eLife assessment

This study presents valuable findings that could be utilized for identifying women at risk for preeclampsia before the onset of the disease. The novel aspect of this study lies in the utilization of exosomes of two different sizes. The data are solid: the methods, data, and analysis broadly support the claims. This work will be of interest to medical researchers and clinicians who work on preeclampsia and women's health.

Reviewer #1 (Public Review):

The authors primary objective in this study was to identify differences between patients with preeclampsia and normal patients with respect to the placental syncytiotrophoblast extracellular vesicle proteome.

A strength of this study is that the authors identified novel STB-EV protein markers that are more abundant in the placenta of patients with preeclampsia compared with normal controls. This contributes a little more to what is already known about STB-EV markers and preeclampsia. If these markers can be shown to be more abundant in maternal plasma of preeclampsia patients, it would be very useful for identifying patients who are at high risk for developing early-onset preeclampsia.

Weaknesses include:<br /> (1) The small sample size. There were only 6 patients in the study group and 6 normal controls. However, this can be considered as a pilot study.<br /> (2) The normal controls were not matched with the study patients and the authors did not state how the controls were selected.<br /> (3) The authors state that the placenta samples were obtained at the time of elective cesarean section. However, it is likely that all the preeclampsia patients were delivered for clinical indications rather than electively. This should be clarified.

Reviewer #2 (Public Review):

Summary:

Preeclampsia is a disorder of pregnancy that affects 4-5% of pregnancies worldwide. Identifying this condition early is clinically relevant as it will help clinicians to make management decisions to prevent adverse outcomes. The placenta holds a key to many pregnancy-related pathologies including preeclampsia and studies have shown many differences in the placenta of women with preeclampsia as compared to controls. However as the placenta cannot be collected directly during pregnancy, the exosomes secreted by it are considered a good alternative to tissue biopsy. In this study, the authors have compared the proteins in different sizes of exosomes from the placenta of women with and without preeclampsia. The idea is to eventually use these as biomarkers for early detection of preeclampsia.

Strengths:

The novelty factor of this study is the use of two different-sized exosomes which has not been achieved earlier.

Weaknesses:

The study measured the proteins at only a single time point after the disease has already occurred. However, the placenta is an ever-changing tissue throughout pregnancy and different proteins can come up at different times in pregnancy. Thus serial measurements are necessary and a single time point measurement. The has not validated the identified biomarkers in plasma or circulating placental exosomes from women with and without preeclampsia. Thus the utility of these findings in real-life situations can not be judged from this work.

eLife assessment

This fundamental study significantly advances our understanding of how parasites evade the host complement immune system. The new cryo-EM structure of the trypanosome receptor ISG65 bound to complement component C3b is highly compelling and well-supported by biochemical experiments. This work will be of broad interest to parasitologists, immunologists, and structural biologists.

Reviewer #1 (Public Review):

The authors set out to use structural biology (cryo-em), SPR and complement convertase assays to understand the mechanism(s) by which ISG65 dampens the cytotoxicity/cellular clearance to/of trypanosmes opsonised with C3b by the innate immune system.

The cryo-EM structure adds significantly the the author's previous crystallographic data because the latter was limited to the C3d sub-domain of C3b. Further, the in vitro convertase assay adds an additional functional dimension to this study.

The authors have achieved their aims and the results support their conclusions.

The role of complement in immunity to T. brucei (or lack thereof) has been a significant question in molecular parasitology for over 30 years. The identification of ISG65 as the C3 receptor and now this study providing mechanistic insights represents a major advance in the field.

The authors have appropriately put their results into perspective with other recent reports on the role of ISG65.

Reviewer #3 (Public Review):

The authors investigate the mechanisms by which ISG65 and C3 recognize and interact with each other. The major strength is the identification of exo-site by determining the cryoEM structure of the complex, which suggests new intervention strategies. This is a solid body of work that has an important impact in parasitology, immunology, and structural biology.

Comments on revised version:

The authors have addressed all the previous concerns.

eLife assessment

This study advances our understanding of the allosteric regulation of anaerobic ribonucleotide reductases (RNRs) by nucleotides, providing valuable new structural insight into class III RNRs containing ATP cones. The cryo-EM structural characterization of the system is solid, but some open questions remain about the interpretation of activity/binding assays and the newly incorporated HDX-MS results. The work will be of interest to biochemists and structural biologists working on ribonucleotide reductases and other allosterically regulated enzymes.

Reviewer #1 (Public Review):

The goal of this study is to understand the allosteric mechanism of overall activity regulation in an anaerobic ribonucleotide reductase (RNR) that contains an ATP-cone domain. Through cryo-EM structural analysis of various nucleotide-bound states of the RNR, the mechanism of dATP inhibition is found to involve order-disorder transitions in the active site. These effects appear to prevent binding of substrate and a radical transfer needed to initiate the reaction.

Strengths of the manuscript include the comprehensive nature of the work - including both numerous structures of different forms of the RNR and detailed characterization of enzyme activity to establish the parameters of dATP inhibition. The manuscript has been improved in a revision by performing additional experiments to help corroborate certain aspects of the study. But these new experiments do not address all of the open questions about the structural basis for mechanism. Additionally, some questions about the strength of biochemical data and fit of binding or kinetic curves to data that were raised by other referees still remain. Some experimental observations are not consistent with the proposed model. For example, why does dATP enhance Gly radical formation when the proposed mechanism of dATP inhibition involves disorder in the Gly radical domain?

The work is impactful because it reports initial observations about a potentially new mode of allosteric inhibition in this enzyme class. It also sets the stage for future work to understand the molecular basis for this phenomenon in more detail.

Reviewer #3 (Public Review):

The manuscript by Bimai et al describes a structural and functional characterization of an anaerobic ribonucleotide reductase (RNR) enzyme from the human microbe, P. copri. More specifically, the authors aimed to characterize the mechanism by how (d)ATP modulates nucleotide reduction in this anaerobic RNR, using a combination of enzyme kinetics, binding thermodynamics, and cryo-EM structural determination, complemented by hydrogen-deuterium exchange (HDX). One of the principal findings of this paper is the ordering of a NxN 'flap' in the presence of ATP that promotes RNR catalysis and the disordering (or increased protein dynamics) of both this flap and the glycyl radical domain (GRD) when the inhibitory effector, dATP, binds. The latter is correlated with a loss of substrate binding, which is the likely mechanism for dATP inhibition. It is important to note that the GRD is remote (>30 Ang) from the binding site of the dATP molecule, suggesting long-range communication of the structural (dis)ordering. The authors also present evidence for a shift in oligomerization in the presence of dATP. The work does provide evidence for new insights/views into the subtle differences of nucleotide modulation (allostery) of RNR, in a class III system, through long-range interactions.

The strengths of the work are the impressive, in-depth structural analysis of the various regulated forms of PcRNR by (d)ATP using cryo-EM. The authors present seven different models in total, with striking differences in oligomerization and (dis)ordering of select structural features, including the GRD that is integral to catalysis. The authors present several, complementary biochemical experiments (ITC, MST, EPR, kinetics) aimed at resolving the binding and regulatory mechanism of the enzyme by various nucleotides. The authors present a good breadth of the literature in which the focus of allosteric regulation of RNRs has been on the aerobic orthologues.

The addition of hydrogen-deuterium exchange mass spectrometry (HDX-MS) complements the results originating from cryo-EM data. Most notably, is the observation of the enhanced exchange (albeit quite subtle) of the GRD domain in the presence of dATP that matches the loss of structural information in this region in the cryo-EM data. The most pronounced and compelling HDX results are seen in the form of dATP-induced protection of peptides immediately adjacent to the b-hairpin at the s-site, where dATP is expected to bind based on cryo-EM. It is clear that the presence of dATP increases the rigidity of this region.

Weaknesses: The discussion of the change in peptide mobility in the N-terminal region is complicated by the presence of bimodal mass spectral features and this may prevent detailed interpretation of the data, especially for select peptide region that shows opposite trends upon nucleotide association. Further, the HDX data in the NxN flap is unchanged upon nucleotide binding (ATP, dATP, or CTP), despite changes observed in the cryo-EM data.

eLife assessment

This useful study tests the hypothesis that Mycobacterium tuberculosis infection increases glycolysis in monocytes, which alters their capacity to migrate to lymph nodes as monocyte-derived dendritic cells. The authors conclude that infected monocytes are metabolically pre-conditioned to differentiate, with reduced expression of Hif1a and a glycolytically exhaustive phenotype, resulting in low migratory and immunologic potential. However, the evidence is incomplete as the use of live and dead mycobacteria still limits the ability to draw firm conclusions. The study will be of interest to microbiologists and infectious disease scientists.

Reviewer #3 (Public Review):

In the revised manuscript by Maio et al, the authors examined the bioenergetic mechanisms involved in the delayed migration of DC's during Mtb infection. The authors performed a series of in vitro infection experiments including bioenergetic experiments using the Agilent Seahorse XF, and glucose uptake and lactate production experiments. Also, data from SCENITH is included in the revised manuscript as well as some clinical data. This is a well written manuscript and addresses an important question in the TB field. A remaining weakness is the use of dead (irradiated) Mtb in several of the new experiments and claims where iMtb data were used to support live Mtb data. Another notable weakness lies in the author's insistence on asserting that lactate is the ultimate product of glycolysis, rather than acknowledging a large body of historical data in support of pyruvate's role in the process. This raises a perplexing issue highlighted by the authors: if Mtb indeed upregulates glycolysis, one would expect that inhibiting glycolysis would effectively control TB. However, the reality contradicts this expectation. Lastly, the examination of the bioenergetics of cells isolated from TB patients undergoing drug therapy, rather than studying them at their baseline state is a weakness.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors examine how temporal expression of the lin-4 microRNA is transcriptionally regulated.

Comments on revised version:

In the revised manuscript, the authors have suitably addressed my original concerns.

Aims achieved: The aims of the work are now achieved.

Impact: This study shows that a single transcription factor (MYRF-1) is important for the regulation of multiple microRNAs that are expressed early in development to control developmental timing.

eLife assessment

This important study shows, based on analyses of single-cell RNA-seq data sets of thymus cells, that transposable elements (TEs) are broadly expressed in thymic stromal cells, especially in medullary thymic epithelial cells and plasamacytoid dendritic cells. The authors also show that at least some TE-derived peptides are presented by MHC-I molecules in the thymus. The study provides solid findings supporting a role of TEs in thymic T-cell selection and immune self-tolerance.

Reviewer #1 (Public Review):

Summary:

Transposable Elements (TEs) are exogenously acquired DNA regions that have played important roles in the evolutional acquisition of various biological functions. TEs may have been important in the evolution of the immune system, but their role in thymocytes has not been fully clarified.

Using the human thymus scRNA dataset, the authors suggest the existence of cell type-specific TE functions in the thymus. In particular, it is interesting to show that there is a unique pattern in the type and expression level of TEs in thymic antigen-presenting cells, such as mTECs and pDCs, and that they are associated with transcription factor activities. Furthermore, the authors suggested that TEs may be non-redundantly regulated in expression by Aire, Fezf2, and Chd4, and that some TE-derived products are translated and present as proteins in thymic antigen-presenting cells. These findings provide important insights into the evolution of the acquired immune system and the process by which the thymus acquires its function as a primary lymphoid tissue.

Strengths:

(1) By performing single-cell level analysis using scRNA-seq datasets, the authors extracted essential information on heterogeneity within the cell population. It is noteworthy that this revealed the diversity of expression not only of known autoantigens but also of TEs in thymic antigen-presenting cells.

(2) The attempt to use mass spectrometry to confirm the existence of TE-derived peptides is worthwhile, even if the authors did not obtain data on as many transcripts as expected.

(3) The use of public data sets and the clearly stated methods of analysis improved the transparency of the results.

Weaknesses:

(1) The authors sometimes made overstatements largely due to the lack or shortage of experimental evidence.

For example in Figure 4, the authors concluded that thymic pDCs produced higher copies of TE-derived RNAs to support the constitutive expression of type-I interferons in thymic pDCs, unlike peripheral pDCs. However, the data was showing only the correlation between the distinct TE expression pattern in pDCs and the abundance of dsRNAs. We are compelled to say that the evidence is totally too weak to mention the function of TEs in the production of interferon. Even if pDCs express a distinct type and amount of TE-derived transcripts, it may be a negligible amount compared to the total cellular RNAs. How many TE-derived RNAs potentially form the dsRNAs? Are they over-expressed in pDCs?<br /> The data interpretation requires more caution to connect the distinct results of transcriptome data to the biological significance.

We contend that our manuscript combines the attributes of a research article (novel concepts) and a resource article (datasets of TEs implicated in various aspects of thymus function). The critical strength of our work is that it opens entirely novel research perspectives. We are unaware of previous studies on the role of TEs in the human thymus. The drawback is that, as with all novel multi-omic systems biology studies, our work provides a roadmap for a multitude of future mechanistic studies that could not be realized at this stage. Indeed, we performed wet lab experiments to validate some but not all conclusions: i) presentation of TE-derived MAPs by TECs and ii) formation of dsRNAs in thymic pDCs. In response to Reviewer #1, we performed supplementary analyses to increase the robustness of our conclusions. Also, we indicated when conclusions relied strictly on correlative evidence and clarified the hypotheses drawn from our observations. Regarding the Reviewer's questions about TE-derived dsRNAs, LINE, LTR, and SINE elements all have the potential to generate dsRNAs, given their highly repetitive nature and bi-directional transcription (1). As ~32% of TE subfamilies are overexpressed in pDCs, we hypothesized that these TE sequences might form dsRNA structures in these cells. To address the Reviewer's concerns regarding the amount of TE-derived RNAs among total cellular RNAs, we also computed the percentage of reads assigned to TEs in the different subsets of thymic APCs (see Reviewer 1 comment #4).<br /> ------

I appreciate the authors' efforts to improve the quality of this valuable paper. The additional data proposed by the authors enhanced the possibility that the non-negligible amount of RNAs in pDCs is derived from TE elements. Their biological roles and significance will be demonstrated in future research.

(2) Lack of generality of specific examples. This manuscript discusses the whole genomic picture of TE expression. In addition, one good way is to focus on the specific example to clearly discuss the biological significance of the acquisition of TEs for the thymic APC functions and the thymic selection.

In Figure 2, the authors focused on ETS-1 and its potential target genes ZNF26 and MTMR3, however, the significance of these genes in NK cell function or development is unclear. The authors should examine and discuss whether the distinct features of TEs can be found among the genomic loci that link to the fundamental function of the thymus, e.g., antigen processing/presentation.

We thank the Reviewer for this highly relevant comment. We investigated the genomic loci associated with NK cell biology to determine if ETS1 peaks would overlap with TE sequences in protein-coding genes' promoter region. Figure 2h illustrates two examples of ETS1 significant peaks overlapping TE sequences upstream of PRF1 and KLRD1. PRF1 is a protein implicated in NK cell cytotoxicity, whereas KLRD1 (CD94) dimerizes with NKG2 and regulates NK cell activation via interaction with the nonclassical MHC-I molecule HLA-E (2, 3). Thus, we modified the section of the manuscript addressing these results to include these new analyses: "Finally, we analyzed publicly available ChIP-seq data of ETS1, an important TF for NK cell development (4), to confirm its ability to bind TE sequences. Indeed, 19% of ETS1 peaks overlap with TE sequences (Figure 2g). Notably, ETS1 peaks overlapped with TE sequences (Figure 2h, in red) in the promoter regions of PRF1 and KLRD1, two genes important for NK cells' effector functions (2, 3)."<br /> ------

I am convinced by the authors' explanation that TE elements may contribute to the functions of NK cells.<br /> However, since I have understood that the main topic of this paper is about the thymus and thymic antigen-presenting cells, the mention of NK cells seems abrupt and unconnected to me. NK cells are a type of innate lymphocyte that arise in the bone marrow, and thymus is dispensable for their development and function. The readers might expect to find something more fundamental regarding the function of the thymus and immunological tolerance.

(3) Since the deep analysis of the dataset yielded many intriguing suggestions, why not add a discussion of the biological reasons and significance? For example, in Figure 1, why is TE expression negatively correlated with proliferation? cTEC-TE is mostly postnatal, while mTEC-TE is more embryonic. What does this mean?

We thank the Reviewer for this comment. To our knowledge, the relationship between cell division and transcriptional activity of TEs has not been extensively studied in the literature. However, a recent study has shown that L1 expression is induced in senescent cells. We therefore added the following sentences to our Discussion: "The negative correlation between TE expression and cell cycle scores in the thymus is coherent with recent data showing that transcriptional activity of L1s is increased in senescent cells (5). A potential rationale for this could be to prevent deleterious transposition events during DNA replication and cell division." We also added several discussion points regarding the regulation of TEs by KZFPs to answer concerns raised by Reviewer 2 (see Reviewer 2 comment #1).<br /> ------

I agree on the possibility suggested by the authors.

(4) To consolidate the experimental evidence about pDCs and TE-derived dsRNAs, one option is to show the amount of TE-derived RNA copies among total RNAs. The immunohistochemistry analysis in Figure 4 requires additional data to demonstrate that overlapped staining was not caused by technical biases (e.g. uneven fixation may cause the non-specifically stained regions/cells). To show this, authors should have confirmed not only the positive stainings but also the negative staining (e.g. CD3, etc.). Another possible staining control was showing that non-pDC (CD303- cell fractions in this case) cells were less stained by the ds-RNA probe.

We thank the Reviewer for this suggestion. We computed the proportion of reads in each cell assigned to two groups of sequences known to generate dsRNAs: TEs and mitochondrial genes (1). These analyses showed that the proportion of reads assigned to TEs is higher in pDCs than other thymic APCs by several orders of magnitude (~20% of all reads). In contrast, reads derived from mitochondrial genes had a lower abundance in pDCs. We included these results in Figure 4 - figure supplement 2 and included the following text in the Results section "To evaluate if these dsRNAs arise from TE sequences, we analyzed in thymic APC subsets the proportion of the transcriptome assigned to two groups of genomic sequences known as important sources of dsRNAs, TEs and mitochondrial genes (1). Strikingly, whereas the percentage of reads from mitochondrial genes was typically lower in pDCs than in other thymic APCs, the proportion of the transcriptome originating from TEs was higher in pDCs (~22%) by several orders of magnitude (Figure 4 - figure supplement 2)." As a negative control for the immunofluorescence experiments, we used CD123- cells. Indeed, flow cytometry analysis of the magnetically enriched CD303+ fraction was around 90% pure, as revealed by double staining with CD123 and CD304 (two additional markers of pDCs): CD123- cells were also CD304-/lo, showing that these cells are non- pDCs. Thus, we decided to compare the dsRNA signal between CD123+ cells (pDCs) and CD123- cells (non-pDCs). The difference between CD123+ and CD123- cells was striking (Figure 4d).<br /> ------

Although the technical concerns about immunostaining were not resolved, it is understandable that it would be difficult to rerun the experiment since the authors used the precious human thymi as the experimental material. Immunostaining co-staining requires careful interpretation so that careful experimental setup is needed.

eLife assessment

This important study provides an example of integrating computational and experimental approaches that lead to new insights into the energy landscape of a model kinase. Compelling use of molecular dynamics simulations and NMR spectroscopy provide a conformational description of active and excited states of the kinase; one of which has not been captured in previously solved crystal structures. Overall, this comprehensive study expands our understanding of the architecture and allosteric features of the conserved bilobal kinase domain structure.

Reviewer #1 (Public Review):

Summary:

The authors use insights into the dynamics of the PKA kinase domain, obtained by NMR experiments, to inform MD simulations that generate an energy landscape of PKA kinase domain conformational dynamics.

Strengths:

The authors integrate strong experimental data through the use of state-of-the-art MD studies and derive detailed insights into allosteric communication in PKA kinase. Comparison of wt kinase with a mutant (F100A) shows clear differences in the allosteric regulation of the two proteins. These differences can be rationalized by NMR and MD results. During the revision process, the authors have addressed the reviewers' comments adequately and have improved the accessibility of the manuscript to a wider audience.

Reviewer #3 (Public Review):

Summary:

Combining several MD simulation techniques (NMR-constrained replica-exchange metadynamics, Markov State Model, and unbiased MD) the authors identified the aC-beta4 loop of PKA kinase as a switch crucially involved in PKA nucleotide/substrate binding cooperatively. They identified a previously unreported excited conformational state of PKA (ES2), this switch controls and characterized ES2 energetics with respect to the ground state. Based on translating the simulations into chemical shits and NMR characterizing of PKA WT and an aC-beta4 mutant, the author made a convincing case in arguing that the simulation-suggested excited state is indeed an excited state observed by NMR, thus giving the excited state conformational details.

Strengths:

This work incorporates extensive simulation works, new NMR data, and in vitro biochemical analysis. It stands out in its comprehensiveness, and I think it made a great case.

Weaknesses:

The manuscript is somewhat difficult to read even for kinase experts, and even harder for the layman. The difficulty partially arises from mixing the technical description of the simulations with the structural interpretation of the results, which is more intuitive, and partially arises from the assumption that readers are familiar with kinase architecture and its key elements (the aC helix, the APE motif, etc).

eLife assessment

The authors describe an important tool, GromovMatcher, that can be used to compare proteomic data from various experimental approaches. The underlying method is innovative, the algorithm is clearly described, and the validation that is presented is convincing.

Reviewer #1 (Public Review):

Summary:

The authors have implemented Optimal Transport algorithm in GromovMatcher for comparing LC/MS features from different datasets. This paper gains significance in the proteomics field for performing meta-analysis of LC/MS data.

Strengths:

The main strength is that GromovMatcher acheives significant performance metrics compared to other existing methods. The authors have done extensive comparisons to claim that GromovMatcher performs well.

Weaknesses:

The authors might need to add the limitation of datasets and thus have tested/validated their tool using simulated data in the abstract as well.

Reviewer #2 (Public Review):

Summary

The goal of untargeted metabolomics is to identify differences between metabolomes of different biological samples.

Untargeted metabolomics identifies features with specific mass-to-charge-ratio (m/z) and retention time (RT). Matching those to specific metabolites based on the model compounds from databases is laborious and not always possible, which is why methods for comparing samples on the level of unmatched features are crucial.<br /> The main purpose of the GromovMatcher method presented here is to merge and compare untargeted metabolomes from different experiments. These larger datasets could then be used to advance biological analyses, for example, for identification of metabolic disease markers.

The main problem that complicates merging different experiments is that m/z and RT vary slightly for the same feature (metabolite).

The main idea behind the GromovMatcher is built on the assumption that if two features match between two datasets (that feature i from dataset 1 matches feature j from dataset 2, and feature k from dataset 1 matches feature l from dataset 2), then the correlations or distances between the two features within each of the datasets (i and k, and j and l) will be similar. The authors then use the Gromov-Wasserstein method to find the best matches matrix from these data.

The variation in m/z between the same features in different experiments is a user-defined value and it is initially set to 0.01 ppm. There is no clear limit for RT deviations, so the method estimates a non-linear deviation (drift) of RT between two studies. GromovMatcher estimates the drift between two studies, and then discards the matching pairs where the drift would deviate significantly from the estimate. It learns the drift from a weighted spline regression.

The authors validate the performance of their GromovMatcher method using a dataset of cord blood. They use 20 different splits and compare the GromovMatcher (both its GM and GMT iterations, whereby GMT version uses the deviation from estimated RT drift to filter the matching matrix) with two other matching methods: M2S and metabCombiner.

The second validation was done using a (scaled and centered) dataset of metabolics from cancer datasets from the EPIC cohort that were manually matched by an expert. This dataset was also used to show that using automated methods can identify more features that are associated with a particular group of samples than what was found by manual matching. Specifically, the authors identify additional features connected to alcohol consumption.

Strengths:

I see the main strength of this work in its combination of all levels of information (m/z, RT, and higher-order information on correlations between features) and using each of the types of information in a way that is appropriate for the measure. The most innovative aspect is using the Gromov-Wasserstein method to match the features based on distance matrices.

The authors of the paper identify two main shortcomings with previously established methods that attempt to match features from different experiments: a) all other methods require fine-tuning of user-defined parameters, and, more importantly, b) do not consider correlations between features. The main strength of the GromovMatcher is that it incorporates the information on distances between the features (in addition to also using m/z and RT).

Weaknesses:

The main weakness is that there seem not to be enough manually curated datasets that could be used for validation. It will, therefore, be important, for the authors, and the field in general to keep validating and improving their methods if more datasets become available.

The second weakness, as emphasized by the authors in the discussion is that the method as it is set up now can be directly used only to compare two datasets. I am confident that the authors will successfully implement novel algorithms to address this issue in the future.

eLife assessment

This study explores the physical principles underlying fluid flow and luminal transport within the endoplasmic reticulum; its important contribution is to highlight the strong physical constraints imposed by viscous dissipation in nanoscopic tubular networks. In particular, the work presents convincing evidence that commonly discussed mechanisms such as tubular contraction are unlikely to be at the origin of the observed transport velocities. As this study is solely theoretical and concerned with order of magnitude estimates, its main conclusions await experimental validation. The work will be of relevance to cell biologists and physicists interested in organelle dynamics.

Reviewer #1 (Public Review):

Theoretical principles of viscous fluid mechanics are used here to assess likely mechanisms of transport in the ER. A set of candidate mechanisms is evaluated, making good use of imaging to represent ER network geometries. Evidence is provided that the contraction of peripheral sheets provides a much more credible mechanism than the contraction of individual tubules, junctions, or perinuclear sheets.

The work has been conducted carefully and comprehensively, making good use of underlying physical principles. There is a good discussion of the role of slip; sensible approximations (low volume fraction, small particle size, slender geometries, pragmatic treatment of boundary conditions) allow tractable and transparent calculations; clear physical arguments provide useful bounds; stochastic and deterministic features of the problem are well integrated.

There are just a couple of areas where more discussion might be warranted, in my view.

(1) The energetic cost of tubule contraction is estimated, but I did not see an equivalent estimate for the contraction of peripheral sheets. It might be helpful to estimate the energetic cost of viscous dissipation in generated flows at higher frequencies. The mechanism of peripheral sheet contraction is unclear: do ATP-driven mechanisms somehow interact with thermal fluctuations of membranes?

(2) Mutations are mentioned in the abstract but not (as far as I could see) later in the manuscript. It would be helpful if any consequences for pathologies could be developed in the text.

Reviewer #2 (Public Review):

Summary:

This study explores theoretically the consequences of structural fluctuations of the endoplasmic reticulum (ER) morphology called contractions on molecular transport. Most of the manuscript consists of the construction of an interesting theoretical flow field (physical model) under various hypothetical assumptions. The computational modeling is followed by some simulations

Strengths:

The authors are focusing their attention on testing the hypothesis that a local flow in the tubule could be driven by tubular pinching. We recall that trafficking in the ER is considered to be mostly driven by diffusion at least at a spatial scale that is large enough to account for averaging of any random flow occurring from multiple directions [note that this is not the case for plants].

Weaknesses:

The manuscript extensively details the construction of the theoretical model, occupying a significant portion of the manuscript. While this section contains interesting computations, its relevance and utility could be better emphasized, perhaps warranting a reorganization of the manuscript to foreground this critical aspect.

Overall, the manuscript appears highly technical with limited conclusive insights, particularly lacking predictions confirmed by experimental validation. There is an absence of substantial conclusions regarding molecular trafficking within the ER.

eLife assessment

This useful manuscript describes a proteomic analysis of plasma from subjects before and after an exercise regime consisting of endurance and resistance exercise. The work identifies a putative new exerkine, CD300LG, and finds associations of this protein with aspects of insulin sensitivity and angiogenesis, but the evidence to support the main claims remains incomplete.

Reviewer #1 (Public Review):

Summary:

In this paper, proteomics analysis of the plasma of human subjects that underwent an exercise training regime consisting of a combination of endurance and resistance exercise led to the identification of several proteins that were responsive to exercise training. Confirming previous studies, many exercise-responsive secreted proteins were found to be involved in the extra-cellular matrix. The protein CD300LG was singled out as a potential novel exercise biomarker and the subject of numerous follow-up analyses. The levels of CD300LG were correlated with insulin sensitivity. The analysis of various open-source datasets led to the tentative suggestion that CD300LG might be connected with angiogenesis, liver fat, and insulin sensitivity. CD300LG was found to be most highly expressed in subcutaneous adipose tissue and specifically in venular endothelial cells. In a subset of subjects from the UK Biobank, serum CD300LG levels were positively associated with several measures of physical activity - particularly vigorous activity. In addition, serum CD300LG levels were negatively associated with glucose levels and type 2 diabetes. Genetic studies hinted at these associations possibly being causal. Mice carrying alterations in the CD300LG gene displayed impaired glucose tolerance, but no change in fasting glucose and insulin. Whether the production of CD300LG is changed in the mutant mice is unclear.

Strengths:

The specific proteomics approach conducted to identify novel proteins impacted by exercise training is new. The authors are resourceful in the exploitation of existing datasets to gain additional information on CD300LG.

Weaknesses:

While the analyses of multiple open-source datasets are necessary and useful, they lead to relatively unspecific correlative data that collectively insufficiently advance our knowledge of CD300LG and merely represent the starting point for more detailed investigations. Additional more targeted experiments of CD300LG are necessary to gain a better understanding of the role of CD300LG and the mechanism by which exercise training may influence CD300LG levels. One should also be careful to rely on external data for such delicate experiments as mouse phenotyping. Can the authors vouch for the quality of the data collected?

Reviewer #2 (Public Review):

Summary:

This manuscript from Lee-Odegard et al reports proteomic profiling of exercise plasma in humans, leading to the discovery of CD300LG as a secreted exercise-inducible plasma protein. Correlational studies show associations of CD300LG with glycemic traits. Lastly, the authors query available public data from CD300LG-KO mice to establish a causal role for CD300LG as a potential link between exercise and glucose metabolism. However, the strengths of this manuscript were balanced by the moderate to major weaknesses. Therefore in my opinion, while this is an interesting study, the conclusions remain preliminary and are not fully supported by the experiments shown so far.

Strengths:

(1) Data from a well-phenotyped human cohort showing exercise-inducible increases in CD300LG.

(2) Associations between CD300LG and glucose and other cardiometabolic traits in humans, that have not previously been reported.

(3) Correlation to CD300LG mRNA levels in adipose provides additional evidence for exercise-inducible increases in CD300LG.

Weaknesses:

(1) CD300LG is by sequence a single-pass transmembrane protein that is exclusively localized to the plasma membrane. How CD300LG can be secreted remains a mystery. More evidence should be provided to understand the molecular nature of circulating CD300LG. Is it full-length? Is there a cleaved fragment? Where is the epitope where the o-link is binding to CD300LG? Does transfection of CD300LG to cells in vitro result in secreted CD300LG?

(2) There is a growing recognition of specificity issues with both the O-link and somalogic platforms. Therefore it is critical that the authors use antibodies, targeted mass spectrometry, or some other methods to validate that CD300LG really is increased instead of just relying on the O-link data.

(3) It is insufficient simply to query the IMPC phenotyping data for CD300LG; the authors should obtain the animals and reproduce or determine the glucose phenotypes in their own hands. In addition, this would allow the investigators to answer key questions like the phenotype of these animals after a GTT, whether glucose production or glucose uptake is affected, whether insulin secretion in response to glucose is normal, effects of high-fat diet, and other standard mouse metabolic phenotyping assays.

(4) I was unable to find the time point at which plasma was collected at the 12-week time point. Was it immediately after the last bout of exercise (an acute response) or after some time after the training protocol (trained state)?

Reviewer #3 (Public Review):

Summary:

This manuscript by Liu et al. presents a case that CAPSL mutations are a cause of familial exudative vitreoretinopathy (FEVR). Attention was initially focused on the CAPSL gene from whole exome sequence analysis of two small families. The follow-up analyses included studies in which CAPSL was manipulated in endothelial cells of mice and multiple iterations of molecular and cellular analyses. Together, the data show that CAPSL influences endothelial cell proliferation and migration. Molecularly, transcriptomic and proteomic analyses suggest that CAPSL influences many genes/proteins that are also downstream targets of MYC and may be important to the mechanisms.

Strengths:

This multi-pronged approach found a previously unknown function for CAPSLs in endothelial cells and pointed at MYC pathways as high-quality candidates in the mechanism.

Weaknesses:

Two issues shape the overall impact for me. First, the unreported population frequency of the variants in the manuscript makes it unclear if CAPSL should be considered an interesting candidate possibly contributing to FEVR, or possibly a cause. Second, it is unclear if the identified variants act dominantly, as indicated in the pedigrees. The studies in mice utilized homozygotes for an endothelial cell-specific knockout, leaving uncertainty about what phenotypes might be observed if mice heterozygous for a ubiquitous knockout had instead been studied.

In my opinion, the following scientific issues are specific weaknesses that should be addressed:

(1) Please state in the manuscript the number of FEVR families that were studied by WES. Please also describe if the families had been selected for the absence of known mutations, and/or what percentage lack known pathogenic variants.

(2) A better clinical description of family 3104 would enhance the manuscript, especially the father. It is unclear what "manifested with FEVR symptoms, according to the medical records" means. Was the father diagnosed with FEVR? If the father has some iteration of a mild case, please describe it in more detail. If the lack of clinical images in the figure is indicative of a lack of medical documentation, please note this in the manuscript.

(3) The TGA stop codon can in some instances also influence splicing (PMID: 38012313). Please add a bioinformatic assessment of splicing prediction to the assays and report its output in the manuscript.

(4) More details regarding utilizing a "loxp-flanked allele of CAPSL" are needed. Is this an existing allele, if so, what is the allele and citation? If new (as suggested by S1), the newly generated CAPSL mutant mouse strain needs to be entered into the MGI database and assigned an official allele name - which should then be utilized in the manuscript and who generated the strain (presumably a core or company?) must be described.

(5) The statement in the methods "All mice used in the study were on a C57BL/6J genetic background," should be better defined. Was the new allele generated on a pure C57BL/6J genetic background, or bred to be some level of congenic? If congenic, to what generation? If unknown, please either test and report the homogeneity of the background, or consult with nomenclature experts (such as available through MGI) to adopt the appropriate F?+NX type designation. This also pertains to the Pdgfb-iCreER mice, which reference 43 describes as having been generated in an F2 population of C57BL/6 X CBA and did not designate the sub-strain of C57BL/6 mice. It is important because one of the explanations for missing heritability in FEVR may be a high level of dependence on genetic background. From the information in the current description, it is also not inherently obvious that the mice studied did not harbor confounding mutations such as rd1 or rd8.

(6) In my opinion, more experimental detail is needed regarding Figures 2 and 3. How many fields, of how many retinas and mice were analyzed in Figure 2? How many mice were assessed in Figure 3?

(7) I suggest adding into the methods whether P-values were corrected for multiple tests.

Reviewer #2 (Public Review):

Summary:

The authors have developed marker selection and k-means (k=2) based binary clustering algorithm for the first-level supervised clustering of the CyTOF dataset. They built a seamless pipeline that offers the multiple functionalities required for CyTOF data analysis.

Strengths:

The strength of the study is the potential use of the pipeline for the CyTOF community as a wrapper for multiple functions required for the analysis. The concept of the first line of binary clustering with known markers can be practically powerful.

Weaknesses:

The weakness of the study is that there's little conceptual novelty in the algorithms suggested from the study and the benchmarking is done in limited conditions.

eLife assessment

This valuable manuscript presents ImmCellTyper, a new toolkit for CyTOF data analysis. The semi-supervised clustering tool, BinaryClust, integrates prior biological knowledge and demonstrates competitive performance in various benchmarks, but there is room for strengthening the evidence base by addressing concerns about incomplete benchmarking results and the limited consideration of CyTOF markers with binary distribution. Overall, the manuscript offers solid potential for enhancing CyTOF data analysis methodologies.

Reviewer #1 (Public Review):

Summary:

This manuscript presented a useful toolkit designed for CyTOF data analysis, which integrates 5 key steps as an analytical framework. A semi-supervised clustering tool was developed, and its performance was tested in multiple independent datasets. The tool was compared to human experts as well as supervised and unsupervised methods.

Strengths:

The study employed multiple independent datasets to test the pipeline. A new semi-supervised clustering method was developed.

Weaknesses:

The examination of the whole pipeline is incomplete. Lack of descriptions or justifications for some analyses.

Reviewer #3 (Public Review):

Summary:

ImmCellTyper is a new toolkit for Cytometry by time-of-flight data analysis. It includes BinaryClust, a semi-supervised clustering tool (which takes into account prior biological knowledge), designed for automated classification and annotation of specific cell types and subpopulations. ImmCellTyper also integrates a variety of tools to perform data quality analysis, batch effect correction, dimension reduction, unsupervised clustering, and differential analysis.

Strengths:

The proposed algorithm takes into account the prior knowledge.<br /> The results on different benchmarks indicate competitive or better performance (in terms of accuracy and speed) depending on the method.

Weaknesses:

The proposed algorithm considers only CyTOF markers with binary distribution.

eLife assessment

This valuable study provides new insight into how non-synaptic interactions affect the activity of adjacent gustatory neurons housed within the same sensillum. The electrophysiological, behavioral, and genetic data supporting the study's conclusions are solid, although the inclusion of additional control experiments would strengthen the study. This work will be of interest to neuroscientists studying chemosensory processing or regulation of neuronal excitability.

Reviewer #1 (Public Review):

Summary:

This study identifies new types of interactions between Drosophila gustatory receptor neurons (GRNs) and shows that these interactions influence sensory responses and behavior. The authors find that HCN, a hyperpolarization-activated cation channel, suppresses the activity of GRNs in which it is expressed, preventing those GRNs from depleting the sensillum potential, and thereby promoting the activity of neighboring GRNs in the same sensilla. HCN is expressed in sugar GRNs, so HCN dampens the excitation of sugar GRNs and promotes the excitation of bitter GRNs. Impairing HCN expression in sugar GRNs depletes the sensillum potential and decreases bitter responses, especially when flies are fed on a sugar-rich diet, and this leads to decreased bitter aversion in a feeding assay. The authors' conclusions are supported by genetic manipulations, electrophysiological recordings, and behavioral assays.

Strengths:

(1) Non-synaptic interactions between neurons that share an extracellular environment (sometimes called "ephaptic" interactions) have not been well-studied, and certainly not in the insect taste system. A major strength of this study is the new insight it provides into how these interactions can impact sensory coding and behavior.

(2) The authors use many different types of genetic manipulations to dissect the role of HCN in GRN function, including mutants, RNAi, overexpression, ectopic expression, and neuronal silencing. Their results convincingly show that HCN impacts the sensillum potential and has both cell-autonomous and nonautonomous effects that go in opposite directions. There are a couple of conflicting or counterintuitive results, but the authors discuss potential explanations.

(3) Experiments comparing flies raised on different food sources suggest an explanation for why the system may have evolved the way that it did: when flies live in a sugar-rich environment, their bitter sensitivity decreases, and HCN expression in sugar GRNs helps to counteract this decrease.

Weaknesses/Limitations:

(1) The genetic manipulations were constitutive (e.g. Ih mutations, RNAi, or misexpression), and depleting Ih from birth could lead to compensatory effects that change the function of the neurons or sensillum. Using tools to temporally control Ih expression could help to confirm the results of this study.

(2) The behavioral experiment shows a striking loss of bitter sensitivity, but it was only conducted for one bitter compound at one concentration. It is not clear how general this effect is. The same is true for some of the bitter GRN electrophysiological experiments that only tested one compound and concentration.

(3) Several experiments using the Gal4/UAS system only show the Gal4/+ control and not the UAS/+ control (or occasionally neither control). Since some of the measurements in control flies seem to vary (e.g., spiking rate), it is important to compare the experimental flies to both controls to ensure that any observed effects are in fact due to the transgene expression.

(4) I was surprised that manipulations of sugar GRNs (e.g. Ih knockdown, Gr64a-f deletion, or Kir silencing) can impact the sensillum potential and bitter GRN responses even in experiments where no sugar was presented. I believe the authors are suggesting that the effects of sugar GRN activity (e.g., from consuming sugar in the fly food prior to the experiment) can have long-lasting effects, but it wasn't entirely clear if this is their primary explanation or on what timescale those long-lasting effects would occur. How much / how long of a sugar exposure do the flies need for these effects to be triggered, and how long do those effects last once sugar is removed?

(5) The authors mention that HCN may impact the resting potential in addition to changing the excitability of the cell through various mechanisms. It would be informative to record the resting potential and other neuronal properties, but this is very difficult for GRNs, so the current study is not able to determine exactly how HCN affects GRN activity.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors start by showing that HCN loss-of-function mutation causes a decrease in spiking in bitter GRNs (bGRN) while leaving sweet GRN (sGRN) response in the same sensillum intact. They show that a perturbation of HCN channels in sweet-sensing neurons causes a similar decrease while increasing the response of sugar neurons. They were also able to rescue the response by exogenous expression. Ectopic expression of HCN in bitter neurons had no effect. Next, they measure the sensillum potential and find that sensillum potential is also affected by HCN channel perturbation. These findings lead them to speculate that HCN in sGRN increases sGRN spiking which in turn affects bGRNs. To test this idea that carried out multiple perturbations aimed at decreasing sGRN activity. They found that decreasing sGRN activity by either using receptor mutant or by expressing Kir (a K+ channel) in sGRN increased bGRN responses. These responses also increase the sensillum potential. Finally, they show that these changes are behaviorally relevant as conditions that increase sGRN activity decrease avoidance of bitter substances.

Strengths:

There is solid evidence that perturbation of sweet GRNs affects bitter GRN in the same sensillum. The measurement of transsynaptic potential and how it changes is also interesting and supports the authors' conclusion.

Weaknesses:<br /> The ionic basis of how perturbation in GRN affects the transepithelial potential which in turn affects the second neuron is not clear.

Reviewer #3 (Public Review):

Ephaptic inhibition between neurons housed in the same sensilla has been long discovered in flies, but the molecular basis underlying this inhibition is underexplored. Specifically, it remains poorly understood which receptors or channels are important for maintaining the transepithelial potential between the sensillum lymph and the hemolymph (known as the sensillum potential), and how this affects the excitability of neurons housed in the same sensilla.

Lee et al. used single-sensillum recordings (SSR) of the labellar taste sensilla to demonstrate that the HCN channel, Ih, is critical for maintaining sensillum potential in flies. Ih is expressed in sugar-sensing GRNs (sGRNs) but affects the excitability of both the sGRNs and the bitter-sensing GRNs (bGRNs) in the same sensilla. Ih mutant flies have decreased sensillum potential, and bGRNs of Ih mutant flies have a decreased response to the bitter compound caffeine. Interestingly, ectopic expression of Ih in bGRNs also increases sGRN response to sucrose, suggesting that Ih-dependent increase in sensillum potential is not specific to Ih expressed in sGRNs. The authors further demonstrated, using both SSR and behavior assays, that exposure to sugars in the food substrate is important for the Ih-dependent sensitization of bGRNs. The experiments conducted in this paper are of interest to the chemosensory field. The observation that Ih is important for the activity in bGRNs albeit expressed in sGRNs is especially fascinating and highlights the importance of non-synaptic interactions in the taste system.

Despite the interesting results, this paper is not written in a clear and easily understandable manner. It uses poorly defined terms without much elaboration, contains sentences that are borderline unreadable even for those in the narrower chemosensory field, and many figures can clearly benefit from more labeling and explanation. It certainly needs a bit of work.

Below are the major points:

(1) Throughout the paper, it is assumed that Ih channels are expressed in sugar-sensing GRNs but not bitter-sensing GRNs. However, both this paper and citation #17, another paper from the same lab, contain only circumstantial evidence for the expression of Ih channels in sGRNs. A simple co-expression analysis, using the Ih-T2A-GAL4 line and Gr5a-LexA/Gr66a-LexA line, all of which are available, could easily demonstrate the co-expression. Including such a figure would significantly strengthen the conclusion of this paper.

(2) Throughout this paper, it is often unclear which class of labellar taste sensilla is being recorded. S-a, S-b, I-a, and I-b sensilla all have different sensitivities to bitters and sugars. Each figure should clearly indicate which sensilla is being recorded. Justification should be provided if recordings from different classes of sensilla are being pooled together for statistics.

(3) In many figures, there is a lack of critical control experiments. Examples include Figures 1C-F (lacking UAS control), Figure 2I-J (lacking UAS control), Figure 4E (lacking the UAS and GAL4 control, and it is also strange to compare Gr64f > RNAi with Gr66a > RNAi, instead of with parental GAL4 and UAS controls.), and Figure 5D (lacking UAS control). Without these critical control experiments, it is difficult to evaluate the quality of the work.

(4) Figure 2A could benefit from more clarification about what exactly is being recorded here. The text is confusing: a considerable amount of text is spent on explaining the technical details of how SP is recorded, but very little text about what SP represents, which is critical for the readers. The authors should clarify in the text that SP is measuring the potential between the sensillar lymph, where the dendrites of GRNs are immersed, and the hemolymph. Adding a schematic figure to show that SP represents the potential between the sensillar lymph and hemolymph would be beneficial.

(5) The sGRN spiking rate in Figure 4B deviates significantly from previous literature (Wang, Carlson, eLife 2022; Jiao, Montell PNAS 2007, as examples), and the response to sucrose in the control flies is not dosage-dependent, which raises questions about the quality of the data. Why are the responses to sucrose not dosage-dependent? The responses are clearly not saturated at these (10 mM to 100 mM) concentrations.

(6) In Figure 4C, instead of showing the average spike rate of the first five seconds and the next 5 seconds, why not show a peristimulus time histogram? It would help the readers tremendously, and it would also show how quickly the spike rate adapts to overexpression and control flies. Also, since taste responses adapt rather quickly, a 500 ms or 1 s bin would be more appropriate than a 5-second bin.

(7) Lines 215 - 220. The authors state that the presence of sugars in the culture media would expose the GRNs to sugar constantly, without providing much evidence. What is the evidence that the GRNs are being activated constantly in flies raised with culture media containing sugars? The sensilla are not always in contact with the food.

(8) Line 223. To show that bGRN spike rates in Ih mutant flies "decreased even more than WT", you need to compare the difference in spike rates between the sorbitol group and the sorbitol + sucrose group, which is not what is currently shown.

(9) To help readers better understand the proposed mechanisms here, including a schematic figure would be helpful. This should show where Ih is expressed, how Ih in sGRNs impacts the sensillum potential, how elevated sensillum potential increases the electrical driving force for the receptor current, and affects the excitability of the bGRNs in the same sensilla, and how exposure to sugar is proposed to affect ion homeostasis in the sensillum lymph.

Author Response

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

Reviewer #1 (Public Review):

Summary:

The authors aim to address a critical challenge in the field of bioinformatics: the accurate and efficient identification of protein binding sites from sequences. Their work seeks to overcome the limitations of current methods, which largely depend on multiple sequence alignments or experimental protein structures, by introducing GPSite, a multi-task network designed to predict binding residues of various molecules on proteins using ESMFold.

Strengths:

• Benchmarking. The authors provide a comprehensive benchmark against multiple methods, showcasing the performances of a large number of methods in various scenarios.

• Accessibility and Ease of Use. GPSite is highlighted as a freely accessible tool with user-friendly features on its website, enhancing its potential for widespread adoption in the research community.

RE: We thank the reviewer for acknowledging the contributions and strengths of our work!

Weaknesses:

• Lack of Novelty. The method primarily combines existing approaches and lacks significant technical innovation. This raises concerns about the original contribution of the work in terms of methodological development. Moreover, the paper reproduces results and analyses already presented in previous literature, without providing novel analysis or interpretation. This further diminishes the contribution of this paper to advancing knowledge in the field.

RE: The novelty of this work is primarily manifested in four key aspects. Firstly, although we have employed several existing tools such as ProtTrans and ESMFold to extract sequence features and predict protein conformations, these techniques were hardly explored in the field of binding site prediction. We have successfully demonstrated the feasibility of substituting multiple sequence alignments with language model embeddings and training with predicted structures, providing a new solution to overcome the limitations of current methods for genome-wide applications. Secondly, though a few methods tend to capture geometric information based on protein surfaces or atom graphs, surface calculation and property mapping are usually time-consuming, while massage passing on full atom graphs is memory-consuming and thus challenging to process long sequences. Besides, these methods are sensitive towards details and errors in the predicted structures. To facilitate large-scale annotations, we have innovatively applied geometric deep learning to protein residue graphs for comprehensively capturing backbone and sidechain geometric contexts in an efficient and effective manner (Figure 1). Thirdly, we have not only exploited multi-task learning to integrate diverse ligands and enhance performance, but also shown its capability to easily extend to the binding site prediction of other unseen ligands (Figure 4 D-E). Last but not least, as a “Tools and Resources” article, we have provided a fast, accurate and user-friendly webserver, as well as constructed a large annotation database for the sequences in Swiss-Prot. Leveraging this database, we have conducted extensive analyses on the associations between binding sites and molecular functions, biological processes, and disease-causing mutations (Figure 5), indicating the potential of our tool to unveil unexplored biology underlying genomic data.

We have now revised the descriptions in the “The geometry-aware protein binding site predictor (GPSite)” section to highlight the novelty of our work in a clearer manner:

“In conclusion, GPSite is distinguished from the previous approaches in four key aspects. First, profiting from the effectiveness and low computational cost of ProtTrans and ESMFold, GPSite is liberated from the reliance on MSA and native structures, thus enabling genome-wide binding site prediction. Second, unlike methods that only explore the Cα models of proteins 25,40, GPSite exploits a comprehensive geometric featurizer to fully refine knowledge in the backbone and sidechain atoms. Third, the employed message propagation on residue graphs is global structure-aware and time-efficient compared to the methods based on surface point clouds 21,22, and memory-efficient unlike methods based on full atom graphs 23,24. Residue-based message passing is also less sensitive towards errors in the predicted structures. Last but not least, instead of predicting binding sites for a single molecule type or learning binding patterns separately for different molecules, GPSite applies multi-task learning to better model the latent relationships among different binding partners.”

• Benchmark Discrepancies. The variation in benchmark results, especially between initial comparisons and those with PeSTo. GPSite achieves a PR AUC of 0.484 on the global benchmark but a PR AUC of 0.61 on the benchmark against PeSTo. For consistency, PeSTo should be included in the benchmark against all other methods. It suggests potential issues with the benchmark set or the stability of the method. This inconsistency needs to be addressed to validate the reliability of the results.

RE: We thank the reviewer for the constructive comments. Since our performance comparison experiments involved numerous competitive methods whose training sets are disparate, it was difficult to compare or rank all these methods fairly using a single test set. Given the substantial overlap between our protein-binding site test set and the training set of PeSTo, we meticulously re-split our entire protein-protein binding site dataset to generate a new test set that avoids any overlap with the training sets of both GPSite and PeSTo and performed a separate evaluation, where GPSite achieves a higher AUPR than PeSTo (0.610 against 0.433). This is quite common in this field. For instance, in the study of PeSTo (Nat Commun 2023), the comparisons of PeSTo with MaSIF-site, SPPIDER, and PSIVER were conducted using one test set, while the comparison with ScanNet was performed on a separate test set.

Based on the reviewer’s suggestion, we have now replaced this experiment with a direct comparison with PeSTo using the datasets from PeSTo, in order to enhance the completeness and convincingness of our results. The corresponding descriptions are now added in Appendix 1-note 2, and the results are added in Appendix 2-table 4. For convenience, we also attach the note and table here:

“Since 340 out of 375 proteins in our protein-protein binding site test set share > 30% identity with the training sequences of PeSTo, we performed a separate comparison between GPSite and PeSTo using the training and test datasets from PeSTo. By re-training with simply the same hyperparameters, GPSite achieves better performance than PeSTo (AUPR of 0.824 against 0.797) as shown in Appendix 2-table 4. Furthermore, when using ESMFold-predicted structures as input, the performance of PeSTo decreases substantially (AUPR of 0.691), and the superiority of our method will be further reflected. As in 24, the performance of ScanNet is also included (AUPR of 0.720), which is also largely outperformed by GPSite.”

Author response table 1.

Performance comparison of GPSite with ScanNet and PeSTo on the protein-protein binding site test set from PeSTo 24

Note: The performance of ScanNet and PeSTo are directly obtained from 24. PeSTo* denotes evaluation using the ESMFold-predicted structures as input. The metrics provided are the median AUPR, median AUC and median MCC. The best/second-best results are indicated by bold/underlined fonts.

• Interface Definition Ambiguity. There is a lack of clarity in defining the interface for the binding site predictions. Different methods are trained using varying criteria (surfaces in MaSIF-site, distance thresholds in ScanNet). The authors do not adequately address how GPSite's definition aligns with or differs from these standards and how this issue was addressed. It could indicate that the comparison of those methods is unreliable and unfair.

RE: We thank the reviewer for the comments. The precise definition of ligand-binding sites is elucidated in the “Benchmark datasets” section. Specifically, the datasets of DNA, RNA, peptide, ATP, HEM and metal ions used to train GPSite were collected from the widely acknowledged BioLiP database [PMID: 23087378]. In BioLiP, a binding residue is defined if the smallest atomic distance between the target residue and the ligand is <0.5 Å plus the sum of the Van der Waal’s radius of the two nearest atoms. Meanwhile, most comparative methods regarding these ligands were also trained on data from BioLiP, thereby ensuring fair comparisons.

However, since BioLiP does not include data on protein-protein binding sites, studies for protein-protein binding site prediction may adopt slightly distinct label definitions, as the reviewer suggested. Here, we employed the protein-protein binding site data from our previous study [PMID: 34498061], where a protein-binding residue was defined as a surface residue (relative solvent accessibility > 5%) that lost more than 1 Å2 absolute solvent accessibility after protein-protein complex formation. This definition was initially introduced in PSIVER [PMID: 20529890] and widely applied in various studies (e.g., PMID: 31593229, PMID: 32840562). SPPIDER [PMID: 17152079] and MaSIF-site [PMID: 31819266] have also adopted similar surface-based definitions as PSIVER. On the other hand, ScanNet [PMID: 35637310] employed an atom distance threshold of 4 Å to define contacts while PeSTo [PMID: 37072397] used a threshold of 5 Å. However, it is noteworthy that current methods in this field including ScanNet (Nat Methods 2022) and PeSTo (Nat Commun 2023) directly compared methods using different label definitions without any alignment in their benchmark studies, likely due to the subtle distinctions among these definitions. For instance, the study of PeSTo directly performed comparisons with ScanNet, MaSIF-site, SPPIDER, and PSIVER. Therefore, we followed these previous works, directly comparing GPSite with other protein-protein binding site predictors.

In the revised “Benchmark datasets” section, we have now provided more details for the binding site definitions in different datasets to avoid any potential ambiguity:

“The benchmark datasets for evaluating binding site predictions of DNA, RNA, peptide, ATP, and HEM are constructed from BioLiP”; “A binding residue is defined if the smallest atomic distance between the target residue and the ligand is < 0.5 Å plus the sum of the Van der Waal’s radius of the two nearest atoms”; “Besides, the benchmark dataset of protein-protein binding sites is directly from 26, which contains non-redundant transient heterodimeric protein complexes dated up to May 2021. Surface regions that become solvent inaccessible on complex formation are defined as the ground truth protein-binding sites. The benchmark datasets of metal ion (Zn2+, Ca2+, Mg2+ and Mn2+) binding sites are directly from 18, which contain non-redundant proteins dated up to December 2021 from BioLiP.”

While GPSite demonstrates the potential to surpass state-of-the-art methods in protein binding site prediction, the evidence supporting these claims seems incomplete. The lack of methodological novelty and the unresolved questions in benchmark consistency and interface definition somewhat undermine the confidence in the results. Therefore, it's not entirely clear if the authors have fully achieved their aims as outlined.

The work is useful for the field, especially in disease mechanism elucidation and novel drug design. The availability of genome-scale binding residue annotations GPSite offers is a significant advancement. However, the utility of this tool could be hampered by the aforementioned weaknesses unless they are adequately addressed.

RE: We thank the reviewer for acknowledging the advancement and value of our work, as well as pointing out areas where improvements can be made. As discussed above, we have now carried out the corresponding revisions in the revised manuscript to enhance the completeness and clearness of our work.

Reviewer #2 (Public Review):

Summary:

This work provides a new framework, "GPsite" to predict DNA, RNA, peptide, protein, ATP, HEM, and metal ions binding sites on proteins. This framework comes with a webserver and a database of annotations. The core of the model is a Geometric featurizer neural network that predicts the binding sites of a protein. One major contribution of the authors is the fact that they feed this neural network with predicted structure from ESMFold for training and prediction (instead of native structure in similar works) and a high-quality protein Language Model representation. The other major contribution is that it provides the public with a new light framework to predict protein-ligand interactions for a broad range of ligands.

The authors have demonstrated the interest of their framework with mostly two techniques: ablation and benchmark.

Strengths:

• The performance of this framework as well as the provided dataset and web server make it useful to conduct studies.

• The ablations of some core elements of the method, such as the protein Language Model part, or the input structure are very insightful and can help convince the reader that every part of the framework is necessary. This could also guide further developments in the field. As such, the presentation of this part of the work can hold a more critical place in this work.

RE: We thank the reviewer for recognizing the contributions of our work and for noting that our experiments are thorough.

Weaknesses:

• Overall, we can acknowledge the important effort of the authors to compare their work to other similar frameworks. Yet, the lack of homogeneity of training methods and data from one work to the other makes the comparison slightly unconvincing, as the authors pointed out. Overall, the paper puts significant effort into convincing the reader that the method is beating the state of the art. Maybe, there are other aspects that could be more interesting to insist on (usability, interest in protein engineering, and theoretical works).

RE: We sincerely appreciate the reviewer for the constructive and insightful comments. As to the concern of training data heterogeneity raised by the reviewer, it is noteworthy that current studies in this field, such as ScanNet (Nat Methods 2022) and PeSTo (Nat Commun 2023), directly compare methods trained on different datasets in their benchmark experiments. Therefore, we have adhered to the paradigm in these previous works. According to the detailed recommendations by the reviewer, we have now improved our manuscript by incorporating additional ablation studies regarding the effects of training procedure and language model representations, as well as case studies regarding the predicted structure’s quality and GPSite-based function annotations. We have also refined the Discussion section to focus more on the achievements of this work. A comprehensive point-by-point response to the reviewer’s recommendations is provided below.

Reviewer #2 (Recommendations For The Authors):

Major comments:

Overall I think the work is slightly deserved by its presentation. Some improvements could be made to the paper to better highlight the significance of your contribution.

RE: We thank the reviewer for recognizing the significance of our work!

• Line 188: "As expected, the performance of these methods mostly decreases substantially utilizing predicted structures for testing because they were trained with high-quality native structures.

This is a major ablation that was not performed in this case. You used the predicted structure to train, while the other did not. One better way to assess the interest of this approach would be to compare the performance of a network trained with only native structure to compare the leap in performance with and without this predicted structure as you did after to assess the interest of some other aspect of your method such as single to multitask.

RE: We thank the reviewer for the valuable recommendation. We have now assessed the benefit of training with predicted instead of native structures, which brings an average AUPR increase of 4.2% as detailed in Appendix 1-note 5 and Appendix 2-table 9. For convenience, we also attach the note and table here:

“We examined the performance under different training and evaluation settings as shown in Appendix 2-table 9. As expected, the model yields exceptional performance (average AUPR of 0.656) when trained and evaluated using native structures. However, if this model is fed with predicted structures of the test proteins, the performance substantially declines to an average AUPR of 0.573. This trend aligns with the observations for other structure-based methods as illustrated in Figure 2. More importantly, in the practical scenario where only predicted structures are available for the target proteins, training the model with predicted structures (i.e., GPSite) results in superior performance than training the model with native structures (average AUPR of 0.594 against 0.573), probably owing to the consistency between the training and testing data. For completeness, the results in Appendix 3-figure 2 are also included where GPSite is tested with native structures (average AUPR of 0.637).”

Author response table 2.

Performance comparison on the ten binding site test sets under different training and evaluation settings

Note: The numbers in this table are AUPR values. “Pep” and “Pro” denote peptide and protein, respectively. “Avg” means the average AUPR values among the ten test sets. “native” and “predicted” denote applying native and predicted structures as input, respectively.

• Line 263: "ProtTrans consistently obtains competitive or superior performance compared to the MSA profiles, particularly for the target proteins with few homologous sequences (Neff < 2)."

This seems a bit far-fetched. If we see clearly in the figure that the performances are far superior for Neff < 2. The performances seem rather similar for higher Neff. Could the author evaluate numerically the significance of the improvement? MSA profiles outperform GPSite on 4 intervals and I don't know the distribution of the data.

RE: We thank the reviewer for the valuable suggestion. We have now revised this sentence to avoid any potential ambiguity:

“As evidenced in Figure 4B and Appendix 2-table 8, ProtTrans consistently obtains competitive or superior performance compared to the MSA profile. Notably, for the target proteins with few homologous sequences (Neff < 2), ProtTrans surpasses MSA profile significantly with an improvement of 3.9% on AUC (P-value = 4.3×10-8).”

The detailed significance tests and data distribution are now added in Appendix 2-table 8 and attached below as Author response-table 3 for convenience:

Author response table 3.

Performance comparison between GPSite and the baseline model using MSA profile for proteins with different Neff values in the combined test set of the ten ligands

Note: Significance tests are performed following the procedure in 12,25. If P-value < 0.05, the difference between the performance is considered statistically significant.

• Line 285: "We first visualized the distributions of residues in this dataset using t-SNE, where the residues are encoded by raw feature vectors encompassing ProtTrans embeddings and DSSP structural properties, or latent embedding vectors from the shared network of GPSite. "

Wouldn't embedding from single-task be more relevant to show the interest of multi-task training here? Is the difference that big when comparing embeddings from single-task training to embeddings from multi-task training? Otherwise, I think the evidence from Figure 4e is sufficient, the interest of multitasking could be well-shown by single-task vs. multi-task AUPR and a few examples or predictions that are improved.

RE: We thank the reviewer for the comment. In the second paragraph of the “The effects of protein features and model designs” section, we have compared the performance of multi-task and single-task learning. However, the visualization results in Figure 4D are related to the third paragraph, where we conducted a downstream exploration of the possibility to extend GPSite to other unseen ligands. This is based on the hypothesis that the shared network in GPSite may have captured certain common ligand-binding mechanisms during the preceding multi-task training process. We visualized the distributions of residues in an unseen carbohydrate-binding site dataset using t-SNE, where the residues are encoded by raw feature vectors (ProtTrans and DSSP), or latent embedding vectors from the shared network trained before. Although the shared network has not been specifically trained on the carbohydrate dataset, the latent representations from GPSite effectively improve the discriminability between the binding and non-binding residues as shown in Figure 4D. This finding indicates that the shared network trained on the initial set of ten molecule types has captured common binding mechanisms and may be applied to other unseen ligands.

We have now added more descriptions in this paragraph to avoid potential ambiguity:

“Residues that are conserved during evolution, exposed to solvent, or inside a pocket-shaped domain are inclined to participate in ligand binding. During the preceding multi-task training process, the shared network in GPSite should have learned to capture such common binding mechanisms. Here we show how GPSite can be easily extended to the binding site prediction for other unseen ligands by adopting the pre-trained shared network as a feature extractor. We considered a carbohydrate-binding site dataset from 54 which contains 100 proteins for training and 49 for testing. We first visualized the distributions of residues in this dataset using t-SNE 55, where the residues are encoded by raw feature vectors encompassing ProtTrans embeddings and DSSP structural properties, or latent embedding vectors from the shared network of GPSite trained on the ten molecule types previously.”

• Line291: "Employing these informative hidden embeddings as input features to train a simple MLP exhibits remarkable performance with an AUC of 0.881 (Figure 4E), higher than that of training a single-task version of GPSite from scratch (AUC of 0.853) or other state-of-the-art methods such as MTDsite and SPRINT-CBH."

Is it necessary to introduce other methods here? The single-task vs multi-task seems enough for what you want to show?

RE: We thank the reviewer for the comment. As discussed above, here we aim to show the potential of GPSite for the binding site prediction of unseen ligand (i.e., carbohydrate) by adopting the pre-trained shared network as a feature extractor. Thus, we think it’s reasonable to also include the performance of other state-of-the-art methods in this carbohydrate benchmark dataset as baselines.

• Line 321: "Specifically, a protein-level binding score can be generated for each ligand by averaging the top k predicted scores among all residues. Empirically, we set k to 5 for metal ions and 10 for other ligands, considering that the binding interfaces of metal ions are usually smaller."

Since binding sites are usually not localized on one single amino-acid, we can expect that most of the top k residues are localized around the same area of the protein both spatially and along the sequence. Is it something you observe and could consider in your method?

RE: We thank the reviewer for the comment. We employed a straightforward method (top-k average) to convert GPSite’s residue-level annotations into protein-level annotations, where k was set empirically based on the distributions of the numbers of binding residues per sequence observed in the training set. We have not put much effort in optimizing this strategy since it mainly serves as a proof-of-concept experiment (Figure 5 A-C) to show the potential of GPSite in discriminating ligand-binding proteins. We have now revised this sentence to better explain how we selected k:

“Specifically, a protein-level binding score indicating the overall binding propensity to a specific ligand can be generated by averaging the top k predicted scores among all residues. Empirically, we set k to 5 for metal ions and 10 for other ligands, considering the distributions of the numbers of binding residues per sequence observed in the training set.”

As for the question raised by the reviewer, we can indeed expect that most of the top k predicted binding residues tend to cluster into several but not necessarily one area. For instance, certain macromolecules like DNA may interact with several protein surface patches due to their elongated structures (e.g., Author esponse-figure 1A). Another case may be a protein binding to multiple molecules of the same ligand type (e.g., Author response-figure 1B).

Author response image 1.

The structures of 4XQK (A) and 4KYW (B) in PDB.

• Line 327: The accuracy of the GPSite protein-level binding scores is further validated by the ROC curves in Figure 5B, where GPSite achieves satisfactory AUC values for all ligands except protein (AUC of 0.608).

Here may be a good place to compare yourself with others, do other frameworks experience the same problem? If so, AUC and AUPR are not relevant here, can you expose some recall scores for example?

RE: We thank the reviewer for the valuable recommendation. We have conducted comprehensive method comparisons in the preceding “GPSite outperforms state-of-the-art methods” section, where GPSite surpasses all existing frameworks across various ligands. Here, the genome-wide analyses of Swiss-Prot in Figure 5 serve as a downstream demonstration of GPSite’s capacity for large-scale annotations. We didn’t compare with other methods since most of them are time-consuming or memory-consuming, thus unavailable to process sequences of substantial quantity or length. For example, it takes about 8 min for the MSA-based method GraphBind to annotate a protein with 500 residues, while it just takes about 20 s for GPSite (see Appendix 3-figure 1 for detailed runtime comparison). It is also challenging for the atom-graph-based method PeSTo to process structures more than 100 kDa (~1000 residues) on a 32 GB GPU as the authors suggested, while GPSite can easily process structures containing up to 2500 residues on a 16 GB GPU.

Regarding the recall score mentioned by the reviewer, GPSite achieves a recall of 0.95 (threshold = 0.5) for identifying protein-binding proteins. This indicates that GPSite can accurately identify positive samples, but it also tends to misclassify negative samples as positive. In our original manuscript, we claimed that “This may be ascribed to the fact that protein-protein interactions are ubiquitous in living organisms while the Swiss-Prot function annotations are incomplete”. To better support this claim, we have now added two examples in Appendix 1-note 7, where GPSite confidently predicted the presences of the “protein binding” function (GO:0005515). Notably, this function was absent in these two proteins in the Swiss-Prot database at the time of manuscript preparation (release: 2023-05-03), but has been included in the latest release of Swiss-Prot (release: 2023-11-08). For convenience, we also attach the note here:

“As depicted in Figure 5A, GPSite assigns relatively high prediction scores to the proteins without “protein binding” function in the Swiss-Prot annotations, leading to a modest AUC value of 0.608 (Figure 5B). This may be ascribed to the fact that protein-protein interactions are ubiquitous in living organisms while the Swiss-Prot function annotations are incomplete. To support this hypothesis, we present two proteins as case studies, both sharing < 20% sequence identity with the protein-binding training set of GPSite. The first case is Aminodeoxychorismate synthase component 2 from Escherichia coli (UniProt ID: P00903). GPSite confidently predicted this protein as a protein-binding protein with a high prediction score of 0.936. Notably, this protein was not annotated with the “protein binding” function (GO:0005515) or any of its GO child terms in the Swiss-Prot database at the time of manuscript preparation (https://rest.uniprot.org/unisave/P00903?format=txt&versions=171, release: 2023-05-03). However, in the latest release of Swiss-Prot (https://rest.uniprot.org/unisave/P00903?format=txt&versions=174, release: 2023-11-08) during manuscript revision, this protein is annotated with the “protein heterodimerization activity” function (GO:0046982), which is a child term of “protein binding”. In fact, the heterodimerization activity of this protein has been validated through experiments in the year of 1996 (PMID: 8679677), indicating the potential incompleteness of the Swiss-Prot annotations. The other case is Hydrogenase-2 operon protein HybE from Escherichia coli (UniProt ID: P0AAN1), which was also predicted as a protein-binding protein by GPSite (score = 0.909). Similarly, this protein was not annotated with the “protein binding” function in the Swiss-Prot database at the time of manuscript preparation (https://rest.uniprot.org/unisave/P0AAN1?format=txt&versions=108). However, in the latest release of Swiss-Prot (https://rest.uniprot.org/unisave/P0AAN1?format=txt&versions=111), this protein is annotated with the “preprotein binding” function (GO:0070678), which is a child term of “protein binding”. In fact, the preprotein binding function of this protein has been validated through experiments in the year of 2003 (PMID: 12914940). These cases demonstrate the effectiveness of GPSite for completing the missing function annotations in Swiss-Prot.”

• Line 381: 'Despite the noteworthy advancements achieved by GPSite, there remains scope for further improvements. Given that the ESM Metagenomic Atlas 34 provides 772 million predicted protein structures along with pre-computed language model embeddings, self-supervised learning can be employed to train a GPSite model for predicting masked sequence and structure attributes, or maximizing the similarity between the learned representations of substructures from identical proteins while minimizing the similarity between those from different proteins using a contrastive loss function training from scratch. Additional opportunities for upgrade exist within the network architecture. For example, a variational Expectation-Maximization (EM) framework 58 can be adopted to handle the hierarchical graph structure inherent in proteins, which contains the top view of the residue graph and the bottom view of the atom graph inside a residue. Such an EM procedure enables training two separate graph neural networks for the two views while simultaneously allowing interaction and mutual enhancement between the two modules. Meta-learning could also be explored in this multi-task scenario, which allows fast adaptation to unseen tasks with limited labels.'

I think this does not belong here. It feels like half of your discussion is not talking about the achievements of this paper but future very specific directions. Focus on the take-home arguments (performances of the model, ability to predict a large range of tasks, interest in key components of your model, easy use) of the paper and possible future direction but without being so specific.

RE: We thank the reviewer for the valuable suggestion. We have now simplified the discussions on the future directions notably:

“Despite the noteworthy advancements achieved by GPSite, there remains scope for further improvements. GPSite may be improved by pre-training on the abundant predicted structures in ESM Metagenomic Atlas, and then fine-tuning on binding site datasets. Besides, the hidden embeddings from ESMFold may also serve as informative protein representations. Additional opportunities for upgrade exist within the network architecture. For example, a variational Expectation-Maximization framework can be adopted to handle the hierarchical atom-to-residue graph structure inherent in proteins. Meta-learning could also be explored in this multi-task scenario, which allows fast adaptation to unseen tasks with limited labels.”

• Overall there is also a lack of displayed structure. You should try to select a few examples of binding sites that were identified correctly by your method and not by others, if possible get some insights on why. Also, some negative examples could be interesting so as to have a better idea of the interest.

RE: We thank the reviewer for the valuable recommendation. We have performed a case study for the structure of the glucocorticoid receptor in Figure 3 D-H to illustrate a potential reason for the robustness of GPSite. Moreover, we have now added a case study in Appendix 1-note 3 and Appendix 3-figure 5 to explain why GPSite sometimes is not as accurate as the state-of-the-art structure-based method. For convenience, we also attach the note and figure here:

“Here we present an example of an RNA-binding protein, i.e., the ribosome biogenesis protein ERB1 (PDB: 7R6Q, chain m), to illustrate the impact of predicted structure’s quality. As shown in Appendix 3-figure 5, ERB1 is an integral component of a large multimer structure comprising protein and RNA chains (i.e., the state E2 nucleolar 60S ribosome biogenesis intermediate). Likely due to the neglect of interactions from other protein chains, ESMFold fails to predict the correct conformation of the ERB1 chain (TM-score = 0.24). Using this incorrect predicted structure, GPSite achieves an AUPR of 0.580, lower than GraphBind input with the native structure (AUPR = 0.636). However, the performance of GraphBind substantially declines to an AUPR of 0.468 when employing the predicted structure as input. Moreover, if GPSite adopts the native structure for prediction, a notable performance boost can be obtained (AUPR = 0.681).”

Author response image 2.

The prediction results of GPSite and GraphBind for the ribosome biogenesis protein ERB1. (A) The state E2 nucleolar 60S ribosome biogenesis intermediate (PDB: 7R6Q). The ribosome biogenesis protein ERB1 (chain m) is highlighted in blue, while other protein chains are colored in gray. The RNA chains are shown in orange. (B) The RNA-binding sites on ERB1 (colored in red). (C) The ESMFold-predicted structure of ERB1 (TM-score = 0.24). The RNA-binding sites are also mapped onto this predicted structure (colored in red). (D-G) The prediction results of GPSite and GraphBind for the predicted and native ERB1 structures. The confidence of the predictions is represented with a gradient of color from blue for non-binding to red for binding.

Minor comments:

• Line 169: "Note that since our test sets may partly overlap with the training sets of these methods, the results reported here should be the upper limits for the existing methods."

Yes, but they were potentially not trained on the most recent structures in that case. These methods could also see improved performance with an updated training set.

RE: We thank the reviewer for the comment. We have now deleted this sentence.

• Line176: "Since 358 of the 375 proteins in our protein-binding site test set share > 30% identity with the training sequences of PeSTo, we re-split our protein-binding dataset to generate a test set of 65 proteins sharing < 30% identity with the training set of PeSTo for a fair evaluation."

Too specific to be here in my opinion.

RE: We thank the reviewer for the comment. We have now moved these details to Appendix 1-note 2. The description in the main text here is now more concise:

“Given the substantial overlap between our protein-binding site test set and the training set of PeSTo, we conducted separate training and comparison using the datasets of PeSTo, where GPSite still demonstrates a remarkable improvement over PeSTo (Appendix 1-note 2).”

• Figure 2. The authors should try to either increase Fig A's size or increase the font size. This could probably be done by compressing the size of Figure C into a single figure.

RE: We thank the reviewer for the suggestion. We have now increased the font size in Figure A. Besides, the figures in the final version of the manuscript should be clearer where we could upload SVG files.

• Have you tried using embeddings from more structure-aware pLM such as ESM Fold embeddings (fine-tuned) or ProstTrans (that may be more recent than this study)?

RE: We thank the reviewer for the insightful comment. We have not yet explored the embeddings from structure-aware pLM, but we acknowledge its potential as a promising avenue for future investigation. We have now added this point in our Discussion section:

“Besides, the hidden embeddings from ESMFold may also serve as informative protein representations.”

Reviewer #3 (Public Review):

Summary

The authors of this work aim to address the challenge of accurately and efficiently identifying protein binding sites from sequences. They recognize that the limitations of current methods, including reliance on multiple sequence alignments or experimental protein structure, and the under-explored geometry of the structure, which limit the performance and genome-scale applications. The authors have developed a multi-task network called GPSite that predicts binding residues for a range of biologically relevant molecules, including DNA, RNA, peptides, proteins, ATP, HEM, and metal ions, using a combination of sequence embeddings from protein language models and ESMFold-predicted structures. Their approach attempts to extract residual and relational geometric contexts in an end-to-end manner, surpassing current sequence-based and structure-based methods.

Strengths

• The GPSite model's ability to predict binding sites for a wide variety of molecules, including DNA, RNA, peptides, and various metal ions.

• Based on the presented results, GPSite outperforms state-of-the-art methods in several benchmark datasets.

• GPSite adopts predicted structures instead of native structures as input, enabling the model to be applied to a wider range of scenarios where native structures are rare.

• The authors emphasize the low computational cost of GPSite, which enables rapid genome-scale binding residue annotations, indicating the model's potential for large-scale applications.

RE: We thank the reviewer for recognizing the significance and value of our work!

Weaknesses

• One major advantage of GPSite, as claimed by the authors, is its efficiency. Although the manuscript mentioned that the inference takes about 5 hours for all datasets, it remains unclear how much improvement GPSite can offer compared with existing methods. A more detailed benchmark comparison of running time against other methods is recommended (including the running time of different components, since some methods like GPSite use predicted structures while some use native structures).

RE: We thank the reviewer for the valuable suggestion. Empirically, it takes about 5-20 min for existing MSA-based methods to make predictions for a protein with 500 residues, while it only takes about 1 min for GPSite (including structure prediction). However, it is worth noting that some predictors in our benchmark study are solely available as webservers, and it is challenging to compare the runtime between a standalone program and a webserver due to the disparity in hardware configurations. Therefore, we have now included comprehensive runtime comparisons between the GPSite webserver and other top-performing servers in Appendix 3-figure 1 to illustrate the practicality and efficiency of our method. For convenience, we also attach the figure here as Author response-figure 3. The corresponding description is now added in the “GPSite outperforms state-of-the-art methods” section:

“Moreover, GPSite is computationally efficient, achieving comparable or faster prediction speed compared to other top-performing methods (Appendix 3-figure 1).”

Author response image 3.

Runtime comparison of the GPSite webserver with other top-performing servers. Five protein chains (i.e., 8HN4_B, 8USJ_A, 8C1U_A, 8K3V_A and 8EXO_A) comprising 100, 300, 500, 700, and 900 residues, respectively, were selected for testing, and the average runtime is reported for each method. Note that a significant portion of GPSite’s runtime (75 s, indicated in orange) is allocated to structure prediction using ESMFold.

• Since the model uses predicted protein structure, the authors have conducted some studies on the effect of the predicted structure's quality. However, only the 0.7 threshold was used. A more comprehensive analysis with several different thresholds is recommended.

RE: We thank the reviewer for the comment. We assessed the effect of the predicted structure's quality by evaluating GPSite’s performance on high-quality (TM-score > 0.7) and low-quality (TM-score ≤ 0.7) predicted structures. We did not employ multiple thresholds (e.g., 0.3, 0.5, and 0.7), as the majority of proteins in the test sets were accurately predicted by ESMFold. Specifically, as shown in Figure 3B, Appendix 3-figure 3 and Appendix 2-table 5, the numbers of proteins with TM-score ≤ 0.7 are small in most datasets (e.g., 42 for DNA and 17 for ATP). Consequently, there is insufficient data available for analysis with lower thresholds, except for the RNA test set. Notably, Figure 3C presents a detailed inspection of the 104 proteins with TM-score < 0.5 in the RNA test set. Within this subset, GPSite consistently outperforms the state-of-the-art structure-based method GraphBind with predicted structures as input, regardless of the prediction quality of ESMFold. Only in cases where structures are predicted with extremely low quality (TM-score < 0.3) does GPSite fall behind GraphBind input with native structures. This result further demonstrates the robustness of GPSite. We have now added clearer explanations in the “GPSite is robust for low-quality predicted structures” section:

“Figure 3B and Appendix 3-figure 3 show the distributions of TM-scores between native and predicted structures calculated by US-align in the ten benchmark datasets, where most proteins are accurately predicted with TM-score > 0.7 (see also Appendix 2-table 5)”; “Given the infrequency of low-quality predicted structures except for the RNA test set, we took a closer inspection of the 104 proteins with predicted structures of TM-score < 0.5 in the RNA test set.”

• To demonstrate the robustness of GPSite, the authors performed a case study on human GR containing two zinc fingers, where the predicted structure is not perfect. The analysis could benefit from more a detailed explanation of why the model can still infer the binding site correctly even though the input structural information is slightly off.

RE: We thank the reviewer for the comment. We have actually explained the potential reason for the robustness of GPSite in the second paragraph of the “GPSite is robust for low-quality predicted structures” section. In summary, although the whole structure of this protein is not perfectly predicted, the local structures of the binding domains of peptide, DNA and Zn2+ are actually predicted accurately as evidenced by the superpositions of the native and predicted structures in Figure 3D and 3E. Therefore, GPSite can still make reliable predictions. We have now revised this paragraph to explain these more clearly:

“Figure 3D shows the structure of the human glucocorticoid receptor (GR), a transcription factor that binds DNA and assembles a coactivator peptide to regulate gene transcription (PDB: 7PRW, chain A). The DNA-binding domain of GR also consists of two C4-type zinc fingers to bind Zn2+ ions. Although the structure of this protein is not perfectly predicted (TM-score = 0.72), the local structures of the binding domains of peptide and DNA are actually predicted accurately as viewed by the superpositions of the native and predicted structures in Figure 3D and 3E. Therefore, GPSite can correctly predict all Zn2+ binding sites and precisely identify the binding sites of DNA and peptide with AUPR values of 0.949 and 0.924, respectively (Figure 3F, G and H).”

• To analyze the relatively low AUC value for protein-protein interactions, the authors claimed that it is "due to the fact that protein-protein interactions are ubiquitous in living organisms while the Swiss-Prot function annotations are incomplete", which is unjustified. It is highly recommended to support this claim by showing at least one example where GPSite's prediction is a valid binding site that is not present in the current Swiss-Prot database or via other approaches.

RE: We thank the reviewer for the valuable recommendation. To support this claim, we have now added two examples in Appendix 1-note 7, where GPSite confidently predicted the presences of the “protein binding” function (GO:0005515). Notably, this function was absent in these two proteins in the Swiss-Prot database at the time of manuscript preparation (release: 2023-05-03), but has been included in the latest release of Swiss-Prot (release: 2023-11-08). For convenience, we also attach the note below:

“As depicted in Figure 5A, GPSite assigns relatively high prediction scores to the proteins without “protein binding” function in the Swiss-Prot annotations, leading to a modest AUC value of 0.608 (Figure 5B). This may be ascribed to the fact that protein-protein interactions are ubiquitous in living organisms while the Swiss-Prot function annotations are incomplete. To support this hypothesis, we present two proteins as case studies, both sharing < 20% sequence identity with the protein-binding training set of GPSite. The first case is Aminodeoxychorismate synthase component 2 from Escherichia coli (UniProt ID: P00903). GPSite confidently predicted this protein as a protein-binding protein with a high prediction score of 0.936. Notably, this protein was not annotated with the “protein binding” function (GO:0005515) or any of its GO child terms in the Swiss-Prot database at the time of manuscript preparation (https://rest.uniprot.org/unisave/P00903?format=txt&versions=171, release: 2023-05-03). However, in the latest release of Swiss-Prot (https://rest.uniprot.org/unisave/P00903?format=txt&versions=174, release: 2023-11-08) during manuscript revision, this protein is annotated with the “protein heterodimerization activity” function (GO:0046982), which is a child term of “protein binding”. In fact, the heterodimerization activity of this protein has been validated through experiments in the year of 1996 (PMID: 8679677), indicating the potential incompleteness of the Swiss-Prot annotations. The other case is Hydrogenase-2 operon protein HybE from Escherichia coli (UniProt ID: P0AAN1), which was also predicted as a protein-binding protein by GPSite (score = 0.909). Similarly, this protein was not annotated with the “protein binding” function in the Swiss-Prot database at the time of manuscript preparation (https://rest.uniprot.org/unisave/P0AAN1?format=txt&versions=108). However, in the latest release of Swiss-Prot (https://rest.uniprot.org/unisave/P0AAN1?format=txt&versions=111), this protein is annotated with the “preprotein binding” function (GO:0070678), which is a child term of “protein binding”. In fact, the preprotein binding function of this protein has been validated through experiments in the year of 2003 (PMID: 12914940). These cases demonstrate the effectiveness of GPSite for completing the missing function annotations in Swiss-Prot.”

• The authors reported that many GPSite-predicted binding sites are associated with known biological functions. Notably, for RNA-binding sites, there is a significantly higher proportion of translation-related binding sites. The analysis could benefit from a further investigation into this observation, such as the analyzing the percentage of such interactions in the training site. In addition, if there is sufficient data, it would also be interesting to see the cross-interaction-type performance of the proposed model, e.g., train the model on a dataset excluding specific binding sites and test its performance on that class of interactions.

RE: We thank the reviewer for the suggestion. We would like to clarify that the analysis in Figure 5C was conducted at “protein-level” instead of “residue-level”. As described in the second paragraph of the “Large-scale binding site annotation for Swiss-Prot” section, a protein-level ligand-binding score was assigned to a protein by averaging the top k residue-level predicted binding scores. This protein-level score indicates the overall binding propensity of the protein to a specific ligand. We gathered the top 20,000 proteins with the highest protein-level binding scores for each ligand and found that their biological process annotations from Swiss-Prot were consistent with existing knowledge. We have now revised the corresponding sentence to explain these more clearly:

“Exploiting the residue-level binding site annotations, we could readily extend GPSite to discriminate between binding and non-binding proteins of various ligands. Specifically, a protein-level binding score indicating the overall binding propensity to a specific ligand can be generated by averaging the top k predicted scores among all residues.”

As for the cross-interaction-type performance raised by the reviewer, we have now conducted cross-type evaluations to investigate the specificity of the ligand-specific MLPs and the inherent similarities among different ligands in Appendix 1-note 6 and Appendix 2-table 10. For convenience, we also attach the note and table here:

“We conducted cross-type evaluations by applying different ligand-specific MLPs in GPSite for the test sets of different ligands. As shown in Appendix 2-table 10, for each ligand-binding site test set, the corresponding ligand-specific network consistently achieves the best performance. This indicates that the ligand-specific MLPs have specifically learned the binding patterns of particular molecules. We also noticed that the cross-type performance is reasonable for the ligands sharing similar properties. For instance, the DNA-specific MLP exhibits a reasonable AUPR when predicting RNA-binding sites, and vice versa. Similar trends are also observed between peptide and protein, as well as among metal ions as expected. Interestingly, the cross-type performance between ATP and HEM is also acceptable, potentially attributed to their comparable molecular weights (507.2 and 616.5, respectively).”

Author response table 4.

Cross-type performance by applying different ligand-specific MLPs in GPSite for the test sets of different ligands

Note: “Pep” and “Pro” denote peptide and protein, respectively. The numbers in this table are AUPR values. The best/second-best result in each test set is indicated by bold/underlined font.

Author Response

eLife assessment

The authors report that optogenetic inhibition of hippocampal axon terminals in retrosplenial cortex impairs the performance of a delayed non-match to place task. The significance of findings elucidating the role of hippocampal projections to the retrosplenial cortex in memory and decision-making behaviors is important. However, the strength of evidence for the paper's claims is currently incomplete.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is a study on the role of the retrosplenial cortex (RSC) and the hippocampus in working memory. Working memory is a critical cognitive function that allows temporary retention of information for task execution. The RSC, which is functionally and anatomically connected to both primary sensory (especially visual) and higher cognitive areas, plays a key role in integrating spatial-temporal context and in goal-directed behaviors. However, the specific contributions of the RSC and the hippocampus in working memory-guided behaviors are not fully understood due to a lack of studies that experimentally disrupt the connection between these two regions during such behaviors.

In this study, researchers employed eArch3.0 to silence hippocampal axon terminals in the RSC, aiming to explore the roles of these brain regions in working memory. Experiments were conducted where animals with silenced hippocampal axon terminals in the RSC performed a delayed non-match to place (DNMP) task. The results indicated that this manipulation impaired memory retrieval, leading to decreased performance and quicker decision-making in the animals. Notably, the authors observed that the effects of this impairment persisted beyond the light-activation period of the opsin, affecting up to three subsequent trials. They suggest that disrupting the hippocampal-RSC connection has a significant and lasting impact on working memory performance.

Strengths:

They conducted a study exploring the impact of direct hippocampal inputs into the RSC, a region involved in encoding spatial-temporal context and transferring contextual information, on spatial working memory tasks. Utilizing eArch3.0 expressed in hippocampal neurons via the viral vector AAV5-hSyn1-eArch3.0, they aimed to bilaterally silence hippocampal terminals located at the RSC in rats pre-trained in a DNMP task. They discovered that silencing hippocampal terminals in the RSC significantly decreased working memory performance in eArch+ animals, especially during task interleaving sessions (TI) that alternated between trials with and without light delivery. This effect persisted even in non-illuminated trials, indicating a lasting impact beyond the periods of direct manipulation. Additionally, they observed a decreased likelihood of correct responses following TI trials and an increased error rate in eArch+ animals, even after incorrect responses, suggesting an impairment in error-corrective behavior. This contrasted with baseline sessions where no light was delivered, and both eArch+ and control animals showed low error rates.

Weaknesses:

While I agree with the authors that the role of hippocampal inputs to the RSC in spatial working memory is understudied and merits further investigation, I find that the optogenetic experiment, a core part of this manuscript that includes viral injections, could be improved. The effects were rather subtle, rendering some of the results barely significant and possibly too weak to support major conclusions.

We thank Reviewer#1 for carefully and critically reading our manuscript, and for the valuable comments provided. The judged “subtlety” of the effects stems from a perspective according to which a quantitatively lower effect bears less biological significance for cognition. We disagree with this perspective and find it rather reductive for several reasons.

Once seen in the context of the animal’s ecology, subtle impairments can be life-threatening precisely because of their subtlety, leading the animal to confidently rely on a defective capacity, for such events as remembering the habitual location of a predator, or food source.

Also, studies in animal cognition often undertake complete, rather than graded, suppression of a given mechanism (in the same sense as that of “knocking out” a gene that is relevant for behaviour), leading to a gravelly, rather that gradually, impaired model system, to the point of not allowing a hypothetical causal link to be mechanistically revealed beyond its mere presence. This often hinders a thorough interpretation of the perturbed factor’s role. If a caricatural analogy is allowed, it would be as if we were to study the role of an animal’s legs by chopping them both off and observing the resulting behaviour.

In our study we conclude that silencing HIPP inputs in RSC perturbs cognition enough to impair behaviour while not disabling the animal entirely, as such allowing for behaviour to proceed, and for our observation of graded, decreased (not absent), proficiency under optogenetic silencing. So rather than weak, we would say the results are statistically significant, and biologically realistic.

Additionally, no mechanistic investigation was conducted beyond referencing previous reports to interpret the core behavioral phenotypes.

We fully agree with this being a weakness, as we wish we could have done more mechanistic studies to find out exactly what is Arch activation doing to HIPP-RSC transmission, which neurons are being affected, and perhaps in the future dissect its circuit determinants. We have all these goals very present and hope we can address them soon.

Reviewer #2 (Public Review):

The authors examine the impact of optogenetic inhibition of hippocampal axon terminals in the retrosplenial cortex (RSP) during the performance of a working memory T-maze task. Performance on a delayed non-match-to-place task was impaired by such inhibition. The authors also report that inhibition is associated with faster decision-making and that the effects of inhibition can be observed over several subsequent trials. The work seems reasonably well done and the role of hippocampal projections to retrosplenial cortex in memory and decision-making is very relevant to multiple fields. However, the work should be expanded in several ways before one can make firm conclusions on the role of this projection in memory and behavior.

We thank Reviewer#2 for carefully and critically reading our manuscript, and for the valuable comments provided.

(1) The work is very singular in its message and the experimentation. Further, the impact of the inhibition on behaviour is very moderate. In this sense, the results do not support the conclusion that the hippocampal projection to retrosplenial cortex is key to working memory in a navigational setting.

As we have mentioned in response to Reviewer#1, the judged “very moderate” effect stems from a perspective according to which a quantitatively lower effect bears less biological significance for cognition, precluding its consideration as “key” for behaviour. We disagree with this perspective and find it rather reductive for several reasons. Once seen in the context of the animal’s ecology, quantitatively lower impairments in working memory are no less key for this cognitive capacity, and can be life-threatening precisely because of their subtlety, leading the animal to confidently rely on a defective capacity, for such events as remembering the habitual location of a predator, or food source. Furthermore, studies in animal cognition often undertake complete, rather than graded, suppression of a given mechanism (in the same sense as “knocking out” a gene that is relevant for behaviour), leading to a gravelly, rather that gradually, impaired model system, to the point of not allowing a hypothetical causal link to be mechanistically revealed beyond its mere presence. This often hinders a thorough interpretation of its role.

In our study we conclude that silencing HIPP inputs in RSC perturbs behaviour enough to impair behaviour while not disabling the animal entirely, as such allowing for behaviour to proceed, and our observation of graded, decreased (not absent), proficiency under optogenetic silencing. So rather than weak, we would say the results are statistically significant, and biologically realistic.

(2) There are no experiments examining other types of behavior or working memory. Given that the animals used in the studies could be put through a large number of different tasks, this is surprising. There is no control navigational task. There is no working memory test that is non-spatial. Such results should be presented in order to put the main finding in context.

It is hard to gainsay this point. The more thorough and complete a behavioural characterization is, the more informative is the study, from every angle you look at it. While we agree that other forms of WM would be quite interesting in this context, we also cannot ignore the fact that DNMP is widely tested as a WM task, one that is biologically plausible, sensitive to perturbations of neural circuitry know to be at play therein, and fully accepted in the field. Faced with the impossibility of running further studies, for lack of additional funding and human resources, we chose to run this task.

A control navigational task would, in our understanding, be used to assess whether silencing HIPP projections to RSC would affect (spatial?) navigation, rather than WM, thus explaining the observed impairment. To this we have the following to say: Spatial Navigation is a very basic cognitive function, one that relies on body orientation relative to spatial context, on keeping an updated representation of such spatial context, (“alas”, as memory), and on guiding behaviour according to acquired knowledge about spatial context. Some of these functions are integral to spatial working memory, as such, they might indeed be affected.

Dissecting the determinants of spatial WM is indeed an ongoing effort, one that was not the intention of the current study, but also one that we have very present, in hope we can address in the future.

A non-spatial WM task would indeed vastly solidify our claims beyond spatial WM, onto WM. We have, for this reason, changed the title of the manuscript which now reads “spatial working memory”.

(3) The actual impact of the inhibition on activity in RSP is not provided. While this may not be strictly necessary, it is relevant that the hippocampal projection to RSP includes, and is perhaps dominated by inhibitory inputs. I wonder why the authors chose to manipulate hippocampal inputs to RSP when the subiculum stands as a much stronger source of afferents to RSP and has been shown to exhibit spatial and directional tuning of activity. The points here are that we cannot be sure what the manipulation is really accomplishing in terms of inhibiting RSP activity (perhaps this explains the moderate impact on behavior) and that the effect of inhibiting hippocampal inputs is not an effective means by which to study how RSP is responsive to inputs that reflect environmental locations.

We fully agree that neural recordings addressing the effect of silencing on RSC neural activity is relevant. We do wish we could have provided more mechanistic studies, to find out exactly what is Arch activation doing to HIPP-RSC transmission, which neurons are being affected, and thus dissecting its circuit determinants. We have all these goals very present and hope we can address them soon. Subiculum, which we mention in the Introduction, is indeed a key player in this complex circuitry, one whose hypothetical influence is the subject of experimental studies which will certainly reveal many other key elements.

(4) The impact of inhibition on trials subsequent to the trial during which optical stimulation was actually supplied seems trivial. The authors themselves point to evidence that activation of the hyperpolarizing proton pump is rather long-lasting in its action. Further, each sample-test trial pairing is independent of the prior or subsequent trials. This finding is presented as a major finding of the work, but would normally be relegated to supplemental data as an expected outcome given the dynamics of the pump when activated.

We disagree that this finding is “trivial”, and object to the considerations of “normalcy”, which we are left wondering about.

In lack of neurophysiological experiments (for the reasons stated above) to address this interesting finding, we chose to interpret it in light of (the few) published observations, such being the logical course of action in scientific reporting, given the present circumstances.

Evidence for such a prolonged effect in the context of behaviour is scarce (to our knowledge only the one we cite in the manuscript). As such, it is highly relevant to report it, and give it the relevance we do in our manuscript, rather than “relegating it to supplementary data”, as the reviewer considers being “normal”.

In the DNMP task the consecutive sample-test pairs are explicitly not independent, as they are part of the same behavioural session. This is illustrated by the simple phenomenon of learning, namely the intra-session learning curves, and the well-known behavioral trial-history effects. The brain does not simply erase such information during the ITI.

(5) In the middle of the first paragraph of the discussion, the authors make reference to work showing RSP responses to "contextual information in egocentric and allocentric reference frames". The citations here are clearly deficient. How is the Nitzan 2020 paper at all relevant here?

Nitzan 2020 reports the propagation of information from HIPP to CTX via SUB and RSC, thus providing a conduit for mnemonic information between the two structures, alternative to the one we target, thus providing thorough information concerning the HIPP-RSC circuitry at play during behaviour.

Alexander and Nitz 2015 precisely cite the encoding, and conjunction, of two types of contextual information, internal (ego-) and external (allocentric).

The subsequent reference is indeed superfluous here.

We thank the Reviewer#2 for calling our attention to the fact that references for this information are inadequate and lacking. We have now cited (Gill et al., 2011; Miller et al., 2019; Vedder et al., 2017) and refer readers to the review (Alexander et al., 2023) for the purpose of illustrating the encoding of information in the two reference frames. In addition, we have substantially edited the Introduction and Discussion sections, and suppressed unnecessary passages.

(6) The manuscript is deficient in referencing and discussing data from the Smith laboratory that is similar. The discussion reads mainly like a repeat of the results section.

Please see above. We thank Reviewer#2 for this comment, we have now re-written the Discussion such that it is less of a summary of the Results and more focused on their implications and future directions.

eLife assessment

The authors report that optogenetic inhibition of hippocampal axon terminals in retrosplenial cortex impairs the performance of a delayed non-match to place task. The significance of findings elucidating the role of hippocampal projections to the retrosplenial cortex in memory and decision-making behaviors is important. However, the strength of evidence for the paper's claims is currently incomplete.

Reviewer #1 (Public Review):

Summary:

This is a study on the role of the retrosplenial cortex (RSC) and the hippocampus in working memory. Working memory is a critical cognitive function that allows temporary retention of information for task execution. The RSC, which is functionally and anatomically connected to both primary sensory (especially visual) and higher cognitive areas, plays a key role in integrating spatial-temporal context and in goal-directed behaviors. However, the specific contributions of the RSC and the hippocampus in working memory-guided behaviors are not fully understood due to a lack of studies that experimentally disrupt the connection between these two regions during such behaviors.

In this study, researchers employed eArch3.0 to silence hippocampal axon terminals in the RSC, aiming to explore the roles of these brain regions in working memory. Experiments were conducted where animals with silenced hippocampal axon terminals in the RSC performed a delayed non-match to place (DNMP) task. The results indicated that this manipulation impaired memory retrieval, leading to decreased performance and quicker decision-making in the animals. Notably, the authors observed that the effects of this impairment persisted beyond the light-activation period of the opsin, affecting up to three subsequent trials. They suggest that disrupting the hippocampal-RSC connection has a significant and lasting impact on working memory performance.

Strengths:

They conducted a study exploring the impact of direct hippocampal inputs into the RSC, a region involved in encoding spatial-temporal context and transferring contextual information, on spatial working memory tasks. Utilizing eArch3.0 expressed in hippocampal neurons via the viral vector AAV5-hSyn1-eArch3.0, they aimed to bilaterally silence hippocampal terminals located at the RSC in rats pre-trained in a DNMP task. They discovered that silencing hippocampal terminals in the RSC significantly decreased working memory performance in eArch+ animals, especially during task interleaving sessions (TI) that alternated between trials with and without light delivery. This effect persisted even in non-illuminated trials, indicating a lasting impact beyond the periods of direct manipulation. Additionally, they observed a decreased likelihood of correct responses following TI trials and an increased error rate in eArch+ animals, even after incorrect responses, suggesting an impairment in error-corrective behavior. This contrasted with baseline sessions where no light was delivered, and both eArch+ and control animals showed low error rates.

Weaknesses:

While I agree with the authors that the role of hippocampal inputs to the RSC in spatial working memory is understudied and merits further investigation, I find that the optogenetic experiment, a core part of this manuscript that includes viral injections, could be improved. The effects were rather subtle, rendering some of the results barely significant and possibly too weak to support major conclusions. Additionally, no mechanistic investigation was conducted beyond referencing previous reports to interpret the core behavioral phenotypes.

Reviewer #2 (Public Review):

The authors examine the impact of optogenetic inhibition of hippocampal axon terminals in the retrosplenial cortex (RSP) during the performance of a working memory T-maze task. Performance on a delayed non-match-to-place task was impaired by such inhibition. The authors also report that inhibition is associated with faster decision-making and that the effects of inhibition can be observed over several subsequent trials. The work seems reasonably well done and the role of hippocampal projections to retrosplenial cortex in memory and decision-making is very relevant to multiple fields. However, the work should be expanded in several ways before one can make firm conclusions on the role of this projection in memory and behavior.

(1) The work is very singular in its message and the experimentation. Further, the impact of the inhibition on behavior is very moderate. In this sense, the results do not support the conclusion that the hippocampal projection to retrosplenial cortex is key to working memory in a navigational setting.

(2) There are no experiments examining other types of behavior or working memory. Given that the animals used in the studies could be put through a large number of different tasks, this is surprising. There is no control navigational task. There is no working memory test that is non-spatial. Such results should be presented in order to put the main finding in context.

(3) The actual impact of the inhibition on activity in RSP is not provided. While this may not be strictly necessary, it is relevant that the hippocampal projection to RSP includes, and is perhaps dominated by inhibitory inputs. I wonder why the authors chose to manipulate hippocampal inputs to RSP when the subiculum stands as a much stronger source of afferents to RSP and has been shown to exhibit spatial and directional tuning of activity. The points here are that we cannot be sure what the manipulation is really accomplishing in terms of inhibiting RSP activity (perhaps this explains the moderate impact on behavior) and that the effect of inhibiting hippocampal inputs is not an effective means by which to study how RSP is responsive to inputs that reflect environmental locations.

(4) The impact of inhibition on trials subsequent to the trial during which optical stimulation was actually supplied seems trivial. The authors themselves point to evidence that activation of the hyperpolarizing proton pump is rather long-lasting in its action. Further, each sample-test trial pairing is independent of the prior or subsequent trials. This finding is presented as a major finding of the work, but would normally be relegated to supplemental data as an expected outcome given the dynamics of the pump when activated.

(5) In the middle of the first paragraph of the discussion, the authors make reference to work showing RSP responses to "contextual information in egocentric and allocentric reference frames". The citations here are clearly deficient. How is the Nitzan 2020 paper at all relevant here?

(6) The manuscript is deficient in referencing and discussing data from the Smith laboratory that is similar. The discussion reads mainly like a repeat of the results section.

Author Response

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Hats off to the authors for taking time to decipher the seemingly subtle but important differences between the Gnai2/3 double mutant and Ptx mutant phenotypes. These results further illustrate the dynamic requirement of Gnai/0 in hair bundle establishment. I have some minor suggestions for the authors to consider and it is up to the authors to decide whether to incorporate them:

We decided to make the current (revised) version the version of record, and we explain why below. Please include these comments in the review+rebuttal material.

(1) The abstract could be modified to reflect the revised interpretations of the results.

Response: the abstract is high-level and the changes in interpretation in the revised manuscript do not modify the message there. Briefly, the abstract only states that Gnai2; Gnai3 double mutants recapitulate two defects previously only observed with pertussis toxin. There is no claim about the timing or dose of GNAI proteins involved.

(2) The three rows of OHCs are like a different beast from each other. Mireille Montcouquiol's lab has demonstrated that there is a differential requirement for Gnai3 in hair bundle orientation among the three rows of OHCs. The results described in this manuscript support this notion as well.

To clarify, Gnai3 inactivation does not affect OHC orientation. Only pertussis toxin, and in this work Gnai2; Gnai3 double mutants, do. The Montcouquiol lab showed different degree of OHC1, OHC2 and OHC3 misorientation upon use of pertussis toxin in vitro using cochlear explants (Ezan et al 2013). We showed the same thing in vivo using transgenic models (Tarchini et al 2013; Tarchini et al 2016). The different OHC responses by row and corresponding citations are mentioned in several locations in the manuscript, including first on line 112 in the Introduction and in Fig. 1C in a graphical summary.

(3) I wonder if "compensate" or "redundancy" may be a better term to use than "rescue" in the Discussion and figure.

Use of “rescue” in the Discussion is line 603 and 604. We think that “rescue” is appropriate to refer to the ability of GNAI2 to compensate for the loss of GNAI1 and GNAI3 in mutant context. We would argue that these different wordings are largely interchangeable and do not change the message.

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Author Response

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

We really appreciate the time the reviewers spent reading and commenting on the original manuscript. Although they were positive already, we decided to spend some time to address the main comments with new experiments as thoroughly as possible in a new manuscript version. We also heavily edited some sections accordingly.: 1) we delayed pertussis toxin activation in hair cells with Atoh1-Cre to show that the resulting misorientation phenotype is delayed compared to FoxG1-Cre results, as also seen in Gnai2; Gnai3 double mutants. It follows that Gnai2; Gnai3 and pertussis mutants do share a similar misorientation profile, and that GNAI proteins are required to normally reverse OHC1-2 (from medial to lateral), but also to maintain the lateral orientation, at least transiently. 2) We experimentally verified that one of our GNAI antibodies can indeed detect GNAI1, and consequently that absence of signal in Gnai2; Gnai3 double mutants is evidence that GNAI1 is not involved in apical hair cell polarization. We believe these changes strengthen the manuscript and its conclusions.

Reviewer #1 (Public Review):

A subclass of inhibitory heterotrimeric guanine nucleotide-binding protein subunits, GNAI, has been implicated in sensory hair cell formation, namely the establishment of hair bundle (stereocilia) orientation and staircase formation. However, the former role of hair bundle orientation has only been demonstrated in mutants expressing pertussis toxin, which blocks all GNAI subunits, but not in mutants with a single knockout of any of the Gnai genes, suggesting that there is a redundancy among various GNAI proteins in this role. Using various conditional mutants, the authors concluded that GNAI3 is the primary GNAI proteins required for hair bundle morphogenesis, whereas hair bundle orientation requires both GNAI2 and GNAI3.

Strength

Various compound mutants were generated to decipher the contribution of individual GNAI1, GNAI2, GNAI3 and GNAIO in the establishment of hair bundle orientation and morphogenesis. The study is thorough with detailed quantification of hair bundle orientation and morphogenesis, as well as auditory functions.

Weakness

While the hair bundle orientation phenotype in the Foxg1-cre; Gnai2-/-; Gnai3 lox/lox (double mutants) appear more severe than those observed in Ptx cKO mutants, it may be an oversimplification to attribute the differences to more GNAI function in the Ptx cko mutants. The phenotypes between the double mutants and Ptx cko mutants appear qualitatively different. For example, assuming the milder phenotypes in the Ptx cKO is due to incomplete loss of GNAI function, one would expect the Ptx phenotype would be reproducible by some combination of compound mutants among various Gnai genes. Such information was not provided. Furthermore, of all the double mutant specimens analyzed for hair bundle orientation (Fig. 8), the hair bundle/kinocilium position started out normally in the lateral quadrant at E17.5 but failed to be maintained by P0. This does not appear to be the case for Ptx cKO, in which all affected hair cells showed inverted orientation by E17.5. It is not clear whether this is the end-stage of bundle orientation in Ptx cKO, and the kinocilium position started out normal, similar to the double mutants before the age of analysis at E17.5. Understanding these differences may reveal specific requirements of individual GNAI subunits or other factors are being affected in the Ptx mutants.

This criticism was very useful and prompted new experiments as well as a change in data presentation and a fundamental rewrite regarding hair cell orientation. These changes are detailed below. Of note, however, please let us clarify that the original manuscript did show that the ptxA orientation phenotype is reproduced to some extent in Gnai2; Gnai3 double mutants (previously Fig. 8 and corresponding text line 505). We showed that OHC1-2 are also inverted in the double mutant, although at a later differentiation stage. We recognize that similarities in hair cell misorientation between ptxA and Gnai2; Gnai3 DKO were not explained and discussed well enough. This part of the manuscript has been re-worked extensively, and we hope that along with new results, comparisons between mutant models are easier to follow and understand. We notably fully adopted the idea that there are qualitative differences between ptxA and Gnai2; Gnai3 mutants, and not only a difference in the remaining “dose” of GNAI activity.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Comments related to clarification of the weakness:

(1) In general, hair bundle orientation in the double mutants is established in the lateral quadrant of the cochlea before being inverted (Fig. 8). These results are intriguing because the lateral orientation is the correct position for these hair bundles normally and Gnai proteins are thought to be required to get the kinocilium to the lateral position. This process appears to proceed normally in the double mutants but the kinocilium reverted to the medial default position over time, which suggests that Gnai2 and Gnai3 are only required for the maintenance and not the establishment of the kinocilium in the lateral position. Is this phenotype qualitatively similar in the Ptx cKO?

We addressed these issues with two types of modifications to the data:

(1) We modified the eccentricity threshold used at E17.5 in Fig. 8 (orientation) to be more stringent, using 0.4 (instead of 0.25 previously) in both controls and mutants. This means that we now only graph the orientation of cells where eccentricity is more marked. The rationale is that at early stages, it is challenging to distinguish immature vs defective near-symmetrical cells. We kept a threshold of 0.25 at P0 when the hair cell apical surface is larger and better differentiated (Fig. 8C-D). Importantly, the dataset remains rigorously identical. This change usefully highlights that a large proportion of OHC1 is in fact inverted (oriented medially) at E17.5 in Gnai2; Gnai3 double mutants at the cochlear mid, as also seen in the ptxA model at the same stage and position (see new Fig. 8A). At the E17.5 base (Fig. 8B), a slightly more mature position, the outcome is unchanged (the majority of OHC1 are inverted using either a 0.25 or 0.4 threshold in double mutants and in ptxA).

Interestingly however, the orientation trend is unchanged for OHC2: OHC2 remain oriented largely laterally (i.e. normally) at the E17.5 mid and base in Gnai2; Gnai3 double mutants even with a raised eccentricity thresholds, whereas by contrast OHC2 in ptxA are inverted at these stage and positions. In the double mutant, OHC2 only become inverted at the P0 base (Fig. 8D). This suggests that there are similarities (OHC1) but also differences (OHC2s) between the two mouse models, and that double mutants show a delay in adopting an inverted orientation compared to ptxA. Of note, OHC2 have been shown to differentiate later than OHC1 (for example, Anniko 1983 PMID:6869851).

(2) To directly test the idea that the misorientation phenotype (inverted OHC1-2) is comparable between the two models but delayed in Gnai2; Gnai3 mutants, we performed a new experiment and added new results in the manuscript. We delayed ptxA action by using Atoh1-Cre (postmitotic hair cells) instead of FoxG1-Cre (otic progenitors). Remarkably, this produced a pattern of OHC1-2 misorientation more similar to Gnai2; Gnai3 mutants: at the E17.5 base and P0 apex, OHC2 were still largely oriented laterally (normally) in Atoh1-Cre; ptxA as in Gnai2; Gnai3 mutants whereas at the P0 base a large proportion of OHC2 were inverted (Fig. 8 Supp 1B). OHC1 were inverted at all stages and positions in the Atoh1-Cre as in the FoxG1-Cre; ptxA model. For Atoh1-Cre; ptxA, we only illustrated OHC1 and OHC2 and did not add E17.5 mid or P0 mid results because other cell types and stage/positions did not provide additional insight. In addition, we are well aware that the full FoxG1-Cre; ptxA and Gnai2; Gnai3 results for 4 cells types (IHC, OHC1-3) and 5 stages/positions is already a lot of data for cell orientation.

These results suggest that:

(a) The normal reversal of OHC1-2 to adopt a lateral orientation needs to be maintained, at least transiently, and that maintenance also relies on GNAI/O (Results starting line 529. Disussion line 621).

(b) ptxA is more severe than Gnai2; Gnai3 when it comes to OHC1-2 orientation (Figure 9, role b). Oppositely, Gnai2; Gnai3 is obviously more severe when it comes to symmetry-breaking (Fig. 9, role a) and hair bundle morphogenesis (Fig. 9, c). It follows that the two early GNAI/O activities are qualitatively different and not just based on dose. This is essentially what this Reviewer correctly pointed out, and we have fully edited both Results and Discussion accordingly. We now speculate that the difference may lie in the identity of the necessary GNAI/O protein for each role. Any GNAI/O proteins acting as a switch downstream of the GPR156 receptor may relay orientation information (Fig. 9, role b), making ptxA a particularly effective disruption strategy since it downregulates all GNAI/O proteins. In contrast, symmetry-breaking may rely more specifically on GNAI2 and GNAI3, and ptxA is not expected to achieve a loss-of-function of GNAI2 and GNAI3 as extensive as a double targeted genetic inactivation of the corresponding genes. Please see new Results starting line 526 and Discussion starting line 603. We consequently abandoned the notion that increased doses of GNAI/O is required for each role, and we also clarify that symmetry-breaking (a) and orientation (b) occur at the same time (Fig. 9).

(2) P0 may not be late enough a stage to access phenotype maturity in the double mutants. For example, it is not clear from the basal PO results whether the IHC will acquire an inverted phenotype or just misorientation in the lateral side.

For context, the OHC1-2 misorientation pattern in the ptxA model at P0 does represent the end stage, as the same pattern is observed in adults (illustrated in Fig. 2A). In addition, OHC1-2 that express ptxA are inverted as soon as they break planar symmetry, and this was established at E16.5 in a previous publication where ptxA and Gpr156 misorientation patterns were compared and shown to be identical (Kindt et al., 2021 Supp. fig. 5C-D). However, we clearly failed to mention these important results in the original manuscript. We now cite Figure 2 for adult defects (line 522), and provide a citation for OHC1-2 inversion being observed from earliest stage of hair cell differentiation (Kindt et al., 2021) (line 519).

The vast majority of Gnai2; Gnai3 double mutants die before weaning but the single specimen we managed to collect at P21 also showed inverted OHC1-2 (representative example in Fig. 2A). Again, we previously failed to point out this important result. We now do so line 214 and 555. This is another evidence that OHC1-2 misorientation is in fact similar in the ptxA and Gnai2; Gnai3 models (but milder and delayed in the latter).

When it comes to IHCs and OHC3s however, the situation is less clear. These cell types are mildly misoriented in ptxA and Gpr156 mutants, but IHCs in particular appear severely misoriented in Gnai2; Gnai3 mutants based on the position of the basal body (Fig. 8). However, very dysmorphic hair bundles can pull on the basal body via the kinocilium and affect its position, which obscures hair cell orientation inferred from the basal body and subsequent interpretations. We do not delve on IHC and OHC3 and their orientation in Gnai2; Gnai3 mutants in the revision since we do not observe similar orientation defects in a different mouse model and lack sufficient adult data.

Suggestions to improve upon the manuscript for readers:

(1) Line 294, indicate on the figure the staining in bare zone and tips of stereocilia on row 1.

Pertains to Figure 4. In A, we now point out the bare zone and stereocilia tips with arrow and arrowheads, respectively (as in other figures).

(2) Fig.8 schematic diagram, the labels of the line and 90o side by side is misleading.

We added black ticks for 0, 90, 180, 270 degree references. In contrast, the hair cell angle represented was switched to magenta.

(3) Fig. 7 legend, redundancy towards the end of the paragraph.

Thank you for catching this issue. A large portion of the legend was indeed accidentally repeated and is now deleted.

(4) Line 490-493, Another plausible explanation is that other factors besides Gnai2 and Gnai3 are involved in breaking symmetry during bundle establishment.

We now acknowledge that other proteins besides GNAI/O may be involved (Discussion line 614). That said, the notion that we do not achieve sufficient and/or early enough GNAI loss is supported for example by the Beer-Hammer 2018 study where no defects in symmetry-breaking or orientation were reported in their Gnai2 flox/flox; Gnai3 flox/flox model (Discussion new Line 637).

(5) Line 518, the base were largely inverted (Figure 8B). Should Fig 8A be cited instead of 8B?

Fig. 8B has graphs for the E17.5 cochlear base where OHC1-2 are inverted in both ptxA and Gnai2;3 DKO models. Fig. 8A has graphs of the E17.5 cochlear mid (less differentiated hair cells) where an inversion was not obvious previously, but is now clear although only partial in Gnai2; Gnai3 DKO (see above; raised eccentricity threshold). In the context of the previous text, this citation was thus correct. However, this section has been heavily modified to better compare Gnai2; Gnai3 DKO and ptxA and is hopefully less confusing in the revised version.

Reviewer #2 (Public Review):

Jarysta and colleagues set out to define how similar GNAI/O family members contribute to the shape and orientation of stereocilia bundles on auditory hair cells. Previous work demonstrated that loss of particular GNAI proteins, or inhibition of GNAIs by pertussis toxin, caused several defects in hair bundle morphogenesis, but open questions remained which the authors sought to address. Some of these questions include whether all phenotypes resulting from expression of pertussis toxin stemmed from GNAI inhibition; which GNAI family members are most critical for directing bundle development; whether GNAI proteins are needed for basal body movements that contribute to bundle patterning. These questions are important for understanding how tissue is patterned in response to planar cell polarity cues.

To address questions related to the GNAI family in auditory hair cell development, the authors assembled an impressive and nearly comprehensive collection of mouse models. This approach allowed for each Gnai and Gnao gene to be knocked out individually or in combination with each other. Notably, a new floxed allele was generated for Gnai3 because loss of this gene in combination with Gnai2 deletion was known to be embryonic lethal. Besides these lines, a new knockin mouse was made to conditionally express untagged pertussis toxin following cre induction from a strong promoter. The breadth and complexity involved in generating and collecting these strains makes this study unique, and likely the authoritative last word on which GNAI proteins are needed for which aspect of auditory hair bundle development.

Appropriate methods were employed by the authors to characterize auditory hair bundle morphology in each mouse line. Conclusions were carefully drawn from the data and largely based on excellent quantitative analysis. The main conclusions are that GNAI3 has the largest effect on hair bundle development. GNAI2 can compensate for GNAI3 loss in early development but incompletely in late development. The Gnai2 Gnai3 double mutant recapitulates nearly all the phenotypic effects associated with pertussis toxin expression and also reveals a role for GNAIs in early movement of the basal body. Although these results are not entirely unexpected based on earlier reports, the current results both uncover new functions and put putative functions on more solid ground.

Based on this study, loss of GNAI1 and GNAO show a slight shortening of the tallest row of stereocilia but no other significant changes to bundle shape. Antibody staining shows no change in GNAI localization in the Gnai1 knockout, suggesting that little to no protein is found in hair cells. One caveat to this interpretation is that the antibody, while proposed to cross-react with GNAI1, is not clearly shown to immunolabel GNAI1. More than anything, this reservation mostly serves to illustrate how challenging it is to nail down every last detail. In turn, the comprehensive nature of the current study seems all the more impressive.

(1) The original manuscript quantified stereocilia properties in Gnai1 and Gnai2 single mutants, and in Gnai1; Gnai2 double mutants using non-parametric t-tests (Mann-Whitney) for comparisons. This approach indeed suggested subtle reduction in row 1 height in IHCs in all 3 mutants. We did not quantify stereocilia features in Gnao1 mutants but could not observe defects (new Fig. 2 Supp. 1E-F). In fact, we could not observe defects in Gnai1 and Gnai2 single mutants, and in Gnai1; Gnai2 double mutants either. For this reason we have been ambivalent about reporting defects for Gnai1 and Gnai2 single and Gnai1; Gnai2 double mutants.

In the revision, we applied a nested (hierarchical) t-test to avoid pseudo-replication (Eisner 2021; PMID: 33464305; https://pubmed.ncbi.nlm.nih.gov/33464305/). In our data, the nested t-tests structure measurements by animal instead of having all stereocilia or other cell measurements treated as independent values. This more stringent approach no longer finds row 1 height reduction significant in single Gnai1 or Gnai2 mutants, or in Gnai1; Gnai2 double mutants. We modified the text accordingly in Results and Discussion. Nested t-tests were applied uniformly across the manuscript and, besides IHC measurements in Fig. 2, now also apply to bare zone surface area in Fig. 6 and eccentricity in Fig. 7. For these experiments in contrast, previous conclusions are not changed. We think that this more careful statistical treatment is a closer representation of the data in term of the conclusions we can safely make.

(2) The reviewer's criticism about antibody specificity is accurate and fair, and is fully addressed in the revised manuscript. First, we provide a phylogeny cartoon as Figure 1A to compare the GNAI/O proteins and highlight how closely related they are in sequence. To validate the assumption that our approach would detect GNAI1 if it were present in hair cells, we took a new dual experimental approach in the revision. First, we electroporated Gnai1, Gnai2 and Gnai3 expression constructs in the E13.5 inner ear and tested whether the two GNAI antibodies used in the study can detect ectopic GNAI1 in Kolliker organ. This revealed that “ptGNAI2” detects GNAI1 very well (in addition to GNAI2), but that “scbtGNAI3” does not detect GNAI1 efficiently (although it does detect GNAI3 very well). To verify in vivo that “ptGNAI2” can detect endogenous GNAI1, we immunolabeled the gallbladder epithelium in Gnai1 mutants and littermate controls using the “ptGNAI2” antibody. Based on IMPC consortium data* about the Gnai1 LacZ mouse strain, Gnai1 is specifically expressed in the adult gallbladder. We could verify that signals detected in the Gnai1 mutants were visually reduced in comparison to littermate controls. We now added this validation step in Results line 309 and the data in Fig. 4 Supp. 1A-B).

*https://www.mousephenotype.org/data/genes/MGI:95771

Reviewer #2 (Recommendations For The Authors):

Minor comments that may marginally improve clarity.

Abstract line 24: delete "nor polarized" because polarization cannot be assessed since the protein is undetectable.

This is a fair point, now deleted.

Consider revising: Lines 80-82; 188-202 (the order in which the mutants were presented was hard to follow for me); 239-240.

Lines 80-82: Used to read as "Ptx recapitulates severe stereocilia stunting and immature-looking hair bundles observed when GPSM2 or both GNAI2 and GNAI3 are inactivated."

Line 88: Was now changed to "Ptx provokes immature-looking hair bundles with severely stunted stereocilia, mimicking defects in Gpsm2 mutants and Gnai2; Gnai3 double mutants".

Lines 188-202: This was the first paragraph describing adult stereocilia defects in the different Gnai/o mouse strains. We completely rewrote the entire section to reflect the order in which the strains appear in Figure 2, hopefully making the text easier to follow because it better matches panels in Fig. 2 . We also made several other modifications to streamline comparisons and better introduce the orientation defects that are later detailed at neonate stages.

Lines 239-240: Used to read "GNAI2 makes a clear contribution since stereocilia defects increase in severity when GNAI loss extends from GNAI3 to both GNAI2 and GNAI3".

Line 247: Was now changed for "GNAI2 makes a clear contribution since Gnai3neo stereocilia defects dramatically increase in severity when GNAI2 is absent as well in Gnai2; Gnai3 double mutants."

Line 164: hardwired is unclear. Conserved?

We modified this sentence as follows: Line 171: "We reasoned that apical HC development is probably highly constrained and less likely to be influenced by genetic heterogeneity compared to susceptibility to disease, for example."

Line 299: It is not clear why GNAI1 is a better target than GNAI3. This phrase is repeated in line 303, I suspect inadvertently. Is there evidence that this antibody detects GNAI1, perhaps in another tissue? Line 308: GNAI1 may also not be detected by this antibody.

Please see point 2 above. We removed these hypothetical statements entirely and we instead now experimentally show that one of the two commercial antibodies used can readily detect GNAI1 (yet does not detect signal in hair cells when GNAI2 and GNAI3 are absent in Fig. 4F).

eLife assessment

This study examines an important aspect of the development of the auditory system, the role of guanine nucleotide-binding protein subunits, GNAIs, in stereociliary bundle formation and orientation, by examining bundle phenotypes in multiple compound GNAI mutants. The experiments are highly rigorous and thorough and include detailed quantifications of bundle morphologies and changes. The depth and care of the study are impressive, with convincing results regarding the roles of GNAIs in stereociliary bundle development. Further, the reviewers believe this to be the definitive study of the role of GNAIs in bundle orientation and development.

Reviewer #1 (Public Review):

A subclass of inhibitory heterotrimeric guanine nucleotide-binding protein subunits, GNAI, has been implicated in sensory hair cell formation, namely the establishment of hair bundle (stereocilia) orientation and staircase formation. However, the former role of hair bundle orientation has only been demonstrated in mutants expressing pertussis toxin, which blocks all GNAI subunits, but not in mutants with a single knockout of any of the Gnai genes, suggesting that there is a redundancy among various GNAI proteins in this role. Using various conditional mutants, the authors concluded that GNAI3 is the primary GNAI proteins required for hair bundle morphogenesis, whereas hair bundle orientation requires both GNAI2 and GNAI3.

Strength

Various compound mutants were generated to decipher the contribution of individual GNAI1, GNAI2, GNAI3 and GNAIO in the establishment of hair bundle orientation and morphogenesis. The study is thorough with detailed quantification of hair bundle orientation and morphogenesis, as well as auditory functions.

The revised manuscript has clarified the phenotypic differences raised between the Gnai2/3 double mutants and Ptx mutant phenotypes and resolved the weakness pointed out in the previous submission. These results further illustrate the dynamic requirement of Gnai/O in hair bundle establishment and is an important contribution to the field.

Reviewer #2 (Public Review):

Jarysta and colleagues set out to define how similar GNAI/O family members contribute to the shape and orientation of stereocilia bundles on auditory hair cells. Previous work demonstrated that loss of particular GNAI proteins, or inhibition of GNAIs by pertussis toxin, caused several defects in hair bundle morphogenesis, but open questions remained which the authors sought to address. Some of these questions include whether all phenotypes resulting from expression of pertussis toxin stemmed from GNAI inhibition; which GNAI family members are most critical for directing bundle development; whether GNAI proteins are needed for basal body movements that contribute to bundle patterning. These questions are important for understanding how tissue is patterned in response to planar cell polarity cues.

To address questions related to the GNAI family in auditory hair cell development, the authors assembled an impressive and nearly comprehensive collection of mouse models. This approach allowed for each Gnai and Gnao gene to be knocked out individually or in combination with each other. Notably, a new floxed allele was generated for Gnai3 because loss of this gene in combination with Gnai2 deletion was known to be embryonic lethal. Besides these lines, a new knockin mouse was made to conditionally express untagged pertussis toxin following cre induction from a strong promoter. The breadth and complexity involved in generating and collecting these strains makes this study unique, and likely the authoritative last word on which GNAI proteins are needed for which aspect of auditory hair bundle development.

Appropriate methods were employed by the authors to characterize auditory hair bundle morphology in each mouse line. Conclusions were carefully drawn from the data and largely based on excellent quantitative analysis. The main conclusions are that GNAI3 has the largest effect on hair bundle development. GNAI2 can compensate for GNAI3 loss in early development but incompletely in late development. The Gnai2 Gnai3 double mutant recapitulates nearly all the phenotypic effects associated with pertussis toxin expression and also reveals a role for GNAIs in early movement of the basal body. This comprehensive study builds on earlier reports, both uncovering new functions and putting previously putative functions on solid ground.

Author Response

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

Reviewer #2 (Public Review):

Major Weaknesses:

The assertion that MOCAT can be rapidly applied in hospital pathology departments seems overstated due to the limited availability of light-sheet microscopes outside research labs. In the first rebuttal letter, authors explain the limitations of other microscopes more readily available in hospitals. This explanation relies on your own investigations and practical experience on the matter, so including them in some part of the manuscript would be beneficial.

We appreciate the reviewer's comments and have added a discussion on the limitations of microscopes that are more readily available in hospitals in our text:

Revised manuscript, line 305-316:

“3.3 Microscopy options for imaging centimeter-sized specimens

Optical sectioning techniques are crucial for obtaining high-quality volumetric images. Techniques such as confocal microscopes, multi-photon microscopy, and light-sheet microscopy filter out-of-focus signals, resulting in sharp images of individual planes. In our study, we used light-sheet microscopy and multi-point confocal (i.e., spinning disc) for imaging centimeter-sized specimens because of their scanning speeds. While two-photon and confocal microscopy offer high-resolution imaging of smaller volumes, they are not ideal for scanning entire tissues because of their prolonged scanning times.”

Non-optical sectioning wide-field fluorescence microscopes, like the Olympus BX series or ZEISS Axio imager series, can also be used to scan samples up to about 3.5mm thick with long working distance objective lenses. In these cases, deconvolution algorithms are required to eliminate out-of-focus signals. However, it should be noted that the epifluorescence system might reduce fluorescent intensity in deeper regions within the samples.”

Refractive index matching is a critical point in the protocol, the one providing final transparency. Authors utilized the commercial solutions NFC1 and NFC2 (Nebulem, Taiwan) with a known refractive index, but for which its composition is non-disclosable. My knowledge on the organic chemistry around refractive index matching is limited, but if users don't really know what is going on in this final step, the whole protocol would rely on a single world-wide provider and troubleshooting would be fishing. I suggest that you try to validate the approach with solutions of known composition, or at least provide the solutions sold by other providers.

We appreciate the reviewer's suggestions. Based on our experience, the CUBIC-R solution developed by Ueda's team also serves as an effective RI-matching solution in the MOCAT pipeline. Its only drawback is the potential reddening of the specimen, likely due to the light-responsive component, antipyrine. We have now added this information to the Methods section:

Revised manuscript, line 492-496:

“Refractive index (RI) matching. Before imaging, the specimens were RI-matched by being immersed in NFC1 (RI = 1.47) and NFC2 (RI = 1.52) solutions (Nebulum, Taipei, Taiwan). Each immersion lasted for one day at room temperature. Alternatively, RI-matching can also be accomplished by immersing specimens in a 1:1 dilution of CUBIC-R[28] for one day, followed by pure CUBIC-R for an additional day.“

Reviewer #2 (Recommendations For The Authors):

A comment on the name of the protocol, MOCAT. I am sorry to bring this now, and not before. But, I strongly recommend another name for the procedure. My concern is that the present name "MOCAT" refers to the problem, and NOT to the actual solution provided by you. See, the problem to solve is: to perform Multiplex labeling Of Centimeter-sized Archived Tissue (MOCAT), but it says nothing about HOW you did it: heat-induced antigen retrieval and Tween20-delipidation for centimeter-scale FFPE specimens. In summary, I strongly recommend that the acronym of the procedure refers more to the "solution" than to the "problem", and for me this is important because otherwise the acronym is not fair with present and future techniques pretending to provide a novel solution to the same problem. Another way to put it is that researchers can own their proposed solutions, but they do not own the problem to be solved.

We appreciate the reviewer's suggestions. In response to their concerns, we have renamed the procedure presented in this study as Heat-Induced FFPE-based Tissue Clearing, with the acronym HIF-Clear. This change reflects the critical step in our procedure. Corresponding updates have also been made in the manuscript.

eLife assessment

The reprocessing and reanalysis of archived samples can yield further insights from past experiments. Here, a useful procedure to perform tissue clearing and immunolabeling on large-scale formalin-fixed paraffin-embedded brain specimens is convincingly evaluated on a set of archival pathology specimens, and its applicability to further such samples is analyzed. This method will be of interest to both neuroscientists and pathologists.

Reviewer #1 (Public Review):

In this study, Lin et al developed a protocol termed HIF-Clear, to perform tissue clearing and labelling on large-scale FFPE mouse brain specimens. They have optimized protocols for dewaxing and adequate delipidation of FFPE tissues to enable deep immunolabelling, even for whole mouse brains. This was useful for the study of disease models such as in an astrocytoma model to evaluate spatial architecture of the tumour and its surrounding microenvironment. It was also used in a traumatic brain injury model to quantify changes in vasculature density and differences in monoaminergic innervation. They have also demonstrated the potential of multi-round immunolabelling using photobleaching, as well as expansion microscopy with FFPE samples using Hif Clear.

Comments on revised version:

The revised manuscript by Lin et al is much improved with a more detailed methods description. There are only a few minor comments for the authors that are still valid:

- Some procedures, including the basic HIF-Clear protocol, seem to produce marked tissue expansion that is not mentioned in the manuscript. Users should take this fact into consideration when making measurements.<br /> - The authors have provided a comparison between mouse and human brain samples in Figure S12. However, it is misleading to mention that the "fluorescent signals are comparable at varying depth" as the figure clearly showed a lack of continuous staining especially for SMI312 at 900um depth, and human brain tissue showed considerably increased background signal (likely due to endogenous lipofuscin which has autofluorescent properties). Also, This is difficult to assess in the present design of the experiment because, at different depths, the tissue and the antigen may change themselves... making it difficult to make a direct staining comparison with other depths.

Reviewer #2 (Public Review):

The manuscript details an investigation aimed at developing a protocol to render centimeter-scale formalin-fixed paraffin-embedded specimens optically transparent and suitable for deep immunolabeling. The authors evaluate various detergents and conditions for epitope retrieval such as acidic or basic buffers combined with high temperatures in entire mouse brains that had been paraffin-embedded for months. They use various protein targets to test active immunolabeling and light-sheet microscopy registration of such preparations to validate their protocol. The final procedure, called MOCAT pipeline, briefly involves 1% Tween 20 in citrate buffer, heated in a pressure cooker at 121 {degree sign}C for 10 minutes. The authors also note that part of the delipidation is achieved by the regular procedure.

Major Strengths<br /> - The simplicity and ease of implementation of the proposed procedure using common laboratory reagents distinguish it favorably from more complex methods.

- Direct comparisons with existing protocols and exploration of alternative conditions enhance the robustness and practicality of the methodology.

Final considerations<br /> The evidence presented supports the effectiveness of the proposed method in rendering thick FFPE samples transparent and facilitating repeated rounds of immunolabeling.

The developed procedure holds promise for advancing tissue and 3D-specific determination of proteins of interest in various settings, including hospitals, basic research, and clinical labs, particularly benefiting neuroscience research.

The methodological findings suggest that MOCAT could have broader applications beyond FFPE samples, differentiating it from other tissue-clearing approaches in that the equipment and chemicals needed are broadly accessible.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

This manuscript aims to understand the biological mechanisms underlying neuropsychiatric symptoms in Parkinson's disease by characterizing subtypes of neurons in the dorsal raphe nucleus and defining their susceptibility to the degeneration of dopaminergic and adrenergic systems in the brain. This study was well-designed, the results were presented beautifully, and the manuscript was well-written. Here are some comments that may help to improve the overall quality of this work.

We thank the reviewer for the kind comments.

Major concerns:

The current study utilized an intrastriatal 6-OHDA injection, which raises the possibility that the observed electrophysiological and morphological changes of DRN5-HT and DRNDA neurons (Figs 3-6) may be due to the direct effects of 6-OHDA to DRN5-HT and DRNDA neurons projecting to the dorsal striatum (at least for DRN5-HT neurons). This possibility requires further clarification and discussion.

6-OHDA is a catecholamine neurotoxin with low selectivity for serotonin neurons. However, changes in the levels of serotonin have been observed with high doses of 6OHDA. In our study, we used lower concentrations of 6-OHDA, which did not affect the levels of serotonin (Suppl. Fig 4D), or the number of DRN5-HT neurons (Suppl. Fig. 5B). Concerning the possible effect of 6-OHDA on DRNDA neurons, we did not observe any modification in the number of these cells in response to the administration of 6-OHDA (Suppl. Fig. 5C), (lines 170-175).

How does the loss of nigrostriatal dopamine neurons affect the electrophysiology and morphology of DRNDA neurons (Figs. 5-6)? What are the potential circuit mechanisms?

The dopaminergic system in the midbrain and the DRN constitute two highly interconnected nuclei and hence there are multiple possible circuit mechanisms that could explain how loss of nigrostriatal dopaminergic neurons affects DRNDA neurons: First, DRNDA neurons are directly innervated by dopaminergic neurons in the SNc and VTA and hence loss of SNc inputs might evoke acute as well as homeostatic changes in DRNDA (Lin et al., 2020; Pinto et al., 2019). Second, midbrain dopaminergic neurons are in turn innervated by the DRN (Watabe-Uchida et al., 2012) and loss of postsynaptic dopaminergic neurons might affect all neuron types in the DRN that target the midbrain. Finally, GABAergic populations in the midbrain have been shown to target DRN5-HT neurons and might potentially also target other local cell types such as DRNDA (Li et al., 2019). Another possible pathway is the bidirectional connection between the striatum and the DRN (Pollak-Dorocic et al, 2014). DA depletion in the striatum may affect the GABAergic projection to the DRN and in turn modify the properties of postsynaptic DRN neurons.

The potential circuit mechanisms are now included in the introduction (lines 58-59).

Whether these intrastriatal 6-OHDA mice exhibited nonmotor deficits (e.g., anxiety) that may be related to the observed changes in the DRN? Such behavioral data would enhance the overall conclusions of this work.

The PD model utilized in this study displays non-motor deficits, including depression- and anxiety-like behavior (Masini et al. 2021, Ztaou et al., 2018). This is now highlighted in the manuscript (lines 167-169).

Minor issues:

The panels of Fig. 2 should be re-labelled to match the descriptions in the main text (L. 142-158).

Fig.2 now matches the descriptions in the main text.

Fig 4D was missing from the figure, which does not match the descriptions in the main text (L. 193-204:)

Fig. 4D includes the parameters describing the dendritic branching and starts with the last graph on the right in the second row of the panel.

Line 409: Extra "as" after "average"

Corrected in revised manuscript.

Fig 3G: Missed asterisks.

Corrected in revised manuscript (Fig. 3G)

Details of how action parameters were quantified should be stated and specified in the methods.

We have now added a section called ‘Quantification of electrophysiological parameters’ in the methods where we explain how the electrophysiological properties are defined and quantified (lines 407-439).

"Parkinson's disease" in the title should be revised to "parkinsonism"

Corrected in revised manuscript.

Reviewer #2 (Recommendations For The Authors):

(1) Throughout the paper, there are numerous inaccuracies and inconsistencies in the figures, which impede the clear understanding of this paper. For example, there are discrepancies between the labeling of the main figures (sub-panels) and the corresponding manuscript (Figure 2, Figure 4).

Corrected in the revised manuscript.

The statistical presentations are inaccurate in several figures (Figure 3E, 3G), making it difficult to distinguish which data is statistically meaningful. Furthermore, the number of cells presented in each figure is ambiguous in the figure legend. It would be better to avoid expressions such as 'n = 28 - 43 cells per group', as in line 456 (Figure 1I). Please provide the exact number of cells for each graph.

We agree with the reviewer, and we have now added the precise n numbers for each panel in the corresponding legends in Fig 1, Fig 3, and Fig 5. Please note that some analysis was restricted to recordings where neurons fired close to their average spontaneous firing frequency (e.g. 1Hz for DRN5-HT) to allow for a fair comparison of the data across groups and that therefore the n numbers vary in different panels.

In some figures, the value of n in the graph seems different from the value of n in the figure legends (Figure 2G-I, Figure 4, Figure 6). Collectively, these inaccurate figures and the manuscript weaken the general credibility of the data presented.

We apologize for the misunderstanding, but in the type of chosen graph, equal values are overlapped. The numbers described in the figure legend are correct.

(2) Some of the authors' claims in this paper are not supported by quantitative analysis, but only by sample recording traces or simple descriptions. For example, in line 97, the authors mentioned, "no differences when comparing TH-positive to TH-negative neurons".

But there are no data actually analyzing these two groups in Supplementary Figure 2A.

In addition, in line 103, there is a claim that "DRN DA neurons showed that they share several properties characteristics of other DA populations located in the SNc and the ventral tegmental area". However, this claim is backed up only by a few sample traces in Figure 1E.

The statement (lines 110-111), "a relative constant action potential (AP) amplitude", is also not supported by appropriate quantitative analysis but only by sample recording traces.

In our study we found a small subset of DAT-tdTomato positive neurons which did not stain positive for TH after the slice recordings. In 5 of 6 of these neurons (recorded in sham), the electrophysiological properties did not differ from other TH-positive neurons. This is visualized in Suppl. Fig 2A. The absence of any statistical difference was also confirmed by a Mann Whiteny U test comparing the TH negative to the TH positive DRNDA neurons (no significant differences in all 6 of 6 properties shown in Suppl. Fig 2A). Additionally, all these cells were DAT-positive, further supporting their classification as dopaminergic neurons. Therefore, we suspect that the lack of TH staining is likely caused by the tissue processing itself. Please note that all our immunohistochemistry was run on slices after several hours of patch-clamping procedures. Finally, including or excluding this small subset of neurons in the present study does not change any of the results presented and data was therefore pooled. We have now clarified this in more detail in the results section and in Suppl. Fig 2A (lines 100-103).

We have moved the comparison of hallmark properties found in DRNDA neurons as well as in dopaminergic neurons in the midbrain from the results section to the discussion (lines 281-283).

The claim that DRN5HT neurons have a comparatively constant action potential amplitude compared to DRNDA neurons is supported by quantitative analysis shown in Fig 1I (left panel, “AP drop rate”), while the representative example traces are shown in Fig 1G.

(3) In the legend of Figure 2, the mouse used in this experiment is mentioned with two different names (wild-type mice in line 463 and sham-lesion mice in line 465). Is this a mistake? Or did the authors intentionally use the brain samples from sham-lesion mice for Figure 2?

Figure 2 shows data in control conditions (Sham-lesion in our case), both from wild-type and Dat-Tomato. The text has been changed to avoid misunderstandings.

(4) While the primary claim of this paper is the differential alterations of DRN 5-HT and DA neurons in a mouse PD model, the observed changes in the DRN neurons of the 'DA only lesion model' are comparatively minor to the 'DA and NA lesions model'. Therefore, it looks like NA depletion has a more critical role in the DRN neurons of 6OHDA-lesion mice than DA depletion. To understand the results of this paper better, it would be great if the authors can provide additional data from the 'NA only lesion model'.

We agree with the reviewer, and we have now added a new set of experiments in which we selectively lesioned noradrenergic cells by injecting 6-OHDA unilaterally into the LC. The new data are presented in supplementary figure 6 in the revised manuscript. We find that selective lesioning of the NA system affects DRNDA and DRN5-HT neurons mildly, suggesting that the concomitant lesion of the DA and NA systems is particularly impactful (possibly because of interactions between these two systems).

(5) In Figure 3B and Figure 5B, only the 6-OHDA+DMI group shows significant differences from the sham group. This finding might be attributed to the effect of DMI itself, not to the nigrostriatal DA degeneration without NA degeneration. Thus, adding the 'DMI-only group' in all experiments will strengthen the conclusion of this paper.

The effect of one acute administration of desipramine was temporally limited to the stereotactic intervention (line 373-375), which was performed several weeks before the electrophysiological and morphological analyses. Given that the half-life of desipramine is approximately 24 hrs (Nagy and Johansson, 1975), we believe that its impact was limited to the neuroprotection of NA-neurons from 6-OHDA toxicity.

(6) DRN 5-HT neurons are known to exhibit cellular heterogeneity, and in particular their electrophysiological properties are quite heterogeneous (Bernat Kocsis. 2006; J.V. Schweimer. et al. 2011). Furthermore, 5-HT neurons in the distinct subregions of the DRN display different membrane properties (LaTasha K. Crawford, 2010). Therefore, not all DRN 5-HT neurons can be regarded as electrophysiologically identical. Given that the molecular identity of all recorded cells was confirmed with neurobiotin in this paper, it would be better to show that recorded cells are not biased toward certain subregions of DRN.

In addition, providing more comprehensive descriptions of the electrophysiological features used in PCA analysis would be beneficial in understanding the electrophysiological profiling of DRN neurons explained in this paper.

Although several studies have revealed electrophysiological and molecular heterogeneity within the DRN5-HT population, we did not observe any significant differences within the DRN5-HT neurons recorded in this study. We compared the properties of DRN5HT neurons recorded more anterior to those recorded in the posterior

DRN as well as neurons found in more ventral locations to those in more dorsal locations (data not shown). We would like to point out that the largest differences within serotonergic neuron populations described by previous studies were often found when comparing those located in the medial raphe nucleus (MRN) to those found in the DRN. Calizo et al., (2011) showed for example significant differences in the input resistance and AHP amplitude between MRN5HT and DRN5HT neurons. These two properties as well as the AP amplitude, AP threshold, AP duration, and tau did however not differ between DRN subregions in their study - and neither in ours. We extended our Suppl. Fig 1 and mapped the location of DRN5HT and DRNDA neurons recorded in sham (Suppl. Fig 1D).

Overall, we’ve sampled neurons along the anterior-posterior and dorsal-ventral axes of the DRN, while on the medial-lateral axis, recorded DRN neurons were located medially.

We agree with the reviewer that a comprehensive description of the electrophysiological features was missing in the manuscript, and we have therefore added a new section in the materials and methods where we explain in detail how each parameter was measured and analyzed (‘Quantification of electrophysiological parameters’, lines 407-439). This section also provides detailed information about the five properties underlying the PCA shown in figure 1 (i.e. delay to the first action potential, action potential drop rate, action potential rise time, duration of the afterhyperpolarization, and capacitance).

(7) Some sample images presented in this paper contain information that can conflict with the previous research. In Figures 4B and 6B, TH expression was significantly increased in the DMI pretreatment group compared to the control group. However, several studies have shown that the administration of DMI decreases TH expression levels (Komori et al.1992; Nestler et al.1990). Therefore, it would be great if the authors further explained how the pretreatment of DMI with 6-OHDA affects TH level within the DRN.

Figure 4B and 6B do not show any quantification of TH expression. The difference observed in the representative pictures is casual and due to the variable expression of TH across the slice. Moreover, as mentioned in the response to point 5, mice were subjected to a single injection of DMI immediately preceding the stereotactic intervention (line 373375). In contrast, the increase in TH expression reported by Komori et al. 1992 and Nestler et al. 1990 was observed in response to chronic (two weeks) administration of DMI.

(8) This paper lacks direct evidence to demonstrate whether DMI pretreatment could effectively protect against NA depletion. Therefore, in addition to TH expression levels, it is important to provide data to confirm the intact NA levels (or NA axons) after DMI treatment.

NA levels in the striatum were measured by Enzyme-linked immunosorbent assay and reported in Suppl.Fig.4 in the revised manuscript.

(9) It would be great if the authors specifically explained why 6-OHDA was injected into the striatum (neither MFB nor SNc) to make a mouse model of PD.

Mice were injected in the dorsal striatum to produce a partial bilateral lesion of the dopamine and noradrenaline systems. This model reproduces the initial stages of PD and also recapitulates several non-motor symptoms of PD, including affective disorders, which may be related to changes in serotonergic and dopaminergic transmission in the dorsal raphe. In contrast, injections in the MFB and SNc quickly produce a severe motor phenotype closer to a late stage of the disease and cannot be done bilaterally. <br /> The striatal model has been successfully used in other publications (Kravitz et al., 2010, Masini et al., 2021, Ztaou et al., 2018, Chen et al., 2014, Branchi et al., 2008, Marques et al. 2019, Tadaiesky et al., 2008, Matheus et al., 2016, Silva et al., 2016).

(10) Supplementary Figures 2 and 3 were erroneously cut on the right side. These figure images should be replaced with the correct ones.

We thank the reviewer for noticing and we have now replaced the figures with the correct ones.

(11) There should be more explanations about tdTomato-positive but non-TH neurons in Supplementary Figure 2. It is strange to regard TH-negative neurons as DA neurons although these neurons have DA neuron-like electrophysiological properties. If these tdTomato-positive but non-TH neurons cannot release DA, can we say these are DA neurons?

In our study we found a small subset of DAT-tdTomato positive neurons which did not stain positive for TH afterwards. In 5 of 6 of these neurons (recorded in sham), the electrophysiological properties did not differ from other TH-positive neurons. This is visualized in Suppl. Fig 2A. The absence of any statistical difference was also confirmed by a Mann Whiteny U test comparing the TH-negative to the TH-positive DRNDA neurons (no significant differences in all 6 of 6 properties shown in SF2A). Additionally, all these cells were DAT-positive, further supporting their classification as dopaminergic neurons. Therefore, we suspect that the lack of TH staining is likely caused by the tissue processing itself. Please note that all our immunohistochemistry was run on slices after several hours of patch-clamping procedures. Finally, including or excluding this small subset of neurons in the present study does not change any of the results presented and data was therefore pooled. We have now clarified this in more detail in the results section and in Suppl. Fig 2A (lines 100-103).

Reviewer #3 (Recommendations For The Authors):

The authors report using a parametric statistical test, the t-test. The t-test makes the assumption that the data are normally distributed. Most biological data is not distributed normally, and with smaller datasets, it is difficult to say whether the underlying distribution would be normally distributed. I would recommend using the non-parametric versions of the same test (eg Mann-Whitney U test), which is likely to give a similar result while being more conservative given the potential for non-normal distribution.

All electrophysiological data were first tested for normality before running the corresponding statistical test (either t-test for normal distributed data or Mann-Whitney U test for non-normally distributed data). The morphological data are now analyzed by the Mann-Whitney U test (lines 484-494).

The authors state that mice were treated with 6-OHDA at 3 months, then brain slices were prepared 3 weeks later, making them about 4 months old. I could not find the age of sham/control mice and 6-OHDA/desipramine mice in the methods section. Were sham/controls and 6-OHDA slices prepared in an interleaved fashion?

Sham and 6-OHDA+DMI mice underwent surgery at 3 months and the brain slices were prepared 3 weeks later, as the 6-OHDA mice. We have now clarified this in the methods (line 381).

While desipramine is relatively selective as a norepinephrine reuptake inhibitor, it also can prevent serotonin reuptake. Could this mechanism also protect DRN neurons from the effects of 6-OHDA?

Even if desipramine has some affinity for the serotonin reuptake, this affinity is 100-fold less than the one described for the noradrenaline reuptake (Richelson and Pfenning, 1984, Gillman, 2007). Moreover, in our study the 6-OHDA injection in the dorsal striatum did not cause any direct damage to the DRN5-HT, as shown by the 5-HT measurement and DRN5-HT counting (Suppl. Fig. 4D, Suppl. Fig. 5A,B), so we can exclude that the effects observed in the DMI+6-OHDA group are related to a protection of the serotonergic system exerted by a single injection of desipramine.

On line 168, the authors use the abbreviation NA for noradrenergic. Was this abbreviation previously defined in the manuscript?

Yes, the abbreviation is defined in the introduction (line 73).

On line 45, the authors cite that the DRN-5HT subpopulation accounts for 30-50% of the DRN neurons. It would be helpful to know approximately what percentage of the DRN neurons belong to the DRNDA subpopulation as well.

To the best of our knowledge, there is unfortunately no detailed analysis of the prevalence of DRNDA neurons in mice available. Previous studies in rats have estimated that this population comprises around 1000 neurons (Descarries et al., 1986). According to Calizo et al. (2011), the number of any non-serotonergic neuron population (releasing dopamine or other neurotransmitters) in the DRN is one third to one tenth less than the number of DRN5-HT neurons. But please note that this study was also performed in rats (line 55).

While I appreciate that the authors did not over-interpret their findings, it would be useful to comment (in the Discussion) on how their findings could/should be used in interpreting other studies using 6-OHDA, as well as the relationship of their findings to loss of 5-HT and/or DRN neurons in Parkinson's Disease itself.

In the manuscript, we refer to the utility of the 6-OHDA model for the study of a wide range of non-motor symptoms. We have now described, in this model, how the loss of midbrain dopaminergic and noradrenergic neurons affects the electrophysiological and morphological properties of DRN5-HT and DRNDA neurons. This information will allow for a more precise assessment of the mechanisms involved in the affective and cognitive aspects of PD symptomatology (lines 354-356).

eLife assessment

This important work provides a convincing dataset of neuronal heterogeneity in the raphe nucleus, including their physiological properties, morphology, and susceptibility to the neurodegeneration of noradrenaline and dopamine systems in the Parkinsonian state. These findings suggest a significant interplay between catecholaminergic systems in healthy and parkinsonian conditions, as well as neuronal structure and function. Such findings provide a strong foundation for basic scientists as well as pre-clinical researchers interested in the role of dorsal raphe neurons in Parkinson's disease.

Reviewer #1 (Public Review):

Summary:

People with Parkinson's disease often experience a variety of nonmotor symptoms, the biological bases of which remain poorly understood. Johansson et al began to study potential roles of the dorsal raphe nucleus (DRN) degeneration in the pathophysiology of neuropsychiatric symptoms in PD.

Strengths:

Boi et al validated a transgenic reporter mouse line that can reliably label dopaminergic neurons in the DRN. This brain region shows severe neurodegeneration and has been proposed to contribute to the manifestation of neuropsychiatric symptoms in PD. Using this mouse line (and others), Boi and colleagues characterized electrophysiological and morphological phenotypes of dopaminergic and serotoninergic neurons in the raphe nucleus. This study involved very careful topographical registration of recorded neurons to brain slices for post hoc immunohistochemical validation of cell identity, making it an elegant and thorough piece of work.

In relevance to PD pathophysiology, the authors evaluated the physiological and morphological changes of DRN serotoninergic and dopaminergic neurons after a partial loss of nigrostriatal dopamine neurons, which serves as a mouse model of early parkinsonian pathology. Moreover, the authors identified a series of physiological and morphological changes of subtypes of DRN neurons that depend on nigral dopaminergic neurodegeneration, LC noradrenergic neurodegeneration, or both. Indeed this works highlights the importance of LC noradrenergic degeneration in PD pathophysiology.

Overall, this is a well-designed study with high significance to the Parkinson's research field.

Reviewer #2 (Public Review):

In this paper, Boi et al. thoroughly classified the electrophysiological and morphological characteristics of serotonergic and dopaminergic neurons in the DRN and examined the alterations of these neurons in the 6-OHDA-induced mouse PD model. Using whole-cell patch clamp recording, they found that 5-HT and dopamine (DA) neurons in the DRN are electrophysiologically well-distinguished from each other. In addition, they characterized distinct morphological features of 5-HT and DA neurons in the DRN. Notably, these specific features of 5-HT and DA neurons in the DRN exhibited different changes in the 6-OHDA-induced PD model. Then the authors utilized desipramine (DMI) to separate the effects of nigrostriatal DA depletion and noradrenalin (NA) depletion which are induced by 6-OHDA. Interestingly, protection from NA depletion by DMI pretreatment reversed the changes in 5-HT neurons, while having a minor impact on the changes in DA neurons in the DRN. These data indicate that the role of NA lesion in the altered properties of DRN 5-HT neurons by 6-OHDA is more critical than the one of DA lesion.

Overall, this study provides foundational data on the 5-HT and DA neurons in the DRN and their potential involvement in PD symptoms. Given the defects of the DRN in PD, this paper may offer insights into the cellular mechanisms that may underlie non-motor symptoms associated with PD. Despite the importance of the primary claim proposed by the authors, however, the interpretation of the authors on some DMI experiments is not explained well.

Reviewer #3 (Public Review):

Summary:

Using ex vivo electrophysiology and morphological analysis, Boi et al. investigate the electrophysiological and morphological properties of serotonergic and dopaminergic subpopulations in the dorsal raphe nucleus (DRN). They performed labor-intensive and rigorous electrophysiology with posthoc immunohistochemistry and neuronal reconstruction to delineate the two major cell classes in the DRN: DRN-DA and DRN-5HT, named according to their primary neurotransmitter machinery. They find that the dopaminergic (DRN-DA) and serotonergic (DRN-5HT) neurons are electrophysiologically and morphologically distinct, and are altered following striatal injection of the toxin 6-OHDA. However, these alterations were largely prevented in DRN-5HT neurons by pre-treatment with desipramine. These findings suggest an important interplay between catecholaminergic systems in healthy and parkinsonian conditions, as well as a relationship between neuronal structure and function.

Strengths:

Large, well-validated dataset that will be a resource for others.<br /> Complementary electrophysiological and anatomical characterizations.<br /> Conclusions are justified by the data.<br /> Relevant for basic scientists interested in DRN cell types and physiology<br /> Relevant for those interested in serotonin and/or DRN neurons in Parkinson's Disease

Weaknesses:

Given the scope of the author's questions and hypotheses, I did not identify any major weaknesses.

Author Response

We are writing this response letter with regards to the insightful feedback you provided on our manuscript titled: "A metabolic modeling-based framework for predicting trophic dependencies in native rhizobiomes of crop plants" submitted for consideration in eLife.

We sincerely appreciate the thorough and constructive reviews, seeing and fitting the intentions behind our work. We intend to fully address all points raised by the reviewers in our revised manuscript. Specifically, we plan to incorporate targeted revisions to address concerns raised during the review process, with focus on process benchmarking and validation of our framework to enhance its reliability and accuracy.

We believe that the current revision would improve the consistency and quality of the framework, making it a suitable tool for the characterization of microbial trophic interactions in diverse biological landscapes.

Thank you once again for both your time and dedication in reviewing our manuscript, as well as the constructive review.

eLife assessment

This important study presents a framework for combining metagenomic and metabolomic data with genome-scale metabolic models to identify trophic interactions in a root microbiome. Its new approach to integrating these sources of data and model-specific trophic exchanges is compelling, but the benchmarking of the model generation process is incomplete and would benefit from a sensitivity and specificity analysis. With a stronger characterization of their method, this paper could be broadly applicable to other microbiomes and used to understand the composition and functioning of a given community, generate hypotheses, and design interventions.

Reviewer #1 (Public Review):

The work by Ginatt et al. uses genome-scale metabolic modeling to identify and characterize trophic interactions between rhizosphere-associated bacteria. Beyond identifying microbial species associated with specific host and soil traits (e.g., disease tolerance), a detailed understanding of the interactions underlying these associations is necessary for developing targeted microbiome-centered interventions for plant health. It has nonetheless remained challenging to define the roles of specific organisms and metabolic species in natural rhizobiomes. Here, the authors combine microbial compositional data obtained through metagenomic sequencing with a new collection of genome-scale models to predict interactions in the native rhizosphere communities of apple rootstocks. To do this, they have established processes to integrate these sources of data and model specific trophic exchanges, which they use to obtain testable hypotheses for targeted modulation of microbiota members in situ.

The authors carry out a careful model curation process based on metagenomic sequencing data and existing model generation tools, which, together with basing the in silico medium composition on known root exudates, strengthens their predictions of interaction network features. Moreover, its reliance on genome-scale models provides a broader basis for linking sequence-based information to predictions of function on a multispecies level beyond rhizosphere microbiomes.

Having generated a set of predicted trophic interactions, the authors carried out a detailed analysis linking features of these interactions to organism taxonomy and broader ecosystem properties. Intriguingly, the organisms predicted to grow in the first iteration of their framework (i.e., on only root exudates) broadly correspond to taxonomic groups experimentally shown to benefit from these compounds. Additionally, the simulations predicted some patterns of vitamin and amino acid secretion that are known to form the basis for interactions in the rhizosphere. Together, these outcomes underscore the applicability of this method to help disentangle trophic interaction networks in complex microbiomes.

The methodology described in this paper represents a useful and promising framework to better understand the complexity of microbial interaction networks in situ. However, the degree to which the predictions can vary according to environmental composition remains difficult to quantify, and the work does not address the sensitivity of the modeling predictions beyond a simulated medium containing 33 root exudates. I find this especially important given that relatively few (84 of 243) species were predicted to grow even after cross-feeding, suggesting that a richer medium could lead to different interaction network structures. While the authors do state the importance of environmental composition and have carefully designed an in silico medium, I believe that simulating a broader set of resource pools would add necessary insight into both the predictive power of the models themselves and trophic interactions in the rhizosphere more generally.

Reviewer #2 (Public Review):

Summary:

The authors present a framework for exploiting shotgun metagenomics and metabolomics data along with constraint-based analysis (CBA) to study, in their case, the dynamics and interactions between the apple rootstock and rhizosphere's microbial community. This study should be considered as a follow-up of Berihu et al. (2022) where the shotgun data were first introduced. A set of 395 Metagenome-Assembled Genomes (MAGs) was derived from those reads and from the latter, using an automatic Genome-scale Metabolic Model (GSMM) reconstruction tool (CarveMe), 243 GSMMs. Metabolomics data from a set of studies were gathered to describe/represent root exudates. Flux Variability Analysis (FVA), a type of constraint-based analysis, was conducted iteratively. Three distinct in silico media were used (optimal, poor, and realistic, with the latter informed by metabolomics data) to examine the potential impact of root exudates on bacterial growth. Additionally, the study investigated the extent to which compounds secreted by bacteria could support the growth of other community members. Further, an exchange network representing all potential metabolic exchanges within the rhizosphere community was built and motifs on it were classified with healthy and/or symptomized soil.

Strengths:

The study provides a great starting point for how one can bring together shotgun metagenomics and other omics technologies such as metabolomics with metabolic modelling approaches. MAGs and the automatic reconstruction of corresponding GSMMs become more and more a common practice and frameworks for their analysis and interpretation are more than needed. The usage of FVA instead of the Flux Balance Analysis allows the authors to get all the range of potentially produced metabolites. The iterative approach can highlight what species are supported by the plant and which need the first to join the community while correlating microbial metabolic interactions with soil performance through differential abundance can bring up valid hypotheses to examine further. On top of that, avoiding modelling approaches that require community objective functions and optimization of that makes the simulation more realistic.

Weaknesses:

There are two main drawback approaches like the one described here, both related only partially to the authors' work yet with great impact in the presented framework. First, the usage of automatic GSMM reconstruction requires great caution. It is indicative of how the semi-curated AGORA models are still considered reconstructions and expect the user to parameterize those in a model. In this study, CarveMe was used. CarveMe is a well-known tool with several pros [1]. Yet, several challenges need to be considered when using it [2]. For example, the biomass function used might lead to an overestimation of auxotrophies. Also, as its authors admit in their reply paper, CarveMe does gap fill in a way [3]; models are constructed to ensure no gaps and also secure a minimum growth. However, curation of such a high number of GSMMs is probably not an option. Further, even if FVA is way more useful than FBA for the authors' aim, it does not yet ensure that when a species secretes one compound (let's say metabolite A), the same flux vector, i.e. the same metabolic functioning profile, secretes another compound (metabolite B) at the same time, even if the FVA solution suggests that metabolite B could be secreted in general.

Besides those challenges, the suggested framework is promising and such approaches can work as the starting point for the next step in microbial ecology studies in general; from soil to marine and host ecosystems. The authors highlight perfectly this angle stating that this framework is currently conceptual and that it can be only used to formulate new hypotheses. Unbiased constraint-based approaches that focus on metabolite exchanges would benefit such approaches.

[1] Mendoza, Sebastián N., et al. "A systematic assessment of current genome-scale metabolic reconstruction tools." Genome biology 20.1 (2019): 1-20.<br /> [2] Price, Morgan. "Erroneous predictions of auxotrophies by CarveMe." Nature Ecology & Evolution 7.2 (2023): 194-195.<br /> [3] Machado, Daniel, and Kiran R. Patil. "Reply to: Erroneous predictions of auxotrophies by CarveMe." Nature Ecology & Evolution 7.2 (2023): 196-197.<br /> [4] Ylva Katarina Wedmark, Jon Olav Vik, Ove Øyås bioRxiv 2023.09.05.556413; doi: https://doi.org/10.1101/2023.09.05.556413

Reviewer #3 (Public Review):

Summary:

This study presents a solid framework for the metabolic modeling of microbial species and resources in the rhizosphere environment. It is an ambitious effort to tackle the huge complexity of the rhizosphere and reveal the plant-microbiota interactions therein. Considering previously published data by Berihu et al., going through a series of steps, the framework then finds associations between an apple tree disease state and both microbes and metabolites. The framework is well explained and motivated. I think that further work should be done to validate the method, both using synthetic data, with a known ground truth and following up on key findings experimentally.

Strengths:

- The manuscript is well written with a good balance between detail and readability. The framework steps are well-motivated and explained.

- The authors faithfully acknowledge the limitations of their approach and do not try to "over-sell" their conclusions.

- The presented framework has the potential for significant discovery if the hypotheses generated are followed up with experimental validation.

Weaknesses:

- When presenting a computational framework, best practices include running it on artificial (synthetic) data where the ground truth is known and therefore the precision and accuracy of the method may be assessed. This is not an optional step, the same way that positive/negative controls in lab experiments are not optional. Without this validation step, the manuscript is severely limited. The authors should ask themselves: what have we done to convince the reader that the framework actually works, at least on our minimal synthetic data?

Justification of claims and conclusions:

The claims and conclusions are sufficiently well justified since the limitations of this approach are acknowledged by the authors.

Author Response

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

Recommendations for the authors:

(1) Substantial revision of the claims and interpretation of the results is needed, especially in the setting of additional data showing enhanced erythrophagocytosis with decreased RBC lifespan.

Thank you for your valuable feedback and suggestion for a substantial revision of the claims and interpretation of our results. We acknowledge the importance of considering additional data that shows enhanced erythrophagocytosis with decreased RBC lifespan. In response, we have revised our manuscript and incorporated additional experimental data to support and clarify our findings.

(1) In our original manuscript, we reported a decrease in the number of splenic red pulp macrophages (RPMs) and phagocytic erythrocytes after hypobaric hypoxia (HH) exposure. This conclusion was primarily based on our observations of reduced phagocytosis in the spleen.

(2) Additional experimental data on RBC labeling and erythrophagocytosis:

• Experiment 1 (RBC labeling and HH exposure)

We conducted an experiment where RBCs from mice were labeled with PKH67 and injected back into the mice. These mice were then exposed to normal normoxia (NN) or HH for 7 or 14 days. The subsequent assessment of RPMs in the spleen using flow cytometry and immunofluorescence detection revealed a significant decrease in both the population of splenic RPMs (F4/80hiCD11blo, new Figure 5A and C) and PKH67-positive macrophages after HH exposure (as depicted in new Figure 5A and C-E). This finding supports our original claim of reduced phagocytosis under HH conditions.

Author response image 1.

-Experiment 2 (erythrophagocytosis enhancement)

To examine the effects of enhanced erythrophagocytosis, we injected Tuftsin after administering PKH67-labelled RBCs. Our observations showed a significant decrease in PKH67 fluorescence in the spleen, particularly after Tuftsin injection compared to the NN group. This result suggests a reduction in RBC lifespan when erythrophagocytosis is enhanced (illustrated in new Figure 7, A-B).

Author response image 2.

(3) Revised conclusions:

• The additional data from these experiments support our original findings by providing a more comprehensive view of the impact of HH exposure on splenic erythrophagocytosis.

• The decrease in phagocytic RPMs and phagocytic erythrocytes after HH exposure, along with the observed decrease in RBC lifespan following enhanced erythrophagocytosis, collectively suggest a more complex interplay between hypoxia, erythrophagocytosis, and RBC lifespan than initially interpreted.

We think that these revisions and additional experimental data provide a more robust and detailed understanding of the effects of HH on splenic erythrophagocytosis and RBCs lifespan. We hope that these changes adequately address the concerns raised and strengthen the conclusions drawn in our manuscript.

(2) F4/80 high; CD11b low are true RPMs which the cells which the authors are presenting, i.e. splenic monocytes / pre-RPMs. To discuss RPM function requires the presentation of these cells specifically rather than general cells in the proper area of the spleen.

Thank you for your feedback requesting a substantial revision of our claims and interpretation, particularly considering additional data showing enhanced erythrophagocytosis with decreased RBC lifespan. In response, we have thoroughly revised our manuscript and included new experimental data that further elucidate the effects of HH on RPMs and erythrophagocytosis.

(1) Re-evaluation of RPMs population after HH exposure:

• Flow cytometry analysis (new Figure 3G, Figure 5A and B): We revisited the analysis of RPMs (F4/80hiCD11blo) in the spleen after 7 and 14 days of HH exposure. Our revised flow cytometry data consistently showed a significant decrease in the RPMs population post-HH exposure, reinforcing our initial findings.

Author response image 3.

Author response image 4.

• In situ expression of RPMs (Figure S1, A-D):

We further confirmed the decreased population of RPMs through in situ co-staining with F4/80 and CD11b, and F4/80 and CD68, in spleen tissues. These results clearly demonstrated a significant reduction in F4/80hiCD11blo (Figure S1, A and B) and F4/80hiCD68hi (Figure S1, C and D) cells following HH exposure.

Author response image 5.

(2) Single-cell sequencing analysis of splenic RPMs:

• We conducted a single-cell sequencing analysis of spleen samples post 7 days of HH exposure (Figure S2, A-C). This analysis revealed a notable shift in the distribution of RPMs, predominantly associated with Cluster 0 under NN conditions, to a reduced presence in this cluster after HH exposure.

• Pseudo-time series analysis indicated a transition pattern change in spleen RPMs, with a shift from Cluster 2 and Cluster 1 towards Cluster 0 under NN conditions, and a reverse transition following HH exposure (Figure S2, B and D). This finding implies a decrease in resident RPMs in the spleen under HH conditions.

(3) Consolidated findings and revised interpretation:

• The comprehensive analysis of flow cytometry, in situ staining, and single-cell sequencing data consistently indicates a significant reduction in the number of RPMs following HH exposure.

• These findings, taken together, strongly support the revised conclusion that HH exposure leads to a decrease in RPMs in the spleen, which in turn may affect erythrophagocytosis and RBC lifespan.

Author response image 6.

In conclusion, our revised manuscript now includes additional experimental data and analyses, strengthening our claims and providing a more nuanced interpretation of the impact of HH on spleen RPMs and related erythrophagocytosis processes. We believe these revisions and additional data address your concerns and enhance the scientific validity of our study.

(3) RBC retention in the spleen should be measured anyway quantitatively, eg, with proper flow cytometry, to determine whether it is increased or decreased.

Thank you for your query regarding the quantitative measurement of RBC retention in the spleen, particularly in relation to HH exposure. We have utilized a combination of techniques, including flow cytometry and histological staining, to investigate this aspect comprehensively. Below is a summary of our findings and methodology.

(1) Flow cytometry analysis of labeled RBCs:

• Our study employed both NHS-biotin (new Figure 4, A-D) and PKH67 labeling (new Figure 4, E-H) to track RBCs in mice exposed to HH. Flow cytometry results from these experiments (new Figure 4, A-H) showed a decrease in the proportion of labeled RBCs over time, both in the blood and spleen. Notably, there was a significantly greater reduction in the amplitude of fluorescently labeled RBCs after NN exposure compared to the reduced amplitude of fluorescently labeled RBCs observed in blood and spleen under HH exposure. The observed decrease in labeled RBCs was initially counterintuitive, as we expected an increase in RBC retention due to reduced erythrophagocytosis. However, this decrease can be attributed to the significantly increased production of RBCs following HH exposure, diluting the proportion of labeled cells.

• Specifically, for blood, the biotin-labeled RBCs decreased by 12.06% under NN exposure and by 7.82% under HH exposure, while the PKH67-labeled RBCs decreased by 9.70% under NN exposure and by 4.09% under HH exposure. For spleen, the biotin-labeled RBCs decreased by 3.13% under NN exposure and by 0.46% under HH exposure, while the PKH67-labeled RBCs decreased by 1.16% under NN exposure and by 0.92% under HH exposure. These findings suggest that HH exposure leads to a decrease in the clearance rate of RBCs.

Author response image 7.

(2) Detection of erythrophagocytosis in spleen:

To assess erythrophagocytosis directly, we labeled RBCs with PKH67 and analyzed their uptake by splenic macrophages (F4/80hi) after HH exposure. Our findings (new Figure 5, D-E) indicated a decrease in PKH67-positive macrophages in the spleen, suggesting reduced erythrophagocytosis.

Author response image 8.

(3) Flow cytometry analysis of RBC retention:

Our flow cytometry analysis revealed a decrease in PKH67-positive RBCs in both blood and spleen (Figure S4). We postulated that this was due to increased RBC production after HH exposure. However, this method might not accurately reflect RBC retention, as it measures the proportion of PKH67-labeled RBCs relative to the total number of RBCs, which increased after HH exposure.

Author response image 9.

(4) Histological and immunostaining analysis:

Histological examination using HE staining and band3 immunostaining in situ (new Figure 6, A-D, and G-H) revealed a significant increase in RBC numbers in the spleen after HH exposure. This was further confirmed by detecting retained RBCs in splenic single cells using Wright-Giemsa composite stain (new Figure 6, E and F) and retained PKH67-labelled RBCs in spleen (new Figure 6, I and J).

Author response image 10.

(5) Interpreting the data:

The comprehensive analysis suggests a complex interplay between increased RBC production and decreased erythrophagocytosis in the spleen following HH exposure. While flow cytometry indicated a decrease in the proportion of labeled RBCs, histological and immunostaining analyses demonstrated an actual increase in RBCs retention in the spleen. These findings collectively suggest that while the overall RBCs production is upregulated following HH exposure, the spleen's capacity for erythrophagocytosis is concurrently diminished, leading to increased RBCs retention.

(6) Conclusion:

Taken together, our results indicate a significant increase in RBCs retention in the spleen post-HH exposure, likely due to reduced residual RPMs and erythrophagocytosis. This conclusion is supported by a combination of flow cytometry, histological staining, and immunostaining techniques, providing a comprehensive view of RBC dynamics under HH conditions. We think these findings offer a clear quantitative measure of RBC retention in the spleen, addressing the concerns raised in your question.

(4) Numerous other methodological problems as listed below.

We appreciate your question, which highlights the importance of using multiple analytical approaches to understand complex physiological processes. Please find below our point-by-point response to the methodological comments.

Reviewer #1 (Recommendations For The Authors):

(1) Decreased BM and spleen monocytes d/t increased liver monocyte migration is unclear. there is no evidence that this happens or why it would be a reasonable hypothesis, even in splenectomized mice.

Thank you for highlighting the need for further clarification and justification of our hypothesized decrease in BM and spleen monocytes due to increased monocyte migration to the liver, particularly in the context of splenectomized mice. Indeed, our study has not explicitly verified an augmentation in mononuclear cell migration to the liver in splenectomized mice.

Nonetheless, our investigations have revealed a notable increase in monocyte migration to the liver after HH exposure. Noteworthy is our discovery of a significant upregulation in colony stimulating factor-1 (CSF-1) expression in the liver, observed after both 7 and 14 days of HH exposure (data not included). This observation was substantiated through flow cytometry analysis (as depicted in Figure S4), which affirmed an enhanced migration of monocytes to the liver. Specifically, we noted a considerable increase in the population of transient macrophages, monocytes, and Kupffer cells in the liver following HH exposure.

Author response image 11.

Considering these findings, we hypothesize that hypoxic conditions may activate a compensatory mechanism that directs monocytes towards the liver, potentially linked to the liver’s integral role in the systemic immune response. In accordance with these insights, we intend to revise our manuscript to reflect the speculative nature of this hypothesis more accurately, and to delineate the strategies we propose for its further empirical investigation. This amendment ensures that our hypothesis is presented with full consideration of its speculative basis, supported by a coherent framework for future validation.

(2) While F4/80+CD11b+ population is decreased, this is mainly driven by CD11b and F4/80+ alone population is significantly increased. This is counter to the hypothesis.

Thank you for addressing the apparent discrepancy in our findings concerning the F4/80+CD11b+ population and the increase in the F4/80+ alone population, which seems to contradict our initial hypothesis. Your observation is indeed crucial for the integrity of our study, and we appreciate the opportunity to clarify this matter.

(1) Clarification of flow cytometry results:

• In response to the concerns raised, we revisited our flow cytometry experiments with a focus on more clearly distinguishing the cell populations. Our initial graph had some ambiguities in cell grouping, which might have led to misinterpretations.

• The revised flow cytometry analysis, specifically aimed at identifying red pulp macrophages (RPMs) characterized as F4/80hiCD11blo in the spleen, demonstrated a significant decrease in the F4/80 population. This finding is now in alignment with our immunofluorescence results.

Author response image 12.

Author response image 13.

(2) Revised data and interpretation:

• The results presented in new Figure 3G and Figure 5 (A and B) consistently indicate a notable reduction in the RPMs population following HH exposure. This supports our revised understanding that HH exposure leads to a decrease in the specific macrophage subset (F4/80hiCD11blo) in the spleen.

We’ve updated our manuscript to reflect these new findings and interpretations. The revised manuscript details the revised flow cytometry analysis and discusses the potential mechanisms behind the observed changes in macrophage populations.

(3) HO-1 expression cannot be used as a surrogate to quantify number of macrophages as the expression per cell can decrease and give the same results. In addition, the localization of effect to the red pulp is not equivalent to an assertion that the conclusion applies to macrophages given the heterogeneity of this part of the organ and the spleen in general.

Thank you for your insightful comments regarding the use of HO-1 expression as a surrogate marker for quantifying macrophage numbers, and for pointing out the complexity of attributing changes in HO-1 expression specifically to macrophages in the splenic red pulp. Your observations are indeed valid and warrant a detailed response.

(1) Role of HO-1 in macrophage activity:

• In our study, HO-1 expression was not utilized as a direct marker for quantifying macrophages. Instead, it was considered an indicator of macrophage activity, particularly in relation to erythrophagocytosis. HO-1, being upregulated in response to erythrophagocytosis, serves as an indirect marker of this process within splenic macrophages.

• The rationale behind this approach was that increased HO-1 expression, induced by erythrophagocytosis in the spleen’s red pulp, could suggest an augmentation in the activity of splenic macrophages involved in this process.

(2) Limitations of using HO-1 as an indicator:

• We acknowledge your point that HO-1 expression per cell might decrease, potentially leading to misleading interpretations if used as a direct quantifier of macrophage numbers. The variability in HO-1 expression per cell indeed presents a limitation in using it as a sole indicator of macrophage quantity.

• Furthermore, your observation about the heterogeneity of the spleen, particularly the red pulp, is crucial. The red pulp is a complex environment with various cell types, and asserting that changes in HO-1 expression are exclusive to macrophages could oversimplify this complexity.

(3) Addressing the concerns:

• To address these concerns, we propose to supplement our HO-1 expression data with additional specific markers for macrophages. This would help in correlating HO-1 expression more accurately with macrophage numbers and activity.

• We also plan to conduct further studies to delineate the specific cell types in the red pulp contributing to HO-1 expression. This could involve techniques such as immunofluorescence or immunohistochemistry, which would allow us to localize HO-1 expression to specific cell populations within the splenic red pulp.

We’ve revised our manuscript to clarify the role of HO-1 expression as an indirect marker of erythrophagocytosis and to acknowledge its limitations as a surrogate for quantifying macrophage numbers.

(4) line 63-65 is inaccurate as red cell homeostasis reaches a new steady state in chronic hypoxia.

Thank you for pointing out the inaccuracy in lines 63-65 of our manuscript regarding red cell homeostasis in chronic hypoxia. Your feedback is invaluable in ensuring the accuracy and scientific integrity of our work. We’ve revised lines 63-65 to accurately reflect the understanding.

(5) Eryptosis is not defined in the manuscript.

Thank you for highlighting the omission of a definition for eryptosis in our manuscript. We acknowledge the significance of precisely defining such key terminologies, particularly when they play a crucial role in the context of our research findings. Eryptosis, a term referenced in our study, is a specialized form of programmed cell death unique to erythrocytes. Similar with apoptosis in other cell types, eryptosis is characterized by distinct physiological changes including cell shrinkage, membrane blebbing, and the externalization of phosphatidylserine on the erythrocyte surface. These features are indicative of the RBCs lifecycle and its regulated destruction process.

However, it is pertinent to note that our current study does not extensively delve into the mechanisms or implications of eryptosis. Our primary focus has been to elucidate the effects of HH exposure on the processes of splenic erythrophagocytosis and the resultant impact on the lifespan of RBCs. Given this focus, and to maintain the coherence and relevance of our manuscript, we have decided to exclude specific discussions of eryptosis from our revised manuscript. This decision aligns with our aim to provide a clear and concentrated exploration of the influence of HH exposure on RBCs dynamics and splenic function.

We appreciate your input, which has significantly contributed to enhancing the clarity and accuracy of our manuscript. The revision ensures that our research is presented with a focused scope, aligning closely with our experimental investigations and findings.

(6) Physiologically, there is no evidence that there is any "free iron" in cells, making line 89 point inaccurate.

Thank you for highlighting the concern regarding the reference to "free iron" in cells in line 89 of our manuscript. The term "free iron" in our manuscript was intended to refer to divalent iron (Fe2+), rather than unbound iron ions freely circulating within cells. We acknowledge that the term "free iron" might lead to misconceptions, as it implies the presence of unchelated iron, which is not physiologically common due to the potential for oxidative damage. To rectify this and provide clarity, we’ve revised line 89 of our manuscript to reflect our meaning more accurately. Instead of "free iron," we use "divalent iron (Fe2+)" to avoid any misunderstanding regarding the state of iron in cells. We also ensure that any implications drawn from the presence of Fe2+ in cells are consistent with current scientific literature and understanding.

(7) Fig 1f no stats

We appreciate your critical review and suggestions, which help in improving the accuracy and clarity of our research. We’ve revised statistic diagram of new Figure 1F.

(8) Splenectomy experiments demonstrate that erythrophagocytosis is almost completely replaced by functional macrophages in other tissues (likely Kupffer cells in the liver). there is only a minor defect and no data on whether it is in fact the liver or other organs that provide this replacement function and makes the assertions in lines 345-349 significantly overstated.

Thank you for your critical assessment of our interpretation of the splenectomy experiments, especially concerning the role of erythrophagocytosis by macrophages in other tissues, such as Kupffer cells in the liver. We appreciate your observation that our assertions may be overstated and acknowledge the need for more specific data to identify which organs compensate for the loss of splenic erythrophagocytosis.

(1) Splenectomy experiment findings:

• Our findings in Figure 2D do indicate that in the splenectomized group under NN conditions, erythrophagocytosis is substantially compensated for by functional macrophages in other tissues. This is an important observation that highlights the body's ability to adapt to the loss of splenic function.

• However, under HH conditions, our data suggest that the spleen plays an important role in managing erythrocyte turnover, as indicated by the significant impact of splenectomy on erythrophagocytosis and subsequent erythrocyte dynamics.

(2) Addressing the lack of specific organ identification:

• We acknowledge that our study does not definitively identify which organs, such as the liver or others, take over the erythrophagocytosis function post-splenectomy. This is an important aspect that needs further investigation.

• To address this, we also plan to perform additional experiments that could more accurately point out the specific tissues compensating for the loss of splenic erythrophagocytosis. This could involve tracking labeled erythrocytes or using specific markers to identify macrophages actively engaged in erythrophagocytosis in various organs.

(3) Revising manuscript statements:

Considering your feedback, we’ve revised the statements in lines 345-349 (lines 378-383 in revised manuscript) to enhance the scientific rigor and clarity of our research presentation.

(9) M1 vs M2 macrophage experiments are irrelevant to the main thrust of the manuscript, there are no references to support the use of only CD16 and CD86 for these purposes, and no stats are provided. It is also unclear why bone marrow monocyte data is presented and how it is relevant to the rest of the manuscript.

Thank you for your critical evaluation of the relevance and presentation of the M1 vs. M2 macrophage experiments in our manuscript. We appreciate your insights, especially regarding the use of specific markers and the lack of statistical analysis, as well as the relevance of bone marrow monocyte data to our study's main focus.

(1) Removal of M1 and M2 macrophage data:

Based on your feedback and our reassessment, we agree that the results pertaining to M1 and M2 macrophages did not align well with the main objectives of our manuscript. Consequently, we have decided to remove the related content on M1 and M2 macrophages from the revised manuscript. This decision was made to ensure that our manuscript remains focused and coherent, highlighting our primary findings without the distraction of unrelated or insufficiently supported data.

The use of only CD16 and CD86 markers for M1 and M2 macrophage characterization, without appropriate statistical analysis, was indeed a methodological limitation. We recognize that a more comprehensive set of markers and rigorous statistical analysis would be necessary for a meaningful interpretation of M1/M2 macrophage polarization. Furthermore, the relevance of these experiments to the central theme of our manuscript was not adequately established. Our study primarily focuses on erythrophagocytosis and red pulp macrophage dynamics under hypobaric hypoxia, and the M1/M2 polarization aspect did not contribute significantly to this narrative.

(2) Clarification on bone marrow monocyte data:

Regarding the inclusion of bone marrow monocyte data, we acknowledge that its relevance to the main thrust of the manuscript was not clearly articulated. In the revised manuscript, we provide a clearer rationale for its inclusion and how it relates to our primary objectives.

(3) Commitment to clarity and relevance:

We are committed to ensuring that every component of our manuscript contributes meaningfully to our overall objectives and research questions. Your feedback has been instrumental in guiding us to streamline our focus and present our findings more effectively.

We appreciate your valuable feedback, which has led to a more focused and relevant presentation of our research. These changes enhance the clarity and impact of our manuscript, ensuring that it accurately reflects our key research findings.

(10) Biotinolated RBC clearance is enhanced, demonstrating that RBC erythrophagocytosis is in fact ENHANCED, not diminished, calling into question the founding hypothesis that the manuscript proposes.

Thank you for your critical evaluation of our data on biotinylated RBC clearance, which suggests enhanced erythrophagocytosis under HH conditions. This observation indeed challenges our founding hypothesis that erythrophagocytosis is diminished in this setting. Below is a summary of our findings and methodology.

(1) Interpretation of RBC labeling results:

Both the previous results of NHS-biotin labeled RBCs (new Figure 4, A-D) and the current results of PKH67-labeled RBCs (new Figure 4, E-H) demonstrated a decrease in the number of labeled RBCs with an increase in injection time. The production of RBCs, including bone marrow and spleen production, was significantly increased following HH exposure, resulting in a consistent decrease in the proportion of labeled RBCs via flow cytometry detection both in the blood and spleen of mice compared to the NN group. However, compared to the reduced amplitude of fluorescently labeled RBCs observed in blood and spleen under NN exposure, there was a significantly weaker reduction in the amplitude of fluorescently labeled RBCs after HH exposure. Specifically, for blood, the biotin-labeled RBCs decreased by 12.06% under NN exposure and by 7.82% under HH exposure, while the PKH67-labeled RBCs decreased by 9.70% under NN exposure and by 4.09% under HH exposure. For spleen, the biotin-labeled RBCs decreased by 3.13% under NN exposure and by 0.46% under HH exposure, while the PKH67-labeled RBCs decreased by 1.16% under NN exposure and by 0.92% under HH exposure.

Author response image 14.

(2) Increased RBCs production under HH conditions:

It's important to note that RBCs production, including from bone marrow and spleen, was significantly increased following HH exposure. This increase in RBCs production could contribute to the decreased proportion of labeled RBCs observed in flow cytometry analyses, as there are more unlabeled RBCs diluting the proportion of labeled cells in the blood and spleen.

(3) Analysis of erythrophagocytosis in RPMs:

Our analysis of PKH67-labeled RBCs content within RPMs following HH exposure showed a significant reduction in the number of PKH67-positive RPMs in the spleen (new Figure 5). This finding suggests a decrease in erythrophagocytosis by RPMs under HH conditions.

Author response image 15.

(4) Reconciling the findings:

The apparent contradiction between enhanced RBC clearance (suggested by the reduced proportion of labeled RBCs) and reduced erythrophagocytosis in RPMs (indicated by fewer PKH67-positive RPMs) may be explained by the increased overall production of RBCs under HH. This increased production could mask the actual erythrophagocytosis activity in terms of the proportion of labeled cells. Therefore, while the proportion of labeled RBCs decreases more significantly under HH conditions, this does not necessarily indicate an enhanced erythrophagocytosis rate, but rather an increased dilution effect due to higher RBCs turnover.

(5) Revised interpretation and manuscript changes:

Given these factors, we update our manuscript to reflect this detailed interpretation and clarify the implications of the increased RBCs production under HH conditions on our observations of labeled RBCs clearance and erythrophagocytosis. We appreciate your insightful feedback, which has prompted a careful re-examination of our data and interpretations. We hope that these revisions provide a more accurate and comprehensive understanding of the effects of HH on erythrophagocytosis and RBCs dynamics.

(11) Legend in Fig 4c-4d looks incorrect and Fig 4e-4f is very non-specific since Wright stain does not provide evidence of what type of cells these are and making for a significant overstatement in the contribution of this data to "confirming" increased erythrophagocytosis in the spleen under HH exposure (line 395-396).

Thank you for your insightful observations regarding the data presentation and figure legends in our manuscript, particularly in relation to Figure 4 (renamed as Figure 6 in the revised manuscript) and the use of Wright-Giemsa composite staining. We appreciate your constructive feedback and acknowledge the importance of presenting our data with utmost clarity and precision.

(1) Amendments to Figure legends:

We recognize the necessity of rectifying inaccuracies in the legends of the previously labeled Figure 4C and D. Corrections have been meticulously implemented to ensure the legends accurately contain the data presented. Additionally, we acknowledge the error concerning the description of Wright staining. The method employed in our study is Wright-Giemsa composite staining, which, unlike Wright staining that solely stains cytoplasm (RBC), is capable of staining both nuclei and cytoplasm.

(2) Addressing the specificity of Wright-Giemsa Composite staining:

Our approach involved quantifying RBC retention using Wright-Giemsa composite staining on single splenic cells post-perfusion at 7 and 14 days post HH exposure. We understand and appreciate your concerns regarding the nonspecific nature of Wright staining. Although Wright stain is a general hematologic stain and not explicitly specific for certain cell types, its application in our study aimed to provide preliminary insights. The spleen cells, devoid of nuclei and thus likely to be RBCs, were stained and observed post-perfusion, indicating RBC retention within the spleen.

(3) Incorporating additional methods for RBC identification:

To enhance the specificity of our findings, we integrated supplementary methods for RBC identification in the revised manuscript. We employed band3 immunostaining (in the new Figure 6, C-D and G-H) and PKH67 labeling (Figure 6, I-J) for a more targeted identification of RBCs. Band3, serving as a reliable marker for RBCs, augments the specificity of our immunostaining approach. Likewise, PKH67 labeling affords a direct and definitive means to assess RBC retention in the spleen following HH exposure.

Author response image 16. same as 10

(4) Revised interpretation and manuscript modifications:

Based on these enhanced methodologies, we have refined our interpretation of the data and accordingly updated the manuscript. The revised narrative underscores that our conclusions regarding reduced erythrophagocytosis and RBC retention under HH conditions are corroborated by not only Wright-Giemsa composite staining but also by band3 immunostaining and PKH67 labeling, each contributing distinctively to our comprehensive understanding.

We are committed to ensuring that our manuscript precisely reflects the contribution of each method to our findings and conclusions. Your thorough review has been invaluable in identifying and rectifying areas for improvement in our research report and interpretation.

(12) Ferroptosis data in Fig 5 is not specific to macrophages and Fer-1 data confirms the expected effect of Fer-1 but there is no data that supports that Fer-1 reverses the destruction of these cells or restores their function in hypoxia. Finally, these experiments were performed in peritoneal macrophages which are functionally distinct from splenic RPM.

Thank you for your critique of our presentation and interpretation of the ferroptosis data in Figure 5 (renamed as Figure 9 in the revised manuscript), as well as your observations regarding the specificity of the experiments to macrophages and the effects of Fer-1. We value your input and acknowledge the need to clarify these aspects in our manuscript.

(1) Clarification on cell type used in experiments:

• We appreciate your attention to the details of our experimental setup. The experiments presented in Figure 9 were indeed conducted on splenic macrophages, not peritoneal macrophages, as incorrectly mentioned in the original figure legend. This was an error in our manuscript, and we have revised the figure legend accordingly to accurately reflect the cell type used.

(2) Specificity of ferroptosis data:

• We recognize that the data presented in Figure 9 need to be more explicitly linked to the specific macrophage population being studied. In the revised manuscript, we ensure that the discussion around ferroptosis data is clearly situated within the framework of splenic macrophages.

• We also provide additional methodological details in the 'Methods' section to reinforce the specificity of our experiments to splenic macrophages.

(3) Effects of Fer-1 on macrophage function and survival:

• Regarding the effect of Fer-1, we agree that while our data confirms the expected effect of Fer-1 in inhibiting ferroptosis, we have not provided direct evidence that Fer-1 reverses the destruction of macrophages or restores their function in hypoxia.

• To address this, we propose additional experiments to specifically investigate the impact of Fer-1 on the survival and functional restoration of splenic macrophages under hypoxic conditions. This would involve assessing not only the inhibition of ferroptosis but also the recovery of macrophage functionality post-treatment.

(4) Revised interpretation and manuscript changes:

• We’ve revised the relevant sections of our manuscript to reflect these clarifications and proposed additional studies. This includes modifying the discussion of the ferroptosis data to more accurately represent the cell types involved and the limitations of our current findings regarding the effects of Fer-1.

• The revised manuscript presents a more detailed interpretation of the ferroptosis data, clearly describing what our current experiments demonstrate and what remains to be investigated.

We are grateful for your insightful feedback, which has highlighted important areas for improvement in our research presentation. We think that these revisions will enhance the clarity and scientific accuracy of our manuscript, ensuring that our findings and conclusions are well-supported and precisely communicated.

Reviewer #2 (Recommendations For The Authors):

The following questions and remarks should be considered by the authors:

(1) The methods should clearly state whether the HH was discontinued during the 7 or 14 day exposure for cleaning, fresh water etc. Moreover, how was CO2 controlled? The procedure for splenectomy needs to be described in the methods.

Thank you for your inquiry regarding the specifics of our experimental methods, particularly the management of HH exposure and the procedure for splenectomy. We appreciate your attention to detail and the importance of these aspects for the reproducibility and clarity of our research.

(1) HH exposure conditions:

In our experiments, mice were continuously exposed to HH for the entire duration of 7 or 14 days, without interruption for activities such as cleaning or providing fresh water. This uninterrupted exposure was crucial for maintaining consistent hypobaric conditions throughout the experiment. The hypobaric chamber was configured to ensure a ventilation rate of 25 air exchanges per minute. This high ventilation rate was effective in regulating the concentration of CO2 inside the chamber, thereby maintaining a stable environment for the mice.

(2) The splenectomy was performed as follows:

After anesthesia, the mice were placed in a supine position, and their limbs were fixed. The abdominal operation area was skinned, disinfected, and covered with a sterile towel. A median incision was made in the upper abdomen, followed by laparotomy to locate the spleen. The spleen was then carefully pulled out through the incision. The arterial and venous directions in the splenic pedicle were examined, and two vascular forceps were used to clamp all the tissue in the main cadre of blood vessels below the splenic portal. The splenic pedicle was cut between the forceps to remove the spleen. The end of the proximal hepatic artery was clamped with a vascular clamp, and double or through ligation was performed to secure the site. The abdominal cavity was then cleaned to ensure there was no bleeding at the ligation site, and the incision was closed. Post-operatively, the animals were housed individually. Generally, they were able to feed themselves after recovering from anesthesia and did not require special care.

We hope this detailed description addresses your queries and provides a clear understanding of the experimental conditions and procedures used in our study. These methodological details are crucial for ensuring the accuracy and reproducibility of our research findings.

(2) The lack of changes in MCH needs explanation? During stress erythropoiesis some limit in iron availability should cause MCH decrease particularly if the authors claim that macrophages for rapid iron recycling are decreased. Fig 1A is dispensable. Fig 1G NN control 14 days does not make sense since it is higher than 7 days of HH.

Thank you for your inquiry regarding the lack of changes in Mean Corpuscular Hemoglobin (MCH) in our study, particularly in the context of stress erythropoiesis and decreased macrophage-mediated iron recycling. We appreciate the opportunity to provide further clarification on this aspect.

(1) Explanation for stable MCH levels:

• Our research identified a decrease in erythrophagocytosis and iron recycling in the spleen following HH exposure. Despite this, the MCH levels remained stable. This observation can be explained by considering the compensatory roles of other organs, particularly the liver and duodenum, in maintaining iron homeostasis.

• Specifically, our investigations revealed an enhanced capacity of the liver to engulf RBCs and process iron under HH conditions. This increased hepatic erythrophagocytosis likely compensates for the reduced splenic activity, thereby stabilizing MCH levels.

(2) Role of hepcidin and DMT1 expression:

Additionally, hypoxia is known to influence iron metabolism through the downregulation of Hepcidin and upregulation of Divalent Metal Transporter 1 (DMT1) expression. These alterations lead to enhanced intestinal iron absorption and increased blood iron levels, further contributing to the maintenance of MCH levels despite reduced splenic iron recycling.

(3) Revised Figure 1 and data presentation

To address the confusion regarding the data presented in Figure 1G, we have made revisions in our manuscript. The original Figure 1G, which did not align with the expected trends, has been removed. In its place, we have included a statistical chart of Figure 1F in the new version of Figure 1G. This revision will provide a clearer and more accurate representation of our findings.

(4) Manuscript updates and future research:

• We update our manuscript to incorporate these explanations, ensuring that the rationale behind the stable MCH levels is clearly articulated. This includes a discussion on the role of the liver and duodenum in iron metabolism under hypoxic conditions.

• Future research could explore in greater detail the mechanisms by which different organs contribute to iron homeostasis under stress conditions like HH, particularly focusing on the dynamic interplay between hepatic and splenic functions.

We thank you for your insightful question, which has prompted a thorough re-examination of our findings and interpretations. We believe that these clarifications will enhance the overall understanding of our study and its implications in the context of iron metabolism and erythropoiesis under hypoxic conditions.

(3) Fig 2 the difference between sham and splenectomy is really marginal and not convincing. Is there also a difference at 7 days? Why does the spleen size decrease between 7 and 14 days?

Thank you for your observations regarding the marginal differences observed between sham and splenectomy groups in Figure 2, as well as your inquiries about spleen size dynamics over time. We appreciate this opportunity to clarify these aspects of our study.

(1) Splenectomy vs. Sham group differences:

• In our experiments, the difference between the sham and splenectomy groups under HH conditions, though subtle, was consistent with our hypothesis regarding the spleen's role in erythrophagocytosis and stress erythropoiesis. Under NN conditions, no significant difference was observed between these groups, which aligns with the expectation that the spleen's contribution is more pronounced under hypoxic stress.

(2) Spleen size dynamics and peak stress erythropoiesis:

• The observed splenic enlargement prior to 7 days can be attributed to a combination of factors, including the retention of RBCs and extramedullary hematopoiesis, which is known to be a response to hypoxic stress.

• Prior research has elucidated that splenic stress-induced erythropoiesis, triggered by hypoxic conditions, typically attains its zenith within a timeframe of 3 to 7 days. This observation aligns with our Toluidine Blue (TO) staining results, which indicated that the apex of this response occurs at the 7-day mark (as depicted in Figure 1, F-G). Here, the culmination of this peak is characteristically succeeded by a diminution in extramedullary hematopoiesis, a phenomenon that could elucidate the observed contraction in spleen size, particularly in the interval between 7 and 14 days.

• This pattern of splenic response under prolonged hypoxic stress is corroborated by studies such as those conducted by Wang et al. (2021), Harada et al. (2015), and Cenariu et al. (2021). These references collectively underscore that the spleen undergoes significant dynamism in reaction to sustained hypoxia. This dynamism is initially manifested as an enlargement of the spleen, attributable to escalated erythropoiesis and erythrophagocytosis. Subsequently, as these processes approach normalization, a regression in spleen size ensues.

We’ve revised our manuscript to include a more detailed explanation of these splenic dynamics under HH conditions, referencing the relevant literature to provide a comprehensive context for our findings. We will also consider performing additional analysis or providing further data on spleen size changes at 7 days to support our observations and ensure a thorough understanding of the splenic response to hypoxic stress over time.

(4) Fig 3 B the clusters should be explained in detail. If the decrease in macrophages in Fig 3K/L is responsible for the effect, why does splenectomy not have a much stronger effect? How do the authors know which cells died in the calcein stained population in Fig 3D?

Thank you for your insightful questions regarding the details of our data presentation in Figure 3, particularly about the identification of cell clusters and the implications of macrophage reduction. We appreciate the opportunity to address these aspects and clarify our findings.

(1) Explanation of cell clusters in Figure 3B:

• In the revised manuscript, we have included detailed notes for each cell population represented in Figure 3B (Figure 3D in revised manuscript). These notes provide a clearer understanding of the cell types present in each cluster, enhancing the interpretability of our single-cell sequencing data.

• This detailed annotation will help readers to better understand the composition of the splenic cell populations under study and how they are affected by hypoxic conditions.

(2) Impact of splenectomy vs. macrophage reduction:

• The interplay between the reduction in macrophage populations, as evidenced by our single-cell sequencing data, and the ramifications of splenectomy presents a multifaceted scenario. Notably, the observed decline in macrophage numbers following HH exposure does not straightforwardly equate to a comparable alteration in overall splenic function, as might be anticipated with splenectomy.

• In the context of splenectomy under HH conditions, a significant escalation in the RBCs count was observed, surpassing that in non-splenectomized mice exposed to HH. This finding underscores the spleen's critical role in modulating RBCs dynamics under HH. It also indirectly suggests that the diminished phagocytic capacity of the spleen following HH exposure contributes to an augmented RBCs count, albeit to a lesser extent than in the splenectomy group. This difference is attributed to the fact that, while the number of RPMs in the spleen post-HH is reduced, they are still present, unlike in the case of splenectomy, where they are entirely absent.

• Splenectomy entails the complete removal of the spleen, thus eliminating a broad spectrum of functions beyond erythrophagocytosis and iron recycling mediated by macrophages. The nuanced changes observed in our study may be reflective of the spleen's diverse functionalities and the organism's adaptive compensatory mechanisms in response to the loss of this organ.

(3) Calcein stained population in Figure 3D:

• Regarding the identification of cell death in the calcein-stained population in Figure 3D (Figure 3A in revised manuscript), we acknowledge that the specific cell types undergoing death could not be distinctly determined from this analysis alone.

• The calcein staining method allows for the visualization of live (calcein-positive) and dead (calcein-negative) cells, but it does not provide specific information about the cell types. The decrease in macrophage population was inferred from the single-cell sequencing data, which offered a more precise identification of cell types.

(4) Revised manuscript and data presentation:

• Considering your feedback, we have revised our manuscript to provide a more comprehensive explanation of the data presented in Figure 3, including the nature of the cell clusters and the interpretation of the calcein staining results.

• We have also updated the manuscript to reflect the removal of Figure 3K/L results and to provide a more focused discussion on the relevant findings.

We are grateful for your detailed review, which has helped us to refine our data presentation and interpretation. These clarifications and revisions will enhance the clarity and scientific rigor of our manuscript, ensuring that our conclusions are well-supported and accurately conveyed.

(5) Is the reduced phagocytic capacity in Fig 4B significant? Erythrophagocytosis is compromised due to the considerable spontaneous loss of labelled erythrocytes; could other assays help? (potentially by a modified Chromium release assay?). Is it necessary to stimulated phagocytosis to see a significant effect?

Thank you for your inquiry regarding the significance of the reduced phagocytic capacity observed in Figure 4B, and the potential for employing alternative assays to elucidate erythrophagocytosis dynamics under HH conditions.

(1) Significance of reduced phagocytic capacity:

The observed reduction in the amplitude of fluorescently labeled RBCs in both the blood and spleen under HH conditions suggests a decrease in erythrophagocytosis. This is indicative of a diminished phagocytic capacity, particularly when contrasted with NN conditions.

(2) Investigation of erythrophagocytosis dynamics:

To delve deeper into erythrophagocytosis under HH, we employed Tuftsin to enhance this process. Following the injection of PKH67-labeled RBCs and subsequent HH exposure, we noted a significant decrease in PKH67 fluorescence in the spleen, particularly marked after the administration of Tuftsin. This finding implies that stimulated erythrophagocytosis can influence RBCs lifespan.

(3) Erythrophagocytosis under normal and hypoxic conditions:

Under normal conditions, the reduction in phagocytic activity is less apparent without stimulation. However, under HH conditions, our findings demonstrate a clear weakening of the phagocytic effect. While we established that promoting phagocytosis under NN conditions affects RBC lifespan, the impact of enhanced phagocytosis under HH on RBCs numbers was not explicitly investigated.

(4) Potential for alternative assays:

Considering the considerable spontaneous loss of labeled erythrocytes, alternative assays such as a modified Chromium release assay could provide further insights. Such assays might offer a more nuanced understanding of erythrophagocytosis efficiency and the stability of labeled RBCs under different conditions.

(5) Future research directions:

The implications of these results suggest that future studies should focus on comparing the effects of stimulated phagocytosis under both NN and HH conditions. This would offer a clearer picture of the impact of hypoxia on the phagocytic capacity of macrophages and the subsequent effects on RBC turnover.

In summary, our findings indicate a diminished erythrophagocytic capacity, with enhanced phagocytosis affecting RBCs lifespan. Further investigation, potentially using alternative assays, would be beneficial to comprehensively understand the dynamics of erythrophagocytosis in different physiological states.

(6) Can the observed ferroptosis be influenced by bi- and not trivalent iron chelators?

Thank you for your question regarding the potential influence of bi- and trivalent iron chelators on ferroptosis under hypoxic conditions. We appreciate the opportunity to discuss the implications of our findings in this context.

(1) Analysis of iron chelators on ferroptosis:

In our study, we did not specifically analyze the effects of bi- and trivalent iron chelators on ferroptosis under hypoxia. However, our observations with Deferoxamine (DFO), a well-known iron chelator, provide some insights into how iron chelation may influence ferroptosis in splenic macrophages under hypoxic conditions.

(2) Effect of DFO on oxidative stress markers:

Our findings showed that under 1% O2, there was an increase in Malondialdehyde (MDA) content, a marker of lipid peroxidation, and a decrease in Glutathione (GSH) content, indicative of oxidative stress. These changes are consistent with the induction of ferroptosis, which is characterized by increased lipid peroxidation and depletion of antioxidants. Treatment with Ferrostatin-1 (Fer-1) and DFO effectively reversed these alterations. This suggests that DFO, like Fer-1, can mitigate ferroptosis in splenic macrophages under hypoxia, primarily by impacting MDA and GSH levels.

Author response image 17.

(3) Potential role of iron chelators in ferroptosis:

The effectiveness of DFO in reducing markers of ferroptosis indicates that iron availability plays a crucial role in the ferroptotic process under hypoxic conditions. It is plausible that both bi- and trivalent iron chelators could influence ferroptosis, given their ability to modulate iron availability within cells. Since ferroptosis is an iron-dependent form of cell death, chelating iron, irrespective of its valence state, could potentially disrupt the process by limiting the iron necessary for the generation of reactive oxygen species and lipid peroxidation.

(4) Additional research and manuscript updates:

Our study highlights the need for further research to explore the differential effects of various iron chelators on ferroptosis, particularly under hypoxic conditions. Such studies could provide a more comprehensive understanding of the role of iron in ferroptosis and the potential therapeutic applications of iron chelators. We update our manuscript to include these findings and discuss the potential implications of iron chelation in the context of ferroptosis under hypoxic conditions. This will provide a broader perspective on our research and its significance in understanding the mechanisms of ferroptosis.

Reviewer #2 (Public Review):

The authors aimed at elucidating the development of high altitude polycythemia which affects mice and men staying in a hypoxic atmosphere at high altitude (hypobaric hypoxia; HH). HH causes increased erythropoietin production which stimulates the production of red blood cells. The authors hypothesize that increased production is only partially responsible for exaggerated red blood cell production, i.e. polycythemia, but that decreased erythrophagocytosis in the spleen contributes to high red blood cells counts.

The main strength of the study is the use of a mouse model exposed to HH in a hypobaric chamber. However, not all of the reported results are convincing due to some smaller effects which one may doubt to result in the overall increase in red blood cells as claimed by the authors. Moreover, direct proof for reduced erythrophagocytosis is compromised due to a strong spontaneous loss of labelled red blood cells, although effects of labelled E. coli phagocytosis are shown.

Comments on latest version:

The authors have partly addressed my comments.

(1) The response to my question regarding unchanged MCH is a kind of "hand waiving" - maybe it would require substantially more extensive work to clarify this issue

(2) The moderate if not marginal difference in normal vs splenectomy argues against a significant role of the spleen - even if the difference was slightly larger in HH

(3) There is still overinterpretation of data. My Q was: Is the reduced phagocytic capacity in Fig 4B significant? Response: "This is indicative of a diminished phagocytic capacity, particularly when contrasted<br /> with NN conditions." I guess that is a "no"

(4) I assume my question with respect to bi- or trivalent iron chelators was misunderstood.

In general, as indicated above, it is an interesting hypothesis which is corroborated by data in several instances. Maybe the scientific community should decide whether it is all in all conclusive.

Reviewer #3 (Public Review):

The manuscript by Yang et al. investigated in mice how hypobaric hypoxia can modify the RBC clearance function of the spleen, a concept that is of interest. Via interpretation of their data, the authors proposed a model that hypoxia causes an increase in cellular iron levels, possibly in RPMs, leading to ferroptosis, and downregulates their erythrophagocytic capacity.

Comments on revised version:

The manuscript has now improved with all the new data, supporting the model proposed by the authors. However, it remains not very easy to follow for the conclusions and experimental details. Some of the most important remaining comments are listed below:

(1) Lines 401-406 - The conclusions in this new fragment sound a bit overstated - the authors do not directly measure erytrophagocytosis capacity, only the total RBC parameters in the circulation. The increase is also very mild biologically between sham and splenectomized mice in HH conditions.

(2) scRNA seq data are still presented in a way that is very difficult to understand. The readers could not see from the graphics that macrophages are depleted. The clusters are not labelled - some clusters in the bin 'macrophahes+DC' seem actually to be more represented in Fig. 3E; Fig. 3F does not correspond to Fig. 3D. It would be maybe more informative to present like in Figure D side by side NN versus HH? The authors could consider moving the data from supplements that relate to RPMs to the main figure and making it consistent for the Clusters - eg, the authors show data for Cluster 0 in the supplement, and the same Cluster is not marked as macrophages in the main figure. This is quite difficult to follow.

(3) Figure 3G has likely mislabeled axis for F4/80 and CD11b - such mistakes should be avoided in a second revised version of the manuscript, and this data is now redundant with the data shown as new Figure 5A.

(4) The data from new Figure 4 should be better mentioned in the main body of the manuscript - all panels are mentioned twice in the text, first speaking about the decline of labelled RBCs and second referring to phagocytic capacity, whereas this figure only illustrates the decline of labelled RBCs, not directly phagocytic capacity of RPMs. What is lacking, as opposed to typical RBC life span assay, is the time '0' ('starting point') - this is particularly important as we can observe a big drop in labelled RBCs for eg 7 days between NN and HH group, actually implying increased removal of labelled RBCs within the first days of hypoxia exposure. What should be better labelled in this figure is that the proportion of RBCs are labelled RBCs not all RBCs (Y axis in individual panels). Overall, the new Figure 4 brings new data to the study, but how it is presented and discussed is not at the 'state-of-the-art' level (eg, missing the time '0') and is not very straightforward to the reader.

(5) In Figure 7, the experiments with Tuftsin are not very easy to follow, especially for the major conclusions. In panels A and B, the focus is the drug itself under NN conditions, with RBC removal as a readout. Then, in the next panels, the authors introduce HH, and then look at the F4/80 and iron staining. What was exactly the major point the authors wanted to make here?

(6) The data from Figure 8 are informative but do not address the individual cell types - eg, a drop in HO1 or FT may be due to the depletion of RPMs. An increase of TFR1 could be due to the retention of RBCs, the same as maybe labile iron. The data from PBMC are only very loosely linked to these phenotypes observed in the total spleen, and the reason for the regulation of the same proteins in PBMC might be different. It goes back to the data in Figure 3A-C, where also total splenocytes are investigated for their viability.

(7) Can the authors provide the data for the purity (eg cell surface markers) of their primary splenic macrophage cultures? Only ensuring that these are macrophages or addressing the readouts from Figure 8 in RPMs could link ferroptosis to RPMs under HH conditions.

(8) All the data are not presented as individual data points which is not widely applied in papers.

(9) No gating strategies are nicely illustrated or described.

Author Response

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

eLife assessment

This valuable study provides insights into the IDA peptide with dual functions in development and immunity. The approach used is solid and helps to define the role of IDA in a two-step process, cell separation followed by activation of innate defenses. The main limitation of the study is the lack of direct evidence linking signaling by IDA and its HAE receptors to immunity. As such the work remains descriptive but it will nevertheless be of interest to a wide range of plant cell biologists.

We thank the reviewers for thoroughly reading our manuscript. We have used their comments and suggestions- to improve the manuscript. Below is a response to the reviewer's comments.

Public Reviews:

Reviewer #1 (Public Review):

The paper titled 'A dual function of the IDA peptide in regulating cell separation and modulating plant immunity at the molecular level' by Olsson Lalun et al., 2023 aims to understand how IDAHAE/HSL2 signalling modulates immunity, a pathway that has previously been implicated in development. This is a timely question to address as conflicting reports exist within the field. IDL6/7 have previously been shown to negatively regulate immune signalling, disease resistance and stress responses in leaf tissue, however IDA has been shown to positively regulate immunity through the shedding of infected tissues. Moreover, recently the related receptor NUT/HSL3 has been shown to positively regulate immune signalling and disease resistance. This work has the potential to bring clarity to this field, however the manuscript requires some additional work to address these questions. This is especially the case as it contracts some previous work with IDL peptides which are perceived by the same receptor complexes.

Can IDA induce pathogen resistance? Does the infiltration of IDA into leaf tissue enhance or reduce pathogen growth? Previously it has been shown that IDL6 makes plants more susceptible. Is this also true for IDA? Currently cytoplasmic calcium influx and apoplastic ROS as overinterpreted as immune responses - these can also be induced by many developmental cue e.g. CLE40 induced calcium transients. Whilst gene expression is more specific is also true that treatment with synthetic peptides, which are recognised by LRR-RKs, can induce immune gene expression, especially in the short term, even when that is not there in vivo function e.g. doi.org/10.15252/embj.2019103894.

We thank the reviewer for the concerns raised and agree that further experiments including pathogen assays would strengthen the link between IDA signaling and immunity and we plan for such experiments in future work. We have however, modified the discussion to include the possible role of IDA induced Ca2+ and ROS during development. We have recently published a preprint (accepted for publication in JXB) ( (Galindo-Trigo et al., 2023, https://doi.org/10.1101/2023.09.12.557497)) strengthening the link between IDA and defense by identifying WRKY transcription factors that regulate IDA expression through a Y1H assay.

This paper shows that receptors other than hae/hsl2 are genetically required to induce defense gene expression, it would have been interesting to see what phenotype would be associated with higher order mutants of closely related haesa/haesa-like receptors. Indeed recently HSL1 has been shown to function as a receptor for IDA/IDL peptides. Could the triple mutant suppress all response? Could the different receptors have distinct outputs? For example for FRK1 gene expression the hae hsl2 mutant has an enhanced response. Could defence gene expression be primarily mediated by HSL1 with subfunctionalisation within this clade?

We agree that it would be interesting to also include HSL1 in our studies. However, the focus of this study has been on HAE and HSL2 and we wanted to explore their role in IDA induced defense responses. Including HSL1 in these studies will require generation of multiple transgenic lines and repeating most of the experiments and are experiments we will consider in a follow up study together with pathogen assays (that would also address the main concern raised in the comment above). We have however, modified the text to include the known function of HSL1 and discuss the possibility of subfunctionalisation of this receptor clade.

One striking finding of the study is the strong additive interaction between IDA and flg22 treatment on gene expression. Do the authors also see this for co-treatment of different peptides with flg22, or is this unique function of IDA? Is this receptor dependent (HAE/HSL1/HSL2)?

This is a good question. Since our study focuses on the IDA signaling pathway we preferentially tested if the additive effect observed between flg22 and mIDA was also observed when mIDA was combined with another peptide involved in defense. The endogenous peptide PIP1, has previously been shown to amplify flg22 signaling (Hou et al 2014, doi:10.1371/journal.ppat.1004331 ). In this study it is shown that co-treatment with flg22 and PIP1 gives increased resistance to Pseudomonas PstDC3000 compared to when plants are treated with each peptide separately. In the same study, the authors also show reduced flg22 induce transcriptional activity of two defense related genes WRKY33 and PR in the receptor like kinase7 (rlk7) mutant (the receptor perceiving PIP1) (). To investigate whether PIP1 would give the same additive effect with mIDA as that observed between flg22 and mIDA, we co-treated seedlings with PIP1 and mIDA. We observed no enhanced transcriptional activity of FRK1, MYB51 and PEP3 in tissue from plants treated with both PIP1 and mIDA peptides compared to single exposure. These results are presented in supplementary figure 11. In conclusion we do not think mIDA acts as a general amplifier of all immune elicitors in plants.

It is interesting how tissue specific calcium responses are in response to IDA and flg22, suggesting the cellular distribution of their cognate receptors. However, one striking observation made by the authors as well, is that the expression of promoter seems to be broader than the calcium response. Indicating that additional factors are required for the observed calcium response. Could diffusion of the peptide be a contributing factor, or are only some cells competent to induce a calcium response?

It is interesting that the authors look for floral abscission phenotypes in cngc and rbohd/f mutants to conclude for genetic requirement of these in floral abscission. Do the authors have a hypothesis for why they failed to see a phenotype for the rbohd/f mutant as was published previously? Do you think there might be additional players redundantly mediating these processes?

It is a possibility that diffusion of the peptide plays a role in the observed response. In a biological context we would assume that the local production of the peptides plays an important role in the cellular responses. In our experimental setup, we add the peptide externally and we can therefore assume that the overlaying cells get in contact with the peptide before cells in the inner tissues and this could be affecting the response recorded However, our results show that there is a differences between flg22 and mIDA induced responses even when the application of the peptides is performed in the same manner, indicating that the difference in the response is not primarily due to the diffusion rate of the peptides but is likely due to different factors being present in different cells. To acquire a better picture of the distribution of receptor expression in the root tissue and to investigate in which cells the receptors have an overlapping expression pattern, we have included results in figure 6 showing plant lines co-expressing transcriptional reporters of FLS2 and HAE or HSL2.

Can you observe callose deposition in the cotyledons of the 35S::HAE line? Are the receptors expressed in native cotyledons? This is the only phenotype tested in the cotyledons.

We thank the reviewer for this valuable comment. We have now conducted callose deposition assay on the 35S:HAE line. And Indeed, we observe callose depositions when cotyledons from a 35S:HAE line is treated with mIDA. We have included these results in figure 4 and have adjusted the text regarding the callose assay accordingly. In addition, we have analyzed the promoter activity of pHAE in cotelydons and we observe weak promoter activity. These results are included as supplementary figure 1d.

Are flg22-induced calcium responses affected in hae hsl2?

The experiment suggested by the reviewer is an important control to ensure that the hae hsl2-Aeq line can respond to a Ca2+ inducing peptide signaling through a different receptor than HAE or HSL2. One would expect to see a Ca2+ response in this line to the flg22 peptide. We performed this experiment and surprisingly we could not detect a flgg22 induced Ca2+ signal in the hae hsl2 mutnt. As it is unlikely that the Ca2+ response triggered by flg22 is dependent on HAE and HSL2 we have to assume that the lack of response is due to a malfunction of the Aeq sensor in this line. As a control to measure the amount of Aeq present in the cells we treat the Aeq seedlings with 2 M CaCl2 and measure the luminescence constantly for 180 seconds (Ranf et al., 2012, DOI10.1093/mp/ssr064). The CaCl2 treatment disrupts the cells and releases the Aeq sensor into the solution where it will react with Ca2+ and release the total possible response in the sample (Lmax) in form of a luminescent peak. When treating the hae hsl2-Aeq line with CaCl2we observe a luminescent peak, indicating the presence of the sensor, however, the response is reduced compared to WT seedlings expressing Aeq. Given the sensitivity of FLS2 to flg22 one would still expect to see a Ca2+ peak in the hae hsl2-Aeq line even if the amount of sensor is reduced. Given that this is not the case, we have to assume that localization or conformation of the sensor is somehow affected in this line or that there is another biological explanation that we cannot explain at the moment.

We have therefore opted on omitting the results using the hae hsl2 Aeq lines from the manuscript and are in the process of mutating HAE and HSL2 by CRISPR-Cas9 in the Aeq background to verify that the mIDA triggered Ca2+ response is dependent on HAE and HSL2.

Reviewer #2 (Public Review):

Lalun and co-authors investigate the signalling outputs triggered by the perception of IDA, a plant peptide regulating organs abscission. The authors observed that IDA perception leads to a transient influx of Ca2+, to the production of reactive oxygen species in the apoplast, and to an increase accumulation of transcripts which are also responsive to an immunogenic epitope of bacterial flagellin, flg22. The authors show that IDA is transcriptionally upregulated in response to several biotic and abiotic stimuli. Finally, based on the similarities in the molecular responses triggered by IDA and elicitors (such as flg22) the authors proposed that IDA has a dual function in modulating abscission and immunity. The manuscript is rather descriptive and provide little information regarding IDA signalling per se. A potential functional link between IDA signalling and immune signalling remains speculative.

We thank the reviewer for the concerns raised and agree that further experiments including pathogen assays would strengthen the link between IDA signaling and immunity and plan for such experiments in future work.

Reviewer #3 (Public Review):

Previously, it has been shown the essential role of IDA peptide and HAESA receptor families in driving various cell separation processes such as abscission of flowers as a natural developmental process, of leaves as a defense mechanism when plants are under pathogenic attack or at the lateral root emergence and root tip cell sloughing. In this work, Olsson et al. show for the first time the possible role of IDA peptide in triggering plant innate immunity after the cell separation process occurred. Such an event has been previously proposed to take place in order to seal open remaining tissue after cell separation to avoid creating an entry point for opportunistic pathogens.

The elegant experiments in this work demonstrate that IDA peptide is triggering the defenseassociated marker genes together with immune specific responses including release of ROS and intracellular CA2+. Thus, the work highlights an intriguing direct link between endogenous cell wall remodeling and plant immunity. Moreover, the upregulation of IDA in response to abiotic and especially biotic stimuli are providing a valuable indication for potential involvement of HAE/IDA signalling in other processes than plant development.

We are pleased that the reviewer finds our findings linking IDA to defense interesting and would like to thank the reviewer for this positive feedback.

Strengths:

The various methods and different approaches chosen by the authors consolidates the additional new role for a hormone-peptide such as IDA. The involvement of IDA in triggering of the immunity complex process represents a further step in understanding what happens after cell separation occurs. The Ca2+ and ROS imaging and measurements together with using the haehsl2 and haehsl2 p35S::HAE-YFP genotypes provide a robust quantification of defense responses activation. While Ca2+ and ROS can be detected after applying the IDA treatment after the occurrence of cell separation it is adequately shown that the enzymes responsible for ROS production, RBOHD and RBOHF, are not implicated in the floral abscission.

Furthermore, IDA production is triggered by biotic and abiotic factors such as flg22, a bacterial elicitor, fungi, mannitol or salt, while the mature IDA is activating the production of FRK1, MYB51 and PEP3, genes known for being part of plant defense process.

Thank you.

Weaknesses:

Even though there is shown a clear involvement of IDA in activating the after-cell separation immune system, the use of p35S:HAE-YFP line represent a weak point in the scientific demonstration. The mentioned line is driving the HAE receptor by a constitutive promoter, capable of loading the plant with HAE protein without discriminating on a specific tissue. Since it is known that IDA family consist of more members distributed in various tissues, it is very difficult to fully differentiate the effects of HAE present ubiquitously.

We agree on this statement. Nevertheless, it is important to note that the responses we have observed are not detectable in WT plants that do not (over)express the HAE receptors. Suggesting that the ROS and callose deposition are induced by the addition of mIDA peptide and not the potential presence of the endogenous IDL peptides.

The co-localization of HAE/HSL2 and FLS2 receptors is a valuable point to address since in the present work, the marker lines presented do not get activated in the same cell types of the root tissues which renders the idea of nanodomains co-localization (as hypothetically written in the discussion) rather unlikely.

Thank you for raising an important aspect of our study. It is true that not all cells in the root which have promoter activity for FLS2 also exhibit promoter activity for either HAE or HSL2. However, we have observed that certain cells in the roots show promoter activity for both receptors. In the revised version of the manuscript, we have included plants expression a transcriptional promoter for both FLS2 and HAE or HSL2 using different fluorescent proteins. We have investigated overlapping promoter activity both at sites of lateral roots, in the tip of the primary root and in the abscission zone. Our results show overlapping expression of the transcriptional reporters in certain cells, indicating that FLS2 and HAE or HSL2 are likely to be found in some of the same cells during plant development. We also observe cells where only one or none of the promoters are active.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Supplementary Figure 3: re-labelling of y axis; 200 than 200,00 for clarity.

This has been addressed.

Supplementary Figure 2: It would be good to include the age of the seedlings used to study calcium influx in the legend.

This has been addressed.

Supplementary Figure 1: rephrase 'IDA induces ROS production in Arabidopsis'.

This has been addressed.

The use of chelating agents to establish the need of calcium from extracellular space is a clear experiment supporting the calcium response phenotype specific to IDA treatment in seedlings. Removing the last asparagine (N) and using it as a peptide that fails to elicit calcium response could simply be because of the peptide is smaller in length or different chemical properties. Therefore, a scrambled sequence would have been a better control.

We thank the reviewer for the suggestion of using a scrambled peptide as a negative control, however we find it unlikely that mIDA∆N69 could induce any activity based on previous work. Results from crystal structure of mIDA bound to the HAE receptor and ligand-receptor interaction studies (10.7554/eLife.15075 ) show that the last asparagine in the mIDA peptide is essential for detectable binding to the HAE receptor and that a peptide lacking this amino acid does not have any activity. We will however, in future experiments also include a scrambled version of the peptide as an additional control.

Reviewer #2 (Recommendations For The Authors):

Please find below specific comments:

(1) Most of the molecular outputs triggered by IDA can be considered as common molecular marks of plant peptides signalling, they do not represent strong evidences of a potential function of IDA in modulating immunity. For instance, perception of CIF peptides, which control the establishment of the Casparian strips, regulate the production of reactive oxygen species, and the transcription of genes associated with immune responses (Fujita et al., The EMBO Journal 2020). It should also be considered that FRK1, whose function remains unknown, may be involved in both immunity and abscission and that the upregulation of FRK1 upon IDA treatment is not indicative of active modulation of immune signalling by IDA.

This is a fair point raised by the reviewer and we now address in the manuscript that ROS and Ca2+ are hallmarks of both plant development and defense. The function of FRK1 is not known however, it is unlikely that the upregulation of FRK1 in response to mIDA plays a role in the developmental progression of abscission as it is not temporally regulated during the abscission process, thus making it an unlikely candidate in the regulation of cell separation (Cai & Lashbrook, 2008, https://doi.org/10.1104/pp.107.110908). We do however agree that further experiments including pathogen assays would strengthen the link between IDA signaling and immunity and plan for such experiments in future work.

(2) It remains unknown whether IDA modulate immunity. For instance, does IDA perception promote resistance to bacteria (bacterial proliferation, disease symptoms)? Is IDA genetically required for plant disease resistance immunity? Is the IDA signalling pathway genetically required for transcriptional changes induced by flg22, such as increase in FRK1 transcripts? In addition, the authors propose that the proposed function of IDA in modulating immune signalling prevents bacterial infection in tissue exposed to stress(es). Does loss of function of IDA or of its corresponding receptors leads to changes in the ability of bacteria to colonise plant root upon stress(es)?

Please see the comment above regarding pathogen assays.

(3) Several aspects of the work appear to correspond to preliminary investigation. For instance, the authors analyse loss of function mutant for genes encoding for Ca2+ permeable channels (CNGCs) which are transcriptionally active during the onset of abscission (Sup. Figure 5). None of the single mutants present an abscission defect. These observations provide no information regarding the identity of the channel(s) involved in IDA-induced calcium influx.

We agree with the reviewer that we have not been able to identify the channels responsible for the IDA-induced calcium influx. Given the redundancy for many of the members of this multigenic family a future approach to identify proteins responsible for the IDA triggered calcium response could be to create multiple KO mutants by CRISPR Cas9.

(4) Using H2DCF-DA, the authors observed a decrease in ROS accumulation in the abscission zone of rbohd/rbohf double KO line (Sup Figure 5c) but describe in the text that ROS production in this zone does not depend on RBOHD and RBOHF (L220). Please clarify.

This has now been clarified in the text.

(5) The authors describe that rbohd/rbohf double KO present a lower petal break-strength, which they describe as an indication of premature cell wall loosening, and that petals of rbohd/rbohf abscised one position earlier than in WT. Yet, the authors postulate that IDA-induced ROS production does not regulate abscission but may regulate additional responses. Instead the data seems to indicate that ROS production by RBOHD and RBOHF regulate the timing of abscission. In addition, it would have been interesting to test whether IDA signalling pathway regulate ROS production in the abscission zone.

The rbohd and rbohf double mutants show several phenotypes associated to developmental stress, the mild phenotype observed with regards to premature abscission (by one position) could be caused by the phenotype of the double mutant rather than related to ROS production. Indeed, it has been suggested that the lignified brace in the AZ dependent on ROS production by the aforementioned RBOHs in necessary for the correct concentration of cell modifying enzymes (Lee et al., 2018, https://doi.org/10.1016/j.cell.2018.03.060). The precocious abscission in this double mutant clearly shows this not to be the case. We have tried to do a ROS burst assay on AZ tissue/flowers with the mIDA peptide but have not been successful with this approach. A ROS sensor expressed in AZ tissue would be a valuable tool to address whether IDA signalling regulates ROS production in AZs.

(6) In Sup. Figure5a, it would be of interest to have a direct comparison of the transcript accumulation of the presented CNGCs and RBOHDs with other of these multigenic families.

The CNGCs and RBOH gene expression profile shown in the figure are the family members expressed during the developmental progress of floral abscission in stamen AZs. Since there is no difference in the temporal expression of the other family members (and most are either not expressed or very weakly expressed in this tissue) it is not possible to do this comparison (Cai & Lashbrook, 2008, https://doi.org/10.1104/pp.107.110908).

(7) L251-253, since IDAdeltaN69 cannot be perceived by its receptors, the absence of induction of pIDA::GUS by IDAdeltaN69 compared to flg22 cannot be seen as a sign of specificity in peptideinduced increase in IDA promotor activity.

We have rephased this in the text

(8) Please provide quantitative and statistical analysis of the calcium measurement presented in sup figure 3.

This has been addressed.

(9) L339-341; This sentence is unclear to me, please rephrase.

We have rephased this in the text

Reviewer #3 (Recommendations For The Authors):

(1) In order to assess the role of CNGCs in abscission process, it would be more interesting to see the effect on the Ca2+ pattern and ROS signaling after application of mIDA on cngc and rbohf rbohd mutants.

We agree in this statement and the studies on mIDA induced ROS and Ca2+ on these mutants will provide valuable information to the regulation of the response. We are in the process of making the lines needed to be able to perform these experiments. However, since it requires crossing of genetically encoded sensors into each mutant, and generation of higher order mutants this is a long process.

(2) With regard to the ROS production (Sup Fig. 1), the application of mIDA can trigger ROS in p35S::HAE:YFP lines, but not in the wild-type plant, which is according to the text "most likely due to the absence of HAE expression" in leaves. The experiment on callose deposition is performed in wild-type cotyledons where no callose deposition could be observed after mIDA treatment (Fig. 4a,b). The conclusion from text is that IDA "is not involved in promoting deposition of callose as a long-term defence response". It appears more likely that neither ROS nor callose can be observed in wild-type plants due to the lack of HAE expression. Therefore, the callose experiment should include the p35S::HAE:YFP lines. The experiment as it is does not allow to draw any conclusion on HAE/IDA involvement in callose formation.

We fully agree with this comment, thank you for pinpointing this out. We have now performed the callose experiment with the 35S:HAE lines. Please see our answer to reviewer #1.

(3) Between Sup Fig. 3 and Sup Fig. 5 two different systems were used to asses the floral stage. An adjustment of the floral stages would be easier to convey the levels of HAE/HSL2 expression and hence potentially with the onset of cell-wall degradation.

We now used the same system to assess floral stages throughout the whole manuscript.

(4) For the Fig. 1 and 2, it will be helpful to mention the genotype used for imaging/quantification of Ca2+.

This has been addressed.

(5) Some of the abbreviations are not introduced as full-text at their first time use in the text, such as: mIDA (Line 68), Ef-Tu (line 85), NADPH (line 77).

The abbreviations have now been introduced.

(6) In the legend of Fig. 5 (lines 897 and 898)- in the figure description, the box plots are identified as light gray and dark gray, while in the panel a of the figure the box plots are colored in red and blue.

Thank you for pointing this out, this has now been corrected.

(7) In figure 1 and 2. the authors write that the number of replicates is 10 (n=10) but data represents a single analysis. Please provide the quantitative ROI analysis, demonstrating that the observed example is representative. This is particularly important since the authors claim very specific changes in pattern of Ca signaling between mIDA and FLG22 treatments (Line 148).

(8) Figure 4: please use alternative scaling on the Y axis instead of breaks.

This has now been fixed.

(9) Figure 5: it is not clear what n=4 refers to when the authors state three independent replicates. In figure 6 they state 4 technical reps and 3 biological reps. Please ensure this is similar across all descriptions.

We have now ensured the correct information in all descriptions.

eLife assessment

This manuscript presents valuable findings on the role of a plant peptide in coordinating developmental and immune responses signaling. The evidence supporting the claims, while mainly descriptive and and somewhat limited due to the main conclusions being drawn from overexpression lines, is mostly solid. The findings are interesting, they align with existing models, and they are of relevance to plant pathologists and developmental biologists.

Reviewer #1 (Public Review):

A descriptive manuscript investigating the ability of a peptide, implicated in development, to induce signalling responses indicative of immunity. The work clearly documents the ability of the synthetic peptide to induce these responses, and open future work to link this back to physiology.

Comments on revised version:

Congratulations to the authors for the improvements to the manuscript.

I still have reservations, as raised by other reviewers, about whether the outputs observed can definitively be classified as immune/defence outputs without assaying an impact upon microbial growth. Indeed, this is challenging to address as many of the outputs are shared by multiple pathways. This is especially the case here as the peptide could have different effects in different tissues or cells with different expression levels of the receptors (e.g. hypothetically - no expression = no effect; weak expression - cell wall loosening and susceptibility; high expression - strong response and 'defence' response). I do however appreciate that the authors have toned down some of the conclusions regarding the defence response and also they included further reference to outputs also being from developmental pathways.

Reviewer #3 (Public Review):

Previously, it has been shown the essential role of IDA peptide and HAESA receptor families in driving various cell separation processes such as abscission of flowers as a natural developmental process, of leaves as a defense mechanism when plants are under pathogenic attack or at the lateral root emergence and root tip cell sloughing. In this work, Olsson et al. show for the first time the possible role of IDA peptide in triggering plant innate immunity after the cell separation process occurred. Such an event has been previously proposed to take place in order to seal open remaining tissue after cell separation to avoid creating an entry point for opportunistic pathogens. The elegant experiments in this work demonstrate that IDA peptide is triggering the defense-associated marker genes together with immune specific responses including release of ROS and intracellular CA2+. Thus, the work highlights an intriguing direct link between endogenous cell wall remodeling and plant immunity. Moreover, the upregulation of IDA in response to abiotic and especially biotic stimuli are providing a valuable indication for potential involvement of HAE/IDA signalling in other processes than plant development.

Comments on revised version:

We thank the authors for addressing our previous comments. Overall, we are satisfied with the improvements and appreciate the hard work that has gone into this manuscript. We wish you all the best on the further publication pathway.

Reviewer #2 (Public Review):

The authors used a whole genome CRISPR screen to identify targetable synthetic lethalities associated with PPM1D mutations, known poor prognosis and currently undruggable factors in leukemia. The authors identified the cytosolic superoxide dismutase (SOD1, Cu/Zn SOD) as a major protective factor in PPMD1 mutant vs. wt cells, and their study investigates associated mechanisms of this protection. Using both genetic depletion and small molecule inhibitors of SOD1, the authors conclude that SOD1 loss exacerbates mitochondrial dysfunction, ROS levels and DNA damage phenotypes in PPM1D mutant cells, decreasing cell growth in AML cells. The data strongly support that PPMD1 mutant cells have high levels of total peroxides and elevated DNA breaks, and that genetic depletion of SOD1 decreases cell growth in two AML cell lines. However, the authors don't explain how superoxide radical (which is not damaging by itself) induces such damage, the on-target effects of the SOD1 inhibitors at the concentrations is not clear, the increase in total hydroperoxides is not supported by loss of SOD1, the changes in mitochondrial function are small, and there is no assessment of how the mitochondrial SOD2 expression or function, which dismutates mitochondrial superoxide, is altered. Overall these studies do not distinguish between signal vs. damaging aspects of ROS in their models and do not rule out an alternate hypothesis that loss of SOD1 increases superoxide production by cytosolic NADPH activity which would significantly alter ROS-driven regulation of kinase/phosphatase signal modulation, affecting cell growth and proliferation as well as DNA repair. Additionally, with the exception of growth defects demonstrated with sgSOD1, the majority of data are acquired using two chemical inhibitors, LCS1 and ATN-224, without supporting evidence that these inhibitors are acting in an on-target manner.

Overall, the authors address an important problem by seeking targetable vulnerabilities in PPM1D mutant AML cells, it is clear SOD1 deletion induces strong growth defects in the AML cell lines tested, most of the approaches are appropriate for the outcomes being evaluated, and the data are technically solid and well-presented. The major weakness lies in which redox pathways and ROS species are evaluated, how the resulting data are interpreted, and gaps in the follow-up experiments. Due to these omissions, as currently presented, the broader impact of these findings are unclear.

These specific concerns are outlined in detail below and I offer some suggestions regarding how to clarify the mechanisms underlying their initial observation of SOD1 synthetic lethality:

(1) Fig. 1 - SOD1 appears to be clustered with several other genes in the volcano plot (including FANC proteins). Did any other ROS-detoxifying enzymes show similar fitness scores? The effects of the SOD1 sgRNA are striking, however it would be useful to see qPCR or immunoblot data confirming robust depletion.

Does SOD1 co-expression in PPM1-mutant patient AML correspond to poorer disease outcomes? This can be evaluated in publicly available patient datasets and would support the idea of SOD1 synthetic lethality.

It would also be useful to know (given the subsequent results) whether expression of the SOD2, the mitochondrial superoxide dismutase, is altered in response to SOD1 loss.

(2) Fig. 2 - What are the relative SOD1 levels in the mutant PPM1D vs. wt. cell lines? The effects of the chemical inhibitors are stronger in MOLM-13 than the other two lines. These data could also point to whether LCS-1 and ATN-224 cytotoxicity is on-target or off-target at these concentrations, which is a key issue not currently addressed in these studies. This is a particular concern as the OCI-AML2 line shows a stronger growth defect with CRISPR SOD1 KO (in Fig 1) but the smallest effects with these chemical inhibitors.

While endogenous mitochondrial superoxide levels are elevated in PPM1D mutant lines, it is entirely unclear why SOD1 inhibition should affect mitochondrial superoxide as it detoxifies cytosolic superoxide. Also unclear why DCFDA signal (which measures total hydroperoxides) is *increased* under SOD1 inhibition - SOD1 dismutates superoxide radicals into hydrogen peroxide, therefore unless SOD2 is compensating for SOD1 loss, one might expect hydroperoxides to be lower (unless some entirely different oxidase is increasing their levels). None of these outcomes appear to be considered. Finally, it is not explained how lipid peroxidation, which requires production of hydroxyl or similarly high potency radicals, is being caused by increased superoxide or peroxides. One possibility is there is an increase in labile iron, in which case this phenotype would be rescued by the iron chelator desferal, and by the lipophilic antioxidant, ferrostatin.

Do the sgSOD1 cells also show similar increases in MitoSox green, DCFDA and BODIPY signal? These experiments would clarify whether the effects with the inhibitors are directly related directly to SOD1 loss or if they represent off-target effects from the inhibitors and/or compensatory changes in SOD2.

(3) Fig. 3 - the effects on mitochondrial respiratory parameters, while statistically significant, do not seem biologically striking. Also, these data are shown for OCI-AML2 cells which show the smallest cytotoxic effects with the SOD1 inhibitors among the 3 lines tested. They do however show the most robust growth defect with sgSOD1. This discrepancy could suggest that mitochondrial dysfunction does not underlie the observed growth defect and/or the inhibitor cytotoxicity is not on-target. Ideally mitochondrial profiling should also be carried out on this cell line with inducible SOD1 depletion. Have the authors assessed whether the mitochondrial Bcl family proteins are affected by the inhibitors?

(4) Fig. 4 - Currently the data in this figure do not support the authors claim that PPM1D-mutant cells have impaired antioxidant defense mechanisms, leading to an elevation in ROS levels and reliance on SOD1 for protection. It should be noted that oxidative stress specifically refers to adverse cellular effects of increasing ROS, not baseline levels of various redox parameters. Ideally levels of GSSG/GSH would be a better measure of potential redox stress tolerance than the total antioxidant capacity assay. Finally, oxidative stress can be assessed by challenging the wt and mutant PPM1D cell lines with oxidant stressors such as paraquat which elevates superoxide or drugs like erastin which elevate mitochondrial ROS. The immunoblot shows negligible changes in the antioxidant proteins assayed. Again, this blot should include SOD2 which is the most relevant antioxidant in the context of mitochondrial superoxide.

(5) Fig. 5 - These data support that DNA breaks are elevated in PPM1D mutant vs. wt cells. However, the data with the chemical SOD1 inhibitor again do not convince that the enhanced levels are due to on-target effects on SOD1. Use of the alkaline comet assay is appropriate for these studies and the 8-oxoguanine data do indicate contributions from oxidative DNA base damage. But these are unlikely to result directly from altered superoxide levels, as this species cannot directly oxidize DNA bases or cause DNA strand breaks.

The following points summarize my specific experimental and textual recommendations:

(1) These studies require an assessment of on-target efficacy of the inhibitors at the relevant concentration ranges. Ideally, they should have minimal effects against SOD1 knockout cell lines (acute challenge at a time point before the growth defects become apparent) and show better efficacy in SOD1-overexpressing lines. Key experiments (changes in superoxide, OCR profiling, DNA alkaline comet assay) would be more convincing if they are carried out with SOD1 knockout lines to compare against the inhibitor effects (3-4 days after introducing sgSOD1 when growth defects are not apparent).

(2) Instead of using NAC, which elevates glutathione synthesis but also has several known side-effects, the authors may want to determine whether Tempol, a SOD mimetic can rescue the effects of SOD1 knockout or inhibition. This would directly prove that SOD1 functional loss underlies the observed growth defect and cytotoxicity from genetic SOD1 knockdown or chemical inhibition.

(3) The complete lack of consideration of SOD2 in these studies is a missed opportunity as it reduces mitochondrial superoxide levels but elevates hydrogen peroxide levels. It would be very interesting to see whether SOD1 inhibition leads to compensatory increases in SOD2. SOD2 can be easily measured by immunoblot. Furthermore, measuring total superoxide via hydroethidium in a flow cytometric assay vs. mitochondrial ROS in PPM1D mut vs. wt cells and under SOD1 knockout would enable a determination of which species dominates (cytosolic or mitochondrial). These experiments are required to fill some logical gaps in interpretation of their redox data.

(4) Given the DNA breaks observed in PPM1D mutant cells, it is highly recommended the authors assess whether iron levels are elevated in mut vs. wt cells and whether desferal can rescue observed SOD1 inhibition defects.

(5) The authors may want to assess whether Rac1 or NADPH oxidase activity is altered in the SOD1 KO in wt vs. PPM1D cells. Their results may be the consequence of compromised ROS-driven survival signaling or DNA repair rather than direct ROS-induced damage, which is not caused directly by superoxide (or hydrogen peroxide).

(6) It is recommended the discussion focus more strongly on how the signaling function of superoxide vs. its reactions with other molecular entities to induce genotoxic outcomes could be contributing to the observed phenotypes. The discussion of FANC proteins, which were targets with similar fitness scores but not experimentally investigated at all, is an unwarranted digression.

Author Response

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

We thank the reviewers for their insightful and constructive comments of our work that have helped to strengthen the manuscript. In response to the additional suggestions provided by the reviewers, we have made revisions by adding or replacing five main figures, three supplementary figures, refining the text, and clarifying certain conclusions. Detailed responses to the reviewers’ points can be found below.

Additional experiments, textual changes, or modulation of claims are needed to address weaknesses in the SOD1 portion of the study. Specifically:

A) These studies require an assessment of the on-target efficacy of the inhibitors at the relevant concentration ranges. Ideally, they should have minimal effects against SOD1 knockout cell lines (an acute challenge at a time point before the growth defects become apparent) and show better efficacy in SOD1-overexpressing lines. Key experiments (changes in superoxide, OCR profiling, DNA alkaline comet assay) would be more convincing if they were carried out with SOD1 knockout lines to compare against the inhibitor effects (3-4 days after introducing sgSOD1 when growth defects are not apparent). In addition, SOD activity should be measured directly following inhibitor treatment.

We agree with the reviewers that the on- vs. off-target effects of the pharmacologic SOD1 inhibitors is a critical point to address. We have validated that SOD activity is reduced following treatment with ATN-224 in Figure 2 – Figure supplement 1A.

Nevertheless, we acknowledge that the potential for off-target effects of these inhibitors cannot be completely ruled out. To address this concern, we have incorporated a discussion regarding the potential off-target effects of both LCS-1 and ATN-224.

B) Assays should be included to support that SOD1 activity is altered. ATN-224 and LCS-1 are used to inhibit SOD1 function in the majority of the experiments, which should be supported by SOD activity assays to confirm SOD inhibition. Further, the concentration of ATN-224 used in this paper (12.5 uM) is beyond the concentration of what has been reported to inhibit SOD1 function in human blood cells. In Figure 4D, the authors demonstrate comparable SOD1 total protein levels in WT and PPM1Dmutant cells. However, the authors should further address whether PPM1D-mutation alters SOD1 activity via SOD activity assays.

We thank the reviewers for these suggestions. We have performed SOD activity assays which confirmed that SOD activity is inhibited upon treatment with ATN-224 at two concentrations (6.25 and 12.5 uM). Although we also did this for LCS-1-treated cells as well, in our hands, we did not see reduced SOD activity. However, LCS-1 has been shown to inhibit SOD activity in other publications including PMID: 21930909 and PMID: 32424294. From these assays, we have also found that PPM1D-mutant cells had increased SOD activity at baseline, despite having similar levels of SOD1 protein. These data have been added to Figure 2–Figure supplement 1A.

C) Some conclusions are not fully supported by the data provided. The authors claimed that "upon inhibition of SOD1, there was an increase in ROS that was specific to the mutant cells" in Figure 2E. Comparison of ROS levels among untreated, ATN-224, and LCS-1 of PPM1D-mutant cells should have been made and the statistics analysis among these groups should have been provided. Moreover, in Figure 2-Figure Supplement 1E, LCS-1 treatment does not increase ROS levels in PPM1D mutant LCLs. Performing these experiments with control and SOD1 deletion cells would have strengthened the results. Along with this point, the authors should comment on why SOD2 is not identified as a top hit in the CRISPR screen, as SOD2 deletion accumulates superoxide in cells.

After performing additional statistical analyses for Figure 2E, we found that the minor increase in ROS levels in the mutant cells after SOD1 inhibition was not statistically significant. We have revised the text accordingly.

As for why SOD2 was not identified as a top hit, we postulate that this may be due to inherent dependency of the WT cell lines on SOD2.

D) Fig. 1 - SOD1 appears to be clustered with several other genes in the volcano plot (including FANC proteins). Did any other ROS-detoxifying enzymes show similar fitness scores? The effects of the SOD1 sgRNA are striking, however, it would be useful to see qPCR or immunoblot data confirming robust depletion.

Thank you for your suggestion. We have validated the loss of SOD1 protein expression after SOD1 sgRNA deletion by immunoblot and have added this data to Figure 1– figure supplement 1D. While other ROS-detoxifying enzymes were not significantly enriched in the top 37 hits, interestingly, the Fanconi Anemia pathway also has roles in counteracting oxidative stress. FA-deficient cells have mitochondrial dysfunction and redox imbalance, and several of the FA family proteins are implicated in mitophagy. Therefore, there may be an interesting interplay between SOD1 and the FA pathway that is worth highlighting in the discussion of our manuscript even though there was no experimental investigation performed.

E) Fig. 2 - What are the relative SOD1 levels in the mutant PPM1D vs. WT. cell lines? The effects of the chemical inhibitors are stronger in MOLM-13 than in the other two lines. These data could also point to whether LCS-1 and ATN-224 cytotoxicity are on-target or off-target at these concentrations, which is a key issue not currently addressed in these studies. This is a particular concern as the OCI-AML2 line shows a stronger growth defect with CRISPR SOD1 KO (in Fig 1) but the smallest effects with these chemical inhibitors. The authors should also include SOD1 levels for Figure 1D and Figure 4Figure supplement 1C.

SOD1 protein expression is similar between WT and PPM1D-mutant cell lines and the loss of SOD1 after SOD1 sgRNA deletion was validated by immunoblot. These data have been added to Figure 1- figure supplement 1D and Figure 4D.

F) Does SOD1 co-expression in PPM1D-mutant patient AML correspond to poorer disease outcomes? This can be evaluated in publicly available patient datasets and would support the idea of SOD1 synthetic lethality.

Unfortunately, there are no publicly available patient datasets with sufficient cases of de novo PPMDmutant AML to assess this question.

G) While endogenous mitochondrial superoxide levels are elevated in PPM1D mutant lines, it is entirely unclear why SOD1 inhibition should affect mitochondrial superoxide as it detoxifies cytosolic superoxide. Also unclear why the DCFDA signal (which measures total hydroperoxides) is increased under SOD1 inhibition - SOD1 dismutates superoxide radicals into hydrogen peroxide, therefore unless SOD2 is compensating for SOD1 loss, one might expect hydroperoxides to be lower (unless some entirely different oxidase is increasing their levels). None of these outcomes appear to be considered. Finally, it is not explained how lipid peroxidation, which requires the production of hydroxyl or similarly high-potency radicals, is being caused by increased superoxide or peroxides. One possibility is there is an increase in labile iron, in which case this phenotype would be rescued by the iron chelator desferal, and by the lipophilic antioxidant, ferrostatin.

We measured intracellular labile iron levels by flow cytometry by staining the cells with FerroOrange at baseline and after SOD1 inhibition with our pharmacologic inhibitors (ATN-224 at 12.5 uM and LCS-1 at 1.25 uM). Across the three leukemia cell lines, we saw variable results in iron levels with no appreciable patterns (see below). Therefore, we cannot make conclusions about the contribution of labile iron to our observed phenotypes.

Author response image 1.

H) Do the sgSOD1 cells also show similar increases in MitoSox green, DCFDA, and BODIPY signal? These experiments would clarify whether the effects of the inhibitors are directly related directly to SOD1 loss or if they represent off-target effects from the inhibitors and/or compensatory changes in SOD2.

We do not observe changes in SOD2 in the several contexts in which we have examined this. We cannot exclude off-target effects of the inhibitors so have clarified this in the text.

I) The authors may want to assess whether Rac1 or NADPH oxidase activity is altered in the SOD1 KO in WT vs. PPM1D cells. Their results may be the consequence of compromised ROS-driven survival signaling or DNA repair rather than direct ROS-induced damage, which is not caused directly by superoxide (or hydrogen peroxide).

We appreciate the reviewer’s recommendations. However, due to time constraints, we regret not being able to assess Rac1 or NADPH oxidase activity. Nevertheless, we recognize the possibility of altered ROS-driven signaling rather than ROS-induced damage as a driver of our phenotype and have incorporated this possibility into our discussion.

J) Fig. 3 - the effects on mitochondrial respiratory parameters, while statistically significant, do not seem biologically striking. Also, these data are shown for OCI-AML2 cells which show the smallest cytotoxic effects with the SOD1 inhibitors among the 3 lines tested. They do however show the most robust growth defect with sgSOD1. This discrepancy could suggest that mitochondrial dysfunction does not underlie the observed growth defect and/or the inhibitor cytotoxicity is not on-target. Ideally, mitochondrial profiling should also be carried out on this cell line with inducible SOD1 depletion. Have the authors assessed whether the mitochondrial Bcl family proteins are affected by the inhibitors?

We assessed a few members of the mitochondrial Bcl-family proteins including MCL-1, BCL-2, and BCL-XL during the revision process. PPM1D-mutant cells have mildly increased expression of these anti-apoptotic proteins at baseline and the expression is not altered by pharmacologic SOD1 inhibition (see Author response image 2 below). Due to time constraints, we were unable to perform seahorse assays and mitochondrial profiling in the SOD1-deletion cells.

Author response image 2.

K) Fig. 4 - Currently the data in this figure do not support the authors' claim that PPM1D-mutant cells have impaired antioxidant defense mechanisms, leading to an elevation in ROS levels and reliance on SOD1 for protection. It should be noted that oxidative stress specifically refers to adverse cellular effects of increasing ROS, not baseline levels of various redox parameters. Ideally, levels of GSSG/GSH would be a better measure of potential redox stress tolerance than the total antioxidant capacity assay. Finally, oxidative stress can be assessed by challenging the wt and mutant PPM1D cell lines with oxidant stressors such as paraquat which elevates superoxide, or drugs like erastin which elevate mitochondrial ROS. The immunoblot shows negligible changes in the antioxidant proteins assayed. Again, this blot should include SOD2 which is the most relevant antioxidant in the context of mitochondrial superoxide.

We measured intracellular glutathione levels by flow cytometry and found that PPM1D-mutant cells had a greater proportion of cells with low levels of GSH. This data has been added as Figure 4D. We have also repeated the western blot to look at the antioxidant proteins catalase, SOD1, and thioredoxin after SOD1-deletion and pharmacologic SOD1 inhibition. We evaluated SOD2 protein levels in these experiments, as suggested. Smooth muscle actin (SMA) is included in the antibody cocktail as a loading control. However, it is unclear to us as to why PPM1D-mutant cells consistently have significantly higher levels of SMA. Therefore, we included a separate loading control, Vinculin. Repeat of these western blots showed a clearer difference between WT and PPM1D-mutant cells in the levels of these antioxidant proteins in which PPM1D-mutant cells have decreased levels of catalase and thioredoxin. These blots also show that SOD2 levels may be mildly increased in the PPM1D-mutant cells at baseline but is not significantly upregulated upon SOD1 inhibition. We have replaced the original immunoblot from Figure 4D with the revised blots that more clearly demonstrate the reduced levels of catalase and thioredoxin, now figure 4E.

L) Fig. 5 - These data support that DNA breaks are elevated in PPM1D mutant vs. wt cells. However, the data with the chemical SOD1 inhibitor again do not convince us that the enhanced levels are due to on-target effects on SOD1. Use of the alkaline comet assay is appropriate for these studies and the 8-oxoguanine data do indicate contributions from oxidative DNA base damage. But these are unlikely to result directly from altered superoxide levels, as this species cannot directly oxidize DNA bases or cause DNA strand breaks.

Thank you to the reviewers for raising this point. We have performed comet assays in SOD1-deletion cells to look at levels of DNA damage. Consistent with the reviewers’ point, we do not see a significant increase in DNA breaks after SOD1 deletion. We have removed the data using the SOD1 inhibitor and instead show the COMET analysis in the PPM1D-mut and SOD1-KO cells (see Figure 5F). We now make the point that increased DNA damage with SOD1 loss cannot explain the vulnerability of the double-mutant cells.

M) Instead of using NAC, which elevates glutathione synthesis but also has several known side effects, the authors may want to determine whether Tempol, a SOD mimetic can rescue the effects of SOD1 knockout or inhibition. This would directly prove that SOD1 functional loss underlies the observed growth defect and cytotoxicity from genetic SOD1 knockdown or chemical inhibition.

This is an excellent suggestion; we have added comments to this effect into the discussion.

N) It is recommended the discussion focus more strongly on how the signaling function of superoxide vs. its reactions with other molecular entities to induce genotoxic outcomes could be contributing to the observed phenotypes. The discussion of FANC proteins, which were targets with similar fitness scores but not experimentally investigated at all, is an unwarranted digression.

Thank you for this recommendation. We have expanded the discussion to focus more on the signaling functions of superoxide. However, considering the role of the Fanconi Anemia pathway in mitigating DNA damage and oxidative stress, we believe the discussion on the FANC proteins is important due to the possible intersection with SOD1. Therefore, we have refined this portion discussion to focus more on the interplay between SOD1 and FA.

O) The complete lack of consideration of SOD2 in these studies is a missed opportunity as it reduces mitochondrial superoxide levels but elevates hydrogen peroxide levels. It would be very interesting to see whether SOD1 inhibition leads to compensatory increases in SOD2. SOD2 can be easily measured by immunoblot. Furthermore, measuring total superoxide via hydroethidium in a flow cytometric assay vs. mitochondrial ROS in PPM1D mut vs. wt cells and under SOD1 knockout would enable a determination of which species dominates (cytosolic or mitochondrial). These experiments are required to fill some logical gaps in the interpretation of their redox data.

During the revision process, we have included SOD2 in our studies and have found that loss of SOD1 via genetic deletion and pharmacologic inhibition does not lead to compensatory increases in SOD2 (Figure 4D). Additionally, we have measured cytoplasmic superoxide levels using dihydroethidium to differentiate between cytoplasmic vs. mitochondrial superoxide. We found that at baseline levels, the mutant cells also harbored more cytoplasmic superoxide. We have added this figure as Figure 2C and moved the original mitochondrial superoxide data to Figure 2-figure supplement 1C.

P) Given the DNA breaks observed in PPM1D mutant cells, it is highly recommended that the authors assess whether iron levels are elevated in mut vs. wt cells and whether desferal can rescue observed SOD1 inhibition defects. Also, it has been reported that PPM1D promotes homologous recombination by forming a stable complex with BRCA1-BARD1, thereby enhancing their recruitment to doublestrand break sites. The authors should comment on why there is no difference in repair via HR in WT and PPM1D mutant cells in Figure 5C.

Please see comment G regarding our findings about iron levels.

The reviewers pose an interesting question as to why there is no difference in HR repair between WT and mutant cells, given the reported role of PPM1D in promoting HR. We have addressed this question in the main text. We believe that several factors can limit the extent of HR enhancement in PPM1D-mutant cells. For example, HR is typically confined to the S/G2 phase and thus may be constrained by cell cycling, among other regulatory mechanisms.

Other comments:

A) The authors described in the Method section that "The CRISPR Screen PPM1D mutant Cas9expressing OCI-AML2 cell lines were transduced with lentivirus library supernatant." The authors need to provide information on whether the MOI of the CRISPR screen has been well controlled to ensure that the majority of the cell population has a single copy of sgRNA transduction.

We performed a lentiviral titer curve prior to the screen to determine the volume of viral supernatant to add for a multiplicity of infection (MOI) of 0.3. This important detail has been added to our Methods.

B) The study convincingly shows differences between parental leukemic cells and the PPM1D mutants but one important control is missing in experiments related to Fig. 2 and 3. All PPM1D mutant clones used in this study were subjected to the blasticidin selection of the transduced cells to generate cells stably expressing Cas9 and subsequently, the clones with successful PPM1D targeting were expanded. The authors should demonstrate that increased ROS production is not just a consequence of the lentiviral transduction and antibiotic selection and that it corresponds to increased PPM1D activity in PPM1D mutant cells. To do that, authors could compare PPM1D clones to parental cells that underwent the same selection procedure (OCI-AML2-Cas9 cells and OCI-AML3-Cas9 cells).

It is true that the parental OCI-AML2 and OCI-AML3 cell lines underwent four days of blasticidin selection to create the stably expressing Cas9 cell lines. However, after the four-day period, the blasticidin was removed from the cell culture media. From there, we induced the PPM1D-mutations into the Cas9-expressing “WT” cell lines using the RNP-based CRISPR/Cas9 delivery method and single cells were then sorted into 96-well plates. Clones were expanded and validated using Sanger sequencing, TIDE analysis, and western blot. In all of our assays, we compare the WT Cas9 cells to the PPM1D-mutant Cas9 cells. Additionally, the cells have been expanded and passaged several times after blasticidin-selection. Therefore, we believe it is unlikely that there are residual ROSinducing effects from the antibiotic treatment.

C) The authors mention that they identified 3530 genes differentially expressed in parental and PPM1D mutant cells (line 267) but it is unclear what was the threshold for statistical significance. They mention FDR<0.05 in the Methods but show GSEA analysis with FDR<0.25 in Figure 4A. Source data for Fig. 4 is missing and the list of differentially expressed genes is not shown.

The source data files for Figures 1 and 4 will be uploaded with the revised manuscript. Upon reviewing the source data, we noticed an error in the number of differentially expressed genes. We have corrected this in line 274 and you will see that this correlates with Figure 4-source data 1. For the thresholds, we used an FDR<0.05 for the differential gene expression analysis, and an FDR <0.25 in the GSEA, which is an appropriate threshold for GSEA. We have clarified these thresholds in the methods section.

D) Include a definition of MFI in Figure legend Fig.2 and also in the Methods section. The unit should be indicated at both the x and y axes.

We have defined MFI in the figure legends and methods sections and have updated the figures accordingly.

E) Legend to Figure 2 - Figure Supplement 1 E should define the grey and pink columns (likely WT and mutants LCLs).

Thank you. We have defined the grey and pink columns as WT and PPM1D-mutant cell lines, respectively for Figure 2 – Figure supplement 2D and E.

F) Reporter assays in Fig. 5 convincingly show that NHEJ capacity is reduced in PPM1D mut cells. In the text, the authors state that this might reflect the impact of PPM1D on LSD1 (line 365). Although this might be the case, other options are equally possible. It would be appropriate to include a reference to the ability of PPM1D to counteract gH2AX and ATM which generate the most upstream signals in DDR.

Thank you to the reviewers for raising this excellent point. We have revised the text to incorporate the impact of PPM1D on yH2AX and ATM on NHEJ.

G) The authors correctly state that truncation of PPM1D leads to protein stabilization (line 85) and that it is present in U2OS cells (line 355). These observations have first been reported by Kleiblova et al 2013 and therefore one reviewer believes that this reference should be included. This study also identified truncating PPM1D mutation in colon adenocarcinoma. HCT116 cells and the role of PPM1D mutation in promoting the growth of colon cancer has subsequently been tested in an animal model (Burocziova et al., 2019).

Thank you. We have added this reference to our text in line 360.

Reviewer #1 (Public Review):

Summary:

Gain-of-function mutations and amplifications of PPM1D are fond across several human cancers and are associated with advanced tumor stage, worse prognosis, and increased lymph node metastasis. This manuscript presents important findings that SOD1 inhibition is a potential strategy to achieve therapeutic synergism for PPM1D-mutant leukemia; and demonstrates the redox landscape of PPM1D-mutant cells.

Strengths:

In this manuscript, Zhang and colleagues investigate the synthetic-lethal dependencies of PPM1D (protein phosphatase, Mg2+/Mn2+ dependent 1D) in leukemia cells using CRISPR/Cas9 screening. They identified that SOD1 (superoxide dismutase-1) as the top hit, whose loss reduces cellular growth in PPM1D-mutant cells, but not wildtype (WT) cells. Consistently, the authors demonstrate that PPM1D-mutant cells are more sensitive to SOD1 inhibitor treatment. By performing different in vitro studies, they show that PPM1D-mutant leukemia cells have elevated level of reactive oxygen species (ROS), decreased basal respiration, increased genomic instability, and impaired non-homologous end-joining repair. These data highlight the potential of SOD1 inhibition as a strategy to achieve therapeutic synergism for PPM1D-mutant leukemia; and demonstrates the redox landscape of PPM1D-mutant cells.

Weaknesses:

While the current study has identified synthetic lethality of PPM1D-mutant leukemia cells upon SOD1 inhibition, the underlying mechanism remains elusive. Although ROS levels have been assessed between wild-type (WT) and PPM1D-mutant leukemia cells, the specific redox alterations induced by SOD1 inhibition in PPM1D mutant versus WT cells have not been elucidated. To address this gap, direct comparisons of ROS levels using various probes should be conducted between PPM1D mutant and WT cells under conditions of SOD1 inhibition.

Reviewer #3 (Public Review):

Summary:

Authors performed a genome-wide CRISPR-based screen for synthetic lethal interactions in leukemic cells expressing a mutant form of PPM1D and identified SOD1. Loss of SOD1 or its inhibition with small molecule compounds reduced survival of the cells containing truncated PPM1D. Further analysis revealed that mitochondria are functionally deficient in PPM1D mutant cells resulting in increased levels of ROS. Surprisingly, expression profiling and reverse phase protein arrays revealed that PPM1D mutant cells did not respond appropriately to the increased levels of ROS. The precise molecular mechanism underlying this phenotype remains currently unclear, nevertheless the study convincingly shows that PPM1D mutant cells are vulnerable to oxidative stress.

Strengths:

Experimental procedures used in the study are appropriate and overall the presented data are very convincing. The study identified an important vulnerability of leukemic cells that carry PPM1D mutation and provides a fundamental background for testing SOD1 inhibitors in preclinical research. In the revised version of the manuscript, authors provide several new experiments that support their former conclusions. In particular, they showed that deletion of SOD1 in AML cells improved survival of the transplanted mice and this effect was more prominent when using cells carrying the mutant PPM1D. Further, they included an important control experiment that showed decreased SOD1 activity after treatment with ATN-224 inhibitor.

Weaknesses:

In the opinion of reviewer, there are no obvious weaknesses in this study. In broader view, the findings presented here using in vitro cultures will need to be validated in vivo by future research. Cell lines used in the study were generated by CRSIPR approaches in AML cells that have already been transformed. In addition, genome editing is inheritably connected with a risk of off target effects. It would therefore be great to identify AML samples carrying the PPM1D mutation that has been naturally selected during the transformation process.

eLife assessment

This useful study aims to quantify associations between regular use of proton-pump inhibitors (PPI) - defined as using PPI most days of the week during the last 4 weeks at one cross-section in time - with several respiratory outcomes (6 different outcomes) up to several years later in time. Weaknesses were identified in the design of the study, such as the measurement of the primary outcome and also the potential of bias which is inherent to the study design, which means the manuscript provides incomplete evidence.

Author Response

Reviewer #1 (Public Review):

Summary:

The current study aims to quantify associations between the regular use of proton-pump inhibitors (PPI) - defined as using PPI most days of the week during the last 4 weeks at one cross-section in time - with several respiratory outcomes up to several years later in time. There are 6 respiratory outcomes included: risk of influenza, pneumonia, COVID-19, other respiratory tract infections, as well as COVID-19 severity and mortality).

Strengths:

Several sensitivity analyses were performed, including i) estimation of the e-value to assess how strong unmeasured confounders should be to explain observed effects, ii) comparison with another drug with a similar indication to potentially reduce (but not eliminate) confounding by indication.

Thank you for pointing out the strengths of our article. We also sincerely thank the reviewer for raising several concerns and providing significant suggestions to improve our manuscript. We will revise our manuscript according to our provisional responses.

Weaknesses:

(1) The main exposure of interest seems to be only measured at one time-point in time (at study enrollment) while patients are considered many years at risk afterwards without knowing their exposure status at the time of experiencing the outcome. As indicated by the authors, PPI are sometimes used for only short amounts of time. It seems biologically implausible that an infection was caused by using PPI for a few weeks many years ago.

We agree with the reviewer, and this is one of the limitations of the UK Biobank data. We might identify potential long-term PPI users by defining the users that have certain indications, since they tend to regularly take PPI for a long period rather than only short amounts of time. We will evaluate the effect modification for the subgroup of potential long-term PPI users.

(2) Previous studies have shown that by focusing on prevalent users of drugs, one often induces several biases such as collider stratification bias, selection bias through depletion of susceptible, etc.

Due to the limitations of the data from the UK Biobank, including the lack of information on the initiation of medications and close follow-up, we can only use prevalent user design to evaluate the associations between PPI use and respiratory outcomes. We will further discuss it in the limitation section.

(3) It seems Kaplan Meier curves are not adjusted for confounding through e.g. inverse probability weighting. As such the KM curves are currently not informative (or the authors need to make clearer that curves are actually adjusted for measured confounding).

We will provide Kaplan Meier curves adjusted for confounding by inverse probability weighting according to the reviewer’s suggestion.

(4) Throughout the manuscript the authors seem to misuse the term multivariate (using one model with e.g. correlated error terms to assess multiple outcomes at once) when they seem to mean multivariable.

We will correct the misused terms throughout the manuscript according to the reviewer’s suggestions.

(5) Given multiple outcomes are assessed there is a clear argument for accounting for multiple testing, which following the logic of the authors used in terms of claiming there is no association when results are not significant may change their conclusions. More high-level, the authors should avoid the pitfall of stating there is evidence of absence if there is only an absence of evidence in a better way (no statistically significant association doesn't mean no relationship exists).

We will revise our interpretation of the results, especially for those without statistically significant associations based on the reviewer’s advice.

(6) While the authors claim that the quantitative bias analysis does show results are robust to unmeasured confounding, I would disagree with this. The e-values are around 2 and it is clearly not implausible that there are one or more unmeasured risk factors that together or alone would have such an effect size. Furthermore, if one would use the same (significance) criteria as used by the authors for determining whether an association exists, the required effect size for an unmeasured confounder to render effects 'statistically non-significant' would be even smaller.

We agree with the reviewer that there might still exist one or more unmeasured risk factors that have effect sizes larger than 2. Therefore, we could not state that the results are robust to unmeasured confounding based on the current analysis, and this would be a limitation of our study. We will add the above information to the discussion section.

(7) Some patients are excluded due to the absence of follow-up, but it is unclear how that is determined. Is there potentially some selection bias underlying this where those who are less healthy stop participating in the UK biobank?

We will provide the details for the determination of absence of follow-up in the UK Biobank and illustrate whether it potentially induced selection bias.

(8) Given that the exposure is based on self-report how certain can we be that patients e.g. do know that their branded over-the-counter drugs are PPI (e.g. guardium tablets)? Some discussion around this potential issue is lacking.

In the data collection of the UK Biobank, the participants can enter the generic or trade name of the treatment on the touchscreen to match the medications they used. We will discuss this important issue in the discussion section.

(9) Details about the deprivation index are needed in the main text as this is a UK-specific variable that will be unfamiliar to most readers.

We will provide details about the deprivation index in the manuscript.

(10) It is unclear how variables were coded/incorporated from the main text. More details are required, e.g. was age included as a continuous variable and if so was non-linearity considered and how?

Age was included as a continuous variable. We will provide information on whether non-linearity was considered in our manuscript.

(11) The authors state that Schoenfeld residuals were tested, but don't report the test statistics. Could they please provide these, e.g. it would already be informative if they report that all p-values are above a certain value.

We will provide the test statistics for the Schoenfeld residuals.

(12) The authors would ideally extend their discussion around unmeasured confounding, e.g. using the DAGs provided in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832226/, in particular (but not limited to) around severity and not just presence/absence of comorbidities.

We will use the DAGs provided by the article (PMC7832226) to extend our discussion around unmeasured confounding, especially the severity of comorbidities.

(13) The UK biobank is known to be highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. The potential problems this might create in terms of collider stratification bias - as highlighted here for example: https://www.nature.com/articles/s41467-020-19478-2 - should be discussed in greater detail and also appreciated more when providing conclusions.

We agree with the reviewer that the highly selective nature of the UK Biobank might create collider stratification bias for the evaluation of COVID-19-related outcomes. We will further discuss this in detail and be cautious when generating conclusions.  

Reviewer #2 (Public Review):

Summary:

Zeng et al investigate in an observational population-based cohort study whether the use of proton pump inhibitors (PPIs) is associated with an increased risk of several respiratory infections among which are influenza, pneumonia, and COVID-19. They conclude that compared to non-users, people regularly taking PPIs have increased susceptibility to influenza, pneumonia, as well as COVID-19 severity and mortality. By performing several different statistical analyses, they try to reduce bias as much as possible, to end up with robust estimates of the association.

Strengths:

The study comprehensively adjusts for a variety of critical covariates and by using different statistical analyses, including propensity-score-matched analyses and quantitative bias analysis, the estimates of the associations can be considered robust.

We thank the reviewer for demonstrating the strengths of our articles. We will further revise our manuscript according to the reviewer’s suggestions.

Weaknesses:

As it is an observational cohort study there still might be bias. Information on the dose or duration of acid suppressant use was not available, but might be of influence on the results. The outcome of interest was obtained from primary care data, suggesting that only infections as diagnosed by a physician are taken into account. Due to the self-limiting nature of the outcome, differences in health-seeking behavior might affect the results.

We will try to adjust or provide discussions about the above factors, including the dose/duration of PPI use, outcome assessment, and health-seeking behavior.

Author Response

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

eLife assessment

This study presents valuable findings on Legionella pneumophila effector proteins that target host vesicle trafficking GTPases during infection and more specifically modulate ubiquitination of the host GTPase Rab10. The evidence supporting the claims of the authors is solid, although it remains unclear how modification of the GTPase Rab10 with ubiquitin supports Legionella virulence and the impact of ubiquitination during LCV formation. The work will be of interest to colleagues studying animal pathogens as well as cell biologists in general.

We greatly appreciate the positive and valuable feedback from the editors and the reviewers. According to their suggestions, we added many new experimental data and implications of our findings in Legionella virulence in terms of the biological process of its replication niche. Please find our point-to-point responses below.

Public Reviews:

Reviewer #1 (Public Review):

In this manuscript, Kubori and colleagues characterized the manipulation of the host cell GTPase Rab10 by several Legionella effector proteins, specifically members of the SidE and SidC family. They show that Rab10 undergoes both conventional ubiquitination and noncanonical phosphoribose-ubiquitination, and that this posttranslational modification contributes to the retention of Rab10 around Legionella vacuoles.

Strengths

Legionella is an emerging pathogen of increasing importance, and dissecting its virulence mechanisms allows us to better prevent and treat infections with this organism. How Legionella and related pathogens exploit the function of host cell vesicle transport GTPases of the Rab family is a topic of great interest to the microbial pathogenesis field. This manuscript investigates the molecular processes underlying Rab10 GTPase manipulation by several Legionella effector proteins, most notably members of the SidE and SidC families. The finding that MavC conjugates ubiquitin to SdcB to regulate its function is novel, and sheds further light into the complex network of ubiquitin-related effectors from Lp. The manuscript is well written, and the experiments were performed carefully and examined meticulously.

Weaknesses

Unfortunately, in its current form this manuscript offers only little additional insight into the role of effector-mediated ubiquitination during Lp infection beyond what has already been published. The enzymatic activities of the SidC and SidE family members were already known prior to this study, as was the importance of Rab10 for optimal Lp virulence. Likewise, it had previously been shown that SidE and SidC family members ubiquitinate various host Rab GTPases, like Rab33 and Rab1. The main contribution of this study is to show that Rab10 is also a substrate of the SidE and SidC family of effectors. What remains unclear is if Rab10 is indeed the main biological target of SdcB (not just 'a' target), and how exactly Rab10 modification with ubiquitin benefits Lp infection.

Reviewer #1 (Recommendations for The Authors):

Major points of concern

(1) The authors show that SdcB increases Rab10 levels on LCVs at later times of infection and conclude that this is its main biological role. An alternative explanation may be that Rab10 is not 'the main' target of SdcB but merely 'a' target, which may explain why the effect of SdcB on Rab10 accumulation on LCV is only detectable after several hours of infection. An unbiased omics-based approach to identify the actual host target(s) of SdcB may be needed to confirm that Rab10 modification by SdcB is biologically relevant.

We totally agree with your comment that SdcB should have multiple targets considering the abundance of ubiquitin observed on the LCVs when SdcB was expressed (Figure 3). However, the effect of SdcB on Rab10 accumulation at the later time point (7 h) (current Figure 4e) was well supported by the new data showing that the SdcB-mediated ubiquitin conjugation to Rab10 was highly detected at this time point (new Figure 4c). We have tried the comprehensive search of interaction partners of the ANK domain of SdcB. This analysis is planned to be included in our on-going study. We therefore decided not to add the data in this manuscript.

(2) The authors show that Rab10 within cell lysate is ubiquitinated and conclude that ubiquitination of Rab10 is directly responsible for its retention on the LCV. What is the underlying molecular mechanism for this retention? Are GAP proteins prevented from binding and deactivating Rab10. This may be worth testing.

It would be a fantastic hypothesis that a Rab10GAP is involved in the regulation of Rab10 localization on the LCV. However, as far as we know, GAP proteins against Rab10 have not been identified yet. It should be an important issue to be addressed when a Rab10GAP will be found.

(3) Related to this, an alternative explanation would be that Rab10 retention is an indirect effect where inactivators of Rab10, such as host cell GAP proteins, are the main target of SidE/C family members and sent for degradation (see point #1). Can the authors show that Rab10 on the LCV is indeed ubiquitinated?

The possible involvement of a putative Rab10GAP is currently untestable as it is not known. To address whether Rab10 located on the LCV is ubiquitinated nor not, we conducted the critical experiments using active Rab10 (QL) and inactive Rab10 (TN) (new Figure 4a, new Figure 4-figure supplement 1). As revealed for Rab1 (Murata et al., Nature Cell Biol. 2006; Ingmundson et al., Nature 2007), Rab10 is expected to be recruited to the LCV as a GDPbound inactive form and converted to a GTP-bound active form on the LCV. The new results clearly demonstrated that GTP-locked Rab10QL is preferentially ubiquitinated upon infection, strongly supporting the model; Rab10 is ubiquitinated “on the LCV” by the SidE and SidC family ligases.

(4) Also, on what residue(s) is Rab10 ubiquitinated? Jeng et. al. (Cell Host Microbe, 2019, 26(4): 551-563)) suggested that K102, K136, and K154 of Rab10 are modified during Lp infection. How does substituting those residues affect the residency of Rab10 on LCVs? Addressing these questions may ultimately help to uncover if the growth defect of a sidE gene cluster deletion strain is due to its inability to ubiquitinate and retain Rab10 on the LCV.

Thank you for the suggestion. We conducted mutagenesis of the three Lys residues of Rab10 and applied the derivative on the ubiquitination analysis (new Figure 1-figure supplement 1). The Lys substitution to Ala residues did not abrogate the ubiquitination upon Lp infection. This result indicates that ubiquitination sites are present in the other residue(s) including the PR-ubiquitination site(s), raising possibility that disruption of sidE genes would be detrimental for intracellular growth of L. pneumophila because of failure of Rab10 retention.

(5) The authors proposed that "the SidE family primarily contributes towards ubiquitination of Rab10". In this case, what is the significance of SdcB-mediated ubiquitination of Rab10 during Lp infection?

We found that the major contribution of SdcB is retention of Rab10 until the late stage of infection. This claim was supported by our new data (new Figure 4c) as mentioned above (response to comment #1).

(6) The contribution of SdcB to ubiquitination of Rab10 relative to SidC and SdcA is unclear. SidC is shown to be unaffected by MavC. In this case, SidC can ubiquitinate Rab10 regardless of the regulatory mechanism of SdcB by MavC. This is not further being examined or discussed in the manuscript.

The effect of intrinsic MavC is apparent at the later stage (9 h) of infection (Figure 7c) when SdcB gains its activity (see above). We therefore do not think that the contribution of MavC on the SidC/SdcA activities, which are effective in the early stage, would impact on Rab10 localization. However, without specific experiments addressing this issue, possible MavC effects on SidC/SdcA would be beyond the scope in this manuscript.

(7) When is Rab10 required during Lp infection? The authors showed that Rab10 levels at LCV are rather stable from 1hr to 7hr post infection. If MavC regulates the activity of SdcB, when does this occur?

While the Rab10 levels on the LCV (~40 %) are stable during 1-7 h post infection (Figure 2b), it reduced to ~20% at 9 h after infection (Figure 7c) (the description was added in lines 304-306). Rab10 seems to be required for optimal LCV biogenesis over the early to late stages, but may not be required at the maturation stage (9 h). We validated the effect of MavC on the Rab10 localization at this time point (Figure 7c). These observations allowed us to build the scheme described in Figure 7d. We revised the illustration in new Figure 7d according to the helpful suggestions from both the reviewers.

(8) Previous analyses by MS showed that ubiquitination of Rab10 in Lp-infected cells decreases over time (from 1 hpi to 8 hpi - Cell Host Microbe, 2019, 26(4): 551-563). How does this align with the findings made here that Rab10 levels on the LCV and likely its ubiquitination levels increase over time?

We carefully compared the Rab10 ubiquitination at 1 h and 7 h after infection (new Figure 1figure supplement 1b). This analysis showed that the level of its ubiquitination decreased over time in agreement with the previous report. Nevertheless, Rab10 was still significantly ubiquitinated at 7 h, which we believe to cause the sustained retention of Rab10 on the LCV at this time point. We added the observation in lines 146-148.

(9) Polyubiquitination of Rab10 was not detected in cells ectopically producing SdcB and SdeA lacking its DUB domain (Figure 7 - figure supplement 2). Does SdcB actually ubiquitinate Rab10 (see also point #5)? Along the same line, it is curious to find that the ubiquitination pattern of Rab10 is not different for LpΔsidC/ΔsdcA compared to LpΔsidC/dsdcA/dsdcB (Figure 1C). The actual contribution of SdcB to ubiquitinating Rab10 compared to SidC/SdcA thus needs to be clarified.

Thank you for the important point. We currently hypothesize that SidC/SdcA/SdcB-mediated ubiquitin conjugation can occur only in the presence of PR-ubiquitin on Rab10 (either directly on the PR-ubiquitin or on other residue(s) of Rab10). Failure to detect the polyubiquitination in the transfection condition (Figure 7-figure supplement 2) suggests that this specific ubiquitin conjugation can occur in the restricted condition, i.e. only “on the LCV”. We added this description in the discussion section (lines 334-335). No difference between the ΔsidCΔsdcA and ΔsidCΔsdcAΔsdcB strains (Figure 1C, 1h infection) can be explained by the result that SdcB gains activity at the later stages (see above).

Minor comments In Figure 4b and 7b, the authors show a quantification of "Rab10-positive LCVs/SdcBpositive LCVs". Whys this distinction? It begs the question what the percentile of Rab10positive/SdcB-negative LCVs might be?

We took this way of quantification as we just wanted to see the effect of SdcB on the Rab10 localization. To distinguish between SdcB-positive and negative LCVs, we would need to rely on the blue color signals of DAPI to visualize internal bacteria, which we thought to be technically difficult in this specific analysis.

The band of FLAG-tagged SdcB was not detected by immunoblot using anti-FLAG antibody (Figure 5). The authors hypothesized that "disappearance of the SdcB band can be caused by auto-ubiquitination, as SdcB has an ability to catalyze auto-ubiquitination with a diverse repertoire of E2 enzymes. This can be easily confirmed by using MG-132 to inhibit proteasomal degradation of polyubiquitinated substrates.

We conducted the experiment using MG-132 as suggested and found that proteasomal degradation is not the cause of the disappearance of the band (new Figure 5-figure supplement 2, added description in lines 228-233). SdcB is actually not degraded. Instead, its polyubiquitination causes its apparent loss by distributing the SdcB bands in the gel.

In Figure 5F, the authors mentioned that "HA-UbAA did not conjugate to SdcB", whereas "shifted band detected by FLAG probing plausibly represents conjugation of cellular intrinsic Ub". The same argument was made in Figure 6B. These claims should be confirmed by immunoblot using anti-Ub antibody.

Thank you. We added the data using anti-Ub antibody (P4D1) (Figure 6f, new third panel).

Figure 7A: In cell producing MavC, SdcB is clearly present on LCV. However, in Figure 5A, SdcB was not detected by immunoblot in cells ectopically expressing MavC-C74A. What is the interpretation for these results?

SdcB was not degraded in the cells, but just its apparent molecular weight shift occurred by polyubiquitination (see above). The detection of SdcB in the IF images (Figure 7a) supported this claim.

Reviewer #2 (Public Review):

This manuscript explores the interplay between Legionella Dot/Icm effectors that modulate ubiquitination of the host GTPase Rab10. Rab10 undergoes phosphoribosyl-ubiquitination (PR-Ub) by the SidE family of effectors which is required for its recruitment to the Legionella containing vacuole (LCV). Through a series of elegant experiments using effector gene knockouts, co-transfection studies and careful biochemistry, Kubori et al further demonstrate that:

(1) The SidC family member SdcB contributes to the polyubiquitination (poly-Ub) of Rab10 and its retention at the LCV membrane.

(2) The transglutaminase effector, MavC acts as an inhibitor of SdcB by crosslinking ubiquitin at Gln41 to lysine residues in SdcB.

Some further comments and questions are provided below.

(1) From the data in Figure 1, it appears that the PR-Ub of Rab10 precedes and in fact is a prerequisite for poly-Ub of Rab10. The authors imply this but there's no explicit statement but isn't this the case?

Yes, we think that it is the case. We revised the description in the text accordingly (lines 326327).

(2) The complex interplay of Legionella effectors and their meta-effectors targeting a single host protein (as shown previously for Rab1) suggests the timing and duration of Rab10 activity on the LCV is tightly regulated. How does the association of Rab10 with the LCV early during infection and then its loss from the LCV at later time points impact LCV biogenesis or stability? This could be clearer in the manuscript and the summary figure does not illustrate this aspect.

Thank you for pointing the important issue. Association of Rab10 with the LCV is thought to be beneficial for L. pneumophila as it is the identified factor which supports bacterial growth in cells (Jeng et al., 2019). We speculate that its loss from the LCV at the later stage of infection would also be beneficial, since the LCV may need to move on to the maturation stage in which a different membrane-fusion process may proceed. As this is too speculative, we gave a simple modification on the part of discussion section (lines 356-358). We also modified the summary figure (revised Figure 7d) as illustrated with the time course.

(3) How do the activities of the SidE and SidC effectors influence the amount of active Rab10 on the LCV (not just its localisation and ubiquitination)

We agree that it is an important point. We tested the active Rab10 (QL) and inactive Rab10 (TN) for their ubiquitination and LCV-localization profiles (new Figure 4ab, new Figure 4figure supplement 1 and 2). These analyses led us to the unexpected finding that the active form of Rab10 is the preferential target of the effector-mediated manipulation. See also our response to Reviewer 1’s comment #3. Thank you very much for your insightful suggestion.

(4) What is the fate of PR-Ub and then poly-Ub Rab10? How does poly-Ub of Rab10 result in its persistence at the LCV membrane rather than its degradation by the proteosome?

We have not revealed the molecular mechanism in this study. We believe that it is an important question to be solved in future. We added the sentence in the discussion section (lines 376378).

(5) Mutation of Lys518, the amino acid in SdcB identified by mass spec as modified by MavC, did not abrogate SdcB Ub-crosslinking, which leaves open the question of how MavC does inhibit SdcB. Is there any evidence of MavC mediated modification to the active site of SdcB?

The active site of SdcB (C57) is required for the modification (Figure 5b), but it is not likely to be the target residue, as the MavC transglutaminase activity restricts the target residues to Lys. It would be expected that multiple Lys residues on SdcB can be modified by MavC to disturb the catalytic activity.

(6) I found it difficult to understand the role of the ubiquitin glycine residues and the transglutaminase activity of MavC on the inhibition of SdcB function. Is structural modelling using Alphafold for example helpful to explain this?

We conducted the Alphafold analysis of SdcB-Ub. Unfortunately, when the Glycine residues of Ub was placed to the catalytic pocket of SdcB, Q41 of Ub did not fit to the expected position of SdcB (K518). Probably, the ternary complex (MavC-Ub-SdcB) would cause the change of their entire conformation. A crystal structure analysis or more detailed molecular modeling would be required to resolve the issue.

(7) Are the lys mutants of SdbB still active in poly-Ub of Rab10?

We performed the experiment and found that K518R K891R mutant of SdcB still has the E3 ligase activity of similar level with the wild-type upon infection (new Figure 6-figure supplement 2) (lines 283-284). The level was actually slightly higher than that of the wildtype. This result may suggest that the blocking of the modification sites can rescue SdcB from MavC-mediated down regulation.

Reviewer #2 (Recommendations For The Authors):

see above

eLife assessment

This important study explores the interplay between Legionella Dot/Icm effectors that modulate ubiquitination of the host GTPase Rab10, which undergoes phosphoribosyl-ubiquitination by the SidE family of effectors, which in turn are required for Rab10 recruitment to the Legionella containing vacuole (LCV). The evidence supporting the claims of the authors is convincing. The study is not only relevant for the microbiology community, but will also be of interest to colleagues in the broader fields of membrane trafficking and general cell biology.

Reviewer #1 (Public Review):

This study presents valuable data on effector proteins (=virulence factors) used by the bacterial pathogen Legionella pneumophila that target host vesicle trafficking GTPases during infection. The evidence supporting the claims of the authors is robust, and the data suggest a sophisticated interplay between multiple effectors with the goal of temporarily exploiting host cell Rab10 during infection.

The authors have done a nice job addressing my earlier concerns. I have no further criticism about the revised paper.

Reviewer #2 (Public Review):

This manuscript explores the interplay between Legionella Dot/Icm effectors that modulate ubiquitination of the host GTPase Rab10. Rab10 undergoes phosphoribosyl-ubiquitination (PR-Ub) by the SidE family of effectors which is required for its recruitment to the Legionella containing vacuole (LCV). Through a series of elegant experiments using effector gene knockouts, co-transfection studies and careful biochemistry, Kubori et al further demonstrate that:

(1) The SidC family member SdcB contributes to the polyubiquitination (poly-Ub) of Rab10 and its retention at the LCV membrane.

(2) The transglutaminase effector, MavC acts as an inhibitor of SdcB by crosslinking ubiquitin at Gln41 to lysine residues in SdcB.

Author Response

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

eLife assessment

This valuable study applies voltage clamp fluorometry to provide new information about the function of serotonin-gated ion channels 5-HT3AR. The authors convincingly investigate structural changes inside and outside the orthosteric site elicited by agonists, partial agonists, and antagonists, helping to annotate existing cryo-EM structures. This work confirms that the activation of 5-HT3 receptors is similar to other members of this well-studied receptor superfamily. The work will be of interest to scientists working on channel biophysics but also drug development targeting ligand-gated ion channels.

Public Reviews:

All reviewers agreed that these results are solid and interesting. However, reviewers also raised several concerns about the interpretation of the data and some other aspects related to data analysis and discussion that should be addressed by the authors. Essential revisions should include:

(1) Please try to explicitly distinguish between a closed pore and a resting or desensitized state of the pore, to help in clarity.

(2) Add quantification of VCF data (e.g. sensor current kinetics, as suggested by reviewer #2) or better clarify/discuss the VCF quantitative aspects that are taken into account to reach some conclusions (reviewer #3).

(3) Review and add relevant foundational work relevant to this study that is not adequately cited.

(4) Revise the text according to all recommendations raised by the reviewers and listed in the individual reviews below.

We have revised the text to address all four points. See the answers to referees’ recommendations.

Reviewer #1 (Public Review):

Summary:

This study brings new information about the function of serotonin-gated ion channels 5-HT3AR, by describing the conformational changes undergoing during ligands binding. These results can be potentially extrapolated to other members of the Cys-loop ligand-gated ion channels. By combining fluorescence microscopy with electrophysiological recordings, the authors investigate structural changes inside and outside the orthosteric site elicited by agonists, partial agonists, and antagonists. The results are convincing and correlate well with the observations from cryo-EM structures. The work will be of important significance and broad interest to scientists working on channel biophysics but also drug development targeting ligand-gated ion channels.

Strengths:

The authors present an elegant and well-designed study to investigate the conformational changes on 5-HT3AR where they combine electrophysiological and fluorometry recordings. They determined four positions suitable to act as sensors for the conformational changes of the receptor: two inside and two outside the agonist binding site. They make a strong point showing how antagonists produce conformational changes inside the orthosteric site similarly as agonists do but they failed to spread to the lower part of the ECD, in agreement with previous studies and Cryo-EM structures. They also show how some loss-of-function mutant receptors elicit conformational changes (changes in fluorescence) after partial agonist binding but failed to produce measurable ionic currents, pointing to intermediate states that are stabilized in these conditions. The four fluorescence sensors developed in this study may be good tools for further studies on characterizing drugs targeting the 5-HT3R.

Weaknesses:

Although the major conclusions of the manuscript seem well justified, some of the comparison with the structural data may be vague. The claim that monitoring these silent conformational changes can offer insights into the allosteric mechanisms contributing to signal transduction is not unique to this study and has been previously demonstrated by using similar techniques with other ion channels.

The referee emphasizes that “some of the comparison with the structural data may be vague”. To better illustrate the structural reorganizations seen in the cryo-EM structures and that are used for VCF data interpretation, we added a new supplementary figure 3. It shows a superimposition of Apo, setron and 5-HT bond structures, with reorganization of loop C and Cys-loop consistent with VCF data.

Reviewer #2 (Public Review):

Summary:

This study focuses on the 5-HT3 serotonin receptor, a pentameric ligand-gated ion channel important in chemical neurotransmission. There are many cryo-EM structures of this receptor with diverse ligands bound, however assignment of functional states to the structures remains incomplete. The team applies voltage-clamp fluorometry to measure, at once, both changes in ion channel activity, and changes in fluorescence. Four cysteine mutants were selected for fluorophore labeling, two near the neurotransmitter site, one in the ECD vestibule, and one at the ECD-TMD junction. Agonists, partial agonists, and antagonists were all found to yield similar changes in fluorescence, a proxy for conformational change, near the neurotransmitter site. The strength of the agonist correlated to a degree with propagation of this fluorescence change beyond the local site of neurotransmitter binding. Antagonists failed to elicit a change in fluorescence in the vestibular the ECD-TMD junction sites. The VCF results further turned up evidence supporting intermediate (likely pre-active) states.

Strengths:

The experiments appear rigorous, the problem the team tackles is timely and important, the writing and the figures are for the most part very clear. We sorely need approaches orthogonal to structural biology to annotate conformational states and observe conformational transitions in real membranes- this approach, and this study, get right to the heart of what is missing.

Weaknesses:

The weaknesses in the study itself are overall minor, I only suggest improvements geared toward clarity. What we are still missing is application of an approach like this to annotate the conformation of the part of the receptor buried in the membrane; there is important debate about which structure represents which state, and that is not addressed in the current study.

Reviewer #3 (Public Review):

Summary:

The authors have examined the 5-HT3 receptor using voltage clamp fluorometry, which enables them to detect structural changes at the same time as the state of receptor activation. These are ensemble measurements, but they enable a picture of the action of different agonists and antagonists to be built up.

Strengths:

The combination of rigorously tested fluorescence reporters with oocyte electrophysiology is a solid development for this receptor class.

Weaknesses:

The interpretation of the data is solid but relevant foundational work is ignored. Although the data represent a new way of examining the 5-HT3 receptor, nothing that is found is original in the context of the superfamily. Quantitative information is discussed but not presented.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Here are some suggestions that may help to improve the manuscript: - Page 6, point 2), typo: "L131W is positioned more profound in each ECD, its side chain (...)"

“profound” have been corrected into “profoundly”

• Fig 1C: Why not compare 5-HT responses for the four sensors studied? If the reason is the low currents elicited by 5-HT on I160C/Y207W sensor, could you comment on this effect that is not observed for the other full agonist tested (mCPBG)?

The point of this figure (Fig 1G) is to show currents that desensitize to follow the evolution of the fluorescence signal during desensitization, that’s why for the I160C/Y207W sensor where 5-HT become a partial agonist we have judge more appropriate to use mCPBG acting as a more potent agonist to elicit currents with clear desensitization component. We have added a sentence in the legend of the figure to explain this choice more clearly.

• Page 9, paragraph 2: "However, concentration-response curves on V106C/L131W show a small yet visible decorrelation of fluorescence and current (...)" Statistical analysis on EC50c and EC50f will help to see this decorrelation.

Statistical analysis (unpaired t test) has been added to figure 3 panel A.

• Page 10, paragraph 1: the authors describe how "different antagonists promote different degrees of local conformational changes". Does it have any relation to the efficacy or potency of these antagonists? Is there any interpretation for this result?

Since setrons are competitive antagonists, the concept of efficacy of these molecules is unclear. Concerning potency, no correlation between affinity and fluorescence variation is observed. For instance, ondansetron and alosetron bind with similar nanomolar affinity to the 5-HT3R (Thompson & Lummis Curr Pharm Des. 2006;12(28):3615-30) but elicit different fluorescence variations on both S204C and I160C/Y207W sensors.

• Fig. 1 panel A, graph to far right: axis label is cut ("current (uA)/..."). Colors of graph A - right are not clearly distinguishable e.g. cyan from green.

The fluorescent green color that describes the mutant has been changed into limon color which is more clearly distinguishable from cyan.

• Why is R219C/F142W not selected in the study? Are the signals comparable to the chosen R219C/F142W?

We have chosen not to select R219C/F142W because the current elicited by this construct was lower than the current elicited by the construct R219C/Y140W. Moreover, the residue F142 belongs to the FPF motif from Cys-loop that is essential for gating (Polovinkin et al, 2018, Nature).

• Fig. 1 legend typo: "mutated in tryptophan”

“in” has been changed by “into”

• Fig. 2: yellow color (graphs in panel B) is very hard to read.

Yellow color has been darkened to yellow/brown to allow easy reading.

• Fig. 4 is too descriptive and undermines the information of the study. It could be improved e.g. by representing specific structures or partial structures involved. As an additional minor comment, some colors in the figure are hard to differentiate, e.g. magenta and purple.

We have added relevant specific structures involved, namely loop C, the Cys-loop and pre-M1 loop to clarify. The intensity of magenta and purple has been increased to help differentiate the two sensor positions.

• Fig S1C: it is confusing to see the same color pattern for the single mutants without the W. I would recommend to label each trace to make it clearer.

Labelling of the traces corresponding to the single mutants has been added.

• Fig S2: Indicating the statistical significance in the graph for the mutants with different desensitization properties compared to the WT receptor will help its interpretation.

The statistical significance of the difference in the desensitization properties has been added to Figure S2.

Reviewer #2 (Recommendations For The Authors):

Overall comments for the authors:

Selection of cysteine mutants and engineered Trp sites is clear and logical. VCF approach with controls for comparing the functionality of WT vs. mutants, and labeled with unlabeled receptor, is well explained and satisfying. The finding that desensitization involves little change in ECD conformation makes sense. It is somewhat surprising, at least superficially, to find that competitive antagonists promote changes in fluorescence in the same 'direction' and amplitude as strong agonists, however, this is indeed consistent with the structural biology, and with findings from other groups testing different labeling sites. Importantly, the team finds that antagonist-binding changes in deltaF do not spread beyond the region near the neurotransmitter site. The finding that most labeling sites in the ECD, in particular those not in/near the neurotransmitter site, fail to report measurable fluorescence changes, is noteworthy. It contrasts with findings in GlyR, as noted by the authors, and supports a mechanism where most of each subunit's ECD behaves as a rigid body.

Specific questions/comments:

I am confused about the sensor current kinetics. Results section 2) states that all sensors share the same current desensitization kinetics, while Results section 5) states that the ECD-TMD site and the vestibule site sensors exhibit faster desensitization. SF1C, right-most panel of R219C suggests the mutation and/or labeling here dramatically changes apparent activation and deactivation rates measured by TEVC. Both activation and deactivation upon washout appear faster in this one example. Data for desensitization are not shown here but are shown in aggregate in earlier panels. It is a bit surprising that activation and deactivation would both change but no effect on desensitization. Indeed, it looks like, in Fig. 1G, that desensitization rate is not consistent across all constructs. Can you please confirm/clarify?

TEVC and VCF recordings in this study show a significant variability concerning both the apparent desensitization and desactivation kinetics. This is illustrated concerning desensitization in TEVC experiments in figure S2, where the remaining currents after 45 secondes of 5-HT perfusion and the rate constants of desensitization are measured on different oocytes from different batches. Therefore, the differences in desensitization kinetics shown in fig 1.G are not significant, the aim of the figure being solely to illustrate that no variation of fluorescence is observed during the desensitization phase. A sentence in the legend of fig 1.G has been added to precise this point. We also revised the first paragraph of result section 5, clearly stating that the slight tendency of faster desensitization of V106C/L131W and R219C/Y140W sensors is not significant.

An alternative to the conclusion-like title of Results section 2) is that the ECD (and its labels) does not undergo notable conformational changes between activated and desensitized states.

This is a good point and we have added a sentence at the end of results section 2 to present this idea.

I find the discussion paragraph on partial agonist mechanisms, starting with "However," to be particularly important but at times hard to follow. Please try to revise for clarity. I am particularly excited to understand how we can understand/improve assignments of cryo-EM structures using the VCF (or other) approaches. As examples of where I struggled, near the top of p. 11, related to the partial agonist discussion, there is an assumption about the pore being either activated, or resting. Is it not also possible that partial agonists could stabilize a desensitized state of the pore? Strictly speaking, the labeling sites and current measurements do not distinguish between pre-active resting and desensitized channel conformations/states. However, the cryo-EM structures can likely help fill in the missing information there- with all the normal caveats. Please try to explicitly distinguish between a closed pore and a resting or desensitized state of the pore, to help in clarity.

We have revised the section, and hope it is clearer now. We notably state more explicitly the argument for annotation of partial agonist bound closed structures as pre-active, mainly from kinetic consideration of VCF experiments. We also mention and cite a paper by the Chakrapani group published the 4th of January 2024 (Felt et al, Nature Communication), where they present the structures of the m5HT3AR bound to partial agonists, with a set of conformations fully consistent with our VCF data.

This statement likely needs references: "...indirect experiments of substituted cysteine accessibility method (SCAM) and VCF experiments suggested that desensitization involves weak reorganizations of the upper part of the channel that holds the activation gate, arguing for the former hypothesis."

Reference Polovinkin et al, Nature, 2018, has been added.

I respectfully suggest toning down this language a little bit: "VCF allowed to characterize at an unprecedented resolution the mechanisms of action of allosteric effectors and allosteric mutations, to identify new intermediate conformations and to propose a structure-based functional annotation of known high-resolution structures." This VCF stands strongly without unclear claims about unprecedented resolution. What impresses me most are the findings distinguishing how agonists/partial agonists/antagonists share a conserved action in one area and not in another, the observations consistent with intermediate states, and the efforts to integrate these simultaneous current and conformation measurements with the intimidating array of EM structures.

We thank the referee for his positive comments. We have removed “unprecedented resolution” and revised the sentences.

It is beyond the scope of the current study, but I am curious what the authors think the hurdles will be to tracking conformation of the pore domain- an area where non-cryo-EM based conformational measurements are sorely needed to help annotate the EM structures.

We fully agree with the referee that structures of the TMD are very divergent between the various conditions depending on the membrane surrogate. We are at the moment working on this region by VCF, incorporating the fluorescent unnatural amino acid ANAP.

Minor:

(1) P. 5, m5-HT3R: Please clarify that this refers to the mouse receptor, if that is correct.

OK, “mouse” has been added.

(2) Fig. 1D, I suggest moving the 180-degree arrow to the right so it is below but between the two exterior and vestibular views.

Ok, it has been done.

(3) Please add a standard 2D chemical structure of MTS-TAMRA, and TAMRA attached to a cysteine, to Fig 1.

A standard chemical structure has been added for the two isomers of MTS-TAMRA.

(4) Please label subpanels in Fig. 1G with the identity of the label site.

The subpanels have been labelled.

Reviewer #3 (Recommendations For The Authors):

This is solid work but I mainly have suggestions about placing it in context.

(1) Abstract "Data show that strong agonists promote a concerted motion of all sensors during activation, "

The concept of sensors here is the fluorescent labels? I did not find this meaningful until I read the significance statement.

We have specified “fluorescently-labelled” before sensors in the abstract.

(2) p4 "each subunit in the 5-HT3A pentamer...." this description would be identical for any pentameric LGIC so the authors should beware of a misleading specificity. This goes for other phrases in this paragraph. However, the summary of the 5HT specific results is very good.

About the description of the structure, we added “The 5-HT3AR displays a typical pLGIC structure, where….”.

(3) This paper is very nicely put together and generally explains itself well. The work is rigorous and comprehensive. But the meaning of quenching (by local Trp) seems straightforward, but it is not made explicit in the paper. Why doesn't simple labelling (single Cys) at this site work? And can we have a more direct demonstration of the advantage of including the Trp (not in the supplementary figure?) All this information is condensed into the first part of figure 1 (the graph in Figure 1A). Figure 1 could be split and the principle of the introduced quenching could be more clearly shown

detailed in a few more sentences the principle of the TrIQ approach. In addition, to be more explicit, the significative differences of fluorescence comparing sensors with and without tryptophan have been added in Figure 1, panel screening and a sentence have been added in the legend of this figure.

(4) p10 "VCF measurements are also remarkably coherent with the atomic structures showing an open pore (so called F, State 2 and 5-HT asymmetric states), "

This statement is intriguing. What do these names or concepts represent? Are they all the same thing? Where do the names come from? What is meant here? Three different concepts, all consistent? Or three names for the same concept?

We have tried to clarify the statement by making reference to the PDB of the structures.

(5) "Fluorescence and VCF studies identified similar intermediate conformations for nAChRs, ⍺1-GlyRs and the bacterial homolog GLIC(21,32-35). "

Whilst this is true, the motivation for such ideas came from earlier work identifying intermediates from electrophysiology alone (such as the flip state (Burzomato et al 2004), the priming state (Mukhatsimova 2009) and the conformational wave in ACh channels grosman et al 2000). It would be appropriate to mention some of this earlier work.

We have incorporated and described these references in the discussion. Of note, we fully quoted these references in our previous papers on the subject (Menny 2017, Lefebvre 2021, Shi 2023), but the referee is right in asking to quote them again.

(6) "A key finding of the study is the identification of pre-active intermediates that are favored upon binding of partial agonists and/or in the presence of loss-of-function mutations. "

Even more fundamental, the idea of a two-state equilibrium for neurotransmitter receptors was discarded in 1957 according to the action of partial agonists.

DEL CASTILLO J, KATZ B (1957) Interaction at end-plate receptors between different choline derivatives. Proc R Soc Lond B Biol Sci

So to discover this "intermediate" - that is, bound but minimal activity - in the present context seems a bit much. It is a big positive of this paper that the results are congruent with our expectations, but I cannot see value in posing the results as an extension of the 2-state equilibrium (for which there are anyway other objections).

As for intermediates being favoured by loss of function mutations, this concept is already well established in glycine receptors (Plested et al 2007, Lape et al 2012) and doubtless in other cases too.

I do get the point that the authors want to establish a basis in 5-HT3 receptors, but these previous works suggest the results are somewhat expected. This should be commented on.

We also agree. We replace “key finding” by “key observation”, quote most of the references proposed, and explicitly conclude that “The present work thus extends this idea to the 5HT3AR, together with providing structural blueprints for cryo-EM structure annotation”.

(7) "In addition, VCF data allow a quantitative estimate of the complex allosteric action of partial agonists, that do not exclusively stabilize the active state and document the detailed phenotypes of various allosteric mutations."

Where is this provided? If the authors are not motivated to do this, I have some doubts that others will step in. If it is not worth doing, it's probably not worth mentioning either.

Language has been toned down by “In addition, VCF data give insights in the action of partial agonists, that do not exclusively stabilize the active state and document the phenotypes of various allosteric mutations."

(8) Figure 1G please mark which construct is which.

This has been added into Figure 1G

eLife assessment

This valuable study applies voltage clamp fluorometry to provide new information about the function of serotonin-gated ion channels 5-HT3AR. The authors convincingly investigate structural changes inside and outside the orthosteric site elicited by agonists, partial agonists, and antagonists, helping to annotate existing cryo-EM structures. This work confirms that the activation of 5-HT3 receptors is similar to other members of this well-studied receptor superfamily. The work will be of interest to scientists working on channel biophysics but also drug development targeting ligand-gated ion channels.

Reviewer #1 (Public Review):

Summary:

This study brings new information about the function of serotonin-gated ion channels 5-HT3AR, by describing the conformational changes undergoing during ligands binding. These results can be potentially extrapolated to other members of the Cys-loop ligand-gated ion channels. By combining fluorescence microscopy with electrophysiological recordings, the authors investigate structural changes inside and outside the orthosteric site elicited by agonists, partial agonists, and antagonists. The results are convincing and correlate well with the observations from cryo-EM structures. The work will be of important significance and broad interest to scientists working on channel biophysics but also drug development targeting ligand-gated ion channels.

Strengths:

The authors present an elegant and well-designed study to investigate the conformational changes on 5-HT3AR where they combine electrophysiological and fluorometry recordings. They determined four positions suitable to act as sensors for the conformational changes of the receptor: two inside and two outside the agonist binding site. They make a strong point showing how antagonists produce conformational changes inside the orthosteric site similarly as agonists do but they failed to spread to the lower part of the ECD, in agreement with previous studies and Cryo-EM structures. They also show how some loss-of-function mutant receptors elicit conformational changes (changes in fluorescence) after partial agonist binding but failed to produce measurable ionic currents, pointing to intermediate states that are stabilized in these conditions. The four fluorescence sensors developed in this study may be good tools for further studies on characterizing drugs targeting the 5-HT3R. The major conclusions of the manuscript seem well justified.

Weaknesses:

Weaknesses have been very well addressed during the review process.

Reviewer #2 (Public Review):

Summary:

This study focuses on the 5-HT3 serotonin receptor, a pentameric ligand-gated ion channel important in chemical neurotransmission. There are many cryo-EM structures of this receptor with diverse ligands bound, however assignment of functional states to the structures remains incomplete. The team applies voltage-clamp fluorometry to measure, at once, both changes in ion channel activity, and changes in fluorescence. Four cysteine mutants were selected for fluorophore labeling, two near the neurotransmitter site, one in the ECD vestibule, and one at the ECD-TMD junction. Agonists, partial agonists, and antagonists were all found to yield similar changes in fluorescence, a proxy for conformational change, near the neurotransmitter site. The strength of the agonist correlated to a degree with propagation of this fluorescence change beyond the local site of neurotransmitter binding. Antagonists failed to elicit a change in fluorescence in the vestibular of the ECD-TMD junction sites. The VCF results further turned up evidence supporting intermediate (likely pre-active) states.

Strengths:

The experiments appear rigorous, the problem the team tackles is timely and important, the writing and the figures are for the most part very clear. We sorely need approaches orthogonal to structural biology to annotate conformational states and observe conformational transitions in real membranes- this approach, and this study, get right to the heart of what is missing.

Weaknesses:

The weaknesses in the study itself are overall minor, I only suggest improvements geared toward clarity. What we are still missing is application of an approach like this to annotate the conformation of the part of the receptor buried in the membrane; there is an important debate about which structure represents which state, and that is not addressed in the current study.

Reviewer #3 (Public Review):

Summary:

The authors have examined the 5-HT3 receptor using voltage clamp fluorometry, which enables them to detect structural changes at the same time as the state of receptor activation. These are ensemble measurements, but they enable an impressive scheme of the action of different agonists and antagonists to be built up. The growing array of structural snapshots of 5-HT3 receptors is used to good effect to understand the results.

Strengths:

The combination of rigorously tested fluorescence reporters with oocyte electrophysiology across a large panel of ligands is a solid development for this receptor type.

Weaknesses:

In their revision, the authors corrected all the weaknesses of the original submission.

Author Response

Provisional response

We would like to thank the reviewers for taking the time to review our manuscript, for providing useful suggestions for improvement, and for highlighting the significance of our approach.

Reviewer #1 (Public Review):

Summary:

The authors demonstrate that it is possible to carry out eQTL experiments for the model eukaryote S. cerevisiae, in "one pot" preparations, by using single-cell sequencing technologies to simultaneously genotype and measure expression. This is a very appealing approach for investigators studying genetic variation in single-celled and other microbial systems, and will likely inspire similar approaches in non-microbial systems where comparable cell mixtures of genetically heterogeneous individuals could be achieved.

Strengths:

While eQTL experiments have been done for nearly two decades (the corresponding author's lab are pioneers in this field), this single-cell approach creates the possibility for new insights about cell biology that would be extremely challenging to infer using bulk sequencing approaches. The major motivating application shown here is to discover cell occupancy QTL, i.e. loci where genetic variation contributes to differences in the relative occupancy of different cell cycle stages. The authors dissect and validate one such cell cycle occupancy QTL, involving the gene GPA1, a G-protein subunit that plays a role in regulating the mating response MAPK pathway. They show that variation at GPA1 is associated with proportional differences in the fraction of cells in the G1 stage of the cell cycle. Furthermore, they show that this bias is associated with differences in mating efficiency.

We thank the reviewer for recognizing the strengths of our overall approach and our dissection of the functional consequences of the W82R variant of GPA1.

Weaknesses:

While the experimental validation of the role of GPA1 variation is well done, the novel cell cycle occupancy QTL aspect of the study is somewhat underexploited. The cell occupancy QTLs that are mentioned all involve loci that the authors have identified in prior studies that involved the same yeast crosses used here. It would be interesting to know what new insights, besides the "usual suspects", the analysis reveals. For example, in Cross B there is another large effect cell occupancy QTL on Chr XI that affects the G1/S stage. What candidate genes and alleles are at this locus?

We thank the reviewer for this suggestion. We plan to expand the section on cell cycle occupancy QTL in our revision.

And since cell cycle stages are not biologically independent (a delay in G1, could have a knock-on effect on the frequency of cells with that genotype in G1/S), it would seem important to consider the set of QTLs in concert.

We thank the reviewer for this suggested clarification. In our revision, we will clarify that the cell cycle occupancy phenotype represents the proportion of cells assigned to a given stage. As the reviewer correctly notes, a change in the proportion of cells in one stage may alter the proportion of cells in other stages, and this could result in cell cycle occupancy QTL for multiple stages. We will make efforts to consider the cell cycle occupancy QTLs in concert in the revised manuscript.

Reviewer #2 (Public Review):

Boocock and colleagues present an approach whereby eQTL analysis can be carried out by scRNA-Seq alone, in a one-pot-shot experiment, due to genotypes being able to be inferred from SNPs identified in RNA-Seq reads. This approach obviates the need to isolate individual spores, genotype them separately by low-coverage sequencing, and then perform RNA-Seq on each spore separately. This is a substantial advance and opens up the possibility to straightforwardly identify eQTLs over many conditions in a cost-efficient manner. Overall, I found the paper to be well-written and well-motivated, and have no issues with either the methodological/analytical approach (though eQTL analysis is not my expertise), or with the manuscript's conclusions.

We thank the reviewer for recognizing the significant contributions our work makes to the field.

393 segregant experiment:

For the experiment with the 393 previously genotyped segregants, did the authors examine whether averaging the expression by genotype for single cells gave expression profiles similar to the bulk RNA-Seq data generated from those genotypes? Also, is it possible (and maybe not, due to the asynchronous nature of the cell culture) to use the expression data to aid in genotyping for those cells whose genotypes are ambiguous? I presume it might be if one has a sufficient number of cells for each genotype, though, for the subsequent one-pot experiments, this is a moot point.

We thank the reviewer for this comment. While we could expand the analysis along these lines, this is not relevant for the subsequent one-pot eQTL experiments, as the reviewer notes, and is therefore beyond the scope of the manuscript. We will make the data available so that anyone interested can try these analyses.

Figure 1B:

Is UMAP necessary to observe an ellipse/circle - I wouldn't be surprised if a simple PCA would have sufficed, and given the current discussion about whether UMAP is ever appropriate for interpreting scRNA-Seq (or ancestry) data, it seems the PCA would be a preferable approach. I would expect that the periodic elements are contained in 2 of the first 3 principal components. Also, it would be nice if there were a supplementary figure similar to Figure 4 of Macosko et al (PMID 26000488) to indeed show the cell cycle dependent expression.

We thank the reviewer for this comment. We too have been following the debate on the utility of UMAP for scRNA-seq, and in our revision we will provide an alternative visualization of the cell cycle. We will also generate a supplementary figure similar to Figure 4 of Macosko et al. to visualize cell-cycle-dependent gene expression.

Aging, growth rate, and bet-hedging:

The mention of bet-hedging reminded me of Levy et al (PMID 22589700), where they saw that Tsl1 expression changed as cells aged and that this impacted a cell's ability to survive heat stress. This bet-hedging strategy meant that the older, slower-growing cells were more likely to survive, so I wondered a couple of things. It is possible from single-cell data to identify either an aging, or a growth rate signature? A number of papers from David Botstein's group culminated in a paper that showed that they could use a gene expression signature to predict instantaneous growth rate (PMID 19119411) and I wondered if a) this is possible from single-cell data, and b) whether in the slower growing cells, they see markers of aging, whether these two signatures might impact the ability to detect eQTLs, and if they are detected, whether they could in some way be accounted for to improve detection.

We thank the reviewer for this comment and suggested analyses. We are not sure whether one can see gene expression signatures of aging in yeast scRNA-seq data. We believe that such analyses are beyond the scope of this work, but we will make the data available so that anyone interested can try them.

AIL vs. F2 segregants:

I'm curious if the authors have given thought to the trade-offs of developing advanced intercross lines for scRNA-Seq eQTL analysis. My impression is that AIL provides better mapping resolution, but at the expense of having to generate the lines. It might be useful to see some discussion on that.

We thank the reviewer for their comment. We will include some discussion of the trade-offs of different experimental designs in our revision.

10x vs SPLit-Seq

10x is a well established, but fairly expensive approach for scRNA-Seq - I wondered how the cost of the 10x approach compares to the previously used approach of genotyping segregants and performing bulk RNA-Seq, and how those costs would change if one used SPLiT-Seq (see PMID 38282330).

We will provide some ballpark estimates of the costs, and we will discuss the trade-offs of different scRNA-seq technologies in our revision

eLife assessment

This manuscript describes the mapping of natural DNA sequence variants that affect gene expression and its noise, as well as cell cycle timing, using as input single-cell RNA-sequencing of progeny from crosses between wild yeast strains. The method represents an important advance in the study of natural genetic variation. The findings, especially given the follow-up validation of the phenotypic impact of a mapped locus of major effect, provide convincing support for the rigor and utility of the method.

Reviewer #1 (Public Review):

Summary:

The authors demonstrate that it is possible to carry out eQTL experiments for the model eukaryote S. cerevisiae, in "one pot" preparations, by using single-cell sequencing technologies to simultaneously genotype and measure expression. This is a very appealing approach for investigators studying genetic variation in single-celled and other microbial systems, and will likely inspire similar approaches in non-microbial systems where comparable cell mixtures of genetically heterogeneous individuals could be achieved.

Strengths:

While eQTL experiments have been done for nearly two decades (the corresponding author's lab are pioneers in this field), this single-cell approach creates the possibility for new insights about cell biology that would be extremely challenging to infer using bulk sequencing approaches. The major motivating application shown here is to discover cell occupancy QTL, i.e. loci where genetic variation contributes to differences in the relative occupancy of different cell cycle stages. The authors dissect and validate one such cell cycle occupancy QTL, involving the gene GPA1, a G-protein subunit that plays a role in regulating the mating response MAPK pathway. They show that variation at GPA1 is associated with proportional differences in the fraction of cells in the G1 stage of the cell cycle. Furthermore, they show that this bias is associated with differences in mating efficiency.

Weaknesses:

While the experimental validation of the role of GPA1 variation is well done, the novel cell cycle occupancy QTL aspect of the study is somewhat underexploited. The cell occupancy QTLs that are mentioned all involve loci that the authors have identified in prior studies that involved the same yeast crosses used here. It would be interesting to know what new insights, besides the "usual suspects", the analysis reveals. For example, in Cross B there is another large effect cell occupancy QTL on Chr XI that affects the G1/S stage. What candidate genes and alleles are at this locus? And since cell cycle stages are not biologically independent (a delay in G1, could have a knock-on effect on the frequency of cells with that genotype in G1/S), it would seem important to consider the set of QTLs in concert.

Reviewer #2 (Public Review):

Boocock and colleagues present an approach whereby eQTL analysis can be carried out by scRNA-Seq alone, in a one-pot-shot experiment, due to genotypes being able to be inferred from SNPs identified in RNA-Seq reads. This approach obviates the need to isolate individual spores, genotype them separately by low-coverage sequencing, and then perform RNA-Seq on each spore separately. This is a substantial advance and opens up the possibility to straightforwardly identify eQTLs over many conditions in a cost-efficient manner. Overall, I found the paper to be well-written and well-motivated, and have no issues with either the methodological/analytical approach (though eQTL analysis is not my expertise), or with the manuscript's conclusions.

I do have several questions/comments.

393 segregant experiment:<br /> For the experiment with the 393 previously genotyped segregants, did the authors examine whether averaging the expression by genotype for single cells gave expression profiles similar to the bulk RNA-Seq data generated from those genotypes? Also, is it possible (and maybe not, due to the asynchronous nature of the cell culture) to use the expression data to aid in genotyping for those cells whose genotypes are ambiguous? I presume it might be if one has a sufficient number of cells for each genotype, though, for the subsequent one-pot experiments, this is a moot point.

Figure 1B:<br /> Is UMAP necessary to observe an ellipse/circle - I wouldn't be surprised if a simple PCA would have sufficed, and given the current discussion about whether UMAP is ever appropriate for interpreting scRNA-Seq (or ancestry) data, it seems the PCA would be a preferable approach. I would expect that the periodic elements are contained in 2 of the first 3 principal components. Also, it would be nice if there were a supplementary figure similar to Figure 4 of Macosko et al (PMID 26000488) to indeed show the cell cycle dependent expression.

Aging, growth rate, and bet-hedging:<br /> The mention of bet-hedging reminded me of Levy et al (PMID 22589700), where they saw that Tsl1 expression changed as cells aged and that this impacted a cell's ability to survive heat stress. This bet-hedging strategy meant that the older, slower-growing cells were more likely to survive, so I wondered a couple of things. It is possible from single-cell data to identify either an aging, or a growth rate signature? A number of papers from David Botstein's group culminated in a paper that showed that they could use a gene expression signature to predict instantaneous growth rate (PMID 19119411) and I wondered if a) this is possible from single-cell data, and b) whether in the slower growing cells, they see markers of aging, whether these two signatures might impact the ability to detect eQTLs, and if they are detected, whether they could in some way be accounted for to improve detection.

AIL vs. F2 segregants:<br /> I'm curious if the authors have given thought to the trade-offs of developing advanced intercross lines for scRNA-Seq eQTL analysis. My impression is that AIL provides better mapping resolution, but at the expense of having to generate the lines. It might be useful to see some discussion on that.

10x vs SPLit-Seq<br /> 10x is a well established, but fairly expensive approach for scRNA-Seq - I wondered how the cost of the 10x approach compares to the previously used approach of genotyping segregants and performing bulk RNA-Seq, and how those costs would change if one used SPLiT-Seq (see PMID 38282330).

Author Response

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

We would like to thank the reviewers for their insightful comments and recommendations. We have extensively revised the manuscript in response to the valuable feedback. We believe the results is a more rigorous and thoughtful analysis of the data. Furthermore, our interpretation and discussion of the findings is more focused and highlights the importance of the circuit and its role in the response to stress. Thank you for helping to improve the presented science.

Key changes made in response to the reviewers comments include:

• Revision of statistical analyses for nearly all figures, with the addition of a new table of summary statistics to include F and/or t values alongside p-values.

• Addition of statistical analyses for all fiber photometry data.

• Examination of data for possible sex dependent effects.

• Clarification of breeding strategies and genotype differences, with added details to methods to improve clarity.

• Addressing concerns about the specificity of virus injections and the spread, with additional details added to methods.

• Modification of terminology related to goal-directed behavior based on reviewer feedback, including removal of the term from the manuscript.

• Clarification and additional data on the use of photostimulation and its effects, including efforts to inactivate neurons for further insight, despite technical challenges.

• Correction of grammatical errors throughout the manuscript.

Reviewer 1:

Despite the manuscript being generally well-written and easy to follow, there are several grammatical errors throughout that need to be addressed.

Thank you for highlighting this issue. Grammatical errors have been fixed in the revised version of the manuscript.

Only p values are given in the text to support statistical differences. This is not sufficient. F and/or t values should be given as well.

In response to this critique and similar comments from Reviewer 2, we re-evaluated our approach to statistical analyses and extensively revised analyses for nearly all figures. We also added a new table of summary statistics (Supplemental Table 1) containing the type of analysis, statistic, comparison, multiple comparisons, and p value(s). For Figures 4C-E, 5C, 6C-E, 7H-I, and 8H we analyzed these data using two-way repeated measures (RM) ANOVA that examined the main effect of time (either number of sessions or stimulation period) in the same animal and compared that to the main effect of genotype of the animal (Cre+ vs Cre-), and if there was an interaction. For Supplemental Figure 7A we also conducted a two-way RM ANOVA with time as a factor and activity state (number of port activations in active vs inactive nose port) as the other in Cre+ mice. For Figures 5D-E we conducted a two-way mixed model ANOVA that accounted and corrected for missing data. In figures that only compared two groups of data (Figures 5F-L, 6F, 8C-D, 8I, and Supp 6F-G) we used two-tailed t-test for the analysis. If our question and/or hypothesis required us to conduct multiple comparisons between or within treatments, we conducted Bonferroni’s multiple comparisons test for post hoc analysis (we note which groups we compared in Supplemental Table 1). For figures that did or did not show a change in calcium activity (Figure 3G, 3I-K, 7B, 7D-E, 8E-F), we compared waveform confidence intervals (Jean-Richard-Dit-Bressel, Clifford, McNally, 2020). The time windows we used as comparison are noted in Supplemental Table 1, and if the comparisons were significant at 95%, 99%, and 99.9% thresholds.

None of prior comparisons in prior analyses that were significant were found to have fallen below thresh holds for significance. Of those found to be not significantly different, only one change was noted. In Figure 6E there was now a significant baseline difference between Cre+ and Cre- mice with Cre- mice taking longer to first engage the port compared to Cre+ mice (p=0.045). Although the more rigorous approach the statistical analyses did not change our interpretations we feel the enhanced the paper and thank the reviewer for pushing this improvement.

Moreover, the fibre photometry data does not appear to have any statistical analyses reported - only confidence intervals represented in the figures without any mention of whether the null hypothesis that the elevations in activity observed are different from the baseline.

This is particularly important where there is ambiguity, such as in Figure 3K, where the spontaneous activity of the animal appears to correlate with a spike in activity but the text mentions that there is no such difference. Without statistics, this is difficult to judge.

Thank you for highlighting this critical point and providing an opportunity to strengthen our manuscript. We added statistical analyses of all fiber photometry data using a recently described approach based on waveform confidence intervals (Jean-Richard-Dit-Bressel, Clifford, McNally, 2020). In the statistical summary (Supplemental Table 1) we note the time window that we used for comparison in each analysis and if the comparisons were significant at 95%, 99%, and 99.9% thresholds. Thank you from highlighting this and helping make the manuscript stronger.

With respect to Figure 3K, we are not certain we understood the spike in activity the reviewer referred to. Figure 3J and K include both velocity data (gold) and Ca2+ dependent signal (blue). We used episodes of velocity that were comparable to the avoidance respond during the ambush test and no significant differences in the Ca2+ signal when gating around changes in velocity in the absence of stressor (Supplemental Table1). This is in contrast to the significant change in Ca2+ signal following a mock predator ambush (Figure 3J). We interpret these data together to indicate that locomotion does not correlate with an increase in calcium activity in SuMVGLUT2+::POA neurons, but that coping to a stressor does. This conclusion is further examined in supplemental Figure 5, including examining cross-correlation to test for temporally offset relationship between velocity and Ca2+ signal in SUMVGLUT2+::POA neurons.

The use of photostimulation only is unfortunate, it would have been really nice to see some inactivation of these neurons as well. This is because of the well-documented issues with being able to determine whether photostimulation is occurring in a physiological manner, and therefore makes certain data difficult to interpret. For instance, with regards to the 'active coping' behaviours - is this really the correct characterisation of what's going on? I wonder if the mice simply had developed immobile responding as a coping strategy but when they experience stimulation of these neurons that they find aversive, immobility is not sufficient to deal with the summative effects of the aversion from the swimming task as well as from the neuronal activation? An inactivation study would be more convincing.

We agree with the point of the reviewer, experiments demonstrating necessity of SUMVGLUT2+::POA neurons would have added to the story here. We carried out multiple experiments aimed at addressing questions about necessity of SuMVGLUT2+::POA neurons in stress coping behaviors, specifically the forced swim assay. Efforts included employing chemogenetic, optogenetic, and tetanus toxin-based methods. We observed no effects on locomotor activity or stress coping. These experiments are both technically difficult and challenging to interpret. Interpretation of negative results, as we obtained, is particularly difficult because of potential technical confounds. Selective targeting of SuMVGLUT2+::POA neurons for inhibition requires a process requiring three viral injections and two recombination steps, increasing variability and reducing the number of neurons impacted. Alternatively, photoinhibition targeting SuMVGLUT2+::POA cells can be done using Retro-AAV injected into POA and a fiber implant over SuM. We tried both approaches. Data obtained were difficult to interpret because of questions about adequate coverage of SuMVGLUT2+::POA population by virally expressed constructs and/or light spread arose. The challenge of adequate coverage to effectively prevent output from the targeted population is further confounded by challenges inherent in neural inhibition, specifically determining if the inhibition created at the cellular level is adequate to block output in the context of excitatory inputs or if neurons must be first engaged in a particular manner for inhibition to be effective. Baseline neural activity, release probability, and post-synaptic effects could all be relevant, which photo-inhibition will potentially not resolve. So, while the trend is to always show “necessary and sufficient” effects, we’ve tried nearly everything, and we simply cannot conclude much from our mixed results. There are also wellestablished problems with existing photo-inhibition methods, which while people use them and tout them, are often ignored. We have a lot of expertise in photo-inhibition optogenetics, and indeed have used it with some success, developed new methods, yet in this particular case we are unable to draw conclusions related to inhibition. People have experienced similar challenges in locus coeruleus neurons, which have very low basal activity, and inhibition with chemogenetics is very hard, as well as with optogenetic pump-based approaches, because the neurons fire robust rebound APs. We have spent almost 2.5 years trying to get this to work in this circuit because reviews have been insistent on this result for the paper to be conclusive. Unfortunately, it simply isn’t possible in our view until we know more about the cell types involved. This is all in spite of experience using the approach in many other publications.

We also employed less selective approaches, such as injecting AAV-DIO-tetanus toxin light chain (Tettox) constructs directly into SuM VGLUT2-Cre mice but found off target effects impacting animal wellbeing and impeding behavioral testing due viral spread to surrounding areas.

While we are disappointed for being unable to directly address questions about necessity of SuMVGLUT2+::POA neurons in active coping with experimental data, we were unable to obtain results allowing for clear interpretation across numerous other domains the reviewers requested. We also feel strongly that until we have a clear picture of the molecular cell type architecture in the SuM, and Cre-drivers to target subsets of neurons, this question will be difficult to resolve for any group. We are working now on RNAseq and related spatial transcriptomics efforts in the SuM and examining additional behavioral paradigm to resolve these issues, so stay tuned for future publications.

Accordingly, we avoid making statements relating to necessity in the manuscript. In spite of having several lines of physiological data with strong robust correlations behavior related to the SuMVGLUT2+::POA circuit.

Nose poke is only nominally instrumental as it cannot be shown to have a unique relationship with the outcome that is independent of the stimuli-outcome relationships (in the same way that a lever press can, for example). Moreover, there is nothing here to show that the behaviours are goal-directed.

Thank you for highlighting this point. Regarding goal-direct terminology, we removed this terminology from the manuscript. Since the mice perform highly selective (active vs inactive) port activation robustly across multiple days of training the behavior likely transitions to habitual behavior. We only tested the valuation of stimuli termination of the final day of training with time limited progressive ratio test. With respect to lever press versus active port activation, we are unclear how using a lever in this context would offer a different interpretation. Lever pressing may be more sensitive to changes in valuation when compared to nose poke port activation (Atalayer and Rowland 2008); however, in this study the focus of the operant behavior is separating innate behaviors for learned action–outcome instrumental learned behaviors for threat response (LeDoux and Daw 2018). The robust highly selective activation of the active port illustrated in Figure 6 fits as an action–outcome instrumental behavior wherein mice learn to engage the active but not inactive port to terminate photostimulation. The first activation of the port occurs through exploration of the arena but as demonstrated by the number of active port activations and the decline in time of the first active port engagement, mice expressing ChR2eYFP learn to engage the port to terminate the stimulation. To aid in illustrating this point we have added Supplemental Figure 7 showing active and inactive port activations for both Cre+ and Cre- mice. This adds clarity to high rate of selective port activation driven my stimulation of SUMVGLUT2+::POA neurons compared to controls. The elimination of goal directed and providing additional data narrows and supports one of the key points of the operant experiment.

With regards to Figure 1: This is a nice figure, but I wonder if some quantification of the pathways and their density might be helpful, perhaps by measuring the intensity of fluorescence in image J (as these are processes, not cell bodies that can be counted)? Mind you, they all look pretty dense so perhaps this is not necessary! However, because the authors are looking at projections in so-called 'stress-engaged regions', the amygdala seems conspicuous by its absence. Did the authors look in the amygdala and find no projections? If so it seems that this would be worth noting.

This is an interesting question but has proven to be a very technically challenging question. We consulted with several leaders who routinely use complimentary viral tracing methods in the field. We were unable to devise a method to provide a satisfactorily meaningful quantitative (as opposed to qualitative) approach to compare SUMVGLUT2+::POA to SuMVGLUT2+ projections. A few limitations are present that hinder a meaningful quantitative approach. One limitation was the need for different viral strategies to label the two populations. Labeling SuMVGLUT2+::POA neurons requires using VGLUT2-Flp mice with two injections into the POA and one into SuM. Two recombinase steps were required, reducing efficiency of overlap. This combination of viral injections, particularly the injections of RetroAAVs in the POA, can induce significant quantitative variability due to tropism, efficacy, and variability of retro-viral methods, and viral infection generally. These issues are often totally ignored in similar studies across the “neural circuit” landscape, but it doesn’t make them less relevant here.

Although people do this in the field, and show quantification, we actually believe that it can be a quite misleading read-out of functionally relevant circuitry, given that neurotransmitter release ultimately is amplified by receptors post-synaptically, and many examples of robust behavioral effects have been observed with low fiber tracing complimentary methods (McCall, Siuda et al. 2017). In contrast, the broader SuMVGLUT2+ population was labeled using a single injection into the SuM. This means there like more efficient expression of the fluorophore. Additionally, in areas that contain terminals and passing fibers understanding and interpreting fluorescent signal is challenging. Together, these factors limit a meaningful quantitative comparison and make an interpretation difficult to make. In this context, we focused on a conservative qualitative presentation to demonstrate two central points. That 1) SuMVGLUT2+::POA neurons are subset of SuMVGLUT2+ neurons that project to specific areas and that exclude dentate gyrus, and they 2) arborize extensively to multiple areas which have be linked to threat responses. We agree that there is much to be learned about how different populations in SuM connect to targets in different regions of the brain and to continue to examine this question with different techniques. A meaningful quantitative study comparing projections is technically complex and, we feel, beyond our ability for this study.

Also, for the reasons above we do not believe that quantification provides exceptional clarity with respect to the putative function of the circuit, glutamate released, or other cotransmitters given known amplification at the post-synaptic side of the circuit.

With regard to the amygdala, other studies on SuM projections have found efferent projections to amygdala (Ottersen, 1980; Vertes, 1992). In our study we were unable to definitively determine projections from SuMVGLUT2+::POA neurons to amygdala, which if present are not particularly dense. For this reason we were conservative and do not comment on this particular structure.

I would suggest removing the term goal-directed from the manuscript and just focusing on the active vs. passive distinction.

We removed the use of goal-directed. Thank you for helping us clarify our terminology.

The effect observed in Figure 7I is interesting, and I'm wondering if a rebound effect is the most likely explanation for this. Did the authors inhibit the VGAT neurons in this region at any other times and observe a similar rebound? If such a rebound was not observed it would suggest that it is something specific about this task that is producing the behaviour. I would like it if the authors could comment on this.

We agree that results showing the change in coping strategy (passive to active) in forced swim after but not during stimulation of SuMVGAT+ neurons is quite interesting (Figure 7I). This experiment activated SuMVGAT+ neurons during a section of the forced swim assay and mice showed a robust shift to mobility after the stimulation of SuMVGAT+ neurons stopped. We did not carry out inhibition of SuMVGAT+ neurons in this manuscript. As the reviewer suggested, strong inhibition of local SuM neurons, including SUMVGLUT2+::POA neurons, could lead to rebound activity that may shift coping behaviors in confusing ways. We agree this is an interesting idea but do not have data to support the hypothesis further at this time.

Reviewer 2

(1) These are very difficult, small brain regions to hit, and it is commendable to take on the circuit under investigation here. However, there is no evidence throughout the manuscript that the authors are reliably hitting the targets and the spread is comparable across experiments, groups, etc., decreasing the significance of the current findings. There are no hit/virus spread maps presented for any data, and the representative images are cropped to avoid showing the brain regions lateral and dorsal to the target regions. In images where you can see the adjacent regions, there appears expression of cell bodies (such as Supp 6B), suggesting a lack of SuM specificity to the injections.

We agree with the reviewer that the areas studied are small and technically challenging to hit. This was one of driving motivations for using multiple tools in tandem to restrict the area targeted for stimulation. Approaches included using a retrograde AAVs to express ChR2eFYP in SUMVGLUT2+::POA neurons; thereby, restricting expression to VGLUT2+ neurons that project to the POA. Targeting was further limited by placement of the optic fiber over cell bodies on SuM. Thus, only neurons that are VGLUT2+, project to the POA, and were close enough to the fiber were active by photostimulation. Regrettably, we were not able to compile images from mice where the fiber was misplaced leading to loss of behavioral effects. We would have liked to provide that here to address this comment. Unfortunately, generating heat maps for injections is not possible for anatomic studies that use unlabeled recombinase as part of an intersectional approach. Also determining the point of injection of a retroAAV can be difficult to accurately determine its location because neurons remote to injection site and their processes are labeled.

Experiments described in Supplemental Figure 6B on VGAT neurons in SuM were designed and interpreted to support the point that SUMVGLUT2+::POA neurons are a distinct population that does not overlap with GABAergic neurons. For this point it is important that we targeted SuM, but highly confined targeting is not needed to support the central interpretation of the data. We do see labeling in SuM in VGAT-Cre mice but photo stimulation of SuMVGAT+ neurons does not generate the behavioral changes seen with activation of SUMVGLUT2+::POA neurons. As the reviewer points out, SuM is small target and viral injection is likely to spread beyond the anatomic boundaries to other VGAT+ neurons in the region, which are not the focus here. The activation would be restricted by the spread of light from the fiber over SuM (estimated to be about a 200um sphere in all directions). We did not further examine projections or localization of VGAT+ neurons in this study but focused on the differential behavioral effects of SUMVGLUT2+::POA neurons.

(2) In addition, the whole brain tracing is very valuable, but there is very little quantification of the tracing. As the tracing is the first several figures and supp figure and the basis for the interpretation of the behavior results, it is important to understand things including how robust the POA projection is compared to the collateral regions, etc. Just a rep image for each of the first two figures is insufficient, especially given the above issue raised. The combination of validation of the restricted expression of viruses, rep images, and quantified tracing would add rigor that made the behavioral effects have more significance.

For example, in Fig 2, how can one be sure that the nature of the difference between the nonspecific anterograde glutamate neuron tracing and the Sum-POA glutamate neuron tracing is real when there is no quantification or validation of the hits and expression, nor any quantification showing the effects replicate across mice? It could be due to many factors, such as the spread up the tract of the injection in the nonspecific experiment resulting in the labeling of additional regions, etc.

Relatedly, in Supp 4, why isn’t C normalized to DAPI, which they show, or area? Similar for G what is the mcherry coverage/expression, and why isn’t Fos normalized to that?

Thank you for highlighting the importance of anatomy and the value of anatomy. Two points based on the anatomic studies are central to our interpretation of the experimental data. First, SUMVGLUT2+::POA are a distinct population within the SuM. We show this by demonstrating they are not GABAergic and that they do not project to dentate gyrus. Projections from SuM to dentate gyrus have been described in multiple studies (Boulland et al., 2009; Haglund et al., 1987; Hashimotodani et al., 2018; Vertes, 1992) and we demonstrate them here for SuMVGLUT2+ cells. Using an intersectional approach in VGLUT2-Flp mice we show SUMVGLUT2+::POA neurons do not project to dentate gyrus. We show cell bodies of SUMVGLUT2+::POA neurons located in SuM across multiple figures including clear brain images. Thus, SUMVGLUT2+::POA neurons are SuM neurons that do not project to dentate gyrus, are not GABAergic, send projections to a distinct subset of targets, most notably excluding dentate gyrus. Second, SUMVGLUT2+::POA neurons arborize sending projections to multiple regions. We show this using a combinatorial genetic and viral approach to restrict expression of eYFP to only neurons that are in SuM (based on viral injection), project to the POA (based on retrograde AAV injection in POA), and VGLUT2+ (VGLUT2-Flp mice). Thus, any eYFP labeled projection comes from SUMVGLUT2+::POA neurons. We further confirmed projections using retroAAV injection into areas identified using anterograde approaches (Supplemental Figure 2). As discussed above in replies to Reviewer 1, we feel limitations are present that preclude meaningful quantitative analysis. We thus opted for a conservative interpretation as outlined.

Prior studies have shown efferent projections from SuM to many areas, and projections to dentate gyrus have received substantial attention (Bouland et al., 2009; Haglund, Swanson, and Kohler, 1984; Hashimotodani et al., 2018; Soussi et al., 2010; Vertes, 1992; Pan and McNaugton, 2004). We saw many of the same projections from SuMVGLUT2+ neurons. We found no projections from SUMVGLUT2+::POA neurons to dentate gyrus (Figure 2). Our description of SuM projection to dentate gyrus is not new but finding a population of neurons in SuM that does not project to dentate gyrus but does project to other regions in hippocampus is new. This finding cannot be explained by spread of the virus in the tract or non-selective labeling.

(3) The authors state that they use male and female mice, but they do not describe the n’s for each experiment or address sex as a biological variable in the design here. As there are baseline sex differences in locomotion, stress responses, etc., these could easily factor into behavioral effects observed here.

Sex specific effects are possible; however, the studies presented here were not designed or powered to directly examine them. A point about experimental design that helps mitigate against strong sex dependent effect is that often the paradigm we used examined baseline (pre-stimulation) behavior, how behavior changed during stimulation, and how behavior returned (or not) to baseline after stimulation. Thus, we test changes in individual behaviors. Although we had limited statistical power, we conducted analyses to examine the effects of sex as variable in the experiments and found no differences among males and females.

(4) In a similar vein as the above, the authors appear to use mice of different genotypes (however the exact genotypes and breeding strategy are not described) for their circuit manipulation studies without first validating that baseline behavioral expression, habituation, stress responses are not different. Therefore, it is unclear how to interpret the behavioral effects of circuit manipulation. For example in 7H, what would the VGLUT2-Cre mouse with control virus look like over time? Time is a confound for these behaviors, as mice often habituate to the task, and this varies from genotype to genotype. In Fig 8H, it looks like there may be some baseline differences between genotypes- what is normal food consumption like in these mice compared to each other? Do Cre+ mice just locomote and/or eat less? This issue exists across the figures and is related to issues of statistics, potential genotype differences, and other experimental design issues as described, as well as the question about the possibility of a general locomotor difference (vs only stress-induced). In addition, the authors use a control virus for the control groups in VGAT-Cre manipulation studies but do not explain the reasoning for the difference in approach.

Thank you for highlighting the need for greater clarity about the breeding strategies used and for these related questions. We address the breeding strategy and then move to address the additional concerns raised. We have added details to the methods section to address this point. For VGLUT2-Cre mice we use litter mates controls from Cre/WT x WT/WT cross. The VGLUT2-Cre line (RRID:IMSR_JAX:028863) (Vong L , et al. 2011) used here been used in many other reports. We are not aware of any reports indicating a phenotype associated with the addition of the IRES-Cre to the Slc17a6 loci and there is no expected impact of expression of VGLUT2. Also, we see in many of the experiments here that the baseline (Figures 4, 5, and 7) behaviors are not different between the Cre+ and Cre- mice. For VGAT-Cre mice we used a different breeding strategy that allowed us to achieve greater control of the composition of litters and more efficient cohorts cohort. A Cre/Cre x WT/WT cross yielded all Cre/WT litters. The AAV injected, ChR2eYFP or eYFP, allowed us to balance the cohort.

Regarding Figure 7H, which shows time immobile on the second day of a swim test, data from the Cre- mice demonstrate the natural course of progression during the second day of the test. The control mice in the VGAT-Cre cohort (Figure 7I) have similar trend. The change in behavior during the stimulation period in the Cre+ mice is caused by the activation of SUMVGLUT2+::POA neurons. The behavioral shift largely, but not completely, returns to baseline when the photostimulation stops. We have no reason to believe a VGLUT2-Cre+ mouse injected with control AAV to express eYFP would be different from WT littermate injected with AVV expressing ChR2eYFP in a Cre dependent manner.

Turning to concerns related to 8H, which shows data from fasted mice quantify time spent interacting with food pellet immediately after presentation of a chow pellet, we found no significant difference between the control and Cre+ mice. We unaware of any evidence indicating that the two groups should have a different baseline since the Cre insertion is not expected to alter gene expression and we are unaware of reports of a phenotype relating to feeding and the presence of the transgene in this mouse line. Even if there were a small baseline shift this would not explain the large abrupt shift induced by the photostimulation. As noted above, we saw shifts in behavior abruptly induced by the initiation of photostimulation when compared to baseline in multiple experiments. This shift would not be explained by a hypothetical difference in the baseline behaviors of litter mates.

(5) The statistics used throughout are inappropriate. The authors use serial Mann-Whitney U tests without a description of data distributions within and across groups. Further, they do not use any overall F tests even though most of the data are presented with more than two bars on the same graph. Stats should be employed according to how the data are presented together on a graph. For example, stats for pre-stim, stim, and post-stim behavior X between Cre+ and Cre- groups should employ something like a two-way repeated measures ANOVA, with post-hoc comparisons following up on those effects and interactions. There are many instances in which one group changes over time or there could be overall main effects of genotype. Not only is serially using Mann-Whitney tests within the same panel misleading and statistically inaccurate, but it cherry-picks the comparisons to be made to avoid more complex results. It is difficult to comprehend the effects of the manipulations presented without more careful consideration of the appropriate options for statistical analysis.

We thank the reviewer for pointing this out and suggesting alterative analyses, we agree with the assessment on this topic. Therefore, we have extensively revised the statical approach to our data using the suggested approach. Reviewer 1 also made a similar comment, and we would like to point to our reply to reviewer 1’s second point in regard to what we changed and added to the new statistical analyses. Further, we have added a full table detailing the statical values for each figure to the paper.

Conceptual:

(6) What does the signal look like at the terminals in the POA? Any suggestion from the data that the projection to the POA is important?

This is an interesting question that we will pursue in future investigations into the roles of the POA. We used the projection to the POA from SuM to identify a subpopulation in SuM and we were surprised to find the extensive arborization of these neurons to many areas associated with threat responses. We focused on the cell bodies as “hubs” with many “spokes”. Extensive studies are needed to understand the roles of individual projections and their targets. There is also the hypothetical technical challenge of manipulating one projection without activating retrograde propagation of action potentials to the soma. At the current time we have no specific insights into the roles of the isolated projection to POA. Interpretation of experiments activating only “spoke” of the hub would be challenging. Simple terminal stimulation experiments are challenged by the need to separate POA projections from activation of passing fibers targeting more anterior structures of the accumbens and septum.

(7) Is this distinguishing active coping behavior without a locomotor phenotype? For example, Fig. 5I and other figure panels show a distance effect of stimulation (but see issues raised about the genotype of comparison groups). In addition, locomotor behavior is not included for many behaviors, so it is hard to completely buy the interpretation presented.

We agree with the reviewer and thank them for highlighting this fundamental challenge in studies examining active coping behaviors in rodents, which requires movement. Additionally, actively responding to threatening stressors would include increased locomotor activity. Separation of movement alone from active coping can be challenging. Because of these concerns we undertook experiments using diverse behavioral paradigms to examine the elicited behaviors and the recruitment of SuMVGLUT2+::POA neurons to stressors. We conducted experiments to directly examine behaviors evoked by photoactivation of SuMVGLUT2+::POA. In these experiments we observed a diversity of behaviors including increased locomotion and jumping but also treading/digging (Figure 4). These are behaviors elicited in mice by threatening and noxious stimuli. An Increase of running or only jumping could signify a specific locomotor effect, but this is not what was observed. Based on these behaviors, we expected to find evidence of increase movement in open field (Figure 5G-I) and light dark choice (Figure 5J-L) assays. For many of the assays, reporting distance traveled is not practical. An important set of experiments that argues against a generic increase in locomotion is the operant behavior experiments, which require the animal to engage in a learned behavior while receiving photostimulation of SuMVGLUT2+::POA neurons (Figure 6). This is particularly true for testing using a progressive ratio when the time of ongoing photostimulation is longer, yet animals actively and selectively engage the active port (Figure 6G-H). Further, we saw a shift in behavioral strategy induce by photoactivation in forced swim test (Figure 7H). Thus, activation of SUMVGLUT2+::POA neurons elicited a range of behaviors that included swimming, jumping, treading, and learned response, not just increased movement. Together these data strongly argue that SuMVGLUT2+::POA neurons do not only promote increased locomotor behavior. We interpret these data together with the data from fiber photometry studies to show SuMVGLUT2+::POA neurons are recruited during acute stressors, contribute to aversive affective component of stress, and promote active behaviors without constraining the behavioral pattern.

Regarding genotype, we address this in comments above as well but believe that clarifying the use of litter mates, the extensive use of the VGLUT2-Cre line by multiple groups, and experimental design allowing for comparison to baseline, stimulation evoked, and post stimulation behaviors within and across genotypes mitigate possible concerns relating to the genotype.

(8) What is the role of GABA neurons in the SuM and how does this relate to their function and interaction with glutamate neurons? In Supp 8, GABA neuron activation also modulates locomotion and in Fig 7 there is an effect on immobility, so this seems pretty important for the overall interpretation and should probably be mentioned in the abstract.

Thank you for noting these interesting findings. We added text to highlight these findings to the abstract. Possible roles of GABAergic neurons in SuM extend beyond the scope of the current study particularly since SuM neurons have been shown to release both GABA and glutamate (Li Y, Bao H, Luo Y, et al. 2020, Root DH, Zhang S, Barker DJ et al. 2018). GABAergic neurons regulate dentate gyrus (Ajibola MI, Wu JW, Abdulmajeed WI, Lien CC 2021), REM sleep (Billwiller F, Renouard L, Clement O, Fort P, Luppi PH 2017), and novelty processing Chen S, He L, Huang AJY, Boehringer R et al. 2020). The population of exclusively GABAergic vs dual neurotransmitter neurons in SuM requires further dissection to be understood. How they may relate to SUMVGLUT2+::POA neurons require further investigation.

Questions about figure presentation:

(9) In Fig 3, why are heat maps shown as a single animal for the first couple and a group average for the others?

Thank you for highlighting this point for further clarification. We modified the labels in the figure to help make clear which figures are from one animal across multiple trials and those that are from multiple animals. In the ambush assay each animal one had one trial, to avoid habituation to the mock predator. Accordingly, we do not have multiple trials for each animal in this test. In contrast, the dunk assay (10 trial/animal) and the shock (5 trials/animal) had multiple trials for each animal. We present data from a representative animal when there are multiple trials per animal and the aggerate data.

Why is the temporal resolution for J and K different even though the time scale shown is the same?

Thank you for noticing this error carried forward from a prior draft of the figure so we could correct it. We replaced the image in 3J with a more correctly scaled heatmap.

What is the evidence that these signal changes are not due to movement per se?

Thank you for the question. There are two points of evidence. First, all the 465 nm excitation (Ca2+ dependent) data was collected in interleaved fashion with 415 nm (isosbestic) excitation data. The isosbestic signal is derived from GCaMP emission but is independent of Ca2+ binding (Martianova E, Aronson S, Proulx CD. 2019). This approach, time-division multiplexing, can correct calcium-dependent for changes in signal most often due to mechanical change. The second piece of evidence is experimental. Using multiple cohorts of mice, we examined if the change in Ca2+ signal was correlated with movement. We used the threshold of velocity of movement seen following the ambush. We found no correlation between high velocity movements and Ca2+ signal (Figure 3K) including cross correlational analysis (Supplemental figure 5). Based on these points together we conclude the change in the Ca2+ signal in SUMVGLUT2+::POA neurons is not due to movement induced mechanical changes and we find no correlation to movement unless a stressor is present, i.e. mock predator ambush or forced swim. Further, the stressors evoke very different locomotor responses fleeing, jumping, or swimming.

(10) In Fig 4, the authors carefully code various behaviors in mice. While they pick a few and show them as bars, they do not show the distribution of behaviors in Cre- vs Cre+ mice before manipulation (to show they have similar behaviors) or how these behaviors shift categories in each group with stimulation. Which behaviors in each group are shifting to others across the stim and post-stim periods compared to pre-stim?

This is an important point. We selected behaviors to highlight in Figure4 C-E because these behaviors are exhibited in response to stress (De Boer & Koolhaas, 2003; van Erp et al., 1994). For the highlighted behaviors, jumping, treading/digging, grooming, we show baseline (pre photostimulation), stimulation, and post stimulation for Cre+ and Cre- mice with the values for each animal plotted. We show all nine behaviors as a heat map in Figure 4B. The panels show changes that may occur as a function of time and show changes induced by photostimulation.

The heatmaps demonstrate that photostimulation of SUMVGLUT2+::POA neurons causes a suppression of walking, grooming, and immobile behaviors with an increase in jumping, digging/treading, and rapid locomotion. After stimulation stops, there is an increase in grooming and time immobile. The control mice show a range of behaviors with no shifts noted with the onset or termination of photostimulation.

Of note, issues of statistics, genotype, and SABV are important here. For example, the hint that treading/digging may have a slightly different pre-stim basal expression, it seems important to first evaluate strain and sex differences before interpreting these data.

We examined the effects of sex as a biological variable in the experiments reported in the manuscript and found no differences among males and females in any of the experiments where we had enough animals in each sex (minimum of 5 mice) for meaningful comparisons. We did this by comparing means and SEM of males and females within each group (e.g. Cre+ males vs Cre+ female, Cre- males vs Cre- females) and then conducted a t-test to see if there was a difference. For figures that show time as a variable (e.g Figure 6C-E), we compared males and females with time x sex as main factors and compared them (including multiple comparisons if needed). We found no significant main effects or interactions between males and females. Because of this, and to maximize statistical power, we decided to move forward to keep males and females together in all the analyses presented in the manuscript. It is worth noting also that the core of the experimental design employed is a change in behavior caused by photostimulation. The mice are also the same strain with only difference being the modification to add an IRES and sequence for Cre behind the coding sequence of the Slc17A6 (VGLUT2) gene.

(11) Why do the authors use 10 Hz stimulation primarily? is this a physiologically relevant stim frequency? They show that they get effects with 1 Hz, which can be quite different in terms of plasticity compared to 10 Hz.

Thank you for the raising this important question. Because tests like open field and forced swim are subject to habituation and cannot be run multiple times per animal a test frequency was needed to use across multiple experiments for consistency. The frequency of 10Hz was selected because it falls within the rate of reported firing rates for SuM neurons (Farrel et al., 2021; Pedersen et al., 2017) and based on the robust but sub maximal effects seen in the real-time place preference assays. Identification of the native firing rates during stress response would be ideal but gathering this data for the identified population remains a dauting task.

(12) In Fig 5A-F, it is unclear whether locomotion differences are playing a role. Entrances (which are low for both groups) are shown but distance traveled or velocity are not.

In B, there is no color in the lower left panel. where are these mice spending their time? How is the entirety of the upper left panel brighter than the lower left? If the heat map is based on time distribution during the session, there should be more color in between blue and red in the lower left when you start to lose the red hot spots in the upper left, for example. That is, the mice have to be somewhere in apparatus. If the heat map is based on distance, it would seem the Cre- mice move less during the stim.

We appreciate the opportunity to address this question, and the attention to detail the reviewer applied to our paper. In the real time place preference test (RTPP) stimulation would only be provided while the animal was on the stimulation side. Mice quickly leave the stimulation side of the arena, as seen in the supplemental video, particularly at the higher frequencies. Thus, the time stimulation is applied is quite low. The mice often retreat to a corner from entering the stimulation side during trials using higher frequency stimulation. Changing locomotor activity along could drive changes in the number entrances but we did not find this. In regard to the heat map, the color scale is dynamically set for each of the paired examples that are pulled from a single trial. To maximize the visibility between the paired examples the color scale does not transfer between the trials. As a result, in the example for 10 Hz the mouse spent a larger amount of time in the in the area corresponding to the lower right corner of the image and the maximum value of the color scale is assigned to that region. As seen in the supplemental video, mice often retreated to the corner of the non-stimulation side after entering the stimulation side. The control animal did not spend a concentrated amount of time in any one region, thus there is a lack of warmer colors. In contrast the baseline condition both Cre+ and Cre- mice spent time in areas disturbed on both sides of arena, as expected. As a result, the maximum value in the heat map is lower and more area are coded in warmer colors allowing for easier visual comparison between the pair. Using the scale for the 10 Hz pair across all leads to mostly dark images. We considered ways to optimized visualization across and within pairs and focused on the within pair comparison for visualization.

(13) By starting with 1 hz, are the experimenters inducing LTD in the circuit? what would happen if you stop stimming after the first epoch? Would the behavioral effect continue? What does the heat map for the 1 hz stim look like?

Relatedly, it is a lot of consistent stimulation over time and you likely would get glutamate depletion without a break in the stim for that long.

Thank you for the opportunity to add clarity around this point regarding the trials in RTPP testing. Importantly, the trials were not carried out in order of increasing frequency of stimulation, as plotted. Rather, the order of trials was, to the extent possible with the number of mice, counterbalanced across the five conditions. Thus, possible contribution of effects of one trial on the next were minimized by altering the order of the trials.

We have added a heat map for the 1 Hz condition to figure 5B.

For experiments on RTPP the average stimulation time at 10Hz was less than 10 seconds per event. As a result, the data are unlikely to be affected by possible depletion of synaptic glutamate. For experiments using sustained stimulation (open field or light dark choice assays) we have no clear data to address if this might be a factor where 10Hz stimulation was applied for the entire trial.

(14) In Fig 6, the authors show that the Cre- mice just don't do the task, so it is unclear what the utility of the rest of the figure is (such as the PR part). Relatedly, the pause is dependent on the activation, so isn't C just the same as D? In G and H, why ids a subset of Cre+ mice shown?

Why not all mice, including Cre- mice?

Thank you for the opportunity to improve the clarity of this section. A central aspect of the experiments in Figure 6 is the aversiveness of SUMVGLUT2+::POA neuron photostimulation, as shown in Figure 5B-F. The aversion to photostimulation drives task performance in the negative reinforcer paradigm. The mice perform a task (active port activation) to terminate the negative reinforcer (photostimulation of SuMVGLUT2+::POA neurons). Accordingly, control mice are not expected to perform the task because SuMVGLUT2+::POA neurons are not activated and, thus the mice are not motivated to perform the task.

A central point we aim to covey in this figure is that while SuMVGLUT2+::POA neurons are being stimulated, mice perform the operant task. They selectively activated the active port (Supplemental Figure 7). As expected, control mice activate the active port at a low level in the process of exploring the arena. This diminishes on subsequent trials as mice habituate to the arena (Figure 6D). The data in Figures 6 C and D are related but can be divergent. Each pause in stimulation requires a port activation of a FR1 test but the number of port activations can exceed the pauses, which are 10 seconds long, if the animal continues to activate the port. Comparing data in Figures 6 C and D revels that mice generally activated the port two to three times for each pause earned with a trend towards greater efficiency on day 4 with more rewards and fewer activations.

The purpose of the progressive ratio test is to examine if photostimulation of SuMVGLUT2+::POA continues to drive behavior as the effort required to terminate the negative stimuli increases. As seen in Figures 6 G and H, the stimulation of SuMVGLUT2+::POA neurons remains highly motivating. In the 20-minute trial we did not find a break point even as the number of port activations required to pause the stimulation exceed 50. We do not show the Cre- mice is Figure 6G and H because they did not perform the task, as seen in Figure 6F. For technical reasons in early trials, we have fully timely time stamped data for rewards and port activations from a subset of the Cre+ mice. Of note, this contains both the highest and lowest performing mice from the entire data set.

Taken together, we interpret the results of the operant behavioral testing as demonstrating that SuMVGLUT2+::POA neuron activation is aversive, can drive performance of an operant tasks (as opposed to fixed escape behaviors), and is highly motivating.

(15) In Fig 7, what does the GCaMP signal look like if aligned to the onset of immobility? It looks like since the hindpaw swimming is short and seems to precede immobility, and the increase in the signal is ramping up at the onset of hindpaw swimming, it may be that the calcium signal is aligned with the onset of immobility.

What does it look like for swimming onset?

In I, what is the temporal resolution for the decrease in immobility? Does it start prior to the termination of the stim, or does it require some elapsed time after the termination, etc?

Thank for the opportunity to addresses these points and improve that clarity of our interpretation of the data. Regarding aligning the Ca2+ signal from fiber photometry recordings to swimming onset and offset, it is important to note that the swimming bouts are not the same length. As a result, in the time prior to alignment to offset of behaviors animals will have been swimming for different lengths of time. In Figure 7 C, we use the behavioral heat map to convey the behavioral average. Below we show the Ca2+ dependent signal aligned at the offset of hindpaw swim for an individual mouse (A) and for the total cohort (B). This alignment shows that the Ca2+ dependent signal declines corresponding to the termination of hindpaw swimming. Because these bouts last less than the total the widow shown, the data is largely included in Figure 7 C and D, which is aligned to onset. Due to the nuance of the difference is the alignment and the partial redundancy, we elected to include the requested alignment to swimming offset in the reply rather in primary figure.

Author response image 1.

Turning to the question regarding swimming onset, the animals started swimming immediately when placed in the water and maintained swimming and climbing behaviors until shifting behaviors as illustrated in Figure 7A and B. During this time the Ca2+-dependent signal was elevated but there is only one trial per animal. This question can perhaps be better addressed in the dunk assay presented in Figure 3C, F and G and Supplemental Figure 4 H and I. Here swimming started with each dunk and the Ca2+ signal increased.

Regarding the question for about figure 7I. We scored for entire periods (2 mins) in aggerate. We noted in videos of the behavior test that there was an abrupt decrease in immobility tightly corresponding to the end of stimulation. In a few animals this shift occurred approximately 15-20s before the end of stimulation. This may relate to the depletion of neurotransmitter as suggested by the reviewer.

Reviewer 3

Major points

(1) Results in Figure 1 suggested that SuM-Vglu2::POA projected not only POA but also to the diverse brain regions. We can think of two models which account for this. One is that homogeneous populations of neurons in SuM-Vglu2::POA have collaterals and innervated all the efferent targets shown in Figure 1. Another is to think of distinct subpopulations of neurons projecting subsets of efferent targets shown in Figure 1 as well as POA. It is suggested to address this by combining approaches taken in experiments for Figure 1 and Supplemental Figure 2.

Thank you for raising this interesting point. We have attempted combining retroAAV injections into multiple areas that receive projections from SUMVGLUT2+::POA neurons. However, we have found the results unsatisfactory for separating the two models proposed. Using eYFP and tdTomato expressing we saw some overlapping expressing in SuM. We are not able to conclude if this indicates separate populations or partial labeling of a homogenous populations. A third option seems possible as well. There could be a mix of neurons projecting to different combinations of downstream targets. This seems particularly difficult to address using fluorophores. We are preparing to apply additional methodologies to this question, but it extends beyond the scope of this manuscript.

(2) Since the authors drew a hypothetical model in which the diverse brain regions mediate the effect of SuM-Vglu2::POA activation in behavioral alterations at least in part, examination of the concurrent activation of those brain regions upon photoactivation of SuM-Vglu2::POA. This must help the readers to understand which neural circuits act upon the induction of active coping behavior under stress.

Thank you for raising this important point. We agree that activating glutamatergic neurons should lead to activation of post synaptic neurons in the target regions. Delineating this in vivo is less straight forward. Doing so requires much greater knowledge of post synaptic partners of SUMVGLUT2+::POA neurons. There are a number of issues that would need to be accounted for. Undertaking two color photo stimulation plus fiber photometry is possible but not a technical triviality. Further, it is possible that we would measure Ca2+ signals in neurons that have no relevant input or that local circuits in a region may shape the signal. We would also lack temporal resolution to identify mono-postsynaptic vs polysynaptic connections. Thus, we would struggle to know if the change in signal was due to the excitatory input from SuM or from a second region. At present, we remain unclear on how to pursue this question experimentally in a manner that is likely to generate clearly interpretable results.

(3) In Figure 4, "active coping behaviors" must be called "behaviors relevant to the active behaviors" or "active coping-like behaviors", since those behaviors were in the absence of stressors to cope with.

Thank you for the suggestion on how to clarify our terminology. We have adopted the active coping-like term.

(4) For the Dunk test, it is suggested to describe the results and methods more in detail, since the readers would be new to it. In particular, the mice could change their behavior between dunks under this test, although they still showed immobility across trials as in Supplemental Figure 4I. Since neural activity during the test was summarized across trials as in Figure 3, it is critical to examine whether the behavior changes according to time.

Thank you for identifying this opportunity to improve our manuscript. We have expanded and added a detailed description of the dunk test in the methods section.

As for Supplemental Figure 4I, we apologize for the confusion because the purpose of this figure is to show that mice remained mobile for the entire 30-second dunk trial. This did not appreciably change over the 10 trials. We have revised this figure to plot both immobile and mobile time to achieve greater clarity on this point.

Minor points

Typos

In Figure 1, please add a serotype of AAVs to make it compatible with other figures and their legends.

In the main text and Figure 2K, the authors used MHb/LHb and mHb/lHb in a mixed fashion. Please make them unified.

In the figure legend of Figure 6, change "SuMVGLUT2+::POA neurons drive" to "SuMVGLUT2+::POA neurons " in the title.

In line 86, please change "Retro-AAV2-Nuc-flox(mCherry)-eGFP" to "AAV5-Nuc-flox(mCherry)eGFP".

In line 80, please change "Positive controls" to "As positive controls, ".

Thank you for taking the time and making the effort to identify and call these out. We have corrected them.

eLife assessment

This important manuscript investigates the role of a subpopulation of glutamatergic neurons in the suprammamillary nucleus that projects to the pre-optic hypothalamus area in active coping but not locomotor activity. They provide solid evidence from experiments using fibre photometry or photostimulation during threatening tasks that these neurons allow animals to produce flexible behaviours in response to stress. This work will be of interest to behavioural and systems neuroscientists.

Joint Public Review:

Summary:

This important manuscript investigates a subpopulation of glutamatergic neurons in the suprammamillary nucleus that projects to the pre-optic hypothalamus area (SuM-VGLUT2+::POA). First, they define the neural circuitry of these neurons, which contact many stress/threat-associated brain regions. Then they employ fibre photometry to measure the activity of these neurons during various threatening tasks and find the responses correlate well with threat stimuli. Finally, they stimulate these neurons and find multiple lines of evidence that mice find this aversive and will act to avoid receiving this stimulation. In sum, they provide solid evidence that this neuronal population represents a new node in stress response circuitry that allows the animal to produce flexible behaviours in response to stress, which will be of interest to neuroscientists across several sub-fields.

Strengths:

Overall this is a solid manuscript tackling an important question. Coping with stress by an animal in danger is essential for survival. This manuscript identifies a novel population of neurons in the murine supramamillary nucleus (SuM) projecting to the pre-optic hypothalamus area among other regions that is involved in this important process. The evidence to support the conclusions is solid.

Specific strengths:

• The topic is novel.

• The manuscript follows a logical structure and neatly moves through the central story. Several potential alternate interpretations are well-controlled for.

• The manuscript employs an array of different tasks to provide converging evidence for their conclusions.

• The authors provide excellent evidence of the specificity of the function of this neuronal population, both from anatomical studies and from behavioural studies (e.g. demonstrating that activity of gabaergic neurons in the same region does not correlate with behaviours in the same way).

• The study is well-powered (sample sizes are good) and the effects are convincing.

Weaknesses:

* Not all of the reviewer comments were addressed in the manuscript itself, although this was acknowledged in the author's responses to reviewers. One key example is as follows:

* The authors did not entirely address comments related to rigor but they at least acknowledged it. For example, in multiple places they argue that WT, purchased mice are probably not different in baseline behavior compared to Vgltu2-IRES-Cre because it is unlikely that adding the IRES-Cre will change behavior. However, they do not acknowledge that transgenic lines are not from the exact same genetic background and generation number, and there is ample evidence in the literature that transgenic mice on a B6J background can differ in basal phenotypes from one another and B6J. In one place they show some basal behavior, at least in heat map form though not quantified. Had the authors decided to apply this more pervasively, it would have made the story even more compelling in terms of a stress/threat-induced phenotype.

Comments on revised version from the Reviewing Editor:

The authors have done a thorough job of answering the reviewer queries, and a good job of explaining why they have not answered a particular point. Indeed, there is so much additional information in response to the reviewers that I hope readers of the manuscript will read the reviews and responses as well! I think they add a lot.

Reviewer #1 (Public Review):

The manuscript investigates the role of the membrane-deforming cytoskeletal regulator protein Abba in cortical development and its potential implications for microcephaly. It is a valuable contribution to the understanding of Abba's role in cortical development. The strengths and weaknesses identified in the manuscript are outlined below:

Clinical Relevance:

The authors identified a patient with microcephaly and a patient with an intellectual disability harboring a mutation in the Abba variant (R671W) adding a clinically relevant dimension to the study.

Mechanistic Insights:

The study offers valuable mechanistic insights into the development of microcephaly by elucidating the role of Abba in radial glial cell proliferation, radial fiber organization, and the migration of neuronal progenitors. The identification of Abba's involvement in the cleavage furrow during cell division, along with its interaction with Nedd9 and positive influence on RhoA activity, adds depth to our understanding of the molecular processes governing cortical development. Though the reported results establish the novel interaction between Abba and Nedd9, the authors have not addressed whether the mutant protein loses this interaction and whether that results in the observed effects.

In Vivo Validation:

The overexpression of mutant Abba protein (R671W) resulting in phenotypic similarities to Abba knockdown effects supports the significance of Abba in cortical development.

Reviewer #2 (Public Review):

Summary:

Carabalona and colleagues investigated the role of the membrane-deforming cytoskeletal regulator protein Abba (MTSS1L/MTSS2) in cortical development to better understand the mechanisms of abnormal neural stem cell mitosis. The authors used short hairpin RNA targeting Abba20 with a fluorescent reporter coupled with in-utero electroporation of E14 mice to show changes to neural progenitors. They performed flow cytometry for in-depth cell cycle analysis of Abba-shRNA impact on neural progenitors and determined an accumulation in the S phase. Using culture rat glioma cells and live imaging from cortical organotypic slides from mice in utero electroporated with Abba-shRNA, the authors found Abba played a prominent role in cytokinesis. They then used a yeast-two-hybrid screen to identify three high-confidence interactors: Beta-Trcp2, Nedd9, and Otx2. They used immunoprecipitation experiments from E18 cortical tissue coupled with C6 cells to show Abba's requirement for Nedd9 localization to the cleavage furrow/cytokinetic bridge. The authors performed a shRNA knockdown of Nedd9 by in-utero electroporation of E14 mice and observed similar results as with the Abba-shRNA. They tested a human variant of Abba using in-utero electroporation of cDNA and found disorganized radial glial fibers and misplaced, multipolar neurons, but lacked the impact of cell division seen in the shRNA-Abba model.

Strengths:

A fundamental question in biology about the mechanics of neural stem cell division.

Directly connecting effects in Abba protein to downstream regulation of RhoA via Nedd9.

Incorporation of human mutation in ABBA gene.

Use of novel technologies in neurodevelopment and imaging.

Weaknesses:

Unexplored components of the pathway (such as what neurogenic populations are impacted by Abba mutation) and unleveraged aspects of their data (such as the live imaging) limit the scope of their findings and leave significant questions about the effect of ABBA on radial glia development.

(1) The claim of disorganized radial glial fibers lacks quantifications.<br /> On page 11, the authors claim that knockdown of Abba leads to changes in radial glial morphology observed with vimentin staining. Here they claim misoriented apical processes, detached end feet, and decreased number of RGP cells in the VZ. However, they do not provide quantification of process orientation to better support their first claim. Measurements of radial glia fiber morphology (directionality, length) and angle of division would be metrics that can be applied to data. Some of these analyses could be done in their time-lapse microscopy images, such as to quantify the number of cell divisions during their period of analysis (though that is short-15 hours).

(2) It is unclear where the effect is:

-In RG or neuroblasts? Is it in cell cleavage that results in the accumulation of cells at VZ (as sometimes indicated by their data like in Figure 2A or 4D)? Interrogation of cell death (such as by cleaved caspase 3) would also help. Given their time-lapse, can they identify what is happening to the RG fiber? The authors describe a change in "migration" but do not show evidence for this for either progenitor or neuroblast populations. Given they have nice time-lapse imaging data, could they visualize progenitor versus young neuron migration? Analysis of neuroblasts (such as with doublecortin expression in the tissue) would also help understand any issues in migration (of neurons v stem cells).

-At cleavage furrow? In abscission? There is high-resolution data that highlights the cleavage furrow as the location of interest (Figure 3A), however, there is also data (Figure 3B) to suggest Abba is expressed elsewhere as well and there is an overall soma decrease. More detail of the localization of Abba during the division process would be helpful for example, could cleavage furrow proteins, such as Aurora B, co-localization (and potentially co-IP) help delineate subpopulations of Abba protein? Furthermore, the FRET imaging is a unique way to connect their mutation with function - could they measure/quantify differences at furrow compared to the rest of soma to further corroborate that the Abba-associated RhoA effect was furrow-enriched?

-The data highlights nicely that a furrow doesn't clearly form when ABBA expression and subsequent RhoA activity are decreased (in Figure 3 or 5A). Does this lead to cells that can't divide because of poor abscission, especially since "rounding" still occurs? Or abnormal progenitors (with loss of fiber or inability to support neuroblast migration)? Or abnormal progression of progenitors to neuroblasts?

(3) Limited to a singular time point of mouse cortical development

On page 13, the authors outline the results of their Y2H screen with the identification of three high-confidence interactors. Notably, they used an E10.5-E12.5 mouse brain embryo library rather than one that includes E14, the age of their in-utero electroporation mice. Many of the authors' claims focus on in-utero electroporation of shRNA-Abba of E14 mice that are then evaluated at E16-18. Justification for the focus on this age range should be included to support that their findings can then be applied to all mouse corticogenesis.

(4) Detail of the effect of the human variant of the ABBA mutation in mice is lacking.

Their identification of the R671W mutation is interesting and the IUE model warrants more characterization, as they did with their original KD experiments.

-Could they show that Abba protein levels are decreased (in either cell lines or electroporated tissue)?

-While time-lapse morphology might not have been performed, more analysis on cell division phenotype (such as plane of division and radial glia morphology) would be helpful.

Reviewer #1 (Public Review):

Summary:

This work proposes a new method, DyNetCP, for inferring dynamic functional connectivity between neurons from spike data. DyNetCP is based on a neural network model with a two-stage model architecture of static and dynamic functional connectivity.

This work evaluates the accuracy of the synaptic connectivity inference and shows that DyNetCP can infer the excitatory synaptic connectivity more accurately than a state-of-the-art model (GLMCC) by analyzing the simulated spike trains. Furthermore, it is shown that the inference results obtained by DyNetCP from large-scale in-vivo recordings are similar to the results obtained by the existing methods (jitter-corrected CCG and JPSTH). Finally, this work investigates the dynamic connectivity in the primary visual area VISp and in the visual areas using DyNetCP.

Strengths:

The strength of the paper is that it proposes a method to extract the dynamics of functional connectivity from spike trains of multiple neurons. The method is potentially useful for analyzing parallel spike trains in general, as there are only a few methods (e.g. Aertsen et al., J. Neurophysiol., 1989, Shimazaki et al., PLoS Comput Biol 2012) that infer the dynamic connectivity from spikes. Furthermore, the approach of DyNetCP is different from the existing methods: while the proposed method is based on the neural network, the previous methods are based on either the descriptive statistics (JSPH) or the Ising model.

Weaknesses:

Although the paper proposes a new method, DyNetCP, for inferring the dynamic functional connectivity, its strengths are neither clear nor directly demonstrated in this paper. That is, insufficient analyses are performed to support the usefulness of DyNetCP.

First, this paper attempts to show the superiority of DyNetCP by comparing the performance of synaptic connectivity inference with GLMCC (Figure 2). However, the improvement in the synaptic connectivity inference does not seem to be convincing. While this paper compares the performance of DyNetCP with a state-of-the-art method (GLMCC), there are several problems with the comparison. For example:

(1) This paper focused only on excitatory connections (i.e., ignoring inhibitory neurons).

(2) This paper does not compare with existing neural network-based methods (e.g., CoNNECT: Endo et al. Sci. Rep. 2021; Deep learning: Donner et al. bioRxiv, 2024).

(3) Only a population of neurons generated from the Hodgkin-Huxley model was evaluated.

Thus, the results in this paper are not sufficient to conclude the superiority of DyNetCP in the estimation of synaptic connections. In addition, this paper compares the proposed method with the standard statistical methods Jitter-corrected CCG (Figure 3) and JPSTH (Figure 4). Unfortunately, these results do not show the superiority of the proposed method. It only shows that the results obtained by the proposed method are consistent with those obtained by the existing methods (CCG or JPSTH). This paper also compares the proposed method with standard statistical methods, such as jitter-corrected CCG (Figure 3) and JPSTH (Figure 4). It only shows that the results obtained by the proposed method are consistent with those obtained by the existing methods (CCG or JPSTH), which does not show the superiority of the proposed method.

In summary, although DyNetCP has the potential to infer synaptic connections more accurately than existing methods, the paper does not provide sufficient analysis to make this claim. It is also unclear whether the proposed method is superior to the existing methods for estimating functional connectivity, such as jitter-corrected CCG and JPSTH. Thus, the strength of DyNetCP is unclear.

eLife assessment

This study presents a useful method for using multi-electrode spike recordings to track the time-varying functional connectivity between neurons. However, the evidence is incomplete: a demonstration of the utility of the method relative to conventional approaches is needed. If such a demonstration is made, this could be a tool for gaining insight into circuit structure.

Reviewer #2 (Public Review):

Summary:

Here the authors describe a model for tracking time-varying coupling between neurons from multi-electrode spike recordings. Their approach extends a GLM with static coupling between neurons to include dynamic weights, learned by a long-short-term-memory (LSTM) model. Each connection has a corresponding LSTM embedding and is read out by a multi-layer perceptron to predict the time-varying weight.

Strengths:

This is an interesting approach to an open problem in neural data analysis. I think, in general, the method would be interesting to computational neuroscientists.

Weaknesses:

It is somewhat difficult to interpret what the model is doing. I think it would be worthwhile to add some additional results that make it more clear what types of patterns are being described and how.

Major Issues:

Simulation for dynamic connectivity. It certainly seems doable to simulate a recurrent spiking network whose weights change over time, and I think this would be a worthwhile validation for this DyNetCP model. In particular, I think it would be valuable to understand how much the model overfits, and how accurately it can track known changes in coupling strength. If the only goal is "smoothing" time-varying CCGs, there are much easier statistical methods to do this (c.f. McKenzie et al. Neuron, 2021. Ren, Wei, Ghanbari, Stevenson. J Neurosci, 2022), and simulations could be useful to illustrate what the model adds beyond smoothing.

Stimulus vs noise correlations. For studying correlations between neurons in sensory systems that are strongly driven by stimuli, it's common to use shuffling over trials to distinguish between stimulus correlations and "noise" correlations or putative synaptic connections. This would be a valuable comparison for Figure 5 to show if these are dynamic stimulus correlations or noise correlations. I would also suggest just plotting the CCGs calculated with a moving window to better illustrate how (and if) the dynamic weights differ from the data.

eLife assessment

In this valuable study, the authors use Staphylococcus aureus to understand how organic acids inhibit bacterial growth. They provide convincing evidence that acetic acid specifically inhibits the activity of the Ddl enzyme and that S. aureus maintains a high intracellular D-ala concentration to circumvent acetate-mediated growth inhibition. This work will be of interest to researchers studying bacteria and antimicrobials.

Reviewer #2 (Public Review):

Summary:

In this manuscript, using Staphylococcus aureus as a model organism, Panda et al. aim to understand how organic acids inhibit bacterial growth. Through careful characterization and interdisciplinary collaboration, the authors present valuable evidence that acetic acid specifically inhibits the activity of Ddl enzyme that converts 2 D-alanine amino acids into D-ala-D-ala dipeptide, which is then used to generate the stem pentapeptide of peptidoglycan (PG) precursors in the cytoplasm. Thus, a high concentration of acetic acid weakens the cell wall by limiting PG-crosslinking (which requires a D-ala portion). However, S. aureus maintains a high intracellular D-ala concentration to circumvent acetate-mediated growth inhibition.

Strengths:

The authors utilized a well-established transposon mutant library to screen for mutants that struggle to grow in the presence of acetic acid. This screen allowed authors to identify that a strain lacking intact alr1, which encodes for alanine racemase (converts L-ala to D-ala), is unable to grow well in the presence of acetic acid. This phenotype is rescued by the addition of external D-ala. Next, the authors rule out the contribution of other pathways that could lead to the production of D-ala in the cell. Finally, by analyzing D-ala and D-ala-D-ala concentrations, as well as muropeptide intermediates accumulation in different mutants, the authors pinpoint Ddl as the specific target of acetic acid. In fact, the synthetic overexpression of ddl alone overcomes the toxic effects of acetic acid. Using genetics, biochemistry, and structural biology, the authors show that Ddl activity is specifically inhibited by acetic acid and likely by other biologically relevant organic acids. Interestingly, this mechanism is different from what has been reported for other organisms such as Escherichia coli (where methionine synthesis is affected). It remains to be seen if this mechanism is conserved in other organisms that are more closely related to S. aureus, such as Clostridioides difficile and Enterococcus faecalis.

Weaknesses:

Although the authors have conclusively shown that Ddl is the target of acetic acid, it appears that the acetic acid concentration used in the experiments may not truly reflect the concentration range S. aureus would experience in its environment. Moreover, Ddl is only significantly inhibited at a very high acetate concentration (>400 mM). Thus, additional experiments showing growth phenotypes at lower organic acid concentrations may be beneficial. Another aspect not adequately discussed is the presence of D-ala in the gut environment, which may be protective against acetate toxicity based on the model provided.

Reviewer #1 (Public Review):

Summary:

The manuscript entitled "Staphylococcus aureus counters organic acid anion-mediated inhibition of peptidoglycan cross-linking through robust alanine racemase activity" by Panda, S et al. reports an extensive biochemical analysis of the result from a Tn screen that identified alr1 as being required for acetic acid tolerance. In the end, they demonstrate that reduced D-Ala pools in the ∆alr1 mutant lead to a drastic reduction in D-Ala-D-Ala dipeptide. They show that this is due to the ability of organic acid anions to limit the D-Ala-D-Ala ligase enzyme Ddl. They demonstrate that:

(1) Acetate exposure in the ∆alr1 results in reduced D-Ala-D-Ala dipeptide, but not the monomers.

(2) Acetate can bind to purified Ddl in vitro.

(3) This binding results in reduced enzyme activity.

(4) Other organic acid anions such as lactate, proprionate, and itaconitate can also inhibit Ddl.

The experiments are clearly described and logically laid out. I have only a few minor comments to add.

Strengths:

The most significant strength is the exceptional experimental data that supports the authors' hypotheses.

Weaknesses:

Only minor weaknesses were identified by this reviewer.

(1) Which allele is alr1, the one upstream of MazEF or the one in the Lysine biosynthetic operon?

(2) Figure 3B. Where does the C3N2 species come from in the WT and why is it absent in the mutants? It is about 25% of the total dipeptide pool.

(3) Figure 3D could perhaps be omitted. I understand that the authors attained statistical significance in the fitness defect, but biologically this difference is very minor. One would have to look at the isotopomer distribution in the Dat overexpressing strain to make sure that increased flux actually occurred since there are other means of affecting activity (e.g. allosteric modulators).

(4) In Figure 4A, why is the complete subunit UDP-NAM-AEKAA increasing in each strain upon acetate challenge if there was such a stark reduction in D-Ala-D-Ala, particularly in the ∆alr1 mutant? For that matter, why are the levels of UDP-NAM-AEKAA in the ∆alr1 mutant identical to that of WT with/out acetate?

(5) Figure 4B. Is there no significant difference between ddl and murF transcripts between WT and ∆alr1 under acetate stress? This comparison was not labeled if the tests were done.

(6) Although tricky, it is possible to measure intracellular acetate. It might be of interest to know where in the Ddl inhibition curve the cells actually are.

Reviewer #2 (Public Review):

In this manuscript, the authors analyze the shapes of cerebral cortices from several primate species, including subgroups of young and old humans, to characterize commonalities in patterns of gyrification, cortical thickness, and cortical surface area. The authors state that the observed scaling law shares properties with fractals, where shape properties are similar across several spatial scales. One way the authors assess this is to perform a "cortical melting" operation that they have devised on surface models obtained from several primate species. The authors also explore differences in shape properties between brains of young (~20 year old) and old (~80) humans. A challenge the authors acknowledge struggling with in reviewing the manuscript is merging "complex mathematical concepts and a perplexing biological phenomenon." This reviewer remains a bit skeptical about whether the complexity of the mathematical concepts being drawn from are justified by the advances made in our ability to infer new things about the shape of the cerebral cortex.

(1) The series of operations to coarse-grain the cortex illustrated in Figure 1 produces image segmentations that do not resemble real brains. The process to assign voxels in downsampled images to cortex and white matter is biased towards the former, as only 4 corners of a given voxel are needed to intersect the original pial surface, but all 8 corners are needed to be assigned a white matter voxel. The reason for introducing this bias (and to the extent that it is present in the authors' implementation) is not provided. The authors provide an intuitive explanation of why thickness relates to folding characteristics, but ultimately an issue for this reviewer is, e.g., for the right-most panel in Figure 2b, the cortex consists of several 4.9-sided voxels and thus a >2 cm thick cortex. A structure with these morphological properties is not consistent with the anatomical organization of typical mammalian neocortex.

(2) For the comparison between 20-year-old and 80-year-old brains, a well-documented difference is that the older age group possesses more cerebral spinal fluid due to tissue atrophy, and the distances between the walls of gyri becomes greater. This difference is born out in the left column of Figure 4b. It seems this additional spacing between gyri in 80 year olds requires more extensive down-sampling (larger scale values in Figure 4a) to achieve a similar shape parameter K as for the 20 year olds. The authors assert that K provides a more sensitive measure (associated with a large effect size) than currently used ones for distinguishing brains of young vs. old people. A more explicit, or elaborate, interpretation of the numbers produced in this manuscript, in terms of brain shape, might make this analysis more appealing to researchers in the aging field.

(3) In the Discussion, it is stated that self-similarity, operating on all length scales, should be used as a test for existing and future models of gyrification mechanisms. Given the lack of association between the abstract mathematical parameters described in this study and explicit properties of brain tissue and its constituents, it is difficult to envision how the coarse-graining operation can be used to guide development of "models of cortical gyrification."

(4) There are several who advocate for analyzing cortical mid-thickness surfaces, as the pial surface over-represents gyral tips compared to the bottoms of sulci in the surface area. The authors indicate that analyses of mid-thickness representations will be taken on in future work, but this seems to be a relevant control for accepting the conclusions of this manuscript.

Reviewer #3 (Public Review):

Summary: Through a rigorous methodology, the authors demonstrated that within 11 different primates, the shape of the brain followed a universal scaling law with fractal properties. They enhanced the universality of this result by showing the concordance of their results with a previous study investigating 70 mammalian brains, and the discordance of their results with other folded objects that are not brains. They incidentally illustrated potential applications of this fractal property of the brain by observing a scale-dependant effect of aging on the human brain.

Strengths:<br /> - New hierarchical way of expressing cortical shapes at different scales derived from previous report through implementation of a coarse-graining procedure<br /> - Investigation of 11 primate brains and contextualisation with other mammals based on prior literature<br /> - Proposition of tool to analyse cortical morphology requiring no fine tuning and computationally achievable<br /> - Positioning of results in comparison to previous works reinforcing the validity of the observation.<br /> - Illustration of scale-dependance of effects of brain aging in the human.

Weaknesses:<br /> - The notion of cortical shape, while being central to the article, is not really defined, leaving some interpretation to the reader<br /> - The organization of the manuscript is unconventional, leading to mixed contents in different sections (sections mixing introduction and method, methods and results, results and discussion...). As a result, the reader discovers the content of the article along the way, it is not obvious at what stages the methods are introduced, and the results are sometimes presented and argued in the same section, hindering objectivity.<br /> To improve the document, I would suggest a modification and restructuring of the article such that: 1) by the end of the introduction the reader understands clearly what question is addressed and the value it holds for the community, 2) by the end of the methods the reader understands clearly all the tools that will be used to answer that question (not just the new method), 3) by the end of the results the reader holds the objective results obtained by applying these tools on the available data (without subjective interpretations and justifications), and 4) by the end of the discussion the reader understands the interpretation and contextualisation of the study, and clearly grasps the potential of the method depicted for the better understanding of brain folding mechanisms and properties.

eLife assessment

In this important paper, the authors propose a computational model for understanding how the dynamics of neural representations may lead to specific patterns of errors as observed in working memory tasks. The paper provides solid evidence showing how a two-area model of sensory-memory interactions can account for the error patterns reported in orientation estimation tasks with delays. By integrating ideas from efficient coding and attractor networks, the resulting theoretical framework is appealing, and nicely captures some basic patterns of behavior data and the distributed nature of memory representation as reported in prior neurophysiological studies. The paper can be strengthened if (i) further analyses are conducted to deepen our understanding of the circuit mechanisms underlying the behavior effects; (ii) the necessity of the two-area network model is better justified; (iii) the nuanced aspects of the behavior that are not captured by the current model are discussed in more detail.

Reviewer #1 (Public Review):

Summary:

Working memory is imperfect - memories accrue errors over time and are biased towards certain identities. For example, previous work has shown memory for orientation is more accurate near the cardinal directions (i.e., variance in responses is smaller for horizontal and vertical stimuli) while being biased towards diagonal orientations (i.e., there is a repulsive bias away from horizontal and vertical stimuli). The magnitude of errors and biases increase the longer an item is held in working memory and when more items are held in working memory (i.e., working memory load is higher). Previous work has argued that biases and errors could be explained by increased perceptual acuity at cardinal directions. However, these models are constrained to sensory perception and do not explain how biases and errors increase over time in memory. The current manuscript builds on this work to show how a two-layer neural network could integrate errors and biases over a memory delay. In brief, the model includes a 'sensory' layer with heterogenous connections that lead to the repulsive bias and decreased error in the cardinal directions. This layer is then reciprocally connected with a classic ring attractor layer. Through their reciprocal interactions, the biases in the sensory layer are constantly integrated into the representation in memory. In this way, the model captures the distribution of biases and errors for different orientations that have been seen in behavior and their increasing magnitude with time. The authors compare the two-layer network to a simpler one-network model, showing that the one-model network is harder to tune and shows an attractive bias for memories that have lower error (which is incompatible with empirical results).

Strengths:

The manuscript provides a nice review of the dynamics of items in working memory, showing how errors and biases differ across stimulus space. The two-layer neural network model is able to capture the behavioral effects as well as relate to neurophysiological observations that memory representations are distributed across the sensory cortex and prefrontal cortex.

The authors use multiple approaches to understand how the network produces the observed results. For example, analyzing the dynamics of memories in the low-dimensional representational space of the networks provides the reader with an intuition for the observed effects.

As a point of comparison with the two-layer network, the authors construct a heterogenous one-layer network (analogous to a single memory network with embedded biases). They argue that such a network is incapable of capturing the observed behavioral effects but could potentially explain biases and noise levels in other sensory domains where attractive biases have lower errors (e.g., color).

The authors show how changes in the strength of Hebbian learning of excitatory and inhibitory synapses can change network behavior. This argues for relatively stronger learning in inhibitory synapses, an interesting prediction.

The manuscript is well-written. In particular, the figures are well done and nicely schematize the model and the results.

Weaknesses:

Despite its strengths, the manuscript does have some weaknesses.

First, as far as we can tell, behavioral data is only presented in schematic form. This means some of the nuances of the effects are lost. It also means that the model is not directly capturing behavioral effects. Therefore, while providing insight into the general phenomenon, the current manuscript may be missing some important aspects of the data.

Relatedly, the models are not directly fit to behavioral data. This makes it hard for the authors to exclude the possibility that there is a single network model that could capture the behavioral effects. In other words, it is hard to support the authors' conclusion that "....these evolving errors...require network interaction between two distinct modules." (from the abstract, but similar comments are made throughout the manuscript). Such a strong claim needs stronger evidence than what is presented. Fitting to behavioral data could allow the authors to explore the full parameter space for both the one-layer and two-layer network architectures.

In addition, directly comparing the ability of different model architectures to fit behavioral data would allow for quantitative comparison between models. Such quantitative comparisons are currently missing from the manuscript.

To help broaden the impact of the paper, it would be helpful if the authors provided insight into how the observed behavioral biases and/or network structures influence cognition. For example, previous work has argued that biases may counteract noise, leading to decreased variance at certain locations. Is there a similar normative explanation for why the brain would have repulsive biases away from commonly occurring stimuli? Are they simply a consequence of improved memory accuracy? Why isn't this seen for all stimulus domains?

Previous work has found both diffusive noise and biases increase with the number of items in working memory. It isn't clear how the current model would capture these effects. The authors do note this limitation in the Discussion, but it remains unclear how the current model can be generalized to a multi-item case.

The role of the ring attractor memory network isn't completely clear. There is noise added in this stage, but how is this different from the noise added at the sensory stage? Shouldn't these be additive? Is the noise necessary? Similarly, it isn't clear whether the memory network is necessary - can it be replaced by autapses (self-connections) in the sensory network to stabilize its representation? In short, it would be helpful for the authors to provide an intuition for why the addition of the memory network facilitates the repulsive bias.

Overall:

Overall, the manuscript was successful in building a model that captured the biases and noise observed in working memory. This work complements previous studies that have viewed these effects through the lens of optimal coding, extending these models to explain the effects of time in memory. In addition, the two-layer network architecture extends previous work with similar architectures, adding further support to the distributed nature of working memory representations.

Reviewer #2 (Public Review):

In this manuscript, Yang et al. present a modeling framework to understand the pattern of response biases and variance observed in delayed-response orientation estimation tasks. They combine a series of modeling approaches to show that coupled sensory-memory networks are in a better position than single-area models to support experimentally observed delay-dependent response bias and variance in cardinal compared to oblique orientations. These errors can emerge from a population-code approach that implements efficient coding and Bayesian inference principles and is coupled to a memory module that introduces random maintenance errors. A biological implementation of such operation is found when coupling two neural network modules, a sensory module with connectivity inhomogeneities that reflect environment priors, and a memory module with strong homogeneous connectivity that sustains continuous ring attractor function. Comparison with single-network solutions that combine both connectivity inhomogeneities and memory attractors shows that two-area models can more easily reproduce the patterns of errors observed experimentally. This, the authors take as evidence that a sensory-memory network is necessary, but I am not convinced about the evidence in support of this "necessity" condition. A more in-depth understanding of the mechanisms operating in these models would be necessary to make this point clear.

Strengths:

The model provides an integration of two modeling approaches to the computational bases of behavioral biases: one based on Bayesian and efficient coding principles, and one based on attractor dynamics. These two perspectives are not usually integrated consistently in existing studies, which this manuscript beautifully achieves. This is a conceptual advancement, especially because it brings together the perceptual and memory components of common laboratory tasks.

The proposed two-area model provides a biologically plausible implementation of efficient coding and Bayesian inference principles, which interact seamlessly with a memory buffer to produce a complex pattern of delay-dependent response errors. No previous model had achieved this.

Weaknesses:

The correspondence between the various computational models is not fully disclosed. It is not easy to see this correspondence because the network function is illustrated with different representations for different models and the correspondence between components of the various models is not specified. For instance, Figure 1 shows that a specific pattern of noise is required in the low-dimensional attractor model, but in the next model in Figure 2, the memory noise is uniform for all stimuli. How do these two models integrate? What element in the population-code model of Figure 2 plays the role of the inhomogeneous noise of Figure 1? Also, the Bayesian model of Figure 2 is illustrated with population responses for different stimuli and delays, while the attractor models of Figures 3 and 4 are illustrated with neuronal tuning curves but not population activity. In addition, error variance in the Bayesian model appears to be already higher for oblique orientations in the first iteration whereas it is only first shown one second into the delay for the attractor model in Figure 4. It is thus unclear whether variance inhomogeneities appear already at the perceptual stage in the attractor model, as it does in the population-code model. Of course, correspondences do not need to be perfect, but the reader does not know right now how far the correspondence between these models goes.

The manuscript does not identify the mechanistic origin in the model of Figure 4 of the specific noise pattern that is required for appropriate network function (with higher noise variance at oblique orientations). This mechanism appears critical, so it would be important to know what it is and how it can be regulated. In particular, it would be interesting to know if the specific choice of Poisson noise in Equation (3) is important. Tuning curves in Figure 4 indicate that population activity for oblique stimuli will have higher rates than for cardinal stimuli and thus induce a larger variance of injected noise in oblique orientations, based on this Poisson-noise assumption. If this explanation holds, one wonders if network inhomogeneities could be included (for instance in neural excitability) to induce higher firing rates in the cardinal/oblique orientations so as to change noise inhomogeneities independently of the bias and thus control more closely the specific pattern of errors observed, possibly within a single memory network.

The main conclusion of the manuscript, that the observed patterns of errors "require network interaction between two distinct modules" is not convincingly shown. The analyses show that there is a quantitative but not a qualitative difference between the dynamics of the single memory area compared to the sensory-memory two-area network, for specific implementations of these models (Figure 7 - Figure Supplement 1). There is no principled reasoning that demonstrates that the required patterns of response errors cannot be obtained from a different memory model on its own. Also, since the necessity of the two-area configuration is highlighted as the main conclusion of the manuscript, it is inconvenient that the figure that carefully compares these conditions is in the Supplementary Material.

The proposed model has stronger feedback than feedforward connections between the sensory and memory modules. This is not a common assumption when thinking about hierarchical processing in the brain, and it is not discussed in the manuscript.

Reviewer #3 (Public Review):

Summary:

The present study proposes a neural circuit model consisting of coupled sensory and memory networks to explain the circuit mechanism of the cardinal effect in orientation perception which is characterized by the bias towards the oblique orientation and the largest variance at the oblique orientation.

Strengths:

The authors have done numerical simulations and preliminary analysis of the neural circuit model to show the model successfully reproduces the cardinal effect. And the paper is well-written overall. As far as I know, most of the studies on the cardinal effect are at the level of statistical models, and the current study provides one possibility of how neural circuit models reproduce such an effect.

Weaknesses:

There are no major weaknesses and flaws in the present study, although I suggest the author conduct further analysis to deepen our understanding of the circuit mechanism of the cardinal effects. Please find my recommendations for concrete comments.

eLife assessment

This important study reports human single-neuron recordings in subcortical structures while participants performed a tactile detection task around the perceptual threshold. The study and the analyses are well conducted and provide solid evidence that the thalamus and the subthalamic nucleus contain neurons whose activity correlates with the task, with stimulus presentation, and even with whether the stimulation is consciously detected or not. The study will be relevant for researchers interested in the role of subcortical structures in tactile perception and the neural correlates of consciousness.

Reviewer #1 (Public Review):

Summary:

A cortico-centric view is dominant in the study of the neural mechanisms of consciousness. This investigation represents the growing interest in understanding how subcortical regions are involved in conscious perception. To achieve this, the authors engaged in an ambitious and rare procedure in humans of directly recording from neurons in the subthalamic nucleus and thalamus. While participants were in surgery for the placement of deep brain stimulation devices for the treatment of essential tremor and Parkinson's disease, they were awakened and completed a perceptual-threshold tactile detection task. The authors identified individual neurons and analyzed single-unit activity corresponding with the task phases and tactile detection/perception. Among the neurons that were perception-responsive, the authors report changes in firing rate beginning ~150 milliseconds from the onset of the tactile stimulation. Curiously, the majority of the perception-responsive neurons had a higher firing rate for missed/not perceived trials. In summary, this investigation is a valuable addition to the growing literature on the role of subcortical regions in conscious perception.

Strengths:

The authors achieved the challenging task of recording human single-unit activity while participants performed a tactile perception task. The methods and statistics are clearly explained and rigorous, particularly for managing false positives and non-normal distributions. The results offer new detail at the level of individual neurons in the emerging recognition of the role of subcortical regions in conscious perception.

Weaknesses:

"Nonetheless, it remains unknown how the firing rate of subcortical neurons changes when a stimulus is consciously perceived." (lines 76-77) The authors could be more specific about what exactly single-unit recordings offer for interrogating the role of subcortical regions in conscious perception that is unique from alternative neural activity recordings (e.g., local field potential) or recordings that are used as proxies of neural activity (e.g., fMRI).

Related comment for the following excerpts:

"After a random delay ranging from 0.5 to 1 s, a "respond" cue was played, prompting participants to verbally report whether they felt a vibration or not. Therefore, none of the reported analyses are confounded by motor responses." (lines 97-99).

"These results show that subthalamic and thalamic neurons are modulated by stimulus onset, irrespective of whether it was reported or not, even though no immediate motor response was required." (lines 188-190).

"By imposing a delay between the end of the tactile stimulation window and the subjective report, we ensured that neuronal responses reflected stimulus detection and not mere motor responses." (lines 245-247).

It is a valuable feature of the paradigm that the reporting period was initiated hundreds of milliseconds after the stimulus presentation so that the neural responses should not represent "mere motor responses". However, verbal report of having perceived or not perceived a stimulus is a motor response and because the participants anticipate having to make these reports before the onset of the response period, there may be motor preparatory activity from the time of the perceived stimulus that is absent for the not perceived stimulus. The authors show sensitivity to this issue by identifying task-selective neurons and their discussion of the results that refer to the confound of post-perceptual processing. Still, direct treatment of this possible confound would help the rigor of the interpretation of the results.

"When analyzing tactile perception, we ensured that our results were not contaminated with spurious behavior (e.g. fluctuation of attention and arousal due to the surgical procedure)." (lines 118-117).

Confidence in the results would be improved if the authors clarified exactly what behaviors were considered as contaminating the results (e.g., eye closure, saccades, and bodily movements) and how they were determined.

The authors' discussion of the thalamic neurons could be more precise. The authors show that only certain areas of the thalamus were recorded (in or near the ventral lateral nucleus, according to Figure S3C). The ventral lateral nucleus has a unique relationship to tactile and motor systems, so do the authors hypothesize these same perception-selective neurons would be active in the same way for visual, auditory, olfactory, and taste perception? Moreover, the authors minimally interpret the location of the task, sensory, and perception-responsive neurons. Figure S3 suggests these neurons are overlapping. Did the authors expect this overlap and what does it mean for the functional organization of the ventral lateral nucleus and subthalamic nucleus in conscious perception?

"We note that, 6 out of 8 neurons had higher firing rates for missed trials than hit trials, although this proportion was not significant (binomial test: p = 0.145)." (lines 215-216).

It appears that in the three example neurons shown in Figure 4, 2 out of 3 (#001 and #068) show a change in firing rate predominantly for the missed stimulations. Meanwhile, #034 shows a clear hit response (although there is an early missed response - decreased firing rate - around 150 ms that is not statistically significant). This is a counterintuitive finding when compared to previous results from the thalamus (e.g., local field potentials and fMRI) that show the opposite response profile (i.e., missed/not perceived trials display no change or reduced response relative to hit/perceived trials). The discussion of the results should address this, including if these seemingly competing findings can be rectified.

The authors report 8 perception-responsive neurons, but there are only 5 recording sites highlighted (i.e., filled-in squares and circles) in Figures S3C and 4D. Was this an omission or were three neurons removed from the perception-responsive analysis?

Could the authors speak to the timing of the responses reported in Figure 4? The statistically significant intervals suggested both early (~160-200ms) to late responses (~300ms). Some have hypothesized that subcortical regions are early - ahead of cortical activation that may be linked with conscious perception. Do these results say anything about this temporal model for when subcortical regions are active in conscious perception?

Reviewer #2 (Public Review):

The authors have studied subpopulations of individual neurons recorded in the thalamus and subthalamic nucleus (STN) of awake humans performing a simple cognitive task. They have carefully designed their task structure to eliminate motor components that could confound their analyses in these subcortical structures, given that the data was recorded in patients with Parkinson's Disease (PD) and diagnosed with an Essential Tremor (ET). The recorded data represents a promising addition to the field. The analyses that the authors have applied can serve as a strong starting point for exploring the kinds of complex signals that can emerge within a single neuron's activity. Pereira et. al conclude that their results from single neurons indicate that task-related activity occurs, purportedly separate from previously identified sensory signals. These conclusions are a promising and novel perspective for how the field thinks about the emergence of decisions and sensory perception across the entire brain as a unit.

Despite the strength of the data that was obtained and the relevant nature of the conclusions that were drawn, there are certain limitations that must be taken into consideration:

(1) The authors make several claims that their findings are direct representations of consciousness identifiable in subcortical structures. The current context for consciousness does not sufficiently define how the consciousness is related to the perceptual task.

(2) The current work would benefit greatly from a description and clarification of what all the neurons that have been recorded are doing. The authors' criteria for selecting subpopulations with task-relevant activity are appropriate, but understanding the heterogeneity in a population of single neurons is important for broader considerations that are being studied within the field.

(3) The authors have omitted a proper set of controls for comparison against the active trials, for example, where a response was not necessary. Please explain why this choice was made and what implications are necessary to consider.

Reviewer #3 (Public Review):

Summary:

This important study relies on a rare dataset: intracranial recordings within the thalamus and the subthalamic nucleus in awake humans, while they were performing a tactile detection task. This procedure allowed the authors to identify a small but significant proportion of individual neurons, in both structures, whose activity correlated with the task (e.g. their firing rate changed following the audio cue signalling the start of a trial) and/or with the stimulus presentation (change in firing rate around 200 ms following tactile stimulation) and/or with participant's reported subjective perception of the stimulus (difference between hits and misses around 200 ms following tactile stimulation). Whereas most studies interested in the neural underpinnings of conscious perception focus on cortical areas, these results suggest that subcortical structures might also play a role in conscious perception, notably tactile detection.

Strengths:

There are two strongly valuable aspects in this study that make the evidence convincing and even compelling. First, these types of data are exceptional, the authors could have access to subcortical recordings in awake and behaving humans during surgery. Additionally, the methods are solid. The behavioral study meets the best standards of the domain, with a careful calibration of the stimulation levels (staircase) to maintain them around the detection threshold, and an additional selection of time intervals where the behavior was stable. The authors also checked that stimulus intensity was the same on average for hits and misses within these selected periods, which warrants that the effects of detection that are observed here are not confounded by stimulus intensity. The neural data analysis is also very sound and well-conducted. The statistical approach complies with current best practices, although I found that, in some instances, it was not entirely clear which type of permutations had been performed, and I would advocate for more clarity in these instances. Globally the figures are nice, clear, and well presented. I appreciated the fact that the precise anatomical location of the neurons was directly shown in each figure.

Weaknesses:

Some clarification is needed for interpreting Figure 3, top rows: in my understanding the black curve is already the result of a subtraction between stimulus present trials and catch trials, to remove potential drifts; if so, it does not make sense to compare it with the firing rate recorded for catch trials.

I also think that the article could benefit from a more thorough presentation of the data and that this could help refine the interpretation which seems to be a bit incomplete in the current version. There are 8 stimulus-responsive neurons and 8 perception-selective neurons, with only one showing both effects, resulting in a total of 15 individual neurons being in either category or 13 neurons if we exclude those in which the behavior is not good enough for the hit versus miss analysis (Figure S4A). In my opinion, it should be feasible to show the data for all of them (either in a main figure, or at least in supplementary), but in the present version, we get to see the data for only 3 neurons for each analysis. This very small selection includes the only neuron that shows both effects (neuron #001; which is also cue selective), but this is not highlighted in the text. It would be interesting to see both the stimulus-response data and the hit versus miss data for all 13 neurons as it could help develop the interpretation of exactly how these neurons might be involved in stimulus processing and conscious perception. This should give rise to distinct interpretations for the three possible categories. Neurons that are stimulus-responsive but not perception-selective should show the same response for both hits and misses and hence carry out indifferently conscious and unconscious responses. The fact that some neurons show the opposite pattern is particularly intriguing and might give rise to a very specific interpretation: if the neuron really doesn't tend to respond to the stimulus when hits and misses are put together, it might be a neuron that does not directly respond to the stimulus, but whose spontaneous fluctuations across trials affect how the stimulus is perceived when they occur in a specific time window after the stimulus. Finally, neuron #001 responds with what looks like a real burst of evoked activity to stimulation and also shows a difference between hits and misses, but intriguingly, the response is strongest for misses. In the discussion, the interesting interpretation in terms of a specific gating of information by subcortical structures seems to apply well to this last example, but not necessarily to the other categories.

Reviewer #3 (Public Review):

Summary:

Chang et al. investigated the mechanisms governing collagen fibrillogenesis, firstly demonstrating that cells within tail tendons are able to uptake exogenous collagen and use this to synthesize new collagen-1 fibrils. Using an endocytic inhibitor, the authors next showed that endocytosis was required for collagen fibrillogenesis and that this process occurs in a circadian rhythmic manner. Using knockdown and overexpression assays, it was then demonstrated that collagen fibril formation is controlled by vacuolar protein sorting 33b (VPS33b), and this VPS33b-dependent fibrillogenesis is mediated via Integrin alpha-11 (ITGA11). Finally, the authors demonstrated increased expression of VPS33b and ITGA11 at the gene level in fibroblasts from patients with idiopathic pulmonary fibrosis (IPF), and greater expression of these proteins in both lung samples from IPF patients and in chronic skin wounds, indicating that endocytic recycling is disrupted in fibrotic diseases.

Strengths:

The authors have performed a comprehensive functional analysis of the regulators of endocytic recycling of collagen, providing compelling evidence that VPS33b and ITGA11 are crucial regulators of this process.

Weaknesses:

Throughout the study, several different cell types have been used (immortalised tail tendon fibroblasts, NIHT3T cells, and HEK293T cells). In general, it is not clear which cells have been used for a particular experiment, and the rationale for using these different cell types is not explained. In addition, some experimental details are missing from the methods.

There is also a lack of functional studies in patient-derived IPF fibroblasts which means the link between endocytic recycling of collagen and the role of VPS33b and ITGA11 cannot be fully established.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors describe a mechanism, by which fluorescently-labelled Collagen type I is taken up by cells via endocytosis and then incorporated into newly synthesized fibers via an ITGA11 and VPS33B-dependent mechanism. The authors claim the existence of this collagen recycling mechanism and link it to fibrotic diseases such as IPF and chronic wounds.

Strengths:

The manuscript is well-written, and experimentally contains a broad variation of assays to support their conclusions. Also, the authors added data of IPF patient-derived fibroblasts, patient-derived lung samples, and patient-derived samples of chronic wounds that highlight a potential in vivo disease correlation of their findings.

The authors were also analyzing the membrane topology of VPS33B and could unravel a likely 'hairpin' like conformation in the ER membrane.

Weaknesses:

Experimental evidence is missing that supports the non-degradative endocytosis of the labeled collagen.

The authors show and mention in the text that the endocytosis inhibitor Dyngo®4a shows an effect on collagen secretion. It is not clear to me how specific this readout is if the inhibitor affects more than endocytosis. This issue was unfortunately not further discussed. The authors use commercial rat tail collagen, it is unclear to me which state the collagen is in when it's endocytosed. Is it fully assembled as collagen fiber or are those single heterotrimers or homotrimers?

The Cy-labeled collagen is clearly incorporated into new fibers, but I'm not sure whether the collagen is needed to be endocytosed to be incorporated into the fibers or if that is happening in the extracellular space mediated by the cells.

In general for the collagen blots, due to the lack of molecular weight markers, what chain/form of collagen type I are you showing here?

Besides the VPS33B siRNA transfected cells the authors also use CRISPR/Cas9-generated KO. The KO cells do not seem to be a clean system, as there is still a lot of mRNA produced. Were the clones sequenced to verify the KO on a genomic level? For the siRNA transfection, a control blot for efficiency would be great to estimate the effect size. To me it is not clear where the endocytosed collagen and VPS33B eventually meet in the cells and whether they interact. Or is ITGA11 required to mediate this process, in case VPS33B is not reaching the lumen?

The authors show an upregulation of ITGA11 and VPS33B in IPF patients-derived fibroblasts, which can be correlated to an increased level of ColI uptake, however, it is not clear whether this increased uptake in those cells is due to the elevated levels of VPS33B and/or ITGA11.

Reviewer #1 (Public Review):

Summary:

The authors describe that the endocytic pathway is crucial for ColI fibrillogenesis. ColI is endocytosed by fibroblasts, prior to exocytosis and formation of fibrils, which can include a mixture of endogenous/nascent ColI chains and exogenous ColI. ColI uptake and fibrillogenesis are regulated by circadian rhythm as described by the authors in 2020, thanks to the dependence of this pathway on circadian-clock-regulated protein VPS33B. Cells are capable of forming fibrils with recently endocytosed ColI when nascent chains are not available. Previously identified VPS33B is demonstrated not to have a role in endocytosis of ColI, but to play a role in fibril formation, which the authors demonstrate by showing the loss of fibril formation in VPS33B KO, and an excess of insoluble fibrils - along-side a decrease in soluble ColI secretion - in VPS33B overexpression conditions. A VPS33B binding protein VIPAS39 is also shown to be required for fibrillogenesis and to colocalise with ColI. The authors thus conclude that ColI is internalised into endosomal structures within the cell, and that ColI, VPS33B, and VIPA39 are co-trafficked to the site of fibrillogenesis, where along with ITGA11, which by mass spectrometric analysis is shown to be regulated by VPS33B levels, ColI fibrils are formed. Interestingly, in involved human skin sections from idiopathic pulmonary fibrosis (IPF) patients, ITGA11 and VPS33B expression is increased compared to healthy tissue, while in patient-derived fibroblasts, uptake of fluorescently-labelled ColI is also increased. This suggests that there may be a significant contribution of endocytosis-dependent fibrillogenesis in the formation of fibrotic and chronic wound-healing diseases in humans.

Strengths:

This is an interesting paper that contributes an exciting novel understanding of the formation of fibrotic disease, which despite its high occurrence, still has no robust therapeutic options. The precise mechanisms of fibrillogenesis are also not well understood, so a study devoted to this complex and key mechanism is well appreciated. The dependence of fibrillogenesis on VPS33B and VIPA39 is convincing and robust, while the distinction between soluble ColI secretion and insoluble fibrillar ColI is interesting and informative.

Weaknesses:

There are a number of limitations to this study in its current state. Inhibition of ColI uptake is performed using Dyngo4a, which although proposed as an inhibitor of Clathrin-dependent endocytosis is known to be quite un-specific. This may not be a problem however, as the endocytic mechanism for ColI also does not seem to be well defined in the literature, in fact, the principle mechanism described in the papers referred to by the authors is that of phagocytosis. It would be interesting to explore this important part of the mechanism further, especially in relation to the intracellular destination of ColI. The circadian regulation does not appear as robust as the authors' last paper, however, there could be a larger lag between endocytosis of ColI and realisation of fibrils. The authors state that the endocytic pathway is the mechanism of trafficking and that they show ColI, VPS33B, and VIPA39 are co-trafficked. However, the only link that is put forward to the endosomes is rather tenuously through VPS33B/VIPA39. There is no direct demonstration of ColI localisation to endosomes (ie. immunofluorescence), and this is overstated throughout the text. Demonstrating the intracellular trafficking and localisation of ColI, and its actual relationship to VPS33B and VIPA39, followed by ITGA11, would broaden the relevance of this paper significantly to incorporate the field of protein trafficking. Finally, the "self-formation" of ColI fibrils is discussed in relation to the literature and the concentration of fluorescently-tagged ColI, however as the key message of the paper is the fibrillogenesis from exocytosed colI, I do not feel like it is demonstrated to leave no doubt. Specific inhibition of intracellular trafficking steps, or following the progressive formation of ColI fibrils over time by immunofluorescence would demonstrate without any further doubt that ColI must be endocytosed first, to form fibrils as a secondary step, rather than externally-added ColI being incorporated directly to fibrils, independent of cellular uptake.

eLife assessment

This important work substantially advances our understanding of how collagen fibrils are built and maintained in a manner regulated by circadian rhythms in intracellular secretory trafficking pathways. The evidence supporting the data are solid, although further data regarding the molecular mechanisms regulating endocytic recycling of collagen would have strengthened the study. The work will be of considerable interest to those who study extracellular matrix assembly or collagen homeostasis.

eLife assessment

This elegant study presents important findings into how small molecules that were originally developed to inhibit the oncogenic kinase, BRAF, instead trigger activation of this kinase target. Compelling and comprehensive evidence supports a new allosteric model to explain the paradoxical activation. This rigorous work will be of great interest to biochemists, structural biologists, and those working on strategies to inhibit kinases in the context of human disease.

Reviewer #1 (Public Review):

Summary:

The authors quantitatively describe the complex binding equilibria of BRAF and its inhibitors resulting in some cases in the paradoxical activation of BRAF dimer when bound to ATP competitive inhibitors. The authors use a biophysical tour de force involving FRET binding assays, NMR, kinase activity assays, and DEER spectroscopy.

Strengths:

The strengths of the study are the beautifully conducted assays that allow for a thorough characterization of the allostery in this complex system. Additionally, the use of F-NMR and DEER spectroscopy provides important insights into the details of the process.

The resulting model for binding of inhibitors and dimerization (Figure 4) is very helpful.

Weaknesses:

This is a complex system and its communication is inherently challenging. It might be of interest to the broader readership to understand the implications of the model for drug development and therapy.

Reviewer #2 (Public Review):

Summary:

This manuscript uses FRET, 19F-NMR, and DEER/EPR solution measurements to examine the allosteric effects of a panel of BRAF inhibitors (BRAFi). These include first-generation aC-out BRAFi, and more recent Type I and Type II aC-in inhibitors. Intermolecular FRET measurements quantify Kd for BRAF dimerization and inhibitor binding to the first and second subunits. Distinct patterns are found between aC-in BRAFi, where Type I BRAFi binds equally well to the first and second subunits within dimeric BRAF. In contrast, Type II BRAFi shows stronger affinity for the first subunit and weaker affinity for the second subunit, an effect named "allosteric asymmetry". Allosteric asymmetry has the potential for Type II inhibitors to promote dimerization while favoring occupancy of only one subunit (BBD form), leading to the enrichment of an active dimer.

Measurements of in vitro BRAF kinase activity correlate amazingly well with the calculated amounts of the half-site-inhibited BBD forms with Type II inhibitors. This suggests that the allosteric asymmetry mechanism explains paradoxical activation by this class of inhibitors. DEER/EPR measurements further examine the positioning of helix aC. They show systematic outward movement of aC with Type II inhibitors, relative to the aC-in state with Type I inhibitors, and further show that helix aC adopts multiple states and is therefore dynamic in apo BRAF. This makes a strong case that negative cooperativity between sites in the BRAF dimer can account for paradoxical kinase activation by Type II inhibitors by creating a half-site-occupied homodimer, BBD. In contrast, Type I inhibitors and aC-out inhibitors do not fit this model, and are therefore proposed to be explained by previously proposed models involving negative allostery between subunits in BRAF-CRAF heterodimers, RAS priming, and transactivation.

Strengths:

This study integrates orthogonal spectroscopic and kinetic strategies to characterize BRAF dynamics and determine how it impacts inhibitor allostery. The unique combination of approaches presented in this study represents a road map for future work in the important area of protein kinase dynamics. The work represents a worthy contribution not only to the field of BRAF regulation but to protein kinases in general.

Weaknesses:

Some questions remain regarding the proposed model for Type II inhibitors and its comparison to Type I and aC-out inhibitors that would be useful to clarify. Specifically, it would be helpful to address whether the activation of BRAF by Type II inhibitors, while strongly correlated with BBD model predictions in vitro, also depends on CRAF via BRAF-CRAF in cells and therefore overlaps with the mechanisms of paradoxical activation by Type I and aC-out inhibitors.

Author Response

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

We are very grateful to both Reviewers, the Reviewing Editor and the Senior Editor for carefully reviewing our manuscript and for providing useful comments and suggestions that further improved the quality of our work. We appreciate that our work is perceived to substantially advance the understanding of osteoblast migration and that the experiments are found to be rigorous and to provide conclusive evidence. We also look forward to reaching a broad audience in the field. Below we provide a point-by-point response to each suggestion made by the reviewers and explain how we included their recommendations in the revised manuscript.

Public Reviews

Reviewer #1 (Public Review):

Summary:

The authors were trying to achieve that Tgif1 expression is regulated by EAK1/2 and PTH in a timedependent manner, and its roles in suppressing Pak3 for facilitating osteoblast adhesion. The authors further tried to show that the Tgif1- Pak3 signaling plays a significant role in osteoblast migration to the site of bone repair and bone remodeling.

Strengths:

• In a previous study, it was demonstrated that Tgif1 is a target gene of PTH, and the absence of Tgif1 failed to increase bone mass by PTH treatment (Saito et al., Nat Commun., 2019). In this study, the authors found that Tgif1-Pak3 signaling prompts osteoblast migration through osteoblast adhesion to prompt bone regeneration. This novel finding provides a better understanding of how Tgif1 expression in osteoblasts regulates adherence, spreading, and migration during bone healing and bone remodeling.

• The authors demonstrated that ERK1/2 and PTH regulate Tgif1 expression in a time-dependent manner and its role in suppressing Pak3 through various experimental approaches such as luciferase assay, ChIP assay, and gene silencing. These results contribute to the overall strength of the article.

We thank the reviewer for acknowledging the novelty of our findings as well as the strength of the manuscript.

Weaknesses:

• The authors need to further justify why they focused on Pak3 in the introduction by mentioning its known function for cell adhesion.

We thank the reviewer for this suggestion. We mention in the introduction that we further investigated Pak3 due to its implication in cell adhesion (page 6, lines 7-8).

• Some results indicated statistically significant but small changes. The authors need to explain in the discussion part why they believe this is the major mechanism or why there may be some other possible mechanisms.

We agree with this comment. We are confident that our work identified an important mechanism by which Tgif1 regulates cellular features of osteoblasts. However, it is certainly possible that other mechanisms may exist as well. We discuss this point in the revised manuscript (page 18, lines 16-17).

• The study does not include enough in vivo data to claim that this mechanism is crucial for bone healing and bone remodeling in vivo.

Re: We agree with this point and have modified the abstract accordingly by replacing “crucial” with “implicated in” as well as the text by changing “crucial” to “important” (page 2, line 9). Furthermore, we discuss this limitation in the revised manuscript (page 18, lines 9-14).

Reviewer #2 (Public Review):

Summary:

Bolamperti S. et al. 2023 investigate whether the expression of TG-interacting factor (Tgif1) is essential for osteoblastic cellular activity regarding morphology, adherence, migration/recruitment, and repair. Towards this end, germ-line Tgif1 deletion (Tgif1-/-) mice or male mice lacking expression of Tgif1 in mature osteoblastic and osteocytic cells (Dmp1-Cre+; Tgif1fl/fl) and corresponding controls were studied in physiological, bone anabolic, and bone fracture-repair conditions. Both Tgif1-/- and Dmp1-Cre+; Tgif1fl/fl exhibited decreased osteoblasts on cancellous bone surfaces and adherent to collagen I-coated plates. Tgif1-/- mice exhibit impaired healing in the tibial midshaft fracture model, as indicated by decreased bone volume (BV/Cal.V), osteoid (OS/BS), and low osteoblasts (number and surface). Likewise, both Tgif1-/- and Dmp1-Cre+; Tgif1fl/fl show impaired PTH 1-34, (100µg/kg, 5x/wk for 3 wks) osteoblast activation in vivo, as detected by increases in quiescent bone surfaces. Mechanistic in vitro studies then utilized primary osteoblasts isolated from Tgif1-/- mice and siRNA Tgif1 knockdown OCY454 cells to further investigate and identify the downstream Tgif1 target driving these osteoblastic impairments. In vitro, Tgif1-/- osteoblastic and Tgif1 knockdown OCY454 cells exhibit decreased migration, abnormal morphology, and decreased focal adhesions/cells. Unexpectantly though, localization assays revealed Tgif1 to primarily concentrate in the nucleus and not to co-localize with focal adhesions (paxillin, talin). Also, the expression of major focal adhesion components (paxillin, talin, FAK, Src, etc.) or the Cdc42 family was not altered by loss of Tgif1 expression. In contrast, PAK3 expression is markedly upregulated by loss of Tgif1. In silico analysis followed by mechanistic molecular assays involving ChIP, siRNA (Tgif1, PAK3), and transfection (rat PAK3 promoter) techniques show that Tgif1 physically binds to a specific site in the PAK3 promoter region. Further, the knockdown of PAK3 rescues the Tgif1-deficient abnormal morphology in OCY454 cells. This is the first study to identify the novel transcriptional repression of PAK3 by Tgif1 as well as the specific Tgif1 binding site within the PAK3 promoter.

Strengths:

This work has a plethora of strengths. The co-authors achieved their aim of eliciting the role of Tgif1 expression in osteoblastic cellular functions (morphology, spreading/attachment, migration).

Further, this work is the first to depict the novel mechanism of Tgif1 transcriptional repression of PAK3 by a thorough usage of mechanistic molecular assays (in silico analysis, ChIP, siRNA, transfection etc.). The conclusions are well supported and justified by these findings, as the appropriate controls, sample sizes (statistical power), statistics, and assays were fully utilized. The claims and conclusions are justified by the data.

Re: We are grateful to this reviewer for recognizing the novelty, strengths, and rigor of our study and for acknowledging that the data convincingly support the conclusions drawn.

Weaknesses:

The discussion section could be expanded with a few sentences regarding limitations to the current study and potential future directions.

Re: In the revised manuscript, we are discussing limitations of the work and describe possible future directions (page 18, line 9-14).

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

(1) The cell spreading and migration assay is quite artificial. Trypsinized osteoblasts and quiescent osteoblasts are totally different. The authors need to cite papers from other groups to justify whether the cell spreading and migration assay is appropriate to achieve the goals of this study.

Re: The reviewer is right that in vitro assays are often artificial and do not necessarily fully reflect in vivo situations. We have taken this aspect into account and discuss it in the revised manuscript (page 18, lines 9-10). In addition, we have included references from other groups who have used similar assays to study cell spreading and migration (Dejaeger M et al., 2017 and Dang et al., 2018).

(2) Page 13 Line 15: The statement "Osteoblasts are greatly impaired in the ability to migrate into the repair zone" is an overstatement. The experiments in Figure 5 do not necessarily reflect osteoblast migration activities. The authors need to rephrase the sentence or need to show observation of earlier time points (e.g., 1 week after fracture) in their bone healing experiments. The number of osteoblasts/surface in Tgif1+/+ and Tgif1-/- mice at different time points during bone healing should be a good indicator for the migration of osteoblasts to the repair site.

Re: We understand the critique that a time course or lineage tracing experiments would provide better evidence for the statement of osteoblast migration into the repair zone. To avoid overinterpretations we have removed the sentence from the revised manuscript.

(3) Page 14, Line 24: Regarding the sentence "The observation that Tgif1 is crucial for osteoblast adherence, spreading, and migration", the authors need to clearly mention this statement is based on the in vitro experiments. The animal studies are not enough to claim that the mechanism is crucial for adherence, spreading, and migration.

Re: We thank the reviewer for pointing out this limitation. We have clarified that the finding that Tgif1 is crucial for osteoblast adherence, spreading and migration was made in vitro (page 14, line 22).

(4) The authors need to demonstrate the suppression of Pak3 expression in PTH-treated mice in vivo, in addition to the in vitro culture system (Fig. 7C and 7D).

Re: We agree with the reviewer that this experiment would be very insightful. However, this is beyond the scope of the current work. Nevertheless, to take this valid point into consideration, we mention it in the discussion as potential future direction (page 18, lines 11-14).

(5) The authors need to demonstrate that the pharmacologic suppression of Pak3 in Tgif1-/- mice reduces the % of quiescent surface/BS in vivo.

Re: This point is also well taken, and we agree that a suppression of Pak3 in Tgif1-deficient mice would be very informative to support our in vitro findings. However, this may also be part of future investigations. This is emphasized in the discussion of the revised manuscript (page 18, lines 11-14).

Figures (Minor)

Fig. 1:

Fig. 1A

Arrows need to indicate a more precise position.

Re: The position of the arrows has been optimized.

Fig. 1DE

What are blue/red bars (genotypes)?

Re: The colors indicate the genotypes. A legend has been added to the revised figure.

Fig. 1K

Quantification data is needed.

Re: Thank you for this suggestion. We added a quantification of the data (Fig. 1L, M; page 8, lines 3-4; page 21, lines 5-6)

Fig. 2A

Show the representative high-magnification image of round (non-spread) cells.

Re: Representative high-magnification images (insets) are provided in the revised figure 2A.

Fig. 5

Red arrows need to indicate a more precise position.

Re: The arrows have been repositioned.

Fig. 6A, C

Red arrows need to indicate a more precise position.

Re: The arrows have been repositioned.

Reviewer #2 (Recommendations For The Authors):

(1) The microscopy images and analyses are excellent.

Re: We thank the reviewer for acknowledging the quality of our microscopy studies.

(2) Since the Tgif1-/- mouse has low osteoclast numbers, is it possible that this is a contributing factor to the delays/impairment in bone healing, given that resorption also has a role in fracture repair? Since the focus of these studies is on osteoblastic cells, this point is a little out of scope. However, would the authors consider exploring this further in the discussion section?

Re: This point is well taken by the reviewer, and we agree that osteoclasts could certainly play a role in the impaired fracture healing. To acknowledge this aspect, we followed the recommendation and discuss this aspect in the revised manuscript (page 16, lines 22-24).

Revisions

Would the authors consider slightly re-wording the title? Tgif1 suppresses PAK3 expression; however, Tgif1-deficiency leads to the unregulated elevation of PAK3 expression.

Re: Thank you for pointing this out. We agree with the reviewer and adapted the title accordingly.

Suggestions

(1) Is it possible that apoptosis and/or anoikis is being induced by Tgif1 deficiency in osteoblastic cells?

Re: We do not have data towards this direction and although Tgif1-deficient osteoblasts are overall viable and well expanding, we cannot fully exclude this possibility.

(2) For the fracture study, any differences in overall callus size? Would it be possible to perform micro-CT imaging with some of these samples?

Re: There is no difference in non-mineralized callus size between Tgif1+/+ and Tgif1-/- mice. However, there is less mineralized bone per callus area in Tgif1-/- mice, confirming an impaired osteoblast phenotype. As suggested by the reviewer, we added representative micro-CT images and the respective information to the revised manuscript (Fig 5F; pages 19-20).

(3) Fracture repair experiment-is PAK3 expression downregulated with fracture injury; and/or, is PAK3 upregulated by loss of Tgif1 expression?

Re: Unfortunately, we do not have data to answer this very interesting question and it would need to be addressed in future studies. This is mentioned in the revised discussion (page 18, lines 12-14).

(4) Fig 7F. within PTH treated cells, is the light blue SCR sphericity statistically different than the light green siTgif1 + siPAK3 ? While the statement of the "lack of both, Tgif1 and PAK3 prevented PTH-induced decrease in cell sphericity" is supported by the lack of differences between dark green vs. light green; is it also possible that this is due to the siPAK3 returning sphericity to control (scr) levels? (i.e. hitting a floor limit of detection).

Re: We thank the reviewer for this thoughtful question. There is no statistically significant difference between light blue and light green. Silencing PAK3 restores the impaired capacity to spread that occurs in the absence of Tgif1 to the level of scr controls (significant difference between dark and light red vs. dark and light green and no difference between either dark or light blue vs. dark or light green). However, unlike in the (scr) controls, in the absence of both Tgif1 and PAK3, the cells do not respond to PTH (statistically significant difference between dark and light blue, no difference between dark and light green). Based on the data, cells can reach sphericity of less than 0.2 and thus it is unlikely that sphericity is “hitting the floor level of detection” in these groups.

eLife assessment

This important work substantially advances our understanding of osteoblast migration to the sites of bone formation and regeneration. The evidence supporting the conclusion is compelling, with rigorous in vitro assays for cellular and biochemical aspects and with appropriate in vivo models. The work will be of broad interest to developmental biologists and bone biologists.

Reviewer #1 (Public Review):

Summary:

The authors were trying to achieve that Tgif1 expression is regulated by EAK1/2 and PTH in a time-dependent manner, and its roles in suppressing Pak3 for facilitating osteoblast adhesion. The authors further tried to achieve that the Tgif1-Pak3 signaling plays a significant role in osteoblast migration to the site of bone repair and bone remodeling.

Strengths:

- In a previous study, they demonstrated that Tgif1 is a target gene of PTH, and the absence of Tgif1 failed to increase bone mass by PTH treatment (Saito et al., Nat Commun., 2019). In this study, they found that Tgif1-Pak3 signaling prompts osteoblast migration through osteoblast adhesion to prompt bone regeneration. This novel finding provides a better understanding of how Tgif1 expression in osteoblasts regulates adherence, spreading, and migration during bone healing and bone remodeling.<br /> - The authors demonstrated that ERK1/2 and PTH regulate Tgif1 expression in a time-dependent manner and its role in suppressing Pak3 through various experimental approaches such as luciferase assay, ChIP assay, and gene silencing. These results contribute to the overall strength of the article.

Weaknesses:

None after substantial revisions especially in vivo parts.

Reviewer #2 (Public Review):

Summary:

Bolamperti S. et al. 2023 investigates whether expression of TG-interacting factor (Tgif1) is essential for osteoblastic cellular activity regarding morphology, adherence, migration/recruitment, and repair. Towards this end, germ-line Tgif1 deletion (Tgif1-/-) mice or male mice lacking expression of Tgif1 in mature osteoblastic and osteocytic cells (Dmp1-Cre+; Tgif1fl/fl) and corresponding controls were studied in physiological, bone anabolic, and bone fracture-repair conditions. Both Tgif1-/- and Dmp1-Cre+; Tgif1fl/fl exhibited decreased osteoblasts on cancellous bone surfaces and adherent to collagen I-coated plates. Tgif1-/- mice exhibit impaired healing in the tibial midshaft fracture model, as indicated by decreased bone volume (BV/Cal.V), osteoid (OS/BS), and low osteoblasts (number and surface). Likewise, both Tgif1-/- and Dmp1-Cre+; Tgif1fl/fl show impaired PTH 1-34, (100 µg/kg, 5x/wk for 3 wks) osteoblast activation in vivo, as detected by increases in quiescent bone surfaces. Mechanistic in vitro studies then utilized primary osteoblasts isolated from Tgif1-/- mice and siRNA Tgif1 knockdown OCY454 cells to further investigate and identify the downstream Tgif1 target driving these osteoblastic impairments. In vitro, Tgif1-/- osteoblastic and Tgif1 knockdown OCY454 cells exhibit decreased migration, abnormal morphology, and decreased focal adhesions/cell. Unexpectantly though, localization assays revealed Tgif1 to primarily concentrate in the nucleus and not to co-localize with focal adhesions (paxillin, talin). Also, expression of major focal adhesion components (paxillin, talin, FAK, Src etc.) or the Cdc42 family was not altered by loss of Tgif1 expression. In contrast, PAK3 expression is markedly upregulated by loss of Tgif1. In silico analysis followed by mechanistic molecular assays involving ChIP, siRNA (Tgif1, PAK3), and transfection (rat PAK3 promoter) techniques show that Tgif1 physically binds to a specific site in the PAK3 promoter region. Further, the knockdown of PAK3 rescues the Tgif1-deficient abnormal morphology in OCY454 cells. This is the first study to identify the novel transcriptional repression of PAK3 by Tgif1 as well as the specific Tgif1 binding site within the PAK3 promoter.

Strengths:

This work has a plethora of strengths. The co-authors achieved their aim in eliciting the role of Tgif1 expression to osteoblastic cellular functions (morphology, spreading/attachment, migration). Further, this work is the first to depict the novel mechanism of Tgif1 transcriptional repression of PAK3 by a through usage of mechanistic molecular assays (In silico analysis, ChIP, siRNA, transfection etc.). The conclusions are well supported and justified by these findings, as the appropriate controls, sample sizes (statistical power), statistics, and assays were fully utilized.

Claims and conclusions justified by data? Yes. absolutely

Weaknesses:

None. All reviewer comments were fully addressed.

Author Response

We would like to thank the reviewers for their positive comments and valuable suggestions for improvements to the manuscript. We intend to revisit the discussion to clarify our interpretation of how azithromycin resistance mutations impact the transmission potential of P. falciparum and expand on the differences between mouse and human malaria. Additionally, we intend to adjust the title to better align with the revised interpretation of the main findings. These changes will be reflected in the revised manuscript to be submitted as the eLife Version of Record.

Author Response

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

General remarks for the Editor and the Reviewers

We would like to thank the Editor and the Reviewers for their feedback. Below we address their comments and present our point-by-point responses as well as the related changes in the manuscript.

In addition to these changes, in a few cases we have found it necessary to move some texts and provide some additional explanations within the manuscript. We emphasize that these amendments have been made for only technical reasons, and do not alter the results and conclusions of the paper, but may help to render the text more coherent and understandable to readers with little knowledge of the subject.

These minor corrections are:

• We extended the Introduction section by a sentence (lines 40-42) that is intended to fit the proposed template directed, non-enzymatic replication mechanism into a more general prebiotic evolutionary context, thus emphasizing its biological relevance. This sentence includes an additional reference (Rosenberger et al., 2021).

• Two very methodologically oriented and repeated descriptions of random sequence generation have been moved to the Methods section (lines 178-185) from the Results section (lines 336-339 and lines 351-354).

• We complemented the Data availability statement with licensing information (lines 684-685).

• Further minor changes (also indicated by red texts) have been implemented to remedy logical and grammatical glitches.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Szathmary and colleagues explore the parabolic growth regime of replicator evolution. Parabolic growth occurs when nucleic acid strain separation is the rate-limiting step of the replication process which would have been the case for non-enzymatic replication of short oligonucleotide that could precede the emergence of ribozyme polymerases and helicases. The key result is that parabolic replication is conducive to the maintenance of genetic diversity, that is, the coexistence of numerous master sequences (the Gause principle does not apply). Another important finding is that there is no error threshold for parabolic replication except for the extreme case of zero fidelity.

Strengths:

I find both the analytic and the numerical results to be quite convincing and well-described. The results of this work are potentially important because they reveal aspects of a realistic evolutionary scenario for the origin of replicators.

Weaknesses:

There are no obvious technical weaknesses. It can be argued that the results represent an incremental advance because many aspects of parabolic replication have been explored previously (the relevant publications are properly cited). Obviously, the work is purely theoretical, experimental study of parabolic replication is due. In the opinion of this reviewer, though, these are understandable limitations that do not actually detract from the value of this work.

We are grateful that this Reviewer appreciates our work. We completely agree that the ultimate validation must come from experiments. It is important to stress that in this field theory often preceded experimental work by decades, and the former often guided the latter. We hope that for the topic of the present paper experiments will follow considerably faster.

Reviewer #2 (Public Review):

Summary:

A dominant hypothesis concerning the origin of life is that, before the appearance of the first enzymes, RNA replicated non-enzymatically by templating. However, this replication was probably not very efficient, due to the propensity of single strands to bind to each other, thus inhibiting template replication. This phenomenon, known as product inhibition, has been shown to lead to parabolic growth instead of exponential growth. Previous works have shown that this situation limits competition between alternative replicators and therefore promotes RNA population diversity. The present work examines this scenario in a model of RNA replication, taking into account finite population size, mutations, and differences in GC content. The main results are (1) confirmation that parabolic growth promotes diversity, but that when the population size is small enough, sequences least efficient at replicating may nevertheless go extinct; (2) the observation that fitness is not only controlled by the replicability of sequences, but also by their GC content; (3) the observation that parabolic growth attenuates the impact of mutations and, in particular, that the error threshold to which exponentially growing sequences are subject can be exceeded, enabling sequence identity to be maintained at higher mutation rates.

Strengths:

The analyses are sound and the observations are intriguing. Indeed, it has been noted previously that parabolic growth promotes coexistence, its role in mitigating the error threshold catastrophe - which is often presented as a major obstacle to our understanding of the origin of life - had not been examined before.

Weaknesses:

Although all the conclusions are interesting, most are not very surprising for people familiar with the literature. As the authors point out, parabolic growth is well known to promote diversity (SzathmaryGladkih 89) and it has also been noted previously that a form of Darwinian selection can be found at small population sizes (Davis 2000).

Given that under parabolic growth, no sequence is ever excluded for infinite populations, it is also not surprising to find that mutations have a less dramatic exclusionary impact.

In the two articles cited (Szathmary-Gladkih 1989 and Davis 2000) the subexponentiality of the system was implemented in a mechanistic way, by introducing the exponent 0 < 𝑝 < 1. Although the behaviour of these models is more or less consistent with experimental findings (von Kiedrowski, 1986; Zielinski and Orgel, 1987), the divergence of per capita growth rates (𝑥̇/𝑥) at very low concentrations–which guarantees the ability to maintain unlimited diversity in the case of infinite population sizes–makes this formal approach partly unrealistic.

To avoid the possible artefacts of this mechanistic approach, and as there are no previous studies analysing the diversity maintaining ability of finite populations of parabolic replicators in an individual-based model context, we implemented a simplified template replication mechanism leading to parabolic growth and analysed the dynamics in an individual-based stochastic model context. The key point of our investigation is that considerable diversity can be maintained in the system even when the population size is quite small.

Regarding the Reviewer’s comment on selection: Darwinian selection can only occur in a simple subexponential dynamics if the ratio of replicabilities diverges, cf. Eq. (8) and the preceding paragraph in Davis, 2000.

Our results also show (Figs. 4B and 4C) that high mutation rates and the error threshold problem can still be considered as a major limiting factor for parabolically replicating systems in terms of their diversity-maintaining ability. In the light of the above, potential mechanisms to relax the error threshold in such systems, one of which is demonstrated in the present study, seem to be important steps to account for the sequence diversification and increase in molecular complexity during the early evolution of RNA replicators.

A general weakness is the presentation of models and parameters, whose choices often appear arbitrary. Modeling choices that would deserve to be further discussed include the association of the monomers with the strands and the ensuing polymerization, which are combined into a single association/polymerization reaction (see also below), or the choice to restrict to oligomers of length L = 10. Other models, similar to the one employed here, have been proposed that do not make these assumptions, e.g. Rosenberger et al. Self-Assembly of Informational Polymers by Templated Ligation, PRX 2021. To understand how such assumptions affect the results, it would be helpful to present the model from the perspective of existing models.

The assumption of one-step polymerization reactions that we used here is a common technique for modelling template replication of sequence-represented replicators [see, e.g., Fontana and Schuster, 1998 (10.1126/science.280.5368.1451), Könnyű et al., 2008 (10.1186/1471-2148-8267), Vig-Milkovics et al, 2019 (10.1016/j.jtbi.2018.11.020) or Szilágyi et al., 2020 (10.1371/journal.pgen.1009155)]. This is because assuming base-to-base polymerisation of the copy would lead to a very large number of different types of intermediates, which a Gillespietype stochastic simulation algorithm could not handle in reasonable computation times, even if the sequences were relatively short. For comparison, in our model, where polymerization is one-step, the characteristic time of a simulation for 𝐿 = 10, 𝑁 = 105 and 𝛿 = 0.01 was 552 hours.

Note that in Rosenberg et al. (PRX 2021), in contrast to a pioneering work [Fernando et al, 2007 (10.1007/s00239-006-0218-4)], sequences of replicators are not represented, which makes this approach completely inapplicable to our case, in which sequence defines the fitness. In sum, we suggest that this valid criticism points to possible future work.

The values of the (many) parameters, often very specific, also very often lack justifications. For example, why is the "predefined error factor" ε = 0.2 and not lower or higher? How would that affect the results?

A general remark. For the more important parameters , several values were used to test the behaviour of the model (see Table 1), but due to the considerable number of parameters, it is impossible to examine all possible combinations. 𝑐+ = 1 fixes the timescale, 𝐿 is set to 10 to obtain reasonable running times (see above).

𝜀 characterizes how replicability decreases as the number of mutations increases. In the manuscript we used the following default vector: 𝜀 = (0.05, 0.2, 1) in which the third element corresponds to the mutation-free sequence, so it must to be 1. The first element determines the baseline replicability (see Methods), which we preferred not to change because it would fundamentally alter the ratio of replication propensities to association and dissociation propensities (as the substantial amount of complementary sequences of the master sequences are of baseline replicability) and thus would alter the reaction kinetics to an extent that it is not comparable with the original results. Therefore, only the second element can be adjusted. Accordingly, we have analysed the behaviour of the model in the cases of a steeper and a more gradual loss of replicability using the following two vectors, respectively: 𝜀, = (0.05, 𝟎. 𝟎𝟓, 1) and 𝜀,, = (0.05, 𝟎. 𝟓, 1). The choice of 𝜀, is chemically more plausible, since for very short oligomers the loss of chemical activity and replicability as a function of the number of mutations can be very sharp. We performed a series of simulations with all possible combinations of 𝛿 = 0.001, 0.005, 0.1 and 𝑁 = 103, 104, 105 for 𝜀′ and 𝜀,,in the constant population and chemostat model context (36 different runs). For other parameters, we took the default values, see Table 1. These values also correspond to the parameters we used in Figures 2 and 6. The results show that the steeper loss of replicability (𝜀,) slightly increases the diversity maintaining ability of the system, whereas the more gradual loss of replicability (𝜀,,) moderately decreases the diversity-maintaining ability of the system, and that these shifts are more pronounced in the constant population size model (Author response image 1) than in the chemostat model (Author response image 2). Altogether, these results confirm that the qualitative outcome of the model is robust in a wide range of loss of replicability (𝜀 vector) values.

Author response image 1.

Replicator coexistence in the constant population model with different loss of replicability (𝜀 vector) values. Within a given combination of 𝛿 and 𝑁 parameter values, the upper panel corresponds to the steeper loss of replicability (𝜀!), the middle panel to the default 𝜀 vector (Figure 2A), and the bottom panel to the more gradual loss of replicability vector (𝜀!!). Within each 𝛿; 𝑁 parameter combination, the same master sequence set was used with the three different 𝜀 vectors for comparability.

Author response image 2.

Replicator coexistence in the chemostat model with different loss of replicability (𝜀 vector) values. Within a given combination of 𝛿 and 𝑁 parameter values, the upper panel corresponds to the steeper loss of replicability (𝜀!), the middle panel to the default 𝜀 vector (Figure 6A), and the bottom panel to the more gradual loss of replicability vector (𝜀!!). Within each 𝛿; 𝑁 parameter combination, the same master sequence set was used with the three different 𝜀 vectors for comparability.

Similarly, in equation (11), where does the factor 0.8 come from?

This factor scales the decay rate of duplex sequences (𝑐"!") as the function of the binding energy

(𝐸b). The value of 0.8 is an arbitrary choice, the value should be in the interval (0,1) and is only relevant in the chemostat model. It is expected to have a similar effect on the dynamics as the duplex decay factor parameter 𝑓, which we have investigated in a wide range of different values (cf. Table 1, Fig. 6), although 𝑓 is independent of the binding energy (𝐸/): increasing/decreasing the 0.8 factor is expected to decrease/increase the average total population size. We have investigated the diversity maintaining ability of the system at smaller (0.6) and larger (0.9) parameter values at different population sizes (𝑁 ≈ 103, 104 and 105) and at different replicability distances (δ = 0.001, 0.005 and 0.01) as shown in Fig. 6. We have found that the number of coexisting master types changes very little in response to changes in this factor. Only two shifts could be detected (underlined): factor 0.9 combined with 𝑁 ≈ 104 and 𝛿 = 0.001 caused the number of surviving master types to decrease by one, while factor 0.9 combined with 𝑁 ≈ 103 and 𝛿 = 0.01 caused the number of surviving master types to increase by one (Author response table 1). Factor 0.6 produced the same number of surviving types as the default (Author response table 1). In summary, the model shows marked robustness to changes in the values of this parameter.

Author response table 1.

Number of coexisting master types in the chemostat model with different binding energy dependent duplex decay rates. Within each 𝛿; 𝑁 parameter combination, the same master sequence set was used with the three different factor values: 0.6, 0.8 (the original) and 0.9 for comparability.

Why is the kinetic constant for duplex decay reaction 1.15e10−8?

Note that this value is the minimum of the duplex decay rate, Table 1 correctly shows the interval of this kinetic constant as: [1.15 ⋅ 10-8, 6.4 ⋅ 10-5]. Both values are derived from the basic parameters of the system and can be computed according to Eq. (11). The minimum: as the parameter set corresponding to this value is: . The maximum: with .

Are those values related to experiments, or are they chosen because specific behaviors can happen only then?

See above.

The choice of the model and parameters potentially impact the two main results, the attenuation of the error threshold and the role of GC content:

Regarding the error threshold, it is also noted (lines 379-385) that it disappears when back mutations are taken into account. This suggests that overcoming the error threshold might not be as difficult as suggested, and can be achieved in several ways, which calls into question the importance of the particular role of parabolic growth. Besides, when the concentration of replicators is low, product inhibition may be negligible, such that a "parabolic replicator" is effectively growing exponentially and an error catastrophe may occur. Do the authors think that this consideration could affect their conclusion? Can simulations be performed?

The assumption of back mutation only provides a theoretical solution to the error threshold problem: back mutation guarantees a positive (non-zero) concentration of a master type, but, since the probability of back mutation is generally very low, this equilibrium concentration may be extremely low, or negligible for typical system sizes. Consequently, back mutation alone does not solve the problem of the error catastrophe: in our system back mutation is present (the probability that a sequence with 𝑘 errors mutates back to a master sequence is 𝜇k(1−𝜇)L-k), and the diversity-maintaining ability is limited. The effect of back mutation decreases exponentially with increasing sequence length.

Regarding the role of the GC content, GC-rich oligomers are found to perform the worst but no rationale is provided.

For GC-rich oligonucleotides the dissociation probability of a template-copy complex is relatively low (cf. Eqs. (9, 10)), thus they have a relatively low number of offspring, cf. lines 557-561: “a relatively high dissociation probability and the consequential higher propensity of being in a simple stranded form provides an advantage for sequences with relatively low GC content in terms of their replication affinity, that is, the expected number of offspring in case of such variants will be relatively high.”. Note that the simulation results shown in Fig. 3A, demonstrate the realization of this effect with prepared sequences (along a GC content gradient).

One may assume that it happens because GC-rich sequences are comparatively longer to release the product. However, it is also conceivable that higher GC content may help in the polymerization of the monomers as the monomers attach longer on the template (as described in Eq. (9)). This is an instance where the choice to pull into a single step the association and polymerization reactions are pulled into a single step independent of GC content may be critical.

It would be important to show that the result arises from the actual physics and not from this modeling choice.

Some more specific points that would deserve to be addressed:

• Line 53: it is said that p "reflects how easily the template-reaction product complex dissociates". This statement is not correct. A reaction order p<1 reflects product inhibition, the propensity of templates to bind to each other, not slow product release. Product release can be limiting, yet a reaction order of 1 can be achieved if substrate concentrations are sufficiently high relative to oligomer concentrations (von Kiedrowski et al., 1991).

We think the key reference is Von Kiedrowski (1993) in this case. Other things being equal, his Table 1 on p. 134 shows that a sufficient increase in 𝐾4, i.e., the stability of the duplex (template and copy) (association rate divided by dissociation rate) throws the system into the parabolic regime. This is what we had in mind. In order to clarify this, we modified the quoted sentence thus: “In this kinetics, the growth order is equal or close to 0.5 (i.e., the dynamics is sub-exponential) because increased stability of the template-copy complex (rate of association divided by dissociation) promotes parabolic growth (von Kiedrowski et al., 1991; von Kiedrowski & Szathmáry, 2001).”

• Population size is a key parameter, and a comparison is made between small (10^3) and large (10^5) populations, but without explaining what determines the scale (small/large relative to what?).

The “small” value (103) corresponds to the smallest meaningful population size, significantly smaller population sizes (e.g. 102) cannot maintain the 10 master types (or any subset of them) and are chemically unrealistic. The “large value” (105) is the largest population size for which simulation times are still acceptable, in the case of 106 the runtimes are in the order of months.

• In the same vein, we might expect size not to be the only important parameter, but also concentration.

With constant volume population size and concentration are strictly coupled.

• Lines 543-546: if understanding correctly, the quantitative result is that the error threshold rises from 0.1 in the exponential case to 0.196 in the parabolic. Are the authors suggesting that a factor of 2 is a significant difference?

In this paragraph we compared the empirical error threshold of our system (which is close to 𝑝"#$ = 0.15) with the error threshold of the well-known single peak fitness landscape (which can be approximated by ) as a reference case. To make the message even clearer we have extended the last sentence (lines 596-597) as follows: “but note that applying this approach to our system is a serious oversimplification”. The 0.196 is simply the probability of error-free replication of a sequence when , but we have removed this sentence (“corresponding to the replication accuracy of a master sequence”) from the manuscript as it seems to be confusing.

• Figure 3C: this figure shows no statistically significant effect?

Thank you for pointing out this. We statistically tested the hypothesis that the GC content between the survived and the extinct master subsets are different. This analysis revealed that the differences between these two groups are statistically significant, which we now included in the manuscript at lines 380-390: “A direct investigation of whether the sequence composition of the master types is associated with their survival outcome was conducted using the data from the constant population model simulation results (Figure 2). In these data, the average GC content was measured to be lower in the surviving master subpopulations than in the extinct subpopulations (Figure 3C). To determine whether this difference was statistically significant, nonparametric, two-sample Wilcoxon rank-sum tests (Hollander & Wolfe, 1999) were performed on the GC content of the extinct-surviving master subsets. The GC content was significantly different between these two groups in all nine investigated parameter combinations of population size (N) and replicability distance (δ) at p<0.05 level, indicating a selective advantage for a lower GC content in the constant population model context. The exact p values obtained from this analysis are shown in Figure 3C.”

• line 542: "phase transition-like species extension (Figure 4B)": such a clear threshold is not apparent.

Thank you for pointing out the incorrect phrasing. As there is no clear threshold in the number of coexisting types as a function of the mutation rate, we removed the “phase transition-like” expression: “However, when finite population sizes and stochastic effects are taken into account, at the largest investigated per-base mutation rate (𝑝mut = 0.15), the summed relative steady-state master frequencies approach zero (Figure 4C) with accelerating species extinction (Figure 4B), indicating that this value is close to the system׳s empirical error threshold.” (lines 589-594).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

On the whole, the work is well done and presented, there are no major recommendations. It seems a good idea to cite and briefly discuss this recent paper: https://pubmed.ncbi.nlm.nih.gov/36996101/ which develops a symbiotic scenario of the coevolution of primordial replicators and reproducers that appears to be fully compatible with the results of the current work.

Thank you for bringing this article to our attention. We have inserted the following sentence at lines 621-624: “The demonstrated diversity-maintaining mechanism of finite parabolic populations can be used as a plug-in model to investigate the coevolution of naked and encapsulated molecular replicators (e.g., Babajanyan et al., 2023).”

The manuscript is well written, but there are some minor glitches that merit attention. For example:

l. 5 "carriers presents a problem, because product formation and mutual hybridization" - "mutual" is superfluous here, delete

l. 13 "amplification. In addition, sequence effects (GC content) and the strength of resource" - hardly "effects" - should be 'features' or 'properties'

l. 41 "If enzyme-free replication of oligomer modules with a high degree of sequence" - "modules" here is only confusing - simply, "oligomers"

l. 44 "under ecological competition conditions with which distinct replicator types with different" - delete "with" etc, there are many such minor glitches that are best corrected.

Thank you for pointing out, we have corrected! Other drafting errors, glitches, superfluous sentences have also been corrected.

Reviewer #2 (Recommendations For The Authors):

None

Editor (Recommendations For The Authors):

In the manuscript, it appears that coexistence is assessed at a given point in time, while figures seem to show that it remains time-dependent. It would be great if the authors could clarify this and/or discuss this.

We appreciate you bringing this to our attention, as we have indeed missed to elaborate on this important point. The steady state characteristic of the coexistence is assessed in our model in the following way: the relative frequency of each master sequence is tested for the condition of ≥ 100- (cut-off relative frequency for survival) in every 2,000th replication step in the interval between 10,000 replication steps before termination and actual termination (10= replication steps). If the above condition is true more than once, we consider the master type in question as survived (we have included this explanation in the Methods section: lines 258-268). Although this relatively narrow time interval can still be regarded as a snapshot of the state of the system, according to our numerical experiences, the resulting measure is a reliable quantitative indicator of the apparent stability of species coexistence in the parabolic dynamics.

Reviewer #1 (Public Review):

Summary:

Szathmary and colleagues explore the parabolic growth regime of replicator evolution. Parabolic growth occurs when nucleic acid strain separation is the rate limiting step of the replication process which would have been the case for non-enzymatic replication of short oligonucleotide that could precede the emergence of ribozyme polymerases and helicases. The key result is that parabolic replication is conducive to the maintenance of genetic diversity, that is, coexistence of numerous master sequences (the Gause principle does not apply). Another important finding is that there is no error threshold for parabolic replication except for the extreme case of zero fidelity.

Strengths:

I find both the analytic and the numerical results to be quite convincing and well described. The results of this work are potentially important because they reveal aspects of a realistic evolutionary scenario for the origin of replicators.

Weaknesses:

There are no obvious technical weaknesses. It can be argued that the results represent an incremental advance because many aspects of parabolic replication have been explored previously (the relevant publications are properly cited). Obviously, the work is purely theoretical, experimental study of parabolic replication is due. In the opinion of this reviewer, though, these are understandable limitations that do not actually detract from the value of this work.

eLife assessment

This study provides a valuable theoretical exploration of non-enzymatic sustained replication of RNA systems, in the parabolic growth regime of the evolution of putative primordial replicators. It provides convincing evidence that parabolic growth mitigates the error threshold catastrophe, thus demonstrating another way in which this regime contributes to the maintenance of genetic diversity. The findings shed light on relevant evolutionary regimes of primordial replicators, with potential applicability to our understanding of the origin of life.

Reviewer #2 (Public Review):

Summary:

A dominant hypothesis concerning the origin of life is that, before the appearance of the first enzymes, RNA replicated non-enzymatically by templating. However, this replication was probably not very efficient, due to the propensity of single strands to bind to each other, thus inhibiting template replication. This phenomenon, known as product inhibition, has been shown to lead to parabolic growth instead of exponential growth. Previous works have shown that this situation limits competition between alternative replicators and therefore promotes RNA population diversity. The present work examines this scenario in an agent-based model of RNA replication, taking into account finite population size, mutations and differences in GC content. The main results are (1) confirmation that parabolic growth promotes diversity, but that when the population size is small enough, sequences least efficient at replicating may nevertheless go extinct; (2) the observation that fitness is not only controlled by the replicability of sequences, but also by their GC content ; (3) the observation that parabolic growth attenuates the impact of mutations and, in particular, that the error threshold to which exponentially growing sequences are subject can be exceeded, enabling sequence identity to be maintained at higher mutation rates.

Strengths:

The analyses are sound and the observations intriguing. Indeed, while it has been noted previously that parabolic growth promotes coexistence, this is the first work to show that it can also mitigate the error threshold catastrophe, which is often presented as a major obstacle to our understanding of the origin of life.

Weaknesses:

A general weakness, which can however be seen as inherent in an agent-based model that aims to be more realistic than earlier, more phenomenological models, is the proliferation of parameters. The choice and values of these parameters are generally justified and, in many cases, several values are tested to assess the robustness of the results, but it can be difficult for the reader to identify the modeling choices that are truly critical from those that are less so.

Author Response

eLife assessment

In this study, the authors offer a theoretical explanation for the emergence of nematic bundles in the actin cortex, carrying implications for the assembly of actomyosin stress fibers. As such, the study is a valuable contribution to the field actomyosin organization in the actin cortex. While the theoretical work is solid, experimental evidence in support of the model assumptions remains incomplete. The presentation could be improved to enhance accessibility for readers without a strong background in hydrodynamic and nematic theories.

To address the weaknesses identified in this assessment, we plan to expand the description of the theoretical model to make it more accessible to a broader spectrum of readers. We will discuss in more detail the relation between the different mathematical terms and physical processes at the molecular scale, as well as the experimental evidence supporting the model assumptions. We will also discuss more explicitly how our results are relevant to different systems exhibiting actomyosin nematic bundles beyond stress fibers.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this article, Mirza et al developed a continuum active gel model of actomyosin cytoskeleton that account for nematic order and density variations in actomyosin. Using this model, they identify the requirements for the formation of dense nematic structures. In particular, they show that self-organization into nematic bundles requires both flow-induced alignment and active tension anisotropy in the system. By varying model parameters that control active tension and nematic alignment, the authors show that their model reproduces a rich variety of actomyosin structures, including tactoids, fibres, asters as well as crystalline networks. Additionally, discrete simulations are employed to calculate the activity parameters in the continuum model, providing a microscopic perspective on the conditions driving the formation of fibrillar patterns.

Strengths:

The strength of the work lies in its delineation of the parameter ranges that generate distinct types of nematic organization within actomyosin networks. The authors pinpoint the physical mechanisms behind the formation of fibrillar patterns, which may offer valuable insights into stress fiber assembly. Another strength of the work is connecting activity parameters in the continuum theory with microscopic simulations.

We thank the referee for these comments.

Weaknesses:

This paper is a very difficult read for nonspecialists, especially if you are not well-versed in continuum hydrodynamic theories. Efforts should be made to connect various elements of theory with biological mechanisms, which is mostly lacking in this paper. The comparison with experiments is predominantly qualitative.

We agree with the referee that the manuscript will benefit from a better description of the theoretical model and the results in relation with specific molecular and cellular mechanisms. We will further emphasize how a number of experimental observations in the literature support our model assumptions and can be explained by our results. A quantitative comparison is difficult for several reasons. First, many of the parameters in our theory have not been measured, and in fact estimates in the literature often rely on comparison with hydrodynamic models such as ours. Second, the effective physical properties of actomyosin gels can vary wildly between cells, which may explain the diversity of forms, dynamics and functions. For these reasons, we chose to delineate regimes leading to qualitatively different emerging architectures and dynamics. In the revised manuscript, we will make this point clearer and will further study the literature to seek quantitative comparison.

It is unclear if the theory is suited for in vitro or in vivo actomyosin systems. The justification for various model assumptions, especially concerning their applicability to actomyosin networks, requires a more thorough examination.

We thank the referee for this comment. Our theory is applicable to actomyosin in living cells. To our knowledge, reconstituted actomyosin gels currently lack the ability to sustain the dynamical steady-states involved in the proposed self-organization mechanism, which balance actin flows with turnover. In addition to actomyosin gels in living cells, in vitro systems based on encapsulated cell extracts can also sustain such dynamical steady states [e.g. https://doi.org/10.1038/s41567-018-0413-4], and therefore our theory may be applicable to these systems as well. Of course, with advancements in the field of reconstituted systems, this may change in the near future. We will explicitly discuss this point in the revised manuscript.

The classification of different structures demands further justification. For example, the rationale behind categorizing structures as sarcomeric remains unclear when nematic order is perpendicular to the axis of the bands. Sarcomeres traditionally exhibit a specific ordering of actin filaments with alternating polarity patterns.

We agree and will avoid the term “sarcomeric”.

Similarly, the criteria for distinguishing between contractile and extensile structures need clarification, as one would expect extensile structures to be under tension contrary to the authors' claim.

We plan to clarify this point by representing in a main figure the stress profiles across dense nematic structures (currently in Supp Fig 2), along with a more detailed description. In short, depending on the parameter regime, the competition between active and viscous stresses in the actin gel determine whether the emergent structures are extensile or contractile. In our system tension is positive in all directions at all times. However, in “contractile” structures, tension is larger along the bundle, whereas in “extensile” structures, tension is larger perpendicular to the bundle. This is consistent with the common expression for active stress of incompressible nematic systems [see e.g. https://doi.org/10.1038/s41467-018-05666-8], that takes the form –zQ, where z is positive for an extensile system, showing that in this case active tension is negative along the nematic direction. This point, also been raised by another referee, will be clarified and connected to existing literature.

Additionally, its unclear if the model's predictions for fiber dynamics align with observations in cells, as stress fibers exhibit a high degree of dynamism and tend to coalesce with neighboring fibers during their assembly phase.

In the present work, we focus on the self-organization of a periodic patch of actomyosin gel. However, in adherent cells boundary conditions play an essential role, e.g. with inflow at the cell edge as a result of polymerization and exclusion at the nucleus. In ongoing work, we are studying with the present model the dynamics of assembly and reconfiguration of dense nematic structures in domains with boundary conditions mimicking in adherent cells, as suggested by the referee. We would like to note, however, that the prominent stress fibers in cells adhered to stiff substrates, so abundantly reported in the literature, are not the only instance of dense nematic actin bundles, and may not be representative of physiologically relevant situations. In the present manuscript, we emphasize the relation of the predicted organizations with those found in different in vivo contexts not related to stress fibers, such as the aligned patterns of bundles in insects (trachea, scales in butterfly wings), in hydra, or in reproductive organs of C elegans; the highly dynamical network of bundles observed in C elegans early embryos; or the labyrinth patters of micro-ridges in the apical surface of epidermal cells in fish. We will further emphasize these points in the revised manuscript.

Finally, it seems that the microscopic model is unable to recapitulate the density patterns predicted by the continuum theory, raising questions about the suitability of the simulation model.

We thank the referee for raising this question, which needs further clarification. The goal of the microscopic model is not to reproduce the self-organized patterns predicted by the active gel theory. The microscopic model lacks essential ingredients, notably a realistic description of hydrodynamics and turnover. Our goal with the agent-based simulations is to extract the relation between nematic order and active stresses for a small homogeneous sample of the network. This small domain is meant to represent the homogeneous active gel prior to pattern formation, and it allows us to substantiate key assumptions of the continuum model leading to pattern formation, notably the dependence of isotropic and deviatoric components of the active stress on density and nematic order (Eq. 7) and the active generalized stress promoting ordering.

We should mention that reproducing the range of out-of-equilibrium mesoscale architectures predicted by our active gel model with agent-based simulations seems at present not possible, or at least significantly beyond the state-of-the-art. We note for instance that parameter regimes in which agent-based simulations of actin gels display extended contractile steady-states are non-generic, as these simulations often lead to irreversible clumping (as do many reconstituted contractile systems), see e.g. https://doi.org/10.1038/ncomms10323 or https://doi.org/10.1371/journal.pcbi.1005277. Very few references report sustained actin flows or the organization of a few bundles (https://doi.org/10.1371/journal.pcbi.1009506). While agent-based cytoskeletal simulations are very attractive because they directly connect with molecular mechanisms, active gel continuum models are better suited to describe out-ofequilibrium emergent hydrodynamics at a mesoscale. We believe that these two complementary modeling frameworks are rather disconnected in the literature, and for this reason, we have attempted substantiate our continuum modeling with discrete simulations. In the revised manuscript, we will better frame the relationship between them.

Reviewer #2 (Public Review):

Summary:

The article by Waleed et al discusses the self organization of actin cytoskeleton using the theory of active nematics. Linear stability analysis of the governing equations and computer simulations show that the system is unstable to density fluctuations and self organized structures can emerge. While the context is interesting, I am not sure whether the physics is new. Hence I have reservations about recommending this article.

We thank the referee for these comments. In the revised manuscript, we will highlight the novelty of the paper in terms of the theoretical model, the mechanism of patterning of dense nematic structures, the nature and dynamics of the resulting architectures, their relation with the experimental record, and the connection with microscopic models.

We will emphasize the fact that nematic architectures in the actin cytoskeleton are characterized by a co-localization of order and density (and strong variations in each of these fields), that recent work shows that isotropic and nematic organizations coexist and are part of a single heterogeneous network, that the emergence and maintenance of nematic order requires active contraction, and that the assembly and maintenance of dense nematic bundles involves convergent flows. None of these key features can be described by the common incompressible models of active nematics. To address this, we develop here a compressible and density dependent model for an active nematic gel. We will carefully justify that the proposed model is meaningful for actomyosin gels, and we will highlight the commonalities and differences with previous models of active nematics.

Strengths:

(i) Analytical calculations complemented with simulations (ii) Theory for cytoskeletal network

Weaknesses:

Not placed in the context or literature on active nematics.

We agree with the referee that the manuscript requires a better contextualization of the work in relation with the very active field of active nematics. In the revised manuscript, we will clearly describe the relation of our model with existing ones.

Reviewer #3 (Public Review):

The manuscript "Theory of active self-organization of dense nematic structures in the actin cytoskeleton" analysis self-organized pattern formation within a two-dimensional nematic liquid crystal theory and uses microscopic simulations to test the plausibility of some of the conclusions drawn from that analysis. After performing an analytic linear stability analysis that indicates the possibility of patterning instabilities, the authors perform fully non-linear numerical simulations and identify the emergence of stripelike patterning when anisotropic active stresses are present. Following a range of qualitative numerical observations on how parameter changes affect these patterns, the authors identify, besides isotropic and nematic stress, also active self-alignment as an important ingredient to form the observed patterns. Finally, microscopic simulations are used to test the plausibility of some of the conclusions drawn from continuum simulations.

The paper is well written, figures are mostly clear and the theoretical analysis presented in both, main text and supplement, is rigorous. Mechano-chemical coupling has emerged in recent years as a crucial element of cell cortex and tissue organization and it is plausible to think that both, isotropic and anisotropic active stresses, are present within such effectively compressible structures. Even though not yet stated this way by the authors, I would argue that combining these two is of the key ingredients that distinguishes this theoretical paper from similar ones. The diversity of patterning processes experimentally observed is nicely elaborated on in the introduction of the paper, though other closely related previous work could also have been included in these references (see below for examples).

We thank the referee for these comments and for the suggestion to emphasize the interplay of isotropic and anisotropic active tension, which is possible only in a compressible gel. We thank the suggestions of the referee to better connect with existing literature.

To introduce the continuum model, the authors exclusively cite their own, unpublished pre-print, even though the final equations take the same form as previously derived and used by other groups working in the field of active hydrodynamics (a certainly incomplete list: Marenduzzo et al (PRL, 2007), Salbreux et al (PRL, 2009, cited elsewhere in the paper), Jülicher et al (Rep Prog Phys, 2018), Giomi (PRX, 2015),...). To make better contact with the broad active liquid crystal community and to delineate the present work more compellingly from existing results, it would be helpful to include a more comprehensive discussion of the background of the existing theoretical understanding on active nematics. In fact, I found it often agrees nicely with the observations made in the present work, an opportunity to consolidate the results that is sometimes currently missed out on. For example, it is known that self-organised active isotropic fluids form in 2D hexagonal and pulsatory patterns (Kumar et al, PRL, 2014), as well as contractile patches (Mietke et al, PRL 2019), just as shown and discussed in Fig. 2. It is also known that extensile nematics, \kappa<0 here, draw in material laterally of the nematic axis and expel it along the nematic axis (the other way around for \kappa>0, see e.g. Doostmohammadi et al, Nat Comm, 2018 "Active Nematics" for a review that makes this point), consistent with all relative nematic director/flow orientations shown in Figs. 2 and 3 of the present work.

We thank the referee for these suggestions. Indeed, in the original submission we had outsourced much of the justification of the model and the relevant literature to a related pre-print, but this is not reasonable. In the revised manuscript, we will discuss our model in the context of the state-of-the-art, emphasizing connections with existing results.

The results of numerical simulations are well-presented. Large parts of the discussion of numerical observations - specifically around Fig. 3 - are qualitative and it is not clear why the analysis is restricted to \kappa<0. Some of the observations resonate with recent discussions in the field, for example the observation of effectively extensile dynamics in a contractile system is interesting and reminiscent of ambiguities about extensile/contractile properties discussed in recent preprints (https://arxiv.org/abs/2309.04224). It is convincingly concluded that, besides nematic stress on top of isotropic one, active self-alignment is a key ingredient to produce the observed patterns.

We thank the referee for these comments. We will expand the description of the results around Figure 3. We are reluctant to extend the detailed analysis of emergent architectures and dynamics to the case \kappa > 0 as it leads to architectures not observed, to our knowledge, in actin networks. We will expand the characterization of emergent contractile/extensile networks by describing the distribution of the different components of the stress tensor across the bundles and will place our results in the context of related recent work.

I compliment the authors for trying to gain further mechanistic insights into this conclusion with microscopic filament simulations that are diligently performed. It is rightfully stated that these simulations only provide plausibility tests and, within this scope, I would say the authors are successful. At the same time, it leaves open questions that could have been discussed more carefully. For example, I wonder what can be said about the regime \kappa>0 (which is dropped ad-hoc from Fig. 3 onward) microscopically, in which the continuum theory does also predict the formation of stripe patterns - besides the short comment at the very end? How does the spatial inhomogeneous organization the continuum theory predicts fit in the presented, microscopic picture and vice versa?

We thank the referee for this compliment. We think that the point raised by the referee is very interesting. It is reasonable to expect that the sign of \kappa will not be a constant but rather depend on S and \rho. Indeed, for a sparse network with low order, the progressive bundling by crosslinkers acting on nearby filaments is likely to produce a large active stress perpendicular to the nematic direction, whereas in a dense and highly ordered region, myosin motors are more likely to effectively contract along the nematic direction whereas there is little room for additional lateral contraction by additional bundling. In the revised manuscript, we envision to further deepen in this issue in two ways. First, we plan to perform additional agent-based simulations in a regime leading to kappa > 0. Second, we will modify the active gel model such that kappa < 0 for low density/order, so that a fibrillar pattern is assembled, and kappa > 0 for high density/order, so that the emergent fibers are highly contractile.

Overall, the paper represents a valuable contribution to the field of active matter and, if strengthened further, might provide a fruitful basis to develop new hypothesis about the dynamic self-organisation of dense filamentous bundles in biological systems.

Author Response

We would like to thank the editorial board and the reviewers for their assessment of our manuscript and their constructive feedback that we believe will make our manuscript stronger and clearer. Please find below our provisional response to the public reviews; these responses outline our plan to address the concerns of the reviewers for a planned resubmission. Our responses are written in red.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this paper, Misic et al showed that white matter properties can be used to classify subacute back pain patients that will develop persisting pain.

Strengths:

Compared to most previous papers studying associations between white matter properties and chronic pain, the strength of the method is to perform a prediction in unseen data. Another strength of the paper is the use of three different cohorts. This is an interesting paper that provides a valuable contribution to the field.

We thank the reviewer for emphasizing the strength of our paper and the importance of validation on multiple unseen cohorts.

Weaknesses:

The authors imply that their biomarker could outperform traditional questionnaires to predict pain: "While these models are of great value showing that few of these variables (e.g. work factors) might have significant prognostic power on the long-term outcome of back pain and provide easy-to-use brief questionnaires-based tools, (21, 25) parameters often explain no more than 30% of the variance (28-30) and their prognostic accuracy is limited.(31)". I don't think this is correct; questionnaire-based tools can achieve far greater prediction than their model in about half a million individuals from the UK Biobank (Tanguay-Sabourin et al., A prognostic risk score for the development and spread of chronic pain, Nature Medicine 2023).

We agree with the reviewer that we might have under-estimated the prognostic accuracy of questionnaire-based tools, especially, the strong predictive accuracy shown by Tangay-Sabourin 2023. In the revised version, we will change both the introduction and the discussion to reflect the the questionnaires based prognostic accuracy reported in the seminal work by TangaySabourin. We do note here, however, that the latter paper while very novel is unique in showing the power of questionnaires. In addition, the questionnaires we have tested in our cohort did not show any baseline differences suggestive of prognostic accuracy.

Moreover, the main weakness of this study is the sample size. It remains small despite having 3 cohorts. This is problematic because results are often overfitted in such a small sample size brain imaging study, especially when all the data are available to the authors at the time of training the model (Poldrack et al., Scanning the horizon: towards transparent and reproducible neuroimaging research, Nature Reviews in Neuroscience 2017). Thus, having access to all the data, the authors have a high degree of flexibility in data analysis, as they can retrain their model any number of times until it generalizes across all three cohorts. In this case, the testing set could easily become part of the training making it difficult to assess the real performance, especially for small sample size studies.

The reviewer raises a very important point of limited sample size and of the methodology intrinsic of model development and testing. We acknowledge the small sample size in the “Limitations” section of the discussion. In the resubmission, we will acknowledge the degree of flexibility that is afforded by having access to all the data at once. However, we will also note that our SLF-FA based model is a simple cut-off approach that does not include any learning or hidden layers and that the data obtained from Open Pain were never part of the “training” set at any point at either the New Haven or the Mannheim site. Regarding our SVC approach we follow standard procedures for machine learning where we never mix the training and testing sets. The models are trained on the training data with parameters selected based on crossvalidation within the training data. Therefore, no models have ever seen the test data set. The model performances we reported reflect the prognostic accuracy of our model. Finally, as discussed by Spisak et al., 1 the key determinant of the required sample size in predictive modeling is the ” true effect size of the brain-phenotype relationship” which we think is the determinant of the replication we observe in this study. As such the effect size in the New Haven and Mannheim data is Cohen’s d >1.

Even if the performance was properly assessed, their models show AUCs between 0.65-0.70, which is usually considered as poor, and most likely without potential clinical use. Despite this, their conclusion was: "This biomarker is easy to obtain (~10 min 18 of scanning time) and opens the door for translation into clinical practice." One may ask who is really willing to use an MRI signature with a relatively poor performance that can be outperformed by self-report questionnaires?

The reviewer is correct, the model performance is poor to fair which limits its usefulness for clinical translation. We wanted to emphasize that obtaining diffusion images can be done in a short period of time and, hence, as such models predictive accuracy improves, clinical translation becomes closer to reality. In addition, our findings are based on old diffusion data and limited sample size coming from different sites and different acquisition sequences. This by itself would limit the accuracy especially that evidence shows that sample size affect also model performance (i.e. testing AUC)1. In the revision, we will re-word the sentence mentioned by the reviewer to reflect the points discussed here. This also motivates us to collect a more homogeneous and larger sample.

Overall, these criticisms are more about the wording sometimes used and the inference they made. I think the strength of the evidence is incomplete to support the main claims of the paper.

Despite these limitations, I still think this is a very relevant contribution to the field. Showing predictive performance through cross-validation and testing in multiple cohorts is not an easy task and this is a strong effort by the team. I strongly believe this approach is the right one and I believe the authors did a good job.

We thank the reviewer for acknowledging that our effort and approach were the right ones.

Minor points:

Methods:

I get the voxel-wise analysis, but I don't understand the methods for the structural connectivity analysis between the 88 ROIs. Have the authors run tractography or have they used a predetermined streamlined form of 'population-based connectome'? They report that models of AUC above 0.75 were considered and tested in the Chicago dataset, but we have no information about what the model actually learned (although this can be tricky for decision tree algorithms).

We apologize for the lack of clarity; we did run tractography and we did not use a predetermined streamlined form of the connectome. We will clarify this point in the methods section.

Finding which connections are important for the classification of SBPr and SBPp is difficult because of our choices during data preprocessing and SVC model development: (1) preprocessing steps which included TNPCA for dimensionality reduction, and regressing out the confounders (i.e., age, sex, and head motion); (2) the harmonization for effects of sites; and (3) the Support Vector Classifier which is a hard classification model2. Such models cannot tell us the features that are important in classifying the groups. Our model is considered a black-box predictive model like neural networks.

Minor:

What results are shown in Figure 7? It looks more descriptive than the actual results.

The reviewer is correct; Figure 7 and supplementary Figure 4 are both qualitatively illustrating the shape of the SLF.

Reviewer #2 (Public Review):

The present study aims to investigate brain white matter predictors of back pain chronicity. To this end, a discovery cohort of 28 patients with subacute back pain (SBP) was studied using white matter diffusion imaging. The cohort was investigated at baseline and one-year follow-up when 16 patients had recovered (SBPr) and 12 had persistent back pain (SBPp). A comparison of baseline scans revealed that SBPr patients had higher fractional anisotropy values in the right superior longitudinal fasciculus SLF) than SBPp patients and that FA values predicted changes in pain severity. Moreover, the FA values of SBPr patients were larger than those of healthy participants, suggesting a role of FA of the SLF in resilience to chronic pain. These findings were replicated in two other independent datasets. The authors conclude that the right SLF might be a robust predictive biomarker of CBP development with the potential for clinical translation.

Developing predictive biomarkers for pain chronicity is an interesting, timely, and potentially clinically relevant topic. The paradigm and the analysis are sound, the results are convincing, and the interpretation is adequate. A particular strength of the study is the discovery-replication approach with replications of the findings in two independent datasets.

We thank reviewer 2 for pointing to the strength of our study.

The following revisions might help to improve the manuscript further.

Definition of recovery. In the New Haven and Chicago datasets, SBPr and SBPp patients are distinguished by reductions of >30% in pain intensity. In contrast, in the Mannheim dataset, both groups are distinguished by reductions of >20%. This should be harmonized. Moreover, as there is no established definition of recovery (reference 79 does not provide a clear criterion), it would be interesting to know whether the results hold for different definitions of recovery. Control analyses for different thresholds could strengthen the robustness of the findings.

The reviewer raises an important point regarding the definition of recovery. To address the reviewers concern we will add a supplementary figure showing the results in the Mannheim data set if a 30% reduction is used as a recovery criterion. We would like to emphasize here several points that support the use of different recovery thresholds between New Haven and Mannheim. The New Haven primary pain ratings relied on visual analogue scale (VAS) while the Mannheim data relied on the German version of the West-Haven-Yale Multidimensional Pain Inventory. In addition, the Mannheim data was pre-registered with a definition of recovery at 20% and is part of a larger sub-acute to chronic pain study with prior publications from this cohort using the 20% cut-off3. Finally, a more recent consensus publication4 from IMMPACT indicates that a change of at least 30% is needed for a moderate improvement in pain on the 0-10 Numerical Rating Scale but that this percentage depends on baseline pain levels.

Analysis of the Chicago dataset. The manuscript includes results on FA values and their association with pain severity for the New Haven and Mannheim datasets but not for the Chicago dataset. It would be straightforward to show figures like Figures 1 - 4 for the Chicago dataset, as well.

We welcome the reviewer’s suggestion; we will therefore add these analyses to the results section of our manuscript upon resubmission

Data sharing. The discovery-replication approach of the present study distinguishes the present from previous approaches. This approach enhances the belief in the robustness of the findings. This belief would be further enhanced by making the data openly available. It would be extremely valuable for the community if other researchers could reproduce and replicate the findings without restrictions. It is not clear why the fact that the studies are ongoing prevents the unrestricted sharing of the data used in the present study.

Reviewer #3 (Public Review):

Summary:

Authors suggest a new biomarker of chronic back pain with the option to predict the result of treatment. The authors found a significant difference in a fractional anisotropy measure in superior longitudinal fasciculus for recovered patients with chronic back pain.

Strengths:

The results were reproduced in three different groups at different studies/sites.

Weaknesses:

The number of participants is still low.

We have discussed this point in our replies to reviewer number 1.

An explanation of microstructure changes was not given.

The reviewer points to an important gap in our discussion. While we cannot do a direct study of actual tissue micro-structure, we will explore further the changes observed in the SLF by calculating diffusivity measures and discuss possible explanations of these changes.

Some technical drawbacks are presented.

We are uncertain if the reviewer is suggesting that we have acknowledged certain technical drawbacks and expects further elaboration on our part. We kindly request that the reviewer specify what particular issues they would like us to address so that we can respond appropriately.

(1) Spisak T, Bingel U, Wager TD. Multivariate BWAS can be replicable with moderate sample sizes. Nature 2023;615:E4-E7.

(2) Liu Y, Zhang HH, Wu Y. Hard or Soft Classification? Large-margin Unified Machines. J Am Stat Assoc 2011;106:166-177.

(3) Loffler M, Levine SM, Usai K, et al. Corticostriatal circuits in the transition to chronic back pain: The predictive role of reward learning. Cell Rep Med 2022;3:100677.

(4) Smith SM, Dworkin RH, Turk DC, et al. Interpretation of chronic pain clinical trial outcomes: IMMPACT recommended considerations. Pain 2020;161:2446-2461.

eLife assessment

This valuable study provides incomplete evidence that white matter diffusion imaging of the right superior longitudinal fasciculus might help to develop a predictive biomarker of chronic back pain chronicity. The results are based on a discovery-replication approach with different cohorts, but the sample size is limited, and the clinical relevance is overstated. The findings will interest researchers interested in the brain mechanisms of chronic pain and in developing brain-based biomarkers of chronic pain.

Reviewer #1 (Public Review):

Summary:

In this paper, Misic et al showed that white matter properties can be used to classify subacute back pain patients that will develop persisting pain.

Strengths:

Compared to most previous papers studying associations between white matter properties and chronic pain, the strength of the method is to perform a prediction in unseen data. Another strength of the paper is the use of three different cohorts. This is an interesting paper that provides a valuable contribution to the field.

Weaknesses:

The authors imply that their biomarker could outperform traditional questionnaires to predict pain: "While these models are of great value showing that few of these variables (e.g. work factors) might have significant prognostic power on the long-term outcome of back pain and provide easy-to-use brief questionnaires-based tools, (21, 25) parameters often explain no more than 30% of the variance (28-30) and their prognostic accuracy is limited.(31)". I don't think this is correct; questionnaire-based tools can actually achieve far greater prediction than their model in about half a million individuals from the UK Biobank (Tanguay-Sabourin et al., A prognostic risk score for the development and spread of chronic pain, Nature Medicine 2023).

Moreover, the main weakness of this study is the sample size. It remains small despite having 3 cohorts. This is problematic because results are often overfitted in such a small sample size brain imaging study, especially when all the data are available to the authors at the time of training the model (Poldrack et al., Scanning the horizon: towards transparent and reproducible neuroimaging research, Nature Reviews in Neuroscience 2017). Thus, having access to all the data, the authors have a high degree of flexibility in data analysis, as they can retrain their model any number of times until it generalizes across all three cohorts. In this case, the testing set could easily become part of the training making it difficult to assess the real performance, especially for small sample size studies.

Even if the performance was properly assessed, their models show AUCs between 0.65-0.70, which is usually considered as poor, and most likely without potential clinical use. Despite this, their conclusion was: "This biomarker is easy to obtain (~10 min 18 of scanning time) and opens the door for translation into clinical practice." One may ask who is really willing to use an MRI signature with a relatively poor performance that can be outperformed by self-report questionnaires?

Overall, these criticisms are more about the wording sometimes used and the inference they made. I think the strength of the evidence is incomplete to support the main claims of the paper.

Despite these limitations, I still think this is a very relevant contribution to the field. Showing predictive performance through cross-validation and testing in multiple cohorts is not an easy task and this is a strong effort by the team. I strongly believe this approach is the right one and I believe the authors did a good job.

Minor points:

Methods:

I get the voxel-wise analysis, but I don't understand the methods for the structural connectivity analysis between the 88 ROIs. Have the authors run tractography or have they used a predetermined streamlined form of 'population-based connectome'? They report that models of AUC above 0.75 were considered and tested in the Chicago dataset, but we have no information about what the model actually learned (although this can be tricky for decision tree algorithms).

Minor:<br /> What results are shown in Figure 7? It looks more descriptive than the actual results.

Reviewer #2 (Public Review):

The present study aims to investigate brain white matter predictors of back pain chronicity. To this end, a discovery cohort of 28 patients with subacute back pain (SBP) was studied using white matter diffusion imaging. The cohort was investigated at baseline and one-year follow-up when 16 patients had recovered (SBPr) and 12 had persistent back pain (SBPp). A comparison of baseline scans revealed that SBPr patients had higher fractional anisotropy values in the right superior longitudinal fasciculus SLF) than SBPp patients and that FA values predicted changes in pain severity. Moreover, the FA values of SBPr patients were larger than those of healthy participants, suggesting a role of FA of the SLF in resilience to chronic pain. These findings were replicated in two other independent datasets. The authors conclude that the right SLF might be a robust predictive biomarker of CBP development with the potential for clinical translation.

Developing predictive biomarkers for pain chronicity is an interesting, timely, and potentially clinically relevant topic. The paradigm and the analysis are sound, the results are convincing, and the interpretation is adequate. A particular strength of the study is the discovery-replication approach with replications of the findings in two independent datasets.

The following revisions might help to improve the manuscript further.

- Definition of recovery. In the New Haven and Chicago datasets, SBPr and SBPp patients are distinguished by reductions of >30% in pain intensity. In contrast, in the Mannheim dataset, both groups are distinguished by reductions of >20%. This should be harmonized. Moreover, as there is no established definition of recovery (reference 79 does not provide a clear criterion), it would be interesting to know whether the results hold for different definitions of recovery. Control analyses for different thresholds could strengthen the robustness of the findings.

- Analysis of the Chicago dataset. The manuscript includes results on FA values and their association with pain severity for the New Haven and Mannheim datasets but not for the Chicago dataset. It would be straightforward to show figures like Figures 1 - 4 for the Chicago dataset, as well.

- Data sharing. The discovery-replication approach of the present study distinguishes the present from previous approaches. This approach enhances the belief in the robustness of the findings. This belief would be further enhanced by making the data openly available. It would be extremely valuable for the community if other researchers could reproduce and replicate the findings without restrictions. It is not clear why the fact that the studies are ongoing prevents the unrestricted sharing of the data used in the present study.

Reviewer #3 (Public Review):

Summary:

Authors suggest a new biomarker of chronic back pain with the option to predict the result of treatment. The authors found a significant difference in a fractional anisotropy measure in superior longitudinal fasciculus for recovered patients with chronic back pain.

Strengths:<br /> The results were reproduced in three different groups at different studies/sites.

Weaknesses:<br /> - The number of participants is still low.<br /> - An explanation of microstructure changes was not given.<br /> - Some technical drawbacks are presented.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Weaknesses:

One important question is needed to further clarify the mechanisms of aberrant Ca2+ microwaves as described below.

Synapsin promoter labels both excitatory pyramidal neurons and inhibitory neurons. To avoid aberrant Ca2+ microwave, a combination of Flex virus and CaMKII-Cre or Thy-1-GCaMP6s and 6f mice were tested. However, all these approaches limit the number of infected pyramidal neurons. While the comprehensive display of these results is appreciated, a crucial question remains unanswered. To distinguish whether the microwave of Ca2+ is caused selectively via the abnormality of interneurons, or just a matter of pyramidal neuron density, testing Flex-GCaMP6 in interneuron specific mouse lines such as PV-Cre and SOM-Cre will be critical.

We agree that unravelling the role of interneurons is important to the understanding of the cellular mechanisms. However, the primary goal of this preprint was to alert the field and those embarking on in vivo Ca2+ imaging to AAV transduction induced artefacts mediated by one of the most widely used viral constructs for Ca2+ imaging in the field. It was important to us to distribute this finding among the community in a timely manner to avoid the unnecessary waste of resources.

We consider a thorough understanding of cell-type specific mechanisms interesting. However, the biological relevance of the Ca2+ waves is as yet unclear and to disentangle exactly which cellular and subcellular factors that drive the aberrant phenomenon will require a large systematic effort which goes beyond our resources. For instance, it will be technically not trivial to separate biologically relevant contributions from technical differences. For instance, the absence of Ca2+ waves under the principal neuron promotor CaMKII may suggest the involvement of interneurons. However, alternate possibilities are a reduced density of expression across principal neurons or that the expression levels between the 2 promoters is different.

The important, take-home message of the preprint, in our opinion, is that users check carefully their viral protocols, adjust the protocols for their specific scientific question and report any issues. We now emphasise the fact that although Ca2+ waves were not observed following conditional expression of syn.GCaMP with CaMKII.cre, this may not be due to a requirement for interneuronal expression but simply reflect differences in final GCaMP expression density and levels between the two transduction procedures (P12, L298-303).

Reviewer #2 (Public Review):

Weaknesses:

Whether micro-waves are associated with the age of mice was not quantified. This would be good to know and the authors do have this data.

We plotted the animal age at the time of injection for all injections of Syn.GCaMP6 into CA1/CA3 and found no correlation in either the occurrence of Ca2+ waves nor the frequency of Ca2+ waves during the age period between 5 – 79 wks (see reviewer Fig1; linear regression fit to the Ca2+ wave frequency against age was not significant: intercept = 1.37, slope = -0.007, p=0.62, n = 14; and generalized linear model relating Ca2+ wave ~ age was not significant: z score = 0.19, deviance above null = 0.04, p = 0.85, n=24). We have now added a statement to this in the revised manuscript (P14 L354-359) and for the reviewers we have added the plots below.

Author response image 1.

Plot of Ca2+ micro-wave frequency (left: number of Ca2+ waves/min) or occurrence (right: yes/no) against the animal age at the time of viral injection. Blue line is linear (left) or logistic (right) fit to the data with 95% confidence level.

The effect of micro-waves on single cell function was not analyzed. It would be useful, for example, if we knew the influence of micro-waves on place fields. Can a place cell still express a place field in a hippocampus that produces micro-waves? What effect might a microwave passing over a cell have on its place field? Mice were not trained in these experiments, so the authors do not have the data.

We agree that these are interesting questions; however, the preprint is focused on describing the GECI expression conditions prone to generating these artefacts. Studying the effects of Ca2+ micro-waves on the circuitry are scientific questions, and would require an experimental framework of testing the aberrant activity on a specific physiological function e.g. place activity or specific oscillations (e.g. sharp-wave activity). Ca2+ microwaves, as the ones described here, have not been reported under physiological conditions or pathophysiological conditions and studying the effects of such artefactual waves on the circuit was not our intention.

With respect to place cell activity, specifically, it is intuitive that during the Ca2+ micro-wave the participating cell’s place field activity would be obscured by the artefactual activity. Cell activity appears to return immediately following the wave suggesting that the cells could exhibit place activity outside their participation in the Ca2+ micro-waves. However, we do not know if the Ca2+ micro-wave activity disrupts the generation or maintenance of place fields. We have now added a brief reference to possible effects on place coding to the paper (P12, L315-317).

The CaMKII-Cre approach for flexed-syn-GCaMP expression shows no micro-waves and is convincing, but it is only from 2 animals, even though both had no micro-waves. In light of the reviewer’s comment, we have added a further 3 animals with conditional expression of GCaMP6m from the DZNE to complement the current dataset with conditional expression of GCaMP6s from UoB (P10, L236 & 239 and revised table 1). Although Ca2+ waves were not observed in any of the in total 5 animals, we still do not know with all certainty whether this approach is completely safe. Time will show if researchers still encounter the phenotype under certain conditions when using this conditional approach.

The authors state in their Discussion that even without observable microwaves, a syn-Ca2+-indicator transduction strategy could still be problematic. This may be true, but they do not check this in their analysis, so it remains unknown

We agree with the reviewer and have now made this point clearer in the revised discussion (P11, L257-258)

Reviewer #3 (Public Review):

Weaknesses:

I believe that the weaknesses of the manuscript are appropriately highlighted by the authors themselves in the discussion. I would, however, like to emphasize several additional points.

As the authors state, the exact conditions that lead to Ca2+ micro-waves are unclear from this manuscript. It is also unclear if Ca2+ micro-waves are specific to GECI expression or if high-titer viral transduction of other proteins such as genetically encoded voltage indicators, static fluorescent proteins, recombinases, etc could also cause Ca2+ micro-waves.

The high expression of other proteins has been shown to result in artefactual phenomenon such as toxicity or fluorescent puncta (for GFP see Hechler et al. 2006; Katayama et al. 2008 for GEVI see Rühl et al. 2021), but we are not aware of reports of micro-waves. Although it is certainly possible that high expression levels of other proteins could lead to waves, we suspect the Ca2+ micro-waves observed in this preprint result from a dysregulation of Ca2+ homeostasis. This is not to suggest that voltage indicators could not result in micro-waves (e.g. Ca2+ homeostasis may be indirectly affected).

The authors almost exclusively tested high titer (>5x10^12 vg/mL) large volume (500-1000 nL) injections using the synapsin promoter and AAV1 serotypes. It is possible that Ca2+ micro-waves are dramatically less frequent when titers are lowered further but still kept high enough to be useful for in vivo imaging (e.g. 1x10^12 vg/mL) or smaller injection volumes are used. It is also possible that Ca2+ micro-waves occur with high titer injections using other viral promoter sequences such as EF1α or CaMKIIα. There may additionally be effects of viral serotype on micro-wave occurrence.

We agree with all points raised by the reviewer. Notably, we used viral transduction protocols with titers and volumes within in the range of those previously used for viral transduction of GCaMP under the synapsin promoter (see P11 L269-275) and we observed Ca2+ micro-waves. As the reviewer suggested, we did find that lowering the titer is an important factor in reducing these Ca2+ micro-waves and there is likely a wide range of approaches that avoid the phenomenon. With regards to viral serotype, we show that micro-waves occurred across AAV1 and 9, but it is possible that other serotypes may avoid the phenomenon.

We reiterate in the abstract of the revised manuscript that expression level is a crucial factor (P2, L40 and P2, L44-45) and now mention that other promoters and induction protocols that result in high Ca2+ indicator expression may result in Ca2+ micro-waves (P12, L291-294.

The number of animals in any particular condition are fairly low (Table 1) with the exception of V1 imaging and thy1-GCaMP6 imaging. This prohibits rigorous comparison of the frequency of pathological calcium activity across conditions.

We have now added 3 more animals with conditional GCaMP6 expression. In total, the study contains 34 animals with viral injection into the hippocampus from different laboratories and under different conditions resulting in multiple groups. As such we are cognizant of the resulting limitations for statistical evaluation.

However, in light of the reviewer’s comment, we have now employed a generalized linear model tested on all the data to examine the relationship between the Ca2+ micro-wave incidence and the different factors. The multivariate GLM did find a significant relationship between Ca2+ micro-wave incidence and both viral dilution and weeks post injection (see below and revised manuscript P8, L189-193).

For injections into CA1 in the hippocampus (n=28), a GLM found no relationship between Ca2+ micro-waves and each of the individual variables x (Ca-wave ~ x) ; viral dilution: z score = 1.14, deviance above null = 1.31, p = 0.254; post injection weeks: : z score = 1.18, deviance above null = 1.44, p = 0.239; injection volume: : z score = -0.76, deviance above null = 0.59, p = 0.45; construct: : z score = 1.18, difference in deviance above null = 1.44, p = 0.239)

However, a multivariable logistic GLM relating dilution and post injection weeks (Ca-wave ~ dilution + p.i_wks) showed that together both variables were significantly related to Ca2+ micro-waves (Deviation above null = 7.5; Dilution: z score = 2.18, p < 0.05; p.i_wks : z score = 2.22, p < 0.05).

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

Results are straightforward and convincing. While a couple of ways to reduce the aberrant microwaves of calcium responses were demonstrated, delving into the functions of interneurons is crucial for a more comprehensive understanding of cellular causality.

As mentioned in the public response, disentangling cellular mechanism from technical requirements will need a large and systematic study. To determine the contribution from interneurons, the use of specific interneuron promoters would be required, and viral titers systematically varied to result in similar cellular GCaMP expression levels as seen under the synapsin promoter condition.

Reviewer #2 (Recommendations For The Authors):

Do the authors think the cells are firing when they participate in a micro-wave, or do they think the calcium influx is due to something else? A discussion point on this would be good.

This is an excellent point raised by the reviewer. We do not know if the elevated cellular Ca2+ during the artifactual Ca2+ micro-wave reflects action potential firing or an increase of Ca2+ from intracellular stores. As already described in the text of the preprint, their optical spatiotemporal profile neither fits with known microseizure progression patterns, nor with spreading depolarization/depression. We have adopted the reviewer’s suggestion and added the following point to the discussion section in the revised preprint (P12, L308-315):

In a limited dataset, we attempted to detect the Ca2+ micro-waves by hippocampal LFP recordings (using a conventional insulated Tungsten wire, diameter ~110µm). We could not identify a specific signature, e.g. ictal activity or LFP depression, which may correspond to these Ca2+ micro-waves. The crucial shortcoming of this experiment of course is that with these LFP recordings, we could not simultaneous perform hippocampal 2-photon microscopy. Thus, it is uncertain if the Ca2+ micro-waves indeed occurred in proximity to our electrode.

The results seem to suggest that micro-waves may involve interneurons as their CaMKII-Cre strategy avoids waves - possibly due to a lack of expression of GECIs in interneurons. It would be great to hear the author's thoughts on this and add a brief discussion point.

As mentioned in public response to Reviewer 1, it is difficult to disentangle cellular mechanisms from technical requirements, and the exact requirements for the Ca2+ micro-waves to occur are still not fully clear. The absence of Ca2+ micro-waves in our CaMKII-Cre dataset may indeed reflect the requirement of interneurons. However, it could just as well be due to a sparse labelling of principle cells or simply reflect differences in the expression levels of GCaMP under the different promotors.

All in all, a more complete understanding of the requirements of such Ca2+ micro-waves will require a community effort. Therefore, it is important that each group check the safety profile of their GECI and report problems to the community.

We have added these points to the revised preprint (P12, L291 and P12, L298)

Plotting the incidence of micro-waves as a function of the age of mice would be a nice addition (the authors have the data).

There was no relationship of Ca2+ micro-wave occurrence or frequency with age over the range of 5-79 wks (see public response) and this has been added to the preprint (P14, L354)

Reviewer #3 (Recommendations For The Authors):

I appreciate the authors raising the awareness of this issue. I had personally observed micro-waves in my own data as well. In agreement with their findings, I found that the occurrence of micro-waves was dramatically lower when I reduced the viral titer. Anecdotally, I also observed voltage micro-waves when virally transducing genetically encoded voltage indicators at similar titers. For that reason, I am skeptical that this issue is exclusive to GECIs.

We find it interesting that the reviewer has also seen artefactual micro-waves following viral transduction of genetically encoded voltage indicators. Without seeing the voltage waves the referee is referring to or the conditions, it is of course difficult to compare with the Ca2+ micro-waves we report. However, this comment again raises the question of mechanism. We believe that in the GECI framework, Ca2+ homeostatic aspects are important. Voltage indicators are based on different sensor mechanisms, and expressed in the cell membrane, but it may very well be that there are overlapping factors between Ca2+ and voltage indicators that could trigger a similar, or even the same phenomenon in the end.

Minor comments:

(1) Line 131-132: I believe the authors only tested for micro-waves in V1. This should be made clear in the results. It could be that micro-waves could occur in other parts of cortex with the same viral titers.

Both V1 and somatosensory cortex were tested as described in the methods (P15, L395-397), we have made this clearer in the revised preprint (P6, L138).

(2) There are no statistics associated with the data from Fig 1e.

We have now added statistics (P5, L126).

(3) The authors may be able to make a stronger claim about the pathological nature of the micro-waves if there are differences in the histology between the injected and non-injected hemispheres. For example, is there evidence of widespread cell death in the injected hemisphere (e.g. lower cell count, smaller hippocampal volume, caspase staining, etc).

We found no evidence of gross morphological changes to the hippocampus following viral transduction with no changes in CA1 pyramidal cell layer thickness or CA1 thickness (pyramidal cell layer thickness: 49 ± 12.5 µm ipsilateral and 50.3 ± 11.1 µm contralateral, n=4, Student’s t-test p=0.89; CA1 thickness: 553.3 ± 14 µm ipsilateral and 555.8 ± 62 µm contralateral, n = 4, Student’s t-test p=0.94; 48 ± 13 weeks post injection at time of perfusion).

We have added this to the preprint (P5, L117-122)

(4) The broader micro-waves in the stratum oriens versus the stratum pyramidale are likely due to the spread of the basal dendrites of pyramidal cells. If the typical size of the basal dendritic arbor of CA1 pyramidal neurons is taken into account, does this explain the wider calcium waves in this layer.

Absolutely, great point, yes, we completely agree on this. It is likely the active neuropil (including dendritic arbour) are contributing to the apparent broader diameter. In addition, as evident in the video 5 cell somata in the stratum Oriens (possibly interneurons) are active and their processes also contribute.

We have now mentioned these points in the revised preprint (P5, L132)

(5) Lines 179-181: Is the difference in the prevalence of micro-waves between viral titers statistically significant?

Although we have a large number of animals in total (n=34) with viral injection into the hippocampus, the number of animals in each condition, given the many factors, is low. We therefore used a generalized linear model to test the relationship between the Ca2+ micro-waves and the variables.

We have now added this analysis to the revised preprint (P8, L189-193)

(6) Lines 200-203: The CA3 micro-waves were only observed at one institution. The current wording is slightly misleading.

We agree and have changed this to be clearer (P9 L216)

Reviewer #4 (Public Review):

Summary:

Masala N et al showed interesting aberrant calcium microwaves in the hippocampus when synapsin promoter driven GCaMPs were expressed for a long period of time. These aberrant hippocampal Ca2+ micro-waves depend on the viral titre of the GECI. The microwave of Ca2+ was not observed when GECI was expressed only a sparse set of neurons.

Strengths:

These findings are important to wide neuroscience community especially when considering a great number of investigators are using similar approaches. Results look convincing and are consistent across several laboratories.

Weaknesses:

Synapsin promoter labels both excitatory pyramidal neurons and inhibitory neurons. To avoid aberrant Ca2+ microwave, a combination of Flex virus and CaMKII-Cre or Thy-1-GCaMP6s and 6f mice were tested. However, all these approaches limit the number of infected pyramidal neurons. While the comprehensive display of these results is appreciated, one additional important test would be more informative. To distinguish whether the microwave of Ca2+ is sufficiently caused via the expression of GCaMP in interneurons, or just a matter of pyramidal neuron density, testing Flex-GCaMP6 in interneuron specific mouse lines such as PV-Cre and SOM-Cre will provide further clarifications.

eLife assessment

This important study provides convincing evidence of artifactual calcium micro-waves during calcium imaging of populations of neurons in the hippocampus using methods that are common in the field. The work raises awareness of these artifacts so that any research labs planning to do calcium imaging in the hippocampus can avoid them by using alternative strategies that the authors propose.

Reviewer #2 (Public Review):

Summary:

The authors describe and quantify a phenomenon in the CA1 and CA3 of the hippocampus that they call aberrant Ca2+ micro-waves. Micro-waves are sometimes seen during 2-photon calcium imaging of populations of neurons under certain conditions. They are spatially confined slow calcium events that start in a few cells and slowly spread to neighboring groups of cells. This phenomenon has been uttered between researchers in the field at conferences, but no one has taken the time to carefully capture and quantify micro-waves and pin down the causes. The authors show that micro-waves are dependent on the viral titre of the genetically encoded calcium indicators (GECIs), the genetic promoter (synapsin), the neuronal subtype (granule cells in the dentate gyrus do not produce micro-waves and they are not seen in the neocortex), and the density of GECI expression. The authors should be commended for their work and for raising awareness to all labs doing any form of calcium imaging in populations of neurons. The authors also come up with alternative approaches to avoid artifactual micro-waves such as reducing the transduction titre (1:2 dilution of virus) and a transduction method employing sparser and cre-dependent GECI expression in principal cells using a CaMKII promoter.

Strengths:

The micro-waves reported in the paper were robustly observed across 4 laboratories and 3 different countries with various experimenters and calcium imaging set-ups. This adds significant strength to the work.

The age of mice used covered a broad range (from 6 to 43 weeks). This is a strength because it covers most ages that are used in labs that regularly do calcium imaging.

Another strength is they used different GCaMP variants (GCaMP6m, GCaMP6s, GCaMP7f), as well as a red indicator: RCaMP. This shows the micro-waves are not an issue with any particular GECI, as the authors suggest.

The authors include many movies of micro-waves. This is extremely useful for researchers in the field to view them in real-time so they can identify them in their own data.

They provide a useful table with specific details of the virus injected, titre, dilution, and other information along with the incidence of micro-waves. A nice look-up table for researchers to see if their viral strategy is associated with a high or low incidence of micro-waves.

Weaknesses:

The effect of mico-waves on single cell function was not analyzed. It would be useful, for example, if we knew the influence of micro-waves on place fields. Can a place cell still express a place field in a hippocampus that produces micro-waves? What effect might a microwave passing over a cell have on its place field? Mice were not trained in these experiments, so the authors do not have the data. However, they do briefly discuss these ideas.

Reviewer #3 (Public Review):

Summary:

The work by Masala and colleagues highlights a striking artifact that can result from a particular viral method for expressing genetically encoded calcium indicators (GECIs) in neurons. In a cross-institutional collaboration, the authors find that viral transduction of GECIs in the hippocampus can result in aberrant slow-traveling calcium (Ca2+) micro-waves. These Ca2+ micro-waves are distinct from previously described ictal activity but nevertheless are likely a pathological consequence of overexpression of virally transduced proteins. Ca2+ micro-waves will most-likely obscure the physiology that most researchers are interested in studying with GECIs, and their presence indicates that the neural circuit is in an unintended pathological state. Interestingly this pathology was not observed using the same viral transduction methods in other brain regions. The authors recommend several approaches that may help other experimenters avoid this confound in their own data such as reducing the titer of viral injections or using recombinase-dependent expression. The intent of this manuscript is to raise awareness of the potential unintended consequences of viral overexpression, particularly for GECIs. A rigorous investigation into the exact causes of Ca2+ micro-waves or the mechanisms supporting them are beyond the authors' intended scope.

Strengths:

The authors clearly demonstrate that Ca2+ micro-waves occur in the CA1 and CA3 regions of the hippocampus following large volume, high titer injections of adeno-associated viruses (AAV1 and AAV9) encoding GECIs. The supplementary videos provide undeniable proof of their existence.

By forming an inter-institutional collaboration, the authors demonstrate that this phenomenon is robust to changes in surgical techniques or imaging conditions.

Weaknesses:

I believe that the weaknesses of the manuscript are appropriately highlighted by the authors themselves in the discussion. The manuscript does not attempt to exhaustively characterize the conditions under which calcium micro-waves occur. Rather, the authors raise awareness of this problem.

Author Response

eLife assessment

The authors used electrophysiology in brain slices and computer modeling and suggest that layer 2/3 pyramidal neurons of the mouse cortex express functional HCN channels, despite little evidence in the past that they are present. The study is useful at the present time, but results are incomplete because the methods, data, and analyses do not always support the conclusions.

Public Reviews:

Reviewer #1 (Public Review):

The manuscript by Oleh et al. uses in vitro electrophysiology and compartmental modeling (via NEURON) to investigate the expression and function of HCN channels in mouse L2/3 pyramidal neurons. The authors conclude that L2/3 neurons have developmentally regulated HCN channels, the activation of which can be observed when subjected to large hyperpolarizations. They further conclude via blockade experiments that HCN channels in L2/3 neurons influence cellular excitability and pathway-specific EPSP kinetics, which can be neuromodulated. While the authors perform a wide range of slice physiology experiments, concrete evidence that L2/3 cells express functionally relevant HCN channels is limited. There are serious experimental design caveats and confounds that make drawing strong conclusions from the data difficult. Furthermore, the significance of the findings is generally unclear, given modest effect sizes and a lack of any functional relevance, either directly via in vivo experiments or indirectly via strong HCN-mediated changes in known operations/computations/functions of L2/3 neurons.

Specific points:

(1) The interpretability and impact of this manuscript are limited due to numerous methodological issues in experimental design, data collection, and analysis. The authors have not followed best practices in the field, and as such, much of the data is ambiguous and/or weak and does not support their interpretations (detailed below). Additionally, the authors fail to appropriately explain their rationale for many of their choices, making it difficult to understand why they did what they did. Furthermore, many important references appear to be missing, both in terms of contextualizing the work and in terms of approach/method. For example, the authors do not cite Kalmbach et al 2018, which performed a directly comparable set of experiments on HCN channels in L2/3 neurons of both humans and mice. This is an unacceptable omission. Additionally, the authors fail to cite prior literature regarding the specificity or lack thereof of Cs+ in blocking HCN. In describing a result, the authors state "In line with previous reports, we found that L2/3 PCs exhibited an unremarkable amount of sag at 'typical' current commands" but they then fail to cite the previous reports.

We thank the reviewer for the thorough examination of our manuscript; however, we strongly disagree with many of the raised concerns for several reasons, as detailed in an initial response below:

To address the lack of certain citations, we would like to emphasize that in the introduction section, we did focus on a several decades-long line of investigation into the HCN channel content of layer 2/3 pyramidal cells (L2/3 PCs), where there has undoubtedly been some controversy as to their functional contribution. We did not explicitly cite papers that claimed to find no/little HCN channels/sag- although this would be a significant list of pubs from some excellent senior investigators, as we wanted to avoid shining a negative light on otherwise excellent publications. However, we plan to address this more clearly in the upcoming revision.

Just to take an example: in the publication mentioned by the reviewer (Kalmbach et al 2018), the investigators did not carry out voltage clamp recordings. Furthermore, the reported input resistance values in the aforementioned paper were far above other reports in mice (Routh et al. 2022, Brandalise et al 2022, Hedrick et al 2012; which were similar and our findings here), suggesting that recordings in Kalmbach were carried out at membrane potentials where HCN activation is less available (Routh, Brager and Johnston 2022).

Another reason for some mixed findings in the field is undoubtedly due to the small/nonexistent sag in L2/3 current clamp recordings in mice. We also found a small sag, and that we have shown to be explained by the following: The ‘sag’ potential is a biphasic voltage response emerging from a relatively fast passive membrane response and a slower Ih activation. In L2/3 PCs, hyperpolarization-activated currents are apparently faster than previously described and are located proximally (our findings here). Therefore, their recruitment in mouse L2/3 PCs is on a similar timescale as the passive membrane response, resulting in a more monophasic response. Again, we plan to include a full set of citations in the updated introduction section, to highlight the importance of HCN channels in L2/3 PCs in mice and other species. The justification for using cesium (i.e., ‘best practices’) is detailed in the next paragraph.

(2) A critical experimental concern in the manuscript is the reliance on cesium, a nonspecific blocker, to evaluate HCN channel function. Cesium blocks HCN channels but also acts at potassium channels (and possibly other channels as well). The authors do not acknowledge this or attempt to justify their use of Cs+ and do not cite prior work on this subject. They do not show control experiments demonstrating that the application of Cs+ in their preparation only affects Ih. Additionally, the authors write 1 mM cesium in the text but appear to use 2 mM in the figures. In later experiments, the authors switch to ZD7288, a more commonly used and generally accepted more specific blocker of HCN channels. However, they use a very high concentration, which is also known to produce off-target effects (see Chevaleyre and Castillo, 2002). To make robust conclusions, the authors should have used both blockers (at accepted/conservative concentrations) for all (or at least most) experiments. Using one blocker for some experiments and then another for different experiments is fraught with potential confounds.

To address the concerns regarding the usage of cesium to block HCN channels, we would like to state that neither cesium nor ZD-7288 are without off-target effects, however in our case the potential off-target effects of external cesium were deemed less impactful, especially concerning AP firing output experiments. Extracellular cesium has been widely accepted as a blocker of HCN channels (Lau et al. 2010, Wickenden et al. 2009, Rateau and Ropert 2005, Hemond et al. 2009, Yang et al. 2015, Matt et al. 2010). However, it is known to act on potassium channels as well, which has mostly been demonstrated with intracellular application (Puil et al. 1981, Fleidervish et al. 2008, Williams et al. 1991, 2008). However, we acknowledge off-target effects and we will better cite the appropriate literature in our manuscript in the revision.

Although we performed internal control experiments during the recordings, these were not included in the manuscript- which we plan to correct in the revision. These are detailed as follows: during our recordings cesium had no significant effect on action potential halfwidth, ruling out substantial blocking of potassium channels, nor did it affect any other aspects of suprathreshold activity. Furthermore, we observed similar effects on passive properties (resting membrane potential, input resistance) following ZD-7288 as with cesium, which we will also update in our figures. We did acknowledge that ZD-7288 is a widely accepted blocker of HCN, and for this reason we carried out some of our experiments using this pharmacological agent instead of cesium. However, these experiments were always supported by complementary findings using external cesium. For example, the effect of ZD-7288 on EPSPs was confirmed by similar synaptic stimulation experiments using cesium. This is important, as synaptic inputs of L2/3 PCs are modulated by both dendritic sodium (Ferrarese et al. 2018) and calcium channels (Landau 2022), therefore the application of ZD-7288 alone may have been difficult to interpret in isolation.

On the other hand, ZD-7288 suffers from its own side effects, such as a substantial effect on sodium channels (Wu et al. 2012) and calcium channels (Sánchez-Alonso et al. 2008, Felix et al. 2003). As our aim was to provide functional evidence for the importance of HCN channels, we deemed these effects unacceptable in experiments where AP firing output (e.g., in cell-attached experiments) was measured.

(3) A stronger case could be made that HCN is expressed in the somatic compartment of L2/3 cells if the authors had directly measured HCN-isolated currents with outside-out or nucleated patch recording (with appropriate leak subtraction and pharmacology). Whole-cell voltage-clamp in neurons with axons and/or dendrites does not work. It has been shown to produce erroneous results over and over again in the field due to well-known space clamp problems (see Rall, Spruston, Williams, etc.). The authors could have also included negative controls, such as recordings in neurons that do not express HCN or in HCN-knockout animals. Without these experiments, the authors draw a false equivalency between the effects of cesium and HCN channels, when the outcomes they describe could be driven simply by multiple other cesium-sensitive currents. Distortions are common in these preparations when attempting to study channels (see Williams and Womzy, J Neuro, 2011). In Fig 2h, cesium-sensitive currents look too large and fast to be from HCN currents alone given what the authors have shown in their earlier current clamp data. Furthermore, serious errors in leak subtraction appear to be visible in Supplementary Figure 1c. To claim that these conductances are solely from HCN may be misleading.

We disagree with the argument that “Whole-cell voltage-clamp in neurons with axons and/or dendrites does not work”. Although this method is not without its confounds (i.e. space clamp), it is still a useful initial measure as demonstrated countless times in the literature. However, the reviewer is correct that the best approach to establish the somatodendritic distribution of ion channels is by direct somatic and dendritic outside-out patches. Due to the small diameter of L2/3 PC dendrites, these experiments haven’t been carried out yet in the literature for any other ion channel either to our knowledge. Mapping this distribution may be outside the scope of the current manuscript, but it was hard for us to ignore the sheer size of the Cs+ sensitive hyperpolarizing currents in whole cell. Thus, we will opt to report this data.

Also, we should point out that space clamp-related errors manifest in the overestimation of frequency-dependent features, such as activation kinetics, and underestimation of steady-state current amplitudes. The activation time constant of our measured currents are somewhat faster than previously reported- reducing major concerns regarding space clamp errors. Furthermore, we simply do not understand what “too large… to be from HCN currents” means. We would like to ask the reviewer to point out what the “serious errors in leak subtraction” are, as the measured currents are similar in shape and correction artifacts to previously reported HCN currents (Meng et al. 2011, Li 2011, Zhao et al. 2019, Yu et al. 2004, Zhang et al. 2008, Spinelli et al. 2018, Craven et al. 2006, Ying et al. 2012, Biel et al. 2009).

Furthermore, we would be grateful if the reviewer would mention the other possible ion channels that are activated at hyperpolarized voltages, have the same voltage dependence as HCN currents, do not show inactivation, influence both input resistance and resting membrane potential, and are blocked by low concentration extracellular cesium.

(4) The authors present current-clamp traces with some sag, a primary indicator of HCN conductance, in Figure 2. However, they do not show example traces with cesium or ZD7288 blockade. Additionally, the normalization of current injected by cellular capacitance and the lack of reporting of input resistance or estimated cellular size makes it difficult to determine how much current is actually needed to observe the sag, which is important for assessing the functional relevance of these channels. The sag ratio in controls also varies significantly without explanation (Figure 6 vs Figure 7). Could this variability be a result of genetically defined subgroups within L2/3? For example, in humans, HCN expression in L2/3 varies from superficial and deep neurons. The authors do not make an effort to investigate this. Regardless of inconsistencies in either current injection or cell type, the sag ratio appears to be rather modest and similar to what has already been reported previously in other papers.

We thank the reviewer for pointing out that our explanation for the modest sag ratio might have not been sufficient to properly understand why this measurement cannot be applied to layer 2/3 pyramidal cells. We will clarify this section in the results section. Briefly: sag potential emerges from a relatively (compared to Ih) fast passive membrane response and a slower HCN recruitment. The opposing polarity and different timescales of these two mechanisms results in a biphasic response called “sag” potential. However, if the timescale of these two mechanisms is similar, the voltage response is not predicted to be biphasic. We have shown that hyperpolarization activated currents in our preparations are fast and proximal, therefore they are recruited during the passive response (see Figure 2g.). This means that although a substantial amount of HCN currents are activated during hyperpolarization, their activation will not result in substantial sag. Therefore, sag ratio measurement is not necessarily applicable to approximate the HCN content of L2/3 PCs. We would like to emphasize that sag ratio measurements are correct in case of other cell types, and our aim is not to discredit the method, but rather to show that it cannot be applied in case of mouse L2/3 PCs.

Our own measurements, similar to others in the literature show that L2/3 PCs exhibit modest sag ratios, however, this does not mean that HCN is not relevant. Ih activation in L2/3 PCs does not manifest in large sag potential but rather in a continuous distortion of steady-state responses (Figure 2b.). The reviewer is correct that L2/3 PCs are non-homogenous, therefore we sampled along the entire L2/3 axis. This yielded some variability in our results (i.e., passive properties); yet we did not observe any cells where hyperpolarizing-activated/Cs+-sensitive currents could not be resolved. As structural variability of L2/3 cells does result in variability in cellular capacitance, we compensated for this variability by injecting cellular capacitance-normalized currents. Our measured cellular capacitances were in accordance with previously published values, in the range of 50-120 pF. Therefore, the injected currents were not outside frequently used values. Together, we would like to state that whether substantial sag potential is present or not, initial estimates of the HCN content for each L2/3 PC should be treated with caution.

(5) In the later experiments with ZD7288, the authors measured EPSP half-width at greater distances from the soma. However, they use minimal stimulation to evoke EPSPs at increasingly far distances from the soma. Without controlling for amplitude, the authors cannot easily distinguish between attenuation and spread from dendritic filtering and additional activation and spread from HCN blockade. At a minimum, the authors should share the variability of EPSP amplitude versus the change in EPSP half-width and/or stimulation amplitudes by distance. In general, this kind of experiment yields much clearer results if a more precise local activation of synapses is used, such as dendritic current injection, glutamate uncaging, sucrose puff, or glutamate iontophoresis. There are recording quality concerns here as well: the cell pictured in Figure 3a does not have visible dendritic spines, and a substantial amount of membrane is visible in the recording pipette. These concerns also apply to the similar developmental experiment in 6f-h, where EPSP amplitude is not controlled, and therefore, attenuation and spread by distance cannot be effectively measured. The outcome, that L2/3 cells have dendritic properties that violate cable theory, seems implausible and is more likely a result of variable amplitude by proximity.

To resolve this issue, we will make a supplementary figure showing elicited amplitudes, which showed no significant distance dependence and minimal variability. We thank the reviewer for suggesting an amplitude-halfwidth comparison control. To address the issue of the non-visible spines, we would like to note that these images are of lower magnification. The presence of dendritic spines was confirmed in every recorded pyramidal cell observed using 2P microscopy.

We would like to emphasize that although our recordings “seemingly” violated the cable theory, this is only true if we assume a completely passive condition. As shown in our manuscript, cable theory was not violated, as the presence of NMDA receptor boosting explained the observed ‘non-Rallian’ phenomenon. We plan to clarify this in the fully revised manuscript.

(6) Minimal stimulation used for experiments in Figures 3d-i and Figures 4g-h does not resolve the half-width measurement's sensitivity to dendritic filtering, nor does cesium blockade preclude only HCN channel involvement. Example traces should be shown for all conditions in 3h; the example traces shown here do not appear to even be from the same cell. These experiments should be paired (with and without cesium/ZD). The same problem appears in Figure 4, where it is not clear that the authors performed controls and drug conditions on the same cells. 4g also lacks a scale bar, so readers cannot determine how much these measurements are affected by filtering and evoked amplitude variability. Finally, if we are to believe that minimal stimulation is used to evoke responses of single axons with 50% fail rates, NMDA receptor activation should be minimal to begin with. If the authors wish to make this claim, they need to do more precise activation of NMDA-mediated EPSPs and examine the effects of ZD7288 on these responses in the same cell. As the data is presented, it is not possible to draw the conclusion that HCN boosts NMDA-mediated responses in L2/3 neurons.

As stated in the figure legends, the control and drug application traces are from the same cell, both in figure 3 and figure 4, and the scalebar is not included as the amplitudes were normalized for clarity. We have address the effects of dendritic filtering above in answer (5), and cesium blockade above in answer (2). To reiterate, dendritic filtering alone cannot explain our observations, and cesium is often a better choice for blocking HCN channels compared to ZD-7288, which blocks sodium channels as well. When an excitatory synaptic signal arrives onto a pyramidal cell in typical conditions, neurotransmitter sensitive receptors transmit a synaptic current to the dendritic spine. This dendritic spine is electrically isolated by the high resistance of the spine neck and due to the small membrane surface of the spine, the synaptic current elicits remarkably large voltage changes. These voltage changes can be large enough to depolarize the spine close to zero millivolts upon even single small inputs (Jayant et al. 2016). Therefore, to state that single inputs arriving to dendritic spines cannot be large enough to recruit NMDA receptor activation is incorrect. This is further exemplified by the substantial literature showing ‘miniature’ NMDA recruitment via stochastic vesicle release alone.

(7) The quality of recordings included in the dataset has concerning variability: for example, resting membrane potentials vary by >15-20 mV and the AP threshold varies by 20 mV in controls. This is indicative of either a very wide range of genetically distinct cell types that the authors are ignoring or the inclusion of cells that are either unhealthy or have bad seals.

Although we are aware of the diversity of L2/3 PCs, resolving further layer depth differences is outside the scope of our current manuscript. However, as shown in Kalmbech et al, resting membrane potential can greatly vary (>15-20 mV) in L2/3 PCs depending on distance from pia. We acknowledge that the variance in AP threshold is large and could be due to genetically distinct cell types. Therefore, we plan to present AP peak/width information in the revision, which showed a significantly smaller variability, therefore validating our recording conditions.

(8) The authors make no mention of blocking GABAergic signaling, so it must be assumed that it is intact for all experiments. Electrical stimulation can therefore evoke a mixture of excitatory and inhibitory responses, which may well synapse at very different locations, adding to interpretability and variability concerns.

We thank the reviewer for pointing out our lack of detail regarding the GABAergic signaling blocker SR 95531. We did include this drug in our recordings of signal summation, so GABAergic responses did not contaminate our recordings. We plan to clarify in the revision.

(9) The investigation of serotonergic interaction with HCN channels produces modest effect sizes and suffers the same problems as described above.

We do not agree with the reviewer that 50% drop in neuronal AP firing responses (Figure 7b) was a modest effect size. Thus we plan to keep this data in the manuscript.

(10) The computational modeling is not well described and is not biologically plausible. Persistent and transient K channels are missing. Values for other parameters are not listed. The model does not seem to follow cable theory, which, as described above, is not only implausible but is also not supported by the experimental findings.

The model was downloaded from the Cell Type Database from the Allen Institute, with only minor modifications including the addition of dendritic HCN channels and NDMA receptors- which were varied along a wide parameter space to find a ‘best fit’ to our observations. These additions were necessary to recapitulate our experimental findings. We agree the model likely does not fully recapitulate all aspects of the dendrites, which as we hope to convey in this manuscript, are not fully resolved in mouse L2/3 PCs. This is a published neuronal model, and despite its potential shortcomings, is one among a handful of open-source neuronal models of fully reconstructed L2/3 PCs. We are open to improvement suggestions.

Reviewer #2 (Public Review):

Summary:

This paper by Olah et al. uncovers a previously unknown role of HCN channels in shaping synaptic inputs to L2/3 cortical neurons. The authors demonstrate using slice electrophysiology and computational modeling that, unlike layer 5 pyramidal neurons, L2/3 neurons have an enrichment of HCN channels in the proximal dendrites. This location provides a locus of neuromodulation for inputs onto the proximal dendrites from L4 without an influence on distal inputs from L1. The authors use pharmacology to demonstrate the effect of HCN channels on NMDA-mediated synaptic inputs from L4. The authors further demonstrate the developmental time course of HCN function in L2/3 pyramidal neurons. Taken together, this a well-constructed investigation of HCN channel function and the consequences of these channels on synaptic integration in L2/3 pyramidal neurons.

Strengths:

The authors use careful, well-constrained experiments using multiple pharmacological agents to asses HCN channel contributions to synaptic integrations. The authors also use a voltage clamp to directly measure the current through HCN channels across developmental ages. The authors also provide supplemental data showing that their observation is consistent across multiple areas of the cerebral cortex.

Weaknesses:

The gradient of the HCN channel function is based almost exclusively on changes in EPSP width measured at the soma. While providing strong evidence for the presence of HCN current in L2/3 neurons, there are space clamp issues related to the use of somatic whole-cell voltage clamps that should be considered in the discussion.

We thank the reviewer for pointing out our careful and well-constrained experiments and for making suggestions. The potential effects of space clamp errors will be detailed in the discussion section (see extended explanations under Reviewer 1).

Reviewer #3 (Public Review):

Summary:

The authors study the function of HCN channels in L2/3 pyramidal neurons, employing somatic whole-cell recordings in acute slices of visual cortex in adult mice and a bevy of technically challenging techniques. Their primary claim is a non-uniform HCN distribution across the dendritic arbor with a greater density closer to the soma (roughly opposite of the gradient found in L5 PT-type neurons). The second major claim is that multiple sources of long-range excitatory input (cortical and thalamic) are differentially affected by the HCN distribution. They further describe an interesting interplay of NMDAR and HCN, serotonergic modulation of HCN, and compare HCN-related properties at 1, 2 and 6 weeks of age. Several results are supported by biophysical simulations.

Strengths:

The authors collected data from both male and female mice, at an age (6-10 weeks) that permits comparison with in vivo studies, in sufficient numbers for each condition, and they collected a good number of data points for almost all figure panels. This is all the more positive, considering the demanding nature of multi-electrode recording configurations and pipette-perfusion. The main strength of the study is the question and focus.

Weaknesses:

Unfortunately, in its present form, the main claims are not adequately supported by the experimental evidence: primarily because the evidence is indirect and circumstantial, but also because multiple unusual experimental choices (along with poor presentation of results) undermine the reader's confidence. Additionally, the authors overstate the novelty of certain results and fail to cite important related publications. Some of these weaknesses can be addressed by improved analysis and statistics, resolving inconsistent data across figures, reorganizing/improving figure panels, more complete methods, improved citations, and proofreading. In particular, given the emphasis on EPSPs, the primary data (for example EPSPs, overlaid conditions) should be shown much more.

However, on the experimental side, addressing the reviewer's concerns would require a very substantial additional effort: direct measurement of HCN density at different points in the dendritic arbor and soma; the internal solution chosen here (K-gluconate) is reported to inhibit HCN; bath-applied cesium at the concentrations used blocks multiple potassium channels, i.e. is not selective for HCN (the fact that the more selective blocker ZD7288 was used in a subset of experiments makes the choice of Cs+ as the primary blocker all the more curious); pathway-specific synaptic stimulation, for example via optogenetic activation of specific long-range inputs, to complement / support / verify the layer-specific electrical stimulation.

We thank the reviewer for their very careful examination of our manuscript and helpful suggestions. We will address the concerns raised in the review and present substantially more raw traces in our figures. Although direct dendritic HCN mapping measurements are likely outside the scope of the current manuscript due to the morphological constraints presented by L2/3 PCs (which explains why no other full dendritic nonlinearity distribution has been described in L2/3 PCs with this method), we will nonetheless supplement our manuscript with additional suggested experiments. For example we plan to include the excellent suggestion of pathway-specific optogenetic stimulation to further validate the disparate effect of HCN channels for distal and proximal inputs. We will also include control measurements using different internal solutions. We agree that ZD-7288 is a widely accepted blocker of HCN channels. However, the off-target effects on sodium channels may have significantly confounded our measurements of AP output using extracellular stimulation. Therefore we chose cesium as the primary blocker for those experiments, but did validate several other Cs+-based results with ZD-7288. These controls will also be represented in a more clear fashion in a new supplementary figure.

eLife assessment

The authors used electrophysiology in brain slices and computer modeling and suggest that layer 2/3 pyramidal neurons of the mouse cortex express functional HCN channels, despite little evidence in the past that they are present. The study is useful at the present time, but results are incomplete because the methods, data, and analyses do not always support the conclusions.

Reviewer #1 (Public Review):

The manuscript by Oleh et al. uses in vitro electrophysiology and compartmental modeling (via NEURON) to investigate the expression and function of HCN channels in mouse L2/3 pyramidal neurons. The authors conclude that L2/3 neurons have developmentally regulated HCN channels, the activation of which can be observed when subjected to large hyperpolarizations. They further conclude via blockade experiments that HCN channels in L2/3 neurons influence cellular excitability and pathway-specific EPSP kinetics, which can be neuromodulated. While the authors perform a wide range of slice physiology experiments, concrete evidence that L2/3 cells express functionally relevant HCN channels is limited. There are serious experimental design caveats and confounds that make drawing strong conclusions from the data difficult. Furthermore, the significance of the findings is generally unclear, given modest effect sizes and a lack of any functional relevance, either directly via in vivo experiments or indirectly via strong HCN-mediated changes in known operations/computations/functions of L2/3 neurons.

Specific points:

(1) The interpretability and impact of this manuscript are limited due to numerous methodological issues in experimental design, data collection, and analysis. The authors have not followed best practices in the field, and as such, much of the data is ambiguous and/or weak and does not support their interpretations (detailed below). Additionally, the authors fail to appropriately explain their rationale for many of their choices, making it difficult to understand why they did what they did. Furthermore, many important references appear to be missing, both in terms of contextualizing the work and in terms of approach/method. For example, the authors do not cite Kalmbach et al 2018, which performed a directly comparable set of experiments on HCN channels in L2/3 neurons of both humans and mice. This is an unacceptable omission. Additionally, the authors fail to cite prior literature regarding the specificity or lack thereof of Cs+ in blocking HCN. In describing a result, the authors state "In line with previous reports, we found that L2/3 PCs exhibited an unremarkable amount of sag at 'typical' current commands" but they then fail to cite the previous reports.

(2) A critical experimental concern in the manuscript is the reliance on cesium, a nonspecific blocker, to evaluate HCN channel function. Cesium blocks HCN channels but also acts at potassium channels (and possibly other channels as well). The authors do not acknowledge this or attempt to justify their use of Cs+ and do not cite prior work on this subject. They do not show control experiments demonstrating that the application of Cs+ in their preparation only affects Ih. Additionally, the authors write 1 mM cesium in the text but appear to use 2 mM in the figures. In later experiments, the authors switch to ZD7288, a more commonly used and generally accepted more specific blocker of HCN channels. However, they use a very high concentration, which is also known to produce off-target effects (see Chevaleyre and Castillo, 2002). To make robust conclusions, the authors should have used both blockers (at accepted/conservative concentrations) for all (or at least most) experiments. Using one blocker for some experiments and then another for different experiments is fraught with potential confounds.

(3) A stronger case could be made that HCN is expressed in the somatic compartment of L2/3 cells if the authors had directly measured HCN-isolated currents with outside-out or nucleated patch recording (with appropriate leak subtraction and pharmacology). Whole-cell voltage-clamp in neurons with axons and/or dendrites does not work. It has been shown to produce erroneous results over and over again in the field due to well-known space clamp problems (see Rall, Spruston, Williams, etc.). The authors could have also included negative controls, such as recordings in neurons that do not express HCN or in HCN-knockout animals. Without these experiments, the authors draw a false equivalency between the effects of cesium and HCN channels, when the outcomes they describe could be driven simply by multiple other cesium-sensitive currents. Distortions are common in these preparations when attempting to study channels (see Williams and Womzy, J Neuro, 2011). In Fig 2h, cesium-sensitive currents look too large and fast to be from HCN currents alone given what the authors have shown in their earlier current clamp data. Furthermore, serious errors in leak subtraction appear to be visible in Supplementary Figure 1c. To claim that these conductances are solely from HCN may be misleading.

(4) The authors present current-clamp traces with some sag, a primary indicator of HCN conductance, in Figure 2. However, they do not show example traces with cesium or ZD7288 blockade. Additionally, the normalization of current injected by cellular capacitance and the lack of reporting of input resistance or estimated cellular size makes it difficult to determine how much current is actually needed to observe the sag, which is important for assessing the functional relevance of these channels. The sag ratio in controls also varies significantly without explanation (Figure 6 vs Figure 7). Could this variability be a result of genetically defined subgroups within L2/3? For example, in humans, HCN expression in L2/3 varies from superficial and deep neurons. The authors do not make an effort to investigate this. Regardless of inconsistencies in either current injection or cell type, the sag ratio appears to be rather modest and similar to what has already been reported previously in other papers.

(5) In the later experiments with ZD7288, the authors measured EPSP half-width at greater distances from the soma. However, they use minimal stimulation to evoke EPSPs at increasingly far distances from the soma. Without controlling for amplitude, the authors cannot easily distinguish between attenuation and spread from dendritic filtering and additional activation and spread from HCN blockade. At a minimum, the authors should share the variability of EPSP amplitude versus the change in EPSP half-width and/or stimulation amplitudes by distance. In general, this kind of experiment yields much clearer results if a more precise local activation of synapses is used, such as dendritic current injection, glutamate uncaging, sucrose puff, or glutamate iontophoresis. There are recording quality concerns here as well: the cell pictured in Figure 3a does not have visible dendritic spines, and a substantial amount of membrane is visible in the recording pipette. These concerns also apply to the similar developmental experiment in 6f-h, where EPSP amplitude is not controlled, and therefore, attenuation and spread by distance cannot be effectively measured. The outcome, that L2/3 cells have dendritic properties that violate cable theory, seems implausible and is more likely a result of variable amplitude by proximity.

(6) Minimal stimulation used for experiments in Figures 3d-i and Figures 4g-h does not resolve the half-width measurement's sensitivity to dendritic filtering, nor does cesium blockade preclude only HCN channel involvement. Example traces should be shown for all conditions in 3h; the example traces shown here do not appear to even be from the same cell. These experiments should be paired (with and without cesium/ZD). The same problem appears in Figure 4, where it is not clear that the authors performed controls and drug conditions on the same cells. 4g also lacks a scale bar, so readers cannot determine how much these measurements are affected by filtering and evoked amplitude variability. Finally, if we are to believe that minimal stimulation is used to evoke responses of single axons with 50% fail rates, NMDA receptor activation should be minimal to begin with. If the authors wish to make this claim, they need to do more precise activation of NMDA-mediated EPSPs and examine the effects of ZD7288 on these responses in the same cell. As the data is presented, it is not possible to draw the conclusion that HCN boosts NMDA-mediated responses in L2/3 neurons.

(7) The quality of recordings included in the dataset has concerning variability: for example, resting membrane potentials vary by >15-20 mV and the AP threshold varies by 20 mV in controls. This is indicative of either a very wide range of genetically distinct cell types that the authors are ignoring or the inclusion of cells that are either unhealthy or have bad seals.

(8) The authors make no mention of blocking GABAergic signaling, so it must be assumed that it is intact for all experiments. Electrical stimulation can therefore evoke a mixture of excitatory and inhibitory responses, which may well synapse at very different locations, adding to interpretability and variability concerns.

(9) The investigation of serotonergic interaction with HCN channels produces modest effect sizes and suffers the same problems as described above.

(10) The computational modeling is not well described and is not biologically plausible. Persistent and transient K channels are missing. Values for other parameters are not listed. The model does not seem to follow cable theory, which, as described above, is not only implausible but is also not supported by the experimental findings.

Taken together, there are serious methodological and analytical concerns that need to be addressed before the authors' claims can be supported. Combined with the small effect sizes and high data variability throughout the paper, this makes it hard to see how the manuscript could make a strong contribution to advancing our understanding of L2/3 cortical pyramidal neuron function.

Reviewer #2 (Public Review):

Summary:

This paper by Olah et al. uncovers a previously unknown role of HCN channels in shaping synaptic inputs to L2/3 cortical neurons. The authors demonstrate using slice electrophysiology and computational modeling that, unlike layer 5 pyramidal neurons, L2/3 neurons have an enrichment of HCN channels in the proximal dendrites. This location provides a locus of neuromodulation for inputs onto the proximal dendrites from L4 without an influence on distal inputs from L1. The authors use pharmacology to demonstrate the effect of HCN channels on NMDA-mediated synaptic inputs from L4. The authors further demonstrate the developmental time course of HCN function in L2/3 pyramidal neurons. Taken together, this a well-constructed investigation of HCN channel function and the consequences of these channels on synaptic integration in L2/3 pyramidal neurons.

Strengths:

The authors use careful, well-constrained experiments using multiple pharmacological agents to asses HCN channel contributions to synaptic integrations. The authors also use a voltage clamp to directly measure the current through HCN channels across developmental ages. The authors also provide supplemental data showing that their observation is consistent across multiple areas of the cerebral cortex.

Weaknesses:

The gradient of the HCN channel function is based almost exclusively on changes in EPSP width measured at the soma. While providing strong evidence for the presence of HCN current in L2/3 neurons, there are space clamp issues related to the use of somatic whole-cell voltage clamps that should be considered in the discussion.

Reviewer #3 (Public Review):

Summary:

The authors study the function of HCN channels in L2/3 pyramidal neurons, employing somatic whole-cell recordings in acute slices of visual cortex in adult mice and a bevy of technically challenging techniques. Their primary claim is a non-uniform HCN distribution across the dendritic arbor with a greater density closer to the soma (roughly opposite of the gradient found in L5 PT-type neurons). The second major claim is that multiple sources of long-range excitatory input (cortical and thalamic) are differentially affected by the HCN distribution. They further describe an interesting interplay of NMDAR and HCN, serotonergic modulation of HCN, and compare HCN-related properties at 1, 2 and 6 weeks of age. Several results are supported by biophysical simulations.

Strengths:

The authors collected data from both male and female mice, at an age (6-10 weeks) that permits comparison with in vivo studies, in sufficient numbers for each condition, and they collected a good number of data points for almost all figure panels. This is all the more positive, considering the demanding nature of multi-electrode recording configurations and pipette-perfusion. The main strength of the study is the question and focus.

Weaknesses:

Unfortunately, in its present form, the main claims are not adequately supported by the experimental evidence: primarily because the evidence is indirect and circumstantial, but also because multiple unusual experimental choices (along with poor presentation of results) undermine the reader's confidence. Additionally, the authors overstate the novelty of certain results and fail to cite important related publications. Some of these weaknesses can be addressed by improved analysis and statistics, resolving inconsistent data across figures, reorganizing/improving figure panels, more complete methods, improved citations, and proofreading. In particular, given the emphasis on EPSPs, the primary data (for example EPSPs, overlaid conditions) should be shown much more.

However, on the experimental side, addressing the reviewer's concerns would require a very substantial additional effort: direct measurement of HCN density at different points in the dendritic arbor and soma; the internal solution chosen here (K-gluconate) is reported to inhibit HCN; bath-applied cesium at the concentrations used blocks multiple potassium channels, i.e. is not selective for HCN (the fact that the more selective blocker ZD7288 was used in a subset of experiments makes the choice of Cs+ as the primary blocker all the more curious); pathway-specific synaptic stimulation, for example via optogenetic activation of specific long-range inputs, to complement / support / verify the layer-specific electrical stimulation.

Author Response

We thank all the reviewers for their comments and insight. We plan to address the comments and recommendations in the revised version of the manuscript. Provisional response on key points are given below.

Reviewer #1 (Public Review):

Summary:

In this manuscript, Chowdhury and co-workers provide interesting data to support the role of G4-structures in promoting chromatin looping and long-range DNA interactions. The authors achieve this by artificially inserting a G4-containing sequence in an isolated region of the genome using CRISPR-Cas9 and comparing it to a control sequence that does not contain G4 structures. Based on the data provided, the authors can conclude that G4-insertion promotes long-range interactions (measured by Hi-C) and affects gene expression (measured by qPCR) as well as chromatin remodelling (measured by ChIP of specific histone markers).

Whilst the data presented is promising and partially supports the authors' conclusion, this reviewer feels that some key controls are missing to fully support the narrative. Specifically, validation of actual G4-formation in chromatin by ChIP-qPCR (at least) is essential to support the association between G4-formation and looping. Moreover, this study is limited to a genomic location and an individual G4-sequence used, so the findings reported cannot yet be considered to reflect a general mechanism/effect of G4-formation in chromatin looping.

Strengths:

This is the first attempt to connect genomics datasets of G4s and HiC with gene expression. The use of Cas9 to artificially insert a G4 is also very elegant.

Weaknesses:

Lack of controls, especially to validate G4-formation after insertion with Cas9. The work is limited to a single G4-sequence and a single G4-site, which limits the generalisation of the findings.

In an earlier study, we reported intracellular G4 formation in the hTERT promoter region in human cells (Sharma et al., Cell Reports, 2021). Exactly the same stretch of DNA was taken for insertion here. This is mentioned in the current manuscript as- “The array of G4-forming sequences used for insertion was previously reported to form stable G4s in human cells.” under the paragraph titled “Insertion of an array of G4s in an isolated locus” in the Results section. As the reviewer points out, we understand that intracellular G4 formation needs to be confirmed upon insertion at the non-native location. These experiments/results will be included in the revised version.

To directly address the second point we are attempting insertion of the same G4-sequence at another locus. Experiments/results on this, and if the insertion is successful, how the insertion affects chromatin organization and nearby gene expression will be included in the revised manuscript.

Reviewer #2 (Public Review):

Summary:

Roy et al. investigated the role of non-canonical DNA structures called G-quadruplexes (G4s) in long-range chromatin interactions and gene regulation. Introducing a G4 array into chromatin significantly increased the number of long-range interactions, both within the same chromosome (cis) and between different chromosomes (trans). G4s functioned as enhancer elements, recruiting p300 and boosting gene expression even 5 megabases away. The study proposes a mechanism where G4s directly influence 3D chromatin organization, facilitating communication between regulatory elements and genes.

Strength:

The findings are valuable for understanding the role of G4-DNA in 3D genome organization and gene transcription.

Weaknesses:

The study would benefit from more robust and comprehensive data, which would add depth and clarity.

(1) Lack of G4 Structure Confirmation: The absence of direct evidence for G4 formation within cells undermines the study's foundation. Relying solely on in vitro data and successful gene insertion is insufficient.

As pointed out in response to the above comment, direct evidence of G4 formation by the stretch of DNA was published by us earlier (Sharma et al., Cell Reports, 2021). We understand here it is important to check/confirm this at the insertion site. These experiments are being initiated.

(2) Alternative Explanations: The study does not sufficiently address alternative explanations for the observed results. The inserted sequences may not form G4s or other factors like G4-RNA hybrids may be involved.

G4 formation at the insertion site will be checked to confirm. It has been reported G4 structures associate with R-loops to strengthen CTCF binding and enhance chromatin looping (Wulfridge et al., 2023). This can discussed further for readers.

(3) Limited Data Depth and Clarity: ChIP-qPCR offers limited scope and considerable variation in some data makes conclusions difficult.

Variation with one of the primers in a few ChIP-qPCR experiments (in Figures 2 and 3D) we have noted. The change however was statistically significant, and consistent with the overall trend across experiments (Figures 2, 3 and 4). Enhancer function, in addition to ChIP, was confirmed using other assays like 3C and RNA expression.

(4) Statistical Significance and Interpretation: The study could be more careful in evaluating the statistical significance and magnitude of the effects to avoid overinterpreting the results.

As pointed out, the manuscript will be revised to ensure we are not overinterpreting any results.

Reviewer #3 (Public Review):

Summary:

This paper aims to demonstrate the role of G-quadruplex DNA structures in the establishment of chromosome loops. The authors introduced an array of G4s spanning 275 bp, naturally found within a very well-characterized promoter region of the hTERT promoter, in an ectopic region devoid of G-quadruplex and annotated gene. As a negative control, they used a mutant version of the same sequence in which G4 folding is impaired. Due to the complexity of the region, 3 G4s on the same strand and one on the opposite strand, 12 point mutations were made simultaneously (G to T and C to A). Analysis of the 3D genome organization shows that the WT array establishes more contact within the TAD and throughout the genome than the control array. Additionally, a slight enrichment of H3K4me1 and p300, both enhancer markers, was observed locally near the insertion site. The authors tested whether the expression of genes located either nearby or up to 5 Mb away was up-regulated based on this observation. They found that four genes were up-regulated from 1.5 to 3-fold. An increased interaction between the G4 array compared to the mutant was confirmed by the 3C assay. For in-depth analysis of the long-range changes, they also performed Hi-C experiments and showed a genome-wide increase in interactions of the WT array versus the mutated form.

Strengths:

The experiments were well-executed and the results indicate a statistical difference between the G4 array inserted cell line and the mutated modified cell line.

Weaknesses:

The control non-G4 sequence contains 12 point mutations, making it difficult to draw clear conclusions. These mutations not only alter the formation of G4, but also affect at least three Sp1 binding sites that have been shown to be essential for the function of the hTERT promoter, from which the sequence is derived. The strong intermingling of G4 and Sp1 binding sites makes it impossible to determine whether all the observations made are dependent on G4 or Sp1 binding. As a control, the authors used Locked Nucleic Acid probes to prevent the formation of G4. As for mutations, these probes also interfere with two Sp1 binding sites. Therefore, using this alternative method has the same drawback as point mutations. This major issue should be discussed in the paper. It is also possible that other unidentified transcription factor binding sites are affected in the presented point mutants.

Since the sequence we used to test the effects of G4 structure formation is highly G-rich, we had to introduce at least 12 mutations to be sure that a stable G4 structure would not form in the mutated control sequence. Sp1 has been reported to bind to G4 structures (Raiber et al., 2012). So, Sp1 binding could also be associated with the G4-dependent enhancer functions observed here. We also appreciate that apart from Sp1, other unidentified transcription factor binding sites might be affected by the mutations we introduced. We will discuss these possibilities in the revised version of the manuscript.

Reviewer #2 (Public Review):

Summary:

Roy et al. investigated the role of non-canonical DNA structures called G-quadruplexes (G4s) in long-range chromatin interactions and gene regulation. Introducing a G4 array into chromatin significantly increased the number of long-range interactions, both within the same chromosome (cis) and between different chromosomes (trans). G4s functioned as enhancer elements, recruiting p300 and boosting gene expression even 5 megabases away. The study proposes a mechanism where G4s directly influence 3D chromatin organization, facilitating communication between regulatory elements and genes.

Strength:

The findings are valuable for understanding the role of G4-DNA in 3D genome organization and gene transcription.

Weaknesses:

The study would benefit from more robust and comprehensive data, which would add depth and clarity.

(1) Lack of G4 Structure Confirmation: The absence of direct evidence for G4 formation within cells undermines the study's foundation. Relying solely on in vitro data and successful gene insertion is insufficient.

(2) Alternative Explanations: The study does not sufficiently address alternative explanations for the observed results. The inserted sequences may not form G4s or other factors like G4-RNA hybrids may be involved.

(3) Limited Data Depth and Clarity: ChIP-qPCR offers limited scope and considerable variation in some data makes conclusions difficult.

(4) Statistical Significance and Interpretation: The study could be more careful in evaluating the statistical significance and magnitude of the effects to avoid overinterpreting the results.

Reviewer #3 (Public Review):

Summary:

This paper aims to demonstrate the role of G-quadruplex DNA structures in the establishment of chromosome loops. The authors introduced an array of G4s spanning 275 bp, naturally found within a very well-characterized promoter region of the hTERT promoter, in an ectopic region devoid of G-quadruplex and annotated gene. As a negative control, they used a mutant version of the same sequence in which G4 folding is impaired. Due to the complexity of the region, 3 G4s on the same strand and one on the opposite strand, 12 point mutations were made simultaneously (G to T and C to A). Analysis of the 3D genome organization shows that the WT array establishes more contact within the TAD and throughout the genome than the control array. Additionally, a slight enrichment of H3K4me1 and p300, both enhancer markers, was observed locally near the insertion site. The authors tested whether the expression of genes located either nearby or up to 5 Mb away was up-regulated based on this observation. They found that four genes were up-regulated from 1.5 to 3-fold. An increased interaction between the G4 array compared to the mutant was confirmed by the 3C assay. For in-depth analysis of the long-range changes, they also performed Hi-C experiments and showed a genome-wide increase in interactions of the WT array versus the mutated form.

Strengths:

The experiments were well-executed and the results indicate a statistical difference between the G4 array inserted cell line and the mutated modified cell line.

Weaknesses:

The control non-G4 sequence contains 12 point mutations, making it difficult to draw clear conclusions. These mutations not only alter the formation of G4, but also affect at least three Sp1 binding sites that have been shown to be essential for the function of the hTERT promoter, from which the sequence is derived. The strong intermingling of G4 and Sp1 binding sites makes it impossible to determine whether all the observations made are dependent on G4 or Sp1 binding. As a control, the authors used Locked Nucleic Acid probes to prevent the formation of G4. As for mutations, these probes also interfere with two Sp1 binding sites. Therefore, using this alternative method has the same drawback as point mutations. This major issue should be discussed in the paper. It is also possible that other unidentified transcription factor binding sites are affected in the presented point mutants.

eLife assessment

This study is valuable in that it aims to demonstrate that genomic insertion of a G4-containing sequence can be sufficient to induce chromosome loops and alter gene expression. However, the evidence remains incomplete because it has not been shown yet that the inserted sequence indeed forms G4 structures in cells and that the observed effects are caused by these structures and not by other elements present in the inserted sequence. Testing of different G4 sequences and other loci would be required to generalize the findings.

Reviewer #1 (Public Review):

Summary:

In this manuscript, Chowdhury and co-workers provide interesting data to support the role of G4-structures in promoting chromatin looping and long-range DNA interactions. The authors achieve this by artificially inserting a G4-containing sequence in an isolated region of the genome using CRISPR-Cas9 and comparing it to a control sequence that does not contain G4 structures. Based on the data provided, the authors can conclude that G4-insertion promotes long-range interactions (measured by Hi-C) and affects gene expression (measured by qPCR) as well as chromatin remodelling (measured by ChIP of specific histone markers).

Whilst the data presented is promising and partially supports the authors' conclusion, this reviewer feels that some key controls are missing to fully support the narrative. Specifically, validation of actual G4-formation in chromatin by ChIP-qPCR (at least) is essential to support the association between G4-formation and looping. Moreover, this study is limited to a genomic location and an individual G4-sequence used, so the findings reported cannot yet be considered to reflect a general mechanism/effect of G4-formation in chromatin looping.

Strengths:

This is the first attempt to connect genomics datasets of G4s and HiC with gene expression. The use of Cas9 to artificially insert a G4 is also very elegant.

Weaknesses:

Lack of controls, especially to validate G4-formation after insertion with Cas9. The work is limited to a single G4-sequence and a single G4-site, which limits the generalisation of the findings.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors establish a recombinant insect cell expression and purification scheme for the antiviral Dicer complex of C. elegans. In addition to Dicer-1, the complex harbors two additional proteins, the RIG-I-like helicase DRH-1, and the dsRNA-binding protein RDE-4. The authors show that the complex prefers blunt-end dsRNA over dsRNAs that contain overhangs. Furthermore, whereas ATP-dependent dsRNA cleavage only exacerbates regular dsRNA cleavage activity, the presence of RDE-4 is essential to ATP-dependent and ATP-independent dsRNA cleavage. Single-particle cryo-EM studies of the ternary C. elegans Dicer complex reveal that the N-terminal domain of DRH-1 interacts with the helicase domain of DCR-1, thereby relieving its autoinhibitory state. Lastly, the authors show that the ternary complex is able to processively cleave long dsRNA, an activity primarily relying on the helicase activity of DRH-1.

Strengths:

First thorough biochemical characterization of the antiviral activity of C. elegans Dicer in complex with the RIG-I-like helicase DRH-1 and the dsRNA-binding protein RDE-4. • Discovery that RDE-4 is essential to dsRNA processing, whereas ATP hydrolysis is not.

Discovery of an autoinhibitory role of DRH-1's N-terminal domain (in analogy to the CARD domains of RIG-I).

First structural insights into the ternary complex DCR-1:DRH-1:RDE-4 by cryo-EM to medium resolution.

Trap experiments reveal that the ternary DCR-1 complex cleaves blunt-ended dsRNA processively. Likely, the helicase domain of DRH-1 is responsible for this processive cleavage.

We thank the reviewer for this accurate and thoughtful summary of the strengths of our study. We note that although ATP hydrolysis is not essential for dsRNA processing, it is essential for promoting an alternative, and dramatically more efficient, cleavage mechanism that is wellsuited for processing viral dsRNA.

Weaknesses:

Cryo-EM Structure of the ternary Dicer-1:DRH-1:RED-4 complex to only medium resolution.

We agree with the reviewer that our structures are only of modest resolution. We continue to work towards a higher resolution structure of this conformationally heterogeneous complex. We do want to emphasize that despite our modest resolution, our structures provide novel insights into how the factors in the antiviral complex interact with each other, and also allow us to compare our findings to other Dicer systems. For example, the dsRNA binding protein RDE-4 binds the Hel2i subdomain, and this is similar to accessory dsRNA binding proteins of other Dicers, including human and Drosophila. Most importantly, for the first time, we uncover the interaction of DRH-1 with C. elegans Dicer; our structures show DRH-1's N-terminal domain interacting with Dicer's helicase domain. This observation spurred our experiments that showed the N-terminal domain of DRH-1, like the analogous domain of RIG-I, enables an autoinhibited conformation. While RIG-I autoinhibition is relieved by dsRNA binding, we do not observe this with C. elegans DRH-1 and speculate that instead it is the interaction with Dicer's helicase domain that relieves autoinhibition.

High-resolution structure of the C-terminal domain of DRH-1 bound to dsRNA does not reveal the mechanism of how blunt-end dsRNA and overhang-containing one are being discriminated.

The cryo-EM structure of DCR1:DRH-1:RDE-4 in the presence of ATP only reveals the helicase and CTD domains of DRH-1 bound to dsRNA. No information on dsRNA termini recognition is presented. The paragraph seems detached from the general flow of the manuscript.

We agree with the reviewer that our paper would be improved with a high-resolution structure of DRH-1 bound to the dsRNA terminus to better understand terminus discrimination. Since we did not obtain a high-resolution structure of DRH-1 bound to the dsRNA terminus, we could not comment on how DRH-1 discriminates termini. However, our structure of DRH-1’s helicase and CTD bound to the middle of the dsRNA does provide important evidence that DRH-1 translocates along dsRNA, which is crucial for our interpretation of DRH-1’s ATPase function in the antiviral complex. Furthermore, our analysis of the DRH-1:dsRNA contacts reveals just how well conserved DRH-1 is with mammalian RLRs.

The antiviral DCR-1:DRH-1:RDE-4 complex shows largely homologous activities and regulation than Drosophila Dicer-2.

It is unclear to us why this is a weakness. In our Discussion in the section “Relationship to previously characterized Dicer activities,” we compare and contrast the C. elegans antiviral complex and the most well characterized antiviral Dicer: Drosophila Dcr2. While it might not be surprising that two invertebrate activities that both must target viral dsRNA have similar enzymatic properties, we find this remarkable given that Dcr2 orchestrates cleavage with a single protein, while two helicases and a dsRNA binding protein cooperate in the C. elegans reaction. Our careful biochemical analyses reveal how the three proteins cooperate. In vivo, C. elegans Dicer must function to cleave pre-miRNAs, endogenous siRNAs as well as viral dsRNA, and we speculate that the use of diverse accessory factors allows C. elegans Dicer to carry out these distinct tasks.

Reviewer #2 (Public Review):

Summary:

To investigate the evolutionary relationship between the RNAi pathway and innate immunity, this study uses biochemistry and structural biology to investigate the trimeric complex of Dicer1, DRH-1 (a RIGI homologue), and RDE-4, which exists in C. elegans. The three subunits were co-expressed to promote stable purification of the complex. This complex promoted ATPdependent cleavage of blunt-ended dsRNAs. A detailed kinetic analysis was also carried out to determine the role of each subunit of the trimeric complex in both the specificity and efficiency of cleavage. These studies indicate that RDE-4 is critical for cleavage while DRC-1 is primarily involved in the specificity of the reaction, and DRH-1 promotes ATP hydrolysis. Finally, a moderate density (6-7 angstrom) cryo-EM structure is presented with attempts to position each of the components.

Strengths:

(1) Newly described methods for studying the C. elegans DICER complex.

(2) New structure, albeit only moderate resolution.

(3) Kinetic study of the complex in the presence and absence of individual subunits and mutations, provides detailed insight into the contribution of each subunit.

Weaknesses:

(1) Limited insight due to limited structural resolution.

(2) No attempts to extend findings to other Dicer or RLR systems.

Overall, we agree with the assessment of this reviewer, and we thank them for their efforts in evaluating our manuscript. Whenever possible we have discussed the similarities and differences of the C. elegans Dicer to other Dicers and RLR systems. We are unsure how we could have expanded upon this further (as suggested in point 2).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor recommendations to the authors:

Page 10: To assess the role of ATP hydrolysis for dsRNA binding, please refrain from using the term "fuzzy band" as a qualitative measure of RNA binding to the ternary complexes.

We searched our entire manuscript and did not find the term “fuzzy band.” We did describe some of the bands in the gel shift assays as “diffuse.” This is an accurate description of the bands we see in our gels and distinguishes them from other more well-defined bands.

Page 13: "positioned internally" - please explain "internally" better here.

We agree with the reviewer that “positioned internally” is confusing. In our revised manuscript we have changed this sentence to (Page 13, line 1):

“Under these conditions, we obtained a 2.9 Å reconstruction of the helicase and CTD domains of DRH-1 bound to the middle region of the dsRNA, rather than its terminus (Figures 4C and S9), suggesting that DRH-1 hydrolyzes ATP to translocate along dsRNA.”

Page 13: Please re-consider the detailed description of the dsRNA:DRH-1 contacts.

We feel it is very important to illustrate and describe these contacts, which will be of interest to those who study mammalian RLRs.

Figure 1C/D: Please write "minus/+ ATP" on top of the gels to make this distinction more clearly visible.

In our original manuscript the gels are labeled with “minus ATP” (panel C) or “5mM ATP” (panel D) on the left to indicate both gels in panel C and both gels in panel D have the same conditions. This is also stated in the figure legend. We have not made revisions in response to this comment because we think it is already clear.

Figure 2: Please explain R = RDE-4 in a clearly visible legend.

We agree with the author that the illustration above the gels was not explained clearly. In our revised manuscript we have added the sentence below to the beginning of Figure legend 2A. “Cartoons indicate complexes and variants, with mutations in DCR-1 (green) and DRH-1 (blue) indicated with the amino acid change, and the presence of RDE-4 (R) represented with a purple circle.”

Figure 4A: Please label the DRH-1 helicase domain and the C-terminal domain.

We agree with the reviewer that we could more clearly define our labeled domains. In the revised manuscript we have added a sentence to the legend of Figure 4A: “The domains of DCR-1, DRH-1, and RDE-4 are color coded the same as in Fig 1A. For simplicity, only domains discussed in the text are labeled.”

Reviewer #2 (Recommendations For The Authors):

This study is complete in that all necessary controls and data are included and the authors are careful in their interpretation so as to not overstate the data or conclusions. The only suggestion is that further extension of the study to address the weaknesses above would increase the breadth of impact of this work.

We thank the reviewer for their thoughtful comments. Weaknesses are addressed above in public reviews, and we will add again that we agree that a higher resolution structure would provide additional insight. In ongoing research, we are working towards this goal.

eLife assessment

To investigate the evolutionary relationship between the RNAi pathway and innate immunity, this valuable study uses biochemistry and structural biology to investigate the trimeric complex of Dicer-1, DRH-1 (a RIGI homologue), and RDE-4, which exists in C. elegans. The results described include rigorous kinetic analysis of the enzymatic activity of the complex and a moderate resolution cryo-EM structure. The results are convincing and add to the broader understanding of the evolution of antiviral defense.

Reviewer #1 (Public Review):

Summary:

The authors establish a recombinant insect cell expression and purification scheme for the antiviral Dicer complex of C. elegans. In addition to Dicer-1, the complex harbors two additional proteins, the RIG-I-like helicase DRH-1 and the dsRNA-binding protein RDE-4. The authors show that the complex prefers blunt-end dsRNA over dsRNAs that contain overhangs. Furthermore, whereas ATP-dependent dsRNA cleavage only exacerbates regular dsRNA cleavage activity, the presence of RDE-4 is essential to ATP-dependent and ATP-independent dsRNA cleavage. Single-particle cryo-EM studies of the ternary C. elegans Dicer complex reveal that the N-terminal domain of DRH-1 interacts with the helicase domain of DCR-1, thereby relieving its autoinhibitory state. Last, the authors show that the ternary complex is able to processively cleave long dsRNA, an activity primarily relying on the helicase activity of DRH-1.

Strengths:

• First thorough biochemical characterization of the antiviral activity of C. elegans Dicer in complex with the RIG-I like helicase DRH-1 and the dsRNA-binding protein RDE-4<br /> • Discovery that RDE-4 is essential to dsRNA processing, whereas ATP hydrolysis is not<br /> • Discovery of an autoinhibitory role of DRH-1's N-terminal domain (in analogy to the CARD domains of RIG-I)<br /> • First structural insights into the ternary complex DCR-1:DRH-1:RDE-4 by cryo-EM to medium resolution<br /> • Trap experiments reveal that the ternary DCR-1 complex cleaves blunt-ended dsRNA processively. Likely, the helicase domain of DRH-1 is responsible for this processive cleavage.

Weaknesses:

• Cryo-EM Structure of the ternary Dicer-1:DRH-1:RED-4 complex to only medium resolution<br /> • High-resolution structure of the C-terminal domain of DRH-1 bound to dsRNA does not reveal the mechanism of how blunt-end dsRNA and overhang-containing one are being discriminated<br /> • The cryo-EM structure of DCR1:DRH-1:RDE-4 in the presence of ATP only reveals the helicase and CTD domains of DRH-1 bound to dsRNA. No information on dsRNA termini recognition is presented. The paragraph seems detached from the general flow of the manuscript.

Reviewer #2 (Public Review):

Summary:

To investigate the evolutionary relationship between the RNAi pathway and innate immunity, this study uses biochemistry and structural biology to investigate the trimeric complex of Dicer-1, DRH-1 (a RIGI homologue) and RDE-4 , which exists in C. elegans. The three subunits were co-expressed to promote stable purification of the complex. This complex promoted ATP-dependent cleavage of blunt-ended dsRNAs. A detailed kinetic analysis was also carried out to determine the role of each subunit of the trimeric complex in both the specificity and efficiency of cleavage. These studies indicate that RDE-4 is critical for cleavage while DRC-1 is primarily involved in the specificity of the reaction, and DRH-1 promotes ATP hydrolysis. Finally, a moderate density (6-7 angstrom) cryo-EM structure of the trimeric complex is provided.

Strengths:

(1) Newly described methods for studying the C. elegans DICER complex<br /> (2) New structure, albeit only moderate resolution<br /> (3) Kinetic study of the complex in the presence and absence of individual subunits and mutations, provide detailed insight into the contribution of each subunit

Weaknesses:

(1) Limited insight due to limited structural resolution.

Author Response

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

We thank the reviewers for their careful comments. We sincerely agree with the comments from both reviewers, and noticed the word “cell transplantation”, throughout the manuscript including the title, was confusing. We revised the manuscript to clarify the aim of the study, and to express the conclusion more straightforwardly.

Response to the reviewers:

We interpret the data of the present study as the color of each RPE cell is a temporal condition which does not necessarily represent the quality (e.g. for cell transplantation) of the cells. We consider this may be applicable not only in vitro but also in vivo, although we do not know whether RPE shows heterogeneous level of pigmentation in vivo.

As our concern for iPSC-RPE is always about their quality for cell transplantation, maybe we haven’t fairly evaluated the scientific significance obtained from the present study.

Another thing we noticed was, although we used the term “cell transplantation” to explain what we meant by “quality” of the cells, we agree this was confusing. The aim of the study was not to show how the pigmentation level of transplant-RPE affects the result of cell transplantation, but to show the heterogeneous gene expression of iPSC-derived RPE cells, and the less correlation of the heterogeneity with pigmentation level. We went through the manuscript, including the title, to more straightforwardly lead this conclusion: the degree of pigmentation had some but weak correlation with the expression levels of functional genes, and the reason for the weakness of the correlation may be because the color is a temporal condition (as we interpreted from the data) that is different from more stable characteristics of the cells.

We agree that “cell transplantation” in the title (and other parts) was misleading. So, we changed the title, and removed the phrase that led as if the aim of the study was to show something about cell transplantation or in vivo results.

Also, to face scientifically significant results obtained from the present study appropriately, we discussed more about the correlation of the pigmentation level with some functional genes, and brought this as one of the conclusions of the manuscript.