing that a model parameter correlates with the data it was fit to does not provide any new information, and cannot support claims like "a prior assumption that control is likely available was reflected in a futile investment of resources in uncontrollable environments." To make that claim, one must collect independent measures of the assumption and the investment.

      Did participants always make two attempts when purchasing tickets? This seems to violate the intuitive model, in which you would sometimes succeed on the first jump. If so, why was this choice made? Relatedly, it is not clear to me after a close reading how the outcome of each trial was actually determined.

      It should be noted that the model is heuristically defined and does not reflect Bayesian updating. In particular, it overestimates control by not using losses with less than 3 tickets (intuitively, the inference here depends on your beliefs about elasticity). I wonder if the forced three-ticket trials in the task might be historically related to this modeling choice.

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      Reply to the reviewers

      Reviewer 1

      Major issue #1. Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities (https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S. cerevisiae, do the authors observe HAC1 mRNA splicing?

      We have not tested whether HAC1 mRNA is processed in S. cerevisiae.

      In addition, as requested by the reviewers, we reassessed our RNA-seq data and compared it with data from (Kimmig et al., 2012) (UPR activation in S. pombe), which added a new layer of data that reinforces the differences between the transcriptomic responses induced by HU and DIA and the canonical UPR. The following information is now included in the paper (page 26, highlighted in blue):

      “We further compared our transcriptomic data with that obtained by Kimmig et al. from DTT- treated S. pombe cells. When we compared the genes that were downregulated in our conditions with the ones described by Kimmig et al. (FC≤-1), we found no similarities between HU treatment (75 mM HU for 150 minutes) and UPR-induced downregulation, and only three genes ( ist2, efn1 and xpa1) all of them encode for transmembrane proteins, were common with DIA treatment (3 mM DIA for 60 minutes). Additionally, ist2 and xpa1, but not efn1, are considered Ire1-dependent downregulated genes and are located in the ER. These results show that HU- or DIA- induced transcriptomic programs are different from UPR, as they do not heavily rely on mRNA decay and favor gene overexpression. Interestingly, we found similarities between genes showed to be upregulated more that twofold by DTT in Kimmig et al., and HU and DIA conditions. When the two N-Cap-inducing conditions were compared with DTT, we found eight common upregulated genes (frp1, plr1, SPCC663.08c, srx1, gst2, str3, caf5 and hsp16) mostly involved in reduction processes and the chaperone Hsp16 which suggests folding stress”.

      Major issue #2. The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S. pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status.

      We agree with the reviewer that it is important to determine the redox and the functional state of the secretory pathway in our conditions to fully understand the cellular consequences of these treatments, especially in the case of HU, as it is routinely used in clinics. In this context, we have already included new data showing that HU or DIA treatment leads to alterations in the Golgi apparatus and in the distribution of secretory proteins (Figures 3A-B). In addition, we are currently performing mass spectrometry experiment to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. Finally, we plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (See below, Reviewer 2, Major issue #1).

      What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?

      We have tested whether the addition of this antioxidant could prevent and/or revert the N-Cap phenotype. We found that NAC in combination with HU increased N-Cap incidence (Figure 5H). As NAC is a GSH precursor and we find that GSH is required to develop the phenotype of N-Cap (Figure 5A-B, D, G), this result further supports that the HU-induced cellular damage might involve ectopic glutathionylation of proteins.

      Unfortunately, we have not tested NAC in combination with DIA, as NAC seems to reduce DIA as soon as they get in contact, as judged by the change in the characteristic orange color of DIA, the same that happens when we combine GSH and DIA (Supplementary Figure 5A-B).

      In this regard, the following information has been added to the manuscript (page 30, highlighted in blue):

      “We also tested GSH addition to the medium in combination with either HU or DIA. When mixed with DIA, we noticed that the color of the culture changed after GSH addition (Figure S5A), which suggests that GSH and DIA can interact extracellularly, thus preventing us from being able to draw conclusions from those experiments. On the other hand, combining GSH with HU increased N-Cap incidence (Figure 5G), as expected based on our previous observations. Additionally, we checked whether the addition of the antioxidant N-acetyl cysteine (NAC), a GSH precursor, impacted upon the N-Cap phenotype. The results were the same as with GSH addition: when combined with HU, NAC increased N-Cap incidence (Figure 5H), whereas in combination, the two compounds interacted extracellularly (Figure S5B). These data align with NAC being a precursor of GSH, as incrementing GSH levels augments the penetrance of the HU-induced phenotype”.

      Major issue #3. The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates?

      DIA is a strong oxidant, and HU treatment results in the production of reactive oxygen species (ROS). Therefore, one hypothesis would be that cytoplasmic chaperone foci represent oxidized and/or misfolded soluble proteins. Indeed, in this revised version of the manuscript we have included data showing that guk1-9-GFP and Rho1.C17R-GFP soluble reporters of misfolding accumulate in cytoplasmic foci upon HU or DIA treatment that colocalize with Hsp104 (Figure 4I-J, pages 23-24 and 29), which demonstrate that cytoplasmic chaperone foci contain misfolded proteins. We have also tested if they contain Vgl1, which is one of the main components of heat shock induced stress granules in S. pombe (Wen et al., 2010). However, we found that HU or DIA-induced foci lacked this stress granule marker, and indeed Vgl1 did not form any foci in response to these treatments. Therefore, our aggregates differ from the canonical stress-induced granules.

      Are those resulting from proficient retrotranslocation or reflux of misfolded proteins from the ER?

      To test whether these cytosolic aggregates result from retrotranslocation from the ER, we plan to use the vacuolar Carboxipeptidase Y mutant reporter CPY*, which is misfolded. This misfolded protein is imported into the ER lumen but does not reach the vacuole. Instead, it is retrotranslocated to the cytoplasm, where it is ubiquitinated and degraded by the proteasome (Mukaiyama et al., 2012). We will analyze by fluorescence microscopy the localization of CPY*´-GFP and Hsp104-containing aggregates upon HU or DIA treatment and with or without proteasome inhibitors. We can also test the levels, processing and ubiquitination of CPY*-GFP by western blot, as ubiquitination of retrotranslocated proteins occurs once they are in the cytoplasm.

      Are those aggregates membrane bound or do they correspond to aggresomes as initially defined? The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol.

      Our results suggest that these aggregates are not bound to ER membranes, as they do not appear in close proximity to the ER area marked by mCherry-AHDL in fluorescence microscopy images.

      To fully rule out this possibility, we have tested whether these Hsp104-aggregates colocalized with ER transmembrane proteins Rtn1 and Yop1, and with Gma12-GFP that marks the Golgi apparatus. In none of the cases the Hsp104-containing aggregates colocalized or were surrounded by membranes. This information will be added to the final version of the manuscript.

      With respect to autophagy, we have tested whether deletion of key genes involved in autophagy affected the N-Cap phenotype. To this end, we used deletions of vac8 and atg8 in strains expressing Cut11-GFP and/or mCherry-AHDL and found that none of them affected N-Cap formation. These data suggest that the core machinery of autophagy is not critical for HU/DIA-induced ER expansion. We plan to include this data in the final version of the manuscript along with the rest of experiments proposed.

      To get deeper insights and to fully rule out a possible contribution of macro-autophagy to the HU- and DIA-induced phenotypes, we plan to analyze by western blot whether GFP-Atg8 is induced and cleaved upon HU or DIA treatments which would be indicative of macroautophagy activation.

      To test whether the cytoplasmic aggregates are the result of an imbalance between ER-expansion and ER-phagy we plan to analyze the localization of GFP-Atg8 and Hsp104-RFP in the atg7Δ mutant, impaired in the core macro-autophagy machinery. In these conditions, the number or size of the cytoplasmic aggregates might be impacted.

      On the other hand, it has been recently shown that an ER-selective microautophagy occurs in yeasts upon ER stress (Schäfer et al., 2020; Schuck et al., 2014). This micro-ER-phagy involves the direct uptake of ER membranes into lysosomes, is independent of the core autophagy machinery and depends on the ESCRT system and is influenced by the Nem1-Spo7 phosphatase. ESCRT directly functions in scission of the lysosomal membrane to complete the uptake of the ER membrane. Interestingly, N-Caps are fragmented in the absence of cmp7 and specially in the absence of vps4 or lem2, the nuclear adaptor of the ESCRT (Figure 3E), We had initially interpreted these results as the need to maintain nuclear membrane identity during the process of ER expansion (Kume et al., 2019); however, the appearance of fragmented ER upon HU treatment in the absence of ESCRT might also be due to an inability to complete microautophagic uptake of ER membranes. To test this hypothesis, we plan to analyze whether the fragmented ER in these conditions co-localize with lysosome/vacuole markers.

      Major issue #4. Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (https://academic.oup.com/nar/article/29/14/3030/2383924), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.

      As stated in (Taricani et al., 2001), hsp16 expression is strongly induced in a cdc22-M45 mutant background. We performed experiments in this mutant that were included in the original version of the manuscript and remain in the current version (Sup. Fig. 2C) and, under restrictive conditions, we do not see spontaneous N-Cap formation. If Hsp16 overexpression and nucleotide depletion were key to the mechanism triggering N-Cap appearance, we would expect this mutant to eventually form N-Caps when placed at restrictive temperature. Furthermore, Taricani et al. show that Hsp16 expression was abolished in a Δatf1 mutant background in the presence of HU, and we found that this mutant is still able to produce N-Caps in HU; therefore, our results strongly suggest that the phenotype of N-cap is independent on the MAPK pathway and on the expression of hsp16.

      Minor issues

      1. __P1 - UPR = Unfolded Protein Response: __Corrected in the manuscript
      2. 2__. P22 - HSP upregulation "might" be indicative of a folding stress:__ Corrected in the manuscript
      3. __ The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors revise the storytelling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.__ We have modified the abstract to better reflect our findings and will further revise our arguments in the final version of the manuscript once we have the results of the experiments proposed

      Reviewer 2

      Major issue #1. The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?

      We have addressed the status of secretion in cells treated with HU or DIA by assessing the morphology of the Golgi apparatus and the localization of several secretory proteins by fluorescence microscopy and found that both HU and DIA treatments impact the secretion system. In addition, we plan on addressing the redox status of ER proteins (Bip1, Pdi or Ero1) by biochemical approaches. Please see the answer to major issue #2 from reviewer 1.

      We will also analyze by western blot the biogenesis and processing of the wildtype vacuolar Carboxypeptidase Y (Cpy1-GFP) and/or alkaline phosphase (Pho8-GFP), two widely used markers to test the functionality of the ER/endomembrane system.

      Major issue #2. Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.

      This same issue arose with reviewer 3, so we decided to change the image of the western blot showing another one with less exposure and added a quantification showing that Bip1-GFP levels remain mostly constant between control conditions and treatments with HU and DIA.

      We have also performed the suggested photobleaching experiment to analyze potential changes in crowding and mobility in Bip1-GFP upon HU treatment. We found that Bip1-GFP signal recovers after photobleaching the perinuclear ER in HU-treated cells that had not yet expanded the ER, showing that Bip1-GFP is dynamic in these conditions. However, Bip1-GFP signal did not recover after photobleaching the whole N-Cap in cells that had fully developed the expanded perinuclear ER phenotype, whereas it did recover when only half of the N-Cap region was bleached. This suggests that Bip1-GFP is mobile within the expanded perinuclear ER but cannot freely diffuse between the cortical and the perinuclear ER once the N-Cap is formed.

      These data have been included in the revised version of the manuscript, in figure 4B, sup. figures 4A-B, and in page 22.

      Major issue #3. It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?

      As all three reviewers had comments about the CHX and Pm-related data, we revised those experiments and noticed a phenotype occurring upon HU+CHX treatment that had gone unnoticed previously and that changed our understanding about the effect of these drugs on the ER. Briefly, we noticed that, although CHX treatment decreases the HU-induced expansion of the perinuclear ER, it indeed induced expansion but in this case in the cortical area of the ER. This means that the phenotype of ER expansion in HU is not being suppressed by addition of CHX, but rather taking place in another area of the ER (cortical ER). We do not understand why this happens; however, these results show that ER expansion is exacerbated both in DIA and HU when combined with CHX. We have included this data in Figures 3C-D and in page 21.

      We also examined the trafficking of secretory proteins that go from the ER to the cell tips and noticed that this transit was affected under both drugs (Figures 3A-B). This suggests that, although there is still protein synthesis when cells are exposed to the drugs (as can be seen by the higher levels of chaperones induced by both stresses (Figure 4C-E)), their protein synthesis capacity is possibly impinged on to certain degree. All this information is now included in the manuscript (page 18).

      Major issue #4. While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).

      Although we have only included experiments using one redox sensor in the manuscript, we had tested the oxidation of several biosensors during HU and DIA exposure monitoring cytoplasmic, mitochondrial and glutathione-specific probes. We have tried to use ER directed probes however, we have not been successful due to oversaturation of the probe in the highly oxidative environment of the ER lumen.

      Although so far we have not been able to directly test the redox status of the ER with optical probes, we plan to test the folding and redox status of several ER proteins and secretory markers by biochemical approaches, so hopefully these experiments will give us more information on this question (See answer to Reviewer 1, Main Issue #2 and Reviewer 2, Main issue #1).

      Major Issue #5. What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci?

      Pm causes premature termination of translation, leading to the release of truncated, misfolded, or incomplete polypeptides into the cytosol and the re-engagement of ribosomes in a new cycle of unproductive translation, as puromycin does not block ribosomes (Aviner, 2020; Azzam & Algranati, 1973). This likely decreases the number of peptides entering the ER that can be targeted by either HU or DIA, decreasing in turn ER expansion. Indeed, we have found that Pm treatment alone results in the formation of multiple cytoplasmic protein aggregates marked by Hsp104-GFP (Figure 4K), consistent with a continuous release of incomplete and misfolded nascent peptides to the cytoplasm. This would explain why Pm treatment suppresses N-Cap formation when cells are treated with either HU or DIA.

      To further test this idea, we analyzed the number and size of Hsp104-containing cytoplasmic aggregates in cells treated with HU or DIA and Pm, where N-Caps are suppressed. As expected, we found an increase in the accumulation of proteotoxicity in the cytoplasm in these conditions. This information has now been added to the paper (Figure 4K, pages 23-24 and 29).

      On the other hand, CHX inhibits translation elongation by stalling ribosomes on mRNAs, preventing further peptide elongation but leaving incomplete polypeptides tethered to the blocked ribosomes. This reduces overall protein load entering the ER by blocking new protein synthesis and stabilizes misfolded proteins bound to ribosomes. Accordingly, it has been shown previously that blocking translation with CHX abolishes cytoplasmic protein aggregation (Cabrera et al., 2020; Zhou et al., 2014). Similarly, we have found that Hsp104 foci are not observed when we add CHX alone or in combination with HU or DIA (Figures 4K-L). These results suggest that cytoplasmic foci that we observe upon HU or DIA treatment likely contain misfolded proteins derived from ongoing translation.

      As this question had also been raised by reviewer 1, we further explored the nature of these cytoplasmic foci (please see answer to Reviewer1, Issue 3). Briefly:

      • We tested whether they colocalize with the foci of Guk1-9-GFP and Rho1.C17R-GFP reporters of misfolding that appear upon HU or DIA treatments and, indeed, Hsp104-containing aggregates colocalize with Guk1-9-GFP and Rho1.C17R-GFP. This information has now been added to the paper (Figure 4I-J, pages 23-24 and 29).
      • We tested whether these foci were membrane bound with several ER transmembrane proteins (Tts1, Yop1, Rtn1) and integral membrane protein Ish1, and in none of the cases we detected membranes surrounding the aggregates. This information will be included in the final version of the paper.
      • We plan to test whether the cytoplasmic foci represent proteins retro-translocated from the ER.
      • We will also test whether autophagy or an imbalance between ER expansion and ER-phagy might contribute to the accumulation of cytoplasmic protein foci. The new data regarding the suppression of cytoplasmic foci by CHX treatment has already been included in the current version of the manuscript in Figure 4K and in the text (page 29).

      The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.

      We agree with the reviewer. We have toned down our statements about the relationship between thiol stress, the cytoplasmic chaperone foci and their relationship with ER expansion. We have removed from the text the statement that cytoplasmic foci are independent from ER expansion and thiol stress and have further revised our claims about CHX and Pm in the main text and the discussion to address these and the other reviewers’ concerns.

      Major Issue #6. Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.

      To address this issue, we plan to analyze the localization of proteins involved in iron-sulfur cluster assembly and/or containing iron-sulfur clusters by in vivo fluorescence microscopy, such as DNA polymerase Dna2 or Grx5, during HU or DIA treatments.

      Related to this, we have found that a subunit of the ribonucleotide reductase (RNR) aggregated in the cytoplasm upon HU exposure (Figure S2B). It is worth noting that RNR is an iron-containing protein whose maturation needs cytosolic Grxs (Cotruvo & Stubbe, 2011; Mühlenhoff et al., 2020). The catalytic site, the activity site (which governs overall RNR activity through interactions with ATP) and the specificity site (which determines substrate choice) are located in the R1 (Cdc22) subunits, which are the ones that aggregate, while the R2 subunits (Suc22) contain the di-nuclear iron center and a tyrosyl radical that can be transferred to the catalytic site during RNR activity (Aye et al., 2015). The fact that a subunit of RNR aggregates could be related to an impingement on its synthesis and/or maturation due to defects in iron-sulfur cluster formation, as it has been recently published that RNR cofactor biosynthesis shares components with cytosolic iron-sulfur protein biogenesis and that the iron-sulfur cluster assembly machinery is essential for iron loading and cofactor assembly in RNR in yeast (Li et al., 2017). This information has been added to the discussion.

      Major Issue #7. The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.

      We modified the language used to describe the experiment in the manuscript, as suggested by the reviewer, to clarify that while DTT is kept in the medium, N-Caps never form. In addition, we have also performed a pre-treatment with DTT; adding 1 mM DTT one hour before, washing the reducing agent out and adding HU to the medium then. The result indicates that pre-treating cells with DTT significantly reduces N-Cap formation after a 4-hour incubation with HU, which suggests that triggering reducing stress “protects” cells from the oxidative damage induced by HU and DIA. This information has been also added to the manuscript (Figure 2J).

      Major Issue #8. For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).

      We have revised and expanded our discussion. In addition, in the final revision of our work we will increase the discussion in the context of the new results obtained.

      Minor points

      1. __ Figure numbering goes from figure 4 to S6 to 5.__ We have updated the numbering of the figures after merging several supplementary figures, so now this issue is fixed.

      __ It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters__.

      Both the Guk1-9-GFP and Rho1.C17R-GFP are two thermosensitive mutants in guanylate kinase and Rho1 GTPase respectively, that have been previously used in S. pombe as soluble reporters of misfolding in conditions of heat stress. During mild heat shock, both mutants aggregate into reversible protein aggregate centers (Cabrera et al., 2020). This information has now been added to the manuscript.

      __ Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?__

      We thank the reviewer for pointing out this mistake. The experiment was performed in 75 mM HU, the legend was correct. It has now been corrected in the manuscript.

      __ The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signaling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.__

      We have included pertinent clarification in the new discussion.

      Reviewer 3

      Major issue #1. It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.

      Regarding the levels of Bip1, we now show in Figure 4 a less exposed image of the western blot, and a quantification of Bip1-GFP intensity from three independent experiments. We find that, in our experimental conditions, neither HU nor DIA treatments significantly altered Bip1 levels.

      With respect to the RNA-Seq, as we mentioned in the major issue 1 from reviewer 1, we reassessed our data to further clarify and add information about ER stress markers induced or repressed by HU and DIA.

      Major issue #2. The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all.

      We have found that puromycin treatment alone results in the formation of cytoplasmic foci containing Hsp104, suggesting that puromycin indeed increases folding stress in the cytoplasm. We have now included this data in Figure 4K (please see Main Issue #5 from Reviewer 2). Pm suppresses the formation of N-caps induced by HU or DIA; however, we have not addressed cell survival or fitness in these conditions and therefore we cannot conclude about being protective.

      In addition, upon the reevaluation of our data, we have realized that CHX treatment suppresses HU-induced perinuclear expansion, although it does not suppress but instead enhances ER expansion in the cortical region. This data has been added to the present version of the manuscript in Figure 3C-D (pages 20-21).

      Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.

      Thapsigargin has been described to be ineffective in yeasts as they lack a (SERCA)‐type Ca2+ pump which is the target of this drug (Strayle et al., 1999). However, deletion of the P5A-type ATPase Cta4, which is required for calcium transport into ER membranes (Lustoza et al., 2011), reduced but did not abolish ER expansion. We also tested the effect of anisomycin. We found that anisomycin in combination with HU or DIA mimicked CHX behavior (ER expansion occurrs in both conditions, exacerbating perinuclear ER expansion in combination with DIA and cortical ER expansion when combined with HU). It is difficult to correlate this result with a role of Ca leakage in ER expansion, as there is no recent information regarding CHX and Ca leakage, although it has been indicated that CHX treatment does not increase cytoplasmic Ca levels (Moses & Kline, 1995). As anisomycin, like CHX, blocks protein synthesis and stabilizes polysomes, what we can conclude from this information is that nascent peptides attached to ribosomes during protein synthesis do promote ER expansion when combined with HU or DIA. This information will be added to the final version of the paper.

      Regarding the downstream effects of HU or DIA treatment on ER proteostasis, we plan to further explore the effect of these drugs on the secretory system (please see major issue #2 from Reviewer 1) and to evaluate the redox state and processing of several key ER and secretory proteins. We have also further explored the nature of the aggregates that appear in the cytoplasm in our experimental conditions, which also shed light into the downstream effects of these drugs in cytoplasmic proteostasis (please see answer to issue #5 from Reviewer 2).

      Major issue #3. The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction?

      We readdressed this topic by analyzing the genes that have been described to be differentially expressed during UPR activation in S. pombe and comparing them with our data by reevaluating our transcriptomic data.. The re-analysis of our RNA-Seq data have allowed us to infer the mechanisms that modulate the ER response to HU or DIA treatment and further separate them from UPR. This information has been added to the paper (page 26). As an alternative approach, we will also analyse the levels of UPR targets by western blot upon HU or DIA treatment

      Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.

      We thank the reviewer for pointing this out. We forgot to include this information which now appears in the M&M section as follows:

      “A gene was considered as differentially expressed when it showed an absolute value of log2FC(LFC)≥1 and an adjusted p-valueIn this regard, we are currently performing proteome-wide mass spectrometry experiments to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. We also plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (see below, and Reviewer 2, Major issue #1).

      We have already tested whether mutant strains with deletions of key enzymes in both cytoplasmic and ER redox systems are able to expand the ER upon HU or DIA treatment. We have found that only pgr1Δ (glutathione reductase), gsa1Δ (glutathione synthetase) and gcs1Δ (glutamate-cysteine ligase) mutants fully suppressed N-Cap formation, which suggests that glutathione has an important role in the phenotype of ER expansion. We have now added the pgr1Δ mutant strain to the main text of the manuscript (Figure 5C, page 30).

      Major issue #5. Figure S5 presents weak ER expansion in fibrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.

      We not only investigated the effects of HU on the ER in mammalian cells, but also of DIA. The results from this experiment mimicked the effect of HU (an increase in ER-ID fluorescence intensity in DIA). We merely excluded this information from the manuscript because we were focusing on HU at that point due to its importance as it is used currently in clinics. In this new version of the manuscript, we have included an extra panel in supplementary figure 5 to show the results from DIA in mammalian cells.

      Minor concerns

      1) Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.

      Although we initially changed the graph, we believe the bar plot disposition facilitates its comprehension and went back to the initial one. Also, as the rest of the graphs similar to 1A are all expressed as bar plots. Therefore, we preferred keeping the figure as it was in the original version. However, we include here the graph with each of the averages of the independent experiments.

      2) It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.

      We have changed the image to show a whole population of cells, with several of them having NPC clusters, and we have indicated the position of SPB in each of them (all colocalizing with the N-Cap).

      3) Figures 1B through 1D do not indicate the HU concentration.

      We thank the reviewer for pointing out this mistake. Figures 1B and 1C represent cells exposed to 15 mM HU for 4 hours, while the graph in 1D shows the results from cells exposed to 75 mM HU over a 4-hour period. This information has been now added to the corresponding figure legend.

      4) I was confused by the photobleaching experiments of Figure S1. How do the authors know that there is complete photobleaching of the cytoplasm or nucleus in the absence of a positive control? If photobleaching is incomplete, they could be measuring motility without compartments rather than transport between compartments, and hence the conclusion that trafficking is unaffected could be wrong.

      Our control is the background of each microscopy image; we make sure that after the laser bleaches a cell, the bleached area coincides with the background noise. That way, we make sure that fluorescence from any remaining GFP is completely removed from the bleached area.

      5) On page 8, they say "exposure to DIA" when they intend HU.

      This has been corrected in the manuscript.

      6) In Figure S3A, the colocalization of INM proteins with the ER are presented. It is not clearly explained what conclusions are meant to be drawn from this figure, but it seems it would have been more useful to compare INM and Cut11, to see whether the NPCs are localizing at the INM or ONM.

      We have added an explanation in the main text to clarify the main conclusions derived from this figure. We think that NPCs localize in a section of the nucleus where the two membranes (INM and ONM) are still bound together.

      7) I had to read Figure 2C's description and caption several times to understand the experiment. A schematic would be helpful. 20 mM HU is low compared to most conditions used. Does repositioning eventually take place for 75 mM HU or 3 mM DIA treatment, or do the cells just die before they get a chance?

      20 mM HU was used in this experiment to provide a time frame suitable for analysis after HU addition, as a higher HU concentration increases the repositioning time. We found that both HU (75mM 4h) and DIA (3mM 4h)-induced ER expansions are reversible upon drug washout. If HU is kept in the media, ER expansions are eventually resolved. However, DIA is a strong oxidant and if it is kept in the media ER expansions are not resolved and cells do not survive.

      8) Figure 2D shows little oxidative consequence from 75 mM HU treatment until 40 min., the same time that phenotypes are observed (Figure 1D). Is this relationship consistent with the kinetics of other concentrations of HU, or of DIA? Seems like a pretty important mechanistic consideration that can rationalize the effects of the two oxidants.

      Thanks to this comment we realized that the numbering underneath Figure 1D (1E in the new version of the manuscript) was wrongly annotated. The original timings shown in the figure were “random”, meaning that the time stablished as 40 minutes was not measuring the passing of 40 minutes since the beginning of the experiment. We have now corrected this panel: the timings are now normalized to the moment when NPCs cluster. The fact that, before, that moment coincided with “40 minutes” does not mean N-Caps appear at that time point in HU (they indeed appear after a >2 hour incubation).

      9) Figure S4 is missing the asterisk on the lower left cell.

      Fixed in the corresponding figure.

      10) How is roundness determined in Figure S4B?

      Roundness in Figure S4B (now S2E) is determined the same way as in Figure 1D, and as is described in the Method section (copied below). A clarification has been added to the legend to address that.

      The ‘roundness’ parameter in the ‘Shape Descriptors’ plugin of Fiji/ImageJ was used after applying a threshold to the image in order to select only the more intense regions and subtract background noise (Schindelin et al., 2012). Roundness descriptor follows the function:

      where [Area] constitutes the area of an ellipse fitted to the selected region in the image and [Major axis] is the diameter of the round shape that in this case would fit the perimeter of the nucleus.

      11) What threshold is used to determine whether cells analyzed in Figures S4C have "small ER" or "large ER"?

      Large ER are considered when their area along the projection of a 3-Z section is over 4 μm2 (more than twice the mean area of the ER in cells with N-Caps in milder conditions). This has now been clarified in the legend of the corresponding figure.

      __12) The authors interpret Figure 4K as indicating that ER expansion is not involved in the generation of punctal misfolded protein aggregates. However, the washout occurs only after the proteins have already aggregated. The proper interpretation is that the aggregates are not reversible by resolution of the stress, and hence are not physically reliant on disulfide bonds. __

      We agree with the reviewer and have modified the interpretation of the indicated figure accordingly (page 29).


      The speculation that these proteins are iron dependent is a stretch; there is no reason to believe that losses of iron metabolism are the most important stress in these cells. It seems at least as likely that oxidizing cysteine-containing proteins in the cytosol or messing with the GSH/GSSG ratio in the cytosol would make plenty of proteins misfold; oxidative stress in budding yeast does activate hsf1. However, this point could be addresses by centrifugation and mass spectrometry to identify the aggregated proteome. It is also surprising that the authors did not investigate ER protein aggregation, perhaps by looking at puncta formation of chaperones beyond BiP. By contrast, the fact that gcs1 deletion prevents ER expansion but does not prevent Hsp104 puncta does support the idea that cytoplasmic aggregation is not dependent on ER expansion.

      To address this suggestion, we plan to analyze the localization of other chaperones and components of the protein quality control such as the ER Hsp40 Scj1 or the ribosome-associated Hsp70 Sks2.

      13) Figure 4L is cited on page 28 when Figure 4K is intended.

      This has been corrected in the text, although new panels have been added and now it is 4N.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The study by Jena et al. addresses important questions on the fundamental mechanisms of genetic adaptation, specifically, does adaptation proceed via changes of copy number (gene duplication and amplification "GDA") or by point mutation. While this question has been worked on (for example by Tomanek and Guet) the authors add several important aspects relating to resistance against antibiotics and they clarify the ability of Lon protease to reduce duplication formation (previous work was more indirect).

      A key finding Jena et al. present is that point mutations after significant competition displace GDA. A second one is that alternative GDA constantly arise and displace each other (see work on GDA-2 in Figure 3). Finally, the authors found epistasis between resistance alleles that was contingent on lon. Together this shows an intricate interplay of lon proteolysis for the evolution and maintenance of antibiotic resistance by gene duplication.

      Strengths:

      The study has several important strengths: (i) the work on GDA stability and competition of GDA with point mutations is a very promising area of research and the authors contribute new aspects to it, (ii) rigorous experimentation, (iii) very clearly written introduction and discussion sections. To me, the best part of the data is that deletion of lon stimulates GDA, which has not been shown with such clarity until now.

      Weaknesses:

      The minor weaknesses of the manuscript are a lack of clarity in parts of the results section (Point 1) and the methods (Point 2).

      We thank the reviewer for their comments and suggestions on our manuscript. We also appreciate the succinct summary of primary findings that the Reviewer has taken cognisance of in their assessment, in particular the association of the Lon protease with the propensity for GDAs as well as its impact on their eventual fate. We have now revised the manuscript for greater clarity as suggested by Reviewer #1.

      Reviewer #2 (Public review):

      Summary:

      In this strong study, the authors provide robust evidence for the role of proteostasis genes in the evolution of antimicrobial resistance, and moreover, for stabilizing the proteome in light of gene duplication events.

      Strengths:

      This strong study offers an important interaction between findings involving GDA, proteostasis, experimental evolution, protein evolution, and antimicrobial resistance. Overall, I found the study to be relatively well-grounded in each of these literatures, with experiments that spoke to potential concerns from each arena. For example, the literature on proteostasis and evolution is a growing one that includes organisms (even micro-organisms) of various sorts. One of my initial concerns involved whether the authors properly tested the mechanistic bases for the rule of Lon in promoting duplication events. The authors assuaged my concern with a set of assays (Figure 8).

      More broadly, the study does a nice job of demonstrating the agility of molecular evolution, with responsible explanations for the findings: gene duplications are a quick-fix, but can be out-competed relative to their mutational counterparts. Without Lon protease to keep the proteome stable, the cell allows for less stable solutions to the problem of antibiotic resistance.

      The study does what any bold and ambitious study should: it contains large claims and uses multiple sorts of evidence to test those claims.

      Weaknesses:

      While the general argument and conclusion are clear, this paper is written for a bacterial genetics audience that is familiar with the manner of bacterial experimental evolution. From the language to the visuals, the paper is written in a boutique fashion. The figures are even difficult for me - someone very familiar with proteostasis - to understand. I don't know if this is the fault of the authors or the modern culture of publishing (where figures are increasingly packed with information and hard to decipher), but I found the figures hard to follow with the captions. But let me also consider that the problem might be mine, and so I do not want to unfairly criticize the authors.

      For a generalist journal, more could be done to make this study clear, and in particular, to connect to the greater community of proteostasis researchers. I think this study needs a schematic diagram that outlines exactly what was accomplished here, at the beginning. Diagrams like this are especially important for studies like this one that offer a clear and direct set of findings, but conduct many different sorts of tests to get there. I recommend developing a visual abstract that would orient the readers to the work that has been done.

      The reviewer’s comments regarding data presentation are well-taken. Since we already had a diagrammatic model that sums up the chief findings of our study (Figure 9), we have now provided schematics in Figures 1, 3, 5 and 8 to clarify the workflow of smaller sections of the study. We hope that these diagrams provide greater clarity with regards to the experiments we have conducted.

      Next, I will make some more specific suggestions. In general, this study is well done and rigorous, but doesn't adequately address a growing literature that examines how proteostasis machinery influences molecular evolution in bacteria.

      While this paper might properly test the authors' claims about protein quality control and evolution, the paper does not engage a growing literature in this arena and is generally not very strong on the use of evolutionary theory. I recognize that this is not the aim of the paper, however, and I do not question the authors' authority on the topic. My thoughts here are less about the invocation of theory in evolution (which can be verbose and not relevant), and more about engagement with a growing literature in this very area.

      The authors mention Rodrigues 2016, but there are many other studies that should be engaged when discussing the interaction between protein quality control and evolution.

      A 2015 study demonstrated how proteostasis machinery can act as a barrier to the usage of novel genes: Bershtein, S., Serohijos, A. W., Bhattacharyya, S., Manhart, M., Choi, J. M., Mu, W., ... & Shakhnovich, E. I. (2015). Protein homeostasis imposes a barrier to functional integration of horizontally transferred genes in bacteria. PLoS genetics, 11(10), e1005612

      A 2019 study examined how Lon deletion influenced resistance mutations in DHFR specifically: Guerrero RF, Scarpino SV, Rodrigues JV, Hartl DL, Ogbunugafor CB. The proteostasis environment shapes higher-order epistasis operating on antibiotic resistance. Genetics. 2019 Jun 1;212(2):565-75.

      A 2020 study did something similar: Thompson, Samuel, et al. "Altered expression of a quality control protease in E. coli reshapes the in vivo mutational landscape of a model enzyme." Elife 9 (2020): e53476.

      And there's a new review (preprint) on this very topic that speaks directly to the various ways proteostasis shapes molecular evolution:

      Arenas, Carolina Diaz, Maristella Alvarez, Robert H. Wilson, Eugene I. Shakhnovich, C. Brandon Ogbunugafor, and C. Brandon Ogbunugafor. "Proteostasis is a master modulator of molecular evolution in bacteria."

      I am not simply attempting to list studies that should be cited, but rather, this study needs to be better situated in the contemporary discussion on how protein quality control is shaping evolution. This study adds to this list and is a unique and important contribution. However, the findings can be better summarized within the context of the current state of the field. This should be relatively easy to implement.

      We thank the reviewer for their encouraging assessment of our manuscript as well as this important critique regarding the context of other published work that relates proteostasis and molecular evolution. Indeed, this was a particularly difficult aspect for us given the different kinds of literature that were needed to make sense of our study. We have now added the references suggested by the reviewer as well as others to the manuscript. We have also added a paragraph in the discussion section (Lines 463-476) that address this aspect and hopefully fill the lacuna that the reviewer points out in this comment.

      Reviewer #3 (Public review):

      Summary:

      This paper investigates the relationship between the proteolytic stability of an antibiotic target enzyme and the evolution of antibiotic resistance via increased gene copy number. The target of the antibiotic trimethoprim is dihydrofolate reductase (DHFR). In Escherichia coli, DHFR is encoded by folA and the major proteolysis housekeeping protease is Lon (lon). In this manuscript, the authors report the results of the experimental evolution of a lon mutant strain of E. coli in response to sub-inhibitory concentrations of the antibiotic trimethoprim and then investigate the relationship between proteolytic stability of DHFR mutants and the evolution of folA gene duplication. After 25 generations of serial passaging in a fixed concentration of trimethoprim, the authors found that folA duplication events were more common during the evolution of the lon strain, than the wt strain. However, with continued passaging, some folA duplications were replaced by a single copy of folA containing a trimethoprim resistance-conferring point mutation. Interestingly, the evolution of the lon strain in the setting of increasing concentrations of trimethoprim resulted in evolved strains with different levels of DHFR expression. In particular, some strains maintained two copies of a mutant folA that encoded an unstable DHFR. In a lon+ background, this mutant folA did not express well and did not confer trimethoprim resistance. However, in the lon- background, it displayed higher expression and conferred high-level trimethoprim resistance. The authors concluded that maintenance of the gene duplication event (and the absence of Lon) compensated for the proteolytic instability of this mutant DHFR. In summary, they provide evidence that the proteolytic stability of an antibiotic target protein is an important determinant of the evolution of target gene copy number in the setting of antibiotic selection.

      Strengths:

      The major strength of this paper is identifying an example of antibiotic resistance evolution that illustrates the interplay between the proteolytic stability and copy number of an antibiotic target in the setting of antibiotic selection. If the weaknesses are addressed, then this paper will be of interest to microbiologists who study the evolution of antibiotic resistance.

      Weaknesses:

      Although the proposed mechanism is highly plausible and consistent with the data presented, the analysis of the experiments supporting the claim is incomplete and requires more rigor and reproducibility. The impact of this finding is somewhat limited given that it is a single example that occurred in a lon strain and compensatory mutations for evolved antibiotic resistance mechanisms are described. In this case, it is not clear that there is a functional difference between the evolution of copy number versus any other mechanism that meets a requirement for increased "expression demand" (e.g. promoter mutations that increase expression and protein stabilizing mutations).

      We thank the reviewer for their in-depth assessment of our work and appreciate their concerns regarding reproducibility and rigor in analysis of our data. We have now incorporated this feedback and provided necessary clarifications/corrections in the revised version of our manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Major Points:

      (1) The authors show that a deletion of lon increases the ability for GDA and they argue that this is adaptive during TMP treatment because it increases the dosage of folA (L. 129). However, the highest frequency of GDA occurred in drug-free conditions (see Figure 1C). This indicates either that GDA is selected in drug-free media and potentially selected against by certain antibiotics. It would help for the authors to discuss this possibility more clearly.

      We thank the reviewer for this astute observation. It is indeed striking that the GDA mutation (i.e. the GDA-2 mutation) selected in a lon-deficient background does not come up in presence of antibiotics. To probe this further, we have now measured the relative fitness of a representative population of lon-knockout from short-term evolution in drug-free LB (population #3) that harbours GDA-2 against its ancestor (marked with DlacZ). These competition experiments were performed in LB (in which GDA-2 emerged spontaneously), as well as in LB supplemented with antibiotics at the concentrations used during the short term evolution.

      Values of relative fitness, w (mean ± SD from 3 measurements), are provided below:

      LB: 1.4 ± 0.2

      LB + Trimethoprim: 1.6 ± 0.2

      LB + Spectinomycin: 0.9 ± 0.2

      LB + Erythromycin: 1.3 ± 0.3

      LB + Nalidixic acid: 1.5 ± 0.2

      LB + Rifampicin: 1.4 ± 0.2

      These data show an increase in relative fitness in drug-free LB as would be expected. Interestingly, we also observe an increase in relative fitness in LB supplemented with antibiotics, except spectinomycin. This result supports the idea that GDA-2 is a “media adaptation” and provides a general fitness advantage to the lon knockout. However, as the reviewer pointed out, we should expect to see GDA-2 emerge spontaneously in antibiotic-supplemented media as well. We think that this does not happen as the fitness advantage of drug-specific mutations (GDAs or point mutations) far exceed the advantage of a media adaptation GDA. As a result, we only see the specific mutations that provide high benefit against the antibiotic at least over the relatively short duration of 20-25 generations. It is noteworthy the GDA-2 mutation does come up in LTMPR1 when it is passaged over >200 generations in drug-free media, but shows fluctuating frequency over time. We expect, therefore, that given enough time we may detect the GDA-2 mutations even in antibiotic-supplemented media.  

      We note, however, that a major caveat in the above fitness calculations is that we cannot be sure that the competing ancestor has no GDA-2 mutations during the course of the experiment. Thus, the above fitness values are only indicative and not definitive. We have therefore not included these data in the revised manuscript.

      (2) It is unclear if the isolates WTMPR1 - 5 and LTMPR1 - 5 were pure clones. The authors write in L.488 "Colonies were randomly picked, cultured overnight in drug-free LB and frozen in 50% glycerol at -80C until further use." And in L. 492 "For long-term evolution, trimethoprim-resistant isolates LTMPR1, WTMPR4 and WTMPR5 were first revived from frozen stocks in drug-free LB overnight." From these descriptions, it is possible that the isolates contained a fraction of cells of other genotypes since colonies are often formed by more than one cell and thus, unless pure-streaked, a subpopulation is present and would in drug-free media be maintained. The possibility of pre-existing subpopulations is important for all statements relating to "reversal".

      This is indeed a valid concern. As far as we can tell all our initial isolates (i.e. WTMPR1-5 and LTMPR1-5) are pure clones at least as far as SNPs are concerned. This is based on whole genome sequencing data that we have reported earlier in Patel and Matange, eLife (2021), where we described the evolution and isolation of WTMPR1-5 and the present study for LTMPR1-5. All SNPs detected were present at a frequency of 100%. For clones with GDAs, however, there is no way to eliminate a sub-population that has a lower or higher gene copy number than average from an isolate. This is because of the inherent instability of GDAs that will inevitably result in heterogeneous gene copy number during standard growth. In this sense, there is most certainly a possibility of a pre-existing subpopulation within each of the clones that may have reversed the GDA. Indeed, we believe that it is this inherent instability that contributes to their rapid loss during growth in drug-free media.

      Minor Points:

      (1) L. 406. "allowing accumulation of IS transposases in E. coli" Please specify that it is the accumulation of transposase proteins (and not genes).

      We have made this change.

      (2) L. 221 typo. Known "to" stabilize.

      We have made this change.

      Reviewer #2 (Recommendations for the authors):

      Most of my suggestions are found in the public review. I believe this to be a strong study, and some slight fixes can solidify its presence in the literature.

      We have attempted to address the two main critiques by Reviewer 2. To simplify the understanding of our data, we have provided small schematics at various points in the paper to clarify the experimental pipelines used by us. We have also provided additional discussion situating our study in the emerging area of proteostasis and molecular evolution. We hope that our revisions have addressed these lacunae in our manuscript.

      Reviewer #3 (Recommendations for the authors):

      Major Points:

      (1) The manuscript is generally a bit difficult to follow. The writing is overly complicated and lacks clarity at times. It should be simplified and improved.

      We have made several revisions to the text, as well as provided schematics in some of our figures which hopefully make our paper easier to understand.

      (2) I cannot find the raw variant summary data for the lon strain evolution experiment in trimethoprim (after 25 generations). Were there any other mutations identified? If not, this should be explicitly stated in the text and the variant output summary from sequencing included as supplemental data.

      We apologise for this oversight. We have now provided these data as Table 1.

      (3) What is the trimethoprim IC50 of the starting (pre-evolution) strains (i.e. wt and lon)? I can't find this information, but it is critical to interpretation.

      We had reported these values earlier in Matange N., J Bact (2020). Wild type and lon-knockout have similar MIC values for trimethoprim, though the lon mutant shows a higher IC50 value. We have now mentioned this in the results section (Line 100-101) and also provided the reference for these data.

      (4) What was the average depth of coverage for WGS? This information is necessary to assess the quality of the variant calling, especially for the population WGS.

      All genome sequencing data has a coverage at least 100x. We have added this detail to the methods section (Line 580-581).

      (5) Five replicate evolution experiments (25 generations, or 7x 10% daily batch transfers) were performed in trimethoprim for the wt and lon strains. Duplication of the folA locus occurred in 1/5 and 4/5 experiments, respectively. It is not entirely clear what type of sampling was actually done to arrive at these numbers (this needs to be stated more clearly), but presumably 1 random colony was chosen at the end of the passaging protocol for each replicate. Based on this result, the authors conclude that folA duplication occurred more frequently in the lon strain, however, this is not rigorously supported by a statistical evaluation. With N=5, one cannot rigorously conclude that a 20% frequency and 80% frequency are significantly different. Furthermore, it's not entirely clear what the mechanism of resistance is for these strains. For example, in one colony sequenced (LTMPR5), it appears no known resistance mechanism (or mutations?) were identified, and yet the IC50 = 900 nM, which is also similar to other strains.

      Indeed, we agree with the reviewer that we don’t have the statistical power to rigorously make this claim. However, since the lon-knockout showed us a greater frequency of GDA across 3 different environments we are fairly confident that loss of lon enhances the overall frequency for GDA mutations. This idea in also supported by a number of previous papers that related GDAs and IS-element transpositions with Lon, viz. Nicoloff et al, Antimicrob Agent Chemother (2007), Derbyshire et al. PNAS (1990), Derbyshire and Grindley, Mol Microbiol (1996). We have therefore not provided further justification in the revised manuscript.

      We had indeed sampled a random isolate from each of the 5 populations and have added a schematic to figure 1 that provides greater clarity.

      Having relooked at the sequencing data for LTMPR1-5 isolates (Table 1), we realised that both LTMPR4 and LTMPR5 harbour mutations in the pitA gene. We had missed this locus during the previous iteration of this manuscript and misidentified an mgrB mutations in LTMPR4. PitA codes for a metal-phosphate symporter. We have observed mutations in pitA in earlier evolution experiments with trimethoprim as well (Vinchhi and Yelpure et al. mBio 2023). Interestingly, in LTMPR5 there was a deletion of pitA, along with 17 other contiguous genes mediated by IS5. To test if loss of pitA is beneficial in trimethoprim, we tested the ability of a pitA knockout to grow on trimethoprim supplemented plates. Indeed, loss of pitA conferred a growth advantage to E. coli on trimethoprim, comparable to loss of mgrB, indicating that the mechanism of resistance of LTMPR5 may be due to loss of pitA. We have added these data to the Supplementary Figure 1 of the revised manuscript and provided a brief description in Lines 103-108. How pitA deficiency confers trimethoprim resistance is yet to be investigated. The mechanism is likely to be by activating some intrinsic resistance mechanism as loss of pitA also conferred a fitness benefit against other antibiotics. This work is currently underway in our lab and hence we do not provide any further mechanism in the present manuscript.

      (6) Although measurement error/variance is reported, statistical tests were not performed for any of the experiments. This is critical to support the rigor and reproducibility of the conclusions.

      We have added statistical testing wherever appropriate to the revised manuscript.

      (7) Lines 150-155 and Figure 2E: Putting a wt copy of mgrB back into the WTMPR4 and LTMPR1 strains would be a better experiment to dissect out the role of mgrB versus the other gene duplications in these strains on fitness. Without this experiment, you cannot confidently attribute the fitness costs of these strains to the inactivation of mgrB alone.

      We agree with the reviewer that our claim was based on a correlation alone. We have now added some new data to confirm our model (Figure 2 E, F). The costs of mgrB mutations come from hyperactivation of PhoQP. In earlier work we have shown that the costs (and benefit) of mgrB mutations can be abrogated in media supplemented with Mg<sup>2+</sup>, which turns off the PhoQ receptor (Vinchhi and Yelpure et al. mBio, 2023). We use this strategy to show that like the mgrB-knockout, the costs of WTMPR4, WTMPR5 and LTMPR1 can be almost completely alleviated by adding Mg<sup>2+</sup> to growth media. These results confirm that the source of fitness cost of TMP-resistant bacteria was not linked to GDA mutations, but to hyperactivation of PhoQP.

      (8) Figure 3F and G: Does the top symbol refer to the starting strain for the 'long-term' evolution? If so, why does WTMPR4 not have the mgrB mutation (it does in Figure 1)? Based on your prior findings, it seems odd that this strain would evolve an mgrB loss of function mutation in the absence of trimethoprim exposure.

      We thank the reviewer for pointing this error out. We have made the correction in the revised manuscript.

      (9) Figure 6A: If the marker is neutral, it should be maintained at 0.1% throughout the 'neutrality' experiment. In both plots, the proportion of some marked strains goes up and then down. This suggests either ongoing evolution (these competitions take place over 105 generations), or noisy data. I suspect these data are just inherently noisy. I don't see error bars in the plots. Were these experiments ever replicated? It seems that replicating the experiments might be able to separate out noise from signal and perhaps clarify this point and better confirm the hypothesis that the point mutants are more fit.

      These experiments were indeed noisy and the apparent enrichment is most likely a measurement error rather than a real change in frequency of competing genotypes. We have now provided individual traces for each of the competing pairs with mean and SD from triplicate observations at each time point.

      (10) Figure 6A: Please indicate which plotted line refers to which 'point mutant' using different colors. These mutants have different trimethoprim IC50s and doubling times, so it would be nice to be able to connect each mutant to its specific data plot.

      We thank the reviewer for this suggestion. We have now colour coded the different strain combinations as suggested.

      (11) Lines 284-285: I disagree that the IC50s are similar. The C-35T mutant has IC50 that is 2x that of LTMPR1. Perhaps more telling is that, compared to the folA duplication strain from the same time-point (which also carries the rpoS mutation), all of the point mutants have greater IC50s (~2x greater). 2-fold changes in IC50 are significant. It would seem that the point-mutants were likely not competing against LTMPR1 at the time they arose, so LTMPR1 might not be the best comparator if it was extinguished from the population early. I'm assuming this is why you chose a contemporary isolate (and, also, rpoS mutant) for the competition experiments. This should be explained more clearly.

      We thank the reviewer for this comment. Indeed, the reviewer is correct about the rationale behind the use of a contemporary isolate and we have provided this clarification in the revised manuscript (Line 287-289). Also, the reviewer is correct in pointing out that a two-fold difference in IC50 cannot be ignored. However, the key point here would be in assessing the differences in growth rates at the antibiotic concentration used during competition (i.e. 300 ng/mL). We are unable to see a direct correlation between the growth rates and enrichment in culture indicating that the observed trends are unlikely to be driven by ‘level of resistance’ alone. We have added these clarifications to the modified manuscript (Lines 299-301)

      Minor Points:

      (1) Line 13: Add a comma before 'Escherichia'

      We have made this change.

      (2) Line 14: Consider changing "mutations...were beneficial in trimethoprim" to "mutations...were beneficial under trimethoprim exposure"

      We have made this change.

      (3) Line 32: Is gene dosage really only "relative to the genome"? Is it not simply its relative copy number generally? Consider changing to "The dosage of a gene, or its relative copy number, can impact its level of expression..."

      We have made this change.

      (4) Line 38: The idea that GDAs are 1000x more frequent than point mutations seems an overgeneralization.

      We agree with the reviewer and have softened our claim.

      (5) Line 50: The term "hard-wired" is confusing. Please be more specific.

      We have modified this statement to “…GDAs are less stable than point mutations….”.

      (6) Line 52-53: What do you mean by "there is also evidence to suggest that...more common in bacteria than appreciated"? Are you implying the field is naïve to this fact? If there is "evidence" of this, then a reference should be included. However, it's not clear why this is important to state in the article. I would consider simply removing this sentence. Less is more in this case.

      We have removed this statement.

      (7) Lines 59-60: Enzymes catalyze reactions. Please also state the substrates for DHFR. Consider, "It catalyzes the NADPH-dependent reduction of dihydrofolate to tetrahydrofolate, and important co-factor for..."

      We have made this change.

      (8) Line 72: Please change to, "In E. coli, DHFR is encoded by folA." You do not need to state this is a gene, as it is implicit with lowercase italics.

      We have made this change.

      (9) Lines 72-86: This paragraph is a bit confusing to read, as it has several different ideas in it. Consider breaking it into two paragraphs at Line 80, "In this study,...". The first paragraph could just review the trimethoprim resistance mechanisms in E. coli and so would change the first sentence (Line 72) to reflect this topic: "In E. coli, DHFR is encoded by folA and several different resistance mechanisms have been characterized." Then, just describe each mechanism in turn. Also, by "hot spots" it would seem you are referring to "point mutations" in the gene that alter the protein sequence and cluster onto the 3D protein structure when mapped? Please be more specific with this sentence for clarity.

      We have made these changes.

      (10) Lines 92-93: Please also state the MIC value of the strain to specifically define "sub-MIC". Alternatively, you could also state the fraction MIC (e.g. 0.1 x MIC).

      We have modified this statement to “…in 300 ng/mL of trimethoprim (corresponding to ~0.3 x MIC) for 25 generations.”

      (11) Lines 95-96. Remove, "These sequencing have been reported earlier, ...(2021)". You just need to cite the reference.

      We have made this change.

      (12) Line 96: Remove the word "gene".

      We have made this change.

      (13) Figure 1 and Figure 4C: The color scheme is tough for those with the most common type of color blindness. Red/green color deficiency causes a lot of difficulty with Red/gray, red/green, green/gray. Consider changing.

      We thank the reviewer for bringing this to our notice. We have modified the colour scheme throughout the manuscript.

      (14) Figure 1: Was there a trimethoprim resistance mechanism identified for LTMPR5?

      As stated by us in response to major comment #7, LTMPR5’s resistance seems to come from a novel mechanism involving loss of the pitA gene.

      (15) Line 349-351: Please briefly define "lower proteolytic stability" as a relative susceptibility to proteolytic degradation and make sure it is clear to the reader that this causes less DHFR. This needs to be clarified because it is confusing how a mutation that causes DHFR proteolytic instability would lead to an increase in trimethoprim IC50. So, you also need to mention that some mutations can cause both increased trimethoprim inhibition and lower proteolytic stability simultaneously. It seems the Trp30Arg mutation is an example of this, as this mutation is associated with a net increase in trimethoprim resistance despite the competing effects of the mutation on enzyme inhibition and DHFR levels.

      We thank the reviewer for this comment and agree that the text in the original manuscript did not fully convey the message. We have made modifications to this section (Lines 359-363) in the revised manuscript in agreement with the reviewer’s suggestions.

    1. Reviewer #1 (Public review):

      Summary:

      The authors seek to understand the role of different ratios of excitatory to inhibitory (EI) neurons, which in experimental studies of the cerebral cortex have been shown to range from 4 to 9. They do this through a simulation study of sparsely connected networks of excitatory and inhibitory neurons.

      Their main finding is that the participation ratio and decoding accuracy increase as the E/I ratio decreases. This suggests higher computational complexity.

      This is the start of an interesting computational study. However, there is no analysis to explain the numerical results, although there is a long literature of reduced models for randomly connected neural networks which could potentially be applied here. (For example, it seems that the authors could derive a mean field expression for the expected firing rate and variance - hence CV - which could be used to target points in parameter space (vs. repeated simulation in Figures 1,2).) The paper would be stronger and more impactful if this was attempted.

      Strengths:

      Some issues I appreciated are:

      (1) The use of a publicly available simulator (Brian), which helps reproducibility. I would also request that the authors supply submission or configuration scripts (if applicable, I don't know Brian).

      (2) A thorough exploration of the parameter space of interest (shown in Figure 2).

      (3) A good motivation for the underlying question: other things being equal, how does the E/I ratio impact computational capacity?

      Weaknesses:

      (1) Lack of mathematical analysis of the network model

      Major issues I recommend that the authors address (not sure whether these are "weaknesses"):

      (1) In "Coding capacity in different layers of visual cortex" the authors measure PR values from layers 2/3 and 4 in VISp and find that layer 2/3 has a higher PR than layer 4.

      But in Dahmen et al. 2020 (https://doi.org/10.1101/2020.11.02.365072 ), the opposite was found (see Figure 2d of Dahmen et al.): layer 2 had a lower PR than layer 4. Can the authors explain how that difference might arise? i.e. were they analyzing the same data sets? If so why the different results? Could it have to do with the way the authors subsample for the E/I ratio?

      From the Methods of that paper: "Visual stimuli were generated using scripts based on PsychoPy and followed one of two stimulus sequences ("brain observatory 1.1" and<br /> "functional connectivity"). We focused on spontaneous neural activity registered while the animal was not performing any task. In each session, the spontaneous activity condition lasted 30 minutes while the animal was in front of a screen of mean grey luminance. We, therefore, analyzed 26 of the original 58 sessions corresponding to the "functional connectivity" subdataset as they included such a period of spontaneous activity. " This suggests to me they may have analyzed recordings with the other stimulus sequence; however, the hypothesis that E/I ratio should modulate dimensionality would not seem to "care" about which stimulus sequence was used.

      (2) In Discussion (pg. 20, line 383): "They showed that brain regions closer to sensory input, like the thalamus, have higher dimensionality than those further away, such as<br /> the visual cortex. " How is this consistent with the hypothesis that "higher dimensionality might be linked to more complex cognitive functions"?

      (3) What is the probability of connection between different populations? e.g. the probability of there being a synaptic connection between any two E cells? I could not find a statement about this. It should be included in the Methods.

      (4) pg. 27, line 540: "Synchronicity within the network" For each cell pair, the authors use the maximum cross-correlation over time lag. I don't think I have seen this before. Can the authors explain why they use this measurement, vs (a) integrated cross-correlation or (b) cross-correlation at some time scale? Also, it seems like this fails to account for neuron pairs for which there is a strong inhibitory correlation.

      (5) "When stimulated, a time-varying input, μext(t), is applied to 2,000 randomly selected excitatory neurons. " I would guess that computing PR would depend on the overlap of the 500 neurons analyzed and this population. Do the authors check or control for that?

      5b) Related: to clarify, are the 500 neurons chosen from the analysis equally likely to be E or I neurons?

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2024-02605

      Corresponding author: Woo Jae, Kim

      1. ____Point-by-point description of the revisions

      Reviewer #1

      General Comment: This study investigates the role of the foraging gene in modulating interval timing behaviors in flies, with a particular focus on mating duration. Using single-cell RNA sequencing and gene knockdown experiments, the research demonstrates the crucial role of foraging gene expression in Pdfr-positive cells for achieving longer mating duration (LMD). The study further identifies key neurons in the ellipsoid body (EB) as essential when the foraging gene is overexpressed, highlighting its specific influence on LMD. The findings suggest that a small subset of EB neurons must express the foraging gene to modulate LMD effectively.

      __Answer:____ __We would like to express our gratitude to the reviewer for their insightful comments and positive feedback on our manuscript. During the revision process, we serendipitously discovered that the heart-specific expression of the foraging gene plays a crucial role in regulating LMD behavior. We have elaborated on the significance of this finding in the revised manuscript and have addressed the reviewer's comments accordingly.

      Comment 1. *(optional) Integration of Neuronal Subsets into a Pathway: The knockdown experiments indicate that a small subset of neurons must express the foraging gene to influence LMD. Could these neurons be integrated into a potential signaling pathway, or being treated as separate components within the brain circuit? How might this integration provide a more cohesive understanding of their role in LMD? *

      Answer: We sincerely thank the reviewer for her/his insightful comments regarding the integration of neuronal subsets into a signaling pathway and their potential role in modulating LMD behavior. During the revision process, we conducted further experiments to address this question. While we were unable to identify a specific small subset of EB neurons expressing foraging, we utilized the recently developed EB-split GAL4 driver line (SS00096), which is restricted to the EB region of the brain, to confirm that foraging expression in the EB is indeed crucial for generating LMD behavior (Fig. 4L-M). This finding underscores the importance of foraging in specific neural circuits within the EB for interval timing.

      Additionally, we discovered that foraging expression in Hand-GAL4-labeled pericardial cells (PCs) of the heart is essential for LMD behavior. These PCs are also partially labeled by fru-GAL4 and 30y-GAL4 drivers, indicating that foraging functions in both neuronal and non-neuronal tissues to regulate interval timing. Importantly, we observed that group-reared males exhibit higher calcium activity in PCs compared to socially isolated males, suggesting that social context-dependent calcium dynamics in the heart play a critical role in modulating LMD behavior.

      These findings highlight a novel integration of neuronal and cardiac mechanisms, where foraging expression in both the EB and heart coordinates calcium dynamics to regulate interval timing. This dual-tissue involvement provides a more cohesive understanding of how foraging integrates social cues with internal physiological states to modulate complex behaviors like LMD. We believe this integration of neuronal and cardiac pathways offers a comprehensive framework for understanding the gene’s pleiotropic roles in behavior. We have included these new findings in the revised manuscript to better address the reviewer’s question and to strengthen the discussion of how foraging functions across tissues to regulate interval timing behaviors.

      Comment 2. Genetic Considerations in Gal4 System Usage (Fig. 1D): In the study, the elavc155-Gal4 transgene, located on chromosome I, produces hemizygous males after crossing, while the repo-Gal4 transgene, located on chromosome III, results in heterozygous males. Is there any evidence suggesting that this genetic configuration could impact the experimental outcomes? If so, what steps could be taken to address potential issues?

      Answer: We appreciate the reviewer’s thoughtful consideration of potential genetic confounds related to the chromosomal locations of the elavc155 and repo-GAL4 transgenes. To address this concern, we conducted additional experiments using the nSyb-GAL4 driver, which is located on the third chromosome, and observed that knockdown of foraging with this driver also disrupts LMD behavior (Fig. S1G). This result aligns with our findings using elavc155 (chromosome I) and repo-GAL4 (chromosome III), indicating that the chromosomal location of the GAL4 transgene does not significantly impact the experimental outcomes.

      Furthermore, our extensive tissue-specific GAL4 screening, which included drivers on different chromosomes, consistently demonstrated that foraging knockdown effects on LMD are robust and reproducible across various genetic configurations. These results suggest that the observed behavioral deficits are due to the loss of foraging function rather than positional effects of the GAL4 transgenes. We thank the Reviewer for raising this important point and have taken care to address it thoroughly in our revised manuscript.

      Comment 3. Discrepancies in lacZ Signal Intensity (Fig. 5A): The observed discrepancies in lacZ signal intensity on the surface of the male brain have been attributed to the dissection procedure. Is it feasible to replace the current data with a new, more consistent dataset? How might improved dissection techniques mitigate these discrepancies?

      Answer____: We thank the reviewer for her/his observation regarding the discrepancies in lacZ signal intensity on the surface of the male brain, which we attributed to variations in the dissection procedure. While replacing the current dataset with a new one is feasible, we have instead shifted our focus to address this concern by leveraging more reliable and validated tissue-specific GAL4 drivers combined with foraging-RNAi.

      During the revision process, we extensively examined multiple foraging-GAL4 lines and found that foraging expression in the brain is limited and often inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To circumvent this issue, we utilized well-characterized tissue-GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior.

      Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment.

      We believe this new analysis addresses the reviewer’s concerns by providing a more robust and consistent approach to studying foraging function, focusing on its role in the heart rather than relying on potentially unreliable brain expression data. We hope these findings meet the reviewer’s expectations and provide a clearer understanding of foraging’s role in mating duration.

      Comment ____4. Rescue Experiment Data (Fig. S2L): Could additional data be provided to demonstrate the rescue effect using the c61-Gal4 driver, similar to what was observed with the 30y-Gal4 driver? How would such data enhance the study's conclusions regarding the specificity and robustness of the foraging gene's role in LMD?

      Answer: We appreciate the reviewer’s suggestion to provide additional rescue experiment data using the c61-GAL4 driver, similar to the results obtained with the 30y-GAL4 driver. While we do not currently have a UAS-for line to perform direct rescue experiments with c61-GAL4, we have conducted extensive follow-up experiments using both 30y-GAL4 driver to further validate the role of foraging in LMD behavior. These experiments consistently demonstrated that foraging knockdown in cells targeted by these drivers disrupts LMD, reinforcing the specificity and robustness of foraging’s role in interval timing.

      Additionally, our revised manuscript includes new findings that highlight the critical role of foraging expression in fru-positive heart neurons for generating male-specific mating investment. These heart neurons exhibit dynamic calcium activity changes in response to social context, further supporting the idea that foraging modulates LMD through both neuronal and non-neuronal mechanisms. While we acknowledge that direct rescue data with c61-GAL4 would strengthen the study, we believe the combination of 30y-GAL4 and c61-GAL4 knockdown results, along with the newly identified role of heart neurons, provides compelling evidence for foraging’s role in LMD.

      In addition, we have confirmed that the 30y-GAL4 driver labels fru-positive heart cells, further supporting the critical role of foraging expression in these cells for generating male-specific mating investment. This finding aligns with our broader results, demonstrating that foraging function in fru-positive heart neurons is essential for modulating interval timing behaviors, particularly LMD. We hope these additional analyses address the reviewer’s concerns and enhance the study’s conclusions regarding the specificity and robustness of foraging function in interval timing behaviors. We have incorporated the following findings into the main text:

      “Therefore, we conclude that the knockdown and genetic rescue effects observed with the Pdfr3A-GAL4 driver (Fig. 3J and 3N) and the 30y-GAL4 driver (Fig. 4A, S2A, and S2L) are attributable to their expression in the heart. In summary, our findings demonstrate that fru-positive heart cells expressing foraging and Pdfr play a critical role in mediating LMD behavior.”


      Reviewer #2

      General Comment: The authors nicely demonstrated that the Drosophila for gene is involved in the plastic LMD behavior that serves as a model for interval timing. For is widely expressed in the body, they have tentatively localized the LMD-relevant for functioning to the ellipsoid body of the central complex.

      Answer: We sincerely thank the reviewer for their positive feedback on our manuscript and their recognition of our findings regarding the role of the foraging gene in modulating plastic LMD behavior as a model for interval timing. In addition to its function in the ellipsoid body (EB) of the central complex, we have identified a novel and critical role for foraging in fru-positive heart neurons. These neurons are essential for regulating male-specific mating investment, as demonstrated by dynamic calcium activity changes in response to social context. This discovery expands our understanding of foraging’s pleiotropic roles, highlighting its function not only in neural circuits but also in non-neuronal tissues, particularly the heart, to modulate interval timing behaviors. We believe these findings provide a more comprehensive view of how *foraging* integrates genetic, neural, and physiological mechanisms to regulate complex behaviors. We hope this additional insight into the role of fru-positive heart neurons further strengthens the manuscript and aligns with the reviewer’s interest in the broader implications of foraging function.


      Major concerns: __ Comment 1.__ Please clarify how a loss-of-function forS allele can be dominant in the presence of overactive forR allele? In the same vein, please clarify how does the forR/forS transgeterozygote supports your hypothesis that high levels of PKG activity disrupt SMD and low levels of it disrupt LMD?

      Answer: We thank the reviewer for her/his insightful questions regarding the dominance of the forS allele in the presence of the overactive forR allele and the implications of the forR/forS transheterozygote phenotype. As the Reviewer noted, the forR allele is associated with higher PKG activity, while the forS allele exhibits lower PKG activity. The disruption of SMD in the presence of a single forR allele can be explained by the excessive PKG activity, which may hyperactivate or desensitize neural circuits required for SMD. Conversely, the forS homozygote disrupts LMD, suggesting that a minimum threshold of PKG activity is necessary for LMD generation.

      The forR/forS transheterozygote, which disrupts both LMD and SMD, presents an intriguing case. Unlike forR/+ or forS/+ heterozygotes, which show intact behaviors due to intermediate PKG activity levels, the forR/forS combination results in conflicting PKG activity levels that likely destabilize shared pathways required for both behaviors. We propose two hypotheses to explain this phenomenon:

      1. Metabolic Disruption: The foraginggene mediates adult plasticity and gene-environment interactions, particularly under conditions of food deprivation (Kent 2009). It influences body fat, carbohydrate metabolism, and gene expression levels, leading to metabolic and behavioral gene-environment interactions (GEI). In forR/forStransheterozygotes, the metabolic changes induced by each allele may accumulate without proper regulatory mechanisms, disrupting the male’s internal metabolic state and impairing the ability to accurately measure interval timing.

      Neuronal Polymorphism: The foraginggene regulates neuronal excitability, synaptic transmission, and nerve connectivity (Renger 1999). The forRand forS alleles may induce distinct neuronal polymorphisms, such as altered synaptic terminal morphology, which could lead to conflicting circuit dynamics in transheterozygotes. This neuronal mismatch may explain why forR/forS flies exhibit disrupted behaviors, unlike heterozygotes with a wild-type allele.

      These findings align with prior studies showing that PKG activity must be tightly regulated within context-dependent ranges for optimal behavior. The foraging gene’s pleiotropic roles, including its influence on metabolic and neural pathways, highlight the importance of allelic balance in maintaining behavioral robustness. The forR/forS transheterozygote phenotype underscores the complexity of foraging’s role in interval timing, where extreme or mismatched PKG activity levels disrupt circuit-specific thresholds critical for distinct behaviors. We hope this explanation clarifies the dominance effects and the role of PKG activity in LMD and SMD, and we have incorporated these insights into the revised manuscript to strengthen our discussion of foraging’s pleiotropic functions.

      We provide a concise explanation of this hypothesis in the Discussion section, as outlined below:

      “The foraging gene plays a critical role in regulating interval timing behaviors, with its allelic variants, rover and sitter, exhibiting distinct effects on LMD and SMD. These differences are primarily driven by their opposing impacts on cGMP-dependent protein kinase (PKG) activity. The forR allele, associated with higher PKG activity, disrupts SMD while maintaining normal LMD (Fig. 1A), suggesting that elevated PKG levels may hyperactivate or desensitize neural circuits specific to SMD processes. Conversely, the forS allele, characterized by lower PKG activity, impairs LMD but not SMD (Fig. 1B), indicating that reduced PKG activity fails to meet the neuromodulatory thresholds required for LMD coordination. The forR/forS transheterozygotes, which disrupt both LMD and SMD (Fig. 1C), reveal a complex interaction between these alleles, likely due to conflicting PKG activity levels or metabolic and neuronal polymorphisms that destabilize shared pathways. This phenomenon underscores the foraging gene’s pleiotropic roles, where allelic balance fine-tunes PKG activity to maintain behavioral robustness, while extreme or mismatched levels disrupt circuit-specific thresholds critical for distinct memory processes [6,10] .

      The foraging gene’s influence on interval timing behaviors extends beyond neural circuits to include metabolic and synaptic regulation. The intact behaviors observed in forR/+ or forS/+ heterozygotes suggest that intermediate PKG activity levels balance circuit dynamics, allowing for normal LMD and SMD. However, the dual deficits in forR/forS transheterozygotes highlight the importance of allelic balance, as conflicting PKG levels may lead to systemic disruptions in both metabolic and neural pathways. This aligns with previous studies showing that foraging mediates adult plasticity and gene-environment interactions, particularly under stress conditions, and regulates synaptic terminal morphology and neuronal excitability [29,77]. The gene’s role in integrating genetic and environmental cues further emphasizes its central role in adaptive behaviors. Collectively, these findings illustrate the complex interplay between PKG activity, neural circuits, and metabolic regulation in shaping interval timing behaviors, highlighting the foraging gene as a key modulator of behavioral plasticity in Drosophila [3,6,77].”

      Comment 2. Please consider removing lines 193-201 & Fig 3G,H, since abruptly and briefly returning to SMD could distract the reader and hinder the flow.

      Answer: We sincerely thank the reviewer for her/his suggestion to improve the flow of the manuscript. In response to reviewer’s feedback, we have removed Figure 3G-H and the related text (lines 193-201) from the main text. While the data on SMD behavior provided additional insights into the role of foraging in gustatory modulation via sNPF-expressing peptidergic neurons, we agree that its inclusion at this point in the manuscript could distract from the primary focus on LMD behavior and interval timing.

      Comment 3. Please use more specific Gal4 drivers to identify the exact subset of the EB-RNs where for function is necessary for LMD. Please note that Taghert lab already identified Pdfr+ EB-RN subset, and in contradiction to your findings, demonstrated that Cry is expressed in these Pdfr+ EB neurons

      Answer: We thank the reviewer for their suggestion to use more specific GAL4 drivers to identify the exact subset of EB ring neurons (EB-RNs) where foraging function is necessary for LMD. In response, we utilized the EB-split-GAL4 driver SS00096, which has been previously employed to map the neuroanatomical ultrastructure of the EB (Turner-Evans 2020). Knockdown of foraging using this refined EB driver disrupted LMD behavior, confirming that foraging function in the EB is indeed crucial for interval timing.

      Regarding the reviewer’s observation about the Taghert lab’s findings on Pdfr+ EB-RNs and the expression of Cry in these neurons, we acknowledge this discrepancy. However, during the revision process, we discovered that foraging and Pdfr are co-expressed not only in EB neurons but also in fru-positive heart neurons, which play a complementary role in modulating LMD behavior. This finding suggests that the apparent contradiction may arise from the dual-tissue involvement of foraging in both EB neurons and heart cells. While foraging function in the EB is critical, its role in heart neurons may provide an additional layer of regulation for interval timing behaviors, potentially compensating for or interacting with EB-related mechanisms.

      We have incorporated these insights into the revised manuscript, emphasizing the importance of both EB and heart neurons in mediating LMD behavior. This dual-tissue perspective offers a more comprehensive understanding of foraging’s role in interval timing and addresses the potential discrepancies highlighted by the reviewer. We hope this clarification resolves the reviewer’s concerns and strengthens the manuscript’s conclusions regarding the neural and non-neural mechanisms underlying foraging function.

      Comment 4. Please clarify how do you think for and Pdfr signaling molecularly interact in these neurons? Since your work doesn't implicate the for+ AL neurons, please remove lines 260-269.Please clarify if the Pdfr+ for+ EB neurons are also fru+.The lacZ staining in Fig5A-B is atypical in having a mosaic-like pattern. Please replace the image.

      Answer: We thank the reviewer for her/his thoughtful questions regarding the molecular interaction between foraging and Pdfr signaling, as well as their observations on the atypical lacZ staining pattern. Below, we address each point in detail:

      1. Molecular Interaction Between foragingand PdfrSignaling: Our tissue-specific driver screening indicates that Pdfr and foraging do not co-express in the same neurons within the brain. Instead, we found that Pdfr and foraging are co-expressed in fru-positive heart cells, suggesting that PDF-Pdfr signaling in these cells modulates calcium activity in pericardial cells (PCs) in a social context-dependent manner. This finding aligns with our previous work showing that PDF signaling is crucial for LMD behavior (Kim 2013). We propose that PDF-Pdfr signaling operates not only through the brain’s sLNv to LNd neuronal circuit but also through a brain-to-heart signaling axis, influencing behaviors and physiological processes across multiple tissues.

      Removal of Lines 260-269: As suggested, we have removed lines 260-269, which discussed for+ AL neurons, as our findings do not implicate these neurons in LMD regulation. This revision helps streamline the manuscript and maintain focus on the relevant neural and cardiac mechanisms.

      Clarification on Pdfr+for+EB Neurons and fru Expression: While our data do not directly address whether Pdfr+ for+ EB neurons are also fru+, we have confirmed that foraging and Pdfr co-express in fru-positive heart cells. This suggests that fru may play a role in integrating foraging and Pdfr signaling in non-neuronal tissues, particularly in the heart, to regulate LMD behavior.

      Replacement of lacZ Staining Images: During the revision process, we extensively examined multiple foraging-GAL4lines and found that foragingexpression in the brain is limited and often inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To circumvent this issue, we utilized well-characterized tissue-GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior. Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment. We believe this new analysis addresses the reviewer’s concerns by providing a more robust and consistent approach to studying foraging function, focusing on its role in the heart rather than relying on potentially unreliable brain expression data. We hope these findings meet the reviewer’s expectations and provide a clearer understanding of foraging’s role in mating duration.

      We hope these revisions meet the Reviewer’s expectations and provide a clearer understanding of the interplay between foraging and Pdfr signaling in interval timing behaviors.

      Comment 5. Please consider removing lines 303-312, since this negative result may dilute your final conclusions without adding strong factual value.

      Answer: We appreciate the reviewer's suggestion regarding lines 303-312. Upon careful consideration, we believe this paragraph provides important context about the roles of dsx-positive and fru-positive cells in foraging behavior. Specifically, it highlights that the foraging function is associated with fru-positive cells rather than dsx-positive cells, which is a key distinction in our study. This information is relevant to understanding the broader implications of our findings, as it underscores the functional specificity of these genes in regulating behavior. However, to address the reviewer's concern, we have revised the paragraph to ensure it is more concise and directly tied to the study's conclusions. We have also integrated additional data from the new manuscript to further strengthen the factual value of this section. We hope this adjustment strikes the right balance between maintaining necessary context and avoiding any dilution of the final conclusions. Thank you for this thoughtful feedback.

      __Minor concerns: __

      __Comment 6. __Minor points: In the intro please mention other interval timing mechanisms and their underlying molecular mechanisms (e.g., CREB work of Crickmore lab). Please provide a better rationale for why you thought for is a good candidate for LMD? In line 124, when you start to talk about larval neurons - please specify which neurons you are referring to. In Fig 2E,G,H - 'glia' should be replaced with 'neurons'.

      Answer: We appreciate the reviewer’s insightful comments regarding our conclusion linking LMD to interval timing behavior. Current research by Crickmore et al. has shed light on how mating duration in Drosophila serves as a powerful model for exploring changes in motivation over time as behavioral goals are achieved. For instance, at approximately six minutes into mating, sperm transfer occurs, leading to a significant shift in the male's nervous system: he no longer prioritizes sustaining the mating at the expense of his own survival. This change is driven by the output of four male-specific neurons that produce the neuropeptide Corazonin (Crz). When these Crz neurons are inhibited, sperm transfer does not occur, and the male fails to downregulate his motivation, resulting in matings that can last for hours instead of the typical ~23 minutes (Thornquist 2020).

      Recent research by Crickmore et al. has received NIH R01 funding (Mechanisms of Interval Timing, 1R01GM134222-01) to explore mating duration in Drosophila as a genetic model for interval timing. Their work highlights how changes in motivation over time can influence mating behavior, particularly noting that significant behavioral shifts occur during mating, such as the transfer of sperm at approximately six minutes, which correlates with a decrease in the male's motivation to continue mating (Thornquist 2020). These findings suggest that mating duration is not only a behavioral endpoint but may also reflect underlying mechanisms related to interval timing.

      In addition to the efforts of Crickmore's group to connect mating duration with a straightforward genetic model for interval timing, we have previously published several papers demonstrating that LMD and SMD can serve as effective genetic models for interval timing within the fly research community. For instance, we have successfully connected SMD to an interval timing model in a recently published paper (Lee 2023), as detailed below:

      "We hypothesize that SMD can serve as a straightforward genetic model system through which we can investigate "interval timing," the capacity of animals to distinguish between periods ranging from minutes to hours in duration.....

      In summary, we report a novel sensory pathway that controls mating investment related to sexual experiences in Drosophila. Since both LMD and SMD behaviors are involved in controlling male investment by varying the interval of mating, these two behavioral paradigms will provide a new avenue to study how the brain computes the ‘interval timing’ that allows an animal to subjectively experience the passage of physical time (Buhusi & Meck, 2005; Merchant et al, 2012; Allman et al, 2013; Rammsayer & Troche, 2014; Golombek et al, 2014; Jazayeri & Shadlen, 2015)."

      Lee, S. G., Sun, D., Miao, H., Wu, Z., Kang, C., Saad, B., ... & Kim, W. J. (2023). Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. PLoS Genetics, 19(5), e1010753.

      We have also successfully linked LMD behavior to an interval timing model and have published several papers on this topic recently (Huang 2024,Zhang 2024,Sun 2024).

      Sun, Y., Zhang, X., Wu, Z., Li, W., & Kim, W. J. (2024). Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. G3: Genes, Genomes, Genetics, jkae255.

      Zhang, T., Zhang, X., Sun, D., & Kim, W. J. (2024). Exploring the Asymmetric Body’s Influence on Interval Timing Behaviors of Drosophila melanogaster. Behavior Genetics, 54(5), 416-425.

      Huang, Y., Kwan, A., & Kim, W. J. (2024). Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. Gene Reports, 36, 101999.

      Finally, in this context, we have outlined in our INTRODUCTION section below how our LMD and SMD models are related to interval timing, aiming to persuade readers of their relevance. We hope that the reviewer and readers are convinced that mating duration and its associated motivational changes such as LMD and SMD provide a compelling model for studying the genetic basis of interval timing in Drosophila.

      “The mating duration (MD) of male fruit flies, Drosophila melanogaster, serves as an excellent model for studying interval timing behaviors. In Drosophila, two notable interval timing behaviors related to mating duration have been identified: Longer-Mating-Duration (LMD), which is observed when males are in the presence of competitors and extends their mating duration [15–17] and Shorter-Mating-Duration (SMD), which is characterized by a reduction in mating time and is exhibited by sexually experienced males [18,19]. The MD of male fruit flies serves as an excellent model for studying interval timing, a process that can be modulated by internal states and environmental contexts. Previous studies by our group (Kim 2013,Kim 2012,Zhang 2024,Lee 2023,Huang 2024) and others (Thornquist 2020,Crickmore 2013,Zhang 2019,Zhang 2021) have established robust frameworks for investigating MD using advanced genetic tools, enabling the dissection of neural circuits and molecular mechanisms that govern interval timing.

      The foraging gene emerged as a strong candidate for regulating LMD due to its well-documented role in behavioral plasticity and decision-making processes (Kent 2009,Alwash 2021,Anreiter 2019). The foraging gene encodes a cGMP-dependent protein kinase (PKG), which has been implicated in modulating foraging behavior, aggression, and other context-dependent behaviors in Drosophila. Its involvement in these processes suggests a potential role in integrating environmental cues and internal states to regulate interval timing, such as LMD. Furthermore, the molecular mechanisms underlying interval timing have been explored in other contexts, such as the work of the Crickmore et al., which has demonstrated the critical role of CREB (cAMP response element-binding protein) in regulating behavioral timing and plasticity. CREB-dependent signaling pathways, along with other molecular players like PKG, provide a broader framework for understanding how interval timing is orchestrated at the neural and molecular levels (Thornquist 2020,Zhang 2016,Zhang 2021,Zhang 2019,Crickmore 2013,Zhang 2023). By investigating foraging in the context of LMD, we aim to uncover how specific genetic and neural mechanisms fine-tune interval timing in response to social and environmental cues, contributing to a deeper understanding of the principles governing behavioral adaptation.”

      When describing larval neurons, we provide specific references to ensure clarity and accuracy, as outlined below:

      “Moreover, the cultured giant neural characteristics of these phenotypes are distinctly different [29].”

      We thank the reviewer for catching this error. We have corrected the incorrect label "Glia" to "Neuron" in Figures 2E, 2G, and 2H.

      Reviewer #3

      General Comment: This manuscript explores the foraging gene's role in mediating interval timing behaviors, particularly mating duration, in Drosophila melanogaster. The two distinct alleles of the foraging gene-rover and sitter-demonstrate differential impacts on mating behaviors. Rovers show deficiencies in shorter mating duration (SMD), while sitters are impaired in longer mating duration (LMD). The gene's expression in specific neuronal populations, particularly those expressing Pdfr (a critical regulator of circadian rhythms), is crucial for LMD. The study further identifies sexually dimorphic patterns of foraging gene expression, with male-biased expression possibly in the ellipsoid body (EB) being responsible for regulating LMD behavior. The findings suggest that the foraging gene operates through a complex neural circuitry that integrates genetic and environmental factors to influence mating behaviors in a time-dependent manner. Additionally, restoring foraging expression in Pdfr-positive cells rescues LMD behavior, confirming its central role in interval timing related to mating.

      Answer: We sincerely thank the reviewer for her/his thoughtful and comprehensive synthesis of our work, as well as their recognition of its key contributions. We are grateful that the reviewer highlighted the central findings of our study, including the allele-specific roles of forR (rover) and forS (sitter) in regulating distinct interval timing behaviors—specifically, the deficiencies of rovers in SMD and sitters in LMD. We also appreciate the reviewer’s emphasis on the sexually dimorphic expression of the *foraging* gene, particularly its male-biased expression in the ellipsoid body (EB), and its critical role in Pdfr-positive neurons for mediating LMD.

      We agree with the reviewer that the interplay between genetic factors (e.g., allelic variation in foraging) and environmental cues (e.g., circadian rhythms via Pdfr pathways) underscores the complexity of interval timing regulation. The rescue of LMD behavior by restoring foraging expression in Pdfr cells further supports our hypothesis that foraging operates through specialized neural circuits to integrate temporal and environmental inputs. This finding aligns with broader studies on interval timing mechanisms, such as the work of the Crickmore lab on CREB-dependent pathways, which have demonstrated how molecular and neural mechanisms converge to regulate behavioral plasticity and timing.

      In the revised manuscript, we will expand on these points to strengthen the discussion of foraging’s pleiotropic roles in time-dependent mating strategies and its potential links to evolutionary fitness. Specifically, we will incorporate additional insights from the new manuscript, including further evidence of how foraging balances behavioral plasticity with metabolic and neural demands, and how its expression in specific neuronal populations, such as the EB, contributes to adaptive behaviors. These updates will provide a more comprehensive understanding of the gene’s role in interval timing and its broader implications for behavioral adaptation. Once again, we thank the Reviewer for their valuable feedback, which has helped us refine and enhance the presentation of our findings.

      __Major concerns: __

      Comment 1. The sexually dimorphic expression of the foraging gene is not convincing. Specifically, the lacZ signal in the male brain is not representative.

      __Answer:____ __We sincerely thank the reviewer for her/his insightful comment regarding the sexually dimorphic expression of the foraging gene. We agree that the lacZ signal in the male brain, as presented, may not be fully representative, and we appreciate the reviewer’s observation regarding the discrepancies in signal intensity, which we attribute to variations in dissection procedures. While replacing the current dataset with a new one is feasible, we have chosen to address this concern by shifting our focus to a more reliable and validated approach using tissue-specific GAL4 drivers combined with foraging-RNAi.

      During the revision process, we conducted an extensive examination of multiple foraging-GAL4 lines and found that foraging expression in the brain is often limited and inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To overcome this limitation, we employed well-characterized tissue-specific GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior.

      Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment.

      By focusing on the heart and leveraging more reliable genetic tools, we believe this new analysis addresses the Reviewer’s concerns and provides a more robust and consistent approach to studying foraging function. We hope these findings meet the reviewer’s expectations and offer a clearer understanding of foraging’s role in mating duration. We are grateful for the Reviewer’s constructive feedback, which has significantly strengthened our study.

      Comment 2____. Key control genotypes are missing.

      Answer: We thank the Reviewer for raising this important point regarding control genotypes. We would like to clarify that all necessary control experiments have indeed been conducted, and the results are included in the manuscript. Detailed descriptions of these controls, including the specific genotypes and experimental conditions, are provided in the Methods section. For example, control experiments were performed to account for genetic background effects, GAL4 driver activity, and RNAi efficiency, ensuring the reliability and specificity of our findings. In the revised manuscript, we have further emphasized these control experiments and their outcomes to ensure transparency and reproducibility. We have also included additional details in the Results section to highlight how these controls validate our key findings. For instance, control genotypes lacking the foraging-RNAi or GAL4 drivers were used to confirm that the observed phenotypes are specifically due to the manipulation of foraging expression.

      We appreciate the Reviewer’s attention to this critical aspect of our study and hope that the additional clarification and emphasis on control experiments in the revised manuscript address their concerns. If there are specific control genotypes or experiments the reviewer would like us to include or elaborate on further, we would be happy to do so. Thank you for this valuable feedback.

      Comment 3____.fru is not expressed in the EB, so the authors may need to reconcile their model in figure 5G.

      Answer: We thank the reviewer for her/his insightful comment regarding the expression of fru in the ellipsoid body (EB) and its relevance to our model in Figure 5G. We agree that fru is not expressed in the EB, and we acknowledge the need to reconcile this aspect of our model. While initial evidence suggested a potential role for the EB in regulating foraging-dependent LMD behavior, further investigation has revealed that neurons outside the EB are more likely to be involved in this process.

      During our revision, we identified fru-positive heart neurons that coexpress Pdfr and foraging, which appear to play a critical role in modulating LMD behavior. These findings suggest that the heart, rather than the EB, may be a key site for foraging function in the context of interval timing and mating duration. Specifically, we demonstrated that calcium activity in these fru+ heart cells is dynamically regulated by social context, further supporting their role in modulating male mating investment.

      In light of these new findings, we revised Figure 5G as new Figure 6H and the accompanying model to reflect the updated understanding that fru+ heart neurons, rather than EB neurons, are central to the regulation of LMD behavior. This adjustment aligns with our broader goal of accurately representing the neural and molecular mechanisms underlying foraging’s role in interval timing. We appreciate the Reviewer’s feedback, which has helped us refine our model and strengthen the manuscript. We hope these revisions address their concerns and provide a clearer and more accurate representation of our findings. Thank you for this valuable input.

      Minor concerns: Comment 4____.

      Line 32, what do you mean by "overall success of the collective"

      Line 124-126: I suggest not using "sitter neurons" or "rover neurons". Line 301, typo with "male-specific".

      Answer: We thank the Reviewer for their careful reading and constructive feedback. We have addressed each of their comments as follows:

      1. Line 32: We agree with the reviewer that the phrase "overall success of the collective" was unclear and have completely revised the Abstract to remove this expression. The updated Abstract now provides a clearer and more concise summary of our findings.

      Lines 124-126: We appreciate the reviewer’s suggestion to avoid using the terms "sitter neurons" or "rover neurons," as they could be misleading. We have revised this phrasing to "neurons of sitter/rover allele" to more accurately reflect the genetic context of our study.

      Line 301: We have corrected the typo with "male-specific" to ensure accuracy and clarity in the text.

      We hope these revisions address the Reviewer’s concerns and improve the overall quality of the manuscript. Thank you for your valuable input, which has helped us refine our work.

      __Strengths and limitations of the study:______ This study presents a significant advancement in understanding the foraging gene's role in regulating mating behaviors through interval timing, and identifies the critical role of Pdfr-expressing neurons in the ellipsoid body for LMD. However, it does not fully explain how these neurons specifically modulate timing mechanisms. The lack of in-depth mechanistic exploration of how these neurons interact with other circuits involved in memory and decision-making leaves gaps in the understanding of the exact pathways influencing interval timing. Also, the study focuses more on LMD behaviors and the neural circuits involved, leaving the mechanisms underlying SMD comparatively underexplored.

      __Answer:____ __We thank the reviewer for her/his thoughtful assessment of the strengths and limitations of our study. We agree that our work represents a significant advancement in understanding the role of the foraging gene in regulating mating behaviors through interval timing, particularly in identifying the critical role of Pdfr-expressing neurons in the ellipsoid body (EB) for long mating duration (LMD). However, we acknowledge that the initial manuscript did not fully elucidate how these neurons specifically modulate timing mechanisms or interact with other neural circuits involved in memory and decision-making.

      In response to this feedback, we have conducted additional experiments and analyses, which are now included in the revised manuscript. Specifically, we identified fru-positive heart neurons that coexpress Pdfr and foraging, and we demonstrated their essential role in LMD using calcium imaging (CaLexA). These findings provide a more comprehensive mechanistic understanding of how foraging influences interval timing through cardiac activity, which is dynamically regulated by social context. This new evidence addresses the reviewer’s concern by offering a clearer picture of the neural and molecular pathways underlying LMD.

      Regarding SMD behavior, we agree that it was comparatively underexplored in the initial manuscript. However, we have extensively studied SMD in other contexts, as highlighted in several of our previously published papers. These studies have investigated the sensory mechanisms, memory processes, peptidergic signaling, and clock gene functions associated with SMD (Zhang 2024,Zhang 2024,Sun 2024,Wong 2019,Kim 2024,Lee 2023). While the current manuscript focuses primarily on LMD, we will include a discussion of these findings to provide a more balanced perspective on the mechanisms underlying both LMD and SMD.

      We believe these revisions address the Reviewer’s concerns and significantly strengthen the manuscript by providing a more detailed mechanistic understanding of foraging’s role in interval timing and mating behaviors. We are grateful for the Reviewer’s constructive feedback, which has helped us improve the depth and clarity of our study. Thank you for your valuable input.

      __Advance:______ This study brings a novel perspective to the foraging gene, previously known for its role in regulating food-search behavior. It demonstrates that foraging is also involved in interval timing, a cognitive process integral to mating behaviors in Drosophila. This discovery challenges the assumption that foraging is solely related to foraging strategies, revealing a broader function in time-based decision-making processes.

      Answer: We sincerely thank the reviewer for her/his insightful comments and for recognizing the novel contributions of our study. We are pleased that the reviewer highlighted how our work expands the understanding of the foraging gene, which was previously primarily associated with food-search behavior. By demonstrating its role in interval timing—a cognitive process critical to mating behaviors in Drosophila—we challenge the conventional assumption that foraging is solely related to foraging strategies. Instead, our findings reveal its broader function in time-based decision-making processes, particularly in the context of mating duration.

      This discovery not only advances our understanding of the pleiotropic roles of foraging but also opens new avenues for exploring how genetic and neural mechanisms integrate temporal and environmental cues to regulate complex behaviors. We are grateful for the reviewer’s support and acknowledgment of the significance of our findings. Thank you for this valuable feedback.

      __Audience:______ The study offers significant value to several specialized research communities, including behavioral genetics and evolutionary biology, especially those using the Drosophila model. This could inform future research on other behaviors that depend on precise timing and decision-making.

      Answer: We sincerely thank the reviewer for her/his thoughtful comment and for recognizing the broad relevance of our study. We are pleased that the reviewer highlighted the significant value our work offers to be specialized research communities, particularly in behavioral genetics and evolutionary biology, as well as to researchers using the Drosophila model. By elucidating the role of the foraging gene in interval timing and its impact on mating behaviors, our findings provide a foundation for future research on other behaviors that rely on precise timing and decision-making. This study not only advances our understanding of the genetic and neural mechanisms underlying interval timing but also opens new avenues for exploring how similar processes may operate in other species or contexts. We hope our work will inspire further investigations into the interplay between genetic variation, neural circuits, and environmental cues in shaping adaptive behaviors. Thank you for your valuable feedback and for acknowledging the potential impact of our research.

  2. Feb 2025
    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      Juvenile Hormone (JH) plays a key role in insect development and physiology. Although the intracellular receptor for JH was identified long ago, a number of studies have shown that part of JH functions should be fulfilled through binding to an unknown membrane receptor, which was proposed to belong to the RTK family. In this study, the authors screened all RTKs from the H. armigera genome for their ability to mediate responses to JH III treatment both in cultured cells and in developing animals. They also present convincing evidence that CAD96CA and FGFR1 directly bind JH III, and that their role might be conserved in other insect species.

      Strengths:

      Altogether, the experimental approach is very complete and elegant, providing evidence for the role of CAD96CA and FGFR1 in JH signalling using different techniques and in different contexts. I believe that this work will open new perspectives to study the role of JH and better understand what is the contribution of signalling through membrane receptors for JH-dependent developmental processes.

      Weaknesses:

      I don't see major weaknesses in this study. However, I think that the manuscript would benefit from further information or discussion regarding the relationship between the two newly identified receptors. Experiments (especially in HEK-293T cells) suggest that CAD96CA and FGFR1 are sufficient on their own to transduce JH signalling. However, they are also necessary since loss-of-function conditions for each of them are sufficient to trigger strong effects (while the other is supposed to be still present).

      Thank you for the suggestion. We have added the discussion in the text: "CAD96CA and FGFR1 have similar functions in JH signaling, including transmitting JH signal for Kr-h1 expression, larval status maintaining, rapid intracellular calcium increase, phosphorylation of transcription factors MET1 and TAI, and high affinity to JH III. CAD96CA and FGFR1 are essential in the JH signal pathway, and loss-of-function for each is sufficient to trigger strong effects on pupation. The difference is that CAD96CA expression has no tissue specificity, and the Fgfr1 gene is highly expressed in the midgut; possibly, it plays a significant role in the midgut. Other possibility is that they play roles by forming heterodimer with each other or other RTKs, which needs to be addressed in future study. CAD96CA and FGFR1 transmit JH III signals in three different insect cell lines, suggesting their conserved roles in other insects.".

      In addition, despite showing different expression patterns, the two receptors seem to display similar developmental functions according to loss-of-function phenotypes. It is therefore unclear how to draw a model for membrane receptor-mediated JH signalling that includes both CAD96CA and FGFR1.

      Thank you for your question. We have modified the figure and the legends to make the conception clear.

      Reviewer #2 (Public Review):

      Summary:

      Juvenile hormone (JH) is a pleiotropic terpenoid hormone in insects that mainly regulates their development and reproduction. In particular, its developmental functions are described as the "status quo" action, as its presence in the hemolymph (the insect blood) prevents metamorphosis-initiating effects of ecdysone, another important hormone in insect development, and maintains the juvenile status of insects. While such canonical functions of JH are known to be mediated by its intracellular receptor complex composed of Met and Tai, there have been multiple reports suggesting the presence of cell membrane receptor(s) for JH, which mediate non-genomic effects of this terpenoid hormone. In particular, the presence of receptor tyrosine kinase(s) that phosphorylate Met/Tai in response to JH and thus indirectly affect the canonical JH signaling pathway has been strongly suggested. Given the importance of JH in insect physiology and the fact that the JH signaling pathway is a major target of insect growth regulators, elucidating the identification and functions of putative JH membrane receptors is of great significance from both basic and applied perspectives. In the present study, the authors identified candidate receptors for such cell membrane JH receptors, CAD96CA and FGFR1, in the cotton bollworm Helicoverpa armigera.

      Strengths:

      Their in vitro analyses are conducted thoroughly using multiple methods, which overall supports their claim that these receptors can bind to JH and mediate their non-genomic effects.

      Weaknesses:

      Results of their in vivo experiments, particularly those of their loss-of-function analyses using CRISPR mutants are still preliminary, and the results rather indicate that these membrane receptors do not have any physiologically significant roles in vivo. More specifically, previous studies in lepidopteran species have clearly and repeatedly shown that precocious metamorphosis is the hallmark phenotype for all JH signaling-deficient larvae. In contrast, the present study showed that Cad96ca and Fgfr1 G0 mutants only showed a slight acceleration in their pupation timing, which is not a typical phenotype one would expect from JH signaling deficiency. This is inconsistent with their working model provided in Figure 6, which indicates that these cell membrane JH receptors promote the canonical JH signaling by phosphorylating Met/Tai.

      If the authors argue that this slight acceleration of pupation is indeed a major JH signaling-deficient phenotype in Helicoverpa, they need to provide more data to support their claim by analyzing CRISPR mutants of other genes involved in JH signaling, such as Jhamt and Met. An alternative explanation is that there is functional redundancy between CAD96CA and FGFR1 in mediating phosphorylation of Met/Tai. This possibility can be tested by analyzing double knockouts of these two receptors.

      Thank you for your question and suggestion. The cadherin 96ca (CAD96CA) and fibroblast growth factor receptor 1 (FGFR1) were finally determined as JH cell membrane receptors by their roles in JH regulated-gene expression, maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and their JH-binding affinity. Their roles as JH cell membrane receptors were further determined by knockdown and knockout of them in vivo and in cell lines, and overexpression of them in mammal HEK-293T heterogeneously. Figure 6 is drafted by these solidate evidences.

      Cad96ca and Fgfr1 G0 mutants caused slight acceleration of pupation is one of the types of evidence of JH signaling-deficient. Othe evidences include a set of gene expression and the block of JH induced-rapid intracellular calcium increase.

      Kr-h1 is a typical indicator gene at the downstream of Jhamt and in JH signaling, so we used it as an indicator to examine JH signaling. Jhamt and Met or other genes might be affected in Cad96ca and Fgfr1 G0 mutants, which can be examined in future study.

      We have discussed the question that Cad96ca and Fgfr1 G0 mutants only showed a slight acceleration in their pupation timing: "Homozygous Cad96ca null Drosophila die at late pupal stages (Wang et al., 2009). However, we found that 86% of the larvae of the Cad96ca mutant successfully pupated in G0 generation, although earlier than the control. Similarly, null mutation of Fgfr1 or Fgfr2 in mouse is embryonic lethal (Arman et al., 1998; Deng et al., 1994; Yamaguchi et al., 1994). In D. melanogaster, homozygous Htl (Fgfr) mutant embryos die during late embryogenesis, too (Beati et al., 2020; Beiman et al., 1996; Gisselbrecht et al., 1996). However, in H. armigera, 91% of larvae successfully pupated in G0 generation after Fgfr1 knockout. The low death rate after Cad96ca and Fgfr1 knockout might be because of following reasons, including the editing efficiency (67% and 61% for Cad96ca mutant and Fgfr1 mutant, respectively), the chimera of the gene knockout at the G0 generation, and the redundant RTKs that play similar roles in JH signaling, similar to the redundant roles of MET and Germ-cell expressed bHLH-PAS (GCE) in JH signaling (Liu et al., 2009), which needs to obtain alive G1 homozygote mutants and double knockout of these two receptors in future study. We indeed observed that the eggs did not hatch successfully after mixed-mating of G0 Cad96ca mutant or Fgfr1 mutant, respectively, but the reason was not addressed further due to the embryonic death. By the similar reasons, most of the Cad96ca and Fgfr1 mutants showed a slight acceleration of pupation (about one day) without the typical precocious metamorphosis (at least one instar earlier) phenotype caused by JH signaling defects (Daimon et al., 2012; Fukuda, 1944; Riddiford et al., 2010) and JH pathway gene deletions (Abdou et al., 2011; Liu et al., 2009). On other side, JH can regulate gene transcription by diffusing into cells and binding to the intracellular receptor MET to conduct JH signal, which might affect the results of gene knockdown and knockout.".

      Currently, the validity of their calcium imaging analysis in Figure 5 is also questionable. When performing calcium imaging in cultured cells, it is critically important to treat all the cells at the end of each experiment with a hormone or other chemical reagents that universally induce calcium increase in each particular cell line. Without such positive control, the validity of calcium imaging data remains unknown, and readers cannot properly evaluate their results.

      Thank you for your question. For Figure 5, our goal was to demonstrate that JH can induce calcium mobilization through CAD96CA and FGFR1. Controls have been established between different experimental groups within the same cell, as well as between different cells. Increasing the positive experimental group would make the results more complex.

      Reviewer #3 (Public Review):

      Summary:

      In this study, Li et al. identified CAD96CA and FGF1 among 20 receptor tyrosine kinase receptors as mediators of JH signaling. By performing a screen in HaEpi cells with overactivated JH signaling, the authors pinpointed two main RTKs that contribute to the transduction of JH. Using the CRISPR/Cas9 system to generate mutants, the authors confirmed that these RTKs are required for normal JH activation, as precocious pupariation was observed in their absence. Additionally, the authors demonstrated that both CAD96CA and FGF1 exhibit a high affinity for JH, and their activation is necessary for the proper phosphorylation of Tai and Met, transcription factors that promote the transcriptional response. Finally, the authors provided evidence suggesting that the function of CAD96CA and FGF1 as JH receptors is conserved across insects.

      Strengths:

      The data provided by the authors are convincing and support the main conclusions of the study, providing ample evidence to demonstrate that phosphorylation of the transducers Met and Tai mainly depends on the activity of two RTKs. Additionally, the binding assays conducted by the authors support the function of CAD96CA and FGF1 as membrane receptors of JH. The study's results validate, at least in H. amigera, the predicted existence of membrane receptors for JH.

      Weaknesses:

      The study has several weaknesses that need to be addressed. Firstly, it is not clear what criteria were used by the authors to discard several other RTKs that were identified as repressors of JH signaling. For example, while NRK and Wsck may not fulfill all the requirements to become JH receptors, other evidence, such as depletion analysis and target gene expression, suggests they are involved in proper JH signaling activation.

      Thank you for your question. We screened the RTKs sequentially, including examining the roles of 20 RTKs identified in the H. armigera genome in JH regulated-gene expression to obtain primary candidates, followed by screening of the candidates by their roles in maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and affinity to JH. WSCK was not involved in the phosphorylation of MET and TAI and was discarded during subsequent screening. NRK did not bind to JH III, did not meet the screening strategy, and was discarded.

      We increased the information in the Introduction: "We screened the RTKs sequentially, including examining the roles of 20 RTKs identified in the H. armigera genome in JH regulated-gene expression to obtain primary candidates, followed by screening of the candidates by their roles in maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and affinity to JH. The cadherin 96ca (CAD96CA) and fibroblast growth factor receptor 1 (FGFR1) were finally determined as JH cell membrane receptors by their roles in JH regulated-gene expression, maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and their JH-binding affinity. Their roles as JH cell membrane receptors were further determined by knockdown and knockout of them in vivo and cell lines, and overexpression of them in mammal HEK-293T heterogeneously.".

      We increased discussion: "This study found six RTKs that respond to JH induction by participating in JH induced-gene expression and intracellular calcium increase, however; they exert different functions in JH signaling, and finally CAD96CA and FGFR1 are determined as JH cell membrane receptors by their roles in JH induced-phosphorylation of MET and TAI and binding to JH III. We screen the RTKs transmitting JH signal primarily by examining some of JH induced-gene expression. By examining other genes or by other strategies to screen the RTKs might find new RTKs functioning as JH cell membrane receptors; however, the key evaluation indicators, such as the binding affinity of the RTKs to JH and the function in transmitting JH signal to maintain larval status are essential.".

      Secondly, the expression of the six RTKs, which, when knocked down, were able to revert JH signaling activation, was mainly detected in the last larval stage of H. amigera. However, since JH signaling is active throughout larval development, it is unclear whether these RTKs are completely required for pathway activation or only needed for high activation levels at the last larval stage.

      Thank you for the question. We knocked down the genes at last larval stage to observe pupation, which is a relatively simple and easily to be observed target to examine the role of the gene in JH-maintained larval status. The results from CRISPR/Cas9 experiments showed: "Most wild-type larvae showed a phenotype of pupation on time. However, in the Cad96ca mutant, 86% of the larvae (an editing efficiency of 67% by TA clone analysis) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 24 h earlier. In the Fgfr1 mutant, 91% of the larvae (an editing efficiency of 61%) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 23 h earlier (Figure 4D and E). The data suggested that CAD96CA and FGFR1 support larval growth and prevent pupation in vivo.".

      Additionally, the mechanism by which different RTKs exert their functions in a specific manner is not clear. According to the expression profile of the different RTKs, one might expect some redundant role of those receptors. In fact the no reversion of phosphorilation of tai and met upon depletion of Wsck in cells with overactivated JH signalling seems to support this idea.

      Nevertheless, and despite the overlapping expression of the different receptors, all RTKs seem to be required for proper pathway activation, even in the case of FGF1 which seems to be only expressed in the midgut. This is an intriguing point unresolved in the study.

      Thank you for your comments. Yes, from our study, different RTKs exert their functions in a specific manner. We have increased discussion: "This study found six RTKs that respond to JH induction by participating in JH induced-gene expression and intracellular calcium increase, however; they exert different functions in JH signaling, and finally CAD96CA and FGFR1 are determined as JH cell membrane receptors by their roles in JH induced-phosphorylation of MET and TAI and binding to JH III. We screen the RTKs transmitting JH signal primarily by examining some of JH induced-gene expression. By examining other genes or by other strategies to screen the RTKs might find new RTKs functioning as JH cell membrane receptors; however, the key evaluation indicators, such as the binding affinity of the RTKs to JH and the function in transmitting JH signal to maintain larval status are essential.".

      Finally, the study does not explain how RTKs with known ligands could also bind JH and contribute to JH signaling activation. in Drosophila, FGF1 is activated by pyramus and thisbe for mesoderm development, while CAD96CA is activated by collagen during wound healing. Now the authors claim that in addition to these ligands, the receptors also bind to JH. However, it is unclear whether these RTKs are activated by JH independently of their known ligands, suggesting a specific binding site for JH, or if they are only induced by JH activation when those ligands are present in a synergistic manner. Alternatively, another explanation could be that the RTK pathways by their known ligands activation may induce certain levels of JH transducer phosphorylation, which, in the presence of JH, contributes to the full pathway activation without JH-RTK binding being necessary.

      Thank you for your professional questions. It is an exciting and challenging to explore the molecular mechanism by which multiple ligands transmit signals through the same receptor. It requires a long-term research plan and in-depth studies. We added discussion in the text: "CAD96CA (also known as Stitcher, Ret-like receptor tyrosine kinase) activates upon epidermal wounding in Drosophila embryos (Tsarouhas et al., 2014) and promotes growth and suppresses autophagy in the Drosophila epithelial imaginal wing discs (O'Farrell et al., 2013). There is a CAD96CA in the genome of the H. armigera, which is without function study. Here, we reported that CAD96CA prevents pupation by transmitting JH signal as a JH cell membrane receptor. We also showed that CAD96CA of other insects has a universal function of transmitting JH signal to trigger Ca2+ mobilization, as demonstrated by the study in Sf9 cell lines of S. frugiperda and S2 cell lines of D. melanogaster.

      FGFRs control cell migration and differentiation in the developing embryo of D. melanogaster (Muha and Muller, 2013). The ligand of FGFR is FGF in D. melanogaste_r (Du et al., 2018_). FGF binds FGFR and triggers cell proliferation, differentiation, migration, and survival (Beenken and Mohammadi, 2009; Lemmon and Schlessinger, 2010). Three FGF ligands and two FGF receptors (FGFRs) are identified in Drosophila (Huang and Stern, 2005). The Drosophila FGF-FGFR interaction is specific. Different ligands have different functions. The activation of FGFRs by specific ligands can affect specific biological processes (Kadam et al., 2009). The FGFR in the membrane of Sf9 cells can bind to Vip3Aa (Jiang et al., 2018). One FGF and one FGFR are in the H. armigera genome, which has yet to be studied functionally. The study found that FGFR prevents insect pupation by transmitting JH signal as a JH cell membrane receptor. Exploring the molecular mechanism and output by which multiple ligands transmit signals through the same receptor is exciting and challenging.".

      Reviewer #1 (Recommendations For The Authors):

      As an experimental suggestion, I will only propose that authors test the double knock-down/knock-out or overexpression of CAD96CA and FGFR1 to give some hints into how redundant/independent the two receptors are.

      Thank you very much for your professional advice. We agree with your point of view that double knockout of CAD96CA and FGFR1 is very important to resolve the redundant/independent of the two receptors, which can make our research more complete. Unfortunately, due to experimental difficulty and time constraints, we did not provide supplementary experiments. In this study, we aim to screen the cell membrane receptors of JH. Therefore, we focused on which RTKs can function as receptors. This article is a preliminary study to identify the cell membrane receptors of JH. To further understand the relationship between the two membrane receptors, we will conduct in-depth research in future work.

      Apart from that, here are some minor points about the manuscript:

      Figure 2A: changing the scale on the y-axis would help to better see the different genotypes (similar to the way it is presented in Figure 5).

      Thanks for your reminding, we have changed the scale in Figure 2A.

      Figure 4J: image settings could be improved to better highlight the green fluorescence.

      Thank you for your advice, we have improved the imaged in Figure 4J.

      In general, the manuscript would benefit from some proofreading since a number of sentences are incorrect.

      Thanks for your reminding, we have carefully revised the manuscript.

      Reviewer #2 (Recommendations For The Authors):

      (1) Although the authors note that there are 21 RTK genes in Drosophila (line 55), I can only see 16 Drosophila RTKs in Figure 1 - Figure Supplement 1. Some important Drosophila RTKs such as breathless are missing. The authors need to redraw the phylogenetic tree.

      Thanks for your reminding, we have presented the new phylogenetic tree in Figure 1-figure supplement 1.

      (2) The accelerated pupation phenotype in Cad96ca and Fgfr1 G0 mutants needs to be better described. In particular, it is critical to examine which developmental stage(s) are shortened in these mutant larvae. Refer to a similar study on a JH biosynthetic enzyme in Bombyx (PMID: 22412378) regarding how to describe the developmental timing phenotype.

      Thank you for your advice. We have re-shown Figure 4E and added the explanation in the text: "In 61 survivors of Cas9 protein plus Cad96ca-gRNA injection, 30 mutants were sequenced, and a mutation efficiency was 49.2%. Similarly, in the 65 survivors of Cas9 protein plus Fgfr1-gRNA injection, 35 mutants were sequenced, and a mutation efficiency was 53.8% (Figure 4C). The DNA sequences, deduced amino acids and off–target were analyzed (Figure 4—figure supplement 1). Most wild-type larvae showed a phenotype of pupation on time. However, in the Cad96ca mutant, 86% of the larvae (an editing efficiency of 67% by TA clone analysis) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 24 h earlier. In the Fgfr1 mutant, 91% of the larvae (an editing efficiency of 61%) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 23 h earlier (Figure 4D and E). The data suggested that CAD96CA and FGFR1 support larval growth and prevent pupation in vivo.".

      (3) The editing efficiency described in lines 211-213 is obscure. Does this indicate the percentage of animals with noisy sequencing spectra or the percentage of mutation rates analyzed by TA cloning?

      Thanks for your reminder. We have revised the description in the text: "In 61 survivors of Cas9 protein plus Cad96ca-gRNA injection, 30 mutants were sequenced, and a mutation efficiency was 49.2%. Similarly, in the 65 survivors of Cas9 protein plus Fgfr1-gRNA injection, 35 mutants were sequenced, and a mutation efficiency was 53.8% (Figure 4C). The DNA sequences, deduced amino acids and off–target were analyzed (Figure 4—figure supplement 1). Most wild-type larvae showed a phenotype of pupation on time. However, in the Cad96ca mutant, 86% of the larvae (an editing efficiency of 67% by TA clone analysis) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 24 h earlier. In the Fgfr1 mutant, 91% of the larvae (an editing efficiency of 61%) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 23 h earlier (Figure 4D and E). The data suggested that CAD96CA and FGFR1 support larval growth and prevent pupation in vivo.".

      (4) In Figures 4F and G, the authors examined expression levels of some JH/ecdysone responsive genes only at 0 hr-old 6th instar larvae. This single developmental stage is not enough for this analysis. In particular, the expression level of Fgfr1 only goes up in the mid-6th instar according to their own data (Figure 1-Figure Supplement 4), so it is critical to examine expression levels of these genes at least throughout the 6th larval instar.

      Thank you for your advice. Indeed, it is essential to detect the expression levels of JH/ecdysone response genes in the whole sixth instar larvae. Because we observed that the mutation has a shorter feeding stage at the sixth instar, we examined the expression level of the JH/ecdysone response gene at the early sixth instar. Due to the number of mutants obtained in the experiment was small and non-destructive sampling could not be performed in sixth instar period, there were no enough samples to test. In the future, we will generate Cad96ca Fgfr1 double mutations to carry out studies and detect the expression level of JH/ecdysone response genes in the whole sixth instar.

      (5) As mentioned above, some important Drosophila RTKs such as breathless are missing in their analyses. As breathless is a close paralog of heartless (Htl), I am sure that Drosophila breathless is also orthologous to Helicoverpa FGFR1. The authors therefore need to analyze breathless in Figure 5B in addition to Htl.

      Thank you for your advice. We added experiments and the results are shown in Figure 5B and Figure 5—figure supplement 1.

      (6) More discussion about the reason why dsNrk and dsWsck can provide resistance to JHIII in Figure 1 is required.

      Thank you for your advice. We added explanation in the discussion: "It is generally believed that the primary role of JH is to antagonize 20E during larval molting (Riddiford, 2008). The knockdown of Cad96ca, Nrk, Fgfr1, and Wsck showed phenotypes resistant to JH III induction and the decrease of Kr-h1 and increase of Br-z7 expression, but knockdown of Vegfr and Drl only decrease Kr-h1, without increase of Br-z7. Br-z7 is involved in 20E-induced metamorphosis in H. armigera (Cai et al., 2014), whereas, Kr-h1 is a JH early response gene that mediates JH action (Minakuchi et al., 2009) and represses Br expression (Riddiford et al., 2010). The high expression of Br-z7 is possible due to the down-regulation of Kr-h1 in Cad96ca, Nrk, Fgfr1 and Wsck knockdown larvae. The different expression profiles of Br-z7 in Vegfr and Drl knockdown larvae suggest other roles of Vegfr and Drl in JH signaling, which need further study."

      Reviewer #3 (Recommendations For The Authors):

      (1) The authors should consider optimizing their experimental approach by depleting the six candidate RTKs in an early larval stage rather than using a sensitized background with JH application in the last larval stage.

      Thank you for your precious suggestion. We knocked down the genes at last larval stage to observe pupation, which is a relatively simple and easily to be observed target to examine the role of the gene in JH-maintained larval status. The results from CRISPR/Cas9 experiments showed: "Most wild-type larvae showed a phenotype of pupation on time. However, in the Cad96ca mutant, 86% of the larvae (an editing efficiency of 67% by TA clone analysis) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 24 h earlier. In the Fgfr1 mutant, 91% of the larvae (an editing efficiency of 61%) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 23 h earlier (Figure 4D and E). The data suggested that CAD96CA and FGFR1 support larval growth and prevent pupation in vivo.". To know the roles of other RTKs in the whole larval development needs future work since a lot of experiments are needed.

      (2) Including a positive control for JH signaling, such as met or tai, would strengthen the assays and provide a benchmark for evaluating the downregulation of target genes and phenotype reversion upon JH application. This addition, especially in Figure 1, would enhance the interpretability of the results.

      Thank you for your suggestion. We agree with your point of view that adding the detection of Met or Tai as a positive control. Our laboratory has reported in previous studies that knockdown of Met leads to decreased expression of genes in the JH signaling pathway and precocious pupation (PMID: 24872508), so we did not repeat this related experiment in this study. In the future, when performg Cad96ca and Fgfr1 double mutant experiments, Met mutant can be generated as a control to provide more references for the interpretation of the results.

      (3) I recommend revising the manuscript to improve readability, particularly in the Results section, where descriptions of the binding part are particularly dense.

      Thank you for your advice. We have carefully revised the manuscript.

      (4) In line 122, please add the reference Wang et al., 2016.

      Thank you for your reminding, we have added the reference in line 125 of the new manuscript.

      (5) The authors should clarify why they chose to test the possible binding to JH of only Cad96CA, FGFR1, and NRK after conducting various assays while including OTK in the study as a negative control. This explanation should be included in the text.

      Thank you for the suggestion. We added the explanation, as described in the text: "We screened the RTKs sequentially, including examining the roles of 20 RTKs identified in the H. armigera genome in JH regulated-gene expression to obtain primary candidates, followed by screening of the candidates by their roles in maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and affinity to JH. The cadherin 96ca (CAD96CA) and fibroblast growth factor receptor 1 (FGFR1) were finally determined as JH cell membrane receptors by their roles in JH regulated-gene expression, maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and their JH-binding affinity. Their roles as JH cell membrane receptors were further determined by knockdown and knockout of them in vivo and cell lines, and overexpression of them in mammal HEK-293T heterogeneously.".

      "Since Cad96CA, FGFR1, and NRK were not only involved in JH-regulated Kr-h1 expression, JH III-induced delayed pupation, and calcium levels increase, but also involved in MET and TAI phosphorylation, we further analyzed their binding affinity to JH III. OTK did not respond to JH III, so we used it as a control protein on the cell membrane to exclude the possibility of nonspecific binding.".

      (6) The observed embryonic lethality of cad96ca and FGF1 mutants in Drosophila contrasts with the ability of the respective mutants in H. armigera to reach the pupal stage. The authors should discuss this significant difference.

      Thank you for the suggestion. We added the explanation in the discussion, as described in the text: "Homozygous Cad96ca null Drosophila die at late pupal stages (Wang et al., 2009). However, we found that 86% of the larvae of the Cad96ca mutant successfully pupated in G0 generation, although earlier than the control. Similarly, null mutation of Fgfr1 or Fgfr2 in mouse is embryonic lethal (Arman et al., 1998; Deng et al., 1994; Yamaguchi et al., 1994). In D. melanogaster, homozygous Htl (Fgfr) mutant embryos die during late embryogenesis, too (Beati et al., 2020; Beiman et al., 1996; Gisselbrecht et al., 1996). However, in H. armigera, 91% of larvae successfully pupated in G0 generation after Fgfr1 knockout. The low death rate after Cad96ca and Fgfr1 knockout might be because of following reasons, including the editing efficiency (67% and 61% for Cad96ca mutant and Fgfr1 mutant, respectively), the chimera of the gene knockout at the G0 generation, and the redundant RTKs that play similar roles in JH signaling, similar to the redundant roles of MET and Germ-cell expressed bHLH-PAS (GCE) in JH signaling (Liu et al., 2009), which needs to obtain alive G1 homozygote mutants and double knockout of these two receptors in future study. We indeed observed that the eggs did not hatch successfully after mixed-mating of G0 Cad96ca mutant or Fgfr1 mutant, respectively, but the reason was not addressed further due to the embryonic death. By the similar reasons, most of the Cad96ca and Fgfr1 mutants showed a slight acceleration of pupation (about one day) without the typical precocious metamorphosis (at least one instar earlier) phenotype caused by JH signaling defects (Daimon et al., 2012; Fukuda, 1944; Riddiford et al., 2010) and JH pathway gene deletions (Abdou et al., 2011; Liu et al., 2009). On other side, JH can regulate gene transcription by diffusing into cells and binding to the intracellular receptor MET to conduct JH signal, which might affect the results of gene knockdown and knockout.".

      (7) Building upon the previous point, it is noteworthy that the cad96ca and FGF1 mutants exhibit only a 24-hour early pupation phenotype, contrasting with the 48-hour early pupation induced by Kr-h1 depletion. This discrepancy suggests that while the function of these RTKs is necessary, it may not be sufficient to fully activate JH signaling. The expression profile of these receptors, primarily observed in the last larval stage, supports this hypothesis.

      Thank you for your suggestion. We added the explanation in the discussion, as described in the text: "Homozygous Cad96ca null Drosophila die at late pupal stages (Wang et al., 2009). However, we found that 86% of the larvae of the Cad96ca mutant successfully pupated in G0 generation, although earlier than the control. Similarly, null mutation of Fgfr1 or Fgfr2 in mouse is embryonic lethal (Arman et al., 1998; Deng et al., 1994; Yamaguchi et al., 1994). In D. melanogaster, homozygous Htl (Fgfr) mutant embryos die during late embryogenesis, too (Beati et al., 2020; Beiman et al., 1996; Gisselbrecht et al., 1996). However, in H. armigera, 91% of larvae successfully pupated in G0 generation after Fgfr1 knockout. The low death rate after Cad96ca and Fgfr1 knockout might be because of following reasons, including the editing efficiency (67% and 61% for Cad96ca mutant and Fgfr1 mutant, respectively), the chimera of the gene knockout at the G0 generation, and the redundant RTKs that play similar roles in JH signaling, similar to the redundant roles of MET and Germ-cell expressed bHLH-PAS (GCE) in JH signaling (Liu et al., 2009), which needs to obtain alive G1 homozygote mutants and double knockout of these two receptors in future study. We indeed observed that the eggs did not hatch successfully after mixed-mating of G0 Cad96ca mutant or Fgfr1 mutant, respectively, but the reason was not addressed further due to the embryonic death. By the similar reasons, most of the Cad96ca and Fgfr1 mutants showed a slight acceleration of pupation (about one day) without the typical precocious metamorphosis (at least one instar earlier) phenotype caused by JH signaling defects (Daimon et al., 2012; Fukuda, 1944; Riddiford et al., 2010) and JH pathway gene deletions (Abdou et al., 2011; Liu et al., 2009). On other side, JH can regulate gene transcription by diffusing into cells and binding to the intracellular receptor MET to conduct JH signal, which might affect the results of gene knockdown and knockout.".

      (8) The expression profile of the RTK hits described in Supplementary Figure 4A appears to be limited to the last larval stage until pupation. The authors should clarify whether these receptors are expressed earlier, and the meaning of the letters in the plot should be described in the figure legend.

      Thank you for the suggestion. We added the explanation in the Figure 1—figure supplement 4 legend, as described in the text: "The expression profiles of Vegfr1, Drl, Cad96ca, Nrk, Fgfr1, and Wsck during development. 5F: fifth instar feeding larvae; 5M: fifth instar molting larvae; 6th-6 h to 6th-120 h: sixth instar at 6 h to sixth instar 120 h larvae; P0 d to P8 d: pupal stage at 0-day to pupal stage at 8-day F: feeding stage; M: molting stage; MM: metamorphic molting stage; P: pupae.".

      We are very sorry, but due to time limitations, we will investigate the expression profile of RTK throughout the larval stage in future work.

      (9) In Figure 4, panels F and G, the levels of Kr-h1 are shown in cad96ca and FGF1 mutants in the last larval stage. The authors should indicate whether Kr-h1 levels are also low in earlier larval stages or only detected in the last larval stage, as this would imply that these RTKs are only required at this stage.

      Thank you for your suggestion. In this study, the Cad96ca and Fgfr1 mutants' feeding stage was shortened in the sixth instar, and they entered the metamorphic molting stage earlier. So, we detected the expression of Kr-h1 in the sixth instar. It is an excellent idea to detect the expression of Kr-h1 at various larvae stages to analyze the stages in which CAD96CA and FGFR1 play a role and to study the relationship between CAD96CA and FGFR1 in future.

      (10) While Figure 5 demonstrates JH-triggered calcium ion mobilization in Sf9 cells and S2 cells, the authors should also include data on JH signaling target genes, such as Kr-h1, for a more comprehensive analysis.

      Thank you for your advice. We added experiments, as described in the text: "To demonstrate the universality of CAD96CA and FGFR1 in JH signaling in different insect cells, we investigated JH-triggered calcium ion mobilization and Kr-h1 expression in Sf9 cells developed from S. frugiperda and S2 cells developed from D. melanogaster. Knockdown of Cad96ca and Fgfr1 (named Htl or Btl in D. melanogaster), respectively, significantly decreased JH III-induced intracellular Ca2+ release and extracellular Ca2+ influx, and Kr-h1 expression (Figure 5A, B, Figure 5—figure supplement 1A and B). The efficacy of RNAi of Cad96ca and Fgfr1 was confirmed in the cells (Figure 5—figure supplement 1C and D), suggesting that CAD96CA and FGFR1 had a general function to transmit JH signal in S. frugiperda and D. melanogaster.".

      (11) The authors should consider improving the quality of images and some plots, particularly enlarging panels showing larval and pupal phenotypes, such as Figure 1B and Supplementary Figure C. Additionally, adding a plot showing the statistical analysis of the phenotype in Supplementary Figure C would enhance clarity. Some plots are overly busy and difficult to read due to small size, such as Figure 1C, Figure 2A, and all the plots in Figure 3. Figure 4E also requires improvement for better readability.

      Thank you for your suggestion. We have adjusted Figure 1B, Figure 1C, Figure 1—figure supplement 1C, Figure 2A and Figure 4E. However, for Figure 3, we have not found a better way to arrange and adapt them, considering the overall arrangement of the results and the page space, so we keep them in their original state.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This work is meant to help create a foundation for future studies of the Central Complex, which is a critical integrative center in the fly brain. The authors present a systematic description of cellular elements, cell type classifications, behavioral evaluations and genetic resources available to the Drosophila neuroscience community.

      Strengths:

      The work contributes new, useful and systematic technical information in compelling fashion to support future studies of the fly brain. It also continues to set a high and transparent standard by which large-scale resources can be defined and shared.

      Weaknesses:

      manuscript p. 1

      "The central complex (CX) of the adult Drosophila melanogaster brain consists of approximately 2,800 cells that have been divided into 257 cell types based on morphology and connectivity (Scheer et al., 2020; Hulse et al. 2021; Wolff et al., 2015)."

      The 257 accumulated cell types have informational names (e.g., PBG2‐9.s‐FBl2.b‐NO3A.b) in addition to their associations with specific Gal4 lines and specific EM Body IDs. All this is very useful. I have one suggestion to help a reader trying to get a "bird's eye view" of such a large amount of detailed and multi-layered information. Give each of the 257 CX cell types an arbitrary number: 1 to 257. In fact, Supplemental File 2 lists ~277 cell types each with a number in sequence, so perhaps in principle, it is there. This could expedite the search function when a reader is trying to cross-reference CX cell type information from the text, to the Figures and/or to the Supplemental Figures. Also, the use of (arbitrary) cell type numbers could expedite the explanation of which cell types are included in any compilation of information (e.g., which ones were tested for specific NT expression).

      In this report we adhered to the nomenclature introduced in Hulse et al. 2021. We agree that the nomenclature of cell types in the CX is imperfect. There are inherent limitations to what can be done with present data. Even between the hemibrain and FAFB/Flywire EM datasets, it was not possible to derive a one-to-one correspondence in many cases, largely because we do not yet have enough information to distinguish between natural variation within a cell type and distinct cell types (see Schlegel et al. 2024).  Moreover, many cell type distinctions depend on connectivity differences that are observable only in EM datasets but not in LM images. Several research groups are currently engaged in a comprehensive and collaborative effort to update the CX nomenclature that will extend over the next few months as additional connectomes become available. This work will require hundreds of hours of effort from anatomical and computational experts in multiple laboratories who have a strong interest in the CX. Since the correspondence between the established Hulse et al nomenclature we use and this new nomenclature will be made clear, it will be easy to transfer our data to that new nomenclature. For all these reasons, we believe we should not unilaterally introduce any new naming systems at this time.

      manuscript p 2

      "Figure 2 and Figure 2-figure supplements 1-4 show the expression of 52 new split-GAL4 lines with strong GAL4 expression that is largely limited to the cell type of interest. .... We also generated lines of lesser quality for other cell types that in total bring overall coverage to more than three quarters of CX cell types."

      This section describes the generation and identification of specific split Gal4 lines, and the presentation is generally excellent. It represents an outstanding compendium of information. My reading of the text suggests ~200 cell types have Gal4 lines that are of immediate use (having high specificity or v close-to-high). Use of an arbitrary number system (mentioned above) could augment that description for the reasons stated. For example, which of the 257 cell types are represented by split Gal4 lines that constitute the ~1/3 representing "high-quality lines "? A second comment relates to this study 's functional analysis of the contributions of CX cell types to sleep physiology. The recent literature contains renewed interest in the specific expression patterns of Gal4 lines that can promote sleep-like behaviors. In particular Gal4 line expression outside the brain (in the VNC and outside the CNS) have been raised as important elements that need be included for interpretation interpretation of sleep regulation. This present study offers useful information about a large number of expression patterns, as well as a basis with which to seek additional information., including mention of VNC expression in many cases However, perhaps I missed it, but I could not find a short description of the over-all strategy used to describe the expression patterns and feel that could be helpful. Were all Gal4 lines studied for expression in the VNC? and in the peripheral NS? It is probably published elsewhere, but even a short reprise would still be useful.

      We added a couple of sentences to clarify that the lines were imaged in the adult female brain and VNC and many were also imaged in males. These data, including the ability to download the original confocal stacks, are contained in an on-line web source cited in the text. We also make clear that we did not assay expression outside of the brain, optic lobes and VNC. Therefore, we cannot rule out expression in the peripheral nervous system (other than detected in the axons of sensory neurons in the CNS) or in muscle or other non-neuronal cell types.

      manuscript p 9

      Neurotransmitter expression in CX cell types

      "To determine what neurotransmitters are used by the CX cell types, we carried out fluorescent in situ hybridization using EASI-FISH (Eddison and Irkhe, 2022; Close et al., 2024) on brains that also expressed GFP driven from a cell-type-specific split GAL4 line. In this way, we could determine what neurotransmitters were expressed in over 100 different CX cell types based on ...."

      Reading this description, I was uncertain whether the >100 cell types mentioned were tested with all the NT markers by EASI-FISH? Also, assigning arbitrary numbers to the cell types (same suggestion as above) could help the reader more readily ascertain which were the ~100 cell types classified in this context.

      The specific probes used for each cell type are indicated in Figure 9 and in Supplemental File 1.

      manuscript p 10

      "Our full results are summarized below, together with our analysis of neuropeptide expression in the same cell types."

      I recommend specifying which Figures and Tables contain the "full results" indicated.

      We changed the wording to read:

      “Our full results are summarized, together with our analysis of neuropeptide expression in the same cell types, in Figures 5 -9 and in Supplemental File 1.”

      NP expression in CX cell types

      Similar to the comments regarding studies of NT expression: were all ~100 cell types tested with each of the 17 selected NPs? Arbitrary numerical identifies could be useful for the reader to determine which cell types/ lines were tested and which were not yet tested.

      We expanded the description in Methods to now read:

      “For neurotransmitters, the specific probes used for each cell type are indicated in Figure 9 and in Supplemental File 1. For neuropeptides, each of the 17 selected NP probes shown in Figure 5—figure supplement 1 was used on all cell types in Figure 9 except those marked by “—” in the neuropeptide column.”

      manuscript p. 11

      "The neuropeptide expression patterns we observed fell into two broad categories."

      This section presents information that is extensive and extremely useful. It supports consideration of peptidergic cell signaling at a circuits level and in a systematic fashion that will promote future progress in this field. I have two comments. First, regarding the categorization of two NP expression patterns, discernible by differences in cell number: this idea mirrors one present in prior literature. Recently the classification of the transcription factor DIMM summarizes this same two-way categorization (e.g., doi: 10.1371/journal.pone.0001896). That included the fact that a single NP can be utilized by cell of either category.

      We inserted a sentence to acknowledge this earlier work:

      “Such large neurosecretory cells often express the transcription factor DIMM (Park et al. 2008).”

      Second, regarding this comment:

      "In contrast, neuropeptides like those shown in Figure 6 appear to be expressed in dozens to hundreds of cells and appear poised to function by local volume transmission in multiple distinct circuits."

      Signaling by NPs in this second category (many small cells) suggests more local diffusion, a smaller geographic expanse compared to "volume" signaling by the sparser larger peptidergic cells. Given this, I suggest re-consideration in using the term "volume" in this instance, perhaps in favor of "local" or "paracrine". This is only a suggestion and in fact rests almost entirely on speculation/ interpretation, as the field lacks a strong empirical basis to say how far NPs diffuse and act. A recent study in the fly brain of peptide co-transmitters (doi: 10.1016/j.cub.2020.04.025) provides an instructive example in which differences between the spatial extents of long-range (peptide 1) versus short-range (peptide 2) NP signaling may be inferred in vivo.

      We have modified the text to now read:

      “those shown in Figure 6 are expressed in dozens to hundreds of cells and appear poised to function by transmission to nearby cells in multiple distinct circuits.”  

      Spab was mentioned (Figure 6 legend) but discarded as a candidate NP to include based on a personal communication, as was Nplp1. The manuscript did not include reasons to do so, nor include a reference to spab peptide. I suggest including explicit reasons to discard candidate NPs.

      While there is strong supportive evidence for many NPs in Drosophila, the fact that other transcripts express NPs is more circumstantial often relying simply on sequence analysis and without convincing evidence for a specific cognate receptor. We note that Spab is not listed as a neuropeptide in the current release of FlyBase. In these cases, we relied on the opinion of individuals with extensive experience in studying Drosophila NPs. The results obtained with the probes for Spab and Nplp1 are still available in Supplemental File 1.

      In Fig 9-supplement 1, neurotransmitter biosynthetic enzymes were measured by RNA-seq for given CX cell types to augment the cell type classification. The same methods could be used to support cell type classification regarding putative peptidergic character (in Figure 9 supplement 2) by measuring expression levels of critical, canonical neuropeptide biosynthetic enzymes. These include the proprotein convertase dPC2 (amon); the carboxypeptidase dCPD/E (silver); and the amidating enzymes dPHM; dPal1; dPal2. PHM is most related to DBM (dopamine beta monooxygenase), the rate limiting enzyme for DA production, and greater than 90% of Drosophila neuropeptides are amidated. If the authors are correct in surmising widespread use of NPs by CX cell types (and I expect they are), there could be diagnostic value to report expression levels of this enzyme set across many/most CX cell types.

      In our admittedly limited experience, most cells express these enzymes and the level we observed in confirmed NP expressing cell types was not reproducibly higher.  (The complete data for all genes for the cell types we assayed are available from our deposition in the NCBI Gene Expression Omnibus with accession number GSE271123.) Given our small sample size we chose not to comment on this in the paper.

      Comment #6

      Screen of effects on Sleep behavior

      This work is large in scope and as suggested likely presents excellent starting points for many follow-up studies. I again suggest assigning stable number identities to the elements described. In this case, not cell types, but split Gal4 lines. This would expedite the cross-referencing of results across the four Supplemental Files 3-6. For example, line SS00273 is entry line #27 in S Files 3 and 4, but line entry #18 in S Files 5 and 6.

      We believe the interested reader can make this correspondence by searching the supplemental files which are excel spreadsheets. We note that both driver lines and cell types have stable identifiers that are used across Figures and Tables: the line numbers (for example, SS00273) for driver lines and the Hulse et al cell type names for cell types.

      manuscript p 26

      Clock to CX

      "Not surprisingly, the connectome reveals that many of the intrinsic CX cell types with sleep phenotypes are connected by wired pathways (Figure 12 and Figure 12-figure supplement 1)."

      Do intrinsic CX cells with sleep phenotypes also connect by wired pathways to CX cells that do not have sleep phenotypes?

      Yes, but we do not have high confidence that negative sleep phenotypes in our assays indicate no role in sleep.

      "The connectome also suggested pathways from the circadian clock to the CX. Links between clock output DN1 neurons to the ExR1 have been described in Lamaze et al. (2018) and Guo et al. (2018), and Liang et al. (2019) described a connection from the clock to ExR2 (PPM3) dopaminergic neurons."

      The introduction to this section indicates a focus on connectome-defined synaptic contacts. Whereas the first two studies cited featured both physiological and anatomic evidence to support connectivity from clock cells to CX, the third did not describe any anatomical connections, and that connection may in fact be due to diffuse not synaptic signaling

      I could not easily discern the difference between Figs 12 and 12-S1? These appear to be highly-related circuit models, wherein the second features more elements. Perhaps spell out the basis for the differences between the two models to avoid ambiguity.

      We clarify the supplemental diagram differs from the one in the main text by the inclusion of additional connections:

      “The strongest of these connections are diagrammed in Figure 12, with Figure 12—figure supplement 1 also showing additional weaker connections.”

      "...the cellular targets of Dh31 released from ER5 are unknown, however previous work (Goda et al., 2017; Mertens et al., 2005; Shafer et al., 2008) has shown that Dh31 can activate the PDF receptor raising the possibility of autocrine signaling."

      Regarding pharmacological evidence for Dh31 activation of Pdfr: strong in vivo evidence was developed in doi: 10.1016/j.neuron.2008.02.018: a strong pdfr mutation greatly reduces response to synthetic dh31 in neurons that normally express Pdfr

      We added the Shafer et al., 2008 reference. 

      manuscript p 30

      "Unexpectedly, we found that all neuropeptide-expressing cell types also expressed a small neurotransmitter."

      Did this conclusion apply only to CX cell types? - or was it also true for large peptidergic neurons? Prior evidence suggests the latter may not express small transmitters (doi: 10.1016/j.cub.2009.11.065). The question pertains to the broader biology of peptidergic neurons, and is therefore outside the strict scope of the main focus area - the CX. However, the text did initially consider peptidergic neurons outside the CX, so the information may be pertinent to many readers.

      We did not look at other cell types in the current study and so cannot provide an answer.

      Reviewer #2 (Public review):

      Summary:

      In this paper, Wolff et al. describe an impressive collection of newly created split-GAL4 lines targeting specific cell types within the central complex (CX) of Drosophila. The CX is an important area in the brain that has been involved in the regulation of many behaviors including navigation and sleep/wake. The authors advocate that to fully understand how the CX functions, cell-specific driver lines need to be created. In that respect, this manuscript will be of very important value to all neuroscientists trying to elucidate complex behaviors using the fly model. In addition, and providing a further very important finding, the authors went on to assess neurotransmitter/neuropeptides and their receptors expression in different cells of the CX. These findings will also be of great interest to many and will help further studies aimed at understanding the CX circuitries. The authors then investigated how different CX cell types influence sleep and wake. While the description of the new lines and their neurochemical identity is excellent, the behavioral screen seems to be limited.

      Strengths:

      (1) The description of dozens of cell-specific split-GAL4 lines is extremely valuable to the fly community. The strength of the fly system relies on the ability to manipulate specific neurons to investigate their involvement in a specific behavior. Recently, the need to use extremely specific tools has been highlighted by the identification of sleep-promoting neurons located in the VNC of the fly as part of the expression pattern of the most widely used dorsal-Fan Shaped Body (dFB) GAL4 driver. These findings should serve as a warning to every neurobiologist, make sure that your tool is clean. In that respect, the novel lines described in this manuscript are fantastic tools that will help the fly community.

      (2) The description of neurotransmitter/neuropeptides expression pattern in the CX is of remarkable importance and will help design experiments aimed at understanding how the CX functions.

      Weaknesses:

      (1) I find the behavioral (sleep) screen of this manuscript to be limited. It appears to me that this part of the paper is not as developed as it could be. The authors have performed neuronal activation using thermogenetic and/or optogenetic approaches. For some cell types, only thermogenetic activation is shown. There is no silencing data and/or assessment of sleep homeostasis or arousal threshold. The authors find that many CX cell types modulate sleep and wake but it's difficult to understand how these findings fit one with the other. It seems that each CX cell type is worthy of its own independent study and paper. I am fully aware that a thorough investigation of every CX neuronal type in sleep and wake regulation is a herculean task. So, altogether I think that this manuscript will pave the way for further studies on the role of CX neurons in sleep regulation.

      (2) Linked to point 1, it is possible that the activation protocols used in this study are insufficient for some neuronal types. The authors have used 29{degree sign} for thermogenetic activation (instead of the most widely used 31{degree sign}) and a 2Hz optogenetic activation protocol. The authors should comment on the fact that they may have missed some phenotypes by using these mild activation protocols.

      Our primary goal was to test the feasibility of using these tools in assessing sleep and wake function of neurons within the CX. In the process we uncovered several new neurons within the DFB-EB network that control sleep and make connections with previously identified sleep regulating neurons. For all single cell type lines and lines with sparse patterns and no VNC expression we present both optogenetics and thermogenetic data. The lines for which we only have thermogenetic but no optogenetic data are those which have multiple cell types or VNC expression. We felt that optogenetic data for these non-specific or contaminated lines would not reliably indicate a role for individual cell types in sleep regulation.

      Many previous studies that have used 31 degrees have done so for shorter durations and often using different times of the day for manipulations. The lack of consistency between studies using this temperature may be due in part to the fact that 31 degrees alters behaviors of flies (including controls) and, for this reason, is usually not used for 24-hour activation durations.

      To keep the screen consistent and ensure we capture changes in both daytime and nighttime sleep we used 29 degrees. The behavior of control flies is not as disrupted or altered at this temperature, and 29 degrees for activation is routinely used in behavioral experiments.

      We similarly selected an optogenetic stimulation protocol that minimizes the response of flies to the red-light pulses. We chose this protocol because we found, in earlier experiments in a different project, that this level of stimulation was able to elicit activation phenotypes across a range of cell types (including several known clock neurons). However, we cannot rule out false negatives in both the TrpA and optogenetic experiments and agree that we might have missed some phenotypes.

      Finally, as the reviewer rightfully points out, a thorough, detailed investigation of each cell type is a herculean task. We screened in both genders with very sparse, and often cell-type-specific, driver lines while using two distinct modes of activation and different methods for assessing sleep. For these reasons, we believe the GAL4 lines we identified provide excellent starting points for the additional investigations that will be required to better understand the roles of specific cell types.

      (3) There are multiple spelling errors in the manuscript that need to be addressed.

      Reviewer #3 (Public review):

      Summary:

      The authors created and characterized genetic tools that allow for precise manipulation of individual or small subsets of central complex (CX) cell types in the Drosophila brain. They developed split-GAL4 driver lines and integrated this with a detailed survey of neurotransmitter and neuropeptide expression and receptor localization in the central brain. The manuscript also explores the functional relevance of CX cell types by evaluating their roles in sleep regulation and linking circadian clock signals to the CX. This work represents an ambitious and comprehensive effort to provide both molecular and functional insights into the CX, offering tools and data that will serve as a critical resource for researchers.

      Strengths:

      (1) The extensive collection of split-GAL4 lines targeting specific CX cell types fills a critical gap in the genetic toolkit for the Drosophila neuroscience community.

      (2) By combining anatomical, molecular, and functional analyses, the authors provide a holistic view of CX cell types that is both informative and immediately useful for researchers across diverse disciplines.

      (3) The identification of CX cell types involved in sleep regulation and their connection to circadian clock mechanisms highlights the functional importance of the CX and its integrative role in regulating behavior and physiological states.

      (4) The authors' decision to present this work as a single, comprehensive manuscript rather than fragmenting it into smaller publications each focusing on separate central complex components is commendable. This decision prioritizes accessibility and utility for the broader neuroscience community, which will enable researchers to approach CX-related questions with a ready-made toolkit.

      Weaknesses:

      While the manuscript is an outstanding resource, it leaves room for more detailed mechanistic exploration in some areas. Nonetheless, this does not diminish the immediate value of the tools and data provided.

      Appraisal:

      The authors have succeeded in achieving their aims of creating well-characterized genetic tools and providing a detailed survey of neurochemical and functional properties in the CX. The results strongly support their conclusions and open numerous avenues for future research. The work effectively bridges the gap between genetic manipulation, molecular characterization, and functional assessment, enabling a deeper understanding of the CX's diverse roles.

      Impact and Utility

      This manuscript will have a significant and lasting impact on the field, providing tools and data that facilitate new discoveries in the study of the CX, sleep regulation, circadian biology, and beyond. The genetic tools developed here are likely to become a standard resource for Drosophila researchers, and the comprehensive dataset on neurotransmitter and neuropeptide expression will inspire investigations into the interplay between neuromodulation and classical neurotransmission.

      Additional Context

      The breadth and depth of the resources presented in this manuscript justify its publication without further modification. By delivering an integrated dataset that spans anatomy, molecular properties, and functional relevance, the authors have created a resource that will serve the neuroscience community for years to come.

      Recommendations for the authors:

      Reviewing Editor:

      The reviewers suggest that a nomenclature, perhaps a numbering system, be adopted for different cell types and Gal4 drivers in order to facilitate reading of the manuscript and cross-referencing.

      We agree that a comprehensive reanalysis of the CX nomenclature is in order, but it is premature for us to attempt that as part of this study. This is best done after additional connectomes are generated to help resolve the degree of variation in morphology and connectivity between the same cell in multiple animals.

      Reviewer #3 (Recommendations for the authors):

      The authors have characterized a large number of split-GAL4 drivers targeting individual or small subsets of CX cell types. This manuscript delivers a detailed anatomical, molecular, and functional mapping of the CX.

      By integrating data on neurotransmitters, neuropeptides, and their receptors, the authors provide a holistic view of CX cell types that will undoubtedly serve as a foundation for future studies.

      The use of these genetic tools to identify CX cell types affecting sleep, as well as those linking the circadian clock to the CX, represents a significant advance. These findings hint at the diverse and integrative roles of the CX in regulating both behavior and physiological states.

      The authors' decision to present this work as a single, comprehensive manuscript rather than fragmenting it into smaller publications each focusing on separate central complex components is commendable. This decision prioritizes accessibility and utility for the broader neuroscience community, which will enable researchers to approach CX-related questions with a ready-made toolkit.

      While the manuscript leaves room for further exploration and mechanistic studies, the breadth and depth of the resources presented are more than sufficient to justify publication in their current form.

      The data on neuropeptide and receptor expression patterns, especially the observation that all examined CX cell types co-express a small neurotransmitter, opens intriguing new avenues of inquiry into the interplay between classical neurotransmission and neuromodulation in this region.

      This manuscript has provided a much-needed resource for the Drosophila neuroscience community and beyond. This work will facilitate important discoveries in CX function, sleep regulation, circadian biology, and more.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      The authors have performed extensive work generating reporter mice and performing single-cell analysis combined with in situ hybridization to arrive at 14 clusters of enterochromaffin (EC) cells. Then, they focus on Piezo channel expression in distal EC cells and find that these channels might play a role in regulating colonic motility. Overall, this is an informative study that comprehensively classifies EC cells in different regions of the small and large intestine. From a functional point of view, however, the authors seem to ignore the fact that the expression of Piezo-2-IRES-Cre is broad, which would raise concerns regarding their physiological conclusions.

      The authors may wish to consider the following specific points: 

      It is surprising that the number of ileal EC cells is less than that of the distal colon, and it would be interesting to know whether the authors can comment about ileal EC cells. It is unclear why ileal ECs were not included in the study, even though they are mentioned in the diagram (Fig. 2c).

      We have discussed the rationale for excluding ileal ECs in the methods section under “Elimination of ileal GFP+ cells”. In our initial scRNA-seq experiment, our yield of epithelial cells and GFP positive cells was low, and a large proportion of these cells appeared to not have fully committed to the EC lineage. Also to note, we have previously seen fewer ECs in the distal ileum than upper small intestine and colon (PMID: 26803512). Given the low yield, and some uncertainty regarding the nature of the ileal EC population sorted by our methods, we considered that data from ileal ECs may not be an accurate representation of ileal EC cell diversity. Thus, we did not use ileal ECs in our second scRNA-seq experiment.

      Based on their analysis, there are 10 EC cell clusters in SI while there are only 4 clusters in the colon. The authors should comment on whether this is reflective of lesser diversity among colonic ECs or due to the smaller number of colonic ECs collected.

      The 4 clusters identified in the colon are consistent with previous a previous publication (Glass et al., Mol. Metab. 2017, PMID: 29031728), supporting the idea that these clusters are representative of the major clusters of colonic ECs. Nonetheless, we anticipate that with greater sample sizes (in any region) further resolution of subtypes could be resolved. 

      The authors previously described that distal colonic EC cells exhibit various morphologies (Kuramoto et al., 2021). Do Ascl1(+) EC cells particularly co-localize with EC cells with long basal processes? Also, to validate the RNA seq data, the authors might show co-localization between Piezo2/Ascl1/Tph1 in distal EC cells. It would be interesting to see whether Ascl1-CreER (which is available in Jax) specifically labels distal colonic EC cells as this could provide a good genetic tool to specifically manipulate distal colonic EC cells.

      We have shown co-localization between Piezo2/Ascl1/Tph1 in Supplementary Figure 6a. Unfortunately we did not study cell morphology in the Ascl1 smRNA-FISH experiments as these used thin cryosections, whereas morphological assessment of EC processes is best performed with thick (>60 µm) sections. It would be interesting if neuronal-like expression profiles correlate with neuronal-like morphology, which could be addressed in future studies with spatial transcriptomics. 

      The authors used Piezo2-IRES-Cre mice, whose expression is rather broad. They might examine the distribution of Chrm3-mCitrine in the intestine (IF/IHC would be straightforward). And if the expression is in other cell types (which is most likely the case), they should justify that the observed phenotype derives from Piezo2-expressing EC cells. Alternatively, they could use Piezo2-Cre;ePetFlp (or Vil-Flp);Chrm3 to specifically express DREADD receptors in distal colonic EC cells. Also, what does 5HT release look like in jejunal EC cells in Piezo-CHRM3 mice?

      Unfortunately we no longer have access to the animals to do these experiments.

      For the same reasons as above, DTR experiments may also be non-specific. For example, based on the IF staining (Fig. 6b,d), there seems to be a loss of Tph1+ cells in the proximal colon of Piezo2-DTR mice, so the effects of the Piezo2-DTR likely extend beyond the distal colon. 

      Figures 6b and d show distal colon, not proximal colon. Our Tph1<sup>+</sup> cell counts indicate there was no loss of Tph1 cells in the proximal colon following intraluminal administrations of DT. 

      It is unclear why the localized loss of Piezo2 in Piezo2-DTR mice alters small intestinal transit (Fig. 6g,h). The authors should discuss the functional differences observed between Piezo2-DTR (intraluminal app) and Vil1Piezo2 KO mice i.e., small intestinal transit, 5HT release, etc. Are these differences due to the residual Piezo2 expression in Piezo2 KO mice? In this context, the authors may want to discuss their findings in the context of recent papers, such as those from the Patapoutian and Ginty groups. 

      We have made the following amendment to speculate on the reason for delayed small intestinal transit in the DTR experiments:

      “There are a several possible explanations for this. Some Piezo2+ cells in the small intestine could have been depleted. Alternatively, 5-HT released from Piezo2+Tph1+ cells in the distal colon may provide feedback to the small intestine to accelerate motility, and thus depletion of these cells would result in slower intestinal transit.” 

      We have also added a comment speculating on why we did not see similar slowing of small intestinal transit in the Villlin-Cre Piezo2 KO:

      “No difference was observed in small intestine transit… in contrast to the DTR experiments, in which small intestinal transit was delayed. This could be due to the depletion of EC cells in the DTR experiments, whereas they are retained in the Villin-Cre Piezo2 KO mice. 5-HT secretion from ECs can be induced by other stimulants (even when Piezo2 is knocked out), and thus colonic 5-HT could be providing feedback to the small intestine to accelerate motility in the Villin-Cre Piezo2 KO mice. Residual Piezo2 expression in these mice could also be contributing to this effect.”

      We have added a comment on neural Piezo2 in the discussion:

      “However, in contrast to Piezo2 signalling in ECs which results in accelerated gut transit, Piezo2 signalling in DRG neurons appears to slow transit (refs: Wolfson et al., Cell 2023; PMID: 37541195; Servin-Venves et al., Cell 2023, PMID: 37541196).”

      Reviewer #2 (Public Review):

      Summary:

      The authors investigated the expression profile of enterochromaffin (EC) cells after creating a new tryptophan hydroxylase 1 (Tph1) GFP-reporter mouse using scRNAseq and confirmative RNAscope analysis. They distinguish 14 clusters of Tph1+ cells found along the gut axis. The manuscript focuses on two of these, (i) a multihormonal cell type shown to express markers of pathogen/toxin and nutrient detection in the proximal small intestine, and (ii) on a EC-cluster in the distal colon, which expresses Piezo2, rendering these cells mechanosensitive. In- and ex- vivo data explore the role of the mechanosensitive EC population for intestinal/colonic transit, using chemogenetic activation, diptheria-toxin receptor dependent cell ablation and conditional gut epithelial specific Piezo2 knock-out. Whilst some of these data are confirmative of previous reports - Piezo2 has been implicated in mechanosensitive serotonin release previously, as referred to by the authors - the data are solid and emphasize the importance of mechanosensitive serotonin release for colonic propulsion. The transcriptomic data will guide future research.

      Strengths:

      The transcriptomic data, whilst confirmative, is more granular than previous data sets. Employing new tools to establish a role of mechanosensitive EC cells for colonic and thus total intestinal transit. 

      Weaknesses: 

      (1) The proposed villus/crypt distribution of the 14 cell types is not verified adequately. The RNAscope and immunohistochemistry samples presented do not allow assessment of whether this interpretation is correct - spatial transcriptomics, now approaching single-cell resolution, would be likely to help verify this claim.

      Spatial transcriptomics would be excellent in validating the spatial distribution of the EC cell types in future studies. In our work, although the villus/crypt cluster annotations are assumptions (based on the differential expression of Neurog3, Tac1, and Sct, which is well supported by the literature), we have validated the spatial segregation of key markers. We quantified the crypt/villus location of Cartpt, Ucn3, and Trpm2 overlap with Tph1 (Figure 2d), Oc3, Cck, and Tph1 (Figure 3d), and TK/5-HT (Supplementary Fig 2d). This work supports our predictions on the spatial distribution of these clusters.

      (2) The physiological function and/or functionality of most of the transcriptomically enriched gene products has not been assessed. Whilst a role for Piezo2 expressing cells for colonic transit is convincingly demonstrated, the nature of the mechanical stimulus or the stimulus-secretion coupling downstream of Piezo2 activation is not clear.

      While we have not investigated the mechanical forces involved in activating Piezo2, we can at least say that physiological mechanical stimulation activates Piezo2, as we measured fecal pellet output in the DTR experiments. 

      Reviewer #2 (Recommendations For The Authors):

      (1) Please state (even more) clearly if/that the apparently GFP+/Tph1+ cells which clustered with the GFP- cells (Suppl. Fig1d/e) were excluded from the subsequent analysis. The detectable Chg-a/b expression in the GFP- cells in Suppl. Fig1f seems to suggest that these (if they have been included in the GFP- group here) are genuine ECs. How do these cells relate to the non-EC cells in Fig1d, which seem to lack Tph1 expression? And given the information in the methods, what %age of these cells derived from the ileum?

      To clarify, data shown in Suppl. Fig 1d/e/f was from our first single cell profiling experiment whereas our subsequent clustering analysis utilizes data from a second (independent) single cell profiling experiment (e.g. Fig1d). 

      In the first profiling experiment, 23% of GFP<sup>+</sup> cells clustered with GFP<sup>-</sup> cells, and for the purposes of Suppl. Figures 1d/e/f, we called these “non-ECs”. In the second profiling experiment (e.g. shown in Fig 1d) we performed a more detailed cluster analysis focusing on only GFP<sup>+</sup> cells. In this second experiment, 19% of GFP<sup>+</sup> cells were identified as “non-EC cells” based on the presence of markers for stem cells, transit amplifying cells (TACs), immature enterocytes, mature enterocytes, colonocytes, T lymphocytes and mucosal mast cells (see Fig 1d and Suppl. Fig 1g). Similar to the first profiling dataset, many of the GFP<sup>+</sup> “non-EC cells” in the second dataset express Tph1, Chga, and Chgb, generally at lower levels than the “EC cells” (Suppl. Fig1i). It is possible that the stem cell and transit amplifying cell clusters are cells that are differentiating into EC cells. However, given that they have not fully committed to the lineage yet, we do not consider it appropriate to classify them as “EC cells”. With regards to the other “non-EC” clusters, we do not think that the expression of EC cell marker genes (Tph1, Chga, and Chgb) is evidence enough to call them genuine “EC cells” given the concurrent expression of markers of other lineages (e.g. enterocyte and mast cell markers Suppl. Fig 1g). The expression of Tph1 in murine mast cells is known, however the expression in enterocytes is unexpected and could be a result of imperfect/incomplete differentiation. Since the ileum was not included in the second profiling experiment we do not think the GFP<sup>+</sup> “non-EC cells” are an artifact from the ileum. 

      We have made some adjustments in the first section of the results to clarify some thoughts on this matter:

      “It is possible that some GFP is expressed in cells that have not yet fully committed to the EC lineage, or that there is some expression in cells outside this lineage, for example, in mast cells. Given the small sample size, we did not further investigate these cells in this dataset. In Supplementary Figures 1 d and f we refer to the GFP<sup>+</sup> cells that clustered with the GFP<sup>-</sup> cells as “non-EC cells”.”

      “It is possible that the stem cell and transit amplifying cell clusters include cells that are in the process of differentiating into EC cells. However, given that they have not fully committed to the lineage, we do not consider it appropriate to classify them as “EC cells” for the purposes of analyzing EC cell types in this study.”

      (2) The authors state: "Notably, OSR2 and HOXB13 were restricted to the ileum and rectum respectively in humans (Fig. 1f)." - the statement regarding OSR2 seems too strong, given that only the ileal part of the human small intestine was examined and that there is a small signal in the proximal colon in Figure 1f.

      Thanks, we have made the following amendment:

      "Notably, OSR2 and HOXB13 were preferentially enriched in the ileum and rectum respectively in these human samples (Fig. 1f)."

      (3) Please clarify Suppl Fig2g/h labelling as villus and crypt enrichment ("...enrichment in villus clusters (g) or crypt clusters (h)."), when enrichment for some genes in cluster 4 is shown in both g and h. Why was duodenal cluster 6 excluded from this subset of data?

      We suspect (although have not proven) that cluster 4 is at a later stage in maturation/migration than cluster, as indicated by a somewhat ‘middle ground’ level of Sct expression, and generally being ‘in between’ the villus clusters and cluster 5 in expression levels of differentially expressed genes shown in Suppl Fig 2g/h. We have added the following comment to the figure legend to clarify this. We have not included cluster 6 as it is transcriptionally quite distinct from the other clusters:

      “Note that cluster 4 shares some features in common with crypt and villus clusters and may represent cells at an intermediate stage of development.”

      (4) "Using smRNA-FISH, we further mapped Olfr558 and Il12a transcripts to a separate subset of EC cells expressing Cpb2 (Fig. 4b,c), confirming the presence of two subpopulations of EC cells associated with different physiological roles in the proximal colon." - Claiming populations with different physiological functionality seems a strong statement given the relatively weak Cpb2 signals observed and that mRNA detection necessarily is a transcriptomic time limited snap-shot. Please reformulate.

      We have made the following revision:

      “Using smRNA-FISH, we further mapped Olfr558 and Il12a transcripts to a separate subset of EC cells expressing Cpb2 (Fig. 4b,c), supporting the idea that there are subpopulations of EC cells in the proximal colon with gene transcripts associated with different physiological roles.”

      (5) What are the white signals in the overlay in Fig5a, given that the Piezo1 probe (white) apparently did not give any staining by itself? Please consider a positive control for the Piezo1 probe.

      The white signals in the overlay are Piezo1 staining that we do observe at what we consider background levels (also visible in the single-channel image).

      (6) "Systematic administration of DT led to lethality in the Piezo2-DTR mice within 12 hours, but not in the Rosa26LSL-DTR or Piezo2-cre mice (data not shown), likely due to the essential function of Piezo2 in respiration" - presumably this should be corrected to "Systemic administration ...".

      Thanks, this has been corrected to "Systemic administration ...".

      (7) "Although gastric emptying (GE) was not affected in the Piezo2-DTR animals after DT treatment, small intestine transit (SIT) time, a measurement to assess the motility of small intestine, presented a small but statistically significant slowdown in the former group (Fig. 6g,h), suggesting that some Piezo2+ cells in the small intestine were depleted." - alternatively there could, of course, be a slowing of SIT in response to slower colonic transit independent of small intestinal epithelial Piezo2 or 5HT - to me this seems more likely given that even proximal colonic cells are spared in Fig6c and this should be discussed.

      Thanks, that is a good point. We have made an amendment, which is shown in response to reviewer 1.

      (8) In the context of the Villin-Cre experiments it should be discussed that other colonic EECs although express Piezo2, which might contribute to the observed phenotypes.

      In our study, 97.7% of Piezo2+ cells in the distal colon had detectable Tph1 expression, suggesting that there is not a significant degree of overlap with other EEC types.

      (9) MC4R is several times referred to as a nutrient-sensing moeity (e.g. in the discussion: "...and receptors associated with nutrient sensing (Casr and Mc4r), ...") - whilst the melanocortin system is important for nutrient homeostasis, MC4R is itself not a "nutrient sensor", a term usually reserved for the detection of macronutrients, such as amino acids, fatty acids, and monosaccharides; please reformulate. 

      We have amended this to “nutrient sensing and homeostasis”.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Major concerns:

      For studies investigating capsaicin binding to KEAP1, the authors used capsaicin concentrations that are toxic to cells (Figures S1D and 4F, G). In vivo studies were performed only in 3 rats per group. The T-test was used for the comparison of more than two groups. Given the well-known issues with the specificity of the NRF2 antibody, the authors should provide appropriate controls, especially for IF and IHC staining.

      We sincerely appreciate your valuable comments. We repeated the experiments about CCK8 (Figure S1d) and Pull-down (Figure 4g), and then updated the results. In September 2022, GES-1 cells were more sensitive to capsaicin (CAP) because Gibco serum from North America was used. Later, in 2024, we changed the serum from Australia(Gibco: 10099-141), and we found that such GES-1 cells raised better, so we re-ran the test, and the IC50 was seen to be 304.8 μM, so concentrations used in this paper has no obvious toxicity to cells. What’s more, we repeated the Pull-down experiment with more reasonable concentrations of 32 μM and 100 μM, and the results were still in line with expectations. In summary, we concluded that the effect of CAP on GES-1 cells is closely related to the cell state, and that treatments of CAP from 32 to 100 μM can hinder the interaction between NRF2 and the Kelch domain of KEPA1. What’s more, at the cellular level, the experimental concentration of CAP was not more than 32 μM, which is a relatively safe concentration for cells.

      Thank you very much for your comments. We also pay attention to using more repetitions to increase the reliability of the experimental results in animal experiments. Therefore, recently we supplemented the experiment of Nfe2l2Knockout mice in Figure 9 (6 mice per group). Additionally, thank you very much for your comments on the use of T-test analysis, we reviewed the statistics and changed them by one-way ANOVA.

      Finally, thanks to your concern about the specificity of NRF2 antibody, we used commercialized NRF2 antibody which have been KO/KD validated (Cat No. 16396-1-AP, Proteintech) and can be used for IF and IHC staining. Each of our fluorescence result was equipped with Western Blotting in its active form at the size of 105-110 KDa for statistical analysis, the trend was consistent with the experimental results of IF and IHC, which fully proves the correctness of the results presented (Figure 2c and Figure S8j).

      Reviewer #2 (Public Review):

      Weaknesses:

      One major weakness of the study is that plausibility is taken as proof for causality. The finding that capsaicin directly binds to Keap1 and releases Nrf2 from its fate of degradation (in vitro) is taken for granted as the sole explanation for the observed improved gastric health upon alcohol exposure (in vivo). There is no consideration or exclusion of any potential unrelated off-target effect of capsaicin, or proteins other than Nrf2 that are also controlled by Keap1. 

      Another point that hampers full appreciation of the capsaicin effect in cells is that capsaicin is not investigated alone, but mostly in combination with alcohol only.

      Thank you very much for this comment. In the introduction, we clarified as follows: “Currently, experiments conducted in rats have demonstrated that red pepper/capsaicin (CAP) had significant protective effects on ethanol-induced gastric mucosal damage, and the mechanism may be related to the promotion of vasodilation(6,7), increased mucus secretion(8) and the release of calcitonin gene-related peptide (CGRP)(9,10). However, it is noteworthy that whether the antioxidant activity of CAP works has not been fully investigated.” Therefore, we also recognize that CAP does not exert its effects through the KEAP1-NRF2 pathway alone. Your advice is very useful. We further explored the TRPV1 and DPP3 to detect the potential off-target effects of CAP respectively. Capsazepine (CAPZ), which is TRPV1 receptor antagonist did not affect the protection of CAP against GES-1 (Fig S4f and S4g), which may indicate that CAP activation of NRF2 does not have to depend on TRPV1. The binding of CAP with DPP3, containing an ETGE motif and can bind to KEPA1, was detected by BLI, and we found that the K<sub>D</sub> between CAP and DPP3 was 1.653 mM(>100 μM), which may indicate the potential off-target effect of CAP is low because CAP had a strong binding force with KEAP1 about 31.45 μM (Fig S4h and S4i).

      Thank you very much for the comment of another point. Multiple experiments have shown that CAP significantly up-regulates NRF2 in the presence of additional stimuli such as EtOH (Figure 1i),  H<sub>2</sub>O<sub>2</sub> (Figure 1l), PS-341(Figure 2e) and DTT (Figure 4d), which pattern is consistent with our understanding of allosteric regulation and as expected. Especially for the experiments of PS-341 and DTT, we had a group that only adds CAP, and it can be seen that the addition of CAP alone did not significantly up-regulate NRF2, which is completely different from traditional NRF2 activators (especially artificially designed covalent binding peptides which have serious side effects).  

      Reviewer #3 (Public Review):

      Weaknesses:

      While the study provides valuable insights into the molecular mechanisms and in vivo effects of CAP, further clinical studies are needed to validate its efficacy and safety in human subjects. The study primarily focuses on the acute effects of CAP on ethanol-induced gastric mucosa damage. Long-term studies are necessary to assess the sustained therapeutic effects and potential side effects of CAP treatment.

      Furthermore, the study primarily focuses on the interaction between CAP and the KEAP1-NRF2 axis in the context of ethanol-induced gastric mucosa damage. It may be beneficial to explore the broader effects of CAP on other pathways or conditions related to oxidative stress. CAP has been known for its interaction with the Transient Receptor Potential Vanilloid type 1 (TRPV1) channel and subsequent NRF2 signaling pathway activation. Those receptors are also expressed within the gastric mucosa and could potentially cross-react with CAP leading to the observed outcome. Including experiments to investigate this route of activation could strengthen the present study.

      While the design of CAP nanoparticles is innovative, further research is needed to optimize the nanoparticle formulation for enhanced efficacy and targeted delivery to specific tissues.

      Addressing these weaknesses through additional research and clinical trials can strengthen the validity and applicability of CAP as a therapeutic agent for oxidative stress-related conditions.

      Thank you very much for these suggestions. We also believe that CAP is very valuable and promising for protecting EtOH induced gastric mucosal injury, and actively promote patent applications and if conditions permit, longer drug research for biosecurity is essential. Because of the inherently new discovery of the binding of CAP and KEAP1, and the important role of NRF2 in various oxidative stress-related diseases, we used Human umbilical cord mesenchymal stem cells (HUC-MSCs) and  H<sub>2</sub>O<sub>2</sub> to explore the potential broader effects of CAP related to oxidative stress in cells (Figure 1l and 1m). At the same time, we also explored TRPV1 related experiments, and we were surprised to find that inhibiting TRPV1 did not affect the effect of CAP (Supplementary Figure 4f and 4g). We hope that more people can read this article and do more interesting research together.

      Recommendations for the authors:

      Reviewing Editor (Recommendations For The Authors):

      Although this study has been conducted in rats, a direct proof that albumin-coated capsaicin nanoparticles act through activation of Nrf2 in protecting gastric mucosa against alcohol toxicity could be well conducted in commercially available Nrf2-deficient mice.

      Thank you very much for your suggestion and the comment is very constructive for us to improve this paper. We purchased Nrf2-deficient mice (Cat. NO. NM-KO-190433) and performed experiments, and the results showed that knockout mice with Nrf2 were more sensitive to EtOH and the effects of CAP were partially eliminated (Figure 9), which further validated the role of Nrf2-related signaling pathway in EtOH-induced gastric mucosal injury and the therapeutic effect of CAP.

      Reviewer #1 (Recommendations For The Authors):

      Minor concerns include proofreading the paper. Actinomycin is not an inhibitor of translation.

      Thank you for your comment. We have revised “Actinomycin” to “Cycloheximide”.

      Reviewer #2 (Recommendations For The Authors):

      - Please have a careful look at your conclusions: just because two effects happen at the same time and may be plausible explanations for each other, it does not mean that they are really in a causative relationship in your given test system (unless unambiguously proven by additional experiments).

      Your suggestions are very constructive for us to improve this paper.

      We further discussed the role of capsaicin with TRPV1, DPP3 and Nrf2deficient mice, hoping to make our conclusions more credible to some extent. 

      - You may want to frankly discuss other targets of capsaicin (e.g. the TrpV1 receptor) that possibly could also account for your observations, and that binding to Keap1 not only releases Nrf2 from proteasomal degradation.

      Thank you for your comment. As a result, we further explored the TRPV1 and DPP3 to detect the potential off-target effects of CAP respectively. Capsazepine (CAPZ), which is TRPV1 receptor antagonist does not affect the protection of CAP against GES-1 (Fig S4f and S4g). DPP3 with an ETGE motif was detected by BLI, and we found that the K<sub>D</sub> between CAP and DPP3 was 1.653 mM, which may indicate the potential off-target effect of CAP is low (Fig S4h and S4i). At the same time, the activation of NRF2 by non-classical pathways such as CAP regulation of DPP3 or other proteins also deserves more discussion and experimental verification.

      - For Figure 1G it does not become entirely clear what has been done (and thus deduction of conclusions is hampered).

      Thank you for your comment. Network targets analysis (Figure 1g) was performed to obtain the potential mechanism of effects of CAP on ROS. Biological effect profile of CAP was predicted based our previous networkbased algorithm:drug CIPHER. Enrichment analysis was conducted based on R package ClusterProfiler v4.9.1 and pathways or biological processes enriched with significant P value less than 0.05 (Benjamini-Hochberg adjustment) were remained for further studies. Then pathways or biological processes related to ROS and significantly enriched were filtered and classified into three modules, including ROS, inflammation and immune expression. Network targets of CAP against ROS were constructed based on above analyses, and finally we combined proteomics to determine the research idea of this paper

      -  Figure 1L: is there a reason/explanation why UC.MSC needs a comparably very high concentration of capsaicin.

      Thank you for your comment. Because the experimental results of 8 μM and 32 μM on this cell were more stable, and the activation effect of NRF2 downstream was more obvious.

      -  Figure 2C: it is surprising that naïve (unstressed /untreated cells) already show a rather high nuclear abundance of Nrf2 (shouldn´t Nrf2 be continuously tagged for degradation by Keap1).

      Thank you for your comment. This is a real experimental result, and we have found in many experiments that the untreated group can also show NRF2 when immunoblotting. We think that this phenomenon may be related to the cell state at that time.

      -  Figure 2E: the claim of synergy between CAP and the proteasome inhibitor is not justified with this single figure.

      Thank you for your comment. Multiple experiments have shown that CAP significantly up-regulates NRF2 in the presence of additional stimuli such as EtOH (Figure 1i),  H<sub>2</sub>O<sub>2</sub> (Figure 1l), PS-341 (Figure 2e) and DTT (Figure 4d), which pattern is consistent with our understanding of allosteric regulation and as expected. However, this synergy does warrant more research.

      -  CHX is cycloheximide (in the main text it is referred to as actinomycin).

      Thank you very much for your comment. We have revised “Actinomycin” to “Cycloheximide”.

      -  Figures 2G-H: why switch to rather high concentrations? Is it due to the overexpression of Keap1?

      Thank you for your comment. At the time of this part of the experiment, we had obtained in vitro data on the interaction of CAP and the Kelch domain of KEAP1 (about 32 μM). To keep the results uniform and valid, we chose a relatively higher concentration.

      -  Figure 2I: in the pics of mitochondria the control mitochondria look way more punctuated (likely fissed) than the ones treated with EtOH or EtOH + CAP. Wouldn´t one expect that EtOH leads to mitochondrial fission and CAP can prevent it?

      Thank you for your comment. MitoTracker® Red CMXRos (M9940, Solarbio, China) is a cell-permeable X-rosamine derivative containing weakly sulfhydryl reactive chloromethyl functional groups that label mitochondria. This product is an oxidized red fluorescent stain (Ex=579 nm, Em=599 nm) that simply incubates the cell and can be passively transported across the cell membrane and directly aggregated on the active mitochondria. Therefore, red does not represent broken mitochondria, but active mitochondria. Quantitative analysis of the mean branch length of mitochondria was calculated using MiNA software (https://github.com/ScienceToolkit/MiNA) developed by ImageJ.

      -  Figure 3C: figure legend is somewhat poor.

      Thank you for your comment. We have revised: “KEAP1-NRF2 interaction was detected with Surface plasmon resonance (SPR) in vitro.”

      -  Figure 3E: given that CAP disrupts Nrf2/Keap1- PPI, why is there no Nrf2 stabilization seen in the fourth lane (input/lysate)?

      Thank you for your comment. The fourth lane may promote the degradation of NRF2 due to overexpression of KEAP1.

      -  Figure 3H: high basal Nrf2 levels in unstressed/untreated HEK WT cells, why?

      Thank you for your comment. This is a real experimental result, and we have found in many experiments that the untreated group can also show NRF2 when immunoblotting in 293T cells. We think that this phenomenon may be related to the cell state at that time.

      -  Figure 3G/I: this data suggests to me that the alcohol-mediated toxicity is Keap1-dependent (rather than the protection by CAP), doesn´t it?

      Thank you for your comment. We can see that KEAP1-KO cells had a high expression of NRF2, which was also in line with our expectations, and EtOH-induced GES-1 damage may be closely related to oxidative stress.

      -  Figure 4a: the inclusion of an additional Keap1 binding protein (one with an ETGE motif) would have been desirable (to get information on specificity/risks of off-target (unwanted) effects of CAP). 

      Thank you for your comment. DPP3 with an ETGE motif was detected by BLI, and we found that the K<sub>D</sub> between CAP and DPP3 was 1.653 mM, which may indicate the potential off-target effect of CAP is low (Fig S4h and S4i).

      -  Figure 4D: why is there no stabilization of Nrf2 by CAP in lane 2 ? How can the DTT-mediated boost on Nrf2 levels be explained?

      Thank you for your comment. Multiple experiments have shown that CAP significantly up-regulates NRF2 in the presence of additional stimuli such as EtOH (Figure 1i),  H<sub>2</sub>O<sub>2</sub> (Figure 1l), PS-341 (Figure 2e) and DTT (Figure 4d), which pattern is consistent with our understanding of allosteric regulation and as expected. However, this synergy does warrant more research.

      -  Figure 4f: 5% DMSO is a rather high solvent concentration, why so high (the solvent alone seems to have quite marked effects).

      Thank you for your comment. Because our maximum concentration was set relatively high, we have also recognized relevant problems and resupplemented the more critical Pull-down experiment (Figure 4g). The current DMSO of 0.2% had no effect on the experimental results.

      -  Figure 5: it should be described in the figure legend which mutant is used. Based on the previous data, I would expect an investigation of mutants carrying amino acid exchanges at the newly identified allosteric site.

      Thank you for your comment. The mutated version involved substitutions at residues Y334A, R380A, N382A, N414A, R415A, Y572A, and S602A (the orthostatic site), which are residues reported to engage NRF2 and classic Keap1 inhibitors. The exploration of newly discovered allosteric sites is worthy of further study.

      -  Figure 6/7: I am not expert enough to judge formulations and histology scores. However, the benefit of the encapsulated capsaicin does not become entirely clear to me, as CAP and IRHSA@CAP mostly do not significantly differ in their elicited response.

      Thank you for your comment. On the one hand, nanomedicine improves the safety of administration: it helps to reduce the intense spicy irritation of CAP itself when administered in the stomach; On the other hand, the dosage of drugs is reduced to a certain extent to achieve better therapeutic effect.

      -  Figure 7: rebamipide was introduced as positive control in the text with an activating effect on Nrf2, but there is no induction of hmox and nqo in Figure 7f, why?

      Thank you for your comment. The effect of addition of positive control drug (Rebamipide) on NRF2 activation is not the focus of this paper. We speculate that the transcription and translation of related genes may not be completely synchronized when Rebamipide was taken at the same time.

      -  Figure 8: the CAP effect on inflammation is visible, however, a clear causal connection between ROS/Nrf2/KEap1 is not given in the presented experiments.

      Thank you for your comment. The simple mechanics of this paper are illustrated in the Graphic diagram. The activation of NRF2 exerts both antiinflammatory and antioxidant functions, which has been reported in many articles, but the causal relationship is still open to exploration.

      Points related to presentation:  

      -  The data with the encapsulated CAP appear a little as a sidearm that does not bolster your main message (maybe take out and elaborate on this topic more extensively in another manuscript).

      -  Revise the introduction on the Nrf2 signaling pathway as it is written at the moment, someone outside the Nrf2 field might have trouble understanding it.

      -  The use of language requires proofreading and revision.

      Thank you for your comment. We rearranged and proofread it.

      Reviewer #3 (Recommendations For The Authors):

      Overall, the manuscript is well-written and the results are presented in a concise and comprehensible manner.

      Some recommendations on the experimental evidence and further suggestions:

      • The authors should state how they assessed the distribution of the data. Description of data with mean and standard deviation as well as comparisons between different groups with t-test assumes that the underlying data is normally distributed.

      Your suggestions are very constructive for us to improve the paper.  The differences in the mean values between the two groups were analyzed using the student’s t-test, while the differences among multiple groups were analyzed using a one-way ANOVA test in the GraphPad Prism software.

      Therefore, we checked and proofread the statistical analysis.

      • Additional experiments further characterising and validating the activation of CAP via direct KELCH1-binding could include parallel experiments with similar agonists like dimethyl fumarate. It would be interesting to know how CAP activation compares to DMF activation.

      Thank you very much for your comment. We believe that the activation of NRF2 by DMF has been widely reported and well-studied, so we did not purchase this drug for comparative study here. If it can be promoted clinically in the future, we may consider comparing with DMF.

      • Also, the knock-down of NRF2 would be a suggested experiment to do because it rules out that the benefit of CAP is independent of KEAP1-NRF2 binding and activation.

      Thank you very much for your suggestions. We purchased Nrf2-deficient mice and performed experiments, and the results showed that knockout mice with Nrf2 were more sensitive to ethanol and the effects of CAP were partially eliminated (Figure 9), which further validated the role of Nrf2-related signaling pathway in alcohol-induced gastric mucosal injury and the therapeutic effect of CAP.

      Some corrections on text and figures:

      • Figure 1b: incorrect spelling of DNA stain. Should be Hoechst33324.

      Thank you very much for your comment. We have revised.

      • Figure 1c: don't put the label inside the plot.

      Thank you very much for your comment. We have revised.

      • Figure 1d: choose less verbose axes titles (this also applies to other figures).

      Thank you very much for your comment. We have revised.

      • Figures 1e and 1f: please state the units.

      Thank you very much for your comment. The enzyme activity of SOD and the content of MDA were compared with that of the control group.

      • Heading 2.2: NRF2-ARE instead of NRF-ARE.

      Thank you very much for your comment. We have revised.

      • Line 118: missing expression after immune.

      Thank you very much for your comment. We have revised.

      • Figure 1g: names of proteins are not readable.

      Thank you very much for your comment. We have revised.

      • Line 120: You performed transcriptomic analyses to identify differentially expressed GENES not proteomic.

      Thank you very much for your comment. This part of the work we do is proteomics.

      • Line 122: Fold change should be stated in both directions, i.e. absolute FC like |FC| > 1. Or did you select only upregulated DEGs? Is it not log2 FC?

      Thank you very much for your comment. We have revised.

      • Figure 1h (and Supplementary Figure 1a): Missing heatmap legend for FC.

      What do the colors show? Sample (column) description missing.

      Thank you very much for your comment. We used red to indicate up-regulation, blue to indicate down-regulation, and the vertical coordinate on the right side were antioxidant genes such as GSS and SOD1, respectively, and the proportion between the treatment group and the model group (CAP + EtOH/EtOH) had been calculated and labeled.

      • Line 145: A Western blot is not a proteomic analysis.

      Thank you very much for your comment. We have revised: “Concurrently, the elevated expression levels of GSS and Trx proteins, which were also downstream targets of NRF2, further validated by western blotting (Figure 1j).”

      • Supplementary Figure 2e-j: expression fold change is not the right quantity. The signal of the actual protein was quantified. And what are you comparing to with the statistics? The stars on one bar are not clear.

      Thank you very much for your comment. The expression level of this part was normalized compared with that of the control group. The significance differentiation analysis is compared with the model group.

      • What was the concentration of  H<sub>2</sub>O<sub>2</sub> used?

      Thank you very much for your comment. 200 μM  H<sub>2</sub>O<sub>2</sub> was used.

      • Figure 2d: use a more precise y-axis label.

      Thank you very much for your comment. We do want to compare the amount of NRF2 entering the nucleus, so the relative expression is compared to the internal reference

      • Figure 2g: missing molecular weight markers.

      Thank you very much for your comment. Since the ubiquitination modification is a whole membrane, and only marking the size of HA and GAPDH is not beautiful enough here.

      • Line 221: lactate is the endproduct of the anaerobic glycolytic pathway.

      Thank you very much for your comment. We have revised.

      • Supplementary Figure 3d: should it be PKM2 (instead of PKM) and LDHA (instead of LDH). Should fit with the text in the manuscript.

      Thank you very much for your comment. We have revised.

      • Supplementary Figures 3 e-f: brackets in y-axis labels are too bold.

      Thank you very much for your comment. We have revised.

      • Figures 3a and b. Brackets should only be used if two conditions are being compared statistically. Remove the one line with ns as it could imply that you have compared the first with the last condition only.

      Thank you very much for your comment. We have revised.

      • Consistent labeling of kDa in figures (no capital K in KDa).

      Thank you very much for your comment. We have revised.

      • Figure 4a. Move kDa on top of 70.

      Thank you very much for your comment. We have revised.

      • Figure 3 g-h: Why 2% EtOH. Used 5% previously?

      Thank you very much for your comment. Because here we changed the 293T cell line, 5% EtOH concentration is too high on this cell.

      • Supplementary Figure b-e: correct typo in y-axis label: expression.

      Thank you very much for your comment. We have revised.

      • Figure 4a: correct x-axis label for temperature unit. Too bold. Not readable.

      Add a clear label and unit for y-axis.

      Thank you very much for your comment. We have revised.

      • Figure 4 b-c: should have a legend explaining colors.

      Thank you very much for your comment. Our Figure legend already contains the meaning of colors: “(b) Computational docking of CAP molecule to KEAP1 surface pockets. The Keap1 protein is represented in gray, while the CAP molecule is shown in yellow. The seven key amino acids predicted to be crucial for the interaction are highlighted in blue. (c) Partial overlap of CAPbinding pocket with KEAP1-NRF2 interface. The KEAP1-NRF2 interaction interface is represented in purple.”

      • Supplementary Figure 5a. Add axis units.

      Thank you very much for your comment. We have revised.

      • Figure 4e: Missing b ions value for number 19.

      Thank you very much for your comment. This part is not missing, but corresponds to 19 of y ions.

      • Figure 7f: adjust brackets - they are too bold.

      Thank you very much for your comment. We have revised.

      • Supplementary Figure 8b-i: labels not readable. c should be spleen.

      Thank you very much for your comment. We have revised.

      • Line 787: specify BH adjustment to Benjamini-Hochberg.

      Thank you very much for your comment. We have revised.

      • Check spelling of µl throughout the Methods section e.g. line 854 - shouldn't be "ul".

      Thank you very much for your comment. We have revised.

      • Line 974: correct spelling of species names: E. coli should be in italics.

      Thank you very much for your comment. We have revised all of these corrections on text and figures. For me, the writing of papers will be more rigorous and careful in the future.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors aimed to quantify feral pig interactions in eastern Australia to inform disease transmission networks. They used GPS tracking data from 146 feral pigs across multiple locations to construct proximity-based social networks and analyse contact rates within and between pig social units.

      Strengths:

      (1) Addresses a critical knowledge gap in feral pig social dynamics in Australia.

      (2) Uses robust methodology combining GPS tracking and network analysis.

      (3) Provides valuable insights into sex-based and seasonal variations in contact rates.

      (4) Effectively contextualizes findings for disease transmission modeling and management.

      (5) Includes comprehensive ethical approval for animal research.

      (6) Utilizes data from multiple locations across eastern Australia, enhancing generalizability.

      Weaknesses:

      (1) Limited discussion of potential biases from varying sample sizes across populations

      This is a really good comment, and we will address this in the discussion as one of the limitations of the study

      (2) Some key figures are in supplementary materials rather than the main text.

      We will move some of our supplementary material to the main text as suggested.

      (3) Economic impact figures are from the US rather than Australia-specific data.

      We included the impact figures that are available for Australia (for FDM), and we will include the estimated impact of ASF in Australia in the introduction.

      (4) Rationale for spatial and temporal thresholds for defining contacts could be clearer.

      We will improve the explanation of why we chose the spatial and temporal thresholds based on literature, the size of animals and GPS errors.

      (5) Limited discussion of ethical considerations beyond basic animal ethics approval.

      This research was conducted under an ethics committee's approval for collaring the feral pigs. This research is part of an ongoing pest management activity, and all the ethics approvals have been highlighted in the main manuscript.

      The authors largely achieved their aims, with the results supporting their conclusions about the importance of sex and seasonality in feral pig contact networks. This work is likely to have a significant impact on feral pig management and disease control strategies in Australia, providing crucial data for refining disease transmission models.

      Reviewer #2 (Public review):

      Summary:

      The paper attempts to elucidate how feral (wild) pigs cause distortion of the environment in over 54 countries of the world, particularly Australia.

      The paper displays proof that over $120 billion worth of facilities were destroyed annually in the United States of America.

      The authors have tried to infer that the findings of their work were important and possess a convincing strength of evidence.

      Strengths:

      (1) Clearly stating feral (wild) pigs as a problem in the environment.

      (2) Stating how 54 countries were affected by the feral pigs.

      (3) Mentioning how $120 billion was lost in the US, annually, as a result of the activities of the feral pigs.

      (4) Amplifying the fact that 14 species of animals were being driven into extinction by the feral pigs.

      (5) Feral pigs possessing zoonotic abilities.

      (6) Feral pigs acting as reservoirs for endemic diseases like brucellosis and leptospirosis.

      (7) Understanding disease patterns by the social dynamics of feral pig interactions.

      (8) The use of 146 GPS-monitored feral pigs to establish their social interaction among themselves.

      Weaknesses:

      (1) Unclear explanation of the association of either the female or male feral pigs with each other, seasonally.

      This will be better explained in the methods.

      (2) The "abstract paragraph" was not justified.

      We have justified the abstract paragraph as requested by the reviewer.

      (3) Typographical errors in the abstract.

      Typographical errors have been corrected in the Abstract.

      Reviewer #3 (Public review):

      Summary:

      The authors sought to understand social interactions both within and between groups of feral pigs, with the intent of applying their findings to models of disease transmission. The authors analyzed GPS tracking data from across various populations to determine patterns of contact that could support the transmission of a range of zoonotic and livestock diseases. The analysis then focused on the effects of sex, group dynamics, and seasonal changes on contact rates that could be used to base targeted disease control strategies that would prioritize the removal of adult males for reducing intergroup disease transmission.

      Strengths:

      It utilized GPS tracking data from 146 feral pigs over several years, effectively capturing seasonal and spatial variation in the social behaviors of interest. Using proximity-based social network analysis, this work provides a highly resolved snapshot of contact rates and interactions both within and between groups, substantially improving research in wildlife disease transmission. Results were highly useful and provided practical guidance for disease management, showing that control targeted at adult males could reduce intergroup disease transmission, hence providing an approach for the control of zoonotic and livestock diseases.

      Weaknesses:

      Despite their reliability, populations can be skewed by small sample sizes and limited generalizability due to specific environmental and demographic characteristics. Further validation is needed to account for additional environmental factors influencing social dynamics and contact rates.

      This is a really good point, and we thank the reviewer for pointing out this issue. We will discuss the potential biases due to sample size in our discussion. We agree that environmental factors need to be incorporated and tested for their influence on social dynamics, and this will be added to the discussion as we have plans to expand this research and conduct, the analysis to determine if environmental factors are influencing social dynamics.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Consider moving some key figures from supplementary materials to the main text to strengthen the presentation of results.

      We included a new figure to strengthen the presentation of results (Figure 3a-b), which shows the node level measures by sex and for direct and indirect networks.

      (2) Expand discussion of limitations, particularly addressing potential biases from varying sample sizes across populations.

      We added more detail and clarity about this potential bias into the limitation section within the discussion: “Different populations in our study had varying numbers of collared individuals, with some populations having only two individuals at certain times. This variability in sample size across populations is a limitation when interpreting the results. Small populations are often the result of a few individuals being trapped and collared, and this does not necessarily reflect the actual number of individuals in those groups.” Moreover, while reviewing the effect of the potential bias, we found that a General Linear Mixed Effect Model (Table 1) was not optimal for analysing the effect of sex on the network measures, and therefore this analysis has been done again using a non-parametric test (Wilcoxon rank-sum test)  for direct and indirect networks based on a 5 metres threshold (Table 1).

      (3) If available, include Australia-specific economic impact data in the introduction.

      We included the impact figures that are available for Australia (for FDM) in the introduction.

      (4) Clarify the rationale for chosen spatial and temporal thresholds for defining contacts.

      This has been added in the methodology: “Direct contact was defined when two individuals interacted either at 2, 5, or 350-metre buffers within a five-minute interval [36]. A previous study used 350 metres as a spatial threshold [16], while others use the approximate average body length of an individual [36]”

      (5) Consider adding a brief discussion of ethical considerations beyond basic animal ethics approval, addressing aspects like animal welfare during collaring and potential environmental impacts.

      Feral pigs are an invasive species in Australia, and managing their population is crucial to protecting native ecosystems. The trapping and collaring of these animals have been conducted following the stringent animal welfare requirements necessary to obtain animal ethics approval in Australia. However, it is important to consider the broader ethical implications. Animal welfare during collaring is a critical aspect and involves minimising stress and physical harm to the animals. The collars used are lightweight and properly fitted only on adults due to welfare issues collaring juveniles.

      (6) Add a statement about data availability/accessibility.

      The GPS data cannot be shared; however, the R codes will be deposited in GitHub (https://github.com/Tatianaproboste/Feral-Pig-Interactions) and the link has been added in the final version.

      (7) Expand on the implications of seasonal variation in contact rates for disease management strategies in the discussion.

      We have added this information in the discussion: “For example, controlling an outbreak during summer would potentially require more resources than an outbreak in other seasons due to the higher number of contact between individuals during summer.”

      Reviewer #2 (Recommendations for the authors):

      The typographical errors in the abstract to be corrected are:

      (1) Line 22: Remove the "are" before "threaten".

      This has been corrected.

      (2) Line 24: Replace the "to" before "extinction" with "into".

      This has been corrected.

      (3) Line 28: Rephrase the sentence.

      ‘Yet social dynamics are known to vary enormously from place to place, so knowledge generated for example in USA and Europe might not easily transfer to locations such as Australia.’

      (3) Line 29: Insert a "comma" after "Here".

      This has been corrected.

      (4) Lines 33 -34: Explain, clearly, the contact rates; is it between females to females or females to males?

      We have improved this phrase and now it reads: “…. with females demonstrating higher group cohesion (female-female) and males acting as crucial connectors between independent groups.”

      (5) Line 36: Make yourselves clear about what you mean by "targeting adult male".

      We believe “targeting adult males” is correct in this context.

      Reviewer #3 (Recommendations for the authors):

      (1) Line 22 and 44, I think are threaten "are" should be removed for better clarity.

      This has been corrected.

      (2) Line 71, the source and not "force" of infection.

      The force of infection is correct here.

      (3) Line 72, population "of".

      This has been corrected.

      (4) Under statistical analysis, the software version should be included.

      R has changed to multiple versions since we started this analysis.

      (5) Terminological consistency: as far as possible try to be consistent with the terms used in the text, such as using "contact rate" instead of "interaction rate" in order not to puzzle the readers.

      We have changed most of the “interactions” to “contact” instead as suggested.

      (6) Correct Typos: Identify typos and grammatical inconsistencies of any kind, especially in those complex sentences that may be hard to follow.

      The typos have been checked.

      (7) Under the methodology, briefly describe why specific thresholds were chosen and any limitations.

      We added the following into the method: “Direct contact was defined when two individuals interacted either at 2, 5, or 350-metre buffers within a five-minute interval [36]. A previous study used 350 metres as a spatial threshold [16], while others use the approximate average body length of an individual [36]”

      (8) The discussion should be strengthened by drawing clear links between the findings and actionable management strategies.

      We have strengthened the discussion by adding more specific actionable management strategies. For example, controlling an outbreak during summer would potentially require more resources than an outbreak in other seasons due to the higher number of contacts between individuals during summer.

      (9) Did you consider additional environmental factors, such as rainfall, food availability, or habitat features, to better understand how these influence seasonal variations in pig interactions and contact rates?

      This is something that we have in mind and will explore in future research. This has been partially explored but is based on how environmental factors and seasons affect the home range (Wilson et al 2023).

      (10) Figure Legends: Add more detailed descriptions in figure legends, especially for those figures showing network metrics or contact rates.

      More information has been added to the figure legends.

      (11) The paper includes too many figures, and thus, it is recommended to simplify or merge some figures where appropriate. In particular, this is recommended for those figures that plot more network measures across thresholds. Adding clear, summarized captions with interpretation on threshold and measure significance would be a great help in interpreting complicated visualizations.

      The figure that shows the comparison between global network measures, including average local transitivity, edge density, global transitivity, mean distance and number of edges for direct and indirect networks has been moved to supplementary material (Figure S3). We also included direct and indirect model-level measures by sex as in Figure 3 and improved the captions of the figures presented in the main document.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this valuable study, the authors found that the macrolide drug rapamycin, which is an important pharmacological tool in the clinic and the research lab, is less specific than previously thought. They provide solid functional evidence that rapamycin activates TRPM8 and develop an NMR method to measure the specific binding of a ligand to a membrane protein.

      Strengths:

      The authors use a variety of complementary experimental techniques in several different systems, and their results support the conclusions drawn.

      Weaknesses:

      Controls are not shown in all cases, and a lack of unity across the figures makes the flow of the paper disjointed. The proposed location of the rapamycin binding pocket within the membrane means that molecular docking approaches designed for soluble proteins alone do not provide solid evidence for a rapamycin binding pocket location in TRPM8, but the authors are appropriately careful in stating that the model is consistent with their functional experiments.

      Impact:

      This work provides still more evidence for the polymodality of TRP channels, reminding both TRP channel researchers and those who use rapamycin in other contexts that the adjective "specific" is only meaningful in the context of what else has been explicitly tested.

      Reviewer #2 (Public Review):

      Summary:

      Tóth and Bazeli et al. find rapamycin activates heterologously-expressed TRPM8 and dissociated sensory neurons in a TRPM8-dependent way with Ca2+-imaging. With electrophysiology and STTD-NMR, they confirmed the activation is through direct interaction with TRPM8. Using mutants and computational modeling, the authored localized the binding site to the groove between S4 and S5, different than the binding pocket of cooling agents such as menthol. The hydroxyl group on carbon 40 within the cyclohexane ring in rapamycin is indispensable for activation, while other rapalogs with its replacement, such as everolimus, still bind but cannot activate TRPM8. Overall, the findings provide new insights into TRPM8 functions and may indicate previously unknown physiological effects or therapeutic mechanisms of rapamycin.

      Strengths:

      The authors spent extensive effort on demonstrating that the interaction between TRPM8 and rapamycin is direct. The evidence is solid. In probing the binding site and the structural-function relationship, the authors combined computational simulation and functional experiments. It is very impressive to see that "within" a rapamycin molecule, the portion shared with everolimus is for "binding", while the hydroxyl group in the cyclohexane ring is for activation. Such detailed dissection represents a successful trial in the computational biology-facilitated, functional experiment-validated study of TRP channel structuralactivity relationship. The research draws the attention of scientists, including those outside the TRP channel field, to previously neglected effects of rapamycin, and therefore the manuscript deserves broad readership.

      Weaknesses:

      The significance of the research could be improved by showing or discussing whether a similar binding pocket is present in other TRP channels, and hence rapalogs might bind to or activate these TRP channels. Additionally, while the finding on TRPM8 is novel, it is worthwhile to perform more comprehensive pharmacological characterization, including single-channel recording and a few more mutant studies to offer further insight into the mechanism of rapamycin binding to S4~S5 pocket driving channel opening. It is also necessary to know if rapalogs have independent or synergistic effects on top of other activators, including cooling agents and lower temperature, and their dependence on regulators such as PIP2.

      Additional discussion that might be helpful:

      The authors did confirm that rapamycin does not activate TRPV1, TRPA1 and TRPM3. But other TRP channels, particularly other structurally similar TRPM channels, should be discussed or tested. Alignment of the amino acid sequences or structures at the predicted binding pocket might predict some possible outcomes. In particular, rapamycin is known to activate TRPML1 in a PI(3,5)P2-dependent manner, which should be highlighted in comparison among TRP channels (PMID: 35131932, 31112550).

      Reviewer #3 (Public Review):

      Summary:

      Rapamycin is a macrolide of immunologic therapeutic importance, proposed as a ligand of mTOR. It is also employed as in essays to probe protein-protein interactions.

      The authors serendipitously found that the drug rapamycin and some related compounds, potently activate the cationic channel TRPM8, which is the main mediator of cold sensation in mammals. The authors show that rapamycin might bind to a novel binding site that is different from the binding site for menthol, the prototypical activator of TRPM8. These solid results are important to a wide audience since rapamycin is a widely used drug and is also employed in essays to probe protein-protein interactions, which could be affected by potential specific interactions of rapamycin with other membrane proteins, as illustrated herein.

      Strengths:

      The authors employ several experimental approaches to convincingly show that rapamycin activates directly the TRPM8 cation channel and not an accessory protein or the surrounding membrane. In general, the electrophysiological, mutational and fluorescence imaging experiments are adequately carried out and cautiously interpreted, presenting a clear picture of the direct interaction with TRPM8. In particular, the authors convincingly show that the interactions of rapamycin with TRPM8 are distinct from interactions of menthol with the same ion channel.

      Weaknesses:

      The main weakness of the manuscript is the NMR method employed to show that rapamycin binds to TRPM8. The authors developed and deployed a novel signal processing approach based on subtraction of several independent NMR spectra to show that rapamycin binds to the TRPM8 protein and not to the surrounding membrane or other proteins. While interesting and potentially useful, the method is not well developed (several positive controls are missing) and is not presented in a clear manner, such that the quality of data can be assessed and the reliability and pertinence of the subtraction procedure evaluated.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major points

      (1) Given the novelty of the STTD NMR approach, please provide more details and data for the reader.

      • I would like to see all of the collected spectra so that readers can see and judge the effect sizes for themselves, perhaps as an additional supplementary figure.

      We agree with the reviewer that the data transparency of the NMR measurements should be improved. We changed panel C of Figure 2 in the main text and provided all the STD and the computed STDD and STTD spectra recorded on one set of experiments. We carried out additional experimental replicas on new samples and addressed the variability of cell samples by rescaling the STD effects based on reference <sup>1</sup>H measurements. We provided supplementary spectra of the reference experiments without saturation (Figure S5) and the obtained STTD spectra from the three parallel NMR sessions (Figure S6).

      • I appreciate the labels for STDD-1, STDD-2, and STTD on the lower two spectra of Figure 2C. Is the top spectrum from STD-1 or is it prior to saturation? In Figure 2C, what do the x1 and x2 notations on the right-hand side of the spectra indicate?

      We showed the top spectrum as an overview and a demonstration of the spectral complexity of the samples. <sup>1</sup>H experiments were run before the STD measurements to assess the sample quality and stability. The demonstrated spectrum on sample 1 (TRPM8 with rapamycin in HEK cells) was recorded with more transients than the corresponding STDs, thus it is only visually comparable with the difference spectra after scaling (2x). Figure 2 was changed and all the spectra were replaced as mentioned before. All the recorded <sup>1</sup>H-experiments without saturation including the one removed are now available in the supplementary information (Figure S5).

      • The STTD NMR results with WT TRPM8 are consistent with rapamycin binding directly to the channel. Testing whether rapamycin binding observed with STTD NMR is disrupted by one of the most compelling mutations (D796A, D802A, G805A, or Q861A) would be a further test of this direct interaction.

      We thank the reviewer for the suggestion and agree that testing the most compelling mutants would be a promising next step. These mutations were generated in plasmid vectors and only transiently transfected into HEK cells. For NMR analysis we would need a high amount of cells stably overexpressing the mutant channels which were not available for experimentation.

      • Given that this is not a methods paper, it is probably outside the scope to further validate the STTD NMR measurements by performing parallel ITC, SPR, MST, or radiolabeled ligand experiments. Nevertheless, I would be excited to see such a comparison since STTD NMR appears to have promise as an experimental technique for assessing ligand binding to membrane proteins that does not require large amounts of purified protein or radioactive isotopes.

      We agree with the reviewer that additional independent biophysical measurements on the interactions are necessary to further validate the STTD methodology. This paper is a preliminary demonstration of the STTD concept and our group is currently working on the challenges of on-cell NMR (e.g., sample and spectral complexity) and the standardization of the proposed workflow.     

      (2) Please clarify the methods used to model of rapamycin binding. Docking can be imprecise in TRP channels, even with a sophisticated docking scheme (Hughes et al., 2019, doi: https://doi.org/10.7554/eLife.49572.001).  

      Thank you for mentioning this point and providing the reference. We have further clarified our methods and included the reference in our discussion, indicating the limitations of our approach.

      • As a positive control, does the docking strategy accurately predict binding of known compounds (menthol, icilin, etc.) to TRPM8 consistent with cryo-EM structures?  

      Yes, the binding site for menthol, based on a similar docking strategy as for rapamycin, is also presented, and matches with predictions from other publications. This is now clarified in the revised manuscript.

      • Why was homology modeling to the human sequence used with the mouse structure but not the avian structure?  

      At this onset of the project, only the avian structure was available, and it was used in the primary docking. Later, to get more precise docking relevant for human TRPM8 pharmacology, we did revert to the then available structure of the mouse ortholog.  

      • How many rapamycin structural clusters were built, and how many structures were there in each cluster? How many were used? "most populated" is unspecific.  

      Thank you for your comment. We have added the following highlighted information to the methods section to address your comment:

      “Representative conformations of rapamycin were identified by clustering of the 1000-membered pools, having the macrocycle backbone atoms compared with 1.0 Å RMSD cut-off. Middle structures of the ten most populated clusters, accounting for more than 90% of the total conformational ensemble generated by simulated annealing, were used for further docking studies. To refine initial docking results and to identify plausible binding sites, the above selected rapamycin structures were docked again, following the same protocol as above, except for the grid spacing which was set to 0.375 Å in the second pass. The resultant rapamycin-TRPM8 complexes were, again, clustered and ranked according to the corresponding binding free energies. Selected binding poses were subjected to further refinement. The three most populated and plausible binding poses were further refined by a third pass of docking, where amino acid side chains of TRPM8, identified in the previous pass to be in close contact with rapamycin (< 4 Å), were kept flexible. Grid volumes were reduced to these putative binding sites including all flexible amino acid side chains (21.0-26.2 Å x 26.2-31.5 Å x 24.8-29.2 Å).”

      However, it is important to clarify that the clusters are not built and their number is not specified by the user. The number of clusters found depends on how similar the structures are in the structural ensemble analyzed by clustering. A high number of clusters indicates a diverse, whereas a low number suggests a uniform structural ensemble. Furthermore, it is arbitrarily controlled by the similarity cutoff specified by the user. If the cutoff is selected well, then the number of structures is different in each cluster. There are some highly populated clusters and a few which only have one structure. The selection of how many cluster representatives are used is usually based on the decision of whether or not the sum of the population of selected clusters sufficiently covers the mapped conformational space.

      • Additionally, the rapamycin poses were generated using a continuum solvent model that is unlikely to replicate the conditions existing in the lipid bilayer or in a lipid-exposed binding pocket as is predicted here. It is therefore possible that the rapamycin poses chosen for docking do not represent the physiological rapamycin binding pose, hampering the ability of the docking algorithm to find an appropriate docking pocket.  

      • Furthermore, accurately docking that may bind to membrane-exposed pockets is a challenging problem, particularly because many scoring algorithms, including those employed by Autodock, do not distinguish between solvent-exposed and membrane-exposed faces of the protein. This affects the predicted binding energies.  

      We appreciate the reviewer's insightful comments. We add a note in discussion part, mentioning these important limitations.  

      • In Figure 4, it appears that the proposed rapamycin binding pocket is located at the interface between two subunits, but only one is shown. Is there any contact with residues in the neighboring subunit? Based on Figure S4, I assume not, but am unsure.

      Based on the estimated distances, we do not think that there are any relevant interactions with residues from neighboring subunits. This is now indicated in the results section.

      • Consider uploading the rapamycin-docked model to a public repository such as Zenodo for readers to examine and manipulate themselves  

      As suggested, the model will be uploaded in a public repository. A link to the file on Zenodo is now included.

      (3) Please discuss the spatial location of the proposed rapamycin binding pocket relative to the vanilloid binding pocket in TRPV1.

      • The mutagenesis indicates that D745, D802, G805, and Q861 are most important for rapamycin sensitivity in TRPM8. Interestingly, the proposed rapamycin binding pocket appears to overlap spatially with the vanilloid binding pocket in TRPV1. Consistent with this, Q861 aligns with E570 in TRPV1, which is a critical residue for resiniferatoxin sensitivity. Indeed, similar to Q861's modeled proximity to the cyclohexyl ring, the hydroxyl group of the vanillyl moity of capsaicin (4DY in 7LR0, for example) is in proximity to E750 in TRPV1. Additionally, searching PubChem by structural similarity suggests that vanillyl head group of the TRP channel modulators capsaicin and eugenol are similar structurally to the trans-2Methoxycyclohexan-1-ol ring. Without overlaying the two structures myself, it is difficult to say more than that, but I encourage the authors to comment on any similarities and differences they observe.

      • If the proposed rapamycin pocket is indeed similar to the location of the vanilloid binding site, the authors may wish to discuss other TRPM channel structures that show ligands and lipids bound to this pocket because this provides evidence that this pocket influences TRPM channel function. For example, how does the proposed rapamycin binding pocket compare to TRPM8 bound to agonist AITC (PDBID 8e4l), TRPM5 bound to inhibitor NDNA (7mbv), and TRPM2 bound to phosphatidylcholine (6co7)?

      • Other TRP channel structures with ligands or lipids modeled in this region include TRPV1 bound to resiniferatoxin, capsaicin, or phosphatidylinositol (7l2j, 7l24, 7l2s, 7l2t, 7l2u, 7lp9, 7lpc, 7lqy, 7mz6, 7mz9, 7mza); TRPV3 bound to phosphatidylcholine (7mij, 7mik, 7mim, 7min, 7ugg); TRPV5 bound to econazole (6b5v) or ZINC9155 (6pbf); TRPV6 bound to piperazine (7d2k, 7k4b, 7k4c, 7k4d, 7k4e, 7k4f) or cholesterol hemisuccinate (7s8c); TRPC6 bound to BTDM (7dxf) or phosphatidylcholine (6uza); and TRP1 bound to PIP2 (6pw5).

      We thank the reviewer for these valuable insights. We have included some additional discussion highlighting the similarities between the proposed rapamycin binding site and some of the other ligandchannel interactions in the TRP superfamily, in particular the well-known vanilloid binding site in TRPV1. However, to keep the discussion focused, we have not fully discussed all the indicated interactions, to best serve the clarity and scope of the manuscript.  

      (4) I would like to see negative control calcium imaging and electrophysiology data with untransfected HEK cells to confirm that the observed activation is mediated by TRPM8 to parallel the TRPM8 KO sensory neuron experiments.  

      This important information is now included in the revised manuscript (Figure S2).

      (5) The DM-nitrophen Ca uncaging experiments are an interesting method to test Ca sensitivity of rapamycin, but the results make these experiments more complex to interpret. Ca has been shown to be an obligate cofactor for icilin sensitivity in TRPM8 under conditions where both the internal and external Ca concentrations are tightly controlled (Kuhn et al., 2009, doi: https://doi.org/10.1074/jbc.M806651200), which is necessary because TRPM8 allows Ca permeation through the pore when open. The large icilin-evoked currents in Figure 5A and 5B indicate that the effective intracellular calcium concentration is not zero prior to calcium uncaging, which may be high enough to mask any Ca-dependence of rapamycin that occurs at low Ca concentrations. Given this ambiguity, the inside-out patch clamp configuration would provide more control over the internal and external Ca concentration than is achieved in the Ca uncaging experiments. Because the authors have already demonstrated their ability to perform such experiments (Figure 2 panel B), it would be nice to see tests of Ca dependence using inside-out patch clamp.

      As was already shown in Figure 2, Rapamycin activates TRPM8 in inside-out patches, and these experiments were performed using calcium-free cytosolic and extracellular solutions. Note that earlier studies have already shown that icilin activates outward TRPM8 currents in the full absence of calcium: see e.g. Janssens et al. eLife, 2016. Chuang et al. 2004. In the case of Icilin, increased calcium further potentiates the current, which is more prominent for the inward current.

      In the Ca uncaging experiments, considering the Kd of DM-nitrophen of 5 nM, we expect that the intracellular calcium concentration before the UV flash would be approximately 15 nM. Taken together, both the inside-out experiments and the flash uncaging experiments confirm that rapamycin responses are not directly regulated by intracellular calcium, contrary to icilin.

      (6) Sequence conservation within TRPM channels could be used in combination with the binding pocket model and mutagenesis to predict rapamycin selectivity for TRPM8 over other TRPMs. For example, some important residues, specifically G805 and Q861, are not conserved in TRPM3, which agrees with the lack of rapamycin sensitivity observed in TRPM3 (Figure S1). Further sequence comparison would provide testable hypotheses for future exploration of rapamycin sensitivity in other TRPMs that could validate the proposed binding pocket.

      Thank you for the suggestion. We now indicate in the discussion that only some of the key residues are conserved and make suggestions for future studies.  

      (7) Please unify the color scheme across the figures to improve clarity.

      • The authors frequently use the colors blue, red, and green to represent menthol and rapamycin in the figures, but they are inconsistent in which one represents menthol and which represents rapamycin. It would be clearer for the audience if, for example, rapamycin is always represented with red and menthol is always represented with blue.  

      Thank you for pointing this out. We have made the coloring schemes more uniform.

      • In Figure 1, panel E, the coloring for Menthol and Pregnenolone Sulfate changes between the TRPM8+/+ and TRPM8-/- panels.  

      Thank you for pointing this out. We have updated the coloring schemes to ensure consistency between the TRPM8+/+ and TRPM8-/- panels.

      • Figure 3 B and E, perhaps color the plot background as a 3-color gradient (blue to white to red) rather than yellow and aqua. Center the white at the WT ratio, keeping the dashed line, with diverging gradients to, for example, blue for mutations that selectively affect menthol sensitivity and red for rapamycin.

      Thank you for the suggestion – we have changed the figure accordingly.  

      • Figure 4 panels A and B use the same color (green) to show two different things (menthol molecule and mutated residues that affect rapamycin sensitivity). It would be clearer for readers to change these colors to agree with a unified color scheme such that, for example, the menthol molecule is colored blue and the rapamycin-neighboring residues are colored red.

      Thank you for the suggestion. We have updated the figure to use a unified color scheme, with the menthol molecule now colored green and the rapamycin-neighboring residues colored cyan, to enhance clarity for readers.

      • I recommend adding a figure or panel that shows side chains for all mutations, colored by menthol/rapamycin selectivity, as indicated by the functional data in Figure 3B and 3E. This will highlight spatial patterns of the selective residues that are discussed in the text.

      Thank you for your suggestion, we added all the side residues in Figure S10.

      Minor points

      (1) It would be nice to have one more concentration data point in the middle of the dose response curve shown in Figure 1 panel B. The response is not saturating at the top or foot of the curve in Figure 1 panel D, precluding a confident fit to a two-state Boltzmann function.

      Instead of adding a single data point to this figure, we performed independent measurements on a plate reader system, comparing concentration responses at room temperature and 37 degrees. These data are now included as Figure S1.   

      (2) The cartoon in Figure 2 panel B should be made more accurate. For example, only the transmembrane helices should be depicted embedded in the membrane, not the whole protein including the intracellular domain. Because the experiment was performed with cells, change the orientation of TRPM8 in the cartoon to show the intracellular domain of the protein facing away from the extracellular side of the membrane where the rapamycin is applied.

      Thank you for this comment. We have corrected the cartoon accordingly

      (3) Perhaps put the yellow circles under or around the carbon atoms to which the identified hydrogen atoms belong in Figure 2 panel E and Figure 4 panel C. I found it difficult to visualize and compare the STTD NMR results with the predicted binding pocket.

      Thank you for the feedback. We have added yellow circles around the carbon atoms corresponding to the identified hydrogen atoms in Figure S9.  

      (4) Regarding the sentence on p. 12 beginning "In agreement with this notion..."

      • Include icilin, Cooling Agent-10, and WS-3 as other cooling agents whose sensitivity has been modulated by mutation of Y745

      • Cryosim-3 responses were not tested in either of the two papers cited; please add citation to Yin et al., 2022, doi: https://doi.org/10.1126/science.add1268 .

      • Other relevant papers include:

      – Malkia et al., 2009, doi: https://doi.org/10.1186/1744-8069-5-62 which includes molecular docking showing the hydroxyl group of menthol interacting with Y745

      – Beccari et al., 2017, doi: https://doi.org/10.1038/s41598-017-11194-0 Figure 5 shows disruption of icilin and Cooling Agent-10 sensitivity by Y745A

      – Palchevskyi et al., 2023, doi: https://doi.org/10.1038/s42003-023-05425-6 Figure 3 shows disruption of icilin, cooling agent-10, WS-3, and menthol sensitivity by Y745A o Plaza-Cayon et al., 2022, https://doi.org/10.1002%2Fmed.21920 Review of TRPM8 mutations

      • typo: Y754H should be Y745H

      Thank you for these suggestions. We have added the above references to the text and corrected the typo.

      (5) The authors use the competitive action of everolimus on rapamycin activation as evidence that the different macrolides are binding to the same binding pocket. In addition, prior work showed that Y745H and N799A mutations (which render TRPM8 insensitive to menthol and icilin, respectively) do not affect TRPM8 sensitivity to the structurally-related compound tacrolimus (Arcas et al., 2019). This is consistent with the docking and mutagenesis results presented here.

      Thank you for this valuable suggestion. We discuss these data in the revised version.

      (6) Rapamycin sensitivity has also been observed in TRPML1 (Zhang et al. 2019, doi: https://doi.org/10.1371/journal.pbio.3000252).

      We added a short reference to this interesting finding in the discussion.

      (7) The whole-cell currents are very large in several of the electrophysiology experiments (for example Figure 3 panel D and Figure S1), which could lead to artifacts of voltage errors as well as ion accumulation/depletion. However, because this paper is not relying on reversal potential measurements or trying to quantify V1/2, these errors are unlikely to affect the qualitative conclusions drawn.

      This is a fair point, but indeed unlikely to affect our main conclusions. Note that we compensated between 70 and 90% of the series resistance, so we don’t expect voltage errors exceeding ~10 mV.

      (8) Ligand sensitivity is frequently species-dependent in TRP channels, so it is interesting that multiple species were used here and that both human and mouse isoforms exhibit rapamycin sensitivity. It should be emphasized that human TRPM8 was used in the calcium imaging and electrophysiology experiments, as well as some docking models, while the mouse isoform was used in the sensory neuron experiments and a mutated avian isoform was used for some docking models.

      This information is available in the Methods and we believe it is clear for the readers.

      (9) Perhaps discuss the unclear mechanism of G805A action in icilin (but not menthol, cold, or praziquantel) sensitivity because it is not in direct contact with the ligand. For example, Yin et al., 2019 propose flexibility allowing Ca binding site and larger binding site for icilin.

      Yin et al. (2019) suggests that the G805A mutation impacts icilin sensitivity by influencing the flexibility of the binding site and possibly affecting calcium binding. In our study, we found that G805A significantly reduces rapamycin sensitivity, likely due to its direct role in the rapamycin binding pocket rather than affecting calcium binding. This is now briefly mentioned in the results section.

      (10) The Figure S1 legend indicates that n=5 for all panels, so please show normalized population IV curves rather than individual examples. Additionally, it would be interesting to see what happens when each agonist is co-applied with rapamycin. Does rapamycin potentiate or inhibit agonist activation in these channels and/or TRPM8?

      We believe that normalized population IVs are not ideal for representing whole-cell currents, considering the substantial variation in current densities. We therefore prefer to show example traces in Figure S3 of the revised version but include mean values of current densities for all tested cells in the text.

      While the effects of co-application of rapamycin with activating ligands could be of interest, we consider this somewhat outside the scope of the present manuscript. The combination of HEK293 cell experiments, along with results obtained in WT and TRPM8-deficient mice does, in our opinion, sufficiently describe the selectivity of rapamycin towards TRPM8 compared to other sensory TRP channels.

      (11) Figure S1 panel A does not contain units for Rapamycin or AITC concentrations.

      Thank you for pointing this out. The units were added to the figure.  

      (12) It would be nice if the authors characterized the different mutations as predicted to contribute to site 1 (D796, H845, Q861, based on Figure S4), site 2 (D796, M801, F847, and R851), and/or site 3 (F847, V849, and R851).

      The indicated mutants were all tested, as shown in Figure 3.

      (13) The numbering scheme in Figure S4 does not appear to match the residue numbers in the rest of the paper for certain residues (HIS-844 rather than H845, PHE-846 rather than F847, VAL-848 rather than V849, ARG-850 rather than R851, and GLN-860 rather than Q861), and labels are often overlapping and difficult to see. I also find the transparent spheres very difficult to distinguish from the transparent background, which makes it difficult to appreciate the STTD NMR data overlay.

      We apologize for the confusing numbering scheme. The lower numbers refer to the initial docking that was done using the avian TRPM8 ortholog. We have made a newer, clearer version of Figure S4 and inserted as Figure S9.  

      (14) Please superpose the Ligplots in Figure S5 panels E and F as described in the LigPlus manual (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/manual/manual.html) to facilitate easier comparison.

      Thank you for the suggestion. We followed the suggestion to superpose the Ligplots as described but found that the result was visually cluttered and difficult to interpret. To avoid confusion, we instead decided to remove panels E and F from Figure S5, as we believe that the visualization in panels A-D is clear and informative.

      (15) Some n values are missing in figure legends.

      We checked all legends, and added n numbers were missing.

      (16) There is an inconsistent specification of error bars as SEM in the figure legends, though it is specified in methods.

      A question for my own edification: Here, you have looked at ligand interactions with the protein by saturating the protein resonances and observing transfer to the ligand. Would it be possible to instead saturate lipid or solute resonances and observe transfer to a ligand? I am curious whether this would be one way to measure equilibrium partitioning of ligand into a membrane and/or determine the effective concentration of a ligand in the membrane. Additionally, could one determine whether the compound is fully partitioned into the center of the membrane or just sitting on the surface?

      The reviewer highlights an interesting aspect. The widely used WaterLOGSY NMR experiment (doi: 10.1023/a:1013302231549) saturates water molecules then the magnetization is transferred to the ligand of interest. Characteristic changes in ligand resonances are observed in the case of a binding event with proteins. On the other hand, the selective saturation of lipids is -while theoretically possible –technically challenging mainly because of the inherent low signal-dispersion of lipids and peak overlapping with ligand resonances. Additionally, lipid systems are more dynamic compared to proteins and ligand-lipid interactions could be weaker and less specific, significantly affecting the sensitivity of STD experiments.

      Reviewer #2 (Recommendations For The Authors):

      Major:

      • Is it feasible to test rapamycin on TRPM8 with single-channel recording? This will allow us to better probe the mechanism of rapamycin activation and compare it with menthol, with parameters of singlechannel conductance and maximal open probability.

      In our experience, it is very difficult to obtain single-channel recordings from TRPM8. The channel expresses at high densities, typically leading to patches contain multiple channels, making a proper analysis of mean open and closed times very difficult. Therefore, we have decided not to include such measurements in the manuscript.

      • The authors classified rapamycin as a type I agonist, the type that stabilizes the open conformation, same as menthol but more prominent. Does that indicate that rapamycin work synergistically (rather than independently) with menthol, because co-application of them can allow them to add to each other in stabilizing the open conformation? I wonder if the authors agree that this could be tested with experiments as in Figure S3, by showing a much more prolonged deactivation with co-application of menthol and rapamycin than applying each alone.

      Thank you for the insightful suggestion. We conducted co-application experiments, and our results show that the deactivation time is indeed significantly prolonged when both compounds are applied together compared to each alone. In fact, very little deactivation is seen when both compounds are co-applied, which made it virtually impossible to perform reliable fits to the deactivation time course for the Menthol+Rapamycin condition. Instead, we have now included summary results showing the percentage of deactivation after 100 ms. We included these findings in FigureS8.  

      • It could be tested whether rapamycin activation of TRPM8 requires or overrides the requirement of PIP2 with inside-out patch by briefly exposing the patch to poly-lysine to sequester PIP2.

      This is certainly a good suggestion for further follow-up studies. However, we considered that examination of the (potential) interaction between ligands and PIP2 was outside the scope of the current manuscript.

      • Figure 1C suggests that the authors test rapamycin when there is a relatively high baseline TRPM8 activation (prior to rapamycin) activation. This raises the possibility that rapamycin is more a potentiator than an activator. I wonder if the following two experiments could address it: (1) perfuse rapamycin while holding at different membrane potentials, wash-off rapamycin in the solution and quickly (in a few seconds) test the activated current magnitude (before rapamycin dissociation), to compare whether a more depolarized membrane potential (high baseline open probability) allows rapamycin to potentiate more. (2) Perform the experiment at a higher temperature (low baseline open probability) and test whether rapamycin EC50 shifts to the right.

      Thank you for the thoughtful suggestion. Overall, we are not really in favor of making a distinction between a potentiator and an activator since it is not really feasible to create a situation where TRPM8 activity is zero. As suggested, we performed the dose response experiment at a higher temperature (37 °C) and observed that rapamycin’s EC<sub>50</sub> shifts to the right FigureS2. This is similar to what has been observed for menthol on TRPM8 and for many other ligands on other temperature-sensitive TRP channels.

      Minor:

      (1) The author should report hill coefficient together with EC50 when showing dose-responses.

      We have added Hill coefficients for all the fits.

      (2) In Figure 1 (E, F), it might be clearer to use Venn-diagram to show whether there is overlapping among rapamycin-, menthol-, and cinnamaldehyde-responsive neurons. According to the authors' explanation, we can predict that rapamycin-insensitive, menthol-sensitive neurons should predominantly be cinnamaldehyde-responsive.

      Thank you for your suggestion. In these experiments, we applied several agonists and the combination of them would result in a visually crowded Venn diagram difficult to interpret. However, we agree, with the reviewer’s suggestion, and discuss the percentage of the cinnamaldehyde+ neurons in the rapa- menthol+ population in Trpm8<sup>-/-</sup> neurons.

      (3) In Figure 3(C), since F847 does not respond to either menthol or rapamycin, it should be excluded from (B). Otherwise it is misleading.

      Thank you for pointing this out. To clarify, we have included a calcium imaging trace for the F847 mutant, demonstrating a clear response to rapamycin in FigureS9. This additional data highlights that F847 does respond to rapamycin, albeit with a more modest response amplitude. This is now also clarified in the results section.  

      (4) The word "potency" in pharmacology usually refers to a smaller EC50 number in dose-dependent experiments. In "Effect of rapamycin analogs on TRPM8" session, the authors use "potency" to refer to response to a single-dose experiment of different compounds. The experiment does not measure potency.

      Thank you for pointing out this mistake. We have corrected the text and replaced “potency” with “efficacy”.

      (5)  "2-methoxyl-" is misspelled in the text body.

      We have corrected the typo.

      (6) It will be nice to include "vehicle" in Figure 6B, or alternatively normalize all individual traces to vehicle. In Figure 6C and D, everolimus has almost no effect with compared to vehicle, and should not be shown as if it had ~8% in Figure 6B.

      We have added the vehicle values to Figure 6B from the same experiments.

      Reviewer #3 (Recommendations For The Authors):

      (1) The NMR method presented here as novel and employed to identify a proposed molecule bound to a membrane protein (TRPM8 in this case) is not well explained and presented. Since several spectra need to be subtracted, the authors should present the raw data and the results of the subtractions step by step. Also, it seems that the height of the peaks in each spectra will be highly variable and thus a reliable criterion employed to scale spectra before subtraction. None of these problems are discussed of described.

      The reviewer is right, that the data transparency should be improved and due to the high molecular complexity of the samples the size of the STD effects should be carefully scaled. We carried out additional experimental replicas on new samples and addressed the inherent sample/peak height variability by rescaling the STD effects based on reference <sup>1</sup>H measurements. We provided supplementary spectra of the reference experiments without saturation (Figure S5) and the computed STTD spectra from three parallel NMR sessions (Figure S6). We changed panel C of Figure 2 in the main text and provided all the STD and the computed STDD and STTD spectra recorded on one set of NMR experiments. We added the following paragraph to the main text: “To address the effect of the inherent variability of cellular samples on peak heights, STD effects were normalized based on the comparison of independent <sup>1</sup>H experiments (Figure S5). Three STTD replicates were computed, unambiguously confirming direct binding to TRPM8 in two datasets (Figure S6 A,B)”.

      Importantly since this signal subtraction method is proposed as a new development, control experiments employing well-established pairs of ligand and membrane protein receptor should be performed to demonstrate the reliability of the method.

      We agree with the reviewer, that the STTD experiment as a new development needs further validation, however, this paper is a preliminary demonstration of a new strategy building on the well-established STD and STDD NMR methodologies. Our group is actively engaged in studying additional biological samples to enhance our understanding of the applicability of STTD NMR. These efforts also aim to address challenges such as sample and spectral complexity by refining and standardizing the proposed workflow.

      (2) The tail currents shown in supplementary figure 3 are clearly not monoexponential. The fit to a single exponential can be seen to be inadequate and thus the comparison of kinetics of control, rapamycin and menthol is incorrect. At least two exponentials should be fitted and their values compared.

      We agree that the decay in the (combined) presence of agonists deviates from a simple monoexponential behavior. While we agree that fitting with two (or more) exponentials would provide a better fit, this also comes with greater variations/uncertainties in the fit parameters. This is particularly the case when inactivation is very slow and incomplete, or when the difference between slow and fast exponential time constants is <5, as seen with rapamycin and rapamycin +menthol. Therefore, we decided to provide monoexponential time constants as a proxy to describe the clear slowing down of activation and deactivation time courses in the presence of Type I agonists.   

      Also related to this aspect, recordings of TRPM8 currents can not be leak subtracted with a p/n protocol, thus a large fraction of the initial tail current must be the capacitive transient. There is no indication in the methods of how was this dealt with for the fitting of tail currents.

      As explained in the methods, capacitive transients and series resistance were maximally compensated. Therefore, we do not agree that a large fraction of the initial tail current must be capacitive. This can also be clearly seen in experiment such as Figure 1C, where the inward tail current is fully abolished in the presence of a TRPM8 antagonist. Likewise, very small and rapidly inactivating tail currents can be seen during voltage steps under control conditions (e.g. Figure S7  and S8 in the revised version).  

      (3) The docking procedure employed, as the authors show, is not appropriate for membrane proteins since it does not include a lipid membrane. It is not clear in the methods section if the MD minimization described applies only to the rapamycin molecule or to rapamycin bound to TRPM8.  

      It is also not clear if the important residue Q861 (and other residues that are identified as interacting with rapamycin) were identified from dockings or proposed based on other evidence.

      (4) Identifying amino acid residues that diminish the response to a ligand, does not uniquely imply that they form a binding site or even interact with said ligand. It is entirely possible that they can be involved in the allosteric networks involved in the activating conformational change. This caveat should be clearly posited by the authors when discussing their results.

      In our study, we identified several residues that significantly reduce the response to rapamycin when mutated, while retaining robust responses to menthol, which indicates that these mutations do not affect crucial conformational changes leading to channel gating. While our cumulative data suggest that these residues may be involved in direct interaction with rapamycin, we recognize the alternative possibility that they allosterically affect rapamycin-induced channel gating. This is now clearly stated in the first paragraph of the discussion.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1 (Public Review):

      • While the title is fair with respect to the data shown, in the summary and the rest of the paper, the comparison between anesthetized and awake conditions is systematically stated, while more caution should be used.

      First, isoflurane is one of the (many) anesthetics commonly used in pre-clinical research, and its effect on the brain vasculature cannot be generalized to all the anesthetics. Indeed, other anesthesia approaches do not produce evident vasodilation; see ketamine + medetomidine mixtures. Second, the imaged awake state is head-fixed and body-constrained in mice. A condition that can generate substantial stress in the animals. In this study, there is no evaluation of the stress level of the mice. In addition, the awake imaging sessions were performed a few minutes after the mouse woke up from isoflurane induction, which is necessary to inject the MB bolus. It is known that the vasodilator effects of isoflurane last a long time after its withdrawal. This aspect would have influenced the results, eventually underestimating the difference with respect to the awake state.

      These limitations should be clearly described in the Discussion.

      Looking at Figure 2e, it takes more than 5' to reach the 5 Millions MB count useful for good imaging. However, the MB count per pixel drops to a few % at that time. This information tells me that (i) repeated measurements are feasible but with limited brain coverage since a single 'wake up' is needed to acquire a single brain section and (ii) this approach cannot fit the requirements of functional ULM that requires to merge the responses to multiple stimuli to get a complete functional image. Of course, a chronic i.v. catheter would fix the issue, but this configuration is not trivial to test in the experimental setup proposed by the authors, hindering the extension of the approach to fULM.

      Thank you for highlighting these limitations, as they address aspects that were not fully considered during the experimental design and manuscript writing. In response, we have added the following paragraphs to the discussion section, addressing these limitations of our study:

      (Line 310) “Although isoflurane is widely used in ultrasound imaging because it provides long-lasting and stable anesthetic effects, it is important to note that the vasodilation observed with isoflurane is not representative of all anesthetics. Some anesthesia protocols, such as ketamine combined with medetomidine, do not produce significant vasodilation and are therefore preferred in experiments where vascular stability is essential, such as functional ultrasound imaging(47). Therefore, in future studies, it would be valuable to design more rigorous control experiments with larger sample sizes to systematically compare the effects of isoflurane anesthesia, awake states, and other anesthetics that do not induce vasodilation on cerebral blood flow.

      Our proposed method enabled repeatable longitudinal brain imaging over a three-week period, addressing a key limitation of conventional ULM imaging and offering potential for various preclinical applications. However, there are still some limitations in this study. 

      One of the limitations is the lack of objective measures to assess the effectiveness of head-fix habituation in reducing anxiety. This may introduce variability in stress levels among mice. Recent studies suggest that tracking physiological parameters such as heart rate, respiratory rate, and corticosterone levels during habituation can confirm that mice reach a low stress state prior to imaging(48). This approach would be highly beneficial for future awake imaging studies. Furthermore, alternative head-fixation setups, such as air-floated balls or treadmills, which allow the free movement of limbs, have been shown to reduce anxiety and facilitate natural behaviors during imaging(30). Adopting these approaches in future studies could enhance the reliability of awake imaging data by minimizing stress-related confounds.

      Another limitation of this study is the potential residual vasodilatory effect of isoflurane anesthesia on awake imaging sessions. The awake imaging sessions were conducted shortly after the mice had emerged from isoflurane anesthesia, required for the MB bolus injections. The lasting vasodilatory effects of isoflurane may have influenced vascular responses, potentially contributing to an underestimation of differences in vascular dynamics between anesthetized and awake state. Future applications of awake ULM in functional imaging using an indwelling jugular vein catheter presents a promising alternative to enable more accurate functional imaging in awake animals, addressing current limitations associated with anesthesia-induced vascular effects.”

      • Statistics are often poor or not properly described. 

      The legend and the text referring to Figure 2 do not report any indication of the number of animals analyzed. I assume it is only one, which makes the findings strongly dependent on the imaging quality of THAT mouse in THAT experiment. Three mice have been displayed in Figure 3, as reported in the text, but it is not clear whether it is a mouse for each shown brain section. Figure 5 reports quantitative data on blood vessels in awake VS isoflurane states but: no indication about the number of tested mice is provided, nor the number of measured blood vessels per type and if statistics have been done on mice or with a multivariate method.

      Also, a T-test is inappropriate when the goal is to compare different brain regions and blood vessel types.

      Similar issues partially apply to Figure 6, too.

      Thank you for bringing this to our attention. 

      We acknowledge that the statistical analyses were not clearly explained in the original version. In the revised manuscript, we have ensured that the statistical methods are clearly described. 

      (Fig.4 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using t-test at each measurement point along the segments.”

      (Fig.6 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using the two one-sided test (TOST) procedure, which evaluates the null hypothesis that the difference between the two weeks is larger than three times the standard deviation of one week.”

      Additionally, we corrected an error in the previous comparison of the violin plots on flow velocities, where a t-test was incorrectly applied; this has now been removed.

      We acknowledge that the original version did not clearly indicate the numbers of animals in the statistical analysis. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. In the revised Figures 4 and 6, we have ensured that each quantitative analysis figure or its caption clearly indicate the specific mice.

      For original Figures 1 and 2, these are presented as case studies to illustrate the methodology. Since the anesthesia time required for tail vein injection for each animal varies slightly, it is challenging to have the consistent time taken for each mouse to recover from anesthesia across all mice. For instance, in Figure 1, the mouse took nearly 500 seconds to recover from anesthesia, but this duration is not consistent across all animals, which is a limitation of the bolus injection technique. We have noted this point in the discussion (discussion on the limitation of bolus injection), and we have also clarified in the results section and figure captions that these figures represent a case study of a single mouse rather than a standardized recovery time for all animals.

      We further clarified this point in the end of the Figure 2 caption:

      (Fig.2 caption) “This figure presents a case study based on the same mouse shown in Fig 1. The x-axis for d-f begins at 500 seconds because, at this point, the mouse’s pupil size stabilized, indicating it had recovered to an awake state. Consequently, ULM images were accumulated starting from this time. It is important to note that not every mouse requires 500 seconds to fully awaken; the time to reach a stable awake state varies across individual mice.” We added the following statement before introducing Figure 1e:

      (Line 93) “Due to differences in tail vein injection timing and anesthesia depth, the time required for each mouse to fully awaken varied. Although it was not feasible to get pupil size stabilized just after 500 seconds for each animal, ULM reconstruction only used the data that acquired after the animal reached full pupillary dilation, to ensure that ULM accurately captures the cerebrovascular characteristics in the awake state.”

      We added the following statement before introducing Figure 2d:

      (Line 139) “To further verify that the proposed MB bolus injection method can help to achieve ULM image saturation shortly after mice awaken from anesthesia, an analysis on the change in MB concentration over time was conducted once pupil size had stabilized (T = 500s).”

      For Figures 3, 4, and 5 (in the revised version, Figures 4 and 5 have been combined into a single Figure 4), the data represents results from three individual mice, with each coronal plane corresponding to a different mouse. In the revised version, we have added labels to indicate the specific mouse in each image to improve clarity. We also recognize that some analyses in the original submission (original Figure 5) may have lacked sufficient statistical power due to the small sample size. Therefore, in the revised version, we have focused only on findings that were consistently observed across the three mice to ensure robust conclusions.

      Reviewer 1 (Recommendations For the Authors):

      • If the study's main goal is to compare awake vs anesthetized ULM, the authors should test at least another anesthetic with no evident vasodilator effect.

      Thank you for this valuable suggestion. We would like to clarify that the primary aim of our study is not to comprehensively compare the effects of anesthesia versus the awake state, as a rigorous comparison would indeed require a more controlled experimental design, including additional anesthetics, a larger cohort of mice, and broader controls to ensure sufficient statistical power. We also add the following statement in the Discussion to clarify this point:

      (Line 314) “Therefore, in future studies, it would be valuable to design more rigorous control experiments with larger sample sizes to systematically compare the effects of isoflurane anesthesia, awake states, and other anesthetics that do not induce vasodilation on cerebral blood flow.”

      We acknowledge that the initial organization of Figures 3–5 placed excessive emphasis on comparisons between the awake and anesthetized states, but without yielding consistently significant findings. Meanwhile, our longitudinal observations in original Figure 6 were underrepresented, despite their potential importance.

      In the revised version, we shifted our focus toward the main goal of awake longitudinal imaging. By consolidating the previous Figures 4 and 5 into the new Figure 4, we emphasize conclusions that are both more consistent and broadly applicable, avoiding areas that may lack sufficient rigor or consensus. Additionally, we expanded the quantitative analysis related to longitudinal imaging, highlighting its role as the ultimate objective of this study. The awake vs. anesthetized ULM comparison was intended to demonstrate the value of awake imaging and introduce the importance of awake longitudinal imaging. In the revised text, we have reframed this comparison to emphasize the specific response to isoflurane rather than a general response to anesthesia. For example, in Figures 3 and 4, we have replaced the original term "Anesthetized" with "Isoflurane". We have also added a discussion noting that isoflurane may induces more vasodilation than other anesthetic agents.

      (Line 310) “Although isoflurane is widely used in ultrasound imaging because it provides long-lasting and stable anesthetic effects, it is important to note that the vasodilation observed with isoflurane is not representative of all anesthetics. Some anesthesia protocols, such as ketamine combined with medetomidine, do not produce significant vasodilation and are therefore preferred in experiments where vascular stability is essential, such as functional ultrasound imaging(47).”

      • The claims made about the proposed experimental protocol to be suitable for the "long-term" (line 255) are not supported by the data and should be modified according to the presented evidence.

      Thank you for your valuable feedback. We agree that our current three-week experimental results do not yet fulfill the requirements for extended longitudinal imaging that may span several months. We have revised the relevant text accordingly. For instance, the phrase “Our proposed method enabled long-term, repeatable longitudinal brain imaging” has been modified to “Our proposed method enabled repeatable longitudinal brain imaging over a threeweek period.” (Similar changes also in Line 67, Line 318, and Line 337) Additionally, we have added the following paragraph in the discussion section to indicate that extending the monitoring period to several months is a meaningful direction for future exploration:

      (Line 337) “In our longitudinal study, consistent imaging results were obtained over a three-week period, demonstrating the feasibility of awake ULM imaging for this duration. However, for certain research applications, a monitoring period of several months would be valuable. Extending the duration of longitudinal awake ULM imaging to enable such long-term studies is a potential direction for future development.”

      Recommendations for improving the writing and presentation:

      • Reporting the number of mice and blood vessels and statistics for each quantitative figure.

      Thank you for highlighting this issue. We acknowledge that the quantitative figures in the previous version lacked clarity in specifying the number of mice, vessels, and associated statistics. In the revised version, we have ensured that each quantitative figure or its caption clearly indicate the specific mice, vessels, and statistical methods used. To further minimize any potential confusion, we have also added Supplementary Figure 1 to clearly label and reference each individual mouse included in the study.

      Minor corrections to the text and figures.

      • Line 22: "vascularity reduction from anesthesia" is not clear, nor it is a codified property of brain vasculature. Explain or rephrase.

      Thank you for your comment. We apologize for any confusion caused by the phrase “vascularity reduction from anesthesia” in the abstract. We agree that this phrasing was unclear without context. To improve clarity, we have revised this statement in the abstract to make it more straightforward and easier to understand. 

      (Line 24) “Vasodilation induced by isoflurane was observed by ULM. Upon recovery to the awake state, reductions in vessel density and flow velocity were observed across different brain regions.” 

      Additionally, we have added a section in the Methods titled Quantitative Analysis of ULM Images to provide a clear definition of vascularity. This section outlines how vascularity is quantified in our study, ensuring that our terminology is well-defined. 

      The following sentence shows the definition of vascularity:

      (Line 547) “Vascularity was defined as the proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal.”

      We have also added an instant definition when it was firstly used in Results part:

      (Line 161) “When comparing vessel density maps, ULM images that are acquired in the awake state demonstrate a global reduction of vascularity, which refers to percentage of pixels that occupied by blood vessels.”

      • Line 76: putting the mice in a tube is also intended "To further reduce animal anxiety and minimize tissue motion" I agree with tissue motion, not with animal anxiety, which, indeed, I expect to be higher than if it could, for example, run on a ball or a treadmill.

      Thank you for pointing this out. We acknowledge the limitations of our setup regarding reducing animal anxiety. We have replaced the original phrase “to further reduce animal anxiety and minimize tissue motion” with “to further minimize tissue motion.” (Line 78) Additionally, we have added the following paragraph in Discussion section to address the limitations of our setup in reducing anxiety.

      (Line 321) “One of the limitations is the lack of objective measures to assess the effectiveness of head-fix habituation in reducing anxiety. This may introduce variability in stress levels among mice. Recent studies suggest that tracking physiological parameters such as heart rate, respiratory rate, and corticosterone levels during habituation can confirm that mice reach a low stress state prior to imaging(48). This approach would be highly beneficial for future awake imaging studies. Furthermore, alternative head-fixation setups, such as air-floated balls or treadmills, which allow the free movement of limbs, have been shown to reduce anxiety and facilitate natural behaviors during imaging(30). Adopting these approaches in future studies could enhance the reliability of awake imaging data by minimizing stress-related confounds.”

      • Line 79: PMP has been used by Sieu et al., Nat Methods, 2015; it should be acknowledged.

      Thank you for highlighting this. We have now included the reference to Sieu et al. Nat Methods, 2015 to appropriately acknowledge their use of PMP. (Line 81)

      • Figure: is there a reason why the plots start at 500 sec? What happened before that time?

      Thank you for your question regarding the starting time in the plots. Figures 1 and 2 are case studies using a single mouse to demonstrate the feasibility of our method. The “zero” timepoint was defined as the moment when anesthesia was stopped, and the microbubble injection began. However, the mouse does not fully recover immediately after anesthesia is stopped. As shown in Figure 1e, there is a period of approximately 500 seconds during which the pupil gradually dilates, indicating recovery. Only after this period does the mouse reach a relatively stable physiological state suitable for ULM imaging, which is why the plots in Figure 2 begin at T = 500 seconds.

      We recognize that this was not sufficiently explained in the main text and figure captions. In the revised manuscript, we have clarified this timing rationale in both the results section and the figure captions. We added the following sentence to the result section to introduce Fig.2d:

      (Line 139) “To further verify that the proposed MB bolus injection method can help to achieve ULM image saturation shortly after mice awaken from anesthesia, an analysis on the change in MB concentration over time was conducted once pupil size had stabilized (T = 500s).”

      We also added the following statement to note that this recover time varies across individual mice:

      (Line 154, Fig.2 caption) “This figure presents a case study based on the same mouse shown in Fig 1. The x-axis for d-f begins at 500 seconds because, at this point, the mouse’s pupil size stabilized, indicating it had recovered to an awake state. Consequently, ULM images were accumulated starting from this time. It is important to note that not every mouse requires 500 seconds to fully awaken; the time to reach a stable awake state varies across individual mice.”

      Reviewer 2 (Public Review):

      • The only major comment (calling for further work) I would like to make is the relative weakness of the manuscript regarding longitudinal imaging (mostly Figure 6), compared to the exhaustive review of the effect of isoflurane on the vasculature (3 rats, 3 imaging planes, quantification on a large number of vessels, in 9 different brain regions). The 6 cortical vessels evaluated in Figure 6 feel really disappointing. As longitudinal imaging is supposed to be the salient element of this manuscript (first word appearing in the title), it should be as good and trustworthy as the first part of the paper. Figure 6c. is of major importance, and should be supported by a more extensive vessel analysis, including various brain areas, and validated on several animals to validate the robustness of longitudinal positioning with several instances of the surgical procedure. Figure 6d estimates the reliability of flow measurements on 3 vessels only. Therefore I recommend showing something similar to what is done in Figures 4 and 5: 3 animals, and more extensive quantification in different brain regions.

      We thank the reviewer for pointing out this issue. We acknowledge that the first version of the manuscript lacked in-depth quantitative analysis in the section on the longitudinal study, which should have been a focal point. It also did not provide a sufficient number of animals to demonstrate the reproducibility of the technique. In this revised version, we have included results from more animals and conducted a more comprehensive quantitative analysis, with the corresponding text updated accordingly. Specifically, we combined the previous Figures 4 and 5 into the current Figure 4 (corresponding revised text from Line 169 to Line 207). The revised Figures 5 and 6

      compare the results of the longitudinal study, presenting data from three mice (corresponding revised text from

      Line 224 to Line 258). Detailed information about the mice used has been added to Supplementary Figure 1, and Supplementary Figure 4 further provides a detailed display of the results for the three mice in longitudinal study. We hope that these adjustments will provide a more thorough validation of the longitudinal imaging.

      Reviewer 2 (Recommendations For The Authors):

      Minor comments:

      • The statistical analyses are not always explained: could they be stated briefly in the legends of each figure, or gathered in a statistical methods section with details for each figure? Be sure to use the appropriate test (e.g. student t-test is used in Fig 5 k whereas normality of distribution is not guaranteed.)

      Thank you for pointing this out. We acknowledge that the statistical analyses were not clearly explained in the original version. In the revised manuscript, we have ensured that the statistical methods are clearly described. 

      (Fig.4 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using t-test at each measurement point along the segments.”

      (Fig.6 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using the two one-sided test (TOST) procedure, which evaluates the null hypothesis that the difference between the two weeks is larger than three times the standard deviation of one week.”

      Additionally, we corrected an error in the previous comparison of the violin plots on flow velocities, where a t-test was incorrectly applied; this has now been removed.

      • The authors use early in the manuscript the term vascularity, e.g. in "vascularity reduction", it is not exactly clear what they mean by vascularity, and would require a proper definition at that moment. If I am correct, a quantification of that "vascularity reduction" (page 5 line 132), is then done in Figures 5 d e f and j.

      Thank you for highlighting this issue. We acknowledge that our initial use of the term “vascularity” may have been unclear and potentially confusing. In the revised manuscript, we have included a clear definition of “vascularity” in the Methods section under Quantitative Analysis of ULM Images (Line 534). 

      The following sentence shows the definition of vascularity:

      (Line 547) “Vascularity was defined as the proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal.”

      We have also added an instant definition when it was firstly used in Results part:

      (Line 161) “When comparing vessel density maps, ULM images that are acquired in the awake state demonstrate a global reduction of vascularity, which refers to percentage of pixels that occupied by blood vessels.”

      • There is very little motion in the images presented, except for the awake "Bregma -4.2 mm" (Figure 3, directional maps), especially in the area including colliculi and mesencephalon, while the cortical vessels do not move. Can you comment on that?

      Thank you for highlighting this important aspect of motion in awake animal imaging. Motion correction is indeed a critical factor in such studies. In the original version of our discussion, we briefly addressed this issue (from Line 342 to Line 346), but we agree that a more detailed discussion is needed.

      To minimize motion artifacts, we conducted habituation to acclimate the animals to the head-fixation setup, which helps reduce anxiety during imaging. With thorough head-fixed habituation, the imaging quality is generally well-preserved. We also applied correlation-based motion correction techniques based on ULM images, which can partially correct for overall brain motion, as stated in the previous version. However, this ULM-images-based correction is limited to addressing only rigid motion.

      In the revised discussion, we have expanded on the limitations of our current motion correction approach and referenced recent work about more advanced motion correction methods:

      (Line 346) “While rigid motion correction is often effective in anesthetized animals, awake animal imaging presents greater challenges due to the more prominent non-rigid motion, particularly in deeper brain regions. This is evidenced in Supplementary Fig. 1 (Mouse 7), where cortical vessels remain relatively stable, but regions around the colliculi and mesencephalon exhibit more noticeable motion artifacts, indicating that displacement is more pronounced in deeper areas. To address these deeper, non-rigid motions, recent studies suggest estimating nonrigid transformations from unfiltered tissue signals before applying corrections to ULM vascular images(16,50). Such advanced motion correction strategies may be more effective for awake ULM imaging, which experiences higher motion variability. The development of more robust and effective motion correction techniques will be crucial to reduce motion artifacts in future awake ULM applications.”

      • Figure 1f maybe flip the color bar to have an upward up and downward down.

      Thank you for your suggestion. This display method indeed makes the images more intuitive. In the revised manuscript, all directional flow color bars have been flipped to ensure that upward flow is displayed as ‘up’ and downward flow as ‘down.’

      • Figure 2b the figure is a bit confusing in what is displayed between dashed lines, solid lines, dots... maybe it would be easier to read with

      - bigger dots and dashed lines in color for each of the 4 series

      - and so in the legend, thin solid lines in the corresponding color for the fit, but no solid line in the legend (to distinguish data/fit)

      - no lines for FWHM as they are not very visible, and the FWHM values are not mentioned for these examples.

      Thank you for your detailed suggestions. We agree that the original Fig. 2b appeared messy and confusing. Based on this feedback and other comments, we decided to replace the FWHM-based vessel diameter measurement with a more stable binarization-based approach. In the revised version, we selected a specific segment of each vessel and measured the diameter by calculating the distance from the vessel’s centerline to both side after binarization. Each point on the centerline of this segment provides a diameter measurement, which can be further used to calculate the mean and standard error. This updated method is more stable and reproducible, providing reliable measurements even for vessels that are not fully saturated. It also facilitates comparison across more vessels, helping to further demonstrate the generalizability of our saturation standard. We believe these adjustments make the revised Fig. 2b clearer and more readable.

      • Page 7, lines 144-147. This passage is not really clear when linking going up or down and going from the stem to the branches that it is specific to Figure 4a (and therefore to this particular location).

      Thank you for your insightful comments on our vessel classification method. We recognize the limitations of the previous approach and, in order to enhance the rigor of the study, we have opted not to continue using this method in the revised manuscript. We have removed all content related to vessel classification based on branchin and branch-out criteria. This includes the original Classification of Cerebral Vessels section in the Methods, the relevant descriptions in the Results section under “ULM reveals detailed cerebral vascular changes from anesthetized to awake for the full depth of the brain”, limitation of this classification method in Discussion section, as well as related content in the original Figures 4 and 5.

      In the revised analysis, for the comparison between arteries and veins, we focus solely on penetrating vessels in the cortex. For these vessels, it is generally accepted that downward-flowing vessels are arterioles, while upwardflowing vessels are venules. Accordingly, in the revised Figures 4 and 6, we analyze arterioles and venules exclusively in the cortex, without relying on the previous classification method that could be considered controversial.

      • Page 11 line 222 "higher vascular density" seems unprecise.

      Thank you for pointing this out. We have revised the sentence to more precisely convey our observations regarding changes in vascular diameter and vascularity within the ROI. We present these findings as evidence of the vasodilation effect under isoflurane, in alignment with existing research. The revised statement is as follows:

      (Line 275) “Statistical analysis from Fig. 4 shows that certain vessels exhibit a larger diameter under isoflurane anesthesia, and the vascularity, calculated as the percentage of vascular area within selected brain region ROIs, is also higher in the anesthetized state. These findings suggest a vasodilation effect induced by isoflurane, consistent with existing research(20,40,41,43,44).

      • Discussion: page 12, lines 257-267: it is not exactly clear how 3D imaging will help for the differentiation of veins/arteries. However, some methods have already been proposed to discriminate between arteries and veins using pulsatility (Bourquin et al., 2022) or 3D positioning when vessels are overlapped (Renaudin et al., 2023). The latter can also help estimate the out-of-plane positioning during longitudinal imaging.

      Bourquin, C., Poree, J., Lesage, F., Provost, J., 2022. In Vivo Pulsatility Measurement of Cerebral Microcirculation in Rodents Using Dynamic Ultrasound Localization Microscopy. IEEE Trans. Med. Imaging 41, 782-792. https://doi.org/10.1109/TMI.2021.3123912

      Renaudin, N., Pezet, S., Ialy-Radio, N., Demene, C., Tanter, M., 2023. Backscattering amplitude in ultrasound localization microscopy. Sci. Rep. 13, 11477. https://doi.org/10.1038/s41598-023-38531-w

      Thank you for pointing this out. We have revised the relevant paragraph in the discussion to clarify the potential advantages of advances in ULM imaging methods, such as those based on pulsatility (as described by Bourquin et al., 2022) or backscattering amplitude (as demonstrated by Renaudin et al., 2023). These established methods could be helpful for longitudinal imaging. Below is the revised text in the discussion section:

      (Line 370) “Advances in ULM imaging methods can benefit longitudinal awake imaging. For instance, dynamic ULM can differentiate between arteries and veins by leveraging pulsatility features(51). 3D ULM, with volumetric imaging array(52,53), enables the reconstruction of whole-brain vascular network, providing a more comprehensive understanding of vessel branching patterns. Meanwhile, 3D ULM also helps to mitigate the challenge of aligning the identical coronal plane for longitudinal imaging, a process that requires precise manual alignment in 2D ULM to ensure consistency. Additionally, this alignment issue can also be alleviated in 2D imaging using backscattering amplitude method, which may assist in estimating out-of-plane positioning during longitudinal imaging(54).”

      Reviewer 3 (Public Review):

      • It is unclear whether multiple animals were used in the statistical analysis.

      Thank you for bringing this to our attention. We acknowledge that the original version did not clearly indicate the use of animals in the statistical analysis. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. In the revised Figures 4 and 6, we have ensured that each quantitative analysis figure or its caption clearly indicate the specific mice.

      • Generalizations are sometimes drawn from what seems to be the analysis of a single vessel.

      Thank you for pointing this out. To enhance the generalizability of our conclusions, we have expanded our analysis beyond single vessels in several parts of the study. For instance, in Figure 2, we analyzed three vessels at different depths within the same brain region of a single mouse, and we have included additional results in the Supplementary Figure 2 to further support these findings. Additionally, we have revised the language in the manuscript to ensure that conclusions are appropriately qualified and avoid overgeneralization.

      In Figures 4 and 6, we extended the analysis from single vessels to larger region-of-interest (ROI) analyses across entire brain regions. Unlike single-vessel measurements, which are susceptible to bias based on specific measurement locations, ROI-based analyses are less influenced by the operator and provide more objective, generalizable insights.

      • The description of the statistical analysis is mostly qualitative.

      We recognize that some aspects of the original statistical analysis (Figures 4 and 5 in the previous version) lacked rigor and description is more qualitative. The revised version of statistical analysis (Figure 4 and Figure 6) presents our findings from multiple dimensions, ranging from individual vessels to individual cortical ROI of arteries and veins, and ultimately to broader brain regions. For instance, as illustrated in the revised Figure 4f, the average cortical arterial flow speed decreases by approximately 20% from anesthesia to wakefulness, while venous flow speed decreases by an average of 40%, with the reduction in venous flow speed being significantly greater than that of arterial flow. We believe that this kind of description offers more quantitative analysis.

      For more examples, please refer to the Results section where Figure 4 (Line 169 to Line 207) and Figure 6 (Line 224 to Line 258) are described. These sections have been extensively rewritten to emphasize quantitative interpretation of the data. Each part of the analysis now focuses more heavily on quantitative analyses that consistently show similar trends across all animals.

      • Some terms used are insufficiently defined.

      • Additional limitations should be included in the discussion.

      • Some technical details are lacking. 

      Thank you for highlighting these issues. In response, we have made several improvements in the revised manuscript to address these issues. We have clarified terms such as “vascularity” (Line 547) and “saturation point” (Line 112) to ensure precision and prevent ambiguity. We have expanded the discussion (Line 310 to Line 377) to include limitations such as motion correction challenges and advances in ULM imaging methods, including dynamic ULM and backscattering amplitude techniques. We have added further details on interleaved sampling (Line 494 to Line 497), ULM tracking (Line 517 to Line 529), and quantitative analysis (Line 535 to Line 551) in the Methods section to provide a clearer understanding of our approach. 

      Please refer to our other responses for more specific adjustments.

      • Without information about whether the results obtained come from multiple animals, it is difficult to conclude that the authors generally achieved their aim. They do achieve it in a single animal. The results that are shown are interesting and could have an impact on the ULM community and beyond. In particular, the experimental setup they used along with the high reproducibility they report could become very important for the use of ULM in larger animal cohorts.

      We thank the reviewer for recognizing the impact of our work. We also acknowledge that there were some issues—specifically, we did not provide sufficient proof of reproducibility. In the revised version, we have included additional animal experiment results to ensure that the conclusions were not drawn from a single animal but are generally representative of our aim. (See supplementary figure 1 for detailed use of the animals) 

      Reviewer 3 (Recommendations For The Authors):

      • The manuscript would be more convincing by removing some of the superlatives used in the text. For instance, shouldn't "super-resolution ultrasound localization microscopy" simply be "ultrasound localization microscopy"? Expressions such as "first study", "essential", and "invaluable", etc could be replaced by more factual terms. The word "significant" is also used sometimes with statistics to back it up and sometimes without.

      Thank you for highlighting this issue. We have removed the superlatives throughout the manuscript to make the language more precise. For instance, we have simplified “super-resolution ultrasound localization microscopy” to “ultrasound localization microscopy” throughout the main text and removed expressions such as “first study” and “invaluable”. We also reviewed all uses of “essential” and “significant,” replacing “essential” with more modest alternatives where it does not indicate a strict requirement. Similarly, where “significant” does not refer to statistical significance, we have used other terms to avoid any ambiguity.

      • The section "Microbubble count serves as a quantitative metric for awake ULM image reconstruction" had several issues that I think should be addressed. Mainly, the authors make the case that after detecting 5 million microbubbles, there is no clear gain in detecting more. The argument is not very convincing as we know many vessels will not have had a microbubble circulate in them within that timeframe, which will be especially true in smaller vessels. While the analysis in Figure 2 shows nicely that the diameter estimate for vessels in the 20-30 um range is stable at 5 million microbubbles, it is not necessarily the case for smaller vessels. A better approach here might be to select, e.g., a total of 5 million detected microbubbles for practical reasons and then to determine which vessel parameters estimation (e.g., diameter, flow velocity) remain stable. In addition:

      a. Terms such as 'complete ULM reconstruction', 'no obvious change', 'ULM image saturation' are not well defined within the manuscript.

      Thank you for pointing out these issues and for offering a more rigorous approach. We completely agree with your suggestion. While our analysis demonstrated stable diameter estimates for vessels with diameter around 20 µm at 5 million microbubbles, this does not necessarily ensure stability for smaller vessels. Therefore, the choice of 5 million microbubbles was primarily for practical reasons. In the revised version, we have provided a more objective description and clarification of this limitation. We also recognize that terms such as “complete ULM reconstruction,” “no obvious change,” and “ULM image saturation” were not well defined and may have caused confusion, reducing the rigor of this manuscript. Based on your feedback, we have clearly defined “ULM image saturation” within the context of our study, removed absolute and ambiguous terms like “complete ULM reconstruction” and “no obvious change”. We revised the entire section accordingly:

      (Line 109) “To facilitate equitable comparison of brain perfusion at different states, a practical saturation point enabling stable quantification of most vessels needs to be established. Our observations indicated that when the cumulative MB count reached 5 million, ULM images achieved a relatively stable state. Accordingly, in this study, the saturation point was defined as a cumulative MB count of 5 million. There are also possible alternatives for ULM image normalization. For example, different ULM images can be normalized to have the same saturation rate. However, the proposed method of using the same number of cumulative MB count for normalization enables the analysis of blood flow distribution across different brain regions from a probabilistic perspective. The following analysis substantiates this criterion.

      Fig. 2a compares ULM directional vessel density maps and flow speed maps generated with 1, 3, 5, and 6 million MBs, using the same animal as shown in Fig. 1. To quantitatively confirm saturation, multiple vessel segments were selected for further analysis. Fig. 2b presents the measured vessel diameter for a specific segment at various MB counts. After binarizing the ULM map, the vessel diameter was measured by calculating the distance from the vessel centerline to the edge. Each point along the centerline of the segment provided a diameter measurement, enabling calculation of the mean and standard error. At low MB counts, vessels appeared incompletely filled, leading to inaccurate estimation of vessel diameter due to incomplete profiles. For example, at 1–2 million MBs, the binarized ULM map displayed a width of only one or two pixels along the segment. As a result, the measurements always yielded the same diameter values (two pixels, ~10um) with a consistently low standard error of the mean across the entire segment. With increased MB counts, the measured vessel diameter gradually rose, ultimately reaching saturation. The plots in Fig. 2b show that vessel diameter stabilized at 5 million MB count. Additionally, Fig. 2c illustrates the changes in flow velocity measured at different cumulative MB counts. The violin plots display the distribution of flow speed estimates for all valid centerline pixels within the selected segment. At low MB counts (1–3 million), flow velocity estimates fluctuated, but they stabilized as the MB count increased (4–6 million MBs). At 5 million MBs, flow velocity estimates were nearly identical to those at 6 million MBs, corroborating previous findings that vessel velocity measurements stabilize as MB count grows(39). To assess the generalizability of the 5 million MB saturation condition, vessel segments from three different mice across various brain regions were examined. The results, shown in Supplementary Fig. 2, confirm that this saturation criterion applies broadly. Although the 5 million MB threshold may not ensure absolute saturation for all vessels, it is generally effective for vessels larger than 15 μm. This MB count threshold was therefore adopted as a practical criterion.” 

      b. The choice of 10 consecutive tracking frames is arbitrary and should be described as such unless a quantitative optimization study was conducted. Was there a gap-filling parameter? What was the maximum linking distance and what is its impact on velocity estimation?

      Thank you for your comment. We acknowledge that the choice of 10 consecutive tracking frames was based on our common practice rather than a specific quantitative optimization. Additionally, with the uTrack algorithm, we set both the gap-filling parameter and maximum linking distance to 10 pixels. Setting these parameters too high could potentially overestimate velocity. These details have now been added to the Methods section for clarity:

      (Line 517) “The choice of 10 consecutive frames (10 ms) was based on established practice but can be adjusted as needed. For the uTrack algorithm, two additional key parameters were specified: the maximum linking distance and the gap-filling distance, both set to 10 pixels (~50 microns). This configuration means that only bubble centroids within 10 pixels of each other across consecutive frames are considered part of the same bubble trajectory. Additionally, when the start and end points of two tracks fall within this threshold, the gap-filling parameter merges them into a single, continuous track. It is important to select these parameters carefully, as overly large values could lead to an overestimation of flow velocity. By setting the maximum linking distance to 10 pixels, we effectively limited the measurable velocity to 50 mm/s, under the assumption that no bubble would exceed a 50-micron displacement within the 1 ms interval between frames. After determining bubble tracks with the specified parameters for uTrack algorithm, accumulating the MB tracks resulted in the flow intensity map. Considering the velocity distribution across the mouse brain, this 50 mm/s limit ensures that the vast majority of blood flow is captured accurately.”

      c. 'The plots (Figure 2b) clearly indicate that the vessel diameter stabilized beyond 5 million MB count.' This is true for one vessel. To generalize that claim, the analysis should be performed quantitatively on a larger sample of vessels in various areas of the brain, across multiple animals.

      Thank you for pointing out this limitation. We agree that conclusions drawn from a single vessel cannot be generalized across all regions. Following your suggestion, we have added Supplementary Figure 2, where we analyzed multiple vessels from different brain regions across three mice. This expanded analysis further confirms that a 5 million MB count is sufficient to stabilize vessel diameter measurements across various samples.

      (Line 133) “To assess the generalizability of the 5 million MB saturation condition, vessel segments from three different mice across various brain regions were examined. The results, shown in Supplementary Fig. 2, confirm that this saturation criterion applies broadly. Although the 5 million MB threshold may not ensure absolute saturation for all vessels, it is generally effective for vessels larger than 15 μm. This MB count threshold was therefore adopted as a practical criterion.” 

      • "Statistical analysis validates the increase in blood flow induced by anesthesia" is a very interesting section but even though a quantitative analysis was conducted in Figure 5, the language used remains mostly qualitative. I think this section should include quantitative conclusions from the statistical analysis to increase the impact of this work.

      Thank you for your valuable feedback. We recognize that some aspects of the original quantitative analysis (Figures 4 and 5 in the previous version) lacked rigor, such as the classification of arteries, veins, and capillaries, and that the data presented in each row of Figure 5 represented only one mouse per coronal section, limiting the generalizability of statistical conclusions.

      In response to the reviewers’ feedback, the revised version incorporates a new approach by merging the previous Figure 4 and Figure 5 into a single, consolidated figure (now Figure 4). This updated figure aims to present our findings from multiple dimensions, ranging from individual vessels to individual cortical ROI of arteries and veins, and ultimately to broader brain regions. We have focused on quantitative analyses that consistently show similar trends across all animals. For instance, as illustrated in the revised Figure 4f, the average cortical arterial flow speed decreases by approximately 20% from anesthesia to wakefulness, while venous flow speed decreases by an average of 40%, with the reduction in venous flow speed being significantly greater than that of arterial flow. We believe that this approach offers more insightful analysis and enhances the overall impact of the study.

      For more examples, please refer to the revised Results section where Figure 4 are described (from Line 169 to Line 212). These sections have been extensively rewritten to emphasize quantitative interpretation of the data. Each part of the analysis now focuses more heavily on quantitative analyses that consistently show similar trends across all animals.

      • In the methods, it is claimed that 6 healthy female C57 mice were used in the study, but it is hard to tell whether more than one animal is shown in the figures. It is also unclear whether the statistics were performed within or across animals. Since one of the major strengths of the manuscript is that it shows the feasibility of performing reproducible measurements using ULM, most figures should be repeated for each individual animal and provided in supplementary data and statistics should be performed across animals.

      Thank you for bringing this to our attention. We acknowledge that the original version did not clearly indicate the use of individual animals. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. Additionally, we included statistics across animals in the revised Figures 4 and 6, and detailed data for each individual mouse are now provided in Supplementary Figures 3 and 4.

      • The effect of aliasing should be discussed given that 1) a high-frequency probe is used along with a correspondingly relatively low frame rate (1000 fps) and 2) Doppler filtering is used to separate upward from downward-moving microbubbles. There will be microbubbles that circulate faster than the Nyquist limit, which will thus appear as moving in the opposite direction in the Doppler spectrum. It would be important to double-check that the effect is not too important and to report this as a limitation in the discussion.

      Thank you for highlighting this important point. Aliasing is indeed a relevant issue to consider, especially for higher flow velocities in large vessels. We have added a discussion on this limitation in the revised manuscript:

      (Line 359) “Based on the maximum linking distance and gap closing parameters outlined in the Methods section, blood flow with velocities below 50 mm/s can be detected. However, the use of a directional filter to estimate flow direction may introduce aliasing. MBs moving at higher velocities may be subject to incorrect flow direction estimation due to aliasing effects. Given that the compounded frame rate is 1000 Hz, with an ultrasound center frequency of 20 MHz and a sound speed of 1540 m/s, the relationship between Doppler frequency and the axial blood flow velocity(12) indicates that aliasing will not occur for axial flow velocities below 19.25 mm/s. In all flow velocity maps presented in this study, the range is limited to a maximum of 15 mm/s, remaining below the critical threshold for aliasing. Additionally, all vessels analyzed in the violin plots for arteriovenous flow comparisons fall within this range. While cortical arterioles and venules generally exhibit moderate flow speeds, aliasing remains a factor to consider when combining directional filtering with velocity analysis.”

      • The method used to classify vessels may be incorrect and may not be needed. I would recommend the authors not use it and describe the vessels as vessels that branch in or out, etc. Applying an arbitrary threshold of 2 to detect capillaries is also not very convincing. I understand that the authors might decide to maintain this nomenclature, in which case I would recommend clearly explaining it at the beginning of the manuscript along with some of the caveats that are already reported in the discussion.

      Thank you for your comments on our vessel classification method. We recognize the limitations of the previous approach and, in order to enhance the rigor of the study, we have opted not to continue using this method in the revised manuscript.

      In the revised analysis regarding artery and vein, we focus solely on penetrating vessels in the cortex. For these vessels, it is generally accepted that downward-flowing vessels are arterioles, while upward-flowing vessels are venules. Accordingly, in the revised Figures 4 and 6, we analyze arterioles and venules exclusively in the cortex, without relying on the previous classification method that could be considered controversial.

      Additionally, we agree that classifying vessels with values below 2 as capillaries was not a robust approach. Thus, we have removed all related analyses from the revised manuscript.

      Minor comments:

      • Line 16: "resolves capillary-scale ..."; it is not clear that the resolution that is achieved in this work is at the capillary scale.

      Thank you for your valuable feedback. We understand that “capillary-scale” may overstate the achieved resolution in our work. To clarify, we have revised the sentence as follows:

      (Line 18) “Ultrasound localization microscopy (ULM) is an emerging imaging modality that resolves microvasculature in deep tissues with high spatial resolution.” 

      This adjustment more accurately reflects the resolution capabilities of ULM as used in our study.

      • Line 22: 'vascularity' is not well defined in the manuscript. Consider defining or using another term.

      Thank you for pointing out the need for clarification on vascularity. We acknowledge that our initial use of the term “vascularity” may have been unclear and potentially confusing. In the revised manuscript, we have included a clear definition of “vascularity” in the Methods section under Quantitative Analysis of ULM Images (Line 534). 

      The following sentence shows the definition of vascularity:

      (Line 547) “Vascularity was defined as the proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal.”

      We have also added an instant definition when it was firstly used in Results part:

      (Line 161) “When comparing vessel density maps, ULM images that are acquired in the awake state demonstrate a global reduction of vascularity, which refers to percentage of pixels that occupied by blood vessels.”

      • Line 30: I'm not convinced the first two sentences are useful.

      Thank you for pointing out this issue. The opening sentence of the article lacked focus and was too broad. We have rewritten the sentence as follows:

      (Line 34) “Sensitive imaging of correlates of activity in the awake brain is fundamental for advancing our understanding of neural function and neurological diseases.”

      • Line 37: 'micron-scale capillaries': this expression is unclear. Capillaries are typically micron-scaled, so it gives the impression that ULM can image ULM at the one-micron scale, which is not the case.

      Thank you for your helpful comment. We agree that “micron-scale capillaries” could be misleading, as it might imply a resolution at the single-micron level. To clarify, we have revised the sentence as follows:

      (Line 40) “ULM is uniquely capable of imaging microvasculature situated in deep tissue (e.g., at a depth of several centimeters).”

      This revised wording more accurately describes ULM’s capability without implying single-micron level resolution.

      • Line 74: I don't think motion-free imaging is possible in the context of awake animals. Consider 'limiting motion' instead.

      Thank you for pointing out the potential issue with the term “motion-free”. We agree that achieving entirely motion-free imaging is challenging, especially in the context of awake animals. In response to your suggestion, we have revised the sentence to better reflect this limitation:

      (Line 76) “To achieve consistent ULM brain imaging while allowing limited movement in awake animals, a headfixed imaging platform with a chronic cranial window was used in this study.”

      This revised wording more accurately conveys our approach to minimizing motion without implying that motion is completely eliminated.

      • Line 134:'clearly reveals decreased vessel diameter' How was that demonstrated?

      • Line 153: 'significant' according to which statistical test?

      • Line 167: 'slight increase', by how much, is it significant?

      • Line 183: 'smaller vessels' the center of the distribution is not at 10mm/s, and velocity is not necessarily correlated with diameter.

      • Line 184: 'more large vessels', see above. What is a large vessel, and how was this measured?

      • Line 205: 'significantly lower', according to which statistical test?

      We acknowledge that the original version did not properly use the terms of statistical analysis. In the revised manuscript, we have deleted the related points, and rewritten the statistical analysis part to ensure the terms are used correctly. Please refer to the revised part of “ULM reveals an increase in blood flow induced by isoflurane anesthesia” (From Line 169 to Line 209). In the revised Figures 4 and 6, we have also ensured that each quantitative analysis figure or its caption is clearly explained.

      •    Line 398: the interleaved sampling scheme should be described in more detail.

      Thank you for pointing out this issue. The previous version did not clearly explain the details of interleaved sampling. We have now added the following paragraph to the Ultrasound imaging sequence section in Methods:

      (Line 494) “Interleaved sampling is employed to capture high-frequency echoes more effectively. With the system’s sampling rate limited to 62.5 MHz, the upper limit of the center frequency of the transducer passband is 15.625 MHz. To mitigate aliasing, two transmissions are sent per angle, staggered in time. This approach effectively doubles the sampling rate, ensuring more accurate image reconstruction.”

      • Figure 1: Which mouse is it? Are these results consistent across all animals?

      • Figure 2: Which mouse is it? Are these results consistent across all animals?

      • Figure 3: Which mouse is it? Are these results consistent across all animals?

      • Figure 4: Which mouse is it? Are these results consistent across all animals?

      • Figure 5: Is it a single mouse or multiple mice? Are these results consistent across all animals?

      We acknowledge that the original version did not clearly indicate the numbers of animals in the statistical analysis. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. In the revised Figures 4 and 6, we have ensured that each quantitative analysis figure or its caption clearly indicate the specific mice.

      For original Figures 1 and 2, these are presented as case studies to illustrate the methodology. Since the anesthesia time required for tail vein injection for each animal varies slightly, it is challenging to have the consistent time taken for each mouse to recover from anesthesia across all mice. For instance, in Figure 1, the mouse took nearly 500 seconds to recover from anesthesia, but this duration is not consistent across all animals, which is a limitation of the bolus injection technique. We have noted this point in the discussion (discussion on the limitation of bolus injection), and we have also clarified in the results section and figure captions that these figures represent a case study of a single mouse rather than a standardized recovery time for all animals.

      We further clarified this point in the end of the Figure 2 caption:

      (Fig.2 caption) “This figure presents a case study based on the same mouse shown in Fig 1. The x-axis for d-f begins at 500 seconds because, at this point, the mouse’s pupil size stabilized, indicating it had recovered to an awake state. Consequently, ULM images were accumulated starting from this time. It is important to note that not every mouse requires 500 seconds to fully awaken; the time to reach a stable awake state varies across individual mice.” We added the following statement before introducing Figure 1e:

      (Line 93) “Due to differences in tail vein injection timing and anesthesia depth, the time required for each mouse to fully awaken varied. Although it was not feasible to get pupil size stabilized just after 500 seconds for each animal, ULM reconstruction only used the data that acquired after the animal reached full pupillary dilation, to ensure that ULM accurately captures the cerebrovascular characteristics in the awake state.”

      We added the following statement before introducing Figure 2d:

      (Line 139) “To further verify that the proposed MB bolus injection method can help to achieve ULM image saturation shortly after mice awaken from anesthesia, an analysis on the change in MB concentration over time was conducted once pupil size had stabilized (T = 500s).”

      For Figures 3, 4, and 5 (in the revised version, Figures 4 and 5 have been combined into a single Figure 4), the data represents results from three individual mice, with each coronal plane corresponding to a different mouse. In the revised version, we have added labels to indicate the specific mouse in each image to improve clarity. We also recognize that some analyses in the original submission (original Figure 5) may have lacked sufficient statistical power due to the small sample size. Therefore, in the revised version, we have focused only on findings that were consistently observed across the three mice to ensure robust conclusions.

      Minor corrections and typos from all reviewers:

      We would like to sincerely thank the reviewers for their careful reading of our manuscript. We appreciate the time and effort taken to point out the minor typographical errors. We have carefully addressed and corrected all the identified typos, as listed below:

      From Reviewer #1:

      • Line 316: "insensate": correct, please.

      (Line 409) “After confirming that the mouse was anesthetized, the head of the animal was fixed in the stereotaxic frame.”

      From Reviewer #3:

      • Line 15: Super-resolution ultrasound localization microscopy -- consider removing super-resolution as it gives the impression that it is different from standard ULM.

      (Line 18) “Ultrasound localization microscopy (ULM) is an emerging imaging modality that resolves microvasculature in deep tissues with high spatial resolution.”

      • Line 39: typo: activities should be activity.

      (Line 41) “ULM can also be combined with the principles of functional ultrasound (fUS) to image whole-brain neural activity at a microscopic scale.”

      • Line 47: typo: over under.

      (Line 50) “Therefore, in neuroscience research, brain imaging in the awake state is often preferred over imaging under anesthesia.”

      Once again, we are grateful for the reviewers’ thorough review and valuable input, which have helped us improve the clarity and precision of the manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank the reviewers for their thoughtful comments.

      Based on their suggestions we will:

      (1) Use more accurate language to describe the hypothalamus regions under investigation in this study. While we aimed to primarily investigate the medial preoptic area (MPOA), our dissections and sequencing data in fact capture several regions of the anterior hypothalamus including the anteroventral periventricular (AVPV), paraventricular (PVN), supraoptic (SON), suprachiasmatic nuclei (SCN), and more. We will revise the language in our manuscript to reflect that our study in fact investigates the cellular evolution of the anterior hypothalamus across behaviorally divergent deer mice.

      (2) Revise our language to clarify that while our study provides a rich dataset for generating hypotheses about which cell types may contribute to behavioral differences, it does not provide any evidence of causal relationships. We hope to investigate this further in future work.

      (3) Clarify specific methodological choices for which reviewers had questions, especially about the hypothalamic regions for which we did histology to validate cell abundance differences and methodological choices related to mapping our cell clusters to Mus cell types.

      Our responses to each reviewer’s specific comments are below.

      Reviewer #1:

      The major limitation of the study is the absence of causal experiments linking the observed changes in MPOA cell types to species-specific social behaviors. While the study provides valuable correlational data, it lacks functional experiments that would demonstrate a direct relationship between the neuronal differences and behavior. For instance, manipulating these cell types or gene expressions in vivo and observing their effects on behavior would have strengthened the conclusions, although I certainly appreciate the difficulty in this, especially in non-musculus mice. Without such experiments, the study remains speculative about how these neuronal differences contribute to the evolution of social behaviors.

      Yes, we agree the study lacks functional experiments. We hope that the dataset is of value for generating hypotheses about how hypothalamic neuronal cell types may govern species-specific social behaviors, and for these hypotheses to be functionally tested by us and others in future work.

      Reviewer #2:

      Some methodology could be further explained, like the decision of a 15% cutoff value for cell type assignment per cluster, or the necessity of a multi-step analysis pipeline for gene enrichment studies.

      A 15% cutoff value for cell type assignment was chosen to include all known homology correspondences between our dataset and the Mus atlas. For example, i14:Avp/Cck cells from the Mus atlas represent Avp cells from the suprachiasmatic nuclei (SCN). Though only 17.3% of cluster 15 maps to i14:Avp/Cck, we know these two clusters correspond based on the expression of Avp and additional SCN marker genes in cluster 15 (Supp Fig 6). We will further explain this cutoff in the revised manuscript.

      Our gene enrichment study includes a multi-step analysis pipeline because we wanted to control for confounders that may be introduced because of gene expression level. Genes that are more highly expressed are more accurately quantified and thus more likely to be identified as differentially expressed. Therefore, we wanted to test for gene enrichments in our set of DE genes against a background of genes with similar expression levels. We will clarify this motivation in the revised manuscript.

      The authors should exercise strong caution in making inferences about these differences being the basis of parental behavior. It is possible, given connections to relevant research, but without direct intervention, direct claims should be avoided. There should be clear distinctions of what to conclude and what to propose as possibilities for future research.

      Yes, we agree that we are unable to make direct claims about neuronal differences being the basis of parental behavior. We will revise our language to be clearer about which relationships we are hypothesizing and what we propose as possibilities for future research.

      Histology is not performed on all regions included in the sequencing analysis.

      We apologize that our language describing the hypothalamic regions included in the sequencing analysis and those included in the histology is unclear. We aimed to dissect the medial preoptic region for the sequencing analysis, but additionally captured parts of the anterior hypothalamus including the paraventricular (PVN), supraoptic (SON), and suprachiasmatic nuclei (SCN), and more.  Our histology was performed across the entire hypothalamus and includes all regions included in the sequencing data. We will revise the manuscript to more accurately describe the hypothalamic regions for which we investigated.

      Reviewer #3:

      My primary concern is that the dataset is limited: 52,121 neuronal nuclei across 24 samples, which does not provide many cells per cluster to analyze comparatively across sex and species, particularly given the heterogeneity of the region dissected. The Supplementary table reports lower UMIs/genes per cell than is typically seen as well. Perhaps additional information could be obtained from the data by not restricting the analyses to cells that can be assigned to Mus types. A direct comparison of the two Peromyscus species could be valuable as would a more complete Peromyscus POA atlas.

      Our dataset reports ~1,500 genes and ~1,000 UMIs per nuclei which is indeed lower than is typically reported in other single nuclei datasets. Some of this discrepancy is due to a lower quality genome and annotated transcriptome available for Peromyscus compared to Mus musculus, which results in a lower mapping rate than is typically reported in Mus studies. However, our dataset was sufficient to identify known peptidergic cell types (Supp Fig 6) and to map homology to Mus cell types for 34 (64%) of our 53 clusters. Additionally, although some of our clusters contain small numbers of cells, our differential abundance analysis accounts for the variance in cell numbers observed across samples and should be robust against any increase in variance due to small numbers. In fact, even differential abundance of very small cell clusters such as oxytocin neurons (cell type 40) was validated by histology.

      We would like to clarify that all analyses were performed on all cell clusters, regardless of whether or not they could be assigned homology to a Mus cell type. All the cell types that we identified as differentially abundant or contained significant sex differences happened to be cell types for which homology to a Mus cell type could be defined. This may arise for a relatively uninteresting reason: cell types that have more distinct transcriptional signatures will be more accurately clustered, leading to more accurate identification of homology as well as more accurate measurements of differential abundance / expression. We will revise language to make this more clear in our manuscript.

      In Supplement 7, it appears that most neurons can be assigned as excitatory or inhibitory, but then so many of these cells remain in the unassigned "gray blob" seen in panel 1E. Clustering of excitatory and inhibitory neurons separately, as in prior cited work in Mus POA (refs 31 and 57) may boost statistical power to detect sex and species differences in cell types. Perhaps the cells that cannot be assigned to Mus contain too few reads to be useful, in which case they should be filtered out in the QC. The technical challenges of a comparative single-cell approach are considerable, so it benefits the scientific community to provide transparency about them.

      We are not certain about why we are unable to cluster and assign homology to many of our cells (i.e. cells in the unassigned “gray blob”). However, we note that even in the Mus atlas, many cells did not belong to obvious clusters by UMAP visualization and that several clusters lacked notable marker genes and were designated simply as “Gaba” and “Glut” clusters. Therefore, it is unsurprising that our own dataset also contains cells that lack the transcriptional signatures needed to be clustered and/or mapped to Mus cell types. We do know, however, that the median number of reads/nuclei is uniform across cell clusters and does not explain why some clusters could not be assigned to Mus. We will add this information to our revised manuscript.

      We do not think that a two-stage clustering (i.e. clustering first by excitatory vs. inhibitory neurons) is expected to gain power to resolve cell types in this case. Excitatory vs. inhibitory neurons are clearly separable on our UMAP (Supp Fig 7) so that information is already being used by our clustering procedure. However, we will explore this further in our revised manuscript to see if doing so will boost statistical power.

      The Calb1 dimorphism as observed by immunostaining, appears much more extensive in P. maniculatus compared to P. polionotus (Figures 3 E and F). This finding is not reflected in the counts of the i20:Gal/Moxd1 cluster. The use of Calb1 staining as a proxy for the Gal/Moxd1 cluster would be strengthened if the number of POA Calb1+ neurons that are found in each cluster was apparent. There may be additional Calb+ neurons in the cells that are not annotated to a Mus cluster. This clarification would add support to the overall conclusion that there is reduced sexual dimorphism in P. polionotus.

      From the Mus MPOA atlas (which includes both single-cell sequencing data and imaging-based spatial information), it is known that the i20:Gal/Moxd1 cluster comprises sexually dimorphic cells that make up both the BNST and the SDN-POA. These sexually dimorphic cells are well-studied and known to be marked by Calb1, which we used in immunostaining as a proxy for i20:Gal/Moxd1.

      However, we would like to clarify that in our study, the immunostaining of Calb1+ neurons and the sequencing counts of the i20:Gal/Moxd1 cluster are not completely reflective of each other because our sequencing dataset only captured the ventral portion of the BNST. Therefore our i20:Gal/Moxd1 counts contain a combination of some Calb1+ BNST cells and likely all Calb1+ SDN-POA cells and is difficult to interpret on its own. Our histology, however, covers the entire hypothalamus and is more reliable for identifying sex and species differences in each region. We will clarify this in the revised manuscript.

      The relationship between the sex steroid receptor expression and the sex bias in gene expression would be improved if the sex bias in sex steroid receptor expression was included in Supplementary Figure 10.

      We will include this in the revised manuscript.

      There is no explanation for the finding that there is a female bias in gene expression across all cell types in P. polionotus.

      We also find this observation interesting but don’t have a good explanation for why at this point. We plan to follow this up in future work.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      This manuscript by Guo and Uusisaari describes a series of experiments that employ a novel approach to address long-standing questions on the inferior olive in general and the role of the nucleoolivary projection specifically. For the first time, they optimized the ventral approach to the inferior olive to facilitate imaging in this area that is notoriously difficult to reach. Using this approach, they are able to compare activity in two olivary regions, the PO and DAO, during different types of stimulation. They demonstrate the difference between the two regions, linked to Aldoc-identities of downstream Purkinje cells, and that there is co-activation resulting in larger events when they are clustered. Periocular stimulation also drives larger events, related to co-activation. Using optogenetic stimulation they activate the nucleoolivary (N-O) tract and observe a wide range of responses, from excitation to inhibition. Zooming in on inhibition they test the assumption that N-O activation can be responsible for suppression of sensoryevoked events. Instead, they suggest that the N-O input can function to suppress background activity while preserving the sensory-driven responses.

      Strengths:

      This is an important study, tackling the long-standing issue of the impossibility to do imaging in the inferior olive and using that novel method to address the most relevant questions. The experiments are technically very challenging, the results are presented clearly and the analysis is quite rigorous. There is quite a lot of room for interpretation, see weaknesses, but the authors make an effort to cover many options.

      Weaknesses:

      The heavy anesthesia that is required during the experiment could severely impact the findings. Because of the anesthesia, the firing rate of IO neurons is found to be 0.1 Hz, significantly lower than the 1 Hz found in non-anesthetized mice. This is mentioned and discussed, but what the consequences could be cannot be understated and should be addressed more. Although the methods and results are described in sufficient detail, there are a few points that, when addressed, would improve the manuscript.

      We sincerely thank the reviewer for their encouraging comments and recognition of our study’s significance. We fully acknowledge the confounding effects of the deep anesthesia used in our experiments, which was necessary to ensure the animals’ welfare while establishing this technically demanding methodology. We elaborate on these effects below and will further clarify them in the revised manuscript.

      Ultimately, the full resolution of this issue will require recordings in awake animals, as we consider our approach an advancement from acute slice preparations but not yet a complete representation of in vivo IO function. However, key findings from our study—such as amplitude modulation with co-activation and the potential role of IO refractoriness in complex spike generation—could be further explored in existing cerebellar cortical recordings from awake, behaving animals. We hope our work will motivate re-examination of such datasets to assess whether these mechanisms contribute to overall cerebellar function.

      Reviewer #1 (Recommendations for the authors):

      On page 10 the authors indicate that 2084 events were included for DAO and 1176 for PO. Is that the total number of events? What was the average and the range per neuron and the average recording duration?

      Thank you for pointing out lack of clarity. The sentence should say "in total, 2084 and 1176 detected events from DAO and PO were included in the study". We will add the averages and ranges of events detected per neuron in different categories, as well as the durations of the recordings (ranging from 120s to 270s) to the tables.

      On page 10 it is also stated that: "events in PO reached larger values than those in DAO even though the average values did not differ". Please clarify that statement. Which parameter + p-value in the table indicates this difference?

      Apologies for omission. Currently the observation is only visible in the longer tail to the right in the PO data in Figure 2B2. We will add the range of values (3.0-75.2 vs 3.1-39.6 for PO and DAO amplitudes, respectively) in text and the tables in the revision.

      Abbreviating airpuff to AP is confusing, I would suggest not abbreviating it.

      Understood. We will change AP to airpuff in the text. In figure labels, at least in some panels, the abbreviation will be necessary due to space constraints.

      What type of pulse was used to drive ChrimsonR? Could it be that the pulse caused a rebound-like phenomenon with the pulse duration that drove the excitation?

      As described on line 229 and in the Methods, we used 5-second trains of 5-ms LED light pulses. Importantly, these stimulation parameters were informed by our extensive in vitro examination of various stimulation patterns (Lefler et al., 2014), which consistently produced stable postsynaptic responses without inducing depolarization or rebound effects. Additionally, Loyola et al. (2024) reported no evidence of rebound activity in IO cells following optogenetic activation of N-O axons in the absence of direct neuronal depolarization. We will incorporate these considerations into the discussion, while also acknowledging that unequivocal confirmation of “direct” rebound excitation would require intracellular recordings, such as patch clamp experiments.

      The authors indicate that the excitatory activity was indistinguishable in shape from other calcium activity, but can anything be said about the timing (the scale bar in Figure 4A2 has no value, is it the same 2s pulse)?

      Apologies for oversight in labeling the scale bar in Figure 4A2 (it is 2s). While we deliberately refrain from making strong claims regarding the origin of the NO-evoked spikes, their timing can be examined in more detail in Figure 4 - Supplement 1, panels C and D. We will make sure this is clearly stated in the revised text.

      Did the authors check for accidental sparse transfection with ChrimsonR of olivary neurons in the post-mortem analysis?

      Good point! However, we have never seen this AAV9-based viral construct to drive trans-synaptic expression in the IO, nor is this version of AAV known to have the capacity for transsynaptic expression in general.

      No sign of retrograde labeling (via the CF collaterals in the cerebellar nuclei) was seen either. Notably, the hSyn promoter used to drive ChrimsonR expression is extremely ineffective in the IO. Thus, we doubt that such accidental labeling could underlie the excitatory events seen upon N-O stimulation. We will add these mentions with relevant references to the discussion of the revised manuscript.

      On page 18 the authors state that: "The lower SS rate was attributed to intrinsic factors of PNs, while the reduced frequency of CSs was speculated to result from increased inhibition of the IO via the nucleo-olivary (N-O) pathway targeting the same microzone." I think I understand what you mean to say, but this is a bit confusing.

      Agreed. We will rephrase this sentence to clarify that a lower SS rate in a given microzone may lead to increased activation of inhibitory N-O axons that target the region of IO that sends CF to the same microzone.

      Is airpuff stimulation not more likely to activate PO dan DAO because of the related modalities (more face vs. more trunk/limbs?), and thereby also more likely to drive event co-activation (as it is stated in the abstract).

      We agree that the specific innervation patterns of different IO regions likely explain the discrepancy between previous reports of airpuff-evoked complex spikes in cerebellar cortical regions targeted by DAO and the absence of airpuff responses in the particular region of DAO accessible via our surgical approach. As in the present dataset virtually no airpuff-evoked events were seen in DAO regions, we are unable to directly compare airpuff-evoked event co-activation between PO and DAO. The higher co-activation for PO was observed for "spontaneous" activity.

      The Discussion addresses the question of why N-O pathway activation does not remove the airpuff response.

      Given the potentially profound effect, I would propose to expand the discussion on the role of aneasthesia, including longer refractory periods but also potential disruption of normal network interactions (even though individually the stimulations work). Briefly indicating what is known about alpha-chloralose would help interpret the results as well.

      We fully agree that the anesthetic state introduces confounding factors that must be considered when interpreting our results. We will expand the discussion to address how anesthesia, particularly alphachloralose as well as tissue cooling, may contribute to prolonged refractory periods and potential disruptions in normal network interactions. However, we recognize that certain aspects cannot be fully resolved without recordings in awake animals. For this reason, we characterize our preparation as an "upgraded" in vitro approach rather than a fully representative in vivo model.

      Please clearly indicate that the age range of P35-45 is for the moment of virus injection and specify the age range for the imaging experiment.

      Apologies for the oversight. We will indicate these age ranges in the results (as they are currently only specified in Methods). The P35-45 range refers to moment of virus injection.

      The methods indicate that a low-pass filter of 1Hz was used. I am sure this helps with smoothing, but does it not remove a lot of potentially interesting information. How would a higher low-pass filter affect the analysis and results?

      We acknowledge that applying a 1 Hz low-pass filter inevitably removes high-frequency components, including potential IO oscillations and fine details such as spike "doublets." However, given the temporal resolution constraints of our recording approach, we prioritized capturing robust, interpretable events over attempting to extract finer features that might be obscured by both the indicator kinetics and imaging speed.

      While a higher cut-off frequency could, in principle, allow more precise measurement of rise times and peak timings, it would also amplify high-frequency noise, complicating automated event detection and reducing confidence in distinguishing genuine neural signals from artifacts. Given these trade-offs, we opted for a conservative filtering approach to ensure stable event detection. Future work, particularly with faster imaging rates and improved sensors (GCaMP8s) will be used to explore the finer temporal structure of IO activity. We will deliberate on these matters more extensively in the revised discussion.

      Reviewer #2 (Public review):

      The authors developed a strategy to image inferior olive somata via viral GCaMP6s expression, an implanted GRIN lens, and a one-photon head-mounted microscope, providing the first in vivo somatic recordings from these neurons. The main new findings relate to the activation of the nucleoolivary pathway, specifically that: this manipulation does not produce a spiking rebound in the IO; it exerts a larger effect on spontaneous IO spiking than stimulus (airpuff)-evoked spiking. In addition, several findings previously demonstrated in vivo in Purkinje cell complex spikes or inferior olivary axons are confirmed here in olivary somata: differences in event sizes from single cells versus co-activated cells; reduced coactivation when activating the NO pathway; more coactivation within a single zebrin compartment.

      The study presents some interesting findings, and for the most part, the analyses are appropriate. My two principal critiques are that the study does not acknowledge major technical limitations and their impact on the claims; and the study does not accurately represent prior work with respect to the current findings.

      We thank the reviewer for recognising the value of the findings in our "reduced" in vivo preparation, and apologize for omissions in the work that led to critique. We will elaborate on these matters below and prepare a revised manuscript.

      The authors use GCaMP6s, which has a tau1/2 of >1 s for a normal spike, and probably closer to 2 s (10.1038/nature12354) for the unique and long type of olivary spikes that give rise to axonal bursts (10.1016/j.neuron.2009.03.023). Indeed, the authors demonstrate as much (Fig. 2B1). This affects at least several claims:

      a. The authors report spontaneous spike rates of 0.1 Hz. They attribute this to anesthesia, yet other studies under anesthesia recording Purkinje complex spikes via either imaging or electrophysiology report spike rates as high as 1.5 Hz (10.1523/JNEUROSCI.2525-10.2011). This discrepancy is not acknowledged and a plausible explanation is not given. Citations are not provided that demonstrate such low anesthetized spike rates, nor are citations provided for the claim that spike rates drop increasingly with increasing levels of anesthesia when compared to awake resting conditions.

      We fully acknowledge that anesthesia is a major confounding factor in our study. Given the unusually invasive nature of our surgical preparation, we prioritized deep anesthesia to ensure the animals’ welfare. This, along with potential cooling effects from tissue removal and GRIN lens contact, likely contributed to the observed suppression of IO activity.

      We recognize that reported complex spike rates under anesthesia vary considerably across studies, and we will expand our discussion to provide a more comprehensive comparison with prior literature. Notably, different anesthetic protocols, levels of anesthesia, and recording methodologies can lead to widely different estimates of firing rates. While we cannot resolve this issue without recordings in awake animals, we will clarify that our observed rates likely reflect both the effects of anesthesia and specific methodological constraints. We will also incorporate additional references to studies examining cerebellar activity under different anesthetic conditions.

      More likely, this discrepancy reflects spikes that are missed due to a combination of the indicator kinetics and low imaging sensitivity (see (2)), neither of which are presented as possible plausible alternative explanations.

      We acknowledge that the combination of slow indicator kinetics and limited optical power in our miniature microscope setup constrains the temporal resolution of our recordings. However, we are confident that we can reliably detect events occurring at intervals of 1 second or longer. This confidence is based on data from another preparation using the same viral vector and optical system, where we observed spike rates an order of magnitude higher.

      That said, we do not make claims regarding the presence or absence of somatic events occurring at very short intervals (e.g., 100-ms "doublets," as described by Titley et al., 2019), as these would likely fall below our temporal resolution. We will clarify this limitation in the revised manuscript to ensure that the constraints of our approach are fully acknowledged.

      While GCaMP6s is not as sensitive as more recent variants (Zhang et al., 2023, PMID 36922596), our previous work (Dorgans et al., 2022) demonstrated that its dynamic range and sensitivity are sufficient to detect both spikes and subthreshold activity in vitro. Although the experimental conditions differ in the current miniscope experiments, we took measures to optimize signal quality, including excluding recordings with a low signal-to-noise ratio (see Methods). This need for high signal fidelity also informed our decision to limit the sampling rate to 20 fps. In future work, we plan to adopt newer GCaMP variants that were not available at the start of this project, which should further improve sensitivity and temporal resolution.

      Many claims are made throughout about co-activation ("clustering"), but with the GCaMP6s rise time to peak (0.5 s), there is little technical possibility to resolve co-activation. This limitation is not acknowledged as a caveat and the implications for the claims are not engaged with in the text.

      As noted in the manuscript (L492-), "interpreting fluorescence signals relative to underlying voltage changes is challenging, particularly in IO neurons with unusual calcium dynamics." We acknowledge that the slow rise time of GCaMP6s ( 0.5 s) limits our ability to precisely resolve the timing of co-activation at very short intervals. However, given the relatively slow timescales of IO event clustering and the inherent synchrony in olivary network dynamics, we believe that the observed co-activation patterns remain meaningful, even if finer temporal details cannot be fully resolved.

      To ensure clarity, we will expand this section to explicitly acknowledge the temporal resolution limitations of our approach and discuss their implications for interpreting co-activation. While the precise timing of individual spikes within a cluster may not be resolvable, the observed increase in event magnitude with coarse co-activation suggests that clustering effects remain functionally relevant even when exact spike synchrony is not detectable at millisecond resolution.

      This finding is consistent with the idea that co-activation enhances calcium influx, leading to larger amplitude events — a relationship that does not require perfect temporal resolution to be observed. The fact that this effect persists across a broad range of clustering windows (as shown in Figure 2 Supplement 2) further supports its robustness. While we cannot make strong claims about precise spike timing within these clusters nor about the mechanism underlying enhanced calcium signal, our results demonstrate that co-activation may influence IO activity in a quantifiable way. We will clarify these points in the revised manuscript to ensure that our findings are appropriately framed given the temporal constraints of our imaging approach.

      The study reports an ultralong "refractory period" (L422-etc) in the IO, but this again must be tempered by the possibility that spikes are simply being missed due to very slow indicator kinetics and limited sensitivity. Indeed, the headline numeric estimate of 1.5 s (L445) is suspiciously close to the underlying indicator kinetic limitation of 1-2 s.

      Our findings suggest a potential refractory period limiting the frequency of events in the inferior olive under our recording conditions. This interpretation is supported by the observed inter-event interval distribution, the inability of N-O stimulation to suppress airpuff-evoked events, and lower bounds reported in earlier literature on complex spike intervals recorded in awake animals under various behavioral contexts. Taking into account the likely cooling of tissue, a refractory period of 1.5s is not unreasonable. Of course, we recognize that the slow decay kinetics of GCaMP6s may cause overlapping fluorescence signals, potentially obscuring closely spaced events. This is in line with data presented in the Chen et al 2013 manuscript describing GCaMp6s (PMID: 36922596; Figure 3b showing events detected with intervals less than 500 ms).

      The consideration of refractoriness only arose late in the project while we were investigating the explanations for lack of inhibition of airpuff-evoked spikes. Future experiments, particularly in awake animals, will be instrumental in validating this interpretation. To ensure that the refractory period is understood as one possible mechanism rather than a definitive explanation, we will rephrase the discussion to clarify that while our data are compatible with a refractory period, they do not establish it conclusively.

      The study uses endoscopic one-photon miniaturized microscope imaging. Realistically, this is expected to permit an axial point spread function (z-PSF) on the order of 40um, which must substantially reduce resolution and sensitivity. This means that if there *is* local coactivation, the data in this study will very likely have individual ROIs that integrate signals from multiple neighboring cells. The study reports relationships between event magnitude and clustering, etc; but a fluorescence signal that contains photons contributed by multiple neighboring neurons will be larger than a single neuron, regardless of the underlying physiology - the text does not acknowledge this possibility or limitation.

      We acknowledge that the use of one-photon endoscopic imaging imposes limitations on axial resolution, potentially leading to signal contributions from neighboring neurons. To mitigate this, we applied CNMFe processing, which allows for the deconvolution of overlapping signals and the differentiation of multiple neuronal sources within shared pixels. However, as the reviewer points out, if two neurons are perfectly overlapping in space, they may be treated as a single unit.

      To clarify this limitation, we will expand the discussion to explicitly acknowledge the impact of one-photon imaging on signal separation and to emphasize that, while CNMFe helps resolve some overlaps, perfect separation is not always possible. As already noted in the manuscript (L495-), "the absence of optical sectioning in the whole-field imaging method can lead to confounding artifacts in densely labeled structures such as the IO’s tortuous neuropil." We will further elaborate on how this factor was considered in our analysis and interpretation.

      Second, the text makes several claims for the first multicellular in vivo olivary recordings. (L11; L324, etc).

      I am aware of at least two studies that have recorded populations of single olivary axons using two-photon Ca2+ imaging up to 6 years ago (10.1016/j.neuron.2019.03.010; 10.7554/eLife.61593). This technique is not acknowledged or discussed, and one of these studies is not cited. No argument is presented for why axonal imaging should not "count" as multicellular in vivo olivary recording: axonal Ca2+ reflects somatic spiking.

      We appreciate the reviewer’s point and acknowledge the important prior work using two-photon imaging to record olivary axonal activity in the cerebellar cortex. However, while axonal calcium signals do reflect somatic spiking, these recordings inherently lack information about the local network interactions within the inferior olive itself.

      A key motivation for our study was to observe neuronal activity within the IO at the level of its gap-junctioncoupled local circuits, rather than at the level of its divergent axonal outputs. The fan-like spread of climbing fibers across rostrocaudal microzones in the cerebellar cortex makes them relatively easy to record in vivo, but it also means that individual imaging fields contain axons from neurons that may be distributed across different IO microdomains. As a result, while previous work has provided valuable insight into olivary output patterns, it has not allowed for the examination of coordinated somatic activity within localized IO neuron clusters.

      With apologies, we recognize that this distinction was not sufficiently emphasized in our introduction. We will clarify this key point and ensure that the important climbing fiber imaging studies are properly cited and contextualized in the revised manuscript.

      Reviewer #2 (Recommendations for the authors):

      The authors state: "we found no reports that examined coactivation levels between Z+ and Z- microzones in cerebellar complex spike recordings" (L359). Multiple papers (that are not cited) using AldolaceC-tdTomato mice with two photon Purkinje dendritic calcium imaging showed synchronization (at similar levels) within but not across z+/z- bands. (2015 10.1523/JNEUROSCI.2170-14.2015, 2023 https://doi.org/10.7554/eLife.86340).

      We apologize for the misleading phrasing. We will rephrase this statement to: "While complex spike coactivation within individual zebrin zones has been extensively studied (references), we found no reports directly comparing the levels of intra-zone co-activation between Z+ and Z microzones."

      Additionally, we will ensure that the relevant studies demonstrating synchronization within zebrin zones, as well as (lack of) interactions between neighboring zones, are properly cited and discussed in the revised manuscript.

      The figures could use more proofreading, and several decisions should be reconsidered:

      Normalizing the amplitude to maximum is not a good strategy, as it can overemphasize noise or extremely small-magnitude signals, and should instead follow standard convention and present in fixed units (3A2, 4B2, and even 2C).

      As noted earlier, we have excluded recordings and cells with high noise or a low signal-to-noise ratio for event amplitudes, ensuring that such data do not influence the color-coded panels. Importantly, all quantitative analyses and traces presented in the manuscript are normalized to baseline noise level, not to maximal amplitude, ensuring that noise or low-magnitude signals do not skew the analysis.

      The decision to use max-amplitude normalization in color-coded panels was made specifically to aid visualization of temporal structure across recordings. This approach allows for clearer comparisons without the distraction of inter-cell variability in absolute signal strength. However, we recognize the potential for confusion and will revise the Results text to explicitly clarify that the color-coded visualizations use a different scaling method than the quantitative analyses.

      x axes with no units: Figures 2B2, 2E1, 3B2, 3C2, 5B2, 5C2, 5D2.

      No colorbar units: 5A3 (and should be shown in real not normalized units).

      No y axis units: 5D1.

      No x axis label or units: 5E1.

      5E3 says "stim/baseline" for the y-axis units and then the first-panel title says "absolute frequencies" meaning it’s *not* normalized and needs a separate (accurate) y-axis with units.

      Illegibly tiny fonts: 2E1, 3E1, etc.

      We will correct all these in the revised manuscript. Thank you for careful reading.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      This study provides a thorough analysis of Nup107's role in Drosophila metamorphosis, demonstrating that its depletion leads to developmental arrest at the third larval instar stage due to disruptions in ecdysone biosynthesis and EcR signaling. Importantly, the authors establish a novel connection between Nup107 and Torso receptor expression, linking it to the hormonal cascade regulating pupariation.

      However, some contradictory results weaken the conclusions of the study. The authors claim that Nup107 is involved in the translocation of EcR from the cytoplasm to the nucleus. However, the evidence provided in the paper suggests it more likely regulates EcR expression positively, as EcR is undetectable in Nup107-depleted animals, even below background levels.

      We appreciate the concern raised in this public review. However, we must clarify that we do not claim that Nup107 regulates the translocation of EcR from the cytoplasm. It is important to note that we posited this hypothesis if Nup107 will regulate EcR nuclear translocation (9<sup>th</sup> line of 2<sup>nd</sup> paragraph on page 6). We have spelled this out more clearly as the 3<sup>rd</sup> sub-section title of the Results section, and in the discussion (8<sup>th</sup> line of 2<sup>nd</sup> paragraph on page 11). Overall, we have expressed surprise that Nup107 is not directly involved in the nuclear translocation of EcR.

      Ecdysone hormone acts through the EcR to induce the transcription of EcR also and creates a positive autoregulatory loop that enhances the EcR level through ecdysone signaling (1). Since Nup107 depletion leads to a reduction in ecdysone levels, it disrupts the transcription autoregulatory EcR expression loop. This can contribute to the reduced EcR levels seen in Nup107-depleted animals.

      Additionally, the link between Nup107 and Torso is not fully substantiated. While overexpression of Torso appears to rescue the lack of 20E production in the prothoracic gland, the distinct phenotypes of Torso and Nup107 depletion-developmental delay in the former versus complete larval arrest in the latter complicate understanding of Nup107's precise role.

      We understand that there are differences in the developmental delay when Tosro and Nup107 depletion is analyzed. However, the two molecules being compared here are very different, and the extent of Torso depletion is not evident in other studies (2). Even if the extent of depletion of Torso and Nup107 is similar, we believe that Nup107, being a more widely expressed protein, induces stronger defects owing to its importance in cellular physiology. We think that RNAi-mediated depletion of Nup107 causes a defect in 20E biosynthesis through the Halloween genes, inducing a developmental arrest.

      To clarify these discrepancies, further investigation into whether Nup107 interacts with other critical signaling pathways related to the regulation of ecdysone biosynthesis, such as EGFR or TGF-β, would be beneficial and could strengthen the findings.

      In summary, although the study presents some intriguing observations, several conclusions are not well-supported by the experimental data.

      We agree with the reviewer’s suggestion. As noted in the literature, five RTKs-torso, InR, EGFR, Alk, and Pvr-stimulate the PI3K/Akt pathway, which plays a crucial role in the PG functioning and controlling pupariation and body size (3). We have checked the torso and EGFR signaling. We rescued Nup107 defects with the torso overexpression, however, constitutively active EGFR (BL-59843) did not rescue the phenotype (data was not shown). Nonetheless, we plan to examine the EGFR pathway activation by measuring the pERK levels in Nup107-depleted PGs.

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Kawadkar et al investigates the role of Nup107 in developmental progression via the regulation of ecdysone signaling. The authors identify an interesting phenotype of Nup107 whole-body RNAi depletion in Drosophila development - developmental arrest at the late larval stage. Nup107-depleted larvae exhibit mis-localization of the Ecdysone receptor (EcR) from the nucleus to the cytoplasm and reduced expression of EcR target genes in salivary glands, indicative of compromised ecdysone signaling. This mis-localization of EcR in salivary glands was phenocopied when Nup107 was depleted only in the prothoracic gland (PG), suggesting that it is not nuclear transport of EcR but the presence of ecdysone (normally secreted from PG) that is affected. Consistently, whole-body levels of ecdysone were shown to be reduced in Nup107 KD, particularly at the late third instar stage when a spike in ecdysone normally occurs. Importantly, the authors could rescue the developmental arrest and EcR mislocalization phenotypes of Nup107 KD by adding exogenous ecdysone, supporting the notion that Nup107 depletion disrupts biosynthesis of ecdysone, which arrests normal development. Additionally, they found that rescue of the Nup107 KD phenotype can also be achieved by over-expression of the receptor tyrosine kinase torso, which is thought to be the upstream regulator of ecdysone synthesis in the PG. Transcript levels of the torso are also shown to be downregulated in the Nup107KD, as are transcript levels of multiple ecdysone biosynthesis genes. Together, these experiments reveal a new role of Nup107 or nuclear pore levels in hormone-driven developmental progression, likely via regulation of levels of torso and torso-stimulated ecdysone biosynthesis.

      Strengths:

      The developmental phenotypes of an NPC component presented in the manuscript are striking and novel, and the data appears to be of high quality. The rescue experiments are particularly significant, providing strong evidence that Nup107 functions upstream of torso and ecdysone levels in the regulation of developmental timing and progression.

      Weaknesses:

      The underlying mechanism is however not clear, and any insight into how Nup107 may regulate these pathways would greatly strengthen the manuscript. Some suggestions to address this are detailed below.

      Major questions:

      (1) Determining how specific this phenotype is to Nup107 vs. to reduced NPC levels overall would give some mechanistic insight. Does knocking down other components of the Nup107 subcomplex (the Y-complex) lead to similar phenotypes? Given the published gene regulatory function of Nup107, do other gene regulatory Nups such as Nup98 or Nup153 produce these phenotypes?

      We thank this public review to raise this concern. Working with a Nup-complex like the Nup107 complex, this concern is anticipated but difficult to address as many Nups function beyond their complex identity. Our observations with all other members of the Nup107-complex, including dELYS, suggest that except Nup107, none of the other Nup107-complex members could induce larval developmental arrest.

      In this study, we primarily focused on the Nup107 complex (outer ring complex) of the NPC. We have not examined other nucleoporins outside of this complex, such as Nup98 and Nup153. However, previous studies have reported that Nup98 and Nup153 interact with chromatin, with these investigations conducted in Drosophila S2 cells (4, 5, 6). In the future, we may check whether Nup98 and Nup153 depletion can produce the arrest phenotype.

      (2) In a related issue, does this level of Nup107 KD produce lower NPC levels? It is expected to, but actual quantification of nuclear pores in Nup107-depleted tissues should be added. These and the above experiments would help address a key mechanistic question - is this phenotype the result of lower numbers of nuclear pores or specifically of Nup107?

      We agree with the concern raised here, and we plan to assess nucleoporin intensity using mAb414 antibody (exclusively FG-repeat Nup recognizing antibody) in the Nup107 depletion background. Our past observations suggest that Nup107-depletion does not affect the overall nuclear pore complex assembly in Drosophila salivary glands (Data is not shown).

      (3) Additional experiments on how Nup107 regulates the torso would provide further insight. Does Nup107 regulate transcription of the torso or perhaps its mRNA export? Looking at nascent levels of the torso transcript and the localization of its mRNA can help answer this question. Or alternatively, does Nup107 physically bind the torso?

      While the concern regarding torso transcript level is genuine, we have already reported in the manuscript that Nup107 levels directly regulate torso expression. When Nup107 is depleted torso levels go down, which in turn controls ecdysone production and subsequent EcR signaling (Figure 6B of the manuscript). However, the exact nature of Nup107 regulation on torso expression is still unclear. Since the Nup107 is known to interact with chromatin (7), it may affect torso transcription. The possibility of a physiologically relevant interaction between Nup107 and the torso in a cellular context is unlikely due to their distinct sub-cellular localizations. If we investigate this further, it will require a significant amount of time for having reagents and experimentation, and currently stands beyond the scope of this manuscript.

      (4) The depletion level of Nup107 RNAi specifically in the salivary gland vs. the prothoracic gland should be compared by RT-qPCR or western blotting.

      Although we know that the Nup107 protein signal is reduced in SG upon knockdown (Figure 3B), we have not compared the Nup107 transcript level in these two tissues (SG and PG). As suggested here, we will knock down Nup107 using SG and PG-specific drivers and quantify the Nup107 depletion level by RT-qPCR.

      (5) The UAS-torso rescue experiment should also include the control of an additional UAS construct - so Nup107; UAS-control vs Nup107; UAS-torso should be compared in the context of rescue to make sure the Gal4 driver is functioning at similar levels in the rescue experiment.

      This is a very valid point, and we took this into account while planning the experiment. To maintain the GAL4 function, we used the Nup107<sup>KK</sup>;UAS-GFP as control alongside the Nup107<sup>KK</sup>;UAS-torso. This approach ensures that GAL4 dilution does not affect observations made in the experiments. It can be noticed in Figure S7 that the presence of GFP signal in prothoracic glands and their reduced size indicates genes downstream to both UAS sequences are transcribed, and GAL4 dilution does not play a role here.

      Minor:

      (6) Figures and figure legends can stand to be more explicit and detailed, respectively.

      We will revisit all figures and their corresponding legends to ensure appropriate and explicit details are provided.

      Reviewer #3 (Public review):

      Summary:

      In this study by Kawadkar et al, the authors investigate the developmental role of Nup107, a nucleoporin, in regulating the larval-to-pupal transition in Drosophila through RNAi knockdown and CRISPR-Cas9-mediated gene editing. They demonstrate that Nup107, an essential component of the nuclear pore complex (NPC), is crucial for regulating ecdysone signaling during developmental transitions. The authors show that the depletion of Nup107 disrupts these processes, offering valuable insights into its role in development.

      Specifically, they find that:

      (1) Nup107 depletion impairs pupariation during the larval-to-pupal transition.

      (2) RNAi knockdown of Nup107 results in defects in EcR nuclear translocation, a key regulator of ecdysone signaling.

      (3) Exogenous 20-hydroxyecdysone (20E) rescues pupariation blocks, but rescued pupae fail to close.

      (4) Nup107 RNAi-induced defects can be rescued by activation of the MAP kinase pathway.

      Strengths:

      The manuscript provides strong evidence that Nup107, a component of the nuclear pore complex (NPC), plays a crucial role in regulating the larval-to-pupal transition in Drosophila, particularly in ecdysone signaling.

      The authors employ a combination of RNAi knockdown, CRISPR-Cas9 gene editing, and rescue experiments, offering a comprehensive approach to studying Nup107's developmental function.

      The study effectively connects Nup107 to ecdysone signaling, a key regulator of developmental transitions, offering novel insights into the molecular mechanisms controlling metamorphosis.

      The use of exogenous 20-hydroxyecdysone (20E) and activation of the MAP kinase pathway provides a strong mechanistic perspective, suggesting that Nup107 may influence EcR signaling and ecdysone biosynthesis.

      Weaknesses:

      The authors do not sufficiently address the potential off-target effects of RNAi, which could impact the validity of their findings. Alternative approaches, such as heterozygous or clonal studies, could help confirm the specificity of the observed phenotypes.

      This is a very valid point raised, and we are aware of the consequences of the off-target effects of RNAi. To assert the effects of authentic RNAi and reduce the off-target effects, we have used two RNAi lines (Nup107<sup>GD</sup> and Nup107<sup>KK</sup>) against Nup107. Both RNAi induced comparable levels of Nup107 reduction, and using these lines, ubiquitous and PG specific knockdown produced similar phenotypes. Although the Nup107<sup>GD</sup> line exhibited a relatively stronger knockdown compared to the Nup107<sup>KK</sup> line, we preferentially used the Nup107<sup>KK</sup> line because the Nup107<sup>GD</sup> line is based on the P-element insertion, and the exact landing site is unknown. Furthermore, there is an off-target predicted for the Nup107<sup>GD</sup> line, where a 19bp sequence aligns with the bifocal (bif) sequence. The bif-encoded protein is involved in axon guidance and regulation of axon extension. However, the Nup107<sup>KK</sup> line does not have a predicted off-target molecule, and we know its precise landing site on the second chromosome. Thus, the Nup107<sup>KK</sup> line was ultimately used in experimentation for its clearer and more reliable genetic background.

      We are also investigating Nup107 knockdown in the prothoracic gland, which exhibits polyteny. Additionally, the number of cells in the prothoracic gland is quite limited, approximately 50-60 cells (8). Given this, there is a possibility that a clonal study may not yield the phenotype. However, we will consider moving forward with this approach also.

      NPC Complex Specificity: While the authors focus on Nup107, it remains unclear whether the observed defects are specific to this nucleoporin or if other NPC components also contribute to similar defects. Demonstrating similar results with other NPC components would strengthen their claims.

      We thank this public review to raise this concern. Working with a Nup-complex like the Nup107 complex, this concern is anticipated but difficult to address as many Nups function beyond their complex identity. Our observations with all other members of the Nup107-complex, including dELYS, suggest that except Nup107, none of the other Nup107-complex members could induce larval developmental arrest. Since the study is primarily focused on the Nup107 complex (outer ring complex) of the NPC, we have not examined other nucleoporins outside of this complex.

      Although the authors show that Nup107 depletion disrupts EcR signaling, the precise molecular mechanism by which Nup107 influences this process is not fully explored. Further investigation into how Nup107 regulates EcR nuclear translocation or ecdysone biosynthesis would improve the clarity of the findings.

      We appreciate the concern raised. Through our observation, we have proposed the upstream effect of Nup107 on the PTTH-torso-20E-EcR axis regulating developmental transitions. We know that Nup107 regulates torso levels, but we do not know if Nup107 directly interacts with torso. We would like to address whether Nup107 exerts control on PTTH levels also.

      We must emphasize that Nup107 does not directly regulate the translocation of EcR. On the contrary, we have demonstrated that EcR translocation is 20E dependent and Nup107 independent. Through our observations, we have argued that Nup107 regulates the expression of Halloween genes required for ecdysone biosynthesis. We are interested in identifying if Nup107 associates directly or through some protein to chromatin to bring about the changes in gene expression required for normal development.

      There are some typographical errors and overly strong phrases, such as "unequivocally demonstrate," which could be softened. Additionally, the presentation of redundant data in different tissues could be streamlined to enhance clarity and flow.

      We thank the reviewer for this observation. We will remove all typographical errors and make reasonable statements based on our conclusions.

      References:

      (1) Varghese, Jishy, and Stephen M Cohen. “microRNA miR-14 acts to modulate a positive autoregulatory loop controlling steroid hormone signaling in Drosophila.” Genes & development vol. 21,18 (2007): 2277-82. doi:10.1101/gad.439807

      (2) Rewitz, Kim F et al. “The insect neuropeptide PTTH activates receptor tyrosine kinase torso to initiate metamorphosis.” Science (New York, N.Y.) vol. 326,5958 (2009): 1403-5. doi:10.1126/science.1176450

      (3) Pan, Xueyang, and Michael B O'Connor. “Coordination among multiple receptor tyrosine kinase signals controls Drosophila developmental timing and body size.” Cell reports vol. 36,9 (2021): 109644. doi:10.1016/j.celrep.2021.109644

      (4) Pascual-Garcia, Pau et al. “Metazoan Nuclear Pores Provide a Scaffold for Poised Genes and Mediate Induced Enhancer-Promoter Contacts.” Molecular cell vol. 66,1 (2017): 63-76.e6. doi:10.1016/j.molcel.2017.02.020

      (5) Pascual-Garcia, Pau et al. “Nup98-dependent transcriptional memory is established independently of transcription.” eLife vol. 11 e63404. 15 Mar. 2022, doi:10.7554/eLife.63404

      (6) Kadota, Shinichi et al. “Nucleoporin 153 links nuclear pore complex to chromatin architecture by mediating CTCF and cohesin binding.” Nature communications vol. 11,1 2606. 25 May. 2020, doi:10.1038/s41467-020-16394-3

      (7) Gozalo, Alejandro et al. “Core Components of the Nuclear Pore Bind Distinct States of Chromatin and Contribute to Polycomb Repression.” Molecular cell vol. 77,1 (2020): 67-81.e7. doi:10.1016/j.molcel.2019.10.017

      (8) Shimell, MaryJane, and Michael B O'Connor. “Endoreplication in the Drosophila melanogaster prothoracic gland is dispensable for the critical weight checkpoint.” microPublication biology vol. 2023 10.17912/micropub.biology.000741. 21 Feb. 2023, doi:10.17912/micropub.biology.000741

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, Fuchsberger et al. demonstrate a set of experiments that ultimately identifies the de novo synthesis of GluA1-, but not GluA2-containing Ca2+ permeable AMPA receptors as a key driver of dopamine-dependent LTP (DA-LTP) during conventional post-before-pre spike-timing dependent (t-LTD) induction. The authors further identify adenylate cyclase 1/8, cAMP, and PKA as the crucial mitigators of these actions. While some comments have been identified below, the experiments presented are thorough and address the aims of the manuscript, figures are presented clearly (with minor comments), and experimental sample sizes and statistical analyses are suitable. Suitable controls have been utilized to confirm the role of Ca2+ permeable AMPAR. This work provides a valuable step forward built on convincing data toward understanding the underlying mechanisms of spike-timing-dependent plasticity and dopamine.

      Strengths:

      Appropriate controls were used.

      The flow of data presented is logical and easy to follow.

      The quality of the data, except for a few minor issues, is solid.

      Weaknesses:

      The drug treatment duration of anisomycin is longer than the standard 30-45 minute duration (as is the 500uM vs 40uM concentration) typically used in the field. Given the toxicity of these kinds of drugs long term it's unclear why the authors used such a long and intense drug treatment.

      In an initial set of control experiments (Figure S 1C-D) we wanted to ensure that protein synthesis was definitely blocked and therefore used a relatively high concentration of anisomycin and a relatively long pre-incubation period. We agree with the Reviewer that we cannot exclude the possibility that this treatment could compromise cell health in addition to the protein synthesis block. Therefore, we carried out an additional experiment with an alternative protein synthesis inhibitor cycloheximide at a lower standard concentration (10 µM) which confirmed a significant reduction in the puromycin signal (Figure S 1A-B). Together these results support the conclusion that puromycin signal is specific to protein synthesis in our labelling assay.

      Furthermore, in the electrophysiology experiments, we used 500 μM anisomycin in the patch pipette solution. Under these conditions, we recorded a stable EPSP baseline for 60 minutes, indicating that the treatment did not cause toxic effects to the cell (Figure S1F). This high concentration would ensure an effective block of local translation at dendritic sites. Nevertheless, we also carried out this experiment with cycloheximide at a lower standard concentration (10 µM) and observed a similar result with both protein synthesis inhibitors (Figure 1F).

      With some of the normalizations (such as those in S1) there are dramatic differences in the baseline "untreated" puromycin intensities - raising some questions about the overall health of slices used in the experiments.

      We agree with the Reviewer that there is a large variability in the normalised puromycin signal which might be due to variability in the health of slices. However, we assume that the same variability would be present in the treated slices, which showed, despite the variability, a significant inhibition of protein synthesis. To avoid any bias by excluding slices with low puromycin signal in the control condition, we present the full dataset.

      The large set of electrophysiology experiments carried out in our study (all recorded cells were evaluated for healthy resting membrane potential, action potential firing, and synaptic responses) confirmed that, generally, the vast majority of our slices were indeed healthy. 

      Reviewer #2 (Public Review):

      Summary:

      The aim was to identify the mechanisms that underlie a form of long-term potentiation (LTP) that requires the activation of dopamine (DA).

      Strengths:

      The authors have provided multiple lines of evidence that support their conclusions; namely that this pathway involves the activation of a cAMP / PKA pathway that leads to the insertion of calcium-permeable AMPA receptors.

      Weaknesses:

      Some of the experiments could have been conducted in a more convincing manner.

      We carried out additional control experiments and analyses to address the specific points that were raised.

      Reviewer #3 (Public Review):

      The manuscript of Fuchsberger et al. investigates the cellular mechanisms underlying dopamine-dependent long-term potentiation (DA-LTP) in mouse hippocampal CA1 neurons. The authors conducted a series of experiments to measure the effect of dopamine on the protein synthesis rate in hippocampal neurons and its role in enabling DA-LTP. The key results indicate that protein synthesis is increased in response to dopamine and neuronal activity in the pyramidal neurons of the CA1 hippocampal area, mediated via the activation of adenylate cyclases subtypes 1 and 8 (AC1/8) and the cAMP-dependent protein kinase (PKA) pathway. Additionally, the authors show that postsynaptic DA-induced increases in protein synthesis are required to express DA-LTP, while not required for conventional t-LTP.

      The increased expression of the newly synthesized GluA1 receptor subunit in response to DA supports the formation of homomeric calcium-permeable AMPA receptors (CP-AMPARs). This evidence aligns well with data showing that DA-LTP expression requires the GluA1 AMPA subunit and CP-AMPARs, as DA-LTP is absent in the hippocampus of a GluA1 genetic knock-out mouse model. Overall, the study is solid, and the evidence provided is compelling. The authors clearly and concisely explain the research objectives, methodologies, and findings. The study is scientifically robust, and the writing is engaging. The authors' conclusions and interpretation of the results are insightful and align well with the literature. The discussion effectively places the findings in a meaningful context, highlighting a possible mechanism for dopamine's role in the modulation of protein-synthesis-dependent hippocampal synaptic plasticity and its implications for the field. Although the study expands on previous works from the same laboratory, the findings are novel and provide valuable insights into the dynamics governing hippocampal synaptic plasticity.

      The claim that GluA1 homomeric CP-AMPA receptors mediate the expression of DA-LTP is fascinating, and although the electrophysiology data on GluA1 knock-out mice are convincing, more evidence is needed to support this hypothesis. Western blotting provides useful information on the expression level of GluA1, which is not necessarily associated with cell surface expression of GluA1 and therefore CP-AMPARs. Validating this hypothesis by localizing the protein using immunofluorescence and confocal microscopy detection could strengthen the claim. The authors should briefly discuss the limitations of the study.

      Although it would be possible to quantify the surface expression of GluA1 using immunofluorescence, it would not be possible to distinguish  between GluA1 homomers and GluA1-containing heteromers. It would therefore not be informative as to whether these are indeed CP-AMPARs. This is an interesting problem, which we have briefly discussed in the Discussion section.

      Additional comments to address:

      (1) In Figure 2A, the representative image with PMY alone shows a very weak PMY signal. Consequently, the image with TTX alone seems to potentiate the PMY signal, suggesting a counterintuitive increase in protein synthesis.

      We agree with the Reviewer that the original image was not representative and have replaced it with a more representative image.

      (2) In Figures 3A-B, the Western blotting representative images have poor quality, especially regarding GluA1 and α-actin in Figure 3A. The quantification graph (Figure 3B) raises some concerns about a potential outlier in both the DA alone and DA+CHX groups. The authors should consider running a statistical test to detect outlier data. Full blot images, including ladder lines, should be added to the supplementary data.

      We have replaced the western blot image in Figure 3A and have also presented full blot images including ladder lines in supplementary Figure S3.

      Using the ROUT method (Q=1%) we identified one outlier in the DA+CHX group of the western blot quantification. The quantification for this blot was then removed from the dataset and the experiment was repeated to ensure a sufficient number of repeats.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) How the authors perform these experiments with puromycin, these are puromycilation experiments - not SuNSET. The SuNSET protocol (surface sensing of translation) specifically refers to the detection of newly synthesized proteins externally at the plasma membrane. I'd advise to update the terminology used.

      We thank the Reviewer for pointing this out. We have updated this to ‘puromycin-based labelling assay’.

      (2) The legend presented in Figure 2F suggests WT is green and ACKO is orange, however, in Figure 2G the WT LTP trace is orange, consider changing this to green for consistency.

      We thank the Reviewer for this suggestion and agree that a matching colour scheme makes the Figure clearer. This has been updated.

      (3) In the results section, it is recommended to include units for the values presented at the first instance and only again when the units change thereafter.

      The units of the electrophysiology data were [%], this is included in the Results section. Results of western blots and IHC images were presented as [a.u.]. While we included this in the Figures, we have not specifically added this to the text of individual results. 

      (4) Two hours pre-treatment with anisomycin vs 30 minutes pretreatment with cycloheximide seems hard to directly compare - as the pharmokinetics of translational inhibition should be similar for both drugs. What was the rationale for the extremely long anisomycin pretreatment? What controls were taken to assess slice health either prior to or following fixation? This is relevant to the below point (5).

      In an initial set of control experiments (Figure S 1C-D) we wanted to ensure that protein synthesis was definitely blocked and therefore used a relatively high concentration of anisomycin and a relatively long pre-incubation period. We agree with the Reviewer that we cannot exclude the possibility that this treatment could compromise cell health in addition to the protein synthesis block. Therefore, we carried out an additional experiment with an alternative protein synthesis inhibitor cycloheximide at a lower standard concentration (10 µM) which confirmed a significant reduction in the puromycin signal (Figure S1A-B). Together these results support the conclusion that puromycin signal is specific to protein synthesis in our labelling assay.

      IHC slices were visually assessed for health. The large set of electrophysiology experiments carried out in our study (all recorded cells were evaluated for healthy resting membrane potential, action potential firing, and synaptic responses) also confirmed that, generally, the vast majority of our slices were indeed healthy. 

      (5) In Supplementary Figure 1, there is a dramatic difference in the a.u. intensities across CHX (B) and AM (D), please explain the reason for this. It is understood these are normalised values to nuclear staining, please clarify if this is a nuclear area.

      We agree with the Reviewer that there is a large variability in normalised puromycin signal which may be due to variability in the health of the slices. However, we assume that the same variability would be present in the treated slices, which showed, despite the variability, a significant effect of protein synthesis inhibition. To prevent introducing bias by excluding slices with low puromycin signal in the control condition, we present the full dataset.

      The CA1 region of the hippocampus contains of a dense layer of neuronal somata (pyramidal cell layer). We normalized against the nuclear area as it provides a reliable estimate of the number of neurons present in the image. This approach minimizes bias by accounting for variation in the number of neurons within the visual field, ensuring consistency and accuracy in our analysis.

      (6) Please clarify the decision to average both the last 5 minutes of baseline recordings and the last 5 minutes of the recording for the normalisation of EPSP slopes.

      The baseline usually stabilises after a few minutes of recording, thus the last 5 minutes were used for baseline measurement, which are the most relevant datapoints to compare synaptic weight change to. After induction of STDP, potentiation or depression of synaptic weights develops gradually. Based on previous results, evaluating the EPSP slopes at 30-40 minutes after the induction protocol gives a reliable estimate of the amount of plasticity.

      Reviewer #2 (Recommendations For The Authors):

      The concentration of anisomycin used (0.5 mM) is very high.

      As described above, in an initial set of control experiments (Figure S 1C-D) we wanted to ensure that protein synthesis was definitely blocked and therefore used a relatively high concentration of anisomycin and a relatively long pre-incubation period. We agree with the Reviewer that this is higher than the standard concentration used for this drug and we cannot exclude the possibility that this treatment could compromise cell health in addition to the protein synthesis block. Therefore, we carried out an additional experiment with an alternative protein synthesis inhibitor cycloheximide at a lower standard concentration (10 µM) which confirmed a significant reduction in the puromycin signal (Figure S1A-B). Together these results support the conclusion that puromycin signal is specific to protein synthesis in our labelling assay.

      Furthermore, in the electrophysiology experiments, we also used 500 µM anisomycin in the patch pipette solution. Under these conditions, we recorded a stable EPSP baseline for 60 minutes, indicating that the treatment did not cause toxic effects to the cell (Figure S1F). This high concentration would ensure an effective block of local translation at dendritic sites. Nevertheless, we also carried out this experiment with cycloheximide at a lower standard concentration (10 µM) and observed a similar result with both protein synthesis inhibitors (Figure 1F).

      The authors conclude that the effect of DA is mediated via D1/5 receptors, which based on previous work seems likely. But they cannot conclude this from their current study which used a combination of a D1/D5 and a D2 antagonist.

      We thank the Reviewer for pointing this out. We agree and have updated this in the Discussion section to ‘dopamine receptors’, without specifying subtypes.

      There is no mention that I can see that the KO experiments were conducted in a blinded manner (which I believe should be standard practice). Did they verify the KOs using Westerns?

      Only a subset of the experiments was conducted in a blinded manner. However, the results were collected by two independent experimenters, who both observed significant effects in KO mice compared to WTs (TF and ZB).

      We received the DKO mice from a former collaborator, who verified expression levels of the KO mice (Wang et al., 2003). We verified DKO upon arrival in our facility using genotyping.

      Maybe I'm misunderstanding but it appears to me that in Figure 1F there is LTP prior to the addition of DA. (The first point after pairing is already elevated). I think the control of pairing without DA should be added.

      We thank the Reviewer for pointing this out. Based on previous results (Brzosko et al., 2015) we would expect potentiation to develop over time once DA is added after pairing, however, it indeed appears in the Figure here as if there was an immediate increase in synaptic weights after pairing. It should be noted, however, that when comparing the first 5 minutes after pairing to the baseline, this increase was not significant (t(9)=1.810, p =0.1037). Nevertheless, we rechecked our data and noticed that this initial potentiation was biased by one cell with an increasing baseline, which had both the test and control pathway strongly elevated. We had mistakenly included this cell in the dataset, despite the unstable conditions (as stated in the Methods section, the unpaired control pathway served as a stability control). We apologise for the error and this has now been corrected (Figure 1F). In addition, we present the control pathway in Figure S1G and I.

      We have also now included the control for post-before-pre pairing (Δt = -20 ms) without dopamine in a supplemental figure (Figure S1E and F).

      The Westerns (Figure 3A) are fairly messy. Also, it is better to quantify with total protein. Surface biotinylation of GluA1 and GluA2 would be more informative.

      We carried out more repeats of Western blots and have exchanged blots in Figure 3A.

      We observed that DA increases protein synthesis, we therefore cannot exclude the possibility that application of DA could also affect total protein levels. Thus quantifying with total protein may not be the best choice here. Quantification with actin is standard practice.

      While we agree with the Reviewer that surface biotinylation of GluA1 and GluA2 could in principle be more informative, we do not think it would work well in our experimental setup using acute slice preparation, as it strictly requires intact cells. Slicing generates damaged cells, which would take up the surface biotin reagents. This would cause unspecific biotinylation of the damaged cells, leading to a strong background signal in the assay.

      In Figure 4 panels D and E the baselines are increasing substantially prior to induction. I appreciate that long stable baselines with timing-dependent plasticity may not be possible but it's hard to conclude what happened tens of minutes later when the baseline only appears stable for a minute or two. Panels A and B show that relatively stable baselines are achievable.

      We agree with the Reviewer that the baselines are increasing, however, when looking at the baseline for 5 minutes prior to induction (5 last datapoints of the baseline), which is what we used for quantification, the baselines appeared stable. Unfortunately, longer baselines are not suitable for timing-dependent plasticity. In addition, all experiments were carried out with a control pathway which showed stable conditions throughout the recording.

      In general, the discussion could be better integrated with the current literature. Their experiments are in line with a substantial body of literature that has identified two forms of LTP, based on these signalling cascades, using more conventional induction patterns.

      We thank the Reviewer for this suggestion and have added more discussion of the two forms of LTP in the Discussion section.

      It would be helpful to include the drug concentrations when first described in the results.

      Drug concentration have now been included in the Results section.

      It is now more common to include absolute t values (not just <0.05 etc).

      While we indicate significance in Figures using asterisks when p values are below the indicated significance levels, we report absolute values of p and t values in the Results section.

      Similarly full blots should be added to an appendix / made available.

      We have now included full blot images in Supplementary Figure S3.

      A 30% tolerance for series resistance seems generous to me. (10-20% would be more typical).

      We thank the Reviewer for their suggestion, and will keep this in mind for future studies. However, the error introduced by the higher tolerance level is likely to be small and would not influence any of the qualitative conclusions of the manuscript.

      Whereas series resistance is of course extremely important in voltage-clamp experiments, changes in series resistance would be less of a concern in current-clamp recordings of synaptic events. We use the amplifier as a voltage follower, and there are two problems with changes in the electrode, or access, resistance. First, there is the voltage drop across the electrode resistance. Clearly this error is zero if no current is injected and is also negligible for the currents we use in our experiments to maintain the membrane voltage at -70 mV. For example, the voltage drop would be 0.2 mV for 20 pA current through a typical 10 MOhm electrode resistance, and a change in resistance of 30% would give less than 0.1 mV voltage change even if the resistance were not compensated. The second problem is distortion of the EPSP shape due to the low-pass filtering properties of the electrode set up by the pipette capacitance and series resistance (RC). This can be a significant problem for fast events, such as action potentials, but less of a problem for the relatively slow EPSPs recorded in pyramidal cells. Nevertheless, we take on board the advice provided by the Reviewer and will use the conventional tolerance of 20% in future experiments.

      Reviewer #3 (Recommendations For The Authors):

      In the references, the entry for Burnashev N et al. has a different font size. Please ensure that all references are formatted consistently.

      We thank the Reviewer for spotting this and have updated the font size of this reference.

    1. Author response:

      eLife Assessment

      Birdsong production depends on precise neural sequences in a vocal motor nucleus HVC. In this useful biophysical model, Daou and colleagues identify specific biophysical parameters that result in sparse neural sequences observed in vivo. While the model is presently incomplete because it is overfit to produce sequences and therefore not robust to real biological variation, the model has the potential to address some outstanding issues in HVC function.

      We are grateful for the extensive supportive comments from the reviewers, including broad, strong appreciation of the novel aspects of our manuscript. We believe these will be only strengthened in the next submission.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The paper presents a model for sequence generation in the zebra finch HVC, which adheres to cellular properties measured experimentally. However, the model is fine-tuned and exhibits limited robustness to noise inherent in the inhibitory interneurons within the HVC, as well as to fluctuations in connectivity between neurons. Although the proposed microcircuits are introduced as units for sub-syllabic segments (SSS), the backbone of the network remains a feedforward chain of HVC_RA neurons, similar to previous models.

      Strengths:

      The model incorporates all three of the major types of HVC neurons. The ion channels used and their kinetics are based on experimental measurements. The connection patterns of the neurons are also constrained by the experiments.

      Weaknesses:

      The model is described as consisting of micro-circuits corresponding to SSS. This presentation gives the impression that the model's structure is distinct from previous models, which connected HVC_RA neurons in feedforward chain networks (Jin et al 2007, Li & Greenside, 2006; Long et al 2010; Egger et al 2020). However, the authors implement single HVC_RA neurons into chain networks within each micro-circuit and then connect the end of the chain to the start of the chain in the subsequent micro-circuit. Thus, the HVC_RA neuron in their model forms a single-neuron chain. This structure is essentially a simplified version of earlier models.

      In the model of the paper, the chain network drives the HVC_I and HVC_X neurons. The role of the micro-circuits is more significant in organizing the connections: specifically, from HVC_RA neurons to HVC_I neurons, and from HVC_I neurons to both HVC_X and HVC_RA neurons.

      We thank Reviewer 1 for their thoughtful comments.

      While the reviewer is correct about the fact that the propagation of sequential activity in this model is primarily carried by HVC<sub>RA</sub> neurons in a feed-forward manner, we need to emphasize that this is true only if there is no intrinsic or synaptic perturbation to the HVC network. For example, we showed in Figures 10 and 12 how altering the intrinsic properties of HVC<sub>X</sub> neurons or for interneurons disrupts sequence propagation. In other words, while HVC<sub>RA</sub> neurons are the key forces to carry the chain forward, the interplay between excitation and inhibition in our network as well as the intrinsic parameters for all classes of HVC neurons are equally important forces in carrying the chain of activity forward. Thus, the stability of activity propagation necessary for song production depend on a finely balanced network of HVC neurons, with all classes contributing to the overall dynamics. Moreover, all existing models that describe premotor sequence generation in the HVC either assume a distributed model (Elmaleh et al., 2021) that dictates that local HVC circuitry is not sufficient to advance the sequence but rather depends upon momentto-moment feedback through Uva (Hamaguchi et al., 2016), or assume models that rely on intrinsic connections within HVC to propagate sequential activity. In the latter case, some models assume that HVC is composed of multiple discrete subnetworks that encode individual song elements (Glaze & Troyer, 2013; Long & Fee, 2008; Wang et al., 2008), but lacks the local connectivity to link the subnetworks, while other models assume that HVC may have sufficient information in its intrinsic connections to form a single continuous network sequence (Long et al. 2010). The HVC model we present extends the concept of a feedforward network by incorporating additional neuronal classes that influence the propagation of activity (interneurons and HVC<sub>X</sub> neurons). We have shown that any disturbance of the intrinsic or synaptic conductances of these latter neurons will disrupt activity in the circuit even when HVC<sub>RA</sub> neurons properties are maintained.

      In regard to the similarities between our model and earlier models, several aspects of our model distinguish it from prior work. In short, while several models of how sequence is generated within HVC have been proposed (Cannon et al., 2015; Drew & Abbott, 2003; Egger et al., 2020; Elmaleh et al., 2021; Galvis et al., 2018; Gibb et al., 2009a, 2009b; Hamaguchi et al., 2016; Jin, 2009; Long & Fee, 2008; Markowitz et al., 2015), all the models proposed either rely on intrinsic HVC circuitry to propagate sequential activity, rely on extrinsic feedback to advance the sequence or rely on both. These models do not capture the complex details of spike morphology, do not include the right ionic currents, do not incorporate all classes of HVC neurons, or do not generate realistic firing patterns as seen in vivo. Our model is the first biophysically realistic model that incorporates all classes of HVC neurons and their intrinsic properties. We tuned the intrinsic and the synaptic properties bases on the traces collected by Daou et al. (2013) and Mooney and Prather (2005) as shown in Figure 3. The three classes of model neurons incorporated to our network as well as the synaptic currents that connect them are based on HodgkinHuxley formalisms that contain ion channels and synaptic currents which had been pharmacologically identified. This is an advancement over prior models that primarily focused on the role of synaptic interactions or external inputs. The model is based on a feedforward chain of microcircuits that encode for the different sub-syllabic segments and that interact with each other through structured feedback inhibition, defining an ordered sequence of cell firing. Moreover, while several models highlight the critical role of inhibitory interneurons in shaping the timing and propagation of bursts of activity in HVC<sub>RA</sub> neurons, our work offers an intricate and comprehensive model that help understand this critical role played by inhibition in shaping song dynamics and ensuring sequence propagation.

      How useful is this concept of micro-circuits? HVC neurons fire continuously even during the silent gaps. There are no SSS during these silent gaps.

      Regarding the concern about the usefulness of the 'microcircuit' concept in our study, we appreciate the comment and we are glad to clarify its relevance in our network. While we acknowledge that HVC<sub>RA</sub> neurons interconnect microcircuits, our model's dynamics are still best described within the framework of microcircuitry particularly due to the firing behavior of HVC<sub>X</sub> neurons and interneurons. Here, we are referring to microcircuits in a more functional sense, rather than rigid, isolated spatial divisions (Cannon et al. 2015). A microcircuit in our model reflects the local rules that govern the interaction between all HVC neuron classes within the broader network, and that are essential for proper activity propagation. For example, HVC<sub>INT</sub> neurons belonging to any microcircuit burst densely and at times other than the moments when the corresponding encoded SSS is being “sung”. What makes a particular interneuron belong to this microcircuit or the other is merely the fact that it cannot inhibit HVC<sub>RA</sub> neurons that are housed in the microcircuit it belongs to. In particular, if HVC<sub>INT</sub> inhibits HVC<sub>RA</sub> in the same microcircuit, some of the HVC<sub>RA</sub> bursts in the microcircuit might be silenced by the dense and strong HVC<sub>INT</sub> inhibition breaking the chain of activity again. Similarly, HVC<sub>X</sub> neurons were selected to be housed within microcircuits due to the following reason: if an HVC<sub>X</sub> neuron belonging to microcircuit i sends excitatory input to an HVC<sub>INT</sub> neuron in microcircuit j, and that interneuron happens to select an HVC<sub>RA</sub> neuron from microcircuit i, then the propagation of sequential activity will halt, and we’ll be in a scenario similar to what was described earlier for HVC<sub>INT</sub> neurons inhibiting HVC<sub>RA</sub> neurons in the same microcircuit.

      We agree that there are no sub-syllabic segments described during the silent gaps and we thank the reviewer to pointing this out. Although silent gaps are integral to the overall process of song production, we have not elaborated on them in this model due to the lack of a clear, biophysically grounded representation for the gaps themselves at the level of HVC. Our primary focus has been on modeling the active, syllable-producing phases of the song, where the HVC network’s sequential dynamics are critical for song. However, one can think the encoding of silent gaps via similar mechanisms that encode SSSs, where each gap is encoded by similar microcircuits comprised of the three classes of HVC neurons (let’s called them GAP rather than SSS) that are active only during the silent gaps. In this case, the propagation of sequential activity is carried throughout the GAPs from the last SSS of the previous syllable to the first SSS of the subsequent syllable. We’ll make sure to emphasize this mechanism more in the revised version of the manuscript.

      A significant issue of the current model is that the HVC_RA to HVC_RA connections require fine-tuning, with the network functioning only within a narrow range of g_AMPA (Figure 2B). Similarly, the connections from HVC_I neurons to HVC_RA neurons also require fine-tuning. This sensitivity arises because the somatic properties of HVC_RA neurons are insufficient to produce the stereotypical bursts of spikes observed in recordings from singing birds, as demonstrated in previous studies (Jin et al 2007; Long et al 2010). In these previous works, to address this limitation, a dendritic spike mechanism was introduced to generate an intrinsic bursting capability, which is absent in the somatic compartment of HVC_RA neurons. This dendritic mechanism significantly enhances the robustness of the chain network, eliminating the need to fine-tune any synaptic conductances, including those from HVC_I neurons (Long et al 2010).

      Why is it important that the model should NOT be sensitive to the connection strengths?

      We thank the reviewer for the comment. While mathematical models designed for highly complex nonlinear biological processes tangentially touch the biological realism, the current network as is right now is the first biologically realistic-enough network model designed for HVC that explains sequence propagation. We do not include dendritic processes in our network although that increases the realistic dynamics for various reasons. 1) The ion channels we integrated into the somatic compartment are known pharmacologically (Daou et al. 2013), but we don’t know about the dendritic compartment’s intrinsic properties of HVC neurons and the cocktail of ion channels that are expressed there. 2) We are able to generate realistic bursting in HVC<sub>RA</sub> neurons despite the single compartment, and the main emphasis in this network is on the interactions between excitation and inhibition, the effects of ion channels in modulating sequence propagation, etc. 3) The network model already incorporates thousands of ODEs that govern the dynamics of each of the HVC neurons, so we did not want to add more complexity to the network especially that we don’t know the biophysical properties of the dendritic compartments.

      Therefore, our present focus is on somatic dynamics and the interaction between HVC<sub>RA</sub> and HVC<sub>INT</sub> neurons, but we acknowledge the importance of these processes in enhancing network resiliency. Although we agree that adding dendritic processes improves robustness, we still think that somatic processes alone can offer insightful information on the sequential dynamics of the HVC network. While the network should be robust across a wide range of parameters, it is also essential that certain parameters are designed to filter out weaker signals, ensuring that only reliable, precise patterns of activity propagate. Hence, we specifically chose to make the HVC<sub>RA</sub>-to-HVC<sub>RA</sub> excitatory connections more sensitive (narrow range of values) such that only strong, precise and meaningful stimuli can propagate through the network representing the high stereotypy and precision seen in song production.

      First, the firing of HVC_I neurons is highly noisy and unreliable. HVC_I neurons fire spontaneous, random spikes under baseline conditions. During singing, their spike timing is imprecise and can vary significantly from trial to trial, with spikes appearing or disappearing across different trials. As a result, their inputs to HVC_RA neurons are inherently noisy. If the model relies on precisely tuned inputs from HVC_I neurons, the natural fluctuations in HVC_I firing would render the model non-functional. The authors should incorporate noisy HVC_I neurons into their model to evaluate whether this noise would render the model non-functional.

      We acknowledge that under baseline and singing settings, interneurons fire in an extremely noisy and inaccurate manner, although they exhibit time locked episodes in their activity (Hahnloser et al 2002, Kozhinikov and Fee 2007). In order to mimic the biological variability of these neurons, our model does, in fact, include a stochastic current to reflect the intrinsic noise and random variations in interneuron firing shown in vivo (and we highlight this in the Methods). If necessary and to make sure the network is resilient to this randomness in interneuron firing, we will investigate different approaches to enhance the noise representation even further and check its effect on sequence propagation.

      Second, Kosche et al. (2015) demonstrated that reducing inhibition by suppressing HVC_I neuron activity makes HVC_RA firing less sparse but does not compromise the temporal precision of the bursts. In this experiment, the local application of gabazine should have severely disrupted HVC_I activity. However, it did not affect the timing precision of HVC_RA neuron firing, emphasizing the robustness of the HVC timing circuit. This robustness is inconsistent with the predictions of the current model, which depends on finely tuned inputs and should, therefore, be vulnerable to such disruptions.

      We thank the reviewer for the comment. The differences between the Kosche et al. (2015) findings and the predictions of our model arise from differences in the aspect of HVC function we are modeling. Our model is more sensitive to inhibition, which is a designed mechanism for achieving precise song patterning. This is a modeling simplification we adopted to capture specific characteristics of HVC function. Hence, Kosche et al. (2015) findings do not invalidate the approach of our model, but highlights that HVC likely operates with several, redundant mechanisms that overall ensure temporal precision.Nevertheless, we will investigate further the effects of the degree of inhibition on song patterning.

      Third, the reliance on fine-tuning of HVC_RA connections becomes problematic if the model is scaled up to include groups of HVC_RA neurons forming a chain network, rather than the single HVC_RA neurons used in the current work. With groups of HVC_RA neurons, the summation of presynaptic inputs to each HVC_RA neuron would need to be precisely maintained for the model to function. However, experimental evidence shows that the HVC circuit remains functional despite perturbations, such as a few degrees of cooling, micro-lesions, or turnover of HVC_RA neurons. Such robustness cannot be accounted for by a model that depends on finely tuned connections, as seen in the current implementation.

      Our model of individual HVC<sub>RA</sub> neurons and as stated previously is reductive model that focuses on understanding the mechanisms that govern sequential neural activity. We agree that scaling the model to include many of HVC<sub>RA</sub> neurons poses challenges, specifically concerning the summation of presynaptic inputs. However, our model can still be adapted to a larger network without requiring the level of fine-tuning currently needed. In fact, the current fine-tuning of synaptic connections in the model is a reflection of fundamental network mechanisms rather than a limitation when scaling to a larger network. Besides, one important feature of this neural network is redundancy. Even if some neurons or synaptic connections are impaired, other neurons or pathways can compensate for these changes, allowing the activity propagation to remain intact.

      The authors examined how altering the channel properties of neurons affects the activity in their model. While this approach is valid, many of the observed effects may stem from the delicate balancing required in their model for proper function.

      In the current model, HVC_X neurons burst as a result of rebound activity driven by the I_H current. Rebound bursts mediated by the I_H current typically require a highly hyperpolarized membrane potential. However, this mechanism would fail if the reversal potential of inhibition is higher than the required level of hyperpolarization. Furthermore, Mooney (2000) demonstrated that depolarizing the membrane potential of HVC_X neurons did not prevent bursts of these neurons during forward playback of the bird's own song, suggesting that these bursts (at least under anesthesia, which may be a different state altogether) are not necessarily caused by rebound activity. This discrepancy should be addressed or considered in the model.

      In our HVC network model, one goal with HVC<sub>X</sub> neurons is to generate bursts in their underlying neuron population. Since HVC<sub>X</sub> neurons in our model receive only inhibitory inputs from interneurons, we rely on inhibition followed by rebound bursts orchestrated by the IH and the I<sub>CaT</sub> currents to achieve this goal. The interplay between the T-type Ca<sup>++</sup> current and the H current in our model is fundamental to generate their corresponding bursts, as they are sufficient for producing the desired behavior in the network. Due to this interplay, we do not need significant inhibition to generate rebound bursts, because the T-type Ca<sup>++</sup> current’s conductance can be stronger leading to robust rebound bursting even when the degree of inhibition is not very strong. We will highlight this with more clarity in the revised version.

      Some figures contain direct copies of figures from published papers. It is perhaps a better practice to replace them with schematics if possible.

      We will replace the relevant figures with schematic representations where possible.

      Reviewer #2 (Public review):

      Summary:

      In this paper, the authors use numerical simulations to try to understand better a major experimental discovery in songbird neuroscience from 2002 by Richard Hahnloser and collaborators. The 2002 paper found that a certain class of projection neurons in the premotor nucleus HVC of adult male zebra finch songbirds, the neurons that project to another premotor nucleus RA, fired sparsely (once per song motif) and precisely (to about 1 ms accuracy) during singing.

      The experimental discovery is important to understand since it initially suggested that the sparsely firing RA-projecting neurons acted as a simple clock that was localized to HVC and that controlled all details of the temporal hierarchy of singing: notes, syllables, gaps, and motifs. Later experiments suggested that the initial interpretation might be incomplete: that the temporal structure of adult male zebra finch songs instead emerged in a more complicated and distributed way, still not well understood, from the interaction of HVC with multiple other nuclei, including auditory and brainstem areas. So at least two major questions remain unanswered more than two decades after the 2002 experiment: What is the neurobiological mechanism that produces the sparse precise bursting: is it a local circuit in HVC or is it some combination of external input to HVC and local circuitry?

      And how is the sparse precise bursting in HVC related to a songbird's vocalizations?

      The authors only investigate part of the first question, whether the mechanism for sparse precise bursts is local to HVC. They do so indirectly, by using conductance-based Hodgkin-Huxley-like equations to simulate the spiking dynamics of a simplified network that includes three known major classes of HVC neurons and such that all neurons within a class are assumed to be identical. A strength of the calculations is that the authors include known biophysically deduced details of the different conductances of the three major classes of HVC neurons, and they take into account what is known, based on sparse paired recordings in slices, about how the three classes connect to one another. One weakness of the paper is that the authors make arbitrary and not well-motivated assumptions about the network geometry, and they do not use the flexibility of their simulations to study how their results depend on their network assumptions. A second weakness is that they ignore many known experimental details such as projections into HVC from other nuclei, dendritic computations (the somas and dendrites are treated by the authors as point-like isopotential objects), the role of neuromodulators, and known heterogeneity of the interneurons. These weaknesses make it difficult for readers to know the relevance of the simulations for experiments and for advancing theoretical understanding.

      Strengths:

      The authors use conductance-based Hodgkin-Huxley-like equations to simulate spiking activity in a network of neurons intended to model more accurately songbird nucleus HVC of adult male zebra finches. Spiking models are much closer to experiments than models based on firing rates or on 2-state neurons.

      The authors include information deduced from modeling experimental current-clamp data such as the types and properties of conductances. They also take into account how neurons in one class connect to neurons in other classes via excitatory or inhibitory synapses, based on sparse paired recordings in slices by other researchers.

      The authors obtain some new results of modest interest such as how changes in the maximum conductances of four key channels (e.g., A-type K<sup>+</sup> currents or Ca-dependent K<sup>+</sup> currents) influence the structure and propagation of bursts, while simultaneously being able to mimic accurately current-clamp voltage measurements.

      Weaknesses:

      One weakness of this paper is the lack of a clearly stated, interesting, and relevant scientific question to try to answer. In the introduction, the authors do not discuss adequately which questions recent experimental and theoretical work have failed to explain adequately, concerning HVC neural dynamics and its role in producing vocalizations. The authors do not discuss adequately why they chose the approach of their paper and how their results address some of these questions.

      For example, the authors need to explain in more detail how their calculations relate to the works of Daou et al, J. Neurophys. 2013 (which already fitted spiking models to neuronal data and identified certain conductances), to Jin et al J. Comput. Neurosci. 2007 (which already discussed how to get bursts using some experimental details), and to the rather similar paper by E. Armstrong and H. Abarbanel, J. Neurophys 2016, which already postulated and studied sequences of microcircuits in HVC. This last paper is not even cited by the authors.

      We thank the reviewer for this valuable comment, and we agree that we did not clarify enough throughout the paper the utility of our model or how it advanced our understanding of the HVC dynamics and circuitry. To that end, we will revise several places of the manuscript and make sure to cite and highlight the relevance and relatedness of the mentioned papers.

      In short, and as mentioned to Reviewer 1, while several models of how sequence is generated within HVC have been proposed (Cannon et al., 2015; Drew & Abbott, 2003; Egger et al., 2020; Elmaleh et al., 2021; Galvis et al., 2018; Gibb et al., 2009a, 2009b; Hamaguchi et al., 2016; Jin, 2009; Long & Fee, 2008; Markowitz et al., 2015; Jin et al., 2007), all the models proposed either rely on intrinsic HVC circuitry to propagate sequential activity, rely on extrinsic feedback to advance the sequence or rely on both. These models do not capture the complex details of spike morphology, do not include the right ionic currents, do not incorporate all classes of HVC neurons, or do not generate realistic firing patterns as seen in vivo. Our model is the first biophysically realistic model that incorporates all classes of HVC neurons and their intrinsic properties.

      No existing hypothesis had been challenged with our model, rather; our model is a distillation of the various models that’s been proposed for the HVC network. We go over this in detail in the Discussion. We believe that the network model we developed provide a step forward in describing the biophysics of HVC circuitry, and may throw a new light on certain dynamics in the mammalian brain, particularly the motor cortex and the hippocampus regions where precisely-timed sequential activity is crucial. We suggest that temporally-precise sequential activity may be a manifestation of neural networks comprised of chain of microcircuits, each containing pools of excitatory and inhibitory neurons, with local interplay among neurons of the same microcircuit and global interplays across the various microcircuits, and with structured inhibition as well as intrinsic properties synchronizing the neuronal pools and stabilizing timing within a firing sequence.

      The authors' main achievement is to show that simulations of a certain simplified and idealized network of spiking neurons, which includes some experimental details but ignores many others, match some experimental results like current-clamp-derived voltage time series for the three classes of HVC neurons (although this was already reported in earlier work by Daou and collaborators in 2013), and simultaneously the robust propagation of bursts with properties similar to those observed in experiments. The authors also present results about how certain neuronal details and burst propagation change when certain key maximum conductances are varied.

      However, these are weak conclusions for two reasons. First, the authors did not do enough calculations to allow the reader to understand how many parameters were needed to obtain these fits and whether simpler circuits, say with fewer parameters and simpler network topology, could do just as well. Second, many previous researchers have demonstrated robust burst propagation in a variety of feed-forward models. So what is new and important about the authors' results compared to the previous computational papers?

      A major novelty of our work is the incorporation of experimental data with detailed network models. While earlier works have established robust burst propagation, our model uses realistic ion channel kinetics and feedback inhibition not only to reproduce experimental neural activity patterns but also to suggest prospective mechanisms for song sequence production in the most biophysical way possible. This aspect that distinguishes our work from other feed-forward models. We go over this in detail in the Discussion. However, the reviewer is right regarding the details of the calculations conducted for the fits, we will make sure to highlight this in the Methods and throughout the manuscript with more details.

      We believe that the network model we developed provide a step forward in describing the biophysics of HVC circuitry, and may throw a new light on certain dynamics in the mammalian brain, particularly the motor cortex and the hippocampus regions where precisely-timed sequential activity is crucial. We suggest that temporally-precise sequential activity may be a manifestation of neural networks comprised of chain of microcircuits, each containing pools of excitatory and inhibitory neurons, with local interplay among neurons of the same microcircuit and global interplays across the various microcircuits, and with structured inhibition as well as intrinsic properties synchronizing the neuronal pools and stabilizing timing within a firing sequence.

      Also missing is a discussion, or at least an acknowledgment, of the fact that not all of the fine experimental details of undershoots, latencies, spike structure, spike accommodation, etc may be relevant for understanding vocalization. While it is nice to know that some models can match these experimental details and produce realistic bursts, that does not mean that all of these details are relevant for the function of producing precise vocalizations. Scientific insights in biology often require exploring which of the many observed details can be ignored and especially identifying the few that are essential for answering some questions. As one example, if HVC-X neurons are completely removed from the authors' model, does one still get robust and reasonable burst propagation of HVC-RA neurons? While part of the nucleus HVC acts as a premotor circuit that drives the nucleus RA, part of HVC is also related to learning. It is not clear that HVC-X neurons, which carry out some unknown calculation and transmit information to area X in a learning pathway, are relevant for burst production and propagation of HVC<sub>RA</sub> neurons, and so relevant for vocalization. Simulations provide a convenient and direct way to explore questions of this kind.

      One key question to answer is whether the bursting of HVC-RA projection neurons is based on a mechanism local to HVC or is some combination of external driving (say from auditory nuclei) and local circuitry. The authors do not contribute to answering this question because they ignore external driving and assume that the mechanism is some kind of intrinsic feed-forward circuit, which they put in by hand in a rather arbitrary and poorly justified way, by assuming the existence of small microcircuits consisting of a few HVC-RA, HVC-X, and HVC-I neurons that somehow correspond to "sub-syllabic segments". To my knowledge, experiments do not suggest the existence of such microcircuits nor does theory suggest the need for such microcircuits.

      Recent results showed a tight correlation between the intrinsic properties of neurons and features of song (Daou and Margoliash 2020, Medina and Margoliash 2024), where adult birds that exhibit similar songs tend to have similar intrinsic properties. While this is relevant, we acknowledge that not all details may be necessary for every aspect of vocalization, and future models could simplify concentrate on core dynamics and exclude certain features while still providing insights into the primary mechanisms.

      The question of whether HVC<sub>X</sub> neurons are relevant for burst propagation given that our model includes these neurons as part of the network for completeness, the reviewer is correct, the propagation of sequential activity in this model is primarily carried by HVC<sub>RA</sub> neurons in a feed-forward manner, but only if there is no perturbation to the HVC network. For example, we have shown how altering the intrinsic properties of HVC<sub>X</sub> neurons or for interneurons disrupts sequence propagation. In other words, while HVC neurons are the key forces to carry the chain forward, the interplay between excitation and inhibition in our network as well as the intrinsic parameters for all classes of HVC neurons are equally important forces in carrying the chain of activity forward. Thus, the stability of activity propagation necessary for song production depend on a finely balanced network of HVC neurons, with all classes contributing to the overall dynamics.

      We agree with the reviewer however that a potential drawback of our model is that its sole focus is on local excitatory connectivity within the HVC (Kornfeld et al., 2017; Long et al., 2010), while HVC neurons receive afferent excitatory connections (Akutagawa & Konishi, 2010; Nottebohm et al., 1982) that plays significant roles in their local dynamics. For example, the excitatory inputs that HVC neurons receive from Uvaeformis may be crucial in initiating (Andalman et al., 2011; Danish et al., 2017; Galvis et al., 2018) or sustaining (Hamaguchi et al., 2016) the sequential activity. While we acknowledge this limitation, our main contribution in this work is the biophysical insights onto how the patterning activity in HVC is largely shaped by the intrinsic properties of the individual neurons as well as the synaptic properties where excitation and inhibition play a major role in enabling neurons to generate their characteristic bursts during singing. This is true and holds irrespective of whether an external drive is injected onto the microcircuits or not. We will however elaborate on and investigate this more during the next submission.

      Another weakness of this paper is an unsatisfactory discussion of how the model was obtained, validated, and simulated. The authors should state as clearly as possible, in one location such as an appendix, what is the total number of independent parameters for the entire network and how parameter values were deduced from data or assigned by hand. With enough parameters and variables, many details can be fit arbitrarily accurately so researchers have to be careful to avoid overfitting. If parameter values were obtained by fitting to data, the authors should state clearly what the fitting algorithm was (some iterative nonlinear method, whose results can depend on the initial choice of parameters), what the error function used for fitting (sum of least squares?) was, and what data were used for the fitting.

      The authors should also state clearly the dynamical state of the network, the vector of quantities that evolve over time. (What is the dimension of that vector, which is also the number of ordinary differential equations that have to be integrated?) The authors do not mention what initial state was used to start the numerical integrations, whether transient dynamics were observed and what were their properties, or how the results depended on the choice of the initial state. The authors do not discuss how they determined that their model was programmed correctly (it is difficult to avoid typing errors when writing several pages or more of a code in any language) or how they determined the accuracy of the numerical integration method beyond fitting to experimental data, say by varying the time step size over some range or by comparing two different integration algorithms.

      We thank the reviewer again. The fitting process in our model occurred only at the first stage where the synaptic parameters were fit to the Mooney and Prather as well as the Kosche results. There was no data shared and we merely looked at the figures in those papers and checked the amplitude of the elicited currents, the magnitudes of DC-evoked excitations etc, and we replicated that in our model. While this is suboptimal, it was better for us to start with it rather than simply using equations for synaptic currents from the literature for other types of neurons (that are not even HVC’s or in the songbird) and integrate them into our network model. However, we will certainly highlight the details of this fitting process in the new submission. We will also highlight more technical details in the Methods regarding the exact number of ODEs, the initial conditions to run them, etc.

      Also disappointing is that the authors do not make any predictions to test, except rather weak ones such as that varying a maximum conductance sufficiently (which might be possible by using dynamic clamps) might cause burst propagation to stop or change its properties. Based on their results, the authors do not make suggestions for further experiments or calculations, but they should.

      We agree that making experimental testable predictions is crucial for the advancement of the model. Our predictions include testing whether eradication of a class of neurons such as HVC<sub>X</sub> neurons disrupts activity propagation which can be done through targeted neuron elimination. This also can be done through preventing rebound bursting in HVC<sub>X</sub> by pharmacologically blocking the I<sub>h</sub> channels. Others include down regulation of certain ion channels (pharmacologically done through ion blockers) and testing which current is fundamental for song production (and there a plenty of test based our results, like the SK current, the T-type Ca<sup>++</sup> current, the A-type K<sup>+</sup> current, etc). We will incorporate these into the revised manuscript to better demonstrate the model's applicability and to guide future research directions.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Structural colors (SC) are based on nanostructures reflecting and scattering light and producing optical wave interference. All kinds of living organisms exhibit SC. However, understanding the molecular mechanisms and genes involved may be complicated due to the complexity of these organisms. Hence, bacteria that exhibit SC in colonies, such as Flavobacterium IR1, can be good models.

      Based on previous genomic mining and co-occurrence with SC in flavobacterial strains, this article focuses on the role of a specific gene, moeA, in SC of Flavobacterium IR1 strain colonies on an agar plate. moeA is involved in the synthesis of the molybdenum cofactor, which is necessary for the activity of key metabolic enzymes in diverse pathways.

      The authors clearly showed that the absence of moeA shifts SC properties in a way that depends on the nutritional conditions. They further bring evidence that this effect was related to several properties of the colony, all impacted by the moeA mutant: cell-cell organization, cell motility and colony spreading, and metabolism of complex carbohydrates. Hence, by linking SC to a single gene in appearance, this work points to cellular organization (as a result of cell-cell arrangement and motility) and metabolism of polysaccharides as key factors for SC in a gliding bacterium. This may prove useful for designing molecular strategies to control SC in bacterial-based biomaterials.

      Strengths:

      The topic is very interesting from a fundamental viewpoint and has great potential in the field of biomaterials.

      Thank you for your comments.

      The article is easy to read. It builds on previous studies with already established tools to characterize SC at the level of the flavobacterial colony. Experiments are well described and well executed. In addition, the SIBR-Cas method for chromosome engineering in Flavobacteria is the most recent and is a leap forward for future studies in this model, even beyond SC.

      We appreciate these comments.

      Weaknesses:

      The paper appears a bit too descriptive and could be better organized. Some of the results, in particular the proteomic comparison, are not well exploited (not explored experimentally). In my opinion, the problem originates from the difficulty in explaining the link between the absence of moeA and the alterations observed at the level of colony spreading and polysaccharide utilization, and the variation in proteomic content.

      We will look at the organisation of the manuscript carefully in the coming, detailed revision, as suggested. In terms of the proteomics, there are clearly a large number of proteins affected by the moeA deletion. In terms of experimental exploration, we chose spreading, structural colour formation and starch degradation to test phenotypically, as the most relevant. For example, in L615-617, we discuss the downregulation of GldL (which is known to be involved Flavobacterial gliding motility [Shrivastava et al., 2013]) in the _moe_A KO as a possible explanation for the reduced colony spreading of moeA mutant. Changes in polysaccharide (starch) utilization were seen on solid medium, as well as in the proteomic profile where we observed the upregulation of carbohydrate metabolism proteins linked to PUL (polysaccharide utilisation locus) operons (Terrapon et al., 2015), such as PAM95095-90 (Figure 8), and other carbohydrate metabolism-related proteins, including a pectate lyase (Table S7) which is involved in starch degradation (Aspeborg et al., 2012). And as noted in L555-566 and Figure 9, starch metabolism was tested experimentally.

      First, the effect of moeA deletion on molybdenum cofactor synthesis should be addressed.

      MoeA is the last enzyme in the MoCo synthesis pathway, thus if only MoeA is absent the cell would accumulate MPT-AMP (molybdopterin-adenosine monophosphatase) (Iobbi-Nivol & Leimkühler, 2013), and the expressed molybdoenzymes would not be functional. In L582-585, we commented how the lack of molybdenum cofactor may affect the synthesis of molybdoenzymes. However, if you meant to analyse the presence of the small molecules, the cofactors, involved in these pathways, that was an assay we were not able to perform. Moreover, in L585-587, we addressed how the deletion of _moe_A affected the proteins encoded by the rest of genes in the operon.

      Second, as I was reading the entire manuscript, I kept asking myself if moeA (and by extension molybdenum cofactor) was really involved in SC or it was an indirect effect. For example, what if the absence of moeA alters the cell envelope because the synthesis of its building blocks is perturbed, then subsequently perturbates all related processes, including gliding motility and protein secretion? It would help to know if the effects on colony spreading and polysaccharide metabolism can be uncoupled. I don't think the authors discussed that clearly.

      The message of the paper is that the moeA gene, as predicted from a previous genomics analysis, is important in SC. This is based on the representation of the _moe_A gene in genomes of bacteria that display SC. This analysis does not predict the mechanism. When knocked out, a significant change in structural colour occurred, supporting this hypothesis. Whether this effect is direct or indirect is difficult to assess, as this referee rightly suggests. In order to follow up this central result, we performed proteomics (both intra- and extracellular). As we observed, the deletion of a single gene generated many changes in the proteomic profile, thus in the biological processes. Based on the known functions of molybdenum cofactor, we could only hypothesize that pterin metabolism is important for SC, not exactly how.

      We intend to discuss the links between gliding/spreading and polysaccharide metabolism more clearly, with reference to the literature, as quite a bit is known here including possible links to SC.

      Reviewer #2 (Public review):

      Summary:

      The authors constructed an in-frame deletion of moeA gene, which is involved in molybdopterin cofactor (MoCo) biosynthesis, and investigated its role in structural colors in Flavobacterium IR1. The deletion of moeA shifted colony color from green to blue, reduced colony spreading, and increased starch degradation, which was attributed to the upregulation of various proteins in polysaccharide utilization loci. This study lays the ground for developing new colorants by modifying genes involved in structural colors.

      Major strengths and weaknesses:

      The authors conducted well-designed experiments with appropriate controls and the results in the paper are presented in a logical manner, which supports their conclusions.

      We appreciate your comment.

      Using statistical tests to compare the differences between the wild type and moeA mutant, and adding a significance bar in Figure 4B, would strengthen their claims on differences in cell motility regarding differences in cell motility.

      Thank you. Figure 4B contains the significance bars that represent the standard deviation of the mean value of the three replicates, but we will modify it to make them more clear.

      Additionally, in the result section (Figure 6), the authors suggest that the shift in blue color is "caused by cells which are still highly ordered but narrower", which to my knowledge is not backed up by any experimental evidence.

      Thanks. We mentioned that the mutant cells are narrower than the wild type based on the estimated periodicity resulting from the goniometry analysis (L427-430). We will now say “likely to be narrower based on the estimated periodicity from the optical analysis” rather than just “narrower” in the revision.

      Overall, this is a well-written paper in which the authors effectively address their research questions through proper experimentation. This work will help us understand the genetic basis of structural colors in Flavobacterium and open new avenues to study the roles of additional genes and proteins in structural colors.

      Much appreciated.

      REFERENCES

      Aspeborg, Henrik, Pedro M. Coutinho, Yang Wang, Harry Brumer, and Bernard Henrissat. "Evolution, substrate specificity and subfamily classification of glycoside hydrolase family 5 (GH5)." BMC evolutionary biology 12 (2012): 1-16.

      lobbi-Nivol, Chantal, and Silke Leimkühler. "Molybdenum enzymes, their maturation and molybdenum cofactor biosynthesis in Escherichia coli." Biochimica et Biophysica Acta (BBA)-Bioenergetics 1827, no. 8-9 (2013): 1086-1101.

      Shrivastava, Abhishek, Joseph J. Johnston, Jessica M. Van Baaren, and Mark J. McBride. "Flavobacterium johnsoniae GldK, GldL, GldM, and SprA are required for secretion of the cell surface gliding motility adhesins SprB and RemA." Journal of bacteriology 195, no. 14 (2013): 3201-3212.

      Terrapon, Nicolas, Vincent Lombard, Harry J. Gilbert, and Bernard Henrissat. "Automatic prediction of polysaccharide utilization loci in Bacteroidetes species." Bioinformatics 31, no. 5 (2015): 647-655.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Reviewer #1:

      (1) To improve the clarity of the work, I suggest a final note to the authors to say more explicitly that objective accuracy has a finer resolution *due to the number of "special circles" per trial* in their task. This task detail got lost in my read of the manuscript, and confused me with respect to the resolution of each accuracy measure.

      We agree with the reviewer that this would be a useful clarification and have therefore added the following statement to the Methods section on p. 20:

      “It should be noted that the OIP has a slightly finer resolution due to the number of special circles per trial.”

      (2) Similarly for clarification, they could point out that their exclusion criteria removes subjects that have lower OIP than their AIP analysis allows (which is good for comparison between OIP and AIP). Thus, it removes the possibility that very poor performing subjects (OIP) are forced to have a higher than actual AIP due to the range).

      We agree this would be a useful statement to add and have included the following sentence in the Supplement on p. 8:

      “Such a restriction of the threshold parameter was intended to increase the comparability between AIP and OIP, and hence improved the calculation of the reminder bias.”


      The following is the authors’ response to the previous reviews.

      Reviewer #1:

      (1) Upon reading their response to the question I had regarding AIP and OIP, a few more questions came up regarding OIP, AIP, how they're calculations differ, and how the latter was computed in R. I hope these help readers to clarify how to interpret these key measures, and the hypotheses that rely upon them.

      Regarding fitting, and in relation to power, is16 queries adequate to estimate an AIP using the R's quickpsy? That is, assuming some noise in the choice process, how recoverable is a true indifference points from 16 trials? If there's a parameter recovery analysis (ie generating choice via the fitting parameters, which will have built-in stochasticity, and seeing how well you recover the parameter) of interest would be helpful. It may help to characterize why the present study might differ from prior studies (maybe a power issue here).

      The reviewer is absolutely correct that we should have provided more detail when describing our fitting procedure for the psychometric curves. We have now addressed this by adding the following statements to the Methods section and Supplement:

      Page 20 in the main manuscript: “Fitting was done using the quickpsy package in R and more detail is given in the Supplement.”

      Pages 8 and 9 in the Supplement: 

      “Psychometric curve fitting

      We used the quickpsy package in R to fit psychometric curves to each participant’s choice data to derive their actual indifference point (AIP), which was operationalised as the threshold parameter when predicting reminder choices from target values. We restricted the possible parameter ranges from 2 to 9 for the threshold parameter and from 1 to 500 for the slope parameter, based on the task’s properties and pilot data. Apart from those parameter ranges, we used only default settings of the quickpsy() function.

      Each participant has only 16 trials (2 for each target value) contribute to the curve fitting. To understand the robustness of the AIP based on such limited data, we conducted a parameter recovery analysis. We simulated 16 trials based on each psychometric function and re-ran the curve fitting based on those simulated choices. There was close correspondence between the actual and recovered threshold parameters (or AIPs) with a correlation of r = 0.97, p < 0.001 (see also Figure S1). In contrast, the slope parameter—which was not central to any of our analyses—exhibited greater variability during the initial fitting. This increased uncertainty likely contributed to its poor recovery in the simulation, as evidenced by a near-zero correlation (r = −0.01, p = 0.82).”

      (2) Along these lines, it would be helpful for the reader to actually see the individual psychometric curve, now how quickpsy was used (did you fit left and right asymptotes), etc, to understand how that fitting procedure works and how the assumptions of the fitting procedure compare to what can be gleaned through seeing the choice curves plotted.

      As stated above, we used default settings of the quickpsy() function and hence assumed symmetric asymptotes at 0 and 1. However, the reviewer mentions “left and right asymptotes”, so maybe this question is about restricting the possible parameter range for the threshold, which we restricted to values from 2 to 9, as described above.

      Regarding the individual curves, we have now include the following statement on page 9 in the Supplement: “Figures S2 to S31 show the individual psychometric curves that were estimated for each participant.” Please refer to the Supplement for the added figures.

      (3) A more full explanation of quickpsy, its parameters, and how choice curves look might also generate interesting further questions to think about with respect to biases and compulsivity. Two individuals might have similar indifference points, but an asymptote might reflect a bias to always have some percent chance of for example to take the reminders even at the lowest offer available for them.

      We agree that this is an interesting focus which we will keep in mind for future studies.

      (4) Regarding comparing OIP to AIP: 

      For OIP, as far as I can understand, the resolution of it is decreased compared to AIP.  Accuracies for OIP can only be 0/4,1/4,2/4,3/4, or 4/4. Yet, the resolution for AIP is the full range of offers (2 to 9) with respect to the parameter of interest (the indifference point). Could this bias the estimation of OIP (for instance, someone who scored 25% might actually be much closer to either 50 or 0, but we can't tell due to resolution?

      As mentioned in response to comment (1), we restricted the parameter range for the thresholds to 2 to 9 to increase comparability. The reviewer is right to point out that the OIP  still has lower resolution than the AIP, which is one of the downsides of having a shortened paradigm (cf. the longer version in Gilbert et al., 2019), which is optimised for online testing, especially if used in combination with additional questionnaires. We have no reason to believe though that this could have led to any bias, especially none that would contribute to the individual differences which are the main focus of our study.

      Gilbert, S. J., Bird, A., Carpenter, J. M., Fleming, S. M., Sachdeva, C., & Tsai, P.-C. (2020). Optimal use of reminders: Metacognition, effort, and cognitive offloading. Journal of Experimental Psychology: General, 149(3), 501–517. https://doi.org/10.1037/xge0000652

      (5) Additionally, it seems like the upper and lower bounds of OIP (0 and 10) differ from AIP (2 and 9). Could this also introduce bias (for example, if someone terrible performance, the mean would artificially be higher under AIP than OIP because the smallest indifference point is 2 under AIP, but could be 0 under OIP.

      See our response to comment (1), we fixed the range to 2 to 9 (which was the range of target values used in our study).

      (6) Finally seeing how CIT actually corresponds to accuracy overall (not a relative measure like AIP compared to OIP) I think would also be helpful as this is related to most points noted above.

      We included the suggested test as an exploratory analysis on pages 42-43 in the Supplement: “Third, we were interested in how the transdiagnostic phenotypes would correspond to performance. We therefore fitted a model which predicted internal accuracy (that is, unaided task performance on trials where no reminders could be used) from AD, CIT, and the other covariates (age, education and gender). We found that neither AD, β = -0.02, SE = 0.05, t = 0.44, p = 0.658, nor CIT, β = -0.03, SE = 0.05, t = -0.66, p = 0.510, predicted internal accuracy.

      The full results can be found in Table S13 as well as in Figure S32.”

    2. Reviewer #1 (Public review):

      Summary:

      Boldt et al test several possible relationships between trandiagnostically-defined compulsivity and cognitive offloading in a large online sample. To do so, they develop a new and useful cognitive task to jointly estimate biases in confidence and reminder-setting. In doing so, they find that over-confidence is related to less utilization of reminder-setting, which partially mediates the negative relationship between compulsivity and lower reminder-setting. The paper thus establishes that, contrary to the over-use of checking behaviors in patients with OCD, greater levels of transdiagnostically-defined compulsivity predicts less deployment of cognitive offloading. The authors offer speculative reasons as to why (perhaps it's perfectionism in less clinically-severe presentations that lowers the cost of expending memory resources), and sets an agenda to understand the divergence in cognitive between clinical and nonclinical samples. Because only a partial mediation had robust evidence, multiple effects may be at play, whereby compulsivity impacts cognitive offloading via overconfidence and also by other causal pathways.

      Strengths:

      The study develops an easy-to-implement task to jointly measure confidence and replicates several major findings on confidence and cognitive offloading. The study uses a useful measure of cognitive offloading - the tendency to set reminders to augment accuracy in the presence of experimentally manipulated costs. Moreover, the utilizes multiple measures of presumed biases -- overall tendency to set reminders, the empirically estimated indifference point at which people engage reminders, and a bias measure that compares optimal indifference points to engage reminders relative to the empirically observed indifference points. That the study observes convergenence along all these measures strengthens the inferences made relating compulsivity to the under-use of reminder-setting. Lastly, the study does find evidence for one of several a priori hypotheses and sets a compelling agenda to try to explain why such a finding diverges from an ostensible opposing finding in clinical OCD samples and the over-use of cognitive offloading.

      Weaknesses:

      Although I think this design and study are very helpful for the field, I felt that a feature of the design might reduce the tasks's sensitivity to measuring dispositional tendencies to engage cognitive offloading. In particular, the design introduces prediction errors, that could induce learning and interfere with natural tendencies to deploy reminder-setting behavior. These PEs comprise whether a given selected strategy will be or not be allowed to be engaged. We know individuals with compulsivity can learn even when instructed not to learn (e.g., Sharp, Dolan and Eldar, 2021, Psychological Medicine), and that more generally, they have trouble with structure knowledge (eg Seow et al; Fradkin et al), and thus might be sensitive to these PEs. Thus, a dispositional tendency to set reminders might be differentially impacted for those with compulsivity after an NPE, where they want to set a reminder, but aren't allowed to. After such an NPE, they may avoid moreso the tendency to set reminders. Those with compulsivity likely have superstitious beliefs about how checking behaviors lead to a resolution of catastrophes, that might in part originate from inferring structure in the presence of noise or from purely irrelevant sources of information for a given decision problem.<br /> It would be good to know if such learning effects exist, if they're modulated by PE (you can imagine PEs are higher if you are more incentivized - e.g., 9 points as opposed to only 3 points - to use reminders, and you are told you cannot use them), and if this learning effect confounds the relationship between compulsivity and reminder-setting.

      A more subtle point, I think this study can be more said to be an exploration than a deductive of test of a particular model -> hypothesis -> experiment. Typically, when we test a hypothesis, we contrast it with competing models. Here, the tests were two-sided because multiple models, with mutually exclusive predictions (over-use or under-use of reminders) were tested. Moreover, it's unclear exactly how to make sense of what is called the direct mechanism, which is supported by the partial (as opposed to complete) mediation.

      Comments on revisions:

      I have the following final comments for your manuscript revisions:

      To improve the clarity of the work, I suggest a final note to the authors to say more explicitly that objective accuracy has a finer resolution *due to the number of "special circles" per trial* in their task. This task detail got lost in my read of the manuscript, and confused me with respect to the resolution of each accuracy measure. Similarly for clarification, they could point out that their exclusion criteria removes subjects that have lower OIP than their AIP analysis allows (which is good for comparison between OIP and AIP). Thus, it removes the possibility that very poor performing subjects (OIP) are forced to have a higher than actual AIP due to the range).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer 1:

      (1) Some conclusions are not completely supported by the present data, and at times the manuscript is disjoint and hard to follow. While the work has some interesting observations, additional experiments and controls are warranted to support the claims of the manuscript.

      Thank you for the comments. We revised some of the claims and conclusions to be more objective and result-supportive.

      (2) While the authors present compelling data that is relevant to the development of anti-bacterial vaccinations, the data does not completely match their assertions and there are places where some further investigation would further the impact of their interesting study.

      We do not fully agree with the reviewer's comments. We have demonstrated that changes in CPS levels during infection are associated with pathogenesis, which will guide future studies on the underlying mechanisms. A significant amount of effort is required for studying mechanisms, which is beyond the scope of this research. We concur with the reviewer that assertions should be made cautiously until further studies are conducted. We have revised these assertions to align with the data and to avoid extrapolating the results (pages 7, lines 126, 133-136; page 11, lines 216-218; page 13, line 264; and page 18, lines 378-383).

      (3) The difference in the pathogenesis of a log phase vs. stationary phage intranasal infection would be interesting. Especially because the bacteria is a part of the natural microbial community of swine tonsils, it is curious if the change in growth phase and therefore CPS levels may be a causative reason for pathogenic invasion in some pigs.

      S. suis is a part of the natural microbial community of swine tonsils but not mouse NALT. It is interesting to know if CPS levels are low in pig tonsils since CPS is hydrophilic and not conducive to bacterial adhesion. In the study, mice were i.n. infected with a high dose of the bacteria, which could increase opportunities for dissemination (acidic acid may not be a contributor since with or without it is similar). S. suis getting into other body compartments from pig tonsils might be triggered by other conditions, such as viral coinfection, nasal cavity inflammation, cold weather, and decreased immunity.

      Experiments with pig blood and phagocytes have shown that genes involved in the synthesis of CPS are upregulated in pig blood. In contrast, these genes are downregulated [1]. In addition, the absence of CPS correlated with increased hydrophobicity and phagocytosis, proposing that S. suis undergoes CPS phase variation and could play a role in the different steps of S. suis infection [2]. We showed direct evidence of encapsulation modulation associated with S. suis pathogenesis in mice. A pig infection model is required to confirm these findings.

      (4) The authors should consider taking the bacteria from NALT/CSF and blood and compare the lag times bacteria from different organs take to enter a log growth phase to show whether the difference in CPS is because S. suis in each location is in a different growth phase. If log phase bacteria were intranasally delivered, would it adapt a stationary phase life strategy? How long would that take? 

      What causes CPS regulation in vivo is not known. CPS changes in different culture stages, indicating that stress, such as nutrition levels, is one of the signals triggering CPS regulation. The microenvironment in the body compartments is far more complex than in vitro, in which host cells, immune factors and others may affect CPS regulation, individually or collectively. The reviewer’ question is important but the suggested experiment is impracticable since bacterial numbers taken from organs are few, and culturing the bacteria in vitro would obliterate the in vivo status.  

      (5) Authors should be cautious about claims about S. suis downregulating CPS in the NALT for increased invasion and upregulating CPS to survive phagocytosis in blood. While it is true that the data shows that there are different levels of CPS in these locations, the regulation and mechanism of the recorded and observed cell wall difference are not investigated past the correlation to the growth phase.

      We lower the tone and change the claim as “suggest a correlation between lower CPS in the NALT and a greater capacity for cellular association, whereas elevated CPS levels in the blood are linked to improved resistance against bactericidal activity. However, the mechanisms behind these associations remain unknown.” (page 7, lines 133-136).

      (6) The mouse model used in this manuscript is useful but cannot reproduce the nasal environment of the natural pig host. It is not clear if the NALTs of pigs and mice have similar microbial communities and how this may affect the pathogenesis of S. Suis in the mouse. Because the authors show a higher infection rate in the mouse with acetic acid, they may want to consider investigating what the mouse NALT microenvironment is naturally doing to exclude more bacterial invasion. Is it simply a host mismatch or is there something about the microbiome or steady-state immune system in the nose of mice that is different from pigs?

      It is a very interesting comment. The mice are SPF level. The microenvironment in SPF mouse NALT should be significantly different from conventional pig tonsils. Although NALT in mice resembles pig tonsils in function, many factors may contribute to the sensitivity to S. suis colonization in the pig nasal cavity, such as the microbiome and local steady-state immune system. More complex microbiota in tonsils could be one of the factors. Analyzing what makes S. suis inclined towards colonization in pig tonsils by SPF and conventional pigs are an ideal experiment to answer the question. 

      (7) Have some concerns regarding the images shown for neuroinvasion because I think the authors mistake several compartments of the mouse nasal cavity as well as the olfactory bulb. These issues are critical because neuroinvasion is one of the major conclusions of this work.

      Thank you for your comments. The olfactory epithelium (OE) is located directly underneath the olfactory bulb in the olfactory mucosa area and lines approximately half of the nasal cavities of the nasal cavity. The remaining surface of the nasal cavity is lined by respiratory epithelium, which lacks neurons. The olfactory receptor neuron in OE is stained green in the images by β-tubulin III, a neuron-specific marker. The respiratory epithelium is colorless due to the absence of nerve cells. Similarly, the green color stained by β-tubulin III identifies the olfactory bulb. The accuracy of the anatomic compartments of the mouse nasal cavity has been checked and confirmed by referring to related literature [3, 4].

      References

      (1) Wu Z, Wu C, Shao J, Zhu Z, Wang W, Zhang W, Tang M, Pei N, Fan H, Li J, Yao H, Gu H, Xu X, Lu C. The Streptococcus suis transcriptional landscape reveals adaptation mechanisms in pig blood and cerebrospinal fluid. RNA. 2014 Jun;20(6):882-98.

      (2) Charland N, Harel J, Kobisch M, Lacasse S, Gottschalk M. Streptococcus suis serotype 2 mutants deficient in capsular expression. Microbiology (Reading). 1998 Feb;144 ( Pt 2):325-332.

      (3) Pägelow D, Chhatbar C, Beineke A, Liu X, Nerlich A, van Vorst K, Rohde M, Kalinke U, Förster R, Halle S, Valentin-Weigand P, Hornef MW, Fulde M. The olfactory epithelium as a port of entry in neonatal neurolisteriosis. Nat Commun. 2018;9(1):4269.

      (4) Sjölinder H, Jonsson AB. Olfactory nerve--a novel invasion route of Neisseria meningitidis to reach the meninges. PLoS One. 2010 Nov 18;5(11):e14034.

      Reviewer 2:

      (1) However, there are serious concerns about data collection and interpretation that require further data to provide an accurate conclusion. Some of these concerns are highlighted below:

      Both reviewers were concerned about some of the interpretations of the results. We modified the interpretations in related lines throughout the manuscript (Please see the related responses to Reviewer 1).

      (2) In figure 2, the authors conclude that high levels of CPS confer resistance to phagocytic killing in blood exposed S. suis. However, it seems equally likely that this is resistance against complement mediated killing. It would be important to compare S. suis killing in animals depleted of complement components (C3 and C5-9).

      We thank the reviewer for the comment. The experiment should be Bactericidal Assay instead of anti-phagocytosis killing. CPS is a main inhibitor of C3b deposition [1]. It interferes with complement-mediated and receptor-mediated phagocytosis; and direct killing. Data in Figure 2C is expressed as “% of bacterial survival in whole blood” for clarity (page 8, Fig. 2C and page 23, lines 489-490).

      (3) Intranasal administration non-CPS antisera provides a nice contrast to intravenous administration, especially in light of the recently identified "blood-olfactory barrier". Can the authors provide any insight into how long and where this antibody would be located after intranasal administration? Would this be antibody mediated cellular resistance, or something akin to simple antibody "neutralization"

      Anti-V5 may not stay long locally following intranasal administration. Efficient reduction of S. suis colonization in NALT supports that anti-V5 could recognize and neutralize the bacteria in NALT quickly, thereby reducing further dissemination in the body. Antibody-mediated phagocytosis may not play a major role because neutrophils are mainly present in the blood but not in the tissues.  

      (4) The micrographs in Figure 7 depict anatomy from the respiratory mucosa. While there is no histochemical identification of neurons, the tissues labeled OE are almost certainly not olfactory and in fact respiratory. However, more troubling is that in figures 7A,a,b,e, and f, the lateral nasal organ has been labeled as the olfactory bulb. This undermines the conclusion of CNS invasion, and also draws into question other experiments in which the brain and CSF are measured.

      We understand the significance of your concerns and appreciate your careful review of Figure 7. The olfactory epithelium (OE) is situated directly beneath the olfactory bulb in the olfactory mucosa area and covers about half of the nasal cavity. This positioning allows information transduction between the olfactory and the olfactory epithelium. The remaining surface of the nasal cavity is lined with respiratory epithelium, which does not contain neurons and primarily serves as a protective barrier. In contrast, the olfactory epithelium consists of basal cells, sustentacular cells, and olfactory receptor neurons. The olfactory receptor neurons are specifically stained green in the images using β-tubulin III, a marker that is unique to neurons. The respiratory epithelium appears colorless due to the lack of nerve cells. Similarly, the green staining with β-tubulin III also highlights the olfactory bulb. The anatomical structures indicated in the images are consistent with those described in the literature [2, 3], confirming that the anatomy of the nasal cavity has been accurately identified.

      (5) Micrographs of brain tissue in 7B are taken from distal parts of the brain, whereas if olfactory neuroinvasion were occurring, the bacteria would be expected to arrive in the olfactory bulb. It's also difficult to understand how an inflammatory process would be developed to this point in the brain -even if we were looking at the appropriate region of the brain -within an hour of inoculation (is there a control for acetic acid induced brain inflammation?). Some explanations about the speed of the immune responses recorded are warranted.

      Thank you for highlighting this issue. Cerebrospinal fluid (CSF) flows into the subarachnoid space surrounding the spinal cord and the brain. There are direct connections from this subarachnoid space to lymphatic vessels that wrap around the olfactory nerves as they cross the cribriform plate towards the nasal submucosa. This connection allows for the drainage of CSF into the nasal submucosal lymphatics in mice [4, 5]. Bacteria may utilize this CSF outflow channel in the opposite direction, which explains the development of brain inflammation in the distal areas of brain tissue adjacent to the subarachnoid space. We have included additional relevant information in the revised manuscript (page 16, lines 323-325).

      (6) The detected presence of S. suis in the CSF 0.5hr following intranasal inoculation is difficult to understand from an anatomical perspective. This is especially true when the amount of S. suis is nearly the same as that found within the NALT. Even motile pathogens would need far longer than 0.5hr to get into the brain, so it's exceedingly difficult to understand how this could occur so extensively in under an hour. The authors are quantifying CSF as anything that comes out of the brain after mincing. Firstly, this should more accurately be referred to as "brain", not CSF. Secondly, is it possible that the lateral nasal organ -which is mistakenly identified as olfactory bulb in figure 7- is being included in the CNS processing? This would explain the equivalent amounts of S. suis in NALT and "CSF".

      The high dose of inoculation used in the experiment may explain the rapid presence of S. suis in the CSF. Mice exhibit low sensitivity to S. suis infection, and the range for the effective intranasal infectious dose is quite narrow. Higher doses lead to the quick death of the mice, while lower doses do not initiate an infection at all. The dose used in this study is empirical and is intended to facilitate the observation of the progression of S. suis infection in mice.

      The NALT tissue and CSF samples are collected separately. After obtaining the NALT tissue, the nasal portion was carefully separated from the rest of the head along the line of the eyeballs. The brain tissue was then extracted from the remaining part of the head to collect the CSF, and it was lacerated to expose the subarachnoid space without being minced. This procedure aims to preserve the integrity of the brain tissue as much as possible. Further details about the CSF collection process can be found in the Materials and Methods section (page 24, lines 508-512).

      (7) To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

      Thank you. We respectfully disagree with the reviewer. We stained neurons using a neuron-specific marker to identify the anatomical structures of the olfactory bulb and olfactory epithelium (in green). We used an S. suis-specific antibody to highlight the bacteria present in these areas (in orange and red). The images, along with the bacteria found in the cerebrospinal fluid (CSF) and the brain inflammation observed early in the infection, strongly support our conclusion regarding brain invasion through the olfactory pathway. Please see the response to question 4 for further clarification.

      References

      (1) Seitz M, Beineke A, Singpiel A, Willenborg J, Dutow P, Goethe R, Valentin-Weigand P, Klos A, Baums CG. Role of capsule and suilysin in mucosal infection of complement-deficient mice with Streptococcus suis. Infect Immun. 2014 Jun;82(6):2460-71.

      (2) Sjölinder H, Jonsson AB. Olfactory nerve--a novel invasion route of Neisseria meningitidis to reach the meninges. PLoS One. 2010 Nov 18;5(11):e14034.

      (3) Pägelow D, Chhatbar C, Beineke A, Liu X, Nerlich A, van Vorst K, Rohde M, Kalinke U, Förster R, Halle S, Valentin-Weigand P, Hornef MW, Fulde M. The olfactory epithelium as a port of entry in neonatal neurolisteriosis. Nat Commun. 2018;9(1):4269.

      (4) Yoon JH, Jin H, Kim HJ, Hong SP, Yang MJ, Ahn JH, Kim YC, Seo J, Lee Y, McDonald DM, Davis MJ, Koh GY. Nasopharyngeal lymphatic plexus is a hub for cerebrospinal fluid drainage. Nature. 2024 Jan;625(7996):768-777.

      (5) Spera I, Cousin N, Ries M, Kedracka A, Castillo A, Aleandri S, Vladymyrov M, Mapunda JA, Engelhardt B, Luciani P, Detmar M, Proulx ST. Open pathways for cerebrospinal fluid outflow at the cribriform plate along the olfactory nerves. EBioMedicine. 2023 May;91:104558.

      Response to Recommendations for the authors:

      Reviewer 1:

      Minor concerns for the manuscript:

      (1) In the introduction, please consider giving a little more background about the bacteria itself and how it causes pathogenesis.

      We appreciate your suggestion. We have included additional background on the virulent factors and the pathogenesis of the bacteria in the introduction to enhance understanding of the results (page 4, lines 63-69).

      (2) Figure 2C would be more correct to say percent survival as the CFUs before and after are what are being compared and not if the bacteria is being phagocytosed or not. Flow cytometry of the leukocytes and a fluorescent S. Suis would show phagocytosis. Unless that experiment is performed, the authors cannot claim that there is a resistance to phagocytosis.

      Thank you for your feedback. We agree with the reviewer that the experiment should be Bactericidal Assay rather than anti-phagocytosis killing. CPS interferes with complement-mediated phagocytosis and direct killing, and receptor-mediated phagocytosis. To enhance clarity, the data in Fig. 2C has been presented as “% of bacterial survival in whole blood” (page 8).  

      (3) There are two different legends present for Figure 1. Please resolve.

      We apologize for the oversight. The redundant figure legend has been removed (page 6).

      (4) There are places such as in lines 194-195, that there are assertions and interpretations about the data that are not directly drawn from the data. These hypotheses are valuable, but please move them to the discussion.

      Thank you for your suggestion. The hypothesis has been moved to the Discussion section (page 19, lines 402 - 405).

      (5) In Figure 4B, higher resolution images would strengthen the ability of non-microbiologists to see the differences in CPS levels in the cell wall.

      We achieved the highest resolution possible for clearer distinctions in CPS levels. To enhance the visualization of the different CPS levels in the images, we revised the description of the CPS changes in Figure 4B within the results section (page 11, lines 208-213).

      (6) In Figure 5 there is no D. Further, the schematics throughout would be easier to parse with the text if the challenge occurred at time 0. Consider revising them for clarity.

      Thank you for highlighting the error. We have removed "i.v + i.n (Fig. 5)" from Figure 5A and made adjustments to the schematic illustrations in Figures 5 and 6 as recommended by the reviewer (page 14).

      (7) What is the control for the serum? The findings for figures 5 and 6 would be much stronger if a non- S. Suis isotype control serum was also infused.

      We used a naive serum as a control to avoid interference from a non-S. suis isotype control that targets other surface molecules of S. suis serotypes.

      (8) Figure 6 legend does not include the anti-CPS treatment.

      Thank you. We have added anti-CPS serum in the legend (page 15, line 249).

      (9) Figure 7 legend does not include the time point for panel 7A.

      Thank you. The time point is shown on Fig.7A (page 17).

      (10) Figure 7 should show OB micrographs or entire brain including the OB.

      The neuron-specific marker, β-tubulin III, identifies the neuro cells in the olfactory bulb (OB) as shown in Fig. 7A. Unfortunately, we were unable to provide an image of the entire brain that includes the OB due to limitations in our section preparation. We apologize for the mislabeled structure in Fig. 7A, which may have caused confusion. We have corrected the labeling for consistency (see page 15, lines 257-260). Additionally, we included a drawing of the sagittal plane of the rodent's nose, depicting the compartments of the OB, olfactory epithelium (OE), nasal cavity (NC), and brain. This illustration, presented in Fig. 7B on page 17, aims to clarify the structural and functional connections between the nasopharynx and the CNS.

      (11) Some conclusions may be better drawn if figures were to be consolidated. As noted above, the data at times feels disjointed and the importance is more difficult for readers to follow because data are presented further apart. Particularly figures 5 and 6 which are similar with different time points and controls of antisera administrative routes; placing these figures together would be an example of increasing continuity throughout the paper.

      Thank you for the valuable suggestion. Figures 5 and 6, along with their related descriptions in the results section, have been combined for better cohesiveness (pages 14-15).

      Reviewer #2:

      To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

      Please refer to our responses to Reviewer 1's Question 7, Reviewer 2's Questions 4 and 7 in the public reviews, and Reviewer 1's Question 10 in the authors' recommendations.

    1. Step 1: Notice how you are feeling.Tuning in to your feelings is very important. When you are notaware of your feelings, it is easy for them to interfere in yourability to build strong, positive, relationships with families.Adele watches her niece’s son, Eduardo, each day—whichshe really enjoys. But her niece, Tasha, is often late to pickhim up and never calls. Adele is really frustrated and angry.She feels it’s very disrespectful and that she is being takenadvantage of. When her niece does eventually show up,Adele is very abrupt and annoyed in her tone. The two adultsbarely communicate. Eduardo glances from one to the otherand looks very tense. Tasha whisks him away and Eduardodoesn’t even say good-bye to his auntie whom he adores.Recognizing the impact on Eduardo, Adele decides to talk toTasha about her feelings and to see about making a plan to helpTasha arrive on time, and at least to call to let Adele know she isrunning late. When Adele takes the approach of partnering withTasha in solving the problem, versus blaming her, Tasha is opento discussing solutions.Step 2: Look at the interaction from thechild’s point of view.Tuning in to the child’s experience can reduce tension and leadto joint problem-solving. Take the example of a child throwing atantrum when their parent comes to pick them up. This situationcan naturally make a parent feel incompetent and embarrassed.But if you look at it from the child’s point of view, you canreframe the issue in a way that doesn’t make the parent feel badand that also helps them understand the complexity of the child’sbehavior: “It seems like Stephanie is trying to tell you, I’m havingso much fun with the dollhouse that I need a little time to adjustto the idea it’s time to leave for the day.” Or, “Stephanie has kepther emotions in all day and now that her safe person is here, shecan really let her feelings out. It is hard to share a day with so manychildren no matter how much fun it is.”In the cases where a child is more cooperative with you than theparent, again, help them see it from the child’s perspective:“Yes, Tony puts his coat on when I ask him to, but that’s because heknows I have to help the other kids too. Kids learn quickly that therules and expectations at home and here can be different. He tellsme all about how you make sure he is zipped up and how youalways check that he has his hat. He talks about you all the time.It is always hardest for parents and families. Children work thingsout with the people they are most connected to.”Step 3: Partner with families.Developing a plan together with families on how to handle achild-rearing issue helps you move forward as partners, insteadof competitors. For example, if you are trying to teach childrennot to hit when they are angry, but the parent hits the child todiscipline them at home, you can:f Use “I” statements. “I know we are both concerned aboutErica hitting other kids when she’s here. I really work with thekids on finding other ways to show angry feelings. I don’t hitthem because when adults hit children when they are angry,it teaches children to hit as well when they are mad.”f Ask for the parent’s perspective. Clarify the parent’s feelingsand beliefs on the issue. Ask questions to learn, not to passjudgment: “What are acceptable ways to you for Erica to expressher angry feelings? What do you do at home? What do you findworks? What doesn’t work? Would you be open to finding ways todiscipline her other than hitting?”f Most important: Look for a place to compromise. Ask theparent if they have ideas for next steps. What can the two ofyou agree on? What can you both work on? For example,“We both agree that Erica needs to find other ways to show heranger besides hitting. One strategy that seems to work here is tohave her stomp her feet as hard as she can to get her mad out.Are you comfortable with that? I also tell her that if she needs abreak, she can curl up on the couch with her teddy bear. Are thesestrategies you think you might want to try at home?” (If not, askthe parent(s) what they would be comfortable with.)Finally, don’t forget to check in.A relationship is a living thing that grows and changes overtime. It’s important to check in with families to see how thingsare going, how your agreed-upon plan is working, and whereyou might need to make some adjustments. Communication is

      When a child acts out during pickup, it may not necessarily be a reflection of poor parenting but rather a sign that the child feels safe expressing their emotions with their caregiver. Recognizing this can help educators support both the child and the parent during transitions.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      DiPeso et al. develop two tools to (i) classify micronucleated (MN) cells, which they call VCS MN, and (ii) segment micronuclei and nuclei with MMFinder. They then use these tools to identify transcriptional changes in MN cells.

      The strengths of this study are:

      (1) Developing highly specialized tools to speed up the analysis of specific cellular phenomena such as MN formation and rupture is likely valuable to the community and neglected by developers of more generalist methods.

      (2) A lot of work and ideas have gone into this manuscript. It is clearly a valuable contribution.

      (3) Combining automated analysis, single-cell labeling, and cell sorting is an exciting approach to enrich phenotypes of interest, which the authors demonstrate here.

      Weaknesses:

      (1) Images and ground truth labels are not shared for others to develop potentially better analysis methods.

      We regret this omission and thank the reviewer for pointing it out. Both the images and ground truth labels for VCS MN and MNFinder are now available on the lab’s github page and described in the README.txt files. VCS MN: https://github.com/hatch-lab/fast-mn. MNFinder: https://github.com/hatch-lab/mnfinder.

      (2) Evaluations of the methods are often not fully explained in the text.

      The text has been extensively updated to include a full description of the methods and choices made to develop the VCS MN and MNFinder image segmentation modules.

      (3) To my mind, the various metrics used to evaluate VCS MN reveal it not to be terribly reliable. Recall and PPV hover in the 70-80% range except for the PPV for MN+. It is what it is - but do the authors think one has to spend time manually correcting the output or do they suggest one uses it as is?

      VCS MN attempts to balance precision and recall with speed to reduce the fraction of MN changing state from intact to ruptured during a single cell cycle during a live-cell isolation experiment. In addition, we chose to prioritize inclusion of small MN adjacent to the nucleus in our positive calls. This meant that there were more false positives (lower PPV) than obtained by other methods but allowed us to include this highly biologically relevant class of MN in our MN+ population. Thus, for a comprehensive understanding of the consequences of MN formation and rupture, we recommend using the finder as is. However, for other visual cell sorting applications where a small number of highly pure MN positive and negative cells is preferred, such as clonal outgrowth or metastasis assays, we would recommend using the slower, but more precise, MNFinder to get a higher precision at a cost of temporal resolution. In addition, MNFinder, with its higher flexibility and object coverage, is recommended for all fixed cell analyses.

      Reviewer #2 (Public review):

      Summary:

      Micronuclei are aberrant nuclear structures frequently seen following the missegregation of chromosomes. The authors present two image analysis methods, one robust and another rapid, to identify micronuclei (MN) bearing cells. The authors induce chromosome missegregation using an MPS1 inhibitor to check their software outcomes. In missegregation-induced cells, the authors do not distinguish cells that have MN from those that have MN with additional segregation defects. The authors use RNAseq to assess the outcomes of their MN-identifying methods: they do not observe a transcriptomic signature specific to MN but find changes that correlate with aneuploidy status. Overall, this work offers new tools to identify MN-presenting cells, and it sets the stage with clear benchmarks for further software development.

      Strengths:

      Currently, there are no robust MN classifiers with a clear quantification of their efficiency across cell lines (mIoU score). The software presented here tries to address this gap. GitHub material (tools, protocols, etc) provided is a great asset to naive and experienced computational biologists. The method has been tested in more than one cell line. This method can help integrate cell biology and 'omics' studies.

      Weaknesses:

      Although the classifier outperforms available tools for MN segmentation by providing mIOU, it's not yet at a point where it can be reliably applied to functional genomics assays where we expect a range of phenotypic penetrance.

      We agree that the MNFinder module has limitations with regards to the degree of nuclear atypia and cell density that can be tolerated. Based on the recall and PPV values and their consistency across the majority conditions analyzed, we believe that MNFinder can provide reliable results for MN frequency, integrity, shape, and label characteristics in a functional genomics assay in many commonly used adherent cell lines. We also added a discussion of caveats for these analyses, including the facts that highly lobulated nuclei will have higher false positive rates and that high cell confluency may require additional markers to ensure highly accurate assignment of MN to nuclei.

      Spindle checkpoint loss (e.g., MPS1 inhibition) is expected to cause a variety of nuclear atypia: misshapen, multinucleated, and micronucleated cells. It may be difficult to obtain a pure MN population following MPS1 inhibitor treatment, as many cells are likely to present MN among multinucleated or misshapen nuclear compartments. Given this situation, the transcriptomic impact of MN is unlikely to be retrieved using this experimental design, but this does not negate the significance of the work. The discussion will have to consider the nature, origin, and proportion of MN/rupture-only states - for example, lagging chromatids and unaligned chromosomes can result in different states of micronuclei and also distinct cell fates.

      We appreciate the reviewer’s comments and now quantify the frequency of other nuclear atypias and MN chromosome content in RPE1 cells after 24 h Mps1 inhibition (Fig. S1). In summary, we find only small increases in nuclear atypia, including multinucleate cells, misshapen nuclei, and chromatin bridges, compared to the large increase in MN formation. This contrasts with what is observed when mitosis is delayed using nocodazole or CENPE inhibitors where nuclear atypia is much more frequent. Importantly, after Mps1 inhibition, RPE1 cells with MN were only slightly more likely to have a misshapen nucleus compared to cells without MN (Fig. S1C).

      Interestingly, this analysis showed that the VCS MN pipeline, which uses the Deep Retina segmenter to identify nuclei, has a strong bias against lobulated nuclei and frequently fails to find them (Fig. S2B). Therefore, the cell populations analyzed by RNAseq were largely depleted of highly misshapen nuclei and differences in nuclear atypia frequency between MN+ and MN- cells in the starting population were lost (Fig. S9A, compare to Fig. S1C). This strongly suggests that the transcript changes we observed reflect differences in MN frequency and aneuploidy rather than differences in nuclei morphology.

      We agree with the reviewer that MN rupture frequency and formation, and downstream effects on cell proliferation and DNA damage, are sensitive to the source of the missegregated chromatin. In the revised manuscript we make clear that we chose Mps1 inhibition because it is strongly biased towards whole chromosome MN (Fig. S1E), limiting signal from DNA damage products, including chromosome fragments and chromatin bridges. This provides a base line to disambiguate the consequences of micronucleation and DNA damage in more complex chromosome missegregation processes, such as DNA replication disruption and irradiation. 

      Reviewer #3 (Public review):

      Summary:

      The authors develop a method to visually analyze micronuclei using automated methods. The authors then use these methods to isolate MN post-photoactivation and analyze transcriptional changes in cells with and without micronuclei of RPE-1 cells. The authors observe in RPE-1 cells that MN-containing cells show similar transcriptomic changes as aneuploidy, and that MN rupture does not lead to vast changes in the transcriptome.

      Strengths:

      The authors develop a method that allows for automating measurements and analysis of micronuclei. This has been something that the field has been missing for a long time. Using such a method has the potential to advance micronuclei biology. The authors also develop a method to identify cells with micronuclei in real time and mark them using photoconversion and then isolate them via FACS. The authors use this method to study the transcriptome. This method is very powerful as it allows for the sorting of a heterogenous population and subsequent analysis with a much higher sample number than could be previously done.

      Weaknesses:

      The major weakness of this paper is that the results from the RNA-seq analysis are difficult to interpret as very few changes are found to begin with between cells with MN and cells without. The authors have to use a 1.5-fold cut-off to detect any changes in general. This is most likely due to the sequencing read depth used by the authors. Moreover, there are large variances between replicates in experiments looking at cells with ruptured versus intact micronuclei. This limits our ability to assess if the lack of changes is due to truly not having changes between these populations or experimental limitations. Moreover, the authors use RPE-1 cells which lack cGAS, which may contribute to the lack of changes observed. Thus, it is possible that these results are not consistent with what would occur in primary tissues or just in general in cells with a proficient cGAS/STING pathway.

      We agree with the reviewer’s assessment of the limitations of our RNA-Seq analysis. After additional analysis, we propose an alternative explanation for the lower expression changes we observe in the MN+ and Mps1 inhibitor RNA-Seq experiments. In summary, we find that VCS MN has a strong bias against highly lobulated nuclei that depletes this class of cells from both the bulk analysis and the micronucleated cell populations (Fig. S9A). Based on this result, we propose that our analysis reduces the contribution of nuclear atypia to these transcriptional changes and that nuclear morphology changes are likely a signaling trigger associated with aneuploidy.

      We believe that this finding strengthens our overall conclusion that MN formation and rupture do not cause transcriptional changes, as suppressing the signaling associated with nuclei atypia should increase sensitivity to changes from the MN. However, we cannot completely rule out that MN formation or rupture cause a broad low-level change in transcription that is obscured by other signals in the dataset.

      As to cGAS signaling, several follow up papers and even the initial studies from the Greenburg lab show that MN rupture does not activate cGAS and does not cause cGAS/STING-dependent signaling in the first cell cycle (see citations and discussion in text). Therefore, we expect the absence of cGAS in RPE1 cells will have no effect in the first cell cycle, but could alter the transcriptional profile after mitosis. Although analysis of RPE1  cGAS+ cells or primary cells in these experiments will be required to definitively address this point, we believe that our interpretation of our RNAseq results is sufficiently backed up by the literature to warrant our conclusion that MN formation and rupture do not induce a transcriptional response in the first cell cycle.

      Reviewer #1 (Recommendations for the authors):

      I do not recommend additional experimental or computational work. Instead, I just recommend adapting the claims of the manuscript to what has been done. I am just asking for further clarification and minor rewriting.

      (1) The manuscript is written like a molecular biology paper with sparse explanations of the authors' reasoning, especially in the development of their algorithms. I was often lost as to why they did things in one way or another.

      The revised manuscript has thorough explanations and additional data and graphics defining how and why the VCS MN and MNFinder modules were developed. We hope that this clears up many of the questions the reviewer had and appreciate their guidance on making it more readable for scientists from different backgrounds.

      (2) Evaluations of their method are often not fully explained, for example:

      "On average, 75% of nuclei per field were correctly segmented and cropped."

      "MN segments were then assigned to 'parent' nuclei by proximity, which correctly associated 97% of MN."

      Were there ground truth images and labels created? How many? For example, I don't know how the authors could even establish a ground-truth for associating MNs to nuclei if MNs happened to be almost equidistant between two nuclei in their images.

      I suggest a separate subsection early in the Results section where the underlying imaging data + labels are presented.

      We added new sections to the text and figures at the beginning of the VCS MN and MNFinder subsections (Fig. S2 and Fig. S5) with specific information about how ground truth images and labels were generated for both modules and how these were broken up for training, validation, and testing.

      We also added information and images to explain how ground truth MN/nucleus associations were derived. In summary, we took advantage of the fact that 2xDendra-NLS is present at low levels in the cytoplasm to identify cell boundaries. This combined with a subconfluent cell population allowed us to unambiguously group MN and nuclei for 98% of MN, we estimate. These identifications were used to generate ground truth labels and analyze how well proximity defines MN/nuclei groups (Fig.s S1 and S2).

      (3) Overall, I find the sections long and more subtitles would help me better navigate the manuscript.

      Where possible, we have added subtitles.

      (4) Everything following "To train the model, H2B channel images were passed to a Deep Retina neural net ..." is fully automated, it seems to me. Thus, there seems to be no human intervention to correct the output before it is used to train the neural network. Therefore, I do not understand why a neural network was trained at all if the pipeline for creating ground truth labels worked fully automatically. At least, the explanations are insufficient.

      We apologize for the initial lack of clarity in the text and included additional details in the revision. We used the Deep Retina segmenter to crop the raw images to areas around individual nuclei to accelerate ground truth labeling of MN. A trained user went through each nucleus crop and manually labeled pixels belonging to MN to generate the ground truth dataset for training, validation, and imaging in VCS MN (Fig. S2A).

      (5) To my mind, the various metrics used to evaluate VCS MN reveal it not to be terribly reliable. Recall and PPV hover in the 70-80% range except for the PPV for MN+. It is what it is - but do the authors think one has to spend time manually correcting the output or do they suggest one uses it as is? I understand that for bulk transcriptomics, enrichment may be sufficient but for many other questions, where the wrong cell type could contaminate the population, it is not.

      Remarks in the Results section on what the various accuracies mean for different applications would be good (so one does not need to wait for the Discussion section).

      One of the strengths of the visual cell sorting system is that any image analysis pipeline can be used with it. We used VCS MN for the transcriptomics experiment, but for other applications a user could run visual cell sorting in conjunction with MNFinder for increased purity while maintaining a reasonable recall or use a pre-existing MN segmentation program that gives 100% purity but captures only a specific subgroup of micronucleated cells (e.g. PIQUE). 

      To maintain readability, especially with the expansion of the results sections, we kept the discussion of how we envision using visual cell sorting for other MN-based applications in the discussion section.

      (6) I am confused about what "cell" is referring to in much of the manuscript. Is it the nucleus + MNs only? Is it the whole cell, which one would ordinarily think it is? If so, are there additional widefield images, where one can discern cell boundaries? I found the section "MNFinder accurately ..." very hard to read and digest for this reason and other ambiguous wording. I suggest the authors take a fresh look at their manuscript and see whether the text can be improved for clarity. I did not find it an easy read overall, especially the computational part.

      After re-examining how “cell” was used, we updated the text to limit its use to the MNFinder arm tasked with identifying MN-nucleus associations where the convex hull defined by these objects is used to determine the “cell” boundary. In all other cases we have replaced cell with “nucleus” because, as the reviewer points out, that is what is being analyzed and converted. We hope this is clearer.

      (7) Post-FACS PPVs are not that great (Figure 3c). It depends on the question one wants to answer whether ~70% PPV is good enough. Again, would be good to comment on.

      We added discussion of this result to the revision. In summary, a likely reason for the reduced PPV is that, although we maintain the cells in buffer with a Cdk1 inhibitor, we know that some proportion of the cells go through mitosis post-sorting. Since MN are frequently reincorporated into the nucleus after mitosis (Hatch et al, 2013; Zhang et al., 2015), we expect this to reduce the MN+ population. Thus, we expect that the PPV in the RNAseq population is higher than what we can measure by analyzing post-sorted cells that have been plated and analyzed later.

      (8) I am thoroughly confused as to why the authors claim that their system works in the "absence of genetic perturbations" and why they emphasize the fact that their cells are non-transformed: They still needed a fluorescent label and they induce MNs with a chemical Mps1 inhibitor. (The latter is not a genetic manipulation, of course, but they still need to enrich MNs somehow. That is, their method has not been tested on a cell population in which MNs occur naturally, presumably at a very low rate, unless I missed something.) A more careful description of the benefits of their method would be good.

      We apologize for the confusion on these points and hope this is clarified in the revision. We were comparing our system, which can be made using transient transfection, if desired, to current tools that disambiguate aneuploidy and MN formation by deleting parts of chromosomes or engineering double strand breaks with CRISPR to generate single chromosome-specific missegregation events. Most of these systems require transformed cancer cells to obtain high levels of recombination. In contrast, visual cell sorting can isolate micronucleated cells from any cell line that can exogenously express a protein, including primary cells and non-transformed cells like RPE1s.

      Other minor points:

      (1) The authors should not refer to "H2B channels" but to "H2B-emiRFP703 channels". It may seem obvious to the authors but for someone reading the manuscript for the very first time, it was not. I was not sure whether there were additional imaging modalities used for H2B/nucleus/chromatin detection before I went back and read that only fluorescence images of H2B-emiRFP703 were used. To put it another way, the authors are detecting fluorescence, not histones -- unless I misunderstood something.

      To address this point, we altered the text to read “H2B-emiRFP703” when discussing images of this construct. For MNFinder some images were of cells expressing H2B-GFP, which has also been clarified.

      (2) If the level of zoom on my screen is such that I can comfortably read the text, I cannot see much in the figure panels. The features that I should be able to see are the size of a title. The image panels should be magnified.

      In the revision, the images are appended to the end at full resolution to overcome this difficulty. Thank you for your forbearance.

      Reviewer #2 (Recommendations for the authors):

      The methods are adequately explained. The Results text narrating experiments and data analysis is clear. Interpretation of a few results could be clarified and strengthened as explained below.

      (1) RNAseq experiments are a good proof of principle. To strengthen their interpretation in Figures 4 and 6, I would recommend the authors cite published work on checkpoint/MPS1 loss-induced chromosome missegregation (PMID: 18545697, PMID: 33837239, PMC9559752) and consider in their discussion the 'origin' and 'proportion' of micronucleated cells and irregularly shaped nuclei expected in RPE1 lines. This will help interpret Figure 6 findings on aneuploidy signature accurately. Not being able to see an MN-specific signature could be due to the way the biological specimen is presented with a mixture of cells with 'MN only' or 'rupture' or 'MN along with misshapen nuclei'. These features may all link to aneuploidy rather than 'MN' specifically.

      We appreciate the reviewer’s suggestion and added a new analysis of nuclear atypia after Mps1 inhibition in RPE1 cells to Fig. S1. Overall, we found that Mps1 inhibition significantly, but modestly, increased the proportion of misshapen nuclei and chromatin bridges. Multinucleate cells were so rare that instead of giving them their own category we included them in “misshapen nuclei.” These results are consistent with images of Msp1i treated RPE1 cells from He et al. 2019 and Santaguida et al. 2017 and distinct from the stronger changes in nuclear morphology observed after delaying mitosis by nocodazole or CENPE inhibition.

      We also found that the Deep Retina segmenter used to identify nuclei in VCS MN had a significant bias against highly lobulated nuclei (Fig. S2B) that led to misshapen nuclei being largely excluded from the RNAseq analyses. As a result we found no enrichment of misshapen nuclei, chromatin bridges, or dead/mitotic nuclear morphologies in MN+ compared to MN- nuclei in our RNASeq experiments (Fig. S9A).

      (2) As the authors clarify in the response letter, one round of ML is unlikely to result in fully robust software; additional rounds of ML with other markers will make the work robust. It will be useful to indicate other ML image analysis tools that have improved through such reiterations. They could use reviews on challenges and opportunities using ML approaches to support their statement. Also in the introduction, I would recommend labelling as 'rapid' instead of 'rapid and precise' method.

      We updated the text to reference review articles that discuss the benefit of additional training for increasing ML accuracy and changed the text to “rapid.”

      (3) The lack of live-cell studies does not allow the authors to distinguish the origin of MN (lagging chromatids or unaligned chromosomes). As explained in 1, considering these aspects in discussion would strengthen their interpretation. Live-cell studies can help reduce the dependencies on proximity maps (Figure S2).

      The revised text includes new references and data (Fig. S1E) demonstrating that Mps1 inhibition strongly biases towards whole chromosome missegregation and that MN are most likely to contain a single centromere positive chromosome rather than chromatin fragments or multiple chromosomes.

      (4) Mean Intersection over Union (mIOU) is a good measure to compare outcomes against ground truth. However, the mIOU is relatively low (Figure 2D) for HeLa-based functional genomics applications. It will help to discuss mIOU for other classifiers (non-MN classifiers) so that they can be used as a benchmark (this is important since the authors state in their response that they are the first to benchmark an MN classifier). There are publications for mitochondria, cell cortex, spindle, nuclei, etc. where IOU has been discussed.

      We added references to classifiers for other small cellular structures. We also evaluated major sources of error in MNFinder found that false negatives are enriched in very small MN (3 to 9 pixels, or about 0.4 µm<sup>2</sup> – 3 µm<sup>2</sup>, Fig. S6B). A similar result was obtained for VCS MN (Fig. S3B). Because small changes in the number of pixels identified in small objects can have outsized effects on mIoU scores, we suspect that this is exerting downward pressure on the mIoU value. Based on the PPV and recall values we identified, we believe that MNFinder is robust enough to use for functional genomics and screening applications with reasonable sample sizes.

      (5) Figure 5 figure legend title is an overinterpretation. MN and rupture-initiated transcriptional changes could not be isolated with this technique where several other missegregation phenotypes are buried (see point 1 above).

      We decided to keep the figure title legend based on our analysis of known missegregation phenotypes in Fig. S1 and S9 showing that there is no difference in major classes of nuclear atypia between MN+ and MN- populations in this analysis. Although we cannot rule out that other correlated changes exist, we believe that the title represents the most parsimonious interpretation.

      Minor comments

      (1) The sentence in the introduction needs clarification and reference. "However, these interventions cause diverse "off-target" nuclear and cellular changes, including chromatin bridges, aneuploidy, and DNA damage." Off-target may not be the correct description since inhibiting MPS1 is expected to cause a variety of problems based on its role as a master kinase in multiple steps of the chromosome segregation process. Consider one of the references in point 1 for a detailed live-cell view of MPS1 inhibitor outcomes.

      We have changed “off-target” to “additional” for clarity.

      (2) In Figure 3 or S3, did the authors notice any association between the cell cycle phase and MN or rupture presence? Is this possible to consider based on FACS outcomes or nuclear shapes?

      Previous work by our lab and others have shown that MN rupture frequency increases during the cell cycle (Hatch et al., 2013; Joo et al., 2023). Whether this is stochastic or regulated by the cell cycle may depend on what chromosome is in the MN (Mammel et al., 2021) and likely the cell line. Unfortunately, the H2B-emiRFP703 fluorescence in our population is too variable to identify cell cycle stage from FACS or nuclear fluorescence analysis.

      (3) Figure 5 - Please explain "MA plot".

      An MA plot, or log fold-change (M) versus average (A) gene expression, is a way to visualize differently expressed genes between two conditions in an RNASeq experiment and is used as an alternative to volcano plots. We chose them for our paper because most of the expression changes we observed were small and of similar significance and the MA plot spreads out the data compared to a volcano plot and allowed a better visualization of trends across the population.

      (4) Page 7: "our results strongly suggest that protein expression changes in MN+ and rupture+ cells are driven mainly by increased aneuploidy rather than cellular sensing of MN formation and rupture.". This is an overstatement considering the mIOU limits of the software tool and the non-exclusive nature of MN in their samples.

      We agree that we cannot rule out that an unknown masking effect is inhibiting our ability to observe small broad changes in transcription after MN formation or rupture. However, we believe we have minimized the most likely sources of masking effects, including nuclear atypia and large scale aneuploidy differences, and thus our interpretation is the most likely one.

      Reviewer #3 (Recommendations for the authors):

      Overall, the authors need to explain their methods better, define some technical terms used, and more thoroughly explain the parameters and rationale used when implementing these two protocols for identifying micronuclei; primarily as this is geared toward a more general audience that does not necessarily work with machine learning algorithms.

      (1) A clearer description in the methods as to how accuracy was calculated. Were micronuclei counted by hand or another method to assess accuracy?

      We significantly expanded the section on how the machine learning models were trained and tested, including how sensitivity and specificity metrics were calculated, in both the results and the methods sections. The code used to compare ground truth labels to computed masks is also now included in the MNFinder module available on the lab github page. 

      (2) Define positive predictive value.

      The text now says “the positive predictive value (PPV, the proportion of true positives, i.e. specificity) and recall (the proportion of MN found by the classifier, i.e. sensitivity)…”.

      (3) Why is it a problem to use the VCS MN at higher magnifications where undersegmentation occurs? What do the authors mean by diminished performance (what metrics are they using for this?).

      We have included a representative image and calculated mIoU and recall for 40x magnification images analyzed by MNFinder after rescaling in Fig. 2A. In summary, VCS MN only correctly labeled a few pixels in the MN, which was sufficient to call the adjacent nucleus “MN+” but not sufficient for other applications, such as quantifying MN area. In addition, VCS MN did much worse at identifying all the MN in 40x images with a recall, or sensitivity, metric of 0.36. We are not sure why. Developing MNFinder provided a module that was well suited to quantify MN characteristics in fixed cell images, an important use case in MN biology.

      (4) The authors should compare MN that are analyzed and not analyzed using these methods and define parameters. Is there a size limitation? Closeness to the main nucleus?

      We added two new figures defining what contributes to module error for both VCS MN (Fig. S3) and MNFinder (Fig. S6). For VCS MN, false negatives are enriched in very large or very small MN and tend to be dimmer and farther from the nucleus than true positives. False positives are largely misclassification of small dim objects in the image as MN. For MNFinder, the most missed class of MN are very small ones (3-9 px in area) and the majority of false positives are misclassifications of elongated nuclear blebs as MN.

      (5) Are there parameters in how confluent an image must be to correctly define that the micronucleus belongs to the correct cell? The authors discussed that this was calculated based on predicted distance. However, many factors might affect proper calling on MN. And the authors should test this by staining for a cytosolic marker and calculating accuracy.

      We updated the text with more information about how the cytoplasm was defined using leaky 2x-Dendra2-NLS signal to analyze the accuracy of MN/nucleus associations (Fig. S2G-H). In addition, we quantified cell confluency and distance to the first and second nearest neighbor for each MN in our training and testing image datasets. We found that, as anticipated, cells were imaged at subconfluent concentrations with most fields having a confluency around 30% cell coverage (Fig. S2E) and that the average difference in distance between the closest nucleus to an MN and the next closest nucleus was 3.3 fold (Fig. S2F). We edited the discussion section to state that the ability of MN/nuclear proximity to predict associations at high cell confluencies would have to be experimentally validated.

      (6) The authors measure the ratio of Dendra2(Red) v. Dendra2 (Green) in Figure 3B to demonstrate that photoconversion is stable. This measurement, to me, is confusing, as in the end, the authors need to show that they have a robust conversion signal and are able to isolate these data. The authors should directly demonstrate that the Red signal remains by analyzing the percent of the Red signal compared to time point 0 for individual cells.

      We found a bulk analysis to be more powerful than trying to reidentify individual cells due to how much RPE1 cells move during the 4 and 8 hours between image acquisitions. In addition, we sort on the ratio between red and green fluorescence per cell, rather than the absolute fluorescence, to compensate for variation in 2xDendra-NLS protein expression between cells. Therefore, demonstrating that distinct ratios remained present throughout the time course is the most relevant to the downstream analysis.

      To address the reviewer’s concern, we replotted the data in Fig. 3B to highlight changes over time in the raw levels of red and green Dendra fluorescence (Fig. S7D). As expected, we see an overall decrease in red fluorescence intensity, and complementary increase in green fluorescence intensity, over 8 hours, likely due to protein turnover. We also observe an increase in the number of nuclei lacking red fluorescence. This is expected since the well was only partially converted and we expect significant numbers of unconverted cells to move into the field between the first image and the 8 hour image.

      (7) The authors isolate and subsequently use RNA-sequencing to identify changes between Mps1i and DMSO-treated cells. One concern is that even with the less stringent cut-off of 1.5 fold there is a very small change between DMSO and MPS1i treated cells, with only 63 genes changing, none of which were affected above a 2-fold change. The authors should carefully address this, including why their dataset sees changes in many more pathways than in the He et al. and Santaguida et al. studies. Is this due to just having a decreased cut-off?

      The reviewer correctly points out that we observed an overall reduction in the strength of gene expression changes between our dataset of DMSO versus Mps1i treated RPE1 cells compared to similar studies. We suggest a couple reasons for this. One is that the log<sub>2</sub> fold changes observed in the other studies are not huge and vary between 2.5 and -3.8 for He et al., 3.3 and -2.3 for Santaguida et al., and -0.8 and 1.6 for our study. This variability is within a reasonable range for different experimental conditions and library prep protocols. A second is that our protocol minimizes a potential source of transcriptional change – nuclear lobulation – that is present in the other datasets.

      For the pathway analysis we did not use a fold-change cut-off for any data set, instead opting to include all the genes found to be significantly different between control and Mps1i treated cells for all three studies. Our read-depth was higher than that of the two published experiments, which could contribute to an increased DEG number. However, we hypothesize that our identification of a broader number of altered pathways most likely arises from increased sensitivity due to the loss of covering signal from transcriptional changes associated with increased nuclear atypia. Additional visual cell sorting experiments sorting on misshapen nuclei instead of MN would allow us to determine the accuracy of this hypothesis.

      (8) Moreover, clustering (in Figure 5E) of the replicates is a bit worrisome as the variances are large and therefore it is unclear if, with such large variance and low screening depth, one can really make such a strong conclusion that there are no changes. The authors should prove that their conclusion that rupture does not lead to large transcriptional changes, is not due to the limitations of their experimental design.

      We agree with the reviewers that additional rounds of RNAseq would improve the accuracy of our transcriptomic analysis and could uncover additional DEGs. However, we believe the overall conclusion to be correct based on the results of our attempt to validate changes in gene expression by immunofluorescence. We analyzed two of the most highly upregulated genes in the ruptured MN dataset, ATF3 and EGR1. Although we saw a statistically significant increase in ATF3 intensity between cells without MN and those with ruptured MN, the fold change was so small compared to our positive control (100x less) that we believe it is it is more consistent with a small increase in the probability of aneuploidy rather than a specific signature of MN rupture.

      (9) The authors also need to address the fact that they are using RPE-1 cells more clearly and that the lack of effect in transcriptional changes may be simply due to the loss of cGAS-STING pathway (Mackenzie et al., 2017; Harding et al., 2017; etc.).

      As we discuss above in the public comments section, the literature is clear that MN do not activate cGAS in the first cell cycle after their formation, even upon rupture. Therefore, we do not expect any changes in our results when applied to cGAS-competent cells. However, this expectation needs to be experimentally validated, which we plan to address in upcoming work.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      The paper explored cross-species variance in albumin glycation and blood glucose levels in the function of various life-history traits. Their results show that

      (1) blood glucose levels predict albumin gylcation rates

      (2) larger species have lower blood glucose levels

      (3) lifespan positively correlates with blood glucose levels and

      (4) diet predicts albumin glycation rates.

      The data presented is interesting, especially due to the relevance of glycation to the ageing process and the interesting life-history and physiological traits of birds. Most importantly, the results suggest that some mechanisms might exist that limit the level of glycation in species with the highest blood glucose levels.

      While the questions raised are interesting and the amount of data the authors collected is impressive, I have some major concerns about this study:

      (1) The authors combine many databases and samples of various sources. This is understandable when access to data is limited, but I expected more caution when combining these. E.g. glucose is measured in all samples without any description of how handling stress was controlled for. E.g glucose levels can easily double in a few minutes in birds, potentially introducing variation in the data generated. The authors report no caution of this effect, or any statistical approaches aiming to check whether handling stress had an effect here, either on glucose or on glycation levels.

      (2) The database with the predictors is similarly problematic. There is information pulled from captivity and wild (e.g. on lifespan) without any confirmation that the different databases are comparable or not (and here I'm not just referring to the correlation between the databases, but also to a potential systematic bias (e.g. captivate-based sources likely consistently report longer lifespans). This is even more surprising, given that the authors raise the possibility of captivity effects in the discussion, and exploring this question would be extremely easy in their statistical models (a simple covariate in the MCMCglmms).

      (3) The authors state that the measurement of one of the primary response variables (glycation) was measured without any replicability test or reference to the replicability of the measurement technique.

      (4) The methods and results are very poorly presented. For instance, new model types and variables are popping up throughout the manuscript, already reporting results, before explaining what these are e.g. results are presented on "species average models" and "model with individuals", but it's not described what these are and why we need to see both. Variables, like "centered log body mass", or "mass-adjusted lifespan" are not explained. The results section is extremely long, describing general patterns that have little relevance to the questions raised in the introduction and would be much more efficiently communicated visually or in a table.

      Reviewer #2 (Public review):

      Summary

      In this extensive comparative study, Moreno-Borrallo and colleagues examine the relationships between plasma glucose levels, albumin glycation levels, diet, and lifehistory traits across birds. Their results confirmed the expected positive relationship between plasma blood glucose level and albumin glycation rate but also provided findings that are somewhat surprising or contradicting findings of some previous studies (relationships with lifespan, clutch mass, or diet). This is the first extensive comparative analysis of glycation rates and their relationships to plasma glucose levels and life history traits in birds that are based on data collected in a single study and measured using unified analytical methods.

      Strengths

      This is an emerging topic gaining momentum in evolutionary physiology, which makes this study a timely, novel, and very important contribution. The study is based on a novel data set collected by the authors from 88 bird species (67 in captivity, 21 in the wild) of 22 orders, which itself greatly contributes to the pool of available data on avian glycemia, as previous comparative studies either extracted data from various studies or a database of veterinary records of zoo animals (therefore potentially containing much more noise due to different methodologies or other unstandardised factors), or only collected data from a single order, namely Passeriformes. The data further represents the first comparative avian data set on albumin glycation obtained using a unified methodology. The authors used LC-MS to determine glycation levels, which does not have problems with specificity and sensitivity that may occur with assays used in previous studies. The data analysis is thorough, and the conclusions are mostly wellsupported (but see my comments below). Overall, this is a very important study representing a substantial contribution to the emerging field of evolutionary physiology focused on the ecology and evolution of blood/plasma glucose levels and resistance to glycation.

      Weaknesses

      My main concern is about the interpretation of the coefficient of the relationship between glycation rate and plasma glucose, which reads as follows: "Given that plasma glucose is logarithm transformed and the estimated slope of their relationship is lower than one, this implies that birds with higher glucose levels have relatively lower albumin glycation rates for their glucose, fact that we would be referring as higher glycation resistance" (lines 318-321) and "the logarithmic nature of the relationship, suggests that species with higher plasma glucose levels exhibit relatively greater resistance to glycation" (lines 386-388). First, only plasma glucose (predictor) but not glycation level (response) is logarithm transformed, and this semi-logarithmic relationship assumed by the model means that an increase in glycation always slows down when blood glucose goes up, irrespective of the coefficient. The coefficient thus does not carry information that could be interpreted as higher (when <1) or lower (when >1) resistance to glycation (this only can be done in a log-log model, see below) because the semi-log relationship means that glycation increases by a constant amount (expressed by the coefficient of plasma glucose) for every tenfold increase in plasma glucose (for example, with glucose values 10 and 100, the model would predict glycation values 2 and 4 if the coefficient is 2, or 0.5 and 1 if the coefficient is 0.5). Second, the semi-logarithmic relationship could indeed be interpreted such that glycation rates are relatively lower in species with high plasma glucose levels. However, the semi-log relationship is assumed here a priori and forced to the model by log-transforming only glucose level, while not being tested against alternative models, such as: (i) a model with a simple linear relationship (glycation ~ glucose); or (ii) a loglog model (log(glycation) ~ log(glucose)) assuming power function relationship (glycation = a * glucose^b). The latter model would allow for the interpretation of the coefficient (b) as higher (when <1) or lower (when >1) resistance in glycation in species with high glucose levels as suggested by the authors.

      Besides, a clear explanation of why glucose is log-transformed when included as a predictor, but not when included as a response variable, is missing.

      We apologize for missing an answer to this part before. Indeed, glucose is always log transformed and this is explained in the text.

      The models in the study do not control for the sampling time (i.e., time latency between capture and blood sampling), which may be an important source of noise because blood glucose increases because of stress following the capture. Although the authors claim that "this change in glucose levels with stress is mostly driven by an increase in variation instead of an increase in average values" (ESM6, line 46), their analysis of Tomasek et al.'s (2022) data set in ESM1 using Kruskal-Wallis rank sum test shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values, not only higher variation.

      Although the authors calculated the variance inflation factor (VIF) for each model, it is not clear how these were interpreted and considered. In some models, GVIF^(1/(2*Df)) is higher than 1.6, which indicates potentially important collinearity; see for example https://www.bookdown.org/rwnahhas/RMPH/mlr-collinearity.html). This is often the case for body mass or clutch mass (e.g. models of glucose or glycation based on individual measurements).

      It seems that the differences between diet groups other than omnivores (the reference category in the models) were not tested and only inferred using the credible intervals from the models. However, these credible intervals relate to the comparison of each group with the reference group (Omnivore) and cannot be used for pairwise comparisons between other groups. Statistics for these contrasts should be provided instead. Based on the plot in Figure 4B, it seems possible that terrestrial carnivores differed in glycation level not only from omnivores but also from herbivores and frugivores/nectarivores.

      Given that blood glucose is related to maximum lifespan, it would be interesting to also see the results of the model from Table 2 while excluding blood glucose from the predictors. This would allow for assessing if the maximum lifespan is completely independent of glycation levels. Alternatively, there might be a positive correlation mediated by blood glucose levels (based on its positive correlations with both lifespan and glycation), which would be a very interesting finding suggesting that high glycation levels do not preclude the evolution of long lifespans.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Line 84: "glycation scavengers" such as polyamines - can you specify what these polyamines do exactly?

      A clarification of what we mean with "glycation scavengers" is added.

      (2) Line 87-89: specify that the work of Wein et al. and this sentence is about birds.

      This is now clarified.

      (3) Line 95: "88 species" add "OF BIRDS". Also, I think it would be nice if you specified here that you are relying on primary data.

      This is now clarified (line 96).

      (4) Line 90-119: I find this paragraph very long and complex, with too many details on the methodology. For instance, I agree with listing your hypothesis, e.g. that with POL, but then what variables you use to measure the pace of life can go in the materials and methods section (so all lines between 112-119).

      This is explained here as a previous reviewer considered this presentation was indeed needed in the introduction.

      (5) Line 122-124: The first sentence should state that you collected blood samples from various sources, and list some examples: zoos? collaborators? designated wild captures? Stating the sample size before saying what you did to get them is a bit weird. Besides, you skipped a very important detail about how these samples were collected, when, where, and using what protocols. We know very well, that glucose levels can increase quickly with handling stress. Was this considered during the captures? Moreover, you state that you had 484 individuals, but how many samples in total? One per individual or more?

      We kindly ask the reviewer to read the multiple supplementary materials provided, in which the questions of source of the samples, potential stress effects and sample sizes for each model are addressed. All individuals contributed with one sample. More details about the general sources employed are given now in lines 125-127.

      (6) Line 135-36: numbers below 10 should be spelled out.

      Ok. Now that is changed.

      (7) Line 136: the first time I saw that you had both wild and captive samples. This should be among the first things to be described in the methods, as mentioned above.

      As stated above, details on this are included in the supplementary materials, but further clarifications have now been included in the main text (question 5).

      (8) Line 137-138: not clear. So you had 46 samples and 9 species. But what does the 3-3-3 sample mean? or for each species you chose 9 samples (no, cause that would be 81 samples in total)?

      This has now been clarified (lines 139-140).

      (9) Line 139-141: what methodological constraints? Too high glucose levels? Too little plasma?

      There were cases in which the device (glucometer) produced an unspecific error. This did not correspond to too high nor too low glucose levels, as these are differently signalled errors. Neither the manual nor the client service provided useful information to discern the cause. This may perhaps be related to the composition of the plasma of certain species, interfering with the measurement. Some clarifications have been added (lines 143-146).

      (10) Line 143: should be ZIMS.

      Corrected.

      (11) Line 120-148: you generally talk about individuals here, but I feel it would be more precise to use 'samples'.

      The use is totally interchangeable, as we never measured more than one sample for a given individual within this study. Besides, in some cases, saying “sample” could result less informative.

      (12) Line 150: missing the final number of measurements for glucose and glycation.

      Please, read the ESM6 (Table ESM6.1), where this information is given.

      (13) Line 154-155: so you took multiple samples from the same individual? It's the first time the text indicates so. Or do you mean technical replicates were not performed on the same samples?

      As previously indicated, each individual included only one sample. Replicates were done only for some individuals to validate the technique, as it would be unfeasible to perform replicates of all of them. This part of the text is referring to the fact that not all samples were analysed at the same time, as it takes a considerable amount of time, and the mass spectrometry devices are shared by other teams and project. Clarifications in this sense are now added (lines 160-163).

      (14) Line 171-172: "After realizing that diet classifications from AVONET were not always suitable for our purpose" - too informal. Try rephrasing, like "After determining that AVONET diet classifications did not align with our research needs...", but you still need to specify what was wrong with it and what was changed, based on what argument?

      The new formulation suggested by the reviewer has now been applied (lines 181-183). The details are given in the ESM6, as indicated in the text. 

      (15) Line 174-176: You start a new paragraph, talking about missing values, but you do not specify what variable are you talking about. you talk about calculating means, but the last variable you mentioned was diet, so it's even more strange.

      We refer to life history traits. It has now been clarified in the text (line 185).

      (16) Line 177: what longevity records? Coming from where? How did you measure longevity? Maximum lifespan ever recorded? 80-90% longevity, life expectancy???

      We refer to maximum lifespan, as indicated in the introduction and in every other case throughout the manuscript. Clarifications have now been introduced (188-190).

      (17) Line 180-183: using ZIMS can be problematic, especially for maximum longevity. There are often individuals who had a wrong date of birth entered or individuals that were failed to be registered as dead. The extremes in this database are often way off. If you want to combine though, you can check the correlation of lifespans obtained from different sources for the overlapping species. If it's a strong correlation it can be ok, but intuitively this is problematic.

      The species for which we used ZIMS were those for which no other databases reported any values. We could try correlations for other species, but this issue is not necessarily restricted to ZIMS, as the primary origin of the data from other databases is often difficultly traceable. Also, ZIMS is potentially more updated that some of the other databases, mainly Amniotes database, from which we rely the most, as it includes the highest number of species in the most easily accessible format.

      (18) Line 181-186: in ZIMS you calculate the average of the competing records, otherwise you choose the max. Why use different preferences for the same data?

      This constitutes a misunderstanding, for which we include clarifications now (line 196). We were referring here to the fact that for maximum lifespan the maximum is always chosen, while for other variables an average is calculated. 

      (19) Line 198: Burn-in and thinning interval is quite low compared to your number of iterations. How were model convergences checked?

      Please, check ESM1.

      (20) Line 201-203: What's the argument using these priors? Why not use noninformative ones? Do you have some a priori expectations? If so, it should be explained.

      Models have now been rerun with no expectations on the variance partitions so the priors are less informative, given the lack of firm expectations, and results are similar. Smaller nu values are also tried.

      (21) Line 217: "carried" OUT.

      Corrected (now in line 229).

      (22) Line 233-234: "species average model" - what is this? it was not described in the methods.

      Please, read the ESM6.

      (23) Line 232-246: (a) all this would be better described by a table or plot. You can highlight some interesting patterns, but describing it all in the text is not very useful I think, (b) statistically comparing orders represented by a single species is a bit odd.

      (a) Figure 1 shows this graphically, but this part was found to be quite short without descriptions by previous reviewers. (b) We recognise this limitation, but this part is not presented as one of the main results of the article, and just constitutes an attempt to illustrate very general patterns, in order to guide future research, as in most groups glycation has never been measured, so this still constitutes the best illustration of such patterns in the literature.

      (24) Line 281: the first time I saw "mass-adjusted maximum lifespan" - what is this, and how was it calculated? It should be described in the methods. But in any case, neither ratios, nor residuals should be used, but preferably the two variables should be entered side by side in the model.

      Please, see ESM6 for the explanations and justifications for all of this.

      (25) Line 281: there was also no mention of quadratic terms so far. How were polynomial effects tested/introduced in the models? Orthogonal polynomials? or x+ x^2?

      Please, read ESM6.

      (26) Table 1. What is 'Centred Log10Body mass', should be added in the methods.

      Please, read ESM6.

      (27) Table 1: what's the argument behind separating terrestrial and aquatic carnivores?

      This was mostly based on the a priori separation made in AVONET, but it is also used in a similar way by Szarka and Lendvai 2024 (comparative study on glucose in birds), where differences in glucose levels between piscivorous and carnivorous are reported. We had some reasons to think that certain differences in dietary nutrient composition, as discussed later, can make this difference relevant.

      (28) Table 1: The variable "Maximum lifespan" is discussed and plotted as 'massadjusted maximum lifespan' and 'residual maximum lifespan'. First, this is confusing, the same name should be used throughout and it should be defined in the methods section. Second, it seems that non-linear effects were tested by using x + x^2. This is problematic statistically, orthogonal polynomials should be used instead (check polyfunction in R). Also, how did you decide to test for non-linear effects in the case of lifespan but not the other continuous predictors? Should be described in the methods again.

      Please, read ESM6. Data exploration was performed prior to carry out these models. Orthogonal polynomials were considered to difficult the interpretation of the estimates and therefore the patterns predicted by the models, so raw polynomials were used. Clarifications have now been included in line 297.

      (29) Figure 2. From the figure label, now I see that relative lifespan is in fact residual. This is problematic, see Freckleton, R. P. (2009). The seven deadly sins of comparative analysis. Journal of evolutionary biology, 22(7), 1367-1375. Using body mass and lifespan side by side is preferred. This would also avoid forcing more emphasis on body mass over lifespan meaning that you subjectively introduce body mass as a key predictor, but lifespan and body size are highly correlated, so by this, you remove a large portion of variance that might in fact be better explained by lifespan.

      Please, read ESM6 for justifications on the use of residuals.

      Reviewer #2 (Recommendations for the authors):

      (1) If the semi-logarithmic relationship (glycation ~ log10(glucose)) is to be used to support the hypothesis about higher glycation resistance in species with high blood glucose (lines 318-321 and 386-388), it should be tested whether it is significantly better than the model assuming a simple linear relationship (i.e., glycation ~ glucose). Alternatively, if the coefficient is to be used to determine whether glycation rate slows down or accelerates with increasing glucose levels, log-log model (log10(glycation) ~ log10(glucose)) assuming power function relationship (glycation = a * glucose^b) should be used (as is for example in the literature about relationships between metabolic rates and body size). Probably the best approach would be to compare all three models (linear, semi-logarithmic, and log-log) and test if one performs significantly better. If none of them, then the linear model should be selected as the most parsimonious.

      Different options (linear, both semi-logarithmic combinations and log-log) have now been tested, with similar results. All of the models confirm the pattern of a significant positive relationship between glucose and glycation. Moreover, when standardizing the variables (both glucose and glycation, either log transformed or not), the estimate of the slope is almost equal for all the models. It is also lower than one, which in the case of both the linear and log-log confirms the stated prediction. The log-log model, showing a much lower DIC than the linear version, is now shown as the final model.

      (2) ESM6, line 46: Please note that Kruskal-Wallis rank sum test in ESM1 shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values (not only higher variation). With this in mind, what is the argument here about increased variation being the main driver of stress-induced change in glucose levels based on? It seems that both the median values and variation differ between baseline and stress-induced levels, and this should be acknowledged here.

      As discussed in the public answers, Kruskal Wallis does not allow to determine differences in mean, but just says that the groups are “different” (implicitly, in their ranksums, which does not mean necessarily in mean), while the Levene test performed signals heteroskedasticity. This makes this feature of the data analytically more grounded. Of course, when looking at the data, a higher mean can be perceived, but nothing can be said about its statistical significance. Still, some subtle changes have been introduced in corresponding section of the ESM6.

      (3) Have you recorded the sampling times? If yes, why not control them in the models? It is at least highly advisable to include the sampling times in the data (ESM5).

      As indicated in ESM6 lines 42-43, we do not have sampling times for most of the individuals (only zebra finches and swifts), so this cannot be accounted for in the models.

      (4) If sampling times will remain uncontrolled statistically, I recommend mentioning this fact and its potential consequences (i.e., rather conservative results) in the Methods section of the main text, not only in ESM6.

      A brief description of this has now been included in the main text (lines 129-132), referencing the more detailed discussion on the supplementary materials. Some subtle changes have also been included in the “Possible effects of stress” section of the ESM6.

      (5) ESM6, lines 52-53: The lower repeatability in Tomasek et al.' study compared to your study is irrelevant to the argument about the conservative nature of your results (the difference in repeatability between both studies is most probably due to the broader taxonomic coverage of the current study). The important result in this context is that repeatability is lower when sampling time is not considered within Tomasek et al's data set (ESM1). Therefore, I suggest rewording "showing a lower species repeatability than that from our data" to "showing lower species repeatability when sampling time is not considered" to avoid confusion. Please also note that you refer here to species repeatability but, in ESM1, you calculate individual repeatability. Nevertheless, both individual and species repeatabilities are lower when not controlling for sampling time because the main driver, in that case, is an increased residual variance.

      We recognize the current confusion in the way the explanation is exposed, and have significantly changed the redaction of the section. However, we would like to indicate that ESM1 shows both species and individual repeatability (for Tomasek et al. 2022 data, for ours only species as we do not have repeated individual values). Changes are now made to make it more evident.

      (6) I recommend providing brief guidelines for the interpretation of VIFs to the readers, as well as a brief discussion of the obtained values and their potential importance.

      Thank you for the recommendation. We included a brief description in lines 230-231. Also in the results section (lines 389-393).

      (7) Line: 264: Please note that the variance explained by phylogeny obtained from the models with other (fixed) predictors does not relate to the traits (glucose or glycation) per se but to model residuals.

      We appreciate the indication, and this has been rephrased accordingly (lines 280-286).

      (8) Change the term "confidence intervals" to "credible intervals" throughout the paper, since confidence interval is a frequentist term and its interpretations are different from Bayesian credible interval.

      Thank you for the remark, this has now been changed.

      (9) Besides lifespan, have you also considered quadratic terms for body mass? The plot in Figure 2A suggests there might be a non-linear relationship too.

      A quadratic component of body mass has not shown any significant effect on glucose in an alternative model. Also, a model with linear instead of log glucose (as performed in other studies) did not perform better by comparing the DICs, despite both showing a significant relationship between glucose and body mass. Therefore, this model remains the best option considered as presented in the manuscript.

      (10) ESM6, lines 115-116: It is usually recommended that only factors with at least 6 or 8 levels are included as random effects because a lower number of levels is insufficient for a good estimation of variance.

      In a Bayesian approach this does not apply, as random and fixed factors are estimated similarly. 

      (11) Typos and other minor issues:

      a) Line 66: Delete "related".

      b) Figure 2: "B" label is missing in the plot.

      c) Reference 9: Delete "Author".

      d) References 15 and 83 are duplicated. Keep only ref. 83, which has the correct citation details.

      e) ESM6, line 49: Change "GLLM" to "GLMM".

      Thank you for indicating this. Now it’s corrected.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Response to Reviewer 2’s comments:

      I am concerned that the results in Figure 8D may not be correct, or that the authors may be mis-interpreting them. From my reading of the paper they cite (Lammers & Flamholz 2023), the equilibrium sharpness limit for the network they consider in Figure 8 should be 0.25. But both solutions shown in Figure 8D fall below this limit, which means that they have sharpness levels that could have been achieved with no energy expenditure. If this is the case, then it would imply that while both systems do dissipate energy, they are not doing so productively; meaning that the same results could be achieved while holding Phi=0.

      I acknowledge that this could be due to a difference in how they measure sharpness, but wanted to raise it here in case it is, in fact, a genuine issue with the analysis.There should be an easy fix for this: just set the sharper "desired response" curve in 8b to be such that it demands non-equilibrium sharpness levels (0.25<S<0.5).

      Thank you for raising this point regarding the interpretation of our results in Figure 8D. We agree that if the equilibrium sharpness limit for this particular network is around 0.25 (as shown by Lammers & Flamholz 2023), then achieving a sharpness below this threshold could, in principle, be accomplished without any energy expenditure. However, in our current design approach, the loss function is solely designed to enforce agreement with a target mean mRNA level at different input concentrations; it does not explicitly constrain energy dissipation, noise, or other metrics. Consequently, the DGA has no built-in incentive to minimize or optimize energy consumption, which means the resulting solutions may dissipate energy without exceeding the equilibrium sharpness limit.

      In other words, the same input–output relationship could theoretically be achieved with \Phi =0 if an explicit constraint or regularization term penalizing energy usage had been included. As noted, adding such a term (e.g., penalizing \Phi^2) is conceptually straightforward but falls outside the scope of this study. Our primary goal is to demonstrate the flexibility of the DGA in designing a desired response, rather than to delve into energy–sharpness trade-offs or other biological considerations

      While we appreciate the suggestion to set a higher target sharpness that exceeds the equilibrium limit, we believe the current example effectively demonstrates the DGA’s ability to design circuits with desired input-output relationships, which is the primary focus of this study. Researchers interested in optimizing energy efficiency, burst size, burst frequency, noise, response time, mutual information, or other system properties can easily extend our approach by incorporating additional terms into the loss function to target these specific objectives.

      We hope this explanation addresses your concern and clarifies that the manuscript provides sufficient context for readers to interpret the results in Figure 8D correctly.


      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      We thank Reviewer #1 for their thoughtful feedback and appreciation of the manuscript's clarity. Our primary goal is to introduce the DGA  as a foundational tool for integrating stochastic simulations with gradient-based optimization. While we recognize the value of providing detailed comparisons with existing methods and a deeper analysis of the DGA’s limitations (such as rare event handling), these topics are beyond the scope of this initial work. Our focus is on presenting the core concept and demonstrating its potential, leaving more extensive evaluations for future research.

      Reviewer #2 (Public review):

      We thank Reviewer #2 for their detailed and constructive feedback. We appreciate the recognition of the DGA as a significant conceptual advancement for stochastic biochemical network analysis and design.

      Weaknesses:

      (1) Validation of DGA robustness in complex systems:

      Our primary goal is to introduce the DGA framework and demonstrate its feasibility. While validation on high-dimensional and non-steady-state systems is important, it is beyond the scope of this initial work. Future studies may improve scalability by employing techniques such as dynamically adjusting the smoothness of the DGA's approximations during simulation or using surrogate models that remain differentiable but more accurately capture discrete behaviors in critical regions, thus preserving gradient computation while improving accuracy.

      (2) Inference accuracy and optimization:

      We acknowledge that the non-convex loss landscape in the DGA can hinder parameter inference and convergence to global minima, as seen in Figure 5A. While techniques like multi-start optimization or second-order methods (e.g., L-BFGS) could improve performance, our focus here is on establishing the DGA framework. We plan to explore better optimization methods in future work to improve the accuracy of parameter inference in complex systems.

      (3) Use of simple models for demonstration:

      We selected well-understood systems to clearly illustrate the capabilities of the DGA. These examples were intended to demonstrate how the DGA can be applied, rather than to solve problems better addressed by analytical methods. Applying DGA to more complex, analytically intractable systems is an exciting avenue for future work, but introducing the method was our main objective in this study.

      Reviewer #3 (Public review):

      We thank the reviewer for their detailed and insightful feedback. We appreciate the recognition of the DGA as a significant advancement for enabling gradient-based optimization in stochastic systems.

      Weaknesses:

      (1) Application beyond steady-state analysis

      We acknowledge the limitation of focusing solely on steady-state properties. To extend the DGA for analyzing transient dynamics, time-dependent loss functions can be incorporated to capture system evolution over time. This could involve aligning simulated trajectories with experimental time-series data or using moment-matching across multiple time points. 

      (2) Numerical instability in gradient computation

      The reviewer correctly highlights that large sharpness parameters (a and b) in the sigmoid and Gaussian approximations can induce numerical instability due to vanishing or exploding gradients. To address this, adaptive tuning of a and b during optimization could balance smoothness and accuracy. Additionally, alternative smoothing functions (e.g., softmax-based reaction selection) and gradient regularization techniques (such as gradient clipping and trust-region methods) could improve stability and convergence.

      Reviewer #1 (recommendations):

      We thank the reviewer for their thoughtful and constructive feedback on our manuscript. Below, we address each of the comments and suggestions raised.

      Main points:

      (1) It would have been useful to have a brief discussion, based on a concrete example, of what can be achieved with the DGA and is totally beyond the reach of the Gillespie algorithm and the numerous existing stochastic simulation methods.

      Thank you for your comment. We would like to clarify that the primary aim of this work is to introduce the DGA and demonstrate its feasibility for tasks such as parameter estimation and network design. Unlike traditional stochastic simulation methods, the DGA’s differentiable nature enables gradient-based optimization, which is not possible with the classical Gillespie algorithm or its variants.

      (2) As often with machine learning techniques, there is a sense of black box, with a lack of mathematical details of the proposed method: as opposite to the exact Gillespie algorithm, whose foundations lie on solid mathematical results (exponentially-distributed waiting times of continuous-time Markov processes), the DGA involves uncontrolled approximations, that are only briefly mentioned in the paper. For instance, it is currently simply noted that "the approximations introduced by the DGA may be pronounced in more complex settings such as the calculation of rare events", without specifying how limiting these errors are. It would be useful to include a clearer and more comprehensive discussion of the limitations of the DGA: When does it work accurately? What are the approximations/errors and can they be controlled? When is it worth paying the price for those approximations/errors, and when is it better to stick to the Gillespie algorithm? Is this notably the case for problems involving rare events? Clearly, these are difficult questions, and the answers are problem specific. However, it would be important to draw the readers' attention on the issues, especially if the DGA is presented as a potentially significant tool in computational and synthetic biology.

      We acknowledge the importance of discussing the limitations of the DGA in more detail. While we have noted that the approximations introduced by the DGA may impact its accuracy in certain scenarios, such as rare-event problems, a deeper exploration of these trade-offs is outside the scope of this work. Instead, we provide sufficient context in the manuscript to guide readers on when the DGA is appropriate.

      (3) The DGA is here introduced and discussed in the context of non-spatial problems (simple gene regulatory networks). However, numerous problems in the life sciences and computational/synthetic biology, involve stochasticity and spatial degrees of freedom (e.g. for problems involving diffusion, migration, etc). It is notoriously challenging to use the Gillespie algorithm to efficiently simulate stochastic spatial systems, especially in the context of rare events (e.g., extinction or fixation problems). It would be useful to comment on whether, and possibly how, the DGA can be used to efficiently simulate stochastic spatial systems, and if it would be better suited than the Gillespie algorithm for this purpose.

      Thank you for pointing this out. Although our current work centers on non-spatial systems, we agree that many biological contexts incorporate both stochasticity and spatial degrees of freedom. Extending the DGA to efficiently simulate such systems would indeed require substantial modifications—for instance, coupling it with reaction-diffusion frameworks or spatial master equations. We believe this is an exciting direction for future research and mention it briefly in the discussion as a potential extension.

      Minor suggestions:

      (1) After Eq.(10): it would be useful to explain and motivate the choice of the ratio JSD/H.

      Done.

      (2) On page 6, just below the caption of Fig.4: it would be useful to clarify what is actually meant by "... convergence towards the steady-state distribution of the exact Gillespie simulation, which is obtained at a simulation time of 10^4".

      Done.

      (3) At the end of Section B on page 7: please clarify what is meant here by "soft directions".

      Done.

      Reviewer #2 (recommendations):

      We thank the reviewer for their thoughtful comments and constructive feedback. Below, we address each of the comments/suggestions.

      Main points:

      (1) Enumerate the conditions under which DGA assumptions hold (and when they do not). There is currently not enough information for the interested reader to know whether DGA would work for their system of interest. Without this information, it is difficult to assess what the true scope of DGA's impact will be. One simple idea would be to test DGA performance along two axes: (i) increasing number of model states and (ii) presence/absence of non-steady state dynamics. I acknowledge that these are very open-ended directions, but looking at even a single instance of each would greatly strengthen this work. Alternatively, if this is not feasible, then the authors should provide more discussion of the attendant difficulties in the main text.

      We agree that a detailed exploration of the conditions under which the DGA assumptions hold would be a valuable addition to the field. However, this paper primarily aims to introduce the DGA methodology and demonstrate its proof-of-concept applications. A comprehensive analysis along axes such as increasing model states or non-steady-state dynamics, while important, would require significant additional simulations and is beyond the scope of this work. In Appendix A, we have discussed the trade-off between accuracy and numerical stability. Additionally, we encourage future users to tune the hyperparameters a and b for their specific systems.

      (2) Demonstrate DGA performance in a more complex biochemical system. Clearly the authors were aware that analytic solutions exist for the 2-state system in Figure 7, but it this is actually also the case (I think) for mean mRNA production rate of the non-equilibrium system in Figure 8. To really demonstrate that DGA is practically viable, I encourage the authors to seek out an interesting application that is not analytically tractable.

      We appreciate the suggestion to validate DGA on a more complex biochemical system. However, the goal of this study is not to provide an exhaustive demonstration of all possible applications but to introduce the DGA and validate it in systems where ground-truth comparisons are available. While the non-equilibrium system in Figure 8 might be analytically tractable, its complexity already provides a meaningful demonstration of DGA’s ability to optimize parameters and design systems. Extending this work to analytically intractable systems is an exciting direction for future studies, and we hope this paper will inspire others to explore these applications.

      (3) Take steps to improve the robustness of parameter optimization and error bar calculations. (3a) When the loss landscape is degenerate, shallow, or otherwise "difficult," a common solution is to perform multiple (e.g. 25-100) inference runs starting from different random positions in parameter space. Doing this, and then taking the parameter set that minimizes the loss should, in theory, lead to a more robust recovery of the optimal parameter set.

      (3b) It seems clear that the Hessian approximation is underestimating the true error in your inference results. One alternative is to use a "brute force" approach like bootstrap resampling to get a better estimate for the statistical dispersion in parameter estimates. But I recognize that this is only viable if the inference is relatively fast. Simply recovering the true minimum will, of course, also help.

      (3a) We acknowledge the challenge posed by degenerate or shallow loss landscapes during parameter optimization. While performing multiple inference runs from different initializations is a common strategy, this approach is computationally intensive. Instead, we rely on standard optimization techniques (e.g., ADAM) to find a robust local minimum. 

      (3b) Thank you for your comment. We agree that Hessian-based error bars can underestimate uncertainty, particularly in degenerate or poorly conditioned loss landscapes. While methods like bootstrap and Monte Carlo can provide more robust estimates, they can be computationally prohibitive for larger-scale simulations. A simpler reason for not using them is the high resource demand from repeated simulations, which quickly becomes infeasible for complex or high-dimensional models. We note these trade-offs between robust estimation and practicality as an important area for further exploration.

      Moderate comments:

      (1) Figure 7: is it possible to also show the inferred kon values? Specifically, it would be of interest to see how kon varies with repressor concentration.

      Thank you for the suggestion. We have updated Figure 7 to include the inferred kon values, showing their variation with the mean mRNA copy number. However, we could not plot them against repressor concentration due to the lack of available data.

      (2) Figure 8B & D: the authors claim that the sharper system dissipates more energy, but doesn't 8D show the opposite of this? More importantly, it does not look like either network drives sharpness levels that exceed the upper equilibrium limit cited in [36]. So it is not clear that it is appropriate to look at energy dissipation here. In fact, it is likely that equilibrium networks could produce the curves in 8B, and might be worth checking.

      Thank you for pointing this out. We realized that the plotted values in Figure 8D were incorrect, as we had mistakenly plotted noise instead of energy dissipation. The plot has now been corrected. 

      (3) Figure 8: I really like this idea of using DGA to "design" networks with desired input-output properties, but I wonder if you could explore more a biologically compelling use-case. Specifically, what about some kind of switch-like logic where, as the activator concentration increases, you have first 0 genes on, then 1 promoter on, then 2 promoters on. This would achieve interesting regulatory logic, and having DGA try to produce step functions would ensure that you force the networks to be maximally sharp (i.e. about double what you're currently achieving).

      Thank you for this intriguing suggestion. While the proposed switch-like logic use case is indeed compelling, implementing such a system would require significant work. This goes beyond the scope of the current study, which focuses on demonstrating the feasibility of DGA for network design with simple input-output properties.

      Minor comments:

      (1) Figure 4B & C: the bar plots do not do a good job conveying the points made by the authors. Consider alternatives, such as scatter plots or box plots that could convey inference uncertainty.

      Done.

      (2) Figure 4B: consider using a log y-axis.

      The y-axis in Figure 4B is already plotted on a log scale.

      (3) Figure 4D is mentioned prior to 4C in the text. Consider reordering.

      Done. 

      (4) Figure 5B: it is difficult to assess from this plot whether or not the landscape is truly "flat," as the authors claim. Flat relative to what? Consider alternative ways to convey your point.

      Thank you for highlighting this ambiguity. By describing the loss landscape as “flat,” we intend to convey its relative insensitivity to parameter variations in certain regions, rather than implying a completely level surface. While we believe Figure 5B still provides a useful qualitative depiction of this behavior, we acknowledge that it does not quantitatively establish “flatness.” In future work, we plan to incorporate more rigorous measures—such as gradient magnitudes or Hessian eigenvalues—to more accurately characterize and communicate the geometry of the loss landscape.

      Reviewer #3 (recommendations):

      We sincerely thank the reviewer for their thoughtful feedback and constructive suggestions, which have helped us improve the clarity and rigor of our manuscript. Below, we address each of the comments.

      (1) Precision is lacking in the introduction section. Do the authors mean the Direct SSA, sorted SSA, which is usually faster, and how about rejection sampling methods?

      Thank you for pointing this out. We have updated the introduction to explicitly mention the Direct SSA.

      (2) When mentioning PyTorch and Jax, would be good to also talk about Julia, as they have fast stochastic simulators.

      We have now mentioned Julia alongside PyTorch and Jax.

      (3) Mentioned references 22-27. Reference 26 is an odd choice; a better reference is from the same author the Automatic Differentiation of Programs with Discrete Randomness, G Arya, M Schauer, F Schäfer, C Rackauckas, Advances in Neural Information Processing Systems, NeurIPS 2022

      We have now cited the suggested reference.

      (4) Page 1, Section: 'To circumnavigate these difficulties, the DGA modifies....' Have you thought about how you would deal with the bias that will be introduced by doing this?

      Thank you for your insightful comment. We acknowledge the potential for bias due to the differentiable approximations in the DGA; however, our analysis has not revealed any systematic bias compared to the exact Gillespie algorithm. Instead, we observe irregular deviations from the exact results as the smoothness of the approximations increases.

      (5) Page 2, first sentence '... traditional Gillespie...' be more precise here - the direct algorithm.

      Thank you for your comment. We believe that the context of the paper, particularly the schematic in Figure 1, makes it clear that we are focusing on the Direct SSA. 

      (6) Page 2, second paragraph: ' In order to simulate such a system...' This doesn't fit here as this section is about tau-leaping. As this approach approximates discrete operations, it is unclear if it would work for large models, snap-shot data of larger scale and if it would be possible to extend it for time-lapse data

      Thank you for your comment. We respectfully disagree that this paragraph is misplaced. The purpose of this paragraph is to explain why the standard Gillespie algorithm does not use fixed time intervals for simulating stochastic processes. By highlighting the inefficiency of discretizing time into small intervals where reactions rarely occur, the paragraph provides necessary context for the Gillespie algorithm’s event-driven approach, which avoids this inefficiency.

      Regarding the applicability of the DGA to larger models, snapshot data, or time-lapse data, we acknowledge these are important directions and have noted them as potential extensions in the discussion section.

      (7) Page 2 Section B: 'In order to make use of modern deep-learning techniques...' It doesn't appear from the paper that any modern deep learning is used.

      Thank you for your comment. Although the DGA does not utilize deep learning architectures such as neural networks, it employs automatic differentiation techniques provided by frameworks like PyTorch and Jax. These tools allow efficient gradient computations, making the DGA compatible with modern optimization workflows.

      (8) Page 3, Fig 1(a). S matrix last row, B and C should swap places: B should be 1 and C is -1.

      Corrected the typo.

      (9) Fig1 needs a more detailed caption.

      Expanded the caption slightly for clarity.

      (10) Page 3 last paragraph: 'The hyperparameter b...' Consequences of this are relevant, for example can we now go below zero. Also, we lose more efficient algorithms here. It would be good to discuss this in more detail that this is an approx.. algorithm that is good for our case study, but for other to use it more tests are needed.

      Thank you for the comment. Appendix A discusses the trade-offs related to a and b, but we agree that more detailed analysis is needed. The hyperparameters are tailored to our case study and must be tuned for specific systems.

      (11) Page 4, Section C, first paragraph, 'The goal of making...' This is snapshot data. Would the framework also translate to time-lapse data? Also, it would be better to make it clearer earlier which type of data are the target of this study.

      Thank you for your suggestion. While the current study focuses on snapshot data and steady-state properties, we believe the DGA could be extended to handle time-lapse data by incorporating multiple recorded time points into its inference objective. Specifically, one could modify the loss function to penalize discrepancies across observed transitions between these time points, effectively capturing dynamic trajectories. We consider this an exciting area for future development, but it lies beyond our present scope.

      (12) Page 4 Section C, sentence '...experimentally measured moments'. Should later be mentioned as error, as moments are imperfect

      Thank you for your comment. We agree that experimentally measured moments are inherently noisy and may not perfectly represent the true system. However, within the context of the DGA, these moments serve as target quantities, and the discrepancy between simulated and measured moments is already accounted for in the loss function. 

      (13) Page 4 Section C, last sentence '...second-order...such as ADAM'. Another formulation would be better as second order can be confusing, especially in the context of parameter estimation

      We have revised the language to avoid confusion regarding “second-order” methods.

      (14) Fig 4(a) a density plot would fit better here

      Fig. 4(a) has been updated to a scatter density plot as suggested.

      (15) Fig 4(c) Would be interesting to see closer analysis of trade of between gradient and accuracy when changing a and b parameters

      Thank you for this suggestion. We acknowledge that an in-depth exploration of these trade-offs could provide deeper insights into the method’s performance. However, for now, we believe the current analysis suffices to highlight the utility of the DGA in the contexts examined.

      (16) Page 6 Section III, first sentence: This fits more to intro. Further the reference list is severely lacking here, with no comparison to other methods for actually fitting stochastic models.

      Thank you for the suggestion. We have added a few references there.

      (17) Page 6, Section A, sentence: '....experimental measured mean...' Why is it a good measure here (moment matching is not perfect), also do you have distribution data, would that not be better? How about accounting for measurement error?

      Thank you for the comment. While we do not have full distribution data, we acknowledge that incorporating experimental measurement error could enhance the framework. A weighted loss function could model uncertainty explicitly, but this is beyond the scope of the current study. 

      (18) Page 7, section B, first paragraph: 'Motivated by this, we defined the...'Why using Fisher-Information when profile-likelihood have proven to be better, especially for systems with few parameters like this.

      Thank you for the suggestion. While profile-likelihood is indeed a powerful tool for parameter uncertainty analysis, we chose Fisher Information due to its computational efficiency and compatibility with the differentiable nature of the DGA framework.

      (19)  Page 7, section C, sentence '...set kR/off=1..'. In this case, we cannot infer this parameter.

      Thank you for the comment. You are correct that setting kR/off = 1 effectively normalizes the rates, making this parameter unidentifiable. In steady-state analyses, not all parameters can be independently inferred because observable quantities depend on relative—rather than absolute—rate values (as evident when setting the time derivative to zero in the master equation). To infer all parameters, one would need additional information, such as time-series data or moments at finite time.

      (20)  Page 7 Section 2. Estimating parameters .... Sentence: '....as can be seen, there is very good agreement..' How many times the true value falls within the CI (because corr 0.68 is not great).

      Thank you for your comment. While a correlation coefficient of 0.68 indicates moderate agreement, the primary goal was to demonstrate the feasibility of parameter estimation using the DGA rather than achieving perfect accuracy. The coverage of the CI was not explicitly calculated, as the focus was on the overall trends and relative agreement.

      (21) Page 7 Section 2. Estimating parameters .... Sentence: 'Fig5(c) shows....' Is this when using exact simulator?

      Thank you for your question. Yes, the exact values in x-axis of Fig. 5(c) are obtained using the exact Gillespie simulation.

      (22) Page 7 Section 3 Estimating parameters for the... Sentence: 'Fig6(a) shows...' Why Cis are not shown?

      Thank you for your comment. CIs are not shown in Fig. 6(a) because this particular case is degenerate, making the calculation and meaningful representation of CIs challenging. 

      (23) Page 10, Sentence: 'As can be seen in Fig 7(b)...' Can you show uncertainty in measured value? It would be good to see something of a comparison against an exact method, at least on simulated synthetic data

      Thank you for the comment. Fig. 7(a) already includes error bars for the experimental data, which account for measurement uncertainty. However, in Fig. 7(b), we do not include error bars for the experimental values due to limitations in the available data.

      (24) Page 12, Section B Loss function '...n=600...' This is on a lower range. Have you tested with n=1000?

      Yes, we have tested with n=1000 and observed no significant difference in the results. This indicates that n=600 is sufficient for the purposes of this study. 

      (25) Fig 8(c) why there are no CI shown?

      Thank you for your comment. CIs were not included in Fig. 8(c) due to degeneracy, which makes meaningful confidence intervals difficult to compute.

      (26) Page 12 Conclusion, sentence: '..gradients via backpropagation...' Actually, by making the function continuous, both forward and reverse mode might be used. And in this case, forward-mode would actually be the fastest by quite a margin

      Thank you for your insightful comment. You are correct that by making the function continuous, both forward-mode and reverse-mode automatic differentiation can be used. We have now mentioned this point in the discussion.

      (27) Overall comment for the Conclusion section: It would be good to discuss how this framework compares to other model-fitting frameworks for models with stochastic dynamics. The authors mention dynamic data and more discussion on this would be very welcomed. Why use ADAM and not something established like BFGS for model fitting? It would be interesting to discuss how this can fit with other SSA algorithms (e.g. in practice sorting SSA is used when models get larger). Also, inference comparison against exact approaches would be very nice. As it is now, the authors truly only check the accuracy of the SSA on 1 model -it would be interesting to see for other models.

      Thank you for your detailed comments. While this study focuses on introducing the DGA and demonstrating its feasibility, we agree that comparisons with other model-fitting frameworks, testing on additional models, and integrating with other SSA variants like sorted SSA are important directions for future work. Similarly, extending the DGA to handle transient dynamics and exploring alternatives to ADAM, such as BFGS, are promising areas to investigate further.

    1. Much of the time, all students should be called on to use what they learn to solve knotty problems that defy a recipe-like answer, even though some will need to go about the task in a different way. Some students may need more scaffolding than others to make and support an argument, for example. Some may benefit from using more advanced resources as they construct their argument. Some may profit from a mini-lesson that recaps how to make and support a solid argument. Some may need to develop their arguments orally and have their work written by a peer or adult. Some may need to use materials in a language other than English, or write initially in a first language and then translate into English. But if we acknowledge that argumentation is a valuable skill, we must commit to helping all students master it by providing the appropriate scaffolding.

      A lot of times in todays time teachers only let their students finish something in the way they want and that’s it. I watch this happen with my older step kids and two of them struggle a bit with the essays. I think the teacher could let them do it another way in like creating a vision board or something they actually understand.

    1. Moreover, all depend on you to make assumptions about people too, drawing upon persona and assumptions of common knowledge, all of which may be untrue of marginalized groups. Design has a long way to go before its methods are truly equitable, focusing on the edge cases and margins of human experience and diversity, rather than on the dominant cases. It’s your responsibility as a designer to look for those methods and demand their use.

      This specific section near the end I think is a good contradiction and reminder to everything that we have been thinking about in the course. Very frequently we have been forcing ourselves to step away from our preferences and adopt an approach that detaches ourselves from the results and prioritizes the users. Even though this is the case I appreciate the focus on still understanding that we have a choice, and are not completely detached from the project we still are allowed to maintain judgment and maintain a level of assumption over the process which helps establish a bit of ownership over it.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This study is useful as it provides further analysis of previously published data to address which specific genes are part of the masculinizing actions of E2 on female zebra finches, and where these key genes are expressed in the brain. However the data supporting the conclusion of masculinizing the song system are incomplete as the current manuscript is a re-analysis of differential gene expression modulated by E2 treatment between male/female zebra finches without manipulation of gene expression. The conclusions (and title) regarding song learning are also incompletely supported with no gene manipulation or song analysis. Importantly, the use of WGCNA for a question of sex-chromosome expression in species without dosage compensation is considered inadequate. As the experimental design did not include groups to directly test for song learning, and there was also no analysis of song performance, these data were also considered inadequate in that regard.

      We are sorry the editor felt the manuscript so incomplete and inadequate. Though the tone of this assessment seems more severe than the below reviewer comments, we are also happy to see that the editor has considered our paper further for a revised publication, based on the reviewer’s comments. We address the editor’s comments as follows:

      While we agree that manipulation of some of the genes we discovered, whose expression levels are E2-sensitive in the song system, would take the study further in validating some proposed hypothesis in the discussion of the paper, we don’t think the outcome of gene manipulations would change the major conclusions from the results of the paper. In this study we performed estrogen hormone manipulations, with causal consequences on gene expression in song nuclei and associated song behavior. In a way this is analogous to gene manipulations, but manipulating directly the action of estrogen. The categories of genes impacted, and the differences among the sex chromosomes wouldn’t change.

      For the comment on WGCNA being inadequate for addressing questions on sex chromosome expression in species without dosage compensation, we think the evidence in our data does not bear that out. One main result of this paper is the separation of Z chromosome transcripts whose expression is most strongly regulated by chromosomal dosage (WGCNA module E) across regions from those subject to additional sources of regulation in song nuclei (other modules). It seems to us that rather than being confounded by the lack of dosage compensation, WGCNA allowed us to better resolve the effects of dosage on different genes within the sex chromosomes. We have added a new figure more directly examining sex chromosome transcript abundance within different modules. Briefly, we found that module E assigned Z chromosome genes exhibited almost exactly the male-biased expression ratio expected from no dosage compensation while the Z chromosome genes in song nuclei assigned to other modules were expressed below the dosage predicted value, consistent with module E containing those genes whose expression are most strongly regulated by dose across all brain regions sampled.

      At its core, WGCNA finds sets of correlated genes. The biological reality of the zebra finch transcriptome is that Z chromosome expression is largely anti-correlated with W chromosome due to dosage. However, this dosage effect is not felt equally by all genes and WGCNA provides an unbiased computational framework which can be used to separate dose from other potential sources of gene regulation. This is why roughly ⅓ of Z chromosome genes are not assigned to module E; for example the growth hormone receptor is assigned to module G based on its correlation with genes upregulated within HVC.

      “As the experimental design did not include groups to directly test for song learning, and there was also no analysis of song performance, these data were also considered inadequate in that regard.”

      Concerning the comment on no analysis on song performance in the paper, all such analyses were conducted on our previous study on the same animals (Choe et al. 2021, Hormones & Behavior). The birds considered here were sacrificed at PHD30, prior to the onset of learned song behavior. However, females treated with E2 the same at the same time and allowed to mature into adulthood, went onto to develop rudimentary song. Further, induction of rudimentary song learning in females following E2 treatment has been well established since the early ‘80s. We have added the following text toward the end of the intro to make this more clear:

      “While the birds for this study were sacrificed prior to the developmental presentation of song behavior, we have previously shown that female finches treated in exactly the say way with E2 go on to produce rudimentary imitative songs as adults (Choe et al 2021), consistent with the known induction of vocal learning in females by E2 (REF).”

      Reviewer #1 (Recommendations For The Authors):

      Overall, this is a wonderfully designed and executed study that takes full advantage of new resources, such as the most complete zebra finch genome assembly yet, as well as the latest methods. I have very few suggestions as to the improvement of the manuscript. They are as follows:

      Results Section:

      In the paragraph "Identification of gene expression modules in song nuclei":

      "The E2-treated females in this study had similarly sized song system nuclei as males, indicating that E2 treatment prevented atrophy."

      Clarify if this comparison is to treated and/or untreated males.

      We thank the reviewer for their comment. The relative differences in the song nuclei sizes between the E2-treated females and the other groups is more complex that our original sentence implied. We have revised the main the text as follows

      “In our previous study, we found that estradiol treatment in PHD30 females caused HVC to enlarge and Area X to appear when it normally does not develop in females, but both at sizes less than in untreated or treated males.The sizes of PHD30 female LMAN RA were already the sizes as seen in males, as the later has not atrophied yet at this age(25).”

      In the paragraph "Sex- and micro-chromosome gene expression across the telencephalon": "These animal and chromosome specific shifts in the transcriptomes could represent the systemic effects of allelic chromosomal structural variation..."

      The authors should clarify the meaning of a"llelic chromosomal structural variation" in this context, as it is an unusual phrase. Major chromosomal structural variation seems unlikely to produce these effects. Is it also possible that animal-specific modules with brain-wide higher could also result from laboratory contamination between all samples from one animal? This is not too likely but perhaps should be acknowledged or ruled out.

      We have removed the word allelic, which was unnecessary. We can’t envision how laboratory contamination could occur such that all of one animal’s samples would be affected to produce the observed result which is module and chromosome specific. An animal wide effect could emerge during sacrifice, but we can think of no reason that would affect these modules and not others. Rather, the most likely explanation is biological natural difference between animals. We have added this consideration of alternative explanations.

      In the section "Candidate gene drivers of HVC specialization in E2-treated females":

      When discussing GHR's role in cell growth and proliferation, the authors' argument could be expanded by including the documented role of GH signaling in anti-apoptotic protection of neurons from rounds of neural pruning during development as documented in the chicken, e.g. • Harvey S, Baudet M-L, Sanders EJ. 2009. Growth Hormone-induced Neuroprotection in the Neural Retina during Chick Embryogenesis. Annals of the New York Academy of Sciences, 1163: 414-416. https://doi.org/10.1111/j.1749-6632.2008.03641.x

      We thank the reviewer for sharing this publication with us.. We have added the following sentence to our discussion with the above citation. “Further, our results are consistent with growth hormone’s known role in avian anti-apoptotic protection, with elevated signaling associated with the survival of chicken neurons during rounds of pruning in the developing

      retina.”

      The authors' argument of the relevance of the passerine GH duplication would be strengthened by citing:

      • Rasband SA, Bolton PE, Fang Q, Johnson PLF, Braun MJ. 2023. Evolution of the Growth Hormone Gene Duplication in Passerine Birds, Genome Biol Evol, 15(3) https://doi.org/10.1093/gbe/evad033. Greatly expands on the Yuri et al. paper cited by characterizing of the molecular evolution of these genes across hundreds of avian species, supporting positive selection on multiple amino acid sites identified in both ancestral and duplicate (passerine) growth hormone.

      • Xie F, London SE, Southey BR et al. 2010. The zebra finch neuropeptidome: prediction, detection and expression. BMC Biol 8, 28. https://doi.org/10.1186/1741-7007-8-28 The authors report significantly different expression of the ancestral GH gene in the adult male zebra finch auditory forebrain after different song exposure experiences.

      We have amended the results section sentence and added all suggested citations. The sentence now reads: “The gene which encodes growth hormone receptor’s ligand, growth hormone, is interestingly duplicated and undergoing accelerated evolution in the genomes of songbirds (Rasband et al 2023); the GH ligand has been found to be upregulated in the zebra finch auditory forebrain following the presentation of familiar song (Xie et al 2010).”

      Figures:

      - Figure 1B. "Duration of sex typing" being a shorter bar compared to the others is not fully explained in the experimental design. Presumably at the end of this time period, the sex is non-invasively, phenotypically evident. I suggest an arrow pointing to the PHD/PHD range when sex is apparent in plumage/anatomy.

      - Figure 4. Caption appears to be truncated; "across all... genes"?

      Fixed

      - Figure 5. For 5E, 5F, 5G, 5H, consider enlarging the plots so overlapping gene symbols are readable. Alternately, smaller numbers or symbols could be used with a key in areas where overlapping symbols are hard to prevent.

      We agree that these are not the easiest to read; we originally offset the symbols in R to minimize overlaps, but it can only do so much for the more crammed panels. We have now added a supplemental .xlsx file with the underlying data from each of the 4 tests for readers that want to examine the data in more detail.

      Reviewer #2 (Recommendations For The Authors):

      Since WGCNA methods will inherently draw together sex-chromosome genes into the same module in systems without dosage compensation, I suggest the authors rerun the WGCNA using only female samples and only male samples. Then identify the composition of modules that differ between E2 and vehicle-treated females and compare these genes to males. Then from male WGCNA identify the composition of modules that differ between E2 and vehicle-treated males and compare to female modules.

      We thank the reviewer for their suggestions. However, we believe it is not as strong as the approach we used, which is grouping data from both sexes in the WGCNA analyses in a study that is looking for sex differences. The reviewer's proposed approach amounts to computing modules twice (once per sex), determining song system specialized modules and E2 responsive modules in both settings, then intersecting the two sets to find corresponding modules, all done to prevent the non-dose compensated sex chromosome genes from being drawn into the same module.

      While WGCNA does group the majority of sex chromosome genes into module E, it does not categorize them all this way (Fig 3). The module classification instead differentiates those sex chromosome genes whose expression are most explained by chromosome dosage / sex across regions (modE) from those whose expression is controlled by other sources of regulation; for an example of the latter, the growth hormone receptor (GHR) is one of several Z chromosome genes classified into modG as its expression better correlates with the genes specialized to HVC than it does with the majority of dosage-dependent Z chromosome genes found in modE. Further, to remove biological sex as a variable in a WGCNA analysis that is focused on sex differences seems counterintuitive.

      Instead, to quantitatively address the reviewer’s concern, we conducted additional analyses, that led to an added new figure, associated text, and tables, that better describes sex/chromosome dosage effects on the abundance (FPKM) and expression ratios of sex chromosome transcripts by module irrespective of brain region (Fig. 5). We find that the Z chromosome genes in modE were expressed at the expected chromosome dosage in the non-vocal surrounding regions (65.06% observed vs 66.6% expected) while in other modules, other Z chromosome genes were expressed at intermediate levels between equal expression and the expected chromosomal dosage. For example, the Z chromosome content of modules D and H exhibited near equal expression between sexes. Within the song system, Z chromosome gene content of modG was highly expressed in males beyond what is expected from chromosome dosage, consistent with modG’s male-specific upregulation in song nuclei relative to surrounds in the absence of E2. These results better demonstrate that in our WGCNA on the combined dataset we are able to separate those Z chromosome genes whose expression is predominantly dosage controlled from those subject to additional regulation such as song system specialization.

      Fig. S3 Legend: 'Black arrow' -> 'Red arrow'

      Change made.

      Fig. S5 - What part of the figure shows the 'human convergent signature'? Also, simply listing the number of genes mapped to a chromosome is misleading to readers unfamiliar with the zebra finch genome, you should either provide the number of genes on each chromosome or present as corrected by that number.

      Fig. S5 was the same type of analyses in Fig. 3 but with an older zebra finch genome assembly, where we had not included the panel a for enrichments with genes convergent in expression between songbird song regions and humans speech brain regions. However, we see that Fig. S5 was not adding any new important information to the paper, so we removed it.

      For the chromosome analyses in Fig. 3b, we provide both the total raw number of module assigned genes broken down by chromosome (The black bar plots on the right) as well as a statistical fold-enrichment value of modules per chromosome. Given the number of genes per chromosome and genes per module in our data, we computed the fold-enrichment for each intersection (observed intersection size / expected intersection size). To test for the significance of these enrichments, we bootstrapped FDR corrected p values for the enrichment of each chromosome-module pairing by randomizing the mapping of genes to modules to construct a null distribution of fold enrichments for each intersection. Our intent was not to describe the size of the chromosomes themselves, information readily available elsewhere, but to show the disproportionate chromosomal origins of the gene sets considered by this study. Performing this enrichment test using all annotated genes per chromosome would artificially increase enrichment values and make the analysis less conservative by confounding the results with the inherent enrichment for “brain function” in the assigned genes relative to all genes.

      At several places you say "we correlated expression of each sex chromosome transcript with sexual dimorphism within each region, such that expressed W genes would be positively correlated and depleted Z chromosome genes would be anticorrelated." What was the sexual dimorphism that was being correlated with? Is this the eigengene?

      We thank you for this comment. Our language was less clear than it could be. We tested for correlations of both the eigengene and the individual gene expression profiles with the biological sex of the animals. We have changed the text to:

      “To do this, we tested for a correlation between the expression of each sex chromosome transcript to the animals’ sex within each brain region. We found that female-enriched transcripts were positively correlated with sex and male-enriched transcripts were anticorrelated (Fig. 4f,g).”

      Fig. 4A: The 'true/false' boxes and animal A-L is confusing and unnecessary. I'd suggest just using M and F (or sex symbols) with a horizontal line below each set of 3 for respective E2 and Veh.

      Change made.

      Reviewer #3 (Recommendations For The Authors):

      General comments:

      After the initial characterization of the datasets and module identification, it is quite hard to follow the logic of the data presentation in the various other Results sections or to clearly understand how they relate to the main stated goal to identify factors related to sex differences in vocal learning. The most relevant findings relate to the presumed actions of hormone treatment and sex chromosome gene dosage in song nuclei, whereas analyses of other brain areas, other chromosomes, or speech-related genes serve more as controls and/or appear as distractions from the main theme. A suggestion to increase the clarity of the presentation and potential impact of the study is to change the order of the presentation, focusing first on the specific analyses and comparisons that most directly speak to the main goals of the study, and then secondarily and more briefly presenting the controls or less related comparisons.

      The reviewer’s suggestion for the results section organization is exactly what we had tried to do. We opened the first paragraph on identification of modules, then presented the song nuclei specific modules, followed by E2-changes to those modules; and the followed by other specific results for the remainder of the paper, including module enrichments to specific chromosomes. The reviewer mentioned our analyses of “other brain areas” (which we assume to mean the non-vocal surround regions), other chromosomes (which we assume means autosomes) and speech-related genes as controls were a distraction in the paper; but within our analysis, these other brain regions are essential controls needed to assess the song-system specificity of any observed sex differences observed from the very first paragraphs of the results; the autosomes were not controls for sex chromosome results, but primary results in of themselves; the overlap with speech-related genes was also not a control, but a novel discovery. We have revised these points in the paper to make them clearer, and revised some of the section titles and transitions between sections to help increase clarity of the main storyline of the paper.

      A related comment is that many of the inferences drawn from the WGCNA analysis were quite complex, thus independent verification of some predictions would be quite valuable. For example, consider the passage: "In non-vocal learning juvenile females, interestingly LMAN was specialized relative to the AN by the same gene modules as in males (B, F, and I) as well as an additional module G (Fig. 2b); RA was specialized by module A as in males, but not module L and by additional modules A and G. In contrast, neither juvenile female HVC nor Area X exhibited significant gene module expression specializations relative to their surrounds." Providing in situ hybridization verification of these regional gene expression predictions with a few representative genes seems quite feasible given the group's expertise and would considerably strengthen confidence in the module-based inferences.

      We performed in-situ independent validation of 36 candidate genes in our first study with this dataset (Choe et al 2021). We now mention this validation in the revised paper. The reviewer’s selection of one of our sentences though made us realize that our grammar used to explain the results was not as clear as it needs to be. We thus cleaned up the grammar of our module descriptions so that it should be communicated with less complexity, the main issue noted by the reviewer.

      Because this is a re-analysis of a previously published dataset, the authors should more explicitly describe somewhere in the Discussion how the present analysis advances the understanding of sex differences in songbird neuroanatomy and behavior beyond the previous analysis.

      We have added an additional sentence into the discussion more clearly separating the results of the current study from our previous work.

      Specific comments:

      Abstract:

      There is evidence (from Frank Johnson's lab) that RA does not completely atrophy in female zebra finches, but is still present with more preserved connectivity than previously thought, possibly related to non-singing function(s). A term like 'marked reduction' of female RA may more accurately reflect the current state of knowledge.

      We have changed the text to “partial atrophy”.

      The term "driver" is undefined and unclear at this point of the paper; a clear definition for "driver" is also lacking in the Intro.

      We now define “driver” or “genetic driver” as understood to mean “a genetic locus whose expression and/or inheritance strongly regulates the trait of interest”.

      When citing the literature on studies that identified "specific genes with specialized up- or down-regulated expression in song and speech circuits relative to the surrounding motor control circuits", the authors should also cite studies from other labs (e.g. Li et al., PNAS, 2007; Lovell et al, Plos One 2008; Lovell et al, BMC Genomics 2018; Nevue et al, Sci Rep. 2020), to be accurate and fair.

      Citations added

      For clarity, the authors should explicitly formulate the hypothesis they are proposing at the end of the Summary.

      We thank the reviewer for this comment. We have replaced the final sentence of the summary with: “We present a hypothesis where reduced dosage and expression of these Z chromosome genes changes the developmental trajectory of female HVC, partially preventable by estrogen treatment, contributing to the loss of song learning behavior.”

      Introduction:

      Vocal learning is arguably the ability to imitate 'vocal' sounds, this could be clarified here.

      We have amended the sentence to “Vocal learning is the ability to imitate heard sounds using a vocal organ…”

      Given they are currently considered sister taxa, can the author briefly explain what is the basis for assuming that songbirds and parrots independently evolved vocal learning?

      Although songbirds and parrots belong to a monophyletic clade, they are not sister taxa. There are two clades separating them that are vocal non-learners. We have cited the reference that demonstrated this (e.g. Jarvis et al 2014 Science).

      Why use Taeniopygia castanotis rather than the more broadly used Taeniopygia guttata?

      Zebra finches were recently reclassified and T.castanotis is now more accurate. The Indonesian Timor zebra finch retained T.guttata while the Australian finch, used here, was classified as T.castanotis.

      The authors state: "...vocal learning is strongly sexually dimorphic in zebra finches and many other vocal learning species" and cite Nottebohm and Arnold, Science, 1978. That landmark paper only shows dimorphism in song nuclei (not learning) in two songbird species. The authors should provide citations for other species and behavior, or modify the statement.

      We have added an additional citation (Odom et al.) to this sentence which covers the phylogeny more broadly.

      The authors refer to the nucleus RA as being located in the lateral intermediate arcopallium (LAI). Other labs have described this domain as the dorsal part of the intermediate arcopallium, thus AId or AID (Mello et al., JCN, 2019; Yuan and Bottjer, J Neurophys 2019; Yuan and Bottjer, eNeuro, 2020; Nevue et al., BCM Genomics, 2020). The authors should acknowledge this discrepancy in nomenclature so that data and conclusions can be more readily compared across studies.

      We thank the reviewer and agree that this is helpful. We have added a note at the first mention of LAI.

      The authors state that data from the gynandromorph bird described by Agate et al implicates "sex chromosome gene expression within the song system" as involved in the song system sexual dimorphism. That study, however, only rules out circulating gonadal steroids, and while suggesting a cell-autonomous mechanism like sex chromosome genes, it does not necessarily exclude other brain-autonomous factors like sex differences in local production of sex steroids.

      We say that this study “implicated” sex chromosome gene expression, which is accurate per the results and discussion of that study. We are unsure what “brain autonomous factors like sex differences in local production of sex steroids” means?. “Brain autonomous” and “local production” in the brain seem contradictory in this context?

      Results:

      The authors state that "the E2-treated females in this study had similarly sized song system nuclei as males, indicating that E2 treatment prevented atrophy". Can they clarify whether the VEH-treated females actually had smaller RAs than E2-treated females or VEH-treated males at this age? This is still quite early in development and it is unclear to what extent RA's marked sexual dimorphism in adults or later developmental ages has already taken place in untreated (or VEH-treated) birds. A related comment is that the authors state later on: "We interpret these findings to indicate that: LMAN and RA atrophy later in juvenile female development..." Does this mean these nuclei actually did not show the marked decreases predicted earlier in the text? Clarifying this point would be helpful.

      We thank the reviewer for pointing out this discrepancy, which reviewer #1 asked for clarification as well. RA size at this age is similar in males and females. However, HVC and Area X is smaller and absent respectively in females and E2 treatment partially prevents this atrophy. The text now reads:

      “In our previous study, we found that estradiol treatment in PHD30 females caused HVC to enlarge and Area X to appear when it normally does not develop in females, but both at sizes less than in untreated or treated males.The sizes of PHD30 female LMAN RA were already the sizes as seen in males, as the later has not atrophied yet at this age(25).”

      The authors acknowledge that area X is absent in untreated and VEH-treated females. Could they please clarify how area X and the surrounding stratal tissue that excludes area X were identified for laser capture dissections in juvenile females?

      We have added the following statement to the main text portion discussing the dissections.

      “In the case of vehicle-treated females which lack Area X, a piece of striatum from the same location of where Area X is found in males was taken. “

      Some passages in Results discussing the authors' interpretation of the modules seem quite speculative and possibly belong instead in the Discussion. For example: "... that module A and G genes could be associated with the start of this atrophy; HVC and Area X are likely the first to atrophy or not develop; and lack of any gene module specialization in them at this age could mean that they would be more sensitive to estrogen prevention of vocal learning loss."

      As suggested, we have removed this text from the results; these ideas were already presented in the Discussion. We have merged the resulting small paragraph with the preceding paragraph.

      The authors state: "To assess the effects of chronic exogenous estrogen on the developing song system, we first performed a control analysis of modules in the E2-treated juvenile males." How can an assessment of estrogen effects be a "control" analysis? Does this refer to a contrast with females? Please clarify the language here.

      The reviewer is correct, that E2 treatment in males should not be considered a control experiment. We removed the word “control”.

      When discussing the GO-enriched terms for module G, it is unclear how the authors reached the conclusion about "proliferative", as the enriched terms do not refer to processes more directly indicative of proliferation like "cell division" or "cell cycle regulation". Rather, these terms seem more related to differentiation and growth, which do not necessarily imply proliferation. The authors also refer to "HVC proliferation" later on in the Discussion. However, there is conclusive evidence from several labs that proliferative events associated with postnatal neuronal addition and/or replacement in song nuclei occur in the subventricular zone, not in song nuclei like HVC itself, and that the growth of song nuclei largely reflects cell survival, as well as growth in size and complexity under the regulation of sex steroids.

      We agree that “proliferative” may have been a poor word choice here. We did not mean to indicate that cell division was occuring in HVC itself. Instead we meant to indicate that HVC is able to accommodate the new born neurons from the SVZ. We have replaced the word “proliferative” throughout. In the instance the reviewer mentions specifically we replaced it with,“...potentially act to integrate and differentiate late born neurons.”

      With regard to module E, referring to a telencephalon-wide sexually dimorphic gene expression program seems quite a stretch, given that only a few regions were sampled and compared between sexes. These related statements should be toned down.

      We have replaced “telencephalon-wide” with “more distributed across the finch telencephalon” and other similar language in each instance.

      The following passage is very speculative and should shortened and/or moved to the Discussion: "Based on the findings in these gene sets, we hypothesize that without excess estrogen in females, HVC expansion is prevented by not specializing the growth and neuronal migration promoting genes in module G to the HVC lineage by late development. This is potentially enacted by depleting necessary gene products from the Z sex chromosome, such as GHR, which are already present in only one copy."

      We have deleted this portion of the text, as the idea is already present in the discussion.

      Figure 5: To this reviewer, the comparisons of sex differences and of female response to E2 are the most relevant and informative ones, whereas the regional differences between song nuclei and surrounds refer to different cell populations and cell types where other processes may be occurring, independently of what occurs in song nuclei. It thus seems like the intersection analysis in panel 5i may be subtracting out important "core genes" in terms of E2 effects and/or sex differences in the most relevant cell populations, i.e. in this case within song nucleus HVC.

      Song learning and the vocal learning brain regions are specialized behaviors and associated nuclei which have a set of hundreds of specialized genes compared to the surrounds. Our previous findings shows that E2 drives the appearance of these specializations in female zebra finches. Thus, we considered this the most interesting question to focus on, which we have further highlighted. Nevertheless, in response to the reviewers suggestion, we have added a .xlsx supplemental file containing the results from each of the individual tests so readers may examine any single comparison, or set of comparisons, in more detail.

      Discussion:

      It is unclear what the term "critical period" refers to in: "during the critical period of atrophy for the female vocal circuit"; please clarify.

      We agree that our language was nebulous. We have replaced it with “as several male song control nuclei begin to expand and female nuclei partially atrophy”

      In: "HVC appeared unspecialized at the level of gene module expression in control females", does "unspecialized" refer to a lack of difference in gene expression when compared to surroundings? Please clarify. The same comment applies to other uses of "unspecialized" in this paragraph.

      Yes, unspecialized means lack of difference in gene expression in the song nucleus. To clarify this point, we have reworked that and the following sentence as follows:

      “HVC appeared unspecialized compared to the surrounding nidopallium at the level of gene module expression in control females, with no significantly differentially expressed MEGs . However, in E2-treated females, HVC exhibited a subset of the observed male HVC gene expression specializations. Similarly, the vehicle-treated female striatum located where Area X would be also lacked any specialized gene module expression, but the E2-treated female Area X exhibited a subset of the male Area X specializations, consistent with the known absence of Area X in vehicle-treated females and presence in E2-treated females.”

      The authors state: "...we surprisingly found that the most specialized genes were disproportionately from the Z chromosome", when discussing module G in HVC. Why is this so surprising? In a sense, this could be taken as consistent with the findings of Friedrich et al, 2022, where sex differences in the RA transcriptome were predominantly Z related on 20 dph. Arguably 20 dph is still quite close to 30 dph in the present study, when compared to 50 dph in Friedrich et al, when autosomes predominate.

      Our bioRxiv was originally posted in July 2021, prior to the publication of Friedrich et al, 2022; however we had previously added to our discussion that several of our results are consistent with the observations of Friedrich et al..

      We have a different interpretation of Z chromosome gene results in Friedrich et al.. While the percentage of specialized genes from the Z chromosome decreased, the absolute number of specialized Z chromosome genes actually increased over this interval. In Fig. 3a from Friedrich et al. it appears that ~28% of Z chromosome genes were sexually dimorphic in their expression in RA at PHD20 but that ~39% of Z chromosome genes were similarly dimorphic at PHD50. We interpret this result as the Z chromosome genes being among the earliest genes differentially expressed between the sexes, not that their differential expression or role ever subsequently decreased. We have reworked this portion of the discussion to make our point more clear:

      “This model of sex chromosome influenced song system development is consistent with recent observations comparing male and female zebra finch transcriptomes from RA at young juvenile (PHD20) and young adult (PHD50) ages in un-manipulated birds (Friedrich et al. 2022)57. While that study proposes that the role of the sex chromosome in maintaining transcriptomic sex differences diminishes across development, as the proportion of specialized genes that originate on the sex chromosomes diminishes, this effect was driven by large increases in differentially expressed autosomal genes rather than by any reduction in sex chromosome dimorphism; the percentage of differentially expressed Z chromosome genes increased from PHD20 (28%) to PHD50 (39%) (Friedrich et al). This leads us to conclude that sexually dimorphic Z chromosome expression at juvenile ages precedes the sexually dimorphic expression of the autosomes seen in adults. This is consistent with our hypothesis that sufficient expression of select Z chromosome gene products (GHR, etc..) is necessary for subsequent autosomal song system specializations (modG).”

      Further, when we write ”When examining the module G HVC specialization induced by E2-treatment in female HVC, we surprisingly found that the most specialized genes were disproportionately from the Z chromosome” we are referring to the upregulation of module G by E2 in female HVC, not the sex difference described in RA by Friedrich et al. which only utilized un-treated RA samples and thus is more likely related to our observations of module E.

      The term "sexual dimorphism" has been more traditionally used for sex differences that are very marked, like features that are highly regressed or absent in one sex, most often in females. Quantitative differences in gene expression, including dosage differences like those related to module E, are more appropriately described as sex differences rather than dimorphisms. That usage would be more consistent with most of the literature, and thus preferable.

      We did a google search for common definitions, and found more the opposite. Sexual dimorphism being used more often as differences of degree (with the zebra finch example as one of the top hits), and sex differences being used often as more absolute differences (like presence vs absence of the Y chromosome). Further, as in the reviewer’s first sentence, the definition of sexual dimorphism is a sex difference. That is, the two phrases can be interchangeable. Thus, we prefer to keep sexual dimorphism.

      Several references are incomplete or seem truncated, like 9 and 10.

      Fixed

      Table S2: Please examine and take into account the W gene curation presented in Table S3 of Friedrich et al., 2022.

      We have added additional supplementals (supplemetal_w_chrom_express.csv and supplemetal_z_chrom_express.csv) of the data provided in new Fig 5 incorporating the curation information from Table S3 from Friedrich et al.

      Data availability:

      Genes for all the main modules identified should be presented in a Supplemental Table, or through a link to a stable data repository.

      We have added an additional Supplemental Table supplemental_gene_module_assignment.csv with this information.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Below is a point-by-point response to reviewers comments. We appreciate the reviewers' thoughtful consideration of the manuscript and __suggestions

      Reviewer #1

      Evidence, reproducibility and clarity

      In this study, Parkinson et al. investigated lung extracellular matrix using imaging mass cytometry (IMC) in mouse models. Overall, the paper is well-written, and the data are clear, although major points outlined below need to be addressed.

      In its current form, the paper appears more like a methods-focused study since, to my understanding, no new biological responses are described. The methods employed are very interesting, particularly the extracellular matrix analysis.

      As the reviewer points out a large part of this manuscript is the development of a novel methodology for analyzing the spatial ECM changes in a model of allergic airway inflammation. However, there are several novel responses described in the manuscript. Firstly, differing spatial organisation of immune cells across different mouse strains has not been shown before, particularly in a model of chronic allergic pathology that shares features of severe steroid-resistant asthma in people. Secondly, we show that specific macrophage-fibroblast interactions are occurring in the subepithelial region during DRA-induced allergic airway inflammation. Finally, we integrate all these established and novel findings with detailed spatial analysis of the cellular ECM environment, something which is sorely needed in the field.

      However, the scope of the study is quite limited, as all the experiments were performed with mouse samples, which are relatively easy to work with, and the cell organisation is simple compared to humans.

      Whilst we appreciate that the dataset in this study is limited, imaging mass cytometry studies, especially when optimizing reagents, are costly, time consuming, and have limited throughput, not to mention the time required to develop new computational tools for data analysis. Investigating cell-matrix changes in mouse data is vitally important for understanding the mechanistic role of pathways and interactions during disease processes. Whilst we have not provided human datasets in this study, staining, data acquisition and analysis has been performed on FFPE samples, making our pipelines applicable to archival tissue banks. Regardless, we are currently preparing a publication showing the applicability of this technique to human samples. Many ECM components are well conserved between humans and mice and the cellular structure and architecture of the lung shares a lot of similarities. Many papers (PMID: 39437149, 38758780, 38581685, and 38142637) have used this imaging technology in the analysis of human cancer, which shows an even more complicated and dense cellular organisation.

      The authors do not discuss how this analysis pipeline could be applied to human samples. Furthermore, the entire paper relies on imaging mass cytometry, and additional techniques could have been used to confirm some of the observations, especially given the availability of mouse samples.

      As mentioned above, we have taken steps to show that this technology is applicable to humans, though this is outside the scope of this already lengthy manuscript. Additionally, Steinbock, the main analysis pipeline, is well published in human datasets (PMID: 38758780, 39905080, 39759522, and 39761010) and the homology between ECM components is strong between mouse and human. The technology itself is completely species agnostic, so there is no reason to think that there would be issues when applying to humans, other than some differences in the marker expression of certain populations, which is well characterised in many cases.

      The reviewer’s comment regarding the use of additional techniques is valid. Firstly, these murine lung pathology samples are derived from the same mouse experiments used in our previous publication (PMID: 33587776), where we have analysed histology, immune mediators and cells using a variety of techniques including flow cytometry and ELISA. We will ensure this point is made clearer in the manuscript. In addition, for revision we plan to compliment IMC data presented with fluorescent immuno-staining to characterize cell populations in greater resolution and also using 3D precision cut lung slices to better characterize and visualize cell populations of interest in greater depth, directly addressing the reviewer’s concerns.

      The introduction mentions the DRA model without providing an explanation of what it involves. Non-specialist readers may not be familiar with this abbreviation, and further clarification should be provided.

      As the DRA model has been characterized previously, we provided references in the text in order to save space. However, we agree with the reviewer and will provide this information up front in the introduction to make the manuscript more approachable for a non-specialist.

      In the methods section, it is not mentioned whether the lungs were inflated before tissue collection, which is crucial for preserving normal cellular organization. The authors should clarify whether this was performed.

      Lungs were inflated prior to tissue collection. We agree that this is important information to include in the methods and we will update the manuscript to reflect this.

      Figure 1 provides a brief summary of the methods employed in the study but could be enriched with additional information. In its current state, it does not provide meaningful insights beyond what is described in the methods section. It would be helpful if the authors clarified whether the mice used were adults and whether both male and female animals were included.

      We agree with the reviewer. The idea behind this figure was to have an approachable introduction to the manuscript. However, in line with the reviewer’s previous comments about focusing more on the biology we will move this to supplementary to keep the importance focused on the biological results. Mouse age and gender were included in the methods of the paper, aligning to the ARRIVE guidelines for reporting animal research. We will additionally clarify that these are adult mice

      Additionally, they could present examples of the cell segmentation approach with zoomed-in images at the cellular level to illustrate the analysis.

      This is a great idea and appreciate the reviewer’s suggestion. We will provide maps (with zoomed inserts) of the cell segmentation and cell classification across representative ROIs to show not only the segmentation but to provide an overview of how the cell types localise across the lung. This addition will also highlight the caveat of IMC around image resolution of 1μm2 which limits the sensitivity of cell segmentation. We will discuss such limitations of the technique in general in the manuscript in response to this and later reviewer comments.

      The first set of data in Figure 2 suggests that C57Bl/6 mice did not respond to allergen treatment, as shown by the non-significant increase in cell numbers. The authors should provide evidence that their model induced inflammation through alternative methods, such as assessing eosinophil counts or pathology.

      We know that these exact animals are allergic as their immunological responses were characterized in a previous publication (PMID: 33587776) demonstrating eosinophil counts and cytokine responses measured by flow cytometry. However, in light of the reviewer’s comment, we will add histological images of the lung to this current manuscript. Such data, together with enhanced expression of RELMα and Ym2 from airway epithelial cells (Sup Fig 6) and the shift from ATI to ATII cells in both C57BL/6 and BALB/c mice after DRA treatment (Fig 5 g) will provide thorough evidence that the DRA model induces allergic airway inflammation and pathology in both mouse strains.

      The UMAP representation indicates significant overlap between cell clusters, which raises concerns about the accuracy of cell segmentation. For example, the heatmap in Supplementary Figure 1 shows endothelial cells expressing markers such as VWF, aSMA, Vimentin, and PDGFRα, suggesting that the cell cluster may contain a mixture of endothelial cells, vascular smooth muscle cells, and fibroblasts.

      UMAP reductions of IMC do not separate as clearly as those from single cell RNAseq or flow cytometry. This is because the staining intensity from IMC is much lower. Rather than being on a log scale, as for single cell or flow cytometry, the values are much closer to linear. Additionally, due to the limitations in IMC resolution and the fact that we did not have distinct membrane markers in our panel, cell mask generation is often non-optimal. This is particularly evident in regions where cells are in close proximity and where the limitations of, an effectively, two-dimensional 5-micron thick tissue section mean that there can be overlap between one cell and another. Whilst we acknowledge that some populations will be a mix of cell types we are limited by the number of markers we can use in IMC, as well as the limitations mentioned above. We have accounted for this by using methodologies to identify and focus on tissue regions (lisaClust) and correlate changes to differences in these regions rather than single cells per se.

      Examples of segmented cells should be shown to validate this approach.

      As per the reviewers comment above, we will provide maps (with zoomed inserts) of the cell segmentation and cell classification across representative ROIs to show not only the segmentation but to provide an overview of how the cell types localised across the lung.

      It is unclear what Figure 2e represents. If it is simply to show that certain clusters can be grouped together, such as AEC, AT1, and AT2 as epithelial cells, this could be conveyed in a simpler way.

      We apologise that the reviewer found Figure 2e confusing. The aim of this figure was to provide a simple diagram to highlight how different classifications of cell types aligned. This was required because there were variations in the specificity of some clusters and to address specific questions it made more sense to analyse cells at a broader level. i.e. merging resting and activated ATI/II cells or grouping specific immune cell clusters into larger groups. We did consider a table, but we did not feel this was a “simpler” way to do it. As it is simply for reference, we will move Figure 2e to supplemental.

      The analysis of extracellular matrix components presented in Figure 3 provides a novel method for studying these acellular structures, which is a challenge in the field. The authors should be commended for their efforts in this area.

      We thank the reviewer for their comment here. We agree that this is a vital area that needs to be addressed as the immunomatrix becomes ever more important in understanding disease pathogenesis. We developed this novel method to begin to understand key spatial interactions between cells and ECM molecules, something missing from the majority of high-dimensional imaging datasets.

      However, the parameters investigated in Figures 4-6 do not report any novel findings. While IMC appears to work effectively to analyse these parameters simultaneously, the induction of immune foci and changes in tissue organisation following allergen challenges are already well-documented in both mouse models and human samples.

      We disagree with the reviewer on this point. Figure 4 shows that immune cell infiltration in the adventitial cuff is different between BALB/c and C57BL/6 mice. This is a new discovery and provides nuance to our previously published data (PMID: 33587776), which showed that in the bronchoalveolar lavage from these same mice there were no differences in immune cell populations at these chronic time points. Therefore, analysis of lavage cells or lung histology in isolation does not provide a full picture of allergic immune responses.

      Figure 5 shows neutrophils localised with alveolar macrophages in the alveolar parenchyma in this chronic DRA model completely distinct from the spatial advential cuff region occupied by other CD11b+ cells. In addition, we show that we can identify perturbations in the alveolar parenchyma by IMC and these correlate with known differences in allergy and asthma such as alterations in ATI/ATII balance, which has also not been shown in this model.

      Figure 6 demonstrates that we can identify a tissue region termed “subepithelial cells” which is the site of where remodelling events are known to occur in asthma and allergic pathology. This ECM-rich region is strongly associated with fibroblasts and immune cells which leads in to figure 7 showing that these cells are interacting.

      In addition to all of this the main focus of this manuscript is to link these analysis parameters to changes in the ECM environment and we have included this in each of these figures showing how these correlates with allergic changes and how they may be important in understanding these processes. In response to this reviewer’s point, we will highlight and make these novel findings clearer within the text of the manuscript.

      In Figure 5, the authors show a decrease in neutrophil numbers in challenged mice. This is unexpected, as this model is widely known to induce strong neutrophil recruitment. The authors should clarify this finding and investigate whether neutrophil chemoattractants are increased in these samples.

      This is a keen observation by the reviewer. We were interested in this finding however as it was not the focus of the paper we did not investigate further. In our previous publication we show that there are increased neutrophil numbers in the BAL of these animals (PMID: 33587776) and as mentioned above, we show in figure 5 that neutrophils are found mainly in the alveolar parenchyma. This perhaps means that they are more sensitive to being washed out in the BAL and perhaps there are differences in their “stickiness” in BALB/c and C57BL/6 animals or during DRA-induced allergy. This is in contrast to eosinophils (likely within our CD11b+ cells) which are found in the adventitial cuff, a region is not likely to be captured by BAL wash, though we know that these cells are actively present in the BAL. Overall, though this is an interesting result it was not the focus of this already lengthy paper and is best investigated in another project.

      When analysing epithelial cells, the authors separate AT1 and AT2 cells based on podoplanin expression. However, data in Supplementary Figure 4b suggest that both cell types express similar levels of podoplanin. The authors do not provide statistical validation for the claim that AT1 cells express higher levels. Additionally, as podoplanin is expressed by various cell types, such as lymphatic endothelial cells, additional markers are required to confirm the identity of AT1 cells.

      Again, the reviewer is entirely correct here. The cells we have identified are labelled as ATI as a best guess and correlate with ATII cells based on anatomical location – though this is likely shared by some of the populations mentioned by the reviewer. The majority of cells in this population are likely ATIs, as they are localized in the alveolar parenchyma and are cells that are not SPC+, though we cannot say for sure without more markers and we were already at the limit of the number of markers that we can run with one IMC panel. It is likely that there are contaminating lymphatic endothelial cells in this cluster. However, these will be a relatively minor population and do not change the main findings presented in the paper. To address this and other comments by the reviewer we plan to include a limitation section to the discussion that highlights exactly these points for future studies.

      The last set of data in Figure 7 is interesting and shows that immune cells interact with a population of S100a4 fibroblasts. This finding could be expanded further, as CD11b and Ly6C are expressed by a variety of immune cells. The authors should include additional staining to identify the specific cell types involved, such as monocytes, eosinophils, or airway macrophages. Furthermore, the authors should speculate on why these fibroblast regions attract immune cells. Are these regions enriched in chemokines or other factors?

      We thank them for this suggestion. To answer this point, we will conduct immunofluorescent imaging to provide further characterization of these cells in greater depth, as we agree, this will be important to consider. To best visualize cells and their interactions in this adventitial region, we plan to use 3D precision cut lung slices from PBS versus DRA mice in combination with confocal imaging. This method will allow us to utilize antibodies and markers that do not work in the FFPE sections such as SiglecF (eosinophils), CD11c (DCs, macrophages), CD64 and CD169 (macrophages).

      The discussion is engaging but focuses more on methodological aspects than new biological insights. Without mechanistic links, it is challenging to draw meaningful biological conclusions.

      We agree that the discussion could be used to reinforce the importance of the biological discoveries we have made (listed previously) in the discussion. However, we also believe that it is important to discuss the methodology as this is a novel way to explore ECM-cell interactions in the tissue as highlighted by the reviewer. There are many limitations to using IMC and similar techniques that should be highlighted for future studies so that we can develop better ways of quantifying the ECM environment during disease.

      Significance

      The study of Parkinson et al. provides interesting methodological insights into the use of imaging mass cytometry (IMC) to analyse lung architecture following inflammation. The application of multiplex antibody staining will leverage important information related to how tissues are adapting to chronic immune response. Here the authors rely entirely on mouse models for their studies and compared two lines of WT animals and the same allergen model. This limits the scope of the study, additional timepoints, sex or age would have improved the manuscript.

      Whilst we appreciate the reviewers points here, we would like to highlight the time involved in generating such datasets, with a lot of careful optimization and experimental design aspects going into each study. Whilst we have also performed staining and analysis using our described method in human FFPE tissue, we are currently looking to further develop analysis tools to assess ECM-cell interactions. Additionally, data acquisition using IMC takes considerable time, and hence it is not feasible run and analysis the number of samples required to address some of the questions proposed by the reviewer.

      We believe our manuscript provides novel methodology to analyse ECM environments within spatial datasets, something that no other spatial datasets have explored to date. Furthermore, we provide numerous new biological findings in relation to how cells are organized within the tissue during allergic pathology and propose immune-fibroblast interactions that may be key for driving ECM remodelling in the lung. Integrating these analyses will be key for further understanding the role of the ECM in disease pathogenesis.

      The applicability of this analysis pipeline to human tissue samples is not discussed, which would significantly enhance the impact of the study. Additionally, complementary techniques, such as flow cytometry or immunohistochemistry, could be used to validate the findings and improve reproducibility. A specialised audience of immunology researchers would be interested by the image analysis approach.

      As mentioned above, this analysis pipeline is easily applied to human samples or any other species as ECM molecule organization is largely conserved across species. Moreover, we have already explored this using human samples. However, adding human data to this manuscript is beyond the scope of this manuscript which was aiming to build one of the first methodologies for incorporating the ECM into this kind spatial analysis from the start in order to make biological discoveries. Regardless, we will add a discussion point on utilizing these pipelines to other species within the discussion of the manuscript.

      Flow cytometry has been published on this model and the exact samples used within this study as mentioned previously (PMID: 33587776), validating some of these findings – we will make this point more clearly in the manuscript. We do appreciate that it would be good to further expand on some findings presented in the manuscript. As such we will expand our immunostaining (as mentioned above) to give more detail on the infiltrating immune cell populations and their interactions with fibroblasts.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required): __

      Summary Parkinson and colleagues provided a highly intriguing manuscript on spatial resolution of cell-ECM interaction in mouse models of allergic airway inflammation. They used IMH to analyse two common mouse strains for allergic airway inflammation with a human relevant allergen mix. The study implements a novel technique to better segment tissue stainings (DeepThresh) and modifies existing tools to assess non-cellular seqmentation, ECM or fibrous structures that is. The study identifies region-specific ECM and confirms cellular proximity with canonical cell markers. Furthermore, clear ECM and cellular differences between the two mouse strains are found. The study concludes that this IMH approach is superior to existing methods as it provides a high spatial resolution of ECM protein - cell interaction.

      Major: ECM Isoform Annotation - The manuscript lacks precise annotation of ECM isoforms, particularly for Collagen I, IV, and VI. This impacts the accuracy of reported associations between ECM environments and cellular interactions.

      We thank the reviewer for this excellent comment and pointing this out. We agree that this is very important and will add this data to the manuscript. All information was included by reference of the antibody clones. However, it is an important point to make and we will account for this during interpretation of the results.

      Spatial Annotation Consistency- The manuscript inconsistently defines and annotates ECM environments (e.g., adventitial collagen, subepithelial & vascular ECM), leading to confusion in spatial correlation analyses.

      We are unsure what the reviewer is exactly referring to here. We have maintained a consistent nomenclature for these annotations throughout the manuscript. If the reviewer has an issue with the names we have provided for the regions; names were chosen these to be more informative than just naming them “region 1, 2, 3…”. Names in the manuscript were based on taking the lung tissue region and the prominent ECM molecules present. Whilst some level of detail will naturally be lost, we considered this the best way to keep data clear and consistent throughout the manuscript. For example, adventitial collagen describes the region predominantly around the adventitial cuff (fig 3c and d; shown in dark blue) that has high levels of Collagen I, III and VI. Yes, HA, laminin and fibronectin are also expressed, but at much lower levels. Regardless, all the information is present within the figures with readers to observe and make their own interpretations. We are happy to consider alternative names if the reviewer were to provide some guidance on what they thought was more appropriate.

      Lack of Supplemental Data- Activated cell types and regions are not clearly defined, and no supplemental data is provided to verify classifications. DeepThresh Validation - The method for removing staining artifacts via DeepThresh lacks clear validation. Complexity - Overlapping marker definitions (e.g., CD11b+ cells and infiltrating cells) need clarification for accurate immune cell characterization.

      We provide heatmaps in the supplementary data which shows the exact marker expression pattern for all of the clusters we define (Sup Fig 1a). Additionally, we provide graphs showing the cellular contribution and spatial distribution of all the regions we defined with lisaClust (Fig 2h & I; Sup Fig 1d). Most activated cells are a feature of a specific clustered cell type only being present in either PBS or DRA treated animals. However, the features which have led to separation these cell types are available in the heatmaps as mentioned (Sup Fig 1a).

      We believe the reviewer may be confused about the purpose of DeepThresh. This algorithm is not for removing staining artifacts. Instead it uses expert annotation of a small training set to generate a method of accurately thresholding images for positive staining in relatively small ROIs which may have diverse structural features with different staining properties. We did not have space in the manuscript to go into this in more detail. However, we appreciate this may not be as clear as needed for readers, and hence, will provide supplementary data showing some example thresholding alongside the original staining in a new edit of the manuscript.

      CD11b+ and infiltrating cells are not an overlapping population, they were separately clustered by the algorithm, but we take the reviewers point that further characterisation could be done. As mentioned in comments from reviewer 1, there is a limitation in the number of markers we can use in IMC, especially with the number of ECM markers we included. Additionally, there are limitations in the appropriate antibodies (carrier-free) that work in FFPE mouse tissue with the antigen retrieval that we use for good, reliable staining of ECM components. As such, we will perform additional immunofluorescence staining in 3D precision cut lung slices to better characterize the CD11b+ population to address comments by both reviewers.

      Minor: Terminology Inconsistency- The manuscript uses inconsistent terminology for ECM components and anatomical regions (e.g., adventitial collagen, immune foci, inflammatory zone).

      This point was directly addressed above in “Major” points and appears to be a duplicate comment.

      ROI Mask Coverage - Statistical insignificance in C57BL/6 ROI mask coverage is not addressed.

      The increase in C57BL/6 mice upon DRA treatment in panel A is not “significant” in the modern sense of the word. However, we would argue that stating it is “not significant” would also be a mistake. We prefer to use p values as an inferential measure of significance in combination with measures such as effect size and variance (PMID: 8465801). We find this more useful considering the vast number of mistakes made when interpreting p values (PMID: 18582619). The importance of not purely relying on p values for clinical research has been reviewed recently here (PMID: 39909800).

      Whilst we appreciate the reviewer’s requirement for significance, we do not want to make sweeping statements based off of a p value of 0.07, especially in only one experiment. Many papers have been published on the pitfalls of stringently adhering to p

      Spelling Error - "Immunte foci" in Figure 4h.

      We thank the reviewer for pointing this out and will correct this.

      Figure 6g Correlation Issue- The matrix environment correlation plot does not align with expected cell-ECM interactions.

      We find it hard to comment on this without more detail of the cell-ECM interactions that the reviewer believes should be occurring. We analysed this in an unbiased way, so we have not forced interactions to appear based on our preconceptions. The regions being analysed in Fig 6g are the resting (PBS) and activated (DRA) airways that contain expected cell populations of airway epithelial cells and a low level of fibroblasts, likely from just under the airway epithelial cells. These cell populations align with AEC-associated matrix, laminin and hyaluronan, and adventitial collagen regions. Perhaps the reviewer is questioning why the airways are associated with adventitial collagens? The reason behind this, is due to adventitial cuff residing adjacent to a proportion of all airways, and hence any ECM associated with the adventitial cuff will likely be included in an airway region. However, as mentioned previously there are limitations to this analysis and we are very likely missing finer details due to issues such as resolution which we have discussed within the point-by-point on numerous occasions, and something we will directly address by adding a limitations section to the discussion of the revised manuscript.

      Color Issues in Figures - ColI and ColIII have the same color in Fig. 3a, making interpretation difficult.

      We agree with the reviewer on this point. The issue we had here was that Col-I and Col-III strongly overlap in these images, whilst one was green and one yellow the effect was to make them look the same in the final images. We will remake these images with clearer colours that better illustrate differences in Col-I and Col-III expression.

      Patch Annotation (Fig. 4i) - The method for defining immune cell patches is unclear.

      Patches refers to an approach that is used to identify interconnected groups of similar cell types and is a method that is based off published data (PMID: 35363540). We will add further method details that explains this process to the revised manuscript.

      Detailed review: Methods: Animal model is suitable for differential analysis of various mouse strain responses to allergic airway inflammation.

      We thank the reviewer for this comment and also agree that the mouse models presented in the manuscript can provide insightful and mechanistic data for investigating human disease.

      Deepthresh matrix thresholding: IMCDenoise is sensitive to clusters of staining artefacts (specks). Please explain how DeepThresh via manual thresholding enables staining artefacts removal/detection. Manual ground truth mapping is common however it is not clear how your approach is performing against another tool. How was manual thresholding controlled (several analysts thresholded same image)?

      As described in a previous comment this is not the function of DeepThresh. Manual annotation for training data was performed by consensus agreement of four independent researchers. In terms of performance against another tool, we are not aware of another tool which performs this function and hence cannot compare. However, we will add additional data showing the validation metrics for the pipeline to make future comparisons easier.

      Antibodies Collagen IV, stains col4a1 - please correct, as isoforms vary throughout tissue. Collagen VI, stains col6a1 - isoforms vary in lung tissue, please state correct isoform throughout the document. Heparan sulfate: Molecular weight? Collagen I - isoform not defined, please state in methods.

      Figure 3 d As a resultant of the choice of antibodies against some particular isoforms of ECM molecules associations of cells, compartments are correct yet do not comply with all isoforms. Col4a1 is a basal membrane collagen from blood vessels; the adventitial area and vascular area are high in Col4a1. Other Col4 isoforms are found more frequently in the alveolar regions (col4a5,a6) and the subepithelial membrane. It is of utmost importance to clearly label the correct isoforms throughout the document.

      This relates to the comment above made by reviewer 2. As mentioned, we agree with this key point and will provide this information from the respective antibody clones.

      However, we are unable to provide details on the molecular weight of heparan sulfate as this will vary depending on location/tissue/condition etc. The antibody recognises 10E4 epitope on HS which is found across a wide variety of tissues and species and will recognise many different sizes of HS and even porcine Heparan. Importantly it is relatively specific, not cross reacting with hyaluronan, keratan sulphate, chondroitin sulphate, or dermatan sulphate which is an issue for certain clones. Whilst the size of the HS is an interesting facet, consideration of changes in sulphation patterns would also be of interest, though these currently cannot be accurately assessed via purely immunostaining-based methodologies and would require more targeted biochemical techniques. In addition to this there are multiple nuances in 10E4 antibody binding (PMID: 15044385 and 11278655) which are interesting, but far beyond the scope of this study. Although captured in the antibody clone information, we will also ensure this is clear in the methods.

      In relation to Col4 isoforms specifically, often antibodies for the ECM are limited because of their repeating structures it is hard to generate specific antibodies. For collagen IV there many clones for Col4a1, but no specific clones for Col4a3/col4a5 etc, suitable for use in FFPE tissues and metal conjugation required for IMC. Therefore, we were very limited in what was available to detect them at all. We will bring this up in the discussion as this is an important point, not just for our data, but also for people attempting to replicate this kind of analysis.

      Figure 2i: The cell-specific marker expression is in part already confounded by region. So vasculature or resting airways show no "resting" fibroblasts as their annotation is linked to activation (indicated by S100A4 expression). Anatomic locations such as airways with remodelling are termed "activated" to explain morphological differences which is acceptable given the model chosen. However, some cell type are not given an anatomical or morpholocial "resting" nomenclature. Only during activation and through location a cell type may aquire e.g. a nomenclature such as "alveolar fibroblast". The correlation blot 2i should provide this basic information. Please explain.

      Our staining approach and analysis have only identified certain activated populations as pointed out by the reviewer. Most of the populations that we have identified as “activated” have been identified primarily only in mice administered DRA. The reason that we have not included “resting” and “activated” populations for all cell types is that these clusters were generated using a clustering algorithm based on the cellular markers used within the study. Each cluster was then simply labelled as best we could, using information from marker expression, published biological data, anatomical location, and sample identity (e.g. PBS or DRA).

      A caveat to using IMC and other similar imaging techniques is that we will miss certain “flavours” of cell populations because we simply do not have the markers, or scope to include markers, with which to identify these cells. This is partly a problem of appropriate antibody availability, but also for many populations there are no specific markers identified in the literature/databases. Single cell RNAseq has provided deep segmentation of some of these populations, but we (and others) have found that often these make poor antibody choices at the protein immunostaining level.

      We are unsure what the reviewer wants adding to plot 2i. This plot shows the cell cluster contribution to different lisaClust defined tissue regions. Hence the presence of alveolar fibroblasts in the resting and activate alveoli region. However, we will include more discussion on the limitations of markers and identification of specific cell populations in the discussion.

      Figure 2h: How do you explain subepithelia to "leak" luminally in C57BL/6 DRA animal?

      We assume the reviewer is referring to the overlap of some grey circles though/over the red airway epithelial cells in the C57BL/6 DRA panel of figure 2h. This figure represents individual cells as circles with the centroid of the circle at the centroid of the cell. Cells are rarely perfect circles and, in this case, it has made it seem like the cell is coming through the airway epithelium, when likely it is a longer cell that sits directly under it. In addition to this, these are effectively 2-dimensional section (5um thick) that capture as small portion of the lung anatomy, hence occasionally this can result in unusual tissue structures that make no sense in the confines of a 2D section, but instead correlate with the larger 3D structure.

      How is an activated airway possible in a Balb/c PBS animal (same for inflammatory adventitia, alveoli)?

      Activated airway simply describes a region that is showing some evidence of activation markers such as RELMα and/or Ym2 etc. PBS itself, as with any other liquid administered into the lungs, will drive a very low level of inflammation, which is why it is used as a control in the animal model. Therefore, it is not surprising that we see a low number of these “activated” cells in PBS animals vice versa for their activated counterparts in DRA treated animals. This is similar for the other regions mentioned.

      How is subepithelia adjacent to immune foci and inflammatory adventitia (Balb/c DRA).

      We are somewhat confused by this question. We have termed the region “subepithelia” because it is mostly found under the airway epithelial cells. We found that this region expands during DRA treatment and covers areas close to the immune foci and inflammatory adventitia, hence they are next to each other.

      As described above, the names of these regions were chosen for simplicity and to communicate its general features. These, regions were identified by detection of nearby regions of cells with similar cellular compositions and the names we a “best fit”.

      Text for fig 3c: Here it should be mentioned that a cell is used as a proxy locator to the ECM region.

      We apologise that this was unclear for the reviewer. Rather than describing it as using the cell as a proxy locator to the ECM region we find it more accurate to think of it as we are characterizing the matrix environment of the cell i.e. what is the cell close to and what is it far away from. We will make this clearer in the results by changing the name to cellular matrix environment, rather than matrix environment.

      Again, in UMAP3b location and ECM molecule a mixed a priori which only can be achieved through proxy loction as in fig 3c or correlation matrix analysis. The UMAP shows ECM molecules in various combinations. Fig3c analysis of anatomic location from images with cell proxies would validate morpho-spatial UMAP annotation. Please make this clear in the manuscript or specify why your approach is superior in its presented format.

      We struggled to ascertain what the reviewer was referring to here and what edits they were suggesting to the revised manuscript. However, this comment seems to assume that we have used cellular location as an input to the UMAP in figure 3b, which is untrue. This UMAP (and associated clustering) shows each cell as a dot which is organised based on its distance to the different matrix components. Effectively showing us how different cells cluster based on their cellular matrix environment, with no input of cellular based markers. We are unsure what the reviewer is referring to on line 486 – as they seem to be describing exactly what figure 3c already is (a spatial map of the UMAP clusters on representative images, which shows that a cells matrix environment does seem to show patterns that align with the general lung anatomy).

      Finally, the reviewer asks us to specify why our approach is superior, but we are unclear what the alternative approach is.

      This methodology is effectively a repurposing of the traditional UMAP and clustering methodology used in many single cell techniques, but instead of applying this to cellular markers we are applying it to a cells matrix environment as quantified by the matrix distances. If the reviewer could clarify this comment we would be happy to revisit it. As mentioned in the previous comment, we will more clearly describe cellular matrix environments in the revised manuscript and this may also help with the confusion.

      Fig 3d: The Matrix Cluster names are in part not correct. Subepithelial & Vascular ECM does not correlate with Vasculature in LisaClust Regions. Also ColIV is not AEC associated, yet subepithelial.

      Respectfully, we completely disagree with the reviewer on this point. In the heatmap (Fig 3d) the Subepithelial & Vascular matrix environment correlates most strongly with the Vasculature and Subepithelial cells as shown by the stronger green-yellow colour in the corresponding cell of the heatmap.

      As mentioned previously in response to another comment by reviewer 2, there could be many reasons that we are not detecting collagen-IV in the AEC associate cell matrix environment. One likely explanation is that this is too fine for the resolution of IMC (1-micron2) or it could be that certain subchains are utilised here that are not recognized by the antibody we managed to optimize. Additionally, AEC-associated matrix environment is comprised of both mouse strains and includes higher representation from DRA treated animals. From our previous work (PMID: 33587776), we have shown that Col-IV expression around the AEC is reduced in DRA versus PBS -treated animals.

      No ECM molecule is inflammatory zone associated. Does this indicate cellular density does not allow to distinguish ECM?

      This is a great point from the reviewer and their explanation is entirely possibly. As mentioned there are huge limitations in the resolution of IMC and so we are likely missing finer matrix structures. There is a huge recruitment of cells within this environment so it could be that we cannot clearly visualise fine ECM structure through this considering we are also looking at a 5-micron thick 2D tissue section. Additionally, cells maybe degrading the ECM in order to infiltrate into the tissue. This is definitely an interesting point to examine in further detail, but would need to be done with a different methodology. We will aim to look at an ECM molecules and its distribution within the inflammatory zone using 3D precision cut lung slices and also immune-staining of tissue sections to see whether we can better resolve this in a revised manuscript.

      Also the term "adventitial collagen" is locating to LisaClust Regions Vasculature, Subepithelial Cells, Resting Airways, Infiltrating Cells, Activated Airways. Adventitial per definition of fig. 2g is around blood vessels extending to airways and around it. The adventitial regions are the ECM rich areas after the fibroblasts (as for blood vessels, PMID: 31522963). The definition used in this study therefore generates morphological overlaps between airways and their basolateral regions and blood vessels. Whilst both morphological regions have an adventitia the Matrix cluster assumes from areas to close by this terminology. As a sensitivity analysis I would suggest to reduce the perimeter around blood vessels to the same borderline as seen in airways. If composition remains similar "adventitial collagen" could be a broader term. Alternatively, if adventitia from airway and blood vessel differ these should be separate terms.

      Whilst the adventitial cuff does refer to the region immediately around a blood vessel in the lung, these structures are slightly more nuanced as blood vessels normally travel through the lung in close association with an airway. This is true all the way down to the close association with the capillaries and the alveolar spaces where gas exchange occurs. Indeed, previous publications (PMID: 30824323) have shown that these adventitial cuffs extend out from around the contiguous area around the blood vessel and associated airway and these can expand during inflammation (PMID: 24631179). This region is rich in Collagen-I and Collagen-III, as we have shown in this manuscript and previously (PMID: 33587776).

      Whilst we agree that there are likely microanatomical niches within this larger structure, our dataset lacks the resolution to study this in more detail. However, as mentioned above we can include matrix markers in our future IF staining to examine this region in more detail. The adventitial collagen environment described in this manuscript and beyond, are vital “meet and greet” spots for immune cell infiltrating into the lungs (PMID: 30824323) as well as being sites of iBALT formation (PMID: 24631179)

      We are unsure what the reviewer means by “…reduce the perimeter around blood vessels to the same borderline as seen in airways.” We have not defined a manual threshold for the border of the airways. These regions were all defined by SNN clustering and not manual segmentation. Whilst this methodology could be developed we do not believe that this dataset has the resolution to answer this question, as mentioned previously.

      Fig 4c: Balb/c and C57bl/6 labels are incorrect (see a,b)

      We thank the reviewer for noticing this incorrect labelling and will update this.

      Fig 4h: Cell type "other is highly present in immune foci and inflammatory adventitia but not further classified and not myeloid. This seems either a difficult definition for myeloid or a significant immune population wasn't stained. How is myeloid defined?

      We define myeloid broadly as CD11b+ or alveolar macs. There were certain populations that were not stained, notably T cells. We were unable to have suitable or reliable staining in FFPE tissue with CD90, TCRa/b, CD3e antibodies via IMC. The same was true for Eosinophil markers (SiglecF, Ccr3, EPO, MBP). The additional experiments we will perform for a revised manuscript (using 3D precision cut lung slices and/or IF staining) should shed further light on these cells. Additionally, as we are not limited by the processing requirements of IMC, we can use a wider range of markers.

      Fig 4l has a vast variety of marker combinations some being very specific within the staining panel, others subsummarise entire groups of cells. It would be very helpful to know if the lables are specific and exclusive or if larger clusters exist, that they then subdivide into specific groups (e.g. Infiltrating cells: any of CD11b, CD44, Ly6C vs. B-cells or CD11b+Lys6C). This graph would profit also from either using markers or cell types only. Your marker set is very distinct and limited so per definition it is either a neutrophil or a Lys6C+. Please decide, explain and provide the other graph as supplement.

      We apologise that this was not clear to the reviewer. Labels are exclusive and represent the clusters that were identified in figure 2 and are at the finest level of detail that we felt we were able to biologically infer from the data. In terms of the reviewer’s first point about infiltrating cells, these are completely separate from the other cell types mentioned. As mentioned in the previous comment line 570, we were simply unable to find working antibodies for some of the common lung populations (a common problem for FFPE sections where antigens are often masked or lost due to fixation and processing) and so are limited to general annotations for these. For the reviewer’s second example of Neutrophils vs Ly6C+ cells, neutrophils were classified by expression of Ly6G, CD11b+, and Ym1 whereas there are many other cell types that express Ly6C, including, but not limited to, dendritic cells, monocytes, eosinophils, and even some T cells.

      We believe that the graph in combination with data in Fig 1c and supplementary Fig 1a, already shows what the reviewer is asking for.

      Fig 5l and sup Fig4i: There is no graph confirming the statement that Ym1 is produced by macrophages. From the graphs in either of the two panels, The AEC are highly associated with Ym1/2 expression or the activated alveoli. Please explain ad amend.

      We assume the reviewer means Fig 5l and sup Fig 5i (as there is no figure sup Fig 4i). Whilst we did not include a graph to show that alveolar macrophages produce Ym1, we did include two references in the text and this has been widely shown in the literature for many years (PMID: 11141507 and 15148607). We are somewhat unclear on the reviewers second point. AEC (airway epithelial cells) can definitely also produce Ym1, though this can often be contentious because of issues with cross-reactivity with its highly homologous sister protein Ym2, which is also produced from airway epithelial cells under Type-2 settings. If the reviewer is referring to AEC (alveolar epithelial cells) then this is true. Activated alveoli are lisaClust regions with lots of alveolar macrophages which was the original statement we made and aligns with sup Fig 5i. Activated alveoli II have less alveolar macrophages and also have less Ym1, which would correlate though there are other cell types which can make Ym1 as well.

      Fig 6g: The correlation plots again show that the matrix environment labels are somewhat confounded. Whilst AEC associated makes perfect sense, adventitial collagen only weakly correlates, yet was part of the adventitial mapping. Cell types like AEC are expected however fibroblasts, especially in resting airways as large constituent cell populations. There are not other, myeloid or lymphoid cells associated with these airways, which under activated conditions seems rather odd. From fig6a it is appearant that the lisaClust has ascribed subepithelial regions to distal parts of the airway separated by blood vessel or parenchyma (C57BL/6 and Balb/c DRA). Also blood vessels are in part other cell types or epithelium (B6 PBS). Is the annotation here the reason for this rather confusing result? Please explain and/or amend.

      We are again somewhat confused by this comment. Adventitial collagen only weakly correlates because it is not within the airway epithelial cells, instead it is adjacent in the subepithelial region which is shown in Fig 6j. We are unsure exactly what the reviewer is referring to in terms of “adventitial mapping” but are happy to comment on this if the reviewer can clarify what they mean.

      We agree with the reviewer that it is somewhat surprising to see so many fibroblasts in the resting and activated airway regions. There is a level of ambiguity here in what lisaClust decides to include in one region vs another. However, what it does show is that there are a large population of fibroblasts around the airway, possibly correlating with peribronchial fibroblasts. We did not observe immune cells in between the airway cells or immediately underneath it. We do not believe this is odd, as from our data it appears that these cells are more likely to be found in the adventitial (including peribronchial as mentioned previously) cuff. Cell are most certainly moving into the airways as shown from the BAL in our previous publication (PMID: 33587776). However, we are unlikely to capture this process in the snapshot of our histology across a relatively small section of the airways covered in our 2D sections.

      In regards to the reviewers comment about figure 6a we agree that some of the regions between the airways and blood vessels have been characterised as subepithelia. As mentioned previously we are happy to consider alternative names but have been unable to come up with an alternative that encompasses the cells and spatial region more accurately and clearly., Regardless, the main purpose of these names is to provide simple nomenclature to follow throughout the manuscript and make these types of analyses accessible to all readers. We believe that these are accurately labelled and have provided information about the constituent cell populations that are present within them, making the data and subsequent analysis transparent for others to view and explore. Our data suggests that the adventitial cuff may fulfil multiple roles during DRA-induced inflammation, some of which are more focused on immune cell recruitment and others which may correlate more with the fibroblast rich subepithelial region.

      The reviewer is entirely correct to point out that some blood vessels were not entirely annotated. We used vWF to manually separate blood vessels from the adjacent smooth muscle layers, which were not separated by the clustering originally. Notably it appears that veins seem to not separate as well as arteries suggesting another marker (e.g. CD31) may help with this, though we were limited in what we could include as mentioned previously. As this is only a small effect, which we do not have a way to correct, and blood vessels were not the focus of this manuscript, we have left the annotation as it is with raw data included.

      __Significance __

      Strength Innovative ECM-Immune Interaction Approach- The study integrates extracellular matrix (ECM) phenotyping with immune cell spatial mapping, providing novel insights into allergic airway inflammation Multiplex Imaging Technology - The use of Imaging Mass Cytometry (IMC) allows high-resolution spatial characterization of both cellular and ECM components. Strain Analysis - The inclusion of BALB/c and C57BL/6 mice enables differentiation of strain-specific ECM and immune responses. Deep-Learning-Based ECM Quantification - DeepThresh offers an advanced computational approach for ECM analysis, enhancing accuracy in defining ECM-cell associations. Comprehensive Tissue Classification- LisaClust clustering facilitates detailed segmentation of lung microenvironments, improving understanding of localized tissue remodeling.

      Limitations ECM Isoform Inconsistencies - The study lacks precise annotation of ECM isoforms, which affects the accuracy of reported ECM-cell interactions. Ambiguous Spatial Correlations- Some ECM clusters, such as "adventitial collagen," overlap inconsistently with anatomical regions, making interpretation challenging. Unvalidated DeepThresh Method - The manuscript does not provide sufficient validation of DeepThresh's ability to remove staining artifacts. Lack of Supplemental Data- Key activated cell types and regions lack supporting data for classification.

      __Advance, gap filled __ Clearly the next step to improve organ compendia such as the lung cell atlas, spatial protein analysis is warranted. scRNA-Seq in particular for ECM molecules is challenging as these molecules are produced in small quantities or have a very slow turn-over. This study has the potential to provide novel deep learning algorithms to include not only cellular markers but consider larger panels of ECM molecules and their spatial orientation in the lung.

      __Audience __ The manuscript is interdisciplinary located between advanced image analysis with deep learning methods, fundamental lung biology and single cell analysis. The readership would entice molecular biologists, bioinformaticians and basic disease model scientists. The manuscript would appeal to clinician scientists and a broader audience if human tissue pendants could be provided validating the methods and outcomes.

      __Own Expertise __ Translational scientist in the field of chronic lung disease, highly familiar with epithelial cells, mouse models, human cohorts and next generation sequencing and imaging of live single cells.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews

      Reviewer #1 (Public Review):

      Summary:

      The authors have created a system for designing and running experimental pipelines to control and coordinate different programs and devices during an experiment, called Heron. Heron is based around a graphical tool for creating a Knowledge Graph made up of nodes connected by edges, with each node representing a separate Python script, and each edge being a communication pathway connecting a specific output from one node to an iput on another. Each node also has parameters that can be set by the user during setup and runtime, and all of this behavior is concisely specified in the code that defines each node. This tool tries to marry the ease of use, clarity, and selfdocumentation of a purely graphical system like Bonsai with the flexibility and power of a purely code-based system like Robot Operating System (ROS).

      Strengths:

      The underlying idea behind Heron, of combining a graphical design and execution tool with nodes that are made as straightforward Python scripts seems like a great way to get the relative strengths of each approach. The graphical design side is clear, selfexplanatory, and self-documenting, as described in the paper. The underlying code for each node tends to also be relatively simple and straightforward, with a lot of the complex communication architecture successfully abstracted away from the user. This makes it easy to develop new nodes, without needing to understand the underlying communications between them. The authors also provide useful and well-documented templates for each type of node to further facilitate this process. Overall this seems like it could be a great tool for designing and running a wide variety of experiments, without requiring too much advanced technical knowledge from the users.

      The system was relatively easy to download and get running, following the directions and already has a significant amount of documentation available to explain how to use it and expand its capabilities. Heron has also been built from the ground up to easily incorporate nodes stored in separate Git repositories and to thus become a large community-driven platform, with different nodes written and shared by different groups. This gives Heron a wide scope for future utility and usefulness, as more groups use it, write new nodes, and share them with the community. With any system of this sort, the overall strength of the system is thus somewhat dependent on how widely it is used and contributed to, but the authors did a good job of making this easy and accessible for people who are interested. I could certainly see Heron growing into a versatile and popular system for designing and running many types of experiments.

      Weaknesses:

      (1) The number one thing that was missing from the paper was any kind of quantification of the performance of Heron in different circumstances. Several useful and illustrative examples were discussed in depth to show the strengths and flexibility of Heron, but there was no discussion or quantification of performance, timing, or latency for any of these examples. These seem like very important metrics to measure and discuss when creating a new experimental system.

      Heron is practically a thin layer of obfuscation of signal passing across processes. Given its design approach it is up to the code of each Node to deal with issues of timing, synching and latency and thus up to each user to make sure the Nodes they author fulfil their experimental requirements. Having said that, Heron provides a large number of tools to allow users to optimise the generated Knowledge Graphs for their use cases. To showcase these tools, we have expanded on the third experimental example in the paper with three extra sections, two of which relate to Heron’s performance and synching capabilities. One is focusing on Heron’s CPU load requirements (and existing Heron tools to keep those at acceptable limits) and another focusing on post experiment synchronisation of all the different data sets a multi Node experiment generates.   

      (2) After downloading and running Heron with some basic test Nodes, I noticed that many of the nodes were each using a full CPU core on their own. Given that this basic test experiment was just waiting for a keypress, triggering a random number generator, and displaying the result, I was quite surprised to see over 50% of my 8-core CPU fully utilized. I don’t think that Heron needs to be perfectly efficient to accomplish its intended purpose, but I do think that some level of efficiency is required. Some optimization of the codebase should be done so that basic tests like this can run with minimal CPU utilization. This would then inspire confidence that Heron could deal with a real experiment that was significantly more complex without running out of CPU power and thus slowing down.

      The original Heron allowed the OS to choose how to manage resources over the required process. We were aware that this could lead to significant use of CPU time, as well as occasionally significant drop of packets (which was dependent on the OS and its configuration). This drop happened mainly when the Node was running a secondary process (like in the Unity game process in the 3rd example). To mitigate these problems, we have now implemented a feature allowing the user to choose the CPU that each Node’s worker function runs on as well as any extra processes the worker process initialises. This is accessible from the Saving secondary window of the node. This stops the OS from swapping processes between CPUs and eliminates the dropping of packages due to the OS behaviour. It also significantly reduces the utilised CPU time. To showcase this, we initially run the simple example mentioned by the reviewer. The computer running only background services was using 8% of CPU (8 cores). With Heron GUI running but with no active Graph, the CPU usage went to 15%. With the Graph running and Heron’s processes running on OS attributed CPU cores, the total CPU was at 65% (so very close to the reviewer’s 50%). By choosing a different CPU core for each of the three worker processes the CPU went down to 47% and finally when all processes were forced to run on the same CPU core the CPU load dropped to 30%.  So, Heron in its current implementation running its GUI and 3 Nodes takes 22% of CPU load. This is still not ideal but is a consequence of the overhead of running multiple processes vs multiple threads. We believe that, given Heron’s latest optimisation, offering more control of system management to the user, the benefits of multi process applications outweigh this hit in system resources. 

      We have also increased the scope of the third example we provide in the paper and there we describe in detail how a full-scale experiment with 15 Nodes (which is the upper limit of number of Nodes usually required in most experiments) impacts CPU load. 

      Finally, we have added on Heron’s roadmap projects extra tasks focusing only on optimisation (profiling and using Numba for the time critical parts of the Heron code).

      (3) I was also surprised to see that, despite being meant specifically to run on and connect diverse types of computer operating systems and being written purely in Python, the Heron Editor and GUI must be run on Windows. This seems like an unfortunate and unnecessary restriction, and it would be great to see the codebase adjusted to make it fully crossplatform-compatible.

      This point was also mentioned by reviewer 2. This was a mistake on our part and has now been corrected in the paper. Heron (GUI and underlying communication functionality) can run on any machine that the underlying python libraries run, which is Windows, Linux (both for x86 and Arm architectures) and MacOS. We have tested it on Windows (10 and 11, both x64), Linux PC (Ubuntu 20.04.6, x64) and Raspberry Pi 4 (Debian GNU/Linux 12 (bookworm), aarch64). The Windows and Linux versions of Heron have undergone extensive debugging and all of the available Nodes (that are not OS specific) run on those two systems. We are in the process of debugging the Nodes’ functionality for RasPi. The MacOS version, although functional requires further work to make sure all of the basic Nodes are functional (which is not the case at the moment). We have also updated our manuscript (Multiple machines, operating systems and environments) to include the above information. 

      (4) Lastly, when I was running test experiments, sometimes one of the nodes, or part of the Heron editor itself would throw an exception or otherwise crash. Sometimes this left the Heron editor in a zombie state where some aspects of the GUI were responsive and others were not. It would be good to see a more graceful full shutdown of the program when part of it crashes or throws an exception, especially as this is likely to be common as people learn to use it. More problematically, in some of these cases, after closing or force quitting Heron, the TCP ports were not properly relinquished, and thus restarting Heron would run into an "address in use" error. Finding and killing the processes that were still using the ports is not something that is obvious, especially to a beginner, and it would be great to see Heron deal with this better. Ideally, code would be introduced to carefully avoid leaving ports occupied during a hard shutdown, and furthermore, when the address in use error comes up, it would be great to give the user some idea of what to do about it.

      A lot of effort has been put into Heron to achieve graceful shut down of processes, especially when these run on different machines that do not know when the GUI process has closed. The code that is being suggested to avoid leaving ports open has been implemented and this works properly when processes do not crash (Heron is terminated by the user) and almost always when there is a bug in a process that forces it to crash. In the version of Heron available during the reviewing process there were bugs that caused the above behaviour (Node code hanging and leaving zombie processes) on MacOS systems. These have now been fixed. There are very seldom instances though, especially during Node development, that crashing processes will hang and need to be terminated manually. We have taken on board the reviewer’s comments that users should be made more aware of these issues and have also described this situation in the Debugging part of Heron’s documentation. There we explain the logging and other tools Heron provides to help users debug their own Nodes and how to deal with hanging processes.

      Heron is still in alpha (usable but with bugs) and the best way to debug it and iron out all the bugs in all use cases is through usage from multiple users and error reporting (we would be grateful if the errors the reviewer mentions could be reported in Heron’s github Issues page). We are always addressing and closing any reported errors, since this is the only way for Heron to transition from alpha to beta and eventually to production code quality.

      Overall I think that, with these improvements, this could be the beginning of a powerful and versatile new system that would enable flexible experiment design with a relatively low technical barrier to entry. I could see this system being useful to many different labs and fields. 

      We thank the reviewer for positive and supportive words and for the constructive feedbacks. We believe we have now addressed all the raised concerns.  

      Reviewer #2 (Public Review):

      Summary:

      The authors provide an open-source graphic user interface (GUI) called Heron, implemented in Python, that is designed to help experimentalists to

      (1) design experimental pipelines and implement them in a way that is closely aligned with their mental schemata of the experiments,

      (2) execute and control the experimental pipelines with numerous interconnected hardware and software on a network.

      The former is achieved by representing an experimental pipeline using a Knowledge Graph and visually representing this graph in the GUI. The latter is accomplished by using an actor model to govern the interaction among interconnected nodes through messaging, implemented using ZeroMQ. The nodes themselves execute user-supplied code in, but not limited to, Python.

      Using three showcases of behavioral experiments on rats, the authors highlighted three benefits of their software design:

      (1) the knowledge graph serves as a self-documentation of the logic of the experiment, enhancing the readability and reproducibility of the experiment,

      (2) the experiment can be executed in a distributed fashion across multiple machines that each has a different operating system or computing environment, such that the experiment can take advantage of hardware that sometimes can only work on a specific computer/OS, a commonly seen issue nowadays,

      (3) he users supply their own Python code for node execution that is supposed to be more friendly to those who do not have a strong programming background.

      Strengths:

      (1) The software is light-weight and open-source, provides a clean and easy-to-use GUI,

      (2) The software answers the need of experimentalists, particularly in the field of behavioral science, to deal with the diversity of hardware that becomes restricted to run on dedicated systems.

      (3) The software has a solid design that seems to be functionally reliable and useful under many conditions, demonstrated by a number of sophisticated experimental setups.

      (4) The software is well documented. The authors pay special attention to documenting the usage of the software and setting up experiments using this software.

      Weaknesses:

      (1) While the software implementation is solid and has proven effective in designing the experiment showcased in the paper, the novelty of the design is not made clear in the manuscript. Conceptually, both the use of graphs and visual experimental flow design have been key features in many widely used softwares as suggested in the background section of the manuscript. In particular, contrary to the authors’ claim that only pre-defined elements can be used in Simulink or LabView, Simulink introduced MATLAB Function Block back in 2011, and Python code can be used in LabView since 2018. Such customization of nodes is akin to what the authors presented.

      In the Heron manuscript we have provided an extensive literature review of existing systems from which Heron has borrowed ideas. We never wished to say that graphs and visual code is what sets Heron apart since these are technologies predating Heron by many years and implemented by a large number of software. We do not believe also that we have mentioned that LabView or Simulink can utilise only predefined nodes. What we have said is that in such systems (like LabView, Simulink and Bonsai) the focus of the architecture is on prespecified low level elements while the ability for users to author their own is there but only as an afterthought. The difference with Heron is that in the latter the focus is on the users developing their own elements. One could think of LabView style software as node-based languages (with low level visual elements like loops and variables) that also allow extra scripting while Heron is a graphical wrapper around python where nodes are graphical representations of whole processes. To our knowledge there is no other software that allows the very fast generation of graphical elements representing whole processes whose communication can also be defined graphically. Apart from this distinction, Heron also allows a graphical approach to writing code for processes that span different machines which again to our knowledge is a novelty of our approach and one of its strongest points towards ease of experimental pipeline creation (without sacrificing expressivity). 

      (2) The authors claim that the knowledge graph can be considered as a self-documentation of an experiment. I found it to be true to some extent. Conceptually it’s a welcoming feature and the fact that the same visualization of the knowledge graph can be used to run and control experiments is highly desirable (but see point 1 about novelty). However, I found it largely inadequate for a person to understand an experiment from the knowledge graph as visualized in the GUI alone. While the information flow is clear, and it seems easier to navigate a codebase for an experiment using this method, the design of the GUI does not make it a one-stop place to understand the experiment. Take the Knowledge Graph in Supplementary Figure 2B as an example, it is associated with the first showcase in the result section highlighting this self-documentation capability. I can see what the basic flow is through the disjoint graph where 1) one needs to press a key to start a trial, and 2) camera frames are saved into an avi file presumably using FFMPEG. Unfortunately, it is not clear what the parameters are and what each block is trying to accomplish without the explanation from the authors in the main text. Neither is it clear about what the experiment protocol is without the help of Supplementary Figure 2A.

      In my opinion, text/figures are still key to documenting an experiment, including its goals and protocols, but the authors could take advantage of the fact that they are designing a GUI where this information, with properly designed API, could be easily displayed, perhaps through user interaction. For example, in Local Network -> Edit IPs/ports in the GUI configuration, there is a good tooltip displaying additional information for the "password" entry. The GUI for the knowledge graph nodes can very well utilize these tooltips to show additional information about the meaning of the parameters, what a node does, etc, if the API also enforces users to provide this information in the form of, e.g., Python docstrings in their node template. Similarly, this can be applied to edges to make it clear what messages/data are communicated between the nodes. This could greatly enhance the representation of the experiment from the Knowledge graph.

      In the first showcase example in the paper “Probabilistic reversal learning.

      Implementation as self-documentation” we go through the steps that one would follow in order to understand the functionality of an experiment through Heron’s Knowledge Graph. The Graph is not just the visual representation of the Nodes in the GUI but also their corresponding code bases. We mention that the way Heron’s API limits the way a Node’s code is constructed (through an Actor based paradigm) allows for experimenters to easily go to the code base of a specific Node and understand its 2 functions (initialisation and worker) without getting bogged down in the code base of the whole Graph (since these two functions never call code from any other Nodes). Newer versions of Heron facilitate this easy access to the appropriate code by also allowing users to attach to Heron their favourite IDE and open in it any Node’s two scripts (worker and com) when they double click on the Node in Heron’s GUI. On top of this, Heron now (in the versions developed as answers to the reviewers’ comments) allows Node creators to add extensive comments on a Node but also separate comments on the Node’s parameters and input and output ports. Those can be seen as tooltips when one hovers over the Node (a feature that can be turned off or on by the Info button on every Node).  

      As Heron stands at the moment we have not made the claim that the Heron GUI is the full picture in the self-documentation of a Graph. We take note though the reviewer’s desire to have the GUI be the only tool a user would need to use to understand an experimental implementation. The solution to this is the same as the one described by the reviewer of using the GUI to show the user the parts of the code relevant to a specific Node without the user having to go to a separate IDE or code editor. The reason this has not been implemented yet is the lack of a text editor widget in the underlying gui library (DearPyGUI). This is in their roadmap for their next large release and when this exists we will use it to implement exactly the idea the reviewer is suggesting, but also with the capability to not only read comments and code but also directly edit a Node’s code (see Heron’s roadmap). Heron’s API at the moment is ideal for providing such a text editor straight from the GUI.

      (3) The design of Heron was primarily with behavioral experiments in mind, in which highly accurate timing is not a strong requirement. Experiments in some other areas that this software is also hoping to expand to, for example, electrophysiology, may need very strong synchronization between apparatus, for example, the record timing and stimulus delivery should be synced. The communication mechanism implemented in Heron is asynchronous, as I understand it, and the code for each node is executed once upon receiving an event at one or more of its inputs. The paper, however, does not include a discussion, or example, about how Heron could be used to address issues that could arise in this type of communication. There is also a lack of information about, for example, how nodes handle inputs when their ability to execute their work function cannot keep up with the frequency of input events. Does the publication/subscription handle the queue intrinsically? Will it create problems in real-time experiments that make multiple nodes run out of sync? The reader could benefit from a discussion about this if they already exist, and if not, the software could benefit from implementing additional mechanisms such that it can meet the requirements from more types of experiments.

      In order to address the above lack of explanation (that also the first reviewer pointed out) we expanded the third experimental example in the paper with three more sections. One focuses solely on explaining how in this example (which acquires and saves large amounts of data from separate Nodes running on different machines) one would be able to time align the different data packets generated in different Nodes to each other. The techniques described there are directly implementable on experiments where the requirements of synching are more stringent than the behavioural experiment we showcase (like in ephys experiments). 

      Regarding what happens to packages when the worker function of a Node is too slow to handle its traffic, this is mentioned in the paper (Code architecture paragraph): “Heron is designed to have no message buffering, thus automatically dropping any messages that come into a Node’s inputs while the Node’s worker function is still running.” This is also explained in more detail in Heron’s documentation. The reasoning for a no buffer system (as described in the documentation) is that for the use cases Heron is designed to handle we believe there is no situation where a Node would receive large amounts of data in bursts while very little data during the rest of the time (in which case a buffer would make sense). Nodes in most experiments will either be data intensive but with a constant or near constant data receiving speed (e.g. input from a camera or ephys system) or will have variable data load reception but always with small data loads (e.g. buttons). The second case is not an issue and the first case cannot be dealt with a buffer but with the appropriate code design, since buffering data coming in a Node too slow for its input will just postpone the inevitable crash. Heron’s architecture principle in this case is to allow these ‘mistakes’ (i.e. package dropping) to happen so that the pipeline continues to run and transfer the responsibility of making Nodes fast enough to the author of each Node. At the same time Heron provides tools (see the Debugging section of the documentation and the time alignment paragraph of the “Rats playing computer games”  example in the manuscript) that make it easy to detect package drops and either correct them or allow them but also allow time alignment between incoming and outgoing packets. In the very rare case where a buffer is required Heron’s do-it-yourself logic makes it easy for a Node developer to implement their own Node specific buffer.

      (4) The authors mentioned in "Heron GUI’s multiple uses" that the GUI can be used as an experimental control panel where the user can update the parameters of the different Nodes on the fly. This is a very useful feature, but it was not demonstrated in the three showcases. A demonstration could greatly help to support this claim.

      As the reviewer mentions, we have found Heron’s GUI double role also as an experimental on-line controller a very useful capability during our experiments. We have expanded the last experimental example to also showcase this by showing how on the “Rats playing computer games” experiment we used the parameters of two Nodes to change the arena’s behaviour while the experiment was running, depending on how the subject was behaving at the time (thus exploring a much larger set of parameter combinations, faster during exploratory periods of our shaping protocols construction). 

      (5) The API for node scripts can benefit from having a better structure as well as having additional utilities to help users navigate the requirements, and provide more guidance to users in creating new nodes. A more standard practice in the field is to create three abstract Python classes, Source, Sink, and Transform that dictate the requirements for initialisation, work_function, and on_end_of_life, and provide additional utility methods to help users connect between their code and the communication mechanism. They can be properly docstringed, along with templates. In this way, the com and worker scripts can be merged into a single unified API. A simple example that can cause confusion in the worker script is the "worker_object", which is passed into the initialise function. It is unclear what this object this variable should be, and what attributes are available without looking into the source code. As the software is also targeting those who are less experienced in programming, setting up more guidance in the API can be really helpful. In addition, the self-documentation aspect of the GUI can also benefit from a better structured API as discussed in point 2 above.

      The reviewer is right that using abstract classes to expose to users the required API would be a more standard practice. The reason we did not choose to do this was to keep Heron easily accessible to entry level Python programmers who do not have familiarity yet with object oriented programming ideas. So instead of providing abstract classes we expose only the implementation of three functions which are part of the worker classes but the classes themselves are not seen by the users of the API. The point about the users’ accessibility to more information regarding a few objects used in the API (the worker object for example) has been taken on board and we have now addressed this by type hinting all these objects both in the templates and more importantly in the automatically generated code that Heron now creates when a user chooses to create a Node graphically (a feature of Heron not present in the version available in the initial submission of this manuscript).  

      (6) The authors should provide more pre-defined elements. Even though the ability for users to run arbitrary code is the main feature, the initial adoption of a codebase by a community, in which many members are not so experienced with programming, is the ability for them to use off-the-shelf components as much as possible. I believe the software could benefit from a suite of commonly used Nodes.

      There are currently 12 Node repositories in the Heron-repositories project on Github with more than 30 Nodes, 20 of which are general use (not implementing a specific experiment’ logic). This list will continue to grow but we fully appreciate the truth of the reviewer’s comment that adoption will depend on the existence of a large number of commonly used Nodes (for example Numpy, and OpenCV Nodes) and are working towards this goal.

      (7) It is not clear to me if there is any capability or utilities for testing individual nodes without invoking a full system execution. This would be critical when designing new experiments and testing out each component.

      There is no capability to run the code of an individual Node outside Heron’s GUI. A user could potentially design and test parts of the Node before they get added into a Node but we have found this to be a highly inefficient way of developing new Nodes. In our hands the best approach for Node development was to quickly generate test inputs and/or outputs using the “User Defined Function 1I 1O” Node where one can quickly write a function and make it accessible from a Node. Those test outputs can then be pushed in the Node under development or its outputs can be pushed in the test function, to allow for incremental development without having to connect it to the Nodes it would be connected in an actual pipeline. For example, one can easily create a small function that if a user presses a key will generate the same output (if run from a “User Defined Function 1I 1O” Node) as an Arduino Node reading some buttons. This output can then be passed into an experiment logic Node under development that needs to do something with this input. In this way during a Node development Heron allows the generation of simulated hardware inputs and outputs without actually running the actual hardware. We have added this way of developing Nodes also in our manuscript (Creating a new Node).

      Reviewer #3 (Public Review):

      Summary:

      The authors present a Python tool, Heron, that provides a framework for defining and running experiments in a lab setting (e.g. in behavioural neuroscience). It consists of a graphical editor for defining the pipeline (interconnected nodes with parameters that can pass data between them), an API for defining the nodes of these pipelines, and a framework based on ZeroMQ, responsible for the overall control and data exchange between nodes. Since nodes run independently and only communicate via network messages, an experiment can make use of nodes running on several machines and in separate environments, including on different operating systems.

      Strengths:

      As the authors correctly identify, lab experiments often require a hodgepodge of separate hardware and software tools working together. A single, unified interface for defining these connections and running/supervising the experiment, together with flexibility in defining the individual subtasks (nodes) is therefore a very welcome approach. The GUI editor seems fairly intuitive, and Python as an accessible programming environment is a very sensible choice. By basing the communication on the widely used ZeroMQ framework, they have a solid base for the required non-trivial coordination and communication. Potential users reading the paper will have a good idea of how to use the software and whether it would be helpful for their own work. The presented experiments convincingly demonstrate the usefulness of the tool for realistic scientific applications.

      Weaknesses:

      (1) In my opinion, the authors somewhat oversell the reproducibility and "selfdocumentation" aspect of their solution. While it is certainly true that the graph representation gives a useful high-level overview of an experiment, it can also suffer from the same shortcomings as a "pure code" description of a model - if a user gives their nodes and parameters generic/unhelpful names, reading the graph will not help much. 

      This is a problem that to our understanding no software solution can possibly address. Yet having a visual representation of how different inputs and outputs connect to each other we argue would be a substantial benefit in contrast to the case of “pure code” especially when the developer of the experiment has used badly formatted variable names.

      (2) Making the link between the nodes and the actual code is also not straightforward, since the code for the nodes is spread out over several directories (or potentially even machines), and not directly accessible from within the GUI. 

      This is not accurate. The obligatory code of a Node always exists within a single folder and Heron’s API makes it rather cumbersome to spread scripts relating to a Node across separate folders. The Node folder structure can potentially be copied over different machines but this is why Heron is tightly integrated with git practices (and even politely asks the user with popup windows to create git repositories of any Nodes they create whilst using Heron’s automatic Node generator system). Heron’s documentation is also very clear on the folder structure of a Node which keeps the required code always in the same place across machines and more importantly across experiments and labs. Regarding the direct accessibility of the code from the GUI, we took on board the reviewers’ comments and have taken the first step towards correcting this. Now one can attach to Heron their favourite IDE and then they can double click on any Node to open its two main scripts (com and worker) in that IDE embedded in whatever code project they choose (also set in Heron’s settings windows). On top of this, Heron now allows the addition of notes both for a Node and for all its parameters, inputs and outputs which can be viewed by hovering the mouse over them on the Nodes’ GUIs. The final step towards GUI-code integration will be to have a Heron GUI code editor but this is something that has to wait for further development from Heron’s underlying GUI library DearPyGUI.

      (3) The authors state that "[Heron’s approach] confers obvious benefits to the exchange and reproducibility of experiments", but the paper does not discuss how one would actually exchange an experiment and its parameters, given that the graph (and its json representation) contains user-specific absolute filenames, machine IP addresses, etc, and the parameter values that were used are stored in general data frames, potentially separate from the results. Neither does it address how a user could keep track of which versions of files were used (including Heron itself).

      Heron’s Graphs, like any experimental implementation, must contain machine specific strings. These are accessible either from Heron’s GUI when a Graph json file is opened or from the json file itself. Heron in this regard does not do anything different to any other software, other than saving the graphs into human readable json files that users can easily manipulate directly.

      Heron provides a method for users to save every change of the Node parameters that might happen during an experiment so that it can be fully reproduced. The dataframes generated are done so in the folders specified by the user in each of the Nodes (and all those paths are saved in the json file of the Graph). We understand that Heron offers a certain degree of freedom to the user (Heron’s main reason to exist is exactly this versatility) to generate data files wherever they want but makes sure every file path gets recorded for subsequent reproduction. So, Heron behaves pretty much exactly like any other open source software. What we wanted to focus on as the benefits of Heron on exchange and reproducibility was the ability of experimenters to take a Graph from another lab (with its machine specific file paths and IP addresses) and by examining the graphical interface of it to be able to quickly tweak it to make it run on their own systems. That is achievable through the fact that a Heron experiment will be constructed by a small amount of Nodes (5 to 15 usually) whose file paths can be trivially changed in the GUI or directly in the json file while the LAN setup of the machines used can be easily reconstructed from the information saved in the secondary GUIs.

      Where Heron needs to improve (and this is a major point in Heron’s roadmap) is the need to better integrate the different saved experiments with the git versions of Heron and the Nodes that were used for that specific save. This, we appreciate is very important for full reproducibility of the experiment and it is a feature we will soon implement. More specifically users will save together with a graph the versions of all the used repositories and during load the code base utilised will come from the recorded versions and not from the current head of the different repositories. This is a feature that we are currently working on now and as our roadmap suggests will be implemented by the release of Heron 1.0. 

      (4) Another limitation that in my opinion is not sufficiently addressed is the communication between the nodes, and the effect of passing all communications via the host machine and SSH. What does this mean for the resulting throughput and latency - in particular in comparison to software such as Bonsai or Autopilot? The paper also states that "Heron is designed to have no message buffering, thus automatically dropping any messages that come into a Node’s inputs while the Node’s worker function is still running."- it seems to be up to the user to debug and handle this manually?

      There are a few points raised here that require addressing. The first is Heron’s requirement to pass all communication through the main (GUI) machine. We understand (and also state in the manuscript) that this is a limitation that needs to be addressed. We plan to do this is by adding to Heron the feature of running headless (see our roadmap). This will allow us to run whole Heron pipelines in a second machine which will communicate with the main pipeline (run on the GUI machine) with special Nodes. That will allow experimenters to define whole pipelines on secondary machines where the data between their Nodes stay on the machine running the pipeline. This is an important feature for Heron and it will be one of the first features to be implemented next (after the integration of the saving system with git). 

      The second point is regarding Heron’s throughput latency. In our original manuscript we did not have any description of Heron’s capabilities in this respect and both other reviewers mentioned this as a limitation. As mentioned above, we have now addressed this by adding a section to our third experimental example that fully describes how much CPU is required to run a full experimental pipeline running on two machines and utilising also non python code executables (a Unity game). This gives an overview of how heavy pipelines can run on normal computers given adequate optimisation and utilising Heron’s feature of forcing some Nodes to run their Worker processes on a specific core. At the same time, Heron’s use of 0MQ protocol makes sure there are no other delays or speed limitations to message passing. So, message passing within the same machine is just an exchange of memory pointers while messages passing between different machines face the standard speed limitations of the Local Access Network’s ethernet card speeds. 

      Finally, regarding the message dropping feature of Heron, as mentioned above this is an architectural decision given the use cases of message passing we expect Heron to come in contact with. For a full explanation of the logic here please see our answer to the 3rd comment by Reviewer 2.

      (5) As a final comment, I have to admit that I was a bit confused by the use of the term "Knowledge Graph" in the title and elsewhere. In my opinion, the Heron software describes "pipelines" or "data workflows", not knowledge graphs - I’d understand a knowledge graph to be about entities and their relationships. As the authors state, it is usually meant to make it possible to "test propositions against the knowledge and also create novel propositions" - how would this apply here?

      We have described Heron as a Knowledge Graph instead of a pipeline, data workflow or computation graph in order to emphasise Heron’s distinct operation in contrast to what one would consider a standard pipeline and data workflow generated by other visual based software (like LabView and Bonsai). This difference exists on what a user should think of as the base element of a graph, i.e. the Node. In all other visual programming paradigms, the Node is defined as a low-level computation, usually a language keyword, language flow control or some simple function. The logic in this case is generated by composing together the visual elements (Nodes). In Heron the Node is to be thought of as a process which can be of arbitrary complexity and the logic of the graph is composed by the user both within each Node and by the way the Nodes are combined together. This is an important distinction in Heron’s basic operation logic and it is we argue the main way Heron allows flexibility in what can be achieved while retaining ease of graph composition (by users defining their own level of complexity and functionality encompassed within each Node). We have found that calling this approach a computation graph (which it is) or a pipeline or data workflow would not accentuate this difference. The term Knowledge Graph was the most appropriate as it captures the essence of variable information complexity (even in terms of length of shortest string required) defined by a Node.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors):

      -  No buffering implies dropped messages when a node is busy. It seems like this could be very problematic for some use cases... 

      This is a design principle of Heron. We have now provided a detailed explanation of the reasoning behind it in our answer to Reviewer 2 (Paragraph 3) as well as in the manuscript. 

      -  How are ssh passwords stored, and is it secure in some way or just in plain text?  

      For now they are plain text in an unencrypted file that is not part of the repo (if one gets Heron from the repo). Eventually, we would like to go to private/public key pairs but this is not a priority due to the local nature of Heron’s use cases (all machines in an experiment are expected to connect in a LAN).  

      Minor notes / copyedits:

      -  Figure 2A: right and left seem to be reversed in the caption. 

      They were. This is now fixed. 

      -  Figure 2B: the text says that proof of life messages are sent to each worker process but in the figure, it looks like they are published by the workers? Also true in the online documentation.  

      The Figure caption was wrong. This is now fixed.

      -  psutil package is not included in the requirements for GitHub

      We have now included psutil in the requirements.

      -  GitHub readme says Python >=3.7 but Heron will not run as written without python >= 3.9 (which is alluded to in the paper)

      The new Heron updates require Python 3.11. We have now updated GitHub and the documentation to reflect this.

      -  The paper mentions that the Heron editor must be run on Windows, but this is not mentioned in the Github readme.  

      This was an error in the manuscript that we have now corrected.

      -  It’s unclear from the readme/manual how to remove a node from the editor once it’s been added.  

      We have now added an X button on each Node to complement the Del button on the keyboard (for MacOS users that do not have this button most of the times).

      -  The first example experiment is called the Probabilistic Reversal Learning experiment in text, but the uncertainty experiment in the supplemental and on GitHub.  

      We have now used the correct name (Probabilistic Reversal Learning) in both the supplemental material and on GitHub

      -  Since Python >=3.9 is required, consider using fstrings instead of str.format for clarity in the codebase  

      Thank you for the suggestion. Latest Heron development has been using f strings and we will do a refactoring in the near future.

      -  Grasshopper cameras can run on linux as well through the spinnaker SDK, not just Windows.  

      Fixed in the manuscript. 

      -  Figure 4: Square and star indicators are unclear.

      Increased the size of the indicators to make them clear.

      -  End of page 9: "an of the self" presumably a typo for "off the shelf"?  

      Corrected.

      -  Page 10 first paragraph. "second root" should be "second route"

      Corrected.

      -  When running Heron, the terminal constantly spams Blowfish encryption deprecation warnings, making it difficult to see the useful messages.  

      The solution to this problem is to either update paramiko or install Heron through pip. This possible issue is mentioned in the documentation.

      -  Node input /output hitboxes in the GUI are pretty small. If they could be bigger it would make it easier to connect nodes reliably without mis-clicks.

      We have redone the Node GUI, also increasing the size of the In/Out points.

      Reviewer #2 (Recommendations For The Authors):

      (1) There are quite a few typos in the manuscript, for example: "one can accessess the code", "an of the self", etc.  

      Thanks for the comment. We have now screened the manuscript for possible typos.

      (2) Heron’s GUI can only run on Windows! This seems to be the opposite of the key argument about the portability of the experimental setup.  

      As explained in the answers to Reviewer 1, Heron can run on most machines that the underlying python libraries run, i.e. Windows and Linux (both for x86 and Arm architectures). We have tested it on Windows (10 and 11, both x64), Linux PC (Ubuntu 20.04.6, x64) and Raspberry Pi 4 (Debian GNU/Linux 12 (bookworm), aarch64). We have now revised the manuscript and the GitHub repo to reflect this.

      (3) Currently, the output is displayed along the left edge of the node, but the yellow dot connector is on the right. It would make more sense to have the text displayed next to the connectors.  

      We have redesigned the Node GUI and have now placed the Out connectors on the right side of the Node.

      (4) The edges are often occluded by the nodes in the GUI. Sometimes it leads to some confusion, particularly when the number of nodes is large, e.g., Fig 4.

      This is something that is dependent on the capabilities of the DearPyGUI module. At the moment there is no way to control the way the edges are drawn.

      Reviewer #3 (Recommendations For The Authors):

      A few comments on the software and the documentation itself:

      - From a software engineering point of view, the implementation seems to be rather immature. While I get the general appeal of "no installation necessary", I do not think that installing dependencies by hand and cloning a GitHub repository is easier than installing a standard package.

      We have now added a pip install capability which also creates a Heron command line command to start Heron with. 

      -The generous use of global variables to store state (minor point, given that all nodes run in different processes), boilerplate code that each node needs to repeat, and the absence of any kind of automatic testing do not give the impression of a very mature software (case in point: I had to delete a line from editor.py to be able to start it on a non-Windows system).  

      As mentioned, the use of global variables in the worker scripts is fine partly due to the multi process nature of the development and we have found it is a friendly approach to Matlab users who are just starting with Python (a serious consideration for Heron). Also, the parts of the code that would require a singleton (the Editor for example) are treated as scripts with global variables while the parts that require the construction of objects are fully embedded in classes (the Node for example). A future refactoring might make also all the parts of the code not seen by the user fully object oriented but this is a decision with pros and cons needing to be weighted first. 

      Absence of testing is an important issue we recognise but Heron is a GUI app and nontrivial unit tests would require some keystroke/mouse movement emulator (like QTest of pytest-qt for QT based GUIs). This will be dealt with in the near future (using more general solutions like PyAutoGUI) but it is something that needs a serious amount of effort (quite a bit more that writing unit tests for non GUI based software) and more importantly it is nowhere as robust as standard unit tests (due to the variable nature of the GUI through development) making automatic test authoring an almost as laborious a process as the one it is supposed to automate.

      -  From looking at the examples, I did not quite see why it is necessary to write the ..._com.py scripts as Python files, since they only seem to consist of boilerplate code and variable definitions. Wouldn’t it be more convenient to represent this information in configuration files (e.g. yaml or toml)?  

      The com is not a configuration file, it is a script that launches the communication process of the Node. We could remove the variable definitions to a separate toml file (which then the com script would have to read). The pros and cons of such a set up should be considered in a future refactoring.

      Minor comments for the paper:

      -  p.7 (top left): "through its return statement" - the worker loop is an infinite loop that forwards data with a return statement?  

      This is now corrected. The worker loop is an infinite loop and does not return anything but at each iteration pushes data to the Nodes output.

      -  p.9 (bottom right): "of the self" → "off-the-shelf"  

      Corrected.

      -  p.10 (bottom left): "second root" → "second route"  

      Corrected.

      -  Supplementary Figure 3: Green start and square seem to be swapped (the green star on top is a camera image and the green star on the bottom is value visualization - inversely for the green square).  

      The star and square have been swapped around.

      -  Caption Supplementary Figure 4 (end): "rashes to receive" → "rushes to receive"  

      Corrected.

    1. As readers, we may seem a bit like TV viewers with remote controls.

      I think this is a great analogy because how often do we flip through channels or scroll through streaming sites until something catches our attention? The same goes for reading, if a paper or text is clustered, has no flow, or is boring, the reader is just going to skip over it.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      (1) This is a valuable manuscript that successfully integrates several data sets to determine genomic interactions with nuclear bodies.

      In this paper we both challenge and/or revise multiple long-standing “textbook” models of nuclear genome organization while also revealing new features of nuclear genome organization. Therefore, we argue that the contributions of this paper extend well beyond “valuable”. Specifically, these contributions include:

      a. We challenge a several decades focus on the correlation of gene positioning relative to the nuclear lamina. Instead, through comparison of cell lines, we show a strong correlation of di4erences in gene activity with di4erences in relative distance to nuclear speckles in contrast to a very weak correlation with di4erences in relative distance to the nuclear lamina. This inference of little correlation of gene expression with nuclear lamina association was supported by direct experimental manipulation of genome positioning relative to the nuclear lamina. Despite pronounced changes in relative distances to the nuclear lamina there was little change relative to nuclear speckles and little change in gene expression.

      b. We similarly challenge the long-standing proposed functional correlation between the radial positioning of genes and gene expression. Here, and in a now published companion paper (doi.org/10.1038/s42003-024-06838-7), we demonstrate how nuclear speckle positioning relative to nucleoli and the nuclear lamina varies among cell types, as does the inverse relationship between genome positioning relative to nuclear speckles and the nuclear lamina. Again, this is consistent with the primary correlation of gene activity being the positioning of genes relative to nuclear speckles and also explains previous observations showing a strong relationship between radial position and gene expression only in some cell types.

      c. We identified a new partially repressed, middle to late DNA replicating type of chromosome domain- “p-w-v fILADs”- by their weak interaction with the nuclear lamina, which, based on our LMNA/LBR KO experimental results, compete with LADs for nuclear lamina association. Moreover, we show that when fLADs convert to iLADs, most conversions are to this p-w-v fiLAD state, although ~ one third are to a normal, active, early replicating iLAD state. Thus, fLADs can convert between repressed, partially repressed, and active states, challenging the prevailing assumption of the division of the genome into two states – active, early replicating A compartment/iLAD regions versus inactive, late replicating, B compartment/LAD regions.

      d. We identified nuclear speckle associated domains as DNA replication initiation zones, with the domains showing strongest nuclear speckle attachment initiating DNA replication earliest in S-phase.

      e. We describe for the first time an overall polarization of nuclear genome organization in adherent cells with the most active, earliest replicating genomic regions located towards the equatorial plane and less expressed genomic regions towards the nuclear top or bottom surfaces. This includes polarization of some LAD regions to the nuclear lamina at the equatorial plane and other LAD regions to the top or bottom nuclear surfaces.

      We have now rewritten the text to make the significance of these new findings clearer.

      (2) Strength of evidence: The evidence supporting the central claims is varied in its strength ranging from solid to incomplete. Orthogonal evidence validating the novel methodologies with alternative approaches would better support the central claims.

      We argue that our work exploited methods, data, and analyses equal to or more rigorous than the current state-of-the-art. This indeed includes orthogonal evidence using alternative methods which both supported our novel methodologies as well as demonstrating their robustness relative to more conventional approaches. This explains how we were able to challenge/revise long-standing models and discover new features of nuclear genome organization. More specifically:

      a. Unlike most previous analyses, we have integrated both genomic and imaging approaches to examine the nuclear genome organization relative to not one, but several di4erent nuclear locales and we have done this across several cell types. To our knowledge, this is the first such integrated approach and has been key to our success in appreciating new features of nuclear genome organization.

      b. The 16-fraction DNA replication Repli-seq data we developed and applied to this project represents the highest temporal mapping of DNA replication timing to date.

      c. The TSA-seq approach that we used remains the most accurate sequence-based method for estimating microscopic distance of chromosome regions to di4erent nuclear locales. As implemented, this method is unusually robust and direct as it exploits the exponential micron-scale gradient established by the di4usion of the free-radicals generated by peroxidase labeling to measure relative distances of chromosome regions to labeled nuclear locales. We had previously demonstrated that TSA-seq was able to estimate the average distances of genomic regions to nuclear speckles with an accuracy of ~50 nm, as validated by light microscopy. The TSA-seq 2.0 protocol we developed and applied to this project maintained the original resolution of TSA-seq to estimate to an accuracy of ~50 nm the average distances of genomic regions from nuclear speckles, as validated by light microscopy, while achieving more than a 10-fold reduction in the required number of cells.

      We have rewritten the text to address the reviewer concerns that led them to their initial characterization of the TSA-seq as novel and not yet validated.

      First, we have added a discussion of how the use of nuclear speckle TSA-seq as a “cytological ruler” was based on an extensive initial characterization of TSA-seq as described in previous published literature. In that previous literature we showed how the conventional molecular proximity method, ChIP-seq, instead showed local accumulation of the same marker proteins over short DNA regions unrelated to speckle distances. Second, we reference our companion paper, now published, and describe how the extension of TSA-seq to measure relative distances to nucleoli was further validated and shown to be robust by comparison to NAD-seq and extensive multiplexed immuno-FISH data. We further discuss how in the same companion paper we show how nucleolar DamID instead was inconsistent with both the NAD-seq and multiplexed immuno-FISH data as well as the nucleolar TSA-seq.

      Third, we have added scatterplots showing exactly how highly the estimated microscopic distances to all three nuclear locales, measured in IMR90 fibroblasts, correlate with the TSA-seq measurements in HFF fibroblasts. This addresses the concern that we were not using the exact same fibroblast cell line for the TSA-seq versus microscopic measurements. The strong correlation already observed would only be expected to become even stronger with use of the exact same fibroblast cell lines for both measurements.

      Fourth, we have addressed the reviewer concern that the nuclear lamin TSA-seq was not properly validated because it did not match nuclear lamin Dam-ID. We have now added to the text a more complete explanation of how microscopic proximity assays such as TSA-seq measure something di4erent from molecular proximity assays such as DamID or NAD-seq. We have added further explanation of how TSA-seq complements molecular proximity assays such as DamID and NAD-seq, allowing us to extract further information than either measurement alone. We also briefly discuss why TSA-seq succeeds for certain nuclear locales using multiple independent markers whereas molecular proximity assays may fail against the same nuclear locales using the same markers. This includes brief discussion from our own experience attempting unsuccessfully to use DamID against nucleoli and nuclear speckles.

      Reviewer #1 (Public Review):

      (1) The weakness of this study lies in the fact that many of the genomic datasets originated from novel methods that were not validated with orthogonal approaches, such as DNAFISH. Therefore, the detailed correlations described in this work are based on methodologies whose efficacy is not clearly established. Specifically, the authors utilized two modified protocols of TSA-seq for the detection of NADs (MKI67IP TSA-seq) and LADs (LMNB1-TSA-seq).

      We disagree with the statement that the TSA-seq approach and data has not been validated by orthogonal approaches. We have now addressed this point in the revised manuscript text:

      a) We added text to describe how previously FISH was used to validate speckle TSA-seq by demonstrating a residual of ~50 nm between the TSA-seq predicted distance to speckles and the distance measured by light microscopy using FISH:

      "In contrast, TSA-seq measures relative distances to targets on a microscopic scale corresponding to 100s of nm to ~ 1 micron based on the measured diffusion radius of tyramide-biotin free-radicals (Chen et al., 2018). Exploiting the measured exponential decay of the tyramide-biotin free-radical concentration, we showed how the mean distance of chromosomes to nuclear speckles could be estimated from the TSA-seq data to an accuracy of ~50 nm, as validated by FISH (Chen et al., 2018)."

      b) We note that we also previously have validated lamina (Chen et al, JCB 2018) and nucleolar (Kumar et al, 2024) TSA-seq and further validated speckle TSA-seq (Zhang et al, Genome Research 2021) by traditional immuno-FISH and/or immunostaining. The overall high correlation between lamina TSA-seq and the orthogonal lamina DamID method was also extensively discussed in the first TSA-seq paper (Chen et al, JCB 2018). Included in this discussion was description of how the di4erences between lamina TSA-seq and DamID were expected, given that DamID produces a signal more proportional to contact frequency, and independent of distance from the nuclear lamina, whereas TSA-seq produces a signal that is a function of microscopic distance from the lamina, as validated by traditional FISH.

      c) We added text to describe how the nucleolar TSA-seq previously was validated by two orthogonal methods- NAD-seq and multiplexed DNA immuno-FISH:

      "We successfully developed nucleolar TSA-seq, which we extensively validated using comparisons with two different orthogonal genome-wide approaches (Kumar et al., 2024)- NAD-seq, based on the biochemical isolation of nucleoli, and previously published direct microscopic measurements using highly multiplexed immuno-FISH (Su et al., 2020)."

      d) We have now added panels A&B to Fig. 7 and a new Supplementary Fig. 7 demonstrating further validation of TSA-seq based on showing the high correlation between the microscopically measured distances of many hundreds of genomic sites across the genome from di4erent nuclear locales and TSA-seq scores. As discussed in response #2 below, we have used comparison of distances measured in IMR90 fibroblasts with TSA-seq scores measured in HFF fibroblasts. We would argue therefore that these correlations are a lower estimate and therefore the correlation between microscopic distances and TSAseq scores would likely have been still higher if we had performed both assays in the exact same cell line.

      (2) Although these methods have been described in a bioRxiv manuscript by Kumar et al., they have not yet been published. Moreover, and surprisingly, Kumar et al., work is not cited in the current manuscript, despite its use of all TSA-seq data for NADs and LADs across the four cell lines.

      The Kumar et al, Communications Biology, 2024 paper is now published and is cited properly in our revision. We apologize for this oversight and confusion our initial omission of this citation may have created. We had been writing this manuscript and the Kumar et al manuscript in parallel and had intended to co-submit. We planned to cross-reference the two at the time we co-submitted, adding the Kumar et al reference to the first version of this manuscript once we obtained a doi from bioRxiv. But we then submitted the Kumar et al manuscript several months earlier, but meanwhile forgot that we had not added the reference to our first manuscript version.

      (3) Moreover, Kumar et al. did not provide any DNA-FISH validation for their methods.

      As we described in our response to Reviewer 1's comment #1, we had previously provided traditional FISH validation of lamina TSA-seq in our first TSA- seq paper as well as validation by comparison with lamina DamID (Chen et al, 2018).

      We also described how the nucleolar TSA-seq was extensively cross-validated in the Kumar et al, 2024 paper by both NAD-seq and the highly multiplexed immuno-FISH data from Su et al, 2020).

      We note additionally that in the Kumar et al, 2024 paper the nucleolar TSA-seq was additionally validated by correlating the predicted variations in centromeric association with nucleoli across the four cell lines predicted by nucleolar TSA-seq with the variations observed by traditional immunofluorescence microscopy.

      (4) Therefore, the interesting correlations described in this work are not based on robust technologies.

      This comment was made in reference to the Kumar et al paper not having been published, and, as noted in responses to points #2 and #3, the paper is now published.

      But we wanted to specifically note, however, that our experience is that TSA-seq has proven remarkably robust in comparison to molecular proximity assays. We've described in our responses to the previous points how TSA-seq has been cross-validated by both microscopy and by comparison with lamina DamID and nucleolar NAD-seq. We note also that in every application of TSA-seq to date, all antibodies that produced good immunostaining showed good TSA-seq results. Moreover, we obtained nearly identical results in every case in which we performed TSA-seq with different antibodies against the same target. Thus anti-SON and antiSC35 staining produced very similar speckle TSA-seq data (Chen et al, 2018), anti-lamin A and anti-lamin B staining produced very similar lamina TSA-seq data (Chen et al, 2018), antinucleolin and anti-POL1RE staining produced very similar DFC/FC nucleolar TSA-seq data (Kumar et al, 2024), and anti-MKI67IP and anti-DDX18 staining produced very similar GC nucleolar TSA-seq data (Kumar et al, 2024).

      This independence of results with TSA-seq to the particular antibody chosen to label a target differs from experience with methods such as ChIP, DamID, and Cut and Run/Tag in which results can differ or be skewed based on variable distance and therefore reactivity of target proteins from the DNA or due to other factors such as non-specific binding during pulldown (ChIP) or differential extraction by salt washes (Cut and Tag).

      Our experience in every case to date is that antibodies that produce similar immunofluorescence staining produce similar TSA-seq results. We attribute this robustness to the fact that TSA-seq is based only on the original immunostaining specificity provided by the primary and secondary antibodies plus the diffusion properties of the tyramide-free radical.

      We've now added the following text to our revised manuscript:

      "As previously demonstrated for both SON and lamin TSA-seq (Chen et al., 2018), nucleolar TSA-seq was also robust in the sense that multiple target proteins showing similar nucleolar staining showed similar TSA-seq results (Kumar et al., 2024); this robustness is intrinsic to TSA-seq being a microscopic rather than molecular proximity assay, and therefore not sensitive to the exact molecular binding partners and molecular distance of the target proteins to the DNA."

      (5) An attempt to validate the data was made for SON-TSA-seq of human foreskin fibroblasts (HFF) using multiplexed FISH data from IMR90 fibroblasts (from the lung) by the Zhuang lab (Su et al., 2020). However, the comparability of these datasets is questionable. It might have been more reasonable for the authors to conduct their analyses in IMR90 cells, thereby allowing them to utilize MERFISH data for validating the TSA-seq method and also for mapping NADs and LADs.

      We disagree with the reviewer's overall assessment that that the use of the IMR90 data to further validate the TSA-seq is questionable because the TSA-seq data from HFF fibroblasts is not necessarily comparable with multiplexed immuno-FISH microscopic distances measured in IMR90 fibroblasts.

      In response we have now added panels to Fig. 7 and Supplementary Fig. 7, showing:

      a) There is very little di4erence in correlation between speckle TSA-seq and measured distances from speckles in IMR90 cells whether we use IMR90 or HFF cells SON TSA-seq data (R<sup>2</sup> = 0.81 versus 0.76) (new Fig. 7A).

      b) There is also a high correlation between lamina (R<sup>2</sup> = 0.62) and nucleolar (R<sup>2</sup> = 0.73) HFF TSA-seq and measured distances in IMR90 cells. Thus, we conclude that this high correlation shows that the multiplexed data from ~1000 genomic locations does validate the TSA-seq. These correlations should be considered lower bounds on what we would have measured using IMR90 TSA-seq data. Thus, the true correlation between distances of loci from nuclear locales and TSA-seq would be expected to be either comparable or even stronger than what we are seeing with the IMR90 versus HFF fibroblast comparisons.

      c) This correlation is cell-type specific (Fig. 7B, new SFig. 7). Thus, even for speckle TSAseq, highly conserved between cell types, the highest correlation of IMR90 distances with speckle TSA-seq is with IMR90 and HFF fibroblast data. For lamina and nucleolar TSA-seq, which show much lower conservation between cell types, the correlation of IMR90 distances is high for HFF data but much lower for data from the other cell types. This further justifies the use of IMR90 fibroblast distance measurements as a proxy for HFF fibroblast measurements.

      Thus, we have added the following text to the revised manuscript:

      "We reasoned that the nuclear genome organization in the two human fibroblast cell lines would be sufficiently similar to justify using IMR90 multiplexed FISH data [43] as a proxy for our analysis of HFF TSA-seq data. Indeed, the high inverse correlation (R= -0.86) of distances to speckles measured by MERFISH in IMR90 cells with HFF SON TSA-seq scores is nearly identical to the inverse correlation (R= -0.89) measured instead using IMR90 SON TSA-seq scores (Fig. 7A). Similarly, distances to the nuclear lamina and nucleoli show high inverse correlations with lamina and nucleolar TSA-seq, respectively (Fig. 7A). These correlations were cell type specific, particularly for the lamina and nucleolar distance correlations, as these correlations were reduced if we used TSA-seq data from other cell types (SFig. 7A). Therefore, the high correlation between IMR90 microscopic distances and HFF TSA-seq scores can be considered a lower bound on the likely true correlation, justifying the use of IMR90 as a proxy for HFF for testing our predictions."

      Reviewer #2 (Public Review):

      Weaknesses:

      (1) The experiments are largely descriptive, and it is difficult to draw many cause-andeffect relationships...The study would benefit from a clear and specific hypothesis.

      This study was hypothesis-generating rather than hypothesis-testing in its goal. Our research was funded through the NIH 4D-Nucleome Consortium, which had as its initial goal the development, benchmarking, and validation of new genomic technologies. Our Center focused on the mapping of the genome relative to different nuclear locales and the correlation of this intranuclear positioning of the genome with functions- specifically gene expression and DNA replication timing. By its very nature, this project took a discovery-driven versus hypothesis-driven scientific approach. Our question fundamentally was whether we could gain new insights into nuclear genome organization through the integration of genomic and microscopic measurements of chromosome positioning relative to multiple different nuclear compartments/bodies and their correlation with functional assays such as RNA-seq and Repliseq.

      Indeed, this study resulted in multiple new insights into nuclear genome organization as summarized in our last main figure. We believe our work and conclusions will be of general interest to scientists working in the fields of 3D genome organization and nuclear cell biology. We anticipate that each of these new insights will prompt future hypothesis-driven science focused on specific questions and the testing of cause-and-effect relationships.

      However, we do want to point out that our comparison of wild-type K562 cells with the LMNA/LBR double knockout was designed to test the long-standing model that nuclear lamina association of genomic loci contributes to gene silencing. This experiment was motivated by our surprising result that gene expression differences between cell lines correlated strongly with differences in positioning relative to nuclear speckles rather than the nuclear lamina. Despite documenting in these double knockout cells a decreased nuclear lamina association of most LADs, and an increased nuclear lamina association of the “p-w-v” fiLADs identified in this manuscript, we saw no significant change in gene expression in any of these regions as compared to wild-type K562 cells. Meanwhile, distances to nuclear speckles as measured by TSA-seq remained nearly constant.

      We would argue that this represents a specific example in which new insights generated by our genomics comparison of cell lines led to a clear and specific hypothesis and the experimental testing of this hypothesis.

      (2) Similarly, the paper would be very much strengthened if the authors provided additional summary statements and interpretation of their results (especially for those not as familiar with 3D genome organization).

      We appreciate this feedback and agree with the reviewer that this would be useful, especially for those not familiar with previous work in the field of 3D genome organization. In an earlier draft, we had included additional summary and interpretation statements in both the Introduction and Results sections. At the start of each Results section, we had also previously included brief discussion of what was known before and the context for the subsequent analysis contained in that section. However, we had thought we might be submitting to a journal with specific word limits and had significantly cut out that text.

      We have now restored this text and, in certain cases, added additional explanations and context.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Figures 1C and D. Please add the units at the values of each y-axis.

      We have done that.

      The representation of Figure 2C lacks clarity and is diJicult to understand. The x-axis labeling regarding the gene fraction number needs clarification.

      We've modified the text to the Fig. 2C legend: "Fraction of genes showing significant di=erence in relative positioning to nuclear speckles (gene fraction, x-axis) versus log2 (HFF FKPM / H1 FKPM) (y-axis);"

      "We next used live-cell imaging to corroborate that chromosome regions close to nuclear speckles, primarily Type I peaks, would show the earliest DNA replication timing." This sentence requires modification as Supplementary Figure 3F does not demonstrate that Type I peaks exhibit the earliest DNA replication timing; it only indicates that the first PCNA foci in S-phase are in proximity to nuclear speckles.

      We've modified the text to: "We next used live-cell imaging to show that chromosome regions close to nuclear speckles show the earliest DNA replication timing; this is consistent with the earliest firing DNA replication IZs, as determined by Repli-seq, aligning with Type 1 peaks that are closely associated with nuclear speckles."

      In Figure 5, the authors employed LaminB1-DamID to quantify LADs in LBR-KO and LMNA/LBR-DKO K562 cells. These are interesting results. However, for these experiments, it is crucial to assess LMNB1 signal at the nuclear periphery via immunofluorescence (IF) to confirm the absence of changes, ensuring that the DamID signal solely reflects contacts with the nuclear lamina. Furthermore, in this instance, their findings should be validated through DNA-FISH.

      Immunostaining of LMNB1 was performed and showed a normal staining pattern as a ring adjacent to the nuclear periphery. Images of this staining were included in the metadata tied to the sequencing data sets deposited on the 4D Nucleome Data portal. We thank the reviewer for bringing up this point, and have added a sentence mentioning this result in the Results Section:

      "Immunostaining against LMNB1 revealed the normal ring of staining around the nuclear periphery seen in wt cells (images deposited as metadata in the deposited sequencing data sets)."

      Because both TSA-seq and DamID have been extensively validated by FISH, as detailed in our previous responses to the public reviewer comments, we feel it is unnecessary to validate these findings by FISH.

      p-w-v-fiLADs should be labelled in Figure 5B.

      We've added labeling as suggested.

      "The consistent trend of slightly later DNA replication timing for regions (primarily p-w-v fiLADs) moving closer to the lamina" is not visible in the representation of the data of Figure 5G.

      We did not make a change as we believed this trend was apparent in the Figure.

      To reduce the descriptive nature of the data, it would be pertinent to conduct H3K9me3 and H3K27me3 ChIP-seq analyses in both the parental and DKO mutant cells. This would elucidate whether p-w-v-fiLADs and NADs anchoring to the nuclear lamina undergo changes in their histone modification profile.

      We believe further analysis of the reasons underlying these shifts in positioning, including such ChIP-seq or equivalent analysis, is of interest but beyond the scope of this publication. We see such measurements as the beginning of a new story but insuJicient alone to determine mechanism. Therefore we believe such experiments should be part of that future study.

      The description of Figure 7 lacks clarity. Additionally, it appears that TSA-seq for NADs and LADs may not be universally applicable across all cell types, particularly in flat cells, whereas DamID scores demonstrate less variation across cell lines, as also stated by the authors.

      TSA-seq is a complement to rather than a replacement for either DamID or NAD-seq. TSAseq reports on microscopic distances whereas both DamID and NAD-seq instead are more proportional to contact frequency with the nuclear lamina or nucleoli, respectively, and insensitive to distances of loci away from the lamina or nucleoli. Thus, TSA-seq provides additional information based on the intrinsic diJerences in what TSA-seq measures relative to molecular proximity methods such as DamID or NAD-seq. The entire point is that the convolution of the exponential point-spread-function of the TSA-seq with the shape of the nuclear periphery allows us to distinguish genomic regions in the equatorial plane versus the top and bottom of the nuclei. The TSA-seq is therefore highly "applicable" when properly interpreted in discerning new features of genome organization. As we stated in the revised manuscript, the lamina DamID and TSA-seq are complementary and provide more information together then either method along. The same is true for the NAD-seq and nucleolar TSA-seq comparison, as described in more detail in the Kumar, et al, 2024 paper.

      Introduction:

      The list of methodologies for mapping genomic contacts with nucleoli (NADs) should also include recent technologies, such as Nucleolar-DamID (Bersaglieri et al., PMID: 35304483), which has been validated through DNA-FISH.

      We did not include nucleolar DamID in the mention in the Introduction of methods for identifying diJerential lamina versus nucleolar interactions of heterochromatin- either from our own collaborative group or from the cited reference- because we did not have confidence in the accuracy of this method in identifying NADs. In the case of the published nucleolar DamID from our collaborative group, published in Wang et al, 2021, we later discovered that despite apparent agreement of the nucleolar DamID with a small number of published FISH localization the overall correlation of the nucleolar DamID with nucleolar localization was poor. As described in detail in the Kumar et al, 2024 publication, this poor correlation of the nucleolar DamID was established using three orthogonal methods- nucleolar TSA-seq, NAD-seq, and multiplexed immuno-FISH measurements from ~1000 genomic locations. Instead, we found that this nucleolar DamID showed high correlation with lamina DamID. We note that many strong NADs are also LADs, which we think is why validation with only several FISH probes is inadequate to demonstrate overall validation of the approach.

      We could not compare our nucleolar-DamID data in human cells with the alternative nucleolar-DamID results cited by the reviewer which were performed in mouse cells. We note that in this paper the nucleolar DamID FISH validation only included several putative NAD chromosome regions and, I believe, one LAD region. However, our initial comparison of the nucleolar DamID cited by the reviewer with unpublished TSA-seq data from mouse ESCs produced by the Belmont laboratory and with NAD-seq data from the Kaufman laboratory shows a similar lack of correlation of the nucleolar DamID signal with nucleolar TSA-seq and NAD-seq, as well as multiplexed immuno-FISH data from the Long Cai laboratory, as we saw in our analysis of own nucleolar DamID data in human cells.

      We have added explanation concerning the lack of correlation of our nucleolar DamID with orthogonal measurements of nucleolar proximity in the added text (below) to our revised manuscript:

      "Nucleolar DamID instead showed broad positive peaks over large chromatin domains, largely overlapping with LADs mapped by LMNB1 DamID (Wang et al., 2021). However, this nucleolar DamID signal, while strongly correlated with lamin DamID, showed poor correlation with either NAD-seq or nucleolar distances mapped by multiplexed immunoFISH (Kumar et al., 2024). We suspect the problem is that with molecular proximity assays the output signals are disproportionally dominated by the small fraction of target proteins juxtaposed in su=icient proximity to the DNA to produce a signal rather than the amount of protein concentrated in the target nuclear body. "

      Our mention of nucleolar TSA-seq was in the context of why we focused on nucleolar TSAseq and excluded our own nucleolar DamID. We chose not to discuss the second nucleolar DamID method cited above 1) because it was not appropriate to our discussion of our own experimental approach and 2) also because we cannot yet make a definitive statement of its accuracy for nucleolar mapping.

      Reviewer #2 (Recommendations For The Authors):

      (1) The authors start the manuscript by describing the 'radial genome organization' model and contrast it with the 'binary model' of genome organization. It would be helpful for the authors to contextualize their results a bit more with regard to these two diJerent models in the discussion.

      We have added several sentences in the first paragraph of the Discussion to accomplish this contextualization. The new paragraph reads:

      "Here we integrated imaging with both spatial (DamID, TSA-seq) and functional (Repli-seq, RNA-seq) genomic readouts across four human cell lines. Overall, our results significantly extend previous nuclear genome organization models, while also demonstrating a cell-type dependent complexity of nuclear genome organization. Briefly, in contrast to the previous radial model of genome organization, we reveal a primary correlation of gene expression with relative distances to nuclear speckles rather than radial position. Additionally, beyond a correlation of nuclear genome organization with radial position, in cells with flat nuclei we show a pronounced correlation of nuclear genome organization with distance from the equatorial plane. In contrast to previous binary models of genome organization, we describe how both iLAD / A compartment and LAD / B compartment contain within them smaller chromosome regions with distinct biochemical and/or functional properties that segregate di=erentially with respect to relative distances to nuclear locales and geometry."

      (2) Data should be provided demonstrating KO of LBR and LMNA - immunoblotting for both proteins would be one approach. In addition, it would be helpful to provide additional nuclear morphology measurements of the DKO cells (volume, surface area, volume of speckles/nucleoli, number of speckles/nucleoli).

      We've added additional description describing the generation and validation of the KO lines:

      "To create LMNA and LBR knockout (KO) lines and the LMNA/LBR double knockout (DKO) line, we started with a parental "wt" K562 cell line, clone #17, expressing an inducible form of Cas9 (Brinkman et al., 2018). The single KO and DKO were generated by CRISPR-mediated frameshift mutation according to the procedure described previously (Schep et al., 2021). The "wt" K562 clone #17 was used for comparison with the KO clones.

      The LBR KO clone, K562 LBR-KO #19, was generated, using the LBR2 oligonucleotide GCCGATGGTGAAGTGGTAAG to produce the gRNA, and validated previously, using TIDE (Brinkman et al., 2014) to check for frameshifts in all alleles as described elsewhere (Schep et al., 2021). The LMNA/LBR DKO, K562 LBR-LMNA DKO #14, was made similarly, starting with the LBR KO line and using the combination of two oligonucleotides to produce gRNAs:

      LMNA-KO1: ACTGAGAGCAGTGCTCAGTG, LMNA-KO2: TCTCAGTGAGAAGCGCACGC.

      Additionally, the LMNA KO line, K562 LMNA-KO #14, was made the same way but starting with the "wt" K562 cell line. Validation was as described above; additionally, for the new LMNA KO and LMNA/LBR DKO lines, immunostaining showed the absence of anti-LMNA antibody signal under confocal imaging conditions used to visualize the wt LMNA staining while the RNA-seq from these clones revealed an ~20-fold reduction in LMNA RNA reads relative to the wt K562 clone."

      As suggested, we also added morphological data for the DKO line in a modified SFig.5.

      (3) The rationale for using LMNB1 TSA-seq and LMNB1 DAMID is not immediately clear. The LMNB1 TSA-seq is more variable across cell types and replicates than the DAMID. Could the authors please compare the datasets a bit more to understand the diJerences? For example, the authors demonstrate that "40-70% of the genome shows statistically significant diJerences in Lamina TSA-seq over regions 100 kb or larger, with most of these regions showing little or no diJerences in speckle TSA-seq scores." If the LMNB1 DAMID data is used for this analysis or Figure 2D, is the same conclusion reached? Also, in Figure 6, the authors conclude that C1 and C3 LAD regions are enriched for constitutive LADs, while C2 and C4 LAD regions are fLADs. This is a bit surprising because the authors and others have previously shown that constitutive LADs have higher LMNB1 contact frequency than facultative LADs (Kind, et al Cell 2015, Figure 3C).

      Indeed, in the first TSA-seq paper (Chen et al, 2018) we did observe that cLADs had the highest LMNB TSA-seq scores; this was for K562 cells with round nuclei in which there is therefore no diJerence in lamina TSA-seq scores produced by nuclear shape over the entire nucleus.

      However, there are diJerences between TSA-seq and DamID in terms of what they measure and we refer the reviewer to the first TSA-seq paper (Chen et al, 2018) that explains in greater depth these diJerences. This first paper explains how DamID is indeed related to contact frequency but how the TSA-seq instead estimates mean distances from the target, in this case the nuclear lamina. This is because the diJusion of tyramide free radicals from the site of their constant HRP production produces an exponential decay gradient of tyramide free radical concentration at steady state.

      We have summarized these diJerences in in text we have added to introduce both DamID and TSA-seq in the second Results section:

      "DamID is a well-established molecular proximity assay; DamID applied to the nuclear lamina divides the genome into lamina-associated domains (LADs) versus nonassociated “inter-LADs” or “iLADs” (Guelen et al., 2008; van Steensel and Belmont, 2017). In contrast, TSA-seq measures relative distances to targets on a microscopic scale corresponding to 100s of nm to ~ 1 micron based on the measured diJusion radius of tyramide-biotin free-radicals (Chen et al., 2018)... While LMNB1 DamID segments LADs most accurately, lamin TSA-seq provides distance information not provided by DamID- for example, variations in relative distances to the nuclear lamina of diJerent iLADs and iLAD regions. These diJerences between the LMNB1 DamID and LMNB TSA-seq signals are also crucial to a computational approach, SPIN, that segments the genome into multiple states based on their varying nuclear localization, including biochemically and functionally distinct lamina-associated versus near-lamina states (Consortium et al., 2024; Wang et al., 2021).

      Thus, lamin DamID and TSA-seq complement each other, providing more information together than either one separately."

      We note that these diJerences in lamina DamID and TSA-seq are crucial to being able to gain additional information by comparing variations in the lamina TSA-seq for LADs in Figs. 6&7. See our response to point (4) below, for further explanation.

      (4) In 7B/C, the authors show that the highest LMNB1 regions in HFF are equator of IMR90s. However, in Figure 7G, their cLAD score indicates that constitutive LADs are not at the equator. This is a bit surprising given the point above and raises the possibility that SON signals (as opposed to LMNB1 signals) might be more responsible for correlation to localization relative to the equator. Hence, it might be helpful if the authors repeat the analyses in Figures 7B/C in regions with diJering LMNB1 signals but similar SON signals (and vice versa).

      Again, this is based on the apparent assumption by the reviewer that DamID and TSA-seq work the same way and measure the same thing. But as explained above in the previous point, this is not true.

      In our first TSA-seq paper (Chen et al, 2018) we showed how we could use the exponential decay point-spread-function produced by TSA, measured directly by light microscopy, to convert sequencing reads from the TSA-seq into a predicted mean distance from nuclear speckles, approximated as point sources. These mean distances predicted from the SON TSA-seq data agreed with measured FISH distances to nuclear speckles to within ~50 nm for a set of DNA probes from diJerent chromosome regions. Moreover, varying TSA staining conditions changed the decay constants of this exponential decay, thus producing diJerences in the SON TSA-seq signals. By using these diJerent exponential decay functions to convert the TSA-seq scores from these independent data sets to estimated distances from nuclear speckles, we again observed a distance residual of ~50 nm; in this case though this distance residual of ~50 nm represented the mean residual observed genome-wide. This gives us great confidence that the TSA-seq is working as we have modeled it.

      As we mentioned in our response to point 3 above, we did see the highest LMNB TSA-seq signal for cLADs in K562 cells with round nuclei (Chen et al, 2018).

      But as we now show in our simulation performed in this paper for Fig. 7, the observed tyramide free radical exponential decay gradient convolved with the flat nuclear lamina shape produces a higher equatorial LMNB1 TSA-seq signal for LADs at the equatorial plane. We confirmed that LADs with this higher TSA-seq signal were enriched at the equatorial plane by mining the multiplexed IMR90 imaging data. Similar mining of the multiplexed FISH IMR90 data showed localization of cLADs away from the equatorial plane.

      We are not clear about the rationale for what the reviewer is suggesting about SON signals "being more responsible for correlation to localization to the equator". We have provided an explanation for the higher lamina TSA-seq scores for LADs near the equator based on the measured spreading of the tyramide free radicals convolved with the eJect of the nuclear shape. This makes a prediction that the observed variation in lamina TSA-seq scores for LADs with similar DamID scores is related to their positioning relative to the equatorial plane as we then validated through our mining of the IMR90 multiplexed FISH data.

      (5) FISH of individual LADs, v-fiLADs, and p-w-v-fiLADs relative to the lamina and speckle would be helpful to understand their relative positioning in control and LBR/LMNA double KO cells. This would significantly bolster the claim that "histone mark enrichments..more precisely revealed the diJerential spatial distribution of LAD regions...".

      Adequately testing these predictions made from the lamina/SON TSA-seq scatterplots by direct FISH measurements would require measurements from large numbers of diJerent chromosome regions through a highly multiplexed immuno-FISH approach. We are not equipped currently in any of our laboratories to do such measurements and we leave this therefore for future studies.

      Rather our statement is based on our use of TSA-seq analyzed through these 2D scatterplots and should be valid to the degree that our TSA-seq measurements do indeed correlate with microscopy derived distances.

      However, we do now include demonstration of a high correlation of speckle, lamina, and nucleolar TSA-seq with highly multiplexed immuno-FISH measurement of distances to these locales in a revised Fig. 7. The high correlation shown between the TSA-seq scores and measured distances does therefore add additional support to our claim that the reviewer is discussing, even without our own multiplexed FISH validation.

      (6) "In contrast, genes within genomic regions which in pair-wise comparisons of cell lines show a statistically significant diJerence in lamina TSA-seq show no obvious trend in their expression diJerences (Figure 2C).". This appears to be an overstatement based on the left panel of 2D.

      We do not follow the reviewer's point. In Fig. 2C we show little bias in the diJerences in gene expression between the two cell types for regions that showed diJerences in lamina TSA-seq. The reviewer is suggesting something otherwise based on their impression, not explicitly stated, of the left panel of Fig. 2D. But we see similar shades of blue extending vertically at low SON values and similar shades of red extending vertically at high SON values, suggesting a correlation of gene expression only with the SON TSA-seq score but not with the LMNB1 TSA-seq score displayed on the y-axis. This is also consistent with the very small and/or insignificant correlation coeJicients measured in our linear model relating diJerences in LMNB1 TSA-seq to diJerences in expression but the large correlation coeJicient observed for SON TSA-seq (Fig. 2E). Thus, we see Fig. 2C-E as self-consistent.

      (7) In the section on "Polarity of Nuclear Genome Organization" - "....Using the IMR90 multiplexed FISH data set [43]...." - The references are not numbered.

      We thank the reviewer for this correction.

      (8) I believe there is an error in the Figure 7B legend. The descriptions of Cluster 1 and 2 do not match those indicated in the figure.

      We again thank the reviewer for this correction.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      The authors present valuable findings on trends in hind limb morphology throughout the evolution of titanosaurian sauropod dinosaurs, the land animals that reached the most remarkable gigantic sizes. The solid results include the use of 3D geometric morphometrics to examine the femur, tibia, and fibula to provide new information on the evolution of this clade and understand the evolutionary trends between morphology and allometry. Further justification of the ontogenetic stages of the sampled individuals would help strengthen the manuscript's conclusions, and the inclusion of additional large-body mass taxa could provide expanded insights into the proposed trends.

      Most of the analyzed specimens, especially from the smaller taxa, come from adult or subadult specimens. None exhibit features that may indicate juvenile status. However, we lack information of the paleohistology that may be a stronger indicator on the ontogenetic status of the individual, and some of operative taxonomic units used in the study come from mean shape of all the sampled specimens.

      Current information on morphological differences between adult and subadult or juvenile specimens indicates that even early juvenile specimens may share same morphological features and overall morphology as the adult (e.g., see Curry-Rogers et al., 2016; Appendix S3). We included a comprehensive analysis of the impact of juvenile specimens as one of the aspects of the intraspecific variability that may alter our results in Appendix S3.

      Public Reviews:

      Reviewer #1:

      Weaknesses:

      Several sentences throughout the manuscript could benefit from citations. For example, the discussion of using hind limb centroid size as a proxy for body mass has no citations attributed. This should be cited or described as a new method for estimating body mass with data from extant taxa presented in support of this relationship. This particular instance is a very important point to include supporting documentation because the authors' conclusions about evolutionary trends in body size are predicated on this relationship.

      We address this issue in the text (Line 32 & 64). Centroid size seems a good indication as it’s the overall size of the entire hind limb, and the length of the femur and tibia is well correlated independently with the body size/mass. Also, as we use few landmarks and only those that are purely type I or II landmarks, with curves of semilandmarks bounded or limited by them, centroid size is not sensible to landmark number differences across the sample in our study (as the centroid size is dependent of the number of landmarks of the current study as well as the physical dimensions of the specimens).

      We have sampled and repeated all the analyses using other proxies like the femoral length and the body mass estimated from the Campione & Evans (2020) and Mazzeta et al. (2004) methods. The comprehensive description of the method is in Appendix S2, the alternative analyses can be accessed in the Appendix S3 and S4; and the code for the alternative analyses can be accessed in the modified Appendix S5. All offer similar results than the ones obtained in our analyses with the body size proxied with the hind limb landmark configuration centroid size.

      An additional area of concern is the lack of any discussion of taphonomic deformation in Section 3.3 Caveats of This Study, the results, or the methods. The authors provide a long and detailed discussion of taphonomic loss and how this study does a good job of addressing it; however, taphonomic deformation to specimens and its potential effects on the ensuing results were not addressed at all. Hedrick and Dodson (2013) highlight that, with fossils, a PCA typically includes the effects of taphonomic deformation in addition to differences in morphology, which results in morphometric graphs representing taphomorphospaces. For example, in this study, the extreme negative positioning of Dreadnoughtus on PC 2 (which the authors highlight as "remarkable") is almost certainly the result of taphonomic deformation to the distal end of the holotype femur, as noted by Ullmann and Lacovara (2016).

      We included a brief commentary in the Caveats of This Study (Line 467) and greatly expanded this issue in the Appendix S3. We followed the methodology proposed by Lefebvre et al. (2020) to discuss the effects of taphonomic deformation in the shape analyses.

      Our shape variables (PCs obtained from the shape PCA) should be viewed as taphomorphospaces as Hedrick and Dodson, as well as the reviewer, points in such cases.

      The analysis of the effects of taphonomy or errors induced by the landmark estimation method indicate that Dreadnoughtus schrani is one of the few sampled taxa that may have a noticeable impact on our analyses due lithostatic deformation. Other taxa like Mendozasaurus neguyelap or Ampelosaurus atacis may also induce some alterations to the PCs. In general, the trends of those PCs slightly altered by taphonomy, where D. scharni is the only sauropod that may alter an entire PC like PC2, did not exhibit phylogenetic signal and are a small proportion of the sample variance.

      The authors investigated 17 taxa and divided them into 9 clades, with only Titanosauria and Lithostrotia including more than two taxa (and four clades are only represented by one taxon). While some of these clades represent the average of multiple individuals, the small number of plotted taxa can only weakly support trends within Titanosauria. If similar general trends could be found when the taxa are parsed into fewer, more inclusive clades, it would support and strengthen their claims. Of course, the authors can only study what is preserved in the fossil record, and titanosaurian remains are often highly fragmentary; these deficiencies should therefore not be held against the authors. They clearly put effort and thought into their choices of taxa to include in this study, but there are limitations arising from this low sample size that inherently limit the confidence that can be placed on their conclusions, and this caveat should be more clearly discussed. Specifically, the authors note that their dataset contains many lithostrotians, but they do not discuss unevenness in body size sampling. As neither their size-category boundaries nor the taxa which fall into each of them are clearly stated, the reader must parse the discussion to glean which taxa are in each size category. It should be noted that the authors include both Jainosaurus and Dreadnoughtus as 'large' taxa even though the latter is estimated to have been roughly five times the body mass of the former, making Dreadnoughtus the only taxon included in this extreme size category. The effects that this may have on body size trends are not discussed. Additionally, few taxa between the body masses of Jainosaurus and Dreadnoughtus have been included even though the hind limbs of several such macronarians have been digitized in prior studies (such as Diamantinasaurus and Giraffititan; Klinkhamer et al. 2018). Also, several members of Colossosauria are more similar in general body size to Dreadnoughtus than Jainosaurus, but unfortunately, they do not preserve a known femur, tibia, and fibula, so the authors could not include them in this study. Exclusion of these taxa may bias inferences about body size evolution, and this is a sampling caveat that could have been discussed more clearly. Future studies including these and other taxa will be important for further evaluating the hypotheses about macronarian evolution advanced by Páramo et al. in this study.

      Sadly, we could not include some larger sized titanosaurians sauropods. As the reviewers points out, the lack of larger sauropods among the sampled taxa may hinder our results, as the “large-bodied” category is filled with some mid-sized taxa and the former Dreadnoughtus schrani which is five times larger than some of them. We tried to include Elaltitan lilloi, digitized for this study and included in preliminary analyses, but the fragmentary status increased greatly the error by the estimation method as there is only a proximal third or mid femur preserved from this taxon. Therefore we opted to exclude it from our database.

      Other taxa considered, as the reviewer suggest, was not readily available for the authors as the time of this study was conducted and including now may have increased the possible bias of our study. Giraffatitan brancai is an Late Jurassic brachiosaurid, which may again increase the number of early-branching titanosauriforms with large body masses while most of the smaller taxa sampled are recovered in deeply-branching macronarians (including Diamantinasaurus matildae if we would have also included it). Future analyses may include a wider sample of the mid to large-bodied titanosaurians, especially lithostrotians, as well as some colossosaurs like Patagotitan mayorum.

      Reviewer #1 (Recommendations For The Authors):

      These are all minor comments that would improve the manuscript.

      - There are a few typos throughout the manuscript such as: line 70 should be 2016 and line 242 should be forelimb.

      Corrected.

      - To me, the most interesting aspect of your study is the diversity and trends recovered in titanosaurian subclades and I would highlight this, not gigantism, in the title if you choose to revise the title.

      It has been addressed. The specificality of some of the tests and the implication to the acquisition of the spread limb posture and gigantism in early-branching taxa is important nonetheless, so we think that it may remain in the title.

      - The abstract should provide more details on the results such as none of the listed trends were statistically significant.

      Many of the trends exhibit phylogenetic signal, but not the allometric components. We have briefly addressed them.

      - Several sentences in the manuscript need citations such as: line 48 the reference to other megaherbivores, line 66 the discussion of poor understanding of the relationship of wide gauge posture and gigantism, and the use of centroid size as an estimate of body mass (see Public Review).

      We changed the line 66 to improve the focus on the current state of the art in the hypothesis of a relationship between arched limbs and in the increase of body size. We included a section relating centroid size as a proxy (due the good correlation between the femur and tibia length and the body mass) and the caveats of using it. We also expanded in the Appendix S2 the use of centroid size and the alternative models.

      - With titanosaur evolution, you mention that they are adapting to new niches and topography (line 64). What support is there for this versus they are adapting to be more successful in their current environment?

      Noted, we have changed the phrase to improved efficiency exploiting of inland environments, as thy can be either opening new inland niches or adapting better to current inland niches that were already exploited for less deeply branching sauropods. However, its testing is beyond the scope of the current work.

      - Line 384-385: the discussion of Rapetosaurus should mention that it is a juvenile and some studies have suggested that titanosaur limbs grow allometrically.

      We have included a small line. Whether Rapetosaurus krausei exhibit allometric growth or not may not change greatly the discussion, maybe only excluding it as morphologically convergent to Lirainosaurus and Muyelensaurus. But if that so, it will be further proof that small-sized titanosaurs exhibit the robust skeleton expected in the giant titanosaurs.

      - I would consider addressing the question of if we are certain enough in our understanding of titanosaurian phylogeny to rule out homology, especially when you discuss the uncertainty of the placement of specific taxa. Also, Diamantinasaurus is not the only titanosaur that has been proposed as a member of both basal and more derived subclades (e.g., Dreadnoughtus).

      We tried to assume a more conservative approach. We could not fully rule out that some of the features observed in the sampled deeply branching lithostrotians, especially saltasauroids, cannot be present in the entire somphospondylan lineage. However, none of the less deeply-branching or early-branching titanosaurs exhibit this kind of morphology. Recent studies propose the possibility that entire groups, included in this study like the Colossosauria, change its position in the phylogeny. However, despite the debated phylogenetic position of Diamantinasaurus or Dreadnoughtus, or even the inclusion of Colossosauria within the saltasauroids and the inclusion of the Ibero-Armorican lithostrotians as putative saltasaurids (Mocho et al. 2024). However, even considering these changes we did not notice any relevant differences in our conclusions about hind limb arched morphology nor about size. Distal hind limb overall robustness should indeed be addressed in the light of shifts in phylogenetic position and include some interesting sauropods like Diamantinasaurus or expand the large-sized Colossosauria or early-branching somphospondyls as it may have profound implications on the morphofunctional adaptations to specific feeding niches, e.g., see current hypotheses about rearing as mentioned in Bates et al. (2016), Ullmann et al. (2017) or Vidal et al. (2020). We had not enough information to conclude the presence of any plesiomorphic condition or analogous feature with our current sample and the debated titanosaurian phylogeny.

      - I understand this is not standard in the field, but your study provides the opportunity to conduct sensitivity testing of the effects of cartilage thickness and user articulation of the bones on PCA results. This would be an inciteful addition to the field of GMM.

      We are currently developing such a comprehensive analysis and several other implications on our past results. However, we feel that it is beyond the scope of the current study. We appreciate the suggestion nonetheless, as it would be a sensitivity test of the impact of several of our assumptions in the final results that is often not considered.

      - In Figure 1, if all the limbs were arranged the same way it would be easier to interpret. Consider flipping panels B and D to match A and C.

      Accepted.

      - In Figures 2-4, the views in C should be labeled in the figure or caption. Oceanotitan is also in the PCA plot but not included in the figure caption. Also, consider changing the names to represent the paraphyletic groupings you are using instead of formal clade names. For example, change 'Titanosauria' to 'Basal Titanosaurs' to reflect that it is not including all titanosaurs in the sample.

      Changes accepted for the shape PCA results. The informal (i.e., paraphyletic) terms such as “Basal Titanosaurs” were only used in the shape analyses as in the RMA, the Titanosauria (and other more inclusive groups) were used as natural groups. Each partial RMA model is based on a sample of all the taxa that are included within that particular clade (e.g., Titanosauria includes both Dreadnoughtus and Saltasaurus; Lithostrotia excludes the former).

      - I am concerned that centroid size does not scale evenly across the wide-ranging body mass of titanosaurs. I do not know if this affects your size trends or their significance, but as I mentioned above Dreadnoughtus is much bigger than most of the taxa included and that isn't as drastically apparent in centroid size (in Figure 5) as it is when taxa are plotted by body mass.

      Main problematic with centroid size of the hind limb is the shift in the body plan of deeply-branching titanosaurs as the Center of Masses is displaced toward the anterior portion of the body and it has been proposed due a large development of the forelimb region (e.g., Bates et al. 2016). However, it would only increase the effects of the phyletic body size reduction, as smaller taxa tend to have a 1:1 fore limb and hind limb ratio, e.g., from our past analyses as in Páramo et al. (2019), and the sacrum is not as beveled as in earlier somphospondyls, e.g., Vidal et al. (2020). The role of the low-browsing feeding habits of deeply-branching lithostrotians shall be explored elsewhere, as it may be the main driving force of this effect. Our point is, the proxy used may have some slight offset due some high-browsing giant early-branching titanosaurs which has a greater cranial region development which increase its body size and mass beyond our bare-minimum estimation based on the hind limb region. But, overall, this offset is assumed to be low. We repeated the analyses with the femoral length as proxy of body size and a mass estimation, including the quadratic equation based on both humeral and femoral lengths, and the results remain similar. Another problem that arises with the use of centroid size is the way it shall be calculated, but as we used an even number of landmarks and curve semilandmarks, and all of them bounded to anatomical features, it remains equal at least for our sample (but cannot be extrapolated to other geometric morphometric studies that do not use the same configurations)

      We appreciate the reviewer concerns nonetheless, as it was on of our own when designing this study, and we in the future will try to expand the analyses, or advise anyone expanding on this study, using total body size/volume estimations following Bates et al. (2016). Which also includes test of the effects of the different whole-body estimation models.

      Cites:

      Bates KT, Mannion PD, Falkingham PL, Brusatte SL, Hutchinson JR, Otero A, Sellers WI, Sullivan C, Stevens KA, Allen V. 2016. Temporal and phylogenetic evolution of the sauropod dinosaur body plan. Royal Society Open Science 3:150636. doi:10.1098/rsos.150636

      Mocho P, Escaso F, Marcos-Fernández F, Páramo A, Sanz JL, Vidal D, Ortega F. 2024. A Spanish saltasauroid titanosaur reveals Europe as a melting pot of endemic and immigrant sauropods in the Late Cretaceous. Commun Biol 7:1016. doi:10.1038/s42003-024-06653-0

      Páramo A, Ortega F, Sanz JL. 2019. A Niche Partitioning Scenario for the Titanosaurs of Lo Hueco (Upper Cretaceous, Spain). International Congress of Vertebrate Morphology (ICVM) - Abstract Volume, Journal of Morphology. Prague. p. S197.

      Ullmann PV, Bonnan MF, Lacovara KJ. 2017. Characterizing the Evolution of Wide-Gauge Features in Stylopodial Limb Elements of Titanosauriform Sauropods via Geometric Morphometrics. The Anatomical Record 300:1618–1635. doi:10.1002/ar.23607

      Vidal D, Mocho P, Aberasturi A, Sanz JL, Ortega F. 2020. High browsing skeletal adaptations in Spinophorosaurus reveal an evolutionary innovation in sauropod dinosaurs. Sci Rep 10:6638. doi:10.1038/s41598-020-63439-0

      Reviewer #2:

      The authors report a quantitative comparative study regarding hind limb evolution among titanosaurs. I find the conclusions and findings of the manuscript interesting and relevant. The strength of the paper would be increased if the authors were to improve their reporting of taxon sampling and their discussion of age estimation and the potential implications that uncertainty in these estimates would have for their conclusions regarding gigantism (vs. ontogenetic patterns).

      Considering the observations made by reviewer #1, we included a data about the impact of ontogenetic patterns and other intraspecific variability in the Appendix S3. We considered to increase the sample but it has not been possible at the time of this study was carried out.

      Reviewer #2 (Recommendations For The Authors):

      I have a few concerns/requests for the authors, that I hope can be easily resolved.

      Comments:

      - What drove taxon sampling?

      Random sampling of somphospondylan sauropods focused on the Lithostrotia clade for the thesis project of one of the authors, APB. Logistics were also one of the bias on our sample, and based on the available titanosaurian material we left out several macronarians that has been already sampled but would further induce a early-branching large sauropod, deeply-branching small sauropod that may alter our results.

      - Which phylogenies were used to create the supertree applied to the analyses? What references were used to time-calibrate the tips and deeper nodes? I couldn't find any reference to this. Additionally, more information regarding the R packages and analytical pipeline would be appreciated: e.g. were measurements used in the analyses log-transformed?

      A comprehensive description of the methodology is provided in Appendix S2.

      - Age estimate: can the author confirm the skeletal maturity of the sampled individuals? If this is not the case, how can the author be sure that the patterns towards gigantism are not reflecting different ontogenetic stages? I believe this should be part of both methods and discussion.

      As commented before, we excluded small, probable juvenile specimens from our sample. We have no paleohistological sample backing the claims of the ontogenetic status of some of the specimens that were included or excluded were calculating the mean shape for the operative taxonomic units. However, we followed a criteria to identify the relative ontogenetic status and it has been included in Appendix S3.

      - The authors used the centroid size for regressions in Figure 6. Although I believe that this is a good variable, would the author be willing to use body mass and log-transformed femur length in addition to what was done? These would be very useful considering that these variables are (relatively) independent from shape/morphology.

      Accepted, we tested our hypotheses with three alternative models based on femoral length, combined femoral and humeral lengths for body mass estimations. Methodology can be found in Appendix S2, results on Appendix S4, code for the alternative methods in Appendix S5.

      - Data access: will stl. Files of the limb elements be shared and freely available? In this case, where the files will be deposited?

      At the time of the current study, some of the sampled specimens cannot be available (material under study) but the mean shapes can be generated after the landmarks and semilandmark curves and the “atlas” mesh.

      - Additionally, outstanding references regarding limb evolution, GMM, role of ontogeny, and evolution of columnar gait are missing. The authors should reinforce the literature review with the following (alphabetical order):

      Bonnan, M. F. (2003). The evolution of manus shape in sauropod dinosaurs: implications for functional morphology, forelimb orientation, and phylogeny. Journal of Vertebrate Paleontology, 23(3), 595-613.

      Botha, J., Choiniere, J. N., & Benson, R. B. (2022). Rapid growth preceded gigantism in sauropodomorph evolution. Current Biology, 32(20), 4501-4507.

      Curry Rogers, K., Whitney, M., D'Emic, M., & Bagley, B. (2016). Precocity in a tiny titanosaur from the Cretaceous of Madagascar. Science, 352(6284), 450-453.

      Day, J. J., Upchurch, P., Norman, D. B., Gale, A. S., & Powell, H. P. (2002). Sauropod trackways, evolution, and behavior. Science, 296(5573), 1659-1659.

      Fabbri, M., Navalón, G., Benson, R. B., Pol, D., O'Connor, J., Bhullar, B. A. S., ... & Ibrahim, N. (2022). Subaqueous foraging among carnivorous dinosaurs. Nature, 603(7903), 852-857.

      Fabbri, M., Navalón, G., Mongiardino Koch, N., Hanson, M., Petermann, H., & Bhullar, B. A. (2021). A shift in ontogenetic timing produced the unique sauropod skull. Evolution, 75(4), 819-831.

      González Riga, B. J., Lamanna, M. C., Ortiz David, L. D., Calvo, J. O., & Coria, J. P. (2016). A gigantic new dinosaur from Argentina and the evolution of the sauropod hind foot. Scientific Reports, 6(1), 19165.

      Lefebvre, R., Allain, R., & Houssaye, A. (2023). What's inside a sauropod limb? First three‐dimensional investigation of the limb long bone microanatomy of a sauropod dinosaur, Nigersaurus taqueti (Neosauropoda, Rebbachisauridae), and implications for the weight‐bearing function. Palaeontology, 66(4), e12670.

      McPhee, B. W., Benson, R. B., Botha-Brink, J., Bordy, E. M., & Choiniere, J. N. (2018). A giant dinosaur from the earliest Jurassic of South Africa and the transition to quadrupedality in early sauropodomorphs. Current Biology, 28(19), 3143-3151.

      Martin Sander, P., Mateus, O., Laven, T., & Knötschke, N. (2006). Bone histology indicates insular dwarfism in a new Late Jurassic sauropod dinosaur. Nature, 441(7094), 739-741.

      Remes, K. (2008). Evolution of the pectoral girdle and forelimb in Sauropodomorpha (Dinosauria, Saurischia): osteology, myology and function (Doctoral dissertation, München, Univ., Diss., 2008).

      Sander, P. M., & Clauss, M. (2008). Sauropod gigantism. Science, 322(5899), 200-201.

      Yates, A. M., & Kitching, J. W. (2003). The earliest known sauropod dinosaur and the first steps towards sauropod locomotion. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270(1525), 1753-1758.

      We appreciate this suggestion and we already used some of the articles in our study but the selection of cites were based also in the available manuscript space enforced by the edition guidelines. We would have like to include several of these works but we had opted to include some of the works that summarize some of them, whereas excluding others.

    1. We can leave; we can conform; or we can express ourselves, argue and protest, and try to change the situation. I am arguing for the last. Becoming visible and using one's voice can be dangerous — even in purportedly free societies like the U.S. — but these strategies can also be successful in the long-run, contributing to local and systemic change.

      This gives the same energy as if you are not a part of the solution you are part of the problem. And to some extent, I think this is absolutely true. We need to be passionate about our children and be able to take care of our students and treat them well and with kindness. I would caution however that it is equaly important to not divide people in our language and educate and empathize with those who may disagree.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This important study identifies the "H-state" as a potential conformational marker distinguishing amyloidogenic from non-amyloidogenic light chains, addressing a critical problem in protein misfolding and amyloidosis. By combining advanced techniques such as small-angle X-ray scattering, molecular dynamics simulations, and H-D exchange mass spectrometry, the authors provide convincing evidence for their novel findings. However, incomplete experimental descriptions, limitations in SAXS data interpretation, and the way HDX MS data is presented aHect the strength and generalizability of the conclusions. Strengthening these aspects would enhance the impact of this work for researchers in amyloidosis and protein misfolding.

      We thank eLife editors and reviewers for their constructive feedback. The manuscript has been improved to provide a more complete description of the experiments and to strengthen the interpretation and presentation of all data. Updated Figures (Figure 2 and Figure 5) and a new Table (Table 2) in the main text provide a more complete and clearer comparison of the SAXS data with MD simulations as well as a clearer representation of the HDX MS data. Additional figures have been added in SI. The text has been extended accordingly and complete materials and methods are now included in the main text. Abstract, introduction and discussion have been revised to improve the overall readability of the manuscript.

      Public Reviews:

      Reviewer #1 (Public review):

      The study investigates light chains (LCs) using three distinct approaches, with a focus on identifying a conformational fingerprint to diHerentiate amyloidogenic light chains from multiple myeloma light chains. The study's major contribution is identifying a low-populated "H state," which the authors propose as a unique marker for AL-LCs. While this finding is promising, the review highlights several strengths and weaknesses. Strengths include the valuable contribution of identifying the H state and using multiple approaches, which provide a comprehensive understanding of LC structural dynamics. However, the study suHers from weaknesses, particularly in interpreting SAXS data, lack of clarity in presentation, and methodological inconsistencies. Critical concerns include high error margins between SAXS profiles and MD fits, unclear validation of oligomeric species in SAXS measurements, and insuHicient quantitative cross-validation between experimental (HDX) and computational data (MD). This reviewer calls for major revisions including clearer definitions, improved methodology, and additional validation, to strengthen the conclusions.

      We thank the reviewer for the supportive comments, in the revised version of the manuscript we have focused on improving the clarity and completeness of our work. We are sorry for example to not have made previously clear enough that the comparison of SAXS with MD simulation was not that shown in the main text in Figure 1 and Table 1 (this is the comparison with single structures) but that reported in the SI (previously Figure S1 and Table S2, showing very good fits). These data have been moved in the main text in the reworked Figure 2 and new Table 2.  We have also improved the presentation of the HDX MS data in Figure 5 and in the text adding also additional analysis in SI. Materials and methods are now completely moved in the main text. We generally revised the manuscript for clarity.

      Reviewer #2 (Public review):

      Summary:

      This well-written manuscript addresses an important but recalcitrant problem - the molecular mechanism of protein misfolding in Ig light chain (LC) amyloidosis (AL), a major life-threatening form of systemic human amyloidosis. The authors use expertly recorded and analyzed smallangle X-ray scattering (SAXS) data as a restraint for molecular dynamics simulations (called M&M) and to explore six patient-based LC proteins. The authors report that a highly populated "H-state" determined computationally, wherein the two domains in an LC molecule acquire a straight rather than bent conformation, is what distinguishes AL from non-AL LCs. They then use H-D exchange mass spectrometry to verify this conclusion. If confirmed, this is a novel and interesting finding with potentially important translational implications.

      We thank the reviewer for the supportive comments.

      Strengths:

      Expertly recorded and analyzed SAXS data combined with clever M&M simulations lead to a novel and interesting conclusion. Regardless of whether or not the CL-CL domain interface is destabilized in AL LCs explored in this (Figure 6) and other studies, stabilization of this interface is an excellent idea that may help protect at least a subset of AL LCs from misfolding in amyloid. This idea increases the potential impact of this interesting study.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The HDX analysis could be strengthened.

      We have extended the analysis and improved the presentation of the HDX data. Figure 5 has been reworked, text has been improved accordingly and additional analysis have been reported in SI.

      Reviewer #3 (Public review):

      Summary:

      This study identifies conformational fingerprints of amyloidogenic light chains, that set them apart from the non-amyloidogenic ones.

      We thank the reviewer for the supportive comments.

      Strengths:

      The research employs a comprehensive combination of structural and dynamic analysis techniques, providing evidence that conformational dynamics at the VL-CL interface and structural expansion are distinguished features of amyloidogenic LCs.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The sample size is limited, which may aHect the generalizability of the findings. Additionally, the study could benefit from deeper analysis of specific mutations driving this unique conformation to further strengthen therapeutic relevance.

      We agree, we tried to maximise the size of the sample and this was the best we could do. With respect to the analysis of the mutations, while we tried to discuss some of them also in view of previous works, because our set covers multiple germlines instead than focusing on a single one, this limit our ability to discuss single point mutations systematically, at the same time the discussion of single points mutations has been the focus of many recent works, while our approach provide a diNerent point of view.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      This study provides an investigation of light chains (LCs) using three distinct approaches, focusing primarily on identifying a conformational fingerprint to distinguish amyloidogenic light chains (AL-LCs) from multiple myeloma light chains (MM-LCs). The authors propose that the presence of a low-populated "H state," characterized by an extended quaternary structure and a perturbed CL-CL interface, is unique to AL-LCs. This finding is validated through hydrogendeuterium exchange mass spectrometry (HDX-MS). The study makes a valuable contribution to understanding the structural dynamics of light chains, particularly with the identification of the H state in AL-LCs. However, significant concerns regarding the interpretation of the SAXS data, clarity in presentation, and methodological rigor must be addressed. I recommend major revisions and resubmission of the work.

      Major concerns:

      (1) A critical concern is how the authors ensure that the SAXS profiles represent only dimeric species, given the high propensity of LCs to aggregate. If higher-order aggregates or monomers were present, this would significantly impact the SAXS data and SAXS-MD integration. Some measurements are bulk SAXS, while others are SEC-SAXS, making the study questionable. The authors need to clarify how only dimeric species were measured for the SEC-SAXS analysis, and all assessments of the dimeric state should be shown in the SI. Additionally, complementary techniques such as DLS or SEC-MALS should be used to verify the oligomeric state of the samples. Without this validation, the SAXS profiles may not be reliable.

      We added SEC-MALS and SEC-SAXS data in the SI (Figures S20 and S21) as well the SAXS curves shown in log-log plot (Figure S1) that display a flat trend at low q that exclude aggregation. SAXS is very sensitive to oligomers and aggregates and our data do not indicate the presence of those species. When we had indication of possible aggregation in the sample we used SEC-SAXS.

      (2) A major problem with the paper is that the claim of the "H state," which is the novelty of the study and serves as a marker of aggregation, is derived from samples where the error between the SAXS profiles and MD fits is extremely high. This casts doubt on whether the structure is indeed resolved by MD. The main conclusion of the paper is derived from weak consistency between experiment and simulation. In AL55, the error between experiment and simulation is greater than 5; for H7, it is higher than 2.8. The residuals show significant error at mid-q values, suggesting that long-range distance correlations (20-10 Å, CL, VL positioning) are not consistent between simulation and experiment. Furthermore, the FES plots of two independent replicas show deviation in the existence of the H state. One shows a minimum in that region, while the other does not. So, how robust is this conclusion? What is the chi-squared value if each replica is used independently? A separate experimental cross-validation is necessary to claim the existence of the H state.

      We apologise for the misunderstanding underlying this reviewer comment. The poor agreement mentioned is not between the SAXS and MD simulations, but with the individual structures, and this disagreement led us to perform MD simulations that are in much better agreement with the data (previously Fig. S1 and Table S2). To avoid this misunderstanding, which would indeed weaken our work, we have now moved both the figure and the table in the main text to the updated Figure 2 and the new Table 2.

      Regarding the robustness of the sampling, we believe that Table 3 (previously Table 2) clearly shows the statistical convergence of the data, diNerences in the presentation of the free energy are purely interpolation issues. The chi-squares of each replicate are reported in Table 2 (previously Table S2).

      (3) There is insuHicient discussion about SAXS computations from MD trajectories. The accuracy of these calculations is crucial to deriving the existing conclusions, and the study's reliance on the PLUMED plugin, which is known to give inaccurate results for SAXS computations, raises concerns. How the solvent is treated in the SAXS computations needs to be explained. Alternative methods like WAXSiS or Crysol should be explored to check whether the SAXS profiles derived from the MD trajectory are consistent across other SAXS computation methods for the major conformers of the proteins.

      We have now clarified that while the SAXS calculation to perform Metainference MD were done using PLUMED (that to our knowledge is as accurate as crysol) SAXS curves used for analysis were calculated using crysol.

      (4) The HDX and MD results do not seem to correlate well, and there is a disconnect between Figure 2 (SAXS profiles) and Figure 5 (HDX structural interpretation). The authors should quantitatively assess residue-level dynamics by comparing HDX signals with MD-derived HDX signals for each protein. This would provide a cross-validation between the experimental and computational data.

      In our opinion our SAXS, MD and HDX MS data provide a consistent picture. Our HDX-MS do not provide per residue data, making a quantitative comparison out of scope. RMSF data do not necessarily need to correlate with the deuterium uptake.

      (5) MD simulations are only used to refine the structure of AlphaFold predictions, but the trajectories could help explain why these structures diHer, what stabilizes the dimer, or what leads to the conformational transition of the H state. A lack of analysis regarding the physical mechanism behind these structural changes is a weakness of the study. The authors should dedicate more eHort to analyzing their data and provide physical insights into why these changes are observed.

      Our aim was to identify a property that could discriminate between AL and MM LCs. We used MD simulations, not to refine structures, but to explore the conformational dynamics of LCs (starting from either X-ray structures, homology or AlphaFold models), because SAXS data suggested that conformational dynamics could discriminate between AL- and MM-LCs. Simulations allowed us to propose a hypothesis, which we tested by HDX MS. While more insight is always welcome, we believe that we have achieved our goal for now. In the discussion, we present additional analysis of the simulations to connect with previous literature, we agree that more analysis can be done, and also for this reason, all our data are publicly available.

      Minor concerns

      (6) The abstract leans heavily on describing the problem and methods but lacks a clear presentation of key results. Providing a concise summary of the main findings (e.g., the identification of the H state) would better balance the abstract.

      We agree with the reviewer and we rewrote the abstract.

      (7) In the abstract, the term "experimental structure" is used ambiguously. Since SAXS also provides an experimental structure, it is unclear what the authors are referring to. This should be clarified.

      We agree with the reviewer and we rewrote the abstract.

      (8) Abbreviations such as VL (variable domain) and CL (constant domain) are not defined, making it harder for readers unfamiliar with the field to follow. Abbreviations should be defined when first mentioned.

      We agree with the reviewer and we rewrote the abstract.

      (9) The introduction provides a good general context but fails to explicitly define the knowledge gap. Specifically, the structural and dynamic determinants of LC amyloidogenicity are not well established, and this study could be framed as addressing that gap.

      We thank the reviewer and we agree this could be better framed, we improved the introduction accordingly.

      (10) The introduction does not present the novel discovery of the H state early enough. The unique contribution of identifying this state as a marker for AL-LCs should be mentioned upfront to guide the reader through the significance of the study.

      We thank the reviewer and we have now made more explicit what we found.

      (11) The therapeutic implications of this research should be highlighted more clearly in the discussion. Examples of how these findings could be utilized in drug design or therapeutic approaches would enhance the study's impact.

      We thank the reviewer, but while we think that the H-state could be targeted for drug design, since we do not have data yet we do not want to stress this point more than what we are already doing.

      (12) There is an overwhelming use of abbreviations such as H3, H7, H18, M7, and M10 without proper introduction. This makes it diHicult for readers to follow the results, and the average reader may become lost in the details. An introductory figure summarizing the sequences under study, along with a schematic of the dimeric structure defining VL and CL domains, would significantly aid comprehension.

      We agree and we tried to better introduce the systems and simplify the language without adding a figure that we think would be redundant.

      (13) In Figure 1, add labels to each SAXS curve to indicate which protein they correspond to. Also, what does online SEC-SAXS mean?

      Done

      (14) The caption of Figure 3 is unclear, particularly with abbreviations like Lb, Ls, G, and H, which are not mentioned in the captions. The authors should define these terms for clarity.

      Done

      (15) The study claims that the dominant structure of the dimer changes between diHerent LCs. However, Figure 5 shows identical structures for all proteins, raising questions about the consistency between the SAXS and HDX data. This inconsistency is a general problem between the MD and HDX sections, where cross-communication and comparisons are not properly addressed.

      We do not claim that the dominant structure of the dimer changes between diNerent LCs, this would also be in contradiction with current literature. We claim a diNerence in a low-populated state. From this point of view using always the same structure is consistent and should simplify the representation of the results. We agree that the manuscript may be not always easy to follow and we thank the reviewer in helping us improving it.

      (16) The authors show I(q) vs q and residuals for each protein. The Kratky plots are not suHicient to compare the SAXS computations with the measured profile.

      Showing Kratky and residuals is a standard and complementary way to present and compare SAXS data to structures. Chi-square values are also reported. Log-log plots have been added to SI in response to previous comments.

      (17) The authors need to explain how they estimate the Rg values (from simulation or SAXS profiles). If they are using simulations, they should compute the Rg values from the simulations for comparison.

      Rg values reported in Table 1 are derived from SAXS. Rg from simulations have been added in Table 2.

      (18) The evolution of the sampling is unclear. The authors need to show the initial starting conformation in each case and the most likely conformation after M&M in the SI, to demonstrate that their approach indeed caused changes in the initial predictions.

      Our approach is not structure refinement and as such the proposed analysis would be misleading. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. DiNerences (or not) between initial and selected configurations will not be particularly informative in this context.

      (19) The authors should also provide a running average of chi-squared values over time to demonstrate that the conformational ensemble converged toward the SAXS profile.

      Our simulations are not driven to improve the agreement with SAXS over time, this is not structure refinement. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. The suggested analysis would be a misinterpretation of our simulations. The comparison with SAXS is provided in Figure 2 and Table 2 as mentioned above.

      (20) The aggregate simulation time of 120 microseconds is misleading, as each replica was only run for 2-3 microseconds. This should be clarified.

      The number reported in the text is accurate and represent the aggregated sampling. The number of replicas for each metainference simulation and their length is reported in Table 2 now moved for clarity from the SI to main text.

      (21) It is not clear how the replicas were weighted to compute the SAXS profiles and FES. There are two independent runs in each case, and each run has about 30 replicas. How these replicas are weighted needs to be discussed in the SI.

      Done

      (22) The methods section is unevenly distributed, with detailed explanations of LC production and purification, while other key methodologies like SAXS+MD integration and HDX are not even mentioned in the main text (they are in the Supporting Information). The authors should provide a brief overview of all methodologies in the main text or move everything to the SI for consistency.

      We agree with the reviewer, all methods are now in main text. 

      Reviewer #2 (Recommendations for the authors):

      (1) Computational M&M evidence is strong (Figure 3) and is supported by SAXS (used as restraints). However, Kratky plots reported in the main MS Figure 1 show significant diHerences between the data and the structural model only for one protein, AL-55. It is hard for the general reader to see how these SAXS data support a clear diHerence between AL and non-AL proteins. If possible, please strengthen the evidence; if not, soften the conclusions.

      We thank the reviewer for the comments. The chi-square (Table 1) and the residuals (Figure 1) are a strong indication of the diNerence. To strengthen the evidence, following also the comment from reviewer 3 we calculated the p-value (<10<sup>-5</sup>) on the significance of the radius of gyration to discriminate AL and MM LCs. We agree that SAXS alone was not enough and this is indeed what prompted us to perform MD simulations.

      (2) HDX MS results are cursory and not very convincing as presented. The butterfly plots in Figure 5 are too small to read and are unlabeled so it is unclear which protein is which.  

      Figure 5 has been reworked for readability. More data have been added in SI. 

      (3) What labeling time was selected to construct these plots and why?

      The deuterium uptakes at 30 min HDX time showed the most pronounced diNerences between diNerent proteins, which were chosen to illustrate the key structural features in the main figure panel (Figure 5).

      How diHerent are the results at other labeling times? Showing uptake curves (with errors) for more than just two peptides in the supplement Figure S12 might be helpful. 

      We found a continuous increase in deuterium uptake as we increased the exchange time from 0.5 to 240 min, which reached saturation at 120 min. Therefore, the exchange follows the same pattern at all time points. Butterfly plots at diNerent HDX times of 0.5 to 240 min are shown in gradient of light blue to dark blue which clearly shows the pattern of deuterium uptake at increasing incubation times (Figure 5). The HDX uptake kinetics of selected peptides with corresponding error bars are shown in Figure S12.

      How redundant are the data, i.e. how good is the peptide coverage/resolution in key regions at the domain-domain interface that the authors deem important? Mapping the maximal deuterium uptake on the structures in Figure 5 is not very helpful. Perhaps mapping the whole range of uptake using a gradient color scheme would be more informative.

      Overall coverage and redundancy for all four proteins are> 90% and > 4.0, respectively, with an average error margin in fractional uptake among all peptides is 0.04-0.05 Da, which suggests that our data is reliable (Table S3). We modified the main panel figures showing the gradient of deuterium uptake in blue-white-red for 0 to 30% of deuterium uptake on the chain A of the dimeric LCs.

      (3) Is the conformational heterogeneity depicted in M&M simulations consistent with HDX results? The authors may want to address this by looking at the EX1/EX2 exchange kinetics for AL vs. non-AL proteins. Do AL proteins show more EX1?

      No, we don’t see any EX1 exchange kinetics in our analysis. This is compatible with the prediction of the H-state that is a native like state and not an unfolded/partially folded state. 

      (4) Perhaps the main conclusion could be softened given the small number of proteins (six), esp. since only four (3 AL and 1 non-AL) could be explored by HDX. Are other HDX MS data of AL LCs from the same Lambda6 family (e.g. PMID: 34678302) consistent with the conclusions that a particular domain-domain interface is weakened in AL vs. non-AL LCs?

      We thank the reviewer for this suggestions. A diNerence in HDX MS data is indeed visible between AL and MM proteins for peptide 33-47 in the suggested paper (Figures 4, S5 and S8). The diNerence is reduced by the mutation identified in the paper as driving the aggregation in that specific case. We now mention this in the discussion.

      (5) Please clarify if the H* state is the same for a covalent vs. non-covalent LC dimer.

      We do not know because our data are only for covalent dimers. But, interestingly, the state is very similar to what was observed for a model kappa light-chain in Weber, et al., we have better highlighted this point in the discussion.

      (6) Please try and better explain why a smaller distance between CL domains in H7 protein and a larger distance in other AL proteins both promote protein misfolding.

      We do not have elements to discuss this point in more detail.

      (7) Please comment on the Kratky plots data vs. model agreement (see comments above).

      Done.

      (8) Please find a better way to display, describe, and interpret the HD exchange MS data.

      We have generated new main text (new Figure 5) and SI figures that we think allow the reader to better appreciated our observations. Corresponding results sections have been also improved.

      Minor points:

      (9) Is the population of the H-state with perturbed CL-CL domain interface, which was obtained in M&M simulations, suHicient to be observable by HDX MS?

      While populations alone are not enough to determine what is observable by HDX MS, a 10% population correspond roughly to 6 kJ/mol of ΔG and is compatible with EX2 kinetics. Previous works suggested that HDX-MS data should be sensitive to subpopulations of the order of 10%, (https://doi.org/10.1016/j.bpj.2020.02.005, https://doi.org/10.1021/jacs.2c06148)

      (10) Typically, an excited intermediate in protein unfolding is a monomer, while here it is an LC dimer. Is this unusual?

      This is a good point, we think that intermediates have mostly been studied on monomeric proteins because these are more commonly used as model systems, but we do not feel like discussing this point.

      (11) Low deuterium uptake is consistent with a rigid structure but may also reflect buried structure and/or structure that moves on a time scale greater than the labeling time.

      We agree.

      Reviewer #3 (Recommendations for the authors):

      (1) The p-value (statistical significance) of Rg diHerence should be computed.

      We thank the reviewer for the suggestion, we calculated the p-value that resulted quite significant.

      (2) The significance of mutations (SHM?) at the interface, such as A40G should be compared with previous observations. (Garrofalo et al., 2021).

      We thank the reviewer for the suggestion, a sentence has been added in the discussion.

    2. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This important study identifies the "H-state" as a potential conformational marker distinguishing amyloidogenic from non-amyloidogenic light chains, addressing a critical problem in protein misfolding and amyloidosis. By combining advanced techniques such as small-angle X-ray scattering, molecular dynamics simulations, and H-D exchange mass spectrometry, the authors provide convincing evidence for their novel findings. However, incomplete experimental descriptions, limitations in SAXS data interpretation, and the way HDX MS data is presented aHect the strength and generalizability of the conclusions. Strengthening these aspects would enhance the impact of this work for researchers in amyloidosis and protein misfolding.

      We thank eLife editors and reviewers for their constructive feedback. The manuscript has been improved to provide a more complete description of the experiments and to strengthen the interpretation and presentation of all data. Updated Figures (Figure 2 and Figure 5) and a new Table (Table 2) in the main text provide a more complete and clearer comparison of the SAXS data with MD simulations as well as a clearer representation of the HDX MS data. Additional figures have been added in SI. The text has been extended accordingly and complete materials and methods are now included in the main text. Abstract, introduction and discussion have been revised to improve the overall readability of the manuscript.

      Public Reviews:

      Reviewer #1 (Public review):

      The study investigates light chains (LCs) using three distinct approaches, with a focus on identifying a conformational fingerprint to diHerentiate amyloidogenic light chains from multiple myeloma light chains. The study's major contribution is identifying a low-populated "H state," which the authors propose as a unique marker for AL-LCs. While this finding is promising, the review highlights several strengths and weaknesses. Strengths include the valuable contribution of identifying the H state and using multiple approaches, which provide a comprehensive understanding of LC structural dynamics. However, the study suHers from weaknesses, particularly in interpreting SAXS data, lack of clarity in presentation, and methodological inconsistencies. Critical concerns include high error margins between SAXS profiles and MD fits, unclear validation of oligomeric species in SAXS measurements, and insuHicient quantitative cross-validation between experimental (HDX) and computational data (MD). This reviewer calls for major revisions including clearer definitions, improved methodology, and additional validation, to strengthen the conclusions.

      We thank the reviewer for the supportive comments, in the revised version of the manuscript we have focused on improving the clarity and completeness of our work. We are sorry for example to not have made previously clear enough that the comparison of SAXS with MD simulation was not that shown in the main text in Figure 1 and Table 1 (this is the comparison with single structures) but that reported in the SI (previously Figure S1 and Table S2, showing very good fits). These data have been moved in the main text in the reworked Figure 2 and new Table 2. We have also improved the presentation of the HDX MS data in Figure 5 and in the text adding also additional analysis in SI. Materials and methods are now completely moved in the main text. We generally revised the manuscript for clarity.

      Reviewer #2 (Public review):

      Summary:

      This well-written manuscript addresses an important but recalcitrant problem - the molecular mechanism of protein misfolding in Ig light chain (LC) amyloidosis (AL), a major life-threatening form of systemic human amyloidosis. The authors use expertly recorded and analyzed smallangle X-ray scattering (SAXS) data as a restraint for molecular dynamics simulations (called M&M) and to explore six patient-based LC proteins. The authors report that a highly populated "H-state" determined computationally, wherein the two domains in an LC molecule acquire a straight rather than bent conformation, is what distinguishes AL from non-AL LCs. They then use H-D exchange mass spectrometry to verify this conclusion. If confirmed, this is a novel and interesting finding with potentially important translational implications.

      We thank the reviewer for the supportive comments.

      Strengths:

      Expertly recorded and analyzed SAXS data combined with clever M&M simulations lead to a novel and interesting conclusion. Regardless of whether or not the CL-CL domain interface is destabilized in AL LCs explored in this (Figure 6) and other studies, stabilization of this interface is an excellent idea that may help protect at least a subset of AL LCs from misfolding in amyloid. This idea increases the potential impact of this interesting study.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The HDX analysis could be strengthened.

      We have extended the analysis and improved the presentation of the HDX data. Figure 5 has been reworked, text has been improved accordingly and additional analysis have been reported in SI.

      Reviewer #3 (Public review):

      Summary:

      This study identifies conformational fingerprints of amyloidogenic light chains, that set them apart from the non-amyloidogenic ones.

      We thank the reviewer for the supportive comments.

      Strengths:

      The research employs a comprehensive combination of structural and dynamic analysis techniques, providing evidence that conformational dynamics at the VL-CL interface and structural expansion are distinguished features of amyloidogenic LCs.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The sample size is limited, which may aHect the generalizability of the findings. Additionally, the study could benefit from deeper analysis of specific mutations driving this unique conformation to further strengthen therapeutic relevance.

      We agree, we tried to maximise the size of the sample and this was the best we could do. With respect to the analysis of the mutations, while we tried to discuss some of them also in view of previous works, because our set covers multiple germlines instead than focusing on a single one, this limit our ability to discuss single point mutations systematically, at the same time the discussion of single points mutations has been the focus of many recent works, while our approach provide a diNerent point of view.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      This study provides an investigation of light chains (LCs) using three distinct approaches, focusing primarily on identifying a conformational fingerprint to distinguish amyloidogenic light chains (AL-LCs) from multiple myeloma light chains (MM-LCs). The authors propose that the presence of a low-populated "H state," characterized by an extended quaternary structure and a perturbed CL-CL interface, is unique to AL-LCs. This finding is validated through hydrogendeuterium exchange mass spectrometry (HDX-MS). The study makes a valuable contribution to understanding the structural dynamics of light chains, particularly with the identification of the H state in AL-LCs. However, significant concerns regarding the interpretation of the SAXS data, clarity in presentation, and methodological rigor must be addressed. I recommend major revisions and resubmission of the work.

      Major concerns:

      (1) A critical concern is how the authors ensure that the SAXS profiles represent only dimeric species, given the high propensity of LCs to aggregate. If higher-order aggregates or monomers were present, this would significantly impact the SAXS data and SAXS-MD integration. Some measurements are bulk SAXS, while others are SEC-SAXS, making the study questionable. The authors need to clarify how only dimeric species were measured for the SEC-SAXS analysis, and all assessments of the dimeric state should be shown in the SI. Additionally, complementary techniques such as DLS or SEC-MALS should be used to verify the oligomeric state of the samples. Without this validation, the SAXS profiles may not be reliable.

      We added SEC-MALS and SEC-SAXS data in the SI (Figures S20 and S21) as well the SAXS curves shown in log-log plot (Figure S1) that display a flat trend at low q that exclude aggregation. SAXS is very sensitive to oligomers and aggregates and our data do not indicate the presence of those species. When we had indication of possible aggregation in the sample we used SEC-SAXS.

      (2) A major problem with the paper is that the claim of the "H state," which is the novelty of the study and serves as a marker of aggregation, is derived from samples where the error between the SAXS profiles and MD fits is extremely high. This casts doubt on whether the structure is indeed resolved by MD. The main conclusion of the paper is derived from weak consistency between experiment and simulation. In AL55, the error between experiment and simulation is greater than 5; for H7, it is higher than 2.8. The residuals show significant error at mid-q values, suggesting that long-range distance correlations (20-10 Å, CL, VL positioning) are not consistent between simulation and experiment. Furthermore, the FES plots of two independent replicas show deviation in the existence of the H state. One shows a minimum in that region, while the other does not. So, how robust is this conclusion? What is the chi-squared value if each replica is used independently? A separate experimental cross-validation is necessary to claim the existence of the H state.

      We apologise for the misunderstanding underlying this reviewer comment. The poor agreement mentioned is not between the SAXS and MD simulations, but with the individual structures, and this disagreement led us to perform MD simulations that are in much better agreement with the data (previously Fig. S1 and Table S2). To avoid this misunderstanding, which would indeed weaken our work, we have now moved both the figure and the table in the main text to the updated Figure 2 and the new Table 2.

      Regarding the robustness of the sampling, we believe that Table 3 (previously Table 2) clearly shows the statistical convergence of the data, diNerences in the presentation of the free energy are purely interpolation issues. The chi-squares of each replicate are reported in Table 2 (previously Table S2).

      (3) There is insuHicient discussion about SAXS computations from MD trajectories. The accuracy of these calculations is crucial to deriving the existing conclusions, and the study's reliance on the PLUMED plugin, which is known to give inaccurate results for SAXS computations, raises concerns. How the solvent is treated in the SAXS computations needs to be explained. Alternative methods like WAXSiS or Crysol should be explored to check whether the SAXS profiles derived from the MD trajectory are consistent across other SAXS computation methods for the major conformers of the proteins.

      We have now clarified that while the SAXS calculation to perform Metainference MD were done using PLUMED (that to our knowledge is as accurate as crysol) SAXS curves used for analysis were calculated using crysol.

      (4) The HDX and MD results do not seem to correlate well, and there is a disconnect between Figure 2 (SAXS profiles) and Figure 5 (HDX structural interpretation). The authors should quantitatively assess residue-level dynamics by comparing HDX signals with MD-derived HDX signals for each protein. This would provide a cross-validation between the experimental and computational data.

      In our opinion our SAXS, MD and HDX MS data provide a consistent picture. Our HDX-MS do not provide per residue data, making a quantitative comparison out of scope. RMSF data do not necessarily need to correlate with the deuterium uptake.

      (5) MD simulations are only used to refine the structure of AlphaFold predictions, but the trajectories could help explain why these structures diHer, what stabilizes the dimer, or what leads to the conformational transition of the H state. A lack of analysis regarding the physical mechanism behind these structural changes is a weakness of the study. The authors should dedicate more eHort to analyzing their data and provide physical insights into why these changes are observed.

      Our aim was to identify a property that could discriminate between AL and MM LCs. We used MD simulations, not to refine structures, but to explore the conformational dynamics of LCs (starting from either X-ray structures, homology or AlphaFold models), because SAXS data suggested that conformational dynamics could discriminate between AL- and MM-LCs. Simulations allowed us to propose a hypothesis, which we tested by HDX MS. While more insight is always welcome, we believe that we have achieved our goal for now. In the discussion, we present additional analysis of the simulations to connect with previous literature, we agree that more analysis can be done, and also for this reason, all our data are publicly available.

      Minor concerns

      (6) The abstract leans heavily on describing the problem and methods but lacks a clear presentation of key results. Providing a concise summary of the main findings (e.g., the identification of the H state) would better balance the abstract.

      We agree with the reviewer and we rewrote the abstract.

      (7) In the abstract, the term "experimental structure" is used ambiguously. Since SAXS also provides an experimental structure, it is unclear what the authors are referring to. This should be clarified.

      We agree with the reviewer and we rewrote the abstract.

      (8) Abbreviations such as VL (variable domain) and CL (constant domain) are not defined, making it harder for readers unfamiliar with the field to follow. Abbreviations should be defined when first mentioned.

      We agree with the reviewer and we rewrote the abstract.

      (9) The introduction provides a good general context but fails to explicitly define the knowledge gap. Specifically, the structural and dynamic determinants of LC amyloidogenicity are not well established, and this study could be framed as addressing that gap.

      We thank the reviewer and we agree this could be better framed, we improved the introduction accordingly.

      (10) The introduction does not present the novel discovery of the H state early enough. The unique contribution of identifying this state as a marker for AL-LCs should be mentioned upfront to guide the reader through the significance of the study.

      We thank the reviewer and we have now made more explicit what we found.

      (11) The therapeutic implications of this research should be highlighted more clearly in the discussion. Examples of how these findings could be utilized in drug design or therapeutic approaches would enhance the study's impact.

      We thank the reviewer, but while we think that the H-state could be targeted for drug design, since we do not have data yet we do not want to stress this point more than what we are already doing.

      (12) There is an overwhelming use of abbreviations such as H3, H7, H18, M7, and M10 without proper introduction. This makes it diHicult for readers to follow the results, and the average reader may become lost in the details. An introductory figure summarizing the sequences under study, along with a schematic of the dimeric structure defining VL and CL domains, would significantly aid comprehension.

      We agree and we tried to better introduce the systems and simplify the language without adding a figure that we think would be redundant.

      (13) In Figure 1, add labels to each SAXS curve to indicate which protein they correspond to. Also, what does online SEC-SAXS mean?

      Done

      (14) The caption of Figure 3 is unclear, particularly with abbreviations like Lb, Ls, G, and H, which are not mentioned in the captions. The authors should define these terms for clarity.

      Done

      (15) The study claims that the dominant structure of the dimer changes between diHerent LCs. However, Figure 5 shows identical structures for all proteins, raising questions about the consistency between the SAXS and HDX data. This inconsistency is a general problem between the MD and HDX sections, where cross-communication and comparisons are not properly addressed.

      We do not claim that the dominant structure of the dimer changes between diNerent LCs, this would also be in contradiction with current literature. We claim a diNerence in a low-populated state. From this point of view using always the same structure is consistent and should simplify the representation of the results. We agree that the manuscript may be not always easy to follow and we thank the reviewer in helping us improving it.

      (16) The authors show I(q) vs q and residuals for each protein. The Kratky plots are not suHicient to compare the SAXS computations with the measured profile.

      Showing Kratky and residuals is a standard and complementary way to present and compare SAXS data to structures. Chi-square values are also reported. Log-log plots have been added to SI in response to previous comments.

      (17) The authors need to explain how they estimate the Rg values (from simulation or SAXS profiles). If they are using simulations, they should compute the Rg values from the simulations for comparison.

      Rg values reported in Table 1 are derived from SAXS. Rg from simulations have been added in Table 2.

      (18) The evolution of the sampling is unclear. The authors need to show the initial starting conformation in each case and the most likely conformation after M&M in the SI, to demonstrate that their approach indeed caused changes in the initial predictions.

      Our approach is not structure refinement and as such the proposed analysis would be misleading. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. DiNerences (or not) between initial and selected configurations will not be particularly informative in this context.

      (19) The authors should also provide a running average of chi-squared values over time to demonstrate that the conformational ensemble converged toward the SAXS profile.

      Our simulations are not driven to improve the agreement with SAXS over time, this is not structure refinement. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. The suggested analysis would be a misinterpretation of our simulations. The comparison with SAXS is provided in Figure 2 and Table 2 as mentioned above.

      (20) The aggregate simulation time of 120 microseconds is misleading, as each replica was only run for 2-3 microseconds. This should be clarified.

      The number reported in the text is accurate and represent the aggregated sampling. The number of replicas for each metainference simulation and their length is reported in Table 2 now moved for clarity from the SI to main text.

      (21) It is not clear how the replicas were weighted to compute the SAXS profiles and FES. There are two independent runs in each case, and each run has about 30 replicas. How these replicas are weighted needs to be discussed in the SI.

      Done

      (22) The methods section is unevenly distributed, with detailed explanations of LC production and purification, while other key methodologies like SAXS+MD integration and HDX are not even mentioned in the main text (they are in the Supporting Information). The authors should provide a brief overview of all methodologies in the main text or move everything to the SI for consistency.

      We agree with the reviewer, all methods are now in main text.

      Reviewer #2 (Recommendations for the authors):

      (1) Computational M&M evidence is strong (Figure 3) and is supported by SAXS (used as restraints). However, Kratky plots reported in the main MS Figure 1 show significant diHerences between the data and the structural model only for one protein, AL-55. It is hard for the general reader to see how these SAXS data support a clear diHerence between AL and non-AL proteins. If possible, please strengthen the evidence; if not, soften the conclusions.

      We thank the reviewer for the comments. The chi-square (Table 1) and the residuals (Figure 1) are a strong indication of the diNerence. To strengthen the evidence, following also the comment from reviewer 3 we calculated the p-value (<10<sup>-5</sup>) on the significance of the radius of gyration to discriminate AL and MM LCs. We agree that SAXS alone was not enough and this is indeed what prompted us to perform MD simulations.

      (2) HDX MS results are cursory and not very convincing as presented. The butterfly plots in Figure 5 are too small to read and are unlabeled so it is unclear which protein is which.

      Figure 5 has been reworked for readability. More data have been added in SI.

      (3) What labeling time was selected to construct these plots and why?

      The deuterium uptakes at 30 min HDX time showed the most pronounced diNerences between diNerent proteins, which were chosen to illustrate the key structural features in the main figure panel (Figure 5).

      How diHerent are the results at other labeling times? Showing uptake curves (with errors) for more than just two peptides in the supplement Figure S12 might be helpful.

      We found a continuous increase in deuterium uptake as we increased the exchange time from 0.5 to 240 min, which reached saturation at 120 min. Therefore, the exchange follows the same pattern at all time points. Butterfly plots at diNerent HDX times of 0.5 to 240 min are shown in gradient of light blue to dark blue which clearly shows the pattern of deuterium uptake at increasing incubation times (Figure 5). The HDX uptake kinetics of selected peptides with corresponding error bars are shown in Figure S12.

      How redundant are the data, i.e. how good is the peptide coverage/resolution in key regions at the domain-domain interface that the authors deem important? Mapping the maximal deuterium uptake on the structures in Figure 5 is not very helpful. Perhaps mapping the whole range of uptake using a gradient color scheme would be more informative.

      Overall coverage and redundancy for all four proteins are> 90% and > 4.0, respectively, with an average error margin in fractional uptake among all peptides is 0.04-0.05 Da, which suggests that our data is reliable (Table S3). We modified the main panel figures showing the gradient of deuterium uptake in blue-white-red for 0 to 30% of deuterium uptake on the chain A of the dimeric LCs.

      (3) Is the conformational heterogeneity depicted in M&M simulations consistent with HDX results? The authors may want to address this by looking at the EX1/EX2 exchange kinetics for AL vs. non-AL proteins. Do AL proteins show more EX1?

      No, we don’t see any EX1 exchange kinetics in our analysis. This is compatible with the prediction of the H-state that is a native like state and not an unfolded/partially folded state.

      (4) Perhaps the main conclusion could be softened given the small number of proteins (six), esp. since only four (3 AL and 1 non-AL) could be explored by HDX. Are other HDX MS data of AL LCs from the same Lambda6 family (e.g. PMID: 34678302) consistent with the conclusions that a particular domain-domain interface is weakened in AL vs. non-AL LCs?

      We thank the reviewer for this suggestions. A diNerence in HDX MS data is indeed visible between AL and MM proteins for peptide 33-47 in the suggested paper (Figures 4, S5 and S8). The diNerence is reduced by the mutation identified in the paper as driving the aggregation in that specific case. We now mention this in the discussion.

      (5) Please clarify if the H* state is the same for a covalent vs. non-covalent LC dimer.

      We do not know because our data are only for covalent dimers. But, interestingly, the state is very similar to what was observed for a model kappa light-chain in Weber, et al., we have better highlighted this point in the discussion.

      (6) Please try and better explain why a smaller distance between CL domains in H7 protein and a larger distance in other AL proteins both promote protein misfolding.

      We do not have elements to discuss this point in more detail.

      (7) Please comment on the Kratky plots data vs. model agreement (see comments above).

      Done.

      (8) Please find a better way to display, describe, and interpret the HD exchange MS data.

      We have generated new main text (new Figure 5) and SI figures that we think allow the reader to better appreciated our observations. Corresponding results sections have been also improved.

      Minor points:

      (9) Is the population of the H-state with perturbed CL-CL domain interface, which was obtained in M&M simulations, suHicient to be observable by HDX MS?

      While populations alone are not enough to determine what is observable by HDX MS, a 10% population correspond roughly to 6 kJ/mol of ΔG and is compatible with EX2 kinetics. Previous works suggested that HDX-MS data should be sensitive to subpopulations of the order of 10%, (https://doi.org/10.1016/j.bpj.2020.02.005, https://doi.org/10.1021/jacs.2c06148)

      (10) Typically, an excited intermediate in protein unfolding is a monomer, while here it is an LC dimer. Is this unusual?

      This is a good point, we think that intermediates have mostly been studied on monomeric proteins because these are more commonly used as model systems, but we do not feel like discussing this point.

      (11) Low deuterium uptake is consistent with a rigid structure but may also reflect buried structure and/or structure that moves on a time scale greater than the labeling time.

      We agree.

      Reviewer #3 (Recommendations for the authors):

      (1) The p-value (statistical significance) of Rg diHerence should be computed.

      We thank the reviewer for the suggestion, we calculated the p-value that resulted quite significant.

      (2) The significance of mutations (SHM?) at the interface, such as A40G should be compared with previous observations. (Garrofalo et al., 2021).

      We thank the reviewer for the suggestion, a sentence has been added in the discussion.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      The manuscript by Dr. Shinkai and colleagues is about the posttranslational modification of a highly important protein, MT3, also known as the growth inhibitory factor. Authors postulate that MT3, or generally all MT isoforms, are sulfane sulfur binding proteins. The presence of sulfane sulfur at each Cys residue has, according to the authors, a critical impact on redox protein properties and almost does not affect zinc binding. They show a model in which 20 Cys residues with sulfane sulfur atoms can still bind seven zinc ions in the same clusters as unmodified protein. They also show that recombinant MT3 (but also MT1 and MT2) protein can react with HPE-IAM, an efficient trapping reagent of persulfides/polysulfides. This reaction performed in a new approach (high temperature and high reagent concentration) resulted in the formation of bis-S-HPE-AM product, which was quantitatively analyzed using LC-MS/MS. This analysis indicated that all Cys residues of MT proteins are modified by sulfane sulfur atoms. The authors performed a series of experiments showing that such protein can bind zinc, which dissociates in the reaction with hydrogen peroxide or SNAP. They also show that oxidized MT3 is reduced by thioredoxin. It gives a story about a new redox-dependent switching mechanism of zinc/persulfide cluster involving the formation of cystine tetrasulfide bridge.

      The whole story is hard to follow due to the lack of many essential explanations or full discussion. What needs to be clarified is the conclusion (or its lack) about MT3 modification proven by mass spectrometry. Figure 1B shows the FT-ICR-MALDI-TOF/MS spectrum of recombinant MT3. It clearly shows the presence of unmodified MT3 protein without zinc ions. Ions dissociate in acidic conditions used for MALDI sample preparation. If the protein contained all Cys residues modified, its molecular weight would be significantly higher. Then, they show the MS spectrum (low quality) of oxidized protein (Fig. 1C), in which new signals (besides reduced apo-MT3) are observed. They conclude that new signals come from protein oxidation and modification with one or two sulfur atoms. If the conclusion on Cys residue oxidation is reasonable, how this protein contains sulfur is unclear. What is the origin of the sulfur if apo-MT does not contain it? Oxidized protein was obtained by acidification of the protein, leading to zinc dissociation and subsequent neutralization and air oxidation. Authors should perform a detailed isotope analysis of the isotopic envelope to prove that sulfur is bound to the protein. They say that the +32 mass increase is not due to the appearance of two oxygen donors. They do not provide evidence. This protein is not a sulfane sulfur binding protein, or its minority is modified. Moreover, it is unacceptable to write that during MT3 oxidation are "released nine molecules of H2". How is hydrogen molecule produced? Moreover, zinc is not "released", it dissociates from protein in a chemical process.

      Thank you for your comment. According to your suggestion, we have rewritten the corresponding sentences below, together with addition of new Fig.1D.

      First, the sentence “which corresponded to the mass of zinc-free apo-GIF/MT3 and indicated that zinc was removed during MS analysis.” was changed to “which corresponded to the mass of zinc-free apo-GIF/MT3 and indicated that zinc dissociates from protein in acidic conditions used for MALDI sample preparation.” in the introduction section. Second, we have added the following sentence “However, FT-ICR-MALDI-TOF/MS analysis failed to detect sulfur modifications in GIF/MT-3 (Fig. 1B), suggesting that sulfur modifications in the protein were dissociated during laser desorption/ionization. Therefore, we postulate that the small amount of sulfur detected in oxidized apo-GIF/MT-3 is derived from the effect of laser desorption/ionization rather than any actual modification of the minority component.” in the discussion section. Third, we have added new Fig. 1D and the corresponding citation in the introduction. Fourth, the sentence “An increase in mass of 32 Da can also result from addition of two oxygen atoms, but we attributed it to one sulfur atom for reasons described later.” was changed to “Note that an increase in mass of 32 Da can also result from addition of two oxygen atoms.”.

      Another important point is a new approach to the HPE-IAM application. Zinc-binding MT3 was incubated with 5 mM reagent at 60°C for 36 h. Authors claim that high concentration was required because apoMT3 has stable conformation. Figure 2B shows that product concentration increases with higher temperature, but it is unclear why such a high temperature was used. Figure 1D shows that at 37°C, there is almost no reaction at 5 mM reagent. Changing parameters sounds reasonable only when the reaction is monitored by mass spectrometry. In conclusion, about 20 sulfane sulfur atoms present in MT3 would be clearly visible. Such evidence was not provided. Increased temperature and reagent concentration could cause modification of cysteinyl thiol/thiolates as well, not only persulfides/polysulfides. Therefore, it is highly possible that non-modified MT3 protein could react with HPE-IAM, giving false results. Besides mass spectrometry, which would clearly prove modifications of 20 Cys, authors should use very important control, which could be chemically synthesized beta- or alfa-domain of MT3 reconstituted with zinc (many protocols are present in the literature). Such models are commonly used to test any kind of chemistry of MTs. If a non-modified chemically obtained domain would undergo a reaction with HPE-IAM under such rigorous conditions, then my expectation would be right.

      Thank you for your comments. Although we have already confirmed that no false-positive results were observed using this method in Fig. 5 (previously Fig. 4), we have conducted additional experiments by preparing chemically synthesized α- and β-domains of GIF/MT-3, as well as recombinant α- and β-domains of GIF/MT-3. As shown in the new Fig. S2A, the chemically synthesized α- and β-domains of GIF/MT-3 detected almost no sulfane sulfur (less than 1 molecule per protein), whereas the recombinant α- and β-domains detected several molecules of sulfane sulfur (more than 5 molecules per protein) (Fig. S2A). Therefore, I would like to emphasize here that the cysteine residue itself cannot be the source of the bis-S-HPE-AM product (sulfane sulfur derivative).

      Accordingly, we have added the following sentence in the results section: “Because this assay was performed at relatively high temperatures (60°C), we also examined the sulfane sulfur levels of several mutant proteins using chemically synthesized α- and β-domains of GIF/MT-3 to eliminate false-positive results. As shown in Fig. S2A, sulfane sulfur (less than 1 molecule per protein) was undetectable in chemically synthesized α- and β-domains of GIF/MT-3, whereas several molecules of sulfane sulfur per protein were detected in recombinant α- and β-domains exhibited (Fig. S2B, left panel). These findings indicated that the sulfane sulfur detected in our assay was derived from biological processes executed during the production of GIF/MT-3 protein. We further analyzed mutant proteins with β-Cys-to-Ala and α-Cys-to-Ala substitutions and found that their sulfane sulfur levels were comparable with those of the α- and β-domains of GIF/MT-3, respectively (Fig. S2B, left panel). Additionally, Ser-to-Ala mutation did not affect the sulfane sulfur levels of GIF/MT-3. The zinc content of each mutant protein was also determined under these conditions (Fig. S2B, right panel).”

      - The remaining experiments provided in the manuscript can also be applied for non-modified protein (without sulfane sulfur modification) and do not provide worthwhile evidence. For instance, hydrogen peroxide or SNAP may interact with non-modified MTs. Zinc ions dissociate due to cysteine residue modification, and TCEP may reduce oxidized residue to rescue zinc binding. Again, mass spectrometry would provide nice evidence.

      Thank you for your comment. We understand that such experiments can also be applied to non-modified proteins (without sulfane sulfur modification). However, the experiments shown in Fig. 4 and Fig. 6 were conducted to investigate the role of sulfane sulfur under oxidative stress conditions, rather than to examine sulfur modification in the protein itself. As mentioned previously, it is difficult to detect sulfur modifications directly in the protein using MALDI-TOF/MS (Fig. 1), as sulfur modifications appear to dissociate during the laser desorption/ionization process.

      - The same is thioredoxin (Fig. 7) and its reaction with oxidized MT3. Nonmodified and oxidized MT3 would react as well.

      Thank you for your comment. We understand that such experiments can also be applied to non-modified MT-3 protein. However, to the best of our knowledge, this is the first report demonstrating that apo-MT-3 can serve as a good substrate for the Trx system. In fact, this experiment is not intended to prove that MT-3 is sulfane sulfur-binding protein. Rather, it demonstrates the novel finding that apo-MT3 serves as an excellent substrate for Trx and that the sulfane sulfur (persulfide structure) remains intact throughout the reduction process.

      - If HPE-IAM reacts with Cys residues with unmodified MT3, which is more likely the case under used conditions, the protein product of such reaction will not bind zinc. It could be an explanation of the cyanolysis experiment (Fig. 6).

      Thank you for your comment. As you pointed out, HPE-IAM reacts with cysteine residues in unmodified MT-3, thereby preventing zinc from binding to the protein. However, we did not use HPE-IAM prior to measuring zinc binding. Instead, HPE-IAM was used solely for determining the sulfane sulfur content in the protein, and thus it cannot explain the results of the cyanolysis experiment.

      - Figure 4 shows the reactivity of (pol)sulfides with TCEP and HPE-IAM. What are redox potentials? Do they correlate with the obtained results?

      Thank you for your comment. However, we must apologize as we do not fully understand the rationale behind determining redox potentials in this experiment. We believe the data itself to be very clear and presenting convincing results.

      - Raman spectroscopy experiments would illustrate the presence of sulfane sulfur in MT3 only if all Cys were modified.

      Yes, that is correct. Since approximately 20 sulfane sulfur atoms are detected in the protein with 20 cysteine residues, we believe that nearly all cysteine residues are modified by sulfane sulfur. Therefore, Raman spectroscopy is considered applicable to our current study.

      - The modeling presented in this study is very interesting and confirms the flexibility of metallothioneins. MT domains are known to bind various metal ions of different diameters. They adopt in this way to larger size the ions. The same mechanism could be present from the protein site. The presence of 9 or 11 sulfur atoms in the beta or alfa domain would increase the size of the domains without changing the cluster structure.

      We truly appreciate your positive evaluation of this work.

      - Comment to authors. Apo-MT is not present in the cell. It exists as a partially metallated species. The term "apo-MT" was introduced to explain that MTs are not fully saturated by metals and function as a metal buffer system. Apo-MT comes from old ages when MT was considered to be present only in two forms: apo-form and fully saturated forms.

      Thank you for your insightful comments. We find it reasonable to understand that apo-MT exists as a partially metallated species within the cell.

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, the authors reveal that GIF/MT-3 regulates zinc homeostasis depending on the cellular redox status. The manuscript technically sounds, and their data concretely suggest that the recombinant MTs, not only GIF/MT-3 but also canonical MTs such as MT-1 and MT-2, contain sulfane sulfur atoms for the Zn-binding. The scenario proposed by the authors seems to be reasonable to explain the Zn homeostasis by the cellular redox balance.

      Strengths:

      The data presented in the manuscript solidly reveal that recombinant GIF/MT-3 contains sulfane sulfur.

      Weaknesses:

      It is still unclear whether native MTs, in particular, induced MTs in vivo contain sulfane sulfur or not.

      Thank you for pointing out the strengths and weaknesses of this manuscript. Based on your suggestions, we have determined the sulfane sulfur content in the native GIF/MT-3 protein, as explained in our response to "Recommendations for the Authors #2."

      Reviewer #3 (Public Review):

      Summary:

      The authors were trying to show that a novel neuronal metallothionein of poorly defined function, GIF/MT3, is actually heavily persulfidated in both the Zn-bound and apo (metal-free) forms of the molecule as purified from a heterologous or native host. Evidence in support of this conclusion is compelling, with both spectroscopic and mass spectrometry evidence strongly consistent with this general conclusion. The authors would appear to have achieved their aims.

      Strengths:

      The analytical data are compelling in support of the author's primary conclusions are strong. The authors also provide some modeling evidence that strongly supports the contention that MT3 (and other MTs) can readily accommodate sulfane sulfur on each of the 20 cysteines in the Zn-bound structure, with little perturbation of the structure. This is not the case with Cys trisulfides, which suggests that the persulfide-metallated state is clearly positioned at lower energy relative to the immediately adjacent thiolate- or trisulfidated metal coordination complexes.

      Weaknesses:

      The biological significance of the findings is not entirely clear. On the one hand, the analytical data are clearly solid (albeit using a protein derived from a bacterial over-expression experiment), and yes, it's true that sulfane S can protect Cys from overoxidation, but everything shown in the summary figure (Fig. 8D) can be done with Zn release from a thiol by ROS, and subsequent reduction by the Trx/TR system. In addition, it's long been known that Zn itself can protect Cys from oxidation. I view this as a minor weakness that will motivate follow-up studies. Fig. 1 was incomplete in its discussion and only suggests that a few S atoms may be covalently bound to MT3 as isolated. This is in contrast to the sulfate S "release" experiment, which I find quite compelling.

      Impact:

      The impact will be high since the finding is potentially disruptive to the metals in the biology field in general and the MT field for sure. The sulfane sulfur counting experiment (the HPE-IAM electrophile trapping experiment) may well be widely adopted by the field. Those of us in the metals field always knew that this was a possibility, and it will interesting to see the extent to which metal-binding thiolates broadly incorporate sulfate sulfur into their first coordination shells.

      Thank you for pointing out the strengths and weaknesses of this manuscript. As you noted, the explanations and discussions regarding Fig. 1 were missing. To address this, we have added the following sentences to the discission section: “However, FT-ICR-MALDI-TOF/MS analysis failed to detect sulfur modifications in GIF/MT-3 (Fig. 1B), suggesting that sulfur modifications in the protein were dissociated during laser desorption/ionization. Therefore, we postulate that the small amount of sulfur detected in oxidized apo-GIF/MT-3 is derived from the effect of laser desorption/ionization rather than any actual modification of the minority component.”

      Reviewer #1 (Recommendations For The Authors):

      Overall, the topic of the study is interesting, but the provided evidence is insufficient to claim that MT3 is a sulfane sulfur-binding protein. Indeed, some recent studies showed that natural and recombinant MT proteins can be modified, but only one or a few cysteine residues were modified. Authors should follow my suggestion and apply mass spectrometry to all performed reactions and, first of all, to freshly obtained protein. I strongly suggest using chemically synthesized and reconstituted domains to test whether the home-developed approach is appropriate. Moreover, native MS and ICP-MS analysis of MT3 would support their claims.

      Thank you for your insightful comments. Following your suggestions, we have prepared chemically synthesized proteins of the α- and β-domains of GIF/MT-3 and conducted additional experiments, as explained in response comments to “Public Review #1”. Regarding the MS analysis, we have also added a discussion on the difficulty of detecting sulfur modifications in the protein.

      Reviewer #2 (Recommendations For The Authors):

      I have some minor points which should be considered by the authors.

      (1) Table 1: In the simulation by MOE, the authors speculated 7 atoms of metal bound to GIF/MT-3. Although a total of 7 atoms of Zn or Cd are actually bound to MTs as a divalent ion, the number of Cu and Hg bound to MTs as a monovalent ion is scientifically controversial. Several ideas have been proposed in the literature, however, "7 atoms of Cu or Hg" could be inappropriate as far as I know. The authors should simulate again using a more appropriate number of Cu or Hg in MTs.

      Thank you for providing this valuable information. We reviewed several papers by the Stillman group and found that the relative binding constants of Cu4-MT, Cu6-MT, and Cu10-MT were determined after the addition of Cu(I) to apo MT-1A, MT-2, and MT-3 (Melenbacher and Stillman, Metallomics, 2024). However, incorporating these copper numbers into our GIF/MT-3 simulation model proved challenging. Therefore, we decided to omit the score value for copper in Table 1.

      On the other hand, some researchers have reported that mercury binds to MT as a divalent ion, and the formation of Hg<sub>7</sub>MT is possible (not just other forms). Therefore, we decided to continue using the score value for mercury shown in Table 1.

      (2) If possible, native MT samples isolated from an experimental animal should be evaluated for the sulfane sulfur content. Canonical MTs, MT-1 and MT-2, are highly inducible by not only heavy metals but also oxidative stress. Under the oxidative stress condition such as the exposure of hydrogen peroxide, it is questionable whether the induced Zn-MTs contain sulfane sulfur or not.

      According to your suggestion, we evaluated the sulfane sulfur content in native GIF/MT-3 samples isolated from mouse brain cytosol (Fig. 10). The measured amount was 3.3 per protein. This suggests that sulfane sulfur in GIF/MT-3 could be consumed under oxidative conditions, as you anticipated. Another possible explanation for the discrepancy between the native form and recombinant protein is likely related to metal binding in the protein. It is generally understood that both zinc and copper bind to GIF/MT-3 in approximately equal proportions in vivo. When we prepared recombinant copper-binding GIF/MT-3 protein, the sulfane sulfur content in the protein was significantly different (approximately 4.0 per protein) compared to the Zn<sub>7</sub>GIF/MT-3 form. Further studies are needed to clarify the relationship between sulfane sulfur binding and the types of metals in the future.

      (3) The biological significance of sulfane sulfur in MTs is still unclear to me.

      Thank you for your comments. To address this question, we have added the following sentence to the discussion section: “The biological significance of sulfane sulfur in MTs lies in its ability to 1) contribute to metal binding affinity, 2) provide a sensing mechanism against oxidative stress, and 3) aid in the regeneration of the protein.”

      (4) According to the widely accepted nomenclature of MT, "MT3" should be amended to "MT-3".

      According to your suggestion, we have amended from MT3 to MT-3 throughout the manuscript.

      Reviewer #3 (Recommendations For The Authors):

      Most of my comments are editorial in nature, largely focused on what I perceive as overinterpretation or unnecessary speculation.

      The authors state in the abstract that the intersection of sulfane sulfur and Zn enzymes "has been overlooked." This is not actually true - please tone down to "under investigated" or something like this.

      Based on your suggestion, we have replaced the term “has been overlooked” with “has been under investigated” in the abstract.

      Line 228: The discussion of Fig. 6C involved too much speculation. I cannot see a quantitative experiment that supports this.

      Based on your suggestion, we have removed Fig. 6C (currently referred to as Fig. 7C). Additionally, we have revised the sentence from “implying that the sulfane sulfur is an essential zinc ligand in apo-GIF/MT3 and that an asymmetric SSH or SH ligand is insufficient for native zinc binding (Fig. 6C)” to “implying the contribution of sulfane sulfur to zinc binding in GIF/MT-3”.

      Line 247 "persulfide in apo-GIF/MT3 seems.." I think the authors mean that the Zn form of the protein is resistant to Trx or TCEP.

      Thank you for pointing this out. We realized that the term “persulfide in apo-GIF/MT3” might be confusing. Therefore, we have replaced it with “persulfide formation derived from apo-GIF/MT3” in the corresponding sentence.

      Molecular modeling: We need more details- were these structures energy-minimized in any way? Can the authors comment on the plethora of S-S dihedral angles in these structures, and whether they are consistent with expectations of covalent geometry? Please add text to explain or even a table that compiles these data.

      Thank you for your comment. Yes, energy minimization calculations for structural optimization were conducted during homology modeling in MOE. In fact, we have already stated in the Methods section that “Refinement of the model with the lowest generalized Born/volume integral (GBVI) score was achieved through energy minimization of outlier residues in Ramachandran plots generated within MOE.” In this model, covalent geometry, including the S-S dihedral angles, is also taken into consideration.

      What is a thermostability score? Perhaps a bit more discussion here and what relationship this has to an apparent (or macroscopic) metal affinity constant.

      The thermostability score is used to compare the thermal stability between the wild-type and mutant proteins. As shown in Equation (1) in the method section, it is calculated by subtracting the energy of the hypothetical unfolded state from the energy of the folded state. Since obtaining the structure of the unfolded state requires extensive computational effort, MOE employs an empirical formula based on two-dimensional structural features to estimate it. The ΔΔG values represent the difference between ΔGf(WT) and ΔGf(Mut). However, because it is difficult to directly determine ΔGf(Mut) and ΔGf(WT), MOE calculates ΔΔG using the thermodynamic cycle equivalence: ΔΔGs =ΔGsf (WT→Mut) - ΔGsu (WT→Mut), as expressed in Equation (1).

      On the other hand, the affinity score represents the interaction energy between the target ligand and the protein. In this study, we calculated the affinity score by selecting metal atoms as the ligands. The interaction energy (E int) is defined as:

      E int = E complex − E receptor − E ligand

      where each term is as follows:

      E complex : Potential energy of the complex.

      E receptor : Potential energy of the receptor alone.

      E ligand : Potential energy of the ligand alone.

      Each potential energy term includes contributions from bonded interactions such as bond lengths and bond angles. However, since there is no structural difference among E receptor, and E ligand, the bonded energy components cancel out. Consequently, E int is determined as:

      E int = ΔEele +ΔEvdW +ΔE sol

      Here, a negative E int indicates that the complex is more stable, while a positive E int implies that the receptor and ligand are more stable in their dissociated states.

      We have revised the sentence "The affinity score was also calculated using MOE software as the difference between the ΔΔGs values of the protein, free zinc, and metal–protein complex” to "The affinity score was also calculated using MOE software as the difference between the potential energy values of the protein, free zinc, and metal–protein complex” to correct the misdescription.

      Lines 278-280: The authors state that they observe a "marked enhancement of metal binding affinity, and rearrangement of zinc ions." I don't see support for this rather provocative conclusion. This is the expectation of course. I would love to see actual experimental data on this point, direct binding titrations with metals performed before and after the release of the sulfate sulfur atoms.

      Thank you for your comments. Although this statement is based on the 3D modeling simulation, we have also experimentally observed that the diminishment of sulfane sulfur in GIF/MT-3 resulted in a decrease in zinc binding levels, as shown in Fig. 7. However, conducting direct binding titration experiments was difficult for us due to the difficulty in preparing pure GIF/MT-3 protein with or without sulfane sulfur. Therefore, we have revised the sentence "marked enhancement of metal binding affinity, and rearrangement of zinc ions" to simply "enhancement of metal binding affinity" to avoid over-speculation.

      Table I- quantitatively lower stability for the Cu complex- the stoichiometry is clearly wrong in this simulation- please redo this simulation with the right stoichiometry or Cu to MT3- consult a Stillman paper.

      Thank you for providing this valuable information. We reviewed several papers by the Stillman group and found that the relative binding constants of Cu4-MT, Cu6-MT, and Cu10-MT were determined after the addition of Cu(I) to apo MT-1A, MT-2, and MT-3 (Melenbacher and Stillman, Metallomics, 2024). However, incorporating these copper numbers into our GIF/MT-3 simulation model proved challenging. Therefore, we decided to omit the score value for copper in Table 1.

      I like the model for reversible metal release mediated by the thioredoxin system (Fig. 8D)- but you can also do this with thiols- nothing really novel here. Has it been generally established that tetraulfides are better substrates for the Trx/TR system? The data shown in Fig. 7B seems to suggest this, but is this broadly true, from the literature?

      There are reports describing that persulfides and polysulfides are reduced by the thioredoxin system. However, it is not well-established that tetraulfides are better substrates for the Trx/TR system. To the best of our knowledge, this is the first report demonstrating that apo-MT-3 can serve as a good substrate for the Trx/TR system. Further research is required to compare the catalytic efficiency between proteins containing disulfide and those with tetraulfide moieties.

      Line 380: Many groups have reported that many proteins are per- or polysulfidated in a whole host of cells using mass spectrometry workflows, and that terminal persulfides can be readily reduced by general or specific Trx/TR systems. This work could be better acknowledged in the context of the authors' demonstration of the reduction of the tetrasulfides, which itself would appear to be novel (and exciting!).

      We truly appreciate your positive evaluation of this work.

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      Reply to the reviewers

      Manuscript number: RC-2024-02824

      Corresponding author(s): Rita tewari

      1. General Statements [optional]

      We wish to thank the reviewers and the Editor for their constructive comments and valuable suggestions to improve our manuscript. We have addressed as far as possible all comments and concerns and we hope that this revised manuscript, with additional new data, will be acceptable for publication. Please find below detailed responses (red text) to all specific points raised by the reviewers

      2. Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      • *

      We would like to thank all the reviewers for using their valuable time to review our manuscript and to provide constructive comments and suggestions. We have now revised the manuscript taking their comments into consideration; our responses to these comments are detailed below (in red).

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Minor comments: In the results section (lines 498-499), the authors describe free kinetochores in many cells without associated spindle microtubules. However, some nuclei appear to have kinetochores, as presented in Figure 6. Could the authors clarify how this conclusion was derived using transmission electron microscopy (TEM) without serial sectioning, as this is not explicitly mentioned in the materials and methods?

      We observed free kinetochores in the ALLAN-KO parasites with no associated spindle microtubules (see Fig. 6Gh), while kinetochores are attached to spindle microtubules in WT-GFP cells (see Fig. 6Gc). To provide further evidence we analysed additional images and found that ALLAN-KO cells have free kinetochores in the centre of nucleus, unattached to spindle microtubules. We provide some more images clearly showing free kinetochores in these cells (new supplementary Fig. S11).

      However, in the ALLAN mutant, this difference is not absolute: in a search of over 50 cells, one example of a cell with a "normal" nuclear spindle and attached kinetochores was observed.

      The use of serial sectioning has limitations for examining small structures like kinetochores in whole cells. The limitations of the various techniques (for example, SBF-SEM vs tomography) are highlighted in our previous study (Hair et al 2022; PMID: 38092766), and we consider that examining a population of randomly sectioned cells provides a better understanding of the overall incidence of specific features.

      Discussion Section:

      Could the authors expand on why SUN1 and ALLAN are not required during asexual replication, even though they play essential roles during male gametogenesis?

      We observed no phenotype in asexual blood stage parasites associated with the sun1 and allan gene deletions. Several other Plasmodium berghei gene knockout parasites with a phenotype in sexual stages, for example CDPK4 (PMID: 15137943), SRPK (PMID: 20951971), PPKL (PMID: 23028336) and kinesin-5 (PMID: 33154955) have no phenotype in blood stages, so perhaps this is not surprising. One explanation may be the substantial differences in the mode of cell division between these two stages. Asexual blood stages produce new progeny (merozoites) over 24 hours with closed mitosis and asynchronous karyokinesis during schizogony, while male gametogenesis is a rapid process, completed within 15 min to produce eight flagellated gametes. During male gametogenesis the nuclear envelope must expand to accommodate the increased DNA content (from 1N to 8N) before cytokinesis. Furthermore, male gametogenesis is the only stage of the life cycle to make flagella, and axonemes must be assembled in the cytoplasm to produce the flagellated motile male gametes at the end of the process. Thus, these two stages of parasite development have some very different and specific features.

      Lines 611-613 states: "These loops serve as structural hubs for spindle assembly and kinetochore attachment at the nuclear MTOC, separating nuclear and cytoplasmic compartments." Could the authors elaborate on the evidence supporting this statement?

      We observed the loops/folds in the nuclear envelope (NE) as revealed by SUN1-GFP and 3D TEM images during male gametogenesis. These folds/loops occur mainly in the vicinity of the nuclear MTOC where the spindles are assembled (as visualised by EB1 fluorescence) and attached to kinetochores (as visualised by NDC80 fluorescence). These loops/folds may form due to the contraction of the spindle pole back to the nuclear periphery, inducing distortion of the NE. Since there is no physical segregation of chromosomes during the three rounds of mitosis (DNA increasing from 1N to 8N), we suggest that these folds provide additional space for spindle and kinetochore dynamics within an intact NE to maintain separation from the cytoplasm (as shown by location of kinesin-8B).

      In lines 621-622, the authors suggest that ALLAN may have a broader role in NE remodelling across the parasite's lifecycle. Could they reflect on or remind readers of the finding that ALLAN is not essential during the asexual stage?

      ALLAN-GFP is expressed throughout the parasite life cycle but as the reviewer points out, a functional role is more pronounced during male gametogenesis. This does not mean that it has no role at other stages of the life cycle even if there is no obvious phenotype following deletion of the gene during the asexual blood stage. The fact that ALLAN is not essential during the asexual blood stage is noted in lines 628-29.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Introduction Line 63: The authors stat: "NE is integral to mitosis, supporting spindle formation, kinetochore attachment, and chromosome segregation..". Seemingly at odds, they also say (Line 69) that 'open' "mitosis is "characterized by complete NE disassembly". The authors could explain better the ideas presented in their quoted review from Dey and Baum, which points out that truly 'open' and 'closed' topologies may not exist and that even in 'open' mitosis, remnants of the NE may help support the mitotic spindle.

      We have modified the sentence in which we discuss current opinions about 'open' and 'closed' mitosis. It is believed that there is no complete disassembly of the NE during open mitosis and no completely intact NE during closed mitosis, respectively. In fact, the NE plays a critical role in the different modes of mitosis during MTOC organisation and spindle dynamics. Please see the modified lines 64-71.

      Results

      Fig 7 is the final figure; but would be more useful upfront.

      We have provided a new introductory figure (Fig 1) showing a schematic of conventional /canonical LINC complexes and evidence of SUN protein functions in model eukaryotes and compare them to what is known in apicomplexans.

      Fig 1D. The authors generated a C-terminal GFP-tagged SUN1 transfectants and used ultrastructure expansion microscopy (U-ExM) and structured illumination microscopy (SIM) to examine SUN1-GFP in male gametocytes post-activation. The immuno-labelling of SUN1-GFP in these fixed cells appears very different to the live cell images of SUN1-GFP. The labelling profile comprises distinct punctate structures (particularly in the U-ExM images), suggesting that paraformaldehyde fixation process, followed by the addition of the primary and secondary antibodies has caused coalescing of the SUN1-GFP signal into particular regions within the NE.

      We agree with the reviewer. Fixation with paraformaldehyde (PFA) results in a coalescence of the SUN1-GFP signal. We have also tried methanol fixation (see below, new Fig. S2), but a similar problem was encountered.

      Given these fixation issues, the suggestion that the SUN1-GFP signal is concentrated at the BB/ nuclear MTOC and "enriched near spindle poles" needs further support.

      These statements seem at odd with the data for live cell imaging where the SUN1-GFP seems evenly distributed around the nuclear periphery. Can the observation be quantitated by calculating the percentage of BB/ nuclear MTOC structures with associated SUN1-GFP puncta? If not, I am not convinced these data help understand the molecular events.

      We agree with the reviewer that whilst the live cell imaging showed an even distribution of SUN1-GFP signal, after fixation with either PFA or methanol, then SUN1-GFP puncta are observed in addition to the peripheral location around the stained DNA (Hoechst) (See the above figure; puncta are indicated by arrows). These SUN1-GFP labelled puncta were observed at the junction of the nuclear MTOC and the basal body (Fig. 2F). Quantification of the distribution showed that these SUN1-GFP puncta are associated with nuclear MTOC in more than 90 % of cells (18 cells examined). Live cell imaging of the dual labelled parasites; SUN1xkinesin-8B (Fig. 2H) and SUN1x EB1 (Fig. 2I) provides further support for the association of SUN1-GFP puncta with BB (kinesin-8B) /nuclear MTOC (EB1).

      The authors then generated dual transfectants and examined the relative locations of different markers in live cells. These data are more informative.

      The authors state; " ..SUN1-GFP marked the NE with strong signals located near the nuclear MTOCs situated between the BB tetrads". The nuclear MTOCs are not labelled in this experiment. The SUN1-GFP signal between the kinesin-8B puncta is evident as small puncta on regions of NE distortion. I would prefer to not describe this signal as "strong". The signal is stronger in other regions of the NE.

      We have modified the sentence on line 213 to accommodate this suggestion.

      Line 219. The authors state; "..SUN1-GFP is partially colocalized with spindle poles as indicated by EB1,.. it shows no overlap with kinetochores (NDC80)." The authors should provide an analysis of the level of overlap at a pixel by pixel level to support this statement.

      We now provide the overlap at a pixel-by-pixel level for representative images, and we have quantified more cells (n>30), as documented in the new Fig. S4A, which is displayed below. We have also modified the sentence on line 219 to reflect these additions.

      The SUN1 construct is C-terminally GFP-tagged. By analogy with human SUN1, the C-terminal SUN domain is expected to be in the NE lumen. That is in a different compartment to EB1, which is located in the nuclear lumen (on the spindle). Thus, the overlap of signal is expected to be minimal.

      We agree with the reviewer that the overlap between EB1 and Sun1 signals is expected to be minimal. We have quantified the data and included it in Supplementary Fig. S4A.

      Similarly, given that EB1 and NDC80 are known to occupy overlapping locations on the spindle, it seems unlikely that SUN1 can overlap with one and not the other.

      We agree with the reviewer's analysis that EB1 and NDC80 occupy overlapping locations on the spindle, although the length of NDC80 is less at the ends of spindles (see below Fig A) as shown in our previous study where we compared the locations of two spindle proteins, ARK2 and EB1, with that of NDC80 (Zeeshan et al, 2022; PMID: 37704606). In the present study we observed that Sun1-GFP partially overlaps with EB1 at the ends of the spindle, but not with NDC80. Please see Fig. B, below.

      I note on Line 609, the authors state "Our study demonstrates that SUN1 is primarily localized to the nuclear side of the NE.." As per Fig 7D, and as discussed above, the bulk of the protein, including the SUN1 domain, is located in the space between the INM and the ONM.

      We appreciate the reviewer's correction; we have now modified the sentence to indicate that the protein is largely localized in the space between the INM and the ONM on line 617.

      Interestingly, as the authors point out, nuclear membrane loops are evident around EB1 and NDC80 focal regions. The data suggests that the contraction of the spindle pole back to the nuclear periphery induces distortion of the NE.

      We agree with the reviewer's suggestion that the data indicate that contraction of spindle poles back to the nuclear periphery may induce distortion of the NE.

      The author should discuss further the overlap of findings of this study with that from a recent manuscript (https://doi.org/10.1016/j.cels.2024.10.008). That Sayers et al. study identified a complex of SUN1 and ALLC1 as essential for male fertility in P. berghei. Sayers et al. also provide evidence that this complex particulate in the linkage of the MTOC to the NE and is needed for correct mitotic spindle formation during male gametogenesis.

      We thank the reviewer for this suggestion. The study by Sayers et al, (2024) was published while our manuscript was under preparation. It was interesting to see that these complementary studies have similar findings about the role of SUN1 and the novel complex of SUN1-ALLAN. Our study contains a more detailed, in-depth analysis both by Expansion and TEM of SUN1. We include additional studies on the role of ALLAN. We discuss the overlap in the findings of the two studies in lines 590-605.

      While the work is interesting, the conclusions may need to be tempered. The authors suggestion that in the absence of KASH-domain proteins, the SUN1-ALLAN complex forms a non-canonical LINC complex (that is, a connection across the NE), that "achieves precise nuclear and cytoskeletal coordination".

      We have toned down the wording of this conclusion in lines 665-677.

      In other organisms, KASH interacts with the C-terminal domain on SUN1, which as mentioned above is located between the INM and ONM. By contrast, ALLAN interacts with the N-terminal domain of SUN1, which is located in the nuclear lumen. The SUN1-ALLAN interaction is clearly of interest, and ALLAN might replace some of the roles of lamins. However, the protein that functionally replaces KASH (i.e. links SUN1 to the ONM) remains unidentified.

      We agree with reviewer, and future studies will need to focus on identifying the KASH replacement that links SUN1 to the ONM.

      It may also be premature to suggest that the SUN1-ALLAN complex is promising target for blocking malaria transmission. How would it be targeted?

      We have deleted the sentence that raised this suggestion.

      While the above datasets are interesting and internally consistent, there are two other aspects of the manuscript that need further development before they can usefully contribute to the molecular story.

      The authors undertook a transcriptomic analysis of Δsun1 and WT gametocytes, at 8 and 30 min post-activation, revealing moderate changes (~2-fold change) in different genes. GO-based analysis suggested up-regulation of genes involved in lipid metabolism. Given the modest changes, it may not be correct to conclude that "lipid metabolism and microtubule function may be critical functions for gametogenesis that can be perturbed by sun1 deletion." These changes may simply be a consequence of the stalled male gametocyte development.

      Following the reviewer's suggestion we have moved these data to the supplementary information (Fig. S5D-I) and toned down their discussion in the results and discussion sections.

      The authors have then undertaken a detailed lipid analysis of the Δsun1 and WT gametocytes, before and after activation. Substantial changes in lipid metabolites might not be expected in such a short period of time. And indeed, the changes appear minimal. Similarly, there are only minor changes in a few lipid sub-classes between Δsun1 and WT gametocytes. In my opinion, the data are not sufficient to support the authors conclusion that "SUN1 plays a crucial role, linking lipid metabolism to NE remodelling and gamete formation."

      In agreement with the reviewer's comments we have moved these data to supplementary information (Fig. S6) and substantially toned down the conclusions based on these findings.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Major comments: My main concern with this manuscript is that the authors do conclude not only that SUN1 is important for spindle formation and basal body segregation, but also that it influences for lipid metabolism and NE dynamics. I don't think the data supports this conclusion, for several reasons listed below. I would suggest to remove this claim from the manuscript or at least tone it down unless more supporting data are provided, in particular showing any change in NE dynamics in the SUN1-KO. Instead I would recommend to focus on the more interesting role of SUN1-ALLAN in bipartite MTOC organisation, which likely explains all observed phenotypes (including those in later stages of the parasite life cycle). In addition, some aspects of the knockout phenotype should be quantified to a bit deeper level.

      In more detail:

      • The lipidomics analysis is clearly the weakest point of the manuscript: The authors state that there are significant changes in some lipid populations between WT and sun1-KO, and between activated and non-activated cells, yet no statistical analysis is shown and the error bars are quite high compared to only minor changes in the means. For some discussed lipids, the result text does not match the graphs, e.g. PA, where the increase upon activation is more pronounced in the SUN1-KO vs WT (contrary to the text), or MAG, which is reduced in the SUN1-KO vs WT (contrary to the text). I don't see the discussed changes in arachidonic acid levels and myristic acid levels in the data either. Even if the authors find after analysis some statistically significant differences between some groups, they should carefully discuss the biological significance of these differences. As it is, I do not think the presented data warrants the conclusion that deletion of SUN1 changes lipid homeostasis, but rather shows that overall lipid homeostasis is not majorly affected by gametogenesis or SUN1 deletion. As a minor comment, if you decide to keep the lipidomics analysis in the manuscript, please state how many replicates were done.

      As detailed above we have moved the lipidomics data to supplementary information (Fig. S6) and substantially toned down the discussion of these data in the results and discussion sections.

      • I can't quite follow the logic why the authors performed transcriptomic analysis of the SUN1 and how they chose their time points. Their data up to this point indicate that SUN1 has a structural or coordinating role in the bipartite MTOC during male gametogenesis. Based on that it is rather unlikely that SUN1 KO directly leads to transcriptional changes within the 8 min of exflagellation. Isn't it more likely that transcriptional differences are purely a downstream effect of incomplete/failed gametogenesis? This is particularly true for the comparison at 30 min, which compares a mixture of exflagellated/emerged gametes and zygotes in WT to a mixture of aberrant, arrested gametes in the knockout, which will likely not give any meaningful insight. The by far most significant GO-term is then also nuclear-transcribed mRNA catabolic process, which is likely not related at all to SUN1 function (and the authors do not even comment on this in the main text). I would therefore suggest removing the 30 min data set from this manuscript. As a minor point, I would suggest highlighting some of the top de-regulated gene IDs in the volcano plots and stating their function. Also, please state how you prepared the cells for the transcriptomes and in how many replicates this was done.

      As suggested by the reviewer we have removed the 30 min post activation data from the manuscript. We have also moved the rest of the transcriptomics data to supplementary information (Fig. S5) and toned down the presentation of this aspect of the work in the results and discussion sections.

      • Live-cell imaging of SUN1-GFP does nicely visualise the NE during gametogenesis, showing a highly dynamic NE forming loops and folds, which is very exciting to see. It would be beneficial to also show a video from the life-cell imaging.

      We have now added videos to the manuscript as suggested by the reviewer. Please see the supplementary Videos S1 and S2.

      In their discussion, the authors state multiple times that NE dynamics are changed upon SUN1 KO. Yet, they do not provide data supporting this claim, i.e. that the extended loops and folds found in the nuclear envelope during gametogenesis are affected in any way by the knockout of SUN1 or ALLAN. What happens to the NE in absence of SUN1? Are there less loops and folds? In absence of a reliable NE marker this may not be entirely easy to address, but at least some SBF-SEM images of the sun1-KO gametocytes could provide insight.

      It was difficult to provide SBF-SEM images as that work is beyond the scope of this manuscript. We will consider this approach in our future work. We re-examined many of our TEM images of SUN1-KO and ALLAN-KO parasites and did find some micrographs showing aberrant nuclear membrane folding ( - I think the exciting part of the manuscript is the cell biological role of SUN1 on male gametogenesis, which could be carved out a bit more by a more detailed phenotyping. Specifically it would be good to quantify

      1) if DNA replication to an octoploid state still occurs in SUN1-KO and ALLAN-KO,

      DNA replication is not affected in the SUN1-KO and ALLAN-KO mutants: DNA content increases to 8N (data added in Fig. 3J and Fig. S10F).

      2) the proportion of anucleated gametes in WT and the KO lines

      We have added these data in Fig. 3K and Fig. S10G

      3) a quantification of the BB clustering phenotype (in which proportion of cells do the authors see this phenotype). This could be addressed by simple fixed immunofluorescence images of the respective WT/KO lines at various time points after activation (or possibly by reanalysis of the already obtained images) and would really improve the manuscript.

      We have reanalysed the BB clustering phenotype and added the quantitative data in Fig. 4E and Fig. S7.

      Especially the claim that emerged SUN1-KO gametes lack a nucleus is currently only based on single slices of few TEM cells and would benefit from a more thorough quantification in both SUN1- and ALLAN-Kos

      We have examined many microgametes (100+ sections). In WT parasites a small proportion of gametes can appear to lack a nucleus if it does not extend all the way to the apical and basal ends (Hair et al. 2022). However, the proportion of microgametes that appear to lack a nucleus (no nucleus seen in any section) was much higher in the SUN1 mutant. In contrast, this difference was not as clear cut in the ALLAN mutant with a small proportion of intact (with axoneme and nucleus) microgametes being observed.

      We have done additional analysis of male gametes, looking for the presence of the nucleus by live cell imaging after DNA staining with Hoechst. Please see the figure below. These data are added in Fig. 3K (for Sun1-KO) and S10G (for Allan-KO).

      • The TEM suggests that in the SUN1-KO, kinetochores are free in the nucleus. Are all kinetochores free or do some still associate to a (minor/incorrectly formed) spindle? The authors could address this by tagging NDC80 in the KO lines.

      Our observation and quantification of the data indicated that 100% of kinetochores were attached to spindle microtubules and that 0% were unattached kinetochores in the WT parasites. However, the exact opposite was found for the SUN1 mutant with 100% unattached kinetochores and 0% attached. The result was not quite as clear cut in the ALLAN mutant, with 98% unattached and 2% attached. An important observation was the lack of separation of the nuclear poles and any spindle formation. Spindle formation was never or very rarely observed in the mutants.

      • Finally, I think it is curious that in contrast to SUN1, ALLAN seems to be less important, with some KO parasite completing the life cycle. Maybe a more detailed phenotyping as above gives some more hints to where the phenotypic difference between the two proteins lies. I would assume some ALLAN-KO cells can still segregate the basal body. Can the authors speculate/discuss in more detail why these two proteins seems to have slightly different phenotypes?

      We agree with the reviewer. Overall, the ALLAN-KO has a less prominent phenotype than that of the Sun1-KO. The main difference is that in the ALLAN-KO mutant some basal body segregation can occur, leading to the production of some fertile microgametocytes, and ookinetes, and oocyst formation (Fig. 8). Approximately 5% of oocysts sporulated to release infective sporozoites that could infect mice in bite back experiments and complete the life cycle. In contrast the Sun1-KO mutant made no healthy oocysts, or infective sporozoites, and could not complete the life cycle in bite back experiments. We have analysed the phenotype in detail and provide quantitative data for gametocyte stages by EM and ExM in Figs. 4 and S8 (SUN1) and Figs. 7 and S11 (ALLAN). We have also performed detailed analysis of oocyst and sporozoite stages and included the data in Fig. 3 (SUN1) and S10 (ALLAN).

      Based on the location, and functional and interactome data, we think that SUN1 plays a central role in coordinating nucleoplasm and cytoplasmic events as a key component of the nuclear membrane lumen, whereas ALLAN is located in the nucleoplasm. Deleting the SUN1 gene may disrupt the connection between INM and ONM whereas the deletion of ALLAN may affect only the INM.

      . Some additional points where the data is not entirely sound yet or could be improved:

      • Localisation of SUN1: There seems to be a discrepancy between SUN1-GFP location as observed by live cell microscopy, and by Expansion Microscopy (ExM), similar for ALLAN-GFP. By live-cell microscopy, the SUN1 localisation is much more evenly distributed around the NE, while the localisation in ExM is much more punctuated, and e.g. in Figure 1E seems to be within the nucleus. Do the authors have an explanation for this? Also, in Fig. 1D there are two GFP foci at the cell periphery (bottom left of the image), which I would think are not SUN1-Foci, as they seem to be outside of the cell. Is the antibody specific? Was there a negative control done for the antibody (WT cells stained with GFP antibodies after ExM)?

      High resolution SIM and expansion microscopy showed that the SUN1-GFP molecules coalesce to form puncta, in contrast to the more uniform distribution observed by live cell imaging. This apparent difference may be due to a better resolution that could not be achieved by live cell imaging. We agree with the reviewer that the two green foci are outside of the cell. As a negative control we have used WT-ANKA cells (which contain no GFP) and the anti-GFP antibody, which gave no signal. This confirms the specificity of the antibody (please see the new Fig. S3).

      • The authors argue that SIM gave unexpected results due to PFA fixation leading to collapse of the NE loops. However, they also fix their ExM cells and their EM cells with PFA and do not observe a collapse, at least from what I see in the two presented images and in the 3D reconstruction. Is there something else different in the sample preparation?

      There was no difference in the fixation process for samples examined by SIM and ExM, but we used an anti-GFP antibody in ExM to visualise the SUN1-GFP, while in SIM the images of GFP signal were collected directly after fixation. We used both PFA and methanol as fixative, and both methods showed a coalescing of the SUN1-GFP signal (please see the new Fig. S2 and S3).

      Can the authors trace their NE in ExM according to the NHS-Ester signal?

      We could trace the NE in the ExM by the NHS-ester signal and observed that the SUN1-GFP signal was largely coincident with the NE (Please see the new Fig. S3B below).

      • Fig 2D: It would be good to not just show images of oocysts but actually quantify their size from images. Also, have the authors determined the sporozoite numbers in SUN1-KO?

      We have measured oocyst size (data added in new Fig. 3) and added the sporozoite quantification data in Fig. 3D.

      • Line 481-483: the authors state that oocyst size is reduced in ALLAN-KO but do not show the data. Please quantify oocyst size or at least show representative images. Also the drastic decrease in sporozoite numbers (Fig. 6D, E) is not mentioned in the text. Please add reference to Fig S7D when talking about the bite back data.

      We have added the oocyst size data in Fig. S10. We mention the changes in sporozoite numbers (now shown in Fig. 7D, E), and refer to the bite back data shown in current Fig. 7E.

      • Fig S1C, 6C: Both WB images are stitched, but this is not clearly indicated e.g. by leaving a small gap between the lanes. Also please show a loading control along with the western blots. Also there seems to be a (unspecific?) band in the control, running at the same height as Allan-GFP WB. What exactly is the control?

      We have provided the original blot showing the bands of ALLAN-GFP and SUN1-GFP. As a positive control, we used an RNA associated protein (RAP-GFP) that is highly expressed in Plasmodium and regularly used in our lab for this purpose.

      • Regarding the crossing experiment: The authors conclude from this cross that SUN1 is only needed in males, yet for this conclusion they would need to also show that a cross with a female line does not rescue the phenotype. The authors should repeat the cross with a male-deficient line to really test if the phenotype is an exclusively male phenotype. In addition, line 270-272 states that no oocysts/sporozoites were detected in sun1-ko and nek4-ko parasites. However, the figure 2E shows only oocysts, not sporozoites, and shows also that sun1-ko does form oocysts, albeit dead ones.

      We have now performed the experiment of crossing the Sun1-KO parasite line with a male deficient line (Hap2-KO) and added the data in Fig. 3I. We have added images showing sporozoites in oocysts.

      • In Fig S1 the authors show that they also generated a SUN1-mCherry line, yet they do not use it in any of the presented experiments (unless I missed it). Would it be beneficial to cross the SUN1-mCherry line with the Allan1-GFP line to test colocalisation (possibly also by expansion microscopy)?

      We did generate a SUN1-mCherry line, with the intent to cross ALLAN-GFP and SUN1-mCherry lines and observe the co-location of the proteins. Despite multiple attempts this cross was unsuccessful. This may have been due to their close proximity such that the addition of both GFP and mCherry was difficult to facilitate a proper protein-protein interaction between either of the proteins.

      • Line 498: "In a significant proportion of cells" - What was the proportion of cells, and what does significant mean in this context?

      Approximately 67% of cells showed the clumping of BBs. We have now added the numbers in Figs. 6H and S11I.

      • The authors should discuss a bit more how their work relates to the work of Sayers et al. 2024, which also identified the SUN1-ALLAN complex. The paper is cited, but only very briefly commented on.

      We have extended this discussion now in lines 590-605.

      Suggestions how to improve the writing and data presentation.

      • General presentation of microscopy images: Considering that large parts of the manuscript are based on microscopy data, their presentation could be improved. Single-channel microscopy images would benefit from being depicted in gray scale instead of color, which would make it easier to see the structures and intensities (especially for blue channels).

      Whilst we agree with the reviewer, sometimes it is difficult to see the features in the merged images. Therefore, we would like to request to be allowed to retain the colours, which can be easily followed in both individual and merged images.

      Also, it would be good to harmonize in which panels arrows are shown (e.g. Fig 1G, where some white arrows are in the SUN1-GFP panel, while others are in the merge panel, but they presumably indicate the same thing.). At the same time, Fig 1H doesn't have any with arrows, even though the figure legend states so.

      We apologise for this lack of consistency, and we have now added arrows wherever they are missing to harmonise in the presentations.

      Fig 3A and S4 show the same experiment but are coloured in different colours (NHS-Eester in green vs grey scale).

      • Are the scale bars of all expansion microscopy images adjusted for the expansion factor?

      Yes, the scale bars are adjusted accordingly.

      • The figure legends would benefit from streamlining, as they have very different style between figures (eg Fig. 6 which has a concise figure legend vs microscopy figures where figure legends are very long and describe not only the figure but the results)

      The figure legends have been streamlined, with removal of the description of results.

      • Line 155-156: The text makes it sound like the expression only happens after activation. is that the case? Are these images activated or non-activated gametocytes?

      They are expressed before activation, but the signal intensifies after activation. Images from before and after activation of gametocytes have been added in Fig. S1F.

      • Line 267: Reference to the original nek4-KO paper missing

      This reference is now included.

      • Line 301: The reference to Figure 2J seems to be a bit arbitrarily placed. Also, this schematic of lipid metabolism is never discussed in relation to the transcriptomic or lipidomic data.

      We have moved these data to supplementary information and modified the text.

      • Line 347-349 states that gametes emerged, but the referenced figure shows activated gametocytes before exflagellation.

      We have corrected the text to the start of exflagellation.

      • Line 588: Spelling mistake in SUN1-domain

      Corrected.

      • Line 726/731: i missing in anti-GFP

      Corrected.

      • Line 787-789: statement of scale bar and number of cells imaged is not at the right position in the figure legend.

      Moved to right place

      • Line 779, 783: "shades of green" should be just "green". Same goes for line 986, 989 with "shades of grey"

      Changed.

      • Line 974, 976: please correct to WT-GFP and dsun1

      Corrected.

      • Line 1041, 1044: WT-GFP instead of WTGFP.

      Corrected to WT-GFP.

      • Fig 1B, D, E, Fig S1G, H: What are the time points of imaging?

      We have added the time points to the images in these figures.

      • Fig 1D/Line 727: the scale of the scale bar on the inset is missing.

      We have added the scale bar.

      • Fig 3 E-G and 6H-J: Please indicate total number of cells/images analysed per quantification, either in the graphs themselves or in the figure legend.

      We indicate now the number of cells analysed in individual figures and also in Fig. S5C and S8C, respectively.

      • Fig 5B: What is NP

      Nuclear Pole (NP), also known as the nuclear/acentriolar MTOC (Zeeshan et al 2022; PMID: 35550346).

      • Fig S1B/D: The legend states that there is an arrow indicating the band, but there is none.

      We have added the arrow.

      • Fig S2C: Is the scale bar really the same for the zygote and the ookinete?

      We have checked this and used the same for both zygote and ookinete.

      • Fig S3C, S7C: which stages was qRT-PCR done on?

      Gametocytes activated for 8 min.

      • Fig. S3D, S7D: According to the figure legend, three independent experiments were performed. How many mice were used per experiment? It would be good to depict the individual data points instead of the bar graph. For S7D, 3 data points are depicted (one in WT, two in allan-KO), what do they mean?

      The bite back experiment was performed using 15-20 mosquitoes infected with WT-GFP and gene knockout lines to feed on one naïve mouse each, in three different experiments. We have now included the data points in the bar diagrams.

      • Fig S3: Panel letters E and G are missing

      We have updated the lettering in current Fig. S5

      • Fig 3D: Please indicate what those boxes are. I presume that these are the insets show in b, e and j, but it is never mentioned. J is not even larger than i. Also, f is quite cropped, it would be good to see the large-scale image it comes from to see where in the nucleus these kinetochores are placed. Were there unbound kinetochores found in WT?

      We mention the boxes in the figure legends. It is rare to find unbound kinetochores in WT parasite. We provide large scale and zoomed-in images of free kinetochores in Fig. S8.

      • Fig S4: Insets are not mentioned in the figure legend. Please add scale bar to zoom-ins

      We now describe the insets in the figure legends and have added scale bars to the zoomed-in images.

      • Fig S5A, B: Please indicate which inset belongs to which sub-panel. Where does Ac stem from?

      We have now included the full image showing the inset (new Fig. S8).

      • Fig S5C and S8C: Change "DNA" to "Nucleus".

      We have changed "DNA" to "Nucleus". Now they are Fig. S8K and S11I.

      Reviewer #3 (Significance (Required)):

      Yet, the statement that SUN1 is also important for lipid homoeostasis and NE dynamics is currently not backed up by sufficient data. I believe that the manuscript would benefit from removing the less convincing transcriptomic and lipidomic datasets and rather focus on more deeply characterising the cell biology of the knockouts. This way, the results would be interesting not only for parasitologists, but also for more general cell biologists.

      We have moved the lipidomics and transcriptomics data to supplementary information and toned down the emphasis on these data to make the manuscript more focused on the cell biology and analysis of the genetic KO data.

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      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      Summary:

      • In this study, authors investigate the impact of pre-membane (prM) and envelope (E) proteins of tick-borne encephalitis virus (TBEV) on viral distribution and tropism, mostly in the brain.*
      • To do so, authors use high resolution imaging of whole mouse brain after infection by either LGTV, a low pathogenic orthoflavivirus also transmitted by ticks, TBEV, or TBEV/LGTV chimeric virus where prM and E of TBEV are inserted in a LGTV background.*
      • Structural and antigenic characterization of the chimeric virus reveal that it remains a low pathogenic virus exhibiting TBEV structural and antigenic features.*
      • Those viruses are then used to infect wt or mavs -/- mice and viral propagation / tropism is explored, revealing that LGTV and LGTVT:prM predominantly infect cerebral cortex while TBEV infects cerebellum.*
      • Authors work at characterizing their viruses is nicely done and convincing, showing that LGTVT:prM replicated just like LGTV, and exhibited increased viral spread in cellulo.*
      • However LGTVT:prM appear to be less pathogenic in vivo and its brain tropism in mavs -/- mice seems to be similar to wt LGTV virus, stressing the fact that the role of structural proteins prM/E is only modest in TBEV specific tropism to cerebellum.*

      Major comments:

      • It is stated in the introduction that prior work on LGTV/TBEV chimera have already been done, and that both LGTV and LGTV/TBEV are neuroinvasive and neurovirulent in animal models. In this study, both LGTV and LGTVT:prM fails to establish infection in wt mouse model. Were previous published data on LGTV and derivatives also only performed in mavs, or ifnar deficient mice? The previous studies referred to in the manuscript (ref 21 and 23) are both using wt mice of younger age, 3.5 and 3 weeks respectively. It is known that age influences immune status, and some of the experiments in these previous studies are performed in even younger animals (3 to 8 days suckling mice) likely for this specific reason. The different mice strains in these studies may also influence their susceptibility to infection.

      • *While LGTV and LGTVT:prME fails to result in symptomatic infection in wt mice in our study, a certain level of localized infection is likely taking place and the outcome will depend on the immune status of the animals (age/immune deficiencies). What we tried to highlight in the manuscript was that the relative pathogenicity (TBEV/LGTV The fact that the whole "tropism" part of the study is performed in mavs -/- mice limits the impact of the study as escape from innate immune response is central in shaping viral tropism. Authors should advertise more this fact (absent from the abstract) and discuss more the links between LGTV / TBEV and innate immune response (escape mechanisms and NS proteins, implication of prM in controlling MDA5, MAVS)

      Thank you for pointing out the lack of clarity. All the tropism studies, figure 4 and 5, were done in adult WT mice infected i.c. to allow the virus to surpass the initial barrier of peripheral immune response and establish infection in the brain. We have now stressed this in the result section and in the relevant figure legends.

      Minor comments:

      • Figures need some re-working:*

      • Figure 1 :

      • 1D : only the difference between TBEV and LGTVT:prME is shown. Plotting the difference LGTV / LGTVT:prM would be a nice upgrade.* Thank you for this suggestion. However, as there is no statistical difference between LGTV and ChLGTV in Fig 1D we have maintained the figure as originally made.

      • Figure 2 : Numbering in the panels is wrong (2j in the text is 2K, 2H is 2I, ...) and should be corrected. Thank you, this has been corrected in the figure.

      • Figure 3 : Route of infection could be added to figure labels for more clarity. Thank you, we have added this to the figure.

      • Figure 4A : Labelling the Mock panel with areas of concern in the brain(Cerebrum, Cerebellum, ...) would help a lot readers not familiar with brain anatomy. We agree that adding these labels improves the clarity and accessibility of the figure and have added this to 4A.

      • Figure 4 E : images are too small to be convincing. What is staining Iba-1 is not mentioned in the figure legend. Thank you, we have added the explanation that microglia were stained by Iba1 and increased the size of the images in Figure 4. Additionally, co-staining of viral antigens and the neuronal marker UCHL-1 has been added as the new Figure 4E and Iba-1 staining moved to 4F.


      Significance

      Prior studies already described the generation and characterization of TBEV/LGTV chimeric viruses. * The main addition of this paper to the field is the use of impressive high-resolution imaging of whole mouse brains, to explore viral infection and tropism in the brain. * However, presented data remain mostly descriptive, and experiments are performed in a model that may not be optimal to study tropism. As the ability of the virus to escape type I interferon participates to tropism, the fact that infections are only performed in mavs -/- mice limits the relevance of those findings.

      We agree that studying tropism in MAVS-/- mice might be misleading and that is why the whole tropism study was performed in adult WT mice, we have clarified in the text that these data are from WT mice. In addition to the significance of this study in highlighting the respective contribution of structural proteins and the immune response in shaping tropism, this study also provides a __well-characterized chimeric virus __with a safety profile comparable to LGTV while retaining key structural and antigenic features of TBEV, model that has already helped advance studies on flavivirus receptor interactions and structural dynamics.

      Reviewer #2

      Evidence, reproducibility and clarity

      In the manuscript entitled "The influence of the pre-membrane and envelope proteins on structure, * pathogenicity and tropism of tick-borne encephalitis virus" Ebba Rosendal and colleagues present a wealth of data regarding generation and characterisation of a chimeric LGTV virus with TBEV structural proteins, comparing this virus to both LGTV and TBEV across a number of different basic and advanced readouts. They present interesting data regarding the ability of the LGTV-TBEV chimera to spread cell-cell, and the prolonged survival of immunocompromised mice compared with LGTV, which the authors associate with reduced replication in the periphery. As well as an overall increased ability of TBEV to replicate in vitro, and lead to mortality in WT mice in vivo, TBEV was found to be able to infect the cerebellum, whilst this region was rarely infected by LGTV and the chimera. The authors also demonstrate the cross-reactivity of these three viruses via neutralization using serum of TBEV vaccinated individuals.*

      General comment: * In general, I am impressed by the amount of work and breadth of techniques included in this manuscript, which I think speaks to the benefit of multidisciplinary collaboration. However, in my opinion, some points are lacking. My primary concerns lie with the in vivo experiments. The comparison of LGTV and the chimera at the same timepoints isn't ideal as the shift in mortality means these animals are at a different stage of disease at different time points. Whilst this is interesting in itself, it leaves questions about viral titres and tropism of i.p. inoculated animals at end points, in addition to the exclusion of serum titre analysis, the strength of discussion regarding peripheral replication and its potential impact on neuroinvasion/virulence is weakened. Further, claims of neuronal infection are made in figure 4 in total absence of a neuron marker. If the authors wish to claim cell-specific tropism, the cell-specific markers must be included. For figures dependent upon fluorescent imaging, further clarification as to what the AU axes indicate would aid in better interpretation of the data, especially regarding comparison of cerebellar layers for TBEV infection (described in more detail in my specific comments). Finally, In general, I think some opportunities are missed to describe the big picture of potential applicability/impact/translatability of the results obtained, especially the conclusions can be expanded to better highlight this.*

      Thank you for these very relevant comments and suggestions. In line with these, we have now added a later timepoint (8 days) for LGTV:prME in IPS1-/- mice to better understand the kinetics of the chimeric virus at later time points (Figure 3). Additionally, we have added a neuronal marker in figure 4. The explanation of quantification of the fluorescence data is described in detail in the material and method, where the concept of this arbitrary unit (AU) used for quantification is described.

      Specific points: * • Line 67: "It" is a bit of a shaky antecedent - assumedly the authors are referring to tropism, but would be good to state this, as they could also be referring to the underlying mechanisms of pathology. i.e. Tropism is determined by....*

      We agree here and have specified this accordingly.

      • Line 70 - Low pathogenicity in which species? All? Humans? The sentence refers to mice as there has not been any human clinical case with LGTV. We have added that to the text.

      • Line 79 - Strange wording - "and which viral factors influence tropism" is sufficient Corrected accordingly.

      • Line 82 - What does "low pathogenic" mean in this context? Good survivability? No clinical signs? We have clarified in the text that this is referring to similarity to the pathogenicity of LGTV.

      • Line 95: Good to mention in the text the cell type in which the foci are seen We agree, this information has been added to the main text in addition to the figure legend.

      • Line 133 - What is the rationale for the different TBEV strains used? (Kuutsalo-14 here but 93/783 before) We compare the structure of our chimeric virus with the previously published Kuutsalo-14 strain (ref 25). The use of 93/783 in this study is to ensure the same strain of TBEV is used as was used to generate LGTV:prME and to compare the chimeric virus to infectious clones of the parental viruses rescued and passaged in the same way as the chimeric virus itself to ensure differences observed is indeed due to the genetic factors.

      • Line 175/Figure 3 - Why these time points and not later ones for the LGTV chimera? I understand the early time points for replication in the periphery, but would also be good to see brain titres around day 14 when the survival of the chimera inoculated mice decreases quite rapidly. Further, imaging at timepoints at which mortality is somewhat comparable (meaning that virus is likely in the brain) would enable additional readouts to characterise neurovirulence such as cell death markers etc. and allow for a more solid comparative characterisation. Thank you for bringing this to our attention. The figure 3E is displaying data for MAVS-/- mice infected with 10^5 FFU, where the some animals meet end-point criteria already around day 7-9. To address this comment, we have added an additional timepoint at day 8 (seven animals) to explore the trend in viral loads in the brain. However, we refrain from analyzing later time points as this would require a high number of starting animals to ensure adequate numbers surviving to e.g. the suggested day 14, which is not in line with RRR.

      • Interestingly, there is not significant increase in viral loads of LGTV:prME infected animals between day 6 and 8. In line with this, IF imaging analysis of brains from later end-point animals (day 10-14) has shown limited staining of viral antigen in the brain (data not included in manuscript but could be provided to reviewers if requested). This suggests that inflammation is driving the pathology in these animals rather than uncontrolled viral replication. This has also been noted in the text. The tropism and imaging is done in WT mice infected i.c.. and the time/infectious dose has been adjusted to ensure similar clinical manifestation as presented in supplemental Figure 2A. These mice are then euthanized around day 5-6 and processed for brain imaging, line 189.

      • Line 174-182/Figure 3 - Why were serum titres not included in these experiments? These would help to strengthen your argument. (also nice to look at neutralisation in this context, though maybe not essential thanks to your data in figure 2). Viral serum titers have been analyzed previously in MAVS-/- mice in Kurhade et al 2016, and they are high at day 2 and go down to almost detection limit day 4, meaning earlier drop than in peripheral organ and was not included in these experiments. For neutralization, the included time points for the experiments in Figure 3E-H the time points are too short for robust detection of IgG antibody responses.

      • Line 183 - Good to overtly state that this is via i.c. inoculation and the justification for use of this route, and that the mice are assumedly WT. I understand LGTV struggles to get to the brain in mice, but is this representative of how neurotropism looks in animals inoculated via a more "natural" route for TBEV? We appreciate the comment and we have clarified that WT mice are i.c. inoculated. Since we wanted to compare the three viruses, we needed to use an inoculation route that is working for all three viruses. While the tropism after peripheral infection of TBEV is a very interesting question, it remains outside the scope of this study as this cannot be compared with LGTV in WT mice.

      • Figure 4B - What could account for the large variation seen in the TBEV group? This is a very good question that is difficult to answer. Although these are inbred mice, we have previously seen that there are differences in infection rate between different mice using whole brain imaging (Chotiwan et al 2023).

      • Line 200-201 - This image doesn't answer the question of tropism, but contributes to that of microglial activation. A neuronal marker should be included to surmise the cell type infected, rather than using staining for a viral protein to indicate cell morphology/type. Also, the justification for use of the microglial marker over neuronal is lacking, especially as microglia are not mentioned anywhere in the discussion. Also, see suggestion regarding cell death markers above. Thank you for this suggestion we have added a neuronal marker. We have also clarified in the text that we confirm the infection pattern in rhinal cortex with confocal microscopy. Microglia activation has been added to the discussion.

      • Line 203/Figure 4E - Are these images quantifiable? Are any differences observed between the viruses? Quantification of microglial activation is sensitive to imaging quality and area of imaging and requires large sample sets to ensure validity in the conclusions. Here we do not observe any clear differences nor claim that the microglia activation is different between the different viral strains.

      • Line 210 - Bit strange to mention figure 4D again after figure 4E, and I also couldn't spot reference to figure 4F? Thank you for pointing this out the Figure 4D should be Figure 4E, this has been corrected.

      • Are both figures 5A and 5C required for the message you wish to get across? I would suggest either only use 5C or only include the white matter/grey matter comparison for TBEV, in combination with 5A. Thank you we have now removed the mock, LGTV and LGTVT:prME from fig 5C to more clearly communicate the message of difference in infection between GM and WM for TBEV specifically.

      • Figure 5D: does the method of quantification you use/the conclusions you arrive at account for cell size/number? The Purkinje cell bodies are very large and the virus signal in these cells looks saturated - however within the granular layer the nuclei are much smaller but have what seem like large foci of NS5 positivity. Though the overall signal is likely much lower, how does relative distribution look when you account for cell size/number or a binary positive/negative quantification? Relatedly, does the primary anti-NS5 antibody have the same affinity for both LGTV and TBEV NS5? The quantification of OPT in figure 5C is not at the level of single cell resolution but rather virus signal over mock. We agree the cells in the cerebellum has different sizes but we do not claim that the Purkinje layer is more infected compared to the granular cell layer, only that Purkinje cells are infected which is relevant in human TBE.

      NS5 antibody is raised against a peptide in the TBEV NS5 protein which is highly conserved. The aa identity between TBEV and LGTV is 93%, we have not seen a difference in the staining between the different viruses using this antibody.

      • Line 242: Please clarify what you mean by "higher infection" - higher titres? Higher fluorescent signal? We have added "as measured by stronger fluorescent signal" to better explain what we mean with higher infection.

      • Line 242: Can you really say anything about replication here? Infection, yes, but the AU readout and lack of multiple time points doesn't allow for much of an insight into replication, especially when TBEV was left out of the comparison in figure 3F, though even this did not look at live virus. We have changed the wording to infected cells.

      • Line 269-271: Exactly what I was wondering and maybe worth discussing a bit more - is there appropriate literature that you could cite here? We were unsure about the specific concern raised by the reviewer in this comment and, therefore, have not made any changes. If the reviewer could clarify their request, we would be happy to address it accordingly.

      • Line 274-275: Also mosquito borne viruses. See nice paper related to impact of TBEV vaccination on ADE for mosquito borne flaviviruses. Very interesting and would increase the impact of this point. https://doi.org/10.1038/s41467-024-45806-x Thank you for this suggestion we have added this point into the discussion.

      • Line 290-291: Are clinical signs associated with cerebellar injury common for TBEV patients? i.e. does this have translatability to human disease and diagnosis? We have now added some information about cerebellum symptoms in human TBE infection to the discussion.

      • Line 308 conclusions; Your point about the potential use of the chimera for vaccine research/to understand cross-reactivity is worth reiterating here, and potentially something about "highlighting the role of non-structural proteins on tropism determination" Thank you for these suggestions we have now added these aspects in the conclusions.

      • Methods: whilst I realise the statistics are described in the figure legends, it is usually customary to include a short statistics section in the methods to indicate which program was used and why certain statistical tests were chosen, e.g. in figure 1 you use both parametric and non-parametric testing. Thank you for this suggestion. We have added a section describing the statistics in the methods.

      Significance

      Broad ranging characterisation of a novel chimera which has potential applications for vaccine/cross-reactivity research and highlights a key role of non-structural proteins in the determination of viral fitness and tropism. Some limitations regarding cell-specific tropism and kinetics of neuroinvasion and neurovirulence. Likely of interest for basic researchers from range of disciplines within arbovirology.

      • Expertise: arboviruses, imaging, neurovirulence, animal models*
      • Limited expertise: in-depth structural biology, therefore my comments on figure 2 are limited.*

      Reviewer #3 (Evidence, reproducibility and clarity (Required)): * SUMMARY: The authors generated an LGTV chimeric virus harboring the prM and ectodomain of E from TBEV. Aim of the study is to understand how the virals structural proteins influence the distribution and tropism of the virus in the brain. They solved the atomic structures of LGTV and the chimeric virus demonstrating that the chimeric virus is structurally and antigenically similar to TBEV. In vivo experiments demonstrate that the chimeric virus is less pathogenic than LGTV. Finally using 3D whole brain OPT imaging techniques the authors demonstrate that the three viruses show a similar viral distribution in cerebral cortex with the rhnial cortex being the primary site of cortical infection for all viruses. In general TBEV exhibit higher infection rates and is more widespread in the brain, particularly in cerebellum, compared to LGTV and the chimeric virus. The authors concluded that the distribution and tropism of LGTV and TBEV are not solely dependent on receptor tropism. *

      MAJOR COMMENTS: * The conclusions are supported by the data.*

      • However, I think the work can be improved if the authors investigate the differences in the antiviral response induced by the chimeric virus compared to LGTV. The authors speculate that the non-structural proteins may play a role in shaping tropism, likely through their immunomodulating role. These data become especially important if you consider that in the experiments of fig 1 the chimeric virus behave similar to the LGTV wt with even an advantage in cell-to-cell spread but in the in vivo experiments with MAVS-/- mice the chimeric virus behave differently, being less pathogenic than LGTV suggesting that the chimeric virus could not escape the antiviral response even in MAVS-/- condition. We thank the reviewer for this suggestion. In line with this we have now added Ifnb1 and Rsad2 RNA levels in different peripheral organs and we see that early on in infection most mice infected with LGTVT:prME show higher upregulation of these genes. These data have been added as a new panel F and G in figure 3.

      • Moreover, in the discussion, line 270 the authors speculate that the observed attenuation could also be due to sub-optial interactions between TBEV prM and C and transmembrane domain of LGTV E. I think it is important to explain and justify why they decided to do not include C protein of TBEV in the chimeric virus, as well as the transmembrane domain of E. The rational for not using the C protein of TBEV is that we did not want to reduce the RNA to C interaction which, could affect the packaging or encapsidation. In line with this, previous research on chimeric flaviviruses has shown that exchanging the prM-E proteins are usually well tolerated while exchanging the C-protein may lead to attenuation or even failure to rescue the virus.

      • Finally, the authors first used A549 cells for studying the kinetics and viral spread of the chimeric virus in vitro. Than they switch to A549-/- cells for studying structure and antigenicity. The different pathogenicity was assessed in Mavs-/- mice but lastly they used mice WT for the 3D whole brain OPT imaging. I found this discrepancy confusing. The authors should justify, including the explanation in the text, why they switch from WT to A549-/- from experiment to experiment. A549 cells were used in the spread and kinetic study because it is an IFN competent cell type which TBEV and LGTV grows well in. The structural studies were performed in A549 MAVS cells because the lack of MAVS results in higher virus titers. The ability of these cells to produce large amount of virus while grown without serum greatly facilitated the purification protocols for cry-EM and mass spectrometry analysis. This has been highlighted in the text of both the material and method and very briefly in the result.

      The pathogenicity with peripheral infection can only be done with MAVS-/- mice as they are more sensitive to LGTV and it is a lethal model. Adult WT mice are resistant to LGTV infection i.p.. As the immune response is important in shaping the tropism, a direct comparison of the viruses is best analyzed in a WT mouse model.

      MINOR COMMENTS:

        • Line 96 - "recombinant parental LGTV" and "recombinant TBEV", the word recombinant is misused in the sentence.* We have removed recombinant.
      • Line 143-144-145 - I believe the authors are referring to Fig 2I and not 2H as written. Moreover, the authors should clarify if all the experiemtns of fig 2 have been performed in A549-/- cells or only the one of fig 2I All experiments in figure 2 are performed in A549 MAVS-/- as highlighted in the material and methods.

      • Line 158 - to be change "Fig 2I" with "fig 2J" Corrected

      • Line 159 - as above: fig 2J to be change with figure 2k Corrected

      *Significance: *

      • The authors designed a chimeric low pathogenic model virus to study the importance of the structural proteins in determing viral tropism and pathogenicity. The strengths of this work is that they combined the use of the chimeric virus with in vivo experiments and 3D whole brain OPT imaging. Integrating together these tools and assays the authors provided an example of complete investigation method for studying neuroinvasive viruses. *

      • My field of expertise: virus-host interaction, at molecular level.*

  3. griersplagueyear.wordpress.com griersplagueyear.wordpress.com
    1. “Subordinates,” Garrett said. “Okay, so under ‘Communication,’ here’s thefirst comment. ‘He’s not good at cascading information down to staff.’ Washe a whitewater rafter, Clark? I’m just curious.”“Yes,” Clark said, “I’m certain that’s what the interviewee was talkingabout. Actual literal cascades.”“This one’s my other favorite. ‘He’s successful in interfacing with clientswe already have, but as for new clients, it’s low-hanging fruit. He takes ahigh-altitude view, but he doesn’t drill down to that level of granularitywhere we might actionize new opportunities.’ ”Clark winced. “I remember that one. I think I may have had a minor strokein the office when he said that.”

      This memory of who they were and that what they did didn't give them meaning. Clark looking back also allows him to reflect on how much he has grown.

    1. Eff orts to study infant mortality have continued to trend toward studying the problem at the molecular level: the missing or defective gene, the environmental toxin. Such eff orts, while personally rewarding to investi-gators, risk irrelevancy and unethical indictment when existing solutions operate at the macroscopic level. Group empowerment socioeco nom ical ly, health education, and abolition of racism have no gene markers, but they do raise a diff erent issue. When infant mortality and disparity are examined in these contexts, there is no question that we know enough. Th e question is: as a resource- rich society facing signifi cant health disparities that can potentially be resolved, are we “good” enough?

      It is interesting to think that when an infant dies, the immediate response is to look at what went wrong with the baby internally, but not necessarily consider external factors. While genetics may play a key role in infant mortality rates, this is not always the case. This passage does a good job at bringing this issue to light, explaining that external factors do play a substantial role in infant mortality rates and should not be ignored. When evaluating factors that lead to an infant's passing, internal and external circumstances should be equally evaluated.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Cheng et al explore the utility of analyte ratios instead of relative abundance alone for biological interpretation of tissue in a MALDI MSI workflow. Utilizing the ratio of metabolites and lipids that have complimentary value in metabolic pathways, they show the ratio as a heat map which enhances the understanding of how multiple analytes relate to each other spatially. Normally, this is done by projecting each analyte as a unique color but using a ratio can help clarify visualization and add to biological interpretability. However, existing tools to perform this task are available in open-source repositories, and fundamental limitations inherent to MALDI MSI need to be made clear to the reader. The study lacks rigor and controls, i.e. without quantitative data from a variety of standards (internal isotopic or tissue mimetic models for example), the potential delta in ionization efficiencies of different species subtracts from the utility of pathway analysis using metabolite ratios.

      We thank the reviewer for comments on the availability of four other commercial and open-source tools for performing ratio imaging: ENVI® Geospatial Analysis Software, MATLAB image processing toolbox, Spectral Python (SPy) and QGIS. We now highlight these in the introduction (page 3 line 80-86). However, in contrast to these target ratio imaging methods, our approach uniquely enables the untargeted discovery of correlated (or anti-correlated) ratios of molecular features, whether the species are structurally known or unknown.

      ENVI® Geospatial Analysis Software and MATLAB image processing toolbox for hyperspectral imaging are both paid programs, limiting free access and software evaluation for the potential application of untargeted ratio-metric imaging. We are able to evaluate the application of MATLAB RatioImage since Weill Cornell Medicine has an institutional subscription for Mathwork-MATLAB. Notably, MATLAB RatioImage computes and displays an individual intensity modulated ratiometric image by choosing a numerator and denominator image. This software tool only images the ratios of selected metabolites from an input list of multiple species and does not allow for the possibility of untargeted ratiometric images of all metabolite pairs.

      While Spectral Python (SPy) and QGIS are both freely-available software packages, and both can perform individual metabolite ratio images, neither allows for untargeted ratiometric imaging of all pairs from a multiple metabolite input list. Table S1 (below) provides a comparison of the ratio imaging tool that we offer in comparison with other previously available tools.

      We appreciate the reviewer’s insightful comments on differential ionization efficiency among metabolites and the importance of using stable isotope internal standard to gain absolute quantification.

      A fundamental advantage of our ratiometric imaging tool is to provide better image contrast for tissue regions with differential ionization efficiency, with the potential to discover new “metabolic” regions that can be revealed by metabolite ratio. Note that comparison for ratio image abundance is limited to tissue groups in the equivalent region which is expected to have similar ionization efficiency for given metabolites. Furthermore, the power of our strategy is to provide untargeted (and targeted) ratio imaging as a hypothesis generation tool and this use does not require absolute quantification. If cost was not an issue, an extensive group of stable isotope standards could theoretically be used for absolute metabolite quantification of target metabolites with known identity.

      Using the tissue mimetic model, we generate calibration curve for stable isotope standards spiked in carboxymethylcellulose (CMC)-embedded brain homogenate cryosections and quantify the concentration of brain glucose, lactate and ascorbate concentrations. Similar ratio images among these metabolites are obtained from abundance data compared to quantified concentration data (Fig S3). While stable isotope standards are often used to obtain quantitative concentration of metabolite/lipid of interest, it is not applicable for untargeted metabolite ratios that include an assessment of structurally undefined species. Nevertheless, our data indicates that absolute quantification is not necessary for the targeted and untargeted ratio imaging described here (Page 6, line 196-205).

      Reviewer #2 (Public Review):

      Summary:

      In the article, "Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging" the authors describe their software package in R for visualizing metabolite ratio pairs. I think the novelty of this manuscript is overstated and there are several notable issues with the figures that prevent detailed assessment but the work would be of interest to the mass spectrometry community.

      Strengths:

      The authors describe a software that would be of use to those performing MALDI MSI. This software would certainly add to the understanding of metabolomics data and enhance the identification of critical metabolites.

      Weaknesses:

      The authors are missing several references and discussion points, particularly about SIMS MSI, where ratio imaging has been previously performed.

      There are several misleading sentences about the novelty of the approach and the limitations of metabolite imaging.

      Several sentences lack rigor and are not quantitative enough.

      The figures are difficult to interpret/ analyze in their current state and lack some critical components, including labels and scale bars.

      We thank reviewer for very helpful comments. The tone of the manuscript has been adjusted to highlight the real novelty of this method in the ease of computing and application to MS specific projects (abstract line 26-30 ). All figures have been updated to include labels and scale bars with improved resolution. References for ratio imaging use of SIMS MSI has been added in the introduction (Page 3, line 80-89).

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major Comments:

      In the Abstract it is stated that: "the research community lacks a discovery tool that images all metabolite abundance ratio pairs." However, the following tools exist that perform this fundamental task.

      A "pixel by pixel" data frame in .csv form has a very similar data structure to many instruments like satellite imaging or other hyperspectral tools. It is true this does not exist in the MALDI-specific context, but it would not be difficult to perform this task on the following programs. Highlight the novelty here is not ratios but the ease of computing them and the application in the specific project. Also, describe the available tools and what shortcomings others lack that this package provides. A supplemental table of MSI data analysis tools and the function of each would be a good addition.

      List of tools to perform band ratio computation with minimal modification:

      (1) ENVI IDL: geospatial imaging tool that allows ratio computation between spectral bands.

      (2) MATLAB image processing toolbox for hyperspectral imaging.

      (3) Spectral Python package (SPy).

      (4) QGIS with plugins can be used for hyperspectral image analysis with a ratio between bands.

      We revised the abstract and introduction to include novelty and comparison to other existing methods listed in Table S1.

      "untargeted R package workflow" - If there are functions used outside the SCiLS Lab API client then write it up and include a GitHub link for open access to fit the mission of eLife.

      As shown in Scheme I. We develop two types of codes for untargeted ratio imaging. The first type uses Scils lab API client to extend the function of targeted and targeted ratio imaging and all related spatial image analysis. This is suitable for Scils lab users. The second type does not require Scils lab API, it allows extracting pixel data from imzml file then proceed targeted and untargeted imaging and analysis. Both codes are now deposit in Github via public access (https://github.com/qic2005/Untargeted-massspectrometry-ratio-imaging.git).

      "across cells and tissue subregions" The value in reporting cell type and tissue type-specific differences in any metric is powerful, but not done in this paper. Only whole samples are compared such as "KO vs WT" and the annotations in Figure 3 are not leveraged for increased biological relevance. This paper treats each image as a homogenization experiment in a practical sense beyond just visually inspecting each image. Remove this claim or do the calculations on region/tissue/cell-type specific differences with the appropriate tools to show the data beyond simple heat map images.

      We have deleted the sentence containing across cells and tissue subregions from the abstract.

      "enhances spatial image resolution" Clarify. The resolution in MALDI is set by the raster size of the pixels which is an instrument parameter and cannot be changed post-acquisition. Image-specific methods to increase resolution exist, but dividing the value in one peak column by another does not change functional resolution in the context of the instruments here.

      We thank reviewer for pointing out this typo. We have changed it to enhance spatial image contrast in the abstract (line 34).

      "pixel-by-pixel imaging of the ratio of an enzyme's substrate to its derived product offers an opportunity to view the distribution of functional activity for a given metabolic pathway across tissue" - Appropriately calibrate the impact of this work and correct this statement to better reflect the capabilities of this approach. Do not oversell the exploration of pathway activity since the raw quantity reported as relative abundance does not provide biologically interpretable pathway information. This is due to unaccounted differences in ionization efficiencies between analytes in a pathway and lack of determination of rate. Without a calibration curve and more techniques on the analytical chemistry side of the project, it is possible a relative abundance of one analyte (like the product of a pathway) could be higher than the relative abundance of another analyte (a precursor), but due to structural differences, the actual quantity of the higher relative abundance species could be significantly different or even lower than its counterpart. Secondly, "functional activity" cannot be assessed in this manner without isotopic labeling or additional techniques. This does not subtract from the overall validity and impact of the work, but highlighting these shortcomings and slight alterations to the claim are important for a multidisciplinary audience.

      Although we show that abundance ratio results in similar image to concentration ratio for brain metabolites such as lactate, glucose and ascorbate, we agree with the reviewer that abundance ratio is different from the absolute concentration ratio in numerical value due to difference in ionization efficiency. We delete the sentence “pixel-by-pixel imaging of the ratio of an enzyme's substrate to its derived product offers an opportunity to view the distribution of functional activity for a given metabolic pathway across tissue" from the abstract. We apologize for not clarifying this application more clearly. We meant to compare pathway activity among the equivalent and similar pixel/regions of tissues from different biological groups, given the assumption that ionization efficiency is identical for equivalent pixel from different tissue sections ( i.e. same cell type and microenvironment), especially for metabolites with similar functional structure in the same pathway. For example, fatty acids with different chain length and phospholipid with same head groups are expected to have similar ionization efficiency in the same tissue pixel/region. We have thereby rewritten this section (Page 7, line 239-247).

      "We further show that ratio imaging minimizes systematic variations in MSI data by sample handling and instrument drift, improves image resolution, enables anatomical mapping of metabotype heterogeneity, facilitates biomarker discovery, and reveals new spatially resolved tissue regions of interest (ROIs) that are metabolically distinct but otherwise unrecognized."

      Instrument drift is not accounted for by ratios as it impacts the process before ratio computation. "metabotype" - spelling?

      Instrument drift here refers to individual ion abundance changes during long data acquisition. Ratio may offer a better read-out than individual metabolite abundance alone. However, for acquired data after total ion normalization, ratio data would not have difference from non-ratio data. Therefore, we delete instrument drift from the sentence (Page 2, line 33, and Page 3, line 99)

      Metabotype is a term widely used for metabolomics field. It is categorized by similar metabolic profiles, which are based on combinations of specific metabolites. https://nutritionandmetabolism.biomedcentral.com/articles/10.1186/s12986-020-00499-z

      Results 3: Justify the claim that the ratio reduces artifacts. A ratio is the value from one m/z area over another and would seem that the quality of the ratio would be always lower than the individually higher quality pixel signal of the two analytes that compose a ratio.

      Ratio images are indeed the heatmaps of pixel-by-pixel ratio data, set by the scale of all ratio values. For very abundant ion pairs, their individual image may not be better than the ratio image, depending on the abundance changes among pixels within tissue sections. Similarly, the quality of ratio image may not be higher than the individual image if distribution of ratios does not change much among pixels in tissue sections. For example, metabolite or lipids in Figures 2 and 5 are abundant, but non-ratio images do not have better quality than ratio images. Furthermore, ratio image provides additional information on how the ratio of the two metabolite pair changes pixel-by pixel in all tissue sections, such additional information could be useful for data interpretation.

      Results 4: The metabolite pairs are biologically sensible but should be clearly stated that they do not account for differences in ionization efficiency between metabolites and cannot provide quantitative pathway analysis with a high degree of biological confidence.

      We apologize for not clarifying this application more clearly. We meant to compare pathway activity among the equivalent and similar pixel/regions of tissues from different biological groups, given the assumption that ionization efficiency is identical for equivalent pixel from different tissue sections ( i.e. same cell type and microenvironment), especially for metabolites with similar functional structure in the same pathway. For example, fatty acids with different chain length and phospholipid with same head groups are expected to have similar ionization efficiency in the same tissue pixel/region. We have thereby rewritten this section (Page 7, 239-247, 254-255).

      Results 4: "cell-type specific metabolic activity at cellular (10 µm) spatial resolution" Prove the cell type differences with IHC coregistration or MALDI IHC if you want to make claims about them. Just visually determining a tissue type of a scan of a slide is inadequate to support this claim.

      We agree with reviewer’s comments. We meant to provide additional information on cellular level metabolic activity such as adenosine nucleotide phosphorylation status (ATP/AMP) ratio at 10µm resolution. Hippocampus neurons provide a good example for depicting this utility. We have rewritten the claim to highlight the role of ratio imaging in providing additional metabolic information (Page 8, line 288-290).

      Minor Comments:

      Table 2 "Aspartiate" spelling

      We have corrected it.

      Describe the process and mathematical background for ratio computation in the Methods section. As this paper introduces a package, describing its underlying functions has value.

      We have added R-script comments to illustrate the untargeted ratio calculation using the R-mathematical function of combination and division between any two metabolite pairs in a data matrix (Page 4, line 139-141)

      "we annotate missing values with 1/5 the minimum value quantified in all pixels in which it was detected" This is explicit (ie only values with exactly 1/5 the value are annotated" - make it clear this is a threshold.

      We apologize for misunderstanding. Missing values are either have no value or have solid zero in their abundance. We first calculate the minimum abundance of a particular m/z among all pixels with detectable abundance ( i.e. excluding non-missing values), then use 1/5 this minimum value as a threshold to annotate missing value (Page 4, 133-139).

      Figure 1: legend scils is branded SCiLS and EXCEL does not need caps lock (Excel).

      Figure 1 legend has been corrected.

      Conflicts of interest "None" - there are Bruker employees on a paper about MALDI method development in a field they dominate.

      We added Joshua Fischer as a Bruker employee.

      Figure 3: The legend does not describe the purple arrow in J.

      Purple arrow description is added to figure legend.

      Figure 5: Fix orientation inconsistencies in G, H, I, and J. Especially in J - they are opposite directions. This is arbitrary and determined in SCiLS lab with simple rotation.

      Orientation has been made consistent in G,H, I and J.

      Figure S8: Provide exact number of biological and technical replicates used to generate this figure.

      Figure S8, now Figure S9, was generated from 4 biological replicates of KO and 4 biological replicates of WT brain section in the ROI7 region. This information has been added to the figure legend.

      Figure S9: Make consistent orientation of all brains

      We have made brain orientations consistent.

      In addition to ionization efficiencies impacting the value of the numeric relative abundance where ratio computation originates from, it should be mentioned how different classes of metabolites are differentially impacted by the euthanasia and collection methods used for various tissue types. For example, it is well established the ATP/AMP ratio can change drastically from tissue collection.

      We have added this to page 8, line 315-319.

      Perform standards to adjust for ionization efficiency between different m/z features.

      Untargeted ratio imaging serves as an add-on MSI data analysis tool with primary use in comparing ratio among equivalent regions/pixels with similar ionization efficiencies. It is a hypothesis generation tool. Standards adjust for ionization efficiency would be a great idea for a more accurate assessment of ratio values. Due to the cost and availability of stable isotope standards for different m/z, we chose glucose, lactate and ascorbate to showcase that abundance ratio and concentration ratio result in similar images among example brain metabolite lactate, glucose and ascorbate (page 6, 196-205).

      Add more controls to support the claims.

      We have 4 biological replicates for each genotype of brain. We have added the number of controls in all figure legends.

      Significantly tone down the claims, it is unclear how knowledgeable the authors are about the current literature of SW regarding MALDI.

      The tone has been significantly tuned down throughout the revised manuscript.

      Reviewer #2 (Recommendations For The Authors):

      Abstract:

      "relative abundance of structurally identified and yet-undefined metabolites across tissue cryosections" is misleading, since tandem MS can be performed in an imaging context and is often also compatible with the same instrument.

      We have deleted this sentence in the abstract.

      Intro:

      Paragraph 1: The authors mention MALDI and DESI, but I would argue that SIMS is more abundantly used than DESI within single-cell applications.

      We have added SIMS to the introduction Page 3, line 67.

      Paragraph 2: While it may not be all detected pairs, there are many examples of ratio imaging in the MALDI MSI and SIMS communities, particularly for bacterial signaling. These would be important examples to reference.

      We have added the application of SIMS ratio imaging to the introduction, page 3, line 74-75.

      Materials :

      Paragraph 1: More specificity on sample size is required. 3 or 4 per group is not specific. Which has four and which has three? Why are they different?

      We have corrected sample numbers for specific genotype in the text and figure legends. The number of sections per group is different due to the availability of fresh-frozen tissues (Page 4, line 115-117).

      Results:

      Paragraph 1: Am I correct in reading that an .imzml can't be used directly? Why not?

      Imaging Mass Spectrometry Markup Language (imzml) is a common data format for mass spectrometry imaging. It was developed to allow the flexible and efficient exchange of large MS imaging data between different instruments and data analysis software (Schramm et al, 2012). It contains two sets of data: the mass spectral data which is stored in a binary file (.ibd file) to ensure efficient storage and the XML metadata (.imzml file) which stores instrumental parameters, sample details. Therefore, it can’t be used directly. We have added this to result 1(Page 5, line 160-169).

      Paragraph 4: "Additionally, nonlipid small molecule metabolites suffer from smearing and/or diffusion during cryosection processing, including over the course of matrix deposition for MALDI-MSI." This is misleading. There are several examples of MALDI MSI of small metabolites that are nonlipids, where smearing or diffusion have not occurred. It would be beneficial to have a more accurate discussion of this instead. The authors should also provide some evidence of this, since they continue to focus on it for the full paragraph and don't provide references.

      We initially meant the poor image quality of small molecule metabolites is due to its interaction with aqueous phase of spraying solution, rapid degradation rate and matrix interference. We have deleted this sentence in the revised version.

      Section 5 Paragraph 2; "However, ratio imaging revealed a much greater aspartate to glutamate ratio in an unusual "moon arc" region across the amygdala and hypothalamus relative to the rest of the coronal brain." Much greater isn't scientifically accurate or descript. Use real numbers and be quantitative.

      We used pixel data from all 8 sections to obtain quantitative changes in the ratio-generated “moon arc” region compared to the rest of coronal brain (page 8, line 331-337). Ratio imaging revealed a average of 1.59-fold increase in aspartate to glutamate ratio in an unusual “moon arc” region across the amygdala and hypothalamus (mean abundance 0.563 in 6345 pixels) relative to the rest of the coronal brain (mean abundance 0.353 in 45742 pixels, Figure 5D). Similar but different arc-like structures are encompassed within the ventral thalamus and hypothalamus, wherein glutamate to glutamine ratio show a 1.63-fold increase in intensity compared to the rest of the brain (mean abundance of 0.695 in 7108 pixels vs 0.428 in 44979 pixels, Figure 5E).

      Section 8 Paragraph 2: "UMAPing" is not scientifically written.

      We have replaced UMAPing with UMAP.

      Figure 2 is difficult to interpret, given the small sizes of the images. Align the images, reduce the white space, clearly label the different tissues, add scale bars, increase size, etc. This applies to all figures, except for 3. This will make it possible to review.

      All figures have been resized by removing extra space between sections.

      Figure 3. There seems to be a change in tissue after section I, so a different diagram would be helpful. SCD has a high abundance in an area that seems to be off of the tissue. Can the authors explain this? Some of the images also appear to be low signal-to-noise. Example spectra in the SI would be helpful, so I can more accurately judge the quality of the data.

      We apologize for the discrepancy. All images are from the same sample. We initially cropped the individual image from multiple page PDF plot, then inserted it in Figure 3. Resizing and cropping inconsistency may lead to the small difference in image size. In the revised version, we plot all images in one page, which eliminates the inconsistency.

      Figure 3 example pixel data, ratio pixel data, mass spectra and ratio images can be downloaded below:

      https://wcm.box.com/s/2d5jch45ar8upjzytljnylt6doewcsqc

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public reviews

      Reviewer #1 (Public review):

      Overall I find the evidence very well presented and the study compelling. It offers an important new perspective on the key properties of neoblasts. I do have some comments to clarify the presentation and significance of the work.

      We thank the reviewer for the positive feedback and plan to improve the presentation of the work.

      Reviewer #2 (Public review):

      However, the absence of a cell-cell feedback mechanism during colony growth and the likelihood of the difference needs to be clarified. Is there any difference in interpreting the results if this mechanism is considered?

      We will improve the description of the model assumptions and the interpretation of the data on the basis of these assumptions.

      Although hnf-4 and foxF have been silenced together to validate the model, a deeper understanding of the tgs-1+ cell type and the non-significant reduction of tgs-1+ neoblasts in zfp-1 RNAi colonies is necessary, considering a high neural lineage frequency.

      We will improve the analysis of this result in light of the experimentally determined frequency of the tgs-1+ neoblast population.

      Recommendations for the authors

      Reviewing Editor Comments:

      After consultation, we have compiled a list of the key changes to be made to the manuscript, along with reviewer-specific recommendations to follow.

      (1) Include a section that explicitly describes the assumptions and limitations of the study, particularly with respect to the following assumptions:

      We thank the reviewers for the comment. We added a description of the model assumptions in the methods section “Assumptions underlying neoblast colony growth model”.

      a) All known types of specialized neoblasts cycle at the same rate (see points from Reviewer 1).

      We thank the reviewers for the comment. The current data used to estimate τ (Lei et al., Dev Cell, 2016) does not allow the direct estimation of individual cycling behaviors. Consequently, we assume that all specialized neoblasts cycle at the same average rate, a simplification supported by the model's accurate prediction of colony growth.

      b) The assumption that any FSTF-like gene would behave like zfp1 or foxF and hnfA genes. The manuscript does not mention that there may be fundamental differences among these different FSTFs that could be uncovered by future work. A strong addition to the paper would be to test other epithelial genes (e.g. p53, chd4, egr5) to show reproducible behavior within a single lineage.

      We thank the reviewers for the comment. Colony size reduction following inhibition of Smed-p53 and failure to produce epidermal progenitors is strongly supported by previous analysis (Wagner et al., Cell Stem Cell, 2012). We refer to this observation in the paper in the section titled: “Inhibition of zfp-1 does not induce overexpression of other lineages in homeostasis”. We added the following sentence to the discussion (Line 460-462): Interestingly, suppression of Smed-p53, a TF expressed in neoblasts and required for epidermal cell production, has resulted in a similar reduction in colony size (Wagner et al., Cell Stem Cell, 2012).

      Of note, Chd4 expression is not limited to specialized neoblasts or to a specific lineage (Scinome et al., Development, 2010), and therefore its inhibition likely has a more complex outcome than an effect on a single lineage. Furthermore, egr-5 is not expressed in neoblasts (Tu et al, eLife, 2015), making this experimental condition more challenging to examine in the context of neoblast colonies at the time points assessed in this study.

      c) The fact that the data used to feed the model relies on radiated animals which are likely to have altered cell cycle rates compared to unirradiated animals (see comment by Reviewer 1). Of note, the model predicts a steady increase in colony size, but colony size does not change between 9dpi and 12dpi.

      We thank the reviewers for the comment. The colony size in control animals increased between 9 and 12 dpi (Fig 3B), as predicted by the model. In zfp-1 (RNAi) animals, the median colony size has also increased over this period, at a slower rate, which we attribute to the increase in q. We attribute the unchanged average colony size to an increase in the frequency of cells failing to proliferate, because of selection of a fate they cannot fully differentiate into.

      d) In light of both reviewers' comments about colony expansion vs. feedback, the authors should discuss how predicted changes to division frequencies might change as homeostasis is reached, or explain how their model accounts for the predicted rate differences under homeostatic conditions in which overall neoblast numbers do not change. Can the model estimate when this transition might occur?

      We thank the reviewers for the comment. Our colony assays are constrained by the animals survival following sub-total irradiation (16 to 20 days). In this timeframe, the neoblast population is overwhelmingly smaller in comparison to non-irradiated animals. Therefore, the animals do not reach homeostasis during the experiment, and the model does not allow to estimate the time the system would need to return to homeostasis.

      (2) In Figure 2D, the assumption is that these adjacent smedwi-1+ cells are sisters. Previous data analyzing this relied on EdU or H3P staining to show a shared division history. When these images were collected is therefore extremely critical to include (the methods suggest 7, 9, or 12 days). The authors should justify why they believe that these adjacent cells are derived from a single neoblast that has divided only once.

      We thank the reviewers for the comment. The images were collected at 7 dpi. We modified the figure legend and the associated methods to include this information. At this early time point, smedwi-1+ cell dyads are spatially separated from other neighboring cells, suggesting that they are the product of a single cell division. Importantly, our data is in complete agreement with previous estimates of symmetric renewal division rate (Raz et al., Cell Stem Cell, 2021; Lei et al, Developmental Cell, 2016).

      (3) Clarify the wording 'pre-selected' in the abstract as described by Reviewer 1.

      We thank the reviewers for the comment, and for clarity we replaced the wording “pre-select” with “select”. 

      (4) Experimental details that are important to the interpretation should be added. For example, how is belonging to a colony defined? This is important because some of the data (e.g. Figure S1A: similar numbers of smedwi-1+ cells are observed at 2dpi and 4dpi, but 4dpi is considered a colony whereas 2dpi is not). The timing of quantification should be included in each figure (it is missing in Figure S2, and Figure 3C and 3D). How the authors distinguish biological vs technical replicates is not mentioned.

      We thank the reviewers for the comment. Subtotal irradiation may result in formation of a spatially-isolated cluster of neoblasts that is not distributed throughout the animal (Wagner et al., Science, 2011). This localized cluster of neoblasts is defined as a neoblast colony (Wagner et al., Science, 2011; Wagner et al., Cell Stem Cell, 2012). The small number of high smedwi-1+ cells observed at 4 dpi in our experiments aligns with this definition (Fig S1A). By contrast, the low smedwi-1 expression detected across the animal 2 dpi does not fit this definition and likely reflects remnants of dying neoblasts resulting from irradiation. The following text was added to the figure legend: “isolated cells expressing low levels of smedwi-1+ were scattered in the planarian parenchyma, likely reflecting remnants of dying neoblasts”.

      (5) Figure 5F appears to use SMEDWI-1 antibody (based on capital letters and increased signal in the brain). Is this the case? The methods do not mention the use of a SMEDWI-1 antibody, and the text indicates that these are progenitors, but SMEDWI-1 protein is well known to not mark neoblasts. If the antibody was used, the authors should not claim that these are neoblasts.

      We thank the reviewers for the comment. The SMEDWI-1 antibody used in the experiments described in Figure 5F indeed labels neoblasts and their progeny (Guo et al., Developmental cell, 2006). The methods section “Immunofluorescence combined with FISH” details the labeling procedure, which combines FISH and IF using this antibody.

      All microscopy images are difficult to see. Perhaps this is because they are formatted as CMYK images. They should be converted to RGB format to make them appear less dull.

      We thank the reviewer for the comment. Improved version of the figures has now been uploaded.

      The terminology used in Figure 5 to describe upregulation should not be "overexpression".  We thank the reviewers for the comment.

      We changed the terminology to “upregulated”.

      Reviewer #1 (Recommendations for the authors):

      I think the authors should include a section that explicitly lays out the assumptions and limitations of the study. For example, I believe that determining tau requires assuming that all different types of specialized neoblasts cycle at the same rates. Also there is the assumption that any FSTF-like gene would behave like zfp1 or foxF and hnfA genes. It seems to remain possible that a future study could find that a subset of FSTFs might indeed exert "either/or" decisions in fating, just not the particular genes under investigation here.

      We thank the reviewer for the comment. We added a description of the model assumptions in the methods section.

      In the abstract, the wording "pre-selected" is somewhat puzzling to me. I would interpret a preselection as a process that defines the next specified state prior to its manifestation. Instead, and as I understand the authors argue this as well, the study provides good evidence that the determination mechanism is random in that subsequent neoblast choices do not likely depend on prior states. So I would suggest changing that wording.

      We thank the reviewer for the comment. We replaced “pre-select” with “select”

      Is it possible to determine the uncertainty in measuring tau the cell cycle time and would this have an impact on subsequent modeling?

      We thank the reviewers for the comment. The current data that was used to estimate tau (Lei et al., Dev Cell, 2016) does not allow us to directly estimate the uncertainty in measuring τ.

      For lines 154-164 I would suggest doing a little more to explicitly write out the logic of determining the growth constants within the main text and not just in methods, for ease of reading.

      We thank the reviewer for the comment, and added explanations for how we determined the growth constant in the text. The text now reads (lines 160-166): “Considering an average cell cycle length of 29.7 hours, we calculated the value of q using the following approach: the probabilities of all cell division outcomes must sum to 1. Our experimental data showed that symmetric renewal (p) and asymmetric division (a) occur at equal rates (i.e., p = a). By fitting these parameters to the experimental data, we determined that the difference between the probabilities of symmetric renewal and symmetric differentiation (i.e., p - q) was = 0.345 (Fig 2E, S1D-E). Therefore, with these criteria, we estimated the probabilities of cell division outcomes in the colony as p = 0.45, a = 0.45, and q = 0.1 (Fig 2G; Methods).”

      Line 192 why does post-mitotic progeny number linearly relate to neoblast number? In clones, a change in q has an exponential effect. I feel like I am missing something.

      We thank the reviewer for the comment. In colonies, 50% of cell divisions result in the production of post-mitotic progeny (asymmetric division). Therefore, the number of produced progenitors in a given cell cycle is linearly correlated with the number of neoblasts. This statement is in line with previous analysis of planarian colony size (Wagner et al., Cell Stem Cell, 2012).

      Line103 it also seems possible, although less likely, that the specified state is not fixed within a given cell cycle and could be that cells that try to switch into zeta-neoblasts mid-cell cycle arrest in proliferation etc just for that time.

      We thank the reviewer for the comment and agree that this is a possibility. However, our observations suggest that incorporating this factor into the model is unnecessary for accurately predicting colony size.

      In terms of the feedback mechanism proposed to operate in homeostasis, I think in the case of zfp-1 it is quite likely that loss of epidermal differentiation results in wound responses (this phenomenon has been documented in egr-5 RNAi in Tu et al 2015 I believe). This could play out differently in the clone assay because the effects of sublethal irradiation on this process would predominate in both control versus zfp1(RNAi) conditions.

      We thank the reviewer for the comment. Our RNA-seq analysis following zfp-1 inhibition did not show overexpression of injury-induced genes at an early time point (6 days; Fig. 5B-C). However, an increase in cycling cells was detected much earlier via EdU labeling (3 days; Fig. 5D). In the case of egr-5 suppression, Tu et al. analyzed injury-induced gene expression at a later stage (21 days of RNAi), where they found significant epidermal defects (see Fig. 5C in Tu et al.). We agree that sublethal irradiation effects likely predominate in colony analysis for both control and zfp-1 (RNAi) animals. In homeostasis, additional factors likely influence cell proliferation and differentiation.

      It seems likely that some of the differences noted between homeostasis versus clone growth could ultimately arise from the different growth parameters under each setting. Could the rate parameters be estimated from prior data in homeostasis as well? It seems to me that with the framework the authors use, homeostasis must involve a net zero change to neoblast abundance (also shown by Wagner 2011 by the sigmoidal curve of neoblast abundance at the endpoint of clone expansion). Therefore, in these conditions p=q by definition. Experimental evidence from Lei 2016 (Figure S7M) suggests asymmetric divisions and symmetric renewing divisions are about equally abundant (5/12 41% sym renewing vs 7/12 69% asymmetric renewing). Therefore, under homeostasis, there would be an estimated p=q=0.3 and a=0.4. Compared to clone growth conditions then, in homeostasis, it seems that roughly the rate of symmetric renewal decreases and the rate of symmetric differentiation also increases. I wonder, could this kind of difference potentially account for the differences between homeostasis versus clone expansion settings? It is also worth noting that the clone expansion context has been used as a sensitized genetic background for identifying effects of gene inhibition on neoblast self-renewal, so perhaps the reason this works is that the rates of selfrenewal are relatively less in homeostasis so that clone expansion represents a case where there is greater demand for self-renewal.

      We thank the reviewer for the comment. We agree that under homeostatic conditions, where the population size remains stable, the average probability of symmetric renewal matches the average probability of symmetric differentiation or elimination. By contrast, during colony expansion, the probability of symmetric renewal exceeds that of symmetric differentiation or elimination. The differences in response to a lineage block between homeostasis and colony expansion can have multiple interpretations. However, data from homeostatic animals does not permit the analysis of individual neoblasts or their specific responses to a lineage block. Consequently, we cannot determine whether the proliferative response following the lineage block during homeostasis is a direct response to the lineage block or an indirect effect resulting from changes in other neoblasts. We discuss these possibilities further in lines 472 - 484.

      In terms of the memory effect, I recall some arguments presented in the Raz 2021 study that were consistent with a slight memory for neoblast specification being retained. I believe this was a minor point from detecting a slightly higher likelihood of identifying 2-cell clones that both took on prog1+ identity compared to the population average. If this is the case, it may be worth the authors commenting on reconciling those observations with their model.

      We thank the reviewer for their comment. Raz et al. (Cell Stem Cell, 2021) reported that in the asymmetric division of a zeta-neoblast, which generates a prog-2+ cell and a neoblast, there was a slightly higher observed frequency of zfp-1 expression in the neoblast compared to the expected rate (Expected: 32%, Observed: 44%). This small increase may reflect a mild memory effect, experimental variability, or both. However, statistical analysis using Fisher's exact test yielded a non-significant p-value (p = 0.1), suggesting that this difference could be attributed to experimental variability. Other data from Raz et al., such as lineage representation in early colonies, also did not show significant memory effects, indicating that any such effects, if present, are minimal and difficult to detect. Therefore, while we do not, and cannot, rule out the presence of minor memory effects, we expect that effects of this magnitude will have minimal impact on our model.

      Reviewer #2 (Recommendations for the authors):

      Figure 2C and 2D:

      Please provide the specific time points for the data presented.

      We thank the reviewer for the comment. The information was added to the figure legend.

      Colony growth and homeostasis:

      It would be beneficial to estimate a time point at which colony growth transitions to a model with a cell-cell feedback mechanism, similar to that observed in homeostasis. This would help in understanding the dynamics and timing of these processes.

      We thank the reviewers for the comment. Our colony assays were constrained by the animals survival following sub-total irradiation (16 to 20 days). Neoblast numbers are substantially reduced compared to unirradiated animals, preventing us from determining the time point at which homeostasis is achieved.

      Methods:

      μl should be μL  

      The text was changed accordingly.

      Line 526: H2O should be H2O

      The text was changed accordingly.

    1. In 2019 the company Facebook (now called Meta) presented an internal study that found that Instagram was bad for the mental health of teenage girls, and yet they still allowed teenage girls to use Instagram. So, what does social media do to the mental health of teenage girls, and to all its other users? The answer is of course complicated and varies. Some have argued that Facebook’s own data is not as conclusive as you think about teens and mental health [m1]. Many have anecdotal experiences with their own mental health and those they talk to. For example, cosmetic surgeons have seen how photo manipulation on social media has influenced people’s views of their appearance: People historically came to cosmetic surgeons with photos of celebrities whose features they hoped to emulate. Now, they’re coming with edited selfies. They want to bring to life the version of themselves that they curate through apps like FaceTune and Snapchat. Selfies, Filters, and Snapchat Dysmorphia: How Photo-Editing Harms Body Image [m2] Comedian and director Bo Burnham has his own observations about how social media is influencing mental health: “If [social media] was just bad, I’d just tell all the kids to throw their phone in the ocean, and it’d be really easy. The problem is it - we are hyper-connected, and we’re lonely. We’re overstimulated, and we’re numb. We’re expressing our self, and we’re objectifying ourselves. So I think it just sort of widens and deepens the experiences of what kids are going through. But in regards to social anxiety, social anxiety - there’s a part of social anxiety I think that feels like you’re a little bit disassociated from yourself. And it’s sort of like you’re in a situation, but you’re also floating above yourself, watching yourself in that situation, judging it. And social media literally is that. You know, it forces kids to not just live their experience but be nostalgic for their experience while they’re living it, watch people watch them, watch people watch them watch them. My sort of impulse is like when the 13 year olds of today grow up to be social scientists, I’ll be very curious to hear what they have to say about it. But until then, it just feels like we just need to gather the data.” Director Bo Burnham On Growing Up With Anxiety — And An Audience [m3] - NPR Fresh Air (10:15-11:20) It can be difficult to measure the effects of social media on mental health since there are so many types of social media, and it permeates our cultures even of people who don’t use it directly. Some researchers have found that people using social media may enter a dissociation state [m4], where they lose track of time (like what happens when someone is reading a good book). Researchers at Facebook decided to try to measure how their recommendation algorithm was influencing people’s mental health. So they changed their recommendation algorithm to show some people more negative posts and some people more positive posts. They found that people who were given more negative posts tended to post more negatively themselves. Now, this experiment was done without informing users that they were part of an experiment, and when people found out that they might be part of a secret mood manipulation experiment, they were upset [m5].

      The chapter's discussion on 'trauma dumping' resonated with me. I've noticed an increase in unfiltered sharing of personal traumas on social media platforms. While it's essential to have spaces for open expression, I'm concerned about the potential emotional burden this places on unsuspecting readers and whether such platforms are suitable for processing deep-seated issues. How can we balance authentic sharing with the need to protect the mental well-being of the broader online community

    2. 13.1. Social Media Influence on Mental Health# In 2019 the company Facebook (now called Meta) presented an internal study that found that Instagram was bad for the mental health of teenage girls, and yet they still allowed teenage girls to use Instagram. So, what does social media do to the mental health of teenage girls, and to all its other users? The answer is of course complicated and varies. Some have argued that Facebook’s own data is not as conclusive as you think about teens and mental health [m1]. Many have anecdotal experiences with their own mental health and those they talk to. For example, cosmetic surgeons have seen how photo manipulation on social media has influenced people’s views of their appearance: People historically came to cosmetic surgeons with photos of celebrities whose features they hoped to emulate. Now, they’re coming with edited selfies. They want to bring to life the version of themselves that they curate through apps like FaceTune and Snapchat. Selfies, Filters, and Snapchat Dysmorphia: How Photo-Editing Harms Body Image [m2] Comedian and director Bo Burnham has his own observations about how social media is influencing mental health: “If [social media] was just bad, I’d just tell all the kids to throw their phone in the ocean, and it’d be really easy. The problem is it - we are hyper-connected, and we’re lonely. We’re overstimulated, and we’re numb. We’re expressing our self, and we’re objectifying ourselves. So I think it just sort of widens and deepens the experiences of what kids are going through. But in regards to social anxiety, social anxiety - there’s a part of social anxiety I think that feels like you’re a little bit disassociated from yourself. And it’s sort of like you’re in a situation, but you’re also floating above yourself, watching yourself in that situation, judging it. And social media literally is that. You know, it forces kids to not just live their experience but be nostalgic for their experience while they’re living it, watch people watch them, watch people watch them watch them. My sort of impulse is like when the 13 year olds of today grow up to be social scientists, I’ll be very curious to hear what they have to say about it. But until then, it just feels like we just need to gather the data.” Director Bo Burnham On Growing Up With Anxiety — And An Audience [m3] - NPR Fresh Air (10:15-11:20) It can be difficult to measure the effects of social media on mental health since there are so many types of social media, and it permeates our cultures even of people who don’t use it directly. Some researchers have found that people using social media may enter a dissociation state [m4], where they lose track of time (like what happens when someone is reading a good book). Researchers at Facebook decided to try to measure how their recommendation algorithm was influencing people’s mental health. So they changed their recommendation algorithm to show some people more negative posts and some people more positive posts. They found that people who were given more negative posts tended to post more negatively themselves. Now, this experiment was done without informing users that they were part of an experiment, and when people found out that they might be part of a secret mood manipulation experiment, they were upset [m5]. 13.1.1. Digital Detox?# Some people view internet-based social media (and other online activities) as inherently toxic and therefore encourage a digital detox [m6], where people take some form of a break from social media platforms and digital devices. While taking a break from parts or all of social media can be good for someone’s mental health (e.g., doomscrolling is making them feel more anxious, or they are currently getting harassed online), viewing internet-based social media as inherently toxic and trying to return to an idyllic time from before the Internet is not a realistic or honest view of the matter. In her essay “The Great Offline,” [m7] Lauren Collee argues that this is just a repeat of earlier views of city living and the “wilderness.” As white Americans were colonizing the American continent, they began idealizing “wilderness” as being uninhabited land (ignoring the Indigenous people who already lived there, or kicking them out or killing them). In the 19th century, as wilderness tourism was taking off as an industry, natural landscapes were figured as an antidote to the social pressures of urban living, offering truth in place of artifice, interiority in place of exteriority, solitude in place of small talk. Similarly, advocates for digital detox build an idealized “offline” separate from the complications of modern life: Sherry Turkle, author of Alone Together, characterizes the offline world as a physical place, a kind of Edenic paradise. “Not too long ago,” she writes, “people walked with their heads up, looking at the water, the sky, the sand” — now, “they often walk with their heads down, typing.” […] Gone are the happy days when families would gather around a weekly televised program like our ancestors around the campfire! But Lauren Collee argues that by placing the blame on the use of technology itself and making not using technology (a digital detox) the solution, we lose our ability to deal with the nuances of how we use technology and how it is designed: I’m no stranger to apps that help me curb my screen time, and I’ll admit I’ve often felt better for using them. But on a more communal level, I suspect that cultures of digital detox — in suggesting that the online world is inherently corrupting and cannot be improved — discourage us from seeking alternative models for what the internet could look like. I don’t want to be trapped in cycles of connection and disconnection, deleting my social media profiles for weeks at a time, feeling calmer but isolated, re-downloading them, feeling worse but connected again. For as long as we keep dumping our hopes into the conceptual pit of “the offline world,” those hopes will cease to exist as forces that might generate change in the worlds we actually live in together. So in this chapter, we will not consider internet-based social media as inherently toxic or beneficial for mental health. We will be looking for more nuance and where things go well, where they do not, and why.

      This chapter talks about how social media affects mental health, especially for teenage girls. It shows that social media can help us connect but also make us feel lonely and anxious. Bo Burnham talks about how people are always worried about how they look online, which can cause a lot of anxiety. The part about “Snapchat Dysmorphia” is pretty shocking because it shows that filters are changing how people see themselves, making them feel unhappy with their real appearance. I found it interesting when Lauren Collee argued that quitting social media isn’t the solution because it ignores the bigger issue of how social media is designed. It made me think about whether social media can be made healthier or if it’s just made to keep us hooked by playing on our fears and insecurities.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript describes the role of PRDM16 in modulating BMP response during choroid plexus (ChP) development. The authors combine PRDM16 knockout mice and cultured PRDM16 KO primary neural stem cells (NSCs) to determine the interactions between BMP signaling and PRDM16 in ChP differentiation.

      They show PRDM16 KO affects ChP development in vivo and BMP4 response in vitro. They determine genes regulated by BMP and PRDM16 by ChIP-seq or CUT&TAG for PRDM16, pSMAD1/5/8, and SMAD4. They then measure gene activity in primary NSCs through H3K4me3 and find more genes are co-repressed than co-activated by BMP signaling and PRDM16. They focus on the 31 genes found to be co-repressed by BMP and PRDM16. Wnt7b is in this set and the authors then provide evidence that PRDM16 and BMP signaling together repress Wnt activity in the developing choroid plexus.

      Strengths:

      Understanding context-dependent responses to cell signals during development is an important problem. The authors use a powerful combination of in vivo and in vitro systems to dissect how PRDM16 may modulate BMP response in early brain development.

      Main weaknesses of the experimental setup:

      (1) Because the authors state that primary NSCs cultured in vitro lose endogenous Prdm16 expression, they drive expression by a constitutive promoter. However, this means the expression levels are very different from endogenous levels (as explicitly shown in Supplementary Figure 2B) and the effect of many transcription factors is strongly dose-dependent, likely creating differences between the PRDM16-dependent transcriptional response in the in vitro system and in vivo.<br />

      We acknowledge that our in vitro experiments may not ideally replicate the in vivo situation, a common limitation of such experiments, our primary aim was to explore the molecular relationship between PRDM16 and BMP signaling in gene regulation. Such molecular investigations are challenging to conduct using in vivo tissues. In vitro NSCs treated with BMP4 has been used a model to investigate NSC proliferation and quiescence, drawing on previous studies (e.g., Helena Mira, 2010; Marlen Knobloch, 2017). Crucially, to ensure the relevance of our in vitro findings to the in vivo context, we confirmed that cultured cells could indeed be induced into quiescence by BMP4, and this induction necessitated the presence of PRDM16. Furthermore, upon identifying target genes co-regulated by PRDM16 and SMADs, we validated PRDM16's regulatory role on a subset of these genes in the developing Choroid Plexus (ChP) (Fig. 7 and Suppl.Fig7-8). Only by combining evidence from both in vitro and in vivo experiments could we confidently conclude that PRDM16 serves as an essential co-factor for BMP signaling in restricting NSC proliferation.

      (2) It seems that the authors compare Prdm16_KO cells to Prdm16 WT cells overexpressing flag_Prdm16. Aside from the possible expression of endogenous Prdm16, other cell differences may have arisen between these cell lines. A properly controlled experiment would compare Prdm16_KO ctrl (possibly infected with a control vector without Prdm16) to Prdm16_KO_E (i.e. the Prdm16_KO cells with and without Prdm16 overexpression.)

      We agree that Prdm16 KO cells carrying the Prdm16-expressing vector would be a good comparison with those with KO_vector. However, despite more than 10 attempts with various optimization conditions, we were unable to establish a viable cell line after infecting Prdm16 KO cells with the Prdm16-expressing vector. The overall survival rate for primary NSCs after viral infection is low, and we observed that KO cells were particularly sensitive to infection treatment when the viral vector was large (the Prdm16 ORF is more than 3kb).

      As an alternative oo assess vector effects, we instead included two other control cell lines, wt and KO cells infected with the 3xNLS_Flag-tag viral vector, and presented the results in supplementary Fig 2.  When we compared the responses of the four lines — wt, KO, wt infected with the Flag vector, KO infected with the Flag vector — to the addition and removal of BMP4, we confirmed that the viral infection itself has no significant impacts on the responses of these cells to these treatments regarding changes in cell proliferation and Ttr induction.

      Given that wt cells and the KO cells, with or without viral backbone infection behave quite similarly in terms of cell proliferation, we speculate that even if we were successful in obtaining a cell line with Prdm16-expressing vector in the KO cells, it may not exhibit substantial differences compared to wt cells infected with Prdm16-expressing vector.

      Other experimental weaknesses that make the evidence less convincing:

      (1) The authors show in Figure 2E that Ttr is not upregulated by BMP4 in PRDM16_KO NSCs. Does this appear inconsistent with the presence of Ttr expression in the PRDM16_KO brain in Figure1C?<br />

      The reviwer’s point is that there was no significant increase in Ttr expression in Prdm16_KO cells after BMP4 treatment (Fig. 2E), but there remained residule Ttr mRNA signals in the Prdm16 mutant ChP (Fig. 1C). We think the difference lies in the measuable level of Ttr expression between that induced by BMP4 in NSC culture and that in the ChP. This is based on our immunostaining expreriment in which we tried to detect Ttr using a Ttr antibody. This antibody could not detect the Ttr protein in BMP4-treated Prdm16_expressing NSCs but clearly showed Ttr signal in the wt ChP. This means that although Ttr expression can be significantly increased by BMP4 in vitro to a level measurable by RT-qPCR, its absolute quantity even in the Prdm16_expressing condition is much lower compared to that in vivo. Our results in Fig 1C and Fig 2E, as well as Fig 7B, all consistently showed that Prdm16 depletion significantly reduced Ttr expression in in vitro and in vivo.

      (2) Figure 3: The authors use H3K4me3 to measure gene activity. This is however, very indirect, with bulk RNA-seq providing the most direct readout and polymerase binding (ChIP-seq) another more direct readout. Transcription can be regulated without expected changes in histone methylation, see e.g. papers from Josh Brickman. They verify their H3K4me3 predictions with qPCR for a select number of genes, all related to the kinetochore, but it is not clear why these genes were picked, and one could worry whether these are representative.

      H3K4me3 has widely been used as an indicator of active transcription and is a mark for cell identity genes. And it has been demonstrated that H3K4me3 has a direct function in regulating transciption at the step of RNApolII pausing release. As stated in the text, there are advantages and disadvantages of using H3K4me3 compared to using RNA-seq. RNA-seq profiles all gene products, which are affected by transcription and RNA stability and turnover. In contrast, H3K4me3 levels at gene promoter reflects transcriptional activity. In our case, we aimed to identify differential gene expression between proliferation and quiescence states. The transition between these two states is fast and dynamic. RNA-seq may not be able to identify functionally relevant genes but more likely produces false positive and negative results. Therefore, we chose H3K4me3 profiling.

      We agree that transcription may change without histone methylation changes. This may cause an under-estimation of the number of changed genes between the conditions. 

      We validated 7 out of 31 genes (Wnt7b, Id3, Mybl2, Spc24, Spc25, Ndc80 and Nuf2). We chose these genes based on two critira: 1) their function is implicated in cell proliferation and cell-cycle regulation based on gene ontology analysis; 2) their gene products are detectable in the developing ChP based on the scRNA-seq data. Three of these genes (Wnt7b, Id3, Mybl2) are not related to the kinetochore. We now clarify this description in the revised text.

      (3) Line 256: The overlap of 31 genes between 184 BMP-repressed genes and 240 PRDM16-repressed genes seems quite small.

      This indicates that in addition to co-repressing cell-cycle genes, BMP and PRDM16 have independent fucntions. For example, it was reported that BMP regulates neuronal and astrocyte differentiation (Katada, S. 2021), while our previous work demonstrated that Prdm16 controls temporal identity of NSCs (He, L. 2021).

      (4) The Wnt7b H3K4me3 track in Fig. 3G is not discussed in the text but it shows H3K4me3 high in _KO and low in _E regardless of BMP4. This seems to contradict the heatmap of H3K4me3 in Figure 3E which shows H3K4me3 high in _E no BMP4 and low in _E BMP4 while omitting _KO no BMP4. Meanwhile CDKN1A, the other gene shown in 3G, is missing from 3E.

      The track in Fig 3G shows the absolute signal of H3K4me3 after mapping the sequencing reads to the genome and normaliz them to library size. Compare the signal in Prdm16_E with BMP4 and that in Prdm16_E without BMP4, the one with BMP4 has a lower peak. The same trend can be seen for the pair of Prdm16_KO cells with or without BMP4.  The heatmap in Fig. 3E shows the relative level of H3K4me3 in three conditions. The Prdm16_E cells with BMP4 has the lowest level, while the other two conditions (Prdm16_KO with BMP4 and Prdm16_E without BMP4) display a higher level. These two graphs show a consistent trend of H3K4me3 changes at the Wnt7b promoter across these conditions.

      (5) The authors use PRDM16 CUT&TAG on dissected dorsal midline tissues to determine if their 31 identified PRDM16-BMP4 co-repressed genes are regulated directly by PRDM16 in vivo. By manual inspection, they find that "most" of these show a PRDM16 peak. How many is most? If using the same parameters for determining peaks, how many genes in an appropriately chosen negative control set of genes would show peaks? Can the authors rigorously establish the statistical significance of this observation? And why wasn't the same experiment performed on the NSCs in which the other experiments are done so one can directly compare the results? Instead, as far as I could tell, there is only ChIP-qPCR for two genes in NSCs in Supplementary Figure 4D.

      In our text, we indicated the genes containing PRDM16 binding peaks in the figures and described them as “Text in black in Fig. 6A and Supplementary Fig. 5A”. We will add the precise number “25 of these genes” in the main text to clarify it. To define a negative control set of genes, we will use BMP-only repressed 184-31 =153 genes (excluding PRDM16-BMP4 co-repressed), and of these 153 genes, we will determine how many have PRDM16 peaks in the E12.5 ChP data, say X. Then we will use binomial test to calculate p-value binom_test(25, 31, X/153, alternative=“greater).

      We are confused with the second part of the comment “And why wasn't the same experiment performed on the NSCs in which the other experiments are done so one can directly compare the results? Instead, as far as I could tell, there is only ChIP-qPCR for two genes in NSCs in Supplementary Figure 4D.” If the reviewer meant why we didn’t sequence the material from sequential-ChIP or validate more taget genes, the reason is the limitation of the material. Sequential ChIP requires a large quantity of the antibodies, and yields little material barely sufficient for a few qPCR after the second round of IP. This yielded amount was far below the minimum required for library construction. The PRDM16 antibody was a gift, and the quantity we have was very limited. We made a lot of efforts to optimize all available commercial antibodies in ChIP and Cut&Tag, but none of them worked.

      (6) In comparing RNA in situ between WT and PRDM16 KO in Figure 7, the authors state they use the Wnt2b signal to identify the border between CH and neocortex. However, the Wnt2b signal is shown in grey and it is impossible for this reviewer to see clear Wnt2b expression or where the boundaries are in Figure 7A. The authors also do not show where they placed the boundaries in their analysis. Furthermore, Figure 7B only shows insets for one of the regions being compared making it difficult to see differences from the other region. Finally, the authors do not show an example of their spot segmentation to judge whether their spot counting is reliable. Overall, this makes it difficult to judge whether the quantification in Figure 7C can be trusted.

      To address these questions, in the revised manuscript we will include an individal channel of Wnt2b and mark the boundaries. We will also provide full-view images and examples of spot segmentation in supplementary figures as space limitation in the main figures.

      (7) The correlation between mKi67 and Axin2 in Figure 7 is interesting but does not convincingly show that Wnt downstream of PRDM16 and BMP is responsible for the increased proliferation in PRDM16 mutants.

      We agree that this result (the correlation between mKi67 and Axin2) alone only suggests that Wnt signaling is related to the proliferation defect in the Prdm16 mutant, and does not necessarily mean that Wnt is downstream of PRDM16 and BMP. Our concolusion is backed up by two additional lines of evidences:  the Cut&Tag data in which PRDM16 binds to regulatory regions of Wnt7b and Wnt3a; BMP and PRDM16 co-repress Wnt7b in vitro.

      An ideal result is that down-regulating Wnt signaling in Prdm16 mutant can rescue Prdm16 mutant phenotype. Such an experiment is technically challenging. Wnt plays diverse and essential roles in NSC regulation, and one would need to use a celltype-and stage-specific tool to down-regulate Wnt in the background of Prdm16 mutation. Moreover, Wnt genes are not the only targets regulated by PRDM16 in these cells, and downregulating Wnt may not be sufficient to rescue the phenotype. 

      Weaknesses of the presentation:

      Overall, the manuscript is not easy to read. This can cause confusion.

      We will revise the text to improve the clarity.

      Reviewer #2 (Public review):

      Summary:

      This article investigates the role of PRDM16 in regulating cell proliferation and differentiation during choroid plexus (ChP) development in mice. The study finds that PRDM16 acts as a corepressor in the BMP signaling pathway, which is crucial for ChP formation.

      The key findings of the study are:

      (1) PRDM16 promotes cell cycle exit in neural epithelial cells at the ChP primordium.

      (2) PRDM16 and BMP signaling work together to induce neural stem cell (NSC) quiescence in vitro.

      (3) BMP signaling and PRDM16 cooperatively repress proliferation genes.

      (4) PRDM16 assists genomic binding of SMAD4 and pSMAD1/5/8.

      (5) Genes co-regulated by SMADs and PRDM16 in NSCs are repressed in the developing ChP.

      (6) PRDM16 represses Wnt7b and Wnt activity in the developing ChP.

      (7) Levels of Wnt activity correlate with cell proliferation in the developing ChP and CH.

      In summary, this study identifies PRDM16 as a key regulator of the balance between BMP and Wnt signaling during ChP development. PRDM16 facilitates the repressive function of BMP signaling on cell proliferation while simultaneously suppressing Wnt signaling. This interplay between signaling pathways and PRDM16 is essential for the proper specification and differentiation of ChP epithelial cells. This study provides new insights into the molecular mechanisms governing ChP development and may have implications for understanding the pathogenesis of ChP tumors and other related diseases.

      Strengths:

      (1) Combining in vitro and in vivo experiments to provide a comprehensive understanding of PRDM16 function in ChP development.

      (2) Uses of a variety of techniques, including immunostaining, RNA in situ hybridization, RT-qPCR, CUT&Tag, ChIP-seq, and SCRINSHOT.

      (3) Identifying a novel role for PRDM16 in regulating the balance between BMP and Wnt signaling.

      (4) Providing a mechanistic explanation for how PRDM16 enhances the repressive function of BMP signaling. The identification of SMAD palindromic motifs as preferred binding sites for the SMAD/PRDM16 complex suggests a specific mechanism for PRDM16-mediated gene repression.

      (5) Highlighting the potential clinical relevance of PRDM16 in the context of ChP tumors and other related diseases. By demonstrating the crucial role of PRDM16 in controlling ChP development, the study suggests that dysregulation of PRDM16 may contribute to the pathogenesis of these conditions.

      Weaknesses:

      (1) Limited investigation of the mechanism controlling PRDM16 protein stability and nuclear localization in vivo. The study observed that PRDM16 protein became nearly undetectable in NSCs cultured in vitro, despite high mRNA levels. While the authors speculate that post-translational modifications might regulate PRDM16 in NSCs similar to brown adipocytes, further investigation is needed to confirm this and understand the precise mechanism controlling PRDM16 protein levels in vivo.

      While mechansims controlling PRDM16 protein stability and nuclear localization in the developing brain are interesting, the scope of this paper is revealing the function of PRDM16 in the choroid plexus and its interaction with BMP signaling. We will be happy to pursuit this direction in our next study.

      (2) Reliance on overexpression of PRDM16 in NSC cultures. To study PRDM16 function in vitro, the authors used a lentiviral construct to constitutively express PRDM16 in NSCs. While this approach allowed them to overcome the issue of low PRDM16 protein levels in vitro, it is important to consider that overexpressing PRDM16 may not fully recapitulate its physiological role in regulating gene expression and cell behavior.

      As stated above, we acknowledge that findings from cultured NSCs may not directly apply to ChP cells in vivo. We are cautious with our statements. The cell culture work was aimed to identify potential mechanisms by which PRDM16 and SMADs interact to regulate gene expression and target genes co-regulated by these factors. We expect that not all targets from cell culture are regulated by PRDM16 and SMADs in the ChP, so we validated expression changes of several target genes in the developing ChP and now included the new data in Fig. 7 and Supplementary Fig. 7. Out of the 31 genes identified from cultured cells, four cell cycle regulators including Wnt7b, Id3, Spc24/25/nuf2 and Mybl2, showed de-repression in Prdm16 mutant ChP. These genes can be relevant downstream genes in the ChP, and other target genes may be cortical NSC-specific or less dependent on Prdm16 in vivo.

      (3) Lack of direct evidence for AP1 as the co-factor responsible for SMAD relocation in the absence of PRDM16. While the study identified the AP1 motif as enriched in SMAD binding sites in Prdm16 knockout cells, they only provided ChIP-qPCR validation for c-FOS binding at two specific loci (Wnt7b and Id3). Further investigation is needed to confirm the direct interaction between AP1 and SMAD proteins in the absence of PRDM16 and to rule out other potential co-factors.

      We agree that the finding of the AP1 motif enriched at the PRDM16 and SMAD co-binding regions in Prdm16 KO cells can only indirectly suggest AP1 as a co-factor for SMAD relocation. That’s why we used ChIP-qPCR to examine the presence of C-fos at these sites. Although we only validated two targets, the result confirms that C-fos binds to the sites only in the Prdm16 KO cells but not Prdm16_expressing cells, suggesting AP1 is a co-factor.  We results cannot rule out the presence of other co-factors.

      Reviewer #3 (Public review):

      Summary:

      Bone morphogenetic protein (BMP) signaling instructs multiple processes during development including cell proliferation and differentiation. The authors set out to understand the role of PRDM16 in these various functions of BMP signaling. They find that PRDM16 and BMP co-operate to repress stem cell proliferation by regulating the genomic distribution of BMP pathway transcription factors. They additionally show that PRDM16 impacts choroid plexus epithelial cell specification. The authors provide evidence for a regulatory circuit (constituting of BMP, PRDM16, and Wnt) that influences stem cell proliferation/differentiation.

      Strengths:

      I find the topics studied by the authors in this study of general interest to the field, the experiments well-controlled and the analysis in the paper sound.

      Weaknesses:

      I have no major scientific concerns. I have some minor recommendations that will help improve the paper (regarding the discussion).

      We will revise the discussion according the suggestions.

    1. Exclusions of LGBTQ-related information signal to students thatsuch people are not respected members of the school community, and in thevacuum of official school silence bias from students can go unchallenged.

      I think that silence is inherently hurtful because it does not convey support and respect for the LGBTQ+ community. In the absence of formal discussion or acknowledgement, students may perceive LGBTQ+ identities as unacceptable or even something to be ignored. Similarly, neutrality can sometimes be seen as a form of neglect by failing to stand up to prejudice and discrimination. In educational settings, if we don't actively advocate for LGBTQ+ students and challenge harmful stereotypes, then this “neutrality” is actually a tacit endorsement of disrespect. Schools should therefore actively promote a culture of inclusion where every student feels respected and understood, rather than exacerbating their marginalization through silence.

    2. Me~bers o[ school communities may believe that sexuality is not anappropriate topic for young people. However, there are significant numbersof LGBTQ and ally students in schools, as well as significant numbers ofsexually aware heterosexual students.

      This is an interesting concept to go by, I think because we live in a society that subconciously supports heterosexual values, by not talking about LGBTQ topics, it is then perceived as not being adressed. In another reality, the presence of sexuality, hetero or homo, should not and would not matter, therefore not needing to be adressed in the curriculum, and I think that is what proponents of that idea really mean.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Response to Reviewers

      We thank the reviewers for their comments and suggestions, which we think are helpful and will improve the manuscript, and intend to address with the changes and planned revisions below.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Bello et al look at the SNP rs28834970 associated with Alzheimer's disease (AD), with C being the risk allele, on chromatin accessibility and expression of a nearby gene, PTK2B, in microglia. Their contention is that the single SNP affects chromatin accessibility and binding of the transcription factor CEBP[beta] in an intronic region of PTK2B and thereby affects PTKB expression. I had a few questions that I think are critical to be addressed. Please note that my numbering of panels is based on the figures, not the legends, which do not seem to quite agree with each other. There are also some figure legends that say "IFNg" while the figures say "LPS", which should be fixed.

      We apologise for the mistake in the figure legend that made this confusing, which we have now revised.

      The abstract says that editing a line that is homozygous for protective alleles to homozygous for risk results in "subtle downregulation of PTK2B expression". It isn't clear to me that the presented data fully supports this contention, which is central to the argument of the paper. In figure 2e, the authors show in both RNAseq and ddPCR that there is numerically lower PTK2B expression but this is not indicated to be statistically significant by one-way paired ANOVA. If there is no nominally significant difference in the edited lines, compared to the proposed significant differences in lines carrying the full risk haplotype (figure 1), then it would not seem sensible to ascribe the effects to the single edited base pair.

      We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, and have substantially reworded the manuscript accordingly.

      Whilst the effects in the eQTL analysis are significant, it is worth noting that this is likely due to the much larger number of donors (133-217) giving greater power to detect the subtle changes in expression (~1.1 to 2 fold in eQTL). This change is of a similar magnitude in our SNP edited lines (~1.2 fold in SNP edited lines) as would be expected of most common regulatory variants so we believe that it could be the primary causal variant. However, we cannot exclude that other variants in the haplotype could contribute to the effect, so have also reworded accordingly to make this clear.

      Given this uncertainty about the overall strength of effect of the single base pair change it would seem important to evaluate the proposed mechanism of CEBPb binding. It wasn't clear whether the ATAC-seq data summarized in the volcano plot in 2C is proposed to be a cause or a consequence of the CEBPb binding change. I am assuming that the 'fold change' estimate here is CC compared to TT, which would be consistent with direction of effect in figure 1, but please clarify.

      We apologise for the mistake in the figure legend that made this confusing, which we have now revised along with clarification in the revised text. It is difficult to be sure whether changes in chromatin accessibility are a cause or consequence of CEBPb binding, but the fact that the binding of CEBPb is increased in the CC allele (Fig 2a, Fig 2c), that the C allele better matches the consensus sequence (Fig 2b) and there is increased chromatin accessibility (Fig 2a, Supp Fig 3b) suggests that CEBPb binding is causing the formation of the region of chromatin accessibility.

      In contrast to the subtle effects at PTK2B, the global transcriptional effects in figure 3 look quite strong. Are any of these changes dependent on PTK2B, that is to say, are they mimicked by partial suppression of PTK2B expression or activity?

      We agree that the downstream effects of the SNP are much stronger than the effects on PTK2B expression, and we have substantially reworded the manuscript to make it clear that we are unsure that the effects of the SNP are all mediated via PTK2B.

      However, we note that there is evidence in the literature of a loss in CCL4 and CCL5 expression upon PTK2B knockout in macrophages (https://www.nature.com/articles/s41467-021-27038-5) and inhibition of PTK2B in monocytes results in a reduction in CCL5 and CXCL1 (https://www.nature.com/articles/s41598-019-44098-2) consistent with our observations.

      Experiments to manipulate PTK2B expression in microglia and readout changes at the RNA level would take a few months to complete, but we would be willing to do this if the reviewer felt this was necessary.

      Finally, in figure 4, it should be clarified as to why lower expression of PTK2B would be expected to have a detrimental effect on Alzheimer's risk. If understood correctly, and again fixing the figure legends would be helpful, the CC edited lines (risk) have lower chemokine induction than the unedited TT lines.

      We apologise for the error in this figure which we have corrected in the revised version. You are correct that the CC lines have a lower chemokine level in both unstimulated and stimulated cells, and we have now discussed further how this may be linked to increased disease risk.

      "Even though overexpression of these chemokines is characteristic of neuroinflammation, correlated with disease progression and found in late stages of AD, knockout of chemokines, such as CCL2, and chemokine receptors, such as CCR2 and CCR5, in mice is associated with increased Aβ deposition and accumulation [47,50-52,107]. It has also been found that patients carrying CCR5Δ32 mutation, which prevents CCR5 surface expression, develop AD at a younger age[108]. Therefore, we hypothesize that in individuals carrying the C/C allele of rs28834970 downregulation of these chemokines in macrophages and microglia harbouring the C/C allele of rs28834970 affects Aβ-induced microglia chemotaxis, leukocytes recruitment and clearance of Aβ, and may increase the risk of developing symptomatic AD"

      Reviewer #1 (Significance (Required)):

      Going from GWAS hits, which represent blocks of high LD inherited variants, to single functional variants is a difficult problem in human genetics. The current paper attempts to isolate the effect of a single variant within an LD block on IPSC derived macrophages and microglia. This idea might be useful in nominating PTK2B as a therapeutic target for AD, although there is some question in my mind as to direction of effect.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      SUMMARY: In this manuscript the authors explore the biological effects of an intronic SNP in the PTK2B gene, previously shown to be associated with late onset Alzheimer's disease (AD) risk. Based on the likely effect of the SNP locus on PTK2B expression in the macrophage lineage, the authors explore the consequences of introducing with the Crispr/Cas9 technique the biallelic SNP base change (C/C vs T/T) in a human IPSC line that is then differentiated into macrophages or microglia. They observe that C/C increases chromatin accessibility and CEBPb binding in comparison to T/T, with a slight decrease in PTK2B expression, significant in macrophages but not in microglia. The authors then investigate the transcriptome changes induced by the C/C mutation and find alteration in many genes, including a decreased expression of a number of cytokine or receptor proteins involved in inflammatory responses. The authors also mention a decreased effect on IFNg-induced reduced mobility but the data are missing (see Figure errors below). Overall the authors propose that the risk SNP is associated with a decreased PTK2B expression and hypothesize a link between this change and a decreased function of macrophages/microglia that may contribute to AD pathology.

      MAJOR COMMENTS

      1- The authors claim that their results show that the investigated SNP has a causal effects in "microglial function" (Title) and in Alzheimer's disease (AD) (Abstract 2nd sentence "Here we validate a causal single nucleotide polymorphism (SNP) associated with an increased risk of Alzheimer's disease". The word "causal" is repeated many times. However the authors should qualify their claim with respect to AD. Their results do show that the SNP has an effect on chromatin accessibility, CEBP binding, PTK2B expression and transcriptome, but the link between these changes is not formally demonstrated and their potential role in AD-like phenotype is not explored. The "causal" role is not formally and logically demonstrated. It remains an interesting, plausible hypothesis and the results provide strong arguments in support of that hypothesis but do not prove it, yet.

      Concerning the title, "causal effects on microglial function" is awkward, anything that has effects is logically "causal" in these effects. The title should be "... has effects on microglial functions" or "... alters microglial function".

      We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, or that they cause AD. We have substantially reworded the manuscript throughout to account for this.

      2- One major difficulty in the results is to link the slight decrease in PTK2B transcript, which is only significant in macrophages, with the rest of the phenotype. Because what matters to make this link is not the mRNA but the protein, and because mRNA levels are often not strictly correlated with the protein levels, the authors should measure the PTK2B/PYK2 protein levels in their differentiated cell lines in basal conditions and following activation (as they do for other readouts) using immunoblotting. A robust and significant diminution in PYK2 protein would strongly support its role in linking PTK2B expression and transcriptome change.

      We have performed preliminary analyses of PTK2B expression by Western blot in these cell lines after differentiation, but were unable to observe a significant change in abundance in the edited cell lines. This is not unexpected given the results at the RNA level, since the effect size of this common regulatory variant is likely very small (estimated to be ~1.2 fold from the eQTL analysis), and likely within the variability of this assay.

      As mentioned above, we have reworded the manuscript to avoid interpreting that the effects of rs28834970 are mediated solely through effects on PTK2B expression. We think that an experiment to manipulate PTK2B levels (see next point) may be a better way to demonstrate whether these effects are mediated through PTK2B expression.

      An optional additional key experiment would be to reverse the transcriptome phenotype by increasing the expression of PTK2B (e.g. by cDNA transfection). Note that these points are important because an alternative hypothesis to explain the effects of C/C mutation on macrophage function would be that the C/C mutation has a long distance effect on other chromatin regions with key role in regulating these cells.

      We agree that this would be a valuable experiment, and are planning additional experiments to investigate the effect of manipulating PTK2B levels (through knockout) on microglia.

      3- The manuscript contains several errors in the figures and figure legends. In Fig. 2 the legends for the figure items are shuffled. Figure 4 and Supplementary Figure 5 are duplicates of the same one. Consequently important data are not presented.

      We apologise for the errors in these figures that were due to a mistake during uploading where the incorrect versions were used. The legends for figure 2 and panels in figure 4 have now been corrected, and show the effects of rs28834970 on microglial migration and chemokine release in the presence or absence of IFNg.

      4- When the number of replicates is small (e.g. n = 3) it is preferable to use non parametric tests (rank analysis, e.g. Mann Whitney's test) rather than t test. This applies to Figures 2D (current legend 2A), 2E (current legend 2B), Figure 4A-C, Supplementary Figures 2A, 2B. In Supplementary Fig 4E (MARCO) the number of replicates (presumably 3 because based on RNAseq) and the used test are not indicated. Is it the RNAseq statistical analysis?

      We thank the reviewer for this comment. We acknowledge that the t-test may lead to inflated false discovery rates. However, it has been shown that for small sample sizes parametric tests have a power advantage compared to non-parametric ones that may outweigh the possibly exaggerated false positives. See https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4#Sec3 which states:

      "In conclusion, when the per-condition sample size is less than 8, parametric methods may be used because their power advantage may outweigh their possibly exaggerated false positives."

      We have also modified the legend of supplementary figure 4E to clarify the number of replicates used.

      5- In addition to the above comment on tests, when the number of replicates is small it is not appropriate (and misleading) to show box plots or bars with SEM. In the indicated figures the individual data points should be shown.

      We now show individual replicates on box plots (Figure 2D, 2E and supp figure 4E).

      MINOR COMMENTS:

      a- Macrophages and microglia are very similar cell types. Could the authors comment more on the differences they observe and how they are related to those previously described?

      We have now referenced the original papers and commented on the markers that we see differentially expressed, notably P2RY12 which is a key homeostatic microglia marker that distinguishes these cells from macrophages.

      b- In Fig. 2A CEBPb cut and run plot, the differences are not limited to the SNP immediate vicinity, there are also visible differences between T/T and C/C plots in at least a 40-kb range. Is it due to multiple interactions of CEBPb? How can the point difference have broad consequences? Please explain this potentially interesting and relevant finding.

      Whilst there may be small changes in CEBPb binding at the second intronic PTK2B chromatin peak, this is not statistically significant given the variability between repeats. In fact, the only significant change we see in CEBPb binding genome-wide is at the locus overlapping the SNP (Fig 2c).

      c- Potentially cis-altered genes near the SNP include CHRNA2 and EPHX2 (see Sup. Fig. 3a). Their expression may not be detected in macrophage lineage. If this is the case please indicate in the text, otherwise please include the corresponding data in Sup. Fig. 3b to show the presence or absence of SNP-induced change.

      You are correct that CHRNA2 and EPHX2 are not expressed in our macrophages or microglia, and we have now explicitly stated this in the revised text.

      d- In general the Figures are not of very high quality and are difficult to read or understand without constantly going back and forth to the legends (which are mislabeled in some instances). To improve:

      . Please increase font size whenever possible.

      . Please improve Fig. 1d by indicating the position of the SNP, numbering the exons (an intermediate scale plot may be necessary and lines on bottom trace are hardly visible).

      . Please indicate the correct color code for T/T and C/C in Fig 3a and b, left panels, which currently doesn't match.

      . Please label the Venn's diagrams comparisons in Sup. Fig. 4b.

      . In the text and legends the Figure items are identified with letters in upper case, in the figures they are in lower case. Please be consistent.

      We have improved the resolution of the images in the pdf and Fig 1d has been revised to include the position of the SNP. The colour code for T/T and C/C is correct in fig 3a and 3b, but since the PCA plots are independently created, we would not always expect the position of the T/T and C/C alleles to be the same. The Venn diagrams in Sup Fig 4b have been updated, and the letters for the figure panels made consistently upper case throughout.

      e- In Fig. 2D and 2E, the Y axes should start at zero to avoid artificially increasing the visual differences. If there is a strong reason not to do so (I don't see any here), the Y axis should be clearly interrupted to avoid confusion.

      We have altered this accordingly.

      f- In the introduction the authors provide some background about previous work about the potential role of PTK2B/PYK2 in AD pathophysiology. The cited preclinical results suggest that PTK2B activity could have a deleterious effect (references in the manuscript). In contrast, some other reports (PMID: 29803828, 33718872) suggest a protective effect of PTK2B/PYK2. Because the evidence in the current manuscript suggests that the risk-associated SNP results in a decreased function of PTK2B/PYK2 (through decreased levels), at least in cells of the macrophage lineage, the authors could broaden their discussion to include these results.

      We have now discussed the conflicting evidence in the revised manuscript.

      Reviewer #2 (Significance (Required)):

      ADVANCE: Late onset Alzheimer's disease is a major medical issue. It has a complex genetic risk component with many associated loci identified in GWAS. Most of these have only a small individual impact on the risk. One of the SNPs associated with increased risk (rs28834970) is located in an intron of the PTK2B gene. Although various reports have investigated the role of the PTK2B gene product, the tyrosine kinase PYK2, in several AD models, the possible link with rs28834970, is unclear.

      An important point is to determine whether TàC SNP corresponding to rs28834970 alters PTK2B expression and how it does so. An alternative hypothesis could be that the SNP has a strong linkage disequilibrium with an unidentified allele in human populations that could be responsible for AD risk. The current manuscript is a significant step forward in addressing that question. By generating a biallelic C/C SNP mutation in a human IPSC line the current study allows to eliminate such linked contribution.

      The strength of the manuscript is to show an effect on chromatin accessibility, CEBP binding and possibly PTK2B transcripts. It also provides interesting evidence of a broad effect of the C/C mutation on the transcriptome of macrophage lineage cells. In its current form the manuscript presents weaknesses that could be improved. These flaws include issues with the presentation discussed above and the uncomplete demonstration that it is the decrease in PTK2B expression that causes the macrophage/microglia phenotype. If these flaws were overcome the paper would represent a significant advance.

      AUDIENCE: The expected audience is specialized in AD with a possible broader range if all weaknesses are addressed.

      REVIEWER EXPERTISE: Basic science close to the field.

    1. Author response:

      The following is the authors’ response to the current reviews.

      We thank Reviewers for highlighting the strengths of our work along with suggestions for future directions.

      We agree with the Reviewers that RPS26 depletion may impact not only RAN translation initiation and codon selection (as showed in the experiments in Figure 4G), but also other mechanisms, such as speed of PIC scanning, as we stated in the discussion. Although, we did provide the data showing that mRNA of exogenous FMR1-GFP does not change upon RPS26 depletion (Figure 3B&C), hence observed effect most likely stems from translation regulation. In addition, an experiment with ASO-ACG treatment (Figure 4G) suggests that near cognate start codon selection or speed of PIC scanning may be a part of the regulation of RAN translation sensitive to RPS26 depletion. In addition, our latest unpublished results (Niewiadomska D. et al., in revision), indicate that FMRpolyG in fusion with GFP is fairly stable, in particular, while derived from long repeats (>90xCGG), suggesting that the protein stability is not at play in RPS26-dependent regulation.

      We would like to stress that in order to avoid bias in result interpretation and to mimic the natural situation, the majority of experiments concerning levels of FMRpolyG were performed in cell models with stable expression of ACG-initiated FMRpolyG. Currently, we do not possess a cell model with stable expression of AUG-initiated FMRpolyG, and the experiments based on transient transfection system would not necessarily be comparable to the results obtained in stable expression system. However, we believe that the experiment presented in Figure 2B serves as a good control for overall translation level upon RPS26 depletion indicating that RPS26 insufficiency does not affect global translation and the observed regulation is specific to some mRNAs including the one encoding FMRpolyG frame. We also show that the level of ca. 80% of identified canonical proteins, including FMRP, did not change upon RPS26 silencing (SILAC-MS, Figure 4A). Indeed, we did not explore the ribosome composition upon RPS26 and TSR2 depletion, although, most likely the pool of functional ribosomes in the cell is sufficient enough to support the basal translation level (SUnSET assays, Figure 2B & 5C). However, we cannot exclude possibility that for some mRNAs, including one encoding for FMRpolyG, the observed effect can be partially caused by lowering the number of fully active ribosomes, especially in experiments with transient transfection experiments where transgene expression is hundreds times higher than for average native mRNA.

      Finally, we agree with the Reviewer that in vitro translation assay would provide the evidence of direct effect of RPS26 on FMRpolyG level, however, we did not manage to overcome technical difficulties in obtaining cellular lysate devoid of RPS26 from vendor companies.


      The following is the authors’ response to the original reviews.

      General Comments

      We thank Reviewers for the critical comments and experimental suggestions. We considered most of the advices in the revised version of the manuscript, which allowed for a more balanced interpretation of the results presented, and further supported major statement of the manuscript that insufficiency of the RPS26 and RPS25 plays a role in modulating the efficiency of noncanonical RAN translation from FMR1 mRNA, which results in the production of toxic polyglycine protein (FMRpolyG). Firstly, performing new experiments, we showed that silencing of the RPS26 and its chaperone protein TSR2, which regulates loading/exchange of RPS26 in maturing small ribosome subunit, did not elicit global translation inhibition. Secondly, we demonstrated that in contrary to RPS26 and RPS25 depletion, silencing the RPS6 protein, a core component of 40S subunit, did not affect FMRpolyG production, further supporting the specific effect of RPS26 and RPS25 on RAN translation regulation of mutant FMR1 mRNA. We also observed that depletion of RPS26, RPS25 and RPS6 had significant negative effect on cells proliferation which is in line with previously published results indicating that insufficiencies of ribosomal proteins negatively affect cell growth. Moreover, we showed that FMRpolyG production is significantly affected by RPS26 depletion while initiated at ACG, but not other near cognate start codons. Importantly, translation of FMRP initiated at canonical AUG codon of the same mRNA upstream the CGGexp was not affected by RPS26 silencing, similarly to vast majority of the human proteome. This implies that RAN translation of FMR1 mRNA mediated by RPS26 insufficiency is likely to be dependent on start codon selection/fidelity. In essence, we provide a series of evidences indicating that cellular amount of 40S ribosomal proteins RPS26 and RPS25 is important factor of CGGrelated RAN translation regulation. Finally, we also decided to tone down our claims. Now, we state that the RPS26/25/TSR2 insufficiency or depletion, affects RAN translation, rather than composition of 40S ribosomal subunit per se influences RAN translation. We have addressed all specific concerns below and made changes to the new version of manuscript.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, Tutak et al use a combination of pulldowns, analyzed by mass spectrometry, reporter assays, and fluorescence experiments to decipher the mechanism of protein translation in fragile X-related diseases. The topic is interesting and important.

      Although a role for Rps26-deficient ribosomes in toxic protein translation is plausible based on already available data, the authors' data are not carefully controlled and thus do not support the conclusions of the paper.

      We sincerely appreciate your rigorous, insightful, and constructive feedback throughout the revision process. We believe your guidance has been instrumental in significantly enhancing the quality of our research. Below, we have addressed your comments pointby-point.

      Strengths:

      The topic is interesting and important.

      Weaknesses:

      In particular, there is very little data to support the notion that Rps26-deficient ribosomes are even produced under the circumstances. And no data that indicate that they are involved in the RAN translation. Essential controls (for ribosome numbers) are lacking, no information is presented on the viability of the cells (Rps26 is an essential protein), and the differences in protein levels could well arise from block in protein synthesis, and cell division coupled to differential stability of the proteins.

      We agree that data presented in the first version of the manuscript did not directly address the following processes: ribosome content, global translation rate and cell viability upon RPS26 depletion. Therefore we addressed some of the issues in the revised version of the manuscript. In particular, we showed that RPS26 and TSR2 knock down did not inhibit global translation (new Figure 2B & 4C), hence we concluded that the changes of FMRpolyG level did not arise from general translational shut down. On the other hand, RPS26, RPS25 and RPS6 depletion negatively affected cells proliferation (new Figure 2A,5D,6C), which is in line with a number of previously published researches (e.g. Cheng et al, 2019; Havkin-Solomon et al, 2023). However, the rate of proliferation abnormalities is limited. We agree that observed effects on RAN translation from mutant FMR1 mRNA may stem from the combination of altered protein synthesis, conditions of the cells but also cis-acting factors of mRNA sequence/structure. In new experiments we showed that single nucleotide substitution of ACG by other near cognate start codons change sensitivity of RAN translation to insufficiency of RPS26 (new Figure 4F). Also the inhibitory effect of antisense oligonucleotide binding to the region of 5’UTR containing ACG initiation codon (ASO_ACG) is different in cells differing in amount of RPS26 (new Figure 4G).

      We also agree that our data only partially supports the role of RPS26-defficient ribosomes in RAN translation. Therefore, we have toned down our claims. Now, we state that the RPS26/25/TSR2 insufficiency or depletion affects RAN translation. We also changed the title of the manuscript to: “Insufficiency of 40S ribosomal proteins, RPS26 and RPS25, negatively affects biosynthesis of polyglycine-containing proteins in fragile-X associated conditions” (Previously it was: “Ribosomal composition affects the noncanonical translation and toxicity of polyglycine-containing proteins in fragile X-associated conditions”.

      Specific points:

      (1) Analysis of the mass spec data in Supplemental Table S3 indicates that for many of the proteins that are differentially enriched in one sample, a single peptide is identified. So the difference is between 1 peptide and 0. I don't understand how one can do a statistical analysis on that, or how it would give out anything of significance. I certainly do not think it is significant. This is exacerbated by the fact that the contaminants in the assay (keratins) are many, many-fold more abundant, and so are proteins that are known to be mitochondrial or nuclear, and therefore likely not actual targets (e.g. MCCC1, PC, NPM1; this includes many proteins "of significance" in Table S1, including Rrp1B, NAF1, Top1, TCEPB, DHX16, etc...).

      The data in Table S6/Figure 3A suffer from the same problem.

      I am not convinced that the mass spec data is reliable.

      We thank Reviewer for the comment concerning MS data; however, we believe that it may stem from misunderstanding of the data presented in Table S3 and S6. Both tables represent the output from MaxQuant analysis (so-called ProteinGroup) of MS .raw files, without any filtering. As stated in the Material&Methods, we applied default parameters suggested by MaxQuant developers to analyze MS data, these include identification of proteins based on at least 1 unique peptide, and thus some of the proteins with only 1 unique peptide are shown in Tables S1 and S3. Reviewer is also right that in this output table common contaminants, such as keratins are included. However, these identifications are denoted as “CON_”, and are further filtered out during statistical analysis in Perseus software. During the statistical analysis we first filtered out irrelevant protein groups identifications, such as contaminants, or only identified by site modifications.

      We have changed the names of Supplementary Table files, giving more detailed description. We hope this will help to avoid misunderstanding for broader public. Secondly, when comparing the data presented in Table S3 and volcano plot presented in Figure 1B, one can notice that indeed the majority of identified proteins are not statistically significant (grey points), thus not selected for further stratification. Lack of significance of these proteins may be partially due to poor MS identification, however, they are not included in the following parts of the manuscript. Further, we selected only eight proteins (out of over 150) for stratification by orthogonal techniques, thus we argue that this step validates the biological relevance of chosen candidate RAN-translation modifiers. One should also keep in mind that pull down samples analyzed by MS often yield lower intensity and identification rates, when comparing to whole cell analysis, as a result of lower protein input or stringent washes used during sample preparation.

      Regarding the data presented in Table S6 (SILAC data), we argue that these data are of very good quality. More than 2,000 proteins were identified in a 125min gradient, with over 80% of proteins that were identified with at least 2 unique peptides. Each of three biological replicates was analyzed three times (technical replicates), giving total of 9 high resolution MS runs. Together, we strongly believe that this data is of high confidence.

      (2) The mass-spec data however claims to identify Rps26 as a factor binding the toxic RNA specifically. The rest of the paper seeks to develop a story of how Rps26-deficient ribosomes play a role in the translation of this RNA. I do not consider that this makes sense.

      Indeed, we identified RPS26 as a protein that co-precipitated with FMR1 containing expanded CGG repeats (Supplementary Figure 1G) and found that depletion of RPS26 hindered RAN translation of FMRpolyG, suggesting that RPS26 positively affects RAN translation. However, we did not state that RPS26 directly interacts with toxic RNA. In order to confirm the specificity of RAN translation regulation by RPS26 insufficiency, we tested whether depletion of other 40S ribosomal protein, RPS6, affects FMRpolyG synthesis. Our experiments showed that there was no any significant effect on RAN translation efficiency post RPS6 silencing (new Figure 5C). Importantly, we showed that RPS26 depletion did not inhibit global translation (new Figure 2B). In addition, mutagenesis of near-cognate start codon (new Figure 4F) and ASO_ACG treatment (new Figure 4G) provided the evidences that modulation of FMRpolyG biosynthesis by RPS26 level may depend on start codon selection. In essence, our data suggest that RPS26 depletion specifically affects synthesis of FMRpolyG, but not FMRP derived from the same FMR1 mRNA with CGGexp. However, we do not claim that the observed effect is the consequence of a direct interaction between RPS26 and 5’UTR of FMR1 mRNA. Downregulation of FMRpolyG biosynthesis could be an outcome of the alteration of ribosomal assembly, decrease of efficiency and fidelity of PIC scanning/initiation or impeded elongation or a combination of all these processes. In the manuscript we presented the results of experiments which tested many of these possibilities.

      (3) Rps26 is an essential gene, I am sure the same is true for DHX15. What happens to cell viability? Protein synthesis? The yeast experiments were carefully carried out under experiments where Rps26 was reduced, not fully depleted to give small growth defects.

      We agree with the Reviewer that RPS26 and DHX15 are essential proteins, similarly to all RNA binding proteins, and caution should be taken during experimental design. To address this, we titrated different concentrations of siRPS26, and found that administration of 5 nM siRPS26, which just partially silenced RPS26, decreased FMRpolyG by around 50% (new Figure 1D). This impact was even greater with 15 nM siRPS26, as we observed around 80% decrease of FMRpolyG.

      Havkin-Solomon et al. (2023), showed that proliferation rate is decreased in cells with mutated C-terminus of RPS26, which is required for contacting mRNA. In accordance with this study, we showed that cells with knocked down RPS26 proliferate less efficiently (new Figure 2A), but depletion of RPS26 did not impact the global translation (new Figure 2B). In addition, our SILAC-MS data indicates that ~80% of proteins with determined expression level were not affected by RPS26 insufficiency, and ~20% of the proteins turned out to be sensitive to RPS26 decrease. Although, these data do not take into account the protein stability.

      (4) Knockdown efficiency for all tested genes must be shown to evaluate knockdown efficiency.

      The current version of the manuscript contains representative western blots with validation of knock-down efficiency (for example in Figure 3B, C, E, Figure 6A) and we included knock-down validations where applicable (Figures 1D, 2B, 4G and 5C).

      (5) The data in Figure 1E have just one mock control, but two cell types (control si and Rps26 depletion).

      Mock control corresponds to the cells treated with lipofectamine reagent and was included in the study to determine the “background” signal from cells treated with delivery agent and reagents used to measure the apoptosis process. These cells were neither expressing FMRpolyG, nor siRNAs. Luminescence signals were normalized to the values obtained from mock control. We added more details describing this assay in the Figure 1 legend.

      (6) The authors' data indicate that the effects are not specific to Rps26 but indeed also observed upon Rps25 knockdown. This suggests strongly that the effects are from reduced ribosome content or blocked protein synthesis. Additional controls should deplete a core RP to ascertain this conclusion.

      We agree that observed effects may stem from reduced ribosome content, however, we argue that this is the only possibility and explanation. Previously, it was shown that RPS25 regulates G4C2-related RAN translation, but knock out of RPS25 does not affect global translation (Yamada S, 2019, Nat. Neuroscience). Similarly, we showed that KD of RPS26 or TSR2 did not reduce significantly global translation rate (SUnSET assay; new Figure 2B and 5C, respectively).

      Moreover, in a new version of manuscript we included a control experiment, where we silenced core ribosomal protein (RPS6) and found that RPS6 depletion did not affect RAN translation from mutant FMR1 mRNA (new Figure 5C), thus strengthening our conclusion about specific RAN translation regulation by the level of RPS26 and RPS25.

      Finally, our observation aligns well with current knowledge about how deficiency of different ribosomal proteins alters translation of some classes of mRNAs (Luan Y, 2022, Nucleic Acids Res; Cheng Z, 2019, Mol Cell). It was shown that depletion of RPS26 affects translation rate of different mRNAs compared to depletion of other proteins of small ribosomal subunit.

      (7) Supplemental Figure S3 demonstrates that the depletion of S26 does not affect the selection of the start codon context. Any other claim must be deleted. All the 5'-UTR logos are essentially identical, indicating that "picking" happens by abundance (background).

      Supplementary Figure 3D represents results indicating that the mutation in -4 position (from G to A) did not affect the RAN translation regardless of RPS26 presence or depletion. However, this result does not imply that RPS26 does not affect the selection of start codon of sequence- or RNA structure-context. We verified this particular -4 position, as it was suggested previously as important RPS26-sensitive site in yeasts (Ferretti M, 2017, Nat Struct Mol Biol). We agree with Reviewer that all 5’UTR logos presented in our paper did not show statistical significance for neither tested position for human mRNAs. On the contrary, we observed that regulation sensitive to RPS26 level depends on the selection of start codon of RAN translation, in particular ACG initiation (new Figure 4F&G). RPS26 depletion affected ACG-initiated but not GTG- or CTG-initiated RAN translation.

      In the previous version of the manuscript, we wrote that we did not identify any specific motifs or enrichment within analyzed transcripts in comparison to the background. On the other hand, we found that the GC-content among analyzed transcripts is higher within 5’UTRs and in close proximity to ATG in coding sequences (Figure 4D), what suggests the importance of RNA stable structures in this region. In addition, we showed that mRNAs encoding proteins responding to RPS26 depletion have shorter than average 5’UTRs (new Figure 4E).

      (8) Mechanism is lacking entirely. There are many ways in which ribosomes could have mRNA-specific effects. The authors tried to find an effect from the Kozak sequence, unsuccessfully (however, they also did not do the experiment correctly, as they failed to recognize that the Kozak sequence differs between yeast, where it is A-rich, and mammalian cells, where it is GGCGCC). Collisions could be another mechanism.

      Indeed, collisions as well as other mechanisms such as skewed start codon fidelity may have an effect on efficiency of FMRpolyG biosynthesis. In the current version of the manuscript, we show that RPS26 amount-sensitive regulation seems to be start codonselection dependent (new Figure 4F&G).

      Reviewer #2 (Public Review):

      Summary:

      Translation of CGG repeats leads to the accumulation of poly G, which is associated with neurological disorders. This is a valuable paper in which the authors sought out proteins that modulate RAN translation. They determined which proteins in Hela cells bound to CGG repeats and affected levels of polyG encoded in the 5'UTR of the FMR1 mRNA. They then showed that siRNA depletion of ribosomal protein RPS26 results in less production of FMR1polyG than in control. There are data supporting the claim that RPS26 depletion modulates RAN translation in this RNA, although for some results, the Western results are not strong. The data to support increased aggregation by polyG expression upon S26 KD are incomplete.

      We thank the Reviewer for critical comments and suggestions. We sincerely appreciate your rigorous, insightful, and constructive feedback throughout the revision process.

      Below each specific point, we addressed the mentioned issues.

      Strengths:

      The authors have proteomics data that show the enrichment of a set of proteins on FMR1 RNA but not a related RNA.

      We thank Reviewer for appreciation of provided MS-screening results, which identified proteins enriched on FMR1 RNA with expanded CGG repeats.

      Weaknesses:

      - It is insinuated that RPS26 binds the RNA to enhance CGG-containing protein expression. However, RPS26 reduction was also shown previously to affect ribosome levels, and reduced ribosome levels can result in ribosomes translating very different RNA pools.

      In previous version of the manuscript we did not state that RPS26 binds directly to RNA with expanded CGG repeats and we did not show the experiment indicating direct interaction between studied RNA and RPS26. What we showed is that RPS26 was enriched on FMR1 RNA MS samples, however, we did not verify whether it is direct or indirect interaction. We also tried to test hypothesis that lack of RPS26 in PIC complex may affect efficiency of RAN translation initiation via specific, previously described in yeast Kozak context (Ferretti M, 2017, Nat Struct Mol Biol). As we described this hypothesis was negatively validated. However, we showed that other features of 5’UTR sequences (e.g. higher GC-content or shorter leader sequence) are potentially important for translation efficiency in cells with depleted RPS26.

      Indeed, RPS26 is involved in 40S maturation steps (Plassart L, 2021, eLife) and its insufficiency or mutations or blocking its inclusion to 40S ribosome may result in incomplete 40S maturation, which subsequently might negatively affect translation per se. However, we did not observe global translation inhibition after RPS26 depletion or depletion of TSR2, the chaperon involved in incorporation/exchange RPS26 to small ribosomal subunit (new Figure 2B and 5C). In addition, our SILAC-MS data indicates that majority of studied proteins (including FMRP, the main product of FMR1 gene) were not affected by RPS26 depletion which can be carefully extrapolated to global translation. In revised manuscript we also showed that relatively low silencing of RPS26 also decreased FMRpolyG production in model cells (new Figure 1D).

      We agree that reduced ribosome levels can result in different efficiency of translation of different RNA pools. We enhance this statement in revised manuscript. However, we also showed that the same mRNA containing different near cognate start codons (single/two nucleotide substitution) specific to RAN translation, or targeting this codon with antisense oligonucleotides resulted in altered sensitivity of FMR1 mRNA translation to RPS26 depletion (new Figure 4F).

      - A significant claim is that RPS26 KD alleviates the effects of FMRpolyG expression, but those data aren't presented well.

      We thank the Reviewer for this comment. In the new version of the manuscript, we have added new microscopic images and improved the explanation of Figure 1E. We have also completed the interpretation of Figure 1F in the main text, figure image as well as figure legend, and we hope that these changes will ameliorate understanding of our data.

      Recommendations For The Authors:

      - A significant claim is that RPS26 KD alleviates the effects of FMR polyG expression, but those data aren't presented well:

      Figure 1D (supporting data in S2) and 2D - the authors need to show representative images of a control that has aggregation and indicate aggregates being counted on an image. The legend states that there are no aggregates, but the quantification of aggregates/nucleus is ~1, suggesting there are at least 1 per cell. It is preferred to show at least a representative of what is quantified in the main figure instead of a bar graph.

      The representative images of control and siRPS26-treated cells are now shown in revised version of Figure 1E. Additionally, we completed the Figure legend concerning this part, as well as extended description of the experiment in Materials&Methods section.

      Figure 1E - it is unclear what luminescence signal is being measured. Is this a dye for an apoptotic marker? More information is needed in the legend.

      This information was added to the legend of modified Figure 1F (previously 1E) as suggested.

      - Some of the Western blots are not very convincing. Better evidence for the changes in bar graphs would improve how convincing the data are:

      Fig 2B. The western for FMR95G in the first model is not very convincing. The difference by eye for the second siRNA seems to give a larger effect than the first for 95G construct but they appear almost the same on the graph. More supporting information for the quantification is needed.

      We provided better explanation for WB quantification in M&M section in the manuscript. Alos, we provided additional blot demonstrating independent biological replicate of the mentioned experiment in supplementary materials (Supplementary Figure S2E).

      Figure 4A, the blots for RPS26 and FMR95G are not convincing. They are quite smeary compared to all of the others shown for these proteins in other figures. Could a different replicate be shown?

      We provided additional blot demonstrating the effect on transiently expressed FMRpolyG affected by depletion of TSR2 in COS7 cell line (Supplementary Figure S4A).

      Figure 5A and 5B blots are not ideal. Could a different replicate be shown? Or show multiple replicates in the supplemental figure?

      We provided additional blots from the same experiment, although data is not statistically significant, most likely due to low quality of normalization factor, which is Vinculin (Supplementary Figure S5A). Nevertheless, the level of FMRpolyG is decreased by ~70% after RPS25 silencing in SH-SY5Y cells.

      Figure 2C. Please use the same y axes for all four Westerns in B and C. One would like to compare 95 and 15 repeats, but it is difficult when the y axes are different.

      Thank you for this comment. The y axis was adjusted as suggested by the Reviewer.

      Figure 3D-The text suggests a significant difference between positive and negative responders that is not clear in the figure.

      In the main body of the manuscript we state that: “We did not observe any significant differences in the frequency of individual nucleotide positions in the 20-nucleotide vicinity of the start codon relative to the expected distribution in the BG”, which is in line with the graph showed in Figure 4D (previously 3D).

      Reviewer #3 (Public Review):

      Tutak et al provide interesting data showing that RPS26 and relevant proteins such as TSR2 and RPS25 affect RAN translation from CGG repeat RNA in fragile X-associated conditions. They identified RPS26 as a potential regulator of RAN translation by RNAtagging system and mass spectrometry-based screening for proteins binding to CGG repeat RNA and confirmed its regulatory effects on RAN translation by siRNA-based knockdown experiments in multiple cellular disease models and patient-derived fibroblasts. Quantitative mass spectrometry analysis found that the expressions of some ribosomal proteins are sensitive to RPS26 depletion while approximately 80% of proteins including FMRP were not influenced. Since the roles of ribosomal proteins in RAN translation regulation have not been fully examined, this study provides novel insights into this research field. However, some data presented in this manuscript are limited and preliminary, and their conclusions are not fully supported.

      (1) While the authors emphasized the importance of ribosomal composition for RAN translation regulation in the title and the article body, the association between RAN translation and ribosomal composition is apparently not evaluated in this work. They found that specific ribosomal proteins (RPS26 and RPS25) can have regulatory effects on RAN translation (Figures 1C, 2B, 2C, 2E, 4A, 5A, and 5B), and that the expression levels of some ribosomal proteins can be changed by RPS26 knockdown (Figure 3B, however, the change of the ribosome compositions involved in the actual translation has not been elucidated). Therefore, their conclusive statement, that is, "ribosome composition affects RAN translation" is not fully supported by the presented data and is misleading.

      We thank the Reviewer for critical comments and suggestions. We agree that the initial title and some statements in the text were misleading and the presented data did not fully support the aforementioned statement regarding ribosomal composition affecting FMRpolyG synthesis. Therefore, in the revised version of the manuscript we included a control experiment indicating that depletion of another core 40S ribosomal protein (RPS6) did not impact the FMRpolyG synthesis (new Figure 5C), which supports our hypothesis that RPS26 and RPS25 are specific CGG-related RAN translation modifiers. To precisely deliver a main message of our work, we changed the title that will indicate the specific effect of RPS26 and RPS25 insufficiency on RAN translation of FMRpolyG. Proposed title: “Insufficiency of 40S ribosomal proteins, RPS26 and RPS25 negatively affects biosynthesis of polyglycine-containing proteins in fragile-X associated conditions”. We also changed all statements regarding “ribosomal composition” in main text of the new version of manuscript.

      (2) The study provides insufficient data on the mechanisms of how RPS26 regulates RAN translation. Although authors speculate that RPS26 may affect initiation codon fidelity and regulate RAN translation in a CGG repeat sequence-independent manner (Page 9 and Page 11), what they really have shown is just identification of this protein by the screening for proteins binding to CGG repeat RNA (Figure 1A, 1B), and effects of this protein on CGG repeat-RAN translation. It is essential to clarify whether the regulatory effect of RPS26 on RAN translation is dependent on CGG repeat sequence or near-cognate initiation codons like ACG and GUG in the 5' upstream sequence of the repeat. It would be better to validate the effects of RPS26 on translation from control constructs, such as one composed of the 5' upstream sequence of FMR1 with no CGG repeat, and one with an ATG substitution in the 5' upstream sequence of FMR1 instead of near-cognate initiation codons.

      We agree that the data presented in the manuscript implies that insufficiency of RPS26 plays a pivotal role in the regulation of CGG-related RAN translation and in the revised version of the manuscript we included a series of experiments indicating that ACG codon selection seems to be an important part of RPS26 level-dependent regulation of polyglycine production (new Figure 4F&G; see point 3 below for more details). Importantly, in the luciferase assay showed on Figure 4F we used the AUG-initiated firefly luciferase reporter as normalization control.

      Moreover, to verify if FMRpolyG response to RPS26 deficiency depends on the type of reporter used, we repeated many experiments using FMRpolyG fused with different tags. The luciferase-based assays were in line with experiments conducted on constructs with GFP tag (new Figure 1D), thus strengthening our previous data. Moreover, in the series of experiments, we show that FMRP synthesis which is initiated from ATG codon located in FMR1 exon 1, was not affected by RPS26 depletion (Figure 3E & 4C), even though its translation occurs on the same mRNA as FMRpolyG. This indicates a specific RPS26 regulation of polyglycine frame initiated from ACG near cognate codon.

      (3) The regulatory effects of RPS26 and other molecules on RAN translation have all been investigated as effects on the expression levels of FMRpolyG-GFP proteins in cellular models expressing CGG repeat sequences Figures 1C, 2B, 2C, 2E, 4A, 5A, and 5B). In these cellular experiments, there are multiple confounding factors affecting the expression levels of FMRpolyG-GFP proteins other than RAN translation, including template RNA expression, template RNA distribution, and FMRpolyG-GFP protein degradation. Although authors evaluated the effect on the expression levels of template CGG repeat RNA, it would be better to confirm the effect of these regulators on RAN translation by other experiments such as in vitro translation assay that can directly evaluate RAN translation.

      We agree that there are multiple factors affecting final levels of FMRpolyG-GFP proteins including aforementioned processes. We evaluated the level of FMR1 mRNA, which turned out not to be decreased upon RPS26 depletion (Figure 3B&C), therefore, we assumed that what we observed, was the regulation on translation level, especially that RPS26 is a ribosomal protein contacting mRNA in E-site. We believe that direct assays such as in vitro translation may be beneficial, however, depletion of RPS26 from cellular lysate provided by the vendor seems technically challenging, if not completely impossible. Instead, we focused on sequence/structure specific regulation of RAN translation with the emphasis on start-codon initiation selection. It resulted in generating the valuable results pointing out the RPS26 role in start codon fidelity (Figure 4F&G). These new results showed that translation from mRNAs differing just in single or two nucleotide substitution in near cognate start codon (ACG to GUG or ACG to CUG), although results in exactly the same protein, is differently sensitive to RPS26 silencing (new Figure 4F). Similar differences were observed for translation efficiency from the same mRNA targeted or not with antisense oligonucleotide complementary to the region of RAN translation initiation codon (new Figure 4G). These results also suggest that stability of FMRpolyG is not affected in cells with decreased level of RPS26.

      (4) While the authors state that RPS26 modulated the FMRpolyG-mediated toxicity, they presented limited data on apoptotic markers, not cellular viability (Figure 1E), not fully supporting this conclusion. Since previous work showed that FMRpolyG protein reduces cellular viability (Hoem G, 2019,Front Genet), additional evaluations for cellular viability would strengthen this conclusion.

      We thank the Reviewer for this suggestion. We addressed the apoptotic process in order to determine the effect of RPS26 depletion on RAN translation related toxicity (Figure 1F). In revised version of the manuscript, we also added the evaluation on how cells proliferation was affected by RPS26, RPS25, RPS6 and TSR2 depletion. Our data indicate that TSR2 silencing slightly impacted the cellular fitness (new Figure 5D), whereas insufficiencies of RPS26, RPS25 and RPS6 had a much stronger negative effect on proliferation (new Figure 2A, 5D, 6C), which is in line with previous data (Cheng Z 2019, Mol Cell; Luan Y, 2022, Nucleic Acids Res). The difference in proliferation rate after treatment with siRPS26 makes proper interpretation of cellular viability assessment very difficult.

      Recommendations For The Authors:

      (1) It would be nice to validate the effects of overexpression of RPS26 and other regulators on RAN translation, not limited to knockdown experiments, to support the conclusion.

      We did not performed such experiments because we believed that RPS26 overexpression may have no or marginal effect on translation or RAN translation. It is likely impossible to efficiently incorporate overexpressed RPS26 into 40S subunits, because the concentration of all ribosomal proteins in the cells is very high.

      (2) It would be better to explain how authors selected 8 proteins for siRNA-based validation (Figure 1C, 1D, S1D) from 32 proteins enriched in CGG repeat RNA in the first screening.

      We selected those candidates based on their functions connected to translation, structured RNA unwinding or mRNA processing. For example, we tested few RNA helicases because of their known function in RAN translation regulation described by other researchers. This explanation was added to the revised version of the manuscript.

      (3) Original image data showing nuclear FMRpolyG-GFP aggregates should be presented in Figure 1D.

      The representative images of control and siRPS26-treated cells are now shown in modified version of Figure 1E and described with more details in the legend.

      (4) Image data in Figure 2A and 2D have poor signal/noise ratio and the resolution should be improved. In addition, aggregates should be clearly indicated in Figure 2D in an appropriate manner.

      The stable S-FMR95xG cellular model is characterized by very low expression of RANtranslated FMR95xG, therefore, it is challenging to obtain microscopic images of better quality with higher GFP signal. In the L-99xCGG model expression of transgene is higher. Therefore, we provided new image in the new version of Figure 3D (former 2D). Moreover, we showed aggregates on the image obtained using confocal microscopy (new Supplementary Figure 2D).

      (5) The detailed information on patient-derived fibroblast (age and sex of the patient, the number of CGG repeats, etc.) in Figure 2F needed to be presented.

      This information was added to the figure legend (Figure 3F; previously 2F) and in the Material and Methods section as suggested.

      (6) It would be better to normalize RNA expression levels of FMR1 and FMR1-GFP by the housekeeping gene in Figure S2C, like other RT-qPCR experimental data such as Figure 2B.

      Normalization of FMR1-GFP to GAPDH is now shown in modified version of Figure S2C (right graph) as requested by the Reviewer.

      (7) It would be better to add information on molecular weight on all Western blotting data.

      (8) Marks corresponding to molecular weight ladder were added to all images.

      Full blots, including protein ladders were deposited in Zenodo repository, under doi: 10.5281/zenodo.13860370

      References

      Cheng Z, Mugler CF, Keskin A, Hodapp S, Chan LYL, Weis K, Mertins P, Regev A, Jovanovic M & Brar GA (2019) Small and Large Ribosomal Subunit Deficiencies Lead to Distinct Gene Expression Signatures that Reflect Cellular Growth Rate. Mol Cell 73: 36-47.e10

      Havkin-Solomon T, Fraticelli D, Bahat A, Hayat D, Reuven N, Shaul Y & Dikstein R (2023) Translation regulation of specific mRNAs by RPS26 C-terminal RNA-binding tail integrates energy metabolism and AMPK-mTOR signaling. Nucleic Acids Res 51: 4415–4428

      Hoem,G., Larsen,K.B., Øvervatn,A., Brech,A., Lamark,T., Sjøttem,E. and Johansen,T. (2019) The FMRpolyGlycine protein mediates aggregate formation and toxicity independent of the CGG mRNA hairpin in a cellular model for FXTAS. Front. Genet., 10, 1–18.

      Luan Y, Tang N, Yang J, Liu S, Cheng C, Wang Y, Chen C, Guo YN, Wang H, Zhao W, et al (2022) Deficiency of ribosomal proteins reshapes the transcriptional and translational landscape in human cells. Nucleic Acids Res 50: 6601–6617

      Plassart L, Shayan R, Montellese C, Rinaldi D, Larburu N, Pichereaux C, Froment C, Lebaron S, O’donohue MF, Kutay U, et al (2021) The final step of 40s ribosomal subunit maturation is controlled by a dual key lock. Elife 10

    1. Author response:

      The following is the authors’ response to the original reviews.

      It would be great if the authors could add clarification about the NMDS analyses and the associated results (Fig. 1, Table 1 and Tables S2-4). The overall aim of these analyses was to see how plot characteristics (e.g. canopy cover) and composition of one taxonomic group were related to the composition of another taxonomic group. The authors quantified species composition by two axes from NMDS. (1) This analysis may yield an interpretation problem: if we only find one of the axes, but not the other, was significantly related to one variable, it would be difficult to determine whether that specific variable is important to the species composition because the composition is co-determined by two axes. (2) It is unclear how the authors did the correlation analyses for Tables S2-4. If correlation coefficients were presented in these tables, then these coefficients should be the same or very similar if we switch the positions of y vs. x. That is, the correlation between host vs. parasite phylogenetic composition would be very close to the correlation between parasite vs. phylogenetic composition, but not as the author found that these two relationships were quite different, leading to the interpretation of bottom-up or top-down processes. It is also unclear which correlation coefficient was significant or not because only one P value was provided per row in these tables. (3) In addition to the issues of multiple axes (point 1), NMDS axes simply define the relative positions of the objects in multi-dimensional space, but not the actual dissimilarities. Other methods, such as generalized dissimilarity modeling, redundancy analysis and MANOVA, can be better alternatives.

      Thank you for the thorough and constructive review. We have taken the concerns and questions raised by the editors and reviewers into account and provided clarification about the NMDS analyses as well as additional analyses to confirm our results. First, we have now added a brief explanation in the manuscript regarding the interpretation of the two NMDS axes and how they relate to species composition. Specifically, we clarified that while NMDS defines the relative positions of objects in multi-dimensional space, the two axes together provide a more comprehensive representation of the community composition, which is not solely determined by either axis independently. Second, we acknowledge that alternative approaches could help further strengthen our conclusions. To address this, we incorporated Mantel tests and PERMANOVA (with ‘adonis2’) as additional validation methods. These analyses allowed us to summarize compositional patterns while testing our hypotheses within the framework of the plot characteristics and taxonomic relationships. We have added these analyses and their results in the manuscript to reinforce our findings.

      In methods: L478-481 “To strengthen the robustness of our findings based on NMDS, we further validated the results using Mantel test and PERMANOVA (with ‘adonis2’) for correlation between communities and relationships between communities and environmental variables.”

      L469-475 “NMDS was used to summarize the variation in species composition across plots. The two axes extracted from the NMDS represent gradients in community composition, where each axis reflects a subset of the compositional variation. These axes should not be interpreted in isolation, as the overall species composition is co-determined by their combined variation. For clarity, results were interpreted based on the relationships of variables with the compositional gradients captured by both axes together."

      In results: L172-177 “The PERMANOVA analysis also highlighted the important role of canopy cover for host and parasitoid community (Table S6-9). The Mantel test revealed a consistent pattern with the NMDS analysis, highlighting a pronounced relationship between the species composition of hosts and parasitoids (Table S10). However, the correlation between the phylogenetic composition of hosts and parasitoids was not significant.”

      In discussion: L257-261 “However, this significant pattern was observed only in the NMDS analysis and not in the Mantel test, suggesting that the non-random interactions between hosts and parasitoids could not be simply predicted by their community similarity and associations between the phylogenetic composition of hosts and parasitoids are more complex and require further investigation in the future.”

      -- One additional minor point: "site" would be better set as a fixed rather than random term in the linear mixed-effects models, because the site number (2) is too small to make a proper estimate of random component.

      Now we treated “site” as a fixed factor in our models, interacting with tree species richness/tree MPD and tree functional diversity to reflect the variation of spatial and tree composition between the two sites. We found the main results did not change, as both sites showed consistent patterns for effects of tree richness/MPD on network metrics, which is more pronounced in one site.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors analyzed how biotic and abiotic factors impact antagonistic host-parasitoid interaction systems in a large BEF experiment. They found the linkage between the tree community and host-parasitoid community from the perspective of the multi-dimensionality of biodiversity. Their results revealed that the structure of the tree community (habitat) and canopy cover influence host-parasitoid compositions and their interaction pattern. This interaction pattern is also determined by phylogenetic associations among species. This paper provides a nice framework for detecting the determinants of network topological structures.

      Strengths:

      This study was conducted using a five-year sampling in a well-designed BEF experiment. The effects of the multi-dimensional diversity of tree communities have been well explained in a forest ecosystem with an antagonistic host-parasitoid interaction.

      The network analysis has been well conducted. The combination of phylogenetic analysis and network analysis is uncommon among similar studies, especially for studies of trophic cascades. Still, this study has discussed the effect of phylogenetic features on interacting networks in depth.

      Weaknesses:

      (1) The authors should examine species and interaction completeness in this study to confirm that their sampling efforts are sufficient.

      (2) The authors only used Rao's Q to assess the functional diversity of tree communities. However, multiple metrics of functional diversity exist (e.g., functional evenness, functional dispersion, and functional divergence). It is better to check the results from other metrics and confirm whether these results further support the authors' results.

      (3) The authors did not elaborate on which extinction sequence was used in robustness analysis. The authors should consider interaction abundance in calculating robustness. In this case, the author may use another null model for binary networks to get random distributions.

      (4) The causal relationship between host and parasitoid communities is unclear. Normally, it is easy to understand that host community composition (low trophic level) could influence parasitoid community composition (high trophic level). I suggest using the 'correlation' between host and parasitoid communities unless there is strong evidence of causation.

      Thank you very much for your thoughtful and constructive review of our manuscript. We have carefully addressed your comments and made several revisions to improve the clarity and robustness of our work.1) We appreciate your suggestion regarding species and interaction completeness. To confirm that our sampling efforts were sufficient, we have now included a figure (Fig. S1) showing the species accumulation curve and the coverage of interactions in our study. This ensures that the data collected provide a comprehensive representation of the system. 2) Regarding the use of only Rao’s Q to assess functional diversity, we acknowledge that multiple metrics of functional diversity exist. However, due to the large number of predictors in our analysis, we decided to streamline our approach and focus on Rao’s Q as it provides a robust measure for our research objectives. We have discussed this decision in the revised manuscript and clarified that, while additional metrics could be informative, we believe Rao’s Q sufficiently captures the key aspects of functional diversity in our study. 3) We have elaborated on the robustness analysis and the null model used in our study. Specifically, we now clarified which extinction sequence (random extinction) was used in our manuscript, and explained interaction abundance was incorporated into the robustness calculations (networklevel function, weighted=TURE; see L506). 4) We have revised the text to clarify the relationship between host and parasitoid communities. As you correctly pointed out, while it is intuitive that host community composition influences parasitoid community composition, we have reframed our analysis to emphasize the correlation between the two communities rather than implying causation without strong evidence. We have revised the manuscript to reflect this distinction.

      Reviewer #2 (Public Review):

      Summary:

      In their manuscript, Multi-dimensionality of tree communities structure host-parasitoid networks and their phylogenetic composition, Wang et al. examine the effects of tree diversity and environmental variables on communities of reed-nesting insects and their parasitoids. Additionally, they look for the correlations in community composition and network properties of the two interacting insect guilds. They use a data set collected in a subtropical tree biodiversity experiment over five years of sampling. The authors find that the tree species, functional, and phylogenetic diversity as well as some of the environmental factors have varying impacts on both host and parasitoid communities. Additionally, the communities of the host and parasitoid showed correlations in their structures. Also, the network metrices of the host-parasitoid network showed patterns against environmental variables.

      Strengths:

      The main strength of the manuscript lies in the massive long-term data set collected on host-parasitoid interactions. The data provides interesting opportunities to advance our knowledge on the effects of environmental diversity (tree diversity) on the network and community structure of insect hosts and their parasitoids in a relatively poorly known system.

      Weaknesses:

      To me, there are no major issues regarding the manuscript, though sometimes I disagree with the interpretation of the results and some of the conclusions might be too far-fetched given the analyses and the results (namely the top-down control in the system). Additionally, the methods section (especially statistics) was lacking some details, but I would not consider it too concerning. Sometimes, the logic of the text could be improved to better support the studied hypotheses throughout the text. Also, the results section cannot be understood as a stand-alone without reading the methods first. The study design and the rationale of the analyses should be described somewhere in the intro or presented with the results.

      Thank you very much for your valuable comments and suggestions on our manuscript! We appreciate your feedback and have made revisions accordingly. Specifically, we have rephrased the interpretation of the results and conclusions to better align with the analyses and avoid overstatements, particularly concerning the top-down control in the system. In addition, we have expanded the methods section by providing more details, especially regarding the statistical approaches, to address the points you raised. To enhance the clarity of the manuscript, we have also ensured that the logic of the text better supports the hypotheses throughout. Please see our point-by-point responses below for additional clarifications.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Line 120: "... and large ecosystems susceptible to global change (add citation here)": Citation(s)?

      Now we provided the missed citations.

      Line 141: Add sampling completeness information.

      Now we provide a new figure about sampling completeness (Fig. S1) in the supplementary materials, showing the adequate sampling effort for our study.

      Line 151: use more metrics in the evaluation of functional diversity

      We used tree functional diversity Rao’s Q, which is an integrated and wildly used metric to represent functional dissimilarity of trees. As our study focus on multiple diversity indices of trees, it would be better to do not pay more attention to one type of diversity. Thank you for your suggestion!

      Line 164: host vulnerability. Although generality and vulnerability are commonly used in network analysis, it is better to link these metrics with the trophic level, like the 'host vulnerability' you used. Thus, you can use 'parasitoid generality' instead of 'generality'.

      Thanks for your suggestion. Now the metrics were labeled with the trophic levels in the full text.

      Line 169: two'.'

      Corrected.

      Line 173: 'parasitoid robustness' Or 'robustness of parasitoids'?

      Now changed it to ‘robustness of parasitoid’.

      Lines 173, 468: For the robustness estimations, maybe use null model for binary networks to get random distributions?

      Thanks for the suggestion. Actually, we have used Patefield null models to compare the randomized robustness and observed, helping to assess whether the robustness of the observed network is significantly different compared to expected by chance. All robustness indices across plots were significantly different from a random distribution, See results section L197-201.

      Line 184: modulating interacting communities of hosts and parasitoids.

      Changed accordingly.

      Line 186: determined host-parasitoid interaction patterns

      Changed accordingly.

      Line 191: Biodiversity loss in this study refers to low trophic levels.

      Now we clarified this point.

      Line 190: understand

      Changed accordingly.

      Lines 215-216: Reorganize these sentences

      Line 227: indirectly influenced by...

      Changed accordingly.

      Line 238: Be more specific. Which type of further study?

      Rephased it more specific.

      Lines 297-299: rewrite this sentence to make it more transparent.

      Now we rewrote the sentence accordingly.

      Line 302: Certain

      Changed accordingly.

      Line 453: effective

      Changed accordingly.

      Finally, the authors should check the text carefully to avoid grammatical errors.

      Thanks, now we have checked the full text to avoid grammatical errors.

      Reviewer #2 (Recommendations For The Authors):

      I feel that the authors have very interesting data and have a solid set of analyses. I do not have major issues regarding the manuscript, though sometimes I disagree with the interpretation of the results and some of the conclusions might be too far-fetched given the analyses and the results. Additionally, the methods section (especially statistics) was lacking some details, but I would not consider it too concerning at this point.

      I feel that the largest caveat of the manuscript remains in the representation of the rationale of the study. I felt the introduction could be more concise and be better focused to back up the study questions and hypotheses. Many times, the sentences were too vague and unspecific, and thus, it was difficult to understand what was meant to be said. The authors could mention something more about how community composition of hosts and parasitoids are expected to change with the studied experimental design regarding the metrices you mention in the introduction (stronger hypotheses). The results section cannot be understood as a stand-alone without reading the methods first. The study design and the rationale of the analyses must be described somewhere in the intro or results, if the journal/authors want to keep the methods last structure. Also, the results and discussion could be more focused around the hypotheses. Naturally, these things can be easily fixed.

      I also disagree with the interpretation of results finding top-down control in the system (it might well be there, but I do not think that the current methods and tests are suitable in finding it). First, the used methodology cannot distinguish parasitoids if the hosts are not there and the probability to detect parasitoid likely depends on the abundance of the host. Thus, the top-down regulation is difficult to prove (is it the parasitoids that have driven the host population down). Secondly, I would be hesitant to say anything about the top-down and bottom-up control in the systems as the data in the manuscript is pooled across five years while the top-down/bottom-up regulation in insect systems usually spans only one season/generation in time (much shorter than five years). Consequently, the analyses are comparing the communities of species that some of most likely do not co-exist (they were found in the same space but not during the same time). Luckily, the top-down/bottom-up effects could potentially be explored by using separately the time steps of the now pooled community data: e.g., does the population of the host decrease in t if the parasitoids are abundant in t-1? There are also other statistical tests to explore these patterns.

      In the manuscript "Phylogenetic composition" refers to Mean Pairwise Distance. I would use "phylogenetic diversity" instead throughout the text. Also, to my understanding, in trees both "phylogenetic composition" and "phylogenetic diversity" are used even though based on their descriptions, they are the same.

      Detailed comments:

      Punctuation needs to be checked and edited at some point (I think copy-pasting had left things in the wrong places). Please check that "-" instead of "-" is used in host-parasitoid.

      1-2 The title is not very matching with the content. "Multi-dimensionality" is not mentioned in the text. "phylogenetic composition" -> "phylogenetic diversity"

      We didn’t find the role of functional diversity of trees in host-parasitoid interactions, but we still have tree richness and phylogenetic diversity. I also disagree with that using phylogenetic diversity to replace phylogenetic composition, because diversity highlights higher or lower phylogenetic distance among communities, while the later highlights the phylogenetic dissimilarity across communities.

      53-57 This sentence is quite vague and because of it, difficult to follow. Consider rephrasing and avoiding unspecified terms such as "tree identity", "genetic diversity", and "overall community composition of higher trophic levels" (at least, I was not sure what taxa/level you meant with them).

      Rephased.

      L58-61 “Especially, we lack a comprehensive understanding of the ways that biotic factors, including plant richness, overall community phylogenetic and functional composition of consumers, and abiotic factors such as microclimate, determining host–parasitoid network structure and host–parasitoid community dynamics.”

      56 I would remove "interact" as no interactions were tested.

      Removed accordingly.

      59-60 This needs rephrasing. I feel "taxonomic and phylogenetic composition should be just "species composition". To better match, what was done: "taxonomic, phylogenetic, and network composition of both host and parasitoid communities" -> "species and phylogenetic diversity of both host and parasitoid communities and the composition their interaction networks"

      Changed accordingly.

      62 Remove "tree composition".

      Done.

      62 Replace "taxonomic" with "species". Throughout the text.

      Done.

      63-64 "Generally, top-down control was stronger than bottom-up control via phylogenetic association between hosts and parasitoids" I disagree, see my comments elsewhere.

      Now we rephased the sentence.

      L68-70 “Generally, phylogenetic associations between hosts and parasitoids reflect non-randomly structured interactions between phylogenetic trees of hosts and parasitoids.”

      68 "habitat structure and heterogeneity" This is too strong and general of a statement based on the results. You did not really measure habitat structure or heterogeneity.

      Now we rephased the statement to avoid strong and general description.

      L71-73 “Our study indicates that the composition of higher trophic levels and corresponding interaction networks are determined by plant diversity and canopy cover especially via trophic phylogenetic links in species-rich ecosystems.”

      69 Specify "phylogenetic links". Trophic links?

      Specified to “trophic phylogenetic links”.

      75-77 The sentence is a bit difficult to follow. Consider rephrasing.

      Now we rephased it.

      L79-82 “Changes in network structure of higher trophic levels usually coincide with variations in their diversity and community, which could be in turn affected by the changes in producers via trophic cascades”

      76 Be more specific about what you mean by "community of trophic levels".

      Specified to “community composition”.

      79 Remove "basal changes of", it only makes the sentence heavier.

      Done.

      81 What is "species codependence"?

      We sim to describe the species co-occurrence depending on their closely relationships. For clarity, now we changed to “species coexistence”

      82 What do you mean by "complex dynamics"?

      Rephased to “mechanisms on dynamics of networks”.

      83 onward: I would not focus so much on top-down/bottom-up as I feel that your current analyses cannot really say anything too strong about these causalities but are rather correlative.

      Thanks, we now removed the relevant contents from the discussion. However, we kept one sentence in the Introduction, because it should be highlighted to make reviewers aware of this (the other text on about this were removed).

      89 Remove "environmental".

      Done.

      90 Specify what you mean by "these forces".

      Done.

      98-99 I have difficulties following the logic here "potential specialization of their hosts may cascade up to impact the parasitoids' presence or absence". Consider rephrasing.

      Now we rephased it.

      L101-102 “…and their host fluctuations may cascade up to impact the parasitoids’ presence or absence.”

      100 Be more specific with "habitat-level changes".

      Specified to “community-level changes”

      100 I do not see why host-parasitoid systems would be ideal to study "species interactions". There are much simpler and easier systems available.

      Changed to “… one of ideal…”

      101-103 "influence of" on what?

      Now we rephased the sentence.

      L104-105 “Previous studies mainly focused on the influence of abiotic factors on host-parasitoid interactions”

      104 Be more specific in "the role of multiple components of plant diversity".

      Now we specified "the role of multiple components of plant diversity".

      L107-108 “…the role of multiple components of plant diversity (i.e. taxonomic, functional and phylogenetic diversity)…”

      106 "diversity associations" of what?

      “diversity associations between host and parasitoids”.

      108 Specify the "direct and indirect effects".

      Now we specified it to “…direct and indirect effects (i.e. one pathway and more pathways via other variables)…”

      110-113 A bit heavy sentence to follow. Consider rephrasing.

      Now we rephased the sentence to make it more readable.

      114 Give an example of "phylogenetic dependences".

      Done. Phylogenetic dependences (e.g. phylogenetic diversity)

      117 Move the "e.g. taxonomic, phylogenetic, functional" within brackets in 117 after "dimensions of biodiversity".

      Done.

      120 "(add citation here)" Yes please!

      Done.

      120-121 Specify "such relationships".

      Done. Specified to “multiple dimensions of biodiversity”

      128-130 This is difficult to follow. Please rephrase.

      Now we rephased the sentence.

      L135-137 “We aimed to discern the primary components of the diversity and composition of tree communities that affect higher trophic level interactions via quantifying the strength and complexity of associations between hosts and parasitoid.”

      131-132 Remove "phylogenetic and". It is redundant to phylogenetic diversity.

      Done.

      128 Tested robustness does not really capture "stability of associations".

      Yes, we agree. Now we rephased the sentence and exclude the “stability” description.

      133 Specify "phylogenetic processes".

      Now we specified “phylogenetic processes”.

      L140-141 “…especially via phylogenetic processes (e.g. lineages of trophic levels diverge and evolve over time)…”

      141 I would like to have more details on the data set somewhere in the results. How many individuals and species were found in each plot (on average)? Was there a lot of temporal variation (e.g. between the seasons)? On how many sites were the insect species found?

      Thanks for your suggestion. Now we provide more details on the data set in the results (L153-156), including mean values of individuals and species in each plot. However, the temporal variation should be studied for another relative independent topic, as our study focus on the general patter of interactions between hosts and parasitoids. Therefore, we would not put more information on temporal changes to make readers get lost in the text.

      153-156 “Among them, we found 56 host species (12 bees and 44 wasps, mean abundance and richness are 400.05 and 45.14, respectively, for each plot) and 50 parasitoid species (38 Hymenoptera and 12 Diptera, mean abundance and richness are 14.07 and 9.05, respectively, for each plot).”

      149 tree -> trees

      Done.

      149 Should there read also some else than "NMDS scores"?

      Thanks! Now we provided more details about “NMDS scores”.

      L161-162 “(NMDS axis scores; i.e. preserving the rank order of pairwise dissimilarities between samples)”

      149 You could mention the amount of variation explained by the first two axes of the NMDSs. Now it is difficult to estimate how much the models actually explain.

      Thanks for your comments! However, we could not directly provide the explanatory power of the two axes, because NMDS is based on rank-order distances rather than linear relationships like in PCA. However, the goodness of fit for the NMDS solution is typically evaluated using the stress value. We provide the stress value in the figure caption.

      150 "tree MPD" is mentioned for the first time. Spell it out.

      Done.

      150 Explain "eastness".

      Done.

      L163-164 “…eastness (sine-transformed radian values of aspect) )”

      151 How was "tree functional diversity" quantified?

      Please see methods. L437-L438.

      160 Specify that you talk about phylogenetic compositions of the host and parasitoid communities here.

      We would keep it refined here, keeping consistent with species composition here. Phylogenetic composition just represents the dissimilarities of phylogenetic linages within a community.

      161 Describe "parafit" test here when first mentioned.

      Done, see methods L485-487.

      182 Keep on referring to tables and figures in the discussion! Also, more clearly discuss your hypotheses. There are lots of discussions on top-down/bottom-up control. It could be good to form a hypothesis on them and predict what kind of patterns would suggest either one and what would you expect to find regarding them.

      Now we referred figures and tables in the discussion. As the contents on top-down and bottom-up control were not fit very well with our study (as also suggested by reviewers), so we rephased the discussion and also clearly discuss our hypotheses in the discussion. See L218, L226, and L237 etc.

      186 "partly determined host-parasitoid networks" Be more specific.

      Done.

      L206-207 “…partly determined host-parasitoid network indices, including vulnerability, linkage density, and interaction evenness.”

      195 Tell what you mean by "other biotic factors".

      Specified it: “…other biotic factors such as elevation and slope…”

      197-198 "It seems likely that these results are based on bee linkages to pollen resources" I would be hesitant to conclude this as the bees most likely forage way beyond the borders of the 30m by 30m study plots.

      Thanks for your concern about this problem. While it is true that bees can forage beyond 30 x 30m, the study focuses on their nesting behavior and activity within this defined area, rather than their entire foraging range. Existing literature shows bees often forage locally when resources are available (e.g. Ebeling et al., 2012 Oecologia; Guo et al., year, Basic and Applied Ecology). Therefore, we are confident that this pattern could be associated with the resources around the trap nests.

      223 "This could be further tested by collecting the food directly used by the wasps (caterpillars)" A bit unnecessary addition.

      Thanks for your suggestion. Yes, this definitely is a good point, but currently we don’t have enough data of caterpillars, but we will follow this in the future.

      232-238 I disagree with the authors on the interpretation of the causality of the results here. I think that the community of parasitoids simply indicates which host species are available, while the host community does not have an as strong effect on parasitoid community as parasitoids do not utilise the whole species pool of the hosts. (Presence of parasitoid tells that the host is around while the presence of the host does not necessarily tell about the presence of the parasitoid.) To me, this would rather indicate a bottom-up than top-down regulation. Similar patterns are also visible in species communities of hosts and parasites.

      Thank you for your suggestion. We agree with you that parasitoids are more depended on hosts, as host could not be always attacked by parasitoids. Now we rephased our explanation to follow this argument.

      L254-256 “Such pattern could be further confirmed by the significant association between host phylogenetic composition and parasitoid phylogenetic composition (Fig. 1c), which suggested that their interactions are phylogenetically structured to some extent.”

      247-266 The logic in this section is difficult to follow. Try rephrasing.

      Now we rephased the section for a clearer logic.

      L270-287 “Tree community species richness did not significantly influence the diversity of hosts targeted by parasitoids (parasitoid generality), but caused a significant increase in the diversity of parasitoids per host species (host vulnerability) (Fig. 3a; Table 2). This is likely because niche differentiation often influences network specialization via potential higher resource diversity in plots with higher tree diversity (Lopez-Carretero et al. 2014). Such positive relationship between host vulnerability and tree species richness suggested that host-parasitoid interactions could be driven through bottom-up effects via benefit from tree diversity. For example, parasitoid species increases more than host diversity with increasing tree species richness (Guo et al. 2021), resulting increasing of host vulnerability at community level. According to the enemies hypothesis (Root 1973), which posits a positive effects of plant richness on natural enemies, the higher trophic levels in our study (e.g. predators and parasitoids) would benefit from tree diversity and regulate herbivores thereby (Staab and Schuldt 2020). Indeed, previous studies at the same site found that bee parasitoid richness and abundance were positively related to tree species richness, but not their bee hosts (Fornoff et al. 2021, Guo et al. 2021). Because our dataset considered all hosts and reflects an overall pattern of host-parasitoid interactions, the effects of tree species richness on parasitoid generality might be more complex and difficult to predict, as we found that neither tree species richness nor tree MPD were related to parasitoid generality.”

      249 "This is likely because niche differentiation often influences network specialization via potential higher resource diversity in plots with higher tree diversity" This is a bit contradicting your vulnerability results as niche differentiation should increase specialization and diversity and specialization should decrease vulnerability (less host per parasitoid).

      Thanks! We understand that the concepts of “generality” and “vulnerability” can be a bit confusing. To clarify, “fewer hosts per parasitoid” actually corresponds to lower generality at the community level.

      332-337 How did you select the species growing on your plots? Or was only species number considered? What was the pool of tree species growing on the selected plots? Was the selection similar at both sites?

      Now we provided more information on the experiment design.

      L354-356 “The species pools of the two plots are nonoverlapping (16 species for each site). The composition of tree species within the study plots is based on a “broken-stick” design (see Bruelheide et al. 2014).”

      342 Remove "centrally per plot"?

      Done.

      346-347 Was the selection of different reed diameters similar in all the plots?

      Diameters and the relative distribution of diameters was similar in all trap nests.

      399 & 432 Are "phylogenetic diversity of the tree communities" and "phylogenetic composition of trees" the same? They are both described as mean pairwise distance.

      These two are actually different, as we use this to distinguish the phylogenetic diversity with communities and rank order of dissimilarities between tree communities. Here, the phylogenetic diversity of the tree communities is mean pairwise phylogenetic distance of species for tree communities. Tree phylogenetic composition is the rank order of pairwise dissimilarities between tree communities based on NMDS.

      400 Do you think that MPD makes any sense with the monocultures (value is always 0)? Does this have a potential to bias your analyses and result?

      We agree your point. However, we do not think that this is a major problem in the analyses. We followed the experimental design and considered low phylogenetic relatedness of tree species in a plot (Likewise in monocultures, the tree species richness is always 1).

      402-405 MNTD is not mentioned before or after this. Consider removing this section.

      We tested the potential effects of MNTD in our models. Now we mentioned it in our results.

      L194-195 “Tree mean nearest taxon distance (MNTD) was unrelated to any network indices.”

      405 "Phylogenetic metrics of trees" Which ones?

      Both tree MPD and MNTD. Now we have noted it in the manuscript. (L432)

      410 Further details on "Rao's Q" and how the functional diversity of the communities was calculated are needed.

      Now more details were provided.

      L435-438 “Specifically, seven leaf traits were used for calculation of tree functional diversity, which was calculated as the mean pairwise distance in trait values among tree species, weighted by tree wood volume, and expressed as Rao's Q”

      413 Specify "higher trophic levels".

      Now we specified the trophic levels.

      L440-441 “…higher trophic levels in our study area, such as herbivores and predators”

      417-424 What about the position of the plots within study sites? Is there potential for edge effects (e.g. bees finding easier the trap nest close to the edge of the experimental forest)? Were there any differences between the two sites? What is the elevation range of the plots?

      Thanks for concerning the details of our study. First, all the plots were randomly distributed within the study sites (see Fig. S2). Admittedly, there are several plots are located in the edges of the site. However, we did not consider the potential edge effects in our analysis. Of course, this will be a good point in our future studies. Moreover, the biggest difference between the two is the non-overlapping tree species pool, and the two study sites are apart from 5 km in the same town. Finally, there is not too distinct elevation gradient across the plots (112 m - 260 m).

      432-434 "The species and phylogenetic composition of trees, hosts, and parasitoids were quantified at each plot with nonmetric multidimensional scaling (NMDS) analysis based on Morisita-Horn distances" This section needs to be more specific and detailed. Did you do the NMDS separately for each plot as suggested in the text?

      We provided more details of the section.

      L462-465 “The minimum number of required dimensions in the NMDS based on the reduction in stress value was determined in the analysis (k = 2 in our case). We centred the results to acquire maximum variance on the first dimension, and used the principal components rotation in the analysis.”

      435 Specify how picante was used (function and arguments)!

      Now we specified the function.

      L465-467 “The phylogenetic composition was calculated by mean pairwise distance among the host or parasitoid communities per plot with the R package “picante” with ‘mpd’ function.”

      436 "standardized values" Of what? How was the standardisation done?

      Now we citied a supplementary table (Table S2) to specify it (see L469). For the standardization, we used ‘scale’ function in R, which standardizes data by centering and scaling data. Specifically, it subtracts the mean and divides by the standard deviation for each variable.

      443 Provide more details on parafit.

      Actually, we have provided the reason why we use the parafit test and the usage.

      L483-486 “We used a parafit test (9,999 permutations) with the R package “ape” to test whether the associations were non-random between hosts and parasitoids. This is widely used to assess host-parasite co-phylogeny by analyzing the congruence between host and parasite phylogenies using a distance-based matrix approach.”

      449-451 Rephrase the sentence.

      Rephased.

      L490-491 “We constructed quantitative host-parasitoid networks at community level with the R package “bipartite” for each plot of the two sites.”

      451 "six" Should this be five?

      Yes, should be five, thanks.

      470-481 What package and function were used for the LMMs?

      As we now used linear models, we do no longer use a R package for LMMs.

      470 "mix" -> mixed

      Changed to linear models.

      472 "six" Should this be five?

      Again, we changed it to five.

      479-481 How did you treat the variables from the two different sites when testing for the correlations to avoid two geographic clusters of data points?

      Now we considered the two study sites as fixed factor in our linear models. Moreover, tree-based variables were additionally included as interaction terms with the study sites.

      501 "mix" -> mixed

      Changed to linear models.

      The panel selection for figures 3 and 4 seems random. Justify it!

      Thank you. To avoid including too many figures in the main text, which could potentially confuse readers, we have selected the key results that are of primary interest. The remaining figures are provided in the appendix for reference.

      533 "Note that axes are on a log scale for tree species richness." Why the log-scale if the analyses were performed with linear fit? Also, the drawn regression lines do not match the model description (non-linear, while a linear model is described in the text). The models should probably be described in more detail.

      We used log-transformed to promote the normality of the data. The drawn regression lines are linear lines, which fit our models.

      539 "Values were adjusted for covariates of the final regression model." How?

      We used residual plot to directly visualizes the relationship between the predictor and the response variable with the fitted regression line, making it easier to assess the model's fit.

      Fig. S4 text does not match the figure.

      Thanks! We now deleted the unmatched text in the figure.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review): 

      Summary: 

      In this work, Noorman and colleagues test the predictions of the "four-stage model" of consciousness by combining psychophysics and scalp EEG in humans. The study relies on an elegant experimental design to investigate the respective impact of attentional and perceptual blindness on visual processing. 

      The study is very well summarised, the text is clear and the methods seem sound. Overall, a very solid piece of work. I haven't identified any major weaknesses. Below I raise a few questions of interpretation that may possibly be the subject of a revision of the text. 

      We thank the reviewer for their positive assessment of our work and for their extremely helpful and constructive comments that helped to significantly improve the quality of our manuscript.

      (1) The perceptual performance on Fig1D appears to show huge variation across participants, with some participants at chance levels and others with performance > 90% in the attentional blink and/or masked conditions. This seems to reveal that the procedure to match performance across participants was not very successful. Could this impact the results? The authors highlight the fact that they did not resort to postselection or exclusion of participants, but at the same time do not discuss this equally important point. 

      Performance was indeed highly variable between observers, as is commonly found in attentional-blink (AB) and masking studies. For some observers, the AB pushes performance almost to chance level, whereas for others it has almost no effect. A similar effect can be seen in masking. We did our best to match accuracy over participants, while also matching accuracy within participants as well as possible, adjusting mask contrast manually during the experimental session. Naturally, those that are strongly affected by masking need not be the same participants as those that are strongly affected by the AB, given the fact that they rely on different mechanisms (which is also one of the main points of the manuscript). To answer the research question, what mattered most was that at the group-level, performance was well matched between the two key conditions. As all our statistical inferences, both for behavior and EEG decoding, rest on this group level. We do not think that variability at the individualsubject level detracts from this general approach.  

      In the Results, we added that our goal was to match performance across participants:

      “Importantly, mask contrast in the masked condition was adjusted using a staircasing procedure to match performance in the AB condition, ensuring comparable perceptual performance in the masked and the AB condition across participants (see Methods for more details).”

      In the Methods, we added:

      “Second, during the experimental session, after every 32 masked trials, mask contrast could be manually updated in accordance with our goal to match accuracy over participants, while also matching accuracy within participants as well as possible.”

      (2) In the analysis on collinearity and illusion-specific processing, the authors conclude that the absence of a significant effect of training set demonstrates collinearity-only processing. I don't think that this conclusion is warranted: as the illusory and nonillusory share the same shape, so more elaborate object processing could also be occurring. Please discuss. 

      We agree with this qualification of our interpretation, and included the reviewer’s account as an alternative explanation in the Discussion section:  

      “It should be noted that not all neurophysiological evidence unequivocally links processing of collinearity and of the Kanizsa illusion to lateral and feedback processing, respectively (Angelucci et al., 2002; Bair et al., 2003; Chen et al., 2014), so that overlap in decoding the illusory and non-illusory triangle may reflect other mechanisms, for example feedback processes representing the triangular shapes as well.”

      (3) Discussion, lines 426-429: It is stated that the results align with the notion that processes of perceptual segmentation and organization represent the mechanism of conscious experience. My interpretation of the results is that they show the contrary: for the same visibility level in the attentional blind or masking conditions, these processes can be implicated or not, which suggests a role during unconscious processing instead. 

      We agree with the reviewer that the interpretation of this result depends on the definition of consciousness that one adheres to. If one takes report as the leading metric for consciousness (=conscious access), one can indeed conclude that perceptual segmentation/organization can also occur unconsciously. However, if the processing that results in the qualitative nature of an image (rather than whether it is reported) is taken as leading – such as the processing that results in the formation of an illusory percept – (=phenomenal) the conclusion can be quite different. This speaks to the still ongoing debate regarding the existence of phenomenal vs access consciousness, and the literature on no-report paradigms amongst others (see last paragraph of the discussion). Because the current data do not speak directly to this debate, we decided to remove  the sentence about “conscious experience”, and edited this part of the manuscript (also addressing a comment about preserved unconscious processing during masking by Reviewer 2) by limiting the interpretation of unconscious processing to those aspects that are uncontroversial:

      “Such deep feedforward processing can be sufficient for unconscious high-level processing, as indicated by a rich literature demonstrating high-level (e.g., semantic) processing during masking (Kouider & Dehaene, 2007; Van den Bussche et al., 2009; van Gaal & Lamme, 2012). Thus, rather than enabling deep unconscious processing, preserved local recurrency during inattention may afford other processing advantages linked to its proposed role in perceptual integration (Lamme, 2020), such as integration of stimulus elements over space or time.”

      (4) The two paradigms developed here could be used jointly to highlight nonidiosyncratic NCCs, i.e. EEG markers of visibility or confidence that generalise regardless of the method used. Have the authors attempted to train the classifier on one method and apply it to another (e.g. AB to masking and vice versa)? What perceptual level is assumed to transfer? 

      To avoid issues with post-hoc selection of (visible vs. invisible) trials (discussed in the Introduction), we did not divide our trials into conscious and unconscious trials, and thus did not attempt to reveal NCCs, or NCCs generalizing across the two paradigms. Note also that this approach alone would not resolve the debate regarding the ‘true’ NCC as it hinges on the operational definition of consciousness one adheres to; also see our response to the previous point the reviewer raised. Our main analysis revealed that the illusory triangle could be decoded with above-chance accuracy during both masking and the AB over extended periods of time with similar topographies (Fig. 2B), so that significant cross-decoding would be expected over roughly the same extended period of time (except for the heightened 200-250 ms peak). However, as our focus was on differences between the two manipulations and because we did not use post-hoc sorting of trials, we did not add these analyses.

      (5) How can the results be integrated with the attentional literature showing that attentional filters can be applied early in the processing hierarchy? 

      Compared to certain manipulations of spatial attention, the AB phenomenon is generally considered to represent an instance of  “late” attentional filtering. In the Discussion section we included a paragraph on classic load theory, where early and late filtering depend on perceptual and attentional load. Just preceding this paragraph, we added this:  

      “Clearly, these findings do not imply that unconscious high-level (e.g., semantic) processing can only occur during inattention, nor do they necessarily generalize to other forms of inattention. Indeed, while the AB represents a prime example of late attentional filtering, other ways of inducing inattention or distraction (e.g., by manipulating spatial attention) may filter information earlier in the processing hierarchy (e.g., Luck & Hillyard, 1994 vs. Vogel et al., 1998).”

      Reviewer #2 (Public Review): 

      Summary: 

      This is a very elegant and important EEG study that unifies within a single set of behaviorally equated experimental conditions conscious access (and therefore also conscious access failures) during visual masking and attentional blink (AB) paradigms in humans. By a systematic and clever use of multivariate pattern classifiers across conditions, they could dissect, confirm, and extend a key distinction (initially framed within the GNWT framework) between 'subliminal' and 'pre-conscious' unconscious levels of processing. In particular, the authors could provide strong evidence to distinguish here within the same paradigm these two levels of unconscious processing that precede conscious access : (i) an early (< 80ms) bottom-up and local (in brain) stage of perceptual processing ('local contrast processing') that was preserved in both unconscious conditions, (ii) a later stage and more integrated processing (200-250ms) that was impaired by masking but preserved during AB. On the basis of preexisting studies and theoretical arguments, they suggest that this later stage could correspond to lateral and local recurrent feedback processes. Then, the late conscious access stage appeared as a P3b-like event. 

      Strengths: 

      The methodology and analyses are strong and valid. This work adds an important piece in the current scientific debate about levels of unconscious processing and specificities of conscious access in relation to feed-forward, lateral, and late brain-scale top-down recurrent processing. 

      Weaknesses: 

      - The authors could improve clarity of the rich set of decoding analyses across conditions. 

      - They could also enrich their Introduction and Discussion sections by taking into account the importance of conscious influences on some unconscious cognitive processes (revision of traditional concept of 'automaticity'), that may introduce some complexity in Results interpretation 

      - They should discuss the rich literature reporting high-level unconscious processing in masking paradigms (culminating in semantic processing of digits, words or even small group of words, and pictures) in the light of their proposal (deeper unconscious processing during AB than during masking). 

      We thank the reviewer for their positive assessment of our study and for their insightful comments and helpful suggestions that helped to significantly strengthen our paper. We provide a more detailed point-by-point response in the “recommendations for the authors” section below. In brief, we followed the reviewer’s suggestions and revised the Results/Discussion to include references to influences on unconscious processes and expanded our discussion of unconscious effects during masking vs. AB.  

      Reviewer #3 (Public Review): 

      Summary: 

      This work aims to investigate how perceptual and attentional processes affect conscious access in humans. By using multivariate decoding analysis of electroencephalography (EEG) data, the authors explored the neural temporal dynamics of visual processing across different levels of complexity (local contrast, collinearity, and illusory perception). This is achieved by comparing the decidability of an illusory percept in matched conditions of perceptual (i.e., degrading the strength of sensory input using visual masking) and attentional impairment (i.e., impairing topdown attention using attentional blink, AB). The decoding results reveal three distinct temporal responses associated with the three levels of visual processing. Interestingly, the early stage of local contrast processing remains unaffected by both masking and AB. However, the later stage of collinearity and illusory percept processing are impaired by the perceptual manipulation but remain unaffected by the attentional manipulation. These findings contribute to the understanding of the unique neural dynamics of perceptual and attentional functions and how they interact with the different stages of conscious access. 

      Strengths: 

      The study investigates perceptual and attentional impairments across multiple levels of visual processing in a single experiment. Local contrast, collinearity, and illusory perception were manipulated using different configurations of the same visual stimuli. This clever design allows for the investigation of different levels of visual processing under similar low-level conditions. 

      Moreover, behavioural performance was matched between perceptual and attentional manipulations. One of the main problems when comparing perceptual and attentional manipulations on conscious access is that they tend to impact performance at different levels, with perceptual manipulations like masking producing larger effects. The study utilizes a staircasing procedure to find the optimal contrast of the mask stimuli to produce a performance impairment to the illusory perception comparable to the attentional condition, both in terms of perceptual performance (i.e., indicating whether the target contained the Kanizsa illusion) and metacognition (i.e., confidence in the response). 

      The results show a clear dissociation between the three levels of visual processing in terms of temporal dynamics. Local contrast was represented at an early stage (~80 ms), while collinearity and illusory perception were associated with later stages (~200-250 ms). Furthermore, the results provide clear evidence in support of a dissociation between the effects of perceptual and attentional processes on conscious access: while the former affected both neuronal correlates of collinearity and illusory perception, the latter did not have any effect on the processing of the more complex visual features involved in the illusion perception. 

      Weaknesses: 

      The design of the study and the results presented are very similar to those in Fahrenfort et al. (2017), reducing its novelty. Similar to the current study, Fahrenfort et al. (2017) tested the idea that if both masking and AB impact perceptual integration, they should affect the neural markers of perceptual integration in a similar way. They found that behavioural performance (hit/false alarm rate) was affected by both masking and AB, even though only the latter was significant in the unmasked condition. An early classification peak was instead only affected by masking. However, a late classification peak showed a pattern similar to the behavioural results, with classification affected by both masking and AB. 

      The interpretation of the results mainly centres on the theoretical framework of the recurrent processing theory of consciousness (Lamme, 2020), which lead to the assumption that local contrast, collinearity, and the illusory perception reflect feedforward, local recurrent, and global recurrent connections, respectively. It should be mentioned, however, that this theoretical prediction is not directly tested in the study. Moreover, the evidence for the dissociation between illusion and collinearity in terms of lateral and feedback connections seems at least limited. For instance, Kok et al. (2016) found that, whereas bottom-up stimulation activated all cortical layers, feedback activity induced by illusory figures led to a selective activation of the deep layers. Lee & Nguyen (2001), instead, found that V1 neurons respond to illusory contours of the Kanizsa figures, particularly in the superficial layers. They all mention feedback connections, but none seem to point to lateral connections. 

      Moreover, the evidence in favour of primarily lateral connections driving collinearity seems mixed as well. On one hand, Liang et al. (2017) showed that feedback and lateral connections closely interact to mediate image grouping and segmentation. On the other hand, Stettler et al. (2002) showed that, whereas the intrinsic connections link similarly oriented domains in V1, V2 to V1 feedback displays no such specificity. Furthermore, the other studies mentioned in the manuscript did not investigate feedback connections but only lateral ones, making it difficult to draw any clear conclusions. 

      We thank the reviewer for their careful review and positive assessment of our study, as well as for their constructive criticism and helpful suggestions. We provide a more detailed point-by-point response in the “recommendations for the authors” section below. In brief, we addressed the reviewer’s comments and suggestions by better relating our study to Fahrenfort et al.’s (2017) paper and by highlighting the limitations inherent in linking our findings to distinct neural mechanisms (in particular, to lateral vs. feedback connections).

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors): 

      -  Methods: it states that "The distance between the three Pac-Man stimuli as well as between the three aligned two-legged white circles was 2.8 degrees of visual angle". It is unclear what this distance refers to. Is it the shortest distance between the edges of the objects? 

      It is indeed the shortest distance between the edges of the objects. This is now included in the Methods.

      -  Methods: It's unclear to me if the mask updating procedure during the experimental session was based on detection rate or on the perceptual performance index reported on Fig1D. Please clarify. 

      It was based on accuracy calculated over 32 trials. We have included this information in the Methods.

      -  Methods and Results: I did not understand why the described procedure used to ensure that confidence ratings are not contaminated by differences in perceptual performance was necessary. To me, it just seems to make the "no manipulations" and "both manipulations" less comparable to the other 2 conditions. 

      To calculate accurate estimates of metacognitive sensitivity for the two matched conditions, we wanted participants to make use of the full confidence scale (asking them to distribute their responses evenly over all ratings within a block). By mixing all conditions in the same block, we would have run the risk of participants anchoring their confidence ratings to the unmatched very easy and very difficult conditions (no and both manipulations condition). We made this point explicit in the Results section and in the Methods section:

      “To ensure that the distribution of confidence ratings in the performancematched masked and AB condition was not influenced by participants anchoring their confidence ratings to the unmatched very easy and very difficult conditions (no and both manipulations condition, respectively), the masked and AB condition were presented in the same experimental block, while the other block type included the no and both manipulations condition.”

      “To ensure that confidence ratings for these matched conditions (masked, long lag and unmasked, short lag) were not influenced by participants anchoring their confidence ratings to the very easy and very difficult unmatched conditions (no and both manipulations, respectively), one type of block only contained the matched conditions, while the other block type contained the two remaining, unmatched conditions (masked, short lag and unmasked, long lag).”

      - Methods: what priors were used for Bayesian analyses? 

      Bayesian statistics were calculated in JASP (JASP Team, 2024) with default prior scales (Cauchy distribution, scale 0.707). This is now added to the Methods.

      - Results, line 162: It states that classifiers were applied on "raw EEG activity" but the Methods specify preprocessing steps. "Preprocessed EEG activity" seems more appropriate. 

      We changed the term to “preprocessed EEG activity” in the Methods and to “(minimally) preprocessed EEG activity (see Methods)” in the  Results, respectively.

      - Results, line 173: The effect of masking on local contrast decoding is reported as "marginal". If the alpha is set at 0.05, it seems that this effect is significant and should not be reported as marginal. 

      We changed the wording from “marginal” to “small but significant.”  

      - Fig1: The fixation cross is not displayed. 

      Because adding the fixation cross would have made the figure of the trial design look crowded and less clear, we decided to exclude it from this schematic trial representation. We are now stating this also in the legend of figure 1.  

      - Fig 3A: In the upper left panel, isn't there a missing significant effect of the "local contrast training and testing" condition in the first window? If not, this condition seems oddly underpowered compared to the other two conditions. 

      Thanks for the catch! The highlighting in bold and the significance bar were indeed lacking for this condition in the upper left panel (blue line). We corrected the figure in our revision.

      - Supplementary text and Fig S6: It is unclear to me why the two control analyses (the black lines vs. the green and purple lines) are pooled together in the same figure. They seem to test for different, non-comparable contrasts (they share neither training nor testing sets), and I find it confusing to find them on the same figure. 

      We agree that this may be confusing, and deleted the results from one control analysis from the figure (black line, i.e., training on contrast, testing on illusion), as the reviewer correctly pointed out that it displayed a non-comparable analysis. Given that this control analysis did not reveal any significant decoding, we now report its results only in the Supplementary text.  

      - Fig S6: I think the title of the legend should say testing on the non-illusory triangle instead of testing on the illusory triangle to match the supplementary text. 

      This was a typo – thank you! Corrected.  

      Reviewer #2 (Recommendations For The Authors): 

      Issue #1: One key asymmetry between the three levels of T2 attributes (i.e.: local contrast; non-illusory triangle; illusory Kanisza triangle) is related to the top-down conscious posture driven by the task that was exclusively focusing on the last attribute (illusory Kanisza triangle). Therefore, any difference in EEG decoding performance across these three levels could also depend to this asymmetry. For instance, if participants were engaged to report local contrast or non-illusory triangle, one could wonder if decoding performance could differ from the one used here. This potential confound was addressed by the authors by using decoders trained in different datasets in which the main task was to report one the two other attributes. They could then test how classifiers trained on the task-related attribute behave on the main dataset. However, this part of the study is crucial but not 100% clear, and the links with the results of these control experiments are not fully explicit. Could the author better clarity this important point (see also Issue #1 and #3). 

      The reviewer raises an important point, alluding to potential differences between decoded features regarding task relevance. There are two separate sets of analyses where task relevance may have been a factor, our main analyses comparing illusion to contrast decoding, and our comparison of collinearity vs. illusion-specific processing.  

      In our main analysis, we are indeed reporting decoding of a task-relevant feature (illusion) and of a task-irrelevant feature (local contrast, i.e., rotation of the Pac-Man inducers). Note, however, that the Pac-Man inducers were always task-relevant, as they needed to be processed to perceive illusory triangles, so that local contrast decoding was based on task-relevant stimulus elements, even though participants did not respond to local contrast differences in the main experiment. However, we also ran control analyses testing the effect of task-relevance on local contrast decoding in our independent training data set and in another (independent) study, where local contrast was, in separate experimental blocks, task-relevant or task-irrelevant. The results are reported in the Supplementary Text and in Figure S5. In brief, task-relevance did not improve early (70–95 ms) decoding of local contrast. We are thus confident that the comparison of local contrast to illusion decoding in our main analysis was not substantially affected by differences in task relevance. In our previous manuscript version, we referred to these control analyses only in the collinearity-vs-illusion section of the Results. In our revision, we added the following in the Results section comparing illusion to contrast decoding:

      “In the light of evidence showing that unconscious processing is susceptible to conscious top-down influences (Kentridge et al., 2004; Kiefer & Brendel, 2006; Naccache et al., 2002), we ran control analyses showing that early local contrast decoding was not improved by rendering contrast task-relevant (see Supplementary Information and Fig. S5), indicating that these differences between illusion and contrast decoding did not reflect differences in task-relevance.”

      In addition to our main analysis, there is the concern that our comparison of collinearity vs. illusion-specific processing may have been affected by differences in task-relevance between the stimuli inducing the non-illusory triangle (the “two-legged white circles”, collinearity-only) and the stimuli inducing the Kanizsa illusion (the PacMan inducers, collinearity-plus-illusion). We would like to emphasize that in our main analysis classifiers were always used to decode T2 illusion presence vs. absence (collinearity-plus-illusion), and never to decode T2 collinearity-only. To distinguish collinearity-only from collinearity-plus-illusion processing, we only varied the training data (training classifiers on collinearity-only or collinearity-plus-illusion), using the independent training data set, where collinearity-only and collinearity-plus-illusion (and rotation) were task-relevant (in separate blocks). As discussed in the Supplementary Information, for this analysis approach to be valid, collinearity-only processing should be similar for the illusory and the non-illusory triangle, and this is what control analyses demonstrated (Fig. S7). In any case, general task-relevance was equated for the collinearity-only and the collinearity-plus-illusion classifiers.  

      Finally, in supplementary Figure 6 we also show that our main results reported in Figure 2 (discussed at the top of this response) were very similar when the classifiers were trained on the independent localizer dataset in which each stimulus feature could be task-relevant.  

      Together, for the reasons described above, we believe that differences in EEG decoding performance across these three stimulus levels did  are unlikely to depend also depend on a “task-relevance” asymmetry.

      Issue #2: Following on my previous point the authors should better mention the concept of conscious influences on unconscious processing that led to a full revision of the notion of automaticity in cognitive science [1 , 2 , 3 , 4]. For instance, the discovery that conscious endogenous temporal and spatial attention modulate unconscious subliminal processing paved the way to this revision. This concept raises the importance of Issue#1: equating performance on the main task across AB and masking is not enough to guarantee that differences of neural processing of the unattended attributes of T2 (i.e.: task-unrelated attributes) are not, in part, due to this asymmetry rather than to a systematic difference of unconscious processing strengtsh [5 , 6-8]. Obviously, the reported differences for real-triangle decoding between AB and masking cannot be totally explained by such a factor (because this is a task-unrelated attribute for both AB and masking conditions), but still this issue should be better introduced, addressed, clarified (Issue #1 and #3) and discussed. 

      We would like to refer to our response to the previous point: Control analyses for local contrast decoding showed that task relevance had no influence on our marker for feedforward processing. Most importantly, as outlined above, we did not perform real-triangle decoding – all our decoding analyses focused on comparing collinearity-only vs. collinearity-plus-illusion were run on the task-relevant T2 illusion (decoding its presence vs. absence). The key difference was solely the training set, where the collinearity-only classifier was trained on the (task-relevant) real triangle and the collinearity-plus-illusion classifier was trained on the (task-relevant) Kanizsa triangle. Thus, overall task relevance was controlled in these analyses.  

      In our revision, we are now also citing the studies proposed by the reviewer, when discussing the control analyses testing for an effect of task-relevance on local contrast decoding:

      “In the light of evidence showing that unconscious processing is susceptible to conscious top-down influences (Kentridge et al., 2004; Kiefer & Brendel, 2006; Naccache et al., 2002), we ran control analyses showing that early local contrast decoding was not improved by rendering contrast task-relevant (see Supplementary Information and Fig. S5), indicating that these differences between illusion and contrast decoding did not reflect differences in task-relevance.”

      Issue #3: In terms of clarity, I would suggest the authors to add a synthetic figure providing an overall view of all pairs of intra and cross-conditions decoding analyses and mentioning main task for training and testing sets for each analysis (see my previous and related points). Indeed, at one point, the reader can get lost and this would not only strengthen accessibility to the detailed picture of results, but also pinpoint the limits of the work (see previous point). 

      We understand the point the reviewer is raising and acknowledge that some of our analyses, in particular those using different training and testing sets, may be difficult to grasp. But given the variety of different analyses using different training and testing sets, different temporal windows, as well as different stimulus features, it was not possible to design an intuitive synthetic figure summarizing the key results. We hope that the added text in the Results and Discussion section will be sufficient to guide the reader through our set of analyses.  

      In our revision, we are now more clearly highlighting that, in addition to presenting the key results in our main text that were based on training classifiers on the T1 data, “we replicated all key findings when training the classifiers on an independent training set where individual stimuli were presented in isolation (Fig. 3A, results in the Supplementary Information and Fig. S6).” For this, we added a schematic showing the procedure of the independent training set to Figure 3, more clearly pointing the reader to the use of a separate training data set.  

      Issue #4: In the light of these findings the authors should discuss more thoroughly the question of unconscious high-level representations in masking versus AB: in particular, a longstanding issue relates to unconscious semantic processing of words, numbers or pictures. According to their findings, they tend to suggest that semantic processing should be more enabled in AB than in masking. However, a rich literature provided a substantial number of results (including results from the last authors Simon Van Gaal) that tend to support the notion of unconscious semantic processing in subliminal processing (see in particular: [9 , 10 , 11 , 12 , 13]). So, and as mentioned by the authors, while there is evidence for semantic processing during AB they should better discuss how they would explain unconscious semantic subliminal processing. While a possibility could be to question the unconscious attribute of several subliminal results, the same argument also holds for AB studies. Another possible track of discussion would be to differentiate AB and subliminal perception in terms of strength and durability of the corresponding unconscious representations, but not necessarily in terms of cognitive richness. Indeed, one may discuss that semantic processing of stimuli that do not need complex spatial integration (e.g.: words or digits as compared to illusory Kanisza tested here) can still be observed under subliminal conditions. 

      We thank the reviewer for pointing us to this shortcoming of our previous Discussion. Note that our data does not directly speak to the question of high-level unconscious representations in masking vs AB, because such conclusions would hinge on the operational definition of consciousness one adheres to (also see response to Reviewer 1). Nevertheless, we do follow the reviewer’s suggestions and added the following in the Discussion (also addressing a point about other forms of attention raised by Reviewer 1):

      “Clearly, these findings do not imply that unconscious high-level (e.g., semantic) processing can only occur during inattention, nor do they necessarily generalize to other forms of inattention. Indeed, while the AB represents a prime example of late attentional filtering, other ways of inducing inattention or distraction (e.g., by manipulating spatial attention) may filter information earlier in the processing hierarchy (e.g., Luck & Hillyard, 1994 vs. Vogel et al., 1998).”

      And, in a following paragraph in the Discussion:

      “Such deep feedforward processing can be sufficient for unconscious high-level processing, as indicated by a rich literature demonstrating high-level (e.g., semantic) processing during masking (Kouider & Dehaene, 2007; Van den Bussche et al., 2009; van Gaal & Lamme, 2012). Thus, rather than enabling high-level unconscious processing, preserved local recurrency during inattention may afford other processing advantages linked to its proposed role in perceptual integration (Lamme, 2020), such as integration of stimulus elements over space or time.  

      Reviewer #3 (Recommendations For The Authors): 

      (1) The objective of Fahrenfort et al., 2017 seems very similar to that of the current study. What are the main differences between the two studies? Moreover, Fahrenfort et al., 2017 conducted similar decoding analyses to those performed in the current study.

      Which results were replicated in the current study, and which ones are novel? Highlighting these differences in the manuscript would be beneficial. 

      We now provide a more comprehensive coverage of the study by Fahrenfort et al., 2017. In the Introduction, we added a brief summary of the key findings, highlighting that this study’s findings could have reflected differences in task performance rather than differences between masking and AB:

      “For example, Fahrenfort and colleagues (2017) found that illusory surfaces could be decoded from electroencephalogram (EEG) data during the AB but not during masking. This was taken as evidence that local recurrent interactions, supporting perceptual integration, were preserved during inattention but fully abolished by masking. However, masking had a much stronger behavioral effect than the AB, effectively reducing task performance to chance level. Indeed, a control experiment using weaker masking, which resulted in behavioral performance well above chance similar to the main experiment’s AB condition, revealed some evidence for preserved local recurrent interactions also during masking. However, these conditions were tested in separate experiments with small samples, precluding a direct comparison of perceptual vs. attentional blindness at matched levels of behavioral performance. To test …”

      In the Results , we are now also highlighting this key advancement by directly referencing the previous study:

      “Thus, whereas in previous studies task performance was considerably higher during the AB than during masking (e.g., Fahrenfort et al., 2017), in the present study the masked and the AB condition were matched in both measures of conscious access.” When reporting the EEG decoding results in the Results section, we continuously cite the Fahrenfort et al. (2017) study to highlight similarities in the study’s findings. We also added a few sentences explicitly relating the key findings of the two studies:

      “This suggests that the AB allowed for greater local recurrent processing than masking, replicating the key finding by Fahrenfort and colleagues (2017). Importantly, the present result demonstrates that this effect reflects the difference between the perceptual vs. attentional manipulation rather than differences in behavior, as the masked and the AB condition were matched for perceptual performance and metacognition.”

      “This similarity between behavior and EEG decoding replicates the findings of Fahrenfort and colleagues  (2017) who also found a striking similarity between late Kanizsa decoding (at 406 ms) and behavioral Kanizsa detection. These results indicate that global recurrent processing at these later points in time reflected conscious access to the Kanizsa illusion.”  

      We also more clearly highlighted where our study goes beyond Fahrenfort et al.’s (2017), e.g., in the Results:

      “The addition of this element of collinearity to our stimuli was a key difference to the study by Fahrenfort and colleagues (2017), allowing us to compare non-illusory triangle decoding to illusory triangle decoding in order to distinguish between collinearity and illusion-specific processing.”

      And in the Discussion:

      “Furthermore, the addition of line segments forming a non-illusory triangle to the stimulus employed in the present study allowed us to distinguish between collinearity and illusion-specific processing.”

      Also, in the Discussion, we added a paragraph “summarizing which results were replicated in the current study, and which ones are novel”, as suggested by the reviewer:

      “This pattern of results is consistent with a previous study that used EEG to decode Kanizsa-like illusory surfaces during masking and the AB (Fahrenfort et al., 2017). However, the present study also revealed some effects where Fahrenfort and colleagues (2017) failed to obtain statistical significance, likely reflecting the present study’s considerably larger sample size and greater statistical power. For example, in the present study the marker for feedforward processing was weakly but significantly impaired by masking, and the marker for local recurrency was significantly impaired not only by masking but also by the AB, although to a lesser extent. Most importantly, however, we replicated the key findings that local recurrent processing was more strongly impaired by masking than by the AB, and that global recurrent processing was similarly impaired by masking and the AB and closely linked to task performance, reflecting conscious access. Crucially, having matched the key conditions behaviorally, the present finding of greater local recurrency during the AB can now unequivocally be attributed to the attentional vs. perceptual manipulation of consciousness.”

      Finally, we changed the title to “Distinct neural mechanisms underlying perceptual and attentional impairments of conscious access despite equal task performance” to highlight one of the crucial differences between the Fahrenfort et al., study and this study, namely the fact that we equalized task performance between the two critical conditions (AB and masking).

      (2) It is not clear from the text the link between the current study and the literature on the role of lateral and feedback connections in consciousness (Lamme, 2020). A better explanation is needed. 

      To our knowledge, consciousness theories such as recurrent processing theory by Lamme make currently no distinction between the role of lateral and feedback connections for consciousness. The principled distinction lies between unconscious feedforward processing and phenomenally conscious or “preconscious” local recurrent processing, where local recurrency refers to both lateral (or horizontal) and feedback connections. We added a sentence in the Discussion:

      “As current theories do not distinguish between the roles of lateral vs. feedback connections for consciousness, the present findings may enrich empirical and theoretical work on perceptual vs. attentional mechanisms of consciousness …”

      (3) When training on T1 and testing on T2, EEG data showed an early peak in local contrast classification at 75-95 ms over posterior electrodes. The authors stated that this modulation was only marginally affected by masking (and not at all by AB); however, the main effect of masking is significant. Why was this effect interpreted as nonrelevant? 

      Following this and Reviewer 1’s comment, we changed the wording from “marginal” to “weak but significant.” We considered this effect “weak” and of lesser relevance, because its Bayes factor indicated that the alternative hypothesis was only 1.31 times more likely than the null hypothesis of no effect, representing only “anecdotal” evidence, which is in sharp contrast to the robust effects of the consciousness manipulations on illusion decoding reported later. Furthermore, later ANOVAs comparing the effect of masking on contrast vs. illusion decoding revealed much stronger effects on illusion decoding than on contrast decoding (BFs>3.59×10<sup>4</sup>).

      (4) The decoding analysis on the illusory percept yielded two separate peaks of decoding, one from 200 to 250 ms and another from 275 to 475 ms. The early component was localized occipitally and interpreted as local sensory processing, while the late peak was described as a marker for global recurrent processing. This latter peak was localized in the parietal cortex and associated with the P300. Can the authors show the topography of the P300 evoked response obtained from the current study as a comparison? Moreover, source reconstruction analysis would probably provide a better understanding of the cortical localization of the two peaks. 

      Figure S4 now shows the P300 from electrode Pz, demonstrating a stronger positivity between 375 and 475 ms when the illusory triangle was present than when it was absent. We did not run a source reconstruction analysis.  

      (5) The authors mention that the behavioural results closely resembled the pattern of the second decoding peak results. However, they did not show any evidence for this relationship. For instance, is there a correlation between the two measures across or within participants? Does this relationship differ between the illusion report and the confidence rating? 

      This relationship became evident from simply eyeballing the results figures: Both in behavior and EEG decoding performance dropped from the both-manipulations condition to the AB and masked conditions, while these conditions did not differ significantly. Following a similar observation of a close similarity between behavior and the second/late illusion decoding peak in the study by Fahrenfort et al. (2017), we adopted their analysis approach and ran two additional ANOVAs, adding “measure” (behavior vs. EEG) as a factor. For this analysis, we dropped the both-manipulations condition due to scale restrictions (as noted in footnote 1: “We excluded the bothmanipulations condition from this analysis due to scale restrictions: in this condition, EEG decoding at the second peak was at chance, while behavioral performance was above chance, leaving more room for behavior to drop from the masked and AB condition.”). The analysis revealed that there were no interactions with condition:

      “The pattern of behavioral results, both for perceptual performance and metacognitive sensitivity, closely resembled the second decoding peak: sensitivity in all three metrics dropped from the no-manipulations condition to the masked and AB conditions, while sensitivity did not differ significantly between these performancematched conditions (Fig. 2C). Two additional rm ANOVAs with the factors measure (behavior, second EEG decoding peak) and condition (no-manipulations, masked, AB)<sup>1</sup> for perceptual performance and metacognitive sensitivity revealed no significant interaction (performance: F</iv><sub>2,58</sub>=0.27, P\=0.762, BF<sub>01</sub>=8.47; metacognition: F</iv><sub>2,58</sub=0.54, P\=0.586, BF<sub>2,58</sub>=6.04). This similarity between behavior and EEG decoding replicates the findings of Fahrenfort and colleagues  (2017) who also found a striking similarity between late Kanizsa decoding (at 406 ms) and behavioral Kanizsa detection. These results indicate that global recurrent processing at these later points in time reflected conscious access to the Kanizsa illusion.”

      (6) The marker for illusion-specific processing emerged later (200-250 ms), with the nomanipulation decoding performing better after training on the illusion than the nonillusory triangle. This difference emerged only in the AB condition, and it was fully abolished by masking. The authors confirmed that the illusion-specific processing was not affected by the AB manipulations by running a rm ANOVA which did not result in a significant interaction between condition and training set. However, unlike the other non-significant results, a Bayes Factor is missing here. 

      We added Bayes factors to all (significant and non-significant) rm ANOVAs.

      (7) The same analysis yielded a second illusion decoding peak at 375-475 ms. This effect was impaired by both masking and AB, with no significant differences between the two conditions. The authors stated that this result was directly linked to behavioural performance. However, it is not clear to me what they mean (see point 5). 

      We added analyses comparing behavior and EEG decoding directly (see our response to point 5).

      (8) The introduction starts by stating that perceptual and attentional processes differently affect consciousness access. This differentiation has been studied thoroughly in the consciousness literature, with a focus on how attention differs from consciousness (e.g., Koch & Tsuchiya, TiCS, 2007; Pitts, Lutsyshyna & Hillyard, Phil. Trans. Roy. Soc. B Biol. Sci., 2018). The authors stated that "these findings confirm and enrich empirical and theoretical work on perceptual vs. attentional mechanisms of consciousness clearly distinguishing and specifying the neural profiles of each processing stage of the influential four-stage model of conscious experience". I found it surprising that this aspect was not discussed further. What was the state of the art before this study was conducted? What are the mentioned neural profiles? How did the current results enrich the literature on this topic? 

      We would like to point out that our study is not primarily concerned with the conceptual distinction between consciousness and attention, which has been the central focus of e.g., Koch and Tsuchiuya (2007). While this literature was concerned with ways to dissociate consciousness and attention, we tacitly assumed that attention and consciousness are now generally considered as different constructs. Our study is thus not dealing with dissociations between attention and consciousness, nor with the distinction between phenomenal consciousness and conscious access, but is concerned with different ways of impairing conscious access (defined as the ability to report about a stimulus), either via perceptual or via attentional manipulations. For the state of the art before the study was conducted, we would like to refer to the motivation of our study in the Introduction, e.g., previous studies’ difficulties in unequivocally linking greater local recurrency during attentional than perceptual blindness to the consciousness manipulation, given performance confounds (we expanded this Introduction section). We also expanded a paragraph in the discussion to remind the reader of the neural profiles of the 4-stage model and to highlight the novelty of our findings related to the distinction between lateral and feedback processes:

      “As current theories do not distinguish between the roles of lateral vs. feedback connections for consciousness, the present findings may enrich empirical and theoretical work on perceptual vs. attentional mechanisms of consciousness (Block, 2005; Dehaene et al., 2006; Hatamimajoumerd et al., 2022; Lamme, 2010; Pitts et al., 2018; Sergent & Dehaene, 2004), clearly distinguishing the neural profiles of each processing stage of the influential four-stage model of conscious experience (Fig. 1A). Along with the distinct temporal and spatial EEG decoding patterns associated with lateral and feedback processing, our findings suggest a processing sequence from feedforward processing to local recurrent interactions encompassing lateral-tofeedback connections, ultimately leading to global recurrency and conscious report.”  

      (9) When stating that this is the first study in which behavioural measures of conscious perception were matched between the attentional blink and masking, it would be beneficial to highlight the main differences between the current study and the one from Fahrenfort et al., 2017, with which the current study shares many similarities in the experimental design (see point 1). 

      We would like to refer the reviewer to our response to point 1), where we detail how we expanded the discussion of similarities and differences between our present study and Fahrenfort et al. (2017).

      (10) The discussion emphasizes how the current study "suggests a processing sequence from feedforward processing to local recurrent interactions encompassing lateral-to-feedback connections, ultimately leading to global recurrency and conscious report". For transparency, it is though important to highlight that one limit of the current study is that it does not provide direct evidence for the specified types of connections (see point 6). 

      We added a qualification in the Discussion section:

      “Although the present EEG decoding measures cannot provide direct evidence for feedback vs. lateral processes, based on neurophysiological evidence, …”

      Furthermore, we added this qualification in the Discussion section:

      “It should be noted that the not all neurophysiological evidence unequivocally links processing of collinearity and of the Kanizsa illusion to lateral and feedback processing, respectively (Angelucci et al., 2002; Bair et al., 2003; Chen et al., 2014), so that overlap in decoding the illusory and non-illusory triangle may reflect other mechanisms, for example feedback processing as well.”

      References

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      Bair, W., Cavanaugh, J. R., & Movshon, J. A. (2003). Time course and time-distance relationships for surround suppression in macaque V1 neurons. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 23(20), 7690–7701.

      Block, N. (2005). Two neural correlates of consciousness. Trends in Cognitive Sciences, 9(2), 46–52.

      Chen, M., Yan, Y., Gong, X., Gilbert, C. D., Liang, H., & Li, W. (2014). Incremental integration of global contours through interplay between visual cortical areas. Neuron, 82(3), 682–694.

      Dehaene, S., Changeux, J.-P., Naccache, L., Sackur, J., & Sergent, C. (2006). Conscious, preconscious, and subliminal processing: a testable taxonomy. Trends in Cognitive Sciences, 10(5), 204–211.

      Hatamimajoumerd, E., Ratan Murty, N. A., Pitts, M., & Cohen, M. A. (2022). Decoding perceptual awareness across the brain with a no-report fMRI masking paradigm. Current Biology: CB. https://doi.org/10.1016/j.cub.2022.07.068

      JASP Team. (2024). JASP (Version 0.19.0)[Computer software]. https://jasp-stats.org/ Kentridge, R. W., Heywood, C. A., & Weiskrantz, L. (2004). Spatial attention speeds discrimination without awareness in blindsight. Neuropsychologia, 42(6), 831– 835.

      Kiefer, M., & Brendel, D. (2006). Attentional Modulation of Unconscious “Automatic” Processes: Evidence from Event-related Potentials in a Masked Priming Paradigm. Journal of Cognitive Neuroscience, 18(2), 184–198.

      Kouider, S., & Dehaene, S. (2007). Levels of processing during non-conscious perception: a critical review of visual masking. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1481), 857–875.

      Lamme, V. A. F. (2010). How neuroscience will change our view on consciousness. Cognitive Neuroscience, 1(3), 204–220.

      Luck, S. J., & Hillyard, S. A. (1994). Electrophysiological correlates of feature analysis during visual search. Psychophysiology, 31(3), 291–308.

      Naccache, L., Blandin, E., & Dehaene, S. (2002). Unconscious masked priming depends on temporal attention. Psychological Science, 13(5), 416–424.

      Pitts, M. A., Lutsyshyna, L. A., & Hillyard, S. A. (2018). The relationship between attention and consciousness: an expanded taxonomy and implications for ‘noreport’ paradigms. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 373(1755), 20170348.

      Sergent, C., & Dehaene, S. (2004). Is consciousness a gradual phenomenon? Evidence for an all-or-none bifurcation during the attentional blink. Psychological Science, 15(11), 720–728.

      Van den Bussche, E., Van den Noortgate, W., & Reynvoet, B. (2009). Mechanisms of masked priming: a meta-analysis. Psychological Bulletin, 135(3), 452–477. van Gaal, S., & Lamme, V. A. F. (2012). Unconscious high-level information processing: implication for neurobiological theories of consciousness: Implication for neurobiological theories of consciousness. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 18(3), 287–301.

      Vogel, E. K., Luck, S. J., & Shapiro, K. L. (1998). Electrophysiological evidence for a postperceptual locus of suppression during the attentional blink. Journal of Experimental Psychology. Human Perception and Performance, 24(6), 1656– 1674.

    1. hile being out and public may work for some stuJentsand may help an entire school community prepare for a student's or facultymember's transition, the choice to remain private also neeJs to be respecteJwith transgender students as with sexual minority students.

      It's sad to think that this isn't already acknowledged and prepared for. Establishing options for students who are apart of a sexual or gender minority and who want to keep their identities private shouldn't be something that we have to fight for. Not only would such changes allow for a more welcoming and inclusive school system, but I would think that these changes would also lead to significantly better academic performance and improvements in the quality of life of said students, along with the students and teachers whom they interact with.

    1. Tasks that are easily copied, completed by cheating, or solved with an answer key, probably should not be factored in as assessment. These lower demand tasks should be seen as practice leading up to proficiency in applying new learning.

      I think that this is tricky in K-12 Math in the current educational system. When factoring in the need to assess many students, mathematical procedural fluency is most easily assessed through written tests, which are easily copied or solved with an answer key. Adding additional tasks such as long written explanations disadvantages those students who may have strong mathematical skill but poor language skill. Although, mathematical communication is also important, it can be done purely symbolically, and this method of communication is usually most like how we use math outside of school. At the same time, we must assess procedural fluency. Without more time spent on Math and more Math integrated into other subjects, my experience is that students don't acquire adequate fluency without motivation to specifically practice those skills for assessment.

      Then again, perhaps tests which are protected against copying and cheating, such as through invigilation, are not the tasks referred to here...

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Weaknesses:<br /> (1) While the overall results are interesting, I am somewhat left confused about how to interpret the difference in the scores derived from different conditions. For example, the authors stated "Comparing the weights for in-group and out-group distractors, the effect of proximity was larger than that of aggression and grooming" in p.8. Does this mean that the proximity is indeed the type of behavior most affected in the out-group condition compared to the in-group condition? The out-group effects are difficult to examine with actual behavioral data, but some in-group effects such as those involving OT can be tested, which possibly provides good insights into interpreting the differences of the weights observed across the experimental conditions.

      Thank you for your thoughtful comments and for highlighting an important aspect of our findings. The statement in page 8 refers to the relative impact of different social behaviors—proximity, aggression, and grooming—on the derived weights for in-group and out-group distractors. Specifically, the data suggest that proximity exerts a stronger influence than aggression or grooming in differentiating the effects of out-group versus in-group distractors. Regarding the out-group condition, we acknowledge that it presents challenges for direct behavioral observation, as interactions involving out-group members are often more difficult to quantify in naturalistic settings. However, we agree with you about the suggestion to test certain in-group effects, particularly those influenced by oxytocin (OT), as they offer a more controlled framework to validate and interpret the observed differences in weights across experimental conditions. In line with this, we examined specific in-group behaviors under OT administration to disentangle their contributions to attentional dynamics (Fig. 4 and Fig. 5 e to h). By integrating controlled experimental manipulations, we think these results could provide deeper insights into how social relationships shape the observed patterns of attention.

      (2) I think it is important to provide how variable spontaneous social interactions were across sessions and how impactful the variability of the interactions is on the SEI and IEI, as it helps to understand how meaningful the differences of weights are across the conditions, but such data are missing. In line with this point, although the conclusions still hold as those data were obtained during the same experimental periods, shouldn't the weights in Fig. 3f and Figs. 4g and 4h (saline) be expected to be similar, if not the same?

      Thank you for your insightful comments. As highlighted, we utilized the entire experimental period as the dataset to evaluate the monkeys' social interactions. The experiments presented in Figures 3 and 4 were designed to examine how social relationships correlate with patterns of social attention under two distinct conditions: without manipulation (Fig. 3) and with nebulized exposure to oxytocin and saline (Fig. 4). Theoretically, the weights observed in the unmanipulated condition and the nebulized saline condition should be similar. However, our results indicate that distractor biases shifted significantly following nebulized saline exposure (Fig. 4) compared to the unmanipulated condition (Fig. 3) (MK: p = 9.3×10<sup>-3</sup>, ML: p = 9.77×10<sup>-4</sup>, MC: p = 9.77×10<sup>-4</sup>, MA: p = 0.09; n<sub>1</sub> = n<sub>2</sub> = 12 experimental days; Two-sided Wilcoxon signed-rank test). This suggests that the nebulization process itself, despite acclimating the monkeys to saline exposure for approximately two weeks prior to the experiments, still influenced their attentional behaviors.

      While the primary goal of nebulization was to assess the effects of oxytocin on social attention, our main conclusions remain robust, even considering the impact of nebulization on distractor biases. We acknowledge that variability in spontaneous social interactions across days or experimental sessions could be an important factor influencing the SEI and IEI. The dynamic nature of social interactions within the colony is likely affected by numerous variables. Future research will aim to integrate these factors into a more comprehensive and dynamic framework to better interpret their influence on social attention metrics.

      Reviewer #2 (Public review):

      Weaknesses:<br /> (1) The study's conclusions are based on observations of only four monkeys, which limits the generalizability of the findings. Larger sample sizes could strengthen the validity of the results.

      Thank you for your valuable comment. We acknowledge that the relatively small sample size could influence the generalizability of the findings.  However, despite this limitation, our work systematically examined multifaceted social relationships among monkeys and their attentional strategies within a well-controlled experimental setup. We reported results across sessions and conditions (e.g., in-group vs. out-group; saline vs. Oxytocin), which strengthens the reliability of the observed effects of social networks within this context. We agree that increasing the sample size would improve the generalizability of the results. Future studies with a larger cohort will be critical for confirming the robustness of our findings and expanding their broader applicability. We have acknowledged this limitation in the revised manuscript and highlighted the potential for further research with larger sample sizes to validate and extend our conclusions.

      (2) The limited set of stimulus images (in-group and out-group faces) may introduce unintended biases. This could be addressed by increasing the diversity of stimuli or incorporating a broader range of out-group members.

      Thank you for your thoughtful comment. We acknowledge that the use of a limited set of six monkey faces as stimuli for in-group and out-group conditions could potentially introduce biases. To address this concern, we conducted an additional analysis to minimize the potential impact of individual images on our findings using the current dataset. Specifically, we randomly excluded one in-group and one out-group image and reanalyzed distractor biases using the remaining two images (Supplementary Fig. 3a). For each subject, this approach generated three sets of two distractors per group, resulting in 81(3<sup>4</sup>) combinations across four monkey subjects, and a total of 81 × 81 subject-distractor pairings. We statistically compared distractor biases between in-group and out-group faces for each combination (Supplementary Fig. 3b). As shown in Supplementary Fig. 3c, 99.30% of the 6,561 combinations demonstrated significantly lower distractor biases towards in-group faces compared to out-group faces (two-sided Wilcoxon signed-rank test, p < 0.05). These results suggest that the observed differences in social attention between in-group and out-group monkeys are unlikely to be driven by specific images within the stimulus set. That said, we agree that increasing the diversity of stimulus images or incorporating a broader range of out-group members would improve the generalizability of the results. We have acknowledged this limitation in the revised manuscript and highlighted the potential for further research to incorporate a more diverse stimulus set to validate and extend our findings.

      “However, these conclusions may be constrained by the relatively small sample size and the homogeneity of stimulus set in the study. Future research focusing on larger, more diverse cohorts and incorporating a broader range of stimuli will enhance the generalizability and applicability of the findings.”

      Reviewer #1 (Recommendations for the authors):

      It is difficult to distinguish "Getting fighted" and "Fighting partner" in Fig. 1b (esp. when printed). I thought Actor showed "Fighting partner" several times in Session 2, but it seems to be "Getting fighted" judging from Figs. 1c and 1d. Is this correct? If so, I would suggest to change the color to improve visibility.

      Thank you for your valuable comment. We apologize for the confusion in the previous version. To improve clarity, we have both terms to “begin fighting” and “being fought”. As shown in Figure 1b, we now explicitly define the identities of the two monkeys as the actor (K) and the partner (L), with all behaviors described from the perspective of the actor. For example, when the actor (K) initiates the fight, it is marked as “begin fighting”, whereas when the partner (L) initiates the fight, the actor (K) is the recipient and labeled as “being fought”. Additionally, we have implemented your suggestion by changing the colors to enhance visibility, especially for the terms “begin fighting” and “being fought”.

      Reviewer #2 (Recommendations for the authors): 

      I have some minor concerns:

      (1) Figure1B, caption for x axis is missing, 4 means 4 days?

      Thank you so much for the comment. We have clarified the x-axis in Figure 1B, where the label "4" corresponds to 4 hours of video typing on each experimental day. The revised figure now includes the appropriate label for better clarity. We appreciate your careful attention to this detail.

      (2) I am slightly concerned about animal safety. How do the experimenters ensure the animals' safety and well-being in cases of aggressive interactions or attacks?

      Thank you for your comment. We share your concern regarding animal safety and take re the well-being of the monkeys in the study. All experimental procedures were reviewed and approved by the Institutional Animal Care and Use Committee at the Institute of Biophysics, Chinese Academy of Sciences (IBP-NHP-002(22)). The monkeys were housed together in the same colony room for over four years, in interconnected cages that allowed for direct physical interaction. Animal behaviors in cages were closely monitored via a live video system to ensure their safety. To prevent potential injuries, a sliding partition system was in place, enabling the isolation of individual animals when necessary, minimizing risks to their well-being.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      his study shows a new mechanism of GS regulation in the archaean Methanosarcina mazei and clarifies the direct activation of GS activity by 2-oxoglutarate, thus featuring another way in which 2-oxoglutarate acts as a central status reporter of C/N sensing.

      Mass photometry and single particle cryoEM structure analysis convincingly show the direct regulation of GS activity by 2-OG promoted formation of the dodecameric structure of GS. The previously recognized small proteins GlnK1 and Sp26 seem to play a subordinate role in GS regulation, which is in good agreement with previous data. Although these data are quite clear now, there remains one major open question: how does 2-OG further increase GS activity once the full dodecameric state is achieved (at 5 mM)? This point needs to be reconsidered.

      Weaknesses:

      It is not entirely clear, how very high 2-OG concentrations activate GS beyond dodecamer formation.

      The data presented in this work are in stark contrast to the previously reported structure of M. mazei GS by the Schumacher lab. This is very confusing for the scientific community and requires clarification. The discussion should consider possible reasons for the contradictory results.

      Importantly, it is puzzling how Schumacher could achieve an apo-structire of dodecameric GS? If 2-OG is necessary for dodecameric formation, this should be discussed. If GlnK1 doesn't form a complex with the dodecameric GS, how could such a complex be resolved there?

      In addition, the text is in principle clear but could be improved by professional editing. Most obviously there is insufficient comma placement.

      We thank Reviewer #1 for the professional evaluation and raising important points. We will address those comments in the updated manuscript and especially improve the discussion in respect to the two points of concern.

      (1) How can GlnA1 activity further be stimulated with further increasing 2-OG after the dodecamer is already fully assembled at 5 mM 2-OG.

      We assume a two-step requirement for 2-OG, the dodecameric assembly and the priming of the active sites. The assembly step is based on cooperative effects of 2-OG and does not require the presence of 2-OG in all 2-OG-binding pockets: 2-OG-binding to one binding pocket also causes a domino effect of conformational changes in the adjacent 2-OG-unbound subunit, as also described for Methanothermococcus thermolithotrophicus GS in Müller et al. 2023. Due to the introduction of these conformational changes, the dodecameric form becomes more favourable even without all 2-OG binding sites being occupied. With higher 2-OG concentrations present (> 5mM), the activity increased further until finally all 2-OG-binding pockets were occupied, resulting in the priming of all active sites (all subunits) and thereby reaching the maximal activity.

      (2) The contradictory results with previously published data on the structure of M. mazei by Schumacher et al. 2023.

      We certainly agree that it is confusing that Schumacher et al. 2023 obtained a dodecameric structure without the addition of 2-OG, which we claim to be essential for the dodecameric form. 2-OG is a cellular metabolite that is naturally present in E. coli, the heterologous expression host both groups used. Since our main question focused on analysing the 2-OG effect on GS, we have performed thorough dialysis of the purified protein to remove all 2-OG before performing MP experiments. In the absence of 2-OG we never observed significant enzyme activity and always detected a fast disassembly after incubation on ice. We thus assume that a dodecamer without 2-OG in Schumacher et al. 2023 is an inactive oligomer of a once 2-OG-bound form, stabilized e.g. by the presence of 5 mM MgCl2.

      The GlnA1-GlnK1-structure (crystallography) by Schumacher et al. 2023 is in stark contrast to our findings that GlnK1 and GlnA1 do not interact as shown by mass photometry with purified proteins. A possible reason for this discrepancy might be that at the high protein concentrations used in the crystallization assay, complexes are formed based on hydrophobic or ionic protein interactions, which would not form under physiological concentrations.

      Reviewer #2 (Public Review):

      Summary:

      Herdering et al. introduced research on an archaeal glutamine synthetase (GS) from Methanosarcina mazei, which exhibits sensitivity to the environmental presence of 2-oxoglutarate (2-OG). While previous studies have indicated 2-OG's ability to enhance GS activity, the precise underlying mechanism remains unclear. Initially, the authors utilized biophysical characterization, primarily employing a nanomolar-scale detection method called mass photometry, to explore the molecular assembly of Methanosarcina mazei GS (M. mazei GS) in the absence or presence of 2-OG. Similar to other GS enzymes, the target M. mazei GS forms a stable dodecamer, with two hexameric rings stacked in tail-to-tail interactions. Despite approximately 40% of M. mazei GS existing as monomeric or dimeric entities in the detectable solution, the majority spontaneously assemble into a dodecameric state. Upon mixing 2-OG with M. mazei GS, the population of the dodecameric form increases proportionally with the concentration of 2-OG, indicating that 2-OG either promotes or stabilizes the assembly process. The cryo-electron microscopy (cryo-EM) structure reveals that 2-OG is positioned near the interface of two hexameric rings. At a resolution of 2.39 Å, the cryo-EM map vividly illustrates 2-OG forming hydrogen bonds with two individual GS subunits as well as with solvent water molecules. Moreover, local side-chain reorientation and conformational changes of loops in response to 2-OG further delineate the 2-OG-stabilized assembly of M. mazei GS.

      Strengths & Weaknesses:

      The investigation studies the impact of 2-oxoglutarate (2-OG) on the assembly of Methanosarcina mazei glutamine synthetase (M mazei GS). Utilizing cutting-edge mass photometry, the authors scrutinized the population dynamics of GS assembly in response to varying concentrations of 2-OG. Notably, the findings demonstrate a promising and straightforward correlation, revealing that dodecamer formation can be stimulated by 2-OG concentrations of up to 10 mM, although GS assembly never reaches 100% dodecamerization in this study. Furthermore, catalytic activities showed a remarkable enhancement, escalating from 0.0 U/mg to 7.8 U/mg with increasing concentrations of 2-OG, peaking at 12.5 mM. However, an intriguing gap arises between the incomplete dodecameric formation observed at 10 mM 2-OG, as revealed by mass photometry, and the continued increase in activity from 5 mM to 10 mM 2-OG for M mazei GS. This prompts questions regarding the inability of M mazei GS to achieve complete dodecamer formation and the underlying factors that further enhance GS activity within this concentration range of 2-OG.

      Moreover, the cryo-electron microscopy (cryo-EM) analysis provides additional support for the biophysical and biochemical characterization, elucidating the precise localization of 2-OG at the interface of two GS subunits within two hexameric rings. The observed correlation between GS assembly facilitated by 2-OG and its catalytic activity is substantiated by structural reorientations at the GS-GS interface, confirming the previously reported phenomenon of "funnel activation" in GS. However, the authors did not present the cryo-EM structure of M. mazei GS in complex with ATP and glutamate in the presence of 2-OG, which could have shed light on the differences in glutamine biosynthesis between previously reported GS enzymes and the 2-OG-bound M. mazei GS.

      Furthermore, besides revealing the cryo-EM structure of 2-OG-bound GS, the study also observed the filamentous form of GS, suggesting that filament formation may be a universal stacking mechanism across archaeal and bacterial species. However, efforts to enhance resolution to investigate whether the stacked polymer is induced by 2-OG or other factors such as ions or metabolites were not undertaken by the authors, leaving room for further exploration into the mechanisms underlying filament formation in GS.

      We thank Reviewer #2 for the detailed assessment and valuable input. We will address those comments in the updated manuscript and clarify the message.

      (1) The discrepancy of the dodecamer formation (max. at 5 mM 2-OG) and the enzyme activity (max. at 12.5 mM 2-OG). We assume that there are two effects caused by 2-OG: 1. cooperativity of binding (less 2-OG needed to facilitate dodecamer formation) and 2. priming of each active site. See also Reviewer #1 R.1). We assume this is the reason why the activity of dodecameric GlnA1 can be further enhanced by increased 2-OG concentration until all catalytic sites are primed.

      (2) The lack of the structure of a 2-OG and ATP-bound GlnA1. Although we strongly agree that this would be a highly interesting structure, it seems out of the scope of a typical revision to request new cryo-EM structures. We evaluate the findings of our present study concerning the 2-OG effects as important insights into the strongly discussed field of glutamine synthetase regulation, even without the requested additional structures.

      (3) The observed GlnA1-filaments are an interesting finding. We certainly agree with the referee on that point, that the stacked polymers are potentially induced by 2-OG or ions. However, it is out of the main focus of this manuscript to further explore those filaments. Nevertheless, this observation could serve as an interesting starting point for future experiments.

      Reviewer #3 (Public Review):

      Summary:

      The current manuscript investigates the effect of 2-oxoglutarate and the Glk1 protein as modulators of the enzymatic reactivity of glutamine synthetase. To do this, the authors rely on mass photometry, specific activity measurements, and single-particle cryo-EM data.

      From the results obtained, the authors convey that glutamine synthetase from Methanosarcina mazei exists in a non-active monomeric/dimeric form under low concentrations of 2-oxoglutarate, and its oligomerization into a dodecameric complex is triggered by higher concentration of 2-oxoglutarate, also resulting in the enhancement of the enzyme activity.

      Strengths:

      Glutamine synthetase is a crucial enzyme in all domains of life. The dodecameric fold of GS is recurrent amongst prokaryotic and archaea organisms, while the enzyme activity can be regulated in distinct ways. This is a very interesting work combining protein biochemistry with structural biology.

      The role of 2-OG is here highlighted as a crucial effector for enzyme oligomerization and full reactivity.

      Weaknesses:

      Various opportunities to enhance the current state-of-the-art were missed. In particular, omissions of the ligand-bound state of GnK1 leave unexplained the lack of its interaction with GS (in contradiction with previous results from the authors). A finer dissection of the effect and role of 2-oxoglurate are missing and important questions remain unanswered (e.g. are dimers relevant during early stages of the interaction or why previous GS dodecameric structures do not show 2-oxoglutarate).

      We thank Reviewer #3 for the expert evaluation and inspiring criticism.

      (1) Encouragement to examine ligand-bound states of GlnK1. We agree and plan to perform the suggested experiments exploring the conditions under which GlnA1 and GlnK1 might interact. We will perform the MP experiments in the presence of ATP. In GlnA1 activity test assays when evaluating the presence/effects of GlnK1 on GlnA1 activity, however, ATP was always present in high concentrations and still we did not observe a significant effect of GlnK1 on the GlnA1 activity.

      (2) The exact role of 2-OG could have been dissected much better. We agree on that point and will improve the clarity of the manuscript. See also Reviewer #1 R.1.

      (3) The lack of studies on dimers. This is actually an interesting point, which we did not consider during writing the manuscript. Now, re-analysing all our MP data in this respect, GlnA1 is likely a dimer as smallest species. Consequently, we will add more supplementary data which supports this observation and change the text accordingly.

      (4) Previous studies and structures did not show the 2-OG. We assume that for other structures, no additional 2-OG was added, and the groups did not specifically analyse for this metabolite either. All methanoarchaea perform methanogenesis and contain the oxidative part of the TCA cycle exclusively for the generation of glutamate (anabolism) but not a closed TCA cycle enabling them to use internal 2-OG concentration as internal signal for nitrogen availability. In the case of bacterial GS from organisms with a closed TCA cycle used for energy metabolism (oxidation of acetyl CoA) like e.g. E. coli, the formation of an active dodecameric GS form underlies another mechanism independent of 2-OG. In case of the recent M. mazei GS structures published by Schumacher et al. 2023, the dodecameric structure is probably a result from the heterologous expression and purification from E. coli. (See also Reviewer #1 R.2). One example of methanoarchaeal glutamine synthetases that do in fact contain the 2-OG in the structure, is Müller et al. 2023.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Specific issues:

      L 141: 2-OG levels increase due to slowing GOGAT reaction (due to Gln limitation as a consequence of N-starvation).... (2-OG also increases in bacteria that lack GDH...)

      As the GS-GOGAT cycle is the major route of ammonium assimilation, consumption of 2-OG by GDH is probably only relevant under high ammonium concentrations.

      In Methanoarchaea, GS is strictly regulated and expression strongly repressed under nitrogen sufficiency - thus glutamate for anabolism is mainly generated by GDH under N sufficiency consuming 2-OG delivered by the oxidative part of the TCA cycle (Methanogenesis is the energy metabolism in methanoarchaea, a closed TCA cycle is not present) thus 2-OG is increasing under nitrogen limitation, when no NH3 is available for GDH.

      L148: it is not clear what is meant by: "and due to the indirect GS activity assay"

      We apologize for not being clear here. The GS activity assay used is the classical assay by Sahpiro & Stadtman 1970 and is a coupled optical test assay (coupling the ATP consumption of the GS activity to the oxidation of NADH by lactate dehydrogenase). Based on the coupled test assay the measurements of low activities show a high deviation. We now added this information in the revised MS respectively.

      L: 177: arguing about 2-OG affinities: more precisely, the 0.75 mM 2-OG is the EC50 concentration of 2-OG for triggering dodecameric formation; it might not directly reflect the total 2-OG affinity, since the affinity may be modulated by (anti)cooperative effects, or by additional sites... as there may be different 2-OG binding sites involved... (same in line 201)

      Thank you for the valuable input. We changed KD to EC50 within the entire manuscript. Concerning possible additional 2-OG binding sites: we did not see any other 2-OG in the cryo-EM structure aside from the described one and we therefore assume that the one described in the manuscript is the main and only one. Considering the high amounts of 2-OG (12.5 mM) used in the structure, it is quite unlikely that additional 2-OG sites exist since they would have unphysiologically low affinities.

      In this respect, instead of the rather poor assay shown in Figure 1D, a more detailed determination of catalytic activation by different 2-OG concentrations should be done (similar to 1A)... This would allow a direct comparison between dodecamerization and enzymatic activation.

      We agree and performed the respective experiments, which are now presented in revised Fig. 1D

      Discussion: the role of 2-OG as a direct activator, comparison with other prokaryotic GS: in other cases, 2-OG affects GS indirectly by being sensed by PII proteins or other 2-OG sensing mechanisms (like 2OG-NtcA-mediated repression of IF factors in cyanobacteria)

      We agree and have added that information in the discussion as suggested.

      290. Unclear: As a second step of activation, the allosteric binding of 2-OG causes a series of conformational.... where is this site located? According to the catalytic effects (compare 1A and 1D) this site should have a lower affinity …

      Thank you very much for pointing this out. Binding of 2-OG only occurs in one specific allosteric binding-site. Binding however, has two effects on the GlnA1: dodecamer assembly and priming of the active site (with two specific EC50, which are now shown in Fig. 1A and D).

      See also public comment #1 (1).

      Reviewer #2 (Recommendations For The Authors):

      The primary concern for me is that mass photometry might lead to incorrect conclusions. The differences in the forms of GS seen in SEC and MP suggest that GS can indeed form a stable dodecamer when the concentration of GS is high enough, as shown in Figure S1B. I strongly suggest using an additional biophysical method to explore the connection between GS and 2-OG in terms of both assembly and activity, to truly understand 2-OG's role in the process of assembly and catalysis.

      We apologize if we did not present this clear enough, however the MP analysis of GlnA1 in the absence of 2-OG showed always (monomers/) dimers, dodecamers were only present in the presence of 2-OG. The SEC analysis in Fig. S1B has been performed in the presence of 12.5 mM 2-OG, we realized this information is missing in the figure legend - we now added this in the revised version. The 2-OG is in addition visible in the Cryo EM structure. Thus, we do not agree to perform additional biophysical methods.

      As for the other experimental findings, they appear satisfactory to me, and I have no reservations regarding the cryoEM data.

      (1) Mass photometry is a fancy technique that uses only a tiny amount of protein to study how they come together. However, the concentration of the protein used in the experiment might be lower than what's needed for them to stick together properly. So, the authors saw a lot of single proteins or pairs instead of bigger groups. They showed in Figure S1B that the M. mazei GS came out earlier than a 440-kDa reference protein, indicating it's actually a dodecamer. But when they looked at the dodecamer fraction using mass photometry, they found smaller bits, suggesting the GS was breaking apart because the concentration used was too low. To fix this, they could try using a technique called analytic ultracentrifuge (AUC) with different amounts of 2-OG to see if they can spot single proteins or pairs when they use a bit more GS. They could also try another technique called SEC-MALS to do similar tests. If they do this, they could replace Figure 1A with new data showing fully formed GS dodecamers when they use the right amount of 2-OG.

      Thank you for this input. In MP we looked at dodecamer formation after removing the 2-OG entirely and re-adding it in the respective concentration. We think that GlnA1 is much more unstable in its monomeric/dimeric fraction and that the complete and harsh removal of 2-OG results in some dysfunctional protein which does not recover the dodecameric conformation after dialysis and re-addition of 2-OG. Looking at the dodecamer-peak right after SEC however, we exclusively see dodecamers, which is now included as an additional supplementary figure (suppl. Fig. 1C). Consequently, we did not perform additional experiments.

      (2) Building on the last point, the estimated binding strength (Kd) between 2-OG and GS might be lower than it really is, because the GS often breaks apart from its dodecameric form in this experiment, even though 2-OG helps keep the pairs together, as seen with cryoEM. What if they used 5-10 times more GS in the mass photometry experiment? Would the estimated bond strength stay the same? Could they use AUC or other techniques like ITC to find out the real, not just estimated, strength of the bond?

      We agree that the term KD is not suitable. We have changed the term KD to EC50 as suggested by reviewer #1, which describes the effective concentration required for 50 % dodecamer assembly. Furthermore, we disagree that the dodecamer breaks apart when the concentrations are as low as in MP experiments. The actual reason for the breaking is rather the harsh dialysis to remove all 2-OG before MP experiments. Right after SEC, the we exclusively see dodecamer in MP (suppl. Fig. S1C). See also #2 (1).

      (3) The fact that the GS hardly works without 2-OG is interesting. I tried to understand the experiment setup, but it wasn't clear as the protocol mentioned in the author's 2021 FEBS paper referred to an old paper from 1970. The "coupled optical test assay" they talked about wasn't explained well. I found other papers that used phosphometry assays to see how much ATP was used up. I suggest the authors give a better, more detailed explanation of their experiments in the methods section. Also, it's unclear why the GS activity keeps going up from 5 to 12.5 mM 2-OG, even though they said it's saturated. They suggested there might be another change happening from 5 to 12.5 mM 2-OG. If that's the case, they should try to get a cryo-EM picture of the GS with lots of 2-OG, both with and without ATP/glutamate (or the Met-Sox-P-ADP inhibitor), to see what's happening at a structural level during this change caused by 2-OG.

      We agree with the reviewer that the GS assay was not explained in detail (since published and known for several years). However, we now added the more detailed description of the assay in the revised MS, which also measures the ATP used up by GS, but couples the generation of ADP to an optical test assay producing pyruvate from PEP with the generated ADP catalysed by pyruvate kinase present in the assay. This generated pyruvate is finally reduced to lactate by the present lactate dehydrogenase consuming NADH, the reduction of which is monitored at 340 nm.

      The still increasing activity of GS after dodecamer formation (max. at 5 mM 2-OG) and the continuously increasing enzyme activity (max. at 12.5 mM 2-OG): See also public reviews, we assume that there are two effects caused by 2-OG: 1. cooperativity of binding (less 2-OG needed to facilitate dodecamer formation) and 2. priming of each active site.

      The suggested additional experiments with and without ATP/Glutamate: Although we strongly agree that this would be a highly interesting structure, it seems out of the scope of a typical revision to request new cryo-EM structures. We evaluate the findings of our present study concerning the 2-OG effects as important insights into the strongly discussed field of glutamine synthetase regulation, even without the requested additional structures.

      (4) Please remake Figure S2, the panels are too small to read the words. At least I have difficulty doing so.

      We assume the reviewer is pointing to Suppl. Fig S3, we now changed this figure accordingly.

      Line 153, the reference Schumacher et al. 23, should be 2023?

      Yes, thank you. We corrected that.

      Line 497. I believe it's UCSF ChimeraX, not Chimera.

      We apologize and corrected accordingly.

      Reviewer #3 (Recommendations For The Authors):

      Recent studies on the Methanothermococcus thermolithotrophicus glutamine synthetase, published by Müller et al., 2024, have identified the binding site for 2-oxoglutarate as well as the conformational changes that were induced in the protein by its presence. In the present study, the authors confirm these observations and additionally establish a link between the presence of 2-oxoglutarate and the dodecameric fold and full activation of GS.

      Curiously, here, the authors could not confirm their own findings that the dodecameric GS can directly interact with the PII-like GlnK1 protein and the small peptide sP26. However, the lack of mention of the GlnK-bound state in these studies is very alarming since it certainly is highly relevant here.

      We agree with the reviewer that we have not observed the interaction with GlnK1 and sP26 in the recent study. Consequently, we speculate that yet unknown cellular factor(s) might be required for an interaction of GlnA1 with GlnK1 and sP26, which were not present in the in vitro experiments using purified proteins, however they were present in the previous pull-down approaches (Ehlers et al. 2005, Gutt et al. 2021). Another reason might be that post-translational modifications occur in M. mazei, which might be important for the interaction, which are also not present in purified proteins expressed in E. coli.

      The manuscript interest could have been substantially increased if the authors had done finer biochemical and enzymatic analyses on the oligomerization process of GS, used GlnK1 bound to known effectors in their assays and would have done some more efforts to extrapolate their findings (even if a small niche) of related glutamine synthetases.

      We thank the reviewer for their valuable encouragement to explore ligand-bound-states of GlnK1. However, in this manuscript we mainly focused on 2-OG as activator of GlnA1 and decided to dedicate future experiments to the exploration of conditions that possibly favor GlnK1-binding.

      In principle, we have explored the ATP bound GlnK1 effects on GlnA1 activity in the activity assays (Fig. 2E) since ATP (3.6 mM) is present. GlnK1 however showed no effects on GlnA1 activity.

      In general, the manuscript is poorly written, with grammatically incorrect sentences that at times, which stands in the way of passing on the message of the manuscript.

      Particular points:

      (1) It is mentioned that 2-OG induces the active oligomeric (dodecamer, 12-mer) state of GlnA1 without detectable intermediates. However, only 62 % of the starting inactive enzyme yields active 12-mers. Note that this is contradicted in line 212.

      Thanks for pointing out this discrepancy. After removing all 2-OG as we did before MP-experiments, GlnA1 doesn’t reach full dodecamers anymore when 2-OG is re-added. This is not because the 2-OG amount is not enough to trigger full assembly, but because the protein is much more unstable in the absence of 2-OG, so we predict that some GlnA1 breaks during dialysis. See also answer reviewer #2 (1) and supplementary figure S1C.

      Is there any protein precipitation upon the addition of 2-OG? Is all protein being detected in the assay, meaning, is monomer/dimer + dodecamer yields close to 100% of the total enzyme in the assay?

      There is no protein precipitation upon the addition of 2-OG, indeed, GlnA1 is much more stable in the presence of 2-OG. In the mass photometry experiments, all particles are measured, precipitated protein would be visible as big entities in the MP.

      Please add to Figure 1 the amount of monomer/dimer during titration. Some debate why there is no full conversion should be tentatively provided.

      We agree with the reviewer and included the amount of monomer/dimer in the figure, as well as some discussion on why it is not fully converted again. GlnA1 is unstable without 2-OG and it was dialysed against buffer without 2-OG before MP measurements. This sample mistreatment resulted in no full re-assembly after re-adding 2-OG (although full dodecamers before dialysis (suppl. Fig. S1C).

      (2) Figure 1B reflects an exemplary result. Here, the addition of 0.1 mM 2-OG seems to promote monomer to dimer transition. Why was this not studied in further detail? It seems highly relevant to know from which species the dodecamer is assembled.

      We thank the reviewer for their comment. However, we would like to point out that, although not shown in the figure, GlnA1 is always mainly present as dimers as the smallest entity. As suggested earlier, we have added the amount of monomers/dimers to Figure 1A, which shows low monomer-counts at all 2-OG concentrations (Fig.1A). Although not depicted in the graph starting at 0.01 mM OG, we also see mainly dimers at 0 mM 2-OG.

      How does the y-axis compare to the number and percentage of counts assigned to the peaks? In line 713, it is written that the percentage of dodecamer considers the total number of counts, and this was plotted against the 2-OG concentration.

      We thank the reviewer for addressing this unclarity. Line 713 corresponds to Figure 1A, where we indeed plotted the percentage of dodecamer against the 2-OG-concentration. Thereby, the percentage of dodecamer corresponds to the percentage calculated from the Gaussian Fit of the MP-dodecamer-peak. In Figure 1 B, however, the y-axis displays the relative amount of counts per mass, multiple similar masses then add up to the percentage of the respective peak (Gaussian Fit above similar masses).

      (3) Lines 714 and 721 (and elsewhere): Why only partial data is used for statistical purposes?

      We in general only show one exemplary biological replicate, since the quality of the respective GlnA1 purification sometimes varied (maximum activity ranging from 5 - 10 U/mg). Therefore, we only compared activities within the same protein purification. For the EC50 calculations of all measurements, we refer to the supplement.

      (4) Lines 192-193: It is claimed that GlnK1 was previously shown to both regulate the activity of GlnA1 and form a complex with GlnA1. Please mention the ratio between GlnK1 and GlnA1 in this complex.

      We now included the requested information (GlnA1:GlnK1 1:1, (Ehlers et al. 2005); His6-GlnA1 (0.95 μM), His6-GlnK1 (0.65 μM); 2:1,4, Gutt et al. 2021).

      It is also known that PII proteins such as GlnK1 can bind ADP, ATP, and 2-OG. Interestingly, however, for various described PII proteins, 2-OG can only bind after the binding of ATP.

      So, the crucial question here is what is the binding state of GlnK1? 

      Were these assays performed in the absence of ATP? This is key to fully understand and connect the results to the previous observations. For example, if the GlnK1 used was bound to ADP but not to ATP, then the added 2-OG might indeed only be able to affect GlnA1 (leading to its activation/oligomerization). If this were true and according to the data reported, ADP would prevent GlnK1 from interacting with any oligomeric form of GlnA1. However, if GlnK1 bound to ATP is the form that interacts with GlnA1 (potentially validating previous results?) then, 2-OG would first bind to GlnK1 (assuming a higher affinity of 2-OG to GlnK1), eventually causing its release from GlnA1 followed by binding and activation of GlnA1.

      These experiments need to be done as they are essential to further understand the process. Given the ability of the authors to produce the protein and run such assays, it is unclear why they were not done here. As written in line 203, in this case, "under the conditions tested" is not a good enough statement, considering what is known in the field and how many more conclusions could easily be taken from such a setup.

      Thanks for the encouragement to investigate the ligand-bound states of GlnK1. We agree and plan to perform the suggested mass photometry experiments exploring the conditions under which GlnA1 and GlnK1 might interact in future work. In GlnA1 activity test assays, when evaluating the presence/effects of GlnK1 on GlnA1 activity, however, ATP was always present in high concentrations and still we did not observe a significant effect of GlnK1 on the GlnA1 activity.

      (5) Figure 2D legend claims that the graphic shows the percentage of dodecameric GlnA1 as a function of the concentration of 2-OG. This is not what the figure shows; Figure 2D shows the dodecamer/dimer (although legend claims monomer was used, in line 732) ratio as a function of 2-OG (stated in line 736!). If this is true, a ratio of 1 means 50 % of dodecamers and dimers co-exist. This appears to be the case when GlnK1 was added, while in the absence of GlnK1 higher ratios are shown for higher 2-OG concentration implying that about 3 times more dodecamers were formed than dimers. However, wouldn´t a 50 % ratio be physiologically significant?

      We apologize for the partially incorrect and also misleading figure legend and corrected it. Indeed, the ratio of dodecamers and dimers is shown. Furthermore, we did not use monomeric GlnA1 (the smallest entity is mainly a dimer, see Fig 1A), however, the molarity was calculated based on the monomer-mass. Concerning the significance of the difference between the maximum ratio of GlnA1 and GlnK1: The ratio does appear higher, but this is mostly because adding large quantities of GlnK1 broadens all peaks at low molecular weight. This happens because the GlnK1 signal starts overlapping with the signal from GlnA1, leading to inflated GlnA1 dimer counts. We therefore do not think that this is biologically significant, especially as the activities do not differ under these conditions.

      (6) Is it possible that the uncleaved GlnA1 tag is preventing interaction with GlnK1? This should be discussed.

      This is of course a very important point. We however realized that Schumacher et al. also used an N-terminal His-tag, so we assume that the N-terminal tag is not hampering the interaction.

      (7) Line 228: Please detail the reported discrepancies in rmsd between the current protein and the gram-negative enzymes.

      The differences in rmsd between our M.mazei GlnA1 structure and the structure of gram-negative enzymes is caused by a) sequence similarity: E.g. M.mazei GlnA1 compared to B.subtilis GlnA have a sequence percent identity of 58.47; b) ligands in the structure: The B.Subtilis structure contains L-Methionine-S-sulfoximine phosphate, a transition state inhibitor, while the M. mazei  structure contains 2OG; c) Methodology: The structural determination methods also contribute to these differences. B. subtilis GlnA was determined using X-ray crystallography, while the M. mazei GlnA1 structure was resolved using Cryo-EM, where the protein behaves differently in ice compared to a crystal.

      (8) Line 747: The figure title claims "dimeric interface" although the manuscript body only refers to "hexameric interface" or "inter-hexamer interface" (line 224). Moreover, the figure 4 legend uses terms such as vertical and horizontal dimers and this too should be uniformized within the manuscript.

      Thank you for your valuable feedback. We have updated both the figure title and the figure legend as well in the main text to ensure consistency in the description.

      (9) Line 752: The description of the color scheme used here is somehow unclear.

      Thanks for pointing this out. We changed the description to make it more comprehensive.

      (10) Please label H14/15 and H14´/H15´in Fig 4C zoom.

      We agree that this has not been very clear. We added helix labels.

      (11) In Figure 4D legend, make sure to note that the binding sites for the substrate are based on homologies with another enzyme poised with these molecules.

      The same should be clear in the text: sites are not known, they are assumed to be, based on homologies (paragraph starting at line 239).

      Concerning this comment we want to point out that we studied the exact same enzyme as the Schumacher group, except that we used 2-OG in our experiments, which they did not.

      (12) Figure 3 appears redundant in light of Figure 4. 

      (13) Line 235: When mentioning F24, please refer to Figure 5.

      Thank you, we changed that accordingly.

      (14) Please provide the distances for the bonds depicted in Figure 4B.

      Thanks for pointing this out, we added distance labels to Figure 4B. For reasons of clarity only to three H-bonds.

      (15) Line 241: D57 is likely serving to abstract a proton from ammonium, what is residue Glu307 potentially doing? The information seems missing in light of how the sentence is built.

      Thanks for pointing this out. According to previous studies both residues are likely involved in proton abstraction - first from ammonium, and then from the formed gamma-ammonium group. Additionally, they contribute in shielding the active site from bulk solvent to prevent hydrolysis of the formed phospho-glutamate.

      (16) Why do the authors assume that increased concentrations of 2-OG are a signal for N starvation only in M. mazei and not in all prokaryotic equivalent systems (line 288)?

      In line 288, we did not claim that this is a unique signal for M. mazei. It is also the central N-starvation signal in Cyanobacteria but not directly perceived by the cyanobacterial GS through binding directly to GS.

      The authors should look into the residues that bind 2-OG and check if they are conserved in other GS. The results of this sequence analysis should be discussed in line with the variable prokaryotic glutamine synthetase types of activity modulation that were exposed in the introduction and Figure 7.

      Please refer to supplementary figure S5, where we already aligned the mentioned glutamine synthetase sequences. Since this was also already discussed in Müller et al. 2024, we did not want to repeat their observations and refer to our supplementary figure in too much detail.

      (17) Figure 5 title: Replace TS by transition state structures of homology enzymes, or alike.

      Thank you for this suggestion. We did not change the title however, since it is not a homologue but the exact same glutamine synthetase from Methanosarcina mazei.

      (18) Line 249: D170 is not shown in Figure 5A or elsewhere in Figure 5.

      Thank you for pointing this out. We added D170 to figure 5A.

      (19) Representative density for the residues binding 2-OG should be provided, maybe in a supplemental figure.

      Thank you for the suggestion. We added the densities of 2-OG-binding residues to figure 4B

      (20) Line 260: Please add a reference when describing the phosphoryl transfer.

      We thank the reviewer for this important point and added that accordingly.

      (21) Line 296: The binding of 2-OG indeed appears to be cooperative, such that at concentrations above its binding affinity to the protein, only dodecamers are seen (under experimental conditions). However, claiming that the oligomerization is fast is not correct when the experimental setup includes 10 minutes of incubation before measurements are done. Please correct this within the entire manuscript.

      A (fast) continuous kinetic assay could have confirmed this point and revealed the oligomerization steps and the intermediaries in the process (maybe monomer/dimers, then dimers/hexamers, and then hexamers/dodecamers). Such assays would have been highly valuable to this study.

      We thank the reviewer for this suggestion, but disagree. It is indeed a rather fast regulation (as activity assays without pre-incubation only takes 1 min longer to reach full activity, see the newly included suppl. Fig S6). Considering other regulation mechanisms like e.g. transcription or translation regulation, an activation that takes only 60 s is actually quite quick.

      (22) Line 305 (and elsewhere in the manuscript): the authors state that 2-OG primes the active site for a transition state. This appears incorrect. The transition state is the highest energy state in an enzymatic reaction progressing from substrate to product. Meaning, the transition state is a state that has a more or less modified form of the original substrate bound to the active site. This is not the case.

      In line 366 an "active open state" appears much more adequate to use. 

      We agree and changed accordingly throughout the manuscript.

      (23) Line 330: Please delete "found". Eventually replace it with "confirmed": As the authors write, others have described this residue as a ligand to glutamine.

      Thanks, we changed that accordingly, although previous descriptions were just based on homologies without the experimental validation.

      (24) The discussion in at various points summarizing again the results. It should be trimmed and improved.

      (25) Line 381: replace "two fast" with "fast"?

      We thank the reviewer for this suggestion, but disagree on this point. We especially wanted to highlight that there are two central nitrogen-metabolites involved in the direct regulation of GlnA1, that means TWO fast direct processes mediated by 2-OG and glutamine.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This manuscript describes an important study of the giant virus Jyvaskylavirus. The characterisation presented is solid, although, in the current form, it is not clear to what extent these findings change our perception of how giant viruses, especially those isolated from a cold environment, function. The work will be of interest to virologists working on giant viruses as well as those working with other members of the PRD1/Adenoviridae lineage.

      Thank you for the revision and positive comments. We decided to submit our revised version of the manuscript with changes made in light of the comments made by the editorial team and the reviewers. We hope that now the manuscript is in a better shape and satisfies all comments received. Major changes made were:

      - We changed the author order considering reviewer 2 comments (point 11). Note that no author was added or removed, we just rearranged the order of authorship.

      - We included a new supplementary table with the Jyvaskylavirus genome annotation. This is now supplementary table 2.

      - We included a supplementary figure 9 to support our changes based on reviewer 2 comments (point 6).

      - Figures 2,5,6,7 and the supplementary figure 2 were updated to accommodate our answers to different reviewer comments.

      - Three new references were added to support some of our changes.

      Below you will find our responses to each specific point raised by the reviewers.

      Public Reviews:

      Reviewer #1 (Public review):

      This study presents Jyvaskylavirus, a new member of the Marseilleviridae family, infecting Acanthamoeba castellanii. The study provides a detailed and comprehensive genomic and structural analysis of Jyvaskylavirus. The authors identified ORF142 as the capsid penton protein and additional structural proteins that comprise the virion. Using a combination of imaging techniques the authors provide new insights into the giant virus architecture and lifecycle. The study could be improved by providing atomic coordinates and refinement statistics, comparisons with available giant virus structures could be expanded, and the novelty in terms of the first isolated example of a giant virus from Finland could be expounded upon.

      The study contributes new structural and genomic diversity to the Marseilleviridae family, hinting at a broader distribution and ecological significance of giant viruses than previously thought.

      Thank you for your constructive comments. We have addressed each point raised in our rebuttal letter and revised the manuscript accordingly. By following your specific comments, we improved the manuscript regarding atomic coordinates, refinement statistics and novelty of finding a Finnish marseillevirus. Details are provided in the specific answers to your points.

      Reviewer #2 (Public review):

      Summary:

      This paper describes the molecular characterisation of a new isolate of the giant virus Jyvaskylavirus, a member of the Marseilleviridae family infecting Acanthamoeba castellanii. The isolate comes from a boreal environment in Finland, showcasing that giant viruses can thrive in this ecological niche. The authors came up with a non-trivial isolation procedure that can be applied to characterise other members of the family and will be beneficial for the virology field. The genome shows typical Marseilleviridae features and phylogenetically belongs to their clade B. The structural characterisation was performed on the level of isolated virion morphology by negative stain EM, virions associated with cells either during the attachment or release by helium microscopy, the visualisation of the virus assembly inside cells using stained thin sections, and lastly on the protein secondary structure level by reconstructing ~6 A icosahedral map of the massive virion using cryoEM. The cryoEM density combined with gene product structure prediction enabled the identification and functional assessment of various virion proteins.

      Strengths:

      The detailed description of the virus isolation protocol is the largest strength of the paper and this reviewer believes it can be modified for isolating various viruses infecting small eukaryotes. The cryoEM map allows us to understand how exceptionally large virions of these viruses are stabilised by minor capsid proteins and nicely demonstrates the integration of medium-resolution cryoEM with protein structure prediction in deciphering virion protein function. The visualisation of ongoing virus assembly inside virus factories brings interesting hypotheses about the process that; however, needs to be verified in the next studies.

      Weaknesses:

      The conclusions from helium microscopy images are overinterpreted, as the native membrane structure cannot be preserved in a fixed and dehydrated sample. In the image, there are many other parts of the curved membrane and a lot of virions, to me it seems the specific position of the highlighted virion could arise by a random chance. The claim that the cells were imaged in the near-original state by this method should be therefore omitted. Also, no mass spectrometry data are presented that would supplement and confirm the identity of virion proteins which predicted models were fitted into the cryoEM density. For a general virology reader outside of the giant virus field, the results presented in the current state might not have enough influence and the section should be rewritten to better showcase the novelty of findings.

      Thank you for your constructive comments. We thank reviewer #2 for highlighting these weaknesses, giving us the opportunity to improve our study. We have removed the claim that the cells were imaged in a near-original state. Additionally, we agree that the positions of the virions on the cell surface could result from a random distribution. However, the specific virion in panel 3C is situated halfway into a crevice, and it cannot be ruled out that this particular one could be in the process of being endocytotically uptaken. This is why we used the term "probably" while referring to this finding. Regarding the mass spectrometry data, while we understand that MS data would provide an additional layer of evidence to validate the specific proteins present in the virion, they would not confirm the precise location or role of these proteins within the virion.

      We have addressed each point raised in our rebuttal letter and revised the manuscript accordingly.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      I have only minor comments which should be relatively simple to address:

      (1) Atomic coordinates should be deposited in the PDB, and refinement statistics for the models provided, for example by expanding Table S2.

      We thank reviewer #1 for the suggestion. In the original submission in the ‘Data availability’ statement we stated that ‘Predicted Jyvaskylavirus PDB models using ModelAngelo and Alphafold have been deposited at BioStudies under the accession number S-BSST1654’. So, atomic coordinates of all predicted models are publicly available at the https://www.ebi.ac.uk/biostudies/ ; for additional clarity we also added the link in the ‘Data availability’ statement in the revised version.

      Our reasoning of not depositing them in the Protein Data Bank associated to our EMD-51613 entry is because they remain predicted models rigid-body fitted into the Jyvaskylavirus density map of 6.3 Å resolution. However, we have added into our BioStudies deposition (BSST1654) the whole Jyvaskylavirus pentameric assembly model (including all identified and predicted major and minor capsid proteins) rigid-body fitted into the Jyvaskylavirus map, and it can be easily downloaded.

      We did not to perform the real-space ‘minimization_global’ refinement of the predicted models corresponding to the ORFs of Melbournevirus (or Jyvaskylavirus) into the corresponding Melbournevirus available densities with entries EMD-37188, 37189, 37190 at ~ 3.5 Å resolution (by block-based reconstruction methods) as these maps were generated and deposited by other authors. Instead, we performed the rigid-body fit-into-map procedure of the individual predicted Jyvaskylavirus models into the previously deposited Melbournevirus maps using ChimeraX, demonstrating a fold-map alignment and assignment (see for example the individual stereo views in Supplementary Figure 6).

      In the revised version, we now provide the refinement statistics for the complete Jyvaskylavirus pentameric assembly (inclusive of peripentonal major capsid and minor capsid proteins) rigid-body fitted as a whole into the Melbournevirus 5-block reconstruction map using PHENIX, resulting into a CC<sub>mask</sub> of 57.3% (this is also stated in Supplementary Figure 7). The same pentameric assembly model was then placed into our lower-resolution 6.3 Å Jyvaskylavirus 3D density map in ChimeraX and rigid-body refined as a whole in PHENIX, yielding a predictably lower CC<sub>mask</sub> of 33%. This pentameric assembly model has now also been included into BioStudies entry.

      The procedure for this rigid body fitting and refinement has been clarified and added to the 'Materials and Methods' section as follows:

      “Then, the corresponding full 3D models were predicted using AlphaFold3 and fitted into the Melbournevirus and Jyvaskylavirus cryoEM density using the fit-into-map routine in ChimeraX together with the peripentonal capsomers (Meng et al 2023). To assess the metric of this fitting (Supplementary Figure 7), the 3.5 Å five-fold Melbournevirus block 3D density (EMDB-37190) was boxed around the pentameric assembly model and refined as a whole using rigid-body refinement in PHENIX, yielding a CC<sub>mask</sub> of 57.3%. The same pentameric model was subsequently fitted into the 6.3 Å Jyvaskylavirus 3D cryo-EM density (previously boxed around the model), resulting in a lower CC<sub>mask</sub> of 33%, consistent with the limited resolution of the capsid map and below regions.”

      (2) The results section 'Jyvaskylavirus three-dimensional architecture' could be expanded to compare and contrast with other giant virus structures, in terms of T-number, diameter, and features on and inside the capsid. This is not essential but would help focus claims of novelty with regard to structure.

      We have added a few lines as indicated by reviewer#1 to contextualize in morphological terms Jyvaskylavirus with other NCLDV viruses as follows:

      “Both the capsid organization and virion size are similar to those of other Marseilleviruses, such as Melbournevirus and Tokyovirus. Pacmanvirus, considered to be at the crossroads between Asfarviridae and Faustoviruses, also possesses the same T number (309) and a comparable diameter to Jyvaskylavirus. In contrast, other giant viruses, such as African swine fever virus (ASFV), representative of the Asfarviridae family, have a T number of 277 and a diameter of approximately 2,100 Å, while PBCV-1, a member of the Phycodnaviridae family, has a T number of 169 and an average diameter of 1,900 Å. All of the above-mentioned viruses have been shown to possess a major capsid protein with a vertical double jelly-roll fold that composes the capsid shell, along with an internal membrane bilayer. Minor capsid proteins have been identified and structurally modelled for the smaller virions ASFV and PBCV-1 (Wang et al. 2019; Shao et al. 2022).”

      (3) The authors highlight one of the main novelties of the virus as being the first to be isolated from Finland. The first isolation of a giant virus from the region is indeed a success but reported isolation experiments for giant viruses are still relatively few. To help shed light on the likely distribution of Jyvaskylavirus-like viruses in the region, and further afield, the genome of Jyvaskylavirus could be searched against relevant available metagenomes.

      In the last decade the interest on finding giant viruses by metagenomics has increased. However, the focus has been on marine environments, where these viruses are shown to be prevalent. Besides the few isolates from the Northern hemisphere mentioned in the manuscript, northern giant viruses were detected in metagenome datasets from glacier samples, epishelf lakes, the permafrost, the Nordic seas and in a deep-sea hydrothermal vent. Most of the genomic hits are for mimivirus-like or phycodnavirus-like sequences. A few marseilleviruses were found in the Loki’s castle deep sea vent, and we have already included these sequences in the analysis shown by the supplementary figure 3. In this case the deep-sea vent viruses clusters outside the conventional clades of the marseilleviridae family, evidencing their uniqueness.

      In response to the suggestion of exploring the distribution of Jyvaskylavirus, we utilized the MGnify-database to search for DNA polymerase (DNApol) and major capsid protein (MCP) sequences. Our findings revealed multiple hits with significantly low E-values (< 1e-80), where both DNApol and MCP were detected from the same studies, indicating the presence of similar virus-like particles (VLPs) globally. Of particular interest was the detection of similar sequences in metagenomes and transcriptomes obtained from drinking water distribution systems of ground and surface waterworks in central and eastern Finland (https://www.ebi.ac.uk/metagenomics/studies/MGYS00005650#overview). We have acknowledged this in the manuscript and cited the appropriated references, as follows:

      Results: “Searching the Jyvaskylavirus major capsid protein and DNA polymerase sequences in the MGnify-database (Richardson et al 2023) yields multiple hits with significantly low E-values (< 1e-80), as expected from the apparent ubiquity of marseilleviruses. Of note was the detection of similar sequences in metagenomes and transcriptomes obtained from drinking water distribution systems of ground and surface waterworks in central and eastern Finland, evidencing that marseilleviruses are prevalent but still unexplored in this region (Tiwari et al 2022)”.

      Discussion: “Marseillevirus DNA polymerase sequences are present in metagenomes from Finnish drinking water distribution systems (Tiwari et al 2022), hinting to a wide distribution of these viruses and still unknown ecological role in Central and Eastern Finland.”

      Reviewer #2 (Recommendations for the authors):

      Apart from the major comments in the weaknesses section, I have these additional minor comments to the authors:

      (1) I do not understand why the authors emphasized the uniqueness of isolating a giant virus from Finland. I think the manuscript would benefit if they rather emphasize that the virus comes from a boreal environment.

      The first giant virus, APMV, was described in 2003. In the following years the apparent ubiquity of these viruses was evidenced by two fronts. Metagenomics made clear that giant viruses are found almost everywhere, biased towards the oceans. Isolation efforts brought new virus groups in evidence but has been so far biased towards central Europe and South America samples. The closest isolated giant viruses to Jyvaskylavirus would be either an uncharacterized Swedish cedratvirus or a few microalgae-infecting mimivirus-like and phycodnaviruses-like isolates from Norway. Among marseilleviruses, Jyvaskylavirus is the northernmost isolate so far. Other marseilleviruses from the northern hemisphere were found in France, India, Japan and Algeria only.

      We still believe that finding a giant virus in Finland is relevant, considering that no other is known to date, be as an isolate or detected by genomics. We have made these observations clearer in the manuscript, giving emphasis to the boreal environment as well.

      (2) All discussed AlphaFold models should be added as Supplementary PDB data.

      We thank reviewer #2 for the suggestion. In the original submission in the ‘Data availability’ statement we stated that ‘Predicted Jyvaskylavirus PDB models using ModelAngelo and Alphafold have been deposited at BioStudies under the accession number S-BSST1654’. So, atomic coordinates of all predicted models are publicly available at the https://www.ebi.ac.uk/biostudies/ ; for additional clarity we also added the link in the ‘Data availability’ statement in the revised version.

      Our reasoning of not depositing them in the Protein Data Bank associated to our EMD-51613 entry is because they remain predicted models rigid-body fitted into the Jyvaskylavirus density map of 6.3 Å resolution. However, we have added into our BioStudies deposition (BSST1654) the whole Jyvaskylavirus pentameric assembly model (including all identified and predicted major and minor capsid proteins) rigid-body fitted into the Jyvaskylavirus map, and it can be easily downloaded.

      We did not to perform the real-space ‘minimization_global’ refinement of the predicted models corresponding to the ORFs of Melbournevirus (or Jyvaskylavirus) into the corresponding Melbournevirus available densities with entries EMD-37188, 37189, 37190 at ~ 3.5 Å resolution (by block-based reconstruction methods) as these maps were generated and deposited by other authors. Instead, we performed the rigid-body fit-into-map procedure of the individual predicted Jyvaskylavirus models into the previously deposited Melbournevirus maps using ChimeraX, demonstrating a fold-map alignment and assignment (see for example the individual stereo views in Supplementary Figure 6).

      In the revised version, we now provide the refinement statistics for the complete Jyvaskylavirus pentameric assembly (inclusive of peripentonal major capsid and minor capsid proteins) rigid-body fitted as a whole into the Melbournevirus 5-block reconstruction map using PHENIX, resulting into a CC<sub>mask</sub> of 57.3% (this is also stated in Supplementary Figure 7).

      The same pentameric assembly model was then placed into our lower-resolution 6.3 Å Jyvaskylavirus 3D density map in ChimeraX and rigid-body refined as a whole in PHENIX, yielding a predictably lower CC<sub>mask</sub> of 33%. This pentameric assembly model has now also been included into BioStudies entry.

      The procedure for this rigid body fitting and refinement has been clarified and added to the 'Materials and Methods' section as follows:

      “Then, the corresponding full 3D models were predicted using AlphaFold3 and fitted into the Melbournevirus and Jyvaskylavirus cryoEM density using the fit-into-map routine in ChimeraX together with the peripentonal capsomers (Meng et al 2023). To assess the metric of this fitting (Supplementary Figure 7), the 3.5 Å five-fold Melbournevirus block 3D density (EMDB-37190) was boxed around the pentameric assembly model and refined as a whole using rigid-body refinement in PHENIX, yielding a CC<sub>mask</sub> of 57.3%. The same pentameric model was subsequently fitted into the 6.3 Å Jyvaskylavirus 3D cryo-EM density (previously boxed around the model), resulting in a lower CC<sub>mask</sub> of 33%, consistent with the limited resolution of the capsid map and below regions.”

      (3) Figure 2A: Could ORFs that encode structural proteins discussed in the paper, be somehow highlighted?

      We have updated Figure2A to include this information.

      (4) Figure 2C: Could be somehow highlighted from these members on which there was conducted structural characterisation (e.g. by some symbol next to the name)?

      We have updated Figure2C to include this information.

      (5) Figure 5A: Could the central bid be shown in a lower threshold (you can retain the threshold for the protein shell)? It would be interesting to see some details of the interior, rather than a massive blob.

      We have decreased the threshold level of the map as suggested.

      (6) Figure 6: the density corresponding to MCPs, minor capsid, and penton proteins respectively could be colour-zoned in Chimera(X). This would better visualise where each entity lies.

      About ORF142 - what other virus protein possesses this fold? Is it similar to the penton protein in other PRD1/Adenoviridae viruses? Maybe some comparison could be presented?

      We have incorporated the feedback from reviewer_#_2 by modifying the corresponding panel A in Figure 6. We have colour-zoned the penton (ORF142), some of the density region corresponding to the MCPs (ORF184) and to the minor cap proteins (ORF121). We have kept in grey the density corresponding to other minor proteins, and those we were able to identify are logically introduced later and shown as individual coloured cartoon tube models fitted into the density in panel A of Figure 7.

      Regarding ORF142, we have included a reference in the Discussion section to a new Supplementary Figure 9, where we provide a side-by-side comparison of the predicted Jyvaskylavirus penton protein model with experimentally derived penton protein models of PRD1 and HCIV-1. In light of this comparison, we have also added a brief clarification in the Discussion as follows:

      “However, in ORF142, the CHEF strands are predicted to be tilted relative to the BIDG strands, with an estimated angle of approximately 60° based on visual inspection (Supplementary Figure 9).”

      (7) Figure 7B: Could the density around the protein be zoned (rather than side view clipped), as this would better showcase how it fits the density?

      Initially, we presented a side view of the clipped surface to highlight the correspondence between the wall-shaped density, characteristic of a low-resolution beta-barrel, and the beta-barrel of the predicted model. Following the Reviewer’s suggestion, we have now surface-zoned the density and provided a stereo view of the density with the model fitted into the map using ChimeraX. While we recognize that stereo views are no longer commonly used in main text figures, we believe they remain valuable for visually assessing the overall match in low-resolution 3D density maps.

      (8) The authors did not try to reconstruct the asymmetric feature of the virion by classifying pentons, which may have identified a special vertex, one they claim might be required for genome packaging in "open particles". I understand the number of particles is low, but even low-resolution classification in C5 might be of interest in the field.

      We thank reviewer #2 for this valuable comment. The potential existence of a unique vertex in Marseilleviruses remains an open and intriguing question. Further investigations, including a significant increase in the number of particles, may help clarify this issue, and we plan to explore this topic in future structural studies.

      (9) Supplementary Figure 2: It would be interesting how the titre changes after the 12 hours, will it plateau? Could you add a bar showing the original titre to the chart showing stability after 109 days? I like the data in this figure and think it should be transferred to the main text.

      The titre at the 12h time point is very close to the titre we often get in our stocks, indicating that indeed it is close to peaking. For comparison: the titre of the 12-hour time point was 10<sup>11.55</sup> TCID50/ml, whereas our stock has a titre of 10<sup>11.66</sup> TCID50/ml. Our growth curve had more time points up to 48h and we lost the later time points due to a higher viral load than predicted, which led to us not being able to count these time points with the dilutions used. Showing the first 12 hours was enough for our initial purpose, which was to show a quick replication cycle for Jyvaskylavirus, in accordance with the other marseilleviruses in which the timing of the replication cycle was observed (see the answer for point 10 below).

      We have added a bar representing the original titre of the stock used for the stability experiment as suggested.

      While preparing the draft we were divided into having the growth and stability figure in the main text or in the supplementary material. Our decision was to move this data to the supplementary material and keep the focus of the main text on the discovery, genome analysis and structural data, as these are the main findings of our work. The specifics regarding stability, growth and other uncharacterized VLPs went to the supplementary material for those in the field who are interested in looking deeper. That being said, we will decide to keep this data as supplementary material if you and the editor agrees.

      (10) In the Discussion, the authors should focus on how our perception of giant viruses changes by this study - compare with other growth curves, stability assays, and structures of giant viruses, showcasing how prevalent those stabilising minor capsid proteins are, etc. My impression is that in the current form, it is just not clear if/how substantial these findings are and such a comparison and putting the results in a bigger picture would considerably increase the impact of the paper.

      Our comparisons with other marseilleviruses were based on genomic and structural characteristics, the two fronts we had data from the literature and databases to compare to. Sadly there is not too much information regarding stability and growth of other isolates that could be used for an in-depth comparison. For example: although marseilleviruses are known to have a fast replication cycle, this has been measured by DAPI staining of DNA inside infected cells to evaluate viral factory formation (Boyer et al 2009), or by time-series observations of viral cycle stages by electron microscopy (Fabre et al 2017), and not by viral titration as done here. We included a mention to these references in the results:

      “A fast replication cycle is a feature also shown for other marseilleviruses (Boyer et al 2009 ; Fabre et al 2017).”

      The literature also does not show virion stability of other isolates, making it impossible to have a comparison with jyvaskylavirus. A comparative study testing different isolates side by side is definitely of relevance and interest, but this would be difficult to be done in a short time due to obtaining other isolates. We believe the results in this manuscript might set some parameters to be used for comparing with other marseilleviruses, by our groups and others, in the future.

      Regarding the prevalence of the minor capsid proteins, we have expanded and clarified the identification of ORFs in Melbournevirus in the ‘Results’ and ‘Discussion’ sections. The revised Supplementary Table 4 has been updated accordingly and referenced in the results to clarify that the identification of Melbourne ORFs was carried out in BLASTp by querying the Jyvaskylavirus minor protein sequences exclusively against the Melbournevirus isolate 1 (NCBI Reference Sequence: NC_025412.1). BLASTp was then performed against the full sequence database, and homologous sequences were primarily retrieved from other marseillaviruses. These results have been compiled in a new Supplementary Table 5.

      However, Supplementary Table 5 also shows that the hits for Melbournevirus are not ranked at the top, and in some cases, they do not appear among the top hits.

      The ‘Results’ section now contains the following text:

      “To this end, we identified the corresponding Jyvaskylavirus ORFs in Melbournevirus through sequence comparison with Melbournevirus isolate 1 (NCBI Reference Sequence: NC_025412.1) (Supplementary Table 34). However, when the identified Jyvaskylavirus ORF sequences were analyzed using BLASTp without restricting the search to the Melbournevirus reference, many hits were observed in other giant viruses, primarily marseillevirus. Remarkably, some of these hits scored higher than those for Melbournevirus, supporting the presence of homologous proteins in these viruses (Supplementary Table 5).”

      The ‘Discussion’ section now contains the following text:

      “Additionally, the observation that the identified Jyvaskylavirus minor capsid protein sequences are shared across other marseillaviruses supports their essential structural and stabilizing roles in these viruses.”

      At the same time, we have modified the ‘Materials and Methods’ section to include a reference to Supplementary Figure 5, where the use of ModelAngelo is mentioned. Additionally, a new Supplementary Figure 10 has been included to clarify how the residues built into the Melbournevirus density using ModelAngelo (without prior knowledge of any sequence) are subsequently matched with the Jyvaskylavirus sequences.

      (11) Based on the author's statement, Iker Arriaga did all the cryoEM experiments. It is strange to me they are not placed higher on the author's list.

      We thank you for this observation and agree with your comment. This manuscript has been in preparation for a few years, and the first draft had the author order defined before the structural data collection and analyses were completed. Iker participation was indeed important and substantial from the first draft to the submitted version and he definitely deserves a better author placement. We have modified the author order to accommodate this. Note that only the author order changed and that no author has been included or removed.

    1. Reviewer #1 (Public review):

      Ejdrup, Gether, and colleagues present a sophisticated simulation of dopamine (DA) dynamics based on a substantial volume of striatum with many DA release sites. The key observation is that a reduced DA uptake rate in the ventral striatum (VS) compared to the dorsal striatum (DS) can produce an appreciable "tonic" level of DA in VS and not DS. In both areas they find that a large proportion of D2 receptors are occupied at "baseline"; this proportion increases with simulated DA cell phasic bursts but has little sensitivity to simulated DA cell pauses. They also examine, in a separate model, the effects of clustering dopamine transporters (DAT) into nanoclusters and say this may be a way of regulating tonic DA levels in VS. I found this work of interest and I think it will be useful to the community. At the same time, there are a number of weaknesses that should be addressed, and the authors need to more carefully explain how their conclusions are distinct from those based on prior models.

      (1) The conclusion that even an unrealistically long (1s) and complete pause in DA firing has little effect on DA receptor occupancy is potentially important. The ability to respond to DA pauses has been thought to be a key reason why D2 receptors (may) have high affinity. This simulation instead finds evidence that DA pauses may be useless. This result should be highlighted in the abstract and discussed more.

      (2) The claim of "DAT nanoclustering as a way to shape tonic levels of DA" is not very well supported at present. None of the panels in Figure 4 simply show mean steady-state extracellular DA as a function of clustering. Perhaps mean DA is not the relevant measure, but then the authors need to better define what is and why. This issue may be linked to the fact that DAT clustering is modeled separately (Figure 4) to the main model of DA dynamics (Figures 1-3) which per the Methods assumes even distribution of uptake. Presumably, this is because the spatial resolution of the main model is too coarse to incorporate DAT nanoclusters, but it is still a limitation. As it stands it is convincing (but too obvious) that DAT clustering will increase DA away from clusters, while decreasing it near clusters. I.e. clustering increases heterogeneity, but how this could be relevant to striatal function is not made clear, especially given the different spatial scales of the models.

      (3) I question how reasonable the "12/40" simulated burst firing condition is, since to my knowledge this is well outside the range of firing patterns actually observed for dopamine cells. It would be better to base key results on more realistic values (in particular, fewer action potentials than 12).

      (4) There is a need to better explain why "focality" is important, and justify the measure used.

      (5) Line 191: " D1 receptors (-Rs) were assumed to have a half maximal effective concentration (EC50) of 1000 nM"<br /> The assumptions about receptor EC50s are critical to this work and need to be better justified. It would also be good to show what happens if these EC50 numbers are changed by an order of magnitude up or down.

      (6) Line 459: "we based our receptor kinetics on newer pharmacological experiments in live cells (Agren et al., 2021) and properties of the recently developed DA receptor-based biosensors (Labouesse & Patriarchi, 2021). Indeed, these sensors are mutated receptors but only on the intracellular domains with no changes of the binding site (Labouesse & Patriarchi, 2021)"<br /> This argument is diminished by the observation that different sensors based on the same binding site have different affinities (e.g. in Patriarchi et al. 2018, dLight1.1 has Kd of 330nM while dlight1.3b has Kd of 1600nM).

      (7) Estimates of Vmax for DA uptake are entirely based on prior fast-scan voltammetry studies (Table S2). But FSCV likely produces distorted measures of uptake rate due to the kinetics of DA adsorption and release on the carbon fiber surface.

      (8) It is assumed that tortuosity is the same in DS and VS - is this a safe assumption?

      (9) More discussion is needed about how the conclusions derived from this more elaborate model of DA dynamics are the same, and different, to conclusions drawn from prior relevant models (including those cited, e.g. from Hunger et al. 2020, etc).

    2. Author response:

      eLife Assessment

      The conclusions of this work are based on valuable simulations of a detailed model of striatal dopamine dynamics. Establishing that a lower dopamine uptake rate can lead to a 'tonic' level of dopamine in the ventral but not dorsal striatum, and that dopamine concentration changes at short delays can be tracked by D1 but not D2 receptor activation, is of value and will be of interest to dopamine aficionados. However, the simulations are incomplete, providing only partial support for the key claims. Several things can be done to strengthen the conclusions, including, for example, but not exclusively, a demonstration of how the results would change as a function of changes in D2 affinity.

      We sincerely thank the Editors and Reviewers for their insightful comments on our manuscript. We are pleased that our simulations are recognized as interesting, sophisticated and valuable. Moreover, we fully agree that many of the findings will be of particular interest to dopamine aficionados. While we maintain that our simulations provide a solid basis for the key claims, we acknowledge that the conclusions can be further strengthened by the revisions suggested below.

      Reviewer #1 (Public review):

      Ejdrup, Gether, and colleagues present a sophisticated simulation of dopamine (DA) dynamics based on a substantial volume of striatum with many DA release sites. The key observation is that a reduced DA uptake rate in the ventral striatum (VS) compared to the dorsal striatum (DS) can produce an appreciable "tonic" level of DA in VS and not DS. In both areas they find that a large proportion of D2 receptors are occupied at "baseline"; this proportion increases with simulated DA cell phasic bursts but has little sensitivity to simulated DA cell pauses. They also examine, in a separate model, the effects of clustering dopamine transporters (DAT) into nanoclusters and say this may be a way of regulating tonic DA levels in VS. I found this work of interest and I think it will be useful to the community. At the same time, there are a number of weaknesses that should be addressed, and the authors need to more carefully explain how their conclusions are distinct from those based on prior models.

      (1) The conclusion that even an unrealistically long (1s) and complete pause in DA firing has little effect on DA receptor occupancy is potentially important. The ability to respond to DA pauses has been thought to be a key reason why D2 receptors (may) have high affinity. This simulation instead finds evidence that DA pauses may be useless. This result should be highlighted in the abstract and discussed more.

      We appreciate that the reviewer finds our work interesting and useful to the community. However, we acknowledge that in the revised version we to need to better describe how our conclusions are different from those reached based on previous models.

      We will also carry out new simulations across a range of D2R affinities to assess how this will affect the finding that even a long pause in DA firing has little effect on DR2 receptor occupancy. As also suggested, the results will be highlighted and further discussed.

      (2) The claim of "DAT nanoclustering as a way to shape tonic levels of DA" is not very well supported at present. None of the panels in Figure 4 simply show mean steady-state extracellular DA as a function of clustering. Perhaps mean DA is not the relevant measure, but then the authors need to better define what is and why. This issue may be linked to the fact that DAT clustering is modeled separately (Figure 4) to the main model of DA dynamics (Figures 1-3) which per the Methods assumes even distribution of uptake. Presumably, this is because the spatial resolution of the main model is too coarse to incorporate DAT nanoclusters, but it is still a limitation.

      We will improve our definitions and descriptions relating to nanoclustering of DAT in the revised version of the manuscript. We fully agree that the spatial resolution of the main model is a limitation and, ideally, that the nanoclustering should be combined with the large-scale release simulations. Unfortunately, this would require many orders of magnitude more computational power than currently available.

      As it stands it is convincing (but too obvious) that DAT clustering will increase DA away from clusters, while decreasing it near clusters. I.e. clustering increases heterogeneity, but how this could be relevant to striatal function is not made clear, especially given the different spatial scales of the models.

      Thank you for raising this important point. While it is true that DAT clustering increases heterogeneity in DA distribution at the microscopic level, the diffusion rate is, in most circumstances, too fast to permit concentration differences on a spatial scale relevant for nearby receptors. Accordingly, we propose that the primary effect of DAT nanoclustering is to decrease the overall uptake capacity, which in turn increases overall extracellular DA concentrations. Thus, homogeneous changes in extracellular DA concentrations can arise from regulating heterogenous DAT distribution. An exception to this would be the circumstance where the receptor is located directly next to a dense cluster – i.e. within nanometers. In such cases, local DA availability may be more directly influenced by clustering effects. This will be further discussed in the revised manuscript.

      (3) I question how reasonable the "12/40" simulated burst firing condition is, since to my knowledge this is well outside the range of firing patterns actually observed for dopamine cells. It would be better to base key results on more realistic values (in particular, fewer action potentials than 12).

      We fully agree that this typically is outside the physiological range. The values are included to showcase what extreme situations would look like.

      (4) There is a need to better explain why "focality" is important, and justify the measure used.

      We will expand on the intention of this measure in the revised manuscript. Thank you for pointing out this lack of clarification.

      (5) Line 191: " D1 receptors (-Rs) were assumed to have a half maximal effective concentration (EC50) of 1000 nM" The assumptions about receptor EC50s are critical to this work and need to be better justified. It would also be good to show what happens if these EC50 numbers are changed by an order of magnitude up or down.

      We agree that these assumptions are critical. Simulations on effective off-rates across a range of EC50 values will be included in the revised version.

      (6) Line 459: "we based our receptor kinetics on newer pharmacological experiments in live cells (Agren et al., 2021) and properties of the recently developed DA receptor-based biosensors (Labouesse & Patriarchi, 2021). Indeed, these sensors are mutated receptors but only on the intracellular domains with no changes of the binding site (Labouesse & Patriarchi, 2021)”

      This argument is diminished by the observation that different sensors based on the same binding site have different affinities (e.g. in Patriarchi et al. 2018, dLight1.1 has Kd of 330nM while dlight1.3b has Kd of 1600nM).

      We sincerely thank the reviewer for highlighting this important point. We fully recognize the fundamental importance of absolute and relative DA receptor kinetics for modeling DA actions and acknowledge that differences in affinity estimates from sensor-based measurements highlight the inherent uncertainty in selecting receptor kinetics parameters. While we have based our modeling decisions on what we believe to be the most relevant available data, we acknowledge that the choice of receptor kinetics is a topic of ongoing debate. Importantly, we are making our model available to the research community, allowing others to test their own estimates of receptor kinetics and assess their impact on the model’s behavior. In our revised manuscript, we will further discuss the rationale behind our parameter choices, including: Our selection of a Kd value of 1000 nM for D1R (based on the observed affinities for D1R sensors) and an extrapolated Koff of 19.5 s<sup>-1</sup> (Labouesse & Patriarchi, 2021). Our use of a Kd value of 7 nM and an extrapolated Koff of 0.2 s<sup>-1</sup> for D2R, consistent with recent binding studies (Ågren et al., 2021).

      (7) Estimates of Vmax for DA uptake are entirely based on prior fast-scan voltammetry studies (Table S2). But FSCV likely produces distorted measures of uptake rate due to the kinetics of DA adsorption and release on the carbon fiber surface.

      We fully agree that this is a limitation of FSCV. However, most of the cited papers attempt to correct for this by way of fitting the output to a multi-parameter model for DA kinetics. If newer literature brings the Vmax values estimated into question, we have made the model publicly available to rerun the simulations with new parameters.

      (8) It is assumed that tortuosity is the same in DS and VS - is this a safe assumption?

      The original paper cited does not specify which region the values are measured in. However, a separate paper estimates the rat cerebellum has a comparable tortuosity index (Nicholson and Phillips, J Physiol. (1981)), suggesting it may be a rather uniform value across brain regions.

      (9) More discussion is needed about how the conclusions derived from this more elaborate model of DA dynamics are the same, and different, to conclusions drawn from prior relevant models (including those cited, e.g. from Hunger et al. 2020, etc).

      As part of our revision, we will expand the current discussion of our finding in the context of previous models in the manuscript

      Reviewer #2 (Public review):

      The work presents a model of dopamine release, diffusion, and reuptake in a small (100 micrometer^2 maximum) volume of striatum. This extends previous work by this group and others by comparing dopamine dynamics in the dorsal and ventral striatum and by using a model of immediate dopamine-receptor activation inferred from recent dopamine sensor data. From their simulations, the authors report two main conclusions. The first is that the dorsal striatum does not appear to have a sustained, relatively uniform concentration of dopamine driven by the constant 4Hz firing of dopamine neurons; rather that constant firing appears to create hotspots of dopamine. By contrast, the lower density of release sites and lower rate of reuptake in the ventral striatum creates a sustained concentration of dopamine. The second main conclusion is that D1 receptor (D1R) activation is able to track dopamine concentration changes at short delays but D2 receptor activation cannot.

      The simulations of the dorsal striatum will be of interest to dopamine aficionados as they throw some doubt on the classic model of "tonic" and "phasic" dopamine actions, further show the disconnect between dopamine neuron firing and consequent release, and thus raise issues for the reward-prediction error theory of dopamine.

      There is some careful work here checking the dependence of results on the spatial volume and its discretisation. The simulations of dopamine concentration are checked over a range of values for key parameters. The model is good, the simulations are well done, and the evidence for robust differences between dorsal and ventral striatum dopamine concentration is good.

      However, the main weakness here is that neither of the main conclusions is strongly evidenced as yet. The claim that the dorsal striatum has no "tonic" dopamine concentration is based on the single example simulation of Figure 1 not the extensive simulations over a range of parameters. Some of those later simulations seem to show that the dorsal striatum can have a "tonic" dopamine concentration, though the measurement of this is indirect. It is not clear why the reader should believe the example simulation over those in the robustness checks, for example by identifying which range of parameter values is more realistic.

      We appreciate that the reviewer finds our work interesting and carefully performed. The reviewer is correct that DA dynamics, including the presence and level of tonic DA, are parameter-dependent in both the dorsal striatum (DS) and ventral striatum (VS). Indeed, our simulations across a broad range of biological parameters were intended to help readers understand how such variation would impact the model’s outcomes, particularly since many of the parameters remain contested. Naturally, altering these parameters results in changes to the observed dynamics. However, to derive possible conclusions, we selected a subset of parameters that we believe best reflect the physiological conditions, as elaborated in the manuscript. This is eventually required in computational modelling of biological systems. In response to the reviewer’s comment, we will place greater emphasis on clarifying which parameter regimes produce a "tonic" versus "non-tonic" DA state in the DS. Additionally, we will underscore that the distinction between tonic and non-tonic states is not a binary outcome but a parameter-dependent continuum—one that our model now allows researchers to explore systematically. Finally, we will highlight how our simulations across parameter space not only capture this continuum but also identify the regimes that produce the most heterogeneous DA signaling, both within and across striatal regions.

      The claim that D1Rs can track rapid changes in dopamine is not well supported. It is based on a single simulation in Figure 1 (DS) and 2 (VS) by visual inspection of simulated dopamine concentration traces - and even then it is unclear that D1Rs actually track dynamics because they clearly do not track rapid changes in dopamine that are almost as large as those driven by bursts (cf Figure 1i).

      We would like to draw the attention also to Fig. S1, where the claim that D1R track rapid changes is supported in more depth. According to this figure, upon coordinated burst firing, the D1R occupancy rapidly increased as diffusion no longer equilibrated the extracellular concentrations on a timescale faster than the receptors – and D1R receptor occupancy closely tracked extracellular DA with a delay on the order of tens of milliseconds. Note that the brief increases in [DA] from uncoordinated stochastic release events from tonic firing in Fig. 1i are too brief to drive D1 signaling, as the DA concentration diffuses into the remaining extracellular space on a timescale of 1-5 ms. This is faster than the receptors response rate, and does not lead to any downstream signaling according to our simulations. This means D1 kinetics are rapid enough to track coordinated signaling on a ~50 ms timescale and slower, but not fast enough to respond to individual release events from tonic activity. In our revised manuscript we will expand the discussion of this topic to provide greater clarity.

      The claim also depends on two things that are poorly explained. First, the model of binding here is missing from the text. It seems to be a simple bound-fraction model, simulating a single D1 or D2 receptor. It is unclear whether more complex models would show the same thing.

      We realize that this is not made clear in the methods and, accordingly, we will update the method section to elaborate on how we model receptor binding. The model simulates occupied fraction of D1R and D2R in every single voxel of the simulation space.

      Second, crucial to the receptor model here is the inference that D1 receptor unbinding is rapid; but this inference is made based on the kinetics of dopamine sensors and is superficially explained - it is unclear why sensor kinetics should let us extrapolate to receptor kinetics, and unclear how safe is the extrapolation of the linear regression by an order of magnitude to get the D1 unbinding rate.

      We chose to use the sensors because it was possible to estimate precise affinities/off-rates from the fluorescent measurements. Although there might some variation in affinities that could be attributable to the mutations introduced in the sensors, the data clearly separated D1R and D2R with a D1R affinity of ~1000 nM and a D1R affinity of ~7 nM (Labouesse & Patriarchi, 2021) consistent with earlier predictions of receptor affinities. From our assessment of the literature we found that this was the most reasonable way to estimate affinities and thereby off-rates. Importantly, the model has been made publicly available, so should new measurements arise, the simulations can be rerun with tweaks to the input parameters.

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      Reply to the reviewers

      Reply to the Reviewers

      I would like to thank the reviewers for their comments and interest in the manuscript and the study.

      Referee #1

      1. I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

      Response: The directional positioning of CTCF-binding sites at chromatin interaction sites was analyzed by CRISPR experiment (Guo Y et al. Cell 2015). We found that the machine learning and statistical analysis showed the same directional bias of the CTCF-binding motif sequence at chromatin interaction sites as the experimental analysis of Guo Y et al. (lines 229-245, Figure 3b, c, d and Table 1). Since CTCF is involved in different biological functions (Braccioli L et al. Essays Biochem. 2019 ResearchGate webpage), the directional bias of binding sites may be reduced in all binding sites including those at chromatin interaction sites (lines 68-73). In our study, we investigated the DNA-binding sites of proteins using the ChIP-seq data of DNA-binding proteins and DNase-seq data. We also confirmed that the DNA-binding sites of SMC3 and RAD21, which tend to be found in chromatin loops with CTCF, also showed the same directional bias as CTCF by the computational analysis.

      1. Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure.

      Response: Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 427 and 817: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

      1. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

      Response: As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality of insulator-associated DNA-binding sites is their overall tendency, and it may be difficult to notice the directionality from each binding site because the directionality may be weaker than that of CTCF, RAD21, and SMC3 as shown in Table 1 and Supplementary Table 2.

      I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. Cell 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study. I have added the statistical summary of the analysis in lines 364-387 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.

      1. The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

      Response: According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 397 - 404: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value 5. Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

      Response: I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

      1. Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

      Response: Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2's comments.

      Referee #2

      1. Introduction, line 95: CTCF appears two times, it seems redundant.

      Response: On lines 91-93, I deleted the latter CTCF from the sentence "and examined the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

      1. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

      Response: Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

      I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.

      1. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

      Response: On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

      1. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

      Response: On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines". On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

      1. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

      Response: The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

      1. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

      Response: As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 493: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

      In Aljahani A et al. Nature Communications 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. Nature Genetics 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.

      FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. Molecular Cell 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. Nucleic acids research 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 548: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.

      1. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

      Response: Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

      The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. Nature Genetics 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. Proc Natl Acad Sci USA 2021 ; Ortabozkoyun H et al. Nature genetics 2022 ; Wang R et al. Nature communications 2022). I have added the following sentences on lines 563-567: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 574-576: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.

      As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. Nature 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. Nature Reviews Molecular Cell Biology 2021). Regarding loop extrusion, the 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. EMBO Journal 2024). I have added the following sentences on lines 535-539: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 569-574: The 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.

      Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 551-559: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.

      1. Do the authors think that the identified DBPs could work in that way as well?

      Response: The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

      Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. Nucleic Acids Research 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. Cell Reports 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 546: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.

      1. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

      Response: Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 576-582: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

      1. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

      Response: Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 531 - 535 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops. To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the Drosophila even skipped locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. PLoS Genetics 2016).

      Referee #3

      1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

      Response: When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 249 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 20 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

      1. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

      Response: As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 917 - 919 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

      1. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

      Response: Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 962 - 964 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 348 - 352: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

      1. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

      Response: The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 585-589: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

      Minor comments:

      1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.

      Response: The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, althought the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

      As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.

      1. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

      Response: In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

      Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 615-620: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.

      Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

    2. Note: This response was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      I would like to thank the reviewers for their comments and interest in the manuscript and the study.

      Reviewer #1

      1) I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

      As the reviewer pointed out, a wet experimental validation of the results of this study would give an opportunity for more biological researchers to have an interest in the study. I plan to promote the wet experimental analysis in collaboration with biological experimental researchers as a next step of this study. The same analysis in this study can be performed in immortalized cells for CRISPR experiment (e.g. Guo Y et al. Cell 2015).

      2) Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure.

      Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 427 and 817: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

      3) Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

      As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality is their overall tendency, and it may be difficult to notice the directionality from each binding site.

      I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. Cell 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study. I have added the statistical summary of the analysis in lines 364-387 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.

      4) The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

      According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 397 - 404: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value < 0.05). The comparison between the splice sites of both ends of first and last introns and those of other introns showed the similar statistical significance of enrichment and number of splice sites with the insulator-associated DNA-binding proteins (Table 2 and Table S9).

      5) Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

      I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

      6) Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

      Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2’s comments.

      Reviewer #2

      1) Introduction, line 95: CTCF appears two times, it seems redundant.

      On lines 91-93, I deleted the latter CTCF from the sentence "We examine the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

      2) Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

      Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

      I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.

      3) Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

      On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

      4) Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

      On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines”. On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

      5) Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

      The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

      6) Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

      As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 493: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

      In Aljahani A et al. Nature Communications 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. Nature Genetics 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.

      I added the following sentence on lines 561-569: The depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. Furthermore, the loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.

      FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. Molecular Cell 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. Nucleic acids research 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 548: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.

      7) In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

      Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

      The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. Nature Genetics 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. Proc Natl Acad Sci USA 2021 ; Ortabozkoyun H et al. Nature genetics 2022 ; Wang R et al. Nature communications 2022). I have added the following sentences on lines 563-567: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 574-576: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.

      As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. Nature 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. Nature Reviews Molecular Cell Biology 2021). Regarding loop extrusion, the ‘loop extrusion’ hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. EMBO Journal 2024). I have added the following sentences on lines 535-539: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 569-574: The ‘loop extrusion’ hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.

      Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 551-559: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.

      8) Do the authors think that the identified DBPs could work in that way as well?

      The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

      Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. Nucleic Acids Research 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. Cell Reports 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 546: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.

      9) Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

      Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 576-582: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

      10) Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

      Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 531 – 535 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops.

      To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the Drosophila even skipped locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. PLoS Genetics 2016).

      Reviewer #3

      1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

      When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 249 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 22 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

      2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

      As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 917 – 919 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

      3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

      Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 962 – 964 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 348 – 352: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

      4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

      The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 585-589: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

      1.PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g.,https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.

      The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, althought the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

      As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.

      2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

      In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

      Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 615-620: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.

      Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

    1. This demonstrates how, once again, no design choice is neutral, and serves all people equally well.

      I found this passage interesting because it echoes a concept form our previous readings. That no design choice is ever truly neutral, and there will always be a group of people who are left out. This is a reminder of the responsibility designers have to be mindful of the impact their choices have on different groups. It’s important to remember that every decision designers make can affect people in different ways, and to consider how our designs may unintentionally exclude or disadvantage certain users. As we work on our project, we need to think about this by using typical page layouts that people are used to and a color palette that works for visually impaired people.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.

      In the study, performed experiments are well described.

      My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.

      Major comments:

      1. What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms. Response: Thank you very much for your insightful comments. 1) To address "what happens to endogenous wild-type full-length P53 in the context of mutant/truncated isoforms," we employed a human A549 cell line expressing endogenous wild-type p53 under DNA damage conditions such as an etoposide treatment1. We choose the A549 cell line since similar to H1299, it is a lung cancer cell line (www.atcc.org). For comparison, we also transfected the cells with 2 μg of V5-tagged plasmids encoding FLp53 and its isoforms Δ133p53 and Δ160p53. As shown in Figure R1A, lanes 1 and 2, endogenous p53 expression, remained undetectable in A549 cells despite etoposide treatment, which limits our ability to assess the effects of the isoforms on the endogenous wild-type FLp53. We could, however, detect the V5-tagged FLp53 expressed from the plasmid using anti-V5 (rabbit) as well as with anti-DO-1 (mouse) antibody (Figure R1). The latter detects both endogenous wild-type p53 and the V5-tagged FLp53 since the antibody epitope is within the N-terminus (aa 20-25). This result supports the reviewer's comment regarding the low level of expression of endogenous p53 that is insufficient for detection in our experiments. (Figure R1 is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

      In summary, in line with the reviewer's comment that 'under normal physiological conditions p53 expression is usually low,' we could not detect p53 with an anti-DO-1 antibody. Thus, we proceeded with V5/FLAG-tagged p53 for detection of the effects of the isoforms on p53 stability and function. We also found that protein expression in H1299 cells was more easily detectable than in A549 cells (Compare Figures R1A and B). Thus, we decided to continue with the H1299 cells (p53-null), which would serve as a more suitable model system for this study.

      2) We agree with the reviewer that 'It is hard to differentiate if aggregation of full-length p53 happens only in overexpression scenario'. However, it is not impossible to imagine that such aggregation of FLp53 happens under conditions when p53 and its isoforms are over-expressed in the cell. Although the exact physiological context is not known and beyond the scope of the current work, our results indicate that at higher expression, p53 isoforms drive aggregation of FLp53. Given the challenges of detecting endogenous FLp53, we had to rely on the results obtained with plasmid mediated expression of p53 and its isoforms in p53-null cells.

      Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.

      Response: Thank you very much for your valuable comments and suggestions. To investigate the potential functional impairment of endogenous wild-type p53 by p53 isoforms, we initially utilized A549 cells (p53 wild-type), aiming to monitor endogenous wild-type p53 expression following DNA damage. However, as mentioned and demonstrated in Figure R1, endogenous p53 expression was too low to be detected under these conditions, making the ChIP assay for analyzing endogenous p53 activity unfeasible. Thus, we decided to utilize plasmid-based expression of FLp53 and focus on the potential functional impairment induced by the isoforms.

      3. On similar lines, authors described:

      "To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)."

      Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.

      Response: Thank you for raising this point regarding the physiological relevance of the ratios used in our study. 1) In the revised manuscript (lines 193-195), we added in this direction that "The elevated Δ133p53 protein modulates p53 target genes such as miR34a and p21, facilitating cancer development2, 3. To mimic conditions where isoforms are upregulated relative to FLp53, we increased the ratios to 1:5 and 1:10." This approach aims to simulate scenarios where isoforms accumulate at higher levels than FLp53, which may be relevant in specific contexts, as also elaborated above.

      2) Regarding the issue of protein expression, where one allele is wild-type and the other is isoform, this assumption is not valid in most contexts. First, human cells have two copies of TPp53 gene (one from each parent). Second, the TP53 gene has two distinct promoters: the proximal promoter (P1) primarily regulates FLp53 and ∆40p53, whereas the second promoter (P2) regulates ∆133p53 and ∆160p534, 5. Additionally, ∆133TP53 is a p53 target gene6, 7 and the expression of Δ133p53 and FLp53 is dynamic in response to various stimuli. Third, the expression of p53 isoforms is regulated at multiple levels, including transcriptional, post-transcriptional, translational, and post-translational processing8. Moreover, different degradation mechanisms modify the protein level of p53 isoforms and FLp538. These differential regulation mechanisms are regulated by various stimuli, and therefore, the 1:1 ratio of FLp53 to ∆133p53 or ∆160p53 may be valid only under certain physiological conditions. In line with this, varied expression levels of FLp53 and its isoforms, including ∆133p53 and ∆160p53, have been reported in several studies3, 4, 9, 10.

      3) In our study, using the pcDNA 3.1 vector under the human cytomegalovirus (CMV) promoter, we observed moderately higher expression levels of ∆133p53 and ∆160p53 relative to FLp53 (Figure R1B). This overexpression scenario provides a model for studying conditions where isoform accumulation might surpass physiological levels, impacting FLp53 function. By employing elevated ratios of these isoforms to FLp53, we aim to investigate the potential effects of isoform accumulation on FLp53.

      4. Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.

      Response: Thank you for your insightful comments. In the experiment with A549 cells (p53 wild-type), endogenous p53 levels were too low to be detected, even after DNA damage induction. The evaluation of the function of endogenous p53 in the presence of isoforms is hindered, as mentioned above. In the revised manuscript, we utilized H1299 cells with overexpressed proteins for apoptosis studies using the Caspase-Glo® 3/7 assay (Figure 7). This has been shown in the Results section (lines 254-269). "The Δ133p53 and Δ160p53 proteins block pro-apoptotic function of FLp53.

      One of the physiological read-outs of FLp53 is its ability to induce apoptotic cell death11. To investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on FLp53-induced apoptosis, we measured caspase-3 and -7 activities in H1299 cells expressing different p53 isoforms (Figure 7). Caspase activation is a key biochemical event in apoptosis, with the activation of effector caspases (caspase-3 and -7) ultimately leading to apoptosis12. The caspase-3 and -7 activities induced by FLp53 expression was approximately 2.5 times higher than that of the control vector (Figure 7). Co-expression of FLp53 and the isoforms Δ133p53 or Δ160p53 at a ratio of 1: 5 significantly diminished the apoptotic activity of FLp53 (Figure 7). This result aligns well with our reporter gene assay, which demonstrated that elevated expression of Δ133p53 and Δ160p53 impaired the expression of apoptosis-inducing genes BAX and PUMA (Figure 4G and H). Moreover, a reduction in the apoptotic activity of FLp53 was observed irrespective of whether Δ133p53 or Δ160p53 protein was expressed with or without a FLAG tag (Figure 7). This result, therefore, also suggests that the FLAG tag does not affect the apoptotic activity or other physiological functions of FLp53 and its isoforms. Overall, the overexpression of p53 isoforms Δ133p53 and Δ160p53 significantly attenuates FLp53-induced apoptosis, independent of the protein tagging with the FLAG antibody epitope."

      **Referees cross-commenting**

      I think the comments from the other reviewers are very much reasonable and logical.

      Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.

      Response: Thank you for these comments. The endogenous p53 protein was undetectable in A549 cells induced by etoposide (Figure R1A). Therefore, we conducted experiments using FLAG/V5-tagged FLp53. To avoid any potential side effects of the FLAG tag on p53 aggregation, we introduced untagged p53 isoforms in the H1299 cells and performed subcellular fractionation. Our revised results, consistent with previous FLAG-tagged p53 isoforms findings, demonstrate that co-expression of untagged isoforms with FLAG-tagged FLp53 significantly induced the aggregation of FLAG-FLp53, while no aggregation was observed when FLAG-tagged FLp53 was expressed alone (Supplementary Figure 6). These results clearly indicate that the FLAG tag itself does not contribute to protein aggregation.

      Additionally, we utilized the A11 antibody to detect protein aggregation, providing additional validation (Figure R3). Given that the fluorescent proteins (~30 kDa) are substantially bigger than the tags used here (~1 kDa) and may influence oligomerization (especially GFP), stability, localization, and function of p53 and its isoforms, we avoided conducting these vital experiments with such artificial large fusions.

      Reviewer #1 (Significance (Required)):

      The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.

      Response: Thank you for your insightful comments. We appreciate your recognition of the significance of our work in providing mechanistic insights into how wild-type FLp53 can be inactivated by truncated isoforms. We agree that these findings have potential for exploring new strategies to restore p53 function as a therapeutic approach against cancer.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.

      This study is innovative, well-executed, and supported by thorough data analysis. However, the authors should address the following points:

        • Introduction on Aggregation and Co-aggregation: Given that the focus of the study is on the aggregation and co-aggregation of the isoforms, the introduction should include a dedicated paragraph discussing this issue. There are several original research articles and reviews that could be cited to provide context.* Response: Thank you very much for the valuable comments. We have added the following paragraph in the revised manuscript (lines 74-82): "Protein aggregation has become a central focus of modern biology research and has documented implications in various diseases, including cancer13, 14, 15. Protein aggregates can be of different types ranging from amorphous aggregates to highly structured amyloid or fibrillar aggregates, each with different physiological implications. In the case of p53, whether protein aggregation, and in particular, co-aggregation with large N-terminal deletion isoforms, plays a mechanistic role in its inactivation is yet underexplored. Interestingly, the Δ133p53β isoform has been shown to aggregate in several human cancer cell lines16. Additionally, the Δ40p53α isoform exhibits a high aggregation tendency in endometrial cancer cells17. Although no direct evidence exists for Δ160p53 yet, these findings imply that p53 isoform aggregation may play a major role in their mechanisms of actions."

      2. Antibody Use for Aggregation: To strengthen the evidence for aggregation, the authors should consider using antibodies that specifically bind to aggregates.

      Response: Thank you for your insightful suggestion. We addressed protein aggregation using the A11 antibody which specifically recognizes amyloid-like protein aggregates. We analyzed insoluble nuclear pellet samples prepared under identical conditions as described in Figure 6B. To confirm the presence of p53 proteins, we employed the anti-p53 M19 antibody (Santa Cruz, Cat No. sc-1312) to detect bands corresponding to FLp53 and its isoforms Δ133p53 and Δ160p53. The monomer FLp53 was not detected (Figure R3, lower panel), which may be attributed to the lower binding affinity of the anti-p53 M19 antibody to it. These samples were also immunoprecipitated using the A11 antibody (Thermo Fischer Scientific, Cat No. AHB0052) to detect aggregated proteins. Interestingly, FLp53 and its isoforms, Δ133p53 and Δ160p53, were clearly visible with Anti-A11 antibody when co-expressed at a 1:5 ratio suggesting that they underwent co-aggregation__.__ However, no FLp53 aggregates were observed when it was expressed alone (Figure R2). These results support the conclusion in our manuscript that Δ133p53 and Δ160p53 drive FLp53 aggregation.

      (Figure R2 is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

      3. Fluorescence Microscopy: Live-cell fluorescence microscopy could be employed to enhance visualization by labeling FLp53 and the isoforms with different fluorescent markers (e.g., EGFP and mCherry tags).

      Response: We appreciate the suggestion to use live-cell fluorescence microscopy with EGFP and mCherry tags for the visualization FLp53 and its isoforms. While we understand the advantages of live-cell imaging with EGFP / mCherry tags, we restrained us from doing such fusions as the GFP or corresponding protein tags are very big (~30 kDa) with respect to the p53 isoform variants (~30 kDa). Other studies have shown that EGFP and mCherry fusions can alter protein oligomerization, solubility and aggregation18, 19. Moreover, most fluorescence proteins are prone to dimerization (i.e. EGFP) or form obligate tetramers (DsRed)20, 21, 22, potentially interfering with the oligomerization and aggregation properties of p53 isoforms, particularly Δ133p53 and Δ160p53.

      Instead, we utilized FLAG- or V5-tag-based immunofluorescence microscopy, a well-established and widely accepted method for visualizing p53 proteins. This method provided precise localization and reliable quantitative data, which we believe meet the needs of the current study. We believe our chosen method is both appropriate and sufficient for addressing the research question.

      Reviewer #2 (Significance (Required)):

      The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.

      Response: We sincerely thank the reviewer for the thoughtful and positive comments on our manuscript and for highlighting the significance of our findings on the p53 isoforms, Δ133p53 and Δ160p53.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript entitled "Δ133p53 and Δ160p53 isoforms of the tumor suppressor protein p53 exert dominant-negative effect primarily by co-aggregation", the authors suggest that the Δ133p53 and Δ160p53 isoforms have high aggregation propensity and that by co-aggregating with canonical p53 (FLp53), they sequestrate it away from DNA thus exerting a dominant-negative effect over it.

      First, the authors should make it clear throughout the manuscript, including the title, that they are investigating Δ133p53α and Δ160p53α since there are 3 Δ133p53 isoforms (α, β, γ), and 3 Δ160p53 isoforms (α, β, γ).

      Response: Thank you for your suggestion. We understand the importance of clearly specifying the isoforms under study. Following your suggestion, we have added α in the title, abstract, and introduction and added the following statement in the Introduction (lines 57-59): "For convenience and simplicity, we have written Δ133p53 and Δ160p53 to represent the α isoforms (Δ133p53α and Δ160p53α) throughout this manuscript."

      One concern is that the authors only consider and explore Δ133p53α and Δ160p53α isoforms as exclusively oncogenic and FLp53 dominant-negative while not discussing evidences of different activities. Indeed, other manuscripts have also shown that Δ133p53α is non-oncogenic and non-mutagenic, do not antagonize every single FLp53 functions and are sometimes associated with good prognosis. To cite a few examples:

      • Hofstetter G. et al. D133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br. J. Cancer 2011, 105, 1593-1599.
      • Bischof, K. et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Sci. Rep. 2019, 9,5244.
      • Knezovi´c F. et al. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol. Oncol. 2019, 37, 578.e1-578.e10.
      • Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369.
      • Gong, L. et al. p53 isoform D133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281.
      • Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028.
      • Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90. Response: Thank you very much for your comment and for highlighting these important studies.

      We agree that Δ133p53 isoforms exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. However, our mission here was primarily to reveal the molecular mechanism for the dominant-negative effects exerted by the Δ133p53α and Δ160p53α isoforms on FLp53 for which the Δ133p53α and Δ160p53α isoforms are suitable model systems. Exploring the oncogenic potential of the isoforms is beyond the scope of the current study and we have not claimed anywhere that we are reporting that. We have carefully revised the manuscript and replaced the respective terms e.g. 'pro-oncogenic activity' with 'dominant-negative effect' in relevant places (e.g. line 90). We have now also added a paragraph with suitable references that introduces the oncogenic and non-oncogenic roles of the p53 isoforms.

      After reviewing the papers you cited, we are not sure that they reflect on oncogenic /non-oncogenic role of the Δ133p53α isoform in different cancer cases. Although our study is not about the oncogenic potential of the isoforms, we have summarized the key findings below:

      • Hofstetter et al., 2011: Demonstrated that Δ133p53α expression improved recurrence-free and overall survival (in a p53 mutant induced advanced serous ovarian cancer, suggesting a potential protective role in this context.
      • Bischof et al., 2019: Found that Δ133p53 mRNA can improve overall survival in high-grade serous ovarian cancers. However, out of 31 patients, only 5 belong to the TP53 wild-type group, while the others carry TP53 mutations.
      • Knezović et al., 2019: Reported downregulation of Δ133p53 in renal cell carcinoma tissues with wild-type p53 compared to normal adjacent tissue, indicating a potential non-oncogenic role, but not conclusively demonstrating it.
      • Gong et al., 2015: Showed that Δ133p53 antagonizes p53-mediated apoptosis and promotes DNA double-strand break repair by upregulating RAD51, LIG4, and RAD52 independently of FLp53.
      • Gong et al., 2016: Demonstrated that overexpression of Δ133p53 promotes efficiency of cell reprogramming by its anti-apoptotic function and promoting DNA DSB repair. The authors hypotheses that this mechanism is involved in increasing RAD51 foci formation and decrease γH2AX foci formation and chromosome aberrations in induced pluripotent stem (iPS) cells, independent of FL p53.
      • Horikawa et al., 2017: Indicated that induced pluripotent stem cells derived from fibroblasts that overexpress Δ133p53 formed non-cancerous tumors in mice compared to induced pluripotent stem cells derived from fibroblasts with complete p53 inhibition. Thus, Δ133p53 overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but it still compromises certain p53-mediated tumor-suppressing pathways. "Overexpressed Δ133p53 prevented FL-p53 from binding to the regulatory regions of p21WAF1 and miR-34a promoters, providing a mechanistic basis for its dominant-negative inhibition of a subset of p53 target genes."
      • Gong, 2016: Suggested that Δ133p53 promotes cell survival under low-level oxidative stress, but its role under different stress conditions remains uncertain. We have revised the Introduction to provide a more balanced discussion of Δ133p53's dule role (lines 62-73):

      "The Δ133p53 isoform exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. Recent studies demonstrate the non-oncogenic yet context-dependent role of the Δ133p53 isoform in cancer development. Δ133p53 expression has been reported to correlate with improved survival in patients with TP53 mutations23, 24, where it promotes cell survival in a non-oncogenic manner25, 26, especially under low oxidative stress27. Alternatively, other recent evidences emphasize the notable oncogenic functions of Δ133p53 as it can inhibit p53-dependent apoptosis by directly interacting with the FLp53 4, 6. The oncogenic function of the newly identified Δ160p53 isoform is less known, although it is associated with p53 mutation-driven tumorigenesis28 and in melanoma cells' aggressiveness10. Whether or not the Δ160p53 isoform also impedes FLp53 function in a similar way as Δ133p53 is an open question. However, these p53 isoforms can certainly compromise p53-mediated tumor suppression by interfering with FLp53 binding to target genes such as p21 and miR-34a2, 29 by dominant-negative effect, the exact mechanism is not known."

      On the figures presented in this manuscript, I have three major concerns:

      *1- Most results in the manuscript rely on the overexpression of the FLAG-tagged or V5-tagged isoforms. The validation of these construct entirely depends on Supplementary figure 3 which the authors claim "rules out the possibility that the FLAG epitope might contribute to this aggregation. However, I am not entirely convinced by that conclusion. Indeed, the ratio between the "regular" isoform and the aggregates is much higher in the FLAG-tagged constructs than in the V5-tagged constructs. We can visualize the aggregates easily in the FLAG-tagged experiment, but the imaging clearly had to be overexposed (given the white coloring demonstrating saturation of the main bands) to visualize them in the V5-tagged experiments. Therefore, I am not convinced that an effect of the FLAG-tag can be ruled out and more convincing data should be added. *

      Response: Thank you for raising this important concern. We have carefully considered your comments and have made several revisions to clarify and strengthen our conclusions.

      First, to address the potential influence of the FLAG and V5 tags on p53 isoform aggregation, we have revised Figure 2 and removed the previous Supplementary Figure 3, where non-specific antibody bindings and higher molecular weight aggregates were not clearly interpretable. In the revised Figure 2, we have removed these potential aggregates, improving the clarity and accuracy of the data.

      To further rule out any tag-related artifacts, we conducted a co-immunoprecipitation assay with FLAG-tagged FLp53 and untagged Δ133p53 and Δ160p53 isoforms. The results (now shown in the new Supplementary Figure 3) completely agree with our previous result with FLAG-tagged and V5-tagged Δ133p53 and Δ160p53 isoforms and show interaction between the partners. This indicates that the FLAG / V5-tags do not influence / interfere with the interaction between FLp53 and the isoforms. We have still used FLAG-tagged FLp53 as the endogenous p53 was undetectable and the FLAG-tagged FLp53 did not aggregate alone.

      In the revised paper, we added the following sentences (Lines 146-152): "To rule out the possibility that the observed interactions between FLp53 and its isoforms Δ133p53 and Δ160p53 were artifacts caused by the FLAG and V5 antibody epitope tags, we co-expressed FLAG-tagged FLp53 with untagged Δ133p53 and Δ160p53. Immunoprecipitation assays demonstrated that FLAG-tagged FLp53 could indeed interact with the untagged Δ133p53 and Δ160p53 isoforms (Supplementary Figure 3, lanes 3 and 4), confirming formation of hetero-oligomers between FLp53 and its isoforms. These findings demonstrate that Δ133p53 and Δ160p53 can oligomerize with FLp53 and with each other."

      Additionally, we performed subcellular fractionation experiments to compare the aggregation and localization of FLAG-tagged FLp53 when co-expressed either with V5-tagged or untagged Δ133p53/Δ160p53. In these experiments, the untagged isoforms also induced FLp53 aggregation, mirroring our previous results with the tagged isoforms (Supplementary Figure 5). We've added this result in the revised manuscript (lines 236-245): "To exclude the possibility that FLAG or V5 tags contribute to protein aggregation, we also conducted subcellular fractionation of H1299 cells expressing FLAG-tagged FLp53 along with untagged Δ133p53 or Δ160p53 at a 1:5 ratio. The results showed (Supplementary Figure 6) a similar distribution of FLp53 across cytoplasmic, nuclear, and insoluble nuclear fractions as in the case of tagged Δ133p53 or Δ160p53 (Figure 6A to D). Notably, the aggregation of untagged Δ133p53 or Δ160p53 markedly promoted the aggregation of FLAG-tagged FLp53 (Supplementary Figure 6B and D), demonstrating that the antibody epitope tags themselves do not contribute to protein aggregation."

      We've also discussed this in the Discussion section (lines 349-356): "In our study, we primarily utilized an overexpression strategy involving FLAG/V5-tagged proteins to investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on the function of FLp53. To address concerns regarding potential overexpression artifacts, we performed the co-immunoprecipitation (Supplementary Figure 6) and caspase-3 and -7 activity (Figure 7) experiments with untagged Δ133p53 and Δ160p53. In both experimental systems, the untagged proteins behaved very similarly to the FLAG/V5 antibody epitope-containing proteins (Figures 6 and 7 and Supplementary Figure 6). Hence, the C-terminal tagging of FLp53 or its isoforms does not alter the biochemical and physiological functions of these proteins."

      In summary, the revised data set and newly added experiments provide strong evidence that neither the FLAG nor the V5 tag contributes to the observed p53 isoform aggregation.

      2- The authors demonstrate that to visualize the dominant-negative effect, Δ133p53α and Δ160p53α must be "present in a higher proportion than FLp53 in the tetramer" and the need at least a transfection ratio 1:5 since the 1:1 ration shows no effect. However, in almost every single cell type, FLp53 is far more expressed than the isoforms which make it very unlikely to reach such stoichiometry in physiological conditions and make me wonder if this mechanism naturally occurs at endogenous level. This limitation should be at least discussed.

      Response: Thank you for your insightful comment. However, evidence suggests that the expression levels of these isoforms such as Δ133p53, can be significantly elevated relative to FLp53 in certain physiological conditions3, 4, 9. For example, in some breast tumors, with Δ133p53 mRNA is expressed at a much levels than FLp53, suggesting a distinct expression profile of p53 isoforms compared to normal breast tissue4. Similarly, in non-small cell lung cancer and the A549 lung cancer cell line, the expression level of Δ133p53 transcript is significantly elevated compared to non-cancerous cells3. Moreover, in specific cholangiocarcinoma cell lines, the Δ133p53 /TAp53 expression ratio has been reported to increase to as high as 3:19. These observations indicate that the dominant-negative effect of isoform Δ133p53 on FLp53 can occur under certain pathological conditions where the relative amounts of the FLp53 and the isoforms would largely vary. Since data on the Δ160p53 isoform are scarce, we infer that the long N-terminal truncated isoforms may share a similar mechanism.

      Figure 5C: I am concerned by the subcellular location of the Δ133p53α and Δ160p53α as they are commonly considered nuclear and not cytoplasmic as shown here, particularly since they retain the 3 nuclear localization sequences like the FLp53 (Bourdon JC et al. 2005; Mondal A et al. 2018; Horikawa I et al, 2017; Joruiz S. et al, 2024). However, Δ133p53α can form cytoplasmic speckles (Horikawa I et al, 2017) when it colocalizes with autophagy markers for its degradation.

      3-The authors should discuss this issue. Could this discrepancy be due to the high overexpression level of these isoforms? A co-staining with autophagy markers (p62, LC3B) would rule out (or confirm) activation of autophagy due to the overwhelming expression of the isoform.

      Response: Thank you for your thoughtful comments. We have thoroughly reviewed all the papers you recommended (Bourdon JC et al., 2005; Mondal A et al., 2018; Horikawa I et al., 2017; Joruiz S. et al., 2024)4, 29, 30, 31. Among these, only the study by Bourdon JC et al. (2005) provided data regarding the localization of Δ133p534. Interestingly, their findings align with our observations, indicating that the protein does not exhibit predominantly nuclear localization in the Figure below. The discrepancy may be caused by a potentially confusing statement in that paper4

      (The Figure from Bourdon JC et al. (2005) is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

      The localization of p53 is governed by multiple factors, including its nuclear import and export32. The isoforms Δ133p53 and Δ160p53 contain three nuclear localization sequences (NLS)4 . However, the isoforms Δ133p53 and Δ160p53 were potentially trapped in the cytoplasm by aggregation and masking the NLS. This mechanism would prevent nuclear import.

      Further, we acknowledge that Δ133p53 co-aggregates with autophagy substrate p62/SQSTM1 and autophagosome component LC3B in cytoplasm by autophagic degradation during replicative senescence33. We agree that high overexpression of these aggregation-prone proteins may induce endoplasmic reticulum (ER) stress and activates autophagy34. This could explain the cytoplasmic localization in our experiments. However, it is also critical to consider that we observed aggregates in both the cytoplasm and the nucleus (Figures 6B and E and Supplementary Figure 6B). While cytoplasmic localization may involve autophagy-related mechanisms, the nuclear aggregates likely arise from intrinsic isoform properties, such as altered protein folding, independent of autophagy. These dual localizations reflect the complex behavior of Δ133p53 and Δ160p53 isoforms under our experimental conditions.

      In the revised manuscript, we discussed this in Discussion (lines 328-335): "Moreover, the observed cytoplasmic isoform aggregates may reflect autophagy-related degradation, as suggested by the co-localization of Δ133p53 with autophagy substrate p62/SQSTM1 and autophagosome component LC3B33. High overexpression of these aggregation-prone proteins could induce endoplasmic reticulum stress and activate autophagy34. Interestingly, we also observed nuclear aggregation of these isoforms (Figure 6B and E and Supplementary Figure 6B), suggesting that distinct mechanisms, such as intrinsic properties of the isoforms, may govern their localization and behavior within the nucleus. This dual localization underscores the complexity of Δ133p53 and Δ160p53 behavior in cellular systems."

      Minor concerns:

      - Figure 1A: the initiation of the "Δ140p53" is shown instead of "Δ40p53"

      Response: Thank you! The revised Figure 1A has been created in the revised paper.

      • Figure 2A: I would like to see the images cropped a bit higher, so the cut does not happen just above the aggregate bands

      Response: Thank you for this suggestion. We've changed the image and the new Figure 2 has been shown in the revised paper.

      • Figure 3C: what ratio of FLp53/Delta isoform was used?

      Response: We have added the ratio in the figure legend of Figure 3C (lines 845-846) "Relative DNA-binding of the FLp53-FLAG protein to the p53-target gene promoters in the presence of the V5-tagged protein Δ133p53 or Δ160p53 at a 1: 1 ratio."

      • Figure 3C suggests that the "dominant-negative" effect is mostly senescence-specific as it does not affect apoptosis target genes, which is consistent with Horikawa et al, 2017 and Gong et al, 2016 cited above. Furthermore, since these two references and the others from Gong et al. show that Δ133p53α increases DNA repair genes, it would be interesting to look at RAD51, RAD52 or Lig4, and maybe also induce stress.

      Response: Thank you for your thoughtful comments and suggestions. In Figure 3C, the presence of Δ133p53 or Δ160p53 only significantly reduced the binding of FLp53 to the p21 promoter. However, isoforms Δ133p53 and Δ160p53 demonstrated a significant loss of DNA-binding activity at all four promoters: p21, MDM2, and apoptosis target genes BAX and PUMA (Figure 3B). This result suggests that Δ133p53 and Δ160p53 have the potential to influence FLp53 function due to their ability to form hetero-oligomers with FLp53 or their intrinsic tendency to aggregate. To further investigate this, we increased the isoform to FLp53 ratio in Figure 4, which demonstrate that the isoforms Δ133p53 and Δ160p53 exert dominant-negative effects on the function of FLp53.

      These results demonstrate that the isoforms can compromise p53-mediated pathways, consistent with Horikawa et al. (2017), which showed that Δ133p53α overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but still affects specific tumor-suppressing pathways. Furthermore, as noted by Gong et al. (2016), Δ133p53's anti-apoptotic function under certain conditions is independent of FLp53 and unrelated to its dominant-negative effects.

      We appreciate your suggestion to investigate DNA repair genes such as RAD51, RAD52, or Lig4, especially under stress conditions. While these targets are intriguing and relevant, we believe that our current investigation of p53 targets in this manuscript sufficiently supports our conclusions regarding the dominant-negative effect. Further exploration of additional p53 target genes, including those involved in DNA repair, will be an important focus of our future studies.

      • Figure 5A and B: directly comparing the level of FLp53 expressed in cytoplasm or nucleus to the level of Δ133p53α and Δ160p53α expressed in cytoplasm or nucleus does not mean much since these are overexpressed proteins and therefore depend on the level of expression. The authors should rather compare the ratio of cytoplasmic/nuclear FLp53 to the ratio of cytoplasmic/nuclear Δ133p53α and Δ160p53α.

      Response: Thank you very much for this valuable suggestion. In the revised paper, Figure 5B has been recreated. Changes have been made in lines 214-215: "The cytoplasm-to-nucleus ratio of Δ133p53 and Δ160p53 was approximately 1.5-fold higher than that of FLp53 (Figure 5B)."

      **Referees cross-commenting**

      I agree that the system needs to be improved to be more physiological.

      Just to precise, the D133 and D160 isoforms are not truncated mutants, they are naturally occurring isoforms expressed in almost every normal human cell type from an internal promoter within the TP53 gene.

      Using overexpression always raises concerns, but in this case, I am even more careful because the isoforms are almost always less expressed than the FLp53, and here they have to push it 5 to 10 times more expressed than the FLp53 to see the effect which make me fear an artifact effect due to the overwhelming overexpression (which even seems to change the normal localization of the protein).

      To visualize the endogenous proteins, they will have to change cell line as the H1299 they used are p53 null.

      Response: Thank you for these comments. We've addressed the motivation of overexpression in the above responses. We needed to use the plasmid constructs in the p53-null cells to detect the proteins but the expression level was certainly not 'overwhelmingly high'.

      First, we tried the A549 cells (p53 wild-type) under DNA damage conditions, but the endogenous p53 protein was undetectable. Second, several studies reported increased Δ133p53 level compared to wild-type p53 and that it has implications in tumor development2, 3, 4, 9. Third, the apoptosis activity of H1299 cells overexpressing p53 proteins was analyzed in the revised manuscript (Figure 7). The apoptotic activity induced by FLp53 expression was approximately 2.5 times higher than that of the control vector under identical plasmid DNA transfection conditions (Figure 7). These results rule out the possibility that the plasmid-based expression of p53 and its isoforms introduced artifacts in the results. We've discussed this in the Results section (lines 254-269).

      Reviewer #3 (Significance (Required)):

      Overall, the paper is interesting particularly considering the range of techniques used which is the main strength.

      The main limitation to me is the lack of contradictory discussion as all argumentation presents Δ133p53α and Δ160p53α exclusively as oncogenic and strictly FLp53 dominant-negative when, particularly for Δ133p53α, a quite extensive literature suggests a not so clear-cut activity.

      The aggregation mechanism is reported for the first time for Δ133p53α and Δ160p53α, although it was already published for Δ40p53α, Δ133p53β or in mutant p53.

      This manuscript would be a good basic research addition to the p53 field to provide insight in the mechanism for some activities of some p53 isoforms.

      My field of expertise is the p53 isoforms which I have been working on for 11 years in cancer and neuro-degenerative diseases

      Response: Thank you very much for your positive and critical comments. We've included a fair discussion on the oncogenic and non-oncogenic function of Δ133p53 in the Introduction following your suggestion (lines 62-73).

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      Referee #1

      Evidence, reproducibility and clarity

      Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.

      In the study, performed experiments are well described.

      My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.

      Major comments:

      1. What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms.
      2. Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.
      3. On similar lines, authors described: "To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)." Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.
      4. Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.

      Referees cross-commenting

      I think the comments from the other reviewers are very much reasonable and logical. Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.

      Significance

      The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer 1 (Public Review):

      O’Neill et al. have developed a software analysis application, miniML, that enables the quantification of electrophysiological events. They utilize a supervised deep learned-based method to optimize the software. miniML is able to quantify and standardize the analyses of miniature events, using both voltage and current clamp electrophysiology, as well as optically driven events using iGluSnFR3, in a variety of preparations, including in the cerebellum, calyx of held, Golgi cell, human iPSC cultures, zebrafish, and Drosophila. The software appears to be flexible, in that users are able to hone and adapt the software to new preparations and events. Importantly, miniML is an open-source software free for researchers to use and enables users to adapt new features using Python.

      Overall this new software has the potential to become widely used in the field and an asset to researchers. However, the authors fail to discuss or even cite a similar analysis tool recently developed (SimplyFire), and determine how miniML performs relative to this platform. There are a handful of additional suggestions to make miniML more user-friendly, and of broad utility to a variety of researchers, as well as some suggestions to further validate and strengthen areas of the manuscript:

      (1) miniML relative to existing analysis methods: There is a major omission in this study, in that a similar open source, Python-based software package for event detection of synaptic events appears to be completely ignored. Earlier this year, another group published SimplyFire in eNeuro (Mori et al., 2024; doi: 10.1523/eneuro.0326-23.2023). Obviously, this previous study needs to be discussed and ideally compared to miniML to determine if SimplyFire is superior or similar in utility, and to underscore differences in approach and accuracy.

      We thank the reviewer for bringing this interesting publication to our attention. We have included SimplyFire in our benchmarking for comprehensive comparison with miniML. The approach taken by SimplyFire differs from miniML in a number of ways. Our results show that miniML provides higher recall and precision than SimplyFire (revised Figure 3). We appreciate that SimplyFire provides a user-interface similar to the commonly used MiniAnalysis software. In addition, the peak-finding-based approach of SimplyFire makes it relatively robust to event shape, which facilitates analysis of diverse data. However, we noted a strong threshold-dependence and long run time of SimplyFire (revised Figure 3 and Figure 3—figure supplement 1). In addition, SimplyFire is not robust against various types of noise typically encountered in electrophysiological recordings. Our extended benchmark analysis thus indicates that AI-based event detection is superior to existing algorithmic approaches, including SimplyFire.

      (2) The manuscript should comment on whether miniML works equally well to quantify current clamp events (voltage; e.g. EPSP/mEPSPs) compared to voltage clamp (currents, EPSC/mEPSCs), which the manuscript highlights. Are rise and decay time constants calculated for each event similarly?

      miniML works equally well for current- and voltage events (Figure 5, Figure 9). In general, events of opposite polarity can be analyzed by simply inverting the data. Transfer learning models may further improve the detection.

      For each detected event, independent of data/recording type, rise times are calculated as 10–90% times (baseline–peak), and decay times are calculated as time to 50% of the peak. In addition, event decay time constants are calculated from a fit to the event average. With miniML being open-source, researchers can adapt the calculations of event statistics to their needs, if desired. In the revised manuscript, we have expanded the Methods section that describes the quantification of event statistics (Methods, Quantification).

      (3) The interface and capabilities of miniML appear quite similar to Mini Analysis, the free software that many in the field currently use. While the ability and flexibility for users to adapt and adjust miniML for their own uses/needs using Python programming is a clear potential advantage, can the authors comment, or better yet, demonstrate, whether there is any advantage for researchers to use miniML over Mini Analysis or SimplyFire if they just need the standard analyses?

      Following the reviewer’s suggestion, we developed a graphical user interface (GUI) for miniML to enhance its usability (Figure 2—figure supplement 2), which is provided on the GitHub repository. Our comprehensive benchmark analysis demonstrated that miniML outperforms existing tools such as MiniAnalysis and SimplyFire. The main advantages are (i) increased reliability of results, which eliminates the need for visual inspection; (ii) fast runtime and easy automation; (iii) superior detection performance as demonstrated by higher recall in both synthetic and real data; (iv) open-source Python-based design. We believe that these advantages make miniML a valuable tool for researchers recording various types of synaptic events, offering a more efficient and reliable solution compared to existing methods.

      (4) Additional utilities for miniML: The authors show miniML can quantify miniature electrophysiological events both current and voltage clamp, as well as optical glutamate transients using iGluSnFR. As the authors mention in the discussion, the same approach could, in principle, be used to quantify evoked (EPSC/EPSP) events using electrophysiology, Ca2+ events (using GCaMP), and AP waveforms using voltage indicators like ASAP4. While I don’t think it is reasonable to ask the authors to generate any new experimental data, it would be great to see how miniML performs when analysing data from these approaches, particularly to quantify evoked synaptic events and/or Ca2+ (ideally postsynaptic Ca2+ signals from miniature events, as the Drosophila NMJ have developed nice approaches).

      In the revised manuscript, we have extended the application examples of miniML. We applied miniML to detect mEPSPs recorded with the novel voltage-sensitive indicator ASAP5 (Figure 9 and Figure 9—figure supplement 1). We performed simultaneous recordings of membrane voltage through electrophysiology and ASAP5 voltage imaging in rat cultured neurons at physiological temperature. Data were analyzed using miniML, with electrophysiology data being used as ground-truth for assessing detection performance in imaging data. Our results demonstrate that miniML robustly detects mEPSPs in current-clamp, and can localize corresponding transients in imaging data. Furthermore, we observed that miniML performs better than template matching and deconvolution on ASAP5 imaging data (Figure 9 and Figure 9—figure supplement 2).

      Reviewer 2 (Public Review):

      This paper presents miniML as a supervised method for the detection of spontaneous synaptic events. Recordings of such events are typically of low SNR, where state-of-the-art methods are prone to high false positive rates. Unlike current methods, training miniML requires neither prior knowledge of the kinetics of events nor the tuning of parameters/thresholds.

      The proposed method comprises four convolutional networks, followed by a bi-directional LSTM and a final fully connected layer which outputs a decision event/no event per time window. A sliding window is used when applying miniML to a temporal signal, followed by an additional estimation of events’ time stamps. miniML outperforms current methods for simulated events superimposed on real data (with no events) and presents compelling results for real data across experimental paradigms and species. Strengths:

      The authors present a pipeline for benchmarking based on simulated events superimposed on real data (with no events). Compared to five other state-of-the-art methods, miniML leads to the highest detection rates and is most robust to specific choices of threshold values for fast or slow kinetics. A major strength of miniML is the ability to use it for different datasets. For this purpose, the CNN part of the model is held fixed and the subsequent networks are trained to adapt to the new data. This Transfer Learning (TL) strategy reduces computation time significantly and more importantly, it allows for using a substantially smaller data set (compared to training a full model) which is crucial as training is supervised (i.e. uses labeled examples).

      Weaknesses:

      The authors do not indicate how the specific configuration of miniML was set, i.e. number of CNNs, units, LSTM, etc. Please provide further information regarding these design choices, whether they were based on similar models or if chosen based on performance.

      The data for the benchmark system was augmented with equal amounts of segments with/without events. Data augmentation was undoubtedly crucial for successful training.

      (1) Does a balanced dataset reflect the natural occurrence of events in real data? Could the authors provide more information regarding this matter?

      In a given recording, the event frequency determines the ratio of event-containing vs. nonevent-containing data segments. Whereas many synapses have a skew towards non-events, high event frequencies as observed, e.g., in pyramidal cells or Purkinje neurons, can shift the ratio towards event-containing data.

      For model training, we extracted data segments from mEPSC recordings in cerebellar granule cells, which have a low mEPSC frequency (about 0.2 Hz, Delvendahl et al. 2019). Unbalanced training data may complicate model training (Drummond and Holte 2003; Prati et al. 2009; Tyagi and Mittal 2020). We therefore decided to balance the training dataset for miniML by down-sampling the majority class (i.e., non-event segments), so that the final datasets for model training contained roughly equal amounts of events and non-events.

      (2) Please provide a more detailed description of this process as it would serve users aiming to use this method for other sub-fields.

      We thank the reviewer for raising this point. In the revised manuscript, we present a systematic analysis of the impact of imbalanced training data on model training (Figure 1—figure supplement 2). In addition, we have revised the description of model training and data augmentation in the Methods section (Methods, Training data and annotation).

      The benchmarking pipeline is indeed valuable and the results are compelling. However, the authors do not provide comparative results for miniML for real data (Figures 4-8). TL does not apply to the other methods. In my opinion, presenting the performance of other methods, trained using the smaller dataset would be convincing of the modularity and applicability of the proposed approach.

      Quantitative comparison of synaptic detection methods on real-world data is challenging because the lack of ground-truth data prevents robust, quantitative analyses. Nevertheless, we compared miniML to common template-based and finite-threshold based methods on four different types of synapses. We noted that miniML generally detects more events, whereas other methods are susceptible to false-positives (Figure 4—figure supplement 1). In addition, we analyzed the performance of miniML on voltage imaging data (Figure 9). Simultaneous recordings of electrophysiological and imaging data allowed a quantitative comparison of detection methods in this dataset. Our results demonstrate that miniML provides higher recall for optical minis recorded using ASAP5 (Figure 9 and Figure 9—figure supplement 2; F1 score, Cohen’s d 1.35 vs. template matching and 5.1 vs. deconvolution).

      Impact:

      Accurate detection of synaptic events is crucial for the study of neural function. miniML has a great potential to become a valuable tool for this purpose as it yields highly accurate detection rates, it is robust, and is relatively easily adaptable to different experimental setups.

      Additional comments:

      Line 73: the authors describe miniML as "parameter-free". Indeed, miniML does not require the selection of pulse shape, rise/fall time, or tuning of a threshold value. Still, I would not call it "parameter-free" as there are many parameters to tune, starting with the number of CNNs, and number of units through the parameters of the NNs. A more accurate description would be that as an AI-based method, the parameters of miniML are learned via training rather than tuned by the user.

      We agree that a deep learning model is not parameter-free, and this term may be misleading. We have therefore changed this sentence in the introduction as follows: "The method is fast, robust to threshold choice, and generalizable across diverse data types [...]"

      Line 302: the authors describe miniML as "threshold-independent". The output trace of the model has an extremely high SNR so a threshold of 0.5 typically works. Since a threshold is needed to determine the time stamps of events, I think a better description would be "robust to threshold choice".

      To detect event localizations, a peak search is performed on the model output, which uses a minimum peak height parameter (or threshold). Extreme values for this parameter do indeed have a small impact on detection performance (Figure 3J). We have changed the description in the introduction and discussion according to the reviewer’s suggestion.

      Reviewer 3 (Public Review):

      miniML as a novel supervised deep learning-based method for detecting and analyzing spontaneous synaptic events. The authors demonstrate the advantages of using their methods in comparison with previous approaches. The possibility to train the architecture on different tasks using transfer learning approaches is also an added value of the work. There are some technical aspects that would be worth clarifying in the manuscript:

      (1) LSTM Layer Justification: Please provide a detailed explanation for the inclusion of the LSTM layer in the miniML architecture. What specific benefits does the LSTM layer offer in the context of synaptic event detection?

      Our model design choice was inspired by similar approaches in the literature (Donahue et al. 2017; Islam et al. 2020; Passricha and Aggarwal 2019; Tasdelen and Sen 2021; Wang et al. 2020). Convolutional and recurrent neural networks are often combined for time-series classification problems as they allow learning spatial and temporal features, respectively. Combining the strengths of both network architectures can thus help improve the classification performance. Indeed, a CNN-LSTM architecture proved to be superior in both training accuracy and detection performance (Figure 1—figure supplement 2). Further, this architecture requires fewer free parameters than comparable model designs using fully connected layers instead. The revised manuscript shows a comparison of different model architectures (Figure 1—figure supplement 2), and we added the following description to the text (Methods, Deep learning model architecture):

      "The combination of convolutional and recurrent neural network layers helps to improve the classification performance for time-series data. In particular, LSTM layers allow learning temporal features."

      (2) Temporal Resolution: Can you elaborate on the reasons behind the lower temporal resolution of the output? Understanding whether this is due to specific design choices in the model, data preprocessing, or post-processing will clarify the nature of this limitation and its impact on the analysis.

      When running inference on a continuous recording, we choose to use a sliding window approach with stride. Therefore, the model output has a lower temporal resolution than the raw data, which is determined by the stride length (i.e., how many samples to advance the sliding window). While using a stride is not required, it significantly reduces inference time (cf. Figure 2—figure supplement 1). We recommend a stride of 20 samples, which does not impact the detection of events. Any subsequent quantification of events (amplitude, area, risetimes, etc.) is performed on raw data. Based on the reviewer’s comment, we have adapted the code to resample the prediction trace to the sampling rate of the original data. This maintains temporal precision and avoids confusion.

      The Methods now include the following statement:

      "To maintain temporal precision, the prediction trace is resampled to the sampling frequency of the raw data."

      (3) Architecture optimization: how was the architecture CNN+LSTM optimized in terms of a number of CNN layers and size?

      We performed a Bayesian optimization over a defined range of hyperparameters in combination with empirical hyperparameter tuning. We now describe this in the Methods section as follows:

      "To optimise the model architecture, we performed a Bayesian optimisation of hyperparameters. Hyperparameter ranges were chosen for the free parameters of all layers. Optimisation was then performed with a maximum number of trials of 50. Models were evaluated using the validation dataset. Because higher number of free parameters tended to increase inference times, we then empirically tuned the chosen hyperparameter combination to achieve a trade-off between number of free parameters and accuracy."

      Recommendations For The Authors

      Reviewing Editor (Recommendations For The Authors):

      Overall suggestions to the authors:

      (1) Directly compare miniML with SimplyFire (which was not cited or discussed in the original manuscript), with both idealized and actual data. Discuss the pros/cons of each software.

      We have conducted an extensive comparison between miniML and SimplyFire using both simulated and actual experimental data. This analysis is now presented in the revised Figure 3, Figure 3—figure supplement 1, and Figure 4—figure supplement 1. In addition, we have included relevant citations for SimplyFire in our manuscript. These additions provide a more comprehensive and balanced view of the available tools in the field, positioning our work within the broader context of existing solutions.

      (2) Generate a better user interface akin to MiniAnalysis or SimplyFire.

      We thank the editor and reviewers for the suggestion to improve the user interface. We have created a user-friendly graphical user interface (GUI) for miniML that is available on our GitHub repository. This GUI is now showcased in Figure 2—figure supplement 2 of the manuscript. The new interface allows users to load and analyze data through an intuitive point-and-click system, visualize results in real-time, and adjust parameters easily without coding knowledge. We have incorporated user feedback to refine the interface and improve user experience. These improvements significantly enhance the accessibility of miniML, making it more user-friendly for researchers with varying levels of programming expertise.

      Reviewer 1 (Recommendations For The Authors):

      Related to point (1) of the Public Review, we have taken the liberty to compare electrophysiological data using miniAnalysis, SimiplyFire, and miniML. In our comparison, we note the following in our experience:

      (1.1) In contrast to both SimplyFire and miniAnalysis, miniML does not currently have a user-friendly interface where the user can directly control or change the parameters of interest, nor does miniML have a user control center, so the user cannot simply type or select the mini manually. Rather, if any parameter needs to be changed, the user needs to read, understand, and change the original source code to generate the preferred change. This level of "activation energy" and required user coding expertise in computer science, which many researchers do not have, renders miniML much less accessible when directly compared to SimplyFire and miniAnalysis. Hence, unless miniML’s interface can be made more user-friendly, this is a major disadvantage, especially when compared to SimplyFire, which has many of the same features as miniML but with a much easier interface and user controls.

      As suggested by the reviewer, we have created a graphical user interface (GUI) for miniML. The GUI allows easy data loading, filtering, analysis, event inspection, and saving of results without the need for writing Python code. Figure 2—figure supplement 2 illustrates the typical workflow for event analysis with miniML using the GUI and a screenshot of the user interface. Code to use miniML via the GUI is now included in the project’s GitHub repository. The GUI provides a simple and intuitive way to analyze synaptic events, whereas running miniML as Python script allows for more customization and a high degree of automatization.

      (1.2) We compared electrophysiological miniature events between miniML, SimplyFire, and miniAnalysis. All three achieved similar mean amplitudes in "wild type" conditions, and conditions in which mini events were enhanced and diminished, so the overall means and utilities are similar, with miniML and SimplyFire being preferred given the flexibility and much faster analysis. We did note a few differences, however. SimplyFire tends to capture a high number of mini-events over miniML, especially in conditions of diminished mini amplitude (e.g., miniML found 76 events, while SimplyFire 587). The mean amplitudes, however, were similar. It seems that in data with low SNR, SimplyFire captures many more events as real minis that are probably noise, while miniML is more selective, which might be an advantage in miniML. That being said, we found SimplyFire to be superior in many respects, not least of which the user interface and experience.

      We appreciate the reviewer’s thorough comparison of miniML, SimplyFire, and MiniAnalysis. While we acknowledge SimplyFire’s user-friendly interface, our study highlights several advantages of AI-based event analysis over conventional algorithmic approaches. Our updated benchmark analysis revealed better detection performance of miniML compared with SimplyFire (revised Figure 3), which had similar performance to deconvolution. As already noted by the reviewer, high false positive rates are a major issue of the SimplyFire approach. Although a minimum amplitude cutoff can partially resolve this problem, detection performance is highly sensitive to threshold setting (revised Figure 3). Another apparent disadvantage of SimplyFire is its relatively slow runtime (Figure 3—figure supplement 1). Finally, we have enhanced miniML’s accessibility by providing a graphical user interface that is easy to use and provides additional functionality.

      Some technical comments:

      (1) Improvements to the dependence version of miniML: There is a need to clarify the dependence version of the python and tensor flow used in this study and in the GitHub. We used Python version 3.8.19 to load the miniML model. However, if Python versions >=3.9, as described on the GitHub provided, it is difficult to have a matched h5py version installed. It is also inaccurate to say using Python >=3.9, because tensor flow version for this framework needs to be around 2.13. However, if using Python >=3.10, it will only allow 2.16 version tensor flow to be the download choice. Therefore, as a Python framework, the dependency version needs to be specified on GitHub to allow researchers to access the model using the entire work.

      Thank you for highlighting this issue. We have now included specific version numbers in the requirements to avoid version conflicts and to ensure proper functioning of the code.

      (2) Due to the intrinsic characteristics of the trained model, every model is only suitable for analyzing data with similar attributes. It is hard for researchers without a strong computer science background to train a new model themselves for their specific data. Therefore, it would be preferred if there were more available transfer learning models on GitHub accessible for researchers to adapt to their data.

      We would like to thank the reviewer for this feedback. Trained models (such as the default model) can often be used on different data (see, e.g., Figure 4, where data from four distinct synaptic preparations were analyzed with the base model, and Figure 5—figure supplement 1). However, changes in event waveform and/or noise characteristics may necessitate transfer learning to obtain optimal results with miniML. We have revised the description and tutorial for model training on the project’s GitHub repository to provide more guidance in this process. In addition, we now provide a tutorial on how to use existing models on out-of-sample data with distinct kinetics, using resampling. We hope these updates to the miniML GitHub repository will facilitate the use of the method.

      Following the suggestion by the reviewer, we have provided the transfer learning models used for the manuscript on the project’s GitHub repository to increase the number of available machine learning models for event detection. In addition, users of miniML are encouraged to supply their custom models. We hope that this will facilitate model exchange between laboratories in the future.

      Reviewer 3:

      I congratulate all authors for the convincing demonstration of their methodology, I do not have additional recommendations.

      We would like to thank the reviewer for the positive assessment of our manuscript.

      References

      Delvendahl, I., Kita, K., & Müller, M. (2019). Rapid and sustained homeostatic control of presynaptic exocytosis at a central synapse. Proceedings of the National Academy of Sciences, 116(47), 23783–23789. https://doi.org/10.1073/pnas.1909675116

      Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017). Long-term recurrent convolutional networks for visual recognition and description. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 677–691. https://doi.org/10.1109/tpami.2016.2599174

      Drummond, C., & Holte, R. C. (2003). C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. https: //api.semanticscholar.org/CorpusID:204083391

      Islam, M. Z., Islam, M. M., & Asraf, A. (2020). A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using x-ray images. Informatics in Medicine Unlocked, 20, 100412. https://doi.org/10.1016/j.imu.2020.100412

      Passricha, V., & Aggarwal, R. K. (2019). A hybrid of deep CNN and bidirectional LSTM for automatic speech recognition. Journal of Intelligent Systems, 29(1), 1261–1274. https://doi.org/10.1515/jisys-2018-0372

      Prati, R. C., Batista, G. E. A. P. A., & Monard, M. C. (2009). Data mining with imbalanced class distributions: Concepts and methods. Indian International Conference on Artificial Intelligence. https://api.semanticscholar.org/CorpusID:16651273

      Tasdelen, A., & Sen, B. (2021). A hybrid CNN-LSTM model for pre-miRNA classification. Scientific Reports, 11(1). https://doi.org/10. 1038/s41598-021-93656-0

      Tyagi, S., & Mittal, S. (2020). Sampling approaches for imbalanced data classification problem in machine learning. In P. K. Singh, A. K. Kar, Y. Singh, M. H. Kolekar, & S. Tanwar (Eds.), Proceedings of icric 2019 (pp. 209–221). Springer International Publishing.

      Wang, H., Zhao, J., Li, J., Tian, L., Tu, P., Cao, T., An, Y., Wang, K., & Li, S. (2020). Wearable sensor-based human activity recognition using hybrid deep learning techniques. Security and Communication Networks, 2020, 1–12. https://doi.org/10.1155/2020/ 2132138

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      This is a new and important system that can efficiently train mice to perform a variety of cognitive tasks in a flexible manner. It is innovative and opens the door to important experiments in the neurobiology of learning and memory.

      Strengths:

      Strengths include: high n's, a robust system, task flexibility, comparison of manual-like training vs constant training, circadian analysis, comparison of varying cue types, long-term measurement, and machine teaching.

      Weaknesses:

      I find no major problems with this report.

      (1) Line 219: Water consumption per day remained the same, but number of trails triggered was more as training continued. First, is this related to manual-type training? Also, I'm trying to understand this result quantitatively, since it seems counter-intuitive: I would assume that with more trials, more water would be consumed since accuracy should go up over training (so more water per average trial). Am I understanding this right? Can the authors give more detail or understanding to how more trials can be triggered but no more water is consumed despite training?

      Thanks for the thoughtful comment. We would like to clarify the phenomenon described in Line 219: As the training advanced, the number of trials triggered by mice per day decreased (rather than increased as you mentioned in the comment) gradually for both manual and autonomous groups of mice (Fig. 2H left). The performance as you mentioned, improved over time, leading to an increased probability of obtaining water and thus relatively stable daily water intake (Fig. 2H left). We believe the stable daily intake is the minimum amount of water required by the mice under circumstance of autonomous behavioral training.

      (2) Figure 2J: The X-axis should have some label: at least "training type". Ideally, a legend with colors can be included, although I see the colors elsewhere in the figure. If a legend cannot be added, then the color scheme should be explained in the caption.

      (3) Figure 2K: What is the purple line? I encourage a legend here. The same legend could apply to 2J.

      (4) Supplementary Figure S2 D: I do not think the phrase "relying on" is correct. Instead, I think "predicted by" or "correlating with" might be better.

      We thank the reviewer for the valuable suggestion. We will address all these points and make the necessary revisions in the next version of our manuscript.

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Yu et al. describes a novel approach for collecting complex and different cognitive phenotypes in individually housed mice in their home cage. The authors report a simple yet elegant design that they developed for assessing a variety of complex and novel behavioral paradigms autonomously in mice.

      Strengths:

      The data are strong, the arguments are convincing, and I think the manuscript will be highly cited given the complexity of behavioral phenotypes one can collect using this relatively inexpensive ($100/box) and high throughput procedure (without the need for human interaction). Additionally, the authors include a machine learning algorithm to correct for erroneous strategies that mice develop which is incredibly elegant and important for this approach as mice will develop odd strategies when given complete freedom.

      Weaknesses:

      (1) A limitation of this approach is that it requires mice to be individually housed for days to months. This should be discussed in depth.

      Thank you for raising this important point. We agree that the requirement for individual housing of mice during the training period is a limitation of our approach, and we appreciate the opportunity to discuss this in more depth. In the revised manuscript, we will add a dedicated section to the Discussion to address this limitation, including the potential impact of individual housing on the mice, the rationale for individual housing in our study, and efforts or alternatives made to mitigate the effects of individual housing.

      (2) A major issue with continuous self-paced tasks such as the autonomous d2AFC used by the authors is that the inter-trial intervals can vary significantly. Mice may do a few trials, lose interest, and disengage from the task for several hours. This is problematic for data analysis that relies on trial duration to be similar between trials (e.g., reinforcement learning algorithms). It would be useful to see the task engagement of the mice across a 24-hour cycle (e.g., trials started, trials finished across a 24-hour period) and approaches for overcoming this issue of varying inter-trial intervals.

      Thank you for your insightful comment regarding the variability in inter-trial intervals and its potential impact on data analysis. We agree that this is an important consideration for continuous self-paced tasks like the autonomous d2AFC paradigm used in our study. In the original manuscript, we have showed the general task engagement across 24-hour cycle (Fig. 2K). The distribution of inter-trial interval was also illustrated (Fig. S3H), which actually shows that most of trials have short intervals (though with extreme long ones). We will include more detailed analysis and discuss the challenges for data analysis.

      Regarding the approaches to mitigate the issue of varying inter-trial interval, we will also discuss strategies to account for and mitigate the effects, including: trial selection, incorporating engagement period (e.g., open only during a fixed 2-hour period each day), etc.

      (3) Movies - it would be beneficial for the authors to add commentary to the video (hit, miss trials). It was interesting watching the mice but not clear whether they were doing the task correctly or not.

      Thanks for the reminder. We will add subtitles to the videos in the next version.

      (4) The strength of this paper (from my perspective) is the potential utility it has for other investigators trying to get mice to do behavioral tasks. However, not enough information was provided about the construction of the boxes, interface, and code for running the boxes. If the authors are not willing to provide this information through eLife, GitHub, or their own website then my evaluation of the impact and significance of this paper would go down significantly.

      Thanks for this important comment. We would like to clarify that the construction methods, GUI, code for our system, PCB and CAD files (newly uploaded) have already been made publicly available on https://github.com/Yaoyao-Hao/HABITS. Additionally, we have open-sourced all the codes and raw data for all training protocols (https://doi.org/10.6084/m9.figshare.27192897). We will continue to maintain these resources in the future.

      Minor concerns:

      Learning rate is confusing for Figure 3 results as it actually refers to trials to reach the criterion, and not the actual rate of learning (e.g., slope).

      Thanks for pointing this out. We will make the revision in the next version.

      Reviewer #3 (Public review):

      Summary:

      In this set of experiments, the authors describe a novel research tool for studying complex cognitive tasks in mice, the HABITS automated training apparatus, and a novel "machine teaching" approach they use to accelerate training by algorithmically providing trials to animals that provide the most information about the current rule state for a given task.

      Strengths:

      There is much to be celebrated in an inexpensively constructed, replicable training environment that can be used with mice, which have rapidly become the model species of choice for understanding the roles of distinct circuits and genetic factors in cognition. Lingering challenges in developing and testing cognitive tasks in mice remain, however, and these are often chalked up to cognitive limitations in the species. The authors' findings, however, suggest that instead, we may need to work creatively to meet mice where they live. In some cases, it may be that mice may require durations of training far longer than laboratories are able to invest with manual training (up to over 100k trials, over months of daily testing) but the tasks are achievable. The "machine teaching" approach further suggests that this duration could be substantially reduced by algorithmically optimizing each trial presented during training to maximize learning.

      Weaknesses:

      (1) Cognitive training and testing in rodent models fill a number of roles. Sometimes, investigators are interested in within-subjects questions - querying a specific circuit, genetically defined neuron population, or molecule/drug candidate, by interrogating or manipulating its function in a highly trained animal. In this scenario, a cohort of highly trained animals that have been trained via a method that aims to make their behavior as similar as possible is a strength.

      However, often investigators are interested in between-subjects questions - querying a source of individual differences that can have long-term and/or developmental impacts, such as sex differences or gene variants. This is likely to often be the case in mouse models especially, because of their genetic tractability. In scenarios where investigators have examined cognitive processes between subjects in mice who vary across these sources of individual difference, the process of learning a task has been repeatedly shown to be different. The authors do not appear to have considered individual differences except perhaps as an obstacle to be overcome.

      The authors have perhaps shown that their main focus is highly-controlled within-subjects questions, as their dataset is almost exclusively made up of several hundred young adult male mice, with the exception of 6 females in a supplemental figure. It is notable that these female mice do appear to learn the two-alternative forced-choice task somewhat more rapidly than the males in their cohort.

      Thank you for your insightful comments and for highlighting the importance of considering both within-subject and between-subject questions in cognitive training and testing in rodent models.

      We acknowledge that our study primarily focused on highly controlled within-subject questions. However, the datasets we provided have showed some evidences for the ‘between-subject’ questions. For example, the large variability in learning rates among mice observed in Fig. 2I, the overall learning rate difference between male and female subjects (Fig. 2D vs. Fig. S2G, as the reviewer already mentioned), the varying nocturnal behavioral patterns (Fig. 2K), etc. While our primary focus was on highly controlled within-subjects questions, we recognize the value of exploring between-subjects differences. In the revised version, we will discuss these points more systematically.

      (2) Considering the implications for mice modeling relevant genetic variants, it is unclear to what extent the training protocols and especially the algorithmic machine teaching approach would be able to inform investigators about the differences between their groups during training. For investigators examining genetic models, it is unclear whether this extensive training experience would mitigate the ability to observe cognitive differences, or select the animals best able to overcome them - eliminating the animals of interest. Likewise, the algorithmic approach aims to mitigate features of training such as side biases, but it is worth noting that the strategic uses of side biases in mice, as in primates, can benefit learning, rather than side biases solely being a problem. However, the investigators may be able to highlight variables selected by the algorithm that are associated with individual strategies in performing their tasks, and this would be a significant contribution.

      Thank you for the insightful comments. We acknowledge that the extensive training experience, particularly through the algorithmic machine teaching approach, could potentially influence the ability to observe cognitive differences between groups of mice with relevant genetic variants. However, our study design and findings suggest that this approach can still provide valuable insights into individual differences and strategies used by the animals during training. First, the behavioral readout (including learning rate, engagement pattern, etc.) as mentioned above, could tell certain number of differences among mice. Second, detailed modelling analysis (with logistical regression modelling) could further dissect the strategy that mouse use along the training process (Fig. S2B). We have actually highlighted some variables selected by the regression that are associated with individual strategies in performing their tasks (Fig. S2C) and these strategies could be different between manual and autonomous training groups (Fig. S2D). We will discuss these points more in the next version of the manuscript.

      (3) A final, intriguing finding in this manuscript is that animal self-paced training led to much slower learning than "manual" training, by having the experimenter introduce the animal to the apparatus for a few hours each day. Manual training resulted in significantly faster learning, in almost half the number of trials on average, and with significantly fewer omitted trials. This finding does not necessarily argue that manual training is universally a better choice because it leads to more limited water consumption. However, it suggests that there is a distinct contribution of experimenter interactions and/or switching contexts in cognitive training, for example by activating an "occasion setting" process to accelerate learning for a distinct period of time. Limiting experimenter interactions with mice may be a labor-saving intervention, but may not necessarily improve performance. This could be an interesting topic of future investigation, of relevance to understanding how animals of all species learn.

      Thank you for your insightful comments. We agree that the finding that manual training led to significantly faster learning compared to self-paced training is both intriguing and important. One of the possible reasons we think is due to the limited duration of engagement provided by the experimenter in the manual training case, which forced the mice to concentrate more on the trails (thus with fewer omitting trials) than in autonomous training. Your suggestion that experimenter interactions might activate an "occasion setting" process is particularly interesting. In the context of our study, we could actually introduce, for example, a light, serving as the cue that prompt the animals to engage; and when the light is off, the engagement was not accessible any more for the mice to simulate the manual training situation. We agree that this could be an interesting topic for future investigation that might create a more conducive environment for learning, thereby accelerating the learning rate.

    1. Author response:

      Reviewer #1 (Public review):

      Wang et al. investigated how sexual failure influences sweet taste perception in male Drosophila. The study revealed that courtship failure leads to decreased sweet sensitivity and feeding behavior via dopaminergic signaling. Specifically, the authors identified a group of dopaminergic neurons projecting to the suboesophageal zone that interacts with sweet-sensing Gr5a+ neurons. These dopaminergic neurons positively regulate the sweet sensitivity of Gr5a+ neurons via DopR1 and Dop2R receptors. Sexual failure diminishes the activity of these dopaminergic neurons, leading to reduced sweet-taste sensitivity and sugar-feeding behavior in male flies. These findings highlight the role of dopaminergic neurons in integrating reproductive experiences to modulate appetitive sensory responses.

      Previous studies have explored the dopaminergic-to-Gr5a+ neuronal pathways in regulating sugar feeding under hunger conditions. Starvation has been shown to increase dopamine release from a subset of TH-GAL4 labeled neurons, known as TH-VUM, in the suboesophageal zone. This enhanced dopamine release activates dopamine receptors in Gr5a+ neurons, heightening their sensitivity to sugar and promoting sucrose acceptance in flies. Since the function of the dopaminergic-to-Gr5a+ circuit motif has been well established, the primary contribution of Wang et al. is to show that mating failure in male flies can also engage this circuit to modulate sugar-feeding behavior. This contribution is valuable because it highlights the role of dopaminergic neurons in integrating diverse internal state signals to inform behavioral decisions.

      An intriguing discrepancy between Wang et al. and earlier studies lies in the involvement of dopamine receptors in Gr5a+ neurons. Prior research has shown that Dop2R and DopEcR, but not DopR1, mediate starvation-induced enhancement of sugar sensitivity in Gr5a+ neurons. In contrast, Wang et al. found that DopR1 and Dop2R, but not DopEcR, are involved in the sexual failure-induced decrease in sugar sensitivity in these neurons. I wish the authors had further explored or discussed this discrepancy, as it is unclear how dopamine release selectively engages different receptors to modulate neuronal sensitivity in a context-dependent manner.

      Our immunostaining experiments showed that three dopamine receptors, DopR1, Dop2R, and DopEcR were expressed in Gr5a<sup>+</sup> neurons in the proboscis, which was consistent with previous findings by using RT-PCR (Inagaki et al 2012). As the reviewer pointed out, we found that DopR1 and Dop2R were required for courtship failure-induced suppression of sugar sensitivity, whereas Marella et al 2012 and Inagaki et al 2012 found that Dop2R and DopEcR were required for starvation-induced enhancement of sugar sensitivity. These results may suggest different internal states (courtship failure vs. starvation) modulate peripheral sensory system via different signaling pathways (e.g. different subsets of dopaminergic neurons; different dopamine release mechanisms; and different dopamine receptors). We will further discuss these possibilities in the revised manuscript.

      The data presented by Wang et al. are solid and effectively support their conclusions. However, certain aspects of their experimental design, data analysis, and interpretation warrant further review, as outlined below.

      (1) The authors did not explicitly indicate the feeding status of the flies, but it appears they were not starved. However, the naive and satisfied flies in this study displayed high feeding and PER baselines, similar to those observed in starved flies in other studies. This raises the concern that sexually failed flies may have consumed additional food during the 4.5-hour conditioning period, potentially lowering their baseline hunger levels and subsequently reducing PER responses. This alternative explanation is worth considering, as an earlier study demonstrated that sexually deprived males consumed more alcohol, and both alcohol and food are known rewards for flies. To address this concern, the authors could remove food during the conditioning phase to rule out its influence on the results.

      We think this is a valid concern. We will conduct courtship conditioning in the absence of food and test if courtship failure can still suppress sugar sensitivity in the revised manuscript.

      (2) Figure 1B reveals that approximately half of the males in the Failed group did not consume sucrose yet Figure 1-S1A suggests that the total volume consumed remained unchanged. Were the flies that did not consume sucrose omitted from the dataset presented in Figure 1-S1A? If so, does this imply that only half of the male flies experience sexual failure, or that sexual failure affects only half of males while the others remain unaffected? The authors should clarify this point.

      Here is a brief clarification of our experimental design and we will further clarify the details in the revised manuscript:

      After the behavioral conditioning, male flies were divided for two assays. On the one hand, we quantified PER responses of individual flies. As shown in Figure 1C, Failed males exhibited decreased sweet sensitivity (as demonstrated by the right shift of the response curve).

      On the other hand, we sought to quantify food consumption of individual flies by using the MAFE assay (Qi et al 2005). When presented with 400 mM sucrose, approximately 100% of the flies in the Naïve and Satisfied groups, and 50% of the flies in the Failed group, extended their proboscis and started feeding (Figure 1B). For these flies, we could quantify the consumed volumes and found there was no change (Figure 1, S1A). We should also note the consistency of these two experiments, e.g. in Figure 1C, only 50-60% of Failed males responded to 400 mM stimulation.  

      These two experiments in combination suggest that sexual failure suppressed sweet sensitivity of the Failed males. Meanwhile, as long as they still initiated feeding, the volume of food consumption remained unchanged. These results led us to focus on the modulatory effect of sexual failure on the sensory system, the main topic of this present study.

      In addition, to further clarify the potential misunderstanding, we plan to examine food consumption by using 800 mM sucrose in the revised manuscript. As shown in Figure 1C, 800 mM sucrose was adequate to induce feeding in ~100% of the flies.

      (3) The evidence linking TH-GAL4 labeled dopaminergic neurons to reduced sugar sensitivity in Gr5a+ neurons in sexually failed males could be further strengthened. Ideally, the authors would have activated TH-GAL4 neurons and observed whether this restored GCaMP responses in Gr5a+ neurons in sexually failed males. Instead, the authors performed a less direct experiment, shown in Figures 3-S1C and D. The manuscript does not describe the condition of the flies used in this experiment, but it appears that they were not sexually conditioned. I have two concerns with this experiment. First, no statistical analysis was provided to support the enhancement of sucrose responses following activation of TH-GAL4 neurons. Second, without performing this experiment in sexually failed males, the authors lack direct evidence to confirm that the dampened response of Gr5a+ neurons to sucrose results from decreased activity in TH-GAL4 neurons.

      We think this is also a valid suggestion. We will directly examine whether activating TH<sup>+</sup> neurons in sexually conditioned males would enhance sugar responses of Gr5a<sup>+</sup> neurons in sexually failed males. We will also add in statistical analysis.

      Nevertheless, we would still argue our current experiments using Naive males (Figure 3, S1C-D) are adequate to show a functional link between TH<sup>+</sup> neurons and Gr5a<sup>+</sup> neurons. Combining with the results that these neurons form active synapses (Figure 3, S1B) and that the activity of TH<sup>+</sup> neurons was dampened in sexually failed males (Figure 3G-I), our current data support the notion that sexual failure suppresses sweet sensitivity via TH-Gr5a circuity.

      (4) The statistical methods used in this study are poorly described, making it unclear which method was used for each experiment. I suggest that the authors include a clear description of the statistical methods used for each experiment in the figure legends. Furthermore, as I have pointed out, there is a lack of statistical comparisons in Figures 3-S1C and D, a similar problem exists for Figures 6E and F.

      We will add detailed information of statistical analysis in each figure legend.

      (5) The experiments in Figure 5 lack specificity. The target neurons in this study are Gr5a+ neurons, which are directly involved in sugar sensing. However, the authors used the less specific Dop1R1- and Dop2R-GAL4 lines for their manipulations. Using Gr5a-GAL4 to specifically target Gr5a+ neurons would provide greater precision and ensure that the observed effects are directly attributable to the modulation of Gr5a+ neurons, rather than being influenced by potential off-target effects from other neuronal populations expressing these dopamine receptors.

      We agree with the reviewer that manipulating Dop1R1 and Dop2R genes (Figure 4) and the neurons expressing them (Figure 5) might have broader impacts. In fact, we have also tested the role of Dop1R1 and Dop2R in Gr5a<sup>+</sup> neurons by RNAi experiments (Figure 6). As shown by both behavioral and calcium imaging experiments, knocking down Dop1R1 and Dop2R in Gr5a<sup>+</sup> neurons both eliminated the effect of sexual failure to dampen sweet sensitivity, further confirming the role of these two receptors in Gr5a<sup>+</sup> neurons.

      (6) I found the results presented in Fig. 6F puzzling. The knockdown of Dop2R in Gr5a+ neurons would be expected to decrease sucrose responses in naive and satisfied flies, given the role of Dop2R in enhancing sweet sensitivity. However, the figure shows an apparent increase in responses across all three groups, which contradicts this expectation. The authors may want to provide an explanation for this unexpected result.

      We agree that there might be some potential discrepancies. However, our current data are not adequate for the clarification given the experiments shown in Figure 6E-F and the apparent control (Figure 3C) were not conducted under identical settings at the same (that’s why we did not directly compare these results). One way to address the issues is to conduct these calcium imaging experiments again with a head-to-head comparison with the control group (Gr5a-GCaMP, +/- Dop1R1 and Dop2R RNAi). We will conduct the experiments and present the data in the revised manuscript.

      (7) In several instances in the manuscript, the authors described the effects of silencing dopamine signaling pathways or knocking down dopamine receptors in Gr5a neurons with phrases such as 'no longer exhibited reduced sweet sensitivity' (e.g., L269 and L288), 'prevent the reduction of sweet sensitivity' (e.g., L292), or 'this suppression was reversed' (e.g. L299). I found these descriptions misleading, as they suggest that sweet sensitivity in naive and satisfied groups remains normal while the reduction in failed flies is specifically prevented or reversed. However, this is not the case. The data indicate that these manipulations result in an overall decrease in sweet sensitivity across all groups, such that a further reduction in failed flies is not observed. I recommend revising these descriptions to accurately reflect the observed phenotypes and avoid any confusion regarding the effects of these manipulations.

      We will change our expressions in the revised manuscript. In brief, we think that these manipulations (suppressing Dop1R1<sup>+</sup> and Dop2R<sup>+</sup> neurons) have two consequences: suppressing the overall sweet sensitivity and eliminating the effect of sexual failure.

      Reviewer #2 (Public review):

      Summary:

      The authors exposed naïve male flies to different groups of females, either mated or virgin. Male flies can successfully copulate with virgin females; however, they are rejected by mated females. This rejection reduces sugar preference and sensitivity in males. Investigating the underlying neural circuits, the authors show that dopamine signaling onto GR5a sensory neurons is required for reduced sugar preference. GR5a sensory neurons respond less to sugar exposure when they lack dopamine receptors.

      Strengths:

      The findings add another strong phenotype to the existing dataset about brain-wide neuromodulatory effects of mating. The authors use several state-of-the-art methods, such as activity-dependent GRASP to decipher the underlying neural circuitry. They further perform rigorous behavioral tests and provide convincing evidence for the local labellar circuit.

      Weaknesses:

      The authors focus on the circuit connection between dopamine and gustatory sensory neurons in the male SEZ. Therefore, it is still unknown how mating modulates dopamine signaling and what possible implications on other behaviors might result from a reduced sugar preference.

      We agree with the reviewer that in the current study, we did not examine how mating experience suppressed the activity of dopaminergic neurons in the SEZ. The current study mainly focused on the behavioral characterization (sexual failure suppresses sweet sensitivity) and the downstream mechanism (TH-Gr5a pathway). We think that examining the upstream modulatory mechanism may be more suitable for a separate future study.

      We believe that a sustained reduction in sweet sensitivity (not limited to sucrose but extend to other sweet compounds, Figure 1, S1B-C) upon sexual failure suggests a generalized and sustained consequence on reward-related behaviors. Sexual failure may thus resemble a state of “primitive emotion” in fruit flies. We will further discuss this possibility in the revised manuscript.

      Reviewer #3 (Public review):

      Summary

      In this work, the authors asked how mating experience impacts reward perception and processing. For this, they employ fruit flies as a model, with a combination of behavioral, immunostaining, and live calcium imaging approaches.

      Their study allowed them to demonstrate that courtship failure decreases the fraction of flies motivated to eat sweet compounds, revealing a link between reproductive stress and reward-related behaviors. This effect is mediated by a small group of dopaminergic neurons projecting to the SEZ. After courtship failure, these dopaminergic neurons exhibit reduced activity, leading to decreased Gr5a+ neuron activity via Dop1R1 and Dop2R signaling, and leading to reduced sweet sensitivity. The authors therefore showed how mating failure influences broader behavioral outputs through suppression of the dopamine-mediated reward system and underscores the interactions between reproductive and reward pathways.

      Concern

      My main concern regarding this study lies in the way the authors chose to present their results. If I understood correctly, they provided evidence that mating failure induces a decrease in the fraction of flies exhibiting PER. However, they also showed that food consumption was not affected (Fig. 1, supplement), suggesting that individuals who did eat consumed more. This raises questions about the analysis and interpretation of the results. Should we consider the group as a whole, with a reduced sensitivity to sweetness, or should we focus on individuals, with each one eating more? I am also concerned about how this could influence the results obtained using live imaging approaches, as the flies being imaged might or might not have been motivated to eat during the feeding assays. I would like the authors to clarify their choice of analysis and discuss this critical point, as the interpretation of the results could potentially be the opposite of what is presented in the manuscript.

      Here is a brief clarification of our experimental design and we will further clarify the details in the revised manuscript:

      After the behavioral conditioning, male flies were divided for two assays. On the one hand, we quantified PER responses of individual flies. As shown in Figure 1C, Failed males exhibited decreased sweet sensitivity (as demonstrated by the right shift of the response curve).

      On the other hand, we sought to quantify food consumption of individual flies by using the MAFE assay (Qi et al 2005). When presented with 400 mM sucrose, approximately 100% of the flies in the Naïve and Satisfied groups, and 50% of the flies in the Failed group, extended their proboscis and started feeding (Figure 1B). For these flies, we could quantify the consumed volumes and found there was no change (Figure 1, S1A). We should also note the consistency of these two experiments, e.g. in Figure 1C, only 50-60% of Failed males responded to 400 mM stimulation.  

      These two experiments in combination suggest that sexual failure suppressed sweet sensitivity of the Failed males. Meanwhile, as long as they still initiated feeding, the volume of food consumption remained unchanged. These results led us to focus on the modulatory effect of sexual failure on the sensory system, the main topic of this present study.

      In addition, to further clarify the potential misunderstanding, we plan to examine food consumption by using 800 mM sucrose instead. As shown in Figure 1C, 800 mM sucrose was adequate to induce feeding in ~100% of the flies.

    1. Friedman is correct, for example when he points out the importance of changes like the rise of India or China, the spatial fragmentation of the production process through offshoring or the lowering of transaction costs that makes more and more services tradable.

      This made me start to rethink whether globalization is a process of "flattening". Indeed, phenomena such as the rise of India and China, as well as production outsourcing, may seem like globalization has increased opportunities for everyone, but I think this is only superficial. There are actually many imbalances behind it, such as some places where cheap labor has become the bottom of the supply chain, while large companies in developed countries have taken the lead.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This valuable study combined whole-head magnetoencephalography (MEG) and subthalamic (STN) local field potential (LFP) recordings in patients with Parkinson's disease undergoing deep brain stimulation surgery. The paper provides solid evidence that cortical and STN beta oscillations are sensitive to movement context and may play a role in the coordination of movement redirection.

      We are grateful for the expert assessment by the editor and the reviewers. Below we provide pointby-point replies to both public and private reviews. We have tried to keep the answers in the public section short and concise, not citing the changed passages unless the point does not re-appear in the recommendations. There, we did include all of the changes to the manuscript, such that the reviewers need not go back and forth between replies and manuscript.

      The reviewer comments have not only led to numerous improvements of the text, but also to new analyses, such as Granger causality analysis, and to methodological improvements e.g. including numerous covariates in the statistical analyses. We believe that the article improved substantially through the feedback, and we thank the reviewers and the editor for their effort.

      Public Reviews

      Reviewer #1 (Public review):

      Summary:

      Winkler et al. present brain activity patterns related to complex motor behaviour by combining wholehead magnetoencephalography (MEG) with subthalamic local field potential (LFP) recordings from people with Parkinson's disease. The motor task involved repetitive circular movements with stops or reversals associated with either predictable or unpredictable cues. Beta and gamma frequency oscillations are described, and the authors found complex interactions between recording sites and task conditions. For example, they observed stronger modulation of connectivity in unpredictable conditions. Moreover, STN power varied across patients during reversals, which differed from stopping movements. The authors conclude that cortex-STN beta modulation is sensitive to movement context, with potential relevance for movement redirection.

      Strengths:

      This study employs a unique methodology, leveraging the rare opportunity to simultaneously record both invasive and non-invasive brain activity to explore oscillatory networks.

      Weaknesses:

      It is difficult to interpret the role of the STN in the context of reversals because no consistent activity pattern emerged.

      We thank the reviewer for the valuable feedback to our study. We agree that the interpretation of the role of the STN during reversals is rather difficult, because reversal-related STN activity was highly variable across patients. Although there seem to be consistent patterns in sub-groups of the current cohort, with some patients showing event-related increases (Fig. 3b) and others showing decreases, the current dataset is not large enough to substantiate or even explain the existence of such clusters. Thus, we limit ourselves to acknowledging this limitation and discussing potential reasons for the high variability, namely variability in electrode placement and insufficient spatial resolution for the separation of specialized cell ensembles within the STN (see Discussion, section Limitations and future directions).

      Reviewer #2 (Public review):

      Summary:

      This study examines the role of beta oscillations in motor control, particularly during rapid changes in movement direction among patients with Parkinson's disease. The researchers utilized magnetoencephalography (MEG) and local field potential (LFP) recordings from the subthalamic nucleus to investigate variations in beta band activity within the cortex and STN during the initiation, cessation, and reversal of movements, as well as the impact of external cue predictability on these dynamics. The primary finding indicates that beta oscillations more effectively signify the start and end of motor sequences than transitions within those sequences. The article is well-written, clear, and concise.

      Strengths:

      The use of a continuous motion paradigm with rapid reversals extends the understanding of beta oscillations in motor control beyond simple tasks. It offers a comprehensive perspective on subthalamocortical interactions by combining MEG and LFP.

      Weaknesses:

      (1) The small and clinically diverse sample size may limit the robustness and generalizability of the findings. Additionally, the limited exploration of causal mechanisms reduces the depth of its conclusions and focusing solely on Parkinson's disease patients might restrict the applicability of the results to broader populations.

      We thank the reviewer for the insightful feedback. We address these issues one by one in our responses to points 2, 4 and 6, respectively.

      (2) The small sample size and variability in clinical characteristics among patients may limit the robustness of the study's conclusions. It would be beneficial for the authors to acknowledge this limitation and propose strategies for addressing it in future research. Additionally, incorporating patient-specific factors as covariates in the ANOVA could help mitigate the confounding effects of heterogeneity.

      Thank you for this comment. The challenges associated with recording brain activity peri-operatively can be a limiting factor when it comes to sample size and cohort stratification. We now acknowledge this in the revised discussion (section Limitations and future directions). Furthermore, we suggest using sensing-capable devices in the future as a measure to increase sample sizes (Discussion, section Limitations and future directions). Lastly, we appreciate the idea of adding patient-specific factors as covariates to the ANOVAs and have thus included age, disease duration and pre-surgical UPDRS score into our models. This did not lead to any qualitative changes of statistical effects.

      (3) The author may consider using standardized statistics, such as effect size, that would provide a clearer picture of the observed effect magnitude and improve comparability.

      Thanks for the suggestion. As measures of effect size, we have added partial eta squared (η<sub>p</sub><sup2</sup>) to the results of all ANOVAs and Cohen’s d to all follow-up t-tests.

      (4) Although the study identifies relevance between beta activity and motor events, it lacks causal analysis and discussion of potential causal mechanisms. Given the valuable datasets collected, exploring or discussing causal mechanisms would enhance the depth of the study.

      We appreciate this idea and have conducted Granger causality analyses in response to this comment. This new analysis reveals that there is a strong cortical drive to the STN for all movements of interest and predictability conditions in the beta band. The detailed results can be viewed on p. 16 in the section on Granger causality. For statistical testing, we conducted an rmANCOVA, similar to those for power and coherence (see p. 46-48 and 54-56 for the corresponding tables), as well as t-tests assessing directionality (Figure 6-figure supplement 2 on p. 35). In the discussion section, we connect these results with prior findings suggesting that the frontal cortex drives the STN in the beta band, likely through hyperdirect pathway fibers (p. 17).

      (5) The study cohort focused on senior adults, who may exhibit age-related cortical responses during movement planning in neural mechanisms. These aspects were not discussed in the study.

      We appreciate the comment and agree that age may have impacted neural oscillatory activity of patients in the present study. We now acknowledge this in the limitations section, and point out that our approach to handling these effects was including age as a covariate in the statistical analyses.

      (6) Including a control group of patients with other movement disorders who also undergo DBS surgery would be beneficial. Because we cannot exclude the possibility that the observed findings are specific to PD or can be generalized. Additionally, the current title and the article, which are oriented toward understanding human motor control, may not be appropriate.

      We thank the reviewer for this comment and fully agree that it cannot be ruled out that the present findings are, in part, specific to PD. We acknowledge this limitation in the Limitations and future directions section (p. 20-21). Indeed, including a control group of patients with other disorders would be ideal, but the scarcity of patients with diseases other than PD who receive STN DBS in our centre makes this an unfeasible option in practical terms. We do suggest that future research may address this issue by extending our approach to different disorders or healthy participants on the cortical level (p. 21). Lastly, we appreciate the idea to adjust the title of the present article. The adjusted title is: “Context-Dependent Modulations of Subthalamo-Cortical Synchronization during Rapid Reversals of Movement Direction in Parkinson’s Disease”.

      That being said, we do believe that our findings at least approximate healthy functioning and are not solely related to PD. For one, patients were on their usual dopaminergic medication and dopamine has been found to normalize pathological alterations of beta activity. Further, the general pattern of movement-related beta and gamma oscillations reported here has been observed in numerous diseases and brain structures, including cortical beta oscillations measured non-invasively in healthy participants.

      Reviewer #3 (Public review):

      Summary:

      The study highlights how the initiation, reversal, and cessation of movements are linked to changes in beta synchronization within the basal ganglia-cortex loops. It was observed that different movement phases, such as starting, stopping briefly, and stopping completely, affect beta oscillations in the motor system.

      It was found that unpredictable cues lead to stronger changes in STN-cortex beta coherence. Additionally, specific patterns of beta and gamma oscillations related to different movement actions and contexts were observed. Stopping movements was associated with a lack of the expected beta rebound during brief pauses within a movement sequence.

      Overall, the results underline the complex and context-dependent nature of motor-control and emphasize the role of beta oscillations in managing movement according to changing external cues.

      Strengths:

      The paper is very well written, clear, and appears methodologically sound.

      Although the use of continuous movement (turning) with reversals is more naturalistic than many previous button push paradigms.

      Weaknesses:

      The generalizability of the findings is somewhat curtailed by the fact that this was performed perioperatively during the period of the microlesion effect. Given the availability of sensing-enabled DBS devices now and HD-EEG, does MEG offer a significant enough gain in spatial localizability to offset the fact that it has to be done shortly postoperatively with externalized leads, with an attendant stun effect? Specifically, for paradigms that are not asking very spatially localized questions as a primary hypothesis?

      We appreciate the reviewer’s feedback and acknowledge the valid point raised on the timing of our measurements. Indeed, sensing-enabled devices offer a valid alternative to peri-operative recordings, circumventing the stun effect. We acknowledge this in the revised discussion, section Limitations and future directions (p. 23): “Additionally, future research could capitalize on sensingcapable devices to circumvent the necessity to record brain activity peri-operatively, facilitating larger sample sizes and circumventing the stun effect, an immediate improvement in motor symptoms arising as a consequence of electrode implantation (Mann et al., 2009).” This alternative strategy, however, was not an option here because we did not have a sufficient number of patients implanted with sensing-enabled devices at the time when the data collection was initialized.

      That being said, we would like to highlight that in the present study, our goal was not to study pathology related to Parkinson’s disease. Rather, we aimed to learn about motor control in general. The stun effect may have facilitated motor performance in our patients, which is actually beneficial to the research goals at hand.

      Further investigation of the gamma signal seems warranted, even though it has a slightly lower proportional change in amplitude in beta. Given that the changes in gamma here are relatively wide band, this could represent a marker of neural firing that could be interestingly contrasted against the rhythm account presented.

      We appreciate the reviewer’s interest and we have extended the investigation of gamma oscillations. We now provide statistics regarding the influence of predictability on gamma power and gamma coherence (no significant effects) and explore Granger causality in the gamma (and beta) band (see comment 4 of reviewer 2). Unfortunately, we cannot measure spiking via the DBS electrode, and therefore we cannot investigate correlations between gamma oscillatory activity and action potentials. We do agree with the reviewer, however, that action potentials rather than oscillations form the basis of motor control in the brain. This view of ours is now reflected in the revised discussion, section Limitations and future directions (p. 21): “Lastly, given the present study’s focus on understanding movement-related rhythms, particularly in the beta range, future research could further explore the role of gamma oscillations in continuous movement and their relation to action potentials in motor areas (Fischer et al., 2020; Igarashi, Isomura, Arai, Harukuni, & Fukai, 2013), which form the basis of movement encoding in the brain.”

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      This is a well-conducted study and overall the results are clear. I only have one minor suggestion for improvement of the manuscript. I found the order of appearance of the results somewhat confusing, switching from predictability-related behavioral effects to primarily stopping and reversal-related neurophysiological effects, back to predictability but starting with coherence. I would suggest that the authors try to follow a systematic order focused on the questions at hand. E.g. perhaps readability could be improved if the results section is split into reversal vs. stopping related effects, reporting behavior, power, and coherence in this order, followed by a predictability section, again reporting behavior, power, and coherence. Obviously, this is an optional suggestion. Apart from that, I just missed a more direct message related to the absence of statistical significance related to STN power changes during reversal. I think this could be made more clear in the text.

      We thank the reviewer for the feedback to our study. In order to ease reading, we modified the order and further added additional sub-titles to the results section. We start with Behavior (p. 4) and then move on to Power (general movement effects on power – movement effects on STN power – movement effects on cortical power – predictability effects on power). Next, we move on to Connectivity (movement effects on connectivity – predictability effects on connectivity – Granger causality). We hope that these adaptations will help guide the reader.

      Additionally, we thank the reviewer for noting that we did not explicitly mention the lack of statistical significance of reversal-related beta power modulations in the STN. We have adapted the section on modulation of STN beta power associated with reversals (p. 8) to: “In the STN, reversals were associated with a brief modulation of beta power, which was weak in the group-average spectrum and did not reach significance (Fig. 3a).”

      Reviewer #2 (Recommendations for the authors):

      (1) The small sample size and variability in clinical characteristics among patients may limit the robustness of the study's conclusions. It would be beneficial for the authors to acknowledge this limitation and propose strategies for addressing it in future research. Additionally, incorporating patient-specific factors as covariates in the ANOVA could help mitigate the confounding effects of heterogeneity.

      Thank you for this comment. The challenges associated with recording brain activity peri-operatively can be a limiting factor when it comes to sample size. We now acknowledge this in the revised discussion, section Limitations and future directions (p. 20):

      “Invasive measurements of STN activity are only possible in patients who are undergoing or have undergone brain surgery. Studies drawing from this limited pool of candidate participants are typically limited in terms of sample size and cohort stratification, particularly when carried out in a peri-operative setting. Here, we had a sample size of 20, which is rather high for a peri-operative study, but still low in terms of absolute numbers.”

      Furthermore, we suggest using sensing-capable devices in the future as a measure to increase sample sizes (p. 21):

      “Additionally, future research could capitalize on sensing-capable devices to circumvent the necessity to record brain activity peri-operatively, facilitating larger sample sizes and circumventing the stun effect, an immediate improvement in motor symptoms arising as a consequence of electrode implantation (Mann et al., 2009).”

      Lastly, we appreciate the idea of adding patient-specific factors as covariates to the ANOVAs and have thus included age, disease duration and pre-surgical UPDRS score into our models. This did not lead to any qualitative changes of statistical effects.

      Revised article

      Methods, Statistical analysis:

      “To account for their potential influence on brain activity, we added age, pre-operative UPDRS score, and disease duration as covariates to all ANOVAs. Covariates were standardized by means of zscoring.”

      (2) The author may consider using standardized statistics, such as effect size, that would provide a clearer picture of the observed effect magnitude and improve comparability.

      Thanks for this useful suggestion. As measures of effect size, we have added partial eta squared (η<sub>p</sub><sup2</sup>) to the results of all ANOVAs and Cohen’s d to all follow-up _t-_tests.

      (3) Although the study identifies relevance between beta activity and motor events, it lacks causal analysis and discussion of potential causal mechanisms. Given the valuable datasets collected, exploring or discussing causal mechanisms would enhance the depth of the study.

      We appreciate this idea and have conducted Granger causality analyses in response to this comment. This new analysis reveals that there is a strong cortical drive to the STN for all movements of interest and predictability conditions in the beta band, but no directed interactions in the gamma band. For statistical testing, we conducted an rmANCOVA, similar to the analysis of power and coherence (see p. 46-48 and 54-56 for the corresponding tables), as well as t-tests assessing directionality (Figure 6 figure supplement 2 on p. 35). In the discussion section, we connect these results with prior findings suggesting that the frontal cortex drives the STN in the beta band, likely through hyperdirect pathway fibers (p. 17).

      Revised article

      Methods Section, Granger Causality Analysis

      “We computed beta and gamma band non-parametric Granger causality (Dhamala, Rangarajan, & Ding, 2008) between cortical ROIs and the STN in the hemisphere contralateral to movement for the post-event time windows (0 – 2 s with respect to start, reversal, and stop). Because estimates of Granger causality are often biased, we compared the original data to time-reversed data to suppress non-causal interactions. True directional influence is reflected by a higher causality measure in the original data than in its time-reversed version, resulting in a positive difference between the two, the opposite being the case for a signal that is “Granger-caused” by the other. Directionality is thus reflected by the sign of the estimate (Haufe, Nikulin, Müller, & Nolte, 2013). Because rmANCOVA results indicated no significant effects for predictability and movement type, and post-hoc tests did not detect significant differences between hemispheres, we averaged Granger causality estimates over movement types, hemispheres and predictability conditions in Figure 6-figure supplement 2.”

      Results, Granger causality

      “In general, cortex appeared to drive the STN in the beta band, regardless of the movement type and predictability condition. This was reflected in a main effect of ROI on Granger causality estimates (F<sub>ROI</sub>(7,9) = 3.443, p<sub>ROI</sub> = 0.044, η<sub>p</sub><sup2</sup> = 0.728; refer to Supplementary File 4 for the full results of the ANOVA). In the hemisphere contralateral to movement, follow-up t-tests revealed significantly higher Granger causality estimates from M1 to the STN (t = 3.609, one-sided p < 0.001, d = 0.807) and from MSMC to the STN (t = 2.051, one-sided p < 0.027, d = 0.459) than the other way around. The same picture emerged in the hemisphere ipsilateral to movement (M1 to STN: t = 3.082, one-sided p = 0.003, d = 0.689; MSMC to STN: t \= 1.833, one-sided p < 0.041, d = 0.410). In the gamma band, we did not detect a significant drive from one area to the other (F<sub>ROI</sub>(7,9) = 0.338, p<sub>ROI</sub> = 0.917, η<sub>p</sub><sup2</sup> = 0.208, Supplementary File 6). Figure 6-figure supplement 2 demonstrates the differences in Granger causality between original and time-reversed data for the beta and gamma band.”

      Discussion, The dynamics of STN-cortex coherence

      “Considering the timing of the increase observed here, the STN’s role in movement inhibition (Benis et al., 2014; Ray et al., 2012) and the fact that frontal and prefrontal cortical areas are believed to drive subthalamic beta activity via the hyperdirect pathway (Chen et al., 2020; Oswal et al., 2021) it seems plausible that the increase of beta coherence reflects feedback of sensorimotor cortex to the STN in the course of post-movement processing. In line with this idea, we observed a cortical drive of subthalamic activity in the beta band.”

      (4) The study cohort focused on senior adults, who may exhibit age-related cortical responses during movement planning in neural mechanisms. These aspects were not discussed in the study.

      We appreciate the comment and agree that age may have impacted neural oscillatory activity of patients in the present study. We now acknowledge this in the limitations section, and point out that our approach to handling these effects was including age as a covariate in the statistical analyses.

      Revised article

      Discussion, Limitations and Future Directions

      “Further, most of our participants were older than 60 years. To diminish any confounding effects of age on movement-related modulations of neural oscillations, such as beta suppression and rebound (Bardouille & Bailey, 2019; Espenhahn et al., 2019), we included age as a covariate in the statistical analyses.”

      (5) Including a control group of patients with other movement disorders who also undergo DBS surgery would be beneficial. Because we cannot exclude the possibility that the observed findings are specific to PD or can be generalized. Additionally, the current title and the article, which are oriented toward understanding human motor control, may not be appropriate.

      We thank the reviewer for this comment and fully agree that it cannot be ruled out that the present findings are, in part, specific to PD. We acknowledge this limitation in the Limitations and future directions section (p. 20-21). Indeed, including a control group of patients with other disorders would be ideal, but the scarcity of patients with diseases other than PD who receive STN DBS makes this an unfeasible option. We do suggest that future research may address this issue by extending our approach to different disorders or healthy participants on the cortical level (p. 21). Lastly, we appreciate the idea to adjust the title of the present article. The adjusted title is: “Context-Dependent Modulations of Subthalamo-Cortical Synchronization during Rapid Reversals of Movement Direction in Parkinson’s Disease”.

      That being said, we do believe that our findings at least approximate healthy functioning and are not solely related to PD. For one, patients were on their usual dopaminergic medication for the study and dopamine has been found to normalize pathological alterations of beta activity. More importantly, the general pattern of movement-related beta and gamma oscillations has been observed in numerous diseases and brain structures, including cortical beta oscillations measured non-invasively in healthy participants. Thus, it is not unlikely that the new aspects discovered here are also general features of motor processing.

      Revised article

      Discussion, Limitations and future directions

      “Furthermore, we cannot be sure to what extent the present study’s findings relate to PD pathology rather than general motor processing. We suggest that our approach at least approximates healthy brain functioning as patients were on their usual dopaminergic medication. Dopaminergic medication has been demonstrated to normalize power within the STN and globus pallidus internus, as well as STN-globus pallidus internus and STN-cortex coherence (Brown et al., 2001; Hirschmann et al., 2013). Additionally, several of our findings match observations made in other patient populations and healthy participants, who exhibit the same beta power dynamics at movement start and stop (Alegre et al., 2004) that we observed here. Notably, our finding of enhanced cortical involvement in face of uncertainty aligns well with established theories of cognitive processing, given the cortex' prominent role in managing higher cognitive functions (Altamura et al., 2010). Yet, transferring our approach and task to patients with different disorders, e.g. obsessive compulsive disorder, or examining young and healthy participants solely at the cortical level, could contribute to elucidating whether the synchronization dynamics reported here are indeed independent of PD and age.”

      Reviewer #3 (Recommendations for the authors):

      Despite the strengths of the "rhythm" account of cognitive processes, the paper could possibly be improved by making it less skewed to rhythms explaining all of the movement encoding.

      Thank you for this comment - the point is well taken. There is a large body of literature relating neural oscillations to spiking in larger neural populations, which itself is likely the most relevant signal with respect to motor control. In our eyes, it is this link that justifies the rhythm account, i.e. we agree with the reviewer that action potentials are the basis of movement encoding in the brain, not oscillations. Unfortunately, we cannot measure spiking with the method at hand.

      To better integrate this view into the current manuscript, we make the following suggestion for future research in the Limitations and future directions section (p. 21): “Lastly, given the present study’s focus on understanding movement-related rhythms, particularly in the beta range, future research could further explore the role of gamma oscillations in continuous movement and their relation to action potentials in motor areas (Fischer et al., 2020; Igarashi, Isomura, Arai, Harukuni, & Fukai, 2013), which form the basis of movement encoding in the brain.”

      In Figure 5 - is the legend correct? Is it really just a 0.2% change in power only? That would be a very surprisingly small effect size.

      We thank the reviewer for noting this. Indeed, the numbers on the scale quantify relative change (post - pre)/pre and should be multiplied by 100 to obtain %-change. We have adjusted the color bars accordingly.

      The dissociation between the effects of unpredictable cues in coherence versus raw power is interesting and could potentially be directly contrasted further in the discussion (here they are presented separately with separate discussions, but this seems like a pretty important and novel finding as beta coherence and power usually go in the same direction).

      We appreciate the reviewer’s interest in our findings on the predictability of movement instructions. In case of coherence, the difference between pre- and post-event was generally more positive in the unpredictable condition, meaning that suppressions (negative pre-post difference) were diminished whereas increases (positive pre-post difference) were enhanced. With respect to power, we also observed less suppression in the unpredictable condition at movement start. Therefore, the direction of change is in fact the same. We made this clearer in the revised version by adapting the corresponding sections of the abstract, results and discussion (see below).

      The only instance of coherence and power diverging (on a qualitative level) was observed during reversals: here, we noted post-event increases in coherence and post-event decreases in M1 power in the group-average spectra. However, when comparing the pre- and post-event epochs statistically by means of permutation testing, the coherence increase did not reach significance. Hence, we did not highlight this aspect.

      Revised version

      Abstract

      “… Event-related increases of STN-cortex beta coherence were generally stronger in the unpredictable than in the predictable condition. … “

      Results, Effects of predictability on beta power  

      “With respect to the effect of predictability of movement instructions on beta power dynamics (research aim 2), we observed an interaction between movement type and condition (F<sub>cond*mov</sub> (2,14) = 4.206, p<sub>cond*mov</sub> = 0.037, η<sub>p</sub><sup2</sup> = 0.375), such that the beta power suppression at movement start was generally stronger in the predictable (M = -0.170, SD = 0.065) than in the unpredictable (M \= -0.154, SD = 0.070) condition across ROIs (t = -1.888, one-sided p \= 0.037, d = -0.422). We did not observe any modulation of gamma power by the predictability of movement instructions (F<sub>cond</sub> (1,15) = 0.792, p<sub>cond</sub> = 0.388, η<sub>p</sub><sup2</sup> = 0.050, Supplementary File 5).”

      Effects of predictability on STN-cortex coherence

      “With respect to the effect of predictability of movement instructions on beta coherence (research aim 2), we found that the pre-post event differences were generally more positive in the unpredictable condition (main effect of predictability condition; F<sub>cond</sub>(1,15) = 8.684, p<sub>cond</sub> = 0.010, η<sub>p</sub><sup2</sup> = 0.367; Supplementary File 3), meaning that the suppression following movement start was diminished and the increases following stop and reversal were enhanced in the unpredictable condition (Fig. 6a). This effect was most pronounced in the MSMC (Fig. 6b). When comparing regionaverage TFRs between the unpredictable and the predictable condition, we observed a significant difference only for stopping (t<sub>clustersum</sub> = 142.8, p = 0.023), suggesting that the predictability effect was mostly carried by increased beta coherence following stops. When repeating the rmANCOVA for preevent coherence, we did not observe an effect of predictability (F<sub>cond</sub>(1,15) = 0.163, p<sub>cond</sub> = 0.692, η<sub>p</sub><sup2</sup> = 0.011), i.e. the effect was most likely not due to a shift of baseline levels. The increased tendency for upward modulations and decreased tendency for downward modulations rather suggests that the inability to predict the next cue prompted intensified event-related interaction between STN and cortex. STN-cortex gamma coherence was not modulated by predictability (F<sub>cond</sub>(1,15) = 0.005, p<sub>cond</sub> = 0.944, η<sub>p</sub><sup2</sup> = 0.000, Supplementary File 5).”

      Discussion, Beta coherence and beta power are modulated by predictability

      “In the present paradigm, patients were presented with cues that were either temporally predictable or unpredictable. We found that unpredictable movement prompts were associated with stronger upward modulations and weaker downward modulations of STN-cortex beta coherence, likely reflecting the patients adopting a more cautious approach, paying greater attention to instructive cues. Enhanced STN-cortex interactions might thus indicate the recruitment of additional neural resources, which might have allowed patients to maintain the same movement speed in both conditions. […]”

      With respect to power, we observed reduced beta suppression in the unpredictable condition at movement start, consistent with the effect on coherence, likely demonstrating a lower level of motor preparation.

      Given that you have a nice continuous data task here - the turning of the wheel, it might be interesting to cross-correlate the circular position (and separately - velocity) of the turning with the envelope of the beta signal. This would be a nice finding if you could also show that the beta is modulated continuously by the continuous movements. In the natural world, we rarely do a continuous movement with a sudden reversal, or stop, most of the time we are in continuous movement. Look at this might also be a strength of your dataset.

      We could not agree more. In fact, having a continuous behavioral output was a major motivation for choosing this particular task. We are very interested in state space models such as preferential subspace identification (Sani et al., 2021), for example. These models relate continuous brain signals to continuous behavioral target variables and should be of great help for questions such as: do oscillations relate to moment-by-moment adaptations of continuous movement? Which frequency bands and brain areas are important? Is angular position encoded by different brain areas/frequency bands than angular speed? These analyses are in fact ongoing. This project, however, is too large to fit into the current article.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study is an important follow-up to their prior work - Wong et al. (2019), starting with clear questions and hypotheses, followed by a series of thoughtful and organized experiments. The method and results are convincing. Experiment 1 demonstrated the sensory preconditioned fear with few (8) or many (32) sound-light pairings. Experiments 2A and 2B showed the role of PRh NMDA receptors during conditioning for online integration, revealing that this contribution is present only after a few sound-light pairings, not after many sound-light pairings. Experiments 3A and 3B showed the contribution of PRh-BLA communication to online integration, again only after a few but not after many. Contrary to Experiments 3A and 3B, Experiments 4A and 4B showed the contribution of PRh-BLA communication to integration at test only after many but not few sound-light pairings.

      Strengths:

      Throughout the manuscript, the methods and results are clearly organized and described, and the use of statistics is solid, all contributing to the overall clarity of the research. The discussion section was also well-written, effectively comparing the current research with the prior work and offering insightful interpretations and potential future directions for this line of research. I have only a limited amount of concerns about some results and some details of experiments/statistics.

      We thank the reviewer for their positive assessment.

      Weaknesses:

      Could you provide further interpretation regarding line 171: the observation that sensory preconditioned fear increased with the number of sound-light pairings? Was this increase due to better sound-light association learning during Stage 1? Additionally, were there any experimental differences between Experiment 1 and the other experiments that might explain why freezing was higher in the P32 group compared to the P8 group? This pattern seemed to be absent in the other experiments. If we consider the hypothesis that the online integration mechanism is more active with fewer pairings and the chaining mechanism at the test is more prominent with many pairings, we wouldn't expect a difference between the P8 and P32 groups. Given the relatively small sample size in Experiment 1, the authors might consider conducting a cross-experiment analysis or something similar to investigate this further.

      We appreciate the reviewer’s point and thank them for the question. The heightened level of sensory preconditioned fear among rats that received many sound-light pairings in the initial control experiment (Group P32) may reflect the combined effects of both mediated learning and chaining at test. We are, however, reluctant to offer a strong interpretation of this result as it was not replicated in the subsequent experiments: i.e., the levels of freezing to the sensory preconditioned stimulus at test were almost identical among vehicle-injected controls that received either few (8) or many (32) sound-light pairings in Experiments 2A and 2B; and this was also true in Experiments 3A and 3B, and again in Experiments 4A and 4B. A key difference between the initial and subsequent experiments is that, in contrast to the initial experiment, rats in subsequent experiments underwent surgery for one reason or another (implantation of cannulas, lesion of the perirhinal cortex). The implication is that surgical interventions in the perirhinal cortex and/or basolateral amygdala might affect the way that rats integrate the sound-light and light-shock associations in sensory preconditioning: i.e., they may force rats to rely on one type of integration strategy or the other. This is, of course, purely speculative – it will be addressed in future research.

      Reviewer #2 (Public review):

      This manuscript builds on the authors' earlier work, most recently Wong et al. 2019, in which they showed the importance of the perirhinal cortex (PRh) during the first-order conditioning stage of sensory preconditioning. Sensory preconditioning requires learning between two neutral stimuli (S2-S1) and subsequent development of a conditioned response to one of the neutral stimuli after pairing of the other stimulus with a motivationally relevant unconditioned stimulus (S1-US). One highly debated question regarding the mechanisms of learning of sensory preconditioning has been whether conditioned responses evoked by the indirectly trained stimulus (S2) occur through a mediated representation at the time of the first-order US training, or whether the conditioned responses develop through a chained evoked representation (S2--> S1 --> US) at the time of test. The authors' prior findings provided strong evidence for PRh being involved in mediated learning during the first-order training. They showed that protein synthesis was required during the first-order S1-US learning to support the conditioned response to the indirectly trained stimulus (S2) at the test.

      One question remaining following the previous paper was whether certain conditions may promote a chaining mechanism over mediated learning, as there is some evidence for chained representations at the time of the test. In this paper, the authors directly address this important question and find unambiguous results that the extent of training during the preconditioning stage impacts the involvement of PRh during the first-order conditioning or stage 2. They show that putative blockade of synaptic changes in PRh, using an NMDA antagonist, disrupts responding to the preconditioned cue at test during shorter duration preconditioning training (8 trials), but not during extended training (32 trials). They also show that this is the case for communication between the PRh and BLA during the same stage of training using a contralateral inactivation approach. This confirms their previous findings in 2019 of connectivity between these regions for the short-duration training, while they observe here for the first time that this is not the case for extended training. Finally, they show that with extended training, communication between BLA and the PRh is required at the final test of the preconditioned stimulus, but not for the short duration training.

      The results are clear and extremely consistent across experiments within this paper as well as with earlier work. The experiments here are thorough, and well-conceived, and address an important and highly debated question in the field regarding the neural and psychological mechanisms underlying sensory preconditioning. This work is highly impactful for the field as the debate over mediated versus chaining mechanisms has been an important topic for more than 70 years.

      We thank the reviewer for their kind assessment.

      Reviewer #3 (Public review):

      The authors tested whether the number of stimulus-stimulus pairings alters whether preconditioned fear depends on online integration during the formation of the stimulus-outcome memory or during the probe test/mobilization phase, when the original stimulus, which was never paired with aversive events, elicits fear via chaining of stimulus-stimulus and stimulus-outcome memories. They found that sensory preconditioning was successful with either 8 or 32 stimulus-stimulus pairings. Perirhinal cortex NMDA receptor blockade during stimulus-outcome learning impaired preconditioning following 8 but not 32 pairings during preconditioning. Therefore, perirhinal cortex NMDA activity is required for online integration or mediated learning. Perirhinal-basolateral amygdala had nearly identical effects with the same interpretation: these areas communicate during stimulus-outcome learning, and this online communication is required for later expressing preconditioned fear. Disconnection prior to the probe test, when chaining might occur, had different effects: it impaired the expression of preconditioned fear in rats that received 32, but not 8, pairings during preconditioning. The study has several strengths and provides a thoughtful discussion of future experiments. The study is highly impactful and significant; the authors were successful in describing the behavioral and neurobiological mechanisms of mediated learning versus chaining in sensory preconditioning, which is often debated in the learning field. Therefore this study will have a significant impact on the behavioral neurobiology and learning fields.

      Strengths:

      Careful, rigorous experimental design and statistics.

      The discussion leaves open questions that are very much worth exploring. For example - why did perirhinal-amygdala disconnection prior to the probe have no effect in the 8-pairing group, when bilateral perirhinal inactivation did (in Wong et al, 2019)? The authors propose that perirhinal cortex outputs bypass the amygdala during the probe test, which is an excellent hypothesis to test.

      The authors provide evidence that both mediated learning and chaining occur.

      Thank you for the positive assessment – we fully intend to identify the circuitry that regulates retrieval/expression of sensory preconditioned fear when it is based on mediated learning in stage 2.

      Weaknesses:

      This is inherent to all neural interference and behavioral experiments: biological/psychological functions do not typically operate binarily. There is no single clear number or parameter at which mediated learning or chaining happens, and both probably happen to some extent. Addressing this is even more difficult given behavioral variability across subjects, implant sites, etc. Thus, this is not so much a weakness particular to this study as much as an existential problem, which the authors were able to work around with careful experimental design and appropriate controls.

      We completely agree with the point raised here and thank the reviewer for their assessment.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) It appears that the method description for Sensory Preconditioning was copied from their previous Wong et al. (2019) paper, which is fine, but in the current research, the authors use 8 or 32 presentations, which is not reflected in the description.

      Thank you for bringing this to our attention. This is now addressed in the method section on page 27 (beginning at line 655):

      “Rats received either eight presentations of the sound and eight of the light in a single session, or 32 presentations of the sound and 32 of the light across four daily sessions. On Day 3, all rats received eight presentations of the sound and eight of the light. Each presentation of the sound was 30 s in duration and each presentation of the light was 10 s in duration. The first stimulus presentation occurred five min after rats were placed into the chambers. The offset of one stimulus co-occurred with the onset of the other stimulus for groups that received paired presentations of the sound and the light, while these stimuli were presented separately for groups that received explicitly unpaired presentations. The interval between each paired presentation was five min while the interval between each separately presented stimulus was 150 s. After the last stimulus presentation, rats remained in the chambers for an additional one min. They were then returned to their home cages. This training was repeated on Days 4-6 for rats that received 32 presentations of the sound and 32 of the light. All rats proceeded to first-order conditioning (details below) the day after their final session of sound and light exposures, which was Day 4 for rats exposed to eight presentations of the sound and light and Day 7 for rats exposed to 32 presentations of the sound and light.”

      (2) Line 148: Could the authors clarify how the "significant linear increase" was assessed? From similar descriptions in later experiments, it seems it was based on a comparison of freezing across the four presentations, but the F(1,26) statistic suggests there seemed to be a half-split test. The same questions exist in all the experiments. Please clarify.

      Conditioning data were analysed using contrasts with repeated measures in ANOVA. The repeated measures (or within-subject) factor was “trial” as all rats were exposed to four light-shock pairings in this stage of training. We examined whether there was a significant linear increase in freezing across trials using a standard within-subject contrast. The specific coefficients for this contrast, given the four trials, were -3, -1, 1, and 3. The reason that the degrees of freedom remain 1 and 26 in this analysis is because the within-subject contrast is part of a set of planned orthogonal contrasts. That is, in any planned analysis of the sort conducted here, the df1 will always be 1, indicating the very nature of the analysis. There was no splitting of the data, or comparisons between the split halves.

      (3) Line 154: Could the authors clarify what is meant by "other main effects and their interactions"? It is not clearly inferable from the context.

      Apologies for the confusion here. “Other main effects” refer to the two between-subject factors in isolation: i.e., the overall comparison of freezing to the light (averaged across the four trials) between groups that received either paired or unpaired stimulus presentations in stage 1 (factor 1 à main effect 1), and between groups that received either eight or 32 sound and light exposures in stage 1 (factor 2 à main effect 2). “Their interaction” refers to the assessment of whether the overall difference in freezing to the light (averaged across the four trials) between Groups P8 and U8 differs from the overall difference in freezing to the light (averaged across the four trials) between Groups P32 and U32. We have edited the text near line 153 to indicate that:

      “The overall comparisons of freezing to the light (averaged across the four conditioning trials) between groups that received either paired or unpaired stimulus presentations in stage 1 (factor 1), and between groups that received either eight or 32 sound and light exposures in stage 1 (factor 2), were not significant (Fs < .45, p > .508). The interaction between these two between-subject factors was also not significant (F < .45, p > .508).”

      (4) The use of sound and light as preconditioned and conditioned cues are counterbalanced. Was there any difference in the increase of freezing during conditioning depending on the type of conditioned cues? Was there any difference in the preconditioned fear? While it is hard to assess statistical significance due to the sample size limit, even observing a trend could be interesting.

      We examined whether the levels of freezing to the conditioned and preconditioned stimuli depend on their physical identity. In general, there was a slight trend towards more freezing to the preconditioned stimulus when it was a tone, and less freezing to the conditioned stimulus when it was a tone. These are, however, simply indications. None of the statistical comparisons between rats for which the preconditioned stimulus was the tone (and, thereby, conditioned stimulus was the light) and rats for which the preconditioned stimulus was the light (and, thereby, conditioned stimulus was the tone) reached the conventional level of significance.

      (5) General suggestion on reporting non-significant statistics: the authors reported a small F statistic value a few times to suggest non-significance. But without clearly specifying degrees of freedom, it is hard to get a sense of statistical significance (e.g. Line 227, largest F<3.10). I recommend adding p values alongside the F statistics and reporting exact statistics whenever possible.

      Apologies for the omission. The p values have now been included alongside all non-significant F statistics.

      (6) Another general suggestion is to use non-parametric statistical testing with such small sample sizes. I recommend using the Kruskal-Wallis H test (the non-parametric equivalent of F-statistic) to replace the ANOVA result. Also, given many tests only involve comparing two independent groups, using Mann-Whitney U test (the non-parametric equivalent of independent t-test) would be sufficient.

      We understand that small sample sizes can occasionally lead to unequal variances between groups, which necessitates the use of non-parametric statistics. However, as non-parametric statistics raise a different set of issues for data analysis (e.g., power) and interpretation, our general view for the type of data collected in this study is that parametric analyses are appropriate and should be retained (particularly in the absence of unequal variances between groups). We hold this view for two reasons. First, the hypotheses tested in the present series were derived from past work in which parametric analyses revealed meaningful patterns of results at the same level of statistical power. Second, the application of these analyses then yielded results consistent with our hypotheses: for the most part, we observed between-group differences where we expected there to be such differences and did not observe between-group differences where we did not expect there to be such differences. As such, we have not switched from a parametric to non-parametric analysis strategy. We do, however, appreciate the suggestion and will apply a non-parametric approach where it is warranted in our future work.

      Reviewer #2 (Recommendations for the authors):

      I have a few very minor comments for the authors regarding the discussion and interpretation of the very nice experimental results.

      (1) In Figures 4 and 5, the authors provide a schematic of the experiment. It's very clearly indicated whether the BLA inactivation is ipsi- or contralateral, but the unilateral PRh lesion isn't mentioned. I'd recommend including that here so that someone reading through the figures can more easily understand the experiment. The hypothesis is clear and the experiment is so well designed that a read through of the figures can relay most information to an experienced reader.

      Thank you for this suggestion – we have included information about the unilateral PRh lesion in the schematic for Figures 4 and 5.

      (2) The authors have an extended description of backward conditioning in the discussion. It seems like the authors are suggesting this as an important future direction, but they never explicitly say this, resulting in a bit of confusion as to what this section refers to. Also, Ward-Robinson and Hall 1996 showed backward sensory preconditioning using a serial auditory-visual association and argued for a mediated solution based on their results. It may be worth citing that paper here.

      Apologies for the lack of clarity. We have revised this point in the discussion (page 18, beginning line 434) and referenced Ward-Robinson and Hall (1996):

      “Why does increasing the number of sound-light pairings change the way that rats integrate the sound-light and light-shock memories? One possibility is that increasing the number of sound-light pairings in stage 1 reduces the ability of each stimulus to activate the memory of the other. This is consistent with findings by Holland (1998), who showed that the likelihood of mediated learning in rats decreases with the amount of training (see also Holland, 2005); but inconsistent with our findings that, after extended training, rats continue to integrate the sound-light and light-shock associations through chaining at the time of testing (as chaining is predicated on the sound activating the memory of the light after extended training). Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993). This order hypothesis is consistent with evidence showing that the way in which animals represent an audio-visual compound changes across repeated compound exposures (e.g., Bellingham & Gillette, 1981; Holmes & Harris, 2009). It can be tested using a so-called “backward” sensory preconditioning protocol, which reverses the order of stimulus presentations in stage 1 (e.g., Ward-Robinson & Hall, 1996). That is, rather than rats being exposed to the “forward” sound-light pairings used here and by Wong et al. (2019), rats in a backward protocol are exposed to light-sound pairings. Increasing the number of light-sound pairings in this protocol should result in rats learning that the light is followed by the sound (light→sound) and that the sound is followed by nothing (sound→nothing). Hence, during the session of light-shock pairings in stage 2, the light should continue to activate the memory of the sound, resulting in formation of the mediated sound-shock association (e.g., Ward-Robinson & Hall, 1996). That is, if our order hypothesis is correct, increasing the number of light-sound pairings in the backward protocol should preserve the likelihood of mediated learning in stage 2 and, if anything, diminish the likelihood of chaining at test in stage 3 (as the sound is never followed by a light). Hence, PRh manipulations that fail to affect fear of the sound when administered after many sound-light pairings (e.g., infusion of DAP5) should disrupt that fear when administered after many light-sound pairings in the backward protocol. This will be assessed in future work.”

      (3) Line 467 in the discussion suggests that the results are surprising that PRh-BLA communication is not needed at test when learning putatively occurs through a mediated mechanism during first-order conditioning. I was a bit surprised by this comment since I was under the assumption that only BLA was required at this point after consolidation of the mediated learning. Holmes et al., 2013 showed that BLA is required for extinction to S2 after first-order conditioning. In that experiment they inactivated BLA during S2- presentations (typically considered the extinction test), and showed that reduction to S2 did not occur the subsequent day, indicating the memory was stored in BLA and may not necessarily require PRh-BLA communication.

      The result noted here was somewhat surprising as our past studies showed that silencing activity in the PRh prior to testing attenuates freezing to a sensory preconditioned stimulus (i.e., an S2). We took this to mean that the PRh is necessary for retrieval/expression of fear to S2 and supposed that this retrieval/expression would be achieved through communication between the PRh and BLA. However, the results of the PRh-BLA disconnection at test show that this communication is not required, leaving us to speculate that retrieval/expression of fear to S2 may be achieved through communication between the PRh and CeA.

      We have edited the opening of the relevant paragraph to clarify why the result noted here was surprising (page 20, beginning line 485):

      “While the PRh and BLA clearly communicate to support mediated learning about the sound, this communication is not required for retrieval/expression of the mediated sound-shock association at the time of testing. This result is somewhat surprising as activity in the PRh is needed for expression of fear to the sound (Holmes et al., 2013; Wong et al., 2019) and raises the question: how does the PRh-dependent sound-shock association come to be expressed in fear responses?”

      (4) The authors reference Holland 1981 and 1998, yet there's not much discussion of these findings. I think there should be a bit more emphasis on these studies since they show how mediated learning greatly depends on the extent of training. Also, it may be worth considering Holland's theory of why mediated conditioning is more effective with shorter training. His theory may be consistent with the authors, but I believe he suggests that early in training a stronger mediated representation is evoked which tends to dissipate with time. I think this is a valid hypothesis to consider in this paper.

      The Holland papers show that rats form mediated associations (Holland, 1981) and that the likelihood of them doing so decreases with the amount of training (Holland, 1998). These findings are paralleled by those reported in the present series of experiments. However, the protocols used by Holland were very different to those used in the present study; and the explanation for his 1998 findings (which is the more relevant of the two papers) simply does not apply to the case of sensory preconditioning.

      To be clear: Holland (1998) exposed rats to either “few” or “many” tone-food pairings in stage 1, tone-lithium chloride pairings in stage 2 and, finally, tested rats with the food alone in stage 3. He predicted and showed that those exposed to few tone-food pairings showed an aversion to the food at test (i.e., they consumed less of the food than controls) whereas those exposed to many tone-food pairings showed no such aversion (i.e., they consumed the same amount of food as the controls). This was taken to mean that, across the series of tone-lithium pairings, the tone activated the memory of food among rats in the few condition, resulting in a mediated food-lithium association; but failed to do so among rats in the many condition, resulting in no food-lithium association. According to Holland, the tone failed to activate the memory of food in the many condition because, by the end of training in stage 1, it was not needed for them to know what to do when the tone was presented: they simply had to run to the magazine to collect the food when delivered. That is, the tone eventually associated with the responses that rats emitted in the training situation, thereby obviating any need for activation of the food memory.

      While this explanation is both elegant and interesting, it cannot be applied to the results obtained in the present study where the initial stage of training involved few or many sound-light pairings. That is, unlike in the Holland study where rats in the many condition eventually learned a stimulus-“run to magazine” association that maintained performance in the absence of any mental image of food, in the present study, any stimulus-response association acquired in stage 1 (e.g., orienting responses towards the sources of the auditory and visual stimuli) cannot have contributed to the expression of sensory preconditioned fear at test. Hence, stimulus-response learning in the many condition cannot be invoked to explain the pattern of results in the present study, even if it adequately explains what-appears-to-be a similar finding in the Holland study.

      Nonetheless, we have included a reference to the general style of explanation that was considered and rejected by Holland in his 1998 and 2005 papers. This appears on page 18 (beginning line 434) and reads:

      “Why does increasing the number of sound-light pairings change the way that rats integrate the sound-light and light-shock memories? One possibility is that increasing the number of sound-light pairings in stage 1 reduces the ability of each stimulus to activate the memory of the other. This is consistent with findings by Holland (1998), who showed that the likelihood of mediated learning in rats decreases with the amount of training (see also Holland, 2005); but inconsistent with our findings that, after extended training, rats continue to integrate the sound-light and light-shock associations through chaining at the time of testing (as chaining is predicated on the sound activating the memory of the light after extended training). Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993)…”

      (5) There is also a Holland 2005 paper in which he tests whether extended training of the initial stimulus associations may result in a reduced associability of those stimuli. This would potentially result in lower mediated learning due to a decreased associability of the mediated representation, thereby explaining why extended training reductions in mediated learning occur. Using a probabilistic design, Holland shows that this reduction in mediated learning is likely not due to a change in associability.

      We appreciate the note re Holland (2005) and have included a reference to it in our General Discussion. We agree with Holland that the reduction in mediated learning across extended training is not due to reduced associability of the retrieved stimulus representation. If this were the case, it would remain to explain why stimulus representations continue to be activated at test, which must occur for successful chaining of the sound-light and light-shock associations upon presentations of the sound alone. This is included in the modified text on page 18 (beginning line 434), which is part of our response to point 4.

      Reviewer #3 (Recommendations for the authors):

      (1) I think the 4th intro paragraph is essentially saying that more pairings during preconditioning encourage chaining as opposed to mediated learning - I might recommend clarifying this a bit. It took me a while to put it together.

      Apologies for the confusion. We have clarified the argument at this point in the Introduction with the following insertion on page 4 (beginning line 84):

      “That is, increasing the number of sound-light pairings may allow rats to encode information about stimulus order in stage 1 and, thereby, shift the locus of integration from mediated conditioning in stage 2 to chaining at test in stage 3 (Holmes et al., 2022).”

      (2) In analyzing test data I am assuming percent freezing is the average of the entire 30s or 10s CS period - could this be clarified?

      This is correct and has been clarified in the section for ‘Scoring and Statistics’ on page 29 (beginning line 708):

      “Freezing data were collected using a time-sampling procedure in which each rat was scored as either ‘freezing’ or ‘not freezing’ every two seconds by an observer blind to the rat’s group allocation. A percentage score was then calculated by dividing the number of samples scored as freezing by the total number of samples. The baseline level of freezing was established by scoring the first two min at the start of each experimental session: i.e., we divided the total number of samples scored as freezing by the total number of observed samples, which was 60. The levels of freezing to the 10 s conditioned stimulus and 30 s preconditioned stimulus were established in a similar manner: we scored the entire period of each stimulus presentation and divided the number of samples scored as freezing by the total number of observed samples, which was 5 for each presentation of the conditioned stimulus and 15 for each presentation of the preconditioned stimulus.”

      (3) Complementary to the above - during the probe test is there a difference during the first/last 2s of the CS? This would be interesting with respect to understanding the associative structure encoded.

      We have previously examined whether freezing responses change across the duration of a 30 s preconditioned stimulus and a 10 s conditioned stimulus. We have never seen any such changes: in our past work and in the present series of experiments, the expression of freezing is largely uniform across each presentation of a preconditioned or conditioned stimulus.

      (4) It is sort of unclear to me why more CS-CS pairings produced stronger preconditioned fear - is it that both mediated learning and chaining occur and giving 32 pairings permits both processes more than 8 pairings?

      This is a very reasonable explanation for the heightened level of sensory preconditioned fear among rats that received many sound-light pairings in the initial control experiment. We are, however, reluctant to offer a strong interpretation of this result as it was not replicated across subsequent experiments in the series: i.e., the levels of freezing to the sensory preconditioned stimulus at test were largely the same among vehicle-injected controls that received either few (8) or many (32) sound-light pairings in Experiments 2A and 2B, and again in Experiments 3A and 3B as well as Experiments 4A and 4B.

      (5) I would suggest individual data points overlaid on the bars, violin plots, or box and whisker plots to provide a better visualization of the data.

      We appreciate the suggestion – these have been included overlaid on bars in each histogram_._

      (6) There are other citations that would strengthen arguments for the idea that unidirectional/temporal associative structure can be acquired during (appetitive) sensory preconditioning: Leising 2007 Learning and Behavior, Hart 2022 Current Biology, for example.

      Thank you for these citations. We have included references to the Leising et al (2007) and Hart et al (2022) papers in our discussion on page 18-19 (beginning line 442):

      “Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993)…”

      Editor's note:

      We agree with the suggestions about full statistical reporting for non-significant results and about putting individual data points, perhaps coded to identify sex, on top of the bar graphs. Both will increase the transparency of the rigor of the work for readers.

      We thank the editors and authors for their suggestions. We have included full statistical reporting for non-significant results and overlaid individual data points on the bars in each histogram.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, Corso-Diaz et al, focus on the NRL transcription factor (TF), which is critical for retinal rod photoreceptor development and function. The authors profile NRL's protein interactome, revealing several RNA-binding proteins (RBPs) among its components. Notably, many of these RBPs are associated with R-loop biology, including DHX9 helicase, which is the primary focus of this study. R-loops are three-stranded nucleic acid structures that frequently form during transcription. The authors demonstrate that R-loop levels increase during photoreceptor maturation and establish an interaction between NRL TF and DHX9 helicase. The association between NRL and RBPs like DHX9 suggests a cooperative regulation of gene expression in a cell-type-specific manner, an intriguing discovery relevant to photoreceptor health. Since DHX9 is a key regulator of R-loop homeostasis, the study proposes a potential mechanism where a cell-type-specific TF controls the expression of certain genes by modulating R-loop homeostasis. This study also presents the first data on R-loop mapping in mammalian retinas and shows the enrichment of R-loops over intergenic regions as well as genes encoding neuronal function factors. While the research topic is very important, there is some concern regarding the data presented: there are substantial data supporting the interaction between NRL and DHX9, including pull-down experiments and proximity labeling assay (PLA), however, the data showing an interaction between NRL and DDX5, another R-loop-associated helicase, are inadequate. Importantly, the data supporting the claim that NRL interacts with R-loops are absolutely insufficient and at best, correlative. The next concerns are regarding the R-loop mapping data analysis and visualization.

      Strengths:

      There is compelling evidence that the NRL transcription factor interacts with several RNA binding proteins, and specifically, sufficient data supporting the interaction of NRL with DHX9 helicase.

      A major strength is the use of the single-stranded R-loop mapping method in the mouse retina.

      Weaknesses:

      (1) Figure S1A: There is a strong band in GST-IP (control IP) for either HNRNPUI1 or HNRNPU, although the authors state in their results that there is a strong interaction of these two RBPs with NRL.

      Under our experimental conditions, most RNA-binding proteins displayed higher binding to glutathione beads (Fig. S1A). However, GST-NRL purifications showed much stronger signals for respective RBPs. In the case of HNRNPU and HNRNPUl1, white bands that are indicative of substrate depletion due to higher protein levels are observed in GST-NRL lanes. Additionally, in Figures 1B and 1C, there is a clear enrichment of HNRNPU and HNRNPUl1 above the background signal. We added this to the text. See page 5.

      Both DHX9 and DDX5 samples have a faint band in the GST-IP.

      RNA-binding proteins may display some background as observed in other studies (e.g. PMID: 32704541). We think that showing the raw data without decreasing the exposure time is useful and that there is a clear enrichment compared to controls.  In addition, we tested the interaction in multiple systems.

      There is an extremely faint band for HNRNPA2B1 in the GST-NRL IP lane. Given this is a pull-down with added benzonase treatment to remove all nucleic acids, these data suggest, that previously observed NRL interactions with these particular RBPs are mediated via nucleic acids. Similarly, there is a loss of band signal for HNRNM in this assay, although it was identified as an NRL-interacting protein in three assays, which again suggests that nucleic acids mediate the interaction.

      Thank you for highlighting this point. We mention in the manuscript that the interaction between HNRNPM and A1 depends on nucleic acids, as noted by the reviewer, since there is no obvious band after the pull-down. We have now added that the interaction of NRL with HNRNPA1B1 is likely dependent on nucleic acids as well, given its weak signal. See page 5.

      (2) The data supporting NRL-DDX5 interaction in rod photoreceptor nuclei is very weak. In Figure 2D, the PLA signal for DDX5-NRL is very weak in the adult mouse retina and is absent in the human retina, as shown in Figure 2H.

      We agree with the reviewer. We think that the signal for DDX5 is weak, and we addressed this in the text. We noted on page 7: “Taken together, these findings suggest a strong interaction between NRL and DHX9 throughout the nuclear compartment in the retina and that a transient and/or more regulated interaction of NRL with DDX5 may require additional protein partners.”  We have modified this sentence to add that the data also suggest transient interaction or the requirement of additional protein partners for stable interaction. See page 7.

      Given that there is no NRL-KO available for the human PLA assay, the control experiments using single-protein antibodies should be included in the assay. Similarly, the single-protein antibody control PLA experiments should be included in the experimental data presented in Figure 2J.

      Thank you for the suggestion. We performed PLAs using both DHX9 and IgG in the human retina and observed no specific amplification signal. Some background is observed outside the nucleus and in the extracellular space. We added these results to the text and to the supplementary information. See page 7 and Fig.S2B.

      (3) The EMSA experiment using a probe containing NRL binding motif within the DHX9 promoter should include incubation with retina nuclear extracts depleted for NRL as a control.

      In EMSA experiments, we used bovine retina to obtain enough protein quantities. As suggested by the reviewer, using NRL depleted extract would increase the specificity of observed gel shift and complement our pre-immune serum as a negative control. However, removal of all the NRL protein using the antibodies available was not feasible. In the future, we will use enough mice to obtain large quantities of protein for this experiment and will collect retinas from Nrl knockout as negative control.

      (4) There is a reduced amount of DHX9 pulled down in NRL-IP in HEK293 cells, but there is no statistically significant difference in the reciprocal IP (DHX9-IP and blotting for NRL) (Figure 4C).

      We believe the reviewer is referring to the data in Figure 4C showing that RNase H treatment led to significantly reduced pulldown of DHX9 as compared to control, but the reciprocal IP in Figure 4D showed no statistical significance between control and RNase H treatment. In Figure 4D, we hypothesize that NRL may account for only a small proportion of DHX9’s interactome, so the change in NRL levels could not be detected due to the sensitivity of our assay. DHX9 likely constitutes a large proportion of NRL’s interactome in HEK293 cells, hence the change in DHX9 level was more obvious when pulling down with NRL. We added this information to the results. See page 8.

      (5) The only data supporting the claim that NRL interacts with R-loops are presented in Figure 5A.

      Additional evidence that NRL interacts with R-loops comes from DRIP-Seq experiments where signals from R-loops overlap with NRL ChIP-Seq signals (Figure 7A). This shows that R-loops and NRL co-occur on multiple genomic regions. In addition, indirect evidence of NRL and R-loops’ interaction is shown in pull down experiments and PLA assays where R-loops influence DHX9 and NRL binding. We clarified this in the discussion. See page 14.

      This is a co-IP of R-loops and then blotting for NRL, DHX9, and DDX5. Here, there is no signal for DDX5, quantification of DHX9 signal shows no statistically significant difference between RNase H treated and untreated samples, while NRL shows a signal in RNase H treated sample. These data are not sufficient to make the statement regarding the interaction of NRL with R-loops.

      Thank you for this comment. We respectfully disagree as we observe statistically significant enrichment for both NRL and DHX9 in these experiments (See Fig5A). Some NRL continues to bind to DNA that is pulled down nonspecifically, which may be expected since NRL is a transcription factor. See for example R-loop binding by the transcription factor Sox2 (PMID: 32704541). However, binding to R-loops is evidenced by an enrichment compared to RNase H-treated sample. We clarified this in Results section (See page 9).

      (6) Regarding R-loop mapping, the data analysis is quite confusing. The authors perform two different types of analyses: either overall narrow and broad peak analysis or strand-specific analysis. Given that the authors used ssDRIP-seq, which is a method designed to map R-loops strand specifically, it is confusing to perform different types of analyses.

      Thank you for highlighting this point. This has enhanced the clarity of the methods and enriched the discussion. We aimed to identify R-loops as accurately as possible. We conducted two types of analyses to capture different aspects of R-loops: one that looks at overall patterns (narrow and broad peaks) and another that focuses on specific strands of DNA.

      Using ssDRIP-seq, which is designed to map R-loops on specific strands, allowed us to examine R-loops formed in only one strand and those formed on both strands. To identify strand-specific R-loops, we filtered our RNase-H enriched peaks for those enriched on one strand compared to the opposite strand. We clarified the analysis in the results section, and Figure 6B. See page 10 and methods section page 25.

      Next, the peak analysis is usually performed based on the RNase H treated R-loop mapping; what does it mean then to have a pool of "Not R-loops", see Figure 6B?

      The “Not R-loop” group refers to peaks called using the opposite strand that are not observed when calling peaks using RNase H as control. We modified this figure for clarity (Figure 6B).

      In that regard, what does the term "unstranded" R-loops mean? Based on the authors' definition, these are R-loops that do not fall within the group of strand-specific R-loops. The authors should explain the reasons behind these types of analyses and explain, what the biological relevance of these different types of R-loops is.

      Thank you for helping us clarify this point. Unstranded R-loops are DNA regions containing DNA:RNA hybrids on both plus and minus strands and possibly representing bidirectional transcription by Pol II. We observed that unstranded R-loops are enriched only in intergenic regions, H3K9me3 regions, and downstream of the transcriptional termination site (TTS). We added to the discussion the possible implications of these enrichments, including regulation of Pol II termination and transcription of long genes.  See Page 13.

      (7) It would be more useful to show the percent distribution of R-loops over the different genomic regions, instead of showing p-value enrichment, see Figure 6C.

      Since most of the genome is non-coding, plotting the distribution as a proportion was not informative since the vast proportion of the data falls in intergenic regions. However, we created a new figure showing observed vs. expected ratio that seems to be more informative and moved the current p-value figure to the supplement in revised version. See Figure 6C and S6D.

      (8) Based on the model presented, NRL regulates R-loop biology via interaction with RBPs, such as DHX9, a known R-loop resolution helicase. Given that the gene targets of NRL TF are known, it would be useful to then analyze the R-loop mapping data across this gene set.

      Thank you for this suggestion. We performed an analysis of R-loops on NRL-regulated genes. Interestingly, NRL target genes have an enrichment of stranded R-loops at the promoter/TSS and unstranded R-loops on the gene body compared to all Ensembl genes (Figure S7B). We added a table containing all NRL-regulated genes we used for this analysis (table S5) and a figure showing this result (Fig. S7B).

      Reviewer #2 (Public review):

      Summary:

      The authors utilize biochemical approaches to determine and validate NRL protein-protein interactions to further understand the mechanisms by which the NRL transcription factor controls rod photoreceptor gene regulatory networks. Observations that NRL displays numerous protein-protein interactions with RNA-binding proteins, many of which are involved in R-loop biology, led the authors to investigate the role of RNA and R-loops in mediating protein-protein interactions and profile the co-localization of R-loops with NRL genomic occupancy.

      Strengths:

      Overall, the manuscript is very well written, providing succinct explanations of the observed results and potential implications. Additionally, the authors use multiple orthogonal techniques and tissue samples to reproduce and validate that NRL interacts with DHX9 and DDX5. Experiments also utilize specific assays to understand the influence of RNA and R-loops on protein-protein interactions. The authors also use state-of-the-art techniques to profile R-loop localization within the retina and integrate multiple previously established datasets to correlate R-loop presence with transcription factor binding and chromatin marks in an attempt to understand the significance of R-loops in the retina.

      Weaknesses:

      In general, the authors provide superficial interpretations of the data that fit a narrative but fail to provide alternative explanations or address caveats of the results. Specifically, many bands are present in interaction studies either in control lanes (GST controls) of Westerns or large amounts of background in PLA experiments.

      We have added additional information to the text regarding the presence of background signals in pull downs. We wish to note that experimental samples always exceeded background signals.  We believe that reporting these raw findings (rather than showing shorter exposures) is valuable for the scientific community. We did not observe any background in the proximity ligation assay (PLA) that exceeded what is typically expected, and the signals were clearly discernible. Cases where signals are weaker, such as with DDX5, have been highlighted. In addition, we added a DHX9-IgG negative control for the human PLA experiment. See page 5 and Fig. S2B.

      Additionally, the lack of experiments testing the functional significance of Nrl interactions or R-loops within the developing retina fails to provide novel biological insights into the regulation of gene regulatory networks other than, 'This could be a potentially important new mechanism'.

      We agree that functional experiments are necessary to understand the molecular mechanisms behind R-loop regulation in the retina; however, we believe it goes beyond the scope of this initial characterization (as this is the first report on R-loops in the retina). We are currently pursuing these studies.

      We performed new analysis on NRL-regulated genes as suggested by reviewer 1. We show that NRL target genes have an enrichment of stranded R-loops at the promoter/TSS and unstranded R-loops on the gene body compared to all Ensembl genes (Figure S7B), providing further evidence of the functional  interaction between NRL and R-loops. See table S5 and Fig. S7B, and discussion.

      Additionally, the authors test the necessity of RNA for NRL/DHX9 interactions but don't show RNA binding of NRL or DHX9 or the sufficiency of RNA to interfere/mediate protein-protein interactions. Recent work has highlighted the prevalence of RNA binding by transcription factors through Arginine Rich Motifs that are located near the DNA binding domains of transcription factors.

      We agree that the role of RNA in these complexes is very exciting, and we are currently pursuing these studies. However, we believe that they fall outside the scope of this initial report on R-loops in the retina.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      There are a couple of minor comments:

      (1) Unfinished sentence; page 11, the end of the first paragraph.

      Thank you for catching this error. We removed the unfinished text.

      (2) Page 6: Figure S2A should be Figure S2.

      In general, the manuscript would benefit from a deeper explanation of the biological relevance of R-loop formation and the connection to NRL TF and the expression of genes regulated by NRL. In this regard, a more substantial description of the model would be useful.

      We have modified the discussion for clarity and included new ideas on possible roles of R-loops in gene regulation of photoreceptors.

      Reviewer #2 (Recommendations for the authors):

      (1) The specificity of interactions needs to be addressed:

      - Figure 1B - HNRNPUI1 bands present in GST control.

      - Figure 1C - Bands present in the Empty Vector control IP for HNRNPU and DHX9.

      - Supplemental Figure 1A - most proteins are present in GST control suggesting prevalent binding to GST and lack of specificity for other interactions.

      Thank you for your comment. RNA-binding proteins can have more background as observed in other studies (e.g. PMID: 32704541) but there is always a higher signal in experimental samples compared to controls. While we agree that we can enhance the conditions for immunoprecipitation (IP) by optimizing washing buffers, exposure and other parameters, we believe the current methods tell the story. We have added additional text explaining this. See page 5.

      (2) Use of the term 'Strongest' interaction - IPs don't directly address the strength of interaction, but depend on levels of expression AND affinity. The strength of interaction should be tested using techniques like an OCTET or SPR assay. One can also quantify the effect that RNA would have in such an assay.

      Thank you for your suggestion. We replaced the term 'stronger' with “higher signal” and “robust” at most places. The source of protein lysates is the same for experiments and controls, thus the amount of protein is consistent in both conditions, and not dependent on level of gene expression.

      (3) In supplemental tables, please use the proper gene names, not the UniProt peptide name. For example, there are no genes named ELAV1-ELAV4. These should be ELAVL1-ELAVL4. A short glance identifies >10 gene name errors.

      Thank you for the suggestion. We updated current gene names in all tables.

      (4) Please provide the rationale for the choice of DNA sequence for the DHX9 nucleotide sequence used for EMSA assays. In the human DHX9 locus, the NRL ChIP-seq peak looks to be contained in Intron1 whereas the NRL ChIP-seq peak in mouse DHX9 looks to be in the proximal upstream promoter. Did the authors choose an evolutionarily conserved sequence in the promoter region that contained the NRL motif or does the probe sequence arise from the sequence that has known NRL binding as assayed by NRL ChIP-seq? A zoomed-in image of the NRL ChIP-seq pile-ups in the DHX9 locus in each species would be beneficial.

      Thank you for this suggestion. The probe was chosen by scanning for NRL binding motifs on the Chip-Seq peak at the human DHX9 promoter. We added a Zoom-in image of the ChIP-Seq or CUT&RUN reads for NRL on both human and mouse retinas. Figure 3D shows NRL binding in both species in regions containing the homologous motif. The sequence is partially conserved and shown in the figure.

      (5) Normalization in RNaseH/RNaseA Co-IP experiments. Why does RNAseH treatment result in increased NRL IP (increased NRL expression?) or does RNaseA treatment cause reduced IP of DHX9? These differences seem to cause a 'denominator' effect, leading the Authors to conclude decreased co-IP of DHX9 with NRL when R-loops are inhibited or increased co-IP of NRL with DHX9 when RNA is degraded. An alternate interpretation would be that inhibiting the R-loop binding of NRL unmasks the epitope for antibody recognition. The authors should test NRL binding to RNA and determine if RNA binding affects the co-IP of NRL with DHX9.

      We agree that removing total RNA by RNase A or R-loops by RNase H may alter the accessibility of our antibodies to the epitopes, resulting in the differences in the level of total protein pulled down. However, we quantified the relative level of the associating protein to the total protein and confirmed, in reciprocal assays, that RNase A treatment led to increased interaction between NRL and DHX9. However, the quantification was not consistent between the reciprocal IPs upon RNase H treatment. We reason that in Figure 4D, as NRL may account for only a small proportion of DHX9’s interactome, the change in NRL level could not be detected due to the sensitivity of our assay. However reciprocally, DHX9 can constitute a larger proportion of NRL’s interactome in HEK293 cells, hence the change in DHX9 level was more obvious. We added this information to the text. See page 8.

      (6) Figure 7 - Malat1 - there doesn't seem to be an overlap of NRL with Stranded R-loop peaks in this image. Nrl seems to flank the region of R-loops.

      We changed Malat1 for Mplkip that shows a direct overlap of Nrl binding and R-loops. See Figure 7C.

      (7) Results end with 'A Model'. Seems like some concluding remarks and references to Figure 8 were mistakenly left out.

      Thank you for catching this typo. We removed the misplaced text.

      (8) Model and Discussion - authors should show raw data for RHO with respect to NRL binding and R-loops. No evidence was provided regarding R-loops (or lack thereof) in the Rhodopsin locus. Additionally, conclusions stating that "R-loops... are specifically depleted from genes, such as Rhodopsin, with high expression levels" go against Figures 7B and 7C. Malat1 is one of the highest expressed genes in the retina and contains R-loops.

      Thank you for helping us clarify our hypothesis. We added a genome browser view of Rhodopsin showing the absence of R-loops (Fig. S8). We hypothesize that R-loops could interfere with achieving higher rates of transcription, however we did not mean to say that all high expressed genes lack R-loops. We have rephrased the discussion to clarify this point.

      (9) Neuronal genes, particularly those involved in synaptic transmission are known to be, on average, longer than most genes (Gabel, 2015; PMID: 25762136). Is it possible that R-loops are detected at genes involved in synaptic function/structure solely because of transcript length, as it takes longer for transcription termination to resolve in genes that are longer? A plot showing R-loop enrichment and transcript length would address this.

      We added a plot showing gene length in relation to R-loops and expression levels. We observed that R-loops are more common over long genes regardless of their expression levels. We also observed that the concomitant presence of stranded and unstranded R-loops is restricted to the longest genes in most cases. We added this to Figure 7D.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, the authors investigate the role of BEND2, a novel regulator of meiosis, in both male and female fertility. Huang et al have created a mouse model where the fulllength BEND2 transcript is depleted but the truncated BEND2 version remains. This mouse model is fertile, and the authors used it to study the role of BEND2 on both male and female meiosis. Overall, the full-length BEND2 appears dispensable for male meiosis. The more interesting phenotype was observed in females. Females exhibit a lower ovarian reserve suggesting that full-length BEND2 is involved in the establishment of the primordial follicle pool.

      Strengths:

      The authors generated a mouse model that enabled them to study the role of BEND2 in meiosis. The role of BEND2 in female fertility is novel and enhances our knowledge of genes involved in the establishment of the primordial follicle pool.

      Weaknesses:

      The manuscript extensively explores the role of BEND2 in male meiosis; however, a more interesting result was obtained from the study of female mice. Only a few experiments were performed using female mice, therefore, more experiments should be performed to complete the story of the role of BEND2 on female fertility. In addition, the title and abstract of the manuscript do not align with the story, as female fertility is only a small portion of the data compared to the male fertility section.

      We appreciate the reviewer’s thoughtful summary, recognition of the strengths of our study, and constructive feedback. In the revised manuscript, we have performed additional experiments to enhance our understanding of the role of BEND2 in female gametogenesis. These new experiments provide further insights into the establishment of the ovarian reserve and the role of BEND2 in female fertility.

      Additionally, we have rewritten the title, abstract, and introduction to better align with the content of the manuscript and to reflect the balance between the male and female fertility results. We believe these changes address the reviewer’s concerns and improve the overall clarity and focus of the manuscript.

      Reviewer #1 (Recommendations For The Authors):

      • I recommend that the authors re-organize their abstract and introduction to accurately reflect the manuscript's primary focus on male fertility. Right now, the title of the manuscript is misleading. The manuscript does not investigate reproductive aging; rather, it primarily describes the depletion of primordial follicle number. The mechanism behind this depletion and whether this phenotype accelerates reproductive aging, are not explored. Clarifying these points will help align the title and content of the manuscript more accurately.

      We thank the reviewer for this suggestion. We agree that the original title and abstract did not fully capture the focus of the study. In response, we have rewritten the title, abstract, and introduction to better align with the results presented, focusing more clearly on the implications of the effects of the full-length BEND2 depletion for spermatogenesis and oogenesis. These revisions ensure that the title, the abstract, and the manuscript's introduction are now more accurately reflective of the work performed.

      • Figure 1: I couldn't find the validation of the polyclonal antibody against BEND2 that the authors generated.

      Regarding this query about the validation of the polyclonal antibody against BEND2, we apologize for any confusion. We would like to clarify that this validation is indeed presented in Figure 2 of our manuscript. To ensure this information is easily accessible, we have revised the text to explicitly mention the validation in Figure 2.

      • Figure 2A: Could you provide the actual numbers for the weight of the mice testis?

      In response to this question regarding Figure 2A and the weights of the mice testis, we have now included this data in a graph in Fig 2A and Table S1 and added this information in the results section.

      • Figure 2C and D: I am confused by the fact that in the WB we can appreciate a high expression of the p75 protein, but the signal is very low in the IF (Figure 2D).

      We thank the reviewer for raising this point. We acknowledge the apparent discrepancy between the strong p75 signal observed in the Western blot (Fig. 2C) and the weaker signal seen in the immunofluorescence (Fig. 2D). We think several factors could contribute to this difference, such as differences in sensitivity and detection methods, epitope accessibility, protein localization or differences in sample preparation, antibody affinity, and experimental conditions between Western blot and IF.

      • In the same figure, the authors also mention that the p75 protein is functional. On what basis do they rely on reaching this conclusion?

      We acknowledge that we cannot definitively confirm the functionality of the p75 protein. Our assumption was based on the observed fertility of the male mice and existing literature indicating that BEND2 is essential for completing meiosis (Ma et al., 2022). However, we understand the importance of clarity in our claims. To avoid any potential confusion, we have revised the sentence to read: "The p75 BEND2 protein—likely corresponding to an exon 11-skipped transcript—is present and might be functional in our mutant testis, based on the observed phenotype (see below)."

      • The phenotype in females is very interesting. The authors conclude that BEND2 influences primordial follicle formation, oocyte quality, fertility, and reproductive aging by (1) performing follicle counts, (2) analyzing the litter size, and (3) analyzing meiotic progression. Given that the authors build their story around these experiments, I strongly encourage them to expand the section on female fertility, or reorganize the manuscript, or be more cautious with some of their conclusions. They might consider performing additional experiments such as:

      - Oocyte quality: To determine whether BEND2 impacts oocyte quality, mice should be stimulated with hormones and oocyte quality should be analyzed (GV, MI, MII progression, spindle morphology and/or fertilization, and embryo development). Does the decrease in primordial follicles correlate with the number of ovulated oocytes, or is the impact only on oocyte quality?

      We appreciate the reviewer's suggestion to assess the impact of BEND2 on oocyte quality. Following the reviewer’s recommendation, we stimulated three control and three mutant mice. We analyzed the number of ovulated oocytes, their fertilization rate, and the percentage of embryos that developed to the blastocyst stage. These new results are included in the revised manuscript (see Results section and new Table 1). Our analyses indicate that for all parameters assessed, control and mutant oocytes behaved similarly. Specifically, there were no significant differences in the number of ovulated oocytes, fertilization rates, or the ability of embryos to progress to the blastocyst stage between the control and mutant groups. These findings suggest that mutant oocyte quality is comparable to control mice of a similar age. We have incorporated these new results into the manuscript.

      - Reproductive aging: A fertility trial would provide more information on whether BEND2 depletion triggers an acceleration of reproductive aging. In addition, the oldest mice used by the authors are 9 months old, and at this point, fertility has not declined yet.

      We appreciate the reviewer's suggestion regarding the assessment of reproductive aging. However, we respectfully disagree with the assertion that fertility has not declined by 9 months of age. In our colony, we have observed a significant decline in fertility around 10 months of age. Specifically, out of 18 10-month-old female mice placed in breeding cages, we observed only three pregnancies within the first 30 days (N.N. and I.R., data not published). Based on these observations, we determined that fertility begins to decline around this age in our colony, which informed our decision to use 9-month-old mice as the oldest age group for our analysis. Thus, this age is appropriate for evaluating the potential effects of BEND2 depletion on reproductive aging in our specific mouse population.

      - The observation that the primordial follicle pool is already diminished in mice that are 1 week old is very interesting. Some experiments that the authors could perform to figure out the mechanism are: (1) Analyzing apoptosis. Are the primordial follicles dying during the pool's establishment, or is this an ongoing apoptotic process throughout the mice's lifespan? (2) If the authors still have ovaries from mice younger than 1 week of age (when the primordial pool is forming), they could perform DDX4 staining and quantify the number of oocytes in follicles and the total number of oocytes. These experiments would provide mechanistic insights into whether BEND2 impacts the formation of the primordial follicle pool or if the pool forms but is then depleted.

      We appreciate the reviewer's suggestion to further explore the mechanism behind the reduced primordial follicle pool. In response, we have analyzed the number of DDX4positive cells (DDX4 labels oocytes) in newborn mutant and wild-type animals. Our results show that mutant ovaries contain significantly fewer oocytes compared to controls (see new Fig. 5). This finding supports the hypothesis that BEND2 is critical for the establishment of a normal ovarian reserve. We are grateful for this suggestion, as these additional data reinforce our conclusion that BEND2 is required to determine a normal ovarian reserve in mice.

      • What is the red signal in Supplementary Figure 1C?

      This image depicts the BEND2 staining pattern in 16 days post-coitum (dpc) wild-type mouse ovaries. To clarify this and prevent any confusion, we have updated the figure legend to explicitly state that the sample shown is from a wild-type mouse.

      • Please spell out the full term of all the acronyms.

      We apologize for the oversight in not fully spelling out some acronyms in the original manuscript. We have carefully reviewed the entire manuscript and have ensured that all acronyms are now spelled out in full upon their first use in the revised version. We want to thank the reviewer for bringing this to our attention.

      • Is Line-1 also dysregulated in the ovary? This was one of the main findings from the male part. It would be interesting to perform the same analysis in the ovary since Line1 has a role in establishing the ovarian reserve (PDMI: 31949138).

      We thank the reviewer for this insightful suggestion. We have analyzed the number of LINE1 and SYCP3-positive cells in wild-type and mutant newborn ovaries (new Fig. S4). Our results show no significant difference between the two genotypes, suggesting that LINE-1 is not dysregulated in newborn Bend2 mutant oocytes. These findings indicate that, at least in the context of the newborn ovary, LINE-1 does not appear to be affected by BEND2 depletion.

      Reviewer #2 (Public Review):

      In their manuscript entitled "BEND2 is a crucial player in oogenesis and reproductive aging", the authors present their findings that full-length BEND2 is important for repair of meiotic double strand break repair in spermatocytes, regulation of LINE-1 elements in spermatocytes, and proper oocyte meiosis and folliculogenesis in females. The manuscript utilizes an elegant system to specifically ablate the full-length form of BEND2 which has been historically difficult to study due to its location on the X chromosome and male sterility of global knockout animals.

      While the manuscript is an overall excellent addition to the field, it would significantly benefit from a few additional experiments, as well as some additional clarification/elaboration.

      The claim that BEND2 is required for ovarian reserve establishment is not supported, as the authors only look at folliculogenesis and oocyte abundance starting at one week of age, after the reserve is formed. Analysis of earlier time points would be much more convincing and would parse the role of BEND2 in the establishment vs. maintenance of this cell population. In spermatocytes, the authors demonstrate a loss of nuclear BEND2 in their mutant but do not comment on the change in localization (which is now cytoplasmic) of the remaining protein in these animals. This may have true biological significance and a discussion of this should be more thoroughly explored.

      We thank the reviewer for their thoughtful feedback and constructive suggestions to improve our manuscript.

      In response to the comment regarding the establishment of the ovarian reserve, we have now analyzed Bend2 mutant and control newborn ovaries. Our results show a significant reduction in the number of DDX4-positive cells in mutant ovaries compared to controls. These findings demonstrate that BEND2 is required for the establishment of the ovarian reserve, as the reduction is evident at birth.

      Regarding the cytoplasmic staining of BEND2 in mutant spermatocytes, we did perform secondary-antibody-only controls using goat anti-rabbit Cy3 to address the specificity of the signal. The staining observed in the Bend2 mutants closely resembles background staining, suggesting that the cytoplasmic signal is nonspecific. Therefore, we do not believe this represents a meaningful change in the localization of BEND2 protein in the mutants. We have clarified this in the revised manuscript to address this point.

      We hope these additional experiments and clarifications strengthen the manuscript and address the reviewer’s concerns.

      Reviewer #2 (Recommendations For The Authors):

      Major points:

      (1) The title of the manuscript does not accurately capture the content of the work. The vast majority of the data presented here is from the male, which is not reflected at all in the title - perhaps considering revising it?

      Thank you for your valuable suggestion. We agree that the original title did not fully reflect the focus of the manuscript. In response, we have revised the title, along with the abstract and introduction, to more accurately capture the content of the study and the emphasis on the male data. These changes ensure that the manuscript more clearly aligns with the results presented.

      (2) In Figure 2D, the authors demonstrate that WT BEND2 expression and localization are lost in the mutant, but staining is still apparent, just in the cytoplasm. Did the authors perform secondary-antibody-only controls to determine if this was background staining or real staining? If real, can they comment on the change in localization of the protein?

      We thank the reviewer for this insightful question. We have indeed performed secondary antibody-only controls using goat anti-rabbit Cy3. The staining observed in the Bend2 mutants closely resembles background staining, suggesting that the signal in the cytoplasm is not specific. Therefore, we do not believe this staining represents any real or meaningful expression of the BEND2 protein in the mutants.

      (3) In Figure S2A, the authors show Ku70 staining and describe that it is similar between the genotypes, but - to my eye - it looks quite distinctly different. It appears to stain in patches in WT SYCP3+ spermatocytes, versus staining in patches in the more mature, SYCP3- germ cells closer to the lumen in the mutant. Can the authors please clarify, or provide arrows to point which foci they are referring to?

      We apologize for the confusion caused by the image provided in the original submission. Upon review, we realized that the mutant image was not fully representative of the staining pattern observed in the majority of mutant samples. We have replaced this image with a new one in the revised manuscript, which more accurately reflects the similarity in Ku70 staining between wild-type and mutant testis. In this updated Figure S2, we have also included arrowheads to indicate the relevant foci, making it clearer to the reader. We have updated the figure legend to correspond with these changes as well.

      (4) The authors state that BEND2 is "required to establish the ovarian reserve during oogenesis" but this has not been demonstrated. The authors do show a reduced density of primordial follicles at one week of age. While this is compelling data, the ovarian reserve is established earlier in the mouse, around postnatal days 0-1, so it is not clear from this manuscript whether BEND2 is required for the maintenance of this population after PND1, leading to reduced numbers by 1 week of age, OR if it is required for the establishment of this population, which would result in reduced numbers of oocytes around the time of birth. This is a critical experiment that should be performed in order to determine which of these possibilities is likely the case. Ideally, looking at embryonic through early postnatal time points during ovarian development would be very helpful.

      We thank the reviewer for raising this important point. As mentioned earlier in response to Reviewer 1, we have performed the experiment suggested by Reviewer 2 and analyzed the number of DDX4-positive cells in newborn ovaries. Our results show that Bend2 mutant ovaries have fewer oocytes at birth than wild-type controls (Fig. 5H). This finding reinforces our conclusion that BEND2 is indeed required to establish the ovarian reserve, as the reduction in oocyte number is evident at the time of birth. We agree that this additional data strengthens our original claim, so we have included these results in the revised manuscript.

      Reviewer #3 (Public Review):

      Summary:

      Huang et al. investigated the phenotype of Bend2 mutant mice which expressed a truncated isoform. This mutant male showed increasing apoptosis due to unrepaired double-strand breaks. However, this mutant male has fertility, and this enabled them to analyze Bend2 function in females. They revealed that Bend2 mutation in females showed decreasing follicle numbers which leads to loss of ovarian reserve.

      Strengths:

      Since their Bend2 mutant males were fertile, they were able to analyze the function of Bend2 in females and they revealed that loss of Bend2 causes less follicle formation.

      Weaknesses:

      Why the phenotype of their mutant male is different from previous work (Ma et al.) is not clear enough although they discuss it.

      We appreciate the reviewer’s comment regarding the differences between our Bend2 mutant male phenotype and the previously reported phenotype by Ma et al., 2022. We believe this discrepancy is due to the fact that the Bend2 locus encodes two BEND2 isoforms: p140 and p80. In contrast to the previous study, where both proteins were ablated by mutation employed (the deletion of exons 12 and 13), our exon 11 deletion specifically ablates p140 expression while allowing the expression of p80 in the testis.

      Based on the distinct phenotypes observed in the two Bend2 mutant mouse models, we hypothesize that p80 is sufficient to fulfill BEND2’s roles in meiosis, which could explain why our Bend2 mutant males remain fertile. We have rewritten the relevant sections in the results and discussion to better articulate this hypothesis and clarify the potential mechanisms behind the observed phenotypic differences.

      We hope these clarifications and additional details adequately address the reviewer’s concerns.

      Reviewer #3 (Recommendations For The Authors):

      (1) The authors showed that Bend2 mutant females had decreased fertility. This may be due to decreased ovarian reserve. Did the authors check if the mutant mice decreased or lost fertility faster than WT? If the authors have the data, please refer to it in the manuscript.

      We followed the breeding performance of a small number of control and Bend2 mutant females, and preliminary observations suggested no clear differences between the two groups. However, due to the limited sample size, we felt that these data were not conclusive enough to be included in the manuscript. We agree that a more thorough analysis of fertility decline over time would be valuable, and we plan to address this question in a future study.

      (2) In Figure 1 A, there is no exon1 in the upper figure.

      We thank the reviewer for pointing this out. We have revised Figure 1A to include exon 1 and ensure the schematic is accurate. The updated figure is included in the revised version of the manuscript.

      (3) Figure 3A, it would be nice to show several tubules of the testis section as well as an enlarged one.

      Following the reviewer's advice, we have revised Figure 3A to include new images showing several tubules and an enlarged view of one section of a tubule. These updates are included in the revised manuscript to better represent the testis sections.

      (4) Please be consistent with the format of the graph, especially Supplemental figures 2C and 4D.

      We have revised the figures, including Supplemental Figures 2C and 4D, to ensure consistency in the format throughout the manuscript. We have made modifications to the figures to align them more closely and improve the overall presentation.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment 

      This study presents valuable finding regarding the role of life history differences in determining population size and demography. The evidence for the claims is still partially incomplete, with concerns about generation times and population structure. Nonetheless, the work will be of considerable interest to biologists thinking about the evolutionary consequences of life history changes.  

      Thank you. We have addressed the generation time and population structure issues in detail in our revision and hope that you, like us, find them to be of sufficiently low concern (i.e., they are not driving the results) that they do not overshadow the main findings and conclusions.

      The opportunity to make in-depth revisions also helped the manuscript in two ways unanticipated by both us and the reviewers. First, KW made a mistake in the original analysis of phylogenetic signal, and catching that error simplifies that aspect of the study (there is none in our measured variables). Second, in June 2024 Hilgers et al. (2024; https://doi.org/10.1101/2024.06.17.599025) posted an important manuscript to bioRxiv noting the possibility of false population size peaks in PSMC analyses using the standard default settings. Our results had three of those, which we have eliminated. N<sub>e</sub>ither of these issues affect the overall conclusions, but their resolution improves the work.  

      Public Reviews: 

      Reviewer #1 (Public Review): 

      Summary: 

      This interesting study applies the PSMC model to a set of new genome sequences for migratory and nonmigratory thrushes and seeks to describe differences in the population size history among these groups. The authors create a set of summary statistics describing the PSMC traces - mean and standard deviation of N<sub>e</sub>, plus a set of metrics describing the shape of the oldest N<sub>e</sub> peak - and use these to compare across migratory and resident species (taking single samples sequenced here as representative of the species). The analyses are framed as supporting or refuting aspects of a biogeographic model describing colonization dynamics from tropical to temperate North and South America. 

      Strengths: 

      At a technical level, the sequencing and analysis up through PSMC looks good and the paper is engaging and interesting to read as an introduction to some verbal biogeographic models of avian evolution in the Pleistocene.

      The core findings - higher and more variable N<sub>e</sub> in migratory species - seem robust, and the biogeographic explanation is plausible.  

      Thanks. We thought so as well. Our analyses go beyond being simply descriptive and test some simple hypotheses, including a biogeographic+ecological expansion opportunity gained in some lineages through the adoption of a seasonal migration life-history strategy.  

      Weaknesses: 

      I did not find the analyses particularly persuasive in linking specific aspects of clade-level PSMC patterns causally to evolutionary driving forces. To their credit, the authors have anticipated my main criticism in the discussion. This is that variation in population size inferred by methods like PSMC is in "effective" terms, and the link between effective and census population size is a morass of bias introduced by population structure and selection so robustly connecting specific aspects of PSMC traces to causal evolutionary forces is somewhere between extremely difficult and impossible.  

      As R1 notes, we do not attempt to link effective population sizes and census sizes (though we do discuss this), and we are also careful to discuss correlated rather than causative factors when going beyond the overarching hypotheses regarding life-history strategy.

      Population structure is the most obvious force that can generate large N<sub>e</sub> changes mimicking the census-sizefocused patterns the authors discuss. The authors argue in the discussion that since they focus on relatively deep time (>50kya at least, with most analyses focusing on the 5mya - 500kya range) population structure is "likely to become less important", and the resident species are usually more structured today (true) which might bias the findings against the observed higher N<sub>e</sub> in migrants.  

      To clarify, the patterns we discuss are entirely related to effective population size, not census size. But, yes, this is why we’ve given population structure its own section in the Discussion.

      But is structure really unimportant in driving PSMC results at these specific timescales? There is no numerical analysis presented to support the claim in this paper. The biogeographic model of increased temperate-latitude land area supporting higher populations could yield high N<sub>e</sub> via high census size, but shifts in population structure (for example, from one large panmictic population to a series of isolated refugial populations as a result of glaciation-linked climate changes) could plausibly create elevated and more variable N<sub>e</sub>. Is it more land area and ecological release leading to a bigger and faster initial N<sub>e</sub> bump, or is it changes in population connectivity over time at expanding range edges, or is the whole single-bump PSMC trace an artifact of the dataset size, or what? The authors have convinced me that the N<sub>e</sub> history of migratory thrushes is on average very different from nonmigrant thrushes, but beyond that it's unclear what exactly we've learned here about the underlying process.  

      We do not argue that population structure is unimportant, only that it is less important as one goes into deeper time. Further, we agree with the reviewer’s observation above that structure is more likely to bias nonmigrant estimates of N<sub>e</sub>. In other words, following Li & Durbin’s (2011) simulations, we interpret that an inflated N<sub>e</sub> due to structure should occur more often among residents. We have clarified this in the revision. We also agree that what we’ve learned about the underlying process is not entirely clear, but as we stated, population structure does not seem to be the main driver, and there is evidence that both biogeographic and ecological factors are involved. With this being the first time that these questions have been asked, we think we’ve made an important advance and that we’ve opened a number of avenues for future study.

      It also important to consider the time scales involved and the sampling regime. Glacial-interglacial cycles averaged ~100 Kyr back to 0.74 Mya and then averaged ~41 Kyr from then back to 2.47 Mya; about 50-60 of these cycles occurred (Lisiecki & Raymo 2005: fig. 4). This probably caused a lot of population structuring and mixing in these lineages. In addition, in the PSMC output from one of our lineages, C. ustulatus swainsonii, we find that there are 54 time segments sampled for the Pleistocene, indicating the inadequacy of this method to reflect fine-scale changes and suggesting that each estimate is capturing a lot of both phenomena, structuring and mixing. We have added this to the revision.

      I generally agree with the authors that "at present there is no way to fully disentangle the effects of population structure and geographic space on our results". But given that, I think there are two options - either we can fully acknowledge that oversimplified demographic models like PSMC cannot be interpreted as supporting evidence of any particular mechanistic or biogeographic hypothesis and stop trying to use them to do that, or we have to do our best to understand specifically which models can be distinguished by the analyses we're employing. 

      Short of developing some novel theory deep in the PSMC model, I think readers would need to see simulations showing that the analyses employed in this paper are capable of supporting or refuting their biogeographic hypothesis before viewing them as strongly supporting a specific biogeographic model. Tools like msprime and stdpopsim can be used to simulate genome-scale data with fairly complex biogeographic models. Running simulations of a thrush-like population under different biogeographic scenarios and then using PSMC to differentiate those patterns would be a more convincing argument for the biogeographic aspects of this paper. The other benefit of this approach would be to nail down a specific quantitative version of the taxon cycles model referenced in the abstract, and it would allow the authors to better study and explain the motivation behind the specific summary statistics they develop for PSMC posthoc analysis.  

      These could very well be fruitful pursuits for future work, but they are beyond the scope of this paper. The impossibility of reconstructing ranges through deep time makes anything other than the very general biogeographic hypothesis we’ve posed an uncertain pursuit. Also, a purely biogeographic approach neglects the likelihood of ecological expansion also being involved. We get at the importance of the latter in the “Geography and evolutionary ecology” section of the Discussion. Below, the editor states that discussions among reviewers indicate that simulations are not warranted at this time. We agree that the complexities involved are substantial, to the point of making direct relevance to this empirical study uncertain (especially in such an among-lineage context). Regarding taxon cycles, we merely point out that that conceptual framework seems relevant given our findings. This was not even remotely anticipated at the outset of the study, so we are reluctant to do anything more than point out its possible relevance in several aspects of the results. Finally, the motivation for the study’s summary statistics were entirely driven by the hypotheses, as given in Methods, and due to an earlier error (noted above), there are no post-hoc analyses in the revision. Sorry for the needless confusion.

      Reviewer #2 (Public Review): 

      Summary: 

      Winker and Delmore present a study on the demographic consequences of migratory versus resident behavior by contrasting the evolutionary history of lineages within the same songbird group (thrushes of the genus Catharus). 

      Strengths: 

      I appreciate the test-of-hypothesis design of the study and the explicit formulation of three main expectations to test. The data analysis has been done with appropriate available tools. 

      Weaknesses: 

      The current version of the paper, with the case study chosen, the results, and the relative discussion, is not satisfying enough to support or reject the hypotheses here considered.  

      Given the stated strengths, the weaknesses noted seem a little incongruous, but we understand from the comments below that the reviewer would like to see the study redesigned and expanded.  

      The authors hypothesized that the wider realized breeding and ecological range characterising migrants versus resident lineages could be a major drive for increased effective population size and population expansion in migrants versus residents. I understand that this pattern (wider range in migrants) is a common characteristic across bird lineages and that it is viewed as a result of adapting to migration. A problem that I see in their dataset is that the breeding grounds range of the two groups are located in very different geographic areas (mainly South versus North America). The authors could have expanded their dataset to include species whose breeding grounds are from the two areas, regardless of their migratory behaviour, as a comparison to disentangle whether ecological differences of these two areas can affect the population sizes or growth rates.

      Because the questions are about the migratory life history strategy and the best way to get at this is in a phylogenetic framework, we’re not sure how we could effectively add species “regardless of their migratory behavior.” Further, we know that migration causes lineages to experience variable ecological conditions that include breeding, migration, and wintering conditions. Obligate migrants are going to have different breeding ranges from their close relatives, and the more distantly related species are, the less likely it is that they respond to particular ecological conditions the same way. So we do not think that an approach that included miscellaneous species from northern and southern regions would strengthen this study. Here, the comparative framework of closely related lineages that possess or lack the trait of interest is a study design strength. We do agree, however, that future work is needed that does encompass more lineages (we would argue in a phylogenetic context), and that disentangling the effects of geography and ecology will also be an important future endeavor. 

      As I understand from previous literature, the time-scale to population growth and estimates of effective population sizes considered in the present paper for the resident versus migratory clades seem to widely predate the times to speciation for the same lineages, which were reported in previous work of the same authors (Everson et al 2019) and others (Termignoni-Garcia et al 2022). This piece of information makes the calculation of species-specific population size changes difficult to interpret in the light of lineages' comparison. It is unclear what the authors consider to be lineage-specific in these estimates, as the clades were likely undergoing substantial admixture during the time predating full isolation.  

      We do recognize that timing estimates vary among studies. Differences among studies in important variables like markers, methods, generation time, and mutation or substitution rates create much of this uncertainty. Also, we are not confident in prior dating efforts in this group, largely because of gene flow and its effects on bringing estimates closer to the present. As we point out (line 485), differences among studies on these issues do not detract from the strengths here for within-study, among-lineage contrasts. In short, the timing could be off in an among-study context (and likely is with prior work, given gene flow), but relative performance of among-lineage N<sub>e</sub> differences is less susceptible to these factors. This was shown fairly well in Li & Durbin’s initial use of the method among human populations. Regarding substantial admixture, PSMC curves often unite at their origins with sister lineages (when they were the same lineage). A good example is with the two C. guttatus E & W curves in Fig. S3, which still have substantial gene flow today (they are subspecies and in contact), yet they show remarkably different N<sub>e</sub> curves through their history. It is not possible to mark a cutoff point for each lineage that represents the cessation of admixture with another lineage (e.g., Everson et al. 2019 showed substantial admixture between three full species in this group); that period can be very long (Price et al. 2008), varies among lineages, and will not be available for deeper lineage divergences in the phylogeny. We therefore chose to use all of the time intervals retrievable from the genomic data in each lineage, considering that this uniform treatment is the best approach for our among-lineage comparison. And note that we were careful to label these as “the lineages’ PSMC inception” (line 190).  

      Regarding the methodological difficulties in interpreting the impact of population structure on the estimates of effective population sizes with the PSMC approach, I would think that performing simulations to compare different scenarios of different degrees of structured populations would have helped substantially understand some of the outcomes.  

      The complexities of such modeling in a system like this are daunting. The different degrees of structuring among all of these lineages across just a single glacial-interglacial cycle would necessitate a lot of guesswork; projecting that back across 50-60 such cycles just in the Pleistocene would probably end up being fiction. Disentangling the effects of structure versus changes in N<sub>e</sub> in a system like this would probably not be possible with that approach and these data. As noted above and below, there was agreement among reviewers and the editor that simulations in this case are not warranted for revision. We have added the nature of the glacialinterglacial cycles and the PSMC sampling time segments to help readers understand this better (see above in response to R1, and lines 272-278).

      Additionally, I have struggled to understand if migratory behaviour in birds is considered to be acquired to relieve species competition, or as a consequence of expanded range (i.e., birds expand their range but their feeding ground is kept where speciation occurred as to exploit a ground with higher quality and abundance of seasonal local resources).  

      The origins of migration have been a struggle for researchers since the subject was taken up. But how the trait was acquired among these species does not really matter for our study. Here, migratory lineages possess different biogeographic+ecological attributes than their close relatives that are sedentary. Our focus is on the presence and absence of this life-history trait.

      The points raised above could be considered to improve the current version of the paper. 

      Thank you. We appreciate the opportunity to guide our revision using your comments.  

      Reviewer #3 (Public Review): 

      Summary: 

      This paper applies PSMC and genomic data to test interesting questions about how life history changes impact long-term population sizes. 

      Strengths: 

      This is a creative use of PSMC to test explicit a priori hypotheses about season migration and N<sub>e</sub>. The PSMC analyses seem well done and the authors acknowledge much of the complexity of interpretation in the discussion. 

      Weaknesses: 

      The authors use an average generation time for all taxa, but the citations imply generation time is known for at least some of them. Are there differences in generation time associated with migration? I am not a bird biologist, but quick googling suggests maybe this is the case (https://doi.org/10.1111/1365-2656.13983). I think it important the authors address this, as differences in generation time I believe should affect estimates of N<sub>e</sub> and growth.  

      Good point. The study cited by the reviewer encompasses a much higher degree of variation in body size and thus generation time. Differences in generation time in similarly sized close relatives, as in our study, should be small, and our approach has been to average those that are known. Unfortunately, generation times are not known for all of these species, but given their similarity in size we can have reasonable confidence in their being similar. We used data from the life-history research available (as cited) to obtain our average; there are not appropriate data for the residents, though. However, there is thought to be a generation time cost to seasonal migration in birds, and Bird et al. (2020) included this in their estimates to provide modeled values for all of the lineages we studied. We’re leery of using modeled values where good data for the nonmigrants in this group don’t exist (and the basis for quantifying this cost is tiny), but we recognize that this second approach is available and could leave some doubt in our results if not pursued. So we re-did everything with the modeled generation times of Bird et al. (2020). As expected, most of the differences are time-related. Importantly, our overall results are not different. We present them as Table S2 and have added the details on this to the Methods.

      The writing could be improved, both in the introduction for readers not familiar with the system and in the clarity and focus of the discussion.  

      We have added a phylogeny (new Fig. 1) to help readers better understand the system, and we’ve re-worked the Discussion to make it clearer what is clarified by our results and what remains unclear.  

      Recommendations for the authors:

      Reviewing Editor comment: 

      I note that discussion among the reviewers made clear that simulations are probably not the right answer given the complexity of the modeling required.  

      We appreciate this conclusion, with which we agree.  

      Reviewer #2 (Recommendations For The Authors): 

      Apologies for the delay with the review, which came at a very busy time. I hope you will find my comments helpful.

      Thanks. Your comments are helpful, and we fully understand how reviews (and our revisions!) have to wait until more pressing needs are addressed.

      I enjoyed reading the manuscript but I believe that the discussion sections could be heavily rewritten for better clarity. The discussion is sometimes redundant and lacks some flow/clarity. In a nutshell, I had the feeling that a bit of everything is thrown in the discussion but clear conclusions are not made.  

      Yes, the Discussion has been difficult to write, because more issues arose in the Results than we anticipated at the outset. We feel that discussing them is relevant, but we agree that much remains unclear. This coupling of paleodemographics with geography and ecology is a new area, which opens some important new (and relevant) areas to consider. So clarity is not possible in some areas. We’ve revised to point out where we do have clarity (e.g., in migrant lineages having different paleodemographic attributes than nonmigrants) and where only further study can provide clarity (e.g., in the roles of geography versus ecology). The journal format does not seem to have secondary subheaders, but we’ve used bold in one place to highlight ‘ecological mechanisms’ to offset that section, one of the more complex. We’ve also added a paragraph in the conclusions to clarify where we have clear takeaways and where uncertainties remain. 

      Reviewer #3 (Recommendations For The Authors): 

      The introduction should engage the reader with biology, not the use of demographic methods or genomics (both of which have been around for more than a decade). I would drop the first paragraph and considerably expand the second. What has previous research on ecology/behavior/genetics found regarding the demographic effects of seasonal migration?

      There are two important aspects to our study: 1) using paleodemographic methods to test hypotheses about adoption of a major life-history trait—an important biological question regardless of system, and so far (surprisingly) unaddressed; and 2) using this novel approach to study the effects of one such trait, seasonal migration. At these timescales, nothing exists on this subject, so there is really nothing to expand with. If there is relevant literature that we’ve missed, we’d be happy to add it.

      What is the missing bit of information or angle the current study addresses (other than just doing it larger and fancier with genomics)?  

      The effects of major life-history traits on paleodemographics has not been addressed before, to our knowledge. The whole context is new, so we’re not doing something “larger and fancier” with genomics. We are doing something that has not been done before: testing hypotheses about the effects of a major life-history trait on population sizes in evolutionary time. We’re not sure how this can be made clearer. To us this seems like a very engaging biological question with wide applicability. We hope that this study is just the first of many to come, in a diversity of biological systems.

      A figure showing the phylogenetic relationships of these taxa which are migratory would help the reader immensely. Although this is shown in Fig S3 I think it might be nice to have a map of the species and their ranges alongside a phylogeny as a main figure early on.  

      Thank you. This is a good suggestion. We can’t fit a phylogeny and all the distribution maps (Fig. S1) onto a page, but we can include a phylogeny as one of the main figures with nonmigrants highlighted. We’ve inserted this as a new Fig. 1. 

      If I understand correctly, the authors' arguments for why migratory species should show more growth hinge on large range size and geographic expansion. Yet they argue in the discussion that these forces are unlikely to be important (L226). I found the discussion on this confusing (e.g. L231 then says maybe it does matter). I think more clarity here would be helpful.

      Our argument and predictions are based both on geographic and ecological expansion. This was clearly stated as our third prediction “3) early population growth would be higher as seasonal migration opens novel ecological and geographic space…” We have gone back through and reiterated the coupling of these two factors. The line mentioned concludes the first paragraph in the section ‘Geography and evolutionary ecology,’ which focuses on the difficulty of decoupling these in this system. As the paragraph relates, geography alone does not seem to be driving our results (we do not argue that it is unimportant). 

      I also would have liked more time in the discussion addressing why variation in N<sub>e</sub> may be higher in migratory lineages.

      In addition to re-clarifying this in the Introduction, we have touched back on this now at line 221: “We attribute the higher variation in N<sub>e</sub> among migrants to be the result of the relative instability of northern biomes compared with tropical ones through glacial-interglacial cycles (e.g., Colinvaux et al., 2000; Pielou, 1991).”

      Minor comments: 

      L 62: Presumably PSMC is limited by the coalescent depth of the genelaogy, which may be younger or older than population "origins" depending on the history of colonization, lineage splitting, gene flow, etc.  

      We were careful to phrase these as “the lineages’ PSMC inception” (line 190), and responded to this issue in more detail above in response to R2’s public review. 

      L 338: I think a few more details on PSMC would be helpful. Was no maskfile used?  

      We did not use a maskfile, choosing instead to generate data of decent coverage and aligning reads to a single closely related relative. 

      Did the consensus fasta include all species?  

      No, we used a single reference high-quality fasta of Catharus ustulatus , as reported (lines 434-37). We have added that “Identical treatment of all lineages in these respects should provide a strong foundation for a comparative study like this among close relatives.” 

      L 361: Fair to assume the authors used a weighted average of N<sub>e</sub> from the output, rather than just averaging the N<sub>e</sub> values from each time segment?  

      No – we used all the values of N<sub>e</sub> produced by PSMC output. The PSMC method uses nonoverlapping portions of the genome in its analyses (which we’ve added to make that clear), and portions in juxtaposition will often provide data for very different periods in the time segments. Further, time segments are uneven within and among taxa, so it is not clear how a uniform and comparable weighting scheme could be implemented. We consider a uniform approach to be of primary importance, including for future comparisons among studies. 

      L 383 "delta" typo

      Thank you for catching this.

      L 93: I'd be tempted to present the questions (how does seasonal migration affect population size trajectory, means, and variation) and rationale before presenting the hypotheses. I found myself reading the hypotheses and wondering "why?"  

      We’ve tried this change in the revision. It makes the hypotheses a little harder to pull out (they are no longer numbered in a short sequence), but it is shorter and solves this concern.  

      L 337 read depth is usually expressed as X (e.g. "23X") rather than bp.

      Changed.

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      Reply to the reviewers

      Reply to the Reviewers

      We sincerely appreciate your insightful and constructive comments from the reviewers, which have significantly enhanced the clarity and rigor of our manuscript.

      Reviewer #1

      Evidence, reproducibility and clarity

      The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

      1) Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).

      2) Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

      These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

      1) The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.

      Response:

      Thanks for your suggestion to avoid confusion. We used the phrase "nodal spacing" instead of "nodal distribution" throughout the revised manuscript.

      2) In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?

      Response:

      As a key distinction, our study focuses specifically on the main trunk of the contralateral projection of NM axons. This projection features a sequential branching structure known as the delay line, where collateral branches form terminal arbors and connect to the ventral dendritic layer of NL neurons. This structural organization plays a critical role in influencing the dynamic range of ITD detection by regulating conduction delays along the NM axon trunk.

      The study by Seidl et al. (2010) is a pioneering work that measured diameter of NM axon using electron microscopy, providing highly reliable data. However, due to the technical limitations of electron microscopy, which does not allow for the continuous tracing of individual axons, it is not entirely clear whether the axons measured in the ventral NL region correspond to terminal arbors of collateral branches or the main trunk of NM axons (see Figure 9E, F in their paper). Instead, they categorized axon diameters based on their distance from NL cell layer, showing that axon diameter increases distally (see Figure 9G in their paper). Notably, the diameters of ventral axons located more than 120 μm away from the NL cell layer is almost identical to those in the midline.

      As illustrated in our Figure 4D and Supplementary Video 2, the main trunk of the contralateral NM projection is predominantly located in these distal regions. Therefore, our findings complement those of Seidl et al. (2010) rather than contradicting them. We made this point as clear as possible in text (page 7, line 7).

      3) The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?

      Response:

      In this study, we examined chick embryos from E9 to just before hatching (E21) and post-hatch chicks up to P9. Chickens begin to perceive sound around E12 and possess sound localization abilities at the time of hatching (Grier et al., 1967) (added to page 4, line 12). Therefore, by E21, the sound localization circuit is largely established.

      On the other hand, additional refinement of the circuit with aging is certainly possible. A key cue for sound localization, interaural time difference (ITD), depends on the distance between the two ears, which increases as the animal grows. As shown in Figure 2G, internodal length increased by approximately 20% between E18 and P9 while maintaining regional differences. Given that NM axons are nearly fully myelinated by E21 (Figure 4D, 6C), this suggests that myelin extends in proportion to the overall growth of the head and brain volume.

      Thus, our study covers not only the early stages of myelination but also the post-functional maturation in the sound localization circuit.

      4) The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?

      Response:

      In this study, we demonstrated that although vesicular release did not affect internodal length, it selectively promoted oligodendrogenesis, thereby supporting the full myelination and hence the pattern of nodal spacing along the NM axons. We believe that this finding falls within the broader scope of 'activity-dependent plasticity' involving oligodendrocytes and nodes.

      As summarized in the excellent review by Bonetto et al. (2021), activity-dependent plasticity in oligodendrocytes encompasses a wide range of phenomena, not limited to changes in internodal length but also including oligodendrogenesis. Moreover, the effects of neuronal activity are not uniform but likely depend on the diversity of both neurons and oligodendrocytes. For example, in the mouse visual cortex, activity-dependent myelination occurs in interneurons but not in excitatory neurons (Yang et al., 2020). Additionally, expression of TeNT in axons affected myelination heterogeneously in zebrafish; some axons were impaired in myelination and the others were not affected at all (Koudelka et al., 2016). In the mouse corpus callosum, neuronal activity influences oligodendrogenesis, which in turn facilitates adaptive myelination (Gibson et al., 2014).

      Thus, rather than refuting the role of activity-dependent plasticity in nodal spacing, our findings emphasize the diversity of underlying regulatory mechanisms. We described these explicitly in text (page 10, line 18).

      Significance

      This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

      Response:

      This paper does not argue against node plasticity, but rather demonstrates that oligodendrocytes in the NL region exhibit a form of plasticity; they proliferate in response to vesicular release from NM axons, yet do not undergo morphological changes, ensuring adequate oligodendrocyte density for the full myelination of the auditory circuit. Thus, activity-dependent plasticity involving oligodendrocytes would contributes in various ways to each neural circuit, which is presumably attributed to the fact that myelination is driven by complex multicellular interactions between diverse axons and oligodendrocytes. Oligodendrocytes are known to exhibit heterogeneity in morphology, function, responsiveness, and gene profiles (Foerster et al., 2019; Sherafat et al., 2021; Osanai et al., 2022; Valihrach et al., 2022), but functional significance of this heterogeneity remains largely unclear. This paper also provides insight into how oligodendrocyte heterogeneity may contribute to the fine-tuning of neural circuit function, adding further value to our findings. Importantly, our study covers the wide range of development in the sound localization circuit, from the pre-myelination (E9) to the post-functional maturation (P9), revealing how the nodal spacing pattern along the axon in this circuit emerges and matures.

      __ __

      Reviewer #2

      Evidence, reproducibility and clarity

      Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

      Major points, detailed below, need to be addressed to overcome some limitations of the study.

      Major comments:

      1) It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.

      Response:

      eTeNT is a widely used genetically encoded silencing tool and constructs similar to the one used in this study have been successfully applied in primates and rodents to suppress target behaviors via genetic dissection of specific pathways (Kinoshita et al., 2012; Sooksawate et al., 2013). However, precisely quantifying the extent of vesicular release inhibition from NM axons in the brainstem auditory circuit is technically problematic.

      One major limitation is that while A3V efficiently infects NM neurons, its transduction efficiency does not reach 100%. In electrophysiological evaluations, NL neurons receive inputs from multiple NM axons, meaning that responses may still include input from uninfected axons. Additionally, failure to evoke synaptic responses could either indicate successful silencing or failure to stimulate NM axons, making a clear distinction difficult. Furthermore, unlike in motor circuits, we cannot assess the effect of silencing by observing behavioral outputs.

      Thus, we instead opted to quantify the precise expression efficiency of GFP-tagged eTeNT in the cell bodies of NM neurons. The proportion of NM neurons expressing GFP-tagged eTeNT was 89.7 {plus minus} 1.6% (N = 6 chicks), which is consistent with previous reports evaluating A3V transduction efficiency in the brainstem auditory circuit (Matsui et al., 2012). These results strongly suggest that synaptic transmission from NM axons was globally silenced by eTeNT at the NL region. We described these explicitly in text (page 8, line 5).

      2) Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?

      Response:

      Figure 6D depicts a representative axon selected from a dense population of GFP-positive axons in a 200-μm-thick slice after A3V-eTeNT infection to bilateral NM. As shown in Supplementary Video 1 and 2, densely labeled GFP-positive axons can be traced along the main trunk. To prevent any misinterpretation, we have revised the description of Figure 6 in the main text and Figure legend (page 31, line 9), and stated the A3V-eTeNT infection efficiency was 89.7 {plus minus} 1.6% in NM neurons, as mentioned above. Based on this efficiency, we interpreted that the global occlusion of vesicular release from most of the NM axons altered the pericellular microenvironment of the NL region, which led to the regional effect on the oligodendrocyte density.

      On the other hand, your question regarding whether sparse expression of eTeNT still has an effect is highly relevant. As we also discussed in our reply to comment 4 by Reviewer #1, the relationship between neuronal activity and oligodendrocytes is highly diverse. In some types of axons, vesicular release is essential for normal myelination, and this process was disrupted by TeNT (Koudelka et al., 2016), suggesting that direct interaction with oligodendrocytes via vesicle release may actively promote myelination in these types of axons.

      To clarify whether the phenotype observed in Figure 6 arises from changes in the pericellular microenvironment at the NL region or from the direct suppression of axon-oligodendrocyte interactions, we plan to add a new Supplementary Figure. Specifically, we will evaluate the node formation on the axon sparsely expressing eTeNT by electroporation into the unilateral NM. Preliminary data indicate that, unlike the results in Figure 6D, sparse eTeNT expression did not contribute to an increase in heminodes and unmyelinated segments. This result would further support our argument that the increase in unmyelinated segments by A3V-eTeNT was due to a disruption of synaptic transmission between NM axons and NL neurons, which in turn altered the pericellular microenvironment at the NL region.

      3) The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:

      __Response: __

      Thank you for your valuable suggestions to improve the rigor of our statistical analyses. We have reanalyzed all statistical tests using R software. In the revised Methods section and Figure Legends, we have clarified the rationale for selecting each statistical test, specified which test was used for each figure, and explicitly defined both n and N. After reevaluation with the Shapiro-Wilk test, we adjusted some analyses to non-parametric tests where appropriate. However, these adjustments did not alter the statistical significance of our results compared to the original analyses.

      3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or Mann-Whitney U test, which are much more common in the field. What is the rationale for the test choice?

      __Response: __

      We have revised the explanation of our statistical test choices to provide greater clarity and precision. For example, in Figure 2G, we first assessed the normality of the data in each of the four groups using the Shapiro-Wilk test, which revealed that some datasets did not follow a normal distribution. Given this, we selected the Kruskal-Wallis test, a commonly used non-parametric test for comparisons across three or more groups. Since the Kruskal-Wallis test indicated a significant difference, we conducted a post hoc Steel-Dwass test to determine which specific group comparisons were statistically significant.

      3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a t-test used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.

      __Response: __

      In the case of Figures 3H-K, we compared oligodendrocyte morphology between regions. However, since the number of sparsely labeled oligodendrocytes differs both between regions and across individual samples, there is no strict correspondence between paired measurements. On the other hand, in Figures 5B, C, and E, we compared the density of labeled cells between regions within the same slice, establishing a direct correspondence between paired data points. For these comparisons, we appropriately used a paired t-test.

      3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)

      __Response: __

      We have specified the statistical tests used for each figure in the Methods section and Figure Legends for better clarity. Additionally, we have revised the descriptions for Figure 4G, L, and M and their corresponding Figure Legends to explicitly indicate that Spearman's rank correlation coefficient (rₛ) was used for evaluation.

      3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).

      __Response: __

      We have now clarified what "n" represents in each figure, as well as the number of animals (N) used in each experiment, in the Figure Legends.

      In this study, developmental stages of chick embryos were defined by HH stage (Hamburger and Hamilton, 1951), minimizing individual variability. Additionally, since our study focuses on the distribution of morphological characteristics of individual cells, averaging measurements per animal would obscure important cellular-level variability and potentially mislead interpretation of data. Furthermore, we employed a strategy of sparse genetic labeling in many experiments, which naturally results in variability in the number of measurable cells per animal. Given the clear distinctions in our data distributions, we believe that averaging per biological replicate is not essential in this case.

      To further ensure the robustness of our statistical analysis, data presented as boxplots were preliminarily assessed using PlotsOfDifferences, a web-based application that calculates and visualizes effect sizes and 95% confidence intervals based on bootstrapping (https://huygens.science.uva.nl/PlotsOfDifferences/; https://doi.org/10.1101/578575). Effect sizes can serve as a valuable alternative to p-values (Ho, 2018; https://www.nature.com/articles/s41592-019-0470-3). The significant differences reported in our study are also supported by clear differences in effect sizes, ensuring that our conclusions remain robust regardless of the statistical approach used.

      If requested, we would be happy to provide PlotsOfDifferences outputs as supplementary source data files, similar to those used in eLife publications, for each figure.

      3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6).

      __Response: __

      We have now incorporated individual data points into the bar graphs in Figures 5 and 6.

      4) The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit?

      __Response: __

      The morphological differences of oligodendrocytes between white and gray matter are well established (i.e. shorter myelin at gray matter), but their correspondence with the nodal spacing pattern along the long axonal projections of cortical neurons is not well understood. Future research may find similarities with our findings. Additionally, as mentioned in the final section of the Discussion, the mammalian brainstem auditory circuit is functionally analogous to the avian ITD circuit. Regional differences in nodal spacing along axons have also been observed in the mammalian system, raising the important question of whether these differences are supported by regional heterogeneity in oligodendrocytes. Investigating this possibility will facilitate our understanding of the underlying logic and mechanisms for determining node spacing patterns along axons, as well as provide valuable insights into evolutionary convergence in auditory processing mechanisms. We described these explicitly in text (page 11, line 32).

      5) Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended.

      __Response: __

      As you summarized in your comment, our results demonstrated that A3V-eTeNT suppressed oligodendrogenesis in the NL region, leading to a reduction in oligodendrocyte density (Figures 6L, M), which caused the emergence of unmyelinated segments. While this is an indirect manipulation of oligodendrocyte density, it nonetheless provides evidence supporting a causal relationship between oligodendrocyte density and nodal spacing.

      The emergence of unmyelinated segments at the NL region further suggests that the myelin extension capacity of oligodendrocytes differs between regions, highlighting regional differences in intrinsic properties of oligodendrocyte as the most prominent determinant of nodal spacing variation. However, as you correctly pointed out, our findings do not establish direct causation.

      In the future, developing methods to artificially manipulate myelin length could provide a more definitive demonstration of causality. Given these considerations, we have modified the title to replace "determine" with "underlie", ensuring that our conclusions are presented with appropriate nuance.

      6) The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:

      6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)

      6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)

      6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812). 6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)

      __Response: __

      Thank you for your insightful suggestions. We have incorporated the relevant references you provided and revised the manuscript accordingly to contextualize our findings within the existing literature.

      Minor comments:

      7) Can the authors amend Fig. 1G with the correct units of measurement, not millimetres.

      __Response: __

      Thank you for your suggestion. We have corrected the units in Figure 1G to µm

      8) The Olig2 staining in Fig 2C does not appear to be nuclear, as would be expected of a transcription factor and as is well established for Olig2, but rather appears to be excluded from the nucleus, as it is in a ring or donut shape. Can the authors comment on this?

      __Response: __

      Oligodendrocytes and OPCs have small cell bodies, often comparable in size to their nuclei. The central void in the ring-like Olig2 staining pattern appears too small to represent the nucleus. Additionally, a similar ring-like appearance is observed in BrdU labeling (Figure 5G), suggesting that this staining pattern may reflect nuclear morphology or other structural features.

      Significance

      In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

      __Response: __

      The main finding of our study is that the primary determinant of the biased nodal spacing pattern in the sound localization circuit is the regional heterogeneity in the morphology of oligodendrocytes due to their intrinsic properties (e.g., their ability to produce and extend myelin sheaths) rather than the density of the cells. This was based on our observations that a reduction of oligodendrocyte density by A3V-eTeNT expression caused unmyelinated segments but did not increase internodal length (Figure 6), further revealing the importance of oligodendrocyte density in ensuring full myelination for the axons with short internodes. Thus, we think that our study could propose the significance of oligodendrocyte heterogeneity in the circuit function as well as in the nodal spacing using experimental manipulation of oligodendrocyte density.

      __ __

      Reviewer #____3

      Evidence, reproducibility and clarity

      The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing. I have some major concerns:

      1) The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.

      __Response: __

      Thank you for your insightful comment regarding the potential role of pre-nodal clusters in determining internodal length. Indeed, studies in zebrafish have suggested that pre-nodal clustering of node components prior to myelination may prefigure internodal length (Vagionitis et al., 2022). We have incorporated a discussion on whether such pre-nodal clusters could contribute to regional differences in nodal spacing in our manuscript (page 9, line 35).

      Whether pre-nodal clusters are detectable before myelination appears to depend on neuronal subpopulation (Freeman et al., 2015). To investigate the presence of pre-nodal clusters along NM axons in the brainstem auditory circuit, we previously attempted to visualize AnkG signals at E13 and E14. However, we did not observe clear structures indicative of pre-nodal clusters; instead, we only detected sparse fibrous AnkG signals with weak Nav clustering at their ends, consistent with hemi-node features. This result does not exclude the possibility of pre-nodal clusters on NM axons, as the detection limit of immunostaining cannot be ruled out. In brainstem slices, where axons are densely packed, nodal molecules are expressed at low levels across a wide area, leading to a high background signal in immunostaining, which may mask weak pre-nodal cluster signals prior to myelination. Regarding the comment on Figure 1D, we assume you are referring to Figure 2D based on the context. The lack of clarity in the high-magnification images in Figure 2D results from both the high background signal and the limited penetration of the MAG antibody. Furthermore, we are unable to verify Neurofascin accumulation at pre-nodal clusters, as there is currently no commercially available antibody suitable for use in chickens, despite our over 20 years of efforts to identify one for AIS research. Therefore, current methodologies pose significant challenges in visualizing pre-nodal clusters in our model. Future advancements, such as exogenous expression of fluorescently tagged Neurofascin at appropriate densities or knock-in tagging of endogenous molecules, may help overcome these limitations.

      However, a key issue to be discussed in this study is not merely the presence or absence of pre-nodal clusters, but rather whether pre-nodal clusters-if present-would determine regional differences in internodal length. To address this possibility, we have added new data in Figure 6I, measuring the length of unmyelinated segments that emerged following A3V-eTeNT expression. If pre-nodal clusters were fixed before myelination and predetermined internodal length, then the length of unmyelinated segments should be equal to or a multiple of the typical internodal length. However, our data showed that unmyelinated segments in the NL region were less than half the length of the typical NL internodal length, contradicting the hypothesis that fixed pre-nodal clusters determine internodal length along NM axons in this region.

      2) The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.

      __Response: __

      As mentioned in our reply to comment 2 by Reviewer #1, the diameter of NM axons was already evaluated using electron microscopy (EM) in the pioneering study by Seidl et al., (2010). Additionally, EM-based analysis makes it difficult to clearly distinguish between the main trunk of NM axons and thin collateral branches at the NL region. Accordingly, we did not do the EM analysis in this revision.

      In Figure 4, we used palGFP, which is targeted to the cell membrane, allowing us to measure axon diameter by evaluating the distance between two membrane signal peaks. This approach minimizes the influence of the blurring of fluorescence signals on diameter measurements. Thus, we believe that our method is sufficient to evaluate the relative difference in axon diameters between regions and hence to show that axon diameter is not the primary determinant of the 3-fold difference in internodal length between regions.

      3) The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.

      __Response: __

      The heterogeneity in oligodendrocyte morphology would reflect differences in gene profiles, which, in turn, may arise from differences in their developmental origin and/or pericellular microenvironment of OPCs. We made this point as clear as possible in Discussion (page 9, line 21).

      Significance

      The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

      1. Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).
      2. Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

      These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

      1. The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.
      2. In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?
      3. The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?
      4. The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?

      Significance

      This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1:

      Regarding the manuscript's clarity, the sentence on page 5, "We also stained VTA sections for Tyrosine hydroxylase (TH) to estimate the rate of ChR2 colocalization with DA neurons," reads awkwardly. Removing the word "rate" could improve clarity.

      We have made the recommended clarifying edit (page 5, lines 30-31).

      Additionally, the anatomical data and findings are largely non-quantitative in nature. However, solid microscopy images are presented to support each claim. Additional quantification would strengthen the paper, specifically the quantification of projection density for each population and the proportion of each subpopulation that projects to their regions of interest.

      To rigorously quantify the projection density of each subpopulation would require a level of exhaustivity our study was not designed for. This is because during microscopy we focused efforts on imaging regions containing dense signals but did not exhaustively image regions receiving apparently weak or no input. While we considered including a semi-quantitative table of projection density, based on the data available we could not discriminate with confidence between, e.g., regions recipient of minimal input versus no input from VTA populations. Thus, while we stand by our descriptive statements we do not expand on those further.

      The authors should consider discussing the possibility that subpopulations of these cells could still be true interneurons especially if cells were looked at the single neuron level of resolution.

      We agree that some of the VTA populations we studied could include subpopulations that are bona fide interneurons. The identification of alternate markers or combinations of markers, or use of single-cell imaging approaches may indeed support this possibility in future. This is discussed in the context of currently available evidence on page 5 lines 32-34, page 11 lines 2-4, page 12 lines 2-11, and page 12 lines 15-16.

      Overall, the paper is well-written and important for the field and beyond.

      Thank you!

      Reviewer #2:

      Weaknesses:

      While the authors use several Cre driver lines to identify GABAergic projection neurons, they then use wild-type mice to show that projection neurons synapse onto neighboring cells within the VTA. This does not seem to lend evidence to the idea that previously described "interneurons" are projection neurons that collateralize within the VTA.

      We think the use of WT mice is a strength because it allows us to measure both GABA and non-GABA synapses made by VTA projections on to the same cells within VTA. However, we have also done this experiment targeting NAc-projecting VTA VGAT-Cre neurons, and VP-projecting VTA MOR-Cre neurons. Consistent with the WT dataset, we find that these defined projection neurons also make intra-VTA synapses. These data are now included as Figure 7.

      More broadly. Our review of the literature finds very little evidence to support the notion of a VTA interneuron as we define it: VTA neurons that makes only local connections. But the absence of evidence need not imply evidence of absence, thus we do not claim that all VTA neurons previously presumed to be interneurons must be projection neurons. We do express confidence in our findings that VTA projection neurons (that include GABA-releasing neurons) make local synapses in VTA. We argue that in the absence of compelling positive evidence for the existence of VTA interneurons, such as a selective marker, “we”, “the field”, should not presume their existence.

      Other suggestions:

      (1) While the authors present evidence that some projection neurons also synapse locally, there is no quantification as to the proportion of each neuronal subtype that collateralizes within the VTA. This would be a useful analysis.

      We agree this would be useful information. But our experiments were not designed to answer this question. Indeed, we have not conceived of a feasible method to discriminate between collateralizing and non-collateralizing VTA projection neurons at the single-cell level, thus we do not know how we would calculate such proportions.

      (2) There is significant interest in the molecular heterogeneity and spatial topography of the VTA. Additional analyses of the spatial topography of labeled projectors would be useful. For example, knowing if Pvalb+ projection neurons are distributed throughout the VTA or located along the midline would be a useful analysis.

      Prior studies and public databases (e.g., Allen brain atlas, GENSAT) allow one to visualize the location of VTA neurons positive for Pvalb and the other markers we investigated (Olson & Nestler, 2007). However, these label the entire population of neurons and thereby include those that project to any of the various projection targets. There are also studies that have used retrograde labeling approaches to map the distribution of labeled VTA cells projecting to one or another target (Beier et al., 2015; Lammel et al., 2008; Margolis et al., 2006). For example, finding that LHb-projecting neurons (a major target of Pvalb+ VTA neurons) are enriched in medial VTA (Root et al., 2014). From this evidence we might infer that Pvalb+ VTA neurons that project to LHb are likely to be medially biased. Future studies may more carefully map the intersection of specific projection targets for each VTA subpopulation.  

      Reviewer #3 (Recommendations For The Authors):

      Weaknesses:

      This study has a few modest shortcomings, of which the first is likely addressable with the authors' existing data, while the latter items will likely need to be deferred to future studies:

      (1) Some key anatomical details are difficult to discern from the images shown. In Figure 1, the low-magnification images of the VTA in the first column, while essential for seeing what overall section is being shown, are not of sufficient resolution to distinguish soma from processes. A supplemental figure with higher-resolution images could be helpful.

      We uploaded a higher resolution file for figure 1.

      Also, where are the insets shown in the second column obtained from? There is not a corresponding marked region on the low-magnification images. Is this an oversight, or are these insets obtained from other sections that are not shown?

      This was an oversight, we added the corresponding marked region to the low-magnification images.

      Lastly, there is a supplemental figure showing the NAc injection sites corresponding to Figure 5, but not one showing VP or PFC injection sites in Figure 6. Why not?

      We added a figure with histology examples for the VP and the PFC injection sites as done for Figure 5, included as Supplemental Figure 3.

      (2) Because multiple ChR2 neurons are activated in the optogenetic experiments, it is not clear how common is it for any specific projection neuron to make local connections. Are the observed synaptic effects driven by just a few neurons making extensive local collateralizations (while other projection neurons do not), or do most VTA projection neurons have local collaterals? I realize this is a complex question, that may not have an easy answer.

      This is a great question but, indeed, we don’t know the answer. As mentioned in response to Reviewer #2, we are not convinced there is a currently feasible way to discriminate between collateralizing and non-collateralizing cells at the single cell level.

      (3) There is something of a conceptual disconnect between the early and later portions of this paper. Whereas Figures 1-4 examine forebrain projections of genetic subtypes of VTA neurons, the optogenetic studies do not address genetic subtypes at all. I do realize that is outside of the scope of the author's intent, but it does give the impression of somewhat different (but related) studies being stitched together. For example, the MOR-expressing neurons seem to project strongly to the VP, but it is not addressed whether these are also the ones making local projections. Also, after showing that PV neurons project to the LHb, the opto experiments do not examine the LHb projection target at all.

      This too was raised by Reviewer #2. While addressing this question for all the populations we investigated feels redundant, we now include optogenetic data showing that NAc-projecting VTA VGAT-Cre and VP-projecting VTA MOR-Cre neurons also make local collaterals (Figure 7). We think this allows us to connect the two approaches to a greater degree. Based on our findings using a dual virus approach to express Syn:Ruby in each population of VTA projection neuron, we think it very likely that we’d continue to find similar results using optogenetics-assisted slice electrophysiology for each population.

      Other suggestions:

      (1) I appreciated the extensive and high-quality anatomical figures shown in Figures 2-4. However, the layout was sometimes left-to-right, and sometimes right-to-left, which felt distracting. At some point, the text refers to "Fig. 3KJ", i.e. with the letters being in backward alphabetical order, and Figures 3I and 3L do not appear mentioned anywhere in the main text, leading me to wonder if that text was intended to read "Fig. 3I-L".

      Thank you for noting this. We have harmonized the layout of Figures 2-4 and adjusted the in-text Figure call-outs.

      Also, the inset in Figure 3J appears to show local collaterals of NTS neurons in the VTA, since there is no soma in that inset. This is interesting, and worth reporting, but is not explained in either the main text or Figure legend.

      We added a more complete description in the result section (page 6 line 25-30).

      (2) Perhaps I missed it, but I could not find any mention of the intensity of the LED light delivered during the optogenetic experiments. While acknowledging that this can be variable, do the authors have at least a rough range?

      We have added this information to the methods, page 17 line 8.

      Editor's Note:

      Should you choose to revise your manuscript, please double check that you have fully reported all statistics including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals.

      We confirm that we have fully reported all statistics including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals.

      Note to Editor and Readers

      While reanalyzing our data for resubmission, we discovered that some of the short-latency optogenetic evoked postsynaptic currents (oPSCs) we detected were erroneously categorized. Specifically, some VTA cells that showed large outward currents (oIPSCs) when held at 0 mV, also had small inward currents when held at -60 mV. These small inward currents were initially categorized as oEPSCs, suggesting these VTA cells received input from populations of VTA projection neurons that released GABA and/or glutamate. However, the kinetics of these small inward currents were slow and aligned with the within-cell kinetics of the oIPSCs, indicating that these were very likely mediated by GABA<SUB>A</SUB> receptors. In one case the opposite was apparent, with a small PSC initially miscategorized as an oIPSC. These miscategorized oEPSCs and oIPSC were presumably detected because our holding potentials were not precisely identical to the reversal potentials for GABA<SUB>A</SUB> and AMPA receptors, respectively. For this reason, we removed these 14 oEPSCs and 1 oIPSCs from our analyses in the revised version. The revised dataset suggests that VTA glutamate projection neurons may be less likely to collateralize widely within VTA compared to GABA projection neurons. But, importantly, this correction does not affect any of our conclusions.

      Citations:

      Beier, K. T., Steinberg, E. E., DeLoach, K. E., Xie, S., Miyamichi, K., Schwarz, L., Gao, X. J., Kremer, E. J., Malenka, R. C., & Luo, L. (2015). Circuit Architecture of VTA Dopamine Neurons Revealed by Systematic Input-Output Mapping. Cell, 162(3), 622-634. https://doi.org/10.1016/j.cell.2015.07.015

      Lammel, S., Hetzel, A., Hackel, O., Jones, I., Liss, B., & Roeper, J. (2008). Unique properties of mesoprefrontal neurons within a dual mesocorticolimbic dopamine system. Neuron, 57(5), 760-773. https://doi.org/10.1016/j.neuron.2008.01.022

      Margolis, E. B., Lock, H., Chefer, V. I., Shippenberg, T. S., Hjelmstad, G. O., & Fields, H. L. (2006). Kappa opioids selectively control dopaminergic neurons projecting to the prefrontal cortex. Proc Natl Acad Sci U S A, 103(8), 2938-2942. https://doi.org/10.1073/pnas.0511159103

      Olson, V. G., & Nestler, E. J. (2007). Topographical organization of GABAergic neurons within the ventral tegmental area of the rat. Synapse, 61(2), 87-95. https://doi.org/10.1002/syn.20345

      Root, D. H., Mejias-Aponte, C. A., Zhang, S., Wang, H. L., Hoffman, A. F., Lupica, C. R., & Morales, M. (2014). Single rodent mesohabenular axons release glutamate and GABA. Nat Neurosci, 17(11), 1543-1551. https://doi.org/10.1038/nn.3823

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      Reply to the reviewers

      Manuscript number: RC-2024-02831

      Corresponding author(s): Charisios Tsiairis

      1. General Statements [optional]

      We are very pleased that all three reviewers found our work to be solid, well-supported by the data, and free of major flaws. It is particularly gratifying that they did not request additional experimental work to support our conclusions. Instead, their comments focused on clarifications, textual improvements, and refinements in data presentation, which we have carefully addressed.

      • *

      We have made revisions to improve the clarity of the manuscript, incorporating insightful suggestions from the reviewers. These include refining key explanations, adjusting figure annotations, and modifying the structure of certain sentences. Additionally, we have addressed specific points regarding statistical significance, genome assembly references, and phylogenetic comparisons, ensuring that all aspects of our study are as precise and informative as possible.

      • *

      We are confident that these revisions have strengthened the manuscript.

      2. Point-by-point description of the revisions

      *Reviewer #1 (Evidence, reproducibility and clarity (Required)): *

      • *

      *Overall, the paper is well-written, the figures are easy to interpret, and the conclusions are well supported by the data. Most of the points discussed below could be addressed with simple text changes. *

      • *

      *General Points: *

      • *

      • The upregulation of Gata3 in response to Zic4 RNAi is relatively modest compared to the more pronounced upregulation of Zic4 following Gata3 knockdown, but this point is not really addressed. While these issues could be simply technical, they might also hint at additional layers of regulation that are not yet fully understood. *

      • *

      The observed differences in upregulation are primarily technical. Expression levels are measured relative to unperturbed tissue, and in the control, Zic4 expression in the foot is detected only at noise levels (see figure 2C). As a result, any increase in Zic4 expression upon Gata3 knockdown appears relatively high when normalized to the minimal control levels. In contrast, Gata3 is already present at detectable levels in control samples from the upper body, head, and tentacles (See Fig 2D). Therefore, while its upregulation following Zic4 RNAi appears more modest, we interpret this as a qualitative indication of increased gene expression in the absence of the opposing transcription factor. That said, we acknowledge the possibility of additional regulatory layers contributing to these differences.

      • *

      • Extending the time course would strengthen the conclusion that, in the Gata3 knockdown, the existing basal disk cells remain stable while body column cells migrating into the region differentiate into tentacle cells. If this hypothesis is correct, one would predict that by approximately 20 days, the basal disk cells would be completely replaced. *

      • *

      This is a valid point; however, the interpretation is complicated by the technical limitations of RNAi-based knockdown rather than a complete knockout of Gata3. Over time, the effect of RNAi diminishes, and we have observed that GFP expression returns within four weeks following GFP RNAi, indicating a temporal limit to RNAi-mediated knockdown. Therefore, while an extended time course would be informative, the transient nature of the knockdown makes it challenging to definitively track long-term cell replacement dynamics.

      • *

      • The conclusion that tentacle cells transdifferentiate into basal disc cells in the Zic4 knockdown may require more nuance, as only the tips of the tentacles express peroxidase. Do the more proximal regions of the tentacle express peduncle markers? *

      • *

      We appreciate the reviewer’s comment. In our previous publication (Vogg et al., 2022), we provided evidence supporting this phenomenon. As demonstrated in our data published there, markers of the peduncle, rather than the basal disc—such as manacle (gene ID 100212761) (Bridge et al., 2000) and Bmp5-8 (gene ID 100206618) (Reinhardt et al., 2004)—are also upregulated, suggesting a transition towards a peduncle-like state. However, we opted not to elaborate on this aspect in the current manuscript to maintain focus and avoid redundancy with previously published findings.

      • *

      *Specific Points: *

      • *

      *Figure 1A, Figure 4E: The pictorial representation of Zic4 expression may need to be revised, as in situ hybridization data from Vogg et al., 2022, suggests that Zic4 is absent from the hypostome and tentacle tips. While in situ hybridization can sometimes lack precision due to variability in staining protocols and subjective decisions on when to stop the reaction, this observation aligns with scRNA-seq data, which also indicates a lack of Zic4 expression in the hypostome and tips of the tentacles. *

      • *

      Our intention was to illustrate the general presence of Zic4 in the oral domain, but we acknowledge the reviewer’s point that this could be misleading regarding its precise expression pattern. To address this concern, we have updated the figure panels to more accurately reflect the available in situ hybridization and scRNA-seq data.

      • *

      *Figure 1 Legend: For panel D, the legend says "data taken from 28" but the references are not numbered. Same problem for panel E legend. *

      • *

      We thank the reviewer for catching this error. We have now corrected the references, replacing the numbering with the first authors' last names and publication dates.

      • *

      Figure 1D: There may be a mistake in the Hydra body part labeling. Is "B" supposed to be "P" for peduncle?

      • *

      We appreciate the reviewer’s observation. The label refers to the budding zone, and we acknowledge our omission in specifying this. We have now updated the figure and its legend to clarify this.

      • *

      *Figure 1 Panel E: Please provide clarification regarding what each box means. Are these 8 replicates of the same condition, or are these the proximal and distal regions of the tentacles as was collected in the Vogg paper? *

      • *

      We appreciate the reviewer’s request for clarification. These conditions are indeed similar to those in the previously published Vogg et al. paper. The boxes in the figure represent proximal and distal tentacle regions, each with four replicates. We have now updated the figure and its legend to make this explicit.

      • *

      *Figure 2A: Consider using the co-expression stats from Fig S2, which are very informative. *

      • *

      *We added the percentage of cells expressing Zic4, Gata3 and both genes on the panel. *

      • *

      *Figure 2E, F: It would be more intuitive to group each experimental sample with its corresponding control. *

      • *

      To make the figure clearer, we modified it and grouped each experimental sample with its corresponding control.

      • *

      *Figure 2C-F: Consider conducting statistical tests of significance between control and treatment groups. *

      • *

      We have now expanded the statistical analyses, ensuring that significance tests are presented in all relevant instances. However, we note that while statistical significance is important, it should be interpreted alongside other factors such as the magnitude of the effect, consistent trends across replicates, and biological relevance. Additionally, high standard deviations in certain conditions may influence absolute p-values, and we encourage consideration of the broader context of the data when interpreting these results.

      • *

      *Figure 2 E - Considering the error bars, Gata3 upregulation in response to Zic4 knockdown does not look significant based on qPCR. Showing the significance of the up-regulation in the RNA-seq data may be more convincing. (I believe RNA-seq to be more reliable anyway). *

      • *

      We understand the reviewer’s concern. The p-value for the qPCR data is slightly above 0.05, primarily due to high standard deviation. As the reviewer notes, qPCR on RNAi samples can be noisy, so the data should be interpreted in context. Importantly, the consistent qualitative increase in Gata3 levels after Zic4 knockdown aligns with the RNA-seq results, which, as the reviewer correctly points out, provide a more reliable measurement. Additionally, qPCR samples include a broader portion of head tissue, likely diluting the Gata3 signal from the tentacles and contributing to the observed variability.

      • *

      *Figure S2: Might be helpful to show co-expression UMAPs here, like what is shown in Figure 2A. *

      • *

      We appreciate the reviewer’s suggestion. However, we believe that displaying co-expression UMAPs for Zic4 would be redundant. Additionally, for genes with greater positional overlap, such as FoxI1 and Nfat5, co-expression UMAPs make visualization more challenging. To ensure clarity and optimize the interpretability of the data, we have chosen to present the expression profiles of each gene separately.

      • *

      *Page 4: "Interestingly, a similar binary choice pattern appears in certain neuronal lineages as well. A recent study demonstrated the involvement of Gata3 in specifying neurons at the aboral end (Primack et al. 2023), suggesting that this cross-regulation between Zic4 and Gata3 may extend beyond the epidermal lineage." Just a note that this paper shows expression, but doesn't show function as the statement implies, so the statement should be changed accordingly. *

      • *

      Indeed, the study does not focus on the functional role of Gata3 in these neurons. We have revised the sentence, replacing "involvement of Gata3 in specifying neurons" with "expression expression of Gata3 in neurons emerging*" to more accurately reflect the study’s findings. *

      • *

      *Page 10: "Transcription Factor Binding site analysis... Hydra promoter sequences were compiled from the NCBI Hydra RP 105 assembly." Authors should provide a repository identifier for the genome they are using. Based on the information provided, it appears the authors are using Genome assembly "Hydra_RP_1.0" RefSeq GCF_000004095.1. However, that genome assembly has been suppressed for the following reason: "superseded by newer assembly for species". Authors should consider updating the reference assembly they are using to map their sequencing data and identify promoter sequences. *

      • *

      We appreciate the reviewer’s concern. However, we have chosen to use the Hydra_RP_1.0 assembly for Figure 1 to maintain consistency with previously published data, which were also mapped to this assembly. Since these publications predate the newer assembly, using the same reference ensures comparability in our analysis. Importantly the assembly used is still downloadable and accessible to every researcher. That said, for the phylogenetic analysis in Figure 2, we have used the latest available genome assemblies and annotations for all species, including Hydra. We have now clarified this in the Methods section.

      • *

      *The paper makes great use of the Hydra scRNA-seq data set! Minor point, when referring to the Hydra scRNA-seq data set, please cite Siebert et al., 2019 (data collection) and Cazet et al., 2023 (analysis that is being used in this paper). *

      • *

      We appreciate the reviewer’s suggestion and have updated the references accordingly to include Siebert et al., 2019, for data collection and Cazet et al., 2023, for the analysis used in this paper.

      • *

      Something to keep in mind: To an audience without expertise in Hydra cell type morphology, the nematocyte marker HCR will likely be more convincing than the actin staining in Figure 3D to identify and quantify nematocytes.

      • *

      We agree with the reviewer that the nematocyte marker HCR provides a more specific identification of nematocytes. This is why we have also used the nematocilin marker in separate samples. However, actin staining adds important information on the morphology of the surrounding epithelial cells, which become indistinguishable from battery cells in Gata3 KDs. Unfortunately, combining actin staining with HCR is technically challenging, as the tissue preparation protocols for these two approaches are not compatible, and we have therefore decided to show both stainings next to each other.

      • *

      *Minor Wording Issues: *

      • *

      *Page 2. "However, the mechanism by which Zic4 prevents the battery cell program from misexpression in normal tentacles remained unclear." Could read more clearly as: However, the mechanism by which Zic4 prevents the misexpression of the battery cell program in normal tentacles remained unclear. *

      • *

      We have made the suggested change.

      • *

      *Page 2. "Potential candidates for this function could be found among TFs with highly enriched binding sites in the dataset, which are themselves Zic4 targets." Could read more clearly as: We reasoned that this intermediary factor, likely a target of Zic4, would be a transcription factor with highly enriched binding sites in the dataset. *

      • *

      We are grateful for the suggestion, we have changed the text accordingly.

      • *

      *p3-4. "Q-PCR performed on dissected oral and aboral body regions confirmed this finding (Fig. 2C-D)" It is unclear which "finding" is being confirmed. *

      • *

      We are referring to the upregulation of gata3 expression in tentacles upon Zic4 knockdown. To make this clearer, we have revised the wording to: “Q-PCR performed on dissected oral and aboral body regions confirmed the upregulation of gata3 upon Zic4 knockdown (Fig. 2C-D).”

      • *

      *Reviewer #1 (Significance (Required)): *

      • *

      *This compelling study from the Tsiairis lab uncovers a double-negative feedback loop between the transcription factors Zic4 and Gata3, functioning as a toggle switch to control oral and aboral fates in Hydra's epidermal lineage. Addressing fundamental questions in developmental biology, this research sheds light on the mechanisms underlying cell fate determination in relationship to their spatial organization. In Hydra, Wnt signaling, a conserved pathway critical for establishing primary body axes, promotes oral fate, emanating from an organizer at the oral end. Hydra body column epidermal cells can differentiate into distinct cell types, including oral battery cells and aboral basal disk cells, but the regulatory mechanisms remained elusive. Recent research from the Tsiairis lab identified Zic4 as a direct Wnt signaling target necessary for repressing basal disk-specific genes. Knocking down Zic4 caused battery cells to transform into basal disk cells, though Zic4 did not directly activate basal disk-specific genes, pointing to an intermediary regulator. This study identifies Gata3 as a key regulator of basal disk gene expression, as it is highly expressed at the aboral end, is inversely correlated with Zic4, and is upregulated in Zic4 knockouts. Functional experiments revealed mutual inhibition between Zic4 and Gata3: knocking down Gata3 led to differentiation of battery cells at the aboral end, while simultaneous knockdowns of Zic4 and Gata3 rescued the phenotypes of individual knockdowns. These findings demonstrate a finely tuned balance between Zic4 and Gata3 in regulating cell fate along the oral-aboral axis in Hydra. This paper therefore offers new insights into the spatial organization of cell type specification in Hydra and into broader principles of cell fate determination. *

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      *We appreciate the reviewer’s thoughtful summary and recognition of our study’s significance. *

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      *Reviewer #2 (Evidence, reproducibility and clarity (Required)): *

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      *Summary: *

      *The authors use the freshwater hydrozoan Hydra as a model to investigate mechanisms of cell fate decisions in the context of terminal epithelial differentiation. The epithelia migrates towards the extremities of the animal and takes on one of two fates: elongated battery cells that house the cnidocytes ( stinging cells ) in the oral ( head ) end of the animal, or more compact secretory basal disc cells at the aboral ( foot ) end. In this manuscript the authors build on previous work that showed the transcription factor Zic4 is necessary for battery cell formation. The authors use in situ hybridization and additional labelling techniques to assess cell fate under a variety of conditions. The authors first screen for Zic4 binding sites in the promoter regions of aboral genes that previously were demonstrated to be up-regulated in response to Zic4 knockdown, and survey publicly available expression databases to identify GATA3 as a candidate transcription factor that shows complementary expression patterns. The authors also screen the promoter regions of Zic4 and GATA3 from a number of other cnidarians and find reciprocal binding sites in all but one case. This is interpreted by the authors as evidence for a Zic4/GATA3 cnidarian regulatory motif. The authors demonstrate that KD of GATA3 results in the opposite phenotype: ectopic differentiation of oral battery cells, and that animals with perturbed GATA3 function fail to regenerate the aboral basal disk cells but rather show oral battery cell phenotype. Further, KD of both genes (Zic4: battery cells and GATA3: pedal disc cells) results in a rescue of the phenotype of either single KD, thereby illustrating that together these two genes function as a negative feedback loop controlling the terminal differentiation of the ectodermal epithelia. *

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      *Major comments: *

      *- Are the key conclusions convincing? *

      *The key conclusions are convincing. *

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      *- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? *

      *The cross species comparison of binding sites is insightful, but is presented very early in the manuscript. This would be better placed as a final piece, to place the Hydra-specific findings in a larger context. *

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      *- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. *

      *No. *

      *- Are the data and the methods presented in such a way that they can be reproduced? *

      *Yes, *

      *- Are the experiments adequately replicated and statistical analysis adequate? *

      *Yes. *

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      *Minor comments: *

      *- Specific experimental issues that are easily addressable. *

      *None. *

      *- Are prior studies referenced appropriately? *

      *Yes. *

      *- Are the text and figures clear and accurate? *

      *Yes. The figures are very nice. *

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      *- Do you have suggestions that would help the authors improve the presentation of their data and conclusions? *

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      *1) Move the phylogenetic comparisons to the end *

      *2) Similarly, in the section on GATA3 KD, present the normal condition first, and then the regeneration experiment results. *

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      We thank the reviewer for their positive assessment and constructive suggestions. Below, we comment on each point:

      • Placement of cross-species comparison: This suggestion concerns the emphasis and structure of the manuscript. We appreciate the reviewer's interest in the evolutionary aspects of our work. However, we believe that moving this analysis to the end would dilute the main message, which is reinforced by the schematic in Figure 4E-F. We aim to conclude with the experimental results demonstrating the minimization of phenotypic consequences when both factors are knocked down. Therefore, we have chosen to retain the cross-species comparison in its current position to emphasize the conservation of the double-negative interaction before presenting the functional consequences of its perturbation.
      • Reordering of Gata3 KD results: We understand the rationale behind this suggestion. However, our sequencing is guided by the fact that foot regeneration deficiency under Gata3 kd has already been documented and presented in previous work (Ferenc et al., 2021). For this reason, we begin with that reference, then build upon it with a deeper examination of the phenotype.
      • *

      We are grateful for the reviewer’s feedback and for recognizing the clarity of our figures and analysis.

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      ***Referee cross-commenting** *

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      *I have read the other two reviews and find that we are all in agreement that the work presented in this manuscript is sound and is a valuable scientific contribution. I would encourage the authors to consider my own suggests for order of presentation of data, to retain a specific to broad theme (normal then regeneration / hydra then comparisons) and to incorporated the detailed corrections highlighted by reviewer 1. *

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      *Regarding reviewer 3's comment regarding SoxA in cnidarians. This is likely true and the nomenclature of the gene likely comes from an automated pipeline to infer gene identities. Unless the authors follow up on this gene, I don't think the onus is on the authors to confirm the identity. *

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      We appreciate Reviewer’s #3 remark about the nuance of transcription factor homology. The situation is exactly as described here by Reviewer #2 - The gene names in Figure 1 are based on the results of NCBI automated homology annotation, which we have now clarified in a note in the legend of Figure 1.

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      *Reviewer #2 (Significance (Required)): *

      • *

      *- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. *

      *This paper is a beautiful illustration of the importance of relative gene expression levels in controlling cell fate decisions. Together with their previous works, the role of both transcription factors in specifying one of two possible terminal fates is very clearly illustrated. The final observation, that a mutual knockdown of both factors leads to a rescue of the polarity of the cell type balance is an excellent example of the importance of relative gene expression levels in controlling homeostatic balance between two mutually exclusive cell fates. *

      *- Place the work in the context of the existing literature (provide references, where appropriate). *

      *The manuscript does a good job of placing the work into the appropriate context. *

      • *

      *- State what audience might be interested in and influenced by the reported findings. *

      *Readers with interest in gene regulation, cell specification, and mechanisms of cell type diversification would find these results of interest. *

      • *

      *- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. *

      *Comparative invertebrate embryogenesis; Single cell transcriptomics; Cell and tissue evolution *

      • *

      We greatly appreciate the reviewer’s positive feedback and recognition of our study's focus on gene expression in cell fate decisions. We're pleased that our findings on the mutual knockdown and the broader context were well received. Thank you for highlighting the relevance of our work to gene regulation and cell specification.

      • *

      *Reviewer #3 (Evidence, reproducibility and clarity (Required)): *

      • *

      *Ferenc et al. have studied the role of transcription factors Zic4 and Gata3 in Hydra epithelial cell fate decision. The Tsiairis team has published a paper recently in which they had studied the role of Zic4 in promoting tentacle formation. Here, they discover a negative feedback loop between Zic4 and Gata3 in the context of epithelial cell differentiation. The authors used computational techniques to identify Zic4 binding sited in Hydra promoters of genes that are upregulated in basal disks, known from a previous study, and identified eight candidate genes. Previous studies were also used to narrow down potential Zic4 targets. They argue that Gata3 appears as a strong candidate to be suppressed by Zic4 in the head and being expressed in the foot. Knockdown experiments, followed by qPCR revealed that Gata3 and Zic4 expression is mutually exclusive such that the one represses the other. Next, they report that Gata3 RNAi results in ectopic battery cells at the lower body column, although basal disk cells maintained their identity following Gata3 knockdown. Finally, knocking down both Gata3 and Zic4 resulted in a more normal phenotype, as predicted if a negative feedback loop existed between the two. *

      • *

      *A minor comment: one of the predicted Zic4 targets is a gene called Sry. Sry is a mammalian male determinant and a SOX-related protein (SoxA). I was wondering if the authors performed phylogenetic analysis or simply took a BLAST hit as the source for this gene's name. I am unaware of SoxA-like genes in cnidarians . Therefore, I would recommend performing a SOX phylogeny and renaming it according to its closest relatives, which probably won't be Sry. *

      • *

      The naming of the gene as Sry was indeed based on the NCBI automated homology annotation, and we have clarified this in the revised manuscript. Since we did not pursue further analysis of this gene, we believe that a deeper phylogenetic analysis may not be necessary and could potentially divert attention from the main focus of our study on Gata3's role.

      • *

      *Reviewer #3 (Significance (Required)): *

      • *

      *This work closes some gaps that remained after publication of previous research by the Tsiairis lab and others. The data are of high quality, solid, and support the authors' conclusions. The manuscript is of general interest for developmental biologists and evodevo workers. *

      • *

      We thank the reviewer for the thoughtful assessment of our work. We appreciate their feedback and the recognition of the quality and significance of our findings.

      • *
    1. This suffers from a sufficient formalisation of the concept of "similarity". Everything is either so similar that characterisation as "identical", similar or different or very different, depending on the frame of reference. By pointing out some resemblense, you cannot make a justified judgement about the similarity or difference of anything. I would suggest that Luhmann didn't write more about his method himself because it would have been generally fruitless for him as everyone around him was doing exactly the same thing. I asked ca. two dozen professors at the very university about their method (btw. at the very university that Luhmann was a professor at). NONE had anything remotely resembling a Luhmann-Zettelkasten. During his lifetime there was quite some interest in his Zettelkasten, hence the visitors, hence the disappointment of the visitors (people made an effort to review his Zettelkasten): (9/8,3) Geist im Kasten? Zuschauer kommen. Sie bekommen alles zu sehen, und nichts als das – wie beim Pornofilm. Und entsprechend ist die Enttäuschung. - From his own Zettelkasten So: The statement that his practice was basically common place (or even a common place book) is not based on sound reasoning (sufficiently precise in the use of the concept "similarity") There is empirical evidence that it was very uncommon. (Which is obvious if you think about the his theoretical reasoning about his Zettelkasten as heavily informed by the very systems theory that he developed. So, a reasoning unique to him)

      Reply to u/FastSascha at https://old.reddit.com/r/Zettelkasten/comments/1ilvvnc/you_need_to_first_define_the_zettlekasten_methoda/mc01tsr/

      The primary and really only "innovation" for Luhmann's system was his numbering and filing scheme (which he most likely borrowed and adapted from prior sources). His particular scheme only serves to provide specific addresses for finding his notes. Regardless of doing this explicitly, everyone's notes have a physical address and can be cross referenced or linked in any variety of ways. In John Locke's commonplacing method of 1685/1706 he provided an alternate (but equivalent method) of addressing and allowing the finding of notes. Whether you address them specifically or not doesn't change their shape, only the speed by which they may be found. This may shift an affordance of using such a system, but it is invariant from the form of the system. What I'm saying is that the form and shape of Luhmann's notes is identical to the huge swath of prior art within intellectual history. He was not doing something astoundingly new or different. By analogy he was making the same Acheulean hand axe everyone else was making; it's not as if he figured out a way to lash his axe to a stick and then subsequently threw it to invent the spear.

      When I say the method was commonplace at the time, I mean that a broad variety of people used it for similar reasons, for similar outputs, and in incredibly similar methods. You can find a large number of treatises on how to do these methods over time and space, see a variety of examples I've collected in Zotero which I've mentioned several times in the past. Perhaps other German professors weren't using the method(s) as they were slowly dying out over the latter half of the 20th century with the rise and ultimate ubiquity of computers which replaced many of these methods. I'll bet that if probed more deeply they were all doing something and the something they were doing (likely less efficiently and involving less physically evident means) could be seen to be equivalent to Luhmann's.

      This also doesn't mean that these methods weren't actively used in a variety of equivalent forms by people as diverse as Aristotle, Cicero, Quintilian, Seneca, Boethius, Thomas Aquinas, Desiderius Erasmus, Rodolphus Agricola, Philip Melancthon, Konrad Gessner, John Locke, Carl Linnaeus, Thomas Harrison, Vincentius Placcius, Gottfried Wilhelm Leibniz, S. D. Goitein, Gotthard Deutsch, Beatrice Webb, Sir James Murray, Marcel Mauss, Claude Lévi-Strauss, Mortimer J. Adler, Niklas Luhmann, Roland Barthes, Umberto Eco, Jacques Barzun, Vladimir Nabokov, George Carlin, Twyla Tharp, Gertrud Bauer, and even Eminem to name but a few better known examples. If you need additional examples to look at, try searching my Hypothesis account for tag:"zettelkasten examples". Take a look at their examples and come back to me and tell me that beyond the idiosyncrasies of their individual use that they weren't all doing the same thing in roughly the same ways and for roughly the same purposes. While the modalities (digital or analog) and substrates (notebooks, slips, pen, pencil, electrons on silicon, other) may have differed, the thing they were doing and the forms it took are all equivalent.

      Beyond this, the only thing really unique about Luhmann's notes were that he made them on subjects that he had an interest, the same way that your notes are different from mine. But broadly speaking, they all have the same sort of form, function, and general topology.

      If these general methods were so uncommon, how is it that all the manuals on note taking are all so incredibly similar in their prescriptions? How is it that Marbach can do an exhibition in 2013 featuring 6 different zettelkasten, all ostensibly different, but all very much the same?

      Perhaps the easier way to see it all is to call them indexed databases. Yours touches on your fiction, exercise, and nutrition; Luhmann's focuses on sociology and systems theory; mine looks at intellectual history, information theory, evolution, and mathematics; W. K. Kellogg's 640 drawer system in 1906 focused on manufacturing, distributing and selling Corn Flakes; Jonathan Edwards' focused on Christianity. They all have different contents, but at the end of the day, they're just indexed databases with the same forms and functionalities. Their time periods, modalities, substrates, and efficiencies have differed, but at their core they're all far more similar in structure than they are different.

      Perhaps one day, I'll write a deeper treatise with specific definitions and clearer arguments laying out the entire thing, but in the erstwhile, anyone saying that Luhmann's instantiation is somehow more unique than all the others beyond the meaning expressed by Antoine de Saint-Exupéry in The Little Prince is fooling themselves. Instead, I suspect that by realizing you're part of a longer, tried-and-true tradition, your own practice will be far easier and more useful.

      The simplicity of the system (or these multiply-named methods) allows for the rise of a tremendous amount of complexity. This resultant complexity can in turn hide the simplicity of the root system.

      “To me, you are still nothing more than a little boy who is just like a hundred thousand other little boys. And I have no need of you. And you, on your part, have no need of me. To you, I am nothing more than a fox like a hundred thousand other foxes. But if you tame me, then we shall need each other. To me, you will be unique in all the world. To you, I shall be unique in all the world..."

      I can only hope people choose to tame more than Luhmann.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      What follows is our revision Plan.

      Manuscript number: RC-2024-02794

      Corresponding author(s): Jo Morris

      [The "revision plan" should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

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      The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the reprint and help them to obtain a balanced view of the paper.

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      If you wish to submit a full revision, please use our "Full Revision" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

      1. General Statements [optional]

      We feel the reviewers understood the paper well and made many reasonable points for improvement.

      In response to Reviewer three's concern about the reliance on SAE2 over-expression, in the 'Significance' section "One limitation is the strong reliance on the use of an actyl-mimicking mutant". We were minded not to rely on the mutant. Hence, the paper contains considerable data onthe HDCAC6 deacteylase, responsible for SEA2 deacetylation. We show that HDAC6 inhibition phenocopies SAE2-K164Q expression and, moreover, that the approaches which rescue the mitotic defects of SAE2-K164Q expression cells also rescue the defects of HDCA6 inhibited cells. These observations, we believe, overcome the concern.

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

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      Revisions.

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      R1: As the authors state, SUMO1 conjugates decrease during mitosis and this is somewhat at odds with the proposed model regarding NuMA. The authors can detect a SUMOylated NuMA conjugate (fig. 4a). To test whether the proposed model is correct, the authors could check: a. Whether this form is indeed SUMO1-NuMA b. Whether it decreases upon expression of the SAE2K164Q variant.

      R2: Figure 4:The authors show a ML792 sensitive high molecular weight smear of NUMA in nocodazole treated cells. It would be very convincing if the authors could demonstrate whether endogenous NUMA is conjugated to SUMO1 or SUMO2 in mitosis by SUMO IPs and whether they can detect a change upon expression of SAE2 variants as in Figure 3a. By replicating this experiment, it would be important to demonstrate the presence of both free and conjugated SUMO paralogs in the input and paralog specific sumoylation in general (smear) and of NUMA in the IP.

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      Response:These are important points. We intend to perform the suggested experiments to address which isoform NuMa is modified by, and what the impact of the variant is.

      R2:Figures 2 C/Supplementary Figure 3c: The enzyme concentrations used in these reactions are much too high. To discriminate between thioester- and isopeptide-linked SUMO, the same samples should be analyzed in the absence (detection of thioester and isopeptide linkages) and presence of high concentrations of DTT (detection of isopeptide-linked SUMO only). The presented assay is problematic as it shows dimeric SUMO and RanGAP1:SUMO bands in the absence of ATP and no UBC9 but SAE2 thioester/isopetide formation in the absence of RanGAP1 (preferentially UBC9 should form a thioester/isopetide bond in this condition as higher molarities of UBC9 over E1 are used). Dimeric SUMO should not be detected unless disulfide bridges are formed between cysteines - this happens when DTT is not present in the reaction - under such conditions, SAE2 and UBC9 can also form disulfide bridges via their catalytic cysteines, impairing their enzymatic activity. In order to interpret the results correctly, it is important to add low concentrations of DTT (~0.1 mM) even in thioester reactions and to distinguish between thioester and isopeptide linkages.

      R2: Figure 2F/ Supplementary Figure 3d: Again, the enzyme concentrations are much too high and need to be reduced to a concentration where mainly RanGAP1 monosumoylation with SUMO1 is detected. As RanGAP1 is the most efficient SUMO substrate known, the enzyme concentrations and reaction time can be greatly reduced to limit the auto-modification of the enzymes and SUMO chain formation. Due to the efficient chain-forming activity of SUMO2, this is more difficult with SUMO2, but can be reduced by limiting the concentration of UBC9 in particular or by using a SUMO2 KallR mutant. In the reaction shown, the authors used only twice the molarity of SUMO compared to the substrate, too low taking into account SUMO2 chain formation, enzyme and substrate modification (The reaction should be limited by enzyme activity not by SUMO2). How can the authors be sure that the band they report as RanGAP1 high MW SUMO2 is indeed RanGAP1 modified and not SAE2 (in comparison to Suppl Figure 3b)?

      Response: We intend to repeat these assays, as suggested by the reviewer, reducing the enzyme concentrations and using low-concentration DTT. With the relevant controls and blots to show the identity of the RanGAP-SUMO2 product. Further, we will show control experiments with and without DTT that demonstrate the sensitivity of the SAE2~SUMO band to the reducing agent.

      R2: Figure 3 nicely shows that ML792-resistant SAE2 variants conjugate SUMO2 equally well, whereas SAE2 K163R is reduced and SAE2 K163Q appears to be abolished in SUMO1 conjugation. However, only high molecular weight SUMO conjugates are shown. What are the levels of free SUMO after overexpression of SAE2 variants and the indicated treatments?

      Response: We will attempt to show free SUMO levels in mitotic cells.

      R2: According to the work of Zhang et al from the Matunis lab (cited as reference 39 in the proposed study), SUMO conjugation is greatly reduced in nocodazole-arrested cells, but is restored after release in G1. Furthermore, SUMO1 and SUMO2 localize to different subcellular regions during mitosis. Have the authors tested whether SAE2 variants differ in their intracellular localization or alter the subcellular localization of SUMO1 and SUMO2 in interphase and mitotic cells?

      Response: We will examine the localisation of the SAE2 variants (see section below for the SUMO proteins).

      R3: It would be helpful if the authors could more clearly separate the two steps catalyzed by the E1. This would be needed to determine whether the accumulation of the SUMO1-AMP intermediate by the K164Q mutant is due to a faster rate of formation or a reduced rate of conversion to the thioester. They could test the AMP formation step in isolation in a straightforward manner by using the double mutant K164Q C173G and measuring a time course of SUMO1-AMP versus SUMO2-AMP build-up. Alternatively, they could try to isolate the second step by adding SUMO1-AMP versus SUMO2-AMP to the E1 de novo - although isolation of the intermediates may be more involved.

      Response: We intend to perform the first approach suggested, making and examining the double mutant's activity as suggested.

      R3: The reason for the isoform selectivity in the context of NuMA SUMOylation remains unresolved. The study would be significantly strengthened if the authors could address the question of whether the mitotic defects come from a lack of NuMA SUMOylation or the wrong type of SUMOylation. In other words, does it matter which isoform of SUMO is attached to NuMA? This could be addressed by also creating a SUMO2 fusion construct and testing whether that suppresses some of the phenotypes observed with the K164Q mutant and upon HDAC6 inhibition.

      Response. This is an excellent suggestion. We intend to make the constructs suggested and perform this experiment for our revision.

      R3. It would be helpful to show a time course of endogenous SAE2 acetylation over the cell cycle, using synchronized cultures.

      Response. We will attempt to gain a view of SAE2 acetylation over the cell cycle, which requires the precipitation of endogenous SAE following synchronisation.

      R3: Fig 2a: The figure would be easier to understand if the same colour scheme was used for S1 versus S2 to aid the comparison.

      Response: We will change this.

      R3: The title is not immediately understandable. "SUMO protein bias for mitotic stability" sounds a bit awkward. It would be clearer to be more explicit about isoforms.

      Response: We have considered: "HDAC6-Dependent Deacetylation of SAE2 enhances SUMO1 Conjugation for Mitotic Integrity", we have not changed it on the current manuscript so as not to confuse the reader - we will change it at the journal level.

      R3: Fig 2b: I don't understand the units of this graph. Why does normalization result in a value of zero, not 1? On this scale, what would a value of 1 signify? How can a value become negative? I would have expected values relative to the WT, with the WT being set to 1 or to 100%. The authors should also show the raw data for this plot.

      Response: The data will be normalised to the WT condition (1 instead of 0), and raw data shown.

      R3: Fig 2c: Please also show representative raw data.

      Response: Representative images will be shown.

      R3: Fig 2d,f: Again, the legend should explain what the plots were normalized to.

      Response: Inserted in the legend for Fig. 2d&f: 'The RanGAP1-SUMO1 products are normalised to the WT SAE1:SAE2:SUMO1-only condition (top) and the RanGAP1-SUMO2 products are normalised to the WT SAE1:SAE2:SUMO2-only condition (bottom).'

      R3 Fig S5b: The authors argue with the hydrogen bonding capacities of the different pairings. However, acetylation at K164 should not necessarily prevent a hydrogen bond to SUMO1-E93, considering that the "NH" group is likely still at a comparable distance to the carboxylate of E93 and could in principle undergo H-bonding unless prevented by the steric bulk introduced by the acetyl group. On the other hand, the K164-E93 interaction is the only electrostatic interaction among the 4 possible combinations. While a contribution is not easy to prove experimentally, I think the possibility of charge-charge interactions having an impact should be considered in the discussion.

      Response: Agreed. The figure will be redrawn, and the possibility will be discussed.

      R1 Fig. 2c: Why does C173G form a thioester with SUMO2 up to 40% of the WT?

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      Response: We believe this arose in measuring background density in the blots in error. We will re-assess the method used.

      R3: Fig 4b: The images have very poor contrast. In addition, the merged image would be clearer if two different colours were used.

      Response: We will change one of the colours.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

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      R1:2. Please clarify the use of Dox addition in the text and legend earlier (is found currently in Supp. Fig 4).

      Response: Inserted before first result using doxycycline: 'Furthermore, we generated U2OS with a doxycycline-inducible (wild-type) WT FLAG-SAE2 or a FLAG-SAE2-K164R mutant.'

      R1.3. Fig. 4f: what is the difference between the first (invisible NUMA) bipolar and the second, NuMA visible bipolar spindle?

      Response: Fig. 4f now annotated with 'Untransfected' and 'GFP-NuMA transfected'.

      R1.4. ML972- should read ML792 on pg 8.

      Response: Corrected.

      R3: All the experiments showing acetylation are done with transfected FLAG-tagged constructs - are they overexpressed?

      Response: Supplemental Figure 4a illustrates that with the exception of the C173G mutant, the remainder WT, and K164-mutants are all expressed at near WT-levels and not over-expressed. The C-G-mutant is highly expressed.

      R3: On page 3, the authors could introduce a justification of why they tested IR treatment.

      Response: now justified.

      R3: The authors repeatedly use the word "codon" when they describe a site in the protein. Codon refers to mRNA, so the word "residue" would be more appropriate when talking about a protein.

      Response: Agreed. Done.

      R3: Page 8: "confirmation" should be "conformation".

      Response: Done.

      R3:Page 8: "While we find a little role for..." - delete "a"

      Response: Done.

      R2: Supplementary Figure 2: Please indicate the size of the marker bands, the fraction numbers and which fractions were pooled for further analysis. Is there any explanation why SAE1:SAE2K164R eluates in two peaks, suggesting two complexes? How different are they in size?

      Response: Ladder markers added to each gel image. Fraction numbers added. Black box indicates fractions pooled. Figure updated with relevant recombinant protein preps generated for updated in vitro experiments. The additional SAE1:SAE2-K164R peak which appeared in the previous manuscript Supp. Fig. 2a eluted in the void volume and so we think it comprised aggregated SAE1:SAE2 protein, more recent preparations do not show it.

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      R3: The authors should include a more detailed discussion of the importance of the absolute and relative concentrations of free SUMO1 versus SUMO2/3 as a possible mechanism to impose isoform bias. Specifically, they should consider the different KM values of the E1 for the isoforms. The literature says that the E1 has a lower KM (higher affinity) for SUMO1 than SUMO2/3 but also a lower kcat (considering both steps of its reaction together), resulting in an approximately equal Kcat/KM. This would mean that at low overall SUMO concentrations, SUMO1 would have an advantage, whereas with rising SUMO concentrations SUMO2/3 would be favoured (which might be particularly important during stress conditions). What part of the curve does the cellular environment reflect?

      Response: Yes, good point. Now included:

      R3: Fig 3g: Could the authors comment on the detrimental effects of both SUMO1 and SUMO2 in the WT background?

      Response: Comment included.

      R3: Fig 3h: typo ("Trasfect")

      Response: Done.

      R3: Fig 4f: The DAPI signal is hardly visible - better contrast would help.

      Response: Improved.

      R3: Fig S2: It would be appropriate to indicate which fractions were actually collected or combined during the purification.

      Response: Ladder markers added to each gel image. Fraction numbers added. Black box indicates the fractions pooled.

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      4. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      • *

      R2: According to the work of Zhang et al from the Matunis lab (cited as reference 39 in the proposed study), SUMO conjugation is greatly reduced in nocodazole-arrested cells, but is restored after release in G1. Furthermore, SUMO1 and SUMO2 localize to different subcellular regions during mitosis. Have the authors tested whether SAE2 variants differ in their intracellular localization or alter the subcellular localization of SUMO1 and SUMO2 in interphase and mitotic cells?

      Response: We have investigated SUMO isoform location. However, in our hands, using a range of SUMO antibodies, we do not see the previously reported localisations in mitotic wild-type cells, and thus, we are not able to assess the impact of the SAE variants. As our phenotypes are restricted to mitosis, we do not consider it worthwhile to look at interphase.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, the authors report on an interesting regulatory mechanism that influences the balance between conjugation of the different SUMO isoforms, SUMO1 versus SUMO2/3. The authors describe that acetylation of a specific residue, K164, in the SUMO activating enzyme (E1) subunit, SAE2, biases the E1's preference towards SUMO2/3. Specifically, they use an acetylation-mimicking K164Q mutation to show that the acetylation state of SAE2 likely affects the affinity of the E1 to SUMO and the rate of thioester formation. With an antibody, they demonstrate the acetylation of SAE2 in cells. Mechanistically, they locate the cause of the isoform bias to a residue in the C-terminus of SUMO in proximity to K164 or SAE2, where SUMO1 carries glutamate, while SUMO2/3 has glutamine. Switching these residues between the SUMO isoforms reverses the isoform preference of the E1. Phenotypically, the SAE2 K164Q mutant induces mitotic problems that the authors attribute to the SUMOylation of the NuMA complex. They assign the deacetylation of SAE1 to HDAC6 and report that deacetylation occurs during mitosis. These results are consistent with a model that SUMO1 modification of the NuMA complex in mitosis is important for mitotic fidelity and that the cell cycle-dependent changes in the acetylation status of SAE2 promote this. Accordingly, fusion of SUMO1 to a NuMA subunit partially overcomes the problems induced by the K164Q mutant or the inhibition of HDAC6.

      Major comments:

      The experiments are largely performed in a well-controlled manner, and overall, the study is very convincing. I would like to suggest a few experiments that would strengthen the authors' conclusions, and there are a few minor issues with some of the figures.

      1. It would be helpful if the authors could more clearly separate the two steps catalyzed by the E1. This would be needed to determine whether the accumulation of the SUMO1-AMP intermediate by the K164Q mutant is due to a faster rate of formation or a reduced rate of conversion to the thioester. They could test the AMP formation step in isolation in a straightforward manner by using the double mutant K164Q C173G and measuring a time course of SUMO1-AMP versus SUMO2-AMP build-up. Alternatively, they could try to isolate the second step by adding SUMO1-AMP versus SUMO2-AMP to the E1 de novo - although isolation of the intermediates may be more involved.
      2. The reason for the isoform selectivity in the context of NuMA SUMOylation remains unresolved. The study would be significantly strengthened if the authors could address the question of whether the mitotic defects come from a lack of NuMA SUMOylation or the wrong type of SUMOylation. In other words, does it matter which isoform of SUMO is attached to NuMA? This could be addressed by also creating a SUMO2 fusion construct and testing whether that suppresses some of the phenotypes observed with the K164Q mutant and upon HDAC6 inhibition.
      3. The authors should include a more detailed discussion of the importance of the absolute and relative concentrations of free SUMO1 versus SUMO2/3 as a possible mechanism to impose isoform bias. Specifically, they should consider the different KM values of the E1 for the isoforms. The literature says that the E1 has a lower KM (higher affinity) for SUMO1 than SUMO2/3 but also a lower kcat (considering both steps of its reaction together), resulting in an approximately equal Kcat/KM. This would mean that at low overall SUMO concentrations, SUMO1 would have an advantage, whereas with rising SUMO concentrations SUMO2/3 would be favoured (which might be particularly important during stress conditions). What part of the curve does the cellular environment reflect?
      4. It would be helpful to show a time course of endogenous SAE2 acetylation over the cell cycle, using synchronized cultures. All the experiments showing acetylation are done with transfected FLAG-tagged constructs - are they overexpressed? Is is not possible to work with endogenous SAE2?

      Minor comments:

      • The title is not immediately understandable. "SUMO protein bias for mitotic stability" sounds a bit awkward. It would be clearer to be more explicit about isoforms.
      • On page 3, the authors could introduce a justification of why they tested IR treatment.
      • The authors repeatedly use the word "codon" when they describe a site in the protein. Codon refers to mRNA, so the word "residue" would be more appropriate when talking about a protein.
      • Page 8: "confirmation" should be "conformation".
      • Page 8: "While we find a little role for..." - delete "a"
      • Fig 2a: The figure would be easier to understand if the same colour scheme was used for S1 versus S2 to aid the comparison.
      • Fig 2b: I don't understand the units of this graph. Why does normalization result in a value of zero, not 1? On this scale, what would a value of 1 signify? How can a value become negative? I would have expected values relative to the WT, with the WT being set to 1 or to 100%. The authors should also show the raw data for this plot.
      • Fig 2c: Please also show representative raw data.
      • Fig 2d,f: Again, the legend should explain what the plots were normalized to.
      • Fig 3g: Could the authors comment on the detrimental effects of both SUMO1 and SUMO2 in the WT background?
      • Fig 3h: typo ("Trasfect")
      • Fig 4b: The images have very poor contrast. In addition, the merged image would be clearer if two different colours were used.
      • Fig 4f: The DAPI signal is hardly visible - better contrast would help.
      • Fig S2: It would be appropriate to indicate which fractions were actually collected or combined during the purification.
      • Fig S5b: The authors argue with the hydrogen bonding capacities of the different pairings. However, acetylation at K164 should not necessarily prevent a hydrogen bond to SUMO1-E93, considering that the "NH" group is likely still at a comparable distance to the carboxylate of E93 and could in principle undergo H-bonding unless prevented by the steric bulk introduced by the acetyl group. On the other hand, the K164-E93 interaction is the only electrostatic interaction among the 4 possible combinations. While a contribution is not easy to prove experimentally, I think the possibility of charge-charge interactions having an impact should be considered in the discussion.

      Significance

      The results presented here are interesting and novel. Importantly, the authors provide a molecular model for a new mechanism of how the SUMO system achieves isoform specificity, which is a still very poorly understood phenomenon. The manuscript makes a significant advance by contributing an important new aspect of how the SUMO conjugation machinery chooses between isoforms. The manuscript is strong by providing very good evidence for its conclusions. One limitation is the strong reliance on the use of an actyl-mimicking mutant; this limitation could be overcome by placing a bit more emphasis on detecting endogenous SAE2 acetylation.

      Audience: The study should be relevant to a broad audience, given the impact of the SUMO system on cellular regulation; after all, the study addresses a very fundamental problem in the field. In addition, it should be of interest to researchers studying regulation of mitosis.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      While CRISPR/Cas technology has greatly facilitated the ability to perform precise genome edits in Leishmania spp., the lack of a non-homologous DNA end-joining (NHEJ) pathway in Leishmania has prevented researchers from performing large-scale Cas-based perturbation screens. With the introduction of base editing technology to the Leishmania field, the Beneke lab has begun to address this challenge (Engstler and Beneke, 2023).

      In this study, the authors build on their previously published protocols and develop a strategy that:

      (1) allows for very high editing efficiency. The cell editing frequency of 1 edit per 70 cells reported in this study represents a 400-fold improvement over the previously published protocol,

      (2) reduces the negative effects of high sgRNA levels on parasite growth by using a weaker T7 promoter to drive sgRNA transcription.

      The combination of these two improvements should open the door to exciting large-scale screens and thus be of great interest to researchers working with Leishmania and beyond.

      We thank reviewer #1 for these encouraging comments.

      Reviewer #2 (Public Review):

      Summary:

      Previously, the authors published a Leishmania cytosine base editor (CBE) genetic tool that enables the generation of functionally null mutants. This works by utilising a CAS9-cytidine deaminase variant that is targeted to a genetic locus by a small guide RNA (sgRNA) and causes cytosine to thymine conversion. This has the potential to generate a premature stop codon and therefore a loss of function mutant.

      CBE has advantages over existing CAS-based knockout tools because it allows the targeting of multicopy gene families and, potentially, the easier generation of pooled loss of function mutants in complex population experiments. Although successful, the first generation of this genetic tool had several limitations that may have prevented its wider adoption, especially in complex genome-wide screens. These include nonspecific toxicity of the sgRNAs, low transfection efficiencies, low editing efficiencies, a proportion of transfectants that express multiple different sgRNAs, and insufficient effectivity in some Leishmania species.

      Here, the authors set out to systematically solve each of these limitations. By trialling different transfection conditions and different CAS12a cut sites to promote sgRNA expression cassette integration, they increase the transfection efficiency 400-fold and ensure that only a single sgRNA expression cassette integrates that edits with high efficiencies. By trialling different T7 promoters, they significantly reduce the non-specific toxicity of sgRNA expression whilst retaining high editing efficiencies in several Leishmania species (Leishmania major, L. mexicana and L. donovani). By improving the sgRNA design, the authors predict that null mutants will be more efficiently produced after editing.

      This tool will find adoption for producing null mutants of single-copy genes, multicopy gene families, and potentially genome-wide mutational analyses.

      Strengths:

      This is an impressive and thorough study that significantly improves the previous iteration of the CBE. The approach is careful and systematic and reflects the authors' excellent experience developing CRISPR tools. The quality of data and analysis is high and data are clearly presented.

      Weaknesses:

      Figure 4 shows that editing of PF16 is 'reversed' between day 6 and day 16 in L. mexicana WTpTB107 cells. The authors reasonably conclude that in drug-selected cells there is a mixed population of edited and non-edited cells, possibly due to mis-integration of the sgRNA expression construct, and non-edited cells outcompete edited cells due to a growth defect in PF16 loss of function mutants. However, this suggests that the CBE tool will not work well for producing mutants with strong fitness phenotypes without incorporating a limiting dilution cloning step (at least in L. mexicana and quite possibly other Leishmania species). Furthermore, it suggests it will not be possible to incorporate genes associated with a growth defect into a pooled drop-out screen as described in the paper. This issue is not well explored in the paper and the authors have not validated their tool on a gene associated with a severe growth defect, or shown that their tool works in a mixed population setting.

      We would like to thank reviewer #2 for this helpful comment and valid point. We have now included a small-scale loss-of-function screen in L. mexicana, targeting nine known essential genes with 24 CBE sgRNAs and 15 non-targeting control sgRNAs. This approach successfully detected all known included growth-associated phenotypes in a pooled screening format. This experiment is now shown in Figure 5 and described in section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”.

      In addition, we would like to re-iterate our initial public response to this comment. We believe that escapes or reversals of mutant phenotypes can be observed also with other genetic tools used for loss-of-function screening, including lentiviral CRISPR approaches in mammalian systems and RNAi in Trypanosoma brucei (e.g. Ariyanayagam et al., 2005 and Schlecker et al., 2005). Notably, in lentiviral delivered CRISPR screens, sgRNA expression cassettes are integrated in random places within the genome and multiple cassettes can be integrated depending on the viral titre. In these type of screens, cells can escape phenotypes through various mechanisms, such as promoter silencing or selection of non-deleterious mutations. Additionally, not every CRISPR guide is efficient in generating a mutant phenotype, and RNAi constructs can also vary in their effectiveness. Despite these challenges, genome-wide loss-of-function screens have been successfully carried out in mammalian cells and Trypanosoma parasites. Therefore, we believe that the observed escape of one mutant phenotype does not preclude the detection of growth-associated or other phenotypes in pooled screens. Moreover, we did not observe a reversal of the mutant phenotype in L. mexicana, L. donovani, and L. major parasites expressing tdTomato from an expression cassette integrated into the 18S rRNA SSU locus (Figure 4). Our now included small scale fitness screen (Figure 5) confirms these assumptions and shows that we can detect “strong” growth associated phenotypes. We would also like to point out that we have recently successfully conducted several genome-wide loss-of-function screens in vivo and in vitro, ultimately confirming the feasibility of this type of screen on a genome-wide scale (manuscript in preparation).

      We have included a discussion of these points under section “Integration of CBE sgRNA expression cassettes via AsCas12a ultra-introduced DSBs increase editing rates” and section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen” in our revised manuscript.

      Although welcome, the improvements to the crRNA CBE design tool are hypothetical and untested.

      We agree that the improvements to the CBE sgRNA design are currently hypothetical. We plan to systematically test our guide design principles in future studies. Since this will require testing hundreds of guides to draw robust conclusions, we believe that this aspect is beyond the scope of the current study. In section “Improved CBE sgRNA design to prioritize edits resulting only in STOP codons” of our revised manuscript we now discuss these future plans.

      The Sanger and Oxford Nanopore Technology analyses on integration sites of the sgRNA expression cassette integration will not detect the mis-integration of the sgRNA expression construct into an entirely different locus.

      We have now re-analysed our ONT data and have extracted all ONT contigs that match the CBE sgRNA expression cassette. All extracted contigs align to the 18S rRNA SSU locus, showing integration of the cassette into this locus. It is important to note that here a population was sequenced and not a clone. Despite this, no contigs could be found that would link the CBE sgRNA expression cassettes to another locus. This is now shown in Figure 4 S2 and described in section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant”.

      Reviewer #3 (Public Review):

      Genetic manipulation of Leishmania has some challenges, including some limitations in the DNA repair strategies that are present in the organism and the absence of RNA interference in many species. The senior author has contributed significantly to expanding the available routes towards Leishmania genetic manipulation by developing and adapting CRISPR-Cas9 tools to allow gene manipulation via DNA double-strand break repair and, more recently, base modification. This work seeks to improve on some limitations in the tools previously described for the latter approach of base modification leading to base change.

      The work in the paper is meticulously described, with solid evidence for most of the improvements that are claimed: Figure1 clearly describes reduced impairment in the growth of parasites expressing sgRNAs via changes in promoters; Figures 2 and 3 compellingly document the usefulness of using AsCas12a for integration after transformation; and Figures 1 and 4 demonstrate the capacity of the combined modifications to efficiently edit a gene in three different Leishmania species. There is little doubt these new tools will be adopted by the Leishmania community, adding to the growing arsenal of approaches for genetic manipulation.

      There are two weaknesses the authors may wish to address, one smaller and one larger.

      (1) The main advance claimed here is in this section title: 'Integration of CBE sgRNA expression cassettes via AsCas12a ultra-introduced DSBs increase editing rates', with the evidence for this presented in Figure 4. It is hard work in the submission to discern what direct evidence there is for editing rates being improved relative to earlier, Cas9-based approaches. Did they directly compare the editing by the new and old approach? If not, can they more clearly explain how they are able to make this claim, either by adding text or a new figure? A side-by-side comparison would emphasise the advance of the new approach more clearly.

      We would like to thank reviewer #3 for this helpful comment. We have directly compared our improved method to our previous base editing method in Figures 1E and 4, demonstrating higher editing rates in a much shorter time. Especially the L. major panel in Figure 4B shows that in a direct comparison between the previously published (Engstler and Beneke, eLife 2023) and our here presented new system, editing can be only observed with the version presented here. However, to clarify the improvements we made, we compare now data from our previous screen done in Engstler and Beneke, eLife 2023 with a loss-of-function screen carried out with our updated method (see Figure 5 and section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”).

      In addition, we also feel that our title might have been misleading in a sense that we claim that Cas12a editing is more efficient than other Cas9 based approaches, which is something that we don’t want to state here. Given that we have now included a small scale CRISPR screen and given that we generally show improved base editing compared to our previous method (improved in terms of less toxicity, more editing in shorter time, higher transfection rates and less species specific variation), we have rephrased our title to: “Improved base editing and functional screening in Leishmania via co-expression of the AsCas12a ultra, a T7 RNA Polymerase, and a cytosine base editor”. 

      (2) The ultimate, stated goal of this work is (abstract) to 'enable a variety of loss-of-function screens', as the older approach had some limitations. This goal is not tested for the new tools that have been developed here; the experiment in Figure 5 merely shows that they can, not unexpectedly, make a gene mutant, which was already possible with available tools. Thus, to what extent is this paper describing a step forward? Why have the authors not run an experiment - even the same one that was described previously in Engstler and Beneke (2023) - to show that the new approach improves on previous tools in such a screen, either in scale or accuracy?

      We have now included a small-scale loss-of-function screen in L. mexicana, targeting nine known essential genes with 24 CBE sgRNAs and 15 non-targeting control sgRNAs. This approach successfully detected all known included growth-associated phenotypes in a pooled screening format. This experiment is now shown in Figure 5 and described in section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”. We believe that this underscores our claims made here and believe therefore that our updated toolbox will indeed enable a variety of loss-of-function screens.

      As pointed out in the comment to reviewer #2, we have recently successfully conducted several genome-wide loss-of-function screens in vivo and in vitro, ultimately confirming the feasibility of this type of screen on a genome-wide scale (manuscript in preparation). Without the improvements presented here, such as the higher transfection and base editing rates, these genome-wide screens could have not been carried out.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I would like to compliment Tom Beneke and his lab on their continued efforts to develop tools to facilitate genome editing in Leishmania.

      I have no doubt that the toolkit presented in this study will be very useful for the community. The submitted paper is very well written and contains all the necessary controls to support the author's claims. There is only one point that left me a bit concerned if this strategy is to be used for large-scale screens, and that is the potential for integration of multiple sgRNA expression cassettes in a single cell.

      We would like to thank reviewer 1 for helpful comments. We have addressed the major concern raised by including a small-scale loss-of-function screen in our revised manuscript. By targeting nine known essential genes with 24 CBE sgRNAs and 15 non-targeting control sgRNAs, this approach successfully detected growth-associated phenotypes in a pooled format (see section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen” and Figure 5). Regarding the point of multiple sgRNA expression cassette integration, please see the next comment below.

      Major points:

      Integration of multiple sgRNA expression cassettes:

      While Illumina-based gDNA-seq is well suited to determine changes in ploidy, I don't think it is sensitive enough to draw conclusions about possible double integration in a small percentage of cells. In fact, the data shown in Figure 4 S1D show a normalized coverage >1.5 for sgRNA cassette and NeoR, suggesting that they may have integrated >1 times in some cells.

      To verify that the integration of the CBE sgRNA expression cassette is specific, we have re-analysed our ONT results and confirmed that only ONT contigs can be detected that link the CBE sgRNA expression to the 18S rRNA locus. No other integration sites can be found. We also do not detect any contigs containing multiple CBE sgRNA expression cassettes. This is now shown in Figure 4 S2 and described in section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant”.

      Nevertheless, it is a valid concern that the sequencing depth is not sufficient to detect small percentage of cells that have integrated the CBE sgRNA expression multiple times. However, in this case we also like to make the point that this small percentage of cells within a screen is likely to be not relevant and we therefore now added a small scale pooled loss-of-function screen, targeting essential genes, to the manuscript (see new Figure 5) to proof our claim. If the integration of multiple sgRNAs into one cell would have any measurable combinatorial effect, the non-targeting controls in our screen would have been depleted as well. However, there is no detectable difference between all 15 included controls in our small-scale screen.

      We have addressed all points in sections “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant“ and “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”.

      To avoid double integration, wouldn't it be easiest to just create an allele-specific "landing pad" on one chromosome? I believe that a double integration rate of ~20% could severely complicate the analysis of any large-scale screen later on.

      We thank the reviewer for this suggestion but we have tried to use an allele-specific "landing pad" and described this already in our first manuscript version (see section “DSBs introduced by AsCas12a ultra increase integration rates of donor DNA constructs”). Specifically, we integrated CBE sgRNA expression cassettes into the neomycin resistance marker contained in the tdTomato expression cassette (Figure 2 S1D, Cas12a crRNA-5 and 6) but this resulted in lower transfection rates (Figure 2F: crRNA-5 1 in ~47,000; crRNA-6 1 in ~32,000) then when using a Cas12a crRNA that targets the 18S rRNA locus directly (Figure 2F: crRNA-4 1 in ~2,000). As we believe a high transfection rate is key for pooled large-scale screens, we therefore pursued further experiments with crRNA-4. However, since a different crRNA can be easily selected for our tool, simply by just changing the Cas12a crRNA during transfection, users can chose a different integration site or other “landing pads” if they want to. We have updated section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant” to clarify these details.

      Also, it is not clear to me how the integration of tdTomato could affect the integration of the sgRNA expression cassette 400 bp downstream.

      As said above, our ONT data clearly shows that we can only see integration into one locus (Figure 4 S1 and S2). Given that the recognition site of crRNA-4 is contained in the homology flank used to integrate tdTomato into the 18S rRNA locus, this may contribute to the effect we observe. But since the homology sequences match the original sequences within the locus, the reasons to why this affects integration of the CBE sgRNA expression cassettes remain also elusive to us. We try to discuss this better now in the section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant”.

      Data accessibility:

      The Illumina and ONT data should be made publicly available.

      ONT and Illumina fastq reads are now available at the European Nucleotide Archive (ENA Accession Number: PRJEB83088)

      Minor point:

      Line 30: It would be easier for readers if the authors could briefly explain what bar-seq is.

      We have added more details:[…] and bar-seq screens, which involve individually deleting, barcoding, and pooling mutants for analysis, have facilitated […].

      Lines 114, 120: I think the authors are referring to Figures 1E and F, not Figures 2E and F.

      Many thanks for picking this up, we have corrected the Figure reference.

      Reviewer #2 (Recommendations For The Authors):

      This has the potential to be a valuable tool for the community if it is efficiently distributed. If the authors have not yet done so they should make their plasmids available to the community via Addgene.

      We have started the deposit process with Addgene and plasmids will be available soon. In the meantime, all plasmid maps are available on our website www.leishbaseedit.net and can be requested for shipment from our lab.

      Line 162-165, 400-401: The potential for using AsCAS12a's intrinsic RNase activity for "multiplexing" would benefit from a little more explanation (i.e. how this would work, and what multiplexing means in this context).

      We have added further details on multiplexing with Cas12a and point out potential applications.

      “For example, Cas12a crRNA arrays with four or more guides can be assembled and transfected to introduce multiple DSBs within one gene. Since Cas12a generates sticky DNA ends that facilitate recombination via microhomology-mediated end joining and homologous recombination (Zhang et al., 2021), this approach could effectively disrupt target genes without requiring the addition of donor DNA and this may provide an alternative approach to our here presented base editing method in the future. Moreover, CBE sgRNAs could be multiplexed by interspacing them with Cas12a direct repeats (DRs), enabling simultaneous targeting of multiple genes in one cell.”

      Line 193-194: can the authors offer an explanation for the reduction in mNG editing observed with 30nt homology flanks?

      We assume this is caused by imprecise recombination events in some cells and have revised the original sentence.

      In several places in the manuscript, it is unclear if an analysis has been done on an individual clone or a population derived from multiple transfected cells. If on mixed population, clarify this and calculate the number of clones that the mixture represents. E.g. lines 195-196 and 221-223 (Sanger sequencing of integration site); Line 333-352 (ONT analysis of CBE expression cassette integration).

      Only when we tested whether multiple CBE sgRNAs are integrated, we generated and analysed clones (Figure 4 S3). In all other experiments we analysed parasite populations. For better clarity, we have where possible indicated this in the revised manuscript (e.g. at the lines requested). 

      Line 259: "site by site" should presumably be "side by side".

      Many thanks for pointing this out. We have changed this typo.

      Lines 315-317: Clarify why the mis-integration of the CBE sgRNA expression cassette might cause a lack of editing (e.g. lack of expression?).

      We have added: “This could potentially result in the silencing of the CBE sgRNA expression or even lead to the deletion of the guide cassette”

      Line 364 - 367: it is unlikely there is the statistical power to state that 2/10 represents lower than the previously observed 38% of double integrants.

      We agree that the statistical power is low and have therefore changed our phrasing to an overall estimation.

      Reviewer #3 (Recommendations For The Authors):

      I suggest that the authors make clearer to the reader the evidence for improved editing efficiency in the new CBE system described here relative to the system described in Engstler and Beneke, 2023. Such clarification could be as simple as an extra paragraph or figure, clearly comparing the editing rates with the two systems in, as far as possible, equivalent conditions.

      We have directly compared our improved method to our previous base editing method in Figures 1E and 4, demonstrating higher editing rates in a much shorter time. Especially the L. major panel in Figure 4B shows that in a direct comparison between the previously published (Engstler and Beneke, eLife 2023) and new system, editing can be only observed with the version presented here. However, to clarify the improvements we made, we compare now data from our previous screen done in Engstler and Beneke, eLife 2023 with a loss-of-function screen carried out with our updated method (see Figure 5 and section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”).

      The significance of this work would be improved by running the type of loss of fitness screen described previously in Engstler and Beneke (2023), thereby showing that the new approach improves on previous tools. Without such data, questions remain about potential confounding effects that might not be anticipated from the targeted experiments provided in the current manuscript.

      We thank the reviewer for this suggestion. The requested experiment is now presented in Figure 5 and described in section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      eLife Assessment

      This important study provides empirical evidence of the effects of genetic diversity and species diversity on ecosystem functions across multi-trophic levels in an aquatic ecosystem. The support for these findings is solid, but a more nuanced interpretation of the results could make the conclusions more convincing. The work will be of interest to ecologists working on multi-trophic relationships and biodiversity.

      Thanks for this new assessment. Here below we reply to the comments that you and the reviewer have made. We understand the critics related to the issue of the interpretation of causal relationships from observational data. We now added an entire paragraph (in the second paragraph of the Discussion) that explicitly call for a cautionary interpretation of our results. We also tried to refrain the use of certain words (e.g., “we demonstrate”) when we think it is hard to conclude. This a tricky exercise as on the one hand we gathered a large and strong database (which had been underlined by the reviewers) that should supposedly strengthen statistical inferences, but on the other hands, the inferences we’ve made are based from observational data, which obviously comes from biases (even if partially controlled statistically). We hope that you’ll find our adding appropriate to find the good balance between a strong dataset and fragile interpretation.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This work used a comprehensive dataset to compare the effects of species diversity and genetic diversity within each trophic level and across three trophic levels. The results stated that species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects. Additionally, these effects were observed only within each trophic level and not across the three trophic levels studied. Although the effects of biodiversity, especially genetic diversity across multi-trophic levels, have been shown to be important, there are still very few empirical studies on this topic due to the complex relationships and difficulty in obtaining data. This study collected an excellent dataset to address this question, enhancing our understanding of genetic diversity effects in aquatic ecosystems.<br /> Strengths:

      The study collected an extensive dataset that includes species diversity of primary producers (riparian trees), primary consumers (macroinvertebrate shredders), and secondary consumers (fish). It also includes genetic diversity of the dominant species in each trophic level, biomass production, decomposition rates, and environmental data. The writing is logical and easy to follow.

      Weaknesses:

      The two main conclusions-(1) species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects, and (2) these effects were observed only within each trophic level, not across the three levels-are overly generalized. Analysis of the raw data shows that species and genetic diversity have different effects depending on the ecosystem function. For example, neither affected invertebrate biomass, but species diversity positively influenced fish biomass, while genetic diversity had no effect. Furthermore, Table S2 reveals that only four effect sizes were significant (P < 0.05): one positive genetic effect, one negative genetic effect, and two negative species effects, with two effects within a trophic level and two across trophic levels. Additionally, using a P < 0.2 threshold to omit lines in the SEMs is uncommon and was not adequately justified. A more cautious interpretation of the results, with acknowledgment of the variability observed in the raw data, would strengthen the manuscript.

      There is actually no objective justification for having chosen p<0.20. This is a subjective threshold that has been chosen to simplify the visual interpretation of causal graphs while highlighting the most biologically relevant links. We have now added a sentence stating explicitly the subjective nature of the threshold. We understand the point you raised regarding the cautionary interpretation of the results. We have now added a paragraph (just before the detailed discussion) explicitly calling for a cautionary interpretation of the results (see l. 414-424). We think this paragraph prevails for the entire discussion. Our message in this paragraph is that inferences that we’ve made can arise from both a biological reality and statistical artefacts. We can not really tease apart at this stage, and our interpretation of the results therefore has to be taken with care. We hope you’ll find the statement adequate.  We prefer advertising the readers from the start rather than including cautionary note all over the discussion. We feel it was more logical and comfortable. We have also modified the text from place to place to avoid strong statement such as “we demonstrated” when we think the demonstration can not be considered as solid.

      Recommendations for the authors:

      Reviewing Editor:

      In addition to the comments from the reviewer, we have the following comments on your paper:

      (1) It would be important to clarify that there could be different interpretations about one of the major findings: for within-trophic BEF relationships, genetic and species diversity have the opposite effects on ecosystem functions (i.e., positive and negative effects for genetic and species diversity, respectively). (1) One possibility is that for each specific ecosystem function, genetic and species diversity have the opposite effects. (2) The other possibility is that genetic diversity has positive effects on some functions, while species diversity has negative effects on other functions. These two possibilities can have quite different implications about the generalizability of the conclusion, mechanisms involved, and practices for ecosystem management. Therefore, it would be important to clarify that the findings from this paper are more about the second rather than the first possibility both in the discussion and conclusion sections.

      Yes, true, this is an important distinction and we agree with your conclusion. We have added a section in the Discussion (l. 537-545) and a note in the Conclusion (l. 625-627).

      (2) Please take special caution when comparing the findings from this observational study vs. previous experimental works. (1) The different ranges of diversity in the observational vs. experimental works, together with the nonlinear nature of the BEF relationship challenge the direct comparisons of their results. That is, even if their true BEF relationship are identical, focusing on different sections of a nonlinear curve can give us different results of the estimated BEF relationships. This challenge is further aggravated when involving both genetic and species diversity because these two facets have different biological meanings as the authors have already noted. Using standardized effect size or explained variance, as this paper did, may partially get around but not truly resolve this issue. It would be important to add clarifications to make the comparisons between genetic and species diversity effects more understandable in a biological or ecological context. One possibility could be to state that both genetic and species diversity measured in this study well represent their natural gradients in this aquatic ecosystem, so that the standardized effect sizes quantify how these natural diversity gradients associate with ecosystem functions. This further points to the issue about the representatives of the genetic diversity sampled from up to 32 individuals for each species per site, which would also need clarification. We suggest the authors to identify these challenges in the discussion, so that future studies can be aware of these or even find alternative solutions. (2) The species diversity effects have quite different meanings between this study and previous observational and experimental studies. The negative effects are for the biomass of one target species from this study, while the species diversity effects are usually for the biomass of all species within a community. These two scenarios are not directly comparable. The negative relationship between species diversity and a target species' biomass can simply arise from a sampling process, for example, given the same community biomass, the more species occur in a community, the less biomass allocated to a single species, without assuming any biological interactions or species differences. And this study cannot exclude this possibility. Note that this null, sampling process is not equal to a negative covariance between biomass of a focal species and biomass of the community involving the species as stated in lines 446-448. To avoid possible mis-interpretation, we suggest the authors to revise or remove the comparison appearing in the paragraph starting from line 515.

      Thanks for these comments. Although we agree with the two points raised by the Editor, we must admit that we found them difficult to answer properly.  See our detailed responses hereafter.

      Point (1): this is true that comparisons with previous studies is tricky, especially when these comparisons also include both genetic and species components. This is a problem (a limit) for almost all comparisons in biology. We added a few lines to warn readers that these comparisons are not without any limits (see l. 414-424). Regarding the fact that « genetic and species diversity measured in this study well represent their natural gradients in this aquatic ecosystem »: all is about scales. The genetic and species diversity measured in this study are obviously representative of communities and populations of the upstream (piedmont) part of the Garonne River basin as our sampling design covers all the east-west gradient. On the other hand, these communities and populations are not representative of the entire Garonne River basin, as we lack all the downstream part of the network. We added a sentence to specify that the sampling communities are specific of this specific ecosystem (rivers from the piedmont, see l. 224-226). Regarding « the issue about the representatives of the genetic diversity sampled from up to 32 individuals », we must admit that we are surprised by this comment as it is a very classical way for estimating genomic diversity. Although there is no clear rule, 30 individuals per site is generally assumed (and has been shown) to be an appropriate sample size (especially given that we used here a genome-wide approach). We added a reference to justify the sample size.

      Point (2): We understand the point raised by the Editors. Regarding your note “Note that this null, sampling process is not equal to a negative covariance between biomass of a focal species and biomass of the community involving the species as stated in lines 446-448.”: this is true, we rephrase this sentence to be more neutral. Regarding the paragraph starting l. 515 (now 550), we refrained to remove this paragraph as it provides some mechanistic explanation for underlying patterns, which we think is important even if incomplete or speculative. The confusion probably arises because here we discuss all type of negative BEFs, including the effect of species diversity on the biomass of the community, on the biomass of focal species (including those from other trophic levels) and the litter degradation. Our discussion is very general, whereas you seem to focus on a specific case of negative species-BEFs. To highlight this further and warn readers about possible conclusions, we added the following sentence: “Given the empirical nature of our study and the fact that our meta-regressive approach includes several types of BEFs (e.g., species richness acting either on the biomass of a single focal species or on the biomass of an entire focal community), it is hard to tease apart specific and underlying mechanisms” (l. 573-576).

      (3) Please clarify how you derived the 95% CI in Fig. 5. For example, how did you involve the uncertainties of each raw effect size (e.g. each black triangle in Fig. 5a) when calculating their mean and 95% CI in each group (e.g., the red triangles and error bars in Fig. 5a)?

      Estimates and 95%-CI from Figure 5 are derived from the mixed-effect models described from l. 314. They are hence marginal effects derived from the models, and 95%-CI include all error terms (fixed and random). We now specify in the Figure caption that estimates and 95%-CI are marginal effects derived from the mixed-effect models.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This paper is an incremental follow-up to the authors' recent paper which showed that Purkinje cells make inhibitory synapses onto brainstem neurons in the parabrachial nucleus which project directly to the forebrain. In that precedent paper, the authors used a mouse line that expresses the presynaptic marker synaptophysin in Purkinje cells to identify Purkinje cell terminals in the brainstem and they observed labeled puncta not only in the vestibular and parabrachial nuclei, as expected, but also in neighboring dorsal brainstem nuclei, prominently the central pontine grey. The present study, motivated by the lack of thorough characterization of PC projections to the brainstem, uses the same mouse line to anatomically map the density and a PC-specific channelrhodopsin mouse line to electrophysiologically assess the strength of Purkinje cell synapses in dorsal brainstem nuclei. The main findings are (1) the density of Purkinje cell synapses is highest in vestibular and parabrachial nuclei and correlates with the magnitude of evoked inhibitory synaptic currents, and (2) Purkinje cells also synapse in the central pontine grey nucleus but not in the locus coeruleus or mesencephalic nucleus.

      Strengths:

      The complementary use of anatomical and electrophysiological methods to survey the distribution and efficacy of Purkinje cell synapses on brainstem neurons in mouse lines that express markers and light-sensitive opsins specifically in Purkinje cells is the major strength of this study. By systematically mapping presynaptic terminals and light-evoked inhibitory postsynaptic currents in the dorsal brainstem, the authors provide convincing evidence that Purkinje cells do synapse directly onto pontine central grey and nearby neurons but do not synapse onto trigeminal motor or locus coeruleus neurons. Their results also confirm previously documented heterogeneity of Purkinje cell inputs to the vestibular nucleus and parabrachial neurons.

      Weaknesses:

      Although the study provides strong evidence that Purkinje cells do not make extensive synapses onto LC neurons, which is a helpful caveat given previous reports to the contrary, it falls short of providing the comprehensive characterization of Purkinje cell brainstem synapses which seemed to be the primary motivation of the study. The main information provided is a regional assessment of PC density and efficacy, which seems of limited utility given that we are not informed about the different sources of PC inputs, variations in the sizes of PC terminals, the subcellular location of synaptic terminals, or the anatomical and physiological heterogeneity of postsynaptic cell types. The title of this paper would be more accurate if "characterization" were replaced by "survey".

      Several of the study's conclusions are quite general and have already been made for vestibular nuclei, including the suggestions in the Abstract, Results, and Discussion that PCs selectively influence brainstem subregions and that PCs target cell types with specific behavioral roles.

      We agree that we did not provide an in-depth characterization of PC synapses onto all identified types of brainstem neurons. With so many types of neurons in the brainstem, this would be a monumental task. Despite this limitation we prefer to keep our original title, since our study makes the following advances:

      • We provide a comprehensive map of all PC synaptic boutons across the brainstem, and corresponding maps of PC synaptic input sizes. The input sizes vary widely, but are often multiple nanoamps, indicating that the cerebellum is an important regulator of activity in these regions. These maps will be indispensable for future investigations of cerebellar outputs.

      • We find that PC projections and the synapses they make are spatially restricted within most target nuclei such as the vestibular and parabrachial nuclei. This suggests that the influence of the cerebellum is spatially segregated within these nuclei, and likely allows the cerebellum to regulate specific behaviors.  While some aspects of these gradients have been described previously, our study is comprehensive, and has a higher degree of specificity than can be achieved with immunohistochemistry. 

      • We discover that PCs form functional synapses in the pontine central grey and nearby nuclei. Much of this region’s function is unknown, but certain subregions are important for micturition and valence. PCs make large synapses onto a small fraction of cells in this region, which suggests that PCs may target specific cell types to control novel nonmotor behaviors.

      • We provide clarification regarding PC projections to the locus coeruleus. Multiple high-profile, highly influential studies using rabies tracing (Schwarz et al., Nature 2015; Breton-Provencher and Sur, Nature Neuroscience 2019; and others) described a prominent PC input to the locus coeruleus. We showed that this projection is essentially nonexistent, both anatomically and functionally. We previously addressed this issue, but the PC-specific optogenetic approach we used here provides the most compelling evidence against a prominent PC-LC connection. This is an important finding for the cerebellum and a cautionary tale for conclusions based solely on viral tracing methods. We will expand on this issue in response to the comments of reviewer #3.

      Reviewer #2 (Public review):

      Summary:

      While it is often assumed that the cerebellar cortex connects, via its sole output neuron, the Purkinje cell, exclusively to the cerebellar nuclei, axonal projections of the Purkinje cells to dorsal brainstem regions have been well documented. This paper provides comprehensive mapping and quantification of such extracerebellar projections of the Purkinje cells, most of which are confirmed with electrophysiology in slice preparation. A notable methodological strength of this work is the use of highly Purkinje cell-specific transgenic strategies, enabling selective and unbiased visualization of Purkinje terminals in the brainstem. By utilizing these selective mouse lines, the study offers compelling evidence challenging the general assumption that Purkinje cell targets are limited to the cerebellar nuclei. While the individual connections presented are not entirely novel, this paper provides a thorough and unambiguous demonstration of their collective significance. Regarding another major claim of this paper, "characterization of direct Purkinje cell outputs (Title)", however, the depth of electrophysiological analysis is limited to the presence/absence of physiological Purkinje input to postsynaptic brainstem neurons whose known cell types are mostly blinded. Overall, conceptual advance is largely limited to confirmatory or incremental, although it would be useful for the field to have the comprehensive landscape presented.

      Strengths:

      (1) Unsupervised comprehensive mapping and quantification of the Purkinje terminals in the dorsal brainstem are enabled, for the first time, by using the current state-of-the-art mouse lines, BAC-Pcp2-Cre and synaptophysin-tdTomato reporter (Ai34).

      (2) Combinatorial quantification with vGAT puncta and synaptophysin-tdTomato labeled Purkinje terminals clarifies the anatomical significance of the Purkinje terminals as an inhibitory source in each dorsal brainstem region.

      (3) Electrophysiological confirmation of the presence of physiological Purkinje synaptic input to 7 out of 9 dorsal brainstem regions identified.

      (4) Pan-Purkinje ChR2 reporter provides solid electrophysiological evidence to help understand the possible influence of the Purkinje cells onto LC.

      Weaknesses:

      (1) The present paper is largely confirmatory of what is presented in a previous paper published by the author's group (Chen et al., 2023, Nat Neurosci). In this preceding paper, the author's group used AAV1-mediated anterograde transsynaptic strategy to identify postsynaptic neurons of the Purkinje cells. The experiments performed in the present paper are, by nature, complementary to the AAV1 tracing which can also infect retrogradely and thus is not able to demonstrate the direction of synaptic connections between reciprocally connected regions. Anatomical findings are all consistent with the preceding paper. The likely absence of robust physiological connections from the Purkinje to LC has also been evidenced in the preceding paper by examining c-Fos response to Purkinje terminal photoinhibition at the PBN/LC region.

      We agree that we previously dealt with the issue of PC-LC synapses (Chen et al., 2023, Nat Neurosci), but our conclusions differed from several high-profile publications (Schwarz et al., Nature 2015; Breton-Provencher and Sur, Nature Neuroscience 2019), and still met considerable resistance. We felt that the optogenetic approach provided the most definitive means of evaluating the presence and strength of PC-LC synapse that will hopefully settle this issue. These experiments also set a standard for future studies assessing the presence of PC synapses onto other target neurons in the brainstem.

      (2) Although the authors appear to assume uniform cell type and postsynaptic response in each of the dorsal brainstem nuclei (as noted in the Discussion, "PCs likely function similarly to their inputs to the cerebellar nuclei, where a very brief pause in firing can lead to large and rapid elevations in target cell firing"), we know that the responses to the Purkinje cell input are cell type dependent, which vary in neurotransmitter, output targets, somata size, and distribution, in the cerebellar and vestibular nuclei (Shin et al., 2011, J Neurosci; Najac and Raman, 2015, J Neurosci; Özcan et al., 2020, J Neurosci). This consideration impacts the interpretation of two key findings: (a) "Large ... PC-IPSCs are preferentially observed in subregions with the highest densities of PC synapses (Abstract)". For example, we know that the terminal sparse regions reported in the present paper do contain Floccular Targeted Neurons that are sparse yet have dense somatic terminals with profound postinhibitory rebound (Shin et al.). Despite their sparsity, these postsynaptic neurons play a distinct and critical role in proper vestibuloocular reflex. Therefore, associating broad synaptic density with "PC preferential" targets, as written in the Abstract, may not fully capture the behavioral significance of Purkinje extracerebellar projections. (b) "We conclude ... only a small fraction of cell. This suggests that PCs target cell types with specific behavioral roles (Abstract, the last sentence)". Prior research has already established that "PCs target cell types with specific behavioral roles in brainstem regions". Also, whether 23 % (for PCG), for example, is "a small fraction" would be subjective: it might represent a numerically small but functionally important cell type population. The physiological characterization provided in the present cell type-blind analysis could, from a functional perspective, even be decremental when compared to existing cell typespecific analyses of the Purkinje cell inputs in the literature.

      We now cite the papers suggested by the reviewer (Shin et al., 2011, J Neurosci; Najac and Raman, 2015, J Neurosci; Özcan et al., 2020, J Neurosci) and add to the discussion.

      (3) The quantification analyses used to draw conclusions about

      (a) the significance of PC terminals among all GABAergic terminals and the fractions of electrophysiologically responsive postsynaptic brainstem neurons may have potential sampling considerations:.

      (a.i) this study appears to have selected subregions from each brainstem nucleus for quantification (Figure 2). However, the criteria for selecting these subregions are not explicitly detailed, which could affect the interpretation of the results.

      Additional explanation has been added to results in the section, “Quantification of PC synapses in the brainstem.”  

      (a.ii) the mapping of recorded cells (Figure 3) seems to show a higher concentration in terminal-rich regions of the vestibular nuclei.

      In Figure 3, we strived to record in an unbiased manner. However, there may have been a slight bias to recordings in areas of lower myelination where patching is easier. We now clarify this issue in the text.

      Reviewer #3 (Public review):

      Summary:

      The manuscript by Chen and colleagues explores the connections from cerebellar Purkinje cells to various brainstem nuclei. They combine two methods - presynaptic puncta labeling as putative presynaptic markers, and optogenetics, to test the anatomical projections and functional connectivity from Purkinje cells onto a variety of brainstem nuclei. Overall, their study provides an atlas of sorts of Purkinje cell connectivity to the brainstem, which includes a critical analysis of some of their own data from another publication. Overall, the value of this work is to both provide neural substrates by which Purkinje cells may influence the brainstem and subsequent brain regions independent of the deep cerebellar nuclei and also, to provide a critical analysis of viral-based methods to explore neuronal connectivity.

      Strengths:

      The strengths lie in the simplicity of the study, the number of cells patched, and the relationship between the presence of putative presynaptic puncta and electrophysiological results. This type of study is important and should provide a foundation for future work exploring cerebellar inputs and outputs. Overall, I think that the critique of viral-based methods to define connectivity, and a more holistic assessment of what connectivity is and how it should be defined is timely and warranted, as I think this is under-appreciated by many groups and overall, there is a good deal of research being published that do not properly consider the issues that this manuscript raises about what viral-based connectivity maps do and do not tell us.

      We thank the reviewer for highlighting this important aspect of this work, and for agreeing with our thesis concerning viral-based connectivity maps.

      Weaknesses:

      While I overall liked the manuscript, I do have a few concerns that relate to interpretation of results, and discussion of technological limitations. The main concerns I have relate to the techniques that the authors use, and an insufficient discussion of their limitations. The authors use a Cre-dependent mouse line that expresses a synaptophysin-tomato marker, which the authors confidently state is a marker of synapses. This is misleading. Synaptophysin is a vesicle marker, and as such, labels axons, where vesicles are present in transit, and likely cell bodies where the protein is being produced. As such, the presence of tdtomato should not be interpreted definitively as the presence of a synapse. The use of vGAT as a marker, while this helps to constrain the selection of putative pre-synaptic sites, is also a vesicle marker and will likely suffer the same limitations (though in this case, the expression is endogenous and not driven by the ROSA locus). A more conservative interpretation of the data would be that the authors are assessing putative pre-synaptic sites with their analysis. This interpretation is wholly consistent with their findings showing the presence of tdtomato in some regions but only sparse connectivity - this would be expected in the event that axons are passing through. If the authors wish to strongly assert that they are specifically assessing synapses, a marker better restricted to synapses and not vesicles may be more appropriate.

      We agree that synaptophysin-tdTomato is an imperfect marker, although it is vastly superior to cytosolic tdTomato.  We found that viral expression of synaptophysin-GFP gives much more punctate labelling, but an appropriate synaptophysin-GFP line is not available. We carefully point out this issue, and threshold the images to avoid faint labeling associated with fibers of passage.  The intersection of VGAT labelling and of the synaptophysin-tdTomato labelling provides us with superior identification of PC boutons.  We will add additional clarification to point out that these are putative presynaptic boutons, but that alone this does not establish the existence or the strength of functional synapses.

      Similarly, while optogenetics/slice electrophysiology remains the state of the art for assessing connectivity between cell populations, it is not without limitations. For example, connections that are not contained within the thickness of the slice (here, 200 um, which is not particularly thick for slice ephys preps) will not be detected. As such, the absence of connections is harder to interpret than the presence of connections. Slices were only made in the coronal plane, which means that if there is a particular topology to certain connections that is orthogonal to that plane, those connections may be under-represented. As such, all connectivity analyses likely are under-representations of the actual connectivity that exists in the intact brain. Therefore, perhaps the authors should consider revising their assessments of connections, or lack thereof, of Purkinje cells to e.g., LC cells. While their data do make a compelling case that the connections between Purkinje cells and LC cells are not particularly strong or numerous, especially compared to other nearby brainstem nuclei, their analyses do indicate that at least some such connections do exist. Thus, rather than saying that the viral methods such as rabies virus are not accurate reflections of connectivity - perhaps a more circumspect argument would be that the quantitative connectivity maps reported by other groups using rabies virus do not always reflect connectivity defined by other means e.g., functional connections with optogenetics. In some cases, the authors do suggest this (e.g."Together, these findings indicate that reliance on anatomical tracing experiments alone is insufficient to establish the presence and importance of a synaptic connection"), but in other cases, they are more dismissive of viral tracing results (e.g. "it further suggests that these neurons project to the cerebellum and were not retrogradely labeled"). Furthermore, some statements are a bit misleading e.g., mentioning that rabies methods are critically dependent on starter cell identity immediately following the citation of studies mapping inputs onto LC cells. While in general, this claim has merit, the studies cited (19-21) use Dbh-Cre to define LC-NE cells which does have good fidelity to the cells of interest in the LC. Therefore, rewording this section in order to raise these issues generally without proximity to the citations in the previous sentence may maintain the authors' intention without suggesting that perhaps the rabies studies from LC-NE cells that identified inputs from Purkinje cells were inaccurate due to poor fidelity of the Cre line. Overall, this manuscript would certainly not be the first report indicating that the rabies virus does not provide a quantitative map of input connections. In my opinion, this is still under-appreciated by the broad community and should be explicitly discussed. Thus, an acknowledgment of previous literature on this topic and how their work contributes to that argument is warranted.

      We have a different take on connectivity and the use of optogenetics.  Based on our years of experience studying synapses in brain slice, axons survive very well even when they are cut. It is not necessary to preserve intact axons that extend for long distances. It is also true that activation of these axons, with either extracellular electrical stimulation or with optogenetics, is sufficient to evoke synaptic inputs. Robust synaptic responses are evoked with optogenetic activation regardless of the slice orientation. We thank the reviewer for raising this issue, and we have added a couple of sentences to clarify this point under the section “Characterization of functional properties of PC synapses in the brainstem.”

      The discussion on starter cell specificity was not referring to the specificity of cre in transgenic animals, but the TVA/G helper proteins that are introduced by AAV and used in conjunction with the rabies virus. The issues related to this have recently been discussed in Elife (Beier, 2022) in addition to citations 58 and 59 in the manuscript. We have more explicitly highlighted this issue in the revised manuscript in the section “Lack of significant PC inputs to LC neurons.”

      Recommendations for the authors:  

      Reviewer #1 (Recommendations for the authors):

      (1) Methods need detail to be replicable, particularly in how PC synapses were identified and automatically counted. It is not clear what was the variation within subregions across mice. How were neurons selected or rejected for recordings and analyses? Was each subregion sampled at equal spacing? Methods for anatomy should mention sagittal sections.

      Wording in Methods section, “Anatomy” was changed to better reflect how PC synapses were identified as colabeled segments of vGAT and tdTomato labeling. 

      Each datapoint in Figure 2D-F was quantification of a region for each section and each mouse. The color of the data point indicates the anterior posterior location of the section. The violin plot quantifies the median and quartile value for all points across sections and mice. The variability captured by the violin point reflects variability across the anterior-posterior axis. 

      Neurons were mostly randomly selected in each slice, and rejected based on unstable holding current or access resistance. Cell locations were recorded and updated with each experiment so that we minimized oversampling easier to patch regions.

      Sagittal sections were added in methods.

      (2) Figure 2D-F what is the black line and grey region?

      Additional text was added in the caption for Figure 2D-F

      (3) MEV is confusing given LAV stands for lateral vestibular - perhaps call it ME5?

      We will remain consistent with the abbreviations in the Allen Brain Reference Atlas.

      Reviewer #2 (Recommendations for the authors):

      (1) What are the criteria for distinguishing large, small, and non-responders?

      Large are in the nA range, small are in the hundreds of pA, and non-responders are effectively zero. Manual curation of these responses indicated that a current amplitude threshold of 45 pA clearly separated non-responders from responders. To be clear, the average response (as stated in text and displayed in Figure 3D) includes all cells.

      (2) p1. "Unexpectedly": it would not be unexpected, rather, expected, because it was reported in Chen et al., 2023, Nat Neurosci.

      The PCG was hinted at, but an actual functional, anatomical connection was not reported in our previous manuscript.

      (3) p1. "We combined electrophysiological recordings with immunohistochemistry to assess the molecular identities of these PC targets": please clarify "these" here. It could be read that it refers to "pontine central gray and nearby subnuclei" but it doesn't make sense. Immuno has only been performed for MeV and LC.

      Corrected

      (4) p1. "but only inhibit a small fraction of cells in many nuclei": as far as I read Fig.3, it seems that ~50% for PBN/VN and ~25% for PCG: would this be "a small fraction"?

      The small fraction of cells was in reference to subnuclei within the PCG, but we agree this statement is too broad to be useful and have eliminated it.

      (5) p2. "conventional tracer": viral tracer is becoming a standard, so dye tracer could be better here.

      Corrected

      (6) p3. "rostral/cauda": typo.

      Corrected.  

      (7) p3. Quantification of PC synapses in the brainstem: it would be helpful to introduce why synapto-tdT alone is not sufficient, and the purpose of adding vGAT immunostaining.

      We have added more on vGAT labeling putative presynaptic sites and quantifying only synaptic labeling instead of axonal tdTomato in the Results, “Quantification of PC synapses in the brainstem.” In addition, vGAT staining allows us to examine the PC contribution to total inhibition in each region.

      (8) p7. "PB and are": typo.

      Corrected. And all instances of PBN were changed to PB

      (9) p7. "they are likely a mix of excitatory and inhibitory inputs 54,55": Bagnall et al., 2009, J Neurosci, would be critically relevant here.

      Added, thank you

      (10) Figures 2-3: Yellow/Blue color scheme is hard to distinguish, and having two colors could be read as implying two distinct regions.

      We are unsure what the reviewer is referring to exactly here, but the colors refer to the sections in 2C (see the color bar on the bottom right of each atlas schematic). The points represent an individual section that was quantified, and thus do represent distinct samples from distinct regions.

      (11) Figure 2D-F: what is indicated by each point?

      Each data point is the number of PC bouton (D), density of bouton (E), or percentage of synaptophysin/vGAT (F) quantified for each region per section. Each color represents a coronally distinct section of a region. Additional text was added into the captions to clarify this and point 10.

      (12) Figure 3E, right: what is the correlation coefficient?

      The correlation coefficient was found to be 0.74

      Reviewer #3 (Recommendations for the authors):

      Some minor grammatical errors and typos need to be cleaned up (e.g. "To quantifying the densities...", "The medial-ventral region of the PBN...have extensive...".

      These errors have been corrected

    1. Author response:

      The following is the authors’ response to the original reviews.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Abstract

      I don't think you need the first two sentences of the abstract. This is not a grant and your results are exciting enough to justify a full basic science-based approach.

      We fully understand this perspective.  However, we prefer to introduce the work in the broader context of sleep medicine.  This manuscript is part of our long-standing efforts to develop cavefish as a model for sleep disorders and we believe this provides important context.

      Last sentence of the abstract: the subject is missing. "That have developed..." who has developed?

      Thank you. We have corrected this error, the sentence now reads “...these findings suggest that cavefish have developed resilience to sleep loss...”

      Introduction

      First paragraph. Worth explaining in a sentence what is the link between DNA damage and ROS.

      We now state ‘Further, chronic sleep loss results in elevated reactive oxygen species (ROS), a known mediatior of DNA damage, in the gut and/or brain that contribute to mortality in Drosophila and mice [11,16].’

      "A. mexicanus exists as blind cave populations and an extant surface population that are interfertile". This needs rephrasing. As it is, it sounds like the surface population is infertile.

      We have rephrased for clarity; the line now reads: “while the surface and cave populations are geographically isolated, they remain interfertile and capable of hybridization in nature as well as laboratory settings”.

      "Further, the evolved differences in DNA repair genes, including links between mechanisms regulating sleep, light responsiveness, and DNA repair across all three cave populations studied to date [27,29]" This sentence is incomplete.

      We have corrected the phrasing, which now reads “...evolved differences in DNA repair genes have been identified across all three cave populations studied to date, including links between mechanisms regulating sleep, light responsiveness, and DNA repair”:

      Figure 1

      I recommend improving the legibility of the figure copying some of the information provided in the legend directly within the figure itself.

      A, B: label in the panel itself what is blue and what is green.

      Thank you, we have made this change.

      C: Make it clear in the figure itself that you are measuring yH2AX. Also, probably you have enough room in the figure to avoid abbreviations for Rhomb, mes, and tele. It may also help if you could add a little cartoon that explains what those three brain regions are.

      We have added text to the y axis indicating that yH2AX fluorescence is being measured, and replaced the abbreviations with eh full names of the regions.

      G: again, explain that DHE is being measured here. And perhaps pick a different colour choice to highlight the difference from C?

      We have added clarifiaction to the y-axis of the figure, but have retained the color scheme for consistency; in all surface-cave comparisons in the manuscript, gray is used for surface fish and red for cavefish.

      In the text: I would recommend adding some quantitative reminder of what is the difference in sleep amount between the two species (cave vs surface).

      We have added the following to highlight the magnitude of the difference in sleep: “Strikingly, cavefish sleep as little as 1-2 hours per day, in contrast to their surface counterparts, which sleep as much as 6-10 hours a day”

      "Together, these findings fortify the notion that cellular stress is elevated in the gut of cavefish relative to surface fish." Were the two populations fed the same diet and raised in the same lab conditions? If this is pinpointed to sleep amount, it's worth ruling out possible confounding factors.

      We have added a sentence to the results underlining this point: “Prior to imaging, both surface and cavefish had been reared in a temperature-controlled incubator, and relied solely on their yolk sac for nutrients; so, differences in gut ROS cannot be attributed to differences in rearing or feeding conditions.”

      Figure 2

      Spell out, somewhere in the figure itself, that the 30s and 60s refer to UV treatment protocols.

      We have added X-axis titles to clarify this in Fig 2 and supp. Fig 1.

      It would be worth providing a cartoon of the experimental setup that shows for instance what time of the day UV was given (it's only specified in the text) and which subsequent sleep period was selected for comparisons.

      We have added arrows to all sleep plots indicating the time of UV treatment, and brackets indicating the time period used for statistical comparisons, as well as text in the figure legends indicating this.

      Figure 3

      A. I don't think this is needed, to be honest, and if you want to keep it, it needs a better legend.

      We have edited the figure legend to increase clarity.

      B. I would make it clear in the figure that this refers to transcriptomics analysis. Perhaps you could change the order and show C, D, and then B.

      We have added text to the figure legend and the results text to more explicitly state that the PCA plot is of transcriptional response. We have however retained the original figure order, as well feel this figure is important to establish that both populations have strong, but distinct responses to the UV treatment.

      Figure 4

      A. Spell it out in the figure itself that you're staining for CPD.

      Thank you, we have made this change.

      B. You are using the same colour combination you had in Figure 1 but for yet another pairing. This is a bit confusing.

      Thank you for bringing this to our attention.  We have added descriptions of the colors in the figure legend.

      Discussion

      "Beyond the Pachón cavefish population, all three other cavefish populations have been found to have reduced sleep (Cite)." Citation missing here.

      Thank you.  We have now clarified this sentence and included a citation.

      Reviewer #2 (Recommendations For The Authors):

      Consideration of Environmental Conditions:

      Evaluate whether the lab conditions, which may more closely resemble surface environments, could influence the observed increase in neuronal DNA damage and gut ROS levels in cavefish. Adjusting these conditions or discussing their potential impact in the manuscript would strengthen the findings.

      We are very excited about these experiments.  We have a paper that will be submitted to BioRxiv this week where we record wild-caught fish, as well as fish in caves.  The conclusion is that sleep loss is present in both populations.  This field work took over 10 years to come together and still lacks the power of the lab based assays.  Nevertheless, we can conclusively say that the phenotypes we have observed for the last ~15 years in the lab are present in a natural setting.  We have included a statement about the need for future work to test these findings in a natural setting.

      Alternative Stressors:

      Given that cavefish are albino and blind (to my knowledge), consider using alternative sources of genotoxic stress beyond UV-induced damage. This could include chemical agents or other forms of environmental stress to provide a more comprehensive assessment of DDR.

      We agree and are enthusiastic about looking more generally at stress.  We note that we have previously found that cavefish rebound following sleep deprivation (McGaugh et al, 2020) suggesting that they are responsive to sleep disruption.  This will be a major research focus area moving forward.

      Broader Stress Responses:

      Investigate whether other forms of stress, such as dietary changes or temperature fluctuations, elicit similar differences in sleep patterns and DDR responses. This could provide additional insights into the robustness of the observed phenomena.

      We fully agree.  This will be the primary focus of this research area moving forward. We hypothesize that cavefish are generally less responsive to their environment.  Unpublished data reveals that temperature stress, circadian changes, and aging (presented here) to little to impact gene expression in surface fish.  We would like to test the hypothesis that transcriptional stability of cavefish contributes to their longevity.

      Potential Protective Mechanisms:

      Discuss the possibility that lower levels of gamma-H2AX in cavefish might be protective, as DDR can lead to cellular senescence or cancer. This perspective could add depth to the interpretation of the results.

      This was the hypothesis underlying this manuscript.  However, we found elevated levels of gamma-H2AX.  We believe there may be additional protective mechanisms that have evolved in cavefish, but cannot identify them to date.  Our hope is future functional studies by our group, as well as other groups’ access to this published work, may help address these questions.

      Strengthening the Sleep-DNA Damage Link:

      Further experiments are needed to directly link sleep differences to the observed variations in DNA damage and DDR. This could involve manipulating sleep patterns in surface fish and cavefish to observe corresponding changes in DNA repair mechanisms.

      We agree.  We have referenced work that conclusively showed this relationship in zebrafish. Our current methods for limiting sleep involves shaking, and this has too many confounds.  We are working on developing genetic tools, and applying the gentle rocking methods used previously in zebrafish to address these questions.

      Clarification of Causal Directionality:

      Address the potential that sleep patterns and DDR responses may both be downstream effects of a common cause or independent adaptations to the cave environment. Clarifying this in the manuscript would provide a more nuanced understanding of the evolutionary adaptations.

      Thank you for this suggestion.  We have now added a paragraph describing how these experiments (and the ones described above) are necessary for understanding the relationship between sleep and DDR.

      Clarification and Presentation:

      Fix the many typos, and improve the clarity of the figures and their legends to ensure they are easily interpretable. Additional context in the discussion section would help readers understand the significance and potential implications of the findings.

      Thank you, we have now included this.

      Reviewer #3 (Recommendations For The Authors):

      There are a number of suggestions that I have made in the public review, but there are a few things that I would like to add here.

      The methods section is missing many important details, for instance, the intensity of the illumination used in the UV exposure in larvae is not reported but is vital for the interpretation/replication of these experiments. In general, this section should be redone with a greater effort to include all important information. Similarly, the figure legends could be greatly improved, with important details like n-number and definition of significance thresholds defined (e.g. see Figures 1, C, and G.)

      We have added greater detail to the methods section to specify the spectral peak and power output of the bulbs used.

      There are a number of passages in the manuscript that do not make sense, which suggests that a future version of record should be carefully proofread. I know that this can be a case of reading multiple versions of a manuscript so many times that one doesn't really see it anymore, but, for example, phrases like "To differentiate between these two possibilities" are confusing to the reader when there has been no introduction of alternate possibilities.

      Thank you for this comment.  We have fixed this mistake and proofread the manuscript.

      Additionally, there are multiple examples of errors in citations/references. A few examples are below:

      "Further, chronic sleep loss results in elevated reactive oxygen species (ROS) in the gut and/or brain that contribute to mortality in Drosophila and mice [11, 16]". Reference 16 does not include mice at all, and reference 11 is Vaccaro et al. 2020, where Drosophila mortality is assessed, but mouse mortality is not.

      We have added the appropriate citations and revised this sentence.

      References 13 and 15 are the same.

      Thank you, we have fixed.

      References 24 and 26 are the same.

      Thank you, we have fixed.

      Public Reviews:

      Reviewer #1 (Publc Review):

      Summary:

      Lloyd et al employ an evolutionary comparative approach to study how sleep deprivation affects DNA damage repair in Astyanax mexicanus, using the cave vs surface species evolution as a playground. The work shows, convincingly, that the cavefish population has evolved an impaired DNA damage response both following sleep deprivation or a classical paradigm of DNA damage (UV).

      Strengths:

      The study employs a thorough multidisciplinary approach. The experiments are well conducted and generally well presented.

      Weaknesses:

      Having a second experimental mean to induce DNA damage would strengthen and generalise the findings.

      Overall, the study represents a very important addition to the field. The model employed underlines once more the importance of using an evolutionary approach to study sleep and provides context and caveats to statements that perhaps were taken a bit too much for granted before. At the same time, the paper manages to have an extremely constructive approach, presenting the platform as a clear useful tool to explore the molecular aspects behind sleep and cellular damage in general. The discussion is fair, highlighting the strengths and weaknesses of the work and its implications.

      We fully agree with this assessment.  We are currently performing experiments to test the effects of additional DNA damaging agents.  We hope to extend these studies beyond DNA-damage agents to look more generally at how animals respond to stress including ROS, sleep deprivation, and high temperature.  This will be a major direction of the laboratory moving forward.

      The manuscript investigates the relationship between sleep, DNA damage, and aging in the Mexican cavefish (Astyanax mexicanus), a species that exhibits significant differences in sleep patterns between surface-dwelling and cave-dwelling populations. The authors aim to understand whether these evolved sleep differences influence the DNA damage response (DDR) and oxidative stress levels in the brain and gut of the fish.

      Summary of the Study:

      The primary objective of the study is to determine if the reduced sleep observed in cave-dwelling populations is associated with increased DNA damage and altered DDR. The authors compared levels of DNA damage markers and oxidative stress in the brains and guts of surface and cavefish. They also analyzed the transcriptional response to UV-induced DNA damage and evaluated the DDR in embryonic fibroblast cell lines derived from both populations.

      Strengths of the Study:

      Comparative Approach:

      The study leverages the unique evolutionary divergence between surface and cave populations of A. mexicanus to explore fundamental biological questions about sleep and DNA repair.

      Multifaceted Methodology:

      The authors employ a variety of methods, including immunohistochemistry, RNA sequencing, and in vitro cell line experiments, providing a comprehensive examination of DDR and oxidative stress.

      Interesting Findings:

      The study presents intriguing results showing elevated DNA damage markers in cavefish brains and increased oxidative stress in cavefish guts, alongside a reduced transcriptional response to UV-induced DNA damage.

      Weaknesses of the Study:

      Link to Sleep Physiology:

      The evidence connecting the observed differences in DNA damage and DDR directly to sleep physiology is not convincingly established. While the study shows distinct DDR patterns, it does not robustly demonstrate that these are a direct result of sleep differences.

      We agree with this assessment.  We are currently working to apply tools developed in zebrafish to examine the physiology of sleep.  While this is important, and our results our promising, we will note that functional analysis of sleep physiology in fish has been limited to zebrafish.  We hope future studies will allow us to integrate approaches that examine the physiology of sleep.

      Causal Directionality:

      The study fails to establish a clear causal relationship between sleep and DNA damage. It is possible that both sleep patterns and DDR responses are downstream effects of a common cause or independent adaptations to the cave environment.

      We agree, however, we note that this could be the case for all animals in which sleep has been linked to DNA damage.  We believe the most likely explanation for Astyanax and other animals studied, is that sleep is that sleep and DDR are downstream/interface with the sleep homeostat.

      Environmental Considerations:

      The lab conditions may not fully replicate the natural environments of the cavefish, potentially influencing the results. The impact of these conditions on the study's findings needs further consideration.

      This is correct. We have considered this carefully.  After nearly a decade of effort,  we have completed analysis of sleep in the wild.  These will be uploaded to BioRxiv within the next week.

      Photoreactivity in Albino Fish:

      The use of UV-induced DNA damage as a primary stressor may not be entirely appropriate for albino, blind cavefish. Alternative sources of genotoxic stress should be explored to validate the findings.

      We have addressed this above.  Future work will examine additional stressors. Both fish are transparent at 6dpf and so it is unlikely that albinism impacts the amount of UV that reaches the brain.

      Assessment of the Study's Achievements:

      The authors partially achieve their aims by demonstrating differences in DNA damage and DDR between surface and cavefish. However, the results do not conclusively support the claim that these differences are driven by or directly related to the evolved sleep patterns in cavefish. The study's primary claims are only partially supported by the data.

      Impact and Utility:

      The findings contribute valuable insights into the relationship between sleep and DNA repair mechanisms, highlighting potential areas of resilience to DNA damage in cavefish. While the direct link to sleep physiology remains unsubstantiated, the study's data and methods will be useful to researchers investigating evolutionary biology, stress resilience, and the molecular basis of sleep.

      Reviewer #3 (Public Review):

      Lloyd, Xia, et al. utilised the existence of surface-dwelling and cave-dwelling morphs of Astyanax mexicanus to explore a proposed link between DNA damage, aging, and the evolution of sleep. Key to this exploration is the behavioural and physiological differences between cavefish and surface fish, with cavefish having been previously shown to have low levels of sleep behaviour, along with metabolic alterations (for example chronically elevated blood glucose levels) in comparison to fish from surface populations. Sleep deprivation, metabolic dysfunction, and DNA damage are thought to be linked and to contribute to aging processes. Given that cavefish seem to show no apparent health consequences of low sleep levels, the authors suggest that they have evolved resilience to sleep loss. Furthermore, as extended wake and loss of sleep are associated with increased rates of damage to DNA (mainly double-strand breaks) and sleep is linked to repair of damaged DNA, the authors propose that changes in DNA damage and repair might underlie the reduced need for sleep in the cavefish morphs relative to their surface-dwelling conspecifics.

      To fulfill their aim of exploring links between DNA damage, aging, and the evolution of sleep, the authors employ methods that are largely appropriate, and comparison of cavefish and surface fish morphs from the same species certainly provides a lens by which cellular, physiological and behavioural adaptations can be interrogated. Fluorescence and immunofluorescence are used to measure gut reactive oxygen species and markers of DNA damage and repair processes in the different fish morphs, and measurements of gene expression and protein levels are appropriately used. However, although the sleep tracking and quantification employed are quite well established, issues with the experimental design relate to attempts to link induced DNA damage to sleep regulation (outlined below). Moreover, although the methods used are appropriate for the study of the questions at hand, there are issues with the interpretation of the data and with these results being over-interpreted as evidence to support the paper's conclusions.

      This study shows that a marker of DNA repair molecular machinery that is recruited to DNA double-strand breaks (γH2AX) is elevated in brain cells of the cavefish relative to the surface fish and that reactive oxygen species are higher in most areas of the digestive tract of the cavefish than in that of the surface fish. As sleep deprivation has been previously linked to increases in both these parameters in other organisms (both vertebrates and invertebrates), their elevation in the cavefish morph is taken to indicate that the cavefish show signs of the physiological effects of chronic sleep deprivation.

      It has been suggested that induction of DNA damage can directly drive sleep behaviour, with a notable study describing both the induction of DNA damage and an increase in sleep/immobility in zebrafish (Danio rerio) larvae by exposure to UV radiation (Zada et al. 2021 doi:10.1016/j.molcel.2021.10.026). In the present study, an increase in sleep/immobility is induced in surface fish larvae by exposure to UV light, but there is no effect on behaviour in cavefish larvae. This finding is interpreted as representing a loss of a sleep-promoting response to DNA damage in the cavefish morph. However, induction of DNA damage is not measured in this experiment, so it is not certain if similar levels of DNA damage are induced in each group of intact larvae, nor how the amount of damage induced compares to the pre-existing levels of DNA damage in the cavefish versus the surface fish larvae. In both this study with A. mexicanus surface morphs and the previous experiments from Zada et al. in zebrafish, observed increases in immobility following UV radiation exposure are interpreted as following from UV-induced DNA damage. However, in interpreting these experiments it is important to note that the cavefish morphs are eyeless and blind. Intense UV radiation is aversive to fish, and it has previously been shown in zebrafish larvae that (at least some) behavioural responses to UV exposure depend on the presence of an intact retina and UV-sensitive cone photoreceptors (Guggiana-Nilo and Engert, 2016, doi:10.3389/fnbeh.2016.00160). It is premature to conclude that the lack of behavioural response to UV exposure in the cavefish is due to a different response to DNA damage, as their lack of eyes will likely inhibit a response to the UV stimulus.

      We believe that in A. mexicanus, like in zebrafish, it is highly unlikely that the effects of UV are mediated through visual processing. Even if this were the case, the timeframe of UV activation is very short compared to the time-scale of sleep measurements so this is unlikely to be a confound.

      Indeed, were the equivalent zebrafish experiment from Zada et al. to be repeated with mutant larvae fish lacking the retinal basis for UV detection it might be found that in this case too, the effects of UV on behaviour are dependent on visual function. Such a finding should prompt a reappraisal of the interpretation that UV exposure's effects on fish sleep/locomotor behaviour are mediated by DNA damage.

      We prefer not to comment on Zada et al, as that is a separate manuscript.

      An additional note, relating to both Lloyd, Xia, et al., and Zada et al., is that though increases in immobility are induced following UV exposure, in neither study have assays of sensory responsiveness been performed during this period. As a decrease in sensory responsiveness is a key behavioural criterion for defining sleep, it is, therefore, unclear that this post-UV behaviour is genuinely increased sleep as opposed to a stress-linked suppression of locomotion due to the intensely aversive UV stimulus.

      We understand this concern and are working on improved methodology for measuring sleep.  However, behavioral measurements are the standard for almost every manuscript that has studied sleep in zebrafish, flies, and worms to date. 

      The effects of UV exposure, in terms of causing damage to DNA, inducing DNA damage response and repair mechanisms, and in causing broader changes in gene expression are assessed in both surface and cavefish larvae, as well as in cell lines derived from these different morphs. Differences in the suite of DNA damage response mechanisms that are upregulated are shown to exist between surface fish and cavefish larvae, though at least some of this difference is likely to be due to differences in gene expression that may exist even without UV exposure (this is discussed further below).

      UV exposure induced DNA damage (as measured by levels of cyclobutene pyrimidine dimers) to a similar degree in cell lines derived from both surface fish and cave fish. However, γH2AX shows increased expression only in cells from the surface fish, suggesting induction of an increased DNA repair response in these surface morphs, corroborated by their cells' increased ability to repair damaged DNA constructs experimentally introduced to the cells in a subsequent experiment. This "host cell reactivation assay" is a very interesting assay for measuring DNA repair in cell lines, but the power of this approach might be enhanced by introducing these DNA constructs into larval neurons in vivo (perhaps by electroporation) and by tracking DNA repair in living animals. Indeed, in such a preparation, the relationship between DNA repair and sleep/wake state could be assayed.

      Comparing gene expression in tissues from young (here 1 year) and older (here 7-8 years) fish from both cavefish and surface fish morphs, the authors found that there are significant differences in the transcriptional profiles in brain and gut between young and old surface fish, but that for cavefish being 1 year old versus being 7-8 years old did not have a major effect on transcriptional profile. The authors take this as suggesting that there is a reduced transcriptional change occurring during aging and that the transcriptome of the cavefish is resistant to age-linked changes. This seems to be only one of the equally plausible interpretations of the results; it could also be the case that alterations in metabolic cellular and molecular mechanisms, and particularly in responses to DNA damage, in the cavefish mean that these fish adopt their "aged" transcriptome within the first year of life.

      This is indeed true.  However, one could also interpret this as a lack of aging.  If the profile does not change over time, the difference seems largely semantic.

      A major weakness of the study in its current form is the absence of sleep deprivation experiments to assay the effects of sleep loss on the cellular and molecular parameters in question. Without such experiments, the supposed link of sleep to the molecular, cellular, and "aging" phenotypes remains tenuous. Although the argument might be made that the cavefish represent a naturally "sleep-deprived" population, the cavefish in this study are not sleep-deprived, rather they are adapted to a condition of reduced sleep relative to fish from surface populations. Comparing the effects of depriving fish from each morph on markers of DNA damage and repair, gut reactive oxygen species, and gene expression will be necessary to solidify any proposed link of these phenotypes to sleep.

      We agree this would be beneficial.  We note that relatively few papers have sleep deprived fish.  While we done have this before in A. mexicanus the assay is less than ideal and likely induces generalizable stress.  We are working on adapting more recently developed methods in zebrafish.

      A second important aspect that limits the interpretability and impact of this study is the absence of information about circadian variations in the parameters measured. A relationship between circadian phase, light exposure, and DNA damage/repair mechanisms is known to exist in A. mexicanus and other teleosts, and differences exist between the cave and surface morphs in their phenomena (Beale et al. 2013, doi: 10.1038/ncomms3769). Although the present study mentions that their experiments do not align with these previous findings, they do not perform the appropriate experiments to determine if such a misalignment is genuine. Specifically, Beale et al. 2013 showed that white light exposure drove enhanced expression of DNA repair genes (including cpdp which is prominent in the current study) in both surface fish and cavefish morphs, but that the magnitude of this change was less in the cave fish because they maintained an elevated expression of these genes in the dark, whereas the darkness suppressed the expression of these genes in the surface fish. If such a phenomenon is present in the setting of the current study, this would likely be a significant confound for the UV-induced gene expression experiments in intact larvae, and undermine the interpretation of the results derived from these experiments: as samples are collected 90 minutes after the dark-light transition (ZT 1.5) it would be expected that both cavefish and surface fish larvae should have a clear induction of DNA repair genes (including cpdp) regardless of 90s of UV exposure. The data in Supplementary Figure 3 is not sufficient to discount this potentially serious confound, as for larvae there is only gene expression data for time points from ZT2 to ZT 14, with all of these time points being in the light phase and not capturing any dynamics that would occur at the most important timepoints from ZT0-ZT1.5, in the relevant period after dark-light transition. Indeed, an appropriate control for this experiment would involve frequent sampling at least across 48 hours to assess light-linked and developmentally-related changes in gene expression that would occur in 5-6dpf larvae of each morph independently of the exposure to UV.

      We agree that this would be useful, however, frequent sampling is not feasible given the experiments presented here and the challenges of working with an emerging model.

      On a broader point, given the effects of both circadian rhythm and lighting conditions that are thought to exist in A. mexicanus (e.g. Beale et al. 2013) experiments involving measurements of DNA damage and repair, gene expression, and reactive oxygen species, etc. at multiple times across >1 24 hour cycle, in both light-dark and constant illumination conditions (e.g. constant dark) would be needed to substantiate the authors' interpretation that their findings indicate consistently altered levels of these parameters in the cavefish relative to the surface fish. Most of the data in this study is taken at only single time points.

      Again, see comment above.  The goal was to identify whether there are differences in DNA Damage response between A. mexcicanus. Extending on this to examine interactions with the circadian system could be a useful path to pursue in the future.

      On a broader point, given the effects of both circadian rhythm and lighting conditions that are thought to exist in A. mexicanus (e.g. Beale et al. 2013) experiments involving measurements of DNA damage and repair, gene expression, and reactive oxygen species, etc. at multiple times across >1 24 hour cycle, in both light-dark and constant illumination conditions (e.g. constant dark) would be needed to substantiate the authors' interpretation that their findings indicate consistently altered levels of these parameters in the cavefish relative to the surface fish. Most of the data in this study is taken at only single time points.

      In summary, the authors show that there are differences in gene expression, activity of DNA damage response and repair pathways, response to UV radiation, and gut reactive oxygen species between the Pachón cavefish morph and the surface morph of Astyanax mexicanus. However, the data presented does not make the precise nature of these differences very clear, and the interpretation of the results appears to be overly strong. Furthermore, the evidence of a link between these morph-specific differences and sleep is unconvincing.

      In summary, the authors show that there are differences in gene expression, activity of DNA damage response and repair pathways, response to UV radiation, and gut reactive oxygen species between the Pachón cavefish morph and the surface morph of Astyanax mexicanus. However, the data presented does not make the precise nature of these differences very clear, and the interpretation of the results appears to be overly strong. Furthermore, the evidence of a link between these morph-specific differences and sleep is unconvincing.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      This paper contains what could be described as a "classic" approach towards evaluating a novel taste stimuli in an animal model, including standard behavioral tests (some with nerve transections), taste nerve physiology, and immunocytochemistry of the tongue. The stimulus being tested is ornithine, from a class of stimuli called "kokumi", which are stimuli that enhance other canonical tastes, increasing essentially the hedonic attributes of these other stimuli; the mechanism for ornithine detection is thought to be GPRC6A receptors expressed in taste cells. The authors showed evidence for this in an earlier paper with mice; this paper evaluates ornithine taste in a rat model.

      Strengths:

      The data show the effects of ornithine on taste: in two-bottle and briefer intake tests, adding ornithine results in a higher intake of most, but not all, stimuli tests. Bilateral nerve cuts or the addition of GPRC6A antagonists decrease this effect. Small effects of ornithine are shown in whole-nerve recordings.

      Weaknesses:

      The conclusion seems to be that the authors have found evidence for ornithine acting as a taste modifier through the GPRC6A receptor expressed on the anterior tongue. It is hard to separate their conclusions from the possibility that any effects are additive rather than modulatory. Animals did prefer ornithine to water when presented by itself. Additionally, the authors refer to evidence that ornithine is activating the T1R1-T1R3 amino acid taste receptor, possibly at higher concentrations than they use for most of the study, although this seems speculative. It is striking that the largest effects on taste are found with the other amino acid (umami) stimuli, leading to the possibility that these are largely synergistic effects taking place at the tas1r receptor heterodimer.

      We would like to thank Reviewer #1 for the valuable comments. Our basis for considering ornithine as a taste modifier stems from our observation that a low concentration of ornithine (1 mM), which does not elicit a preference on its own, enhances the preference for umami substances, sucrose, and soybean oil through the activation of the GPRC6A receptor. Notably, this receptor is not typically considered a taste receptor. The reviewer suggested that the enhancement of umami taste might be due to potentiation occurring at the TAS1R receptor heterodimer. However, we propose that a different mechanism may be at play, as an antagonist of GPRC6A almost completely abolished this enhancement. In the revised manuscript, we will endeavor to provide additional information on the role of ornithine as a taste modifier acting through the GPRC6A receptor.

      Reviewer #2 (Public review):

      Summary:

      The authors used rats to determine the receptor for a food-related perception (kokumi) that has been characterized in humans. They employ a combination of behavioral, electrophysiological, and immunohistochemical results to support their conclusion that ornithine-mediated kokumi effects are mediated by the GPRC6A receptor. They complemented the rat data with some human psychophysical data. I find the results intriguing, but believe that the authors overinterpret their data.

      Strengths:

      The authors examined a new and exciting taste enhancer (ornithine). They used a variety of experimental approaches in rats to document the impact of ornithine on taste preference and peripheral taste nerve recordings. Further, they provided evidence pointing to a potential receptor for ornithine.

      Weaknesses:

      The authors have not established that the rat is an appropriate model system for studying kokumi. Their measurements do not provide insight into any of the established effects of kokumi on human flavor perception. The small study on humans is difficult to compare to the rat study because the authors made completely different types of measurements. Thus, I think that the authors need to substantially scale back the scope of their interpretations. These weaknesses diminish the likely impact of the work on the field of flavor perception.

      We would like to thank Reviewer #2 for the valuable comments and suggestions. Regarding the question of whether the rat is an appropriate model system for studying kokumi, we have chosen this species for several reasons: it is readily available as a conventional experimental model for gustatory research; the calcium-sensing receptor (CaSR), known as the kokumi receptor, is expressed in taste bud cells; and prior research has demonstrated the use of rats in kokumi studies involving gamma Glu-Val-Gly (Yamamoto and Mizuta, Chem. Senses, 2022).

      We acknowledge that fundamentally different types of measurements were conducted in the human psychophysical study and the rat study. Kokumi can indeed be assessed and expressed in humans; however, we do not currently have the means to confirm that animals experience kokumi in the same way that humans do. Therefore, human studies are necessary to evaluate kokumi, a conceptual term denoting enhanced flavor, while animal studies are needed to explore the potential underlying mechanisms of kokumi. We believe that a combination of both human and animal studies is essential, as is the case with research on sugars. While sugars are known to elicit sweetness, it is unclear whether animals perceive sweetness identically to humans, even though they exhibit a strong preference for sugars. In the revised manuscript, we will incorporate additional information to address the comments raised by the reviewer. We will also carefully review and revise our previous statements to ensure accuracy and clarity.

      Reviewer #3 (Public review):

      Summary:

      In this study, the authors set out to investigate whether GPRC6A mediates kokumi taste initiated by the amino acid L-ornithine. They used Wistar rats, a standard laboratory strain, as the primary model and also performed an informative taste test in humans, in which miso soup was supplemented with various concentrations of L-ornithine. The findings are valuable and overall the evidence is solid. L-Ornithine should be considered to be a useful test substance in future studies of kokumi taste and the class C G protein-coupled receptor known as GPRC6A (C6A) along with its homolog, the calcium-sensing receptor (CaSR) should be considered candidate mediators of kokumi taste.

      Strengths:

      The overall experimental design is solid based on two bottle preference tests in rats. After determining the optimal concentration for L-Ornithine (1 mM) in the presence of MSG, it was added to various tastants, including inosine 5'-monophosphate; monosodium glutamate (MSG); mono-potassium glutamate (MPG); intralipos (a soybean oil emulsion); sucrose; sodium chloride (NaCl); citric acid and quinine hydrochloride. Robust effects of ornithine were observed in the cases of IMP, MSG, MPG, and sucrose, and little or no effects were observed in the cases of sodium chloride, citric acid, and quinine HCl. The researchers then focused on the preference for Ornithine-containing MSG solutions. The inclusion of the C6A inhibitors Calindol (0.3 mM but not 0.06 mM) or the gallate derivative EGCG (0.1 mM but not 0.03 mM) eliminated the preference for solutions that contained Ornithine in addition to MSG. The researchers next performed transections of the chord tympani nerves (with sham operation controls) in anesthetized rats to identify the role of the chorda tympani branches of the facial nerves (cranial nerve VII) in the preference for Ornithine-containing MSG solutions. This finding implicates the anterior half-two thirds of the tongue in ornithine-induced kokumi taste. They then used electrical recordings from intact chorda tympani nerves in anesthetized rats to demonstrate that ornithine enhanced MSG-induced responses following the application of tastants to the anterior surface of the tongue. They went on to show that this enhanced response was insensitive to amiloride, selected to inhibit 'salt tastant' responses mediated by the epithelial Na+ channel, but eliminated by Calindol. Finally, they performed immunohistochemistry on sections of rat tongue demonstrating C6A positive spindle-shaped cells in fungiform papillae that partially overlapped in its distribution with the IP3 type-3 receptor, used as a marker of Type-II cells, but not with (i) gustducin, the G protein partner of Tas1 receptors (T1Rs), used as a marker of a subset of type-II cells; or (ii) 5-HT (serotonin) and Synaptosome-associated protein 25 kDa (SNAP-25) used as markers of Type-III cells.

      Weaknesses:

      The researchers undertook what turned out to be largely confirmatory studies in rats with respect to their previously published work on Ornithine and C6A in mice (Mizuta et al Nutrients 2021).

      The authors point out that animal models pose some difficulties of interpretation in studies of taste and raise the possibility in the Discussion that umami substances may enhance the taste response to ornithine (Line 271, Page 9). They miss an opportunity to outline the experimental results from the study that favor their preferred interpretation that ornithine is a taste enhancer rather than a tastant.

      At least two other receptors in addition to C6A might mediate taste responses to ornithine: (i) the CaSR, which binds and responds to multiple L-amino acids (Conigrave et al, PNAS 2000), and which has been previously reported to mediate kokumi taste (Ohsu et al., JBC 2010) as well as responses to Ornithine (Shin et al., Cell Signaling 2020); and (ii) T1R1/T1R3 heterodimers which also respond to L-amino acids and exhibit enhanced responses to IMP (Nelson et al., Nature 2001). While the experimental results as a whole favor the authors' interpretation that C6A mediates the Ornithine responses, they do not make clear either the nature of the 'receptor identification problem' in the Introduction or the way in which they approached that problem in the Results and Discussion sections. It would be helpful to show that a specific inhibitor of the CaSR failed to block the ornithine response. In addition, while they showed that C6A-positive cells were clearly distinct from gustducin-positive, and thus T1R-positive cells, they missed an opportunity to clearly differentiate C6A-expressing taste cells and CaSR-expressing taste cells in the rat tongue sections.

      It would have been helpful to include a positive control kokumi substance in the two-bottle preference experiment (e.g., one of the known gamma-glutamyl peptides such as gamma-glu-Val-Gly or glutathione), to compare the relative potencies of the control kokumi compound and Ornithine, and to compare the sensitivities of the two responses to C6A and CaSR inhibitors.

      The results demonstrate that enhancement of the chorda tympani nerve response to MSG occurs at substantially greater Ornithine concentrations (10 and 30 mM) than were required to observe differences in the two bottle preference experiments (1.0 mM; Figure 2). The discrepancy requires careful discussion and if necessary further experiments using the two-bottle preference format.

      We would like to thank Reviewer #3 for the valuable comments and helpful suggestions. We propose that ornithine has two stimulatory actions: one acting on GPRC6A, particularly at lower concentrations, and another on amino acid receptors such as T1R1/T1R3 at higher concentrations. Consequently, ornithine is not preferable at lower concentrations but becomes preferable at higher concentrations. For our study on kokumi, we used a low concentration (1 mM) of ornithine. The possibility mentioned in the Discussion that 'the umami substances may enhance the taste response to ornithine' is entirely speculative. We will reconsider including this description in the revised version. As the reviewer suggested, in addition to GPRC6A, ornithine may bind to CaSR and/or T1R1/T1R3 heterodimers. However, we believe that ornithine mainly binds to GPRC6A, as a specific inhibitor of this receptor almost completely abolished the enhanced response to umami substances, and our immunohistochemical study indicated that GPRC6A-expressing taste cells are distinct from CaSR-expressing taste cells (see Supplemental Fig. 3). We conducted essentially the same experiments using gamma-Glu-Val-Gly in Wistar rats (Yamamoto and Mizuta, Chem. Senses, 2022) and compared the results in the Discussion. The reviewer may have misunderstood the chorda tympani results: we added the same concentration (1 mM) used in the two-bottle preference test to MSG (Fig. 5-B). Fig. 5-A shows nerve responses to five concentrations of plain ornithine. In the revised manuscript, we will strive to provide more precise information reflecting the reviewer’s comments.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The behavioral effects found with the CPRC6A antagonists are not entirely convincing, as the antagonist is seemingly just mixed up in the solution with the stimuli. There are no control experiments demonstrating that the antagonists do not have a taste themselves.

      We mixed the antagonists into both liquids used in the two-bottle preference test to eliminate any potential taste effects of the antagonists themselves. In the electrophysiological experiments, the antagonist was incorporated into the solution after confirming that it did not elicit any appreciable response in the taste nerve.

      (2) The effects of ornithine found with quinine did not have a satisfying explanation - if there is some taste cell-taste cell modulation that accounts for the taste enhancement, why is the quinine less aversive? Why is it not enhanced like the other compounds?

      The effects of ornithine on quinine responses remain difficult to explain. A previous study (Tokuyama et al., Chem Pharm Bull, 2006) proposed that ornithine prevents bitter substances from binding to bitter receptors, although this hypothesis lacks definitive evidence. In the present study, our findings suggest that the binding of quinine to bitter receptors is essential, as another agonist, gallate, also enhanced the preference for quinine, but this effect was abolished by EGCG, a GPRC6A antagonist (see Supplemental Fig. 2).

      (3) Unless I am missing something, there appears to be no quantitative analysis of the immunocytochemical data, just assertions.

      We have made quantitative analyses in the revised text, and the following sentences have been added: “Approximately 11% of GPRC6A-positive cells overlapped with IP3R3 (9 double-positive cells/80 GPRC6A-positive cells), while approximately 8.3% of IP3R3-positive cells expressed GPRC6A (9 double-positive /109 IP3R3-positive cells). In addition, GPRC6A-positive cells were unlikely to colocalize with a-gustducin, another marker for a subset of type II cells, in single taste cells (0 double-positive cell/93 GPRC6A-positive cells). Regarding type III cell markers, GPRC6A-positive cells were unlikely to colocalize with 5-HT in single taste cells (0 double-positive cell/75 GPRC6A-positive cells).”

      (4) The hallmarks of Kokumi taste include descriptors such as "thickness", and "mouthfeel", which sound like potential somatosensory attributes. Perhaps the authors should consider this possibility for at least some of the effects found.

      The term kokumi, a Japanese word, refers to a phenomenon in which the flavor of complexly composed food is enhanced through certain processes, making them more delicious. To date, kokumi has been described using the representative terms thickness, mouthfulness, and continuity, originally introduced in the first paper on kokumi by Ueda et al. (1990). However, these terms are derived from Japanese and may not fully convey the nuances of the original language when translated into these simple English words. In particular, thickness is often interpreted as referring to physical properties such as viscosity or somatosensory sensations. Since kokumi inherently lacks somatosensory elements, this revised paper adopts alternative terms and explanations for the three components of kokumi to prevent misunderstanding and confusion.

      Therefore, to clarify that kokumi attributes are inherently gustatory, thickness is replaced with intensity of whole complex tastes (rich flavor with complex tastes), emphasizing the synergistic effects of a variety of tastes rather than the mere enhancement of a single flavor. Mouthfulness is clarified as not referring to mouthfeel (the tactile sensation a food gives in the mouth) but rather as spread of taste and flavor throughout the oral cavity, describing how the flavor fills the mouth. Continuity is replaced with persistence of taste (lingering flavor).

      (5) I don't think the human experiment (S1) belongs to the paper, even as a supplementary bit of data. It's only 17 subjects, they are all female, and we don't know anything about how they were selected, even though it states they are all students/staff at Kio. Were any of them lab members? Were they aware of the goals of the experiment? Could simply increasing the amount of solute in the soup make it seem thicker? This (sparse) data seems to have been shoehorned into the paper without enough detail/justification.

      Despite the reviewer’s suggestion, we would like to include the human experiment because the rationale of the present study is to confirm, through a human sensory test, that the kokumi of a complex solution (in this case, miso soup) is enhanced by the addition of ornithine. This is followed by basic animal experiments to investigate the underlying mechanisms. Therefore, this human study serves an important role.

      The total number of participants increased to 22 (19 women and three men) following an additional experiment with 5 new participants. New results have been shown in Supplemental Figure 1 with statistical analyses. The rewritten parts are as follows:

      We recruited 22 participants (19 women and three men, aged 21-28 years) from Kio University who were not affiliated with our laboratory, including students and staff members. All participants passed a screening test based on taste sensitivity. According to the responses obtained from a pre-experimental questionnaire, we confirmed that none of the participants had any sensory abnormalities, eating disorders, or mental disorders, or were taking any medications that may potentially affect their sense of taste. All participants were instructed not to eat or drink anything for 1 hour prior to the start of the experiment. We provided them with a detailed explanation of the experimental procedures, including safety measures and personal data protection, without revealing the specific goals of the study.

      (6) The introduction could be more concise - for example, when describing Kokumi stimuli such as ornithine and its possible receptors, the authors do not need to add the detail about how this stimulus was deduced from adding clams to the soup. Details like this can be reserved for the discussion.

      Thank you for this comment. We have tried to shorten the Introduction.

      (7) Line 86: awkward phrasing - this doesn't need to be a rhetorical question.

      We have deleted the sentence.

      (8) Supplementary Figure 1: The labels on the figure say "Miso soup in 1 mM Orn" when the Orn is dissolved into the soup.

      Thank you for pointing out our mistake. We have changed the description, such as “1 mM Orn in miso soup”.

      Reviewer #2 (Recommendations for the authors):

      Major concerns

      (1) The impact of "kokumi" taste ligands on food perception appears to be profound in humans. This observation is fascinating because it implies that molecules like ornithine impact a variety of flavor perceptions, some of which are non-gustatory in nature (e.g., spread, mouthfulness and harmony). What remains unclear is whether "kokumi" ligands produce analogous sensations in rodents. If they don't, then rodents are an inappropriate model system for studying the impact of kokumi on flavor perceptions. The authors fail to address this key issue, and uncritically assume that kokumi ligands produce sensations like thickness, mouthfulness, and continuity in rodents. For this reason, the authors' reference to GPRC6A as a kokumi receptor is inappropriate.

      Thank you very much for the valuable comments. The term kokumi refers to a phenomenon in which the flavor of complexly composed foods is enhanced through certain processes, making them more delicious. It is an important concept in the field of food science, which studies how to make prepared dishes more enjoyable. Kokumi is also considered a higher-order, profound cognitive function evaluated by humans who experience a wide variety of foods. However, it is unclear whether animals, particularly experimental animals, can perceive kokumi in the same way humans do.

      To date, kokumi has been described using the representative terms thickness, mouthfulness, and continuity, originally introduced in the first paper on kokumi by Ueda et al. (1990). However, these terms are derived from Japanese and may not fully convey the nuances of the original language when translated into these simple English words. In particular, thickness is often interpreted as referring to physical properties such as viscosity or somatosensory sensations. Since kokumi inherently lacks somatosensory elements, this revised paper adopts alternative terms and explanations for the three components of kokumi to prevent misunderstanding and confusion.

      Therefore, to clarify that kokumi attributes are inherently gustatory, thickness is replaced with intensity of whole complex tastes (rich flavor with complex tastes), emphasizing the synergistic effects of a variety of tastes rather than the mere enhancement of a single flavor. Mouthfulness is clarified as not referring to mouthfeel (the tactile sensation a food gives in the mouth) but rather as spread of taste and flavor throughout the oral cavity, describing how the flavor fills the mouth. Continuity is replaced with persistence of taste (lingering flavor).

      Rodents are thought to possess basic taste functions similar to humans, such as the expression of taste receptors, including kokumi receptors, in taste cells. Regardless of whether rodents can perceive kokumi, findings from studies on rodents may provide insights into aspects of the kokumi concept as experienced by humans.

      Indeed, the results of this study indicate that ornithine enhances umami, sweetness, fat taste, and saltiness, leading to the enhancement of complex flavors—referred to as intensity of whole taste. The activation of various taste cells, resulting in the enhancement of multiple tastes, may contribute to the sensation of flavors spreading throughout the oral cavity. Furthermore, the strong enhancement of MSG and MPG suggests that glutamate contributes to the mouthfulness and persistence of taste characteristic of kokumi.

      (2) A related concern is that the authors did not make any measurements that model kokumi sensations documented in the literature. For example, they would need to develop behavioral/electrophysiological measurements that reflect the known effects of kokumi ligands on flavor perception (i.e., increases in intensity, spread, continuity, richness, harmony, and punch). For example, ornithine is thought to produce more "punch" (i.e., a more rapid rise in intensity). This could be manifested as a more rapid rise in peripheral taste response or a more rapid fMRI response in the taste cortex. Alternatively, ornithine is thought to increase "continuity" (i.e., make the taste response more persistent). This response would presumably be manifested as a peripheral taste response that adapts more slowly or a more persistent fMRI response. As it stands, the authors have documented that ornithine increases (i) the preference of rats for some chemical stimuli, but not others; and (ii) the response of the CT nerve to some but not all taste stimuli.

      In animal experiments, it is challenging to examine each attribute of kokumi. The increase of complex tastes can be investigated through behavioral experiments and neural activity recordings. However, phenomena such as spread or harmony, which arise from profound human judgments, are difficult to validate in animal studies.

      While it was possible to examine persistence through neural responses to tastants, all stimuli were rinsed at 30 seconds after onset of stimulation, so the exact duration of persistence was not investigated. However, since the MSG response was enhanced approximately 1.5 times with the addition of ornithine, it is strongly suggested that the duration might also have been prolonged.

      Regarding punch, no differences were observed in the neural responses when ornithine was added, likely because the phasic response already had a rapid onset.

      In the context of fMRI studies, there has been a report that adding glutathione to mixtures of umami and salt solutions increases responses (Goto et al. Chem Senses, 2016). However, research specifically examining the attributes of kokumi has not yet been reported.

      (3) The quality of the SNAP-25 immunohistochemistry is poor (see Figure 7D), with lots of seemingly nonspecific staining in and outside the taste bud.

      The quality of the SNAP-25 is not poor. It is known that SNAP-25 labels not only type III cells but also the dense network of intragemmal nerve fibers (Tizzano et al., Immunohistochemical Analysis of Human Vallate Taste Buds. Chem Senses.40:655-60, 2015). Therefore, lots of seemingly nonspecific staining is due to intense SNAP-25-immunoreactivity of the nerve fibers.

      (4) The authors need to drastically scale back the scope of their conclusions. What they can say is that ornithine appears to enhance the taste responses of rats to a variety of taste stimuli and that this effect appears to be mediated by the GPRC6A receptor. They cannot use their data to address kokumi effects in humans, as they have not attempted to model any of these effects. Given the known problems with pharmacological blocking agents (e.g., nonspecificity), the authors would significantly strengthen their case if they could generate similar results in a GPRC6A knockout mouse.

      Our research approach begins with confirming in humans that the addition of ornithine to complex foods (such as miso soup) induces kokumi. Based on this confirmation, we conduct fundamental studies using animal models to investigate the peripheral taste mechanisms underlying the expression of kokumi.

      It is possible that the key to kokumi expression lies in the enhancement of desirable tastes (particularly umami) and the suppression of unpleasant tastes. Moving forward, we will deepen our fundamental research on the action of ornithine mediated through GPRC6A, including studies using knockout mice.

      (5) The introduction is too long. Much of the discussion of kokumi perception in humans should either be removed or shortened considerably.

      Following the reviewer’s suggestion, the introduction has been shortened.

      (6) I recommend that the authors break up the Methods and Results sections into different experiments. This would enable the authors to provide separate rationales for each procedure. For instance, the authors conducted a variety of different behavioral procedures (e.g., long- and short-term preference tests, and preference tests with and without GPRC6A receptor antagonists).

      Rather than following the reviewer’s suggestion, we have added subheadings to describe the purpose of each experiment. This approach would help readers better understand the experimental flow, as each experiment is relatively straightforward.

      (7) The inclusion of the human data is odd for two reasons. First, the measurements used to assess the impact of ornithine on flavor perception in humans were totally different than those used in rats. This makes it impossible to compare the human and rat datasets. Second, the human study was rather limited in scope, had small effect sizes, and had a lot of individual variation. For these reasons, the human data are not terribly helpful. I recommend that the authors remove the human data from this paper, and publish them as part of a more extensive study on humans.

      Despite the reviewer’s suggestion, we would like to include the human experiment because the rationale of the present study is to confirm, through a human sensory test, that the kokumi of a complex solution (in this case, miso soup) is enhanced by the addition of ornithine. This is followed by basic animal experiments to investigate the underlying mechanisms. Therefore, this human study serves an important role. The considerable variation in the scores suggests that evaluating the three kokumi attributes is challenging and likely influenced by differences in judgment criteria among participants.

      The total number of participants increased to 22 (19 women and three men) following an additional experiment with 5 new participants. New results have been shown in Supplemental Figure 1 with statistical analyses. The rewritten parts are as follows:

      We recruited 22 participants (19 women and three men, aged 21-28 years) from Kio University who were not affiliated with our laboratory, including students and staff members. All participants passed a screening test based on taste sensitivity. According to the responses obtained from a pre-experimental questionnaire, we confirmed that none of the participants had any sensory abnormalities, eating disorders, or mental disorders, or were taking any medications that may potentially affect their sense of taste. All participants were instructed not to eat or drink anything for 1 hour prior to the start of the experiment. We provided them with a detailed explanation of the experimental procedures, including safety measures and personal data protection, without revealing the specific goals of the study.

      (8) While the use of English is generally good, there are many instances where the English is a bit awkward. I recommend that the authors ask a native English speaker to edit the text.

      Thank you for this comment. The text has been edited by a native English speaker.

      Minor concerns

      (1) Lines 13-14: The authors state that "the concept of 'kokumi' has garnered significant attention in gustatory physiology and food science." This is an exaggeration. Kokumi has generated considerable interest in food science but has yet to generate much interest in gustatory physiology.

      We have rewritten this part: “The concept of “kokumi” has generated considerable interest in food science but kokumi has not been well studied in gustatory physiology.”

      (2) Line 20: The use of "specific taste" is unclear in this context. The authors indicate (in Figure 5A) that 1 mM ornithine generates a CT nerve response. They also reveal (in Figure 1A) that rats do not prefer 1 mM ornithine over water. The results from a preference test do not provide insight into whether a solution can be tasted; they merely demonstrate a lack of preference for that solution. Based on these data, the authors cannot infer that 1 mM ornithine cannot be tasted.

      We agree with the reviewer’s comment. Ornithine at 1 mM concentration may have a weak taste because this solution elicited a small neural response (Fig. 5-A). We have rewritten the text: “… at a concentration without preference for this solution.”

      (3) Line 44: Sensory information from foods enters the oral and the nasal cavity.

      The nasal cavity has been added.

      (5) Lines 59: The terms "thickness", "mouthfulness" and "continuity" are not intuitive in English, and may reflect, at least in part, a failure in translation. The word thickness implies a tactile sensation (e.g., owing to high viscosity), but the authors use it to indicate a flavor that is more intense and onsets more quickly. The word mouthfulness is supposed to indicate that a flavor is experienced throughout the oral cavity. The problem here is that this happens with all tastants, independent of the presence of substances like ornithine. Indeed, taste buds occur in a limited portion of the oral epithelium, but we nevertheless experience tastes throughout the oral cavity, owing to a phenomenon called tactile referral (see the following reference: Todrank and Bartoshuk, 1991, A taste illusion: taste sensation localized by touch" Physiology & Behavior 50:1027-1031). The word continuity does not imply that the taste is long-lasting or persistent.

      These three attributes were originally introduced by Ueda et al. (1990), who translated Japanese terms describing the profound characteristics of kokumi, which are deeply rooted in Japanese culinary culture. However, these simply translated terms have caused global misunderstanding and confusion, because they sound like somatosensory rather than gustatory descriptions. Therefore, to clarify that kokumi attributes are inherently gustatory, in the revised version we use the terms “intensity of whole complex tastes (rich flavor with complex tastes)” instead of thickness, “mouthfulness (spread of taste and flavor throughout the oral cavity),” and “persistence of taste (lingering flavor)” instead of continuity.

      The results of this study indicate that ornithine enhances umami, sweetness, fat taste, and saltiness, leading to the enhancement of complex flavors—referred to as intensity of whole taste. The activation of various taste cells, resulting in the enhancement of multiple tastes, may contribute to the sensation of flavors spreading throughout the oral cavity. Furthermore, the strong enhancement of MSG and MPG suggests that glutamate contributes to the mouthfulness and persistence of taste characteristic of kokumi.

      (6) Figure legends: The authors provide results of statistical comparisons in several of the figures. They need to explain what statistical procedures were performed. As it stands, it is impossible to interpret the asterisks provided.

      We have explained statistical procedures in each Figure legend.

      (7) I did not see any reference to the sources of funding or any mention of potential conflicts of interest.

      We have added the following information:

      Funding: JSPS KAKENHI Grant Numbers JP17K00935 (to TY) and JP22K11803(to KU).

      Declaration of interests: The authors declare that they have no competing interests.

      Reviewer #3 (Recommendations for the authors):

      (1) I suggest that the authors increase their level of interest in glutathione and gamma-glutamyl peptides. This might include an appropriate gamma-glutamyl control substance in the two-bottle preference study (see Public Review). It might also include more careful attention to the work that identified glutathione as an activator of the CaSR (Wang et al., JBC 2006) and the nature of its binding site on the CaSR which overlaps with its site for L-amino acids (Broadhead et al., JBC 2011). This latter article also identified S-methyl glutathione, in which the free-SH group is blocked, as a high-potency activator of the CaSR. It would be expected to show comparable potency to gamma-glu-Val-Gly in assays of kokumi taste.

      We have appropriately referenced glutathione and gamma-Glu-Val-Gly, potent agonists of CaSR, where necessary. In our previous study (Yamamoto and Mizuta, Chem Senses, 2022), we examined the additive effects of these substances on basic taste stimuli in rodents, and the results were compared in greater detail with those obtained from the addition of ornithine in the present study. We have also discussed the potential binding of ornithine to other receptors, including CaSR and T1R1/T1R3 heterodimers.

      (2) Figures:

      -None of the figures were labelled with their Figure numbers. I have inferred the Figure numbers from the legends and their positions in the pdf.

      We are sorry for this inconvenience.

      - The labelling of Figure 1 and Figure 2 are problematic. In Figure 1 it should be made clear that the horizontal axes refer to the Ornithine concentration. In Figure 2 it should be made clear that the horizontal axes refer to the tastant concentrations (MSG, IMP, etc) and that the Ornithine concentrations were fixed at either zero or 1.0 mM.

      We are sorry for the lack of information about the horizontal axes. We have explained the horizontal axes in figure legends in Figs. 1 and 2. The labelling of both figures has also been modified to make this clear.

      - Figure 3B: 'Control' should appear at the top of this panel since the panels that follow all refer to it.

      Following the reviewer’s suggestion, we have added ‘Control’ at the top of Figure 3B.

      - Figure 5A. Provide a label for the test substance, presumably Ornithine.

      Yes, we have added ‘Ornithine’.

      - Figure 7 would be strengthened by the inclusion of immunohistochemistry analyses of the CaSR.

      We are sorry that we did not analyze immunohistochemistry for the CaSR because a previous study precisely had analyzed the CaSR expression on taste cells in rats. We have analyzed co-expression of GPRC6A and CaSR (see Supplemental Figure 3).

      (3) Other Matters:

      - Line 38: list the five basic taste modalities here.

      Yes, we have included the five basic taste modalities here.

      - Line 107: 'even if ... kokumi ... is less developed in rodents' - if there is evidence that kokumi is less developed in rodents it should be cited here.

      We cannot cite any references here because no studies have compared the perception of kokumi between humans and rodents.

      - Line 308: 'recently we conducted experiments in rats using gallate ...' - the authors appear to imply that they performed the research in Reference 43, however, I was unable to find an overlap between the two lists of authors.

      We are not doing a similar study as the research in Reference 43 (40 in the revised paper). Following the result that gallate is an agonist of GPRC6A as shown by Reference 43, we were interested in doing similar behavioral experiments using gallate instead of ornithine.

      The sentences have been rewritten to avoid misunderstanding.

      - Line 506: the sections are said to be 20 mm thick - should this read 20 micrometers?

      Thank you. We have changed to 20 micrometers.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Serra et al have conducted transcriptomic analyses for thalamic Sox2 and Nr2f1 cKO mice, revealing gene regulatory networks underlying development and functions of dLGN which plays pivotal roles in visual sensation. The findings are also potentially important for understanding vision disability in human. Their conclusions are mostly supported by the data, but some reinforcement and additional explanations may further improve the paper.

      *We thank the reviewer for their appreciation of our work, and the constructive comments.

      *

      Major points:

      1. Although they showed that Sox2 does not regulate Nr2f1 by immunostaining in Fig.1, it would be reinforced by the RNA-seq results. What about evidence for regulation of Sox2 by Nr2f1? I could not find.

      *We have now highlighted, in Fig.1D, the requested RNAseq results from Table S1, showing a very limited reduction of expression of Nr2f1 in Sox2 mutant and of Sox2 in Nr2f1 mutants. We further added ISH results confirming this data (Fig. 4A). *

      The onset of and specificity among the thalamic nuclei of Sox2 and Nr2f1 expression would better be mentioned in the beginning. As far as I remember, both genes are quite widely expressed in the thalamic nuclei, not necessarily specific to dLGN.

      We previously reported in Mercurio et al 2019 (ref. 7) that Sox2 is highly expressed in the dorsal thalamus (precursor to the sensory thalamic nuclei) at least from E15.5 and is later expressed in all the sensory thalamic nuclei, though not in surrounding regions (Mercurio et al 2019 Fig.1). A similar expression pattern was previously reported for Nr2f1 in Chou et al 2013 (ref. 6). A brief mention of this point is now present in Introduction.

      Mechanistically, how Sox2 function becomes distinct in neural stem cells and neurons would be of a great interest (e.g., changes in binding partner). But, it might be too much for the present package.

      *We agree on the interest of this point. We note that SOX2 binding sites in neurons (but not in stem cells), as detected by CUT&RUN, are enriched for SOX2 and RORA/NRF binding sites. The co-presence of SOX and NRF potential binding motifs (Fig. 2F-G), suggests the possibility of direct physical interaction between SOX2 and NR2F1 mediating joint binding to DNA. This is interesting and will be experimentally addressed in a follow up study. *

      Minor points: 1. Explanation for the values in Fig.3A in the text or the figure legend would be helpful for readers unfamiliar with MuSiC.

      We clarified the figure legend, better explaining how the plotted were computed and their meaning.

      Since Ror-alpha is also expressed layer 4 in the cortex, some explanations for these phenotypes being caused by thalamic defects may be provided. I know that expression of Sox2 and Ror-alpha do not overlap in layer 4, though.

      *In fact, we propose that downregulation of RORa in layer 4 maybe caused by reduced thalamic afferents to layer 4, possibly also acting through a reduced delivery of VGF to the cortex; in fact, as the reviewer correctly states Sox2 itself is not expressed in the cortex. *

      Why did the authors use two types of Sox2 antibodies in Fig.4A?

      We strive to replicate our CUT&RUN data such that we can rely only on the reproducible binding events. We have often noted that – being CUT&RUN a “challenging” application for antibodies – different antibodies yield non-fully overlapping binding profiles. While we do not have a clear explanation for this, we consider more robust converging on those binding events that are obtained by two independent antibodies, when such tools are available. This, in our opinion and experience, drastically decreases the chance of stumbling upon false positive hits.

      Quatification for Fig.1A, Fig.2A and 2B may be necessary for the current publication standards.

      The requested quantification has been added in Fig. S1A and in Fig. 4C.

      In Introduction, NRF1 or NRF is somewhat confusing because there is a different gene named NRF (Nuclear respiratory factor).

      *We corrected this. *

      Reference 14 is identical to 44.

      *We corrected this. *

      Reviewer #1 (Significance (Required)):

      This work provides a basis of gene regulatory network involved in development and function of dLGN neurons, which may also be important for understanding mechanisms of vision disability in human caused by genetic mutations. Although I am not an expert in this particular field (GRNs in thalamic neurons), a series of the authors' works certainly establish a molecular basis of the roles of Sox2 ranging from neural stem/progenitor cells to neurons. Limitations of the current study in my opinion would be that it only lists up candidate genes for the functions or cause of visual sensations or defects, and thus experimental proof awaits actual biological experiments. Although the results and conclusion provided by the authors are reasonable and convincing, conceptual advance may be limited to some extent. Readers in both basic and clinical researches will be interested in that vision disability caused by mutations in Sox2 and Nr2f1 could be explained by synapse-related genes, axon guidance molecules, or secreting factors like VGF, albeit not with big surprise. My research expertise would be in the field of brain development, particularly in regionalization and morphogenesis of the brain. Yet, I am not particularly familiar with transcriptomic analyses in general.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In the current manuscript, Serra, Mercurio, and colleagues carried out Ror-alpha-Cre specific conditional mutant analysis of Sox2 and Nr2f1 in the thalamus/dLGN. The workflow primarily focused on potential mechanisms underlying transcriptional regulation. With RNA-Seq, the authors identified multiple "common" targets shared by both Sox2 and Nr2f1 factors. In parallel, the authors also carried out CUT-RUN analysis for Sox2 binding patterns in dLGN chromatin.

      The current work is built upon the intellectual framework of two papers: the past work led by the senior author in 2019, as well as an earlier work by Chou /O'Leary 2013, in terms of genetic reagents and anatomical and functional analysis. While the newly performed experiments may open some new avenues for future investigation, the current manuscript did NOT vigorously validate bioinformatics predictions using experimental approaches. The current dataset did NOT present any functional and anatomical analysis, esp. in terms of the target gene functions back to the same circuits/connections (thalamus-cortex). The manuscript presented in the current format offers limited biological insights into the neurobiology of dLGN. The limited experimental data also indicated that the manuscript may not be suitable for a very general readership.

      We thank the reviewer for pointing out contributions as well as limitations of our work. We are convinced that our work does indeed open up " new avenues for future investigation", reporting for the first time hundreds of targets of SOX2 and NR2F1 as well as hundreds of direct SOX2 binding sites in dLGN neurons that will contribute to future investigations.

      Major points: 1. Unless I missed anything - I was not sure why the current Figure 1/ Tables 1&2 took a sharp pause without any in situ/histochemical validations of the "prominent" downstream targets - at minimum, the authors should validate the common targets, including VGF among others;

      We now validated the downregulation VGF and Sox5 at the RNA level by ISH confirming SOX5 downregulation by IF. These data are presented in the new Fig. 4, in results page 5 and discussion page 7.

      Could the over-expression of any targets (Sox5, etc) reverse the loss of Sox2-phenotypes, esp. in terms of the establishment of thalamic-cortical connections, as assayed by Fig 2A (as well as Mercurio, 2019, Figure4)? Having such an assay would significantly boost the significance of the current study.

      The experiment suggested by the reviewer would undoubtly be interesting to address Sox5 contribution to the mutant phenotype; unfortunately, this is too demanding for the present paper.

      However, for the sake of data interpretation, we propose that the mutant phenotypes observed rather result from the global deregulation of a set of genes, not just of a single gene. Indeed, we discuss the potential contribution of several different genes, among those co-regulated by SOX2 and NR2F1. From this point of view, we don't necessarily expect the contribution of a specific gene to be prominent. In fact, we believe an interesting result emerging from our work is the identification of a rather numerous set of genes collectively responding to both Sox2 and Nr2f1 mutation, many of which may contribute to the shared phenotypes of the two mutants.

      Figure 3 is presented in a very inconvenient manner for any reviewers/future readers to understand and interpret. The plots in B and C are what matter the most, while the raw data in 3A could be included in a table. The presentation and comparison of this figure need some significant work.

      We have now modified Fig. 3 as requested and moved the raw data to the Supplementary material (Table S4).

      The Cut-n-Run assays offered several dLGN unique (non-neurogenesis) targets. However, the study paused at bioinformatics prediction without experimental validations as well, including the dLGN peaks near Vgf and Sox5.

      We are not sure we understand the reviewer's question. The " dLGN unique (non-neurogenesis) targets" that we report are not the results of a bioinformatics prediction, but of the CUT&RUN experiment itself including the dLGN peaks near Vgf and Sox5. In addition, we experimentally validated the downregulation of Vgf and Sox5 by in situ hybridization in the new Figure 4.

      Minor points: For general readers, (1) please explicitly document whether Ror-alpha-Cre does NOT(?) impact the retina and cortex;

      This is now mentioned in results in agreement with the results in Chou et al. 2013 and Mercurio et al. 2019.

      Chou et al mentions explicitly absence of Rora Cre activity in the cortex and this is also in agreement with our own results in Mercurio et al. 2019. As to the retina, we reported not observing any retinal phenotypes in Sox2 mutants in agreement with the absence of any Sox2 deletion within the retina, that would have caused a drastic phenotype as reported in Taranova et al. 2006.

      (2) please explain when Ror-alpha-Cre expression timing - is it solely post-mitotic in the dLGN? The authors may have taken these for granted, esp. given Mercurio 2019 and Chou 2013, but such information may help readers outside the field.

      The onset of Rora Cre activity is at a stage in which dLGN neurogenesis is completed and most if not all cells are postmitotic as reported in Chou et al. 2013. This point is now more explicitly mentioned in results.

      Reviewer #2 (Significance (Required)):

      The manuscript offers limited new information to general readers. It might be a good dataset for researchers specialized in transcriptional regulation in terms of finding useful/relevant information to design future experiments. However, the study did NOT offer any histological and functional assays based on bioinformatics tests.

      • General assessment: The strengths were a careful analysis of dLGN in early development using both RNA-Seq and Cut-n-Run with a focus on Sox2's post-mitotic role. The limitations were that the study was lack of histological validations and functional tests of the candidate genes.

      We now added histological validation of selected targets as requested in the new Fig. 4.

      • Advance: The advance of the study is limited, though the experiments were carefully launched.

      • Audience: Very limited audience with a specialty in transcription factors in visual system development.

      The reviewer is an expert in neurodevelopment using the mouse genetics approach, with primary interests in studying the retina and retino-recipient zone development.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary:

      This manuscript investigates the role of Sox2 and Nr2f1 on dLGN development. The authors perform RNA-seq on thalamus-specific conditional knock outs of Sox2 and Nr2f1. The author compile lists of the genes that showed the greatest change in detection between control mice (3 and 3) and mutant mice (3 and 3). The authors find significant overlap in the lists of genes most altered in the mutants and argue that this overlap is consistent with the two transcription factors regulating the same gene network. The authors also perform a CUT&RUN analysis of Sox2 binding sites and find overlap in the list of genes that Sox2 binds to and the genes with altered expression levels in the Sox2-cKO. Regulation of neuron-specific cellular components are highly represented in both the list of binding sites and genes with altered expression levels.

      The RNA-seq data and binding site data are valuable resources for researchers trying to understand the development of the dLGN and should be published. However, I am not confident that author's interpretations of their data are supported by what is provided in the manuscript.

      Major comments:

      Issues with the statistical logic

      -Lack of statistical significance is not evidence of equality. The fact that Sox2 and Nr2f1 do not pass the FDR threshold is not evidence that they are unchanged in the two conditional knock-outs.

      The meaning of statistical testing and significance in this context is assessing if, and how much, the observed changes in expression in RNA-Seq estimated transcript levels can be due only to experimental variability (not significant) or, vice versa, if there is an additional biological factor (the knock-out of Sox2 or Nr2f1, in this case) behind the changes observed. Clearly, the more “significant” (lower) are the p-value/FDR values associated with changes observed for a gene, the more likely is that the gene transcript levels are affected by the knock outs. Vice versa, if the change is reported to be “not significant”, there isn’t enough evidence – at least from a statistical point of view - that the observed changes in transcript levels are due to the effect of the knock outs. Three replicates per condition are required in order to estimate variance – which is gene specific and estimates what is the “natural” range of variability of each gene due only to experimental variability (and not generated by the knock-outs).

      We now report the RNAseq data for Sox2 and Nr2f1 in Fig. 1D and complete them with ISH data in the new Fig. 4. The results are consistent with a limited reduction Nr2f1 in the Sox2 mutants and Sox2 in the Nr2f1 mutants. Though we cannot rule out that they might contribute to some extent to the mutant phenotype, we document a stronger downregulation, in both mutants, of a vast set of other genes (Fig. 1C) onto which our analysis focuses.

      -Many arguments are based on the result that Sox2 knock out has a "strong" effect on a gene. FDR and p-values do not provide evidence about effect size beyond "not 0". Average TPN values are provided but, without sorting through thousands of values in the supplementary data, it is not possible to judge the reliability of a claimed effect size. Finally, no biological reference is given for what should be considered a strong effect size besides the relative values within the knockout experiment. I would like to see the replicates for the relevant TPN data presented in the main text and I would like to see the variance between those replicates considered in the author's conclusions. Space in the tables could be saved by reporting fewer digits in the fold changes.

      See previous point. The more “significant” are the changes of transcript levels according to statistical testing, the “stronger” the effect of the knock out on them, where by “strong” we mean a more relevant variation of transcript levels. However, since we realized that this term could cause confusion in the reader, we rephrased the relevant parts. Variance is taken into account in the computation of pvalues/FDRs, so the same difference in mean TPM values for two different genes can result to more/less significant according to the estimated variance of the values.

      -The authors identify 469 dLGN specific SOX2 binding sites by subtracting the 248 high confidence binding sites identified in non-dLGN cells from the 717 high confidence binding sites identified in dLGN. This subtraction is basically a comparison of p-values with the false assumption that lack of statistical significance means there was no change. The quantitation required to make the claim would be a direct comparison of the two data sets for each binding site.

      *We appreciate the concern from the reviewer. CUT&RUN, especially when performed in vivo versus cell lines, has a high intrinsic variability between experiments, and even between technical replicates (DOI: 10.1093/nar/gkae180). While it would be possible to, for example, run DiffBind (built for ChIP-seq), on the dLGN data versus the NS data, these are not, in our opinion, directly comparable as they were not performed in the same batch, on the same type of material (dissected mouse tissue versus cultured cells) or even with the same batches of reagents. Thus, to quantify them in terms of signal at specific loci, without taking into account things like global background, local background, and overall signal to noise ratio, we do not believe is correct. There are many attempts in the field to better quantify CUT&RUN data (spike-in yeast or E. coli DNA at different moments, spike-in drosophila nuclei, etc.) but there remains to be determined a general consensus on what is best or trustworthy. The best way we could do the comparison, with our data as it was generated, was as pointed out above, by comparing the statistically significant events in the dLGN versus those in the NS, that way each dataset is considered independently before the overlap is performed. To help alleviate the reviewers concerns, we have provided here, for the reviewer, signal profiles and heatmaps of the dLGN only regions in both dLGN and NS CUT&RUN. *

      Non-quantitative issues:

      -It is known that both the Sox2 and Nr2f1 mutants have similar dLGN phenotypes. How, then, can we know if individual changes in gene expression reflect direct regulation by Sox2 and Nr2f1 or the dramatically altered state of the dLGN? The binding data would add to the argument of direct regulation, but it is difficult to judge the specificity of the binding data.

      The timepoint of the RNAseq analyses was chosen to precede any phenotypic changes detected in the dLGN based on our previous analyses reported in Mercurio et al. 2019 as stated in Results page 3.

      * * -The authors argue that a decrease in layer 4 of the cortex argues that Vgf1 is a likely link between Sox2 and cortical development. However, some decrease in layer 4 thickness is a given if the number of thalamocortical cells in dLGN is reduced.

      We agree with the Reviewer. The possible contribution of VGF has been rephrased considering a possible wider contribution of thalamic afferents in general.

      -Immuno fluorescence is used to support the idea that the number of cells strongly expressing Sox5 is reduced in the Sox2 cKO. The image shows a reduced patch of Sox5 labeling. However, the dLGN is generally reduced in the Sox2 cKO so it is not clear if there is a difference in the proportion of cells expressing Sox5. The sample size also appears to be 1.

      The time of this analysis was chosen to precede dLGN size reduction in mutants, as clearly shown in our previous work Mercurio et al. 2019 and further confirmed by the new ISH for Sox2 and Nr2f1 presented in the new Fig. 4.

      The sample size is n=4 as reported in the Figure legend.

      Minor

      Introduction:

      -Writing could be improved.

      -Descriptions of effects of Sox2 or Nr2fl using RORalpha-Cre use words like "reduced", "significant", "important". It is unclear what the actual effects or effect sizes are.

      We revised the wording for this point.

      RESULTS

      -What is "Three independent pools of mutant and control dissected visual thalami"? Three mice for each condition (twice for control)?

      -Why are there two groups of 3 control mice each and not one group of 6?

      As reported in Materials and Methods " RNA sequencing was performed on three independent samples for both mutant and control dLGN. Each sample was composed of dLGNs from three animals of the same genotype pooled together."

      *Thalami from 3 mice represent an adequate amount of RNA to perform a single experiment of RNAseq. 3 x 3 represents a biological triplicate for the RNAseq experiment. * Section 2

      -For the model in which the probability of genes changing in the same direction is calculated, are all genes assumed to have the same chance of passing the FDR? Gene variance and detection rate will be correlated between conditions. I would suggest a more conservative comparison. What is the correlation of fold change for genes that pass FDR? Of 514 that change in both, 481 go in the same direction and 33 go in a different direction. If everything is random, the number would be 257/257. The claim of four times random overlap does not seem like the conservative estimate.

      Genes were selected with the same FDR thresholds in both experiments. The assumption is anyway more simple: the probability of a gene to have a significant change (passing the FDR threshold) in one experiment does not influence its probability to change also in the other, and vice versa. That is, we compute the probability to have a given number of up- or down-regulated genes in common in the two experiments assuming that the two experiments were independent from one another. From another point of view, this is the usual strategy employed in order to assess whether the overlap between two gene sets obtained by two different genome-wide experiments can be considered to be random or not, that is, if the number of genes in the overlap is close to random expected values they can be considered to be independent from one another.

      Section 3

      -I don't see any basis to judge the p-values in Fig 1D. How do these changes compare to what you would from other dramatic manipulations of neural tissue? Can figure 1D compare to changes in non-neuronal standard? How about metabolism and cell death?

      The graph shown represents the most significantly enriched functional annotations (GO annotations, pathways, etc.) among the deregulated genes as computed by Enrichr, one of the many tools developed for this task. And as for all the tools performing this analysis, the p-value means “the probability of having the same number of genes sharing the same functional annotation in a set of genes chosen at random”, computed with the same strategy employed for the overlap between the two deregulated gene sets described before.

      Section "Deconvolution..."

      -It is great that results for each replicate is presented.

      We thank the reviewer.

      * * -There are too many significant digits in Fig 3A given the variance.

      This has been adjusted as suggested.

      -Why do the NR2F1 mutants look more like the Sox2 controls (in terms of excitatory Neurons) than the NR2F1 controls do?

      *The graphical presentation of the data in Fig. 3 has been improved, and the numerical data (former panel A) have been moved to the supplementary materials (Table S4) as recommended. *

      Controls for Nr2f1 and Sox2 mutants have similar values for excitatory neurons, as expected, see Table S4. Fig. 3 shows the variation between each knock-out and its respective control experiments, and although excitatory neurons are reduced in both mutants the extent of reduction is greater in the Sox2 mutant.

      Section "CUT&RUN..."

      -How many overlaps (Figure 4B) would you expect by chance?

      *This is an extremely difficult number to calculate. It is possible to, for example, generate a random set of genomic fragments of similar length, and check how many of them overlap. This would however be extremely unfair, as CUT&RUN is naturally biased towards open chromatin, and thus would preferentially contain these types of regions in a “randomly” digested set. Additionally, data analysis and mapping biases further increase what overlaps would often occur. To circumvent this, we i) use an IgG control, which should identify and remove regions that are nonspecifically digested and sequenced during the experiment, and ii) performed our analysis after first removing sets of known artifact regions (Nordin et al 2023, ref. 43). *

      -Fig 4J needs more description. What does the first full pie represent?

      *We have added more description in the figure legend, it now reads: *

      1. *Schematic depiction of CUT&RUN and RNA-seq overlap, showing Sox2 peak associated genes that are transcribed ( > 5 TPM, 784/1102) and those that are differentially expressed (DEG) in Sox2 mutant dLGN (FDR -Please include the denominator in the binding event argument. It is difficult to judge the specificity of the effect in this section.

      We apologize but we don't understand this comment.

      Reviewer #3 (Significance (Required)):

      The mouse dorsal lateral geniculate nucleus (dLGN) is an important model system for understanding vision and the development of visual circuitry. A considerable literature exists on the role of activity dependent development and molecular gradients in shaping the synaptic connections between the retina and the dLGN. Less is known about the transcriptional networks that regulate dLGN development. Mutations in the transcription factors Sox2 and NR2F1 are associated with severe vision defects and conditional knockout of Sox2 has been shown to cause dramatic defects in dLGN development. The data provided in the current study adds to our understanding of how these transcription factors influence gene expression and circuit formation in the dLGN. Their work points to changes in VGF expression and fewer thalamocortical cells as the most salient effects of Sox2 deletion. These results increase our understanding of the transcriptional networks underlying dLGN development and several visual pathologies.

      I think the manuscript should be helpful to researchers interested in the dLGN or researchers interested in the transcription factors important for neural circuit development. My own expertise covers dLGN development but not transcription factors and the interpretation of RNA-seq data. My impression was that the biggest contribution of this manuscript was in obtaining gene expression levels in the Sox2 conditional knockout with multiple RNA-seq replicates. The impact of the paper, as written, is lessened by the fact that the confidence gained by replicating the analysis is not leveraged in the main text of the manuscript.

      Performing a RNA-Seq analysis in replicates is common practice, and as we detailed in our replies to the reviewer’s comments the goal of replicates is to have reliable estimations of the parameters needed (mean, variance of each gene) for the subsequent statistical analyses. So, we leveraged the information obtained from the replicates in order to identify with high confidence with genes could be considered to be affected by the knock-outs.

      Much of the results, interpretation, and discussion depend on sorting strong effects on genes from weak ones without presenting replicates for effect size or confidence intervals. The replicate data is available in the supplementary data and should be a good resource for future research.

      As discussed in the previous responses, the statistical evaluations usually performed on estimated transcript levels and their variance can be translated into a more qualitative evaluation of the effect of the knock-outs performed – the larger is the impact on transcript levels of a gene with respect to its estimated variance (variability) the stronger the effect is assumed to be. Confidence intervals are not usually employed in this context – the “confidence” with which the experimental setting can be assumed to affect gene expression is summarized by the p-values and the subsequent FDR values.

    1. There’s no need to reinvent the wheel. Learn from what has been tried and is currently in use, map it out in a competitive analysis, and leverage your findings to differentiate your solution from the competition. And if you are new to a particular vertical, i.e. financial technology, then a competitive analysis will be imperative to grow your understanding of the basic features and functions of a financial technology platform. Understanding the landscape of competitors not only helps inform your design decisions but it also helps inform the overall product strategy.

      I think that the importance of understanding how much is needed to succeed is more important than wanting to succeed. The reason I believe this is that many times I think we as individuals believe we need a grand solution or something very impressive to solve an issue when in reality it may be a small adjustment. What I mean by that is that it's important to understand the scope, because it can save you time, and also help you get to the solution. That's why reinventing the wheel is not such a big deal.

    1. Others involve getting us to ask ourdoctors about these conditions and drugs and developing relationships with us so that we keep taking our meds

      I think many doctors use medications as a default 'fix' to a problem which may (or may not) be related to them trying to push us to take more medications. It is possible but in my opinion I don't think they are pushing drugs for the sake of it. I agree that the chronic use of medicine is on the rise and has been for years, but I also think that this push for medicine has to do with our life expectancy growing. Many of these professionals are trying to maximize our lifespans, so the idea of 'getting ahead' of these known ailments is the priority.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this work, Harpring et al. investigated divisome assembly in Chlamydia trachomatis serovar L2 (Ct), an obligate intracellular bacterium that lacks FtsZ, the canonical master regulator of bacterial cell division. They find that divisome assembly is initiated by the protein FtsK in Ct by showing that it forms discrete foci at the septum and future division sites. Additionally, knocking down ftsK prevents divisome assembly and inhibits cell division, further supporting their hypothesis that FtsK regulates divisome assembly. Finally, they show that MreB is one of the last chlamydial divisome proteins to arrive at the site of division and is necessary for the formation of septal peptidoglycan rings but does not act as a scaffold for division assembly as previously proposed.

      Strengths:

      The authors use microscopy to clearly show that FtsK forms foci both at the septum as well as at the base of the progenitor cell where the next septum will form. They also show that the Ct proteins PBP2, PBP3, MreC, and MreB localize to these same sites suggesting they are involved in the divisome complex.

      Using CRISPRi the authors knock down ftsK and find that most cells are no longer able to divide and that PBP2 and PBP3 no longer localized to sites of division suggesting that FtsK is responsible for initiating divisome assembly. They also performed a knockdown of pbp2 using the same approach and found that this also mostly inhibited cell division. Additionally, FtsK was still able to localize in this strain, however PBP3 did not, suggesting that FtsK acts upstream of PBP2 in the divisome assembly process while PBP2 is responsible for the localization of PBP3.

      The authors also find that performing a knockdown of ftsK also prevents new PG synthesis further supporting the idea that FtsK regulates divisome assembly. They also find that inhibiting MreB filament formation using A22 results in diffuse PG, suggesting that MreB filament formation is necessary for proper PG synthesis to drive cell division.

      Overall the authors propose a new hypothesis for divisome assembly in an organism that lacks FtsZ and use a combination of microscopy and genetics to support their model that is rigorous and convincing. The finding that FtsK, rather than a cytoskeletal or "scaffolding" protein is the first division protein to localize to the incipient division site is unexpected and opens up a host of questions about its regulation. The findings will progress our understanding of how cell division is accomplished in bacteria with non-canonical cell wall structure and/or that lack FtsZ.

      Weaknesses:

      No major weaknesses were noted in the data supporting the main conclusions. However, there was a claim of novelty in showing that multiple divisome complexes can drive cell wall synthesis simultaneously that was not well-supported (i.e. this has been shown previously in other organisms). In addition, there were minor weaknesses in data presentation that do not substantially impact interpretation (e.g. presenting the number of cells rather than the percentage of the population when quantifying phenotypes and showing partial western blots instead of total western blots).

      We agree with the weaknesses identified by the reviewer. We removed the statements in the Results and Discussion that multiple independent divisome complexes can simultaneously direct PG synthesis. We presented the data in Figs. 3-5 as % of the cells in the population, and complete western blots are shown in Supp. Fig. S1.

      Reviewer #2 (Public review):

      Summary:

      Chlamydial cell division is a peculiar event, whose mechanism was mysterious for many years. C. trachomatis division was shown to be polar and involve a minimal divisome machinery composed of both homologues of divisome and elongasome components, in the absence of an homologue of the classical division organizer FtsZ. In this paper, Harpring et al., show that FtsK is required at an early stage of the chlamydial divisome formation.

      Strengths:

      The manuscript is well-written and the results are convincing. Quantification of divisome component localization is well performed, number of replicas and number of cells assessed are sufficient to get convincing data. The use of a CRISPRi approach to knock down some divisome components is an asset and allows a mechanistic understanding of the hierarchy of divisome components.

      Weaknesses:

      The authors did not analyse the role of all potential chlamydial divisome components and did not show how FtsK may initiate the positioning of the divisome. Their conclusion that FtsK initiates the assembly of the divisome is an overinterpretation and is not backed by the data. However, data show convincingly that FtsK, if perhaps not the initiator of chlamydial division, is definitely an early and essential component of the chlamydial divisome.

      The following statement has been included in the Discussion (pg. 16 of the revised manuscript)  “Although we focused our study on a subset of the divisome and elongasome proteins that Chlamydia expresses (bolded in Fig. 6G), our results support our conclusion that chlamydial budding is dependent upon a hybrid divisome complex and that FtsK is required for the assembly of this hybrid divisome. At this time, we cannot rule out that other proteins act upstream of FtsK to initiate divisome assembly in this obligate intracellular bacterial pathogen.”

      We will soon be submitting another manuscript that addresses how FtsK specifies the site of divisome assembly. This work is too extensive to be included in this manuscript.

      Reviewer #3 (Public review):

      Summary:

      The obligate intracellular bacterium Chlamydia trachomatis (Ct) divides by binary fission. It lacks FtsZ, but still has many other proteins that regulate the synthesis of septal peptidoglycan, including FtsW and FtsI (PBP3) as well as divisome proteins that recruit and activate them, such as FtsK and FtsQLB. Interestingly, MreB is also required for the division of Ct cells, perhaps by polymerizing to form an FtsZ-like scaffold. Here, Harpring et al. show that MreB does not act early in division and instead is recruited to a protein complex that includes FtsK and PBP2/PBP3. This indicates that Ct cell division is organized by a chimera between conserved divisome and elongasome proteins. Their work also shows convincingly that FtsK is the earliest known step of divisome activity, potentially nucleating the divisome as a single protein complex at the future division site. This is reminiscent of the activity of FtsZ, yet fundamentally different.

      Strengths:

      The study is very well written and presented, and the data are convincing and rigorous. The data underlying the proposed localization dependency order of the various proteins for cell division is well justified by several different approaches using small molecule inhibitors, knockdowns, and fluorescent protein fusions. The proposed dependency pathway of divisome assembly is consistent with the data and with a novel mechanism for MreB in septum synthesis in Ct.

      Weaknesses:

      The paper could be improved by including more information about FtsK, the "focus" of this study. For example, if FtsK really is the FtsZ-like nucleator of the Ct divisome, how is the Ct FtsK different sequence-wise or structurally from FtsK of, e.g. E. coli? Is the N-terminal part of FtsK sufficient for cell division in Ct like it is in E. coli, or is the DNA translocase also involved in focus formation or localization? Addressing those questions would put the proposed initiator role of FtsK in Ct in a better context and make the conclusions more attractive to a wider readership.

      We will be submitting another manuscript soon that details the conserved domain organization of FtsK from different bacteria, and the role of the various domains of chlamydial FtsK (including the N-terminus and the C-terminal translocase domain) in directing its localization in dividing Chlamydia. We have added text to the discussion (pg. 16 of the revised manuscript) that describes the sequence homology of chlamydial FtsK to FtsK from E. coli.

      Another weakness is that the title of the paper implies that FtsK alone initiates divisome assembly. However, the data indicate only that FtsK is important at an early stage of divisome assembly, not that it is THE initiator. I suggest modifying the title to account for this--perhaps "FtsK is required to initiate....".

      We agree with the reviewer and modified the title to “FtsK is Critical for the Assembly of the Unique Divisome Complex of the FtsZ-less Chlamydia trachomatis”. We have also modified the text throughout to indicate that FtsK is required for the assembly of the hybrid divisome of Chlamydia

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Suggestions for improvement (mostly minor):

      (1) For several of the graphs, the authors plot the number of cells with a given phenotype on the y-axis, but then describe percentages of cells in the text. It would make it clearer if all the graphs had the percentage of cells on the y-axis instead.

      We have modified the figures to indicate the percentage of cells on the y-axis with a given phenotype.

      (2) In Figures 3, 4, and 5 the authors show separate graphs for plus/minus drug or inducer. These should be on the same graph as they are directly comparing these two different conditions. Having them on separate graphs makes it less clear whether these differences are significant between the two conditions

      We modified Fig. 4 to show +/- inducer in ftsk and pbp2 knockdown strains in the same graph.  Regarding Figures 3 and 5, we believe the figures in the original submission effectively demonstrate the +/- drug conditions, so these figures remain unchanged in the revised manuscript.

      (3) In Figure 2 the authors show microscopy of the colocalization of FtsK with several other divisome proteins from Ct. Quantification of the colocalization of FtsK with these other proteins would provide a more holistic understanding of their colocalization and help further support their argument that FtsK initiates the assembly of the divisome.

      Supp. Fig. S4A of the revised manuscript contains images showing the colocalization of FtsK with the fusions at the septum and the base of dividing cells, and the colocalization of FtsK with the fusions that are only at the base of dividing cells. Supp. Fig. S4B quantified the percentage of dividing cells where FtsK overlaps the localization of each of the fusions at the septum, at the septum and the base, and at the base alone.

      (4) In Figure 6 the authors mention that the PG ring was at a slight angle relative to the MOMP-stained septum. What is the significance of this? The authors mention it several times but do not explain its relevance to divisome assembly. It is not really evident in the images presented.

      We mention in the discussion pgs. 17-18 of the revised manuscript that “The relevance of the angled orientation of PG and MreC rings relative to the MOMP-stained septum in division intermediates is unclear. However, it appears to be a conserved feature of the cell division process and may arise because the divisome proteins are often positioned slightly above or below the plane of the MOMP-stained septum. The positioning of divisome proteins above or below the septum is indicated in Figs. 1 and 2.

      We included cartoons in Fig. 6C of the revised manuscript to assist the reader in visualizing the angled orientation of the PG ring relative to the MOMP-stained septum.

      (5) In line 270 the authors claim that "these are the first data in any system to suggest that septal PG synthesis/modification is simultaneously directed by multiple independent divisome complexes." However, their experiments do not demonstrate that multiple divisome complexes are active at the same time. They show that multiple foci of FtsK etc. are present at sites where PG synthesis has occurred, but that does not necessarily mean that each focus/complex was actively synthesizing PG at the same time. Moreover, similar approaches were used to support a claim that septal PG synthesis is directed by multiple discrete divisome complexes previously (e.g. in Figure 1 of Bisson-Filho et al. 2017 (PMID: 28209898) in Bacillus subtilis and in Perez et al 2021 (PMID: 33269494) in Streptococcus pneumoniae). This claim is not central to the main conclusions of the study and could just be removed.

      This statement has been removed from the Results and the Discussion.

      (6) In Figure 6B the authors see three distinct FtsK foci. Why is this the only place in the manuscript where they see three foci? They mentioned previously that they saw foci at the septum and at the base of the progenitor mother cell, but why are there three foci here?

      The vast majority of dividing cells displayed one foci at the septum and/or the base.  Representative images were chosen that reflected the localization profiles observed in the majority of cells. While we observed cells with  multiple foci, as shown in Figure 6C, these cells were relatively rare   (~2% of cells for all the divisome proteins in 3 independent experiments).  Since  the number of cells with multiple foci were relatively rare, we chose to group these cells with the cells that had single foci at the septum, the septum and base, or base alone categories in the quantification shown in Fig. 2C. This is stated in the legend of Fig. 2 of the revised manuscript.

      (7) The Discussion section is lacking a couple of things that would put the data in a broader context. Can the authors speculate on how FtsK knows how to find the division site? I.e. what might be upstream of FtsK localization? Additionally, the authors do not talk about the FtsK sequence or domains at any point in the paper. Does Ct FtsK have a similar sequence/structure to FtsKs from other bacteria? Are there any differences in sequence/structure that might tell us about its function in Ct?

      We will be submitting another manuscript soon that examines how the site of assembly of the divisome is defined in dividing Chlamydia. This manuscript will also define the localization of the different sub-domains of chlamydial FtsK during cell division.  For this manuscript, we added a paragraph in the Discussion (pg. 16 of the revised manuscript) that states the domain organization is conserved in FtsK proteins from different bacteria. This paragraph includes information regarding the % sequence identity of the C-terminus and the N-terminus of chlamydial FtsK when compared to E. coli FtsK.

      (8) For Supplementary Figure S1B-C. The authors should show the full blots rather than just the single band of the protein of interest to show that the antibodies are specific. Additionally, the authors should include a loading control to show that they loaded the same amount of protein for each sample.

      We have included the full blots in Supp. Fig. S1 of the revised manuscript. We do not see the need for including a loading control for these blots because we are not making arguments about the relative level of the proteins that were assayed. We only use the blots to show that the fusion proteins are primarily a single species of the predicted molecular mass.

      (9) In Supplementary Figure S4A the authors use RT-qPCR to measure ftsK and pbp2 transcript levels. Since they have antibodies against these proteins, they should also include Western blots to show that the proteins are not being produced when targeted using CRISPRi.

      We have included data in Supp. Fig. S5E of the resubmission that indicates foci of FtsK and PBP2 could not be detected following the knockdown of ftsk and pbp2. We feel that these data support our conclusion that the induced expression of dCas12 in the the ftsk and pbp2 knockdown strains results in the downregulation of the endogenous FtsK and PBP2 polypeptides.

      (10) In lines 261-262 the authors say that "PG organization was the same or differed at the septum." What is the PG organization being compared to? Same or different from what?

      We agree with the reviewer that the text in lines 261-262 in the original submission was confusing.  The text has been modified.

      (11) Lines 201-215 the authors refer to Supplementary Figure S3 throughout this section, but they should refer to Supplementary Figure S4.

      This has been corrected.

      Reviewer #2 (Recommendations for the authors):

      I am not convinced that this paper shows that FtsK initiates the assembly of the divisome since the authors did not analyse the role and localization of all other chlamydial divisome components. Out of the ten homologues of divisome and elongasome components encoded by C. trachomatis genome, only five are investigated in this study. There is no explanation about how these five were chosen.

      We state on pg. 16 of the revised manuscript that “Although we focused our study on a subset of the divisome and elongasome proteins that Chlamydia expresses (bolded in Fig. 6G), our results support our conclusion that chlamydial budding is dependent upon a hybrid divisome complex and that FtsK is required for the assembly of this hybrid divisome. At this time, we cannot rule out that other proteins act upstream of FtsK to initiate divisome assembly in this obligate intracellular bacterial pathogen.

      Results convincingly indicate that FtsK is an early divisome component, but proofs are lacking to indicate that it initiates the divisome formation. Indeed, the authors do not show how FtsK would be the first protein to selectively accumulate at a given location to initiate the divisome formation. For this reason, the model they propose at the end of their study is not backed by sufficient data, to my opinion.

      We agree with the reviewer that our data does not show that FtsK initiates divisome assembly. The title of the manuscript has been modified to “FtsK is Critical for the Assembly of the Unique Divisome Complex of the FtsZ-less Chlamydia trachomatis” and the text throughout has been modified to indicate that FtsK is the first protein we assayed that associates with nascent divisomes at the base of dividing cells. We will soon be submitting another manuscript that details how FtsK is recruited to a specific site to initiate nascent divisome assembly, This work is too extensive to be included in this manuscript.

      There are also discrepancies in the number of cells analysed to quantify the localization of divisome components, ranging from 50 to 250 cells. The authors could better explain why there are such variations.

      There were differences in the number of cells analyzed in the various experiments, but in every instance the effect of inhibitors (A22 and mecillinam) or ftsk and pbp2 knockdown on divisome assembly was statistically significant.

      There are a few mistakes in the text regarding figure numbering (Figure S4 is mentioned as S3 in the text). Figures 5B and D are not specifically cited.

      These mistakes have been corrected in the revised manuscript.

      Line 261-262: the sentence starting "Our imaging analysis.." is not clear to me.

      We agree with the reviewer that the text in lines 261-262 was confusing.  The text has been modified (pg. 14 of the revised manuscript).

      Line 270-271: there are insufficient proofs to say that there are multiple independent divisome complexes. This is in my opinion an overinterpretation of the data, since there is no proof that these complexes are independent.

      This statement has been removed from the text.

      A few details are lacking in the figure legends:

      Figure 2C: when was the expression of the different mCherry and 6xHis constructs induced?

      The onset and length of the induction of the fusions have been included in the legend of Fig. 2.

      Bars are sometimes mentioned as uM and should be um. Bars sizes, number of replicates, and/or meaning of the error bars are lacking in legends of Figures S2, S3, and S4

      This has been corrected in the revised manuscript.

      The consistency of Figures could be improved between Figures 3A, 4A, B, and 5A. The results of treated cells could be always shown as dark grey. It would help the reader.

      We have used consistent coloring in Figs. 3-5 to indicate the treated cells.

      Reviewer #3 (Recommendations for the authors):

      (1) Lines 113-118: do Ct cells increase in size as they get closer to starting division? If so, could a pseudo-time course (demograph) be done to bolster the evidence that the base foci formed mainly in predivisional cells and not newborn cells? This evidence might be more convincing than the data in Figures 1F and G.

      Chlamydial cells in the population were heterogeneous in size at the timepoint we are studying. This observation is consistent with previous reports in the literature (Liechti et al.,2021). While we agree that a pseudo-time course could potentially bolster the evidence about when FtsK foci appear, we believe our current analysis sufficiently demonstrates that basal foci of FtsK appear prior to the appearance of new buds at the base of dividing cells.

      (2) Figure 3E: It looks like MreC localization to foci doesn't strictly require MreB polymerization. Is this known for E. coli or other species?

      To our knowledge, MreC assembly into a filament has not been shown to be dependent upon MreB in other bacteria.  In Caulobacter crescentus, MreC forms a helical structure that is not dependent upon MreB or MreB filament formation (Dye et al., 2005. PNAS; Divakaruni et al., 2005. PNAS).

      (3) Figure 5E: why is nearly half of PBP2 and PBP3 still localized to foci at the membrane even after treatment with mecillinam? This suggests, as the authors mention, that mecillinam reduces the efficiency of localization to the divisome but does not eliminate it. Any ideas why?

      At this time, we do not know why inhibiting the catalytic activity of PBP2 with mecillinam does not fully prevent the association of PBP2 with the chlamydial divisome. We have included a statement in the Results (pg. 13 of the revised manuscript) that inhibiting the catalytic activity of PBP2 prevents it from efficiently associating with or maintaining its association with polarized divisome complexes.

      (4) Line 262-263: This sentence is confusing-please rephrase. The same as what? Differed from what?

      We agree with the reviewer. The wording in lines 262-263 of the original submission has been modified.  

      (5) Lines 265-267 and Figure 6: Adding cartoon schematics might help readers visualize cell orientations in Fig. 6 (especially 6B).

      Cartoons have been added to Fig. 6C (Fig. 6B in the original submission) to orient the reader.

      (6) Line 294-298: Do the authors think that the residual 5-10% of PG foci after FtsK knockdown is due to the ability of residual FtsK to organize divisomes?

      We show that knockdown of FtsK is not complete, and while we cannot be certain, it is likely, that the PG foci detected in FtsK knockdown cells is due to the ability of the residual FtsK to organize divisomes that direct PG synthesis.

      (7) Do the authors have any evidence that FtsK foci are mobile like treadmilling FtsZ?

      We have not performed real-time imaging studies, and we currently have no evidence that FtsK foci are mobile.

      (8) FtsK foci here are reminiscent of mobile foci formed by the FtsK-like SpoIIIE at the Bacillus subtilis sporulation septum. This might be a good idea to mention in the Discussion. Is it possible that Ct FtsK is also involved in coordinating chromosome partitioning through the developing septum? (That is another reason why it would be useful to know if the translocase domain was dispensable for localization/activity).

      We are currently preparing another manuscript that documents the contribution of the various domains of FtsK to its localization profile and whether the division defect in ftsk knockdown cells can be suppressed by specific subdomains of FtsK. This manuscript not only will include these data, it will also include experiments that address how the site of polarized budding is defined. In the revised manuscript, we have included a description of how the domain organization of chlamydial FtsK is similar to E. coli FtsK (pg. 16 of revision). Chlamydial FtsK also has a similar domain organization as SpoIIIE from B. subtilis. The C-terminal catalytic domain of SpoIIIE is 45% identical to chlamydial FtsK. The N-terminus of SpoIIIE is predicted to encode 4 transmembrane spanning helices, like chlamydial FtsK. However, the N-terminus of SpoIIIE shares no sequence homology with the N-terminus of chlamydial FtsK.  We have not included the similar domain organization of SpoIIIE and chlamydial FtsK in the revised manuscript.

      (9) It seems that FtsK foci localize to a particular spot opposite from the active septum, although how this spot is specified is not clear. Is there any geometric clue for FtsK's localization like there is for Min-specified FtsZ localization?

      As mentioned above, we are currently preparing another manuscript that documents our efforts to understand how the site of polarized budding is defined.  This analysis is too extensive to include in this study.

      (10) As mentioned in the Summary, do the authors know whether the N-terminal membrane binding part of FtsK (FtsKn) sufficient for localization/divisome assembly in Ct as it is in other species? Oullette et al. 2012 showed that FtsKn could interact with MreB in BACTH.

      We are currently preparing another manuscript that documents the contribution of the various domains of FtsK to its localization profile.

      (11) The previous BACTH result with MreB and FtsKn implies that this interaction is direct, yet the current data suggest that this is not the case. Can the authors comment on this? Is this due to bridging effects inherent in the BACTH system?

      We have not presented any data to indicate that FtsK and MreB do not interact. We have only shown that FtsK localization is not dependent upon MreB filament formation (Fig. 3).

      (12) The FtsZ-independent role of FtsK in nucleating the divisome suggests that Ct FtsK may differ from other FtsKs structurally - can this be explored, perhaps with AlphaFold 3?

      As mentioned above, we have included a paragraph in the discussion of the revised manuscript (pg. 16 of the revised manuscript) that states the domain organization of chlamydial FtsK is similar to E.coli FtsK. This conserved domain organization is evident when we view the structures of the proteins using Alphafold.

      (13) Typo on line 559: should be HeLa.

      This has been corrected.

    1. Just as the technology of printing altered and reduced the power of medieval guilds and the social power structure, so too will cryptologic methods fundamentally alter the nature of corporations and of government interference in economic transactions. Combined with emerging information markets, crypto anarchy will create a liquid market for any and all material which can be put into words and pictures. And just as a seemingly minor invention like barbed wire made possible the fencing-off of vast ranches and farms, thus altering forever the concepts of land and property rights in the frontier West, so too will the seemingly minor discovery out of an arcane branch of mathematics come to be the wire clippers which dismantle the barbed wire around intellectual property. Arise, you have nothing to lose but your barbed wire fences!

      digital IP as crypto backed media... NFTs didnt really take off, but i believe thats because its hard to not reproduce digital media. which leads to the dissoultion of IP, especially market backed IP. I keep saying all digial media should be freely accesible. maybe opening access enables a digial footprint that is similar to DCMA. Is there an open source version of DMCA. how does that tech work.

      I also dont think people are much concerned with privacy in optimal conditions. there isnt much i do in private or public that warrents true privacy. most systems i contribute to digitally are not op secret.

      i think this manifesto reminds me of the radical versions of left/right concepts thomas sowell probs surface in a conflict of visions. it assumes that the avg person cares or benefits from this.

      when it comes to tool usse, i think e should remember this is a specialized tool. for example the avg person probs wont ever use a beat machine, but it does allow the newbie to create loops and digi beats.

      i say that to say its unlikely the avg person will build a beat machine. i certainly might. but most wont. and most dont actually benefit from the capability to do so. the same can be said of the plethora of yogurt brands that democratize access to whatever flavor you want.

      that is a bit silly of an example. but we should know now that technology does not solve human collaboration. and while the mediums of our conceptual thinking are two way forces having more yogurt flavors doesnt really mean much.

      i am curious to see if crypto lands outside of the market based domains, and more into civilian infrastructure tech, warehouse tech, etc. Its just hard to believe that tools communicate anything other than their capapbilities as devloped by humans.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      (1) The overall conclusion, as summarized in the abstract as "Together, our study documents the diversification of locomotor and oculomotor adaptations among hunting teleost larvae" is not that compelling. What would be much more interesting would be to directly relate these differences to different ecological niches (e.g. different types of natural prey, visual scene conditions, height in water column etc), and/or differences in neural circuit mechanisms. While I appreciate that this paper provides a first step on this path, by itself it seems on the verge of stamp collecting, i.e. collecting and cataloging observations without a clear, overarching hypothesis or theoretical framework.

      There are limited studies on the prey capture behaviors of larval fishes, and ours is the first to compare multiple species systematically using a common analysis framework. Our analysis approach could have uncovered a common set of swim kinematics and capture strategies shared by all species; but instead, we found that medaka used a monocular strategy rather than the binocular strategy of cichlids and zebrafish. Our analysis similarly could have revealed first-feeding larvae of all species go through a “bout” stage, which was previously proposed as important for sensorimotor decision making (Bahl et al., 2019), but instead we found that medaka and some cichlids have more continuous swimming from an early life stage. Finally, the rate at which prey capture kinematics evolves is not known. Our approach could have revealed rapid diversification of feeding strategies in cichlids (similarly to how adult feeding behavior evolves), but instead we found smaller differences within cichlids than between cichlids and medaka.

      (2) The data to support some of the claims is either weak or lacking entirely.

      Highlighted timestamps in videos, new stats in fig 1H and fig 2, updated supplementary figures now provide additional support for claims.

      - It would be helpful to include previously published data from zebrafish for comparison.

      We appreciate the suggestion. Mearns et al. (2020) provided a comprehensive account of prey capture in zebrafish larvae in an almost identical setup with similar analyses. We do not feel it is necessary to recount all the findings in that paper here. There are many studies on prey capture in zebrafish from the past 20 years, and reproducing these here would not add anything to that extensive pre-existing literature.

      - Justification is required for why it is meaningful to compare hunting strategies when both fish species and prey species are being varied. For instance, artemia and paramecia are different sizes and have different movement statistics.

      We added text explaining why different food was chosen for medaka/cichlids. There is no easy way to stage match fishes as evolutionarily diverged as cichlids, medaka, and zebrafish. Size is a reasonable metric within a species, but there is no guarantee that sizematched larvae of two different species are at the same level of maturity. Therefore, we thought the most appropriate stage to address is when larvae first start feeding, as this enables us to study innate prey capture behavior before any learning or experience-dependent changes have taken place. Given that zebrafish, medaka and cichlid larvae are different sizes when they first start feeding, it was necessary to study their hunting behavior to different prey items.

      - It would be helpful in Figure 1A to add the abbreviations used elsewhere in the paper. I found it slightly distracting that the authors switch back and forth in the paper between using "OL" and "medaka" to refer to the same species: please pick one and then remain consistent.

      Medaka is the common name for the japanese rice fish, O. latipes. Cichlilds do not have common names are only referred to by their scientific names. Since readers are more likely to be familiar with the common name, medaka, we now use medaka (OL) throughout the manuscript, which we hope makes the text clearer.

      - The conceptual meaning of behavioral segmentation is somewhat unclear. For zebrafish, the bouts already come temporally segmented. However in medaka for instance, swimming is more continuous, and the segmentation is presumably more in terms of "behavioral syllables" as have been discussed for example mouse or drosophila behavior (in the last row of Figure S1 it is not at all obvious why some of the boundaries were placed at their specific locations). It's not clear whether it's meaningful to make an equivalence between syllables and bouts, and so whether for instance Figure 1H is making an apples-to-apples comparison.

      We clarified the text to say we are comparing syllables, rather than bouts.

      - The interpretation of 1H is that "medaka exhibited significantly longer swims than cichlids"; however this is not supported by the appropriate statistical test. The KS test only says that two probability distributions are different; to say that one quantity is larger than another requires a comparison of means.

      Updated Fig 1H; boostrap test (difference of medians) and re plotted data as violin plots.

      (2) The data to support some of the claims is either weak or lacking entirely.

      Highlighted timestamps in videos, new stats in fig 1H and fig 2, updated supplementary figures now provide additional support for claims.

      - I think the evidence that there are qualitatively different patterns of eye convergence between species is weak. In Figure 2A I admire the authors addressing this using BIC, and the distributions are clearly separated in LA (the Hartigan dip test could be a useful additional test here). However for LO, NM, and AB the distributions only have one peak, and it's therefore unclear why it's better to fit them with two Gaussians rather than e.g. a gamma distribution. Indeed the latter has fewer parameters than a two-gaussian model, so it would be worthwhile to use BIC to make that comparison. The positions of the two Gaussians for LO, NM, and AB are separated by only a handful of degrees (cf LA, where the separation is ~20 degrees), which further supports the idea that there aren't really two qualitatively different convergence states here.

      Added explanation to text.

      - Figure S2 is unfortunately misleading in this regard. I don't claim the authors aimed to mislead, but they have made the well-known error of using colors with very different luminances in a plot where size matters (see e.g.

      https://nam12.safelinks.protection.outlook.com/?url=https%3A %2F%2Fwww.r-project.org%2Fconferences%2FDSC2003%2FProceedings%2FIhaka.pdf&data=05%7C02%7Cdme arns%40princeton.edu%7C17ae2b44f0f246f15ddd08dc9b8e2 01c%7C2ff601167431425db5af077d7791bda4%7C0%7C0%7

      C638556282750568814%7CUnknown%7CTWFpbGZsb3d8ey

      JWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJ XVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Ll4J4Xo39JEtKb %2FNnRWNoyedZAu5aAOMq0lHJCwsfXI%3D&reserved=0).

      Thus, to the eye, it appears there's a big valley between the red and blue regions, but actually, that valley is full of points: it's really just one big continuous blob.

      Kernel density estimation of eye convergence angles were added to Figure S2. The point we wish to make is that there is higher density when both eyes are rotated invwards (converged) in cichlids, but not medaka (O. latipes). The valley between converged and unconverged states being full of points is due to (1) slight variation with placement of key points in SLEAP, which blurs the boundary between states and (2) the eye convergence angle must pass through the valley in order to become converged, so necessarily there are points in between the two extremes of eye convergence.

      - In Figure 2D please could the authors double-check the significance of the difference between LO and NM: they certainly don't look different in the plot.

      Thank for for flagging this. We realize the way we previously reported the stats was open to misinterpretation. We have updated figure 2C, D and F to use letters to indicate statistical groupings, which hopefully makes it clearer which species are statistically different from each other.

      - In Figure 2G it's not clear why AB is not included. It is mentioned that the artemia was hard to track in the AB videos, but the supplementary videos provided do not support this.

      The contrast of the artemia in the AB videos is sufficiently different from the other cichlid videos that our pre-trained YOLO model fails. Retraining the model would be a lot of extra work and we feel like a comparison of three species is sufficient to address the sensorimotor transformations that occur over the course of prey capture in cichlids.

      - The statement "Zebrafish larvae have a unique swim repertoire during prey capture, which is distinct from exploratory swim bouts" is not supported by the work of others or indeed the authors' own work. In Figure 4F all types of bouts can occur at any time, it's just the probability at which they occur that varies during prey capture versus other times (see also Mearns et al (2020) Figure S4B).

      The point is well taken that there probably is not a hard separation between spontaneous and prey capture swims based on tail kinematics alone, which is also shown in Marques et al. (2018). However, we think that figure 2I of Mearns et al., which plots the probability of swims being drawn from different parts of the behavior space during prey capture (eyes converged) or not (eyes unconverged), shows that the repertoire of swims during the two states is substantially different. Points are blue or red; there are very few pale blue/pale red points in that figure panel. Figure S4B is showing clustered data, and clustering is a notoriously challenging problem for which there exists no perfect solution (Kleinberg, 2002). The clusters in Mearns et al. incorporated information about transition structure, as this was necessary for obtaining interpretable clusters for subsequent analyses. However, a different clustering approach could have yielded different boundaries, which may have shown more (or less) separation of bout types during prey capture/exploratory swimming. Therefore, we have updated the text to say that zebrafish perferentially perform different swim types during prey capture and exploration, and re-interpreted the behavior of cichlids similarly.

      - More discussion is warranted of the large variation in the number of behavioral clusters found between species (11-32). First, how much is this variation really to be trusted? I appreciate the affinity propogation parameters were the same in all cases, but what parameters "make sense" is somewhat dependent on the particular data set. Second, if one does believe this represents real variation, then why? This is really the key question, and it's unsatisfying to merely document it without trying to interpret it.

      Extended paragraph with more interpretation.

      - What is the purpose of "hovers"? Why not stay motionless? Could it be a way of reducing the latency of a subsequent movement? Is this an example of the scallop theorem?

      Added a couple of sentences speculating on function.

      - I'm not sure "spring-loaded" is a good term here: the tension force of a coiled tail is fairly negligible since there's little internal force actively trying to straighten it.

      Rewrote this part to highlight that fish spring toward the prey, without the implication that tension forces in the tail are responible for the movement. However, we are not aware of any literature measuring passive forces within the tail of fishes. Presumably the notochord is relatively stiff and may provide an internal force trying to straighten the tail.

      - There are now several statements for which no direct evidence is presented. We shouldn't have to rely on the author's qualitative impressions of what they observed: show us quantitative analysis.

      * "often hover"

      * "cichlids often alternate between approaches and hover swims"

      * "over many hundreds of milliseconds"

      * "we have also observed suction captures and ram-like attacks"

      * "may swim backwards"

      * "may expel prey from their mouth"

      * "cichlid captures often occur in two phases"

      Added references to supplementary videos with timestamps to highlight these behaviors.

      - I don't find it plausible that sated fish continue hunting prey that they know they're not going to eat just for the practice.

      Removed the speculation.

      - In Figure 3 is it not possible to include medaka, based on the hand-tracked paramecia?

      The videos are recorded at high frame rate, so it would be a lot of additional work to track these manually. Furthermore, earlier in prey capture it is very difficult to tell by watching videos which prey the medaka are tracking, especially as single paramecia can drift in and out of focus in the videos. Since there is no eye convergence, it is very difficult to ascertain for certain when tracking a given prey begins. In Fig 4, it was only possible to track paramecia by hand since it is immediately prior to the strike and from the video it is possible to see which paramecium the fish targeted. Our analyses of heading changes was performed over the 200 ms prior to a strike, which we think is a conservative enough cutoff to say that fish were probably pursuing prey in this window (it is shorter than the average behavioral syllable duration in medaka).

      - Figure 3 (particularly 3D) suggests the interesting finding that LA essentially only hunt prey that is directly in front of them (unlike LO and NM, the distribution of prey azimuth actually seems to broaden slightly over the duration of hunting events).

      This is worthy of discussion.

      We offer a suggestion for the many instances of prey capture being initiated in the central visual field in LA later in the manuscript when we discuss spitting behavior. We have added text to make this point earlier in the manuscript. The increase in azimuthal range at the end of prey capture may be due to abort swims (e.g. supp. vid. 1, 00:21). The widening of azimuthal angles is present in LO and NM also and is not unique to LA.

      - The reference Ding et al (2016) is not in the reference list.

      Wrong paper was referenced. Should be Ding 2019, which has been added to bibliography.

      - I am not convinced that medaka exhibit a unique side-swing behavior. I agree there is this tendency in the example movie, however, the results of the quantification (Figure 4) are underwhelming. First, cluster 5 in 4K appears to include a proportion of cases from LA and AB. These proportions may be small, but anything above zero means this is not unique to medaka. Second, the heading angle (4N) starts at 4 degrees for LA and 8 degrees for medaka. This difference is genuine but very small, much smaller than what's drawn in the schematic (4M). I'm not sure it's justifiable to call a difference of 4 degrees a qualitatively different strategy.

      We have changed the text to highlight that side swing is highly enriched in medaka. Comparing 4J to 3B we would argue that there is a qualitative difference in the strategy used to capture prey in the cichlid larvae we study here and medaka. We agree that further work is required to understand distance estimation behaviors in different species. In this manuscript, we use heading angle as a proxy for how prey position might change on the retina over a hunting sequence. But as the heading and distance are changing over time, the actual change in angle on the retina for prey may be much larger than the ~8 degree shift reported here. The actual position of the prey is also important here, which, for reasons mentioned above, we could not track. Given the final location of prey in the visual field prior to the strike (Fig 4J), the most parsimonious explanation of the data is that the prey is always in the monocular visual field. In cichlids, the prey is more-or-less centered in the 200 ms preceding the strike. While it is true theat the absolute difference in heading is 4 degrees, when converted to an angular velocity (4N, right), the medaka (OL) effectively rotate twice as fast as LA (20 deg/s vs 40 deg/s), which we think is a substantial difference and evidence of a different targeting strategy.

      - 4K: This is referred to in the caption as a confusion matrix, which it's not.

      Fixed.

      - 4N right panel: how many fish contributed to the points shown?

      Added to figure legend (n=113, LA; n=36, OL). Same data in left and right panels.

      - In the Discussion it is hypothesized that medaka use their lateral line in hunting more than in other species. Testing this hypothesis (even just compared to one other species) would be fairly straightforward, and would add significant interest to the paper overall.

      We agree that this is an interesting experiment for follow up studies, but it is beyond the scope of the current manuscript as we do not have the appropriate animal license for this experiment.

      Reviewer 2:

      The paper is rather descriptive in nature, although more context is provided in the discussion. Most figures are great, but I think the authors could add a couple of visual aids in certain places to explain how certain components were measured.

      Added new supplemental figure (Supp Fig 2)

      Figure 1B- it could be useful to add zebrafish and medaka to the scientific names (I realize it's already in Figure A but I found myself going back and forth a couple of times, mostly trying to confirm that O. latipes is medaka).

      Added common names to 1B, sprinkled reminders of OL/medaka throughout text.

      Figure 1G. I wasn't sure how to interpret the eye angle relative to the midline. Can they rotate their eyes or is this due to curvature in the 'upper' body of the fish? Adding a schematic figure or something like that could help a reader who is not familiar with these methods. Related to this, I was a bit confused by Figure 2A. After reading the methods section, I think I understand - but I little cartoon to describe this would help. It also reminds the reader (especially if they don't work with fish) that fish eyes can rotate. I also wanted to note that initially, I thought convergence was a measure of how the two eyes were positioned relative to the prey given the emphasis given on binocular vision, and only after reading certain sections again did I realize convergence was a measure of eye rotation/movement.

      New supplemental figure explaining how eye tracking is performed

      Figure 3. It was not immediately clear to me what onset, middle, and end represented - although it is explained in the caption. I think what tripped me up is the 'eye convergence' title in the top right corner of Figure 3A.

      Updated figure with schematic illustrating that time is measured relative to eye convergence onset and end.

      The result section about attack swim, S-strike, capture spring, etc. was a bit confusing to read and could benefit from a couple of concise descriptions of these behaviors. For example, I am not familiar with the S strike but a couple of paragraphs into this section, the reader learns more about the difference between S strike vs. attack swim. This can be mentioned in the first paragraph when these distinct behaviors are mentioned.

      Added description of behavior earlier in text.

      Figure 4. Presents lots of interesting data! I wonder if using Figure 1E could help the reader better understand how these measurements were taken.

      New supplemental figure added, explaining how tail tracking is performed.

      I probably overlooked this, but I wonder why so many panels are just focused on one species.

      Added explanation to the text.

      Is the S-shaped capture strategy the same as an S strike?

      Clarified in text to say "S-strike-like". This is a description of prey capture from adult largemouth bass in New et al. (2002). From the still frames shown in that paper, the kinematics looks similar to an S-strike or capture spring. The important point we wish to make is that tail is coiled in an S-shape prior to a strike, which indicates this that a kinematically similar behavior exists fishes beyond just larval cichlids and zebrafish.

      At the end of the page, when continuous swimming versus interrupted swimming is discussed, please remind the reader that medaka shows more continuous swimming (longer bouts).

      Added "while medaka swim continuously with longer bouts ("gliding")".

      After reading the discussion, it looks like many findings are unique. For example, given that medaka is such a popular model species in biology, it strikes me that nobody has ever looked into their hunting movements before. If their findings are novel, perhaps they should state so it is clear that the authors are not ignoring the literature.

      We have highlighted what we believe to be the novelty of our findings (first description of prey capture in larval cichlids and medaka). To our knowledge, we are first to describe hunting in medaka; but there is an extensive literature on medaka dating back to the early 20th century, some of which is only published in Japanese. We have done our best to review the literature, but we cannot rule out that there are papers that we missed. No English language article or review we found mentions literature on hunting behavior in medaka larvae.

      Reviewer 3:

      More evidence is needed to assess the types of visual monocular depth cues used by medaka fish to estimate prey location, but that is beyond the scope of this compelling paper. For example, medaka may estimate depth through knowledge of expected prey size, accommodation, defocus blur, ocular parallax, and/or other possible algorithms to complement cues from motion parallax.

      Added sentence to discussion highlighting that other cues may also contribute to distance estimation in cichlids and medakas. Follow-up studies will require new animal license.

      None. It's quite nice, timely, and thorough work! For future work, one could use 3D pose estimation of eye and prey kinematics to assess the dynamics of the 2D image (prey and background) cast onto the retina. This sort of representation could be useful to infer which monocular depth cues may be used by medaka during hunting.

      Great suggestion for follow up studies. Bolton et al. and Mearns et al. both find changes in z associated with prey capture, and it would be interesting to see how other fish species use the full 3-dimensional water column during prey capture, especially considering the diversity of hunting strategies in adult cichlids (ranging from piscivorous species, like LA, to algar grazers).

      In Figure 4N, you use "change in heading leading up to a strike as a proxy for the change in visual angle of the prey for cichlids and medaka." This proxy makes sense, but you also have the eye angles and (in some cases) the prey positions. One could estimate the actual change in visual angle from this information, which would also allow one to measure whether the fish are trying to stabilize the position of the prey on a high-acuity patch of the retina during the final moments of the hunt. This information may also shed light on which monocular depth cues are used.

      As addressed in comment to reviewer 1, this would require actually manually tracking individual paramecia over hundreds of frames. It is not possible to determine exactly when hunting begins in medaka, and it is prone to errors if medaka switch between targets over the course of a hunting episode. This question is better addressed with psychophysics experiments in embedded animals where it is possible to precisely control the stimulus, but this requires new animal licenses and is beyond the scope of this paper.

      In Figure 5, you could place the prey object a little farther from the D. rerio fish for the S-strike diagram.

      Fixed.

      Figure 4F legend should read "...at the peak of each bout."

      Fixed.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Thank you for your constructive feedback and recognition of our work. We followed your suggestion and improved the accuracy of the language used to interpret some of our findings. 

      Summary:

      The present study by Mikati et al demonstrates an improved method for in-vivo detection of enkephalin release and studies the impact of stress on the activation of enkephalin neurons and enkephalin release in the nucleus accumbens (NAc). The authors refine their pipeline to measure met and leu enkephalin using liquid chromatography and mass spectrometry. The authors subsequently measured met and leu enkephalin in the NAc during stress induced by handling, and fox urine, in addition to calcium activity of enkephalinergic cells using fiber photometry. The authors conclude that this improved tool for measuring enkephalin reveals experimenter handling stress-induced enkephalin release in the NAc that habituates and is dissociable from the calcium activity of these cells, whose activity doesn't habituate. The authors subsequently show that NAc enkephalin neuron calcium activity does habituate to fox urine exposure, is activated by a novel weigh boat, and that fox urine acutely causes increases in met-enk levels, in some animals, as assessed by microdialysis.

      Strengths:

      A new approach to monitoring two distinct enkephalins and a more robust analytical approach for more sensitive detection of neuropeptides. A pipeline that potentially could help for the detection of other neuropeptides.

      Weaknesses:

      Some of the interpretations are not fully supported by the existing data or would require further testing to draw those conclusions. This can be addressed by appropriately tampering down interpretations and acknowledging other limitations the authors did not cover brought by procedural differences between experiments.

      We have taken time to go through the manuscript ensuring we are more detailed and precise with our interpretations as well as appropriately acknowledging limitations. 

      Reviewer #2 (Public Review):

      Thank you for your constructive and thorough assessment of our work. In our revised manuscript, we adjusted the text to reflect the references you mentioned regarding the methionine oxidation procedure. Additionally, we expanded the methods section to include the key details of the statistical tests and procedures that you outlined. 

      Summary:

      The authors aimed to improve the detection of enkephalins, opioid peptides involved in pain modulation, reward, and stress. They used optogenetics, microdialysis, and mass spectrometry to measure enkephalin release during acute stress in freely moving rodents. Their study provided better detection of enkephalins due to the implementation of previously reported derivatization reaction combined with improved sample collection and offered insights into the dynamics and relationship between Met- and Leu-Enkephalin in the Nucleus Accumbens shell during stress.

      Strengths:

      A strength of this work is the enhanced opioid peptide detection resulting from an improved microdialysis technique coupled with an established derivatization approach and sensitive and quantitative nLC-MS measurements. These improvements allowed basal and stimulated peptide release with higher temporal resolution, lower detection thresholds, and native-state endogenous peptide measurement.

      Weaknesses:

      The draft incorrectly credits itself for the development of an oxidation method for the stabilization of Met- and Leu-Enk peptides. The use of hydrogen peroxide reaction for the oxidation of Met-Enk in various biological samples, including brain regions, has been reported previously, although the protocols may slightly vary. Specifically, the manuscript writes about "a critical discovery in the stabilization of enkephalin detection" and that they have "developed a method of methionine stabilization." Those statements are incorrect and the preceding papers that relied on hydrogen peroxide reaction for oxidation of Met-Enk and HPLC for quantification of oxidized Enk forms should be cited. One suggested example is Finn A, Agren G, Bjellerup P, Vedin I, Lundeberg T. Production and characterization of antibodies for the specific determination of the opioid peptide Met5-Enkephalin-Arg6-Phe7. Scand J Clin Lab Invest. 2004;64(1):49-56. doi: 10.1080/00365510410004119. PMID: 15025428.

      Thank you for highlighting this. It was not our intention to imply that we developed the oxidation method, rather that we were able improve the detection of metenkephalin by oxidation of the methionine without compromising the detection resolution of leu-enkephalin, enabling the simultaneous detection of both peptides. We have addressed this is the manuscript and included the suggested citation. 

      Another suggestion for this draft is to make the method section more comprehensive by adding information on specific tools and parameters used for statistical analysis:

      (1) Need to define "proteomics data" and explain whether calculations were performed on EIC for each m/z corresponding to specific peptides or as a batch processing for all detected peptides, from which only select findings are reported here. What type of data normalization was used, and other relevant details of data handling? Explain how Met- and Leu-Enk were identified from DIA data, and what tools were used.

      Thank you for pointing out this source of confusion. We believe it is because we use a different DIA method than is typically used in other literature. Briefly, we use a DIA method with the targeted inclusion list to ensure MS2 triggering as opposed to using large isolation widths to capture all precursors for fragmentation, as is typically done with MS1 features. For our method, MS2 is triggered based on the 4 selected m/z values (heavy and light versions of Leu and Met-Enkephalin peptides) at specific retention time windows with isolation width of 2 Da; regardless of the intensity of MS1 of the peptides. 

      (2) Simple Linear Regression Analysis: The text mentions that simple linear regression analysis was performed on forward and reverse curves, and line equations were reported, but it lacks details such as the specific variables being regressed (although figures have labels) and any associated statistical parameters (e.g., R-squared values). 

      Additional detail about the linear regression process was added to the methods section, please see lines 614-618. The R squared values are also now shown on the figure. 

      ‘For the forward curves, the regression was applied to the measured concentration of the light standard as the theoretical concentration was increased. For plotting purposes, we show the measured peak area ratios for the light standards in the forward curves. For the reverse curves, the regression was applied to the measured concentration of the heavy standard, as the theoretical concentration was varied.’

      (3) Violin Plots: The proteomics data is represented as violin plots with quartiles and median lines. This visual representation is mentioned, but there is no detail regarding the software/tools used for creating these plots.

      We used Graphpad Prism to create these plots. This detail has been added to the statistical analysis section. See line 630.

      (4) Log Transformation: The text states that the data was log-transformed to reduce skewness, which is a common data preprocessing step. However, it does not specify the base of the logarithm used or any information about the distribution before and after transformation.

      We have added the requested details about the log transformation, and how the data looked before and after, into the statistical analysis section. We followed convention that the use of log is generally base 10 unless otherwise specified as natural log (base 2) or a different base. See lines 622-625

      ‘The data was log10 transformed to reduce the skewness of the dataset caused by the variable range of concentrations measured across experiments/animals. Prior to log transformation, the measurements failed normality testing for a Gaussian distribution. After the log transformation, the data passed normality testing, which provided the rationale for the use of statistical analyses that assume normality.’

      (5) Two-Way ANOVA: Two-way ANOVA was conducted with peptide and treatment as independent variables. This analysis is described, but there is no information regarding the software or statistical tests used, p-values, post-hoc tests, or any results of this analysis.

      Information about the two-way ANOVA analysis has been added to the statistical analysis section. Additionally, more detailed information has been added to the figure legends about the statistical results. Please see lines 625-628.

      ‘Two-way ANOVA testing with peptide (Met-Enk or Leu-Enk) and treatment (buffer or stress for example) as the two independent variables. Post-hoc testing was done using Šídák's multiple comparisons test and the p values for each of these analyses are shown in the figures (Figs. 1F, 2A).’ 

      (6) Paired T-Test: A paired t-test was performed on predator odor proteomic data before and after treatment. This step is mentioned, but specific details like sample sizes, and the hypothesis being tested are not provided.

      The sample size is included in the figure legend to which we have included a reference. We have also included the following text to highlight the purpose of this test. See lines 628-630

      A paired t-test was performed on the predator odor proteomic data before and after odor exposure to test that hypothesis that Met-Enk increases following exposure to predator odor  (Fig. 3F). These analyses were conducted using Graphpad Prism.

      (7) Correlation Analysis: The text mentions a simple linear regression analysis to correlate the levels of Met-Enk and Leu-Enk and reports the slopes. However, details such as correlation coefficients, and p-values are missing.

      We apologize for the use of the word correlation as we think it may have caused some confusion and have adjusted the language accordingly. Since this was a linear regression analysis, there is no correlation coefficient. The slope of the fitted line is reported on the figures to show the fitted values of Met-Enk to Leu-Enk. 

      (8) Fiber Photometry Data: Z-scores were calculated for fiber photometry data, and a reference to a cited source is provided. This section lacks details about the calculation of zscores, and their use in the analysis. 

      These details have been added to the statistical analysis section. See lines 634-637

      ‘For the fiber photometry data, the z-scores were calculated as described in using GuPPy which is an open-source python toolbox for fiber photometry analysis. The z-score equation used in GuPPy is z=(DF/F-(mean of DF/F)/standard deviation of DF/F) where F refers to fluorescence of the GCaMP6s signal.’

      (9) Averaged Plots: Z-scores from individual animals were averaged and represented with SEM. It is briefly described, but more details about the number of animals, the purpose of averaging, and the significance of SEM are needed.

      We have added additional information about the averaging process in the statistical analysis section. See lines 639-643.

      ‘The purpose of the averaged traces is to show the extent of concordance of the response to experimenter handling and predator odor stress among animals with the SEM demonstrating that variability. The heatmaps depict the individual responses of each animal. The heatmaps were plotted using Seaborn in Python and mean traces were plotted using Matplotlib in Python.’

      A more comprehensive and objective interpretation of results could enhance the overall quality of the paper.

      We have taken this opportunity to improve our manuscript following comments from all the reviewers that we hope has resulted in a manuscript with a more objective interpretation of results. 

      Reviewer #3 (Public Review):

      Thank you for your thoughtful review of our work. To clarify some of the points you raised, we revised the manuscript to include more detail on how we distinguish between the oxidized endogenous and standard signal, as well as refine the language concerning the spatial resolution. We also edited the manuscript regarding the concentration measurements. We conducted technical replicates, so we appreciate you raising this point and clarify that in the main text. 

      Summary:

      This important paper describes improvements to the measurement of enkephalins in vivo using microdialysis and LC-MS. The key improvement is the oxidation of met- to prevent having a mix of reduced and oxidized methionine in the sample which makes quantification more difficult. It then shows measurements of enkephalins in the nucleus accumbens in two different stress situations - handling and exposure to predator odor. It also reports the ratio of released met- and leu-enkephalin matching what is expected from the digestion of proenkephalin. Measurements are also made by photometry of Ca2+ changes for the fox odor stressor. Some key takeaways are the reliable measurement of met-enkephalin, the significance of directly measuring peptides as opposed to proxy measurements, and the opening of a new avenue into the research of enkephalins due to stress based on these direct measurements.

      Strengths:

      -Improved methods for measurement of enkephalins in vivo.

      -Compelling examples of using this method.

      -Opening a new area of looking at stress responses through the lens of enkephalin concentrations.

      Weaknesses:

      (1) It is not clear if oxidized met-enk is endogenous or not and this method eliminates being able to discern that.

      We clarified our wording in the text copied below to provide an explanation on how we distinguish between the two. Even after oxidation, the standard signal has a higher m/z ratio due to the presence of the Carbon and Nitrogen isotopes as described in the Chemicals section of the methods ‘For Met Enkephalin, a fully labeled L-Phenylalanine (<sup>13</sup>C<sub>9</sub>, <sup>15</sup>N) was added (YGGFM). The resulting mass shift between the endogenous (light) and heavy isotope-labeled peptide are 7Da and 10Da, respectively.’, so they can still be differentiated from the endogenous signal. We have clarified the language in the results section. See lines 82-87. 

      ‘After each sample collection, we add a consistent known concentration of isotopically labeled internal standard of Met-Enk and Leu-Enk of 40 amol/sample to the collected ISF for the accurate identification and quantification of endogenous peptide. These internal standards have a different mass/charge (m/z) ratio than endogenous Met- and Leu-Enk. Thus, we can identify true endogenous signal for Met-Enk and Leu-Enk (Suppl Fig. 1A,C) versus noise, interfering signals, and standard signal (Suppl. Fig. 1B,D).’

      (2) It is not clear if the spatial resolution is really better as claimed since other probes of similar dimensions have been used.

      Apologies for any confusion here. To clarify we primarily state that our approach improves temporal resolution and in a few cases refer to improved spatiotemporal resolution, which we believe we show. The dimensions of the microdialysis probe used in these experiments allow us to target the nucleus accumbens shell and as well as being smaller – especially at the membrane level - than a fiber photometry probe. 

      (3) Claims of having the first concentration measurement are not quite accurate.

      Thank you for your feedback. To clarify, we do not claim that we have the first concentration measurements, rather we are the first to quantify the ratio of Met-Enk to Leu-Enk in vivo in freely behaving animals in the NAcSh. 

      (4) Without a report of technical replicates, the reliability of the method is not as wellevaluated as might be expected.

      We have added these details in the methods section, please see lines 521-530. 

      ‘Each sample was run in two technical replicates and the peak area ratio was averaged before concentration calculations of the peptides were conducted. Several quality control steps were conducted prior to running the in vivo samples. 1) Two technical replicates of a known concentration were injected and analyzed – an example table from 4 random experiments included in this manuscript is shown below. 2) The buffers used on the day of the experiment (aCSF and high K+ buffer) were also tested for any contaminating Met-Enk or Leu-Enk signals by injecting two technical replicates for each buffer. Once these two criteria were met, the experiment was analyzed through the system. If either step failed, which happened a few times, the samples were frozen and the machines were cleaned and restarted until the quality control measures were met.’

      Recommendations For The Authors:

      Reviewer #1 (Recommendations For The Authors):

      • The authors should provide appropriate citations of a study that has validated the Enkephalin-Cre mouse line in the nucleus accumbens or provide verification experiments if they have any available.

      Thank you for your comment. We have added a reference validating the Enk-Cre mouse line in the nucleus accumbens to the methods section and is copied here. 

      D.C. Castro, C.S. Oswell, E.T. Zhang, C.E. Pedersen, S.C. Piantadosi, M.A. Rossi, A.C. Hunker, A. Guglin, J.A. Morón, L.S. Zweifel, G.D. Stuber, M.R. Bruchas, An endogenous opioid circuit determines state-dependent reward consumption, Nature 2021 598:7882 598 (2021) 646–651. https://doi.org/10.1038/s41586-02104013-0.

      • Better definition of the labels y1,y2,b3 in Figures 1 and S1 would be useful. I may have missed it but it wasn't described in methods, results, or legends.

      Thank you for this comment. We have added this information to Fig.1 legend ‘Y1, y2, b3 refer to the different elution fragments resulting from Met-Enk during LC-MS.

      • It is interesting that the ratio of KCl-evoked release is what changes differentially for Met- vs Leu. Leu enk increases to the range of met-enk. There is non-detectable or approaching being non-detectable leu-enk (below the 40 amol / sample limit of quantification) in most of the subjects that become apparent and approach basal levels of met-enkephalin. This suggests that the K+ evoked response may be more pronounced for leu-enk. This is something that should be considered for further analysis and should be discussed.

      Thank you for this astute observation, and you make a great point. We have added some discussion of this finding in the results and discussion sections see lines 111112 and lines 253-257. 

      ‘Interestingly, Leu-Enk showed a greater fold change compared to baseline than did Met-Enk with the fold changes being 28 and 7 respectively based on the data in Fig.1F.’

      ‘We also noted that Leu-Enk showed a greater fold increase relative to baseline after depolarization with high K+ buffer as compared to Met-Enk. This may be due to increased Leu-Enk packaging in dense core vesicles compared to Met-Enk or due to the fact that there are two distinct precursor sources for Leu-Enk, namely both proenkephalin and prodynorphin while Met-Enk is mostly cleaved from proenkephalin (see Table 1 [48]).’

      • For example in 2E, it would be helpful to label in the graph axis what samples correspond to the manipulation and also in the text provide the reader with the sample numbers. The authors interpret the relationship between the last two samples of baseline and posthandling stress as the following in the figure legend "the concentration released in later samples is affected; such influence suggests that there is regulation of the maximum amount of peptide to be released in NAcSh. E. The negative correlation in panel d is reversed by using a high K+ buffer to evoke Met-Enk release, suggesting that the limited release observed in D is due to modulation of peptide release rather than depletion of reserves." However, the correlations are similar between 2D and E and it appears that two mice are mediating the difference between the two groups. The appropriate statistical analysis would be to compare the regressions of the two groups. Statistics for the high K+ (and all other graphs where appropriate) need to be reported, including the r2 and p-value.

      Thank you for your constructive critique. To elucidate the effect of high K+, we have plotted the regression line and reported the slope for Fig. 2E. Notably, the slope is reduced by a factor of 2 and appears to be driven by a large subset of the animals. The statistics for the high K+ graph are shown on the figure (Fig 1F) which test the hypothesis of whether high K+ leads to the release of Leu-Enk and Met-Enk respectively compared to baseline with aCSF. We have added the test statistics to the figure legend for additional clarity. Fig. 1G has no statistics because it is only there to elucidate the ratio between Met-Enk and Leu-Enk in the same samples. We did not test any hypotheses related to whether there are differences between their levels as that is not relevant to our question. The correlation on the same data is depicted in Fig. 1H, and we have added the R<sup>2</sup> value per your request. 

      • The interpretation that handling stress induces enkephalin release from microdialysis experiments is also confounded by other factors. For instance, from the methods, it appears that mice were connected and sample collection started 30 min after surgery, therefore recovery from anesthesia is also a confounding variable, among other technical aspects, such as equilibration of the interstitial fluid to the aCSF running through the probe that is acting as a transmitter and extracellular molecule "sink". Did the authors try to handle the mice post hookup similar to what was done with photometry to have a more direct comparison to photometry experiments? This procedural difference, recording from recently surgerized animals (microdialysis) vs well-recovered animals with photometry should be mentioned in addition to the other caveats the authors mention.

      Thank you for your comment. We are aware of this technical limitation, and it is largely why we sought to conduct the fiber photometry experiments to get at the same question. As you requested, we have included additional language in the discussion to acknowledge this limitation and how we chose to address it by measuring calcium activity in the enkephalinergic neurons, which would presumably be the same cell population whose release we are quantifying using microdialysis. See lines 262-273.  

      ‘Our findings showed a robust increase in peptide release at the beginning of experiments, which we interpreted as due to experimenter handling stress that directly precedes microdialysis collections. However, there are other technical limitations to consider such as the fact that we were collecting samples from mice that were recently operated on. Another consideration is that the circulation of aCSF through the probe may cause a sudden shift in oncotic and hydrostatic forces, leading to increased peptide release to the extracellular space. As such, we wanted to examine our findings using a different technique, so we chose to record calcium activity from enkephalinergic neurons - the same cell population leading to peptide release. Using fiber photometry, we showed that enkephalinergic neurons are activated by stress exposure, both experimenter handling and fox odor, thereby adding more evidence to suggest that enkephalinergic neurons are activated by stress exposure which could explain the heightened peptide levels at the beginning of microdialysis experiments.’

      • The authors should provide more details on handling stress manipulation during photometry. For photometry what was the duration of the handling bout, what was the interval between handling events, and can the authors provide a description of what handling entailed? Were mice habituated to handling days before doing photometry recording experiments?

      Thank you for your suggestion. We have addressed all of your points in the methods section. See lines 564-570. 

      ‘The handling bout which mimicked traditional scruffing lasted about 3-5 seconds. The mouse was then let go and the handling was repeated another two times in a single session with a minimum of 1-2 minutes between handling bouts. Mice were habituated to this manipulation by being attached to the fiber photometry rig, for 3-5 consecutive days prior to the experimental recording. Additionally, the same maneuver was employed when attaching/detaching the fiber photometry cord, so the mice were subjected to the same process several times.’

      • For the novel weigh boat experiments, the authors should explicitly state when these experiments were done in relation to the fox urine, was it a different session or the same session? Were they the same animals? Statements like the following (line 251) imply it was done in the same animals in the same session but it should be clarified in the methods "We also showed using fiber photometry that the novelty of the introduction of a foreign object to the cage, before adding fox odor, was sufficient to activate enkephalinergic neurons."

      As shown in supplementary figure 4, individual animal data is shown for both water and fox urine exposure (overlaid) to depict whether there were differences in their responses to each manipulation – in the same animal. And yes, you are correct, the animals were first exposed to water 3 times in the recording session and then exposed to fox urine 3 times in the same session. We have added that to the methods section describing in vivo fiber photometry. See lines 575-576.  

      • Statistical testing would be needed to affirm the conclusions the authors draw from the fox urine and novel weigh boat experiments. For example, it shows stats that the response attenuates, that it is not different between fox urine and novel (it looks like the response is stronger to the fox urine when looking at the individual animals), etc. These data look clear but stats are formally needed. Formal statistics are also missing in other parts of the manuscript where conclusions are drawn from the data but direct statistical comparisons are not included (e.g. Fig 2.G-I).

      The photometry data is shown as z-scores which is a formal statistical analysis. ANOVA would be inappropriate to run to compare z-scores. We understand that this is erroneously done in fiber photometry literature, however, it remains incorrect. The z-scores alone provide all the information needed about the deviation from baseline. We understand that this is not immediately clear to readers, and we thank you for allowing us to explain why this is the case. We have added test statistics to figure legends where hypothesis testing was done and p-values were reported. 

      • Did the authors try to present the animals with repeated fox urine exposure to see if this habituates like the photometry?

      No, we did not do that experiment due to the constrained timing within which we had to run our microdialysis/LC-MS timeline, but it is a great point for future exploration. 

      • It would be useful to present the time course of the odor experiment for the microdialysis experiment.

      The timeline is shown in Fig.1a and Fig.3e. To reiterate, each sample is 13 minutes long.

      • Can the authors determine if differences in behavior (e.g. excessive avoidance in animals with with one type of response) or microdialysis probe location dictate whether animals fall into categories of increased release, no release, or no-detection? From the breakdown, it looks like it is almost equally split into three parts but the authors' descriptions of this split are somewhat misleading (line 210). " The response to predator odor varies appreciably: although most animals show increased Met-Enk release after fox odor exposure, some show continued release with no elevation in Met-Enk levels, and a minority show no detectable release".

      Thank you for your constructive feedback. We do not believe the difference in behavior is correlated with probe placement. The hit map can be found in suppl. Fig 3 and shows that all mice included in the manuscript had probes in the NAcSh. We purposely did not distinguish between dorsal and ventral because of our 1 mm membrane would make it hard to presume exclusive sampling from one subregion. That is a great point though, and we have thought about it extensively for future studies. We have edited the language to reflect the almost even split of responses for Met-Enk and appreciate you pointing that out. 

      • Overall, given the inconsistencies in experimental design and overall caveats associated, I think the authors are unable to draw reasonable conclusions from the repeated stressor experiments and something they should either consider is not trying to draw strong conclusions from these observations or perform additional experiments that provide the grounds to derive those conclusions.

      We have included additional language on the caveats of our study, and our use of a dual approach using fiber photometry and microdialysis was largely driven by a

      desire to offer additional support of our conclusions. We expected pushback about our conclusions, so we wanted to offer a secondary analysis using a different technique to test our hypothesis. To be honest the tone of this comment and content is not particularly constructive (especially for trainees) nor does it offer a space to realistically address anything. This work took multiple years to optimize, it was led by a graduate student, and required a multidisciplinary team. As highlighted, we believe it offers an important contribution to the literature and pushes the field of peptide detection forward.  

      Reviewer #2 (Recommendations For The Authors):

      A more comprehensive and objective interpretation of results could enhance the overall quality of the paper. The manuscript contains statements like "we are the first to confirm," which can be challenging to substantiate and may not significantly enhance the paper. It's essential to ensure that novelty statements are well-founded. For example, the release of enkephalins from other brain regions after stress exposure is well-documented but not addressed in the paper. Similarly, the role of the NA shell in stress has been extensively studied but lacks coverage in this manuscript.

      We have edited the language to reflect your feedback. We have also included relevant literature expanding on the demonstrated roles of enkephalins in the literature. We would like to note that most studies have focused on chronic stress, and we were particularly interested in acute stress. See lines 129-134.

      ‘These studies have included regions such as the locus coeruleus, the ventral medulla, the basolateral nucleus of the amygdala, and the nucleus accumbens core and shell. Studies using global knockout of enkephalins have shown varying responses to chronic stress interventions where male knockout mice showed resistance to chronic mild stress in one study, while another study showed that enkephalin-knockout mice showed delayed termination of corticosteroid release. [33,34]’ 

      Finally, not a weakness but a clarification suggestion: the method description mentions the use of 1% FA in the sample reconstitution solution and LC solvents, which is an unusually high concentration of acid. If this concentration is intentional for maintaining the peptides' oxidation state, it would be beneficial to mention this in the text to assist readers who might want to replicate the method.

      This is correct and has been clarified in the methods section

      Reviewer #3 (Recommendations For The Authors):

      -The Abstract should state the critical improvements that are made. Also, quantify the improvements in spatiotemporal resolution.

      Thank you for your comment. We have edited the abstract to reflect this. 

      - The use of "amol/sample" as concentration is less informative than an SI units (e.g., pM concentration) and should be changed. Especially since the volume used was the same for in vivo sampling experiments.

      Thank you for your comment. We chose to report amol/sample because we are measuring such a small concentration and wanted to account for any slight errors in volume that can make drastic differences on reported concentrations especially since samples are dried and resuspended.  

      -Please check this sentence: "After each collection, the samples were spiked with 2 µL of 12.5 fM isotopically labeled Met-Enkephalin and Leu-Enkephalin" This dilution would yield a concentration of ~2 fM. In a 12 uL sample, that would be ~0.02 amol, well below the detection limit. (note that fM would femtomolar concentration and fmol would be femtomoles added).

      -"liquid chromatography/mass spectrometry (LC-MS) [9-12]"... Reference 9 is a RIA analysis paper, not LC-MS as stated.

      Thank you for catching these. We have corrected the unit and citation. 

      -Given that improvements in temporal resolution are claimed, the lack of time course data with a time axis is surprising. Rather, data for baseline and during treatment appear to be combined in different plots. Time course plots of individuals and group averages would be informative.

      Due to the expected variability between individual animal time course data, where for example, we measure detectable levels in one sample followed by no detection, it was very difficult to combine data across time. Therefore, to maximize data inclusion from all animals that showed baseline measurements and responses to individual manipulations, we opted to report snapshot data. Our improvement in temporal resolution refers to the duration of each sample rather than continuous sampling, so those two are unrelated. Thank you for your feedback and allowing us to clarify this.

      - I do not understand this claim "We use custom-made microdialysis probes, intentionally modified so they are similar in size to commonly used fiber photometry probes to avoid extensive tissue damage caused by traditional microdialysis probes (Fig. 1B)." The probes used are 320 um OD and 1 mm long. This is not an uncommon size of microdialysis probes and indeed many are smaller, so is their probe really causing less damage than traditional probes?

      Thank you for your comment. We are only trying to make the point that the tissue damage from these probes is comparable to commonly used fiber photometry probes. We only point that out because tissue damage is used as a point to dissuade the usage of microdialysis in some literature, and we just wanted to disambiguate that. We have clarified the statement you pointed out.  

      -The oxidation procedure is a good idea, as mentioned above. It would be interesting to compare met-enk with and without the oxidation procedure to see how much it affects the result (I would not say this is necessary though). It is not uncommon to add antioxidants to avoid losses like this. Also, it should be acknowledged that the treatment does prevent the detection of any in vivo oxidation, perhaps that is important in met-enk metabolism?

      The comparison between oxidized and unoxidized Met-Enk detection is in figure 1C. 

      -It would be a best practice to report the standard deviation of signal for technical replicates (say near in vivo concentrations) of standards and repeated analysis of a dialysate sample to be able to understand the variability associated with this method. Similarly, an averaged basal concentration from all rats.

      Thank you for your comment. We have included a table showing example quality control standard injections from 4 randomly selected experiments included in the manuscript that were run before and after each experiment and descriptive statistics associated with these technical replicates. We also added some detail to the methods section to describe how quality control is done. See lines 521-530. 

      ‘Each sample was run in two technical replicates and the peak area ratio was averaged before concentration calculations of the peptides were conducted. Several quality control steps were conducted prior to running the in vivo samples. 1) Two technical replicates of a known concentration were injected and analyzed – an example table from 4 random experiments included in this manuscript is shown below. 2) The buffers used on the day of the experiment (aCSF and high K+ buffer) were also tested for any contaminating Met-Enk or Leu-Enk signals by injecting two technical replicates for each buffer. Once these two criteria were met, the experiment was analyzed through the system. If either step failed, which happened a few times, the samples were frozen and the machines were cleaned and restarted until the quality control measures were met.’

      EDITORS NOTE

      Should you choose to revise your manuscript, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.

      Thank you for your suggestion. We have included more detail about statistical analysis in the figure legends per this comment and reviewer comments.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Responses to Reviewer #1:

      We thank the reviewer for these additional comments, and more generally for their extensive engagement with our work, which is greatly appreciated. Here, we respond to the three points in their latest review in turn.

      The results of these experiments support a modest but important conclusion: If sub-optimal methods are used to collect retrospective reports, such as simple yes/no questions, inattentional blindness (IB) rates may be overestimated by up to ~8%.

      It is true, of course, that we think the field has overstated the extent of IB, and we appreciate the reviewer characterizing our results as important along these lines. Nevertheless, we respectfully disagree with the framing and interpretation the reviewer attaches to them. As explained in our previous response, we think this interpretation — and the associated calculations of IB overestimation ‘rates’ — perpetuates a binary approach to perception and awareness which we regard as mistaken.

      A graded approach to IB and visual awareness 

      Our sense is that many theorists interested in IB have conceived of perception and awareness as ‘all or nothing’: You either see a perfectly clear gorilla right in front of you, or you see nothing at all. This is implicit in the reviewer’s characterization of our results as simply indicating that fewer subjects fail to see the critical stimulus than previously assumed. To think that way is precisely to assume the orthodox binary position about perception, i.e., that any given subject can neatly be categorized into one of two boxes, saw or didn’t see.

      Our perspective is different. We think there can be degraded forms of perception and awareness that fall neatly into neither of the categories “saw the stimulus perfectly clearly” or “saw nothing at all”. On this graded conception, the question is not: “What proportion of subjects saw the stimulus?” but: “What is the sensitivity of subjects to the stimulus?” This is why we prefer signal detection measures like d′ over % noticing and % correct. This powerful framework has been successful in essentially every domain to which it has been applied, and we think perception and visual awareness are no exception. We understand that the reviewer may not think the same way about this foundational issue, but since part of our goal is to promote a graded approach to perception, we are keen to highlight our disagreement here and so resist the reviewer’s interpretation of our results (even to the extent that it is a positive one!).

      Finally, we note that given this perspective, we are correspondingly inclined to reject many of the summary figures following below in Point (1) by the reviewer. These calculations (given in terms of % noticing and not noticing) make sense on the binary conception of awareness, but not on the SDT-based approach we favor. We say more about this below. 

      (1) In experiment 1, data from 374 subjects were included in the analysis. As shown in figure 2b, 267 subjects reported noticing the critical stimulus and 107 subjects reported not noticing it. This translates to a 29% IB rate if we were to only consider the "did you notice anything unusual Y/N" question. As reported in the results text (and figure 2c), when asked to report the location of the critical stimulus (left/right), 63.6% of the "non-noticer" group answered correctly. In other words, 68 subjects were correct about the location while 39 subjects were incorrect. Importantly, because the location judgment was a 2-alternative-forced-choice, the assumption was that if 50% (or at least not statistically different than 50%) of the subjects answered the location question correctly, everyone was purely guessing. Therefore, we can estimate that ~39 of the subjects who answered correctly were simply guessing (because 39 guessed incorrectly), leaving 29 subjects from the nonnoticer group who were correct on the 2AFC above and beyond the pure guess rate. If these 29 subjects are moved from the non-noticer to the noticer group, the corrected rate of IB for Experiment 1 is 20.86% instead of the original 28.61% rate that would have been obtained if only the Y/N question was used. In other words, relying only on the "Y/N did you notice anything" question led to an overestimate of IB rates by 7.75% in Experiment 1.

      In the revised version of their manuscript, the authors provided the data that was missing from the original submission, which allows this same exercise to be carried out on the other 4 experiments.  

      (To briefly interject: All of these data were provided in our public archive since our original submission and remain available at https://osf.io/fcrhu. The difference now is only that they are included in the manuscript itself.)

      Using the same logic as above, i.e., calculating the pure-guess rate on the 2AFC, moving the number of subjects above this pure-guess rate to the non-noticer group, and then re-calculating a "corrected IB rate", the other experiments demonstrate the following:

      Experiment 2: IB rates were overestimated by 4.74% (original IB rate based only on Y/N question = 27.73%; corrected IB rate that includes the 2AFC = 22.99%)

      Experiment 3: IB rates were overestimated by 3.58% (original IB rate = 30.85%; corrected IB rate = 27.27%)

      Experiment 4: IB rates were overestimated by ~8.19% (original IB rate = 57.32%; corrected IB rate for color* = 39.71%, corrected IB rate for shape = 52.61%, corrected IB rate for location = 55.07%)

      Experiment 5: IB rates were overestimated by ~1.44% (original IB rate = 28.99%; corrected IB rate for color = 27.56%, corrected IB rate for shape = 26.43%, corrected IB rate for location = 28.65%)

      *note: the highest overestimate of IB rates was from Experiment 4, color condition, but the authors admitted that there was a problem with 2AFC color guessing bias in this version of the experiment which was a main motivation for running experiment 5 which corrected for this bias.

      Taken as a whole, this data clearly demonstrates that even with a conservative approach to analyzing the combination of Y/N and 2AFC data, inattentional blindness was evident in a sizeable portion of the subject populations. An important (albeit modest) overestimate of IB rates was demonstrated by incorporating these improved methods.

      We appreciate the work the reviewer has put into making these calculations. However, as noted above, such calculations implicitly reflect the binary approach to perception and awareness that we reject. 

      Consider how we’d think about the single subject case where the task is 2afc detection of a low contrast stimulus in noise. Suppose that this subject achieves 70% correct. One way of thinking about this is that the subject fully and clearly sees the stimulus on 40% of trials (achieving 100% correct on those) and guesses completely blindly on the other 60% (achieving 50% correct on those) for a total of 40% + 30% = 70% overall. However, this is essentially a ‘high threshold’ approach to the problem, in contrast to an SDT approach. On an SDT approach — an approach with tremendous evidential support — on every trial the subject receives samples from probabilistic distributions corresponding to each interval (one noise and one signal + noise) and determines which is higher according to the 2afc decision rule. Thus, across trials, they have access to differentially graded information about the stimulus. Moreover, on some trials they may have significant information from the stimulus (perhaps, well above their single interval detection criterion) but still decide incorrectly because of high noise from the other spatial interval. From this perspective, there is no nonarbitrary way of saying whether the subject saw/did not see on a given trial. Instead, we must characterize the subject’s overall sensitivity to the stimulus/its visibility to them in terms of a parameter such as d′ (here, ~ 0.7).

      We take the same attitude to the subjects in our experiments (and specifically to our ‘super subject’). Instead of calculating the proportion of subjects who saw or failed to see the stimulus (with some characterized as aware and some as unaware), we think the best way to characterize our results is that, across subjects (and so trials also), there was differential graded access to information from the stimulus, and this is best represented in terms of the group-level sensitivity parameter d′. This is why we frame our results as demonstrating that subjects traditionally considered inattentionally blind exhibit significant residual visual sensitivity to the critical stimulus.

      (2) One of the strongest pieces of evidence presented in this paper was the single data point in Figure 3e showing that in Experiment 3, even the super subject group that rated their non-noticing as "highly confident" had a d' score significantly above zero. Asking for confidence ratings is certainly an improvement over simple Y/N questions about noticing, and if this result were to hold, it could provide a key challenge to IB. However, this result can most likely be explained by measurement error.

      In their revised paper, the authors reported data that was missing from their original submission: the confidence ratings on the 2AFC judgments that followed the initial Y/N question. The most striking indication that this data is likely due to measurement error comes from the number of subjects who indicated that they were highly confident that they didn't notice anything on the critical trial, but then when asked to guess the location of the stimulus, indicated that they were highly confident that the stimulus was on the left (or right). There were 18 subjects (8.82% of the high-confidence non-noticer group) who responded this way. To most readers, this combination of responses (high confidence in correctly judging a stimulus feature that one is highly confident in having not seen at all) indicates that a portion of subjects misunderstood the confidence scales (or just didn't read the questions carefully or made mistakes in their responses, which is common for experiments conducted online).

      In the authors' rebuttal to the first round of peer review, they wrote, "it is perfectly rationally coherent to be very confident that one didn't see anything but also very confident that if there was anything to be seen, it was on the left." I respectfully disagree that such a combination of responses is rationally coherent. The more parsimonious interpretation is that a measurement error occurred, and it's questionable whether we should trust any responses from these 18 subjects.

      In their rebuttal, the authors go on to note that 14 of the 18 subjects who rated their 2AFC with high confidence were correct in their location judgment. If these 14 subjects were removed from analysis (which seems like a reasonable analysis choice, given their contradictory responses), d' for the high-confidence non-noticer group would most likely fall to chance levels. In other words, we would see a data pattern similar to that plotted in Figure 3e, but with the first data point on the left moving down to zero d'. This corrected Figure 3e would then provide a very nice evidence-based justification for including confidence ratings along with Y/N questions in future inattentional blindness studies.

      We appreciate the reviewer’s highlighting of this particular piece of evidence as amongst our strongest. (At the same time, we must resist its characterization as a “single data point”: it derives from a large pre-registered experiment involving some 7,000 subjects total, with over 200 subjects in the relevant bin — both figures being far larger than a typical IB experiment.) We also appreciate their raising the issue of measurement error.

      Specifically, the reviewer contends that our finding that even highly confident non-noticers exhibit significant sensitivity is “most likely … explained by measurement error” due to subjects mistakenly inverting our confidence scale in giving their response. In our original reply, we gave two reasons for thinking this quite unlikely; the reviewer has not addressed these in this revised review. First, we explicitly labeled our confidence scale (with 0 labeled as ‘Not at all confident’ and 3 as ‘Highly confident’) so that subjects would be very unlikely simply to invert the scale. This is especially so as it is very counterintuitive to treat “0” as reflecting high confidence. More importantly, however, we reasoned that any measurement error due to inverting or misconstruing the confidence scale should be symmetric. That is: If subjects are liable to invert the confidence scale, they should do so just as often when they answer “yes” as when they answer “no” – after all the very same scale is being used in both cases. This allows us to explore evidence of measurement error in relation to the large number of high-confidence “yes” subjects (N = 2677), thus providing a robust indicator as to whether subjects are generally liable to misconstrue the confidence scale. Looking at the number of such high confidence noticers who subsequently respond to the 2afc question with low confidence (a pattern which might, though need not, suggest measurement error), we found that the number was tiny. Only 28/2677 (1.05%) of high-confidence noticers subsequently gave the lowest level of confidence on the 2afc question, and only 63/2677 (2.35%) subjects gave either of the two lower levels of confidence. For these reasons, we consider any measurement error due to misunderstanding the confidence scale to be extremely minimal.

      The reviewer is correct to note that 18/204 (9%) subjects reported both being highly confident that they didn't notice anything and highly confident in their 2afc judgment, although only 14/18 were correct in this judgment. Should we exclude these 14? Perhaps if we agree with the reviewer that such a pattern of responses is not “rationally coherent” and so must reflect a misconstrual of the scale. But such a pattern is in fact perfectly and straightforwardly intelligible. Specifically, in a 2afc task, two stimuli can individually fall well below a subject’s single interval detection criterion — leading to a high confidence judgment that nothing was presented in either interval. Quite consistent with this, the lefthand stimulus may produce a signal that is much higher than the right-hand stimulus — leading to a high confidence forced-choice judgment that, if something was presented, it was on the left. (By analogy, consider how a radiologist could look at a scan and say the following: “We’re 95% confident there’s no tumor. But even on the 5% chance that there is, our tests completely rule out that it’s a malignant one, so don’t worry.”) 

      (3) In most (if not all) IB experiments in the literature, a partial attention and/or full attention trial is administered after the critical trial. These control trials are very important for validating IB on the critical trial, as they must show that, when attended, the critical stimuli are very easy to see. If a subject cannot detect the critical stimulus on the control trial, one cannot conclude that they were inattentionally blind on the critical trial, e.g., perhaps the stimulus was just too difficult to see (e.g., too weak, too brief, too far in the periphery, too crowded by distractor stimuli, etc.), or perhaps they weren't paying enough attention overall or failed to follow instructions. In the aggregate data, rates of noticing the stimuli should increase substantially from the critical trial to the control trials. If noticing rates are equivalent on the critical and control trials, one cannot conclude that attention was manipulated in the first place.

      In their rebuttal to the first round of peer review, the authors provided weak justification for not including such a control condition. They cite one paper that argues such control conditions are often used to exclude subjects from analysis (those who fail to notice the stimulus on the control trial are either removed from analysis or replaced with new subjects) and such exclusions/replacements can lead to underestimations of inattentional blindness rates. However, the inclusion of a partial or full attention condition as a control does not necessitate the extra step of excluding or replacing subjects. In the broadest sense, such a control condition simply validates the attention manipulation, i.e., one can easily compare the percent of subjects who answered "yes" or who got the 2AFC judgment correct during the critical trial versus the control trial. The subsequent choice about exclusion/replacement is separate, and researchers can always report the data with and without such exclusions/replacements to remain more neutral on this practice.

      If anyone were to follow-up on this study, I highly recommend including a partial or full attention control condition, especially given the online nature of data collection. It's important to know the percent of online subjects who answer yes and who get the 2AFC question correct when the critical stimulus is attended, because that is the baseline (in this case, the "ceiling level" of performance) to which the IB rates on the critical trial can be compared.

      We agree with the reviewer that future studies could benefit from including a partial or full attention condition. They are surely right that we might learn something additional from such conditions. 

      Where we differ from the reviewer is in thinking of these conditions as “controls” appropriate to our research question. This is why we offered the justification we did in our earlier response. When these conditions are used as controls, they are used to exclude subjects in ways that serve to inflate the biases we are concerned with in our work. For our question, the absence of these conditions does not impact the significance of the findings, since such conditions are designed to answer a question which is not the one at the heart of our paper. Our key claim is that subjects who deny noticing an unexpected stimulus in a standard inattentional blindness paradigm nonetheless exhibit significant residual sensitivity (as well as a conservative bias in their response to the noticing question); the presence or absence of partial- or full-attention conditions is orthogonal to that question.

      Moreover, we note that our tasks were precisely chosen to be classic tasks widely used in the literature to manipulate attention. Thus, by common consensus in the field, they are effective means to soak up attention, and have in effect been tested in partial- and full-attention control settings in a huge number of studies. Second, we think it very doubtful that subjects in a full-attention trial would not overwhelmingly have detected our critical stimuli. The reviewer worries that they might have been “too weak, too brief, too far in the periphery, too crowded by distractor stimuli, etc.” But consider E5 where the stimulus was a highly salient orange or green shape, present on the screen for 5 seconds. The reviewer also suggests that subjects in the full-attention control might not have detected the stimulus because they “weren't paying enough attention overall”. But evidently if they weren’t paying attention even in the full-attention trial this would be reason for thinking that there was inattentional blindness even in this condition (a point made by White et al. 2018) and certainly not a reason for thinking there was not an attentional effect in the critical trial. Lastly, the reviewer suggests that a full-attention condition would have helped ensure that subjects were following instructions. But we ensured this already by (as per our pre-registration) excluding subjects who performed poorly in the relevant primary tasks.

      Thus, both in principle and in practice, we do not see the absence of such conditions as impacting the interpretation of our findings, even as we agree that future work posing a different research question could certainly learn something from including such conditions.

      Responses to Reviewer #2:

      We note that this report is unchanged from an earlier round of review, and not a response to our significantly revised manuscript. We believe our latest version fully addresses all the issues which the reviewer originally raised. The interested reader can see our original response below. We again thank the reviewer for their previous report which was extremely helpful.

      —-

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This study presents valuable findings to the field interested in inattentional blindness (IB), reporting that participants indicating no awareness of unexpected stimuli through yes/no questions, still show above-chance sensitivity to specific properties of these stimuli through follow-up forced-choice questions (e.g., its color). The results suggest that this is because participants are conservative and biased to report not noticing in IB. The authors conclude that these results provide evidence for residual perceptual awareness of inattentionally blind stimuli and that therefore these findings cast doubt on the claim that awareness requires attention. Although the samples are large and the analysis protocol novel, the evidence supporting this interpretation is still incomplete, because effect sizes are rather small, the experimental design could be improved and alternative explanations have not been ruled out.

      We are encouraged to hear that eLife found our work “valuable”. We also understand, having closely looked at the reviews, why the assessment also includes an evaluation of “incomplete”. We gave considerable attention to this latter aspect of the assessment in our revision. In addition to providing additional data and analyses that we believe strengthen our case, we also include a much more substantial review and critique of existing methods in the IB literature to make clear exactly the gap our work fills and the advance it makes. (Indeed, if it is appropriate to say this here, we believe one key aspect of our work that is missing from the assessment is our inclusion of ‘absent’ trials, which is what allows us to make the crucial claims about conservative reporting of awareness in IB for the first time.) Moreover, we refocus our discussion on only our most central claims, and weaken several of our secondary claims so that the data we’ve collected are better aligned with the conclusions we draw, to ensure that the case we now make is in fact complete. Specifically, our two core claims are (1) that there is residual sensitivity to visual features for subjects who would ordinarily be classified as inattentionally blind (whether this sensitivity is conscious or not), and (2) that there is a tendency to respond conservatively on yes/no questions in the context of IB. We believe we have very compelling support for these two core claims, as we explain in detail below and also through revisions to our manuscript.

      Given the combination of strengthened and clarified case, as well as the weakening of any conclusions that may not have been fully supported, we believe and hope that these efforts make our contribution “solid”, “convincing”, or even “compelling” (especially because the “compelling” assessment characterizes contributions that are “more rigorous than the current state-of-the-art”, which we believe to be the case given the issues that have plagued this literature and that we make progress on).

      Reviewer #1 (Public review):

      Summary:

      In the abstract and throughout the paper, the authors boldly claim that their evidence, from the largest set of data ever collected on inattentional blindness, supports the views that "inattentionally blind participants can successfully report the location, color, and shape of stimuli they deny noticing", "subjects retain awareness of stimuli they fail to report", and "these data...cast doubt on claims that awareness requires attention." If their results were to support these claims, this study would overturn 25+ years of research on inattentional blindness, resolve the rich vs. sparse debate in consciousness research, and critically challenge the current majority view in cognitive science that attention is necessary for awareness.

      Unfortunately, these extraordinary claims are not supported by extraordinary (or even moderately convincing) evidence. At best, the results support the more modest conclusion: If sub-optimal methods are used to collect retrospective reports, inattentional blindness rates will be overestimated by up to ~8% (details provided below in comment #1). This evidence-based conclusion means that the phenomenon of inattentional blindness is alive and well as it is even robust to experiments that were specifically aimed at falsifying it. Thankfully, improved methods already exist for correcting the ~8% overestimation of IB rates that this study successfully identified.

      We appreciate here the reviewer’s recognition of the importance of work on inattentional blindness, and the centrality of inattentional blindness to a range of major questions. We also recognize their concerns with what they see as a gap between our data and the claims made on their basis. We address this in detail below (as well as, of course, in our revised manuscript). However, from the outset we are keen to clarify that our central claim is only the first one the reviewer mentions — and the one which appears in our title — namely that, as a group, participants can successfully report the location, color, and shape of stimuli they deny noticing, and thus that there is “Sensitivity to visual features in inattentional blindness”. This is the claim that we believe is strongly supported by our data, and all the more so after revising the manuscript in light of the helpful comments we’ve received.

      By contrast, the other claims the reviewer mentions, concerning awareness (as opposed to residual sensitivity–which might be conscious or unconscious) were intended as both secondary and tentative. We agree with the referee that these are not as strongly supported by our data (and indeed we say so in our manuscript), whereas we do think our data strongly support the more modest — and, to us central — claim that, as a group, inattentionally blind participants can successfully report the location, color, and shape of stimuli they deny noticing. 

      We also feel compelled to resist somewhat the reviewer’s summary of our claims. For example, the reviewer attributes to us the claim that “subjects retain awareness of stimuli they fail to report”; but while that phrase does appear in our abstract, what we in fact say is that our data are “consistent with an alternative hypothesis about IB, namely that subjects retain awareness of stimuli they fail to report”. We do in fact believe that our data are consistent with that hypothesis, whereas earlier investigations seemed not to be. We mention this only because we had used that careful phrasing precisely for this sort of reason, so that we wouldn’t be read as saying that our results unequivocally support that alternative.

      Still, looking back, we see how we may have given more emphasis than we intended to some of these more secondary claims. So, we’ve now gone through and revised our manuscript throughout to emphasize that our main claim is about residual sensitivity, and to make clear that our claims about awareness are secondary and tentative. Indeed, we now say precisely this, that although we favor an interpretation of “our results in terms of residual conscious vision in IB … this claim is tentative and secondary to our primary finding”. We also weaken the statements in the abstract that the reviewer mentions, to better reflect our key claims.

      Finally, we note one further point: Dialectically, inattentional blindness has been used to argue (e.g.) that attention is required for awareness. We think that our data concerning residual sensitivity at least push back on the use of IB to make this claim, even if (as we agree) they do not provide decisive evidence that awareness survives inattention. In other words, we think our data call that claim into question, such that it’s now genuinely unclear whether awareness does or does not survive inattention. We have adjusted our claims on this point accordingly as well.

      Comments:

      (1) In experiment 1, data from 374 subjects were included in the analysis. As shown in figure 2b, 267 subjects reported noticing the critical stimulus and 107 subjects reported not noticing it. This translates to a 29% IB rate, if we were to only consider the "did you notice anything unusual Y/N" question. As reported in the results text (and figure 2c), when asked to report the location of the critical stimulus (left/right), 63.6% of the "non-noticer" group answered correctly. In other words, 68 subjects were correct about the location while 39 subjects were incorrect. Importantly, because the location judgment was a 2-alternative-forced-choice, the assumption was that if 50% (or at least not statistically different than 50%) of the subjects answered the location question correctly, everyone was purely guessing. Therefore, we can estimate that ~39 of the subjects who answered correctly were simply guessing (because 39 guessed incorrectly), leaving 29 subjects from the nonnoticer group who may have indeed actually seen the location of the stimulus. If these 29 subjects are moved to the noticer group, the corrected rate of IB for experiment 1 is 21% instead of 29%. In other words, relying only on the "Y/N did you notice anything" question leads to an overestimate of IB rates by 8%. This modest level of inaccuracy in estimating IB rates is insufficient for concluding that "subjects retain awareness of stimuli they fail to report", i.e. that inattentional blindness does not exist.

      In addition, this 8% inaccuracy in IB rates only considers one side of the story. Given the data reported for experiment 1, one can also calculate the number of subjects who answered "yes, I did notice something unusual" but then reported the incorrect location of the critical stimulus. This turned out to be 8 subjects (or 3% of the "noticer" group). Some would argue that it's reasonable to consider these subjects as inattentionally blind, since they couldn't even report where the critical stimulus they apparently noticed was located. If we move these 8 subjects to the non-noticer group, the 8% overestimation of IB rates is reduced to 6%.

      The same exercise can and should be carried out on the other 4 experiments, however, the authors do not report the subject numbers for any of the other experiments, i.e., how many subjects answered Y/N to the noticing question and how many in each group correctly answered the stimulus feature question. From the limited data reported (only total subject numbers and d' values), the effect sizes in experiments 2-5 were all smaller than in experiment 1 (d' for the non-noticer group was lower in all of these follow-up experiments), so it can be safely assumed that the ~6-8% overestimation of IB rates was smaller in these other four experiments. In a revision, the authors should consider reporting these subject numbers for all 5 experiments.

      We now report, as requested, all these subject numbers in our supplementary data (see Supplementary Tables 1 and 2 in our Supplementary Materials).

      However, we wish to address the larger question the reviewer has raised: Do our data only support a relatively modest reduction in IB rates? Even if they did, we still believe that this would be a consequential result, suggesting a significant overestimation of IB rates in classic paradigms. However, part of our purpose in writing this paper is to push back against a certain binary way of thinking about seeing/awareness. Our sense is that the field has conceived of awareness as “all or nothing”: You either see a perfectly clear gorilla right in front of you, or you see nothing at all. Our perspective is different: We think there can be degraded forms of awareness that fall into neither of those categories. For that reason, we are disinclined to see our results in the way that the reviewer suggests, namely as simply indicating that fewer subjects fail to see the stimulus than previously assumed. To think that way is, in our view, to assume the orthodox binary position about awareness. If, instead, one conceives of awareness as we do (and as we believe the framework of signal detection theory should compel us to), then it isn’t quite right to think of the proportion of subjects who were aware, but rather (e.g.) the sensitivity of subjects to the relevant stimulus. This is why we prefer measures like d′ over % noticing and % correct. We understand that the reviewer may not think the same way about this issue as we do, but part of our goal is to promote that way of thinking in general, and so some of our comments below reflect that perspective and approach.

      For example, consider how we’d think about the single subject case where the task is 2afc detection of a low contrast stimulus in noise. Suppose that this subject achieves 70% correct. One way of thinking about that is that the subject sees the stimulus on 40% of trials (achieving 100% correct on those) and guesses blindly on the other 60% (achieving 50% correct on those) for a total of 40% + 30% = 70% overall. However, this is essentially a “high threshold” approach to the problem, in contrast to an SDT approach. On an SDT approach (an approach with tremendous evidential support), on every trial the subject receives samples from probabilistic distributions corresponding to each interval (one noise and one signal + noise) and determines which is higher according to the 2afc decision rule. Thus, across trials they have access to differentially graded information about the stimulus. Moreover, on some trials they may have significant information from the stimulus (perhaps, well above their single interval detection criterion) but still decide incorrectly because of high noise from the other spatial interval. From this perspective, there is no non-arbitrary way of saying whether the subject saw/did not see on a given trial. Instead, we must characterize the subject’s overall sensitivity to the stimulus/its visibility to them in terms of a parameter such as d′ (here, ~ 0.7).

      We take the same attitude to our super subject. Instead of saying that some subjects saw/failed to see the stimuli, instead we suggest that the best way to characterize our results is that across subjects (and so trials also) there was differential graded access to information from the stimulus best represented in terms of the group-level sensitivity parameter d′.

      We acknowledge that (despite ourselves) we occasionally fell into an all-too-natural binary/high threshold way of thinking, as when we suggested that our data show that “inattentionally blind subjects consciously perceive these stimuli after all” and “the inattentionally blind can see after all." (p.17) We have removed such problematic phrasing as well as other problematic phrasing as noted below.

      (2) Because classic IB paradigms involve only one critical trial per subject, the authors used a "super subject" approach to estimate sensitivity (d') and response criterion (c) according to signal detection theory (SDT). Some readers may have issues with this super subject approach, but my main concern is with the lack of precision used by the authors when interpreting the results from this super subject analysis.

      Only the super subject had above-chance sensitivity (and it was quite modest, with d' values between 0.07 and 0.51), but the authors over-interpret these results as applying to every subject. The methods and analyses cannot determine if any individual subject could report the features above-chance. Therefore, the following list of quotes should be revised for accuracy or removed from the paper as they are misleading and are not supported by the super subject analysis: "Altogether this approach reveals that subjects can report above-chance the features of stimuli (color, shape, and location) that they had claimed not to notice under traditional yes/no questioning" (p.6)

      "In other words, nearly two-thirds of subjects who had just claimed not to have noticed any additional stimulus were then able to correctly report its location." (p.6)

      "Even subjects who answer "no" under traditional questioning can still correctly report various features of the stimulus they just reported not having noticed, suggesting that they were at least partially aware of it after all." (p.8)

      "Why, if subjects could succeed at our forced-response questions, did they claim not to have noticed anything?" (p.8)

      "we found that observers could successfully report a variety of features of unattended stimuli, even when they claimed not to have noticed these stimuli." (p.14)

      "our results point to an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects consciously perceive these stimuli after all... they show sensitivity to IB stimuli because they can see them." (p.16)

      "In other words, the inattentionally blind can see after all." (p.17)

      We thank the reviewer for pointing out how these quotations may be misleading as regards our central claim. We intended them all to be read generically as concerning the group, and not universally as claiming that all subjects could report above-chance/see the stimuli etc. We agree entirely that the latter universal claim would not be supported by our data. In contrast, we do contend that our super-subject analysis shows that, as a group, subjects traditionally considered intentionally blind exhibit residual sensitivity to features of stimuli (color, shape, and location) that they had all claimed not to notice, and likewise that as a group they could succeed at our forced-choice questions. 

      To ensure this claim is clear throughout the paper, and that we are not interpreted as making an unsupported universal claim we have revised the language in all of the quotations above, as follows, as well as in numerous other places in the paper.

      “Altogether this approach reveals that subjects can report above-chance the features of stimuli (color, shape, and location) that they had claimed not to notice under traditional yes/no questioning” (p.6) => “Altogether this approach reveals that as a group subjects can report above-chance the features of stimuli (color, shape, and location) that they had all claimed not to notice under traditional yes/no questioning” (p.6)

      “Even subjects who answer “no” under traditional questioning can still correctly report various features of the stimulus they just reported not having noticed, suggesting that they were at least partially aware of it after all.” (p.8) => “... even subjects who answer “no” under traditional questioning can, as a group, still correctly report various features of the stimuli they just reported not having noticed, indicating significant group-level sensitivity to visual features. Moreover, these results are even consistent with an alternative hypothesis about IB, that as a group, subjects who would traditionally be classified as inattentionally blind are in fact at least partially aware of the stimuli they deny noticing.” (p.8)

      “Why, if subjects could succeed at our forced-response questions, did they claim not to have noticed anything?” (p.8) => “Why, if subjects could succeed at our forcedresponse questions as a group, did they all individually claim not to have noticed anything?” (p.8)

      “we found that observers could successfully report a variety of features of unattended stimuli, even when they claimed not to have noticed these stimuli.” (p.14) => “we found that groups of observers could successfully report a variety of features of unattended stimuli, even when they all individually claimed not to have noticed those stimuli.” (p.14)

      “our results point to an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects consciously perceive these stimuli after all... they show sensitivity to IB stimuli because they can see them.” (p.16) => “our results just as easily raise an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects may retain a degree of awareness of these stimuli after all.” (p.16) Here deleting: “they show sensitivity to IB stimuli because they can see them.”

      “In other words, the inattentionally blind can see after all.” (p.17) => “In other words, as a group, the inattentionally blind enjoy at least some degraded or partial sensitivity to the location, color and shape of stimuli which they report not noticing.” (p.17)

      In one case, we felt the sentence was correct as it stood, since it simply reported a fact about our data:

      “In other words, nearly two-thirds of subjects who had just claimed not to have noticed any additional stimulus were then able to correctly report its location.” (p.6)

      After all, if subjects were entirely blind and simply guessed, it would be true to say that 50% of subjects would be able to correctly report the stimulus location (by guessing).

      In addition to these and numerous other changes, we also added the following explicit statement early in the paper to head-off any confusion on this point: “Note that all analyses reported here relate to this super subject as opposed to individual subjects”. 

      (3) In addition to the d' values for the super subject being slightly above zero, the authors attempted an analysis of response bias to further question the existence of IB. By including in some of their experiments critical trials in which no critical stimulus was presented, but asking subjects the standard Y/N IB question anyway, the authors obtained false alarm and correct rejection rates. When these FA/CR rates are taken into account along with hit/miss rates when critical stimuli were presented, the authors could calculate c (response criterion) for the super subject. Here, the authors report that response criteria are biased towards saying "no, I didn't notice anything". However, the validity of applying SDT to classic Y/N IB questioning is questionable.

      For example, with the subject numbers provided in Box 1 (the 2x2 table of hits/misses/FA/CR), one can ask, 'how many subjects would have needed to answer "yes, I noticed something unusual" when nothing was presented on the screen in order to obtain a non-biased criterion estimate, i.e., c = 0?' The answer turns out to be 800 subjects (out of the 2761 total subjects in the stimulus-absent condition), or 29% of subjects in this condition.

      In the context of these IB paradigms, it is difficult to imagine 29% of subjects claiming to have seen something unusual when nothing was presented. Here, it seems that we may have reached the limits of extending SDT to IB paradigms, which are very different than what SDT was designed for. For example, in classic psychophysical paradigms, the subject is asked to report Y/N as to whether they think a threshold-level stimulus was presented on the screen, i.e., to detect a faint signal in the noise. Subjects complete many trials and know in advance that there will often be stimuli presented and the stimuli will be very difficult to see. In those cases, it seems more reasonable to incorrectly answer "yes" 29% of the time, as you are trying to detect something very subtle that is out there in the world of noise. In IB paradigms, the stimuli are intentionally designed to be highly salient (and unusual), such that with a tiny bit of attention they can be easily seen. When no stimulus is presented and subjects are asked about their own noticing (especially of something unusual), it seems highly unlikely that 29% of them would answer "yes", which is the rate of FAs that would be needed to support the null hypothesis here, i.e., of a non-biased criterion. For these reasons, the analysis of response bias in the current context is questionable and the results claiming to demonstrate a biased criterion do not provide convincing evidence against IB.

      We are grateful to the reviewer for highlighting this aspect of our data. We agree with several of these points. For example, it is indeed striking that — given the corresponding hit rate — a false alarm rate of 29% would be needed to obtain an unbiased criterion. At the same time, we would respectfully push back on other points above. In our first experiment that uses the super-subject analysis, for example, d′ is 0.51 and highly significant; to describe that figure, as the reviewer does, as “slightly above zero” seemed not quite right to us (and all the more so given that these experiments involve very large samples and preregistered analysis plans). 

      We also respectfully disagree that our data call into question the validity of applying SDT to classic yes/no IB questioning. The mathematical foundations of SDT are rock solid, and have been applied far more broadly than we have applied them here. In fact, in a way we would suggest that exactly the opposite attitude is appropriate: rather than thinking that IB challenges an immensely well-supported, rigorously tested and broadly applicable mathematical model of perception, we think that the conflict between our SDT-based model of IB and the standard interpretation constitutes strong reason to disfavor the standard interpretation. Several points are worth making here.

      First, it is already surprising that 11.03% of our subjects in E2 (46/417) and 7.24% of our subjects in E5 (200/2761) E5 reported noticing a stimulus when no stimulus was present. But while this may have seemed unlikely in advance of inquiry, this is in fact what the data show and forms the basis of our criterion calculations. Thus, our criterion calculations already factor in a surprising but empirically verified high false alarm rate of subjects answering “yes” when no stimulus was presented and were asked about their noticing. (We also note that the only paper we know of to report a false alarm rate in an IB paradigm, though not one used to calculate a response criterion, found a very consistent false alarm rate of 10.4%. See Devue et al. 2009.)

      Second, while the reviewer is of course correct that a common psychophysical paradigm involves detection of a “threshold-level”/faint stimulus in noise, it is widely recognized that SDT has an extremely broad application, being applicable to any situation in which two kinds of event are to be discriminated (Pastore & Scheirer 1975) and being “almost universally accepted as a theoretical account of decision making in research on perceptual detection and recognition and in numerous extensions to applied domains” quite generally (Estes 2002, see also: Wixted 2020). Indeed, cases abound in which SDT has been successfully applied to situations which do not involve near threshold stimuli in noise. To pick two examples at random, SDT has been used in studying acceptability judgments in linguistics (Huang and Ferreira 2020) and the assessment of physical aggression in childstudent interactions (Lerman et al. 2010; for more general discussion of practical applications, see Swets et al. 2000). Given that the framework of SDT is so widely applied and well supported, and that we see no special reason to make an exception, we believe it can be relied on in the present context.

      Finally, we note that inattentional blindness can in many ways be considered analogous to “near threshold” detection since inattention is precisely thought to degrade or even abolish awareness of stimuli, meaning that our stimuli can be construed as near threshold in the relevant sense. Indeed, our relatively modest d′ values suggest that under inattention stimuli are indeed hard to detect. Thus, even were SDT more limited in its application, we think it still would be appropriate to apply to the case of IB.

      (4) One of the strongest pieces of evidence presented in the entire paper is the single data point in Figure 3e showing that in Experiment 3, even the super subject group that rated their non-noticing as "highly confident" had a d' score significantly above zero. Asking for confidence ratings is certainly an improvement over simple Y/N questions about noticing, and if this result were to hold, it could provide a key challenge to IB. However, this result hinges on a single data point, it was not replicated in any of the other 4 experiments, and it can be explained by methodological limitations. I strongly encourage the authors (and other readers) to follow up on this result, in an in-person experiment, with improved questioning procedures.

      We agree that our finding that even the super-subject group that rated their non-noticing as “highly confident” had a d' score significantly above zero is an especially strong piece of evidence, and we thank the reviewer for highlighting that here. At the same time, we note that while the finding is represented by a single marker in Figure 3e, it seemed not quite right to call this a “single data point”, as the reviewer does, given that it derives from a large pre-registered experiment involving some 7,000 subjects total, with over 200 subjects in the relevant bin — both figures being far larger than a typical IB experiment. It would of course be tremendous to follow up on this result – and we certainly hope our work inspires various follow-up studies. That said, we note that recruiting the necessary numbers of in person subjects would be an absolutely enormous, career-level undertaking – it would involve bringing more than the entire undergraduate population at our own institution, Johns Hopkins, into our laboratory! While those results would obviously be extremely valuable, we wouldn’t want to read the reviewer’s comments as implying that only an experiment of that magnitude — requiring thousands upon thousands of in-person subjects — could make progress on these issues. Indeed, because every subject can only contribute one critical trial in IB, it has long been recognized as an extremely challenging paradigm to study in a sufficiently well-powered and psychophysically rigorous way. We believe that our large preregistered online approach represents a major leap forward here, even if it involves certain trade-offs.

      In the current Experiment 3, the authors asked the standard Y/N IB question, and then asked how confident subjects were in their answer. Asking back-to-back questions, the second one with a scale that pertains to the first one (including a tricky inversion, e.g., "yes, I am confident in my answer of no"), may be asking too much of some subjects, especially subjects paying half-attention in online experiments. This procedure is likely to introduce a sizeable degree of measurement error.

      An easy fix in a follow-up study would be to ask subjects to rate their confidence in having noticed something with a single question using an unambiguous scale:

      On the last trial, did you notice anything besides the cross?

      (1): I am highly confident I didn't notice anything else

      (2): I am confident I didn't notice anything else

      (3): I am somewhat confident I didn't notice anything else

      (4): I am unsure whether I noticed anything else

      (5): I am somewhat confident I noticed something else

      (6): I am confident I noticed something else

      (7): I am highly confident I noticed something else

      If we were to re-run this same experiment, in the lab where we can better control the stimuli and the questioning procedure, we would most likely find a d' of zero for subjects who were confident or highly confident (1-2 on the improved scale above) that they didn't notice anything. From there on, the d' values would gradually increase, tracking along with the confidence scale (from 3-7 on the scale). In other words, we would likely find a data pattern similar to that plotted in Figure 3e, but with the first data point on the left moving down to zero d'. In the current online study with the successive (and potentially confusing) retrospective questioning, a handful of subjects could have easily misinterpreted the confidence scale (e.g., inverting the scale) which would lead to a mixture of genuine high-confidence ratings and mistaken ratings, which would result in a super subject d' that falls between zero and the other extreme of the scale (which is exactly what the data in Fig 3e shows).

      One way to check on this potential measurement error using the existing dataset would be to conduct additional analyses that incorporate the confidence ratings from the 2AFC location judgment task. For example, were there any subjects who reported being confident or highly confident that they didn't see anything, but then reported being confident or highly confident in judging the location of the thing they didn't see? If so, how many? In other words, how internally (in)consistent were subjects' confidence ratings across the IB and location questions? Such an analysis could help screen-out subjects who made a mistake on the first question and corrected themselves on the second, as well as subjects who weren't reading the questions carefully enough.

      As far as I could tell, the confidence rating data from the 2AFC location task were not reported anywhere in the main paper or supplement.

      We are grateful to the reviewer for raising this issue and for requesting that we report the confidence rating data from our 2afc location task in Experiment 3. We now report all this data in our Supplementary Materials (see Supplementary Table 3).

      We of course agree with the reviewer’s concern about measurement error, which is a concern in all experiments. What, then, of the particular concern that some subjects might have misunderstood our confidence question? It is surely impossible in principle to rule out this possibility; however, several factors bear on the plausibility of this interpretation. First, we explicitly labeled our confidence scale (with 0 labeled as ‘Not at all confident’ and 3 as ‘Highly confident’) so that subjects would be very unlikely simply to invert the scale. This is especially so as it is very counterintuitive to treat “0” as reflecting high confidence. However, we accept that it is a possibility that certain subjects might nonetheless have been confused in some other way.

      So, we also took a second approach. We examined the confidence ratings on the 2afc question of subjects who reported being highly confident that they didn't notice anything.

      Reassuringly, the large majority of these high confidence “no” subjects (~80%) reported low confidence of 0 or 1 on the 2afc question, and the majority (51%) reported the lowest confidence of 0. Only 18/204 (9%) subjects reported high confidence on both questions. 

      Still, the numbers of subjects here are small and so may not be reliable. This led us to take a third approach. We reasoned that any measurement error due to inverting or misconstruing the confidence scale should be symmetric. That is: If subjects are liable to invert the confidence scale, they should do so just as often when they answer “yes” as when they answer “no” – after all the very same scale is being used in both cases. This allows us to explore evidence of measurement error in relation to the much larger number of highconfidence “yes” subjects (N = 2677), thus providing a much more robust indicator as to whether subjects are generally liable to misconstrue the confidence scale. Looking at the number of such high confidence noticers who subsequently respond to the 2afc question with low-confidence, we found that the number was tiny. Only 28/2677 (1.05%) of highconfidence noticers subsequently gave the lowest level of confidence on the 2afc question, and only 63/2677 (2.35%) subjects gave either of the two lower levels of confidence. In this light, we consider any measurement error due to misunderstanding the confidence scale to be extremely minimal.

      What should we make of the 18 subjects who were highly confident non-noticers but then only low-confidence on the 2afc question? Importantly, we do not think that these 18 subjects necessarily made a mistake on the first question and so should be excluded. There is no a priori reason why one’s confidence criterion in a yes/no question should carry over to a 2afc question. After all, it is perfectly rationally coherent to be very confident that one didn’t see anything but also very confident that if there was anything to be seen, it was on the left. Moreover, these 18 subjects were not all correct on the 2afc question despite their high confidence (4/18 or 22% getting the wrong answer). 

      Nonetheless, and again reassuringly, we found that the above-chance patterns in our data remained the same even excluding these 18 subjects. We did observe a slight reduction in percent correct and d′ but this is absolutely what one should expect since excluding the most confident performers in any task will almost inevitably reduce performance.

      In this light, we consider it unlikely that measurement error fully explains the residual sensitivity found even amongst highly confident non-noticers. That said, we appreciate this concern. We now raise the issue and the analysis of high confidence noticers which addresses it in our revised manuscript. We also thank the reviewer for pressing us to think harder about this issue, which led directly to these new analyses that we believed have strengthened the paper.

      (5) In most (if not all) IB experiments in the literature, a partial attention and/or full attention trial (or set of trials) is administered after the critical trial. These control trials are very important for validating IB on the critical trial, as they must show that, when attended, the critical stimuli are very easy to see. If a subject cannot detect the critical stimulus on the control trial, one cannot conclude that they were inattentionally blind on the critical trial, e.g., perhaps the stimulus was just too difficult to see (e.g., too weak, too brief, too far in the periphery, too crowded by distractor stimuli, etc.), or perhaps they weren't paying enough attention overall or failed to follow instructions. In the aggregate data, rates of noticing the stimuli should increase substantially from the critical trial to the control trials. If noticing rates are equivalent on the critical and control trials one cannot conclude that attention was manipulated.

      It is puzzling why the authors decided not to include any control trials with partial or full attention in their five experiments, especially given their online data collection procedures where stimulus size, intensity, eccentricity, etc. were uncontrolled and variable across subjects. Including such trials could have actually helped them achieve their goal of challenging the IB hypothesis, e.g., excluding subjects who failed to see the stimulus on the control trials might have reduced the inattentional blindness rates further. This design decision should at least be acknowledged and justified (or noted as a limitation) in a revision of this paper.

      We acknowledge that other studies in the literature include divided and full attention trials, and that they could have been included in our work as well. However, we deliberately decided not to include such control trials for an important reason. As the referee comments, the main role of such trials in previous work has been to exclude from analysis subjects who failed to report the unexpected stimulus on the divided and/or full attention control trials.

      (For example, as Most et al. 2001 write: “Because observers should have seen the object in the full-attention trial (Mack & Rock, 1998), we used this trial as a control … Accordingly, 3 observers who failed to see the cross on this trial were replaced, and their data were excluded from the analyses.") As the reviewer points out, excluding such subjects would very likely have ‘helped' us. However, the practice is controversial. Indeed, in a review of 128 experiments, White et al. 2018 argue that the practice has “problematic consequences” and “may lead researchers to understate the pervasiveness of inattentional blindness". Since we wanted to offer as simple and demanding a test of residual sensitivity in IB as possible, we thus decided not to use any such exclusions, and for that reason decided not to include divided/full attention trials. 

      As recommended, we discuss this decision not to include divided/full attention trials and our logic for not doing so in the manuscript. As we explain, not having those conditions makes it more impressive, not less impressive, that we observed the results we in fact did — it makes our results more interpretable, not less interpretable, and so absence of such conditions from our manuscript should not (in our view) be considered any kind of weakness.

      (6) In the discussion section, the authors devote a short paragraph to considering an alternative explanation of their non-zero d' results in their super subject analyses: perhaps the critical stimuli were processed unconsciously and left a trace such that when later forced to guess a feature of the stimuli, subjects were able to draw upon this unconscious trace to guide their 2AFC decision. In the subsequent paragraph, the authors relate these results to above-chance forced-choice guessing in blindsight subjects, but reject the analogy based on claims of parsimony.

      First, the authors dismiss the comparison of IB and blindsight too quickly. In particular, the results from experiment 3, in which some subjects adamantly (confidently) deny seeing the critical stimulus but guess a feature at above-chance levels (at least at the super subject level and assuming the online subjects interpreted and used the confidence scale correctly), seem highly analogous to blindsight. Importantly, the analogy is strengthened if the subjects who were confident in not seeing anything also reported not being confident in their forced-choice judgments, but as mentioned above this data was not reported.

      Second, the authors fail to mention an even more straightforward explanation of these results, which is that ~8% of subjects misinterpreted the "unusual" part of the standard IB question used in experiments 1-3. After all, colored lines and shapes are pretty "usual" for psychology experiments and were present in the distractor stimuli everyone attended to. It seems quite reasonable that some subjects answered this first question, "no, I didn't see anything unusual", but then when told that there was a critical stimulus and asked to judge one of its features, adjusted their response by reconsidering, "oh, ok, if that's the unusual thing you were asking about, of course I saw that extra line flash on the left of the screen". This seems like a more parsimonious alternative compared to either of the two interpretations considered by the authors: (1) IB does not exist, (2) super-subject d' is driven by unconscious processing. Why not also consider: (3) a small percentage of subjects misinterpreted the Y/N question about noticing something unusual. In experiments 4-5, they dropped the term "unusual" but do not analyze whether this made a difference nor do they report enough of the data (subject numbers for the Y/N question and 2AFC) for readers to determine if this helped reduce the ~8% overestimate of IB rates.

      Our primary ambition in the paper was to establish, as our title suggests, residual sensitivity in IB. The ambition is quite neutral as to whether the sensitivity reflects conscious or unconscious processing (i.e. is akin to blindsight as traditionally conceived). We were evidently not clear about this, however, leading to two referees coming away with an impression of our claims that is different than we intended. We have revised our manuscript throughout to address this. But we also want to emphasize here that we take our data primarily to support the more modest claim that there is residual sensitivity (conscious or unconscious) in the group of subjects who are traditionally classified as inattentionally blind. We believe that this claim has solid support in our data.

      We do in the discussion section offer one reason for believing that there is residual awareness in the group of subjects who are traditionally classified as inattentionally blind. However, we acknowledge that this is controversial and now emphasize in the manuscript that this claim “is tentative and secondary to our primary finding”. We also emphasize that part of our point is dialectical: Inattentional blindness has been used to argue (e.g.) that attention is required for awareness. We think that our data concerning residual sensitivity at least push back on the use of IB to make this claim, even if they do not provide decisive evidence (as we agree) that awareness survives inattention. (Cf. here, Hirshhorn et al. 2024 who take up a common suggestion in the field that awareness is best assessed by using both subjective and objective measures, with claims about lack of awareness ideally being supported by both; our data suggest at a minimum that in IB objective measures do not neatly line up with subjective measures.)

      We hope this addresses the referee’s concern that we dismiss the “the comparison of IB and blindsight too quickly”. We do not intend to dismiss that comparison at all, indeed we raise it because we consider it a serious hypothesis. Our aim is simply to raise one possible consideration against it. But, again, our main claim is quite consistent with sensitivity in IB being akin to “blindsight”.

      We also agree with the referee that a possible explanation of why some subjects say they do not notice something unusual in IB paradigms, is not because they didn’t notice anything but because they didn’t consider the unexpected stimulus sufficiently unusual. However, the reviewer is incorrect that we did not mention this interpretation; to the contrary, it was precisely the kind of concern which led us to be dissatisfied with standard IB methods and so motivated our approach. As we wrote in our main text: “However, yes/no questions of this sort are inherently and notoriously subject to bias…   For example, observers might be under-confident whether they saw anything (or whether what they saw counted as unusual); this might lead them to respond “no” out of an excess of caution.” On our view, this is exactly the kind of reason (among other reasons) that one cannot rely on yes/no reports of noticing unusual stimuli, even though the field has relied on just these sorts of questions in just this way.

      We do not, however, think that this explanation accounts for why all subjects fail to report noticing, nor do we think that it accounts for our finding of above-chance sensitivity amongst non-noticers. This is for two critical reasons. First, whereas the word “unusual” did appear in the yes/no question in our Experiments 1-3, it did not appear in our Experiments 4 and 5 on dynamic IB. (In both cases, we used the exact wording of such questions in the experiments we were basing our work on.) And, of course, we still found significant residual sensitivity amongst non-noticers in Experiments 4 and 5. Second, in relation to our confidence experiment, we think it unlikely that subjects who were highly confident that they did not notice anything unusual only said that because they thought what they had seen was insufficiently unusual. Yet even in this group of subjects who were maximally confident that they did not notice anything unusual, we still found residual sensitivity.

      (7) The authors use sub-optimal questioning procedures to challenge the existence of the phenomenon this questioning is intended to demonstrate. A more neutral interpretation of this study is that it is a critique on methods in IB research, not a critique on IB as a manipulation or phenomenon. The authors neglect to mention the dozens of modern IB experiments that have improved upon the simple Y/N IB questioning methods. For example, in Michael Cohen's IB experiments (e.g., Cohen et al., 2011; Cohen et al., 2020; Cohen et al., 2021), he uses a carefully crafted set of probing questions to conservatively ensure that subjects who happened to notice the critical stimuli have every possible opportunity to report seeing them. In other experiments (e.g., Hirschhorn et al., 2024; Pitts et al., 2012), researchers not only ask the Y/N question but then follow this up by presenting examples of the critical stimuli so subjects can see exactly what they are being asked about (recognition-style instead of free recall, which is more sensitive). These follow-up questions include foil stimuli that were never presented (similar to the stimulus-absent trials here), and ask for confidence ratings of all stimuli. Conservative, pre-defined exclusion criteria are employed to improve the accuracy of their IB-rate estimates. In these and other studies, researchers are very cautious about trusting what subjects report seeing, and in all cases, still find substantial IB rates, even to highly salient stimuli. The authors should consider at least mentioning these improved methods, and perhaps consider using some of them in their future experiments.

      The concern that we do not sufficiently discuss the range of “improved” methods in IB studies is well-taken. A similar concern is raised by Reviewer #2 (Dr. Cohen). To address the concern, we have added to our manuscript a substantial new discussion of such improved methods. However, although we do agree that these methods can be helpful and may well address some of the methodological concerns which our paper raises, we do not think that they are a panacea. Thus, our discussion of these methods also includes a substantial discussion of the problems and pitfalls with such methods which led us to favor our own simple forced-response and 2afc questions, combined with SDT analysis. We think this approach is superior both to the classic approach in IB studies and to the approach raised by the reviewers.

      In particular, we have four main concerns about the follow up questions now commonly used in the field:

      First, many follow up questions are used not to exclude people from the IB group but to include people in the IB group. Thus, Most et al. 2001 asked follow up questions but used these to increase their IB group, only excluding subjects from the IB group if they both reported seeing and answered their follow ups incorrectly: “Observers were regarded as having seen the unexpected object if they answered 'yes' when asked if they had seen anything on the critical trial that had not been present before and if they were able to describe its color, motion, or shape." This means that subjects who saw the object but failed to see its color, say, would be treated as inattentionally blind. This has the purpose of inflating IB rates, in exactly the way our paper is intended to critique. So, in our view this isn’t an improvement but rather part of the approach we take issue with.

      Second, many follow up questions remain yes/no questions or nearby variants, all of which are subject to response bias. For example, in Cohen’s studies which the reviewer mentions, it is certainly true that “he uses a carefully crafted set of probing questions to conservatively ensure that subjects who happened to notice the critical stimuli have every possible opportunity to report seeing them.” We agree that this improves over a simple yes/no question in some ways. However, such follow up probes nonetheless remain yes/no questions, subject to response bias, e.g.:

      (1) “Did you notice anything strange or different about that last trial?”

      (2) “If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?”

      (3) “If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?”

      (4) “Did you notice anything different about the colors in the last scene?”

      Indeed, follow up questions of this kind can be especially susceptible to bias, since subjects may be reluctant to “take back” their earlier answers and so be conservative in responding positively to avoid inconsistency or acknowledgement of earlier error. This may explain why such follow up questions produce remarkable consistency despite their rather different wording. Thus, Simons and Chabris (1999) report: “Although we asked a series of questions escalating in specificity to determine whether observers had noticed the unexpected event, only one observer who failed to report the event in response to the first question (“did you notice anything unusual?'') reported the event in response to any of the next three questions (which culminated in “did you see a ... walk across the screen?''). Thus, since the responses were nearly always consistent across all four questions, we will present the results in terms of overall rates of noticing.” Thus, while there are undoubtedly merits to these follow ups, they do not resolve problems of bias.

      This same basic issue affects the follow up question used in Pitts et al. 2012 which the reviewer mentions. Pitts et al. write: “If a participant reported not seeing any patterns and rated their confidence in seeing the square pattern (once shown the sample) as a 3 or less (1 = least confident, 5 = most confident), she or he was placed in Group 1 and was considered to be inattentionally blind to the square patterns.” The confidence rating follow-up question here remains subject to bias. Moreover, and strikingly, the inclusion criterion used means that subjects who were moderately confident that they saw the square pattern when shown (i.e. answered 3) were counted as inattentionally blind (!). We do not think this is an appropriate inclusion criterion.

      The third problem is that follow up questions are often free/open-response. For instance, Most et al. (2005) ask the follow up question: "If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess." This is a much more difficult and to that extent less sensitive question than our binary forced-response/2afc questions. For this reason, we believe our follow up questions are more suitable for ascertaining low levels of sensitivity.

      The fourth and final issue is that whereas 2afc questions are criterion free (in that they naturally have an unbiased decision rule), this is in fact not true of n_afc questions in general, nor is it true in general of _delayed n-alternative match to sample designs. Thus, even when limited response options are given, they are not immune to response biases and so require SDT analysis. Moreover, some such tasks can involve decision spaces which are often poorly understood or difficult to analyze without making substantial assumptions about observer strategy. 

      This last point (as well as the first) is relevant to Hirshhorn et al. 2024. Hirshhorn et al. write that they “used two awareness measures. Firstly, participants were asked to rate stimulus visibility on the Perceptual Awareness Scale (PAS, a subjective measure of awareness: Ramsøy & Overgaard, 2004), and then they were asked to select the stimulus image from an array of four images (an objective measure: Jakel & Wichmann, 2006).”

      While certainly an improvement on simple yes/no questioning, the PAS remains subject to response bias. On the other hand, we applaud Hirshhorn et al.’s use of objective measures in the context of IB which of course our design implements. However, while Hirshhorn et al. 2024 suggest that their task is a spatial 4afc following the recommendation of this design by Jakel & Wichmann (2006), it is strictly a 4-alternative delayed match to sample task, so it is doubtful if it can be considered a preferred psychophysical task for the reasons Jakel & Wichmann offer. Regardless, the more crucial point is that observers in such a task might be biased towards one alternative as opposed to another. Thus, use of d′ (as opposed to percent correct as in Hirshhorn et al. 2024) is crucial in assessing performance in such tasks.

      For all these reasons, then, while we agree that the field has taken significant steps to move beyond the simple yes/no question traditionally used in IB studies (and we have revised our manuscript to make this clear); we do not think it has resolved the methodological issues which our paper seeks to highlight and address, and we believe that our approach contributes something additional that is not yet present in the literature. We have now revised our manuscript to make these points much more clearly, and we thank the reviewer for prompting these improvements.

      Reviewer #2 (Public review):

      In this study, Nartker et al. examine how much observers are conscious of using variations of classic inattentional blindness studies. The key idea is that rather than simply asking observers if they noticed a critical object with one yes/no question, the authors also ask follow-up questions to determine if observers are aware of more than the yes/no questions suggest. Specifically, by having observers make forced choice guesses about the critical object, the authors find that many observers who initially said "no" they did not see the object can still "guess" above chance about the critical object's location, color, etc. Thus, the authors claim, that prior claims of inattentional blindness are mistaken and that using such simple methods has led numerous researchers to overestimate how little observers see in the world. To quote the authors themselves, these results imply that "inattentionally blind subjects consciously perceive these stimuli after all... they show sensitivity to IB stimuli because they can see them."

      Before getting to a few issues I have with the paper, I do want to make sure to explicitly compliment the researchers for many aspects of their work. Getting massive amounts of data, using signal detection measures, and the novel use of a "super subject" are all important contributions to the literature that I hope are employed more in the future.

      We really appreciate this comment and that the reviewer found our work to make these important contributions to the literature. We wrote this paper expecting not everyone to accept our conclusions, but hoping that readers would see the work as making a valuable contribution to the literature promoting an underexplored alternative in a compelling way. Given that this reviewer goes on to express some skepticism about our claims, it is especially encouraging to see this positive feedback up top!

      Main point 1: My primary issue with this work is that I believe the authors are misrepresenting the way people often perform inattentional blindness studies. In effect, the authors are saying, "People do the studies 'incorrectly' and report that people see very little. We perform the studies 'correctly' and report that people see much more than previously thought." But the way previous studies are conducted is not accurately described in this paper. The authors describe previous studies as follows on page 3:

      "Crucially, however, this interpretation of IB and the many implications that follow from it rest on a measure that psychophysics has long recognized to be problematic: simply asking participants whether they noticed anything unusual. In IB studies, awareness of the unexpected stimulus (the novel shape, the parading gorilla, etc.) is retroactively probed with a yes/no question, standardly, "Did you notice anything unusual on the last trial which wasn't there on previous trials?". Any subject who answers "no" is assumed not to have any awareness of the unexpected stimulus.

      If this quote were true, the authors would have a point. Unfortunately, I do not believe it is true. This is simply not how many inattentional blindness studies are run. Some of the most famous studies in the inattentional blindness literature do not simply as observes a yes/no question (e.g., the invisible gorilla (Simons et al. 1999), the classic door study where the person changes (Simons and Levin, 1998), the study where observers do not notice a fight happening a few feet from them (Chabris et al., 2011). Instead, these papers consistently ask a series of follow-up questions and even tell the observers what just occurred to confirm that observers did not notice that critical event (e.g., "If I were to tell you we just did XYZ, did you notice that?"). In fact, after a brief search on Google Scholar, I was able to relatively quickly find over a dozen papers that do not just use a yes/no procedure, and instead as a series of multiple questions to determine if someone is inattentionally blind. In no particular order some papers (full disclosure: including my own):

      (1) Most et al. (2005) Psych Review

      (2) Drew et al. (2013) Psych Science

      (3) Drew et al. (2016) Journal of Vision

      (4) Simons et al. (1999) Perception

      (5) Simons and Levin (1998) Perception

      (6) Chabris et al. (2011) iPerception

      (7) Ward & Scholl (2015) Psych Bulletin and Review

      (8) Most et al. (2001) Psych Science

      (9) Todd & Marois (2005) Psych Science

      (10) Fougnie & Marois (2007) Psych Bulletin and Review

      (11) New and German (2015) Evolution and Human Behaviour

      (12) Jackson-Nielsen (2017) Consciousness and cognition

      (13) Mack et al. (2016) Consciousness and cognition

      (14) Devue et al. (2009) Perception

      (15) Memmert (2014) Cognitive Development

      (16) Moore & Egeth (1997) JEP:HPP

      (17) Cohen et al. (2020) Proc Natl Acad Sci

      (18) Cohen et al. (2011) Psych Science

      This is a critical point. The authors' key idea is that when you ask more than just a simple yes/no question, you find that other studies have overestimated the effects of inattentional blindness. But none of the studies listed above only asked simple yes/no questions. Thus, I believe the authors are mis-representing the field. Moreover, many of the studies that do much more than ask a simple yes/no question are cited by the authors themselves! Furthermore, as far as I can tell, the authors believe that if researchers do these extra steps and ask more follow-ups, then the results are valid. But since so many of these prior studies do those extra steps, I am not exactly sure what is being criticized.

      To make sure this point is clear, I'd like to use a paper of mine as an example. In this study (Cohen et al., 2020, Proc Natl Acad Sci USA) we used gaze-contingent virtual reality to examine how much color people see in the world. On the critical trial, the part of the scene they fixated on was in color, but the periphery was entirely in black and white. As soon as the trial ended, we asked participants a series of questions to determine what they noticed. The list of questions included:

      (1) "Did you notice anything strange or different about that last trial?"

      (2) "If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?"

      (3) "If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?"

      (4) "Did you notice anything different about the colors in the last scene?"

      (5) We then showed observers the previous trial again and drew their attention to the effect and confirmed that they did not notice that previously.

      In a situation like this, when the observers are asked so many questions, do the authors believe that "the inattentionally blind can see after all?" I believe they would not say that and the reason they would not say that is because of the follow-up questions after the initial yes/no question. But since so many previous studies use similar follow-up questions, I do not think you can state that the field is broadly overestimating inattentional blindness. This is why it seems to me to be a bit of a strawman: most people do not just use the yes/no method.

      We appreciate this reviewer raising this issue. As he (Dr. Cohen) states, his “primary issue” concerns our discussion of the broader literature (which he worries understates recent improvements made to the IB methodology), rather than, e.g., the experiments we’ve run. We take this concern very seriously and address it comprehensively here.

      A very similar issue is identified by Reviewer #1, comment (7). To review some of what we say in reply to them: To address the concern we have added to our manuscript a substantial new discussion of such improved methods. However, although we do agree that these methods can be helpful and may well address some of the methodological concerns which our paper raises, we do not think that they are a panacea. Thus, our discussion of these methods also includes a substantial discussion of the problems and pitfalls with such methods which led us to favor our own simple forced-response and 2afc questions, combined with SDT analysis. We think this approach is superior both to the classic approach in IB studies and to the approach raised by the reviewers.

      In particular, we have three main concerns about the follow up questions now commonly used in the field:

      First, many follow up questions are used not to exclude subjects from the IB group but to include subjects in the IB group. Thus, Most et al. (2001) asked follow up questions but used these to increase their IB group, only excluding subjects from the IB group if they both reported seeing and failed to answer their follow ups correctly: “Observers were regarded as having seen the unexpected object if they answered 'yes' when asked if they had seen anything on the critical trial that had not been present before and if they were able to describe its color, motion, or shape." This means that subjects who saw the object but failed to describe it in these respects would be treated as inattentionally blind. This is problematic since failure to describe a feature (e.g., color, shape) does not imply a complete lack of information concerning that feature; and even if a subject did lack all information concerning these features of an object, this would not imply a complete failure to see the object. Similarly, Pitts et al. (2012) asked subjects to rate their confidence in their initial yes/no response from 1 = least confident to 5 = most confident, and used these ratings to include in the IB group those who rated their confidence in seeing at 3 or less. This is evidently problematic, since there is a large gap between being under confident that one saw something and being completely blind to it. More generally, using follows up to inflate IB rates in such ways raises precisely the kinds of issues our paper is intended to critique. So in our view this isn’t an improvement but rather part of the approach we take issue with.

      Second, many follow up questions remain yes/no questions or nearby variants, all of which are subject to response bias. For example, in the reviewer’s own studies (Cohen et al. 2020, 2011; see also: Simons et al., 1999; Most et al., 2001, 2005; Drew et al., 2013; Memmert, 2014) a series of follow up questions are used to try and ensure that subjects who noticed the critical stimuli are given the maximum opportunity to report doing so, e.g.:

      (1) “Did you notice anything strange or different about that last trial?”

      (2) “If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?”

      (3) “If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?”

      (4) “Did you notice anything different about the colors in the last scene?”

      We certainly agree that such follow up questions improve over a simple yes/no question in some ways. However, such follow up probes nonetheless remain yes/no questions, intrinsically subject to response bias. Indeed, follow up questions of this kind can be especially susceptible to bias, since subjects may be reluctant to “take back” their earlier answers and so be conservative in responding positively to avoid inconsistency or acknowledgement of earlier error. This may explain why such follow up questions produce remarkable consistency despite their rather different wording. Thus, Simons and Chabris (1999) report: “Although we asked a series of questions escalating in specificity to determine whether observers had noticed the unexpected event, only one observer who failed to report the event in response to the first question (“did you notice anything unusual?'') reported the event in response to any of the next three questions (which culminated in “did you see a ... walk across the screen?''). Thus, since the responses were nearly always consistent across all four questions, we will present the results in terms of overall rates of noticing.” Thus, while there are undoubtedly merits to these follow ups, they do not resolve problems of bias.

      It is also important to recognize that whereas 2afc questions are criterion free (in that they naturally have an unbiased decision rule), this is not true of n_afc nor delayed _n-alternative match to sample designs in general. Performance in such tasks thus requires SDT analysis – which itself may be problematic if the decision space is not properly understood or requires making substantial assumptions about observer strategy.

      Third, and finally, many follow up questions are insufficiently sensitive (especially with small sample sizes). For instance, Todd, Fougnie & Marois (2005) used a 12-alternative match-tosample task (see similarly: Fougnie & Marois, 2007; Devue et al., 2009). And Most et al. (2005) asked an open-response follow-up: “If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess.” These questions are more difficult and to that extent less sensitive than binary forced-response/2afc questions of the sort we use in our own studies – a difference which may be critical in uncovering degraded perceptual sensitivity.

      For all these reasons, then, while we agree that the field has taken significant steps to move beyond the simple yes/no question traditionally used in IB studies (and we have revised our manuscript to make this clear); we do not think it has resolved the methodological issues which our paper seeks to highlight and address, and we believe that our approach of using 2afc or forced-response questions combined with signal detection analysis is an important improvement on prior methods and contributes something additional that is not yet present in the literature. We have now revised our manuscript to make these points much clearer.

      Other studies that improve on the standard methodology

      This reviewer adds something else, however: A very helpful list of 18 papers which include follow ups and that he believes overcome many of the issues we raise in our paper. To just state our reaction bluntly: We are familiar with every one of these papers (indeed, one of them is a paper by one of us!), and while we think these are all very valuable contributions to the literature, it is our view that none of these 18 papers resolves the worries that led us to conduct our work.  

      Here we briefly comment on the relevant pitfalls in each case. We hope this serves to underscore the importance of our methodological approach.

      (1) Most et al. (2005) Psych Review

      Either a 2-item or 5-item questionnaire was used. The 2-item questionnaire ran as follows:

      (1) On the last trial, did you see anything other than the 4 circles and the 4 squares (anything that had not been present on the original two trials)? Yes No 

      (2) If you did see something on the last trial that had not been present during the original two trials, please describe it in as much detail as possible.

      This clearly does not substantially improve on the traditional simple yes/no question. Moreover, the second question (as well as being open-ended) was used to include additional subjects in the IB group, in that participants were counted as having seen the object only if they responded “yes” to Q1 and in addition “were able to report at least one accurate detail” in response to Q2. In other words, either a subject says “no” (and is treated as unaware), or says “yes” and then is asked to prove their awareness, as it were. If anything, this intensifies the concerns we raise, by inflating IB rates. 

      The 5-item questionnaire looked like this: 

      (1) On the last trial, did you see anything other than the black and white L’s and T’s (anything that had not been present on the first two trials)?

      (2) If you did see something on the last trial that had not been present during the first two trials, please describe it.

      (3) If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (4) If you did see something during the last trial that had not been present in the first two trials, please draw an arrow on the “screen” below showing the direction in which it was moving. If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (5) If you did see something during the last trial that had not been present during the first two trials, please circle the shape of the object below [4 shapes are presented to choose from]. If you did not see anything, please guess. (Please indicate whether you did see something or are guessing)

      Q5 was not used for analysis purposes. (It suffers from the second issue raised above.) Q1 is the traditional y/n question. Qs 2&3 are open ended. It is unclear how responses to Q4 were analyzed (at the limit it could be considered a helpful, forced-choice question – though it again would suffer from the second issue raised above). However, as noted with respect to the 2-item questionnaire, these responses were not used to exclude people from the IB group but to include people in it. So again, this approach does not in any way address the issues we are concerned about, and if anything, only makes them worse. 

      (2)  Drew et al. (2013) Psych Science

      All follow ups were yes/no: “we asked a series of questions to determine whether they noticed the gorilla: ‘Did the final trial seem any different than any of the other trials?’, ‘Did you notice anything unusual on the final trial?’, and, finally, ‘Did you see a gorilla on the final trial?’”. So, this paper essentially implements the standard methodology we mention (and criticize). 

      (3)  Drew et al. (2016) Journal of Vision

      Follow up questions were used, but the reported procedure does not provide sufficient details to evaluate them (we are only told: “After the final trial, they were asked: ‘On that last trial of the task, did you notice anything that was not there on previous trials?’ They then answered questions about the features of the unexpected stimulus on a separate screen (color, shape, movement, and direction of movement).”). It is not clear that these follow ups were used to exclude any subjects from the analysis. Finally, given that the unexpected object could be the same color as the targets/distractors, it is clear that biases would have been introduced which would need to be considered (but which were not).

      (4)  Simons & Chabris (1999) Perception

      All follow ups were yes/no: “observers were … asked to provide answers to a surprise series of additional questions. (i) While you were doing the counting, did you notice anything unusual on the video? (ii) Did you notice any- thing other than the six players? (iii) Did you see anyone else (besides the six players) appear on the video? (iv) Did you see a gorilla [woman carrying an umbrella] walk across the screen? After any “yes'' response, observers were asked to provide details of what they noticed. If at any point an observer mentioned the unexpected event, the remaining questions were skipped.” As noted previously, the analyses in fact did not use these questions to exclude subjects since answers were so consistent.

      (5)  Simons and Levin (1998) Perception

      This is a change detection paradigm, not a study of inattentional blindness. And in any case, one yes/no follow up was used: “Did you notice that I'm not the same person who approached you to ask for directions?”

      (6)  Chabris et al. (2011) iPerception

      Two yes/no questions were asked: “we asked whether the subjects had seen anything unusual along the route, and then whether they had seen anyone fighting.” It seems that follow up questions (a request to describe the fight) were asked only of those who said yes.

      This is in fact a common procedure – follow up questions only being asked of the “yes” group. As discussed, it is sometimes used to increase rates of IB, compounding the problem we identify in our paper. So this is another example of a follow-up question that makes the problem we identify worse, not better.

      (7) Ward & Scholl (2015) Psych Bulletin and Review

      Two yes/no questions were used: “...observers were asked whether they noticed ‘anything … that was different from the first three trials’ — and if so, to describe what was different. They were then shown the gray cross and asked if they had noticed it—and if so, to describe where it was and how it moved. Only observers who explicitly reported not noticing the cross were counted as ‘nonnoticers’ to be included in the final sample (N = 100).” In each case, combining the traditional noticing question with a request to describe and identify may have induced conservative response biases in the noticing question, since a subject might consider being able to describe or identify the unexpected stimulus a precondition of giving a positive answer to the noticing question.

      (8) Most et al. (2001) Psych Science

      The same 5-item questionnaire discussed above in relation to Most et al. (2005) was used: 

      (1) On the last trial, did you see anything other than the black and white L’s and T’s (anything that had not been present on the first two trials)?

      (2)   If you did see something on the last trial that had not been present during the first two trials, please describe it.

      (3) If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (4) If you did see something during the last trial that had not been present in the first two trials, please draw an arrow on the “screen” below showing the direction in which it was moving. If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (5) If you did see something during the last trial that had not been present during the first two trials, please circle the shape of the object below [4 shapes are presented to choose from]. If you did not see anything, please guess. (Please indicate whether you did see something or are guessing)

      Q5 was not used for analysis purposes. (It suffers from the second issue raised above.) Q1 is the traditional yes/no question. Qs 2&3 are open ended. It is unclear how responses to Q4 were analyzed (at the limit it could be considered a helpful, forced-choice question – though it again would suffer from the second issue raised above). However, as noted with respect to the two item questionnaire in Most et al. 2005, these responses were not used to exclude people from the IB group but to include people in it. So again this approach does not in any way address the issues we are concerned about, and if anything only makes them worse.

      (9) Todd, Fougnie & Marois (2005) Psych Science

      “participants were probed with three questions to determine whether they had detected the critical stimulus ... .The first question assessed whether subjects had seen anything unusual during the trial; they responded ‘‘yes’’ or ‘‘no’’ by pressing the appropriate key on the keyboard. The second question asked participants to select which stimulus they might have seen among 12 possible objects and symbols selected from MacIntosh font databases. The third question asked participants to select the quadrant in which the critical stimulus may have appeared by pressing one of four keys, each of which corresponded to one of the quadrants.”

      These follow ups were used to include people in the IB group: “In keeping with previous studies (Most et al., 2001), participants were considered to have detected the critical stimulus successfully if they (a) reported seeing an unexpected stimulus and (b) correctly selected its quadrant location.” In line with our third point about sensitivity, the object identity test transpired to be “too difficult even under full-attention conditions … Thus, performance with this question was not analyzed further.”

      (10) Fougnie & Marois (2007) Psych Bulletin and Review

      Same exact methods and problems as with Todd & Marois (2005) Psych Science, just discussed.

      (11) New and German (2015) Evolution and Human Behaviour

      “After the fourth trial containing the additional experimental stimulus, the participant was asked, “Did you see anything in addition to the cross on that trial?” and which quadrant the additional stimulus appeared in. They were then asked to identify the stimulus in an array which in Experiment 1 included two variants chosen randomly from the spider stimuli and the two needle stimuli. Participants in Experiment 2 picked from all eight stimuli used in that experiment.”

      Our second concern about response biases and the need for appropriate SDT analysis of the 4/8 alternative tasks applies to all these questions. We also note that analyses were only performed on groups separately (those who detected/failed to detect, those who located/failed to locate, and those who identified/failed to identify) and on the group which did all three/failed to do any one of the three. Especially in light of the fact that some subjects could clearly detect the stimulus without being able to identity it (e.g.), the most stringent test given our concerns (which were not obviously New and German’s comparative concerns), would be to consider the group which could not detect, identify or localize.

      (12) Jackson-Nielsen (2017) Consciousness and cognition

      This is a very interesting example of a follow-up which used a 3-AFC recognition test:

      “participants were immediately asked, ‘‘which display looks most like what you just saw?’ from 3 alternatives”. However, though such an objective test is definitely to be preferred in our view to an open-ended series of probes, the 3-AFC test administered clearly had issues with response biases, as discussed, and actually yielded significantly below chance performance in one of the experiments.

      (13) Mack et al. (2016) Consciousness and cognition

      The follow ups here were essentially yes/no combined with an assessment of surprise. Participants were asked to enter letters into a box, and if they did so “were immediately asked by the experimenter whether they had noticed anything different about the array on this last trial and if they did not, they were told that there had been no letters and their responses to that news were recorded. Clearly, if they expressed surprise, this would be compelling evidence that they were unaware of the absence of the letters. Those observers who did not enter letters and realized there were no letters present were considered aware of the absence.” So, this again has all of the same problems we identify, considering subjects unaware because they expressed surprise.

      (14) Devue et al. (2009) Perception

      An 8-alternative task was used. The authors were primarily interested in a comparative analysis and so did not use this task to exclude subjects. We note that an 8 alternative task is very demanding – compare the 12-alternative task used in Todd, Fougnie & Marois (2005). There was an attempt to investigate biases in a separate bias trial, however SDT measures were not used.

      (15) Memmert (2014) Cognitive Development

      “After watching the video and stating the number of passes, participants answered four questions (following Simons & Chabris, 1999): (1) While you were counting, did you perceive anything unusual on the video? (2) Did you perceive anything other than the six players? (3) Did you see anyone else (besides the six players) appear on the video? (4) Did you notice a gorilla walk across the screen? After any “yes” reply, children were asked to provide details of what they noticed. If at any point a child mentioned the unexpected event, the remaining questions were omitted.” All of these follow-up questions are yes/no judgments, used to determine awareness in exactly the way we critique as problematic.

      (16) Moore & Egeth (1997) JEP:HPP

      This study (which includes one of us, Egeth, as author) did use forced choice questions. In one case, the question was 2-alternative, in the other it was 4-alternative. In the latter case, SDT would have been appropriate but was not used. In the former case, it may have been that a larger sample would have revealed evidence of sensitivity to the background pattern (as it stood 55% answered the 2-alternative question correctly). Although these results have been replicated, unfortunately the replication in Wood and Simons 2019 used a 6-alternative recognition task and this was not analyzed using SDT. We also note that the task is rather difficult in this study. Wood and Simons report: “Exclusion rates were much higher than anticipated, primarily due to exclusions when subjects failed to correctly report the pattern on the full-attention trial; we excluded 361 subjects, or 58% of our sample.”

      (17) Cohen et al. (2020) Proc Natl Acad Sci

      While this paper improves over a simple yes/no question in some ways, especially in that it used the follow up questions to exclude subjects from the unaware (IB) group, the follow up probes nonetheless remain yes/no questions, subject to response bias, e.g.:

      (1) “Did you notice anything strange or different about that last trial?”

      (2) “If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?”

      (3) “If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?”

      (4) “Did you notice anything different about the colors in the last scene?”

      Follow up questions of this kind can be especially susceptible to bias, since subjects may be reluctant to “take back” their earlier answers and so be conservative in responding positively to avoid inconsistency or acknowledgement of earlier error. This may explain why such follow up questions can produce remarkable consistency despite their rather different wording. 

      (18) Cohen et al. (2011) Psych Science

      Here are the probes used in this study:

      (1) Did you notice anything different on that trial?

      (2) Did you notice something different about the background stream of images?

      (3) Did you notice that a different type of image was presented in the background that was unique in some particular way?

      (4) Did you see an actual photograph of a natural scene in that stream?

      (5) If I were to tell you that there was a photograph in that stream, can you tell me what it was a photograph of?

      Qs 1-4 are yes/no. Q5 is yes/no with an open-ended response. After this, a 5 or 6-alternative recognition test was administered. So again, this faces the same issues, since y/n questions are subject to bias in the way we have described, and many-alternative tests are more problematic than 2afc tests.

      In summary

      We really appreciate the care that went into compiling this list, and we agree that these papers and the improved methods they contain are relevant. But as hopefully made clear above, the approaches in each of these papers simply don’t solve the foundational issues our critique is aimed at (though they may address other issues). This is why we felt our new approach was necessary. And we continue to feel this way even after reading and incorporating these comments from Dr. Cohen.

      Nevertheless, there is clearly lots for us to do in light of these comments. And so as noted earlier we have now added a very substantial new section to our discussion section to more fairly and completely portray the state of the art in this literature. This is really to our benefit in the end, since we now not only better acknowledge the diverse approaches present, but also set up ourselves to make our novel contribution exceedingly clear.

      Main point 2: Let's imagine for a second that every study did just ask a yes/no question and then would stop. So, the criticism the authors are bringing up is valid (even though I believe it is not). I am not entirely sure that above chance performance on a forced choice task proves that the inattentionally blind can see after all. Could it just be a form of subliminal priming? Could there be a significant number of participants who basically would say something like, "No I did not see anything, and I feel like I am just guessing, but if you want me to say whether the thing was to the left or right, I will just 100% guess"? I know the literature on priming from things like change and inattentional blindness is a bit unclear, but this seems like maybe what is going on. In fact, maybe the authors are getting some of the best priming from inattentional blindness because of their large sample size, which previous studies do not use.

      I'm curious how the authors would relate their studies to masked priming. In masked priming studies, observers say the did not see the target (like in this study) but still are above chance when forced to guess (like in this study). Do the researchers here think that that is evidence of "masked stimuli are truly seen" even if a participant openly says they are guessing?

      We’re grateful to the reviewer for raising this question. As we say in response to Reviewer #1, our primary ambition in the paper is to establish, as our title suggests, residual sensitivity in IB. The ambition is quite neutral as to whether the sensitivity reflects conscious or unconscious processing (i.e. is akin to blindsight as traditionally conceived, or what the reviewer here suggests may be happening in masked priming). Since we were evidently insufficiently clear about this we have revised our manuscript in several places to clarify that we take our data primarily to support the more modest claim that there is residual sensitivity (conscious or unconscious) in the group of subjects who are traditionally classified as inattentionally blind. We believe that this claim has much more solid support in our data than our secondary and tentative suggestion about awareness.

      This said, we do consider masked priming studies to be susceptible to the critique that performance may reflect degraded conscious awareness which is unreported because of conservative response criteria. There is good evidence that response criteria tend to be conservative near threshold (Björkman et al. 1993; see also: Railo et al. 2020), including specifically in masked priming studies (Sand 2016, cited in Phillips 2021). So, we consider it a perfectly reasonable hypothesis that subjects who say they feel they are guessing in fact have conscious access to a degraded signal which is insufficient to reach a conservative response criterion but nonetheless sufficient to perform above chance in 2afc detection. Of course, we appreciate that this hypothesis is controversial, so it is not one we argue for in our paper (though we are happy to share our feelings about it here).

      Main point 3: My last question is about how the authors interpret a variety of inattentional blindness findings. Previous work has found that observers fail to notice a gorilla in a CT scan (Drew et al., 2013), a fight occurring right in front of them (Chabris et al., 2011), a plane on a runway that pilots crash into (Haines, 1991), and so forth. In a situation like this, do the authors believe that many participants are truly aware of these items but simply failed to answer a yes/no question correctly? For example, imagine the researchers made participants choose if the gorilla was in the left or right lung and some participants who initially said they did not notice the gorilla were still able to correctly say if it was in the left or right lung. Would the authors claim "that participant actually did see the gorilla in the lung"? I ask because it is difficult to understand what it means to be aware of something as salient as a gorilla in a CT scan, but say "no" you didn't notice it when asked a yes/no question. What does it mean to be aware of such important, ecologically relevant stimuli, but not act in response to them and openly say "no" you did not notice them?

      Our view is that in such cases, observers may well have a “degraded” percept of the relevant feature (gorilla, plane, fight etc.). But crucially we do not suggest that this percept is sufficient for observers to recognize the object/event as a gorilla, plane, fight etc. Our claim is only that, in our studies at least, observers (as a group) do have enough information about the unexpected stimuli to locate them, and discriminate certain low level features better than chance. Crudely, it may be that subjects see the gorilla simply as a smudge or the plane as a shadowy patch etc. (One of us who is familiar with the gorilla CT scan stimuli notes that the gorilla is in fact rather hard to see even when you know which slide it is on, suggesting that they are not as “salient” as the reviewer suggests!) 

      More precisely, in the paper we write that in our view perhaps “...unattended stimuli are encoded in a partial or degraded way. Here we see a variety of promising options for future work to investigate. One is that unattended stimuli are only encoded as part of ensemble representations or summary scene statistics (Rosenholtz, 2011; Cohen et al., 2016). Another is that only certain basic “low-level” or “preattentive” features (see Wolfe & Utochkin, 2019 for discussion) can enter awareness without attention. A final possibility consistent with the present data is that observers can in principle be aware of individual objects and higher-level features under inattention but that the precision of the corresponding representations is severely reduced. Our central aim here is to provide evidence that awareness in inattentional blindness is not abolished. Further work is needed to characterize the exact nature of that awareness.” We hope this sheds light on our perspective while still being appropriately cautious not to go too far beyond our data.

      Overall: I believe there are many aspects of this set of studies that are innovative and I hope the methods will be used more broadly in the literature. However, I believe the authors misrepresent the field and overstate what can be interpreted from their results. While I am sure there are cases where more nuanced questions might reveal inattentional blindness is somewhat overestimated, claims like "the inattentionally blind can see after all" or "Inattentionally blind subjects consciously perceive thest stimuli after all" seem to be incorrect (or at least not at all proven by this data).

      Once again, we would like to thank this reviewer for his feedback, which obviously comes from a place of tremendous expertise on these issues. We appreciate his assessment that our studies are innovative and that our methodological advances will be of use more broadly. We also hear the reviewer loud and clear about the passages in question, which on reflection we agree are not as central to our case as the other claims we make (regarding residual sensitivity and conservative responding), and so we have now edited them accordingly to refocus our discussion on only those claims that are central and supported. Thank you for making our paper stronger!

      Reviewer #3 (Public review):

      Summary:

      Authors try to challenge the mainstream scientific as well as popularly held view that Inattentional

      Blindness (IB) signifies subjects having no conscious awareness of what they report not seeing (after being exposed to unexpected stimuli). They show that even when subjects indicate NOT having seen the unexpected stimulus, they are at above chance level for reporting features such as location, color or movement of these stimuli. Also, they show that 'not seen' responses are in part due to a conservative bias of subjects, i.e. they tend to say no more than yes, regardless of actual visibility. Their conclusion is that IB may not (always) be blindness, but possibly amnesia, uncertainty etc.

      We just thought to say that we felt this was a very accurate summary of our claims, and in ways underscore the modesty we had hoped to convey. This is especially true of the reviewer’s final sentence: “Their conclusion is that IB may not (always) be blindness, but possibly amnesia, uncertainty etc.”; as we noted in response to other reviewers, our claim is not that IB doesn’t exist, that subjects are always conscious of the stimulus, etc.; it is only that the cohort of IB subjects show sensitivity to the unattended stimulus in ways that suggest they are not as blind as traditionally conceived. Thank you for reading us as intended!

      Strengths:

      A huge pool of (25.000) subjects is used. They perform several versions of the IB experiments, both with briefly presented stimuli (as the classic Mack and Rock paradigm), as well as with prolonged stimuli moving over the screen for 5 seconds (a bit like the famous gorilla version), and all these versions show similar results, pointing in the same direction: above chance detection of unseen features, as well as conservative bias towards saying not seen.

      We’re delighted that the reviewer appreciated these strengths in our manuscript!

      Weaknesses:

      Results are all significant but effects are not very strong, typically a bit above chance. Also, it is unclear what to compare these effects to, as there are no control experiments showing what performance would have been in a dual task version where subjects have to also report features etc for stimuli that they know will appear in some trials

      The backdrop to the experiments reported here is the “consensus view” (Noah & Mangun, 2020) according to which inattention completely abolishes perception, such that subjects undergoing IB “have no awareness at all of the stimulus object” (Rock et al., 1992) and that “one can have one’s eyes focused on an object or event … without seeing it at all” (Carruthers, 2015). In this context, we think our findings of significant above-chance sensitivity (e.g., d′ = 0.51 for location in Experiment 1; chance, of course, would be d′ = 0 here) are striking and constitute strong evidence against the consensus view. We of course agree that the residual sensitivity is far lower than amongst subjects who noticed the stimulus. For this reason, we certainly believe that inattention has a dramatic impact on perception. To that extent, our data speak in favor of a “middle ground” view on which inattention substantially degrades but crucially does not abolish perception/explicit encoding. We see this as an importantly neglected option in a literature which has overly focused on seen/not seen binaries (see our section ‘Visual awareness as graded’).

      Regarding the absence of a control condition, we think those conditions wouldn’t have played the same role in our experiments as they typically play in other experiments. As Reviewer #1 comments, the main role of such trials in previous work has been to exclude from analysis subjects who failed to report the unexpected stimulus on the divided and/or full attention control trials. As Reviewer #1 points out, excluding such subjects would very likely have ‘helped’ us. However, the practice is controversial. Indeed, in a review of 128 experiments, White et al. 2018 argue that the practice has “problematic consequences” and “may lead researchers to understate the pervasiveness of inattentional blindness". Since we wanted to offer as simple and demanding a test of residual sensitivity in IB as possible, we thus decided not to use any exclusions, and for that reason decided not to include divided/full attention trials.

      As recommended, we discuss this decision not to include divided/full attention trials and our logic for not doing so in the manuscript. As we explain, not having those conditions makes it more impressive, not less impressive, that we observed the results we in fact did — it makes our results more interpretable, not less interpretable, and so absence of such conditions from our manuscript should not (in our view) be considered any kind of weakness.

      There are quite some studies showing that during IB, neural processing of visual stimuli continues up to high visual levels, for example, Vandenbroucke et al 2014 doi:10.1162/jocn_a_00530 showed preserved processing of perceptual inference (i.e. seeing a kanizsa illusion) during IB. Scholte et al 2006 doi: 10.1016/j.brainres.2005.10.051 showed preserved scene segmentation signals during IB. Compared to the strength of these neural signatures, the reported effects may be considered not all that surprising, or even weak.

      We agree that such evidence of neural processing in IB is relevant to — and perhaps indeed consistent with — our picture, and we’re grateful to the reviewer for pointing out further studies along those lines. Previously, we mentioned a study from Pitts et al., 2012 in which, as we wrote, “unexpected line patterns have been found to elicit the same Nd1 ERP component in both noticers and inattentionally blind subjects (Pitts et al., 2012).” We have added references to both the studies which the reviewer mentions – as well as an additional relevant study – to our manuscript in this context. Thank you for the helpful addition.

      We do however think that our studies are importantly different to this previous work. Our question is whether processing under IB yields representations which are available for explicit report and so would constitute clear evidence of seeing, and perhaps even conscious experience. As we discuss, evidence for this kind of processing remains wanting: “A handful of prior studies have explored the possibility that inattentionally blind subjects may retain some visual sensitivity to features of IB stimuli (e.g., Schnuerch et al., 2016; see also Kreitz et al., 2020, Nobre et al., 2020). However, a recent meta-analysis of this literature (Nobre et al., 2022) argues that such work is problematic along a number of dimensions, including underpowered samples and evidence of publication bias that, when corrected for, eliminates effects revealed by earlier approaches, concluding “that more evidence, particularly from well-powered pre-registered experiments, is needed before solid conclusions can be drawn regarding implicit processing during inattentional blindness” (Nobre et al., 2022).” Our paper is aimed at addressing this question which evidence of neural processing can only speak to indirectly.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations for the authors):

      (1) Please report all of the data, especially the number of subjects in each experiment that answered Y/N and the numbers of subjects in each of the Y and N groups that guessed a feature correctly/incorrectly on the 2AFC tasks. And also the confidence ratings for the 2AFC task (for comparison with the confidence ratings on the Y/N questions).

      We now report all this data in our (revised) Supplementary Materials. We agree that this information will be helpful to readers.

      (2) Consider adding a control condition with partial attention (dual task) or full attention (single task) to estimate the rates of seeing the critical stimulus when it's expected.

      This is the only recommendation we have chosen not to implement. The reason, as we explain in detail above (especially in response to Reviewer #1 comment 5), is that this would not in fact be a “control condition” in our studies, and indeed would only inflate the biases we are concerned with in our work. As the referee comments, the main role of such trials in previous work has been to exclude from analysis subjects who failed to report the unexpected stimulus on the divided and/or full attention control trials. And the practice is controversial: Indeed, in a review of 128 experiments, White et al. 2018 argue that the practice has “problematic consequences” and “may lead researchers to understate the pervasiveness of inattentional blindness" (emphasis added). So, our choice not to have such conditions ensures an especially stringent test of our central claim. Not having those conditions (and their accompanying exclusions) makes our results more interpretable, not less interpretable, and so the absence of such conditions from our manuscript should not (in our view) be considered any kind of weakness.

      We have added a paragraph to our “Design and analytical approach” section explaining the logic behind our deliberate decision not to include divided or full attention trials in our experiments. (For even fuller discussion, see our response to Reviewer #1’s comment 5 above.)

      (3) Consider revising the interpretations to be more precise about the distinction between the super subject being above chance versus each individual subject who cannot be at chance or above chance because there was only a single trial per subject.

      We have now done this throughout the manuscript, as discussed above. We have also added a substantive additional discussion to our “Design and analytical approach” section discussing what should be said about individual subjects in light of our group level data.

      This was a very helpful point, and greatly clarifies the claims we wish to make in the paper. Thank you for this comment, which has certainly made our paper stronger.

      Reviewer #2 (Recommendations for the authors):

      I would be curious to hear the authors' response to two points:

      (1) What do they have to say about prior studies that do more than just ask yes/no questions (and ask several follow-ups)? Are those studies "valid"?

      A very substantial new discussion of this important point has been added. As you will see above, we comment on every one of the 18 papers this reviewer raised (as well as the general argument made); we contend that while many of these papers improve on past methodology in various ways, most in fact do “just ask yes/no questions”, and none of them makes the methodological advance we offer in our manuscript. However, this discussion has helped us clarify that very advance, and so working through this issue has really helped us improve our paper and make its relation to existing literature that much clearer. Thank you for raising this crucial point.

      (2) Do the authors think it is possible that in many cases, people are just guessing about a critical item's location or color and this is at least in part a form of priming?

      We have clarified our discussion in numerous places to further emphasize that our main point concerns above-chance sensitivity, not awareness. Given this, we take very seriously the hypothesis that something like priming of a kind sometimes proposed to occur in cases of blindsight or other putative cases of unconscious perception could be what is driving the responses in non-noticers.

      Reviewer #3 (Recommendations for the authors):

      (1) Control dual task version with expected stimuli would be nice

      We have added a paragraph to our “Design and analytical approach” section explaining the logic behind our deliberate decision not to include divided or full attention trials, which would not in fact be a “control” task in our experiments. For full discussion, see our response to Reviewer 3 above, as well as our summary here in the Recommendations for Authors section in responding to Reviewer 1, recommendation (2).

      (2) Please do a better job in discussing and introducing experiments about neural signatures during IB.

      A discussion of Vandenbroucke et al. 2014 and Scholte et al. 2006 has been added to our discussion of neural signatures in IB, as well as an additional reference to an important early study of semantic processing in IB (Rees et al., 1999). Thank you for these very helpful suggestions!

    1. Annotation #1: Thoughts

      "World inequality, however, cannot be explained by climate or diseases, or any version of the geography hypothesis. Just think of Nogales. What separates the two parts is not climate, geography, or disease environment, but the U.S.-Mexico border." In this passage the author is debunking various aspects of the geographic theory, which attributes difference in economic success to geographic conditions. The author is essentially saying that while theories about climate and disease impacting a countries productivity and consequently economic development may seem plausible. When looking at actual events in history, we can see that even countries with exactly the same geographies still face completely different circumstances economically. This connects to Singapore as we can see this phenomenon occur here as well, with Singapore being significantly better off economically than its neighbours such as Thailand, Cambodia, etc.

      Annotation #2: Question “But mostly no, because those aspects of culture often emphasized—religion, national ethics, African or Latin values—are just not important for understanding how we got here and why the inequalities in the world persist.”

      This passage is introducing the culture theory, a theory that cites cultural differences as the source of inequality in economic growth across the world. This specific quote elaborates on why Robinson doesn’t believe that culture is a significant cause of the difference in economic growth across the world, stating that aspects of culture such as “religion, national ethics and African or Latin values” are not important. This idea made me wonder about the impact a societies intrinsic cultural values can have when looked at from a larger scale: How do a society’s intrinsic cultural values influence its long-term economic growth and development on a global scale? When connecting this to Singapore and it’s own rapid economic development, I wonder if certain cultural values such as value of education in many asian cultures or respect for the law, influenced the way Singapore as a country was able to develop economically on a global scale.

      **Annotation #3: Epiphanies ** “China, despite many imperfections in its economic and political system, has been the most rapidly growing nation of the past three decades. Chinese poverty until Mao Zedong’s death had nothing to do with Chinese culture; it was due to the disastrous way Mao organized the economy and conducted politics.”

      This passage further analyzing why the culture theory doesn’t properly explain the economic growth of certain countries over others.This specific quote shifts the focus from cultural explanations of poverty to highlighting how government policies and political decisions can drastically impact economic outcomes. It made me think a lot more about the significant influence that historical and political events have on countries, and how decisions from decades ago can still have lasting impact on countries. What interested me most was how some countries, like China, were able to recover and achieve significant growth, while others, such as certain African nations, continue to struggle. This made me reflect on how institutions and governance propel a country towards prosperity, and connecting it to Singapore made me wonder the unique aspects of it's institutions that lead it to be so economically successful.

    2. Annotation 1: "History illustrates that there is no simple or enduring connection between climate or geography and economic success."

      The authors here are arguing that geographical factors like climate and location do not provide a reliable or enduring explanation for economic success or failure. This emphasises that history demonstrates other factors, like colonisation and political institutions play a much larger role in shaping economic outcomes. While geography may influence certain conditions, it is not the primary factor in explaining economic growth or development.

      Annotation 2: "Though it is not politically correct to articulate in public, many people still maintain that Africans are poor because they lack a good work ethic, still believe in witchcraft and magic, or resist new Western technologies."

      Why do certain negative cultural perceptions persist, despite evidence showing that these beliefs are not the underlying causes of poverty? How will these misunderstandings about different cultures chance in the future, especially if these perceptions have historically shaped policies and economic outcomes?

      Annotation 3: "To understand world inequality we have to understand why some societies are organised in very inefficient and socially undesirable ways. Nations sometimes do manage to adopts efficient instituations and achieve prosperity, but alas, these are the rare cases. Most economists and policymakers have focused on "getting it right,"whole what is really needed is an explanation for why poor nations "get it wrong." Getting it wrong is mostly not about ignorance or culture. As we will show, poor countries are poor because those in power make choices that create poverty. They get it wrong not by mistake or ignorance but on purpose."

      The author is telling us that poverty is often the result of a strategic decision made by elites who benefit from the status quo. This makes me think about the role of politics and power in economic development, rather than focusing purely on economic policies or advice. Corruption and poor governance in certain nations create economic inequality and prevent sustainable growth, strengthening the notion that improving economic growth is not just about providing better economic advice or policies but also addressing the deep rooted political issues that prevent those policies from being implemented effectively. The real challenge lies in political reform and ensuring that those in power are incentivised to act in the interests of economic prosperity for all.

      Corruption is a Global Problem for Development

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Hahn et al use bystander BRET, NanoBiT assays, and APEX2 proteomics to investigate endosomal signaling of CCR7 by two agonists, CCL19 and CCL21. The authors suggest that CCR7 signals from early endosomes following internalisation. They use spatial proteomics to try to identify novel interacting partners that may facilitate this signaling and use this data to specifically enhance a Rac1 signaling pathway. Many of the results in the first few figures showing simultaneous recruitment of Barr and G proteins by CCR7 have been shown previously (Laufer et al, 2019, Cell Reports), as has signaling from endomembranes, and Rac1 activation at intracellular sites. The new findings are the APEX2 proteomics studies, which could be useful to the scientific community. Unfortunately, the authors only follow up on a single finding, and the expansion of this section would improve the manuscript.

      First of all, we would like to thank the reviewer for helping with the manuscript. The summary is mostly accurate except for the statement that simultaneous recruitment of barr and G protein to CCR7 has been shown before. It should also be noted that it has not been demonstrated that CCR7 activates G proteins from endosomes previously nor has the functional role of this signaling mechanism. However, that CCR7 activity at endomembranes is associated with Rac1 signaling was demonstrated in the Laufer et al. study as the reviewer correctly points out.

      Strengths:

      (1) The APEX2 resource will be valuable to the GPCR and immunology community. It offers many opportunities to follow up on findings and discover new biology. The resource could also be used to validate earlier findings in the current manuscript and in previous manuscripts. Was there enrichment of early endosomal markers, Barr and Gi as this would provide further evidence for their earlier claims regarding endosomal signaling? Previous studies have suggested signaling from the TGN, so it is possible that the different ligands also direct to different sites. This could easily be investigated using the APEX2 data.

      Thank you for your comment. We do in fact observe enrichment of TGN/Golgi markers in response to chemokine stimulation, which we now have highlighted in the manuscript (fourth paragraph on page 7).

      (2) The results section is well written and can be followed very easily by the reader.

      We are glad that the reviewer found the results section very readable.

      (3) Some findings verify previous studies (e.g. endomembrane signalling). This should be acknowledged as this shows the validity of the findings of both studies.

      This is correct. We have now included more discussion of previous work related to CCR7 signaling at endomembranes (thirdparagraph on page 10).

      Weaknesses:

      (1) The findings are interesting although the studies are almost all performed in HEK293 cells. I understand that these are commonly used in GPCR biology and are easy to transfect and don't express many GPCRs at high concentrations, but their use is still odd when there are many cell-lines available that express CCR7 and are more reflective of the endogenous state (e.g. they are polarised, they can perform chemotaxis/ migration). Some of the findings within the study should also be verified in more physiologically relevant cells. At the moment only the final figure looks at this, but findings need to be verified elsewhere.

      We thank the reviewer for raising this point and giving us an opportunity to elaborate in further detail. The major goal of our study was to investigate whether CCR7 activates G protein from endosomes, the underlying mechanism, and functions of this potential signaling mechanism. The reason we chose CCR7 as our model receptor was that it belongs to a group of GPCRs, the chemokine receptors, that most often have features associated with the ability to promote endosomal G protein activation (phosphorylation site clusters in the C-terminal region).

      Specific detection of G protein activation at distinct subcellular compartments is currently very challenging in truly endogenous systems despite new innovative biosensors that are available (not just related to CCR7, but GPCRs in general). To our knowledge, most if not all studies that detect direct activation of G protein at a specific compartment whether at the plasma membrane, endosome, Golgi, or other compartments, have overexpressed either the receptor, G protein, or both. This is why we choose the HEK293 cell system for most of our experiments, which are easy to manipulate. That being said, we did confirm major findings in an indirect manner using Jurkat T-cells, which express CCR7 endogenously and are physiological relevant. Our hope is that in the future we will be able to use highly sensitive biosensors to directly confirm our findings in such a cell system as the reviewer wisely suggests.

      (2) The authors acknowledge that the kinetic patterns of the signals at the early endosome are not consistent with the rates of internalisation. They mention that this could be due to trafficking elsewhere. This could be easily looked at in their APEX2 data. Is there evidence of proximity to markers of other membranes? Perhaps this could be added to the discussion. Similarly, previous studies have shown that CCR7 signaling may involve the TGN. Was there enrichment of these markers? If not, this could also be an interesting finding and should be discussed. It is also possible that the Rab5 reporter is just not as efficient as the trafficking one, especially as in later figures the very convincing differences in the two ligands are not as robust as the differences in trafficking.

      Excellent point. We have now highlighted the possibility of CCR7 being further trafficked to the trans-Golgi network (TGN) as possible explanation for the transient translocation of activated CCR7 to the early endosome in Fig. 1G-H (second paragraph on page 3).

      Furthermore, in the APEX2 experiment we observe enrichment of proteins involved in lysosomal trafficking (LAMP1, VPS16, VAMP7, WDR91, and PP4P1) by CCL19 stimulation at 25 min, and recycling endosomes/TGN markers (SNX6, RAB7L, and GGA) by CCL21 stimulation at 25 min. In addition to this, several markers of TGN/Golgi (SNX3, COG5, YIF1A, SC22B, and AP3S1) were enriched as well in response to both CCL19 and CCL21 stimulation. We have now included a statement in the manuscript, which describes the likely trafficking of CCR7 to the TGN/Golgi in response to CCL19 and CCL21 stimulation (fourth paragraph on page 7).

      (3) In the final sentence of paragraph 2 of the results the authors state that the internalisation is specific to CCR7 as there isn't recruitment to V2R. I'm not sure this is the best control. The authors can only really say it doesn't recruit to unrelated receptors. The authors could have used a different chemokine receptor which does not respond to these ligands to show this.

      The point with this control experiment was to demonstrate that the loss of NanoBiT signal in response to CCL19 in CCR7-SmBiT/LgBiT-CAAX expressing cells, but not in V2R-SmBiT/LgBiT-CAAX expressing cells, was a result of bona fide CCR7 internalization rather than potential artifactual effects of CCL19 on the NanoBiT system. Our intent was not to demonstrate specificity of CCL19 among chemokine receptors, which already has been thoroughly tested in previous studies. We have now modified the sentence (second paragraph on page 3) “Moreover, CCL19/CCL21-stimulation of receptor internalization to endosomes is specific to CCR7 as none of the chemokines promote internalization or trafficking to endosomes of the vasopressin type 2 receptor (V<sub>2</sub>R)-SmBiT construct (Fig. S1E-F)” to “Moreover, CCL19/CCL21-stimulation did not promote internalization or trafficking to endosomes of the vasopressin type 2 receptor (V<sub>2</sub>R)-SmBiT construct, which validates that these chemokines act specifically via the CCR7-SmBiT system (Fig. S1E-F).”

      (4) The miniGi-Barr1 and imaging showing co-localisation could be more convincing if it was also repeated in a more physiological cell line as in the final figure. Imaging of CCR7, miniGi, and Barr1 would also provide further evidence that the receptor is also present within the complex.

      We agree with the reviewer’s assessment. However, as mentioned above it is currently extremely challenging to detect endogenous G protein coupling/activation to endogenous receptors. In addition, we are not sure if overexpressing fluorophore-tagged receptor, miniG, and barr1 in a physiological-relevant cell line would provide truly physiological conditions as the expression of these proteins still would be artificially high. This is why we chose to conduct these mechanistic experiments in HEK293 cells and then indirectly verify key findings in an endogenous and physiological-relevant cell line.

      (5) The findings regarding Rac1 are interesting, although an earlier paper found similar results (Laufer et al, 2019, Cell Reports), so perhaps following up on another APEX2-identified protein pathway would have been more interesting. The authors' statement that Rac1 is specifically activated, and RhoA and Cdc42 are not, is unconvincing from the current data. Only a single NanoBiT assay was used, and as raw values are not reported it is difficult for the reader to glean some essential information. The authors should show evidence that these reporters work well for other receptors (or cite previous studies) and also need evidence from an independent (i.e. non-NanoBiT or BRET) assay.

      The major focus of the study was to investigate whether CCR7 can activate G protein after having been internalized into endosomes via formation of CCR7-Gi/o-barr megaplexes, and to dissect out potential functions of said endosomal G protein signaling. To do this, we used CCL19 and CCL21 which stimulate G protein to the same extent but differ in their ability of promote barr recruitment and receptor internalization with CCL19 being superior to CCL21. To this end, we found that CCL19 also promote endosomal G protein activation to a greater extent than CCL21, and therefore, we specifically looked for proteins enriched by CCL19 in our APEX experiment. This led us to some Rho GTPase regulators that were differentially enriched by CCL19 and CCL21. We agree that there were other interesting effectors related to CCR7 biology identified in the APEX experiment such as EYA2, GRIP2, and EI24. However, those proteins were enriched similar by CCL19 and CCL21 challenge, and thus, do not seem to be activated specifically at endosomes. Following the same argument, we also did not observe any difference in the activity of RhoA or Cdc42 when stimulated with CCL19 or CCL21, so we cannot conclude that these signaling proteins are activated specifically in endosomes. On the other hand, Rac1 was stimulated to a larger degree by CCL19 than CCL21, its activity was inhibited by the Gi/o inhibitor PTX and endocytosis inhibitors Dyngo-4a and PitStop2. CCR7-mediated Rac1 signaling was also inhibited by expression of a dominant negative dynamin mutant that inhibits receptor internalization, and Rac1 was not activated by an internalization-deficient CCR7-DS/T mutant. Finally, the involvement of Rac1 in CCR7 mediated chemotaxis of Jurkat T cells was also demonstrated. We believe that these findings together provide strong basis for the claim that endosomal Gi/o protein signaling by CCR7 activates Rac1.

      Following the reviewer’s suggestion, we have now included experiments to show that the activation of RhoA, Rac1, and Cdc42 by CXCR4 also can be detected by the NanoBiT biosensors (Fig. S7D-F). We have also added the appropriate references to the original studies where these biosensors were developed in the results section (first paragraph on page 8).

      (6) At present, the studies in Figure 7 do not go beyond those in the previous Laufer et al study in which they showed blocking endocytosis affected Rac1 signalling. The authors could show that Rac1 signalling is from early endosomes to improve this, otherwise, it could be from the TGN as previously reported.

      The major purpose of Figure 7 was to indirectly confirm findings from HEK293 cells experiments and to tie them to physiological functions. Our experiments using Jurkat T-cells show that CCL19 promote stronger chemotactic response than CCL21 despite similar Gi/o response. In addition, we showed that CCR7-mediated Gi/o activation, receptor endocytosis, as well as Rac1 activity, are required to drive chemotaxis. The Laufer et al. study did not investigate whether CCR7 activates G protein after having been internalized into endosomes via formation of CCR7-Gi/o-barr megaplexes, and thus, did not focus on functional outcomes of this signaling mechanism. Based on this, we believe our work provides new and valuable knowledge to the field.

      Reviewer #2 (Public Review):

      Summary:

      This manuscript describes a comprehensive analysis of signalling downstream of the chemokine receptor CCR7. A comprehensive dataset supports the authors' hypothesis that G protein and beta-arrestin signalling can occur simultaneously at CCR7 with implications for continued signalling following receptor endocytosis.

      We would like to thank the reviewer for helping with the manuscript. We agree on all points made and have now updated the manuscript accordingly.

      Strengths:

      The experiments are well controlled and executed, employing a wide range of assays using - in the main - CCR7 transfectants. Data are well presented, with the authors' claims supported by the data. The paper also has an excellent narrative which makes it relatively easy to follow. I think this would certainly be of interest to the readership of the journal.

      We appreciate the positive assessment of strengths.

      Weaknesses:

      Since the authors show a differential enrichment of RhoGTPases by CCR7 stimulation with CCL19 versus CCL21, I think that they also need to show that the Gi/o coupling of HEK-292-CCR7-APEX2 cells to both CCL19 and CCL21 is not perturbed by the modification. Currently, the authors only show data for CCL19 signalling, which leaves the potential for a false negative finding in terms of CCL21 signalling being selectively impaired. This should be relatively easy to do and should strengthen the authors' conclusions.

      We agree with the reviewer and have now included experiments to show that both CCL19- and CCL21-mediated CCR7-APEX2 stimulation leads to Gi/o activation (Fig. S4C). In addition, our proteomics experiments show strong effects of both CCL19 and CCL21 stimulation, which suggest that the receptor is activated by both ligands.

      The authors conclude the discussion by suggesting that their findings highlight endosomal signalling as a general mechanism for chemokine receptors in cell migration. I think this is an overreach. The authors chose several studies of CXC chemokine receptors to support their argument that C-terminal truncation or mutation of the C-terminal phosphorylation sites impairs endocytosis and chemotaxis (refs 40-42). However, in some instances e.g. at the related chemokine receptor CCR4, C-terminal removal of these sites impairs endocytosis but promotes chemotaxis (Nakagawa et al, 2014); Anderson et al, 2020). I therefore think that either the final statement needs to be tempered down or the counterargument discussed a little.

      We appreciate the reviewer highlighting this point. We have now modified the concluding sentence from “Thus, the findings from our study highlight endosomal G protein signaling by chemokine receptors as a potential general mechanism that regulates key aspects of cell migration” to “Thus, the findings from our study highlight endosomal G protein signaling by some chemokine receptors as a potential mechanism that regulates key aspects of cell migration.” We hope that the temper level of this sentence is more appropriate.

      References:

      Anderson, C. A. et al. A degradatory fate for CCR4 suggests a primary role in Th2 inflammation. J Leukocyte Biol 107, 455-466 (2020).

      Nakagawa, M. et al. Gain-of-function CCR4 mutations in adult T cell leukaemia/lymphoma. Journal of Experimental Medicine 211, 2497-2505 (2014).

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) The results section is well written, although the introduction needs more information on what is known about CCR7 trafficking and endomembrane signaling. I understand this is because the authors wanted to focus on GPCR signaling, but the study will equally be of interest to researchers in the immunology and chemokine fields, and therefore more CCR7-focussed discussion in the introduction would be useful. Similarly, the discussion would benefit from more discussion of previous studies of CCR7 trafficking and endomembrane signaling (in particular the Laufer et al paper) to acknowledge that many of the findings within this paper verify previous studies.

      We have now included additional immunology/endomembrane background information about CCR7 at the place where the receptor is introduced (first paragraph on page 3). We have also expanded our discussion of our work in relation to the Laufer et al. study (third paragraph on page 10).

      (2) On page 5, the authors state that 'The response to chemokine stimulation was not observed in mock transfected HEK293 cells'. Figure S4D does not have a legend so it is difficult to see what they mean by mock transfected. Do they mean not transfecting with anything or not with the receptor? The better control would be transfecting the reporters but not the receptor. This may have been done, but the wording needs clarifying and S4D needs a legend.

      Thanks for pointing this out. We believe the reviewer refers to Figure S2D and we have now highlighted/clarified the legend better. Mock transfected conditions refer to HEK293 cells transfected with the reporter, but not the receptor. This is written in the legend as “(D) Change in luminescence signal generated between SmBiT-barr1 and LgBiT-miniGi in response to 100 nM CCL19 or 100 nM CCL21 in mock transfected HEK293 cells (no CCR7)”, which we believe should be clear to the audience.

      (3) The validation of the APEX2 receptor construct relies on a single assay with one ligand. The authors should show that the receptor expresses at the cell surface, is internalised normally, and that both ligands activate the receptor.

      We have now included additional data to show that (1) the receptor is expressed at the cell surface, (2) that the CCR7-APEX2 recruits barr1 to the plasma membrane, (3) that this association leads to barr1 translocation to the early endosomes as an indirect measurement of receptor internalization, and (4) that both CCL19- and CCL21-stimulation inhibit forskolin induced cAMP production (Fig.S4A-C, and described in fifth paragraph on page 6).

      (4) The APEX2 section is very short, especially as this is novel data. It lacks some important information, e.g. when the authors state that 'we identified a total of 579 proteins', is this in total for both ligands, separately or were some shared? More information on each ligand separately and combined would make this clearer.

      We have now specified that the identified total proteins enriched from our APEX2 approach is when the cells are stimulated with either CCL19 or CCL21 (third paragraph on page 7). Furthermore, we have included a Venn diagram in Fig. S5C to show how many proteins were enriched by CCL19 or CCL21 stimulation and how many of those were shared at different time points.

      (5) The discussion would benefit from some further work. The current first two paragraphs just reiterate the introduction and don't discuss the current paper so could be removed completely. The Laufer et al study needs much more discussion as they report many of the findings of the current paper (signaling following endocytosis, Rac1 endomembrane signaling) five years ago. The APEX2 findings that are discussed, though interesting, are not followed up by further experimental evidence and there is little discussion of why the two ligands have different responses or what the physiological effects could be.

      We appreciate the reviewer’s effort in helping with the discussion. To this end, we have now expanded our discussion of the mentioned paper further as suggested (third paragraph on page 10). We agree that the findings from our APEX experiment are interesting, but the focus of this study relates to proteins enriched specifically at endosomes. Several of the most enriched proteins did not show this localization bias, which is why these proteins were not further investigated.

      Minor changes:

      (1) The authors should remove the word 'recent' at the start of the first sentence of the third paragraph. Endosomal signaling by GPCRs was described 15 years ago so cannot really be seen as recent anymore.

      We have now adjusted the manuscript accordingly.

      (2) Tukey defaulted to Turkey in some places.

      We thank the reviewer for pointing out these typos, which now have been corrected.

      Reviewer #2 (Recommendations For The Authors):

      Minor Points:

      (1) ACKRs do not couple to G proteins so it is peculiar to see them in this table. I would limit the table to the conventional CCR1-10, CXCR1-6 and XCR1. The ligand for XCR1 is XCL1 which is absent from the table.

      We have now modified the table accordingly.

      (2) CCL19 (formerly known as ELC) has been long known to be a more efficacious and potent ligand in chemotaxis assays (Bardi et al, 2001). This earlier reference should be added to the citations in the preceding statement on page 10.

      This is an important study showing that CCL19 is more efficacious than CCL21 in promoting chemotaxis and that this has been known for decades. We have now included the reference accordingly (reference 59 in second paragraph on page 11).

      (3) Figure 6, Panel Q. I think the legends for CCR7 and CCR7 delta ST might be flipped.

      We thank the reviewer for pointing out this error. We have now corrected the figure panel.

      (4) Figure S5 (or 5) might benefit from simple Venn diagrams showing the numbers of differentially enriched proteins following treatment with the two ligands at different time points.

      We have included a Venn diagram in Fig. S5C to show how many proteins were enriched by CCL19 or CCL21 stimulation and how many of those where shared.

      Reference:

      Bardi, G., Lipp, M., Baggiolini, M. & Loetscher, P. The T cell chemokine receptor CCR7 is internalized on stimulation with ELC, but not with SLC. European Journal of Immunology 31, 3291-3297 (2001).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      Understanding the mechanisms of how organisms respond to environmental stresses is a key goal of biological research. Assessment of transcriptional responses to stress can provide some insights into those underlying mechanisms. The researchers quantified traits, fitness, and gene expression (transcriptional) response to salinity stress (control vs stress treatments) for 130 accessions of rice (three replicates for each accession), which were grown in the field in the Philippines. This experimental design allowed for many different types of downstream analyses to better understand the biology of the system. These analyses included estimating the strength of selection imposed on transcription in each environment, evaluating possible trade-offs in gene expression, testing whether salinity induces transcriptional decoherence, and conducting various eQTL-type analyses.

      Strengths:

      The study provides an extensive analysis of gene expression responses to stress in rice and offers some insights into underlying mechanisms of salinity responses in this important crop system. The fact that the study was conducted under field conditions is a major plus, as the gene expression responses to soil salinity are more realistic than if the study was conducted in a greenhouse or growth chamber. The preprint is generally well-written and the methods and results are mostly well-described.

      Weaknesses:

      While the study makes good use of analyzing the dataset, it is not clear how the current work advances our understanding of gene regulatory evolution or plant responses to soil salinity generally. Overall, the results are consistent with other prior studies of gene expression and studies of selection across environmental conditions. Some of the framing of the paper suggests that there is more novelty to this study than there is in reality. That said, the results will certainly be useful for those working in rice and should be interesting to scientists interested in how gene expression responses to stress occur under field conditions. I detail other concerns I had about the preprint below:

      The abstract on lines 33-35 illustrates some of my concerns about the overstatement of the novelty of the current study. For example, is it really true that the role of gene expression in mediating stress response and adaptation is largely unexplored? There have been numerous studies that have evaluated gene expression responses to stresses in a wide range of organisms. Perhaps, I am missing something critically different about this study. If so, I would recommend that the authors reword this sentence to clarify what gap is being filled by this study. Further, is it really the case that none of them have evaluated how the correlational structure of gene expression changes in response to stresses in plants, as implied in lines 263-265? Don't the various modules and PC analyses of gene expression get at this question?

      We have re-worded these sentences, and highlighted the novelty of our work.

      There were some places in the methods of the preprint that required more information to properly evaluate. For example, more information should be provided on lines 664-668 about how G, E, and GxE effects were established, especially since this is so central to this study. What programs/software (R? SAS? Other?) were used for these analyses? If R, how were the ANOVAs/models fit? What type of ANOVA was used? How exactly was significance determined for each term? Which effects were considered fixed and which were random? If the goal was to fit mixed models, why not use an approach like voom-limma (Law et al. 2014 Genome Biology)? More details should also be added to lines 688-709 about these analyses, including what software/programs were used for these analyses.

      We have added more details in the methods. Also, although we could in priciple use voom-limma to fit our mixed model, to be able to partition variance into G, E and G×E, we need to use the function fitExtractVarPartModel (from package VariancePartition) which requires all categorical variables to be modeled as random effects. Therefore, we couldn’t model environment as a fixed effect.

      One thing that I found a bit confusing throughout was the intermixing of different terms and types of selection. In particular, there seemed to be some inconsistencies with the usage of quantitative genetics terms for selection (e.g. directional, stabilizing) vs molecular evolution terms for selection (e.g. positive, purifying). I would encourage the authors to think carefully about what they mean by each of these terms and make sure that those definitions are consistently applied here.

      We have defined the selection terms used in the study and used these terms consistently throughout the manuscript.

      It would be useful to clarify the reasons for the inherent bias in the detection of conditional neutrality (CN) and antagonistic pleiotropy (AP; Lines 187-196). It is also not clear to me what the authors did to deal with the bias in terms of adjusting P-value thresholds for CN and AP the way it is currently written. Further, I found the discussion of antagonistic pleiotropy and conditional neutrality to be a bit confusing for a couple of reasons, especially around lines 489-491. First of all, does it really make sense to contrast gene expression versus local adaptation, when lots of local adaptation likely involves changes in gene expression? Second, the implication that antagonistic pleiotropy is more common for local adaptation than the results found in this study seems questionable. Conditional neutrality appears to be more common for local adaptation as well: see Table 2 of Wadgymar et al. 2017 Methods in Ecology and Evolution. That all said, it is always difficult to conclude that there are no trade-offs (antagonistic pleiotropy) for a particular locus, as the detecting trade-offs may only manifest in some years and not others and can require large sample sizes if they are subtle in effect.

      We have now explained the cause of the inherent bias in the detection of CN, and also elaborated on how we deal with this bias. Also, we have edited our discussion and added relevant citations to indicate both conditional neutrality and antagonistic pleiotropy can lead to local adaptations and added the caveat regarding detecting antagonistic pleiotropy.

      Reviewer #2 (Public Review):

      The authors investigate the gene expression variation in a rice diversity panel under normal and saline growth conditions to gain insight into the underlying molecular adaptive response to salinity. They present a convincing case to demonstrate that environmental stress can induce selective pressure on gene expression, which is in agreement to their earlier study (Groen et al, 2020). The data seems to be a good fit for their study and overall the analytic approach is robust.

      (1) The work started by investigating the effect of genotype and their interaction at each transcript level using 3'-end-biased mRNA sequencing, and detecting a wide-spread GXE effect. Later, using the total filled grain number as a proxy of fitness, they estimated the strength of selection on each transcript and reported stronger selective pressure in a saline environment. However, this current framework relies on precise estimation of fitness and, therefore can be sensitive to the choice of fitness proxy.

      We now acknowledge this caveat in the discussion.

      (2) Furthermore, the authors decomposed the genetic architecture of expression variation into cis- and trans-eQTL in each environment separately and reported more unique environment-specific trans-eQTLs than cis-. The relative contribution of cis- and trans-eQTL depends on both the abundance and effect size. I wonder why the latter was not reported while comparing these two different genetic architectures. If the authors were to compare the variation explained by these two categories of eQTL instead of their frequency, would the inference that trans-eQTLs are primarily associated with expression variation still hold?

      We have now also reported the effect sizes for both cis- and trans-eQTLs in the two environments and showed that the trans-eQTLs have higher effect sizes as compared to cis-eQTLs, indicating that they are able to explain higher proportion of variation in transcript abundances in the two environments.

      (3) Next, the authors investigated the relationship between cis- and trans-eQTLs at the transcript level and revealed an excess of reinforcement over the compensation pattern. Here, I struggle to understand the motivation for testing the relationship by comparing the effect of cis-QTL with the mean effect of all trans-eQTLs of a given transcript. My concern is that taking the mean can diminish the effect of small trans-eQTLs potentially biasing the relationship towards the large-effect eQTLs.

      We wanted to estimate compensating vs reinforcing effects, which essentially entails identifying genes that have opposing directionality of cis and trans-effects. To get the total trans-effect we decided to take the mean effect of trans-eQTLs. This mean was only used to identify the compensating/reinforcing genes and although the mean effects diminishes the effect of small trans-eQTLs, this mean was not used in downstream analyses.

      Reviewer #3 (Public Review):

      In this work, the authors conducted a large-scale field trial of 130 indica accessions in normal vs. moderate salt stress conditions. The experiment consists of 3 replicates for each accession in each treatment, making it 780 plants in total. Leaf transcriptome, plant traits, and final yield were collected. Starting from a quantitative genetics framework, the authors first dissected the heritability and selection forces acting on gene expression. After summarizing the selection force acting on gene expression (or plant traits) in each environment, the authors described the difference in gene expression correlation between environments. The final part consists of eQTL investigation and categorizing cis- and trans-effects acting on gene expression.

      Building on the group's previous study and using a similar methodology (Groen et al. 2020, 2021), the unique aspect of this study is in incorporating large-scale empirical field works and combining gene expression data with plant traits. Unlike many systems biology studies, this study strongly emphasizes the quantitative genetics perspective and investigates the empirical fitness effects of gene expression data. The large amounts of RNAseq data (one sample for each plant individual) also allow heritability calculation. This study also utilizes the population genetics perspective to test for traces of selection around eQTL. As there are too many genes to fit in multiple regression (for selection analysis) and to construct the G-matrix (for breeder's equation), grouping genes into PCs is a very good idea.

      Building on large amounts of data, this study conducted many analyses and described some patterns, but a central message or hypothesis would still be necessary. Currently, the selection analysis, transcript correlation structure change, and eQTL parts seem to be independent. The manuscript currently looks like a combination of several parallel works, and this is reflected in the Results, where each part has its own short introduction (e.g., 185-187, 261-266, 349-353). It would be great to discuss how these patterns observed could be translated to larger biological insights. On a related note, since this and the previous studies (focusing on dry-wet environments) use a similar methodology, one would also wonder what the conclusions from these studies would be. How do they agree or disagree with each other?

      We acknowledge that the manuscript currently presents some analyses in a somewhat independent manner. Although it would be ideal to have a central hypothesis/message, our study is meant to broadly outline the various responses and fitness effects of salinity stress in rice. Throughout the manuscript, we have also included comparisons between our findings and that of our previous studies on drought stress to highlight any consistent themes or novel insights.

      Many analyses were done separately for each environment, and results from these two environments are listed together for comparison. Especially for the eQTL part, no specific comparison was discussed between the two environments. It would be interesting to consider whether one could fit the data in more coherent models specifically modeling the X-by-environment effects, where X might be transcripts, PCs, traits, transcript-transcript correlation, or eQTLs.

      We do plan to consider fitting models that explicitly incorporate X-by-environment interactions to provide a more detailed understanding of the genetics of plasticity between the two environments, but it is beyond the scope of this paper. This will be explored in a separate report.

      As stated, grouping genes into PCs is a good idea, but although in theory, the PCs are orthogonal, each gene still has some loadings on each PC (ie. each PC is not controlled by a completely different set of genes). Another possibility is to use any gene grouping method, such as WGCNA, to group genes into modules and use the PC1 of each module. There, each module would consist of completely different sets of genes, and one would be more likely to separate the biological functions of each module. I wonder whether the authors could discuss the pros and cons of these methods.

      We recognize that individual genes can contribute to multiple PCs, and this is precisely why we choose PCA clustering over WGCNA where one gene can belong to only one module. Our aim was to recognize all biological processes that could be under selection in either environment, and since one gene can be involved in various different processes, we wanted to identify the contribution of these genes to different processes which can be done effectively by a PCA analyses.

      Reviewer #4 (Public Review):

      The manuscript examines how patterns of selection on gene expression differ between a normal field environment and a field environment with elevated salinity based on transcript abundances obtained from leaves of a diverse panel of rice germplasm. In addition, the manuscript also maps expression QTL (eQTL) that explains variation in each environment. One highlight from the mapping is that a small group of trans-mapping regulators explains some gene expression variation for large sets of transcripts in each environment. The overall scope of the datasets is impressive, combining large field studies that capture information about fecundity, gene expression, and trait variation at multiple sites. The finding related to patterns indicating increased LD among eQTLs that have cis-trans compensatory or reinforcing effects is interesting in the context of other recent work finding patterns of epistatic selection. However, other analyses in the manuscript are less compelling or do not make the most of the value of collected data. Revisions are also warranted to improve the precision with which field-specific terminology is applied and the language chosen when interpreting analytical findings.

      Selection of gene expression:

      One strength of the dataset is that gene expression and fecundity were measured for the same genotypes in multiple environments. However, the selection analyses are largely conducted within environments. The addition of phenotypic selection analyses that jointly analyze gene expression across environments and or selection on reaction norms would be worthwhile.

      We do plan to consider fitting models that explicitly incorporate G×E interactions to provide a more detailed understanding of the genetics of plasticity between the two environments, but it is beyond the scope of this paper. This will be explored in a separate report.

      Gene expression trade-offs:

      The terminology and possibly methods involved in the section on gene expression trade-offs need amendment. I specifically recommend discontinuing reference to the analysis presented as an analysis of antagonistic pleiotropy (rather than more general trade-offs) because pleiotropy is defined as a property of a genotype, not a phenotype. Gene expression levels are a molecular phenotype, influenced by both genotype and the environment. By conducting analyses of selection within environments as reported, the analysis does not account for the fact that the distribution of phenotypic values, the fitness surface, or both may differ across environments. Thus, this presents a very different situation than asking whether the genotypic effect of a QTL on fitness differs across environments, which is the context in which the contrasting terms antagonistic pleiotropy and conditional neutrality have been traditionally applied. A more interesting analysis would be to examine whether the covariance of phenotype with fitness has truly changed between environments or whether the phenotypic distribution has just shifted to a different area of a static fitness surface.

      We recognize that pleiotropy is a property of a genotype, and not phenotype, but since our phenotype (gene expression) is strongly coupled with the genotype, we choose to call trade-offs as antagonistic pleiotropy. That being said, we did test whether the covariance of gene expression with phenotype significantly varies between environments, and found that to indeed be the case.

      Biological processes under selection / Decoherence: PCs are likely not the most ideal way to cluster genes to generate consolidated metrics for a selection gradient analysis. Because individual genes will contribute to multiple PCs, the current fractional majority-rule method applied to determine whether a PC is under direct or indirect selection for increased or decreased expression comes across as arbitrary and with the potential for double-counting genes. A gene co-expression network analysis could be more appropriate, as genes only belong to one module and one can examine how selection is acting on the eigengene of a co-expression module. Building gene co-expression modules would also provide a complementary and more concrete framework for evaluating whether salinity stress induces "decoherence" and which functional groups of genes are most impacted.

      We recognize that individual genes can contribute to multiple PCs, and this is precisely why we choose PCA clustering over WGCNA where one gene can belong to only one module. Our aim was to recognize all biological processes that could be under selection in either environment, and since one gene can be involved in various different processes, we wanted to identify the contribution of these genes to different processes which can be done effectively by a PCA analyses. But again as pointed out by the reviewer, our PCs did contain contribution (even negligible) of each gene, so to identify the ‘primary’ biological processes represented by the PCs, we chose the majority rule. As for testing decoherence, we agree that a co-expression module analyses would have provided additional support to the specific test performed in our manuscript, but since it would just be additional support, we choose to not add it in the manuscript.

      But based on the recommendation of the reviewer(s), we did perform a WGCNA analyses and found a total of 14 and 13 modules in normal and saline conditions, of which 0 and 2 modules (with no significant GO enrichment) were under directional selection. This supports our reasoning of potentially missing on identification of processes under selection.

      Selection of traits:

      Having paired organismal and molecular trait data is a strength of the manuscript, but the organismal trait data are underutilized. The manuscript as written only makes weak indirect inferences based on GO categories or assumed gene functions to connect selection at the organismal and molecular levels. Stronger connections could be made for instance by showing a selection of co-expression module eigengene values that are also correlated with traits that show similar patterns of selection, or by demonstrating that GWAS hits for trait variation co-localize to cis-mapping eQTL.

      We did perform a GWAS for all the traits collected in both normal and saline environment, and only found significant hits for fecundity (in both normal and saline environment) and chlorophyll_a content (in the saline environment). But these regions did not overlap with any candidate genes or cis-mapping eQTL. Hence we choose to mention it in the manuscript. Additionally, using the WGCNA modules, we found that the only two module under selection in the saline environment were not significantly correlated with any of the traits measured.

      Genetic architecture of gene expression variation:

      The descriptive statistics of the eQTL analysis summarize counts of eQTLs observed in each environment, but these numbers are not broken down to the molecular trait level (e.g., what are the median and range of cis- and trans-eQTLs per gene). In addition, genetic architecture is a combination of the numbers and relative effect sizes of the QTLs. It would be useful to provide information about the relative distributions of phenotypic variance explained by the cis- vs. trans- eQTLs and whether those distributions vary by environment. The motivation for examining patterns of cis-trans compensation specifically for the results obtained under high salinity conditions is unclear to me. If the lines sampled have predominantly evolved under low salinity conditions and the hypothesis being evaluated relates to historical experience of stabilizing selection, then my intuition is that evaluating the eQTL patterns under normal conditions provides the more relevant test of the hypothesis.

      We have added the median number of eQTLs per gene in each environment. Additionally, we recognize that genetic architecture is a combination id numbers and effect size, and we have added information regarding the effect sizes of eQTLs by type and by environment as recommend by another reviewer. We did explore the distributions of phenotypic variance explained by the cis- vs. trans- eQTLs as recommended here, and found that trans-eQTLs explain more phenotypic variance than cis-eQTLs in both environments and that the distribution of either type of eQTL does not vary by environment. We are choosing to not add this in the main text due to space limitations. Lastly, we examined the patterns of cis-trans compensation/reinforcement under both normal and salinity conditions and have compared and contrasted the results from both in the main text.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Lines 126: I would recommend citing those who originally developed the 3' end targeted RNA sequencing methods (e.g. Meyer et al 2011 Molecular Ecology).

      We have cited the recommended paper.

      Lines 128-130: It would be useful to include a description here of what models were fit to the data to partition out G, E, and GxE effects.

      Due to space limitations, we have in brief added a sentence to this effect.

      Line 139: I would suggest changing "found little" to "no" since the test was not significant.

      The sentence has been modified to say no evidence.

      Line 313: I think you mean directional selection instead of positive selection.

      We have corrected the text

      Lines 362-363: Would the authors also expect an enrichment of reinforcing genes for most scenarios where that has been divergent selection, such as local adaptation among populations?

      Based on our hypothesis, we would indeed expect an enrichment of reinforcing genes for scenarios of local adaptation where different alleles are maintained in different populations due to local adaptation.

      Reviewer #3 (Recommendations For The Authors):

      Figures 1d-e are not mentioned in the Results.

      The figures have been referenced in appropriate places.

      Lines 41-45: Terms such as reinforcement and compensation need to be explained in this specific context. Also "different selection regimes" is a bit broad and vague.

      Due to word-count limitation, we are choosing to not elaborate the terms reinforcement and compensation in the abstract (since these are commonly used in the literature, and we have also defined these in the main text). Additionally, we now explicitly state the selection pressures associated with cis and trans eQTLs.

      Table 1: Please explain S and C in the footnote.

      We have added the recommended footnote

      Figures: Some panel labels (a, b, c...) are mingled with the graphs.

      We are re-made our figure such that the panel labels do not mingle with the plots.

      Lines 588-591: font.

      Modified

      Lines 620-633: Please describe how these RNAseq libraries were allocated/pooled into different sequencing lanes to avoid potential batch effects among sequencing lanes.

      The sequencing was performed on the same Illumina NextSeq 500 machine and we have added the sequencing libraries/pool plan in the methods (lines 688-689). 

      Lines 690-692: At the beginning of this paragraph, it was mentioned that the un-standardized coefficients were estimated. But here, it seems like the transcript data were already standardized in the data preparation step. What do lines 687-688 refer to? Further standardizing those estimated coefficients so that the whole distribution has mean=0 and sd=1?

      Thank you pointing out our oversight. We checked our scripts and data preparation did not include transcript standardization, and we have removed the above line from the manuscript.

      Lines 705-711: Please explain why assigning the positive/negative selection status for each gene is important. "Positive selection" here is defined as genes whose increased expression also increases fitness, but traditionally positive selection was defined as "the derived state is favored over the ancestral state". For a gene whose ancestral expression is high but lower expression increases fitness in this experiment, could we also say this gene is under positive selection? Given that we don't know the ancestral state here, maybe the authors could explain whether this definition is necessary. Also, given that many genes positively or negatively regulate each other in a pathway, it is also unclear whether it is necessary to assign the positive/negative status for a PC using the majority rule (lines 710-711).

      We have now defined the different selection terms with respect to our study and use them consistently throughout the manuscript.

      Lines 711-715: If I understand correctly, PCs were used as traits, and by definition PCs should all be orthogonal. Is this section saying only retaining PCs whose correlation < 0.6 with each other? What is the rationale?

      PCA were performed on transcript abundance and the resulting orthogonal PCs explaining over 0.5% variance were all retained for selection analyses.

      We also performed selection analyses on the functional traits measured in the field, but since these functional traits are correlated (and as such would not satisfy the independent variable requirement of regression analyses), we retained only those functional traits which had a Pearson correlation coefficient < 0.6.

      Line 729: Please briefly describe what CLIP is doing.

      We have added the required description.

      Lines 736-741: The accession numbers do not add up to 125.

      Thank you for catching our oversight. We have edited the text, and now the numbers add upto 125.

      Line 796: Please remind readers where these 247k SNPs come from. Supposedly all accessions have been whole-genome sequenced, so the total number of SNPs should be larger than this.

      We have detailed method detailing how the SNPs were obtained and processed in the lines preceding this. Indeed the number of SNPs would have been much bigger, but the stringent cutoffs and linkage disequilibrium pruning reduced our dataset to about 247k SNPs.

      Lines 154-160: This is a bit confusing. The authors first mentioned, for the raw selection differentials, the mean and variance differ between environments, meaning they are misleading (why?). The next sentence then says non-standardized selection differentials will be used.

      The mean and variance for transcript abundances vary between the two environments. Because traits are usually measured in different scales, it is recommended to standardize trait values using variance or mean before estimating selection coefficients. Multiplying this variance (or mean) standardized selection differential with heritability gives the expected response to selection in standard deviation (or mean) units. But if the trait variance (or mean) varies between traits or environments, it leads to a conflation between the standardized selection differential and trait variance (or mean), which can be misleading. So to avoid this, and given that our traits (transcript abundance in this case) were all measured on the same scale, we chose to not standardize our trait values and estimated raw selection differentials.

      Figure 1 c-e: Please explain how the horizontal axis values were obtained. Is it assuming these selection differentials have a normal distribution of mean=0 & sd=1?

      Yes, horizontal axis represents theorical quantile for selection differential assuming they have a normal distribution with mean=0 and sd=1. This has been added to the figure legend.

      Line 162-168: Please clarify this part. What does “general trend towards stronger positive compared to negative selection on gene expression” mean? Does it mean the whole distribution of S is significantly different from 0, the difference in the number of genes in the S>0 vs S<0 category, or the a-bit-higher median |S| in the S>0 vs S<0 category? If it is the last one, are the small differences biological meaningful (0.053 vs. 0.047 for control & 0.051 vs. 0.050 for salt conditions), given that the authors defined |S|<0.1 as neutral?

      By “general trend towards stronger positive compared to negative selection on gene expression”, we mean that more transcripts were under positive directional selection as compared to negative directional selection. We have also clarified this in the text now.

      Line 177-178: This sentence implies disruptive selection is more important than stabilizing selection in the saline environment, but the test was not significant (line 176).

      Although there was no significant difference in the magnitude of stabilizing vs disruptive selection within the saline environment, the number of transcripts experiencing stronger disruptive selection in the saline condition was greater than the number of transcripts experiencing disruptive selection in the normal conditions. And so comparing between conditions, disruptive selection plays an important role in the saline conditions.

      Line 188-190: How CN vs. AP was statistically defined was not mentioned in the Methods section.

      We have added in the main text within the Results section.

      Line 203-214: How do these results fit with the previous observations that almost all transcripts have significant heritability?

      Although we do find that all but three transcripts have a have significant genetic effect (and thus have significant heritability), the median broad-sense heritability for 51 antagonistically pleiotropic genes is 0.23. Give that, we would only be able to detect SNPs regulating gene expression with high effect size since our sample size is n=130. Additionally, we used a very stringent criteria (FDR < 0.001) to define eQTLs. These two factors in combination could lead to us not being able to detect significant eQTLs for AP genes.

      Line 246-250: Please explain why the current conclusion would be opposite from the previous study. Supposedly the PCA, G matrix, and breeder’s equation were done for each environment separately. It makes sense that the G matrix and response to selection could be different between saline and drought treatments, but for the control treatments in the two studies, do they still differ? Why? Also in Table S7, it would be nice to show the % variation explained by each PC.

      Although both our studies had largely overlapping samples, about 20% samples were unique to each study. Additionally, although the site where the study was performed was the same across the two studies, we found significant temporal differences in gene expression due to micro-environmental differences. Both these factors can lead to changes in direct and indirect selection and its response, and we are examining these differences as part of a separate study. We also highlight these caveats in our discussion.

      Information on percent explained by each PCs is given in Table S5.

      Figure 2b: The vertical axis was labeled as “selection gradient”, but I think the responses to selection (D, I, T) have different units.

      We have re-labeled the vertical axis as “selection”.

      Reviewer #4 (Recommendations For The Authors):

      The manuscript mixes terminology for selection from quantitative genetics with that from population genetics. This is problematic, and the adjectives positive and negative should be replaced as descriptors of selection by instead rewording, for example, positive directional selection as directional selection for higher transcript abundance.

      Lines 193-196: The phrasing here reads as if the selection is solely acting on the presence/absence of expression rather than on quantitative variation in expression. During revision, it would be worth considering including an analysis of genes that parses genes that show the presence/absence of variation of expression within or across environments separately from genes that are expressed to non-trivial levels in both environments.

      We have modified the sentence in question now. Also, we pre-processed RNA-seq data to remove all transcripts with low expression signals (sigma signal < 20), and further retained only transcripts that had non-trivial expression in at least 10% of the population, which we believe represents presence/absence of variation of expression within or across environments.

      Lines 216-231: Is this analysis solely for directional selection? Not clear since previous sections examined both directional and stabilizing selection.

      Yes, we performed this analysis for only directional selection, and have clarified this in the text too.

      Lines 224-226: The meaning of this sentence is unclear and should be written more concretely.

      We have rephrased the sentence to be more clear.

      Lines 232-241: The description of the scientific logic here could be read as implying that genes interacting in networks are the sole source of indirect selection. I recommend revising the language to indicate this cause is one of several potential causes.

      We have reworded the sentence such that we indicate selection acting on interacting genes is just one of the causes of indirect selection.

      The strength of the conclusions of the decoherence analysis should be evaluated in light of caveats with such analyses (see Cai and Des Marais New Phytologist 2023).

      We have added the caveat with relevant citation in the manuscript.

      Rename this section as "Selection on Organismal Traits", as the previous sections have also been investigating selection on traits, just molecular traits.

      We have renamed the section as recommended

      Lines 314-318: Rewrite for clarity. Most environments select for an optimal phenotype; it is just the case here that the phenotypic distribution in the high salinity environment overlaps with the optimum.

      We have rephrased and clarified the statement.

      Lines 343-345: Rephrase to "These results indicate that natural variation in gene regulation under..."

      Rephrased.

      Line 354: "most" reads as too strong a descriptor here if the majority is ~60%.

      We have reworded the sentence to read “more than half”

      Lines 359-361: It is unclear to me how this interpretation follows from the above analysis.

      We have reworded the sentence so that the claim follows our analysis.

      Line 372: Is the expectation here more specifically one of epistatic selection? Other processes could stochastically lead to the genetic fixation of compensatory/reinforcing variants, but I think only epistasis for fitness would cause the interesting patterns of LD observed.

      The expectation here is that certain cis and trans variants only exists to compensate/reinforce, potentially through epistasis. We have clarified this in the text.

      Line 405: Change "adaptive organismal responses of organisms" to "organismal responses." As written, the sentence reads as being about plasticity rather than evolutionary responses, which are by populations, not organisms. None of the analyses included the manuscript test specifically test for adaptive plasticity.

      Rephrased.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, a chromosome-level genome of the rose-grain aphid M. dirhodum was assembled with high quality, and A-to-I RNA-editing sites were systematically identified. The authors then demonstrated that: 1) Wing dimorphism induced by crowding in M. dirhodum is regulated by 20E (ecdysone signaling pathway); 2) an A-to-I RNA editing prevents the binding of miR-3036-5p to CYP18A1 (the enzyme required for 20E degradation), thus elevating CYP18A1 expression, decreasing 20E titer, and finally regulating the wing dimorphism of offspring.

      Strengths:

      he authors present both genome and A-to-I RNA editing data. An interesting finding is that a A-to-I RNA editing site in CYP18A1 ruin the miRNA binding site of miR-3036-5p. And loss of miR-3036-5p regulation lead to less 20E and winged offspring.

      Weaknesses:

      How crowding represses the miR-3036-5p is still unclear.

      Reviewer #2 (Public Review):

      Summary:

      Environmental influences on development are ubiquitous, affecting many phenotypes in organisms. However molecular genetic and cellular mechanisms transducing environmental signals are still only barely understood. This study examines part of one such intracellular mechanism in a polyphenic (or dimorphic) aphid.

      Strengths:

      While other published reports have linked phenotypic plasticity to RNA editing before, this study reports such an interaction in insects. The study uses a wide array of molecular tools to identify connections upstream and downstream of the RNA editing to elucidate the regulatory mechanism, which is illuminating.

      Weaknesses:

      While this system is intriguing, this report does not foster confidence in its conclusions. Many of the analyses seem based on very small sample sizes. It is itself problematic that sample sizes are not obvious in most figures, although based on Methods section covering RNAseq, they seem to be either 3, 6 or 9, depending on whether stages were pooled, but that point is not made clear. With such small sample sizes, statistical tests of any kind are unreliable. Besides the ambiguity on sample sizes, it's unclear what error bars or whiskers show in plots throughout this study. When sample sizes are small estimates of variance are not reliable. Student's t-test is not appropriate for comparisons with such small sample sizes. Presently, it is not possible to replicate the tests shown in Figures 3, 4 and 6. (Besides the HT-seq reads, other data should also be made publicly available, following the journal's recommendations.) Regardless, effect sizes in some comparisons (Fig 3J, 4A-C, 6E, H) are clearly not large, making confidence in conclusions low. The authors should be cautious about over-interpreting these data.

      We appreciate very much for the reviewers’ time spent on our manuscript and the referees for the valuable suggestions and comments.

      To Reviewer #1:

      At present, researches on miRNAs mainly focus on its role in gene regulation by binding to the mRNA of target genes, “how miRNAs are regulated” has received less attention.

      Recent researches indicated that the expression of miRNAs is also regulated at the transcriptional or post transcriptional level. Transcriptional regulation including changes in the promoter of microRNA genes, and post-transcriptional mechanisms such as changes in miRNA processing and stability can both affect the final expression level of miRNAs.

      This article did not address how crowding treatment regulates miRNA expression. But this will be a very interesting issue, and we will pay attention to it in our future research.

      Thank you for this suggestion.

      To Reviewer #2:

      (1) “Transgenerational wing dimorphism was observed in M. dirhodum in which crowding of the parent (100 mother aphids in a 10 cm³ tube) increased the winged offspring (Fig 3E).” In this experiment, over 250 offsprings were used to calculate the proportion of winged and wingless individuals in normal (277), crowding (255) and crowding+20E (272) groups, respectively.

      “The RNAi-mediated knockdown of CYP18A1 and ADAR2 can significantly increase the titer of 20E (Fig. 4E) and reduce the number of winged offspring by 29.6% and 24.4% (Fig. 4F), respectively.” In this experiment, over 245 offsprings were used to calculate the proportion of winged and wingless individuals in dsEGFP (273), dsCYP18A1(248), and dsADAR2 (250) groups, respectively.

      “miR-3036-5p agomir and antagomir treatments could affect the proportion of winged offspring under normal conditions (Fig. 6F), but have no effect on the wing dimorphism of offspring under crowded conditions (Fig. 6L).” In this experiment, over 235 offsprings were used to calculate the proportion of winged and wingless individuals in each group, respectively.

      So I think our conclusion that crowding treatment, A-to-I RNA editing, and miRNAs could affect the wing dimorphism of offspring in M. dirhodum is very reliable. Because the number of aphids we use to count the results is sufficient.

      (2) The quantitative PCR method is used to detect changes in gene expression levels of CYP18A1 and ADAR2 after treatment with crowding, 20E, dsRNA, miRNA agomir and antagomir, and the results are shown in Fig. 3J, 4A-C, 5B, 6B, H, respectively. 5 biological replicates (more than 100 aphids were used for each biological replicate) were used in each sample, which might be sufficient for qPCR experiments. And among these biological replicates, the differences in gene expression levels are relatively small.

      (3) The titer of 20E was detected after treatment with crowding, 20E, dsRNA, miRNA agomir and antagomir, and the results are shown in Fig. 3I, 4E, 6E, K, respectively. 8 biological replicates (more than 100 aphids were used for each biological replicate) were used in each sample.

      The number of biological replicates used in each analysis and the number of aphids included in each biological replicate have been added in the Materials and Methods section. Thank you very much for pointing out this important issue.

      Reviewer #1 (Recommendations For The Authors):

      Several questions:

      (1) This study was conducted on the rose-grain aphid M. dirhodum. However, pea aphid Acyrthosiphon pisum seems to be a better object in wing dimorphism and development studies. Have the authors also identified the A-to-I RNA editing on pea aphids or other aphids?

      Wheat is one of the main grain crops in China as well as in the world. Metopolophium dirhodum is one of the most important wheat aphids around China, and has posed a significant threat to grain production. The current study was conducted to determine the regulatory mechanism of wing dimorphism on M. dirhodum, which might be of great significance to better control this pest in wheat production.

      Surely the pea aphid offers more established experimental tools and genomic resources. However, with the development of high-throughput sequencing technology, the chromosome level genomes of many insect species have been assembled. That means any of various insects might be studied as a model species, and not limited to Drosophila melanogaster, Acyrthosiphon pisum, etc.

      We didn’t identify the A-to-I RNA editing on pea aphids or other aphids. A recent study has shown that editing events are poorly conserved across different Xenopus species. Even sites that are detected in both X. laevis and X. tropicalis show largely divergent editing levels or developmental profiles. In protein-coding regions, only a small subset of sites that are found mostly in the brain are well conserved between frogs and mammals. The conservation of RNA editing in aphids is still unknown, and we will continue to pay attention to this issue in our future research works.

      Reference: Nguyen TA, Heng JWJ, Ng YT, Sun R, Fisher S, Oguz G, Kaewsapsak P, Xue S, Reversade B, Ramasamy A, Eisenberg E, Tan MH. Deep transcriptome profiling reveals limited conservation of A-to-I RNA editing in Xenopus. BMC Biology. 2023, 21(1):251.

      (2) "Two miRNA-target prediction software programs, miRanda and RNAhybrid, were used to identify the miRNAs that potentially act on CYP18A1. The results showed that miR-3036-5p could bind to the sequence containing edited position (editing site 528) of CYP18A1 in M. dirhodum." Is there any other miRNA that can also act on CYP18A1, thereby regulating its expression?

      The predicted results indicate that there are several other miRNAs can act on CYP18A1, but none of them can bind to this editing site (editing site 528). Therefore, we did not pay attention to other miRNAs.

      (3) 11678 A-to-I RNA-editing sites were systematically identified in M. dirhodum. Does that mean RNAi-mediated knockdown of ADAR2 may affect the RNA-editing and expression of a large number of genes? Please clarify.

      It is of course possible that RNAi-mediated knockdown of ADAR2 may affect the RNA-editing and expression of a large number of genes. A-to-I RNA editing was also observed in 5 other genes that involved in 20E biosynthesis and signaling pathway, but no evident difference was identified for the RNA editing and expression levels of these 5 genes after crowding treatment (Fig. S2, Table S5). That means the A-to-I RNA editing of CYP18A1 might be crucial in 20E-mediated wing dimorphism in M. dirhodum.

      (4) It is interesting that "the transcriptional level of ADAR2 was 2.19 fold higher in the crowding+20E treatment parent than that in the normal group, but no significant difference was identified between the crowding and normal groups". ADAR2 can be induced by 20E, rather than crowding. How should the author explain? It seems that 20E induction can also cause many RNA editing events.

      20-hydroxyecdysone (20E) can affect the growth and development, molting, metamorphosis, and reproductive processes of insects. According to this result, 20E induction can also cause RNA editing events by regulating the expression of ADAR2, and which may provide valuable references for the future study on 20E. Meanwhile, we will also continue to pay attention to this issue in our future research works.

      (5) Authors provided a lot of text to describe the genome assembly. I don't think it's necessary, authors can make appropriate deletions.

      Thank you for this suggestion. This is the first high-quality chromosome-level genome of M. dirhodum, which will be very helpful for the cloning, functional verification, and evolutionary analysis of genes in this important species or even other Hemiptera insects. Therefore, I think it is necessary to provide a detailed description. We will also make appropriate deletions in the “Result and Discussion” sections.

      Reviewer #2 (Recommendations For The Authors):

      Additional concerns

      - With an existing genome sequence available for the peas aphid *Acyrthosiphon pisum*, why have these authors chosen to use the rose-grain aphid for this study? It would be helpful to address any limitations in *Acyrthosiphon pisum* or advantages in *Metopolophium dirhodum* that explain that decision.

      Wheat is one of the main grain crops in China as well as in the world. Metopolophium dirhodum is one of the most important wheat aphids around China, and has posed a significant threat to grain production. The current study was conducted to determine the regulatory mechanism of wing dimorphism on M. dirhodum, which might be of great significance to better control this pest in wheat production.

      Surely the pea aphid offers more established experimental tools and genomic resources. However, with the development of high-throughput sequencing technology, the chromosome level genomes of many insect species have been assembled. That means any of various insects might be studied as a model species, and not limited to Drosophila melanogaster, Acyrthosiphon pisum, etc.

      - In Figure 5E, what anatomy is being shown in FISH? Moreover, this represents a single sample. It would be preferable to include a supplemental figure with comparable images from at least 3 additional specimens.

      It is the whole aphid body, and we have already uploaded additional 2 FISH images to the supplementary material Fig. S5. Thank you for this suggestion.

      - L190: Conservation alone seems inadequate to conclude that a chromosome functions as a sex chromosome. It would be fine to note the homology between Chr1 and the X of other Aphidini, but there are other explanations for that. Inference that Chr 1 is a sex chromosome might come from observations in karyotypes (by relative size comparisons or ideally from FISH) or from comparison of reads mapped to the chromosomes, suggesting Chr1 is hemizygous in males.

      Karyotype analysis experiment was not conducted in this research, so here the sex chromosome was determined based on chromosome homology between M. dirhodum and A. pisum genome. We have made appropriate modifications to the description in the article. Thank you for this suggestion.

      - L205: It's unclear to me how to interpret RNA editing results, based on RNAseq data, that map to "intergenic regions", especially when this is such a large fraction (37.3%) of the total result. Does this suggest a fundamental problem with the analysis, that so much RNAseq data maps to parts of the genome that are not annotated as genes?

      Non-coding RNA regions often account for a large proportion in the genome, and this RNAseq data is mapped to non-coding RNA transcription regions (37.3%) between protein-coding genes (intergenic regions).

      - L288-290: What degrees of confidence are attached to the predictions of these miRNA targets?

      There is no clear research indicating the accuracy of miRNA target prediction software. However, by comprehensively utilizing multiple prediction tools and experimental verification, the accuracy and reliability of prediction can be significantly improved.

      Actually, the prediction of miRNA targets is only a preliminary identification step, and we have subsequently demonstrated that miR-3036-5p can act on CYP18A1 through dual-luciferase reporter assay, RNA immunoprecipitation and FISH, etc.

      - L296-298: The mechanism proposed in this study seems to imply that miR-3036-5p should be absent (not expressed) in aphids under crowded conditions. Therefore, relative realtime PCR is not particularly useful here. Finding that the miR relative expression is reduced by 48.8% is meaningless, because in *relative* expression, zero has no special meaning. In this case, absolute quantitative PCR, measuring actual transcript numbers, would be far more informative.

      miR-3036-5p is not absent in aphids under crowded conditions. Only a significant decrease of miR-3036-5p in expression level under crowded conditions was identified compared to normal feeding conditions (Fig. 5B). So it should be reasonable to use relative quantitative methods for expression level analysis.

      - L361: Isn't alternative mRNA splicing a more common post-transcriptional modification?

      I'm very sorry, this sentence has been modified to “A-to-I RNA editing is one of the most prevalent forms of posttranscriptional modification in animals, plants, and other organisms.” Thank you for this suggestion.

      - L372: "Functional wing polymorphism is commonly observed in insects as a form of adaptation and a source of variation for natural selection (14)." The relationship between plastic phenotypic variation and natural selection is complex, and there is a large theoretical literature in evolutionary biology and evo-devo on this topic, but it is not a focus in the cited review by Zhang et al.. It would be helpful if the authors could expand on this idea with reference to some of this literature (e.g. Levins 1968; Harrison 1980; Moran 1992; Roff 1996; West-Eberhard 2003; Zera 2009).

      I have changed the citation and expanded on this idea. “Wing polymorphism is commonly observed in insects, resulting from variation in both genetic factors and environmental factors (Zera 2009).”

      - L404: Use the word "accurate" seems inappropriate in this context. Both morphs are equally "accurate".

      This sentence has been modified to “resulting in the alteration of CYP18A1 expression and wing dimorphism of offspring regulated by miR-3036-5p”, Thank you for this suggestion.

      - L412: Reference 67 seems irrelevant to this point.

      References have been changed and added.

      67. E.J. Duncan, C.B. Cunningham, P.K. Dearden. Phenotypic plasticity: what has DNA methylation got to do with it? Insects. 13(2):110 (2022).

      68. K.J. Rangan, S.L. Reck-Peterson, RNA recoding in cephalopods tailors microtubule motor protein function. Cell 186, 2531-2543 (2023).

      - L443: Is this referring to "mixed stage" aphids?

      Yes. To make it clearer, this sentence has been modified to “Approximately 200 mg of fresh M. dirhodum with mixed stages (including first- to fourth-instar nymphs and winged and wingless adults)”.

      - L483: What mass or number of individual aphids was used? I assume multiple individuals were pooled?

      Each sample contains approximately 200 aphids.

      - L499: Why was k = 17 used? The default is k = 21.

      The selection of k is usually an odd number between 15 and 21, which ensures that the types of k-mers can cover the genome while being small enough to avoid erroneous effects. Therefore, using 17 is very reasonable.

      - L574: what does it mean "multiple editing types"? What different types are possible? Are you referring to things other than A-to-I editing?

      That means besides A-to-I, this locus may also have other editing situations, such as A-to-C. If this situation occurs, it will be discarded.

      - L635: Which luciferase construct or plasmid has been used in this experiment? Citation to that source is necessary.

      PmirGLO vector (Promega, Leiden, Netherlands) was used in this experiment, and a reference has been added.

      B. Zhu, L. Li, R. Wei, P. Liang, X. Gao. Regulation of GSTu1-mediated insecticide resistance in Plutella xylostella by miRNA and lncRNA. PLoS Genetics. 17(10), e1009888 (2021).

      - L644: Did cDNA synthesis employ random primers or a poly-dT primer?

      This kit provides mixed primers, including random and poly-dT primers. (PrimeScript™ RT reagent Kit with gDNA Eraser (Perfect Real Time), Takara Biotechnology, Dalian, China).

      - Fig 4D: Seems like this panel should be divided to cover the two sites, as in Fig 3F. Right now the x-axis labels seem redundant.

      Done. Thank you for this suggestion.

      - Fig 7: Consider adding ADAR2 to this figure.

      Done. Thank you for this suggestion.

      - Table 1: It would be helpful to represent this data in a figure where the phylogenetic relationships among the species can be shown.

      The phylogenetic relationships among the species were shown in Fig. 1D, and the table here may present genome information in more detail.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife assessment

      “This work presents valuable data demonstrating that a camelid single-domain antibody can selectively inhibit a key glycolytic enzyme in trypanosomes via an allosteric mechanism. The claim that this information can be exploited for the design of novel chemotherapeutics is incomplete and limited by the modest effects on parasite growth, as well as the lack of evidence for cellular target engagement in vivo.”

      We agree with this assessment. In this re-worked version, we implemented the textual changes suggested by the reviewers and performed additional in silico work. The reviewers also presented valuable suggestions for additional experiments. However, we currently don’t have dedicated hands and funding for this project, which renders it impossible for us to perform additional “wet lab” experiments at this stage. We have thus not included new experimental “wet lab” data. Finally, the claim that our results may be exploited for the design of novel chemotherapeutics perhaps came across stronger than we intended to. We still believe our findings indicate a potential for such an endeavor, but this clearly requires further investigation and experimental evidence. We have softened this statement by removing it from the abstract and have edited the discussion to end as follows.

      “Based on the presented results, we propose that sdAb42 may pinpoint a site of vulnerability on trypanosomatid PYKs that could potentially be exploited for the design of novel chemotherapeutics. Indeed, antibodies (or fragments thereof) are valuable drug discovery tools. Antibodies (and camelid sdAbs especially) are known for their ability to "freeze out" specific conformations of highly dynamic antigens, thereby exposing target sites of interest, which could be exploited for rational drug design (the development of so-called "chemo-superiors", (Lawson, 2012; Khamrui et al., 2013; van Dongen et al., 2019)). While the design of a "chemo-superior" inspired on the sdAb42-mediated allosteric inhibition mechanism will require further investigation, the results presented here provide a foundation to fuel such an endeavour.”

      REVIEWER 1:

      Summary:

      The authors identified nanobodies that were specific for the trypanosomal enzyme pyruvate kinase in previous work seeking diagnostic tools. They have shown that a site involved in the allosteric regulation of the enzyme is targeted by the nanobody and using elegant structural approaches to pinpoint where binding occurs, opening the way to the design of small molecules that could also target this site.

      Strengths:

      The structural work shows the binding of a nanobody to a specific site on Trypanosoma congolense pyruvate kinase and provides a good explanation as to how binding inhibits enzyme activity. The authors go on to show that by expressing the nanobodies within the parasites they can get some inhibition of growth, which albeit rather weak, they provide a case on how this could point to targeting the same site with small molecules as potential trypanocidal drugs.

      Weaknesses:

      The impact on growth is rather marginal. Although explanations are offered on the reasons for that, including the high turnover rate of the expressed nanobody and the difficulty in achieving the high levels of inhibition of pyruvate kinase required to impact energy production sufficiently to kill parasites, this aspect of the work doesn't offer great support to developing small molecule inhibitors of the same site.

      Recommendations for authors:

      Generally, the paper is very well written and the figures and their legends are clear.

      Comment 1.1: I thought the Introduction could give more focus to the need for new drugs for veterinary trypanosomiasis. The reality is that with fexinidazole now available and acoziborole soon to be available, with <1,000 cases of human African trypanosomiasis in each of the last five years, the case for needing new drugs is difficult to make. For Animal trypanosomiasis, however, the need for novel drugs is much more pressing.

      We agree with this comment and have included an additional section in the Introduction’s second paragraph, which reads as follows.

      “Hence, there is a need for alternative compounds, preferably with novel modes of action and/or designed based on mechanistic insights of the target’s structure-function relationship (Field et al., 2017; De Rycker et al., 2018). This need is especially pressing for AAT, which strongly impedes sustainable livestock rearing in Sub-Saharan Africa. AAT results in drastic reductions of draft power, meat, and milk production by the infected animals (small and large ruminants), and its control relies mainly on vector control and chemotherapy, with only few drugs currently available. The lack of routine field diagnosis has resulted in the misuse of trypanocidal drugs, thereby accelerating the rise of parasite resistance and further exacerbating the problem (Richards et al., 2021). As such, AAT-inflicted annual losses are estimated at around $5 billion (and the necessity to invest another $30 million each year to control AAT through chemotherapy), thereby having a devastating impact on the socio-economic development of Sub-Saharan Africa (Fetene et al., 2021). In contrast, HAT is perceived as a minor threat as it has reached a post-elimination phase as a public health problem with less than 1,000 yearly documented cases (Franco et al., 2022). In addition, new and effective drugs for HAT treatment have recently become available (De Rycker et al., 2023). HAT control currently relies on case detection and treatment, and vector control (Büscher et al., 2017).”

      Comment 1.2: A few pedantic things can be tidied up too, for example on line 61 it is stated tsetse flies are part of the life cycle for all trypanosomes while some veterinary species e.g. T. evansi and some T.vivax strains use other biting flies for transmission. I'd also add in the Introduction that pyruvate kinase is not a glycosomal enzyme (it is covered in the legend to figure 1 but I think it is quite important to clarify in the Introduction too so as to assure readers aren't wondering if "intrabodies" can get targeted there.

      We agree with this comment and have included an additional section in the Introduction’s third paragraph to expand on the life cycles of African trypanosomes, which reads as follows.

      “African trypanosomes are extracellular parasites that have a bipartite life cycle involving insect vectors and mammals as hosts (Radwanska et al., 2018). Most HAT (T. brucei gambiense and T. b. rhodesiense) and AAT (T. b. brucei and T. congolense) causing trypanosomes are uniquely vectored by tsetse flies (Glossina spp.) and are confined to Sub-Saharan Africa. T. b. evansi and T. vivax (both causative agents of AAT) have expanded beyond the tsetse belt due to their ability to be mechanically transmitted by a variety of biting flies (Glossina, Stomoxys, and Tabanus spp.). Finally, T. b. equiperdum infects equids and represents an exception as it is transmitted directly from animal to animal through sexual contact.”

      The introduction now also explicitly mentions that pyruvate kinase is not a glycosomal enzyme.

      Comment 1.3: The introduction would also be a good place to include some more information on what is known about the allosteric effectors of pyruvate kinase in trypanosomes, and emphasize where gaps in knowledge exist too.

      We agree with this comment and have included an additional section in the Introduction’s third paragraph, which reads as follows.

      “Pyruvate kinase (PYK) represents another attractive glycolytic target. This non-glycosomal enzyme catalyses the last step of the glycolysis (the irreversible conversion of phosphoenolpyruvate (PEP) to pyruvate; Figure 1A). The importance of this reaction is two-fold: i) the generation of ATP through the transfer of a phosphoryl group from PEP to ADP and ii) the formation of pyruvate, a crucial metabolite of the central metabolism. Like most PYKs, trypanosomatid PYKs are homotetramers. The PYK monomer is a ∼55 kDa protein organized into four domains termed ’N’, ’A’, ’B’, and ’C’ (Figure 1B). The A domain constitutes the largest part of the PYK monomer and is characterized by an (𝛼/𝛽)8-TIM barrel fold that contains the active site. Together with the N-terminal domain, it is also involved in the formation of the PYK tetramer AA’ dimer interfaces. The B domain is known as the flexible ’lid’ domain that shields the active site during enzyme-mediated phosphotransfer. Finally, the C domain harbors the binding pocket for allosteric effectors and stabilizes the PYK tetramer by formation of CC’ dimer interfaces. Because of their role in ATP production and distribution of fluxes into different metabolic branches, the activity of trypanosomatid PYKs is tightly regulated through an allosteric mechanism known as the "rock and lock" model (Morgan et al., 2010, 2014; Pinto Torres et al., 2020). In this model (which is detailed in Figure 1C), the binding of substrates and/or effectors (and analogs thereof) to the active and effector sites, respectively, trigger a conformational change from the enzymatically inactive T state to the catalytically active R state. Known effector molecules for trypanosomatid PYKs are fructose 2,6-bisphosphate (F26BP), fructose 1,6-bisphosphate (F16BP) and sulfate (SO<sub>4</sub><sup>2-</sup>), with F26BP being the most potent one (van Schaftingen et al., 1985; Callens and Opperdoes, 1992; Ernest et al., 1994; Tulloch et al., 2008). Interestingly, trypanosomatid PYKs seem to be largely unresponsive to the allosteric regulation of enzyme activity by free amino acids (Callens et al., 1991), which contrasts with human PYKs (Chaneton et al., 2012; Yuan et al., 2018). Known trypanosomatid PYK inhibitors impair enzymatic activity through occupation of the PYK active site (Morgan et al., 2011).”

      In the Results, although I am not qualified to analyse the structural data in detail I am confident in the ability of the authors to do so.

      Comment 1.4: Differences in nanobody binding kinetics to the T. congolense enzyme when compared to T. brucei and Leishmania enzymes are attributed to the relatively few amino acid differences in those sites. It is desirable to test site-directed mutagenesis of those residues.

      This is a highly valuable suggestion from the reviewer. However, we currently don’t have dedicated hands and funding for this project, which renders it impossible for us to perform additional experiments at this stage.

      Comment 1.5: In the section on slow-binding inhibition kinetics (lines 194-220) I found it difficult to follow whether it was just the R<>T transition that slowed nanobody inhibition, or whether competition with effectors at the site would also impact on those inhibition kinetics. Can this be clarified?

      Since the sdAb42 epitope is located relatively far away from both active and effector sites (~20 and ~40 Å, respectively), it seems highly unlikely the observed “slow-binding inhibition” kinetics are the result of a competition between sdAb42 on one hand and substrates and/or effectors on the other for enzyme binding. Instead, given that sdAb42 selectively binds and locks the enzyme’s inactive T state, these data can be explained by the idea that sdAb42 can only bind to trypanosomatid PYKs after having undergone an R- to T-state transition. To clarify this matter, we slightly reformulated the discussion as indicated below. We also included a small discussion on the observation that there is a 400-fold difference between the Kd and the IC50.

      “Since the sdAb42 epitope is located relatively far away from both active and effector sites (~20 and ~40 Å, respectively), it seems highly unlikely that the observed “slow-binding inhibition” kinetics are the result of a direct competition between sdAb42 and substrates and/or effectors. Instead, given that sdAb42 selectively binds and locks the enzyme’s inactive T state, these data can be explained by the idea that sdAb42 can only bind to trypanosomatid PYKs after having undergone an R- to T-state transition. An additional observation in this context, is the 400-fold difference between the K<sub>D</sub> and IC<sub>50</sub> values. Although we currently do not have a mechanistic explanation, similar differences have been observed for the sdAb-mediated allosteric inhibition of other kinases (Singh et al., 2022).”

      For the intrabody expression work, the reference cited on line 230 actually points to a growing ability to genetically modify T. congolense. However, it is justifiable to work on T.brucei given the much wider availability and advanced status of the genetic tools.

      The growth inhibition data shown in Figure 7 is weak, albeit significant and the case is made as to why that might be.

      Comment 1.6: The authors do point to the fact that inhibiting other parts of the glycolytic pathway might be helpful in getting a better growth inhibitory effect. It would be useful, in this regard, to test the ability of the PFK inhibitors in the Macnae et al. paper in the intrabody expressing line, and possibly other inhibitors e.g. 2-deoxy-D-glucose to see if these combinations do have the desired impacts. Also, looking at the metabolome of the intrabody expressors under induction could also give some further insights into changes in flux (although perhaps not on its own given the weak effects on the growth seen).

      This is a highly valuable suggestion from the reviewer. However, we currently don’t have dedicated hands and funding for this project, which renders it impossible for us to perform additional experiments at this stage. We would like to point out that, in our experience, studying the effect of enzyme inhibition on the metabolome is usually only useful shortly after adding the onset of inhibition. The system adapts to the lowered flux and relevant changes are mostly transient. Since the induced expression of sdAb42 is almost certainly slow, we expect the metabolic changes will be minimal.

      REVIEWER 2:

      Summary:

      In this work, the authors show that the camelid single-chain antibody sdAb42 selectivity inhibits Trypanosome pyruvate kinase (PYK) but not human PYK. Through the determination of the crystal structure and biophysical experiments, the authors show that the nanobody binds to the inactive T-state of the enzyme, and in silico analysis shows that the binding site coincides with an allosteric hotspot, suggesting that nanobody binding may affect the enzyme active site. Binding to the T-state of the enzyme is further supported by non-linear inhibition kinetics. PYK is an important enzyme in the glycolytic pathway, and inhibition is likely to have an impact on organisms such a trypanosomes, that heavily rely on glycolysis for their energy production. The nanobody was generated against Trypanosoma congolense PYK, but for technical reasons the authors progressed to testing its impact on cell viability in Trypanosoma brucei brucei. First, they show that sdA42 is able to inhibit Tbb PYK, albeit with lower potency. Cell-based experiments next show that expression of sdA42 has a modest, and dose-dependent effect on the growth rate of Tbb. The authors conclude that their data indicates that targeting this allosteric site affects cell growth and is a valuable new option for the development of new chemotherapeutics for trypanosomatid diseases.

      Strengths:

      The work clearly shows that sdA42A inhibits Trypanosome and Leishmania PYK selectively, with no inhibition of the human orthologue. The crystal structure clearly identifies the binding site of the nanobody, and the accompanying analysis supports that the antibody acts as an allosteric inhibitor of PYK, by locking the enzyme in its apo state (T-state).

      Weaknesses:

      (1) The most impactful claim of this work is that sdAb42-mediated inhibition of PYK negatively affects parasite growth and that this presents an opportunity to develop novel chemotherapeutics for trypanosomatid diseases. For the following reasons I think this claim is not sufficiently supported:

      Comment 2.1: The authors do not provide evidence of target-engagement in cells, i.e. they do not show that sdA42A binds to, or inhibits, Tbb PYK in cells and/or do not provide a functional output consistent with PYK inhibition (e.g. effect on ATP production). Measuring the extent of target engagement and inhibition is important to draw conclusions from the modest effect on growth.

      The authors do not explore the selectivity of sdA42A in cells. Potentially sdA42A may cross-react with other proteins in cells, which would confound interpretation of the results.

      We understand the reviewer’s concern. While it is theoretically possible that sdAb42 may non-specifically (cross-)react with other proteins in the cell, this would be highly unlikely based on the following arguments. First, many studies have employed sdAbs as intrabodies and reported on specific sdAb-mediated effects (outstanding reviews on the topic are Cheloha et al. (PMID 32868455) and Soetens et al. (PMID 33322697)). Second, it has been demonstrated that selecting sdAbs from an immune library through phage display or “bacteriomatch” (a bacterial system similar to yeast two hybrid) yields highly similar results (Pellis et al., PMID 22583807), thereby indicating that sdAbs interact specifically with their target antigens in an intracellular environment. Third, we identified TcoPYK as the target for sdAb42 by employing sdAb42 as bait in a pull-down from a parasite whole cell lysate (Pinto Torres et al., PMID 29899344). The pull-down fractions were analysed by SDS-PAGE and we observed a clear prominent band, which was further analysed by mass spectrometry and revealed TcoPYK as the target with great certainty. Even though the affinity of sdAb42 for TbrPYK is lower, it still remains high (nM affinity) and we expect it to bind TbrPYK with high specificity.

      Regarding measuring the effect on ATP production, we would like to state that such experiments are not obvious. Instead of measuring ATP levels, one should measure ATP turnover as ATP levels may not necessarily be decreased. The latter was observed to be the case for the specific inhibition of trypanosomal PFK (Nare et al. PMID 36864883). The specific trypanosomal PFK inhibitor inhibits motility (and growth) of T. congolense IL3000 at concentrations that only slightly affect ATP levels. One could think of repeating the sdAb42 experiments in a T. congolense model. However, T. congolense BSF metabolism is more complicated than that of T. brucei BSF. First, the T. congolense glucose metabolic network is more expanded, allowing a lower glucose consumption rate to produce ATP and metabolites for growth. Second, pyruvate is not excreted but further metabolised, in part in the mitochondrion. Steketee et al. (PMID 34310651) have shown that T. congolense also takes up pyruvate from the medium. One can thus check if (increased) external pyruvate (partially) rescues the growth inhibition by sdAb42. It will not provide proof, but maybe an indication. As mentioned above, we are currently unable to perform such additional experiments due to lack of dedicated hands and funding.

      Comment 2.2: sdA42A only affects minor growth inhibition in Tbb. The growth defect is used as the main evidence to support targeting this site with chemotherapeutics, however based on the very modest effect on the parasites, one could reasonably claim that PYK is actually not a good drug target. The strongest effect on growth is seen for the high expressor clone in Figure 4a, however here the uninduced cells show an unusual profile, with a sudden increase in growth rate after 4 days, something that is not seen for any of the other control plots. This unexplained observation accentuates the growth difference between induced and uninduced, and the growth differences seen in all other experiments, including those with the highest expressors (clones 54 and 55) are much more modest. The loss of expression of sdA42A over time is presented as a reason for the limited effect, and used to further support the hypothesis that targeting the allosteric site is a suitable avenue for the development of new drugs. However, strong evidence for this is missing.

      We agree that the growth effect of sdAb42 expression is modest, and we have provided several explanations as to why this could be the case. In addition, as mentioned at the start of this rebuttal, the claim that our results may be exploited for the design of novel chemotherapeutics was perhaps expressed stronger than we intended to. We still believe our findings indicate a potential for such an endeavor, but this clearly requires further investigation and experimental evidence as mentioned by the reviewer.

      We, however, disagree that PYK would not be a good drug target. Its potential to serve as a drug target is related to its fundamentally important role in trypanosomal glycolysis and not to the features of sdAb42. Steketee et al. (PMID 34310651) have shown that glycolysis is essential for T. congolense BSF, despite a lower glycolytic flux than in T. brucei BSF. The T. congolense glucose metabolic network is more expanded, allowing a lower glucose consumption rate to produce ATP and metabolites for growth. Also here, PYK is thus almost certainly essential and from that perspective a good drug target.

      Comment 2.3: For chemotherapeutic interventions to be possible, a ligandable site is required. There is no analysis provided of the antibody binding site to indicate that small molecule binding is indeed feasible.

      We agree with the reviewer’s comment and have included APOP analysis on the TcoPYK T state crystal structure (see also reply to Comment 3.1). Briefly, APOP works by detecting pockets and then perturbing each pocket in the protein's elastic network (GNM) by adding stiffer springs between the surrounding residues. The pockets are scored and ranked based on the calculated shifts in the eigenvalues of the global GNM modes and their local hydrophobic densities, thereby also considering the pocket’s surface accessibility, which renders it suitable for the identification of allosteric (and druggable) pockets. The APOP analysis identifies pockets overlapping with the sdAb42 epitope as highly ranking allosteric ligand binding pockets. The data have been summarized in an additional supplementary figure (Figure 4 – figure supplement 1). The manuscript also contains details on the performed APOP analysis in the Materials and Methods section.

      Comment 2.4: The authors comment on the modest growth inhibition, and refer to the need to achieve over 88% reduction in Vmax of PYK to see a strong effect, something that may or may not be achieved in the cell-based model (no target-engagement or functional readout provided). The slow binding model and switch of species are also raised as potential explanations. While these may be plausible explanations, they are not tested which leaves us with limited evidence to support targeting the allosteric site on PYK.

      In our understanding of this remark, we believe it be related to Comments 2.1 and 2.2 and thus refer to our answers formulated above.

      Comment 2.5: The evidence to support an allosteric mechanism is derived from structural studies, including the in silico allosteric network predictions. Unfortunately, standard enzyme kinetics mode of inhibition studies are missing. Such studies could distinguish uncompetitive from non-competitive behaviour and strengthen the claim that sdAb42 locks the enzyme complex in the apo form.

      We agree with the referee that a thorough kinetic analysis could distinguish between uncompetitive (i.e., sdAb only binds to the enzyme if substrate is bound) or non-competitive (i.e., sdAb can bind to apo enzyme and substrate-bound enzyme) inhibition. In both cases, however, this would correspond to an allosteric mechanism of inhibition. Although such a thorough kinetic analysis would be interesting in its own right, we would like to argue that this type of very detailed kinetics is outside the scope of this paper. This is especially the case taking into account that this analysis could be complicated by the slow-onset inhibition behavior.

      Comment 2.6: As general comment, the graphical representation of the data could be improved in line with recent recommendations: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128, https://elifesciences.org/inside-elife/5114d8e9/webinar-report-transforming-data-visualisation-to-improve-transparency-and-reproducibility.

      - Bar-charts for potency are ideally presented as dot plots, showing the individual data points, or box plots with datapoints shown.

      - Images in Figure 7 show significant heterogeneity of nanobody expression, but the extent of this can not be gleaned from Figure 7B. It would be much better to use box plots or violin plots for each cell line on this figure panel. The same applies to Figure 10.

      We thank the reviewer for these suggestions but have taken the decision not to act upon these as the other reviewers explicitly mentioned that our figures are very clear.

      Recommendations for authors:

      Please find below some minor comments:

      Comment 2.7: Line 24: "increasing number of drug failures": This does not really reflect the current situation for human African trypanosomiasis, with NECT treatment retaining efficacy, fexinidazole now being registered, and acoziborole progressing towards registration. It may be worth considering focusing the introduction more on Nagana, as all Trypanosome species used in the paper are animal infective, and the nanobody was discovered for T. congolense.

      We refer to our answer formulated in response to Comment 1.1.

      Comment 2.8: Line 55: "alarming number of reports describing ..." While resistance is a big problem, this mainly applies to malaria, bacterial and fungal diseases. For kinetoplastids, the number of reports describing resistance in the clinic is pretty limited. However, the drug discovery pipeline for these diseases is sparse, so I definitely agree there is a need to develop new compounds with differentiated mechanisms.

      We agree with the reviewer and have slightly adapted our wording here as follows.

      “Unfortunately, a number of reports describe treatment failure or parasite resistance to the currently available drugs (De Rycker et al., 2018).”

      Comment 2.9: This manuscript is about pyruvate kinase, but the enzyme is not properly introduced. I suggest a short paragraph introducing PYK at line 77 (without duplicating Figure 1), covering its role in glycolysis, the importance of pyruvate, any essentiality data from the literature, and any known inhibitors.

      We refer to our answer formulated in response to Comment 1.3.

      Comment 2.10: Figure 6: For the top insets it would be useful to somehow show the increasing antibody concentration, either by using a changing intensity or size for each line.

      We thank the reviewer for this suggestions, but decided not to act upon it as we found that the inclusion of this information in the figure made it “too crowded”, which is why we opted to provide this information in the figure legend.

      “Only a subset of the traces is shown for the sake of clarity. The following curves are shown (from bottom to top): TcoPYK (0.15 nM sdAb42, 500 nM sdAb42, 750 nM sdAb42, 1000 nM sdAb42, 1500 nM sdAb42, 2000 nM sdAb42, no enzyme control), LmePYK (5 nM sdAb42, 750 nM sdAb42, 1250 nM sdAb42, 1500 nM sdAb42, 2500 nM sdAb42, 3000 nM sdAb42, no enzyme control), and TbrPYK (1 nM sdAb42, 1000 nM sdAb42, 1750 nM sdAb42, 2000 nM sdAb42, 3500 nM sdAb42, 4000 nM sdAb42, no enzyme control).”

      Comment 2.11: You refer to the curves as biphasic, but they look like 1st order kinetics, and there are no clear 1st and 2nd phases (or at least they are not marked). It may be more appropriate to label these as non-linear.

      We agree that the term “biphasic” is potentially an over-simplification of the actual situation. What we mean is that the formation of product as a function of time ([P] versus [t] curve) is not linear at short time ranges but evolves from an initial “weakly inhibited” rate (v<sub>0</sub>) to a “strongly inhibited” steady-state rate (v<sub>ss</sub>). This conversion from v<sub>0</sub> to v<sub>ss</sub> indeed occurs in a fashion following single exponential behavior. With the term “biphasic” we thus meant a non-linear phase (before v<sub>ss</sub> is reached) followed by a linear phase (after v<sub>ss</sub> is reached). To avoid any confusion, we replaced the term “biphasic” by “non-linear”.

      Comment 2.12: IC50s - would be useful to provide a comparison with IC50s generated in the pre-incubation experiments - is the antibody less potent without pre-incubation? I could not find IC50s for the pre-incubation experiments shown in Figure 2.

      We determined an IC50 value for sdAb42 against TcoPYK under pre-incubation conditions, but initially decided not to include this into the manuscript. We agree with the reviewer that a comparison between IC50 values determined under pre- and post-incubation conditions would be of interest, and have therefore included the pre-incubation IC50 data for TcoPYK in Figure 2 (panel B). The data indeed show that sdAb42 far more efficiently inhibits an enzyme that is not continuously cycling between R and T states (IC50 values of 15 nM and 359 nM under pre- and post-incubation conditions, respectively). This is now discussed in the results section of the manuscript. We did not determine IC50 values for sdAb42 against TbrPYK and LmePYK under pre-incubation conditions, but suspect that a similar observation will be made upon comparing these values to IC50 under post-incubation conditions.

      REVIEWER 3:

      Summary:

      Out of the 20 Neglected Tropical Diseases (NTD) highlighted by the WHO, three are caused by members of the trypanosomatids, namely Leishmanaisis, Trypanosomiasis, and Chagas disease. Trypanosomal glycolytic enzymes including pyruvate kinase (PyK) have long been recognised as potential targets. In this important study, single-chain camelid antibodies have been developed as novel and potent inhibitors of PyK from the T, congolense. To gain structural insight into the mode of action, binding was further characterised by biophysical and structural methods, including crystal structure determination of the enzyme-nanobody complex. The results revealed a novel allosteric mechanism/pathway with significant potential for the future development of novel drugs targeting allosteric and/or cryptic binding sites.

      Strengths:

      This paper covers an important area of science towards the development of novel therapies for three of the Neglected Tropical Diseases. The manuscript is very clearly written with excellent graphics making it accessible to a wide readership beyond experts. Particular strengths are the wide range of experimental and computational techniques applied to an important biological problem. The use of nanobodies in all areas from biophysical binding experiments and X-ray crystallography to in-vivo studies is particularly impressive. This is likely to inspire researchers from many areas to consider the use of nanobodies in their fields.

      Weaknesses:

      There is no particular weakness, but I think the computational analysis of allostery, which basically relies on a single server could have been more detailed.

      Recommendations for authors:

      Overall an excellent paper, there are just a couple of points that the authors could consider, if time allows.

      Comment 3.1: As mentioned above the computational analysis of allostery appears to be based on a single server based on coordinates alone with no in-depth analysis. It would be extremely interesting to see if more sophisticated methods based on elastic network model and/or molecular dynamics simulation gave similar results. I realize that this would require quite a lot of work though.

      We agree with the reviewer’s comment and have complemented the perturbation analysis (previously presented in the manuscript) with dGNM and APOP analyses to identify allosteric communication pathways and allosteric binding pockets, respectively. dGNM, which is based on transfer entropy, allowing for a detailed characterization of the dynamic coupling and information transfer between residues. Meanwhile, APOP employs a perturbation-based approach to detect and rank allosteric pockets. The findings are in good agreement with the previously presented perturbation data and have been summarized in an additional supplementary figure (Figure 4 – figure supplement 1). The manuscript also contains details on the performed transfer entropy and APOP analyses in the Materials and Methods section.

      Comment 3.2: The figures are excellent and really help the reader - with the exception of the screenshots (Figure 8). Using pymol or chimera (or any other more expensive commercial package) would really help the reader and will not take much time.

      We agree with the referee that this is not the most beautiful figure. However, we find the quality and clarity of the figure to be adequate for its purpose (i.e., a supplemental figure).

      Comment 3.3: Finally, I would have liked to see at least the PDB validation files. This is a highly regarded and experienced team, nevertheless, the resolution is rather mediocre. As the crystal coordinates were used as input for the computational, any experimental inaccuracies will affect the computational results.

      We agree with the reviewer that we could have provided the validation report together with the submitted manuscript and we apologise for the inconvenience. The validation reports will be released together with the structures following final manuscript publication. Regarding the resolution of the crystal structures, we agree with the reviewer’s comment, but we obviously employed data sets from our best diffracting crystals and could not obtain a higher resolution despite our best efforts.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Reviews):

      Summary:

      This study examines to what extent this phenomenon varies based on the visibility of the saccade target. Visibility is defined as the contrast level of the target with respect to the noise background, and it is related to the signal-to-noise ratio of the target. A more visible target facilitates the oculomotor behavior planning and execution, however, as speculated by the authors, it can also benefit foveal prediction even if the foveal stimulus visibility is maintained constant. Remarkably, the authors show that presenting a highly visible saccade target is beneficial for foveal vision as the detection of stimuli with an orientation similar to that of the saccade target is improved, the lower the saccade target visibility, the less prominent the effect.  

      Strengths:

      The results are convincing and the research methodology is technically sound.

      Weaknesses:

      Discussion on how this phenomenon may unfold in natural viewing conditions when the foveal and saccade target stimuli are complex and are constituted by different visual properties is lacking. Some speculations regarding feedforward vs feedback neural processing involved in the phenomenon and the speed of the feedforward signal in relation to the visibility of the target, are not well justified and not clearly supported by the data.

      We thank the reviewer for their comment. In general, we tried to address conceptual points only briefly in this Research Advance if we had discussed them in depth in our main article which this advance will be linked to (Kroell & Rolfs, 2022: https://elifesciences.org/articles/78106). However, the reviews showed us that this rendered our theoretical reasoning in the current manuscript appear incomplete. In the revised Discussion section, we have elaborated on several conceptual questions. In particular, we expand on the transferability of our findings to natural viewing conditions:

      “Foveal prediction in natural visual environments

      As noted above, human observers typically move their eyes towards the most conspicuous objects in their environment (‘t Hart, Schmidt, Roth, & Einhäuser, 2013). Foveal prediction seems to benefit from this strategy as the strength of the predicted signal increases with the conspicuity of the eye movement target. Nonetheless, natural visual environments as well as naturalistic viewing behavior pose several challenges for the foveal prediction mechanism (see Kroell & Rolfs, 2022, for an initial discussion). 

      First, naturalistic saccade target stimuli will likely exhibit complex shapes and, more often than not, will include feature conjunctions rather than isolated features. Previous findings suggest that the foveal feedback mechanism is capable of operating at this level of complexity: High-level peripheral information such as the category of novel, rendered objects (Williams et al., 2008) has been successfully decoded from activation in foveal retinotopic cortex. If, indeed, temporal objectspecific areas such as area TE send feedback, the foveal prediction mechanism may even be specialized for the transfer of complex visual properties.

      Second, foveal input will often be of high contrast in natural visual environments. If fed-back predictive signals can influence foveal perception in the presence of high-contrast feedforward input remains to be established. In our main investigation (Kroell & Rolfs, 2022; Figure 2B) as well as in previous studies (Hanning & Deubel, 2022b), pre-saccadic foveal detection performance decreased markedly in the course of saccade preparation, presumably because visuospatial attention gradually shifted towards the saccade target and away from the foveal location. This presaccadic decrease in foveal sensitivity may boost the relative weight of fed-back signals by attenuating the conspicuity of high-contrast feedforward input. In other words, the strength of feedforward input to the fovea is reduced gradually across saccade preparation. At the same time, the strength of the fed-back predictive signal should profit from the high contrast of naturalistic saccade targets.

      Third, while foveal and peripheral information was congruent on 50% of all ‘probe present’ trials in our investigation, peripheral and foveal features will often be weakly correlated or even uncorrelated in natural environments (see Samonds, Geisler, & Priebe, 2018). Again, the presaccadic attenuation of foveal feedforward processing may allow fed-back peripheral signals to influence perception even if they are uncorrelated with foveal information. Moreover, in piloting variations of our paradigm, we observed that the subjective impression of perceiving the saccade target at the pre-saccadic foveal location is most pronounced if the foveal noise region is replaced with a black Gaussian blob at certain time points before saccade onset (unpublished phenomenological accounts). In consequence, fed-back signals do not seem to require correlated feedforward input to influence perception. Quantitative evidence, however, remains to be established.

      Lastly, pre-saccadic foveal input is likely less relevant during natural viewing behavior than it is in our task. It is possible that this task-induced prioritization of the foveal location facilitated the emergence of congruency effects. In a previous experiment (Kroell & Rolfs, 2022; Figure 1D), however, the perceptual probe could appear anywhere on a horizontal axis of 9 dva length around the fixation location. Despite this spatial unpredictability, congruency effects peaked at the presaccadic foveal location, even after peripheral baseline performances had been raised to a foveal level through an adaptive increase in probe opacity. On a similar note, the orientation of the saccade target is irrelevant to the behavioral task in our design, mirroring naturalistic situations: The eye movement can be planned and executed based on local contrast variations alone, and observers are never required to report on the orientation of the peripheral target stimulus. Ultimately, however, an influence of task demands on visual processing can only be fully excluded through techniques that provide a direct readout of perceptual contents without requiring overt responses. In psychophysical investigations, a prediction of saccade target motion may be read out from observers’ eye velocities (Kroell, Mitchell, & Rolfs, 2023; Kwon, Rolfs, & Mitchell, 2019). In electroencephalographic (EEG) and electrophysiological studies, foveal predictions should manifest in early visually evoked potentials (e.g., Creel, 2019) and increased firing rates of featureselective foveal neurons in early visual areas, respectively. In conclusion, previous findings (Williams et al., 2008), the assumed properties of the neuronal feedback mechanism (Williams et al., 2008; Bullier, 2001) and characteristics of our current and previous experimental paradigms collectively suggest that foveal feature predictions are likely to transfer to naturalistic environments and viewing situations. Experimental evidence remains to be established.”

      We have furthermore modified the Abstract to emphasize the connection of the current manuscript to the main article.

      With respect to the reviewer’s point that “speculations regarding feedforward vs feedback neural processing involved in the phenomenon and the speed of the feedforward signal in relation to the visibility of the target, are not well justified”: 

      Again, we understand that we should have elaborated on our theoretical reasoning in this Research Advance. The assumption that our initial findings rely on neuronal feedback to foveal retinotopic cortex is derived from Williams et al.’s (2008) seminal findings: In an fMRI study, the category of peripherally presented objects could be decoded from voxels in foveal retinotopic cortex, suggesting that peripheral visual information was available to neurons with strictly foveal receptive fields. We extended these findings to saccade preparation, suggesting that feedback from higher-order, non-retinotopically organized visual areas may transmit information without the requirement of efference copies (see Kroell, 2023; Dissertation; https://doi.org/10.18452/27204, pp. 54-59): Irrespective of the vector of the upcoming saccade, the features of the attended saccade target would invariably be relayed to foveal retinotopic cortex. Ultimately, only anatomical and functional studies in non-human primates can conclusively establish the role of feedback connections in the observed foveal prediction effects. At present, however, this parsimonious model could account for all of our current and previous findings, that is, a temporally, spatially and feature-specific anticipation of saccade target properties in the presaccadic center of gaze. Nonetheless, we are open to considering any other mechanism that may account for our findings, and have integrated the explanation provided by the reviewer into the paragraph on potential thalamic mechanisms (see the reviewer’s Major Point 1).

      Concerning the point that the “some speculations regarding feedforward vs feedback neural processing […] and the speed of the feedforward signal in relation to the visibility of the target are not well justified and not clearly supported by the data”: 

      Theoretical considerations on the impact of peripheral target contrast on feedforward processing speed were a main motivation for the current study. We apologize if our theoretical reasoning was incomplete and have added additional references and elaborations to the Introduction: 

      “In particular, neuronal response latencies decrease systematically as the contrast of visual input increases. While this phenomenon is reliably observed at varying stages of the visual processing hierarchy—such as the lateral geniculate nucleus (Lee, Elepfandt, & Virsu, 1981b), primary visual cortex (e.g., Albrecht, 1995; Carandini & Heeger, 1994; Carandini, Heeger, & Movshon, 1997; Carandini, Heeger, & Senn, 2002), and anterior superior temporal sulcus (STSa; Oram, Xiao, Dritschel, & Payne, 2002; van Rossum, van der Meer, Xiao, & Oram, 2008)—influences of contrast on neuronal response latency are particularly pronounced in higher-order visual areas: A doubling of stimulus contrast has been shown to decrease the latency of V1 neurons by 8 ms, compared to a reduction of 33 ms in area STSa (Oram et al., 2002; van Rossum et al., 2008). Assuming that the peripheral target is processed in a bottom-up fashion until it reaches higher-order object processing areas, the time point at which peripheral signals are available for feedback should be dictated by the temporal dynamics of visual feedforward processing.”

      Concerning the interpretation of the observed time courses, and regarding the reviewer’s Major points 3 & 6, we substantially revised the Results and Discussion section. In brief, we deemphasized the claim/interpretation of faster enhancement with increasing target opacity and instead focus on describing the oscillatory pattern mentioned by the reviewer. We provide a more temporally resolved pre-saccadic time course using a moving-window analysis and discuss all suggested and further alternative explanations (i.e., saccade-locked perceptual or attentional oscillations, longer signal accumulation intervals for low-contrast information, oscillatory nature of feedback signaling). Details and full revised paragraphs are provided in the response to this reviewer’s Major points 3 & 6.

      Unfortunately, there is no line numbering in the manuscript version I downloaded so I cannot refer to the specific lines of text here.

      We apologize for the inconvenience and have added line numbers.

      Major:

      (1) The authors speculate that the phenomenon of pre-saccadic foveal prediction arises from feedback connections from higher-order visual areas, which relay relevant saccade target features to the foveal retinotopic cortex. These feedback signals are then presumably combined with feedforward foveal input to the early visual cortex and facilitate the detection of target-congruent features at the center of gaze. This interpretation is sensible, however, it may not be the only plausible scenario. The thalamus receives copies of feedforward and feedback connections between all visual areas and is a likely candidate hub for combining information across visual space. In this latter case, the phenomenon of pre-saccadic foveal prediction may not arise from feedback from higher-order visual areas, but rather from a combination of signals occurring at the level of the thalamus. The authors should either acknowledge this possibility and the fact that this phenomenon is not necessarily the result of a feedback loop, or they should explain their rationale for excluding this scenario.

      We thank the reviewer for their highly thoughtful suggestion, and for alerting us to relevant literature. We have added the following paragraph to the Discussion section. In brief, we discuss the thalamic pulvinar as either an intermediate modulatory region or as the final receiver of the fed-back signal. Yet, we assume that—to solve the combinatorial issue associated with a transfer of feature information before saccades with any possible direction and amplitude—the contribution of non-retinotopic, higherorder object processing areas is likely required. 

      “Neural implementation of foveal prediction

      Based on the body of our findings as well as previous literature, we suggested a parsimonious feedback mechanism to underly the observed effects: the preparation of a saccadic eye movement, and the concomitant shift of pre-saccadic attention (e.g., Kowler, Anderson, Dosher, & Blaser, 1995; Deubel & Schneider, 1996), selects the peripheral target stimulus among competing information. Higher-order visual areas feed selected feature input back to early retinotopic areas— specifically, to neurons with foveal receptive fields. Fed-back feature information combines with congruent, foveal feedforward input, resulting in the enhancement effects we observe. Especially in the context of active vision, this feedback mechanism is appealing as it resolves a combinatorial issue associated with feature-specific information transfer before saccades. Consider a simplified case in which, right before a saccadic eye movement, the activation of a feature-selective neuron that encodes a certain retinal location is transferred to a neuron within the same brain area that will encode said retinal location after saccade landing. For this mechanism to function for any possible saccade direction and amplitude, most neurons would need to be connected to most other neurons (or, in a simplified version, to neurons with foveal receptive fields) in a given brain area. Assuming an information transmission via feedback rather than horizontal connections significantly reduces this dimensionality: Higher-order visual areas that encode object properties (largely) detached from retinotopic or spatiotopic reference frames selectively transfer feature information to neurons with foveal receptive fields, irrespective of the vector of the upcoming saccade. This parsimonious mechanism would have shortcomings. In particular, foveal feedback should become less effective during saccade sequences where several peripheral targets are simultaneously attended. Feature information at both attended target locations may be fed back in temporal succession or weighted and erroneously combined into a single fed-back signal. In most cases, however, foveal feedback may reasonably achieve what established transsaccadic mechanisms struggle to explain: An anticipation of the features of a single saccade target—which typically constitutes the currently most relevant object in the visual field—in foveal vision. 

      While direct feedback connections from higher-order to early visual areas would constitute the most straightforward implementation, it is conceivable that feedback signals are relayed through and modulated by subcortical areas. In particular, the thalamic pulvinar has been identified as a connection hub for visual processing that receives copies of feedforward and feedback connections from different visual areas and may even combine information across visual space (Cortes, Ladret, Abbas-Farishta, & Casanova, 2024). In the case of foveal prediction, thalamic neurons may receive fed-back signals from higher-order areas and enhance those signals before passing them on to cortical neurons with foveal receptive fields. Perhaps, a modification of foveal activation within the thalamic pulvinar itself is sufficient to influence perception. To the best of our understanding, however, the fed-back signal must originate in non-retinotopic, higher-order object processing areas to reduce the number of necessary neuronal connections.”

      (2) The results presented are very compelling. I wonder to which extent they generalize to situations in which the foveal input and the peripheral input are more heterogenous (e.g., faces or complex objects composed of many different features, orientations, and other visual properties). I think the current research raises a number of interesting questions. In general, it would be important for the readers to elaborate more on how the mechanism of pre-saccadic foveal prediction may play out in normal viewing conditions or in conditions in which the foveal input is completely irrelevant to the task.

      We agree and have reiterated this point in the current manuscript (see our first reply to “Weaknesses”). We also explicitly refer to Kroell & Rolfs (2022) for an extensive initial discussion of this question.

      (3) On page 10 the authors state that their data suggest that foveal enhancement emerges in earlier stages of saccade preparation as target opacity increases. However, this is not clear from the figures, when performance is locked to saccade onset (Fig 3 C), for the highest opacity targets performance seems to oscillate, however, the authors do not comment on that. There is literature showing how saccades can reset perceptual oscillations, and maybe what is observed here is just a stronger performance oscillation when the saccade target is more visible. Why would performance drop systematically 75 ms before saccade onset and then increase again 25 ms before the onset? Can the authors elaborate more on this?

      In response to this comment, we inspected the pre-saccadic time course of enhancement effects in a more temporally resolved fashion and, indeed, observed pronounced oscillations for the two higher target opacity conditions (see Results): 

      “Especially at higher target opacities, the temporal development of foveal enhancement appears to exhibit an oscillatory pattern. To inspect this incidental observation in a more temporally resolved fashion, we determined mean enhancement values in a boxcar window of 50 ms duration sliding along all saccade-locked probe offset time points (step size = 10 ms; x-axis values in Figure 4 indicate the latest time point in a certain window). We then fitted 6th order polynomials (with no constraints on parameters) to the resulting time courses and compared the fitted values against zero using bootstrapping (see Methods). The average foveal enhancement across target opacities reached significance starting 115 ms before saccade onset (gray curve in Figure 4; all ps < .046). For every individual target opacity condition, we observed significant enhancement immediately before saccade onset, although only very briefly for the lowest opacity (-2–0 ms for 25%; -39–0 ms for 39%, -106–0 ms for 59% &  -13–0 ms for 90%; all ps < .050; yellow to dark red curves in Figure 4). Especially for the higher two target opacities, we observed a local maximum preceding eye movement onset by approximately 80 ms. Interestingly, assuming a peak in enhancement in approximately 80 ms intervals (i.e., at x-axis values of -80 and 0 ms in Figure 4) would correspond to an oscillation frequency of 12.5 Hz. In contrast to rapid feedforward processing, feedback signaling is associated with neural oscillations in the alpha and beta range (i.e., between 7 and 30 Hz; Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle et al., 2015).”

      We had observed an oscillatory pattern in multiple previous investigations, and in both Hit Rates to foveal orientation content and reflexive gaze velocities in response to peripheral motion information. So far, we have been unsure how to explain it. The literature on thalamic visual processing mentioned by the reviewer alerted us to the oscillatory nature of feedback signaling itself. Interestingly, the temporal frequency range of feedback oscillations includes the frequency of ~12.5 Hz observed in our data. We have included this and alternative explanations in the Discussion section (see below). Throughout, we highlight that we are aware that our analysis approach is purely descriptive and that the potential explanations we give are speculative.

      “Moreover, foveal congruency effects appear to exhibit an oscillatory pattern, with peaks in a medium saccade preparation stage (~80 ms before the eye movement) and immediately before saccade onset. We have noticed this pattern in several investigations with substantially different visual stimuli and behavioral readouts. For instance, using a full-screen dot motion paradigm, we observed a pre-saccadic, small-gain ocular following response to coherent motion in the saccade target region (Kroell, Rolfs, & Mitchell, 2023, conference abstract; Kroell, 2023, dissertation). Predictive ocular following first reached significance ~125 ms before the eye movement, then decreased and subsequently ramped up again ~25 ms before saccade onset. Several explanatory mechanisms appear conceivable. Unlike rapid feedforward processing, feedback propagation has been shown to follow an oscillatory rhythm in the alpha and beta range, that is, between 7 and 30 Hz (Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle, et al., 2015). In our case, it is possible that the object-processing areas that send feedback to retinotopic visual cortex do so at a temporal frequency of ~12.5 Hz. At higher stimulus contrasts, feedforward signals may be fed back instantaneously and without the need for signal accumulation in feedbackgenerating areas. The resulting perceptual time courses may reflect innate temporal feedback properties most veridically. Alternatively, the initial enhancement peak may be related to the sudden onset of the saccade target stimulus and not to movement preparation itself. In this case, the initial peak should become particularly apparent if enhancement is aligned to the onset of the target stimulus. Yet, Figure 3 and Figure 4 suggest more prominent oscillations in saccade-locked time courses. In accordance with this, perceptual and attentional processes have been shown to exhibit oscillatory modulations that are phase-locked to action onset (e.g., Tomassini, Spinelli, Jacono, Sandini, & Morrone, 2015; Hogendoorn, 2016; Wutz, Muschter, van Koningsbruggen, Weisz, & Melcher, 2016; Benedetto & Morrone, 2017; Tomassini, Ambrogioni, Medendorp, & Maris, 2017; Benedetto, Morrone & Tomassini, 2019). Whether the oscillatory pattern of foveal enhancement, as well as its increased prominence at higher target contrasts, relies on innate temporal properties of feedback signaling, signal accumulation, saccade-locked oscillatory modulations of feedforward processing or attention, or a combination of these factors, one conclusion remains: task-induced cognitive influences suggested to underlie the considerable variability in temporal characteristics of foveal feedback during passive fixation (e.g., Fan et al., 2016; Weldon et al., 2016; 2020) are not the only possible explanation. Low-level target properties such as its luminance contrast modulate the resulting time course and should be equally considered, at least in our paradigm.”

      In the revised Abstract, we removed our claim on an earlier emergence of enhancement at higher opacities and have added this summary instead:

      “Second, the time course of foveal enhancement appeared to show an oscillatory pattern that was particularly pronounced at higher target opacities. Interestingly, the temporal frequency of these oscillations corresponded to the frequency range typically associated with neural feedback signaling.”

      (4) What was the average difference in latency between short and long latencies? It would be good to report it in the main text.

      We apologize for the oversight. The difference was 61 ms, with latencies of md = 247±18 ms for short- and md = 308±18 ms for long-latency saccades. We have added this information to the main text.

      (5) From the saccade latency graphs in Figure S1 it seems there is some variability in the latency of saccades across subjects, I wonder if there is a correlation between saccade latency and the magnitude of the foveal prediction effect across subjects.

      We had inspected a connection between saccade latency and congruency in our first investigation (Kroell & Rolfs, 2022; not reported) and observed that participants with lower latencies tended to show more enhancement, albeit non-significantly. Likewise, we observed a non-significant negative correlation between the median saccade latency and the mean foveal prediction effect (across opacities and time points) in the current investigation, r \= -0.22, p \= .572. While our study involved a small number of observers (n = 9), the analysis approach illustrated in Figure 2 A-C instead makes use of the large number of trials collected per participant (mean n = 2841 trials per observer) and demonstrates a reliable influence of saccade latency on an individual-observer level.

      (6) Page 14, the authors state that their findings suggest that the feedforward processing of the peripheral saccade target is accelerated when it is presented at high contrast. I find this a bit too speculative, both in terms of assuming that there is a feedforward vs a feedback process (see my point 1) and in terms of speculating that the feedforward process is accelerated as I do not see a clear hint of this in the data (see my point 3) and it is a bit of a stretch to speculate on delays or accelerations of neural processing. It is possible that the feedforward signal is always delivered at the same speed but it is weaker in one case and the effect needs more time to build up.

      We fully agree and hope to have addressed the reviewer’s arguments in the sections preceding this point. We included the reviewer’s last sentence in the Discussion section as well: 

      “Alternatively, or in addition, it is conceivable that weaker feedforward signals require a longer accumulation interval before the feedback process can be initiated.”

      Minor:

      (1) I think the description of the linear mixed-effects model can go in the supplemental methods, if possible, and its results can be briefly mentioned in the text.

      In previous work, we have been asked to move linear mixed-effects model descriptions from supplemental to main method (or even results) sections for clarity. We have followed this suggestion ever since and, due to the relevance of the models for the interpretation of the presented results, would like to keep their description in the methods section.

      (2) This is just a minor point, but I would suggest using a different word instead of opacity (maybe visibility?).

      We had gone back and forth on this. We decided to use the term ‘conspicuity’ when we discuss our findings conceptually and the term ‘opacity’ when we refer to the experimental manipulation (since we directly manipulate the transparency, i.e., 1-opacity, of the target patch against the background). To compute the slopes in Figures 2 and 5, we ordered observers’ performances by the linearly spaced opacity conditions. Since the term ‘opacity’ is closest to both the experimental manipulation and the variable entered into analysis, we would like to adhere to this terminology. However, we have added an explicit note to the end of our introduction to avoid confusion: 

      “Throughout the paper, we use the term ‘opacity’ when we refer to the experimental manipulation (that is, a variation of the transparency, i.e., 1-opacity of the target patch against the background noise) and the term ‘conspicuity’ when we discuss our findings conceptually.”

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, the authors ran a dual task. Subjects monitored a peripheral location for a target onset (to generate a saccade to), and they also monitored a foveal location for a foveal probe. The foveal probe could be congruent or incongruent with the orientation of the peripheral target. In this study, the authors manipulated the conspicuity of the peripheral target, and they saw changes in performance in the foveal task. However, the changes were somewhat counterintuitive.

      Strengths:

      The authors use solid analysis methods and careful experimental design.

      Weaknesses:

      I have some issues with the interpretation of the results, as explained below. In general, I feel that a lot of effects are being explained by attention and target-probe onset asynchrony etc, but this seems to be against the idea put forth by the authors of "foveal prediction for visual continuity across saccades". Why would foveal prediction be so dependent on such other processes? This needs to be better clarified and justified.

      We address the described weaknesses in the respective sections below. In general, as we point out in response to Reviewer 1 as well, the current submission is a Research Advance article meant to supplement our main article (Kroell & Rolfs, 2022, https://doi.org/10.7554/eLife.78106). To comply with the eLife recommendations for Research Advance submissions, we addressed conceptual points only briefly, especially if they had been explained in detail in our main article. To make the nature and format of the current submission as explicit as possible, and to emphasize its connection to our previous work, we refer to the submission format in our abstract and introduction now.

      Specifics:

      The explanation of decreased hit rates with increased peripheral target opacity is not convincing. The authors suggest that higher contrast stimuli in the periphery attract attention. But, then, why are the foveal results occurring earlier (as per the later descriptions in the manuscript)? And, more importantly, why would foveal prediction need to be weaker with stronger pre-saccadic attention to the periphery? What is the function of foveal prediction? What of the other interpretation that could be invoked in general for this type of task used by the authors: that the dual task is challenging and that subjects somehow misattribute what they saw in the peripheral task when planning the saccade. i.e. foveal hit rates are misperceptions of the peripheral target. When the peripheral target is easier to see, then the foveal hit rate drops.

      We will address these comments one by one:

      The authors suggest that higher contrast stimuli in the periphery attract attention. But, then, why are the foveal results occurring earlier (as per the later descriptions in the manuscript)?

      We consider these observations to rely on separate processes. Already in the main publication (Kroell & Rolfs, 2022), we had observed a continuous decrease of target-congruent and target-incongruent foveal Hit Rates (HRs) during saccade preparation, and suggested that this decrease (similarly observed in Hanning & Deubel, 2022b is likely caused by the pre-saccadic shift of visuospatial attention to the target. In other words, as attentional resources shift towards the periphery, foveal detection performance is hampered, irrespective of peripheral and foveal feature (in-)congruency. In the current investigation, we again observed a pronounced pre-saccadic decrease of foveal HRs, irrespective of foveal probe orientation. Our argument that high-contrast peripheral saccade targets attract more attention relies on the clear observation that this decrease becomes more pronounced as the contrast of the saccade target increases. To the best of our judgment and experience with doing the task ourselves, this interpretation appears very conceivable. We explain this rationale in the Abstract and the Results sections of the manuscript (see below).

      Our hypotheses and interpretations concerning the time course of foveal prediction refer to the difference between target-congruent and target-incongruent foveal HRs (i.e., to predictive foveal feature enhancement). Irrespective of the general, feature-unspecific decrease of foveal detection performances, we had hypothesized that the peripheral target is processed faster if it exhibits a high contrast. This assumption is based on temporal processing properties of many visual neurons that we have expanded on in our revision: 

      “In particular, neuronal response latencies decrease systematically as the contrast of visual input increases. While this phenomenon is reliably observed at varying stages of the visual processing hierarchy—such as the lateral geniculate nucleus (Lee et al., 1981b), primary visual cortex (e.g., Albrecht, 1995; Carandini et al., 1997, 2002; Carandini and Heeger, 1994), and anterior superior temporal sulcus (STSa; Oram et al., 2002; van Rossum et al., 2008)— influences of contrast on neuronal response latency are particularly pronounced in higher-order visual areas: A doubling of stimulus contrast has been shown to decrease the latency of V1 neurons by 8 ms, compared to a reduction of 33 ms in area STSa (Oram et al., 2002; van Rossum et al., 2008). Assuming that the peripheral target is processed in a bottom-up fashion until it reaches higher-order object processing areas, the time point at which peripheral signals are available for feedback should be dictated by the temporal dynamics of visual feedforward processing.”

      Of note, both reviewers asked us to explore the oscillatory nature of the difference between targetcongruent and target-incongruent HRs. We will post our changes in response to the reviewer’s remark below.

      And, more importantly, why would foveal prediction need to be weaker with stronger pre-saccadic attention to the periphery?

      We hope that our previous reply has cleared up that the opposite is true: In general, and irrespective of the feature congruency of target and foveal probe, foveal HRs decrease as target contrast increases. As we have stated in our Abstract and Results, “foveal Hit Rates for target-congruent and incongruent probes decreased as target opacity increased, presumably since attention was increasingly drawn to the target the more salient it became. Crucially, foveal enhancement defined as the difference between congruent and incongruent Hit Rates increased with opacity”. This finding did not appear counterintuitive to us and was, in fact pre-registered as a main hypothesis (see https://osf.io/wceba). 

      We are unsure if this goes beyond the reviewer’s concern but we, in fact, speculate in the revised Discussion section as well as in our original eLife article that the overall, feature-unspecific decrease in foveal detection performances may aid feature-specific foveal prediction: 

      “This pre-saccadic decrease in foveal sensitivity may boost the relative weight of fed-back signals by attenuating the conspicuity of high-contrast feedforward input. In other words, the strength of feedforward input to the fovea is reduced gradually across saccade preparation. At the same time, the strength of the fed-back predictive signal should profit from the high contrast of naturalistic saccade targets.”

      What is the function of foveal prediction?

      Please refer to the section ‘What is the function of foveal prediction?’ in our main article. We have pasted this paragraph below for the reviewer’s convenience. 

      “What is the function of foveal prediction?

      As stated above, previous investigations on foveal feedback required observers to make peripheral discrimination judgments. We, in contrast, did not ask observers to generate a perceptual judgment on the orientation of the saccade target. Instead, detecting the target was necessary to perform the oculomotor task. While the identification of local contrast changes would have sufficed to direct the eye movement, the orientation of the target enhanced foveal processing of congruent orientations. The automatic nature of foveal enhancement showcases that perceptual and oculomotor processing are tightly intertwined in active visual settings: planning an eye movement appears to prioritize the features of its target; commencing the processing of these features before the eye movement is executed may accelerate post- saccadic target identification and ultimately provide a head start for corrective gaze behavior (Deubel et al., 1982; Ohl and Kliegl, 2016; Tian et al., 2013).”

      What of the other interpretation that could be invoked in general for this type of task used by the authors: that the dual task is challenging and that subjects somehow misattribute what they saw in the peripheral task when planning the saccade. i.e. foveal hit rates are misperceptions of the peripheral target. When the peripheral target is easier to see, then the foveal hit rate drops.

      Alternative explanations in general: In our main article, we ruled out—either through direct experimentation or by considering relevant properties of our findings—the following alternative explanations: i) spatially global feature-based attention to the target orientation, ii) a multiplicative combination of spatial and feature-based attention, and iii) shifts of decision criterion. While dual tasks (i.e., simultaneous oculomotor planning and perceptual detection) are standard in psychophysical investigations of active vision, we acknowledge the potential influence of an explicit foveal task in the revised manuscript, and in response to both reviewers: 

      “Lastly, pre-saccadic foveal input is likely less relevant during natural viewing behavior than it is in our task. It is possible that this task-induced prioritization of the foveal location facilitated the emergence of congruency effects. In a previous experiment (Kroell & Rolfs, 2022; Figure 2D), the perceptual probe could appear anywhere on a horizontal axis of 9 dva length around the screen center. Despite this spatial unpredictability, however, congruency effects peaked at the pre-saccadic foveal location, even after peripheral baseline performances had been raised to a foveal level through an adaptive increase in probe opacity. Ultimately, an influence of task demands on visual processing can only be fully excluded through techniques that provide a direct readout of perceptual contents without requiring keyboard responses. In psychophysical investigations, a prediction of saccade target motion may be read out from observers’ eye velocities (Kroell, Mitchell, & Rolfs, 2023; Kwon, Rolfs, & Mitchell, 2019). In electroencephalographic (EEG) and neurophysiological studies, foveal predictions should manifest in early visual evoked potentials (e.g., Creel, 2019) and increased firing rates of feature-selective foveal neurons in early visual areas, respectively.”

      Difficulty of the task: Concerning the perceptual detection task, every experimental session was preceded by an adaptive staircase procedure that adjusted the transparancy of the foveal probe—and, thus, task difficulty—depending on the respective observer’s performance (see Methods for details). Concerning the oculomotor task, observers were able to perform accurate saccades with typical movement latencies for all target opacity conditions (see Results, Supplements & Figure S1). In general, we are unsure how high task difficulty could produce a feature-, temporally and spatially specific enhancement of both filtered and incidental target-congruent foveal orientation information. In fact, a main finding of our current submission is that foveal HRs decrease as the target becomes easier to see and the oculomotor task thus becomes easier to perform.

      Perceptual confusion of target and probe stimulus: We observe a specific increase in HRs for foveal probes that exhibit the same orientation as the peripheral saccade target. Just like in our main article, a response is defined as a ‘Hit’ if a foveal probe is presented and the observer generates a ‘present’ judgment. To our understanding, the suggestion that a confusion of target and probe stimuli may account for these effects necessarily implies that this confusion hinges on the congruency between peripheral and foveal feature inputs. In other words, peripheral and foveal signals should be more readily “confused” if they exhibit similar features. We assume that peripheral feature information is fed back to neurons with foveal receptive field and combines with feature-congruent feedforward input. Whether this combination of signals can be described as low-level perceptual “confusion” likely depends on individual linguistic judgments (it would certainly be a novel description of feedback-feedforward interactions). Perhaps a defining difference between the reviewer’s concern and our assumed mechanism is the spatial specificity of the resulting congruency effects. We suggest that only neurons with foveal receptive fields receive feature information via feedback. And indeed, we demonstrate a clear spatial specificity of congruency effects around the pre-saccadic foveal location, even after parafoveal performances had been raised to a foveal level by an adaptive increase in probe opacity (see Kroell & Rolfs, 2022; Figure 2C & Figure 3). In other words, observers’ perception is altered in their pre-saccadic center of gaze while the target is presented peripherally. We struggle to conceive a

      scenario in which a confusion of signals should be feature-specific as well as specific to an interaction between peripheral and foveal signals without being meaningful at the same time. If the reviewer is referring to confusions on the response or decision level, we would like to point them towards the Discussion section ‘Can our findings be explained by established mechanisms other than foveal prediction?’ in our main article. In this paragraph, we provide detailed arguments for a dissociation between our findings and shifts in decision criterion that would exceed the scope of a Research Advance. 

      When the peripheral target is easier to see, then the foveal hit rate drops.

      We agree. Target-congruent and incongruent foveal HRs decreased as the contrast of the probe increased. However, and as we stated in response to the reviewer’s first comment, the difference between target-congruent and target-incongruent foveal HRs (and, thus, foveal enhancement of the target orientation) increased with peripheral target contrast.

      The analyses of Fig. 3C appear to be overly convoluted. They also imply an acknowledgment by the authors that target-probe temporal difference matters. Doesn't this already negate the idea that the foveal effects are associated with the saccade generation process itself? If the effect is related to target onset, how is it interpreted as related to a foveal prediction that is associated with the saccade itself? 

      We indeed conducted analyses that can reveal an influence of target presentation duration at probe onset, the saccade preparation stage at probe offset, as well as a combination of both factors. The fact that target presentation duration may have an influence on foveal prediction would not negate a simultanous influence of saccade preparation and vice versa. In the main article, we directly investigated the influence of saccade preparation on foveal enhancement by introducing a passive fixation condition (Kroell & Rolfs, 2022; Figure 5). At identical target-probe offset durations, pre-saccadic foveal enhancement was significantly more pronounced and accelerated compared to enhancement during passive fixation. We have added a purely saccade-locked time course (uncorrected by targetprobe interval) to our Results section and to Figure 3 (second row). We still believe that the target-locked, saccade-locked and combined analysis are informative for future investigations and would like to present them all for completeness.

      Also, the oscillatory nature of the effect in Fig. 3C for 59% and 90% opacity is quite confusing and not addressed. The authors simply state that enhancement occurs earlier before the saccade for higher contrasts. But, this is not entirely true. The enhancement emerges then disappears and then emerges again leading up to the saccade. Why would foveal prediction do that?

      In response to this comment and a suggestion by Reviewer 1, we inspected the pre-saccadic time course of enhancement effects in a more temporally resolved fashion and, indeed, observed pronounced oscillations for the two higher target opacity conditions (see Results): 

      “Especially at higher target opacities, the temporal development of foveal enhancement appears to exhibit an oscillatory pattern. To inspect this incidental observation in a more temporally resolved fashion, we determined mean enhancement values in a boxcar window of 50 ms duration sliding along all saccade-locked probe offset time points (step size = 10 ms; x-axis values in Figure 4 indicate the latest time point in a certain window). We then fitted 6th order polynomials to the resulting time courses and compared the fitted values against zero using bootstrapping (see Methods). The average foveal enhancement across target opacities reached significance starting 115 ms before saccade onset (gray curve in Figure 4; all ps < .046). For every individual target opacity condition, we observed significant enhancement immediately before saccade onset, although only very briefly for the lowest opacity (-2–0 ms for 25%; -39–0 ms for 39%, -106–0 ms for 59% &  -13–0 ms for 90%; all ps < .050; yellow to dark red curves in Figure 4). Especially for the higher two target opacities, we observed a local maximum preceding eye movement onset by approximately 80 ms. Interestingly, assuming a peak in enhancement in approximately 80 ms intervals (i.e., at x-axis values of -80 and 0 ms in Figure 4) would correspond to an oscillation frequency of 12.5 Hz. In contrast to rapid feedforward processing, feedback signaling is associated with neural oscillations in the alpha and beta range (i.e., between 7 and 30 Hz; Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle et al., 2015).”

      We had observed an oscillatory pattern in multiple previous investigations, and in both Hit Rates to foveal orientation content and reflexive gaze velocities in response to peripheral motion information. So far, we have been unsure how to explain it. The literature on thalamic visual processing mentioned by the reviewer alerted us to the oscillatory nature of feedback signaling itself. Interestingly, the temporal frequency range of feedback oscillations includes the frequency of ~12.5 Hz observed in our data. We have included this and alternative explanations in the Discussion section (see below). We are aware, and acknowledge in the manuscript, that our analysis approach is purely descriptive, and that the potential explanations we give are speculative. 

      “Moreover, foveal congruency effects appeared to exhibit an oscillatory pattern, with peaks in a medium saccade preparation stage (~80 ms before the eye movement) and immediately before saccade onset. We have noticed this pattern in several investigations with substantially different visual stimuli and behavioral readouts. For instance, using a full-screen dot motion paradigm, we observed a pre-saccadic, small-gain ocular following response to coherent motion in the saccade target region (Kroell, Rolfs, & Mitchell, 2023, conference abstract; Kroell, 2023, dissertation). Predictive ocular following first reached significance ~125 ms before the eye movement, then decreased and subsequently ramped up again ~25 ms before saccade onset. Several explanatory mechanisms appear conceivable. Unlike rapid feedforward processing, feedback propagation has been shown to follow an oscillatory rhythm in the alpha and beta range, that is, between 7 and 30 Hz (Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle, et al., 2015). In our case, it is possible that the object-processing areas that send feedback to retinotopic visual cortex do so at a temporal frequency of ~12.5 Hz. At higher stimulus contrasts, feedforward signals may be fed back instantaneously and without the need for signal accumulation in feedback-generating areas. The resulting perceptual time courses may reflect innate temporal feedback properties most veridically. Alternatively, the initial enhancement peak may be related to the sudden onset of the saccade target stimulus and not to movement preparation itself. In this case, the initial peak should become particularly apparent if enhancement is aligned to the onset of the target stimulus. Yet, Figure 3 and Figure 4 suggest more prominent oscillations in saccade-locked time courses. In accordance with this, perceptual and attention processes have been shown to exhibit oscillatory modulations that are phase-locked to action onset (e.g., Tomassini, Spinelli, Jacono, Sandini, & Morrone, 2015; Hogendoorn, 2016; Wutz, Muschter, van Koningsbruggen, Weisz, & Melcher, 2016; Benedetto & Morrone, 2017; Tomassini, Ambrogioni, Medendorp, & Maris, 2017; Benedetto, Morrone & Tomassini, 2019). Whether the oscillatory pattern of foveal enhancement, as well as its increased prominence at higher target contrasts, relies on innate temporal properties of feedback  signaling, signal accumulation, saccade-locked oscillatory modulations of feedforward processing or attention, or a combination of these factors, one conclusion remains: task-induced cognitive influences suggested to underlie the considerable variability in temporal characteristics of foveal feedback during passive fixation (e.g., Fan et al., 2016; Weldon et al., 2016; 2020) are not the only possible explanation. Low-level target properties such as its luminance contrast modulate the resulting time course and should be equally considered, at least in our paradigm.”

      The interpretation of Fig. 4 is also confusing. Doesn't the longer latency already account for the lapse in attention, such that visual continuity can proceed normally now that the saccade is actually eventually made? In all results, it seems that the effects are all related to the dual nature of the task and/or attention, rather than to the act of making the saccade itself. Why should visual continuity (when a saccade is actually made, whether with short or long latency) have different "fidelity"? And, isn't this disruptive to the whole idea of visual continuity in the first place?

      We are unsure if we grasp the unifying concern behind these remarks. For the reviewer’s point on the dual-task nature of our paradigm, please consider our answer above. Perhaps it is important to note that we do not (and would never) claim that foveal prediction is the only mechanism underlying visual continuity. We believe that multiple mechanisms, including but not limited to pre-saccadic shifts of attention, predictive remapping of attention pointers and the perception of intra-saccadic signals interact and jointly contribute to visual continuity. It appears highly conceivable that, like most processes in biological systems, motor and perceptual performances are subject to fluctuations. We argue that saccade latencies as well as the magnitude of foveal prediction constitute read-outs of these variations. We also suggest that those read-outs are innately correlated beyond their common moderator of, perhaps, attentional state; we have previously presented clear evidence for a link between eye movement preparation and foveal prediciton (Kroell & Rolfs, 2022; Figure 2). To the best of our judgment, we consider it reasonable that the effectiveness of movement-contingent perceptual processes varies with the effectiveness (in programming or execution) of the very movement motivating them. We present evidence for this assumption in our submission. We would also like to make clear that we do not assume our vision to fail entirely, even if every single well-known mechanism of visual continuity were to break down at once. Upon saccade landing, the visual system receives reliable visual input. Nonetheless, the visual system has undeniably developed mechanisms to optimize this process. We believe foveal prediciton to rank among them.

      Small question: is it just me or does the data in general seem to be too excessively smoothed?

      We did not apply any smoothing to either the analysis or visualization of our data in the initial manuscript.

      Every observer completed a large number of trials (mean n = 2841 trials per observer; total trial number > 25,500), which likely contributes to the clarity of our data. To inspect the oscillatory pattern of enhancement in a more temporally resolved fashion (in response to the reviewer’s point above), we applied a moving window analysis in this revision. Due to overlapping window borders, this analysis introduces a certain degree of smoothing. Nonetheless, data patterns are comparable to the time course with only few non-overlapping time bins (Figure 3B; second row). In general, we have described all steps of our analysis routine extensively in the Methods section and will make our data publicly available upon publication of the Reviewed Preprint. 

      General comment: it is important to include line numbers in manuscripts, to help reviewers point to specific parts of the text when writing their comments. Otherwise, the peer review process is rendered unnecessarily complicated for the reviewers.

      We apologize and have added line numbers.

    1. may not have any correlation to the world as humans understand it. Like a good bullshitter, it’s better at form and style than substance

      we can see how this happens over time leading to inaccuracies against the truth "from people", I think this leads to a shift in how we talk as we all try to become more human.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1:

      The analysis of the dormancy rates is interesting and offers some intriguing questions related to the higher dormancy rate found for the L2 isolates and lower for the L3 ones. It will be interesting in the future to expand the data generated in this advanced in vitro plaAorm to in vivo studies.

      Indeed, an increased dormancy propensity of L2 isolates was previously reported in broth culture and associated to specific genetic polymorphisms. The opposite phenotype observed in the L3 isolates is indeed particularly intriguing and was not described to date. Hence, we fully agree that it would be very interesting to find out whether these phenotypes are also observed in vivo.

      The authors propose that ‘strains exhibiting greater proliferative capacity are more prone to induce macrophage apoptosis, thereby contributing to the extent of the granulomatous response.’ It would be interesting to know what happens if the macrophage apoptotic response is blocked.

      This is an interesting suggestion that would deserve a dedicated comprehensive investigation covering other cell death pathways. Even though the trend is significant, the correlation coefficient is rather low in this interaction, which looks a fortiori due to substantial inter-host variability in the apoptotic propensity of macrophages from individual donors to a given strain. In addition, such blocking experiments may require performing isolated macrophage infections that would fall outside of the scope of this study, or considering the extent and the contribution of the apoptosis of other cell subsets. 

      In contrast to macrophage apoptosis, T cell activation correlated with less replicative bacteria. Are these two findings related, ie, are the granulomas showing more (apoptotic) macrophages the ones with a lower percentage of activated T cells? This would shed light on what distinguishes granulomas that are protective from those that support bacterial growth. 

      Indeed, a significant negative correlation between macrophage apoptosis induction and T cell activation can be observed, specifically with activated CD4 T cells expressing CD38 (rS \= -0.36, p < 0.05) or CD69 (rS = -0.40, p < 0.01). We have added this additional result in the manuscript text (line 217).

      It would also be interesting to know the functional impact of blocking early CXCL9 or IL1b on the outcome of granulomatous response/bacteria growth.

      We have performed the suggested early blocking experiments and added the expected negative effect on granuloma formation upon neutralization of IL-1b (current Fig. 6E) in the revised version of the manuscript, and furthermore discussed the null effect on bacterial growth of the treatment with an anti-CXCL-9 specific antibody (current Fig. 6H).

      The authors acknowledge the absence of neutrophils in this model. However, this could be discussed in more detail, as neutrophils play an important part in TB pathogenesis as shown in different models of infection and human TB. 

      We concur and have expanded the importance of neutrophils in TB pathogenesis (including references) in the discussion section (line 260). 

      Related to neutrophils and TB pathogenesis, another important player is type I IFN. The multiplex assay used included IFN-alpha, was this molecule detected? If so, was there any difference in the levels of type I IFN detected among the different infections?

      We agree and that is why we had originally included IFN-α in our screen. However, this cytokine remained under the limit of quantification at both studied time points, preventing us to draw conclusions on the effect of Mtb strain diversity on the secretion of type I IFNs in in vitro granulomas.

      Reviewer #2:

      In Figure 1b/c, it is not clear what comparisons are being made to give the p-value annotations.

      In Figure 2a/b, it is not clear what comparisons are being made to give the p-value annotations.

      In Figure 3a, again it is not clear what comparisons are being made to give the p-value annotation.

      The p-values formerly present on the upper le] corner of the panels were resulting from either Friedman (Figures 1C, 2A and 3A) or Kruskal-Wallis (Figures 1B and 2B) tests and indicated whether there was a significant difference between the analyzed groups overall. To avoid confusion, those values have been removed to only leave the post-test comparison between specific groups.  

      In the results narrative related to Figure 1 (lines 93-103), the authors refer to lineage heterogeneity without providing any objective quantification of this - I suggest they do so, by providing variance or standard deviations. 

      Thank you very much for this relevant suggestion, we have now included the coefficients of variation as a quantitative measure of the within-lineage heterogeneity in the manuscript (line 97). 

      I also suggest the authors explain what the data points actually represent in this figure - do I assume each data point = cfu from a well of 'granuloma'? Are they all from the same donor PBMC? What is the sample N for each lineage? If the data are not from the same donor PBMC, I think more informative to present the results of paired statistical analyses, stratified by donor cells. In addition, the authors should include a summary table of the demographic characteristics of the donors (at least sex, ethnicity, and age). If the data are derived from a single donor, I'd advocate providing data from at least one further donor.

      In the new supplementary figure requested by Reviewer 3 Figure 1—figure supplement 1 (actual CFU data on days 1 and 8 p.i. used to calculate the growth rate) it is now indicated that bacterial load was quantified as CFU per well.

      Regarding the number of donors used, as stated in the Material and Methods section (current line 418) and depicted by the four different shapes used when data are grouped by individual infecting strain, all figures in our manuscript have been generated using PBMCs from 4 independent donors. For greater clarity, “n = 4” has now been included in the figure legends. Regarding the statistical analyses, paired statistical analyses stratified by donor were already performed in the original version of the manuscript whenever appropriate. 

      As stated in the methods section, the buffy coats used for PBMC isolation are anonymized so demographic data are unavailable.

      The premise of the analysis in Figure tic and the results narrative ("This finding suggests that an increased ability to enter dormancy is not necessarily associated with a more pronounced growth phenotype", line 132) is not clear to me. Why would increased dormancy relate to increased growth in the same context? I suggest this analysis be removed.

      We apologize for the confusion in our original statement. We now rephrased it as “This finding suggests that an increased tendency to remain in a metabolically active state is not necessarily associated with a more pronounced growth phenotype”.

      In Figure 3b, I think it may be more informative if the data points from the same donor were linked. Likewise in Figure 3c, I'd like to see a donor-paired statistical analysis.

      For all figures, the choice of using individual symbols to identify data points from the same donor but not connecting lines was made to provide a neater image. Nevertheless, we have now modified the figure linking the data points from the same donor. The statistical analysis performed is always donor-paired whenever appropriate. 

      The casual inference suggested in the results narrative between ‘macrophage apoptosis’ and granulomatous response line 173-175) is not tested directly by the experiment – I suggest the authors exclude this statement.

      Fair point, the statement has been removed.

      To what extent have the authors considered whether variation in T cell responses between lineages may be confounded by variation in Mtb reactive T cell frequencies in donor PBMC. Can this be disentangled at all? This should be acknowledged as a potential limitation of the study.

      We did characterize the presence of mycobacterial antigen-specific reactive T cells in the PBMCs from the investigated donors. To do so, we performed in vitro stimulations with purified protein derivative (PPD) or an ESAT-6/CFP-10 peptide pool and quantified the frequency of IFN-γ-positive CD4 T cells by flow cytometry. The percentage of IFN-γg-positive CD4 T cells recalled by PPD stimulation ranged from 0.02% to 0.13%, while no ESAT6/CFP-10 reactive T cells were detected. As such, we can akest that the PBMC donors never encountered Mtb even though some levels of memory recalled by PPD may be due to cross-reactivity with BCG or pre-exposure to non-tuberculous mycobacteria. We have now added a panel in Figure 5—figure supplement 2 representing the frequency of mycobacteria-specific CD4 T cells and, as suggested, discussed the impact on the extent of the T cell responses observed in granulomas in the revised version of the manuscript.  Nevertheless, the observed MTBC strain-specific trends are consistent across the donors, as depicted in Figure 5B and Figure 5—figure supplement 2A-B.

      Moreover, the experimental design does not really test cause and effect for the relationship between T cell proliferation/activation and bacterial growth. What is the impact of T-cell depletion from PBMC on bacterial growth?

      The increased TB susceptibility of HIV patients demonstrated that T cells play a critical part in the control of Mtb infection. We agree and did envisage such a depletion experiment. However, depleting T cells from PBMCs would imply removing up to 70% of the cells present in the specimen, which would lead to a situation from which results cannot be compared to the original sample and therefore would not be interpretable. 

      Reviewer #3:

      Data presentation:

      - In Figure 1 (replication rate), actual cumulative CFU means from each strain for both days 1 and 8 with statistical analysis should be presented as panels in this figure.

      Agreed. We are providing the requested representation of the data and the corresponding paired statistical analysis as supplementary material Figure 1—figure supplement 1.

      - In Figure 2 (dormancy), a panel comparing the mean number of bacteria that are single positive for either Auramine-O, Nile Red, or are double positive should be included for each strain, with statistical analysis. Representative photomicrographs of phenotypes from the staining should also be included. Electron microscopy could be conducted to compare the presence of intermediate lipid inclusions within organoidbound mycobacteria.

      As requested, percentages of single stained as well as double positive bacilli in each sample are now represented in Figure 2—figure supplement 1. In addition, we have now also followed the request and included a photomicrograph picturing representative Mtb staining phenotypes. Lastly, it would certainly be very elegant to visualize the presence of Mtb lipid inclusions within cellular aggregates by electron microscopy. However, we do not currently have the means for such investigations and the implementation of such a protocol under BSL3 conditions appears unrealistic in the context of this study.  

      - In Figure 3 (granulomatous response), the number, circularity, and size of immune aggregates are presented as "granuloma score" in which the mean ratio of size to circularity is divided by the number of inclusions. To their credit, in Supplementary Figure 2, the authors provide the data in a straighAorward manner. However, the granuloma score metric is reduced as the number of observed "granulomas" increases, which is counterintuitive. Additionally, circularity is not a definitive aspect of human granulomas (Wells et al., Am J Respir Crit Care Med, 2021, PMID: 34015247). I am skeptical that the "granuloma score" is an accurate predictor granulomatous inflammation. Is there precedent for this metric in the literature? If so, a reference should be provided. A high magnification inset of 1 representative granuloma from each strain should be included in Figure 3A.

      As requested, insets of a representative average granuloma for each strain have been included in Figure 3A. The formulation of the “granuloma score” has no precedent and cannot be referenced. By doing so, we meant to integrate within one single parameter the visual differences represented in the current Figure 3— figure supplement 2. We intentionally sought to assign the highest score to the massive aggregation that some strains may promote unlike some that trigger several small, dispersed and diffused aggregates.

      - In Figure 4 (macrophage apoptosis), a panel showing the percentage of dual Annexin V and 7-AAD positive cells should be included to provide the reader with the relative scope of ongoing apoptotic vs necrotic/secondary necrotic death in the model. If the data is readily available, including a control of uninfected PBMCs would also allow the reader to evaluate donor-dependent differences of in vitro cell death at baseline.

      No significant differences were observed in the percentage of dual Annexin V- and 7-AAD-positive macrophages (necrosis/secondary necrosis) between the MTBC strains at this time-point. Nevertheless, we have disclosed this result in the revised manuscript as Figure 4—figure supplement 2.

      - In Figures 5 and 6 (lymphocyte activation and soluble mediator secretion), panels showing unscaled data should be included. Panels depicting the unscaled immunoassay protein readings (pg/mL) by strain for CXCL9, granzyme B, and TNF with statistical analysis should be included in Figure 6.

      As requested, unscaled lymphocyte activation and soluble mediator data have been included as Figure 5— figure supplement 2 and Figure 6—figure supplement 1, respectively (replacing former supplementary figures 5 and 7). In addition, updated Figure 6G panel now depicts correlation analysis with the unscaled cytokine concentrations.

      The DosR-regulon:

      The authors hypothesize that differences in the prevalence of the dormancy metrics (acid-fastness or lipid inclusion prevalence, are due to strain-specific increases in expression of the DosR regulon within the model's hypoxic conditions (lines 107-114, 126-127). The claim that their model is equipped to evaluate dosR-dependent mycobacterial phenotypes was also previously proposed (Arbués et el., 2021) and should be tested. A comparison of the dosR-dependent gene expression of each strain in PBMC aggregates and broth culture by qRT-PCR would test this idea at a very basic level.

      We agree. Actually, a similar request was made during the revision of our first in vitro granuloma study for which such qPCR data were generated and presented in Fig. 1 D (PMID: 32069329). In addition, the work of Kapoor et al., who originally developed the in vitro granuloma model also demonstrated the induction of most of the DosR regulated genes by qPCR (PMID: 23308269). We trust that the reviewer will agree that this does not need to be repeated.

      The modern Beijing lineage strain L2C:

      The authors claim (Line 101-102) that the results of Figure 1 "confirm the higher virulence propensities of strains from modern lineages". From the data presented, it appears that strain L2C (Modern-Beijing) dominates the modern vs ancestral and inter/intra-lineage phenotypes of replication, dormancy, and apoptosis. Are significant differences between modern and ancestral lineages or between strains simply a facet of the distinct profile of L2C? Do the statistical differences disappear when the L2C group is excluded?

      Indeed, among the modern lineages’ isolates, L2C exhibits a hypervirulent profile in terms of bacterial replication. However, the difference between modern and ancestral strains remains statistically significant when L2C is excluded from the analysis (p = 0.002). That is also the case when we analyze the proportion of dormant bacteria. Exclusion of L2C strain results in a Kruskal-Wallis overall p = 0.005, and p = 0.0002 when we compare L2 vs. L3. Lastly, regarding the percentage of apoptotic macrophages, if we use L2B (instead of L2C) to compare, the difference is still significant vs. L1A (p = 0.008) although there is no longer a trend for L2A (p = 0.1).

      "Dormancy":

      Dormancy is definitively a non-replicative state, where bacterial growth is absent. The authors' findings and claims appear to be incompatible with that definition, which they acknowledge (Lines 130-135). The lack of correlation between growth and dormancy in their model is supported with reference to Figure 2C, a Spearman's analysis of dormancy ratio with growth rate (inclusive of all strains under consideration). The figure supports a model where "dormancy" and "growth rate" are disjunct but also appears to show high "dormancy" accompanying increasing "growth" in the L2C group. How are strains able to grow if they are in a non-replicative state? Are the "growth rate" assays actually measures of survival? Are there different rates of infectivity? Are the bacteria growing cellularly in the serum-rich ECM, etc. etc? We need to see the hard CFU and Nile Red, and Auramine-O data to contextualize these findings. Alternatively, could the accumulation of inclusions in the model not be a reliable dormancy metric (Fines et al., BioRxiv [Preprint], 2023, PMID: 37609245)?

      We fully agree. The Nile red profiles are always relative and only depict the proportion of the population that has entered a dormant state. Nevertheless, dormancy can be dynamic and bacteria may swi]ly resuscitate in that model. Furthermore, and as depicted in Figure 2—figure supplement 1, despite showing an increased tendency to enter a dormant-like state, a considerable population of lineage 2 bacilli still remains metabolically active and in a replicative state. The referred preprint is very interesting and we will follow it up closely.

      Specificity of responses to PBMC aggregation:

      The authors claim that their results "reveal a broad spectrum of granulomatous responses" (Line 73) but do not show any aggregation specificity of PBMC responses beyond the model's intrinsic metrics of area and circularity. To establish that their phenotypes such as lymphocyte activation, cytokine release, cell death, or mycobacterial acid-fastness/lipid inclusion prevalence, are aspects of the granulomatous response the authors could infect PBMCs from the same donors with the same strains and perform the same assays using established Mtb-PBMC models in which the cells do not aggregate. This would answer many important questions, for example, does the rate of macrophage infection account for variability in apoptosis percentage? Phagocytosis assay and quantification of stained intracellular mycobacteria within recently infected PBMCs could be conducted to determine if phenotypes are an aspect of granulomatous aggregation or due to strain-specific differences in cellintrinsic macrophage immunity. It would also be very informative to know what percentage of PBMCs and mycobacteria are granuloma-bound in the ECM.

      We are not aware of Mtb-PBMC models in which the cells do not aggregate. We previously compared PBMC infection models in the presence or absence of the collagen matrix and cells also spontaneously coalesced around infection foci (PMID: 34603299). Regarding the last point, the melting step of the collagen matrix requires enzymatic digestion and pipetting that dislocate the aggregates. Accordingly, we cannot distinguish the bacteria that would remain within the matrix compared to those replicating within cellular aggregates. However, we did resolve this question by demonstrating that the bacteria were not able to grow in the absence of cells in this culture condition (Supplementary material, PMID: 34603299)

      Minor recommendations

      - The term TNF-a should be replaced with TNF throughout the manuscript.

      We acknowledge that the term TNF-a can be interchangeable with TNF. However, we chose to use the TNFα terminology to differentiate it from lymphotoxin α, which is also referred to as TNF-β.

      - The authors cite studies conducted in murine and NHP models to support the claim that "understanding of immune protective traits in TB remains insufficient and yet dominated by data from mouse and non-human primate studies" (Lines 63-64) but ignore an abundance of data from other in vivo and in vitro models that have provided numerous valuable insights in the field of TB immunology. This line should be revised or omired.

      For us, the term “dominate” implies that these models are widely used, not that they are the only ones. Other models indeed provided additional relevant data. We are citing the lung-on-chip model of McKinney’lab and the in vitro granuloma model of Elkigton’s lab (line 66). We would be very happy to include more references upon further specifications even though we cannot build an extensive review here.

      - The authors claim that their model "encompasses, with the exception of neutrophils, all immune cell types involved in TB" (Lines 67-68). To support this claim, they should provide additional references or data demonstrating that the PBMC aggregates include, eosinophils, mast cells, dendritic cells, yolk-sac-derived alveolar macrophages, and Langhan's giant cells.

      With the aim of providing a more accurate and detailed information regarding the cell types present in the model, the sentence has been reformulated as: “The model encompasses all PBMC-derived cell types involved in TB immune responses, but lacks granulocytes (i.e. neutrophils, eosinophils, basophils and mast cells)” (line 260). Noteworthy, the presence of multinucleated giant cells was reported in Kapoor’s paper describing the in vitro granuloma model for the first time (PMID: 23308269).

      -  As an additional note, the title can be improved and made more broadly accessible by revising the use of the acronyms CXCL9, granzyme B, and TNF-α.

      To render the title more broadly accessible we propose to replace the listed acronyms by “soluble immune mediators”, but we remain opened to more appropriate and specific suggestions.

      Answers to the reviewers’ public comments

      Reviewer #1:

      First of all, we would like to thank the reviewers for their feedback and suggestions to improve our manuscript. To strengthen the findings of our study, we have performed and added results from IL-1b and CXCL9 blocking experiments evaluating the impact on the granulomatous response and bacterial load, respectively. In the revised version of the manuscript, while we discuss the null effect on bacterial growth of the treatment with an anti-CXCL-9 antibody and the potential reason behind it, we are now reporting a negative effect on the magnitude of granuloma formation upon neutralization of IL-1b that the correlation analysis had initially suggested.

      Reviewer #2:

      The revised version of our manuscript incorporates now all the points detailed in the private answers to the reviewer, including clarifications on the statistical tests performed, additional supplementary materials to transparently disclose the raw data behind the normalization approach, as well as flow cytometry data on the immune memory status of the blood donors. In addition, and as stated in the answer to reviewer #1, to test causal relationship between some host and pathogen traits, we have now performed and provided data and interpretation of IL-1b and CXCL9 blocking experiments.

      Reviewer #3:

      We are thankful and concur with these constructive comments and insights. We have now consistently revisited the statistics in the figures to improve clarity and included new supplementary figures reporting the raw data that were missing in the initial version of the manuscript. In addition, and as mentioned in the answers to reviewers #1 and #2, we have now performed and added IL-1β and CXCL9 blocking experiments to test causal relationship between specific host and pathogen traits. In particular, we are now reporting a negative effect on the magnitude of granuloma formation upon neutralization of IL-1β that the correlation analysis had initially suggested.

      More specifically, regarding the point that our method for bacterial collection calls into question whether all Mtb plated for CFU assay resided within granulomatous aggregates, we previously reported that Mtb growth strictly required the presence of human cells in our culture conditions (Supplementary material, Arbués et al, 2021, PMID: 34603299). In the presence of cells, our microscopy read-out does allow us to observe extra-cellular growth if infections are carried on beyond an 8-day limit, which we applied in the current study to exclude this particular caveat. 

      Concerning the apparently conflicting observation that those strains displaying an increased tendency to enter a dormant-like state are the ones exhibiting the highest replication rates, we would like to point out that a considerable population of bacilli still remains metabolically active and in a replicative state. For instance, and as depicted in Figure 2—figure supplement 1, despite showing an increased tendency to enter a dormant-like state, a considerable population of lineage 2 bacilli does remain metabolically active. Moreover, dormancy can be dynamic and bacteria may swi]ly resuscitate.

      Regarding the mentioned limitations of our study that we have discussed in the revised version of our manuscript, we fully concur that PBMC-based in vitro granuloma models lack tissue structure as well as some important stromal and immune cellular players. Nevertheless, we and others demonstrated the particular relevance of the 3-dimensional infection approach within a matrix of collagen and fibronectin by providing mechanistical insights into Mtb resuscitation previously associated to treatment with various immunomodulatory drugs (Arbués et al., 2020, PMID: 32069329; Tezera et al., 2020, PMID: 32091388).

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      1. General Statements [optional]

      Our manuscript initially entitled “Ribosomal RNA synthesis by RNA polymerase I is regulated by premature termination of transcription” investigates the regulation of the initial steps of ribosome biogenesis – the synthesis of large ribosomal RNA precursor by RNA polymerase I.

      In our manuscript, we demonstrate for the first time that RNA Polymerase I (Pol I) can prematurely release nascent transcripts at the 5' end of ribosomal DNA transcription units in vivo. This achievement was made possible by comparing wild-type Pol I with a mutant form of Pol I, hereafter called SuperPol previously isolated in our lab (Darrière at al., 2019). By combining in vivo analysis of rRNA synthesis (using pulse-labelling of nascent transcript and cross-linking of nascent transcript - CRAC) with in vitro analysis, we could show that Superpol reduced premature transcript release due to altered elongation dynamics and reduced RNA cleavage activity. Such premature release could reflect regulatory mechanisms controlling rRNA synthesis. Importantly, This increased processivity of SuperPol is correlated with resistance with BMH-21, a novel anti-cancer drugs inhibiting Pol I, showing the relevance of targeting Pol I during transcriptional pauses to kill cancer cells. This work offers critical insights into Pol I dynamics, rRNA transcription regulation, and implications for cancer therapeutics.

      We sincerely thank the three reviewers for their insightful comments and recognition of the strengths and weaknesses of our study. Their acknowledgment of our rigorous methodology, the relevance of our findings on rRNA transcription regulation, and the significant enzymatic properties of the SuperPol mutant is highly appreciated. We are particularly grateful for their appreciation of the potential scientific impact of this work. Additionally, we value the reviewer’s suggestion that this article could address a broad scientific community, including in transcription biology and cancer therapy research. These encouraging remarks motivate us to refine and expand upon our findings further.

      All three reviewers acknowledged the increased processivity of SuperPol compared to its wild-type counterpart. However, two out of three questions our claims that premature termination of transcription can regulate ribosomal RNA transcription. This conclusion is based on SuperPol mutant increasing rRNA production. Proving that modulation of early transcription termination is used to regulate rRNA production under physiological conditions is beyond the scope of this study. Therefore, we propose to change the title of this manuscript to focus on what we have unambiguously demonstrated:

      “Ribosomal RNA synthesis by RNA polymerase I is subjected to premature termination of transcription”.

      Reviewer 1 main criticisms centers on the use of the CRAC technique in our study. While we address this point in detail below, we would like to emphasize that, although we agree with the reviewer’s comments regarding its application to Pol II studies, by limiting contamination with mature rRNA, CRAC remains the only suitable method for studying Pol I elongation over the entire transcription units. All other methods are massively contaminated with fragments of mature RNA which prevents any quantitative analysis of read distribution within rDNA. This perspective is widely accepted within the Pol I research community, as CRAC provides a robust approach to capturing transcriptional dynamics specific to Pol I activity.

      We hope that these findings will resonate with the readership of your journal and contribute significantly to advancing discussions in transcription biology and related fields.

      2. Description of the planned revisions

      Despite numerous text modification (see below), we agree that one major point of discussion is the consequence of increased processivity in SuperPol mutant on the “quality” of produced rRNA. Reviewer 3 suggested comparisons with other processive alleles, such as the rpb1-E1103G mutant of the RNAPII subunit (Malagon et al., 2006). This comparison has already been addressed by the Schneider lab (Viktorovskaya OV, Cell Rep., 2013 - PMID: 23994471), which explored Pol II (rpb1-E1103G) and Pol I (rpa190-E1224G). The rpa190-E1224G mutant revealed enhanced pausing in vitro, highlighting key differences between Pol I and Pol II catalytic rate-limiting steps (see David Schneider's review on this topic for further details).

              Reviewer 2 and 3 suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Pol I mutant with decreased rRNA cleavage have been characterized previously, and resulted in increased error-rate. We already started to address this point. Preliminary results from *in vitro* experiments suggest that SuperPol mutants exhibit an elevated error rate during transcription. However, these findings remain preliminary and require further experimental validation to confirm their reproducibility and robustness. We propose to consolidate these data and incorporate into the manuscript to address this question comprehensively. This could provide valuable insights into the mechanistic differences between SuperPol and the wild-type enzyme. SuperPol is the first pol I mutant described with an increased processivity *in vitro* and *in vivo*, and we agree that this might be at the cost of a decreased fidelity.
      

      Regulatory aspect of the process:

      To address the reviewer’s remarks, we propose to test our model by performing experiments that would evaluate PTT levels in Pol I mutant’s or under different growth conditions. These experiments would provide crucial data to support our model, which suggests that PTT is a regulatory element of Pol I transcription. By demonstrating how PTT varies with environmental factors, we aim to strengthen the hypothesis that premature termination plays an important role in regulating Pol I activity.

      We propose revising the title and conclusions of the manuscript. The updated version will better reflect the study's focus and temper claims regarding the regulatory aspects of termination events, while maintaining the value of our proposed model.

      __ __

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Some very important modifications have now been incorporated:



      Statistical Analyses and CRAC Replicates:

      Unlike reviewers 2 and 3, reviewer 1 suggests that we did not analyze the results statistically. In fact, the CRAC analyses were conducted in biological triplicate, ensuring robustness and reproducibility. The statistical analyses are presented in Figure 2C, which highlights significant findings supporting the fact WT Pol I and SuperPol distribution profiles are different. We CRAC replicates exhibit a high correlation and we confirmed significant effect in each region of interest (5’ETS, 18S.2, 25S.1 and 3’ ETS, Figure 1) to confirm consistency across experiments. We finally took care not to overinterpret the results, maintaining a rigorous and cautious approach in our analysis to ensure accurate conclusions.

      CRAC vs. Net-seq:

      Reviewer 1 ask to comment differences between CRAC and Net-seq. Both methods complement each other but serve different purposes depending on the biological question on the context of transcription analysis. Net-seq has originally been designed for Pol II analysis. It captures nascent RNAs but does not eliminate mature ribosomal RNAs (rRNAs), leading to high levels of contamination. While this is manageable for Pol II analysis (in silico elimination of reads corresponding to rRNAs), it poses a significant problem for Pol I due to the dominance of rRNAs (60% of total RNAs in yeast), which share sequences with nascent Pol I transcripts. As a result, large Net-seq peaks are observed at mature rRNA extremities (Clarke 2018, Jacobs 2022). This limits the interpretation of the results to the short lived pre-rRNA species. In contrast, CRAC has been specifically adapted by the laboratory of David Tollervey to map Pol I distribution while minimizing contamination from mature rRNAs (The CRAC protocol used exclusively recovers RNAs with 3′ hydroxyl groups that represent endogenous 3′ ends of nascent transcripts, thus removing RNAs with 3’-Phosphate, found in mature rRNAs). This makes CRAC more suitable for studying Pol I transcription, including polymerase pausing and distribution along rDNA, providing quantitative dataset for the entire rDNA gene.

      CRAC vs. Other Methods:

      Reviewer 1 suggests using GRO-seq or TT-seq, but the experiments in Figure 2 aim to assess the distribution profile of Pol I along the rDNA, which requires a method optimized for this specific purpose. While GRO-seq and TT-seq are excellent for measuring RNA synthesis and co-transcriptional processing, they rely on Sarkosyl treatment to permeabilize cellular and nuclear membranes. Sarkosyl is known to artificially induces polymerase pausing and inhibits RNase activities which are involved in the process. To avoid these artifacts, CRAC analysis is a direct and fully in vivo approach. In CRAC experiment, cells are grown exponentially in rich media and arrested via rapid cross-linking, providing precise and artifact-free data on Pol I activity and pausing.

      Pol I ChIP Signal Comparison:

      The ChIP experiments previously published in Darrière et al. lack the statistical depth and resolution offered by our CRAC analyses. The detailed results obtained through CRAC would have been impossible to detect using classical ChIP. The current study provides a more refined and precise understanding of Pol I distribution and dynamics, highlighting the advantages of CRAC over traditional methods in addressing these complex transcriptional processes.

      BMH-21 Effects:

      As highlighted by Reviewer 1, the effects of BMH-21 observed in our study differ slightly from those reported in earlier work (Ref Schneider 2022), likely due to variations in experimental conditions, such as methodologies (CRAC vs. Net-seq), as discussed earlier. We also identified variations in the response to BMH-21 treatment associated with differences in cell growth phases and/or cell density. These factors likely contribute to the observed discrepancies, offering a potential explanation for the variations between our findings and those reported in previous studies. In our approach, we prioritized reproducibility by carefully controlling BMH-21 experimental conditions to mitigate these factors. These variables can significantly influence results, potentially leading to subtle discrepancies. Nevertheless, the overall conclusions regarding BMH-21's effects on WT Pol I are largely consistent across studies, with differences primarily observed at the nucleotide resolution. This is a strength of our CRAC-based analysis, which provides precise insights into Pol I activity.

      We will address these nuances in the revised manuscript to clarify how such differences may impact results and provide context for interpreting our findings in light of previous studies.

      Minor points:

      Reviewer #1:

      • In general, the writing style is not clear, and there are some word mistakes or poor descriptions of the results, for example: On page 14: "SuperPol accumulation is decreased (compared to Pol I)". • *On page 16: "Compared to WT Pol I, the cumulative distribution of SuperPol is indeed shifted on the right of the graph." *

      We clarified and increased the global writing style according to reviewer comment.

      • *There are also issues with the literature, for example: Turowski et al, 2020a and Turowski et al, 2020b are the same article (preprint and peer-reviewed). Is there any reason to include both references? Please, double-check the references. *

      This was corrected in this version of the manuscript.

      • *In the manuscript, 5S rRNA is mentioned as an internal control for TMA normalisation. Why are Figure 1C data normalised to 18S rRNA instead of 5S rRNA? *

      Data are effectively normalized relative to the 5S rRNA, but the value for the 18S rRNA is arbitrarily set to 100%.

      • Figure 4 should be a supplementary figure, and Figure 7D doesn't have a y-axis labelling.

      The presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. In the absence of these subunits (which can vary depending on the purification batch), Pol I pausing, cleavage and elongation are known to be affected. To strengthen our conclusion, we really wanted to show the subunit composition of the purified enzyme. This important control should be shown, but can indeed be shown in a supplementary figure if desired.

      Y-axis is figure 7D is now correctly labelled

      • *In Figure 7C, BMH-21 treatment causes the accumulation of ~140bp rRNA transcripts only in SuperPol-expressing cells that are Rrp6-sensitive (line 6 vs line 8), suggesting that BHM-21 treatment does affect SuperPol. Could the author comment on the interpretation of this result? *

      The 140 nt product is a degradation fragment resulting from trimming, which explains its lower accumulation in the absence of Rrp6. BMH21 significantly affects WT Pol I transcription but has also a mild effect on SuperPol transcription. As a result, the 140 nt product accumulates under these conditions.

      Reviewer #2:

      • *pp. 14-15: The authors note local differences in peak detection in the 5'-ETS among replicates, preventing a nucleotide-resolution analysis of pausing sites. Still, they report consistent global differences between wild-type and SuperPol CRAC signals in the 5'ETS (and other regions of the rDNA). These global differences are clear in the quantification shown in Figures 2B-C. A simpler statement might be less confusing, avoiding references to a "first and second set of replicates" *

      According to reviewer, statement has been simplified in this version of the manuscript.


      • *Figures 2A and 2C: Based on these data and quantification, it appears that SuperPol signals in the body and 3' end of the rDNA unit are higher than those in the wild type. This finding supports the conclusion that reduced pausing (and termination) in the 5'ETS leads to an increased Pol I signal downstream. Since the average increase in the SuperPol signal is distributed over a larger region, this might also explain why even a relatively modest decrease in 5'ETS pausing results in higher rRNA production. This point merits discussion by the authors. *

      We agree that this is a very important discussion of our results. Transcription is a very dynamic process in which paused polymerase is easily detected using the CRAC assay. Elongated polymerases are distributed over a much larger gene body, and even a small amount of polymerase detected in the gene body can represent a very large rRNA synthesis. This point is of paramount importance and, as suggested by the reviewer, is now discussed in detail.


      • *A decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Have the authors observed any evidence supporting this possibility? *

      Reviewer suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. We already started to address this point. Preliminary results from in vitro experiments suggest that SuperPol mutants exhibit an elevated error rate during transcription. However, these findings remain preliminary and require further experimental validation to confirm their reproducibility and robustness. We propose to consolidate these data and incorporate into the manuscript to address this question comprehensively.


      • *pp. 15 and 22: Premature transcription termination as a regulator of gene expression is well-documented in yeast, with significant contributions from the Corden, Brow, Libri, and Tollervey labs. These studies should be referenced along with relevant bacterial and mammalian research. *

      According to reviewer suggestion, we referenced these studies.


      • *p. 23: "SuperPol and Rpa190-KR have a synergistic effect on BMH-21 resistance." A citation should be added for this statement. *

      This represents some unpublished data from our lab. KR and SuperPol are the only two known mutants resistant to BMH-21. We observed that resistance between both alleles is synergistic, with a much higher resistance to BMH-21 in the double mutant than in each single mutant (data not shown). Comparing their resistance mechanisms is a very important point that we could provide upon request. This was added to the statement.


      • *p. 23: "The released of the premature transcript" - this phrase contains a typo *

      This is now corrected.


      Reviewer #3:

      • *Figure 1B: it would be opportune to separate the technique's schematic representation from the actual data. Concerning the data, would the authors consider adding an experiment with rrp6D cells? Some RNAs could be degraded even in such short period of time, as even stated by the authors, so maybe an exosome depleted background could provide a more complete picture. Could also the authors explain why the increase is only observed at the level of 18S and 25S? To further prove the robustness of the Pol I TMA method could be good to add already characterized mutations or other drugs to show that the technique can readily detect also well-known and expected changes. *

      The precise objective of this experiment is to avoid the use of the Rrp6 mutant. Under these conditions, we prevent the accumulation of transcripts that would result from a maturation defect. While it is possible to conduct the experiment with the Rrp6 mutant, it would be impossible to draw reliable conclusions due to this artificial accumulation of transcripts.


      • *Figure 1C: the NTS1 probe signal is missing (it is referenced in Figure 1A but not listed in the Methods section or the oligo table). If this probe was unused, please correct Figure 1A accordingly. *

      __We corrected Figure 1A. __


      • *Figure 2A: the RNAPI occupancy map by CRAC is hard to interpret. The red color (SuperPol) is stacked on top of the blue line, and we are not able to observe the signal of the WT for most of the position along the rDNA unit. It would be preferable to use some kind of opacity that allows to visualize both curves. Moreover, the analysis of the behavior of the polymerase is always restricted to the 5'ETS region in the rest of the manuscript. We are thus not able to observe whether termination events also occur in other regions of the rDNA unit. A Northern blot analysis displaying higher sizes would provide a more complete picture. *

      We addressed this point to make the figure more visually informative. In Northern Blot analysis, we use a TSS (Transcription Start Site) probe, which detects only transcripts containing the 5' extremity. Due to co-transcriptional processing, most of the rRNA undergoing transcription lacks its 5' extremity and is not detectable using this technique. We have the data, but it does not show any difference between Pol I and SuperPol. This information could be included in the supplementary data if asked.


      • *"Importantly, despite some local variations, we could reproducibly observe an increased occupancy of WT Pol I in 5'-ETS compared to SuperPol (Figure 1C)." should be Figure 2C. *

      Thanks for pointing out this mistake. it has been corrected.


      • *Figure 3D: most of the difference in the cumulative proportion of CRAC reads is observed in the region ~750 to 3000. In line with my previous point, I think it would be worth exploring also termination events beyond the 5'-ETS region. *

      We agree that such an analysis would have been interesting. However, with the exception of the pre-rRNA starting at the transcription start site (TSS) studied here, any cleaved rRNA at its 5' end could result from premature termination and/or abnormal processing events. Exploring the production of other abnormal rRNAs produced by premature termination is a project in itself, beyond this initial work aimed at demonstrating the existence of premature termination events in ribosomal RNA production.


      • *Figure 4: should probably be provided as supplementary material. *

      As lmentioned earlier (see comments), ____the presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. This important control should be shown, but can indeed be shown in a supplementary figure if desired.


      • *"While the growth of cells expressing SuperPol appeared unaffected, the fitness of WT cells was severely reduced under the same conditions." I think the growth of cells expressing SuperPol is slightly affected. *

      We agree with this comment and we modified the text accordingly.


      • *Figure 7D: the legend of the y-axis is missing as well as the title of the plot. *

      Legend of the y-axis and title of the plot are now present.


      • The statements concerning BMH-21, SuperPol and Rpa190-KR in the Discussion section should be removed, or data should be provided.

      This was discussed previously. See comment above.


      • *Some references are missing from the Bibliography, for example Merkl et al., 2020; Pilsl et al., 2016a, 2016b. *

      Bibliography is now fixed

      __ __

      4. Description of analyses that authors prefer not to carry out

      Does SuperPol mutant produces more functional rRNAs ?

      As Reviewer 1 requested, we agree that this point requires clarification. In cells expressing SuperPol, a higher steady state of (pre)-rRNAs is only observed in absence of degradation machinery suggesting that overproduced rRNAs are rapidly eliminated. We know that (pre)-rRNas are unable to accumulate in absence of ribosomal proteins and/or Assembly Factors (AF). In consequence, overproducing rRNAs would not be sufficient to increase ribosome content. This specific point is further address in our lab but is beyond the scope of this article.

      __Is premature termination coupled with rRNA processing __

      We appreciate the reviewer’s insightful comments. The suggested experiments regarding the UTP-A complex's regulatory potential are valuable and ongoing in our lab, but they extend beyond the scope of this study and are not suitable for inclusion in the current manuscript.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      In the manuscript "Ribosomal RNA synthesis by RNA polymerase I is regulated by premature termination of transcription", Azouzi and co-authors investigate the regulatory mechanisms of ribosomal RNA (rRNA) transcription by RNA Polymerase I (RNAPI) in the budding yeast S. cerevisiae. They follow up on exploring the molecular basis of a mutant allele of the second largest subunit of RNAPI, RPA135-F301S, also dubbed SuperPol, that they had previously reported (Darrière et al, 2019), and which was shown to rescue Rpa49-linked growth defects, possibly by increasing rRNA production.

      Through a combination of genomic and in vitro approaches, the authors test the hypothesis that RNAPI activity could be subjected to a Premature Transcription Termination (PPT) mechanism, akin to what is observed for RNA Polymerase II (RNAPII), and which is suggested to be an important step for the quality control of rRNA transcripts. SuperPol is proposed to lack such a regulatory mechanism, due to an increased processivity. In agreement, SuperPol is shown to be resistant to BMH-21, a drug previously shown to impair RNAPI elongation.

      Overall, the experiments are performed with rigor and include the appropriate controls and statistical analysis. Both the figures and the text present the data clearly. The Material and Methods section is detailed enough. The reported results are interesting; however, I am not fully convinced of the existence of PPT of RNAPI, and even less of its utmost importance. The existence of PPT of RNAPI would entail an intended regulatory mechanism. The authors propose that PPT could serve as quality control step for the UTP-A complex loading on the rRNA 5'-end. While this hypothesis is enticing and cautiously phrased by the authors, the lack of evidence showing a specific regulatory function (such as UTP-A loading checkpoint or else) limits these termination events to possibly abortive actions of unclear significance. The auhors may want to consider comparisons to other processive alleles, such as the rpb1-E1103G mutant of the RNAPII subunit (Malagon et al, 2006) or the G1136S allele of E. coli RNAP (Bar-Nahum et al., 2005). While clearly mechanistically distinct, these mutations result in similarly processive enzymes that achieve more robust transcription, possibly at the cost of decreased fidelity. Indeed, an alternative possibility explaining these transcripts could be that they originate from unsuccessful resumption of transcription after misincorporation (see below).

      I suggest reconsidering the study's main conclusions by limiting claims about the regulatory function of these termination events (the title of the manuscript should be changed accordingly). Alternatively, the authors should provide additional investigation on their regulatory potential, for example by assessing if indeed this quality control is linked to the correct assembly of the UTP-A complex. The expectation would be that SuperPol should rescue at least to some extent the defects observed in the absence of UTP-A components. Moreover, the results using the clv3 substrate suggest the possibility that SuperPol might simply be more able to tolerate mismatches, thus be more processive in transcribing, because not subjected to proof-reading mechanisms, similarly to what observed in Schwank et al., 2022. This could explain many of the observations, and I think it is worth exploring by assessing the fidelity of the enzyme, especially in the frame of suggesting a regulatory function for these termination events.

      Minor comments

      1. Figure 1B: it would be opportune to separate the technique's schematic representation from the actual data. Concerning the data, would the authors consider adding an experiment with rrp6D cells? Some RNAs could be degraded even in such short period of time, as even stated by the authors, so maybe an exosome depleted background could provide a more complete picture. Could also the authors explain why the increase is only observed at the level of 18S and 25S? To further prove the robustness of the Pol I TMA method could be good to add already characterized mutations or other drugs to show that the technique can readily detect also well-known and expected changes.
      2. Figure 1C: the NTS1 probe signal is missing (it is referenced in Figure 1A but not listed in the Methods section or the oligo table). If this probe was unused, please correct Figure 1A accordingly.
      3. Figure 2A: the RNAPI occupancy map by CRAC is hard to interpret. The red color (SuperPol) is stacked on top of the blue line, and we are not able to observe the signal of the WT for most of the position along the rDNA unit. It would be preferable to use some kind of opacity that allows to visualize both curves. Moreover, the analysis of the behavior of the polymerase is always restricted to the 5'ETS region in the rest of the manuscript. We are thus not able to observe whether termination events also occur in other regions of the rDNA unit. A Northern blot analysis displaying higher sizes would provide a more complete picture.
      4. "Importantly, despite some local variations, we could reproducibly observe an increased occupancy of WT Pol I in 5'-ETS compared to SuperPol (Figure 1C)." should be Figure 2C.
      5. Figure 3D: most of the difference in the cumulative proportion of CRAC reads is observed in the region ~750 to 3000. In line with my previous point, I think it would be worth exploring also termination events beyond the 5'-ETS region.
      6. Figure 4: should probably be provided as supplementary material.
      7. "While the growth of cells expressing SuperPol appeared unaffected, the fitness of WT cells was severely reduced under the same conditions." I think the growth of cells expressing SuperPol is slightly affected.
      8. Figure 6B: can the authors explain why most of bands detected in their Pol I TMA assay in Figure 6B are unchanged? It is unclear to me why only the 18S and 25S bands are decreased following BMH-21 treatment. Moreover, this experiment lacks the corresponding quantification and statistical tests.
      9. Figure 7D: the legend of the y-axis is missing as well as the title of the plot.
      10. The statements concerning BMH-21, SuperPol and Rpa190-KR in the Discussion section should be removed, or data should be provided.
      11. Some references are missing from the Bibliography, for example Merkl et al., 2020; Pilsl et al., 2016a, 2016b.

      Significance

      Azouzi and co-authors' work builds on their previous study (Darrière et al, 2019) of RPA135-F301S (SuperPol), a mutant allele of the second largest RNAPI subunit, which was shown to compensate for Rpa49 loss, potentially by increasing rRNA production. The work advances the mechanistic understanding of the the SuperPol allele, demonstrating the increased processivity of this enzyme compared to its wild-type counterpart. Such increased processivity "desensitizes" RNAPI from abortive transcription cycles, the existence of which is clearly shown, though the biological significance of this phenomenon remains unclear. The lack of evidence for a regulatory mechanism behind these early termination events is, in my opinion, a limitation of this study, as it does not allow for differentiation between an intended regulatory process and a byproduct of an imperfect system.

      This work is of interest for researchers studying transcription regulation, particularly those interested in understanding RNAPI's role and fidelity. Demonstrating PPT as a regulatory quality control for RNAPI could point to common strategies in between RNAPI and RNAPII regulation, where premature termination has been extensively documented. However, without evidence of a specific regulatory function, these findings may currently be limited to descriptive insights.

      My expertise lies is RNAPII transcription, transcription termination, and genomic approaches to studying transcription.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This manuscript describes the impact of modulating signaling by a key regulatory enzyme, Dual Leucine Zipper Kinase (DLK), on hippocampal neurons. The results are interesting and will be important for scientists interested in synapse formation, axon specification, and cell death. The methods and interpretation of the data are solid, but the study can be further strengthened with some additional studies and controls.

      We greatly appreciate the thorough review and thoughtful suggestions from the reviewers and editors on our original manuscript. We provide point-to-point response below.  We added new studies on P10 mice and controls as suggested, and made revision of figures and texts for clarification. The revised manuscript includes three new supplemental figures; major text revision is copied under response.

      Reviewer #1 (Public Review):

      Summary:

      In this work, Ritchie and colleagues explore functional consequences of neuronal over-expression or deletion of the MAP3K DLK that their labs and others have strongly implicated in both axon degeneration, neuronal cell death, and axon regeneration. Their recent work in eLife (Li, 2021) showed that inducible over-expression of DLK (or the related LZK) induces neuronal death in the cerebellum. Here, they extend this work to show that inducible over-expression in Vglut1+ neurons also kills excitatory neurons in hippocampal CA1, but not CA3. They complement this very interesting finding with translatomics to quantify genes whose mRNAs are differentially translated in the context of DLK over-expression or knockout, the latter manipulation having little to no effect on the phenotypes measured. The authors note that several genes and pathways are differentially regulated according to whether DLK is over-expressed or knocked out. They note DLK-dependent changes in genes related to synaptic function and the cytoskeleton and ultimately relate this in cultured neurons to findings that DLK over-expression negatively impacts synapse number and changes microtubules and neurites, though with a less obvious correlation.

      Strengths:

      This work represents a conceptual advance in defining DLK-dependent changes in translation. Moreover, the finding that DLK may differentially impact neuronal death will become the basis for future studies exploring whether DLK contributes to differential neuronal susceptibility to death, which is a broadly important topic.

      We thank the reviewer for the comments on the value of our work.

      Weaknesses:

      This seems like two works in parallel that the authors have not yet connected. First is that DLK affects the translation of an interesting set of genes, and second, that DLK(OE) kills some neurons, disrupts their synapses, and affects neurite growth in culture.

      Specific questions:

      (1) Is DLK effectively knocked out? The authors reference the floxxed allele in their 2016 work (PMID: 27511108), however, the methods of this paper say that the mouse will be characterized in a future publication. Has this ever been published? The major concern is that here the authors show that Cre-mediated deletion results in a smaller molecular weight protein and the maintenance of mRNA levels.

      We apologize for out-of-date citation of the DLK(cKO)<sup>fl/fl</sup> mice.  The DLK(cKO)<sup>fl/fl</sup> mice have been published in (Li et al., 2021; Saikia et al., 2022); excision of the flox-ed exon was verified using several Cre drivers (Pv-Cre, AAV-Cre, and VGlut1-Cre in this study).  The flox-ed exon contains the initiation ATG and 148 amino acids.  By western blot analysis using antibodies against C-terminal peptides of DLK on cerebellar extracts (in Li et al., 2021) and hippocampal extracts (this study), the full-length DLK protein was significantly reduced (Fig 1A-B); DLK is expressed in other hippocampal cells, in addition to glutamatergic neurons, explaining remaining full-length DLK detected. 

      Our Ribo-seq of VGlut1-Cre; DLK(cKO)<sup>fl/fl</sup> detected remaining Dlk mRNAs lacking the floxed exon (Fig.S1C), which has several candidate ATG at amino acid 223 and after (Fig.S1C1). We detected a very faint band for smaller molecular weight proteins on western blots, only when the membrane was exposed under 5X longer exposure using Pico PLUS Chemiluminescent Substrate (Thermo Scientific, 34580) and a Licor Odyssey XF Imager (revised Fig. S1B). This smaller molecular weight protein might be produced using any candidate ATGs, but would represent an N-terminal truncated DLK protein lacking the ATP binding site and ~1/4 of the kinase domain, i.e. not a functional kinase. 

      The revised manuscript has updated citation for DLK(cKO)<sup>fl/fl</sup>. Revised Fig.S1B includes images of a western blot under normal exposure vs longer exposure of western blots using anti-DLK antibodies. New Fig.S1C1 shows effects of floxed exon on DLK.

      (2) Why does DLK(OE) not kill CA3 neurons? The phenomenon is clear but there is no link to gene expression changes. In fact, the highlighted transcript in this work, Stmn4, changes in a DLK-dependent manner in CA3.

      We agree that this is a very interesting question not answered by our gene expression analysis.  While we verified Stmn4 expression levels to correlate to the levels of DLK, we do not think that increased Stmn4 per se in DLK(iOE) is a major factor accounting for CA1 death vs CA3 survival. Several published studies have also reported regulation of Stmn4 mRNAs in other cell types, in the contexts of cell death (Watkins et al., 2013; Le Pichon et al., 2017) and axon regeneration and cytoskeleton disruption (Asghari Adib et al., 2024; DeVault et al., 2024; Hu et al., 2019;  Shin et al., 2019). As Stmns have significant expression and function redundancy, conventional knockdown or overexpression of individual Stmn generally does not lead to detectable effects on cellular function. As CA3 neurons are widely known for their dense connections and show resilience to NMDA-mediated neurotoxicity (Sammons et al., 2024; Vornov et al., 1991), we speculate that the differential vulnerability of CA1 and CA3 under DLK(iOE) is a reflection of both the intrinsic property, such as gene expression, and also their circuit connection. 

      In the revised manuscript, we have included following statement on pg 18:

      ‘While our data does not pinpoint the molecular changes explaining why CA3 would show less vulnerability to increased DLK, we may speculate that DLK(iOE) induced signal transduction amplification may differ in CA1 vs CA3. CA1 genes appear to be more strongly regulated than CA3 genes, consistent with our observation that increased c-Jun expression in CA1 is greater than that in CA3. Other parallel molecular factors may also contribute to resilience of CA3 neurons to DLK(iOE), such as HSP70 chaperones, different JNK isoforms, and phosphatases, some of which showed differential expression in our RiboTag analysis of DLK(iOE) vs WT (shown in File S2. WT vs DLK(iOE) DEGs). Together with other genes that show dependency on DLK, the DLK and Jun regulatory network contributes to the regional differences in hippocampal neuronal vulnerability under pathological conditions.’

      Further we state in ‘Limitation of our study’ on pg 20:

      ‘Our analysis also does not directly address why CA3 neurons are less vulnerable to increased DLK expression. Future studies using cell-type specific RiboTag profiling and other methods at a refined time window will be required to address how DLK dependent signaling interacts with other networks underlying hippocampal regional neuron vulnerability to pathological insults.’

      We hope our data will stimulate continued interests for testable hypothesis in future studies.

      (3) Why are whole hippocampi analyzed to IP ribosome-associated mRNAs? The authors nicely show a differential effect of DLK on CA1 vs CA3, but then - at least according to their methods ¬- lyse whole hippocampi to perform IP/sequencing. Their data are therefore a mix of cells where DLK does and does not change cell death. The key issue is whether DLK does/does not have an effect based on the expression changes it drives.

      At the time of planning the Ribo-Tag experiment several years ago, we focused on the hippocampal glutamatergic neurons. Due to technical difficulty in micro-dissecting individual hippocampal regions from this early timepoint, we opted to use whole hippocampi to isolate ribosome-associated mRNAs. We agree with the reviewer that it is important to sort out DLK-dependent general gene expression changes vs those specific to a particular cell type where DLK impacts its survival. With emerging CA1, CA3 and other cell-type specific Cre drivers and advanced RNAseq technology, we hope that our work will stimulate broad interest in these questions in future studies. 

      In the revised manuscript, we have included new analysis comparing our Vglut1-RiboTag profiling (P15) with CamK2-RiboTag (for CA1) and Grik4-RiboTag (for CA3) (P42) published in Traunmüller et al., 2023 (GSE209870). We find that >80% of the top ranked genes in their CamK2-RiboTag (for CA1) and Girk4-RiboTag (for CA3) were detected in our VGlut1-RiboTag (revised methods and Supplemental Excel File S3). CA1-enriched genes tended to be expressed higher in DLK(cKO), compared to control, whereas CA3-enriched genes showed less significant correlation to DLK expression levels. Additionally, many genes known to specify CA1 fate do not show significant downregulation in DLK(iOE). This analysis, along with other data in our manuscript, is consistent with an idea that DLK does not regulate neuronal fate.

      In the revised manuscript, we presented this additional analysis in Fig. S6K-L, and expanded text description on page 9:

      ‘Additionally, we compared our Vglut1-RiboTag datasets with CamK2-RiboTag and Grik4-RiboTag datasets from 6-week-old wild type mice reported by (Traunmüller et al., 2023; GSE209870). We defined a list of genes enriched in CamK2-expressing CA1 neurons relative to Grik4-expressing CA3 neurons (CA1 genes), and those enriched in Grik4-expressing CA3 neurons (CA3 genes) (File S3). When compared with the entire list of Vglut1-RiboTag profiling in our control and DLK(cKO), we found CA1 genes tended to be expressed more in DLK(cKO) mice, compared to control (Fig.S6K), while CA3 genes showed a slight enrichment in control though the trend was less significant, and were less clustered towards one genotype (Fig.S6L). Moreover, many CA1 genes related to cell-type specification, such as FoxP1, Satb2, Wfs1, Gpr161, Adcy8, Ndst3, Chrna5, Ldb2, Ptpru, and Ntm, did not show significant downregulation when DLK was overexpressed. These observations imply that DLK likely specifically down-regulates CA1 genes both under normal conditions and when overexpressed, with a stronger effect on CA1 genes, compared to CA3 genes. Overall, the informatic analysis suggests that decreased expression of CA1 enriched genes may contribute to CA1 neuron vulnerability to elevated DLK, although it is also possible that the observed down-regulation of these genes is a secondary effect associated with CA1 neuron degeneration’.

      (4) Is the subtle decrease in synapse number (Basson/Homer co-loc.) in the DLK (OE) simply a function of neurons (and their synapses, presumably) having died? At the P15 time point that the authors choose because cell death is minimal, there is still a ~25% reduction in CA1 thickness (Figure 2B), which is larger than the ~15% change in synapses (Figure 5H) they describe.

      We thank reviewer for the question. To address this, we have analyzed synapses in the CA1 region at P10 in DLK(iOE) mice when there was no detectable loss of neurons. At P10, we did not detect significant changes in Bassoon, Homer1, or colocalized puncta in CA1 (Fig.S11A-F). In P15 DLK(iOE) mice, Homer1 puncta were slightly smaller (Fig.5L) and showed a significant decrease in CA1 SR (Fig.5I).

      In the revised manuscript we have also redone our statistical analysis of synapses, using mice rather than ROIs (revised Fig. 5), as recommended by R3. We also analyzed synapses in CA3, and found no significant differences in P10 or P15 (Fig.S12).  We would interpret the data to mean that the effects of DLK(OE) on synapses in CA1 may represent an early step in neuronal death. We hope that future studies will shed clarity on this question.

      Reviewer #2 (Public Review):

      This manuscript describes the impact of deleting or enhancing the expression of the neuronal-specific kinase DLK in glutamatergic hippocampal neurons using clever genetic strategies, which demonstrates that DLK deletion had minimal effects while overexpression resulted in neurodegeneration in vivo. To determine the molecular mechanisms underlying this effect, ribotag mice were used to determine changes in active translation which identified Jun and STMN4 as DLK-dependent genes that may contribute to this effect. Finally, experiments in cultured neurons were conducted to better understand the in vivo effects. These experiments demonstrated that DLK overexpression resulted in morphological and synaptic abnormalities.

      Strengths:

      This study provides interesting new insights into the role of DLK in the normal function of hippocampal neurons. Specifically, the study identifies:

      (1) CA1 vs CA3 hippocampal neurons have differing sensitivity to increased DLK signaling.

      (2) DLK-dependent signaling in these neurons is similar to but distinct from the downstream factors identified in other cell types, highlighted by the identification of STMN4 as a downstream signal.

      (3) DLK overexpression in hippocampal neurons results in signaling that is similar to that induced by neuronal injury.

      The study also provides confirmatory evidence that supports previously published work through orthogonal methods, which adds additional confidence to our understanding of DLK signaling in neurons. Taken together, this is a useful addition to our understanding of DLK function.

      We thank the reviewer for careful reading and positive comments.

      Weaknesses:

      There are a few weaknesses that limit the impact of this manuscript, most of which are pointed out by the authors in the discussion. Namely:

      (1) It is difficult to distinguish whether the changes in the translatome identified by the authors are DLK-dependent transcriptional changes, DLK-dependent post-transcriptional changes or secondary gene expression changes that occur as a result of the neurodegeneration that occurs in vivo. Additional expression analysis at earlier time points could be one method to address this concern.

      We appreciate the reviewer’s comment, and have performed new analysis on c-Jun and p-c-Jun levels in CA1, CA3, and DG in P10 DLK(OE) mice. Our data suggest that in CA3 elevations in p-c-Jun and c-Jun occur separately from cell death in a DLK-dependent manner, though the high elevation of both p-c-Jun and c-Jun in CA1 correlates with cell death.

      The data is presented in revised Fig.S7A,B, and described in revised text on pg 9-10:

      ‘In control mice, glutamatergic neurons in CA1 had low but detectable c-Jun immunostaining at P10 and P15, but reduced intensity at P60; those in CA3 showed an overall low level of c-Jun immunostaining at P10, P15 and P60; and those in DG showed a low level of c-Jun immunostaining at P10 and P15, and an increased intensity at P60 (Fig.S7A,C,E). In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice at P10 when no discernable neuron degeneration was seen in any regions of hippocampus, only CA3 neurons showed a significant increase of immunostaining intensity of c-Jun, compared to control (Fig.S7A). In P15 mice, we observed further increased immunostaining intensity of c-Jun in CA1, CA3, and DG, with the strongest increase (~4-fold) in CA1, compared to age-matched control mice (Fig.S7C). The overall increased c-Jun staining is consistent with RiboTag analysis.’

      Also, on pg.10:

      In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice, we observed increased p-c-Jun positive nuclei in CA1 at P10, and strong increase in CA1 (~10-fold), CA3 (~6-fold), and DG (~8-fold) at P15 (Fig.S7B,D).

      (2) Related to the above, it is difficult to conclusively determine from the current data whether the changes in synaptic proteins observed in vivo are a secondary result of neuronal degeneration or a primary impact on synapse formation. The in vitro studies suggest this has the potential to be a primary effect, though the difference in experimental paradigm makes it impossible to determine whether the same mechanisms are present in vitro and in vivo.

      We appreciate the comment, which is related to R1 point 4. We have performed further analysis and revised the text on pg.12 with the following text:

      ‘To assess effects of DLK overexpression on synapses, we immunostained hippocampal sections from both P10 and P15, with age-matched littermate controls. Quantification of Bassoon and Homer1 immunostaining revealed no significant differences in CA1 SR and CA3 SR and SL in P10 mice of _<_i>Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> and control (Fig.S11A-F, S12A-J). In P15, Bassoon density and size in CA1 SR were comparable in both mice (Fig 5G, H, K), while Homer1 density and size were reduced in DLK(iOE) (Fig.5G,I, L). Overall synapse number in CA1 SR was similar in DLK(iOE) and control mice (Fig.5J). Similar analysis on CA3 SR and SL detected no significant difference from control (Fig.S12M-V).’

      We would interpret the data to mean that the effects of DLK(OE) on synapses in CA1 may represent an early step in neuronal death. We hope that future studies will shed clarity on this question.

      Additionally, to address whether the same mechanisms are present in vitro, we have performed further analysis on cultured hippocampal neurons. As described in the Methods, we made hippocampal neuron cultures from P1 pups of the following crosses:

      For control: Vglut1<sup>Cre/+</sup> X Rosa26<sup>tdT/+</sup> 

      For DLKcKO: Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>  X Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>;Rosa26<sup>tdT/+</sup> 

      For DLKiOE: H11-DLK<sup>iOE/iOE</sup> X Vglut1<sup>Cre/+</sup>;Rosa26<sup>tdT/+</sup> 

      Dissociated cells from a given litter were pooled into the same culture. Because there were different proportions of neurons with our genotype of interest in each culture, it is not simple to know whether DLK was causing significant cell death.

      On pg 13, we stated our observation:

      ‘We did not notice an obvious effect of DLK(iOE) or DLK(cKO) on neuron density in cultures at DIV2. To assess neuronal type distribution in our cultures, we immunostained DIV14 neurons with antibodies for Satb2, as a CA1 marker (Nielsen et al., 2010), and Prox1, as a marker of DG neurons (Iwano et al., 2012). We did not observe significant differences in the proportion of cells labeled with each marker in DLK(cKO) or DLK(iOE) cultures (Fig.S13E). These data are consistent with the idea that DLK signaling does not have a strong role in neuron-type specification both in vivo and in vitro’.

      (3) The phenotype of DLK cKO mice is very subtle (consistent with previous reports) and while the outcome of increased DLK levels is interesting, the relevance to physiological DLK signaling is less clear. What does seem possible is that increased DLK may phenocopy other neuronal injuries but there are no real comparisons to directly address this in the manuscript. It would be helpful for the authors to provide this analysis as well as a table with all of the translational changes along with fold changes.

      Thank you for the suggestion. The fold changes of genes showing significantly altered expression in DLK(cKO) and DLK(iOE) are provided in the excel files (Supplementary excel File S1 WT vs DLK(cKO) DEGs and File S2. WT vs DLK(iOE) DEGs, highlighted columns B and F).  

      On pg 6, we revised the text as following to include comparison of DLK levels in other physiological conditions and our mice:

      ‘Several studies have reported that DLK protein levels increase under a variety of conditions, including optic nerve crush (Watkins et al., 2013), NGF withdrawal (~2 fold) (Huntwork-Rodriguez et al., 2013; Larhammar et al., 2017), and sciatic nerve injury (Larhammar et al., 2017). Induced human neurons show increased DLK abundance about ~4 fold in response to ApoE4 treatment (Huang et al., 2019). Increased expression of DLK can lead to its activation through dimerization and autophosphorylation (Nihalani et al., 2000)’.

      And,

      ‘Additional analysis at the mRNA level (supplemental excel, File S2. WT vs DLK(iOE) DEGs) and at the protein level (Fig.S8E) suggest that the increase in DLK abundance was around 3 times the control level. The localization patterns of DLK protein appeared to vary depending on region of hippocampus and age of animals in both control and Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice (Fig.S3C).’

      In Discussion, we state (pg. 16): ‘The levels of DLK in our DLK(iOE) mice model appear comparable to those reported under traumatic injury and chronic stress.’

      (4) For the in vivo experiments, it is unclear whether multiple sections from each animal were quantified for each condition. More information here would be helpful and it is important that any quantification takes multiple sections from each animal into account to account for natural variability.

      We apologize this was unclear in the original manuscript.

      In the revised methods, under Confocal imaging and quantification (pg 33), we stated: “For brain tissue, three sections per mouse were imaged with a minimum of three mice per genotype for data analysis.”

      In revised figure legends, we made it clear that multiple sections from each animal have been used for quantification in all instances, i.e. “Each dot represents averaged thickness from 3 sections per mouse, N≥4 mice/genotype per timepoint.” 

      In Fig.1F-H: “Each dot represents averaged intensity from 3 sections per mouse”

      In Fig.S3B “Data points represent individual mice, averages taken across 3 sections per mouse”

      Reviewer #3 (Public Review):

      Dr Jin and colleagues revisit DLK and its established multifactorial roles in neuronal development, axonal injury, and neurodegeneration. The ambitious aim here is to understand the DLK-dependent gene network in the brain and, to pursue this, they explore the role of DLK in hippocampal glutamatergic neurons using conditional knockout and induced overexpression mice. They produce evidence that dorsal CA1 and dentate gyrus neurons are vulnerable to elevated expression of DLK, while CA3 neurons appear unaffected. Then they identify the DLK-dependent translatome featured by conserved molecular signatures and cell-type specificity. Their evidence suggests that increased DLK signaling is associated with possible STMN4 disruptions to microtubules, among else. They also produce evidence on cultured hippocampal neurons showing that expression levels of DLK are associated with changes in neurite outgrowth, axon specification, and synapse formation. They posit that downstream translational events related to DLK signaling in hippocampal glutamatergic neurons are a generalizable paradigm for understanding neurodegenerative diseases.

      Strengths

      This is an interesting paper based on a lot of work and a high number of diverse experiments that point to the pervasive roles of DLK in the development of select glutamatergic hippocampal neurons. One should applaud the authors for their work in constructing sophisticated molecular cre-lox tools and their expert Ribotag analysis, as well as technical skill and scholarly treatment of the literature. I am somewhat more skeptical of interpretations and conclusions on spatial anatomical selectivity without stereological approaches and also going directly from (extremely complex) Ribotag profiling patterns to relevance based on immunohistochemistry and no additional interventions to manipulate (e.g. by knocking down or blocking) their top Ribotag profile hits. Also, it seems to this reviewer that major developmental claims in the paper are based on gene translational profiling dependent on DLK expression, not DLK activation, despite some evidence in the paper that there is a correlation between the two. Therefore, observed patterns and correlations may or may not be physiologically or pathologically relevant. Generalizability to neurodegenerative diseases is an overreach not justified by the scope, approach, and findings of the paper.

      We thank the reviewer for the encouraging and constructive comments on the manuscript.

      Weaknesses and Suggestions:

      The authors state that the rationale for the translatomic studies is to "to gain molecular understanding of gene expression associated with DLK in glutamatergic neurons" and to characterize the "DLK-dependent molecular and cellular network", However, a problem with the experimental design is the selection of an anatomical region at a time point featured by active neurodegeneration. Therefore, it is not straightforward that the differentially expressed genes or pathways caused by DLK overexpression changes could be due to processes related to neurodegeneration. Indeed, the authors find enrichment of signals related to pathways involved in extracellular matrix organization, apoptosis, unfolded protein responses, the complement cascade, DNA damage responses, and depletion of signals related to mitochondrial electron transport, etc., all of which could be the consequence of neurodegeneration regardless of cause. A more appropriate design to discover DLK-dependent pathways might be to look at a region and/or a time point that is not confounded by neurodegeneration.

      We appreciate reviewer’s comment. We included our thoughts in ‘Limitation of the study’ (pg 20):

      ‘Future studies using cell-type specific RiboTag profiling and other methods at a refined time window will be required to address how DLK dependent signaling interacts with other networks underlying hippocampal regional neuron vulnerability to pathological insults.’

      In a related vein, the authors ask "if the differentially expressed genes associated with DLK(iOE) might show correlation to neuronal vulnerability" and, to answer this question, they select the set of differentially expressed genes after DLK overexpression and assess their expression patterns in various regions under normal conditions. It looks to me that this selection is already confounded by neurodegeneration which could be the cause for their downregulation. Therefore, such gene profiles may not be directly linked to neuronal vulnerability. A similar issue also relates to the conclusion that "...the enrichment of DLK-dependent translation of genes in CA1 suggests that the decreased expression of these genes may contribute to CA1 neuron vulnerability to elevated DLK".

      We agree with the reviewer’s concern that it is difficult to separate neurodegenerative consequences from changes caused by DLK solely based on our translatomics studies on P15 DLK(iOE) mice.  As responded to reviewer 1 (point 4) and reviewer 2 (point 1), we have included new analysis of P10 mice (Fig.S7A,B) when neurons did not show detectable sign of degeneration.

      We consider several lines of evidence supporting that some differentially expressed genes in DLK(iOE) vs control may likely be specific for increased DLK signaling.

      First, the genes identified in DLK(iOE) vs control represent a small set of genes (260), which is comparable to other DLK dependent datasets (Asghari Adib et al., 2024) but shows cell-type specificity.

      Second, our analysis using rank-rank hypergeometric overlap (RRHO) detects a significant correlation between upregulated genes from DLK(iOE) vs downregulated genes in DLK(cKO), and vice versa, suggesting that expression of a similar set of genes is depended on DLK (Fig.3C, S6C-E). Consistently, GO term analysis using the list of genes coordinately regulated by DLK, derived from our RRHO analysis, leads to identification of similar GO terms related to up- and downregulated genes as using DLK(iOE)-RiboTag data alone. SynGO analysis of DLK(iOE) regulated genes and DLK(cKO) regulated genes also identified similar synaptic processes regulated by significantly regulated genes (Fig.3F and S6J).  

      Third, we performed additional analysis comparing our Vglut1-RiboTag dataset with CamK2-RiboTag and Grik4-RiboTag datasets from 6-week-old wild type mice reported by (Traunmüller et al., 2023; GSE209870). We observed >80% overlap among the top ranked genes (revised Methods). We described this analysis on pg 9 and Fig. S6K-L (and Supplemental Excel File S3):

      ‘Additionally, we compared our Vglut1-RiboTag datasets with CamK2-RiboTag and Grik4-RiboTag datasets from 6-week-old wild type mice reported by (Traunmüller et al., 2023; GSE209870). We defined a list of genes enriched in CamK2-expressing CA1 neurons relative to Grik4-expressing CA3 neurons (CA1 genes), and those enriched in Grik4-expressing CA3 neurons (CA3 genes) (File S3). When compared with the entire list of Vglut1-RiboTag profiling in our control and DLK(cKO), we found CA1 genes tended to be expressed more in DLK(cKO) mice, compared to control (Fig.S6K), while CA3 genes showed a slight enrichment in control though the trend was less significant, and were less clustered towards one genotype (Fig.S6L). Moreover, many CA1 genes related to cell-type specification, such as FoxP1, Satb2, Wfs1, Gpr161, Adcy8, Ndst3, Chrna5, Ldb2, Ptpru, and Ntm, did not show significant downregulation when DLK was overexpressed. These observations imply that DLK likely specifically down-regulates CA1 genes both under normal conditions and when overexpressed, with a stronger effect on CA1 genes, compared to CA3 genes. Overall, the informatic analysis suggests that decreased expression of CA1 enriched genes may contribute to CA1 neuron vulnerability to elevated DLK, although it is also possible that the observed down-regulation of these genes is a secondary effect associated with CA1 neuron degeneration.’

      To understand the role and relevance of the DLK overexpression model, there should be a discussion of to what extent it corresponds to endogenous levels of DLK expression or DLK-MAPK pathway activation under baseline or pathological conditions.

      We appreciate the suggestion, which is similar to R2 point 3. We have revised the text and discussion to include how DLK levels may be altered in other physiological conditions vs our mice.

      Pg. 6: ‘Several studies have reported that DLK protein levels increase under a variety of conditions, including optic nerve crush (Watkins et al., 2013), NGF withdrawal (~2 fold) (Huntwork-Rodriguez et al., 2013; Larhammar et al., 2017), and sciatic nerve injury (Larhammar et al., 2017). Induced human neurons show increased DLK abundance about ~4 fold in response to ApoE4 treatment (Huang et al., 2019). Increased expression of DLK can lead to its activation through dimerization and autophosphorylation (Nihalani et al., 2000)’.

      And,

      ‘Additional analysis at the mRNA level (supplemental excel, File S2. WT vs DLK(iOE) DEGs) and at the protein level (Fig.S8E) suggest that the increase in DLK abundance was around 3 times the control level. The localization patterns of DLK protein appeared to vary depending on region of hippocampus and age of animals in both control and Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice (Fig.S3C).’

      In Discussion (pg. 16): ‘The levels of DLK in our DLK(iOE) mice model appear comparable to those reported under traumatic injury and chronic stress.’

      The authors posit that "dorsal CA1 neurons are vulnerable to elevated DLK expression, while neurons in CA3 appear largely resistant to DLK overexpression". This statement assumes that DLK expression levels start at a similar baseline among regions. Do the authors have any such data? Ideally, they should show whether DLK expression and p-c-Jun (as a marker of downstream DLK signaling) are the same or different across regions in both WT and overexpression mice. For example, what are the DLK/p-c-Jun expression levels in regions other than CA1 in Supplementary Figures 2-3 and how do they compare with each other? Normalization to baseline for each region does not allow such a comparison. Also, in Supplementary Figure 6, analyses and comparisons between regions are done at a time point when degeneration has already started. Ideally, these should be done at P10.

      We thank the reviewer for raising these points. In the revised manuscript we have included protein expression analysis of DLK (Fig S3), c-Jun, and p-c-Jun at P10 (Fig. S7).

      We provided a quantification of DLK immunostaining intensity in CA1 and CA3 in Fig.S3D,E and find roughly comparable levels between regions.

      Pg. 6: ‘Additional analysis at the mRNA level (supplemental excel, File S2. WT vs DLK(iOE) DEGs) and at the protein level (Fig.S8E) suggest that the increase in DLK abundance was around 3 times the control level. The localization patterns of DLK protein appeared to vary depending on region of hippocampus and age of animals in both control and Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice (Fig.S3C).’

      We provided our quantifications without normalization to baseline in each region for c-Jun and p-c-Jun, and revised the text accordingly:

      Pg. 9-10: ‘In control mice, glutamatergic neurons in CA1 had low but detectable c-Jun immunostaining at P10 and P15, but reduced intensity at P60; those in CA3 showed an overall low level of c-Jun immunostaining at P10, P15 and P60; and those in DG showed a low level of c-Jun immunostaining at P10 and P15, and an increased intensity at P60 (Fig.S7A,C,E). In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice at P10 when no discernable neuron degeneration was seen in any regions of hippocampus, only CA3 neurons showed a significant increase of immunostaining intensity of c-Jun, compared to control (Fig.S7A). In P15 mice, we observed further increased immunostaining intensity of c-Jun in CA1, CA3, and DG, with the strongest increase (~4-fold) in CA1, compared to age-matched control mice (Fig.S7C). The overall increased c-Jun staining is consistent with RiboTag analysis’.

      Pg. 10: ‘In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice, we observed increased p-c-Jun positive nuclei in CA1 at P10, and strong increase in CA1 (~10-fold), CA3 (~6-fold), and DG (~8-fold) at P15 (Fig.S7B,D).

      Illustration of proposed selective changes in hippocampal sector volume needs to be very carefully prepared in view of the substantial claims on selective vulnerability. In 2A under P15 and especially P60, it is difficult to see the difference - this needs lower magnification and a lot of care that anteroposterior levels are identical because hippocampal sector anatomy and volumes of sectors vary from level to level. One wonders if the cortex shrinks, too. This is important.

      Thank you for raising the point. We have provided images to view the anteroposterior level in Fig.S2A-C. We have noticed cortex in DLK(OE) mice to become thinner, along with expansion of ventricles in some animals at later timepoints (Fig.S2C).

      One cannot be sure that there is selective death of hippocampal sectors with DLK overexpression versus, say, rearrangement of hippocampal architecture. One may need stereological analysis, otherwise this substantial claim appears overinterpreted.

      We appreciate the comment.

      In the revised manuscript, we included a new supplemental figure (Fig. S2) showing lower magnification images of coronal sections, and used cautionary wording, such as ‘CA3 is less vulnerable, compared to CA1’, to minimize the impression of over-interpretation.  By NeuN staining, at P10, P15, P60, we did not observe detectable difference in overall hippocampus architecture, apart from noted cell death of CA1 and DG and associated thinning of each of the layers. At 46 weeks, some animals showed differences in the overall shape of dorsal hippocampus, though this appeared to reflect a disproportionately large CA3 region compared to other regions (Fig S2). Increased GFAP staining (Fig.S5A-C) was detected in CA1 but not in CA3, and microglia by IBA1 staining (Fig.S5E) also displayed less reactivity in CA3, compared to CA1. Thus, based on NeuN staining, GFAP staining, IBA1 staining and analysis of the differentially regulated genes, we infer that the effect of DLK(iOE) in CA1 is different than the effect on CA3.

      Is the GFAP excess reflective of neuroinflammation? What do microglial markers show? The presence of neuroinflammation does not bode well with apoptosis. Speaking of which, TUNEL in one cell in Supplementary Figure 4E is not strong evidence of a more widespread apoptotic event in CA1.

      We have included staining data for the microglia marker IBA1. Both GFAP and IBA1 showed evidence of reactivity particularly in the CA1 region (S5A-E), supporting the differential vulnerability in different regions, though whether cell death is primarily due to apoptosis is unclear.

      We agree that our data of sparse TUNEL staining at P15 (Fig S5F,G) do not rule out whether other mechanisms of cell death may also occur.  We have included this in our limitations (pg.20) “While we find evidence for apoptosis, other forms of cell death may also occur.”

      In several places in the paper (as illustrated in Figure 4B, Supplementary Figure 2B, etc.): the unit of biological observation in animal models is typically not a cell, but an organism, in which averaged measures are generated. This is a significant methodological problem because it is not easy to sample neurons without involving stereological methods. With the approach taken here, there is a risk that significance may be overblown.

      We appreciate the reviewer’s point. We used same region for quantification of RNAscope, genotype-blind when possible. We revised the graphs to show mean values for individual mice in Fig.4B, 4C, and Fig.S3B (previously Fig.S2B).

      Other Comments and Questions:

      Supplementary Figure 9: The authors state that data points are shown for individual ROIs - ideally, they should also show averages for biological replicates. Can the authors confirm that statistical analyses are based on biological replicates (mice) and not ROIs?

      We have revised the graphs to show averages from individual mice in Fig.5B-D, F5E-F (previously Fig.S9G-I), Fig.5H-J, and Fig.5K-L (previously Fig.S9J-L)  and Fig.S10B,C,E,F (previously Fig.S9B,C, E,F). The statistical analyses are based on biological replicates of mice.

      For in vitro experiments, what is the effect of DLK overexpression on neuronal viability and density? Could these variables confound effects on synaptogenesis/synapse maturation?

      As described in the Methods, we made hippocampal neuron cultures from P1 pups of the following crosses:

      For control: Vglut1<sup>Cre/+</sup> X Rosa26<sup>tdT/+</sup> 

      For DLKcKO: Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>  X Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>;Rosa26<sup>tdT/+</sup> 

      For DLKiOE: H11-DLK<sup>iOE/iOE</sup> X Vglut1<sup>Cre/+</sup>;Rosa26<sup>tdT/+</sup> 

      Dissociated cells from a given litter were pooled into the same culture. Because there were different proportions of neurons with our genotype of interest in each culture, it is not simple to know whether DLK was causing significant cell death.

      On pg 13, we stated our observation:

      ‘We did not notice an obvious effect of DLK(iOE) or DLK(cKO) on neuron density in cultures at DIV2. To assess neuronal type distribution in our cultures, we immunostained DIV14 neurons with antibodies for Satb2, as a CA1 marker (Nielsen et al., 2010), and Prox1, as a marker of DG neurons (Iwano et al., 2012). We did not observe significant differences in the proportion of cells labeled with each marker in DLK(cKO) or DLK(iOE) cultures (Fig.S13E). These data are consistent with the idea that DLK signaling does not have a strong role in neuron-type specification both in vivo and in vitro’.

      We cannot rule out whether variable factors in our cultures may confound effects on synaptogenesis/synapse maturation, and would hope future studies will shed clarity.

      Correlations between c-jun expression and phosphorylation are extremely important and need to be carefully and convincingly documented. I am a bit concerned about Supplementary Figure 6 images, especially 6B-CA1 (no difference between control and KO, too small images) and 6D (no p-c-Jun expression at all anywhere in the hippocampus at P15?).

      At P10, P15, and P60 we stained for p-c-Jun using the Rabbit monoclonal p-c-Jun (Ser73) (D47G9) antibody from Cell Signaling (cat# 3270) at a 1:200 dilution and imaged using an LSM800 confocal microscope with a 20x objective. We observed p-c-Jun to be quite low generally in control animals. We have replaced the images in Fig.S7F (previously S6D), and adjusted the brightness/contrast to enable better visualization of the low signal in Fig.S7B,D,F (previously Fig.S6B,D).

      We revised our text to present the data carefully as stated above:

      Pg. 9-10: ‘In control mice, glutamatergic neurons in CA1 had low but detectable c-Jun immunostaining at P10 and P15, but reduced intensity at P60; those in CA3 showed an overall low level of c-Jun immunostaining at P10, P15 and P60; and those in DG showed a low level of c-Jun immunostaining at P10 and P15, and an increased intensity at P60 (Fig.S7A,C,E). In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice at P10 when no discernable neuron degeneration was seen in any regions of hippocampus, only CA3 neurons showed a significant increase of immunostaining intensity of c-Jun, compared to control (Fig.S7A). In P15 mice, we observed further increased immunostaining intensity of c-Jun in CA1, CA3, and DG, with the strongest increase (~4-fold) in CA1, compared to age-matched control mice (Fig.S7C). The overall increased c-Jun staining is consistent with RiboTag analysis’.

      Pg. 10: ‘In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice, we observed increased p-c-Jun positive nuclei in CA1 at P10, and strong increase in CA1 (~10-fold), CA3 (~6-fold), and DG (~8-fold) at P15 (Fig.S7B,D).

      Recommendations for the authors:

      Several major and minor reservations were raised. The major issues are the need for more information about the over-expression of DLK and a need to extrapolate to an in vivo condition with DLK. A considerable amount of useful information is presented with some very nicely done experiments but it is not yet a coherent or integrated story. The lack of impact of DLK overexpression in some neurons is perhaps the most impactful observation of the study and would be great to have more information around the differential transcriptional/signaling response in these cell types. There is also a need for more experimental details and to address several questions about the mouse genetic and translatome analysis. They are valid concerns that require attention by the authors.

      We thank the editors and reviewers for their thoughtful evaluation and suggestions.  We hope that the editors and reviewers find that the new data and text changes in our revised manuscript, along with above point-to-point response, have addressed the concerns and strengthened our findings.

      Minor points:

      (1)The authors state that deletion of DLK has no effect on CA1 at 1yr, however, the image of CA1 in Figure S1D shows substantially fewer NeuN+ neurons. Is this a representative field of view?

      We have re-examined images, and observed no effect on hippocampal morphology at 1 yr. We now included representative images in the revised Fig S1D.

      (2) Is the DLK protein section staining in Figure 2C a real signal? The staining looks like speckles and is purely somatic. Axonal staining is widely expected based on the literature and the authors' own work. There should be a specificity control.

      To our knowledge, axonal staining of DLK reported in the literature is mostly based on cultured DRG neurons. In addition to the reported axonal localization, DLK is present in the cell soma, near the golgi (Hirai et al., 2002), and in the post-synaptic density (Pozniak et al., 2013).

      In the revised manuscript, we addressed this point by including controls with no primary antibody, and using an antibody against the closely related kinase, LZK. These additional data are shown in (Fig.S3C,D) (previously Fig.S2C), supporting that DLK protein staining represents real signal.  At P10 and P15, DLK immunostaining around CA3 showed axonal staining of the mossy fibers, as well as in the soma and dendritic layers (Fig.S3C,D). A similar pattern was also seen in primary cultured neurons (Fig 6A).

      (3) The protein expression of DLK in the transgenic overexpressor (Figure S7C) looks, to the resolution of this blot, to be at least 50kD heavier than 'WT' DLK. Can the authors explain this discrepancy?

      The Cre-induced DLK(iOE) transgene has T2A and tdTomato in-frame to C-terminus of DLK. It is known that T2A ‘self-cleavage’ is often incomplete. DLK-T2A-tdTomato would be about 50 kD bigger than WT DLK. We now include the transgene design in revised Fig S1D, and also stated in figure legend of Fig.S8C (previously S7C) that ‘Larger molecular weight band of DLK in Vglut1<sup>Cre/+</sup>;H11-DLKiOE/+ would match the predicted molecular weight of DLK-T2A-tdTomato if T2A-peptide induced ‘self-cleavage’ due to ribosomal skipping is ineffective (Fig.S1D).’

      (4) Expression changes in DLK affect various aspects of neurites in CA1 cultures (Figure 6), and changes in DLK also modestly affect STMN4 (and 2, perhaps indirectly) levels (Figure S7C), but there is no indication that DLK acts via STMN4 to cause these changes. It is not clear what to make of these data. Of note, Stmn4 levels change in response to DLK in CA3, without DLK affecting cell death in this region.

      We appreciate and agree with the comment. Other studies (Asghari Adib et al., 2024; DeVault et al., 2024; Hu et al., 2019; Larhammar et al., 2017; Le Pichon et al., 2017; Shin et al., 2019; Watkins et al., 2013) reported expression changes in Stmn4 mRNAs in other cell types and cellular contexts, which appeared to depend on DLK. Hippocampal neurons express multiple Stmns (Fig.S8A). While we present our analysis on the effects of DLK dosage on Stmn4, and also Stmn2, we do not think that DLK-induced changes of Stmn4 expression per se is a major factor underlying CA1 cell death vs CA3 survival.

      In the revised manuscript, we addressed this point in ‘Limitation of our study’ (pg 20):

      ‘Additional experiments will be needed to elucidate in vivo roles of STMN4 and its interaction with other STMNs’.

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      Reply to the reviewers

      • Reviewer #1 (Evidence, reproducibility and clarity (Required)): Summary:

      In this manuscript, Hammond et al. study robustness of the vertebrate segmentation clock against morphogenetic processes such as cell ingression, cell movement and cell division to ask whether the segmentation clock and morphogenesis are modular or not. The modularity of these two would be important for evolvability of the segmenting system. The authors adopt a previously proposed 3D model of the presomitic mesoderm (Uriu et al. 2021 eLife) and include new elements; diKerent types of cell ingression, tissue compaction and cell cycles. Based on the results of numerical simulations that synchrony of the segmentation clock is robust, the authors conclude that there is a modularity in the segmentation clock and morphogenetic processes.

      The presented results support the conclusion. The manuscript is clearly written. I have several comments that could help the authors further strengthen their arguments.

      Major comment:

      [Optional] In both the current model and Uriu et al. 2021, coupling delay in phase oscillator model is not considered. Given that several previous studies (e.g. Lewis 2003, Herrgen et al. 2010, Yoshioka-Kobayashi et al. 2020) suggested the presence of coupling delays in Delta- Notch signaling, could the authors analyze the eKect of coupling delay on robustness of the segmentation clock against morphogenetic processes?

      Response: We thank the reviewer for the suggestion. Owing to the computational demands of including such a delay in the model, we cannot feasibly repeat every simulation analysed here in the presence of delay, and would like to note that the increased computational demand that delays put on the simulations is also the reason why Uriu et al 2021 did not include it, as stated in their published exchange with reviewers. However, analogous to our analysis in figure 7, we can analyse how varying the position of progenitor cell ingression aKects synchrony in the presence of the coupling delay measured in zebrafish by Herrgen et al. (2010). We show this analysis in a new figure 8 (8B, specifically), on page 21, and discuss its implications in the text on pages 20- 22. Our analysis reveals that the model cannot recover synchrony using the default parameters used by Uriu et al. (2021) and reveal a much stronger dependence on the rate of cell mixing (vs) than shown in the instantaneous coupling case (cf. figure 7). However, by systematically varying the value of the delay we find that a relatively minor increase in the delay is suKicient to recover synchrony using the parameter set of Uriu et al. (see figure 8C). Repeating this across the three scenarios of cell ingression we see that the combination of coupling strength and delay determine the robustness of synchrony to varying position of cell ingression. This suggests that the combination of these two parameters constrain the evolution of morphogenesis.

      Minor comments:

      • PSM radius and oscillation synchrony are both denoted by the same alphabet r. The authors should use different alphabets for these two to avoid confusion.

      Response: We thank the reviewer for spotting this. This has now been changed throughout to rT, as shorthand for ‘radius of tissue’.

      • page 5 Figure 1 caption: (x-x_a/L) should be (x-x_a)/L.

      Response: We thank the reviewer for spotting this. This has now been corrected.

      • Figure 3C: Description of black crosses in the panels is required in the figure legend.

      Response: Thank you for spotting this. The legend has now been corrected.

      • Figure 3C another comment: In this panel, synchrony r at the anterior PSM is shown. It is true that synchrony at anterior PSM is most relevant for normal segment formation. However, in this case, the mobility profile is changed, so it may be appropriate to show how synchrony at mid and posterior PSM would depend on changes in mobility profile. Is synchrony improved by cell mobility at the region where cell ingression happens?

      Response: We thank the reviewer for the suggestion. We have now plotted the synchrony along the AP axis for varying motility profiles, and this can be seen in figure 3 supplement 1, and is briefly discussed in the text on page 11. We show that while the synchrony varies with x-position (as already expected, see figure 2), there is no trend associated with the shape of the motility profile.

      • In page 12, the authors state that "the results for the DP and DP+LV cases are exactly equal for L = 185 um, as .... and the two ingression methods are numerically equivalent in the model". I understood that in this case two ingression methods are equivalent, but I do not understand why the results are "exactly" equal, given the presence of stochasticity in the model.

      Response: These results can be exactly equal despite the simulations being stochastic because they were both initialised using the same ‘seed’ in the source code. However, we now see that this might be confusing to the reader, and we have re-generated this figure but this time initialising the simulations for each ingression scenario using a diKerent seed value. This is now reflected in the text on page 12 and in figure 4.

      • The authors analyze the eKect of cell density on oscillation synchrony in Fig. 4 and they mention that higher density increases robustness of the clock by increasing the average number of interacting neighbours. I think it would be helpful to plot the average number of neighbouring cells in simulations as a function of density to quantitatively support the claim.

      Response: We thank the reviewer for their suggestion. Distributions of neighbour numbers for exemplar simulations with varying density can now be found in figure 4 supplementary figure 1 and are referred to in the text on page 11.

      • The authors analyze the eKect of PSM length on synchrony in Fig. 4. I think kymographs of synchrony r as shown in Fig. 2D would also be helpful to show that indeed cells get synchronized while advecting through a longer PSM.

      Response: We thank the reviewer for their suggestion and agree that visualising the data in this way is an excellent idea. We have generated the suggested kymographs and added them to figure 4 as supplements 2 and 4, and discussed these results in the text on page 12.

      • I understand that cells in M phase can interact with neighboring cells with the same coupling strength kappa in the model, although their clocks are arrested. If so, this aspect should be also mentioned in the main text in page 16, as this coupling can be another noise source for synchrony.

      Response: We agree this is an important clarification. We explicitly state this, and briefly justify our choice, in the text on page 16.

      • Figure 5-figure supplement 2: panel labels A, B, C are missing.

      Response: Thank you for bringing this to our attention. These have now been added.

      • Figure 5-figure supplement 3: panel labels A, B, C are missing.

      Response: Thank you for bringing this to our attention. These have now been added.

      • Reviewer #1 (Significance (Required)):

      Synchronization of the segmentation clock has been studied by mathematical modeling, but most previous studies considered cells in a static tissue without morphogenesis. In the previous study by Uriu et al. 2021, morphogenetic processes such as cell advection due to tissue elongation, tissue shortening, and cell mobility were considered in synchronization. The current manuscript provides methodological advances in this aspect by newly including cell ingression, tissue compaction and cell cycle. In addition, the authors bring a concept of modularity and evolvability to the field of the vertebrate segmentation clock, which is new. On the other hand, the manuscript confirms that the synchronization of the segmentation clock is robust by careful simulations, but it does not propose or reveal new mechanisms for making it robust or modular. The main targets of the manuscript will be researchers working on somitogenesis and evolutionary biologists who are interested in evolution of developmental systems. The manuscript will also be interested by broader audiences, like developmental biologists, biophysicists, and physicists and computer scientists who are working on dynamical systems.

      Response: We thank the reviewer for their interest in our manuscript and for acknowledging us as one of the first to address the modularity and evolvability of somitogenesis. We hope that this work will encourage others to think about these concepts in this system too. In the original submission, we identified a high enough coupling strength as the main mechanism underlying the identified modularity in somitogenesis. Since, we have included an analysis of the coupling delay and find that it is the interplay between coupling strength and coupling delay that mediate the identified modularity, allowing PSM morphogenesis and the segmentation clock to evolve independently in regions of parameter space that are constrained and determined by the interplay between these two parameters. We have now added an extra figure (figure 8) where we explore this interplay and have discussed it at length in the last section of the results and in the discussion. We again thank the reviewer for encouraging us to include delays in our analysis.

      • Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      SUMMARY

      The manuscript from Hammond et al., investigates the modularity of the segmentation clock and morphogenesis in early vertebrate development, focusing on how these processes might independently evolve to influence the diversity of segment numbers across vertebrates.

      Methodology | The study uses a previously published computational model, parameterized for zebrafish, to simulate and analyse the interactions between the segmentation clock and the morphogenesis of the pre-somitic mesoderm (PSM). Their model integrates cell advection, motility, compaction, cell division, and the synchronization of the embryo clock. Three alternative scenarios of PSM morphogenesis were modeled to examine how these changes aKect the segmentation clock.

      Model System | The computational model system combines a representation of cell movements and the phase oscillator dynamics of the segmentation clock within a three-dimensional horseshoe-shaped domain mimicking the geometry of the vertebrate embryo PSM. The parameters used for the mathematical model are mostly estimated from previously published experimental findings.

      Key Findings and Conclusions | (1) The segmentation clock was found to be broadly robust against variations in morphogenetic processes such as cell ingression and motility; (2) Changes in the length of the PSM and the strength of phase coupling within the clock significantly influenced the system's robustness; (3) The authors conclude that the segmentation clock and PSM morphogenesis exhibited developmental modularity (i.e. relative independence), allowing these two phenomena to evolve independently, and therefore possibly contributing to the diverse segment numbers observed in vertebrates.

      MAJOR COMMENTS

      1. The key conclusion drawn by the authors (that there is robustness, and therefore modularity, between the morphogenetic cellular processes modeled and the embryo clock synchronization) stems directly from the modeling results appropriately presented and discussed in the manuscript. The model comprises some strong assumptions, however all have been clearly explained and the parameterization choices are supported by experimental findings, providing biological meaning to the model. Estimated parameters are well explained and seem reasonable assumptions (from the embryology perspective).

      Response: We thank the reviewer for their positive comments about our work

      1. This study, as is, achieves its proposed goal of evaluating the potential robustness of the embryo clock to changes in (some) morphogenetic processes. The authors do not claim that the model used is complete, and they properly identify some limitations, including the lack of cell-cell interactions. Given the recognized importance of cellular physical interactions for successful embryo development, including them in the model would be a significant addition in future studies.

      Response: We would like to clarify that the model does include cell-cell interactions as cells interact with their neighbours’ clock phase to synchronise and to avoid occupying the same physical space.

      1. The authors have deposited all the code used for analysis in a public GitHub repository that is updated and available for the research community.

      Response: We support open source coding practices.

      1. In page 6, the authors justify their choice of clock parameters for cells ingressing the PSM: "As ingressing cells do not appear to express segmentation clock genes (Mara et al. (2007)), the position at which cells ingress into the PSM can create challenges for clock patterning, as only in the 'oK' phase of the clock will ingressing cells be in-phase with their neighbours."

      However, there are several lines of evidence (in chick and mouse), that some oscillatory clock genes are already being expressed as early as in the gastrulation phase (so prior to PSM ingression) (Feitas et al, 2001 [10.1242/dev.128.24.5139]; Jouve et al, 2002 [10.1242/dev.129.5.1107]; Maia-Fernandes at al, 2024 [10.1371/journal.pone.0297853])

      Question: Is this also true in zebrafish? (I.e. is there any recent experimental evidence that the clock genes are not expressed at ingression, since the paper cited to support this assumption is from 2007). If they are expressed in zebrafish (as they are in mouse and chick), then the cell addition should have random clock gene periods when they enter the PSM and not start all with a constant initial phase of zero. Probably this will not impact the results since the cells will also be out of phase with their neighbours when they "ingress", however, it will model more closely the biological scenario (and avoid such criticism).

      Response: We thank the reviewer for their comments. While it is known that in zebrafish the clock begins oscillating during epiboly and before the onset of segmentation (Riedel-Kruse et al., 2007), to our knowledge no-one has examined whether posteriorly or laterally ingressing progenitor cells express clock genes prior to their ingression into the PSM, which occurs later in development than the first oscillations which give rise to the first somites. We have not found any published evidence of her/hes gene expression in the dorsal donor tissues or lateral tissues surrounding the PSM, however we acknowledge that this has not been actively studied before and our assumption relies on an absence of evidence, rather than evidence of absence.

      However, we agree with the reviewer that one should include such an analysis for completeness, and we have now generated additional simulations where progenitor cells ingress with a random clock phase. This data is presented in figure 2 supplement 1 and mentioned in the main text on page 9.

      MINOR COMMENTS:

      1. The citations are appropriate and cover the major labs that have published work related to this study (although with some overrepresentation of the lab that published the model used).

      Response: We have cited the vast literature on somitogenesis to the best of our ability and do recognise that the work of the Oates lab appears prominently, but this is probably because their experimental data were originally used to parametrise the model in Uriu et al. 2021.

      The text is clear, carefully written, and both the methods and the reasoning behind them are clearly explained and supported by proper citations.

      Response: We are very glad to see that the reviewer found that the manuscript was clearly presented.

      1. The figures are comprehensive, properly annotated, with explanatory self-contained legends. I have no comments regarding the presentation of the results.

      Response: Thank you

      Minor suggestions:

      1. Page 26: In the Cell addition sub-section of the Methods section, correct all

      instances where the word domain is used, but subdomain should be used (for clarity and coherence with the description of the model, stated as having a single domain comprising 3 subdomains).

      Response: We thank the reviewer for raising this, this is a good point. We have now corrected to ‘subdomain’ where appropriate.

      1. Page 32: Table 1. Parameter values used in our work, unless otherwise stated -> Suggestion: Add a column with the individual citations used for each parameter (to facilitate the confirmation of each corresponding reference).

      Response: Thank you for the suggstion, we have now done this (see table 1 page 36).

      **Referee Cross-commenting**

      I carefully read the reports provided by my fellow reviewers. My cross-comments aim to enhance the collective evaluation of the manuscript by Hammond et al.

      • On reviewer #1's Comments:

      I agree with Reviewer #1's overall evaluation of the manuscript's value and relevance, and with their general comments. I particularly support the suggestion to optionally include coupling delays known to influence the clock's period, as this would improve the model's completeness and benefit the research community. I also view this as an optional but desirable addition, not mandatory.

      Response: As per reviewer #1’s suggestion, we have now included this analysis (figure 8).

      In Fig. 4, I agree that showing kymographs, similar to Fig. 2D, for each PSM length would greatly improve the visualization of the results, given the relevance of this result to the manuscript's main message.

      Response: As per reviewer #1’s suggestion, we have now included such an analysis (figure 4 supplements 2 and 4) and agree with both reviewers that they improve the communication of our results.

      The remaining minor comments are useful and relevant to improving the manuscript.

      • On reviewer #3's Comments:

      Although I agree with Reviewer #3 that the paper is somewhat lengthy, I find the detailed description of the model in its biological context necessary and welcomed by the embryology research community. Without this detail, the paper might be too 'dry' and lose part of its audience. Conversely, focusing mostly on embryology without detailing the model parameters and simulation findings would deprive it of novelty and critical insights.

      Response: We thank Reviewer #2 for this assessment, which we agree with. Nonetheless we have sought to streamline our writing throughout to increase clarity without reducing the content.

      Overall, I find Reviewer #3's suggestions scientifically interesting, particularly comments 3, 4, and 5, which express legitimate questions for future study. However, I find them tangential to the main question addressed in this manuscript, which pertains to the modularity of the segmentation clock and morphogenesis. Therefore, I do not see them as significant improvements for the authors to implement in the current study.

      Response: We thank Reviewer #2 for their comments here and refer them to our responses to Reviewer #3.

      I would like to know how the authors respond to comments 1 and 2, which I do not have the expertise to evaluate.

      Response: We have now addressed these concerns in our response to Reviewer #3. Please see below.

      I agree with comment 6 that a brief mention of the known pathways/gene networks to which the assumptions apply (in zebrafish) would be a good addition. However, I do not think a detailed discussion is needed, as specific genes/networks can be diKerent for diKerent organisms.

      Response: We now justify this assumption in the methods on page 32.

      I disagree with comment 7, as Fig. 3 shows that the clock is robust to changes in cell ingression regime across all cell motility profiles tested. This is an important result for the manuscript's take home message, and should remain in the main text, not as a supplementary figure.

      Response: We agree with Reviewer #2 and have included this in our response to Reviewer #3.

      Finally, regarding Reviewer #3's concern about the incompleteness of the results, I find the results robust given the formalism chosen and within the scenarios where the assumptions hold. I believe this concern applies to the formalism (which is a choice) and not to the quality or relevance of the work presented in the manuscript. Additionally, some of the model's limitations have been adequately addressed by the authors.

      Response: We thank Reviewer #2 for their comments.

      • Reviewer #2 (Significance (Required)): GENERAL ASSESSMENT

      • This study uses a previously published model to simulate alternative scenarios of morphogenetic parameters to infer the potential independence (termed here modularity) between the segmentation clock and a set of morphogenetic processes, arguing that such modularity could allow the evolution of more flexible body plans, therefore partially explaining the variability in the number of segments observed in the vertebrates. This question is fundamental and relevant, yet still poorly researched. This work provides a comprehensive simulation with a model that tries to simplify the many morphogenetic processes described in the literature, reducing it to a few core fundamental processes that allow drawing the conclusions seeked. It provides theoretical insight to support a conceptual advance in the field of evolutionary vertebrate embryology.

      ADVANCE

      • This study builds on a model recently published by Uriu et al. (eLife, 2021) that incorporates quantitative experimental data within a modeling framework including cell and tissue-level parameters, allowing the study of multiscale phenomena active during zebrafish embryo segmentation. Uriu's publication reports many relevant and often non-intuitive insights uncovered by the model, most notably the description of phase vortices formed by the synchronizing genetic oscillators interfering with the traveling-wave front pattern.

      However, this model can be further explored to ask additional questions beyond those described in the original paper. A good example is the present study, which uses this mathematical framework to investigate the potential independence between two of the modeled processes, thereby extracting extra knowledge from it. Accordingly, the present study represents a step forward in the direction of using relevant theoretical frameworks to quantitatively explore the landscape of complex molecular hypotheses in silico, and with it shed some light on fundamental open questions or inform the design of future experiments in the lab.

      • The study incorporates a wide range of existing literature on the developmental biology of vertebrates. It comprehensively cites prior work, such as the foundational studies by Cooke and Zeeman on the segmentation clock and the role of FGF signaling in PSM development as discussed by Gomez et al. The literature properly covers the breadth of knowledge in this field.

      AUDIENCE

      • Target audience | This study is relevant for fundamental research in developmental biology, specifically targeting researchers who focus on early embryo development and morphogenesis from both experimental and theoretical perspectives. It is also relevant for evolutionary biologists investigating the genetic factors that influence vertebrate evolution, as well as to computational biologists and bioinformatics researchers studying developmental processes and embryology.

      Developmental researchers studying the segmentation clock in other vertebrate model organisms (namely mouse and chick), will find this publication especially valuable since it provides insights that can help them formulate new hypotheses to elucidate the molecular

      mechanisms of the clock (for example finding a set of evolutionarily divergent genes that might interfere with PSM length). Additionally, this study provides a set of cellular parameters that have yet to be measured in mouse and chick, therefore guiding the design of future experiments to measure them, allowing the simulation of the same model with sets of parameters from diKerent vertebrate model organisms, therefore testing the robustness of the findings reported for zebrafish.

      MY EXPERTISE

      My areas of research (relevant for this study): Vertebrate embryo clock oscillations in Gallus gallus; Computational biology.

      I can evaluate the relevance and validity of the model, critically evaluate its outputs and parameters, and the significance of the model assumptions for drawing relevant biological insights; however, I am not an expert on this mathematical formalism.

      • Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Verd and colleagues explored how various biologically relevant factors influence the robustness of clock dynamics synchronization among neighboring cells within the context of somatogenesis, adapting a mathematical model presented by Urio et. al in 2021 in a similar context. Specifically they show that clock dynamics is robust to diKerent biological mechanisms such as cell infusion, cellular motility, compaction-extension and cell-division. On the other hand , the length of Presomitic Mesoderm (PSM) and density of cells in it has a significant role in the robustness of clock dynamics. While the manuscript is well-written and provides clear descriptions of methods and technical details, it tends to be somewhat lengthy. Below are the comments I would like the authors to address:

      1. The authors mention that "...the model is three dimensional and so can quantitatively recapture the rates of cell mixing that we observe in the PSM". I am not convinced with this justification of using a 3D model. None of the eKects the authors explore in this manuscript requires a three dimensional model or full physical description of the cellular mechanics such as excluded volume interaction etc. A one-dimensional model characterized by cell position along the arclength of PSM and somatic region and segmentation clock phase θ can incorporate all the physics authors described in this manuscript as well as significantly computationally cheap allowing the authors to explore the eKect of diKerent parameters in greater detail.

      Response: One of the main objectives of the work we present in this manuscript is to assess how the evolution of PSM morphogenesis affects, or does not affect, segment patterning. The PSM is a three-dimensional tissue with diKering cell rearrangement dynamics along its anterior-posterior axis. In addition, PSM dimension, density, the rearrangement rate, and patterns of cell ingression all vary across vertebrate species, and they are functional, especially cell mixing as it promotes synchronisation and drives elongation. In order to answer questions on the modularity of somitogenesis we therefore consider it absolutely necessary to include a three-dimensional representation of the PSM thatcaptures single cells and their movements. In addition, this will allow us, as Reviewer #2 also pointed out, to reparametrize our model using species-specific data as it becomes available.

      While the reviewer is right in that lower dimensional representations would be computationally more efficient, and are generally more tractable, it would not be possible to represent cell mixing in one dimension, as this happens in three dimensions. One could perhaps encode the synchrony-promoting eKect of cell mixing via some coupling function κ(x) that increases towards the posterior, however it is unclear what existing biological data one could use to parameterise this function or determine its form. Cell mixing can be modelled in a two-dimensional framework, however this cannot quantitatively recapture the rate of cell mixing observed in vivo, which is an advantage of this model.

      Furthermore, it is unclear how one would simulate processes such as compaction- extension using a one-dimensional model. The two diKerent scenarios of cell ingression which we consider can also not be replicated in a one-dimensional model, as having a population of cells re-acquiring synchrony on the dorsal surface of the tissue while new material is added to the ventral side, creating asynchrony, is qualitatively diKerent than a one-dimensional scenario where cells are introduced continuously along the spatial axis.

      I am not sure about the justification for limiting the quantification of phase synchrony in a very limited (one cell diameter wide) region at one end of the somatic part (Page 33 below Fig. 9). From my understanding of the manuscript, the segments appear in significant length anterior to this region. Wouldn't an ensemble average of multiple such one cell diameter wide regions in the somatic region be a more accurate metric for quantifying synchrony?

      Response: Indeed, such a metric (e.g. as that used by Uriu et al. to quantify synchrony along the x- axis) would be more accurate for determining synchrony within the PSM. However, as per the clock and wavefront model of somitogenesis, only synchrony at the very anterior of the PSM (or at the wavefront, equivalently) is functional for somitogenesis and thus evolution. Therefore, we restrict our analysis to the anterior-most region of the PSM. We now further justify this in the main text on page 9.

      While studying the eKect of cellular ingression, the authors study three discrete modes- random, DP and DP+LV and show that in the DP+LV mode the clock synchrony becomes aKected. I would like the authors to explore this in a continuous fashion from a pure DP ingression to Pure LV ingression and intermediates.

      Response: We thank the reviewer for this suggestion; this is a very interesting question. We are currently working on a related computational and experimental project to address the question of how PSM morphogenesis can change over evolutionary time to evolve the diKerent modes that we see across species. As part of this work, we are running precisely the simulations suggested by the reviewer to find regions of parameter space in which all the relevant morphogenetic processes can freely evolve. While interesting, this work is however outside the scope of the current manuscript.

      While studying the effect of length and density of cells in PSM on cellular synchrony, the authors restrict to 3 values of density and 6 values of PSM length keeping the other parameter constant. I would be interested to see a phase diagram similar to Fig. 7 in the two-dimensional parameter space of L and ρ0. I am curious if a scaling relation exists for the parameter values that partition the parameter space with and without synchrony.

      Response: We thank the reviewer for their suggestion and agree that this would constitute an interesting addition to the manuscript. We have now generated these data, which are shown in figure 4 supplement 5 and mentioned on page 13. We see no clear relationship between these two variables when co-varying in the presence of random ingression.

      Both in the abstract and introduction, the authors discuss at a great length about the variability in the number of segments. I am curious how the number and width of the segments observed depend on different parameters related to cellular mechanics and the segmentation clock ?

      Response: We thank the reviewer for this question. It was not clear to us if this was something the reviewer wants us to address in the study’s background and introduction, or an analysis we should include in the results. Therefore, we have responded to both comprehensively below:

      The prevailing conceptual framework for understanding this is the clock and wavefront model (Cooke and Zeeman, 1976), which posits that the somite length is inversely proportional to the frequency of the clock relative to the speed of the wavefront, and that the total number of segments is the relative frequency multiplied by the total duration of somitogenesis.

      Experimentally we know that the frequency is determined in part by the coupling strength (Liao, Jorg, and Oates, 2016), and from comparative embryological studies (Gomez et al., 2008; Steventon et al., 2016) we know that changes in the elongation dynamics of the PSM correlate with changes in somite number, presumably by altering the total duration of somitogenesis (Gomez et al., 2009). These changes in elongation are thought to be driven by the changes in cell and tissue mechanics we test in our manuscript.

      Within our model, we cannot in general predict how the number of segments responds to changes in either clock parameters or cell mechanical parameters, as we lack understanding of what causes somitogenesis to cease; this is thus not encoded in our model and segmentation can in principle proceed indefinitely. Therefore, we have not performed this analysis.

      Similarly, we have not included an analysis of somite length. This is for two reasons: 1) as per the clock and wavefront model, the frequency at the PSM anterior (which we analyse) is equivalent to this measurement, as we assume (in general) the wavefront ($x = x_{a}$) is inertial. 2) the length of the nascent somite is not thought to be of much relevance to the adult phenotype, and by extension evolution. Somites undergo cell division and growth soon after their patterning by the segmentation clock, therefore their final size does not majorly depend on the dynamics of the segmentation clock. Rather, the main function of the clock is to control their number (and polarity).

      The authors assume that the phase dynamics of the chemical network may be described by an oscillator with constant frequency. For the completeness of the manuscript, the author should discuss in detail, for which chemical networks this is a good assumption.

      Response: We thank the reviewer for their suggestion and now justify this assumption in the methods on page 31.

      Such an assumption is appropriate for the segmentation clock, as the clock in the posterior of the PSM is thought to oscillate with a constant frequency, at least for the majority of somitogenesis although the frequency of somite formation slows towards the end of this process in zebrafish (Giudicelli et al., 2007, PLoS Biol.). In addition, PSM cells isolated and cultured in the presence of FGF (thus replicating the signalling environment of the posterior PSM) will continue to exhibit her1 oscillations with an apparently constant frequency (Webb et al., 2016).

      We note that such formulations are widely used within the segmentation clock literature (e.g. Riedel-Kruse et al., 2007, Morelli et al., 2009).

      Figure 3 and the associated text shows no eKect of the cellular motility profile in the synchrony of the segmentation clock. This may be moved to the supplementary considering the length of this manuscript.

      Response: Thank you for the suggestion. However, we would argue that the lack of eKect is a crucial result when discussing modularity. Reviewer #2 agrees with this assessment.

      • Reviewer #3 (Significance (Required)):

      The manuscript answers some important questions in the synchrony of segmentation clock in the vertebrates utilizing a model published earlier. However, the presented result is incomplete in some aspects (points 2 to 5 of section A) and that could be overcome by a more detailed analysis using a simpler one dimensional (point 1 of section A). I believe this manuscript could be of interest to an intersecting audience of developmental biologists, systems biologists, and physicists/engineers interested in dynamical systems.

      My research interests are building physics and engineering based models of cell and tissue scale biological phenomena.