Author Response

Reviewer #1 (Public Review):

The paper is very well written, the question is interesting, and the analyses are innovative. However, I do have concerns about the overall approach. My main concern is about looking at asymmetries in the low dimensional representation of connectivity. A secondary concern has to do with looking at the parcellated connectome. I explain these concerns in succession below.

We thank the Reviewer for the appreciation of our work and the insightful comments, which we have addressed below. The page numbers are corresponding to the clean version of the manuscript.

The first concern is to me quite a fundamental issue: looking at connectivity in a low dimensional space, that of the laplacian eigenvectors. There are two issues with this. The first one, which is less important than the second, is that the authors have a reference embedding to which they align other embeddings using a procrustes method with no scaling. While the 3D embedding is still optimally representing the connectivity (because distances don't change under rotations), we can no longer look at one axis at a time, which is what the authors do when they look at G1. In this case, G1 is representative of the connectivity of the reference matrix (LL), but not the others.

But even if the authors only projected their matrices onto a single G1 dimension with no procrustes (and only sign flipping if necessary), there is still a major issue. One implicit assumption of this whole approach is that if there is a change in connectivity somewhere in the original matrix, the same "nodes" of the matrix will change in the embedding. This is not the case. Any change in the original matrix, even if it is a single edge, will affect the positions of all the nodes in the embedding. That is because the embedding optimises a global loss function, not a local one.

To make this point clear, consider the following toy example. Say we have 4 brain regions A,B,C,D. Let us say that we have the following connectivity:

In the Left Hemisphere: A-B-C-D

In the Right Hemisphere: A-B=C-D

So the connection between B and C is twice as strong in the right hemi, and everything else remains the same.

The low dimensional embedding of both will look like this:

Left: ... A ... B ....... C ... D ...

Right A... ... ... B ... C ... ... ... D

Note how B,C are closer to each other in the RIGHT, but also that A,D have moved away from each other because the eigenvector has to have norm 1.

So if we were to calculate an asymmetry index, we would say that:

A is higher on the LEFT

B is higher on the RIGHT

C is higher on the LEFT

D is higher on the RIGHT

So we have found asymmetry in all of our regions. But in fact the only thing that has changed is the connection between B and C.

This illustrates the danger of using a global optimisation procedure (like low-dim embedding) to analyse and interpret local changes. One has to be very careful.

We thank the Reviewer for the detailed description of the first concern. We agree that low-dimensional embeddings describe global embedding of local features, rather than local phenomena. Moreover, we indeed assume that the connectivity embedding of a given node gives us information about its position along ‘gradients’ relative to other nodes and their respective embedding. Thus, indeed, when a single node (node X) has a different connectivity profile in the right hemisphere relative to the left, this will also have some impact on the embeddings of all nodes showing a relevant (i.e., top 10%) connection to node X.

To evaluate whether asymmetry could be observed in average connectivity within functional networks, an alternative approach to measure asymmetry was taken by computing average connectivity within different functional networks. Following we compared the within-network connectivity between left and right. We have now added this conceptual analysis to our results robustness analysis section. In short, we observed that transmodal networks (DMN, FPN, and language network) showed higher connectivity in the left hemisphere but other networks showed higher connectivity in the right hemisphere. Thus, this indicates that observations made with respect to asymmetry of functional gradients are similar to those observed for within-network functional asymmetry between the left and right hemispheres. We have now detailed the outcome of this analysis in our Result section and Supplementary Materials.

Results, p.14.: “As low-dimensional embedding is a global approach to summarize functional connectivity we reiterated our analysis by evaluating asymmetry of within network functional connectivity in the current sample. Observations made with respect to asymmetry of functional gradients are similar to those observed for within-network functional asymmetry between the left and right hemispheres.”

“To further explore functional connectivity asymmetry between left and right hemispheres, we calculated the LL within network FC and RR within network FC (Figure 2-figure supplement 5). It showed that connections in the left hemisphere and right hemisphere were relatively equal in the global scale. However, for the local differences, networks showed significant subtle leftward or rightward asymmetry (vis1: t = -5.203, P < 0.001; vis2: t = -22.593, P < 0.001; SMN: t = -8.262, P < 0.001; CON: t = -32.715, P < 0.001; DAN: t = -11.272, P < 0.001; Lan.: t = 33.827, P < 0.001; FPN: t = 24.439, P < 0.001; Aud.: t = 0.191, P = 0.849; DMN: t = 11.303, P < 0.001; PMN: t = -35.719, P < 0.001; VMN: t = -11.056, P < 0.001; OAN: t = 0.311, P = 0.756).”

Irrespectively, we have further highlighted that such a global interpretation for asymmetry of areas is still meaningful, given that a node is always placed in a global context. We have now further explained that our metrics give insights in local embedding of global phenomena in the introduction, p. 3.

Introduction, p. 3: “These low-dimensional gradient embeddings describe global embedding of local features, rather than local phenomena. Thus, interpretation for asymmetry of areas is under a global context.”

My second concern is about interpreting the brain asymmetry as differences in connectivity, as opposed to differences in other things like regional size. The authors use a parcellated approach, where presumably the parcels are left-right symmetric. If one area is actually larger in one hemisphere than in the other, the will manifest itself in the connectivity values. To mitigate this, it may be necessary to align the two hemispheres to each other (maybe using spherical registration) using connectivity prior to applying the parcellation.

Thanks for this nice idea. We have now computed the differences of the mean rsfMRI connectome along the first gradient at the vertex level using 100 random subjects, as we have the data mapped to a symmetric template (fs_LR_32k), indicating that each vertex has a symmetric counterpart in the right hemisphere. Our results show left-right asymmetry as language/default mode-visual-frontoparietal vertices, which is consistent with the main results of the parcel-based approach. We have also added this response to the Supplementary materials.

Though overall findings are consistent, spherical registration may also have new issues. Total anatomical spatial symmetry may not provide functional comparability at the vertex level between left and right hemisphere. For example, during language tasks in the current sample, the activated frontal region in the left hemisphere is larger than the activated contralateral region in the right hemisphere. In the current study, we aimed to evaluate asymmetry between functionally and structurally homologous regions, as described by the Glasser atlas. In case of the resting state fMRI data, we used the region-wise symmetric multimodal parcellation (Glasser et al., 2016). This parcellation ensures the functional contralateral regions in both hemispheres. A previous study (Williams et al., 2021) investigated the structural and functional asymmetry in newborn infants. They used spherical registration (make fs_LR symmetric) for structural asymmetry but not for functional asymmetry. As such spheric registration may hide functional information, we think spherical registration may be more suitable for structural studies.

To address the concern regarding the alignment of hemispheres, we used joint alignment for LL and RR to compare the results between this and the Procrustes alignment technique (Pearson r=0.930, P_spin<0.001), below is the figure of asymmetry along the principal gradient (upper: joint alignment, below: Procrustes alignment) indicating convergence between both approaches. We have reported this information in the Supplementary Materials.

Lastly, we do agree that parcel size might be an important issue influencing the asymmetry pattern. To test for such an effect, we performed the correlation between the rank of parcel size (left-right)/(left+right) and rank of asymmetry index. It suggests only a small insignificant correlation along G1 (Spearman r_intra=0.130, P_spin=0.105; Spearman r_inter=0.130, P_spin=0.084). Of note, there is a systematic difference in parcel size as a function of sensory-association hierarchy, indicating that the link between parcel-size and asymmetry may vary as a function of sensory vs associative regions.

Reviewer #2 (Public Review):

Using recently-developed functional gradient techniques, this study explored human brain hemispheric asymmetry. The functional gradient is a hot technique in recent years and has been applied to study brain asymmetries in two papers of 2021. Compared to previous studies, the current study further evaluated the degree of genetic control (heritability) and evolutionary conservation for such gradient asymmetries by using human twin data and monkey's fMRI data. These investigations are of value and do provide interesting data. However, it suffers from a lack of specific hypotheses/questions/motivations underlying all kinds of analyses, and the rich observational or correlational results seem not to offer significant improvement of theoretical understanding about brain asymmetries or functional gradient. In addition, given the limited number of twins in HCP project (for a heritability estimation), the limited number of monkeys (20 monkeys), and the relatively poor quality of monkeys' resting functional MRI data, the results and conclusion should be taken cautiously. Below are major concerns and suggestions.

We thank the Reviewer for the evaluation of our work and the helpful suggestions.

The gradient from resting-state functional connectome has been frequently used but mainly at the group level. The current study essentially applied the gradient comparison (i.e., gradient score) at the individual level. Biological interpretation for individual gradient score at the parcel level as well as its comparability between individuals and between hemispheres should be resolved. This is the fundamental rationale underlying the whole analyses.

We thank the Reviewer for this remark, and are happy to provide further rationale for using and comparing individual gradients scores to evaluate individual variation in asymmetry and associated heritability. Though gradients from resting-state functional connectivity have been frequently used at the group level, various studies have also studied individual differences. For example, using linear mixed models to compare gradient scores between left and right across subjects (Liang et al., 2021), applying the individual gradient scores to compare disease and controls (Dong et al., 2020, 2021; Hong et al., 2019; Park et al., 2021), and link individual hippocampal gradients to memory recollection (Przeździk et al., 2019). Together, these studies show individual variations of local gradients, indicating changes in node centrality and hubness (Hong et al., 2019), and connectivity profile distance (Y. Wang et al., 2021). Of note, low-dimensional embeddings describe global embedding of local features, rather than local phenomena. Thus, interpretation for asymmetry of areas is under a global context. The biological interpretation for individual gradients would be to what degree the system segregated and integrated has changed patterns of ongoing neural activity (Mckeown et al., 2020). It reflects that individuals have different functional boundaries between anatomical regions. Whereas, individual neurons are embedded under the global-local boundaries through a cortical wiring space consisting of intricate long- and short-range white matter fibers (Paquola et al., 2020).

Introduction, p. 4: “We applied the individual gradient scores to study the asymmetry, consistent with prior studies (Gonzalez Alam et al., 2021; Liang et al., 2021). Individual variation along the gradients reflects a global change across subjects in the functional connectome integration and segregation, and it is under genetic control (Valk et al., 2021). Moreover, to what degree the system segregated and integrated relates to patterns of ongoing neural activity (Mckeown et al., 2020), and different individuals have different functional boundaries between anatomical regions.”

Results, p. 5: “Next, individual gradients were computed for each subject and the four different FC modes and aligned to the template gradients with Procrustes rotation. It rotates a matrix to maximum similarity with a target matrix minimizing sum of squared differences. As noted, Procrustes matching was applied without a scaling factor so that the reference template only matters for matching the order and direction of the gradients. Therefore, it allows comparison between individuals and hemispheres. The individual mean gradients showed high correlation with the group gradients LL (all Pearson r > 0.97, P spin < 0.001).”

Only the first three gradients are used but why? What about the fourth gradient? Specific theoretical interpretation is needed. At the individual level, is it ensured that the first gradients of all individuals correspond to each other? In this study, it is unclear whether we should or should not care about the G2 and G3. The results of G2 and G3 showed up randomly to some degree.

In the current study we focused on the principal gradient in the main analysis, given its association with sensory-transmodal hierarchy, microstructure, and evolutionary alterations (Margulies et al., 2016; Paquola et al., 2019; Xu et al., 2020).

Conversely, gradient 2 reflects the dissociation between visual and sensory-motor networks and gradient 3 is linked to task-positive, control, versus ‘default’ and sensory-motor regions. We analyzed asymmetry and its heritability of the first three gradients (explaining respectively 23.3%, 18.1%, and 15.0% of the variance of the rsFC matrix). However, we extracted the first ten gradients to maximize the degree of fit (Margulies et al., 2016; Mckeown et al., 2020). We have now also shown G4-10 mean asymmetry results as a supplementary figure. To ensure correspondence of gradients across individuals, we aligned the individual gradients to the group level template with Procrustes rotation. Procrustes rotation rotates a matrix to maximum similarity with a target matrix minimizing sum of squared differences. The approach is typically used in comparison of ordination results and is particularly useful in comparing alternative solutions in multidimensional scaling. Figure S1 shows the mean gradients across subjects of each FC mode, which is close to the Figure 1D template gradient space.

Results, p. 5: “The current study analyzed asymmetry and its heritability of the first three gradients explaining most variance (Figure 1d). As they all have reasonably well described functional associations (G1: unimodal-transmodal gradient with 24.1%, G2: somatosensory-visual gradient with 18.4%, G3: multi-demand gradient with 15.1%). However, given we extracted ten gradients to maximize the degree of fit 26,52. We stated mean asymmetry of G4-10 in Figure 1-figure supplement 1.”

The intra-hemispheric gradient is institutive. However, it is hard to understand what the inter-hemispheric gradient means. From the data perspective, yes you can do such gradient comparison between the LR and RL connectome but what does this mean? Why should we care about such asymmetry? From the introduction to the discussion, the authors simply showed the data of inter-hemispheric gradients without useful explanation. This issue should be solved.

We are happy to further clarify. The LR and RL connectivity reflects cross-hemispheric functional signal interaction via corpus callosum, whose structural asymmetry is usually studied (Karolis et al., 2019). Such intra-hemispheric connections, compared to the inter-hemispheric connections, have been suggested to reflect the inhibition of corpus callosum, and underlie hemispheric specialization. Different information relies on hemispheric specialization (e.g., visual, motor, and crude information) and/or inter-hemispheric information transfer (e.g., language, reasoning, and attention) (Gazzaniga, 2000). To clarify and motivate the analysis of both intra- and inter-hemispheric asymmetry in functional gradients, we have now added further detail in the introduction, p. 5.

Here is text: Introduction, p. 4. “The full FC matrix contains both intra-hemispheric and inter-hemispheric connections. Intra-hemispheric connections, compared to the inter-hemispheric connections, have been suggested to reflect the inhibition of corpus callosum and may underlie hemispheric specializations involving language, reasoning, and attention. Conversely, inter-hemispheric connectivity may reflect information transfer between hemispheres, for example a wide range of modal and motor information, and crude information concerning spatial locations 48. Previous studies have reported intra-hemispheric FC to study gradient asymmetry 6,38. By having the callosum related to association white matter fibers, one hemisphere could develop for new functions while the other hemisphere could continue to perform the previous functions for both hemispheres 48. Therefore, in addition to the intra-hemispheric FC gradients, we depicted the inter-hemispheric FC, which is abnormal in patients with schizophrenia 23,49 and autism 24.”

as well as Discussion, p. 16 “Conversely, the transmodal frontoparietal network was located at the apex of rightward preference, possibly suggesting a right-ward lateralization of cortical regions associated with attention and control and ‘default’ internal cognition 62,63. The observed dissociation between language and control networks is also in line with previous work suggesting an inverse pattern of language and attention between hemispheres 3,64. Such patterns may be linked to inhibition of corpus callosum 65, promoting hemispheric specialization. It has been suggested that such inter-hemispheric connections set the stage for intra-hemispheric patterns related to association fibers 48. Future research may relate functional asymmetry directly to asymmetry in underlying structure to uncover how different white-matter tracts contribute to asymmetry of functional organization.”

and Discussion, p.18 “Though overall intra- and inter-hemispheric connectivity showed a strong spatial overlap in humans, we also observed marked differences between both metrics across our analysis. For example, although we found both intra- and inter-hemispheric differences in gradient organization to be heritable, only for intra-hemispheric asymmetry we found a correspondence between degree of asymmetry and degree of heritability. Similarly comparing asymmetry observed in human data to functional gradient asymmetry in macaques, we only observed spatial patterning of asymmetry was conserved for intra-hemispheric connections. Whereas intra-hemispheric asymmetry relates to association fibers, commissural fibers underlie inter-hemispheric connections 77 It has been suggested that there is a trade-off within and across mammals of inter- and intra-hemispheric connectivity patterns to conserve the balance between grey and white-matter 76. Consequently, differences in asymmetry of both ipsi- and contralateral functional connections may be reflective of adjustments in this balance within and across species. Secondly, previous research studying intra- and inter-hemispheric connectivity and associated asymmetry has indicated a developmental trajectory from inter- to intra-hemispheric organization of brain functional connectivity, varying from unimodal to transmodal areas 78,79. It is thus possible that a reduced correspondence of asymmetry and heritability in humans, as well as lack of spatial similarities between humans and macaques for inter-hemispheric connectivity may be due to the age of both samples (young adults in humans, adolescents in macaques). Further research may study inter- and intra-hemispheric asymmetry in functional organization as a function of development in both species to further disentangle heritability and cross-species conservation and adaptation.”

When aligning intra-hemispheric gradient, choosing averaged LL mode as the reference may introduce systematic bias towards left hemisphere. Such an issue also applies to LR-RL gradient alignment as well as cross-species gradient alignment. This methodological issue should be solved.

We thank the Reviewer for raising this point. Indeed, we also used RR as reference, the results were virtually identical. We have stated this in the Results, p. 13. Regarding the cross-species alignment, we averaged the left and right hemispheres to reduce the systematic bias. It showed that the correlation and comparison results remained robust. Now we have updated the method and corresponding results (p.10). Here is the text:

Results (p.15): “We also set the RR FC gradients as reference, the first three of which explained 22.8%, 18.8%, and 15.9% of total variance. We aligned each individual to this reference. It suggested all results were virtually identical (Pearson r > 0.9, P spin < 0.001).”

Results (p.10): “To reduce a possible systematic hemispheric bias during the cross-species alignment, we averaged the left and right hemisphere. We found that the macaque and macaque-aligned human AI maps of G1 were correlated positively for intra-hemispheric patterns (Pearson r = 0.345, P spin = 0.030). For inter-hemispheric patterns, we didn’t observe a significant association (Pearson r = -0.029, P spin = 0.858)”

The sample size of monkey (i.e., 20) is far less than human subjects (> 1000). Such limitation raises severe concern on the validity of the currently observed gradient asymmetry pattern in the monkey group, as well as the similarity results with human gradient asymmetry pattern. Despite the marginal significance of G1 inter-hemisphere gradient between humans and monkeys, I feel overall there is no convincingly meaningful similarity between these two species. However, the authors' discussion and conclusion are largely based on strong inter-species similarity in such asymmetry. The conclusion of evolutionary conservation for gradient asymmetry, therefore, is not well supported by the results.

We agree with your comments. Although it is a small sample compared to humans, in NHP studies, it is a relatively decent sample size (most of the studies have N<10). Of note, recent work suggested that the individual variation pattern can be captured using 4 subjects in both human and macaques (Ren et al., 2021).

To overcome potential overinterpretation of our findings, we have now changed the title to a more descriptive format: “Heritability and cross-species comparisons of asymmetry of human cortical functional organization”

And further detailed findings already in the Abstract; “These asymmetries were heritable in humans and, for intra-hemispheric asymmetry of functional connectivity, showed similar spatial distributions in humans and macaques, suggesting phylogenetic conservation.”

We have pointed out the small sample size in the limitation. Please find the text below: Discussion, p. 18: “Due to the small sample size of macaques, it is important to be careful when interpreting our observations regarding asymmetry in macaques, and its relation to asymmetry patterning observed in humans. Therefore, further study is needed to evaluate the asymmetry patterns in macaques using large datasets 53,79”

And nuanced the conclusion, p.19: “This asymmetry was heritable and, in the case of organization of intra-hemispheric connectivity, showed spatial correspondence between humans and macaques. At the same time, functional asymmetry was more pronounced in language networks in humans relative to macaques, suggesting adaptation.”

Author Response

Reviewer #2 (Public Review):

Weaknesses:

1) It is surprising that certain enzymes with established depalmitoylation activity were excluded from BrainPalmSeq data-base (e.g. ABHD4, ABHD11, ABHD12, ABHD6)

We have now included additional depalmitoylating enzymes in our database and manuscript.

2) Albeit not essential it will be of great interest to include in the established database enzymes necessary for synthesis of ACYL-CoA (e.g. ACSL enzymes). One improvement may include the ability of future researchers to add such curated analysis to the platform within future research studies.

We agree with the reviewer there are many expansions of our gene set that would be interesting to include. Given the size of the current manuscript however, for brevity we have decided at present to curate data for the core set of genes that directly regulate dynamic palmitoylation. We have also added a ‘Contact Us’ feature to the website, so that repeatedly requested genes or datasets can be added in future.

3) The experimental validation presented in figure 6 relies on over-expression of substrates and ZDHHC enzymes. This setup is known to often provide unspecific S-acylation events which result from excess enzyme or substrate availability. Hence, such validation would be greatly strengthened by loss of function experiments.

We have now done loss-of-function experiments and included results in major discussion point 1 above. If the editors/reviewers think it is appropriate to add to the manuscript, we will comply. However, as our negative data does not negate the fact that ZDHHC9 is able to palmitoylate the myelin proteins tested, but merely suggests it may not be necessary for protein palmitoylation in vivo, we do not think it strengthens the manuscript.

4) The authors relevantly use in-situ hybridization images from the Allen Brain atlas to validate their predictions. Although it is understandable that an extensive experimental validation of the predictions here established would be out of the scope of the current study, this work could be improved by validating the RNA expression at the protein level of certain abundant ZDHHC enzymes in available neuro-associated cell types.

We have now validated RNA expression at the protein level for a few palmitoylating and depalmitoylating enzymes.

5) It would be interesting if the authors would further compare the predicted association clusters (e.g. figure 1), substrates (figures 1 and 2), and S-acylation pairs (figure 4) here determine, with previous determined ZDHHC enzyme associations described in different cell types and biological systems. Alternatively, further relevant validation could include testing whether further established ZDHHC-ZDHHC cascades (e.g. ZDHHC3-7) can be also detected with specific cells or regions of the CNS.

On our website, all expression data can be downloaded below the heatmaps for each study, and the cell type expression relationships between any 2 genes can be plotted by the user to reveal cell types (if any) within which genes are co-expressed. In response to this comment and that of Reviewer 3 below, we have now performed such analysis on ZDHHC5/ZDHHC20 and ZDHHC6/ZDHHC16, which are to our knowledge the best established ZDHHC cascades. We have included these plots in new Figure 1 – figure supplement 2, along with discussion on line 172. Similar analysis has been performed on the known ZDHHC-accessory protein pairs (see below).

6) Figure 3B: it is not clear why the cluster of zdhhcs with high layer specific expression displayed at the top of the graph does not follow the low-to-high expression scale of the table.

The expression data in this figure is grouped by hierarchical clustering, rather than in order of low-to-high expression, in order to be consistent with Figure 2B. While we believe this is the better way to display the data, we are willing to modify if the editors/reviewers have a strong preference.

7) Figure 4D: the more relevant potential cooperative pairs (ZDHHCs-APTs) could be highlighted in more contrasted colours.

We thank the reviewer for this suggestion but at this stage would prefer to keep the color scheme as it is so that readers are better able to formulate their own hypotheses when observing these figures.

Reviewer #3 (Public Review):

Weaknesses:

1) There is a vast amount of data available and the description and discussion of this could be endless, but there are a few points that could be brought out in more detail. For example, the correlation (or lack of correlation) of expression of the proposed zDHHC-PAT accessory proteins with their cognate zDHHCs. The dominance of a relatively small number of zDHHC enzymes (20, 2, 17, 3, 21, 8) in the CNS also merits some discussion. Is the combination of a high-capacity, low-specificity enzyme (zDHHC3) with others that are regarded as more 'specific'? I believe none of these are ER-resident - they represent Golgi and PM?

The reviewer brings up many interesting questions. Indeed, we were hopeful that this type of mining of RNAseq data would bring to light many questions that can be followed up on in future publications.

We have addressed the correlation in expression of accessory proteins with their cognate ZDHHCs with new data.

We are unsure how to address the dominance of a relatively small number of ZDHHC enzymes (20, 2, 17, 3, 21, 8) in the CNS, beyond highlighting this expression pattern. We believe that interpretation of the expression of this in any way (e.g. co-expression of high-capacity, low-specificity enzymes (ZDHHC3) with more 'specific' ZDHHCs) would merely be speculative. However, we are open to adding further discussion with some guidance from the reviewer.

An early example of the verbification of Steven Pinker's name. Here it indicates the view of predominantly privileged men to argue that because the direction of history has been so positive, that those without power shouldn't complain.

I've also heard it used to generally mean a preponderance of evidence on a topic, as seen in Pinker's book The Better Angels of Our Nature, but still not necessarily convincingly prove one's thesis.

Aristotelian criticism is largely concerned with wondering how effective an artifact is in reaching its intended audience. In this case, Senator Smith never mentions Sen. McCarthy by name, but given the historical context, it is obvious she refers to him and his supporters in condemning "the Senate and its members". If one were to measure her success in doing so, as mentioned in Lorraine Boissoneault's Smithsonian article, stating , "The one person who didn’t forget Smith’s speech was McCarthy himself. 'Her support for the United Nations, New Deal programs, support for federal housing and social programs placed her high on the list of those against whom McCarthy and his supporters on local levels sought revenge,' writes Gregory Gallant in Hope and Fear in Margaret Chase Smith’s America. When McCarthy gained control of the Permanent Subcommittee on Investigations (which monitored government affairs), he took advantage of the position to remove Smith from the group, replacing her with acolyte Richard Nixon, then a senator from California." This may not have been an intended effect, but shows nonetheless the significance and degree to which Smith's speech was able to reach her audience. Unfortunately however, it's hard to say how effective entirely her speech would have been, as popularity of her speech waned as the Korean War broke out later the same month, inclining many to take a more right-wing, anti-communist approach favored by McCarthy and many other Republicans.

Aristotelian criticism is largely concerned with wondering how effective an artifact is in reaching its intended audience. In this case, Senator Smith never mentions Sen. McCarthy by name, but given the historical context, it is obvious she refers to him and his supporters in condemning "the Senate and its members". If one were to measure her success in doing so, as mentioned in Lorraine Boissoneault's Smithsonian article , "The one person who didn’t forget Smith’s speech was McCarthy himself. 'Her support for the United Nations, New Deal programs, support for federal housing and social programs placed her high on the list of those against whom McCarthy and his supporters on local levels sought revenge,' writes Gregory Gallant in Hope and Fear in Margaret Chase Smith’s America. When McCarthy gained control of the Permanent Subcommittee on Investigations (which monitored government affairs), he took advantage of the position to remove Smith from the group, replacing her with acolyte Richard Nixon, then a senator from California." This may not have been an intended effect, but shows nonetheless the significance and degree to which Smith's speech was able to reach her audience. Unfortunately however, it's hard to say how effective entirely her speech would have been, as popularity of her speech waned as the Korean War broke out later the same month, inclining many to take a more right-wing, anti-communist approach favored by McCarthy and many other Republicans.

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): Excellent quality of cell biology and biochemistry. the additional supports are needed for the claim of actin elongation using different formin variants.

Reviewer #1 (Significance (Required)): Ingrid Billault-Chaumartin and co-authors described interesting research that provides insights on formin-isoform specific function in fission yeast and a new role of Fus1 FH2 domain in cell-cell fusion event. While three formin isoforms have different localization, research proposed an additional dissection in their functional differences by having different functions in C-terminus, including FH1 FH2 and formin C-terminus. The work also described additional factors that regulate cell fusions from autotrophy effect and formin expression level, in addition to the well-accepted formin biochemical activities. Here are my comments regarding the strengths of the work and improvements that could further strengthen the story.

Major comments 1. Fig.1 shows Cdc12C could recapitulate Fus1 function by ~80% if fused with Fus1C, whereas deletion of the C-terminal tail of Cdc12 following FH2 introduces drastic dysfunction. Together with Fig. 3, these results indicate Cdc12 Cter plays more important roles than Fus1 Cter for there respective functions. Such results suggested a Cter-mediated mechanism that differentiates the functions of three fission yeast formin isoforms. The authors examined contributions from the difference in FH1 (Figs 4,5) and FH2 residues (Fig. 6). Whereas the obvious phenotype of Cter was not further investigated and not much discussed. The Cter of budding yeast formins interacts with nucleation-promoting factors, Bud6 and Aip5. Although S. Pombe does not have orthologs of budding yeast Bud6 and Aip5, I wonder would the author discuss the potential contribution of Cter in differentiating S. Pombe formins.

The reviewer is correct that the C-terminal tail region of Cdc12 beyond the FH1-FH2 domains has a strong influence on the ability of Cdc12C to replace Fus1C. This is one reason why we specifically investigated the possible role of Fus1 C-terminal tail, which is much shorter than that of Cdc12. We found that Fus1 C-terminal tail plays only very minor role in regulating Fus1 function, as described in Figure 3. We note that contrary to what the reviewer states, Bud6 exists in S. pombe and binds the C-terminal tail of the formin For3 (see Martin et al, MBoC 2007), but whether it binds Fus1 is unknown. We have expanded our discussion to include a paragraph on the role of formin C-termini.

Because the manuscript is focused on the function of Fus1 formin, we did not explore further the role of the Cdc12 C-terminal tail. It was previously shown that this region of Cdc12 contains an oligomerization domain that promotes actin bundling (Bohnert et al, Genes and Dev 2013). It is thus likely that this helps Cdc12 FH1-FH2 perform well in replacement of Fus1. In fact, it is likely that oligomerization boosts formin function, as we have discovered that Fus1 N-terminus contains a disordered region that fulfils exactly this function. This is described in a distinct manuscript under review elsewhere and just deposited on BioRxiv (Billault-Chaumartin et al, BioRxiv 2022; DOI: 10.1101/2022.05.05.490810). We have now cited this point in the discussion.

1. Here, the study focuses on the FH1 between Fus1 and Cdc12 to understand their different functions in actin polymerization. FH1 mediated actin elongation through its interaction with profilin via polyP. The transfer rate of G-actin from profilin and profilin sliding depends on the polyP patterns regarding the length of each polyp motif and their distance to FH2 (Naomi Courtemanche and Thomas D. Pollard, JBC, 2012). To better understand the mechanisms by which these engineered FH1 variants on both Fus1 and Cdc12 in Fig. 4, the author may want to list the sequence of these engineered FH1 domains, including the information of the number and length of polyp motifs, and discuss these patterns.

This list and discussion were available in the initial paper that characterized each of the constructs in vitro (Scott et al, MBoC 2011). We have now re-drawn it in a supplemental figure for convenience (as also answered in response to minor point 2), which is already provided in the revised manuscript as Figure S1. (Previous supplementary figures are re-numbered S1>S2, S2>S3 and S3>S4).

1. Figs.4,5 cell biology results do not directly support the point of specific elongation rate unless the LifeAct-labeled actin cable elongation speed could be followed and quantified. The fluorescent tagging of tropomyosin does not show the actin cable pattern, which makes it very difficult to be used to study actin cable dynamics, such as elongation. Therefore, I feel the data in current Fig. 4 and Fig. 5 could not claim the differences in actin elongation without a quantitative comparison of elongation rate. I suggest a CK666 treatment to increase the visibility of the actin cable pattern of LifeAct, used before in both fission and budding yeasts, which would allow the author to quantify the actin cable elongation rate. Another way is to use the TIRF assay used in this study, which would give a better quantitation of formin nucleation and profilin-aided elongation.

We respectfully disagree with the reviewer on this point. All the constructs we use in vivo have been characterized in vitro and their elongation rate carefully measured (Scott et al, MBoC 2011). These values are thus known and can be directly compared to our results in vivo.

Of course, it would be fantastic to be able to directly measure formin elongation rates in vivo, but we are not aware that this has been done in any system. The proxy experiments that the reviewer suggests would be good ones, but each faces technical challenges that make them impossible in our system. First, because the fusion focus is a structure that forms in response to cell-cell pheromonal communication, we cannot add CK-666 or any other drug during this phase, as this perturbs the pheromone signal. Indeed, we had shown that simple buffer wash leads to loss of the fusion focus (see Dudin et al, Genes and Dev 2016). Second, the fusion focus is at the contact site between partner cells, i-e somewhat distant (1-2µm) from the coverslip during imaging. It is thus impossible to use TIRF. Finally, the fusion focus is a tightly packed actin structure. This is the reason why (rather than use of the tropomyosin marker) we cannot image single actin filaments (or even bundles) of which we could follow the dynamics as has been done to measure the retrograde flow of actin cables in yeast.

What we have done is to use a better tropomyosin tag, mNeonGreen-Cdc8, which was just described (Hatano et al, BioRxiv 2022; DOI: 10.1101/2022.05.19.492673) to quantify amounts of linear actin. Although this is not a measure of elongation rate, it would give some sense about amounts of polymer assembled. We have obtained images with mNeonGreen-Cdc8 of all experiments previously conducted with GFP-Cdc8 and have replaced them in Figure 4C, Figure 5E, Figure 6E and Figure S2B. We have also quantified the relevant strains. The relative intensities of mNeonGreen-Cdc8 at the fusion focus at fusion time reflect remarkably well the measured elongation rates of the various formin constructs characterized in vitro. These data are now provided as new panels Figure 4F and Figure 5F.

1. I appreciated the detailed biochemical dissections of multiple aspects of WTFus1 and Fus1R1054E, although the biochemical assays could not identify the mechanism by which R1054E causes the cell fusion. In many cases, the formin functions are diverse in diverse biological processes and sophisticated that cannot be explained well only from its biochemical activities in actin polymerization, such as the bundling, nucleation, and elongation studied in this story regarding fusion. This exciting information allows us to think of more possibilities that might regulate formin function rather than a direct change of formin activities in actin polymerization. I think a discussion of different aspects of functional regulation of formin might inspire society to investigate new possibilities to solve the mysteries. For example, the changes in formin behaviors and functions could be regulated by stress-induced formin turnover by degradation, cell signaling-regulated formin clustering and complex assembly, and their potential relevance to recruit protein constituents for fusion progression.

We have added a paragraph on the role of Fus1 C-terminus. If you feel we should expand more on the diverse modes of regulation of formins, we could, but we have so far kept the discussion centred around the points of investigation in this paper, whose aim was to probe how changes in nucleation and elongation rates, rather than other regulations, affect the in vivo function of Fus1.

Minor comments. 1. There are two types of "C", one includes FH1/FH2 and one following FH2, used in the manuscript, and it is a bit confusing. Better to differentiate them that allows an easy following. Fig. 1 uses Cdc12C-deltaC, Fig. 3 uses Fus1-delta Cter.

We have updated the nomenclature to make this clearer: the C-terminal region beyond the FH1-FH2 domains is now called Cter throughout the manuscript.

1. It's better to specify the amino acid position on the schematic of formins, such as panel A in many figures. It's always more informative to compare formin activities by considering the domain lengths, especially for the C-terminal tail that is variable in lengths and sequences. With similar thoughts, I suggest a supplementary figure that lists the sequence of all FH1 domains variants and Cter domains, such as the FH2 domain in Fig. S1.

We have made a supplementary figure (new Figure S1) listing all constructs with specific aa positions as well as the FH1 domain variants and their sequences (see also answer to point 2 above). We have not added the sequence of the Cter domains in this figure, as these are extremely divergent and not particularly informative at this point.

1. "n" for the statistic needs to be provided for Fig. S3.

We have added the information to the legend of the figure (now Fig S4).

1. The SDS-PAGE staining gel of the purified recombinant proteins for biochemical assays should be provided, particularly for these newly reported mutant variants.

This is now provided as new panel S4C. We show the purified recombinant Cdc122FH1-Fus1FH2 proteins, which are the newly reported ones.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): In this study, Billaut-Chaumartin and colleagues investigate the molecular specialization of the S. pombe formin, Fus1. The authors systematically modulate the actin filament elongation and nucleation activities of Fus1 by expressing chimeric constructs that contain Formin Homology 1 and 2 domains from two other formins with known polymerization activities. By characterizing the architecture of the fusion focus and the efficiency of cell fusion, they find that both the elongation and nucleation properties of Fus1 are specifically tailored for its cellular role. Comparison of formin constructs with similar elongation and nucleation activities also reveals that the Fus1 FH2 domain possesses a specific property that promotes efficient cell fusion. Using sequence alignment and homology modeling, the authors identify R1054 as the residue that confers this novel, fusion-specific activity to Fus1, despite producing no effect on its bundling or polymerization properties in vitro.

Overall, this study is well motivated, and the results support the conclusions that are drawn. I have only minor suggestions, as described below.

Minor comments: (1) The schematic diagrams of the chimeric formin constructs are very helpful. However, it is difficult to distinguish the colors from one another, especially in the case of the Cdc12FH1-Fus1FH2 variant, which requires discernment of the relatively small purple region within the dark blue molecule. Would it be possible to modify the colors to increase their contrast? Similarly, the blue and gray data sets in Figure 3B are very difficult to discern.

We have changed the colours to improve contrasts.

(2) The affinities (Kd) with which the formins bind the barbed ends as described in the second-to-last paragraph on page 8, in Figure Legend 7G, and in the "Analysis of pyrene data" section of the Materials and Methods should be defined as dissociation "constants", rather than dissociation "rates". Also, these affinities are lacking units in the following sentence on page 8.

We have corrected this. The unit is nM.

(3) When comparing the TIRF micrographs in Figure S3A, it looks as though both formins (but especially the R1054E variant) nucleate more filaments in the presence of profilin than in its absence. Is this a reproducible effect? If so, can the authors provide an explanation for this?

There is strong variability in the filament numbers observed by TIRF in replicate experiments, which makes it difficult to use this technique to determine the nucleation efficiency. This may be due for instance to the stickiness of the glass, which may influence the number of observed filaments. We have measured the number of filaments after 130s of polymerization for each condition to test whether there are any significant differences between conditions despite overall variability. The measurements suggest that the addition of profilin increases the number of actin filaments. However, these results should be taken very carefully due to the experimental variations (very large error bars). Additionally, because Fus1-associated filaments are very short in absence of profilin, it is quite likely that this influences their crowding at the glass surface compared to longer filaments (in presence of profilin). Since in TIRF we can only observe the filaments at the glass surface, we may miss a portion of short Fus1-bound actin filaments in absence of profilin.

For these reasons, and because the possible role of profilin in modulating nucleation efficiency by formins is not the object of the work here, would thus prefer not to include this graph in the manuscript.

Reviewer #2 (Significance (Required)): This study contributes a key advancement towards understanding how the polymerization activities of formins are tailored to support diverse and specific cellular functions. The results in this study nicely complement and expand upon similar recent work that dissected the polymerization requirements of the formin Cdc12, which mediates cytokinetic ring assembly in S. pombe, and For2, which drives the assembly of apical networks that are necessary for polarized growth in Physcomitrella patens. As such, this work will likely be of significant interest to scientists who study mechanisms of actin dynamics regulation. The identification of R1054 as a residue that confers a novel regulatory activity to the FH2 domain of Fus1 will also likely be of great interest to biochemists and other scientists who study formins at the molecular level.

My expertise is in the field of formins and actin polymerization.

Reviewer #1 (Public Review): 

In this article Farrell et al. leverage existing datasets which measure frailty longitudinally in mice and humans to model 'robustness' (the ability to resist damage) and 'resilience' (the ability to recover from damage), their dynamics across age, and their relative contributions to overall frailty and mortality. The concept of separating damage/robustness from recovery/resilience is valid and has many important applications including better assessment and prediction of effective intervention strategies. I also appreciate the authors' sophisticated attempts to effectively model longitudinal data, which is a challenge in the field. The use of human and mouse data is another strength of the study, and it is quite interesting to see overlapping trends between the two species. 

While I find the rationale sound and appreciate the approach taken at a high level, there are a few key considerations of the specific data used which are lacking. The authors conceptualize resilience based on studies which primarily use short time scales and dynamic objective measures (ex. complete blood cell counts in Pyrkov et al.) often in conjunction with an acute stress stimulus. For example, they heavily cite Ukraintseva et al. who define resilience as "the ability to quickly and completely recover after deviation from normal physiological state or damage caused by a stressor or an adverse health event." 

Given these definitions, the human data used seem to fit within this framework, but we should carefully consider the mouse data. The mouse frailty index is a very useful tool for efficiently measuring the organismal state in large cohorts. A tradeoff for quickly measuring a broad range of health domains is that the individual measurements are low resolution (categorical) and involve inherent subjectivity (which may be considered part of the measurement error). Some transitions in individual components are due to random measurement error and I believe this is especially likely with decreases (or 'resilience' transitions). 

The reason I think the resilience transitions are subject to high measurement error is that I am skeptical as to whether many of the deficits in the mouse index are reversible under normal physiologic conditions. For example, it is exceptionally unlikely for a palpable/visible tumor to resolve in an aged mouse over the time scales studied here, thus any reversal that was observed is very likely due to random measurement error. Other components which I have doubts about reversibility are alopecia, loss of fur color, loss of whiskers, tumors, kyphosis, hearing loss, cataracts, corneal capacity, vision loss, rectal prolapse, genital prolapse. 

In summary, I applaud the authors' efforts in generating complex models to better understand longitudinal aging data. This is an important area that needs further development. I appreciate their conceptualization of resilience and robustness and think this framework has an important place in aging research. I also appreciate their cross-species approach. However, the authors may have over-conceptualized and made some assumptions about the mouse data which may not be valid. It will be important to assess the results with careful consideration of the time scales of the underlying biology and the resolution and measurement error inherent to these tools.

Benjamin Franklin manufactured the first recorded abortion debate in America to help sell his newspapers and to crush a rival.

The typical student note taking method of transcribing, using (or often not using at all), and keeping notes is doomed to failure.

Many students make the mistake of not making their own actual notes. By this I don't mean they're not writing information down. In fact many are writing information down, but we can't really call these notes. Notes by definition ought to transform something seen or heard into one's own words. Without the transformation, these students think that they're taking notes, but in reality they're focusing their efforts on being transcriptionists. They're attempting to capture something for later consumption. This is a deadly trap! By only transcribing, they're not taking advantage of transforming information by putting ideas down in their own words to test their understanding. Often worse, even if they do transcribe notes, they don't revisit them. If they do revisit them, they're simply re-reading them and not actively working with them. Only re-reading them will lead to the illusion that they're learning something when in fact they're falling into the mere-exposure effect.

Students who are acting as transcriptionists would be better off simply reading a textbook and taking notes directly from that.

A note that isn't revisited or revised, may as well be a note not taken. If we were to consider a spectrum of useful, valuable, and worthwhile notes, these notes would be at the lowest end of the spectrum.

link to: https://hypothes.is/a/QgkL6IkIEeym7OeN9v9New

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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Reply to the reviewers

From the start, the authors would like to thank all the reviewers for their careful and constructive consideration of our manuscript. We have now made several changes to the paper and believe it to be better for the feedback.

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

In this study, Rees et al. perform an RNA-seq circadian time course experiment in the recently formed allopolyploid wheat. Through comparisons with other circadian transcriptomic datasets in other species it appears that the period of rhythmic genes is much more variable in wheat with a shift to longer periods compared to the other species examined. Interestingly, by analyzing circadian parameters among expressed genes, they find evidence that this newly formed allopolyploid already shows signs of divergence in circadian traits among homoeologs. A thorough comparison with circadian regulated genes in Arabidopsis reveals overlap in phasing of genes involved in certain biological processes such as photosynthesis and light signaling whereas genes involved in starch metabolism were found to have different levels of rhythmicity and phasing. This dataset will be a great resource for the community and enable new predictions about the influence of polyploidy on the circadian control of important crop improvement traits and the circadian regulation of gene expression.

Major Comments

1. The results section starts with very little explanation of the experiment. It would help to provide a little more detail at the start of the results to explain the context for the experiment and what was done, when samples were collected and for how long. For the methods section, it isn't until line 650 that it is clearly stated that the sampling started at ZT0. It would be better to put this in the plant materials and growth condition section.

Thank you for highlighting the need for this context, we agree that the manuscript is improved by an introduction to the experiments. We have now included an “Experimental context” section in the results and have taken the opportunity to explain how the full 0-68h and 24-68h datasets are used within our analysis. Ln 74-82. We have also edited the Methods as suggested Ln 610-615.

The low proportion of circadian regulated genes is likely due to the very low cutoff for calling a gene expressed, especially when there are three days of repeated timepoints. If a gene is expressed across the time course it should have values above TPM 0 for at least 3 time points in order for it to be expressed each day. I'd also be suspicious of a gene with a TPM value less than 0.5. Comparing these types of numbers is always challenging due to the various cutoffs used. Along those lines, why was a different filtering scheme used for Arabidopsis (line 657)?

We completely agree that the proportion of genes described as rhythmic changes a great deal with the threshold at which you exclude low expression transcripts as well as the window over which measurements are taken and the q-value cut-off for rhythmicity. We performed an analysis to test the effects of applying a pre-filtering step to exclude low-expression genes and discuss our findings in Supplementary Note 1. Briefly, we removed genes with expression less than 0.1 TPM in six or more timepoints and again ran Metacycle to define numbers of rhythmic genes. Our results are discussed in Supplementary Note 1 and are presented in Supplementary Table 1. Regardless of the cut-offs applied, Arabidopsis and wheat data was treated identically, and our findings reported in the main results were consistent with those reported in the Supplementary analysis. Thank you for raising this point, as we have now improved our description of this analysis in the main text (Ln 92-95).

Regarding the different filtering schemes, the filtering mentioned by Reviewer 1 was applied to both Arabidopsis and wheat data for a stricter retention of rhythmic genes, as part of the pre-WGCNA clustering analysis. Filtering to retain genes with >0.5TPM across 3 timepoints was applied to reduce lowly expressed genes, that act as background 'noise' when defining clusters. We applied this across 3 timepoints rather than the WGCNA suggestion of 90% of samples - because the patterns of expression in our rhythmically filtered datasets were cyclical in nature.

In reference to the shortening of the period every day, this should be interpreted with caution. Period estimate of a single cycle are not very reliable and the SD for each day is around 3h so it is difficult to draw any conclusions about changes in period each day. One option would be to only include genes with an SD less than 1h or alternatively to remove the discussion surrounding the comparison of period across the three days and focus on the period results for the full 24h-68h window shown in 1b. While 2 days is better it is still not ideal for calling period; however, your first day will still have a strong diurnal driven pattern that will likely skew your circadian period.

Thank you for your comments. Our question here was to determine whether the mean period lengths of rhythmic transcripts in wheat were always immediately longer upon transfer to constant light, or whether they got progressively longer over time. Upon reading the reviewer’s comment, we realize that the explanation provided of how we conducted this analysis was misleading. Our approach was to take a 44h sliding window (almost 2 days) and measure period at 0-44h, 12-56h and 24-68h. We have now added the previously missing statistics that support our findings in the main text, and which hopefully show the significance of the period changes over time (supplementary note 2). One of the most surprising findings from this analysis was that the periods in the first window were the longest 28.61h (SD=3.421), suggesting that the diel (driven) oscillation had little impact upon immediate transfer to free run. Our interpretation is that the mean period initially lengthens trying to follow the missing dusk signal, before the free-running endogenous period asserts itself in later cycles (Ln 129-128).

Line 87-93: If the dusk cue is important for clock expression you would think this would be biased towards genes that peak later in the day or near dusk. This argument should be connected better to the period results discussed on lines 98-101.

Following on from our statement above, we have now combined our hypothesis for why wheat transcripts expressed at dusk have longer periods with the discussion about longer periods upon transfer to constant light. We agree that the two processes are likely to be connected and have now placed them together in Ln 129-128.

1. Lines 650-652 of the Methods mentions that one of the main interests was the response to transfer to L:L, but this isn't mentioned in the introduction and doesn't come up much in the Results section. Most of the expression comparisons are focused on the 24-68h window. It also isn't clearly explained why the first day in LL is still a diurnal cycle. This would be helpful for non-circadian readers who may wonder why the first day is not included in all the analyses.

We believe this point is now also addressed by the addition of an Experimental Context section in the results (Ln 74-82), in response to the reviewer’s previous comment.

1. The phase comparisons shown in Figure suppl 4 are confusing. Suppl. Note 3 states that the period from the 24-68h data window was used to establish the bins but then the phase is shown for 3 different windows for each column? When calculating the phase for each of those 3 windows which period was used as the denominator in the phase calculation? Was it the period that matches the window used to calculate phase? What does the plot look like if phase is called on the same window used to calculate period (24-68)? What method was used to call phase in Suppl. Fig 4? As shown in Suppl Fig. 3 the method can influence the phase distributions. The methods suggest that the phase was determined with Metacycle but then FFT and MESA were used to verify. What does this mean verify, were they adjusted if FFT/MESA didn't agree?

We agree that this Figure was unnecessarily complicated. We have now simplified Supplementary Figure 4 so that only the phases from 24-68h are presented. We have also clarified the legend to explain why we used FFT-NLLS to improve accuracy of Metacycle predictions.

It is difficult to interpret the value of the period and phase comparisons shown in Fig. 1b, c, e and f after the preceding section about how variable the period and phase is across days. It is also surprising that the full 3 days were used to calculate the circadian statistics considering the first day is still under diurnal control. Do the ratios remain the same if the statistics are performed only on the 24h-68h window? For consistency with the rest of the paper and avoid confusion it would be best to have all circadian parameters measured using the same time window (24h-68h).

Thank you for your comments, we can see how our logic in using the different data windows was not clear enough. As mentioned above, we have now explained the use of the full and shortened data windows in Experimental context section (Ln 74-82). Fig 1c is a comparison between different circadian datasets and as such we have only compared periods across 24-68h window. Similarly, Fig 1b is a global analysis of periods in rhythmic genes in comparison with Arabidopsis and so is again measured from 24-68h. We have now clarified this in the Figure legend for 1b.

For comparisons of homoeologs within wheat triads, our question was in identifying homoeologs which behaved differently when placed under free-running conditions. We therefore still feel justified in using the full 0-68h dataset to identify homoeolog periods and phases which indicate differential circadian regulation, but we have now clarified that we are using the full dataset for the triad analysis in the results (Ln 140).

Fig 1h-m. How were those genes chosen? It would help to see the SD of the replicates shown, since this is just showing one triad. It would be helpful to see a plot that represents the full set of triads rather than just one that looks best. If normalized to a standard phase they could be put on the same plot. For example, panel j is meant to show the 8h lag of subgenome D. If the data is normalized so that A and B are set to the same phase all the triads could be displayed with shaded SD bars to show the variation. Something like this would be a better representation of the data rather than showing just one example.

Fig. 1h-m are case-studies illustrating the different forms of circadian imbalance between homoeologs. We agree that it is helpful to see the standard deviation as error bars on these triad plots and have added it as suggested. In line with another Reviewer 2’s suggestion we have removed Fig 1k and have replaced this with a comparison of mean normalised data for Triad 408 and Triad 2454, highlighting the difference between imbalanced rhythmicity and imbalanced amplitudes between homoeologs. Fig 1 I and m do not have error bars as adding standard deviations to mean normalised data wasn’t appropriate.

Thank you for your suggestion on how to display the different phases between homoeologs. We feel that if we were to plot all of the triads displaying imbalanced phases, the differences in period length and accompanying noise differences would make the plot so busy as to be unreadable. We hope that the pie charts Fig 1 d-g give a global overview of the proportions of triads with circadian imbalance, but agree with the point that it is useful to allow readers to view triads of their own preference. Therefore, we have now provided the replicate level TPM data with the triad IDs annotated (Supplementary File 12) and Supplementary file 11 provides the classification of each triad alongside Metacycle statistics, ortholog identification and cluster information discussed elsewhere in the paper. Readers can now look up a triad or gene of interest and see how it was classified and what the expression looks like over the full dataset.

It is surprising that there aren't more comparisons with the B. rapa dataset, especially when discussing the clock genes that show balanced or imbalanced expression. Are they similar in B. rapa and does it support your hypothesis that unbalance for certain genes are selected against?

While we agree that a thorough, multiple species, comparative transcriptomic analysis is undoubtably of interest for the future, we feel it is beyond the scope of the questions being addressed in this paper. We do compare paralogs defined as “similar” in the Greenham dataset with homoeologs described as “balanced” in our dataset and find that genes involved with “photosynthesis” and “generation of precursor metabolites and energy” tend to be common between the two groups, potentially suggesting conservation of balance for certain types of genes (Ln 206-217).

Figure 2 networks. Why were these specific modules selected? Is it actually appropriate to directly compare these modules? I do see that some of the comparisons have high correlations from panel a, but not all. For example, in panel b the W9 and A9 modules have a correlation value of 0.92, which seems appropriate. However, panel c (modules W3 and A2) have a correlation of 0.42, which seems far too low to make any sort of comparison meaningful.

The modules were selected to simplify the comparison of genes expressed in the dawn, midday, dusk, and night. We were interested in identifying common GO-enrichment in genes peaking throughout the day, although as you have identified, the differences in period length between Arabidopsis and wheat made this difficult. Our reasons for comparing module W3 with module A2, were that, even though their eigengenes are not highly correlated per se, when period length is taken into account, both modules peak during the subjective day (CT 6.34h and 6.19h) and they share commonly enriched GO terms which make sense for day peaking genes.

Further, as described in methods comments, using a cutHeight as low as 0.15 will likely lead to some number of genes in any given module that do not necessarily "share" a similar expression pattern. These genes could have a pattern that has very low correlation to their module eigengene and were only placed in that module because the pattern was "less similar" to other module eigengenes. The current expression plots in this figure follow a clear pattern, but I suspect this would be even more apparent if the genes within these modules had a higher correlation to the module eigengene. Perhaps the current genes in these modules could just be filtered to have a higher correlation score?

Thank you for your comments, we have now made changes to the Results and Methods to clarify our approach (Ln 237-239 and Ln738-765). Merging modules with highly correlated module eigengenes (ME) is the final step in constructing our co-expression networks. To do this, as the reviewer describes - we used the WGCNA default parameter of a mergeCutHeight() of 0.15. This results in the merging of modules with highly correlated ME as the 0.15 mergeCutHeight() refers to the dissimilarity metric of 1 minus the eigengene correlation. So for WGCNA, a mergeCutHeight() of 0.15 corresponded to a correlation of 0.85. For the wheat modules, we took the additional step of merging closely related modules (mergeCloseModules()) using a cutHeight of 0.25, again a dissimilarity metric of 1 minus the eigengene correlation (corresponding to a correlation of 0.75). Reducing the stringency of the cutHeight to merge highly correlated wheat modules enabled us to more easily compare significantly correlated wheat and Arabidopsis co-expression modules to identify groups of genes in wheat and Arabidopsis expressed at similar times in the day, and enable the comparison of whether similar phased transcripts in wheat and Arabidopsis had similar biological roles.

Lines 327-334: I am not following the connection between 'response to abiotic stimulus' and the photoreceptor and light signaling proteins. At the start of this section (line 308) the authors say that the GO analysis was only done on rhythmically expressed genes but the reference to only one PHYA being rhythmic and yet multiple genes are shown in the plot in fig. S16. Does this mean that all the genes were shown and not just the rhythmic ones? This would explain why many of the PHY and CRY genes don't seem to have rhythms. This should be clarified better in the text or indicated in the plot which ones were called rhythmic. Since the first day following transfer is still the diel pattern from the entrainment condition, what does the PHY and CRY expression look like? Does it appear rhythmic under diel but lose rhythmicity in LL? It should be noted in the text that arrhythmicity in circadian conditions doesn't mean there isn't rhythmicity under diel conditions. This could be an additional explanation apart from the current one in the text that the regulation is at the level of protein stability/localization. Overall, this entire section is very long and entirely based on data shown in the supplemental material. I do appreciate having the individual gene plots that supplement Figure 4 and would suggest either providing a main figure to highlight a small subset of genes or pathways in this section or shorten it and focus on the results shown in the main figures.

Upon reading the reviewer’s comment, we realize that we should have made our motivations and processes clearer within this section. We used the data filtered for rhythmicity to conduct the GO-enrichment analysis and then used that to identify processes which should be of interest for further investigation. We have now added an additional sentence (Ln 352-354) to explain this more clearly. We then considered the orthologs of well-known Arabidopsis gene networks and extracted their expression from our circadian dataset, whether rhythmic or not. Supplementary Table 10 contains all of the genes we investigated, their expression and their MetaCycle statistics. We have also indicated here which genes are plotted in which Supplementary Figure 18-20. The reasons for plotting non-rhythmic genes in some cases was that it illustrates the differences between circadian control in Arabidopsis versus wheat (as is the case for the PHY and CRY genes). We understand that it is useful to see at a glance which genes are classified as rhythmic or arrhythmic, so have now highlighted each row in Supplementary Table 10 to make this more intuitive, and added a read me tab.

Regarding your point about oscillation under diel cycles, we agree that some transcripts will show rhythmic behaviour under entraining environments but not under constant conditions, and may perform time-of-day specific functions. However, these transcripts are likely to not be regulated by the circadian clock (at the transcriptional level) and so are not discussed in the context of a circadian transcriptome.

For your interest, here is the full expression of PHY and CRY transcripts starting at ZT0:

[Image]

It is difficult to say for definite, but it seems likely that some of these photoreceptors will have rhythmic patterns of expression under diel cycles, but these rhythms do not endogenously persist under constant conditions.

We appreciate your feedback that this section would benefit from cutting down of text and addition of a Figure to illustrate the text. We have now cut some of this section down and created a new main figure based on some of the oscillation plots from Supplementary Figure 18 and 19. We chose examples that reflect a conservation of relationships between transcripts of different peak phases, as we find it interesting that both species have similar patterns. (Main Figure 4, Ln 361--363, 382).

1. Primary metabolism section: in terms of the supplemental figure, similar to the previous one I think it would declutter the plots if the genes that are not rhythmic were left out and simply indicate below the plot that they didn't meet the rhythmicity cutoff. This is another area where there is more discussion surrounding the supplemental figures than the main figure 4.

One of the overall findings of this section was that many of the genes involved in Starch and T6P metabolism which are rhythmically expressed in Arabidopsis are not rhythmically expressed in wheat. We feel removing these genes from the results would detract from the importance of this finding. We have now edited Supplementary Table 10 to highlight which genes are classified as rhythmic. We have also added in a sentence to the start of this section which lays out our motivations for this analysis, summarises our findings and better connects the text with an explanation of Fig. 5 (Ln 408-430).

For all gene expression figures there should be SD or SE shown either as bars or ribbons to represent the variation in replicates.

Although we agree that error bars are informative for showing variation between replicates (and have added them to Fig. 1 to show differences within wheat triads) we feel that adding error bars to the gene expression plots in Fig. 3, Fig 4 and Supplementary Fig 19-20 would make these plots difficult to read, particularly where the wheat homeologs are very similar. The purpose of these gene expression plots is to compare circadian profiles in Arabidopsis and wheat orthologs rather than to claim significant differences in expression at any particular timepoint. This is fairly common in other circadian biology studies:

https://www.pnas.org/doi/10.1073/pnas.1408886111 ,

https://www.jbc.org/article/S0021-9258(17)49454-3/fulltext#seccestitle20 , https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0169923 , https://www.science.org/doi/10.1126/science.290.5499.2110?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,

https://www.frontiersin.org/articles/10.3389/fgene.2021.664334/full,

https://www.science.org/doi/full/10.1126/science.1161403

The replication level information for each gene has now been made available in Supplementary file 12.

1. It would be very helpful to include the code used to generate the networks and perform the cross-correlation of eigengenes across networks should be included in the Methods. This will also save you from responding to email requests!

Thank you for your comment, Code for the cross-correlation analysis, Loom plots and WGCNA network construction is now available from our groups GitHub repository: https://github.com/AHallLab/circadian_transcriptome_regulation_paper_2022/tree/main

Minor Comments

1. Figure 1, panel d: - The "unbalanced" triads that are depicted by the lighter shading; do these in fact have a different cutoff than the original rhythmic homoeologs? In the figure it says qThank you for bringing this to our attention, this has now been corrected.

Hard to directly compare the GO term overlap in Figure 2f. Might be better to only show the results for the 4 pairs shown in b-e and put them side by side in the bubble plot.

Thank you for this feedback, We have tried to make this plot easier to understand without losing any of the available information. Hopefully it is now more intuitive to understand which columns are being compared. We have changed the coloured lines to make them slightly wider, put the modules in corresponding coloured boxes and highlighted GO-slim terms shared by modules being compared.

1. Line 314 -316 don't see supp tables 10, 11

Our apologies, these files were missed previously from the upload are now available.

1. For the selection of B. rapa circadian paralogs with similar and differential expression patterns (starting line 714), the authors choose a hard cut off of 0.001 (differentially patterned) OR 0.1 (similarly patterned). What happens to the genes that are between these two cut offs or is this a typo. Since all the other cutoffs for rhythmicity was set at 0.01 it seems likely that this is a typo.

We have now clarified this in the methods, (Ln 807-822). This is not a typo, but it is a different method to the Metacycle approach we have used for our wheat data. We defined similar/different paralogs as characterized in Greenham et al, (2020) using DiPALM p-values. We chose these DiPALM p-value cut-offs as they gave us approximately equal numbers of paralogs in each category, which represent tails of similarly expressed or differently expressed circadian genes. We checked these cut-offs by calculating average Pearson’s correlation statistics between paralogs and found that differential Brassica paralogs had a mean Pearson correlation coefficient of 0.31 (SD = 0.43) and similar Brassica paralogs had a mean Pearson correlation of 0.75 (SD= 0.23) which confirms that the DiPALM method of defining expression patterns makes sense in the context of this analysis.

Line 681. Should be supplemental Figure 6 not 9.

1. References to most supplemental figures are not the correct number.

2. Labels above the plots in Supp Fig5 do not match the legend.

We apologise for these mistakes. We realize that we had mistakenly submitted an earlier draft of the Supplementary materials file, which was missing Supplementary Figure 5, 6 and 9 which therefore shifted the order of the remaining figures. This is now updated.

1. Suppl table 7 should be as a separate .csv file or similar to be able to see the full table.

This is a good suggestion, and we have added this.

1. Line 723 should be B. rapa not B. napus.

Thank you for catching this! Corrected.

1. Figure 4. There is no explanation for what the black boxes represent in the figure legend.

Thank you for your comment. Figure 4 (new Figure 5) has now been updated.

Reviewer #1 (Significance (Required)):

This study provides new insight into the circadian regulation of the transcriptome in a new allopolyploid. It adds a valuable resource to a growing collection of circadian studies in important crops and will greatly improve our efforts to learn more about the circadian control of important crop improvement traits. The dataset will be of interest to other plant circadian biologists as well as the general plant biology community who focus on monocot crops. My expertise is more on the transcriptomic side and I do not have the expertise to evaluate the phylogenetic work presented in this study.

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

Summary Rees et al. present an RNAseq time course of bread wheat. Its recent polyploidisation is one motivation for this study as gene expression dosage is known to be important for clock function in other plants. The time course covers 3 days at sampling intervals of 4h of 2-week old wheat plants (all aerial tissues), in triplicates. The subsequent analysis of the RNAseq data includes analysis of the generated data by itself (e.g. GO analysis, rhythmicity, period and phase analysis, rhythmicity of transcription factor families as well as TF binding sites) as well as thorough comparison with published datasets of other species (Arabidopsis, Brassica rapa, Brachypodium dystachion). One of the key findings is that the mean period length and the period spread are larger in wheat than in these other species). Circadian clock genes largely have similar dynamics in wheat compared to Arabidopsis. In addition, one focus is the analysis of the dynamics of three genes of one triad and imbalance / balance of such triads. To the surprise of the authors, circadian regulated and clock genes were not necessarily balanced. Silencing is one of their explanation for imbalance of circadian genes as arrhythmic genes of one triad are typically those with the lowest expression level. Finally, the authors point out more examples of rhythmic processes and genes (photoreceptors and signalling, auxin, carbon metabolism) and their commonalities and differences with Arabidopsis.

Major comments - The key conclusions and the data are convincing

We thank the reviewer for their supportive comments.

• line 120 and figure 1: In my opinion, q > 0.05 is not a good definition of arrhythmicity as non-significant q-values can result from either noise in spite of rhythmicity or from arrhythmicity. A more statistically sound way to detect arrhythmicity could for example be two-one-side tests (for example in the R package 'equivalence', e.g. see usage for time courses by Noordally et al. 2018, https://www.biorxiv.org/content/10.1101/287862v1).

Thank you for pointing us in the direction of this package, we agree that choosing methods for circadian quantification and q-value cut-offs is always tricky and different approaches will perform better for noisier or non-sinusoidal waveforms. For future work, we will investigate the application of the suggested method in circadian rhythmicity analysis. However, we believe that the criteria used in this paper for rhythmicity quantification is suitable for addressing our questions, and overall, we are satisfied that rhythms with a q-value of >0.05 would also be classified by eye as being arrhythmic, and rhythms with a q-value Many other studies have used meta2d B.H q-values as a metric of rhythmicity: e.g. (https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-022-03565-1 , https://link.springer.com/content/pdf/10.1186%2Fs12915-022-01258-7 , https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8782462/pdf/pcbi.1009762.pdf )

• lines 480-484 and intro: In the introduction, the authors write that expression levels of clock components are important for the function of the clock, and that this is one motivation for the current study where polyploidisation is expected to affect the expression levels of clock genes and their outputs. I wonder what answers or speculations this study provides in the end, or whether such answers / speculations should be made clearer. For example, do the authors think that the higher variability of periods in wheat could be a consequence of lower robustness (in addition to possible spatial differences that are mentioned) due to polyploidisation? Is anything known about the period of rhythms of close wheat relatives that did not undergo polyploidisation? Did you look at dampening over the time course in wheat vs. Arabidopsis?

The point above is an interesting one, and we thank the reviewer for raising it. We agree that the high variability of periods in wheat may be a product of polyploidisation, as functional redundancy between homoeologs may allow a tolerance for less tightly regulated, non-dominantly expressed circadian transcripts. We have now added this hypothesis to our discussion: Ln536-550.

In our comparative analysis of period distributions, we looked at periods of transcripts from a diploid relative of hexaploid wheat, Brachypodium distachyon. In Brachypodium, period lengths have around the same SD as in Arabidopsis but the mean period length is slightly longer (Supplementary table 2). We have now edited our results to make the relationship between wheat and Brachypodium clearer (ln 109-110).

Minor comments:

Introduction - lines 49: it is unclear what is meant by ppd-1 at this position of the sentence

We agree this was unclear and have revised it to “notably the ppd-1 locus within TaPRR3/7” Ln 52

• line 54/55: clarify that this refers to Arabidopsis thaliana

Corrected.

Results - line 69 and 76: cite references for these tools here (not only in the methods section)

Corrected.

• line 90-93: Why wouldn't the same thing happen on subsequent subjective evenings?

Thank you for your comments. We have now combined our hypothesis for why wheat transcripts expressed at dusk have longer periods with the discussion about longer periods upon transfer to constant light. We think that the two processes are likely to be connected and have now placed them together in Ln 126-131.

The behaviour of mean period lengths of wheat transcripts upon transfer to constant light was unexpected and we believe is quite interesting. One explanation is that the influence of the ongoing light zeitgeber when dusk was expected causes a delay in the expression of evening peaking genes which are delayed by the continuous light signal. Then, on subsequent evenings the influence of the diel dusk signal is ‘forgotten’ as the governance of the endogenous clock takes over. The very long period observed at 0-24h (28.61h) may be due to a phase shift rather than an intrinsic lengthening of period per se. Whether this trait is unique to wheat or can also be seen in other plant species is, to our knowledge, unknown.

• line 118: what is your defined cutoff for significance of the Chi square test (p=0.03 not regarded significant?)

The reviewer is completely right, we have now clarified this. Ln 145-149

• figure 1h,i: In order for the reader to see whether A and D (Figure 1h) or A (figure 1i) are indeed arrhythmic, one would need to see plots with a normalisation as done in figure 1m for 1l.

We have now removed the triad showing one rhythmic gene and two arhythmic genes (as Fig. 1h already illustrates this type of circadian imbalance) and replaced this with a side by side comparison of how imbalance in rhythmicity differs from imbalance in relative amplitude as suggested.

• figure 1h-m (and others with circadian time course traces): could a measure of variation (e.g. SD, SEM, confidence interval) be plotted as a shaded region around the curves (unless they're so small that they are there but not visible)?

We have now added error bars to these plots to show standard deviation between replicates, in Fig. 1 h, j, k and l. We could not think of an accurate way to display this information for the mean normalised data (Fig 1. i and m) so have not put error bars on these plots.

• line 139 (also in 737 and 450): give reference to Ramirez-Gonzalez et al in the same style as the rest of the manuscript (number)

Thank you for raising this, we believe we have corrected all in-text citations (both narrative and fully parenthetical form) for consistency with the APA format used by the majority of Review Commons affiliate journals.

• Clustering (modules): What is the reason for choosing 9 clusters? Was this number optimised or chosen for other reasons?

WGCNA uses an unsupervised clustering algorithm that works within the supplied parameters to determine the optimum number of clusters to explain the dataset, without prior specification of the number of clusters. We have amended the manuscript text to clarify this Ln237-239.

• lines 280 - 284: The TaELF3-1D phenotype could be explained a bit better to the non-wheat specialist, for example by mentioning in the beginning of this set of sentences.

Done (Ln 314-318).

• The authors present an analysis of TF binding sites. Can they say something about binding sites in a less sophisticated manner, such as on some very well-known motifs in promoters like the evening element?

We agree that this is a very interesting question, and one that we may investigate in more detail with our data in the future. In this paper, we performed a global analysis of wheat TFBS predicted from orthologous Arabidopsis TF targets. These targets have been experimentally validated in Arabidopsis using DAP-seq, but we have not validated that these binding sites exist in wheat promoters. We therefore took a tentative approach, and presented only enrichments at the superfamily level rather than talking about specific regulatory motifs.

The evening element would fit most likely fit within the MYB or MYB-related TFBS superfamily, however the diversity of transcription factors in this family means that there is significant enrichment of these TFBS in multiple modules throughout the day (Supplementary Figure 11). In summary, a more in depth TFBS analysis of known circadian motifs is of great interest, but we feel would be a substantial work in its own right.

• Figure 1h-l: If known or meaningful, it would be interesting to know the gene identities behind the triads shown, as in supplementary figure 5.

These triads were selected as case studies to exemplify the ways in which we were defining imbalanced circadian triads. They have no particular relevance to the figure, but out of curiosity, these are the closest Arabidopsis orthologs for the triads displayed in Fig. 1:

Triad 408 has highest identity to a hypothetical protein (AT4G26415).

Triad 2454 is similar to AT3G07600, a heavy metal transport/detoxification superfamily protein

Triad 13405 is similar to AT3G22360, encoding an ALTERNATIVE OXIDASE 1B, AOX1B

Triad 10854 is similar to NSE4A, a δ-kleisin component of the SMC5/6 complex, possibly involved in synaptonemal complex formation (AT1G51130).

Information about wheat gene names in each triad and their Arabidopsis orthologs can be viewed in Supplementary Table 11, so that readers can search for genes of particular interest to them.

• Figure 4 and text: The illustration of starch metabolism is very helpful. However, I think the paper would benefit from giving a better reason for the selection of this specific set of processes, for example by relating these findings to functional differences in starch metabolism in the two species (in contrast to Arabidopsis, wheat stores little starch in leaves but uses fructans as main reserve carbohydrate)? Are there known differences in the dynamics of starch degradation during the night?

The reviewer raises an interesting point, and we have now clarified in our results that the stated differences between starch regulation in Arabidopsis and wheat was part of the motivation behind studying this pathway. Starch is at the centre of plant primary metabolism as a carbon storage source and is arguably one of the most important features that breeders look for in regard to grain filling and yields. Additionally, it is of interest to circadian biologists as starch (as well as sucrose) have been shown to transiently cycle and to be regulated by the circadian clock. However, in wheat, carbon storage primarily uses sucrose rather than starch, and we have now added sucrose to Figure 5 to place it in this context. We think your suggestion has now improved our explanation for why we focused on starch in the manuscript, and we are grateful for your input (Ln 408-421).

We also agree that the differences in the ways that Arbaidopsis and wheat utilise starch versus sucrose, and perhaps the role that fructans have in as a reserve carbohydrate and in protection against freezing in wheat may be one of the reasons we are seeing differences in circadian regulation of starch. We have now added this to our discussion (Ln 584-592).

• Figure 4: triose-phosphates can be transported in and out of the chloroplast, as is illustrated in the figure. However, the illustration looks as though they are converted to hexose phosphates during the transport process. In order to be consistent with other transport processes of the figure (maltose and glucose), triose-phosphate should be repeated on the cytosolic side.

We have now amended this (new Fig. 5). Thank you for your feedback.

Methods - line 543: if I understand correctly that triplicates were collected and analysed for each time point, '18 samples' is mis-leading (18 time points would be more accurate).

We agree this was badly worded. Changed Ln 615.

Supplementary - Supplementary figure 3: x axis label very small and contains typo

Now corrected. Also enlarged axis for Supplementary Figure 2.

• Supplementary table 1: Romanowski et al 2020 (add year), or use ref. number citation style as in the rest of the manuscript

Thank you for raising this, we have now hopefully corrected all in text citations (both narrative and fully parenthetical form) to be consistent with APA format used by the majority of Review commons affiliate journals.

• Supplementary table 9, primary metabolism: does bold highlighting of Arabidopsis accession numbers have a meaning or is it accidental?

We apologise that this was unclear. We have corrected this. Supplementary Table 10 now also has a “Read me” tab which explains that table.

Reviewer #2 (Significance (Required)):

I believe this is a precious, carefully generated and analysed dataset which many biologists will benefit from, beyond wheat or circadian specialists. The dataset expands the knowledge of circadian transcriptome regulation to an important crop and contributes a resource of which only a handful of others exist in other species. Many high impact papers on RNAseq include some follow-up on candidates, for example in Romanowski et al 2020, which is admittedly easier to do in Arabidopsis than wheat due to the availability of genetic resources.

My expertise: Plant circadian clock (Arabidopsis), dataset analysis (but not specifically for RNAseq)

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

This manuscript is based on the analysis of a single experiment consisting in transcriptomic profiling of one (hexaploid) wheat genotype along 3 days (samples taken every 4 hours). The experiment is performed in constant light conditions, allowing detection of transcripts controlled by the circadian clock. The bioinformatic analysis studies the dynamics of the different homoeologous transcript in the polyploid genome and compares cycling transcripts in wheat with what is known from Arabidopsis.

The manuscript is well written, the methods are correct, the analysis performed is sufficiently extensive and the figures are clear. The manuscript finds interesting expression patterns among homeologous genes, and goes into detail on important differences in circadian regulation of relevant gene families between Arabidopsis and wheat. The work is purely descriptive and does not aim at associations with physiological phenotypes, but the bioinformatic analysis is very thorough and uncovers interesting examples.

Only one caveat: For what I gather, there is no replication in the RNA-seq experiment, although the exact method does not appear in the text. From the Methods section: "tissue was sampled every 4h for 3 days (18 samples in total)" and "At each timepoint, we sampled the entire aerial tissue from 3 replicate plants". Whether these samples were pooled or not is not described. The "Data Availability" section links to 18 RNA-seq paired end libraries, which suggest that the replicates were pooled, although some type of barcoding might have been used. The text should mention if the replicates were pooled or not, and, if so, what was the method used for poling (tissue, RNA or libraries). Even in the case of no biological replication the manuscript brings interesting insights into wheat transcriptomics and circadian biology. The editor (or the rules of the journal) should decide if they accept articles with no "real" biological replication (I am sure we all understand by now the benefits and limitations of pooling biological replicates into a single RNA-seq library).

There was replication within the RNA sequencing experiment, and we apologise that this was unclear from our manuscript. Each timepoint consisted of three independent biological replicates. We have now created a new “Experimental context” section in the results to explain this (Ln 74-82) and have clarified in the methods how our data was processed (Ln 609-615 and 636-638).

We have now included an additional matrix with TPMs at the replicate level to assist readers in looking at specific genes of interest (Supplementary Table 12).

Minor comments:

The description of the experimental setup in the first sentence of the Results section is too brief. Could you please talk about for how long the experiment was running? At what intervals the samples were taken? What conditions were used?

We apologise that this was unclear. We hope that the new Experimental Context section, added in response to comments from several reviewers, makes this much clearer, alongside the clarification in the methods (Ln 609-615 and 636-638).

Line 280: "...due *to* an introgression..."

Corrected. Ln 315

The legend of Figure 3l says elf4 instead of elf3

We thank the reviewer for noticing this mistake that we have now corrected.

Line 306 "says Supplementary Note 7 instead of Supplementary Note 7

We are not sure what is to be corrected here!

Reviewer #3 (Significance (Required)):

This works advances our knowledge on how genome wide expression levels are controlled by the circadian clock in polyploids. Although previous works had performed similar analyses in other polyploid plants, this is the first time this is done in an hexaploid. This work is a starting step to understand gene regulation in this important crop, and have interest for researchers working in fundamental and applied plant biology.

Thank you for your positive comments and your feedback in improving this manuscript. We would like to clarify that to our knowledge, this work presents the first analysis of a circadian transcriptome in a polyploid crop. The work by Greenham et al, although undoubtably providing insight into circadian regulation of ancient paralogs, was performed in the diploid Brassica rapa.

So much wisdom and stuff to think about here.

Author Response

Reviewer #1 (Public Review):

1) The connectivity patterns along the anterior-posterior hippocampal axis broadly follow an anterior-posterior cortical bias, such that posterior regions, e.g. the visual cortex, are preferentially connected to the hippocampal tail, and anterior regions, e.g. the temporal pole, are preferentially connected to the hippocampal head. The authors focus on the twenty regions with the highest connectivity profiles, which appears to capture the majority of all connections. However, some of the present structural connectivity patterns differ in interesting ways from previously described cortical networks reported in resting-state fMRI studies. Most notably, the medial PFC and orbitofrontal regions combined account for less than 1% of all connections in the present investigation (Table S1 & S2). This is an interesting contrast to functional investigations which tend to find that these regions cluster with the aHPC (e.g., Adnan et al. 2016 Brain Struct Func; Barnett et al. 2021 PLoS Biol; Robinson et al. 2016 NeuroImage). In contrast, the present DWI results suggesting preferential pHPC-medial parietal connectivity dovetail with those observed in fMRI studies. It seems important to discuss why these differences may arise: whether this is a differentiation between structural and functional networks, or whether this is due to a difference in methods.

We thank Reviewer 1 for making this important point and agree that these observations are deserving of further expansion. We have now included additional text where we place the surprising observation of sparse connectivity between PFC regions and the hippocampus more firmly in the context of recent evidence and argue that these observations suggest a potential differentiation between structural and functional networks.

We have included the following text in the discussion (pp. 16-17, lines 439-457);

“While many of our observed anatomical connections dovetail nicely with known functional associations, patterns of anatomical connectivity strength did not always mirror well characterised functional associations between the hippocampus and cortical areas. For example, a surprising observation from our study was that only weak patterns of anatomical connectivity were observed between the hippocampus and the ventromedial prefrontal cortex (vmPFC) and other frontal cortical areas. This lies in contrast to well documented functional associations between these regions (46-48). Our observation, however, supports a growing body of evidence that direct anatomical connectivity between the hippocampus and areas of the PFC may be surprisingly sparse in the human brain. For example, Rosen and Halgren (49) recently reported that long range connections between the hippocampus and functionally related frontal cortical areas may constitute fewer than 10 axons/mm2 and more broadly observed that axon density between spatially distant but functionally associated brain areas may be much lower than previously thought. Our observation of sparse anatomical connectivity between the hippocampus and PFC mirrors this recent work and suggests a potential differentiation between structural and functional networks as they relate to the hippocampus. It remains possible, however, that methodological factors may contribute to these differences. We return to this point later in the discussion. A future dedicated study aimed at assessing whether the well characterised functional associations between the hippocampus and vmPFC are driven by sparse direct connections or primarily by intermediary structures is necessary to address this issue in an appropriate level of detail.”

2) While the analytic pipeline is described in sufficient detail in the Methods, it is somewhat unclear to a non-DWI expert what the major methodological advance is over prior approaches. The authors refer to a tailored processing pipeline and 'an advance in the ability to map the anatomical connectivity (p. 5), but it's not immediately clear what these entail. It would be useful to highlight the key methodological differences or advances in the Introduction to help with the interpretation of the similarities and differences with previous connectivity findings.

We have now included a brief description in the Introduction highlighting the key methodological advances used in the current study.

We have included the following text in the Introduction (pp. 4-5, lines 130-144);

“In typical fibre-tracking studies, we cannot reliably ascertain where streamlines would naturally terminate, as they have been found to also display unrealistic terminations, such as in the middle of white matter or in cerebrospinal fluid (39). While methods have been proposed to ensure more meaningful terminations (40), for example, with terminations forced at the grey matter-white matter interface (gmwmi), this approach is still not appropriate for characterising terminations within complex structures like the hippocampus. A key methodological advance of our approach was to remove portions of the gmwmi inferior to the hippocampus (where white matter fibres are known to enter/leave the hippocampus). This allowed streamlines to permeate the hippocampus in a biologically plausible manner. Importantly, we combined this with a tailored processing pipeline that allowed us to follow the course of streamlines within the hippocampus and identify their ‘natural’ termination points. These simple but effective methodological advances allowed us to map the spatial distribution of streamline ‘endpoints’ within the hippocampus. We further combined this approach with state-of-the-art tractography methods that incorporate anatomical information (40) and assign weights to each streamline (41) to achieve quantitative connectivity results that more faithfully reflect the biological accuracy of the connection’s strength (39).”

3) Related to the point above, it was a bit unclear to me how the present connections map onto canonical white matter tracts. In Fig., 4A, the tracts are shown for a single participant, but it would be helpful to map or quantify know how many of the connections for a given hippocampal subregion are associated with a given tract to provide a link to prior work or clarify the approach. A fairly large body of prior research on hippocampal white matter connectivity has focused on the fornix, but it's a little difficult to align these prior findings with the connectivity density results in the current paper.

We thank Reviewer 1 for this comment and agree this would be an interesting avenue to pursue. However, the reliable segmentation of white matter fibre bundles is currently an area of contention in the DWI community. This pervasive and problematic issue was highlighted in a recently published large multi-site study that revealed a high degree of variability in how white matter bundles are defined, even from the same set of whole-brain streamlines (Schilling et al., 2021, Neuroimage. Nov; 243:118502. https://pubmed.ncbi.nlm.nih.gov/34433094/). This means that, even if we were to choose a particular method to segment white matter bundles, our results would not be readily translatable to those reported in previous DWI studies. This significantly limits meaningful comparison and/or interpretation. Indeed, such an approach may paradoxically take away from the detailed characterisations we have achieved in the current study. As highlighted in that study, it is now paramount that consensus is reached in this field to define criteria to reliably and reproducibly define white matter fibre bundles. Once that is achieved, we plan to conduct a follow-up study to characterise this in more detail, with bundles that will be able to be reliably reproduced by others.

4) Finally, on a more speculative note: based on the endpoint density maps, there seems to be a lot of overlap between the EDMs associated with different cortical regions (which makes sense given the subregion results). Does this effectively mean that the same endpoints may be equally connected with multiple different cortical regions? Part of the answer can be found in Fig. 3D showing the combined EDM for three different regions, but how spatially unique is each endpoint? This is likely not a feasible question to address analytically but it might be helpful to provide some more context for what these maps represent and how they might relate to differences across individuals.

The primary aim of the current analysis was to characterise broad patterns of endpoint density captured by our averaged group level analysis. However, Reviewer 1 is astute in assuming that, although there is overlap in the group averaged endpoint density maps (EDMs) associated with different cortical areas, at the single participant level, there are both overlaps and spatial uniqueness in the location of individual endpoints. For example, while group level analysis revealed that area V1 and area V2 showed preferential connectivity with overlapping regions of the posterior medial hippocampus, when visualising individual endpoints associated with each of these areas at the single participant level, we can see that some endpoints overlap while others display spatially unique patterns (see image below). Although a more in-depth analysis of individual variability in these patterns was beyond the scope of this investigation (as noted on Page14; Lines 379-381), we agree with Reviewer 1 that this is an important point to note in the manuscript. We have, therefore, included additional text touching on this and have included a new Supplementary Figure (Page 42; also see below) to emphasise that, at the single participant level, different cortical areas display both overlapping and spatially unique endpoints within specific regions of the hippocampus (using areas V1 and V2 as an example).

We have included the following text in the Results section (pp. 14, lines 370-379);

“Finally, while we observed clear overlaps in the group averaged EDMs associated with specific cortical areas, a closer inspection of individual endpoints at the single participant level revealed that endpoints associated with different cortical areas displayed both overlapping and spatially unique characteristics within these areas of overlap. For example, at the group level, areas V1 and V2 showed preferential connectivity with overlapping regions of the posterior medial hippocampus (see Supplementary Figure S5) while, at the single participant level, individual endpoints associated with each of these areas display both overlapping and spatially unique patterns (see Supplementary Figure S6). This suggests that, while specific cortical areas display overlapping patterns of connectivity within specific regions of the hippocampus, subtle differences in how these cortical regions connect within these areas of overlap likely exist.”

Reviewer #2 (Public Review):

Dalton and colleagues present an interesting and timely manuscript on diffusion weighted imaging analysis of human hippocampal connectivity. The focus is on connectivity differences along the hippocampal long axis, which in principle would provide important insights into the neuroanatomical underpinnings of functional long axis differences in the human brain. In keeping with current models of long-axis organisation, connectivity profiles show both discrete areas of higher connectivity in long axis portions, as well as an anterior-to-posterior gradient of increasing connectivity. Endpoint density mapping provided a finer grained analysis, by allowing visualisation of the spatial distribution of hippocampal endpoint density associated with each cortical area. This is particularly interesting in terms of the medial-lateral distribution with hippocampal head, body and tail. Specific areas map to precise hippocampal loci, and some hippocampal loci receive inputs from multiple cortical areas.

This work is well-motivated, well-written and interesting. The authors have capitalised on existing data from the Human Connectome Project. I particularly like the way the authors try to link their findings to human histological data, and to previous NHP tracing results.

Many thanks.

1) There are some important surprises in the results, particularly the relatively strong connectivity between hippocampus and early visual areas (including V1) and low connectivity with areas highly relevant from functional perspectives, such as the medial prefrontal cortex (rank order by strength of connectivity 7th and 78th of all cortical structures, respectively). This raises a concern that the fibre tracking method may be joining hippocampal connections with other tracts. In particular, given the anatomical proximity of the lateral geniculate nucleus to the body and tail of the hippocampus, the reported V1 connectivity potentially reflects a fusion of tracked fibres with the optic radiation. In visualizing the putative posterior hippocampus-to-V1 projection (Figure 4B, turquoise), the tract does indeed resemble the optic radiation topography. Although care was taken to minimise the hippocampus mask 'spilling' into adjacent white matter, this was done with focus on the hippocampal inferior margin, whereas the different components of the optic radiation lie lateral and superior to the hippocampus.

We agree with Reviewer 2 that our observations relating to area V1 could be the result of limitations inherent to current tracking methodology. Indeed, probabilistic tracking can result in tracks mistakenly ‘jumping’ between fibre bundles. Unfortunately, primarily due to limitations in image resolution, we do not believe that we can categorically rule this possibility out in the current dataset beyond the measures we have already taken in our analysis pipeline. We have now included additional text in the Discussion acknowledging and emphasising this possible limitation of our study.

We have included the following text in the Discussion section (Page 25; Lines 694-699);

“Also, we cannot rule out that some connections observed in the current study may result from limitations inherent to current probabilistic fibre-tracking methods whereby tracks can mistakenly ‘jump’ between fibre bundles (e.g. for connections between the posterior medial hippocampus and area V1 due to the proximity to the optic radiation), especially in “bottleneck” areas. Again, future work using higher resolution data may allow more targeted investigations necessary to confirm or refute the patterns we observed here.”

Beyond the possibility of tracks jumping between fibre bundles, we feel it is important to emphasise that an integral part of our analysis was the detailed attention we took to minimise mask ‘spillage’ of the entire hippocampus mask. It is not the case that we primarily focussed on inferior portions of the hippocampus as stated by Reviewer 2. Equal focus was paid to medial, lateral and superior portions of the mask which lie adjacent to visual thalamic nuclei, the optic radiation posteriorly and a number of other structures. We can see that our description relating to this lacked the necessary detail to convey this important point clearly and we apologise for the confusion. We have, therefore, included additional text in the Methods section clarifying this further.

We have included the following text in the Methods section (Page 26; Lines 751-755);

“We took particular care to ensure that all boundaries of the hippocampus mask (including inferior, superior, medial and lateral aspects) did not encroach into adjacent white or grey matter structures (e.g., amygdala, thalamic nuclei). This minimised the potential fusion of white matter tracts associated with other areas with our hippocampus mask.”

These points notwithstanding, our results support recently observed structural and functional associations between the posterior hippocampus and early visual processing areas. We agree that these findings are potentially of great conceptual importance for how we think about the hippocampus and its connectivity with primary sensory cortices in the human brain and we have now included a brief comment relating to this in the Discussion.

We have included the following text in the Discussion (Page 23-24; Lines 638-644);

“However, this observation supports recent reports of similar patterns of anatomical connectivity as measured by DWI in the human brain (38) and functional associations between these areas (43, 60). Collectively, these findings are potentially of great conceptual importance for how we think about the hippocampus and its connectivity with early sensory cortices in the human brain and open new avenues to probe the degree to which these regions may interact to support visuospatial cognitive functions such as episodic memory, mental imagery and imagination.”

2) A second concern pertains to the location of endpoint densities within the hippocampus from the cortical mantle. These are almost entirely in CA1/subiculum/presubiculum. It is, however, puzzling why, in Supp Figure 2, the hippocampal endpoints for entorhinal projections is really quite similar to what is observed for other cortical projections (e.g., those from area TF). One would expect more endpoint density in the superior portions of the hippocampal cross section in head and body, in keeping with DG/CA3 termination. I note that streamlines were permitted to move within the hippocampus, but the highest density of endpoints is still around the margins.

We agree with Reviewer 2 that, in relation to the entorhinal cortex, we would expect to see more endpoint density in areas aligning with the dentate gyrus (DG) and CA3 regions of the hippocampus. We noted in the discussion that “Despite the high-quality HCP data used in this study, limitations in spatial resolution likely restrict our ability to track particularly convoluted white-matter pathways within the hippocampus and our results should be interpreted with this in mind”. We believe that this limitation applies to pathways between the entorhinal cortex and DG/CA3. We have now included additional text specifically noting that this limitation likely affects our ability to track streamlines as they relate to DG/CA3. A targeted investigation of this effect using higher resolution diffusion MRI data may help address this issue, and this will be the subject of future work.

We have included the following text in the Discussion (Page 25; Lines 690-693);

“Indeed, this may explain the surprising lack of endpoint density observed in the DG/CA4-CA3 regions of the hippocampus where we would expect to see high endpoint density associated with, for example, the entorhinal cortex which is known to project to these regions. Future dedicated studies using higher resolution data are needed to assess these pathways in greater detail.”

3) On a related point, the use of "medial" and "lateral" hippocampus can be confusing. In the head, CA2/3 is medial to CA1, but so are subicular subareas, just that the latter are inferior.”

We agree that applying the terms ‘medial’ and ‘lateral’ to our three-dimensional representations can lead to some ambiguities and confusion. We have included a new description defining our use of these terms in the Results section.

We have included the following text in the Results section (Page 10; Lines 268-273).

“In relation to nomenclature, our use of the term ‘medial’ hippocampus refers to inferior portions of the hippocampus aligning with the distal subiculum, presubiculum and parasubiculum. Our use of the term ‘lateral’ hippocampus refers to inferior portions of the hippocampus aligning with the proximal subiculum and CA1. In instances that we refer to portions of the hippocampus that align with the DG or CA3/2 we state these regions explicitly by name”.

Discussion, revision and decision

Decision

Verified with reservations: The content is scientifically sound, but has shortcomings that could be improved by further studies and/or minor revisions.

Dr. Bañuelos: Verified manuscript

Dr. Morris: Verified with reservations

Revision

Response to Reviewer 1 (Dr. Bañuelos)

1. Most importantly, I would like to see an introduction that explains the authors’ general arguments about grading changes – including the trajectory of these changes at Dalhousie and why this arc contributes to our knowledge of the history of higher education more broadly. Then, the authors might continually remind us of the arc they present at the outset of their paper – especially when they are highlighting a piece of evidence that illustrates their central argument. To me, the quotes from students and faculty responding to grading changes are among the most interesting parts of the paper and placing these in additional context should make them shine even more brightly!

Our Response: Thank you so much for your thoughtful review. We have added a larger new introduction section of the paper (paragraphs 1-5 in the latest draft are new) that outlines the general importance of the topic, the Canadian context, details on Dalhousie University, and our overall thesis statement (i.e., most decisions were to improve the external communication value of grades). Moreover, we have added three new student quotes form the Dalhousie Gazette to build a stronger picture for student reactions, and to build a better case for our overall thesis statement (i.e., that changes in grading were often to increase the external communication value of grades). Moreover, throughout we have added some details on the overall funding trajectory for institutions in Canada that created some pressure to standardize grading. We think that these changes have improved the manuscript.

1. I’d like to read a little more about Dalhousie itself – why it is either a remarkable or unremarkable place to study changes in grading policies. Is it representative of most Canadian universities and thus, a good example of how grading changes work in this national context? Is it unlike any other institution of higher education and thus, tells us something important about grades that we could not learn from other case studies? I don’t think this kind of description needs to be particularly long, but it should be a little more involved than the brief sentences the authors currently include (p.3, paragraph 1) and should explain the choice of this case.

Our Response: This comment revealed that two additional pieces of context were needed for the introduction: (a) some national context for higher education policy in Canada and (b) some extended description of Dalhousie University when compared to other universities in Canada. To this end, two new paragraphs have been added to the paper (paragraphs 2 & 3 in the current draft).

Notably, Jones (2014) notes that “Canada may have the most decentralized approach to higher education than any other developed country on the planet” (pg 20). With this in mind, any historical review of education policy is by necessity specific to province and institution – that is, the information can be placed in its context, but resists wide generalization to the country as a whole. In the newest draft, we tried to describe the national, provincial, and institutional context in some more detail in paragraphs 2 & 3.

1. I’d also like to know more about the archival materials the authors used. The authors mention that they drew from “Senate minutes, university calendars, and student newspapers” (p. 3), but what kinds of conversations about grades did these materials include? At various points, the authors engage in “speculation” (e.g. p.4) about why a particular change occurred. This is just fine and, in fact, it’s good of the authors to remind us that they are not really sure why some of these shifts happened. But, they might go one step further and tell us why they have to speculate. Were explicit discussions of grading changes – including in inter- and intradepartmental letters and memo, reports, and other documents – not available in these archives? Why are these important discussions absent from the historical record?

Our Response: We have added a new paragraph (paragraph 4) to the paper discussing the sources in some more detail. It is true that the verbatim discussions are frequently absent from the record, especially earlier in history – or if they exist, we have not found them! Instead, we frequently are reviewing meeting minutes or committee reports, which are summaries of discussions. As we now note in the paper, “Thus, the sources used showed what policy changes were implemented, when they were implemented, and a general sense of whether there was opposition to changes; however, there were notable gaps in faculty and student reactions to grade policy changes, as these reactions were frequently not written down and archived.”

This gap was most apparent in the Senate minutes around the 1940s, where I (the first author) could not find any direct discussions of why changes were implemented. Under the 1937-1947 heading, we more clearly indicate that the rationale for the changes was absent from the Senate minutes during this period. I add some further speculation on why these records might be absent, based on summaries from Waite (1998b); specifically, the university president of the time often made unilateral decisions, circumventing Senate, which might account for why the changes are absent from the records.

This will hopefully make the limitations of what can be learned from this approach more apparent.

1. At various points, the authors make references to the outside world – for example, WWII (p. 5), the Veteran’s Rehabilitation Act (pp. 6-7), and British versus American grading schemas (p. 6). But, these references are brief and seem almost off-handed. I know space is limited, but putting these grading changes in their broader context might help make the case for why this study is interesting and important. Are the changes in the 1940s, for example, related to the ascendance of one national graduate education model over another (e.g. American versus British)? Are there any data on how many Canadian undergraduates enrolled in British versus American graduate programs over time? If so, I would share any information you might have on these broader trends.

Our Response: To our knowledge, there isn’t any comparable report to what we’ve written here documenting the transition from British “divisions” to American “letter grades” in Canadian Universities, making our report novel in this regard. It might well be that a similar historical arc exists in many of the 223 public and private universities in Canada, but we don’t believe such data exists in any readily accessible way – excepting perhaps undergoing a similar deep dive into historical documents at each respective institution! So, we do not have the answer to your question: “Are there any data on how many Canadian undergraduates enrolled in British versus American graduate programs over time?” However, we did add one reference which provided a snapshot point of comparison in 1960, noting in the paper “Baldwin (1960) notes that the criteria for “High First Class” grades in the humanities was around 75-80% at Universities of Toronto, Alberta, and British Columbia in 1960, suggesting that Dalhousie’s system was similar to other research-intensive universities around this time.” That said, there are a few major national events related to the funding of universities in Canada that we have elaborated on in the text to address the spirit of your recommendation for describing the national context:

a) In the “Late 1940s” section of the paper, we added: “Though Dalhousie had an unusually high proportion of veterans enrolled relative to other maritime universities during this period (Turner, 2011), the Veteran’s Rehabilitation Act was a turning point for large increases in enrollment and government funding Canada-wide, at least until the economic recession of the 1970s (Jones, 2014).”

b) In the 1990s, there were major government cuts to funding, creating challenging financial times for the university. We discuss the funding pressures that likely contributed to standardization of grading during this time by saying the following in the 1980s-2000s section: “Starting in in the 1980s-1990s there were major government cuts to university funding nation-wide, with the cuts becoming more severe in the 1990s (Jones, 2014; Higher Education Strategy Associates, 2021). Because of the nature of the funding formulas, cuts in Nova Scotia were especially deep. Beyond tuition increases, university administrators knew that obtaining external research grants, Canada Research Chairs, and scholarship funding was one of the few other ways for a university to balance budgets, so there was extra pressure to be competitive in these pools. […] The increased standardization was likely related to increased financial pressures at this time – standardization is an oft-employed tool to deal with ever-increasing class sizes with no additional resources.”

c) In the 2010s section of the paper, we added context to how universities in country-wide have become increasingly dependent on tuition fees for funding: “Following the 2008 recession, federal funding decreased again (Jones, 2014; Higher Education Strategy Associates, 2021); however, this time universities tended to balance budgets by increasing tuition and international student fees. This trend towards increased reliance on tuition for income is especially pronounced in Nova Scotia, which has the highest tuition rates in the country (Higher Education Strategy Associates, 2021). Thus, the university moved closer to a “consumer” model of education, so it makes sense that a driving force for standardization was student complaints.”

1. This is a very nitpicky concern that doesn’t fit well elsewhere, so please take it with a grain of salt. I was surprised at the length of the reference list – it seemed quite short for a historical piece! I wonder, again, if more description of the archival material - including why you looked at these sources, in particular, and what was missing from the record – would help explain this and further convince the reader that you have all your bases covered.

Our Response: In the introduction section, paragraph 4, we describe our sources in more detail including what is likely missing from the record and why we used them. Regarding the length of the reference list, we did add ~12 new references to the list in the course of making various revisions, which partially addresses your concern. Beyond this though, it’s worth noting that some of the sources more extensive than they seem, even though they don’t take up much space in the reference list (e.g., there is one entry for course calendars, but this covers ~100 documents reviewed!). Moreover, there were many dead-ends in the archives that are not cited (e.g., reviewing 10 years of Senate minutes in the 1940s produced little of relevance), so the reference list is curated to only those sources where relevant materials were found.

Reviewer response to revisions

The new introduction to the piece addresses many of my previous questions about the authors’ general arguments, the Dalhousie context, and the source material. Thank you for addressing these! Reading this version, it is much clearer that the key argument is that standardized, centralized grading practices were “to improve the external communication value of the grades, rather than for pedagogical reasons” (p. 6). I also really enjoyed the added quotes from students in the Dalhousie Gazette.

The authors’ response to Reviewer 2 really gave me a better sense of why they wrote this piece and also helped me to more clearly put my finger on what was troubling me in the first round. It still reads a little like a report for an internal audience – which is just fine and, in fact, can be extremely useful for historians of the future. But, as Reviewer 2 notes, this means it does not really seem like a piece of historical scholarship. I do worry that shaping it into this form would take an extensive revision and might not be in the spirit of what the authors intended to do.

A different version of this article might start with this idea that grades were standardized for external audiences and in response to financial pressures. It would then develop a richer story behind the sudden importance of these external audiences and the nature (i.e. source, type) of financial pressures Dalhousie was facing. It would highlight the impact such changes had on students and their future careers/graduate experiences. It could then connect these trends to other similar changes for external audiences and the increasing interconnectedness of American, Canadian, and British systems through graduate education. It might even turn to sociological theories of organizational change and adaptation and make an argument for when (historically) similar forms of decoupling were likely to occur in the Canadian higher education system. Finally, it might connect these grading changes to current trends – including accusations of grade inflation and accepted best practices for measuring learning outcomes.

But, it doesn’t seem that the authors necessarily want to do this, which I can understand and respect. I think there is enormous value in a piece of scholarship like this existing – both for internal audiences and for future historians. Indeed, imagine if every university had a detailed history of its grading policies like this available somewhere online! Comparing such practices across institutions would certainly tell us a lot about why grading currently looks the way it does.

Decision changed

Verified manuscript: The content is scientifically sound, only minor amendments (if any) are suggested.

Response to Reviewer 2 (Dr. Morris)

The authors dove headfirst into Dalhousie’s archives, unpacking the subtle shifts in grading policy. Their work seems to be comparable to archaeologists, digging deep beneath mountains of primary sources to find nuggets of clues into Dalhousie’s grading evolution. I particularly liked when the authors were able to link these changes to student voices, as seen in moments when they referenced student publications.

Ultimately, I kept coming back to one main comment that I wrote in the margins: “So what?” I would humbly suggest that the authors reflect on why this history matters to them. Granted, they do this in the conclusion, where they touch on Schneider & Hutt’s argument that grades evolved to increasingly be a form of external communication with audiences beyond school communities. Sure. But I want more. I wanted to see a new insight that this microhistory of Dalhousie significant to the history of Canada or the history of education more generally.

If the authors are so inclined, there might be several approaches to transform this manuscript. I would suggest the following. First, instead of tracing the entire history of grading at the institution, choose one moment of change that you think is the most important. Perhaps in the 1920s and the lack of transparency in grading, or the post-war shift toward American grading. Second, show me – don’t tell me – what Dalhousie was like at this moment. Paint a picture of the institution with details about student demographics, curriculum, educational goals, the broader town, etc. Make the community come alive. Show me what makes Dalhousie unique from other institutions of higher ed. Once you establish that picture, perhaps you could link the change in grading practices to subtle changes at the university community, thereby establishing a before and after snapshot. This will require considerable amounts of work, and the skills of a historian. You will have to find primary and secondary sources that go far beyond what you’ve relied on thus far.

In the end, I found myself wanting the authors to humanize this manuscript, meaning I wanted them to show me that changes in grading practices have tangible effects on real-life human beings. A humanization of their research would mean going narrower and deeper; or, in other words, eliminating much of what they have documented.

However, if that is too tall of an order, I would ask that the authors clarify for themselves who this manuscript is for. Is this a chronicling of facts for an internal audience at Dalhousie’s faculty, alumni, and students? Fine. But my guess is that even members of the Dalhousie community want to read something relatable.

I am suggesting revisions, although not because of objective errors. History is more of an art, in my opinion. With that in mind, I would suggest that the authors paint a more vivid picture (metaphorically) of Dalhousie, showing me how changes one moment of change in grading practices impacted the lives of human beings.

Our Response: Thank you very much for taking the time to read our paper and provide your thoughts and recommendations. It may be helpful to begin by describing why I (the first author) decided to write this paper. Ultimately, I wrote this paper to satisfy my own personal curiosity and to connect with other people at my own place of employment by exploring our shared history. At present day, Dalhousie has a letter grading scheme with a standardized percentage conversion scheme that all instructors used. I wanted to know why this particular scheme was used, but I quickly realized that nobody at Dalhousie really knew how we ended up grading this way! There was an institutional memory gap, and a puzzle that was irresistible to me. So, I wrote this paper for the most basic of all academic reasons: Pure curiosity. I do very much recognize that the subject matter is very niche, perhaps too niche for a traditional journal outlet. Thus, my publishing plan is to self-publish a manuscript to the Education Resources Information Center (ERIC) database and a preprint server as a way of sharing my work with others who might be interested in what I found. Nonetheless, I believe in the importance and value of peer review, especially since I am writing in a field different than most of my scholarly work. That is why I chose PeerRef as a place to submit, so that I could undergo rigorous peer review to improve the work while still maintaining the niche subject matter and focus that drives my passion and curiosity for the project. Of course, if you feel the whole endeavor is so flawed that it precludes publication anywhere, then we can consider this a “rejection” and I will not make any further edits through PeerRef.<br /> The core of your critique suggested that I should write a fundamentally different paper on different subject matter. While I don’t necessarily disagree that the kind of paper you describe might have broader appeal, it would no longer answer the core research question I wanted an answer to: How has Dalhousie’s grading changed over time? So, I must decline to rewrite the paper to focus on a single timeframe as recommended. All this said, I did try my best to address the spirit of your various concerns to improve the quality of the manuscript. Below, I will outline the various major changes to the manuscript that we made to improve the manuscript along the lines you described, while maintaining our original vision for the structure and focus of the paper. The specific changes are outline below:

a) Two new paragraphs (now paragraphs 1-2 of the revised manuscript) were added to explain the “so what” part of the question. Specifically, we describe why we think the subject matter might be of interest to others and summarize the general dearth of historical information on grading practices in Canada as a whole.

b) Consistent with recommendations from the other reviewer, we now state a core argument (i.e., that most major grading changes were implemented to improve the external communication value of the grades) earlier in the introduction in paragraph 5 and describe how various pieces of evidence throughout the manuscript tie back to that core theme.

c) In an attempt to “humanize” the manuscript more, we added more student quotes from the Dalhousie Gazette throughout the paper so that readers can get a better sense of how students thought about grading practices at various times throughout history. Specifically, three new quotes were added in the following sections: 1901-1936, late 1940s, 1950s-1970s. We also added this short note about the physical location where grades used to be posted: “Naturally, this physical location was dreaded by students, and was colloquially referred to as “The Morgue” (Anonymous Dalhousie Gazette Author, 1937).”

d) Early in the paper, we describe why we chose Dalhousie and the potential audience of interest: “As employees of Dalhousie, we naturally chose this institution as a case study due to accessibility of records and because it has local, community-level interest. The audience was intended to be members of the Dalhousie community; however, it may also be a useful point of comparison for other institutions, should similar histories be written.”

e) We have described some of the limitations of our sources in paragraph 4, which may explain why the manuscript takes the form it does – it has conformed to the information that is available!

f) We have linked events at Dalhousie to the national context in some more detail, by detailing some national events related to the funding of universities in Canada. See our response to Reviewer 1, #4 above for more details on the specific changes.

g) Consistent with your stylistic recommendations, we have changed various spots throughout the paper from the present tense (e.g., “is”) to the past tense (e.g., “was”), and were careful in our new additions to maintain the past tense, when appropriate. If there are any spots that we missed, let us know the page number / section, and we will make further changes, as necessary.

h) We retained the first person in our writing – this may be discipline-specific, but in Psychology (the first author’s home discipline), first person is acceptable in academic writing. If you feel strongly about this, we can go through the manuscript and remove all instances of the first person, but we would prefer to keep it, if at all possible.

Hopefully this helps address the spirit of your concerns, and I look forward to hearing your thoughts in the second round of reviews.

Decision changed

Verified with reservations: The content is scientifically sound, but has shortcomings that could be improved by further studies and/or minor revisions.

Author Response

Reviewer #1 (Public Review):

We thank the reviewer for a very constructive evaluation of our work and for a fair summary of its main strengths. We have addressed her/his main concerns as follows:

1) The experiments involve an invasive neurosurgical procedure used to perform hippocampal imaging, which removes the ipsilateral overlying somatosensory cortex, and it is not possible to evaluate from the data provided that this surgery does not disrupt network function, especially given the focus on movement-related activity patterns.

We thank the reviewer for bringing up this important issue. Indeed, our experimental access to early hippocampal activity with 2-photon calcium imaging relies on a quite invasive procedure. However, the many control experiments we have performed indicate that early hippocampal dynamics were not significantly altered by the surgery. First, our extracellular electrophysiological recordings from a sample of 6 mice (ranging from P6 to P11, Figure 1- figure supplement 1C) show that the frequency of early sharp waves (eSW) was slightly but not significantly reduced in the ipsilateral hemisphere compared to the contralateral one. Of note, a similar “non-significant” decrease had been previously reported by another group (Graf et al 2021 Fig S6C). As suggested by the reviewer, we can speculate that this slight decrease may result from a reduction of the sensory feedback re-afference originating from the right limbs. Indeed, we observed that movements of the right limbs (contralateral to the window implant) elicited a slightly smaller response than those from the left limbs. This observation has been added to Figure 1 - Supplement 1E and described in the results (lines 128-134) and discussion (lines 314-320).

We have performed additional control experiments using EMG nuchal electrodes in two pups aged P5 and P6. We observed that, an hour following the surgery (corresponding to the recovery time in our experimental procedure), the composition of the sleep-wake cycle (with 70 to 80 % of active sleep) was comparable to previous reports (Jouvet-Mounier, 1969, Fig 4). This quantification was added to Figure 1- figure supplement 1B (lines 82-86).

2) State-dependent parameters are not adequately described, controlled, and examined quantitatively to ensure that data from similar behavioral states is being used for analysis across ages. Network activity from wakefulness, REM/active sleep and NREM/quiet sleep should not be presumed to be indistinguishable.

We would like to point out that our analysis across ages focused on the population response following animal movements, and not across all behavioral states. That said, it is true that two types of movements can be distinguished, namely the twitches and the complex ones. To take this behavioral heterogeneity into account, we have now separately quantified the hippocampal activation following twitches (movement during active sleep) and complex movement (during wakefulness). We show in Figure 2 - figure supplement 1B that the hippocampal response to twitches and complex movements is similar across ages. Thus, even if the amount of time spent in each behavioral state is modified over the developmental period that we have studied, we are pretty confident that it does not impact the transition we have described in the relationship between animal movements and hippocampal activity. Additionally, we were able to combine in one P5 mouse pup 2p-imaging with nuchal EMG recordings and separately computed the PMTH for movements observed during REM or wakefulness (Figure 2 - figure supplement 1C). We show that CA1 hippocampal neurons were activated time-locked to movement in both behavioral states, with only the amplitude of the population response differing between wakefulness than during REM. This point is now included in the result section (lines 148-152) and discussed (lines 324-327).

3) Currently employed statistics are not rigorous, unified, or sensitive, and do not support all of the authors' claims. Data shown suggest potentially significant changes that have not been identified due to suboptimal statistical approach and/or underpowering.

We obviously agree with this reviewer that rigorous statistics should be employed and can certify that the data analyzed in the submitted manuscript was carefully examined following that principle. We feel that his/her strong criticism regarding that point was not fully justified. In particular, we do not understand why statistical tests should be “unified” across different figures of the paper. Rather, statistical tests should be adapted to the sample size and distribution. Of course, the same tests were used for similar datasets. This revised manuscript now contains further description and justification of all the tests included in every figure panels.

4) The authors use an artificial neural network approach to infer cell classification (pyramidal cell vs. interneuron). From the data provided, it is not possible to adequately evaluate whether these 'inferred' interneurons represent the same population as conventionally labeled interneurons.

We thank the reviewer for this important remark and apologize for the lack of detailed description of our method to ‘infer’ interneurons. This method was previously published (Denis et al., 2020), and designed to identify interneurons from their calcium fluorescence signals in the absence of a reporter. Most importantly, this cell type classifier was trained and tested on a dataset in which interneurons were labeled using a reporter mouse line (GAD 76-Cre). This dataset is included in this article. This means that all the ‘labelled’ interneurons included here were also used for the training and the test dataset. As for the activity classifier, the training and test data sets covered all the developmental ages used in the study. Thus, the previously published statistics (accuracy/sensitivity) of this classifier should well account for the present analysis. This method is now described in better detail in the results (line 183) and methods parts (lines 616-619). We now also illustrate in the figures how this classifier can infer interneurons with 91% precision (split up of prediction vs ground truth in test data are reported from Denis et al) and that these ‘infered’ interneurons are activated with movement just as genetically ‘labeled’ interneurons (Figure 3 - figure supplement 1B-E).

5) Functional GABAergic activity is not assessed across development (only at P9-10), limiting mechanistic conclusions that can be drawn.

We thank the reviewer for this comment that reveals some lack of clarity in the previous description of our experiments. Indeed, functional GABAergic activity was also assessed before P9, however, given that there are no GABAergic axons in the CA1 pyramidal layer at early stages (for both CCK cf. Morozov and Freund 2003, and prospective PV cells cf. Figure 4A,B), there is no signal to be measured either. We have now added a new figure (Figure 4 - figure supplement 1) to clarify this point. In agreement with our Syt2 longitudinal quantification, we show, using tdTomato expression in the Gad67cre driver mouse line, that GABAergic perisomatic innervation is only visible after p9. This matches as well our attempted imaging experiments using axon enriched GCaMP in mice before P9.

6) The present analyses are almost exclusively focused on movement-related epochs, substantially limiting conclusions that can be drawn as to what neural dynamics are actually occurring during epochs that the authors propose comprise internal representations.

We agree with this reviewer that our study is focusing on movement-related episodes and that we are not assessing hippocampal representations, especially since the pups are recorded in conditions that minimize external environmental influences. Still, we observe that there is a switch in the distribution of spontaneous activity in CA1 after P9, with most activity occurring outside from the synchronous calcium events and detached from movement. The exact nature of this activity remains to be studied, however, it is most likely not evoked by extrinsic phasic inputs and rather represents local dynamics. We have now removed reference to ‘internal representations” or “internal models” in the two previous instances of use i(abstract and discussion) and replaced them, when possible by “self-referenced” representations alluding to self-generated-movement-triggered activity.

Reviewer #2 (Public Review):

The study by Dard et al aims to uncover the post-natal emergence of mature network dynamics in the hippocampus, with a particular focus on how pyramidal cells and interneurons change their response to spontaneous limb movement. Several previous studies have investigated this topic using electrophysiology, but this study is the first to utilize 2-photon calcium imaging, enabling the recording of hundreds of individual neurons, and discrimination between pyramidal cell and interneuron activity. The aims of the study are of broad interest to all neuroscientists studying development (including neurodevelopmental disorders) and the basic science of network dynamics.

The main conclusions of the study are that (1) in early life, most pyramidal cell activity occurs in bursts synchronized to spontaneous movement, (2) by P12, pyramidal cell activity is largely desynchronized from spontaneous movement, and indeed movement triggers an inhibition in the pyramidal network (approximately 2-4sec following movement), (3) unlike pyramidal cells, interneuron activity remains positively modulated by movement, throughout the period P1-P12, (4) the changes in pyramidal cell activity are achieved by means of increases in perisomatic inhibition, between P8 and P10.

It should be noted that conclusion (1) and to some extent conclusion (2) have already been reported, by previous studies using electrophysiology (as clearly acknowledged by the authors).

A principal strength of this manuscript is the extremely high quality of the data that the authors are able to use in support of (1) and (2), with very large numbers of neurons being analyzed to clearly delineate the relationship between neural activity and movement. The finding that pyramidal cells become inhibited following movement is novel, I believe. Furthermore, this study offers the first description of the development of interneuron activity, in this experimental context.

The main weakness of the manuscript is that the authors cannot provide direct functional evidence for the conclusion (4). As shown by the analysis in support of conclusion (3), interneuron activity with respect to movement does not actually change during the developmental period being studied, making it prima facie unlikely that this is the cause of changes in pyramidal network responses to movement. To overcome this, the study describes the activity of GABA-ergic axon terminals in the pyramidal cell layer at P9-10, but it appears that due to technical problems this was not possible in younger animals. It, therefore, remains unknown if the functional inhibitory inputs to pyramidal cells are changing over the ages studied.

We thank this reviewer for acknowledging the broad interest of the study, its novelty, and the high quality of our dataset. The main concern raised by this reviewer (lack of axonal activity experiments in younger pups) was in fact a misunderstanding of the experiments performed and we apologize for this lack of clarity. Reviewer #2 is correct in that the relationship between interneuron activity and movement does not change over the developmental period studied. However, we have only included GABAergic axonal imaging after P9, not due to a technical problem but rather because there are no GABAergic axons in the pyramidal layer before (we see GABAergic neurites only outside the layer). We have now dedicated a new supplementary figure (Figure 4 - figure supplement 1) to explain why we could not image GABAergic axons in the pyramidal cell layer at earlier developmental stages.

The study does describe increases in the protein synaptotagmin-2, in the pyramidal cell layer, between P3 and P11, but in my opinion, this molecular evidence for increases in perisomatic inhibition does not match the (very high) standards of neuronal function/activity reported elsewhere in the manuscript.

In the absence of parvalbumin expression in early development, synaptotagmin-2 has been described as the best marker of prospective PV boutons in the cortex (Someijer et al. 2012). This molecular marker has been used in other studies (Modol et al. Neuron 2020, Sigal et al. PNAS 2019). We respectfully disagree with this reviewer, and think that quantification from immunohistochemistry experiments is as high of a standard as functional imaging as it is the only way to describe the anatomical structure of active neuronal processes.

Reviewer #3 (Public Review):

Dard and colleagues use both in vivo calcium imaging and computational modelling to explore the relationship between the early movement of CA1 hippocampal activity in neonatal mice.

The manuscript represents a significant technical advance in that the authors have pioneered the use of multiphoton imaging to record activity in the hippocampus of awake neonates. Overall the presentation of the data is convincing although I would recommend a number of tweaks to the figures and the inclusion of some raw data to better direct and inform non-expert readers. I also believe that the assessment of long-range inputs using pseudo-rabies virus should be present in the main body of the manuscript as opposed to supplemental material. The computational modeling supports their idea but does not exclude other possibilities. Further, it is not clear to what extent the strengthening of local excitatory input onto the interneurons - the dominant route of recurrent input in the hippocampus, is important; something that the authors acknowledge in the discussion.

Overall, I believe the paper adds to our knowledge of the timeline of development and further identified the postnatal day (P)9-P10 window as important in emergent cortical processing. The fact that this is linked to an increase in GABAergic innervation has implications for our understanding of both normal and dysfunctional brain development.

We thank the reviewer for his constructive comments and helpful suggestions. As suggested, this revised version now includes some raw-data and better descriptions to guide non-expert readers. Regarding the inclusion of rabies-tracing experiments in the main part of the MS, we would like to state here that there are still a number of limitations with the use of this method during development (incubation time, spatial precision of the injection site, etc. ) that limit the interpretation and quantification of the results. As a result, we have decided to remain only qualitative, focusing on identifying the brain regions that could send projections onto CA1 pyramidal cells and interneurons. We believe that this type of description is more suited for a supplementary figure than a principal figure, but will be happy to change this, if the reviewer and editors think otherwise.

I think this statement is important because it shows that the argument is not as simple as, "Online courses are bad and in-person classes are good". It shows that, while plenty of students do just fine learning online, the online courses themselves lack a lot of the edge that an in-person course can give a student. This is an important observation because we can use this research to optimize the way we learn online moving forward!

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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Reply to the reviewers

Manuscript number: RC-2021-01219

Corresponding author(s): Rajan, Akhila

1) General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

The goal of this study is to:

• Define how prolonged exposure to a high-sugar diet (HSD) regime alters both the lipid landscape and feeding behavior.
• Determine how changes in lipid classes within the adipose tissue regulates feeding behavior. Key findings:

In this study, by taking an unbiased systems level and genetic approach, we reveal that phospholipid status of the fat tissue controls global satiety sensing.

Impact of Key findings:

By uncovering a critical role for adipose tissue phospholipid balance as a key regulator of organismal feeding, our work raises the possibility that the rate-limiting enzymes in phospholipid synthesis, including Pect, are potential targets for therapeutic interventions for obesity and feeding disorders.

Peer review comments:

This study has immensely benefited from the thoughtful peer-review of three reviewers. As per their recommendations, we have performed a major revision by performing additional experiments (see summary table below in next section) and strived to address the major concerns raised. Based on our reading, there were two major concerns that overlapped between all three reviewers raised. They are as follows:

• Does the genetic disruption of Pect in fly fat body alter phospholipid levels? Two reviewers (#2 and #3) recommended that we perform lipidomic analyses on adult flies with adipose tissue specific knockdown of For the revised version, we have completed this lipidomic experiment, and present results as a new main Figure 6, Supplemental S7 and S9.
• Is the dampened HSD induced hunger-driven feeding (HDF) behavior because of increased baseline feeding (#1 and #3)? In addition, reviewer #1, asked us whether HSD flies experience an energy-deficit? In other words, we were asked to uncouple whether what we observed was HSD-driven allostasis or indeed, as we had interpreted, that HSD dampened hunger-driven feeding response.

Hence, they recommended that we:

1. Re-analyze our hunger-driven feeding datasets and present non-normalized data (also requested by Reviewer #3) and show baseline feeding behavior on HSD. To address this, we have completed this analysis and present our results in Figure 1B-D and S1.
2. Determine whether the HSD fed flies display an energy deficit on starvation. To this end, we performed an assayed starvation-induced fat mobilization on HSD, results for this are now presented on Figure 1E-G and S2. Conclusions after the revision:

First, it is important to note here that the additional experiments have not caused a significant revision of the major conclusions of the original version of our study. In fact, we hope that the revised version provides clarity and further substantiation to our original arguments.

• The lipidomics experiments on Pect fat-specific knock-down flies show that reducing Pect in fat-body causes a significant reduction in certain PE lipid species (PE 36.2 specifically- Figure 6B). This is consistent with a prior report on lipidomics of the Pect null allele by Tom Clandinin’s group (PMID: 30737130). Furthermore, we note that when Pect is knocked down in the fat body, there is a significant increase in two other classes of phospholipids LPC and LPE (Figure 6A). Together, this suggests that an imbalance in phospholipid composition in the absence of Pect activity in fat.
• The starvation-induced fat mobilization experiments show that despite being fed a prolonged HSD, adult flies sense starvation and effectively mobilize fat stores, at a level comparable to Normal food (NF) fed adult flies, suggesting that even despite HSD exposure, adult flies experience an energy deficit on starvation.
• In our non-normalized data, we find that the baseline feeding events are not significantly altered between HSD and NF-fed flies (Figure 1D). This suggests that the effects we observe are not due to an increase in the “denominator”, but a dampening of hunger-driven feeding on HSD. With regard to our original version, all three peer-reviewers found that the study was interesting, significant, important, and novel – Reviewer #1: “The work is potentially novel and interesting”; #2 : “I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The conclusions are mostly convincing”; #3: “This manuscript demonstrates how fat body Pect levels affect HSD induced changes in hunger-driven feeding response. I agree with all the reviewers points; potentially very interesting”. But had requested that we provide further substantiation and clarification.

We sincerely hope that the peer-reviewers find that our revised version with additional new experimental datasets, improved data visualization, and the presentation of non-normalized raw data points, makes this study clear, compelling, and well-substantiated.

• Point-by-point description of the revisions This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

Below we summarize in Part A, the key experiments that were performed to address the major concerns. In Part B, we provide a point-point response to each reviewer with embedded datasets.

Part a:

We performed several new experiments, including:

• To address the primary concern of Reviewer #1 regarding whether the HSD flies have a similar energy deficit to Normal food (NF) fed flies, we performed analysis of stored neutral fat Triacylglycerol (TAG) reserves and how HSD fed flies mobilized fat stores on starvation. We present these results in Figure 1E-G, S2. These results show that HSD-flies despite accumulating more TAG (S2), breakdown a similar amount of fat reserves as NF-fed flies on starvation at any time-point (Figure 1E-G). This suggests that HSD-fed flies do sense and respond to energy deficit.
• To address concerns of reviewer #2 and #3 on whether Pect genetic manipulation affects specific phospholipid classes, we performed lipidomic analyses. The table below summarizes the new 3 new figures and 4 supplemental figures (blue text are all new figure numbers and figure panels) and three new Supplementary files as per reviewer’s request.

Figure #

Main point

New datasets in revision

Companion Supplement

1

HSD alters feeding behavior, but flies still breakdown TAG on starvation.

TAG storage and breakdown over longitudinal HSD shows that HSD and NF fed flies show similar levels of TAG breakdown on starvation, despite consistently elevated TAG on HSD. This supports the idea that flies do sense starvation even on HSD, but there is a uncoupling of the feeding behavior after Day 14. Revised the data representation of Figure 1 to show non-normalized data over time. S1 and S2 companions are new in the revision. Panels 1D to 1E are new for the revision.

S1- Raw data of feeding events plotted.

S2 Elevated TAG at all time points.

2

HSD causes insulin resistance

S3A added to show that insulin transcript levels remain the same in response to reviewer #3’s concerns.

S3

3

Phospholipid concentration raw data from lipidomic on Day 7 and Day 14 HSD suggest that PC, PE levels are increased on Day 14 HSD.

Figure 3 revamped to show new data visualization and non-normalized raw data to address Reviewer #2’s major concerns. S4A and S4B added. In addition Supplementary File 1 and 2 provided with raw lipidomics data as per reviewer #2’s request.

S4.

S4A- non normalized raw data of all other lipid classes on HSD.

S4B- fatty acid species data on Day 14 added as per request of rev.#2.

4

HSD regulate Apo-I levels in the IPCs and phenocopies Pect KD.

Added Figure 4A to show that HSD phenocopies Pect-KD in terms of delivery to brain

S5 showing the validation of the Apo-I antibody.

S6 validation of Pect KD and over-expression and Pect mRNA levels dysregulation on HSD.

5

Pect RNAi is insulin resistant

N/A

N/A

6

Pect knockdown shows significant increase in LPC and LPE, and a non-significant reduction in PC, PE levels. Specifically, the PE lipid class PE36.2 is downregulated.

Fig 6, S7, S9 are completely new based on reviewer #2 and #3 requests. In addition Supplementary File 3 provided with raw lipidomics data as per reviewer #2’s request

S7, S8, S9#.

S7- new Pect KD other classes

S8- new PE classes for day 14 and Pect associated classes.

S9- Pect OE lipidomics

7

Pisd and Pect activity in adipocytes are required for hunger-driven feeding behavior in normal diets

Pisd RNAi data was moved from supplement to main figure.

N/A

Note on revised text: We have revised text not only in the results section, but also as per reviewer #2’s recommendation, we have revamped our introduction and discussion as well. Since the manuscript has been significantly revised to include a main figure 6, fully altered Figure 1 and 3, multiple new supplemental figures, the changes in text are extensive. Hence, they are unmarked in the main text. Nonetheless, we hope that the reviewers will be able to evaluate these changes, as we have provided the specific locations in text and embed key figures in the point-point response below.

__Part B: __Point-Point responses to reviewer comments.

Reviewer #1 comments in Blue, author response in black.

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

In this manuscript, Kelly et al. show that the difference between the feeding behavior of fed and starved flies (hunger-driven feeding; HDF) is absent in animals fed a high-sugar diet (HSD) for two weeks or more. The disappearance of HDF with HSD coincides with changes in phospholipid profiles caused by HSD. Furthermore, RNAi-mediated downregulation of Pect in the fat body-a key enzyme in the PE biosynthesis pathway-phenocopies physiological effects of HSD. Moreover, downregulation or overexpression in the fat body abolishes or induces HDF, respectively, abolishes or induces HDF, respectively, independent of HSD treatment.

Overall, the manuscript is well-written and the phenotypes are clear. However, I have major concerns regarding the authors' interpretation of the data and their conclusion. Most importantly, while it is clear that the authors' high-sugar dietary treatment affects feeding behavior and physiology, I am not convinced that the changes can be considered "hunger-driven"-which is central to the main point of the manuscript. Therefore, it is my recommendation that the authors substantially revise the manuscript by either showing additional/re-analyzed data that rule out alternative hypotheses, or rewriting the manuscript keeping alternative interpretations in mind.

We are thankful to this reviewer for their thoughtful critique, and constructive and specific suggestions on how we can redress these concerns. We have taken on board the concerns of this reviewer regarding our interpretation of whether the changes in feeding behavior can be considered hunger-driven or not. Based on their advice, we have made significant changes by addressing: i) does HSD increased baseline feeding- we now show non-normalized raw data and data supports conclusion that baseline feeding is not higher; ii) whether HSD- fed flies can sense an energy deficit at levels similar to NF fed flies- we show that HSD flies sense energy deficit. We have provided detailed response below, and we hope the reviewer finds the additional datasets and re-analyzed data are consistent with the interpretation that prolonged HSD dampens starvation induced feeding. In addition to this key concern this reviewer has made a many other salient points that we have addressed with additional data or by clarifying the text.

Major comments: 1) The data do not sufficiently show that the long-term HSD regime disrupts "hunger-sensing." The manuscript should address alternative hypotheses by showing raw instead of normalized data, rewriting the manuscript with a new central conclusion, or running additional experiments that actually show a defect in hunger-driven response. a. The main results that the authors rely on for the argument is that the ratio of feeding events that the starved and non-starved flies eat is different between the groups fed normal or HSD. However, because the authors only show normalized data (normalized to non-starved flies; Fig. 1), it is difficult to tell whether the change is due to a chronically increased feeding in non-starved HSD flies-maybe in perpetual hunger-like allostasis-or dampened starvation response. Indeed, the data shown in Fig S1 show that flies fed HSD for as short as 5 days show more frequent feeding events compared to age-matched controls fed normal food. It is possible that because the HSD-fed flies eat more than NF-fed flies, even without being starved, the ratio of starved/non-starved feeding is lower in the HSD-fed group-due to changes in the denominator, rather than the numerator.

We have taken onboard this concern regarding presenting only normalized data, and that clouded the interpretation and left open other possibilities. In the completely revised figure 1 and S1. We now show non-normalized data, as a function of time. First we note that HSD-fed flies, do not show higher baseline feeding that NF fed flies, except on Day 10 of HSD, when there is a modest but significant elevation (Figure 1D).

Nonetheless, on Day 10 HSD, flies still display increased hunger-driven feeding HDF (Figure 1C), it is only after Day 14 HSD that HSD dampens the starvation induced feeding.

1. It is also possible that the HSD-fed flies are simply not in as big an energy deficit physiologically, due to the increased fat deposits they've accumulated (as the authors show later in the manuscript). It may take longer for the fat HSD flies to reach substantial energy deficiency than the NF flies, but they still may eventually be able to appropriately respond to hunger, just like NF flies. In such case, it would be a misnomer to call this behavioral change a 'defect in hunger-driven feeding behavior.' Maybe an experiment with a dose-response curve of "hunger driven feeding response" as a function of duration of starvation would help? Prompted by this reviewers question, we asked whether HSD fed flies, that have a higher baseline neutral fat store (Triacylglycerol-TAG) level, and if HSD-fed flies can sense energy deficit. For this, we revisited the longitudinal assays for neutral fat triacylglycerol (TAG) storage that our lab had generated, along with the HSD-HDF studies. We now present this evidence as Figure 1E-1G and Figure S2. Overall, our experiments point to the idea that adult flies fed HSD, are able to sense and mobilize TAG stores effectively throughout the 28-day time point that we analysed.

First as shown in Figure S2, flies fed HSD display an increase in TAG levels. But it is to be noted that while TAG stores increase, the increase is not linear with time. This suggests that adult flies exposed to HSD store excess energy as TAG, but the increased TAG stores stay within a certain range despite the length of HSD exposure. This suggests that adult flies on HSD still display TAG homeostasis.

Next, to directly address the reviewers point about HSD fed flies not sensing an energy deficit, we subject HSD-fed flies to an overnight starvation, same regime as used in the overnight feeding experiments, and asked whether they mobilize TAG. We noted that flies exposed to HSD breakdown TAG throughout the 28-day exposure at statistically significant levels for Day 3- Day 28, except on 14 and 21 days (Figure 1F). While there is TAG mobilization on Day 14 and 21, the difference is not statistically significant. Nonetheless, we note the same levels TAG breakdown for normal lab food (NF) fed flies on Day 14 and 21 (Figure 1E). Overall, HSD fed flies sense and display energy deficit, as measured by TAG store mobilization, throughout the 28 days of HSD exposure, at levels comparable to NF-fed flies (Figure 1G).

Taken together, these results suggest that while HSD-fed flies experience an energy deficit on starvation, at levels comparable to NF-fed flies, throughout the 28-day time point assayed. But, their starvation driven feeding-response is dampened by Day 14 and by Day 28, the HSD-fed flies display more feeding events than HSD starved flies. These results are consistent with the interpretation that in HSD-fed flies the starvation-induced feeding behavior becomes desynchronized from the starvation induced TAG-mobilization, suggesting that there is an absence of hunger-driven feeding.

2) How can you be sure that lower Dilp5 immunofluorescence is indicative of increased Dilp5 secretion? Wouldn't decreased production of dilp5 also have the same results?

It has been shown previously in HSD fed larvae are hyperinsulinemic, i.e., they have 55% increase in circulating Dilp2 ( PMID: 22567167). Additionally, we have shown that ectopic activation of the insulin-producing neurons by expressing TRPA1, an ion channel that activates neurons, reduces Dilp5 accumulation without a change in Dilp5 mRNA levels (PMID: 32976758), suggesting that reduced Dilp5 accumulation, without alterations to mRNA levels is a proxy for increased secretion. Now, in response to this concern, in the revised manuscript, we have added qPCR data of Dilp2 and 5 (Figure S3A), which show no difference in expression levels after 14 days on HSD. Therefore, there is no dip in Dilp5 mRNA production. Given that Dilp2 and Dilp5 mRNA levels remain the same, but we see reduced Dilp5 accumulation, we interpret this to mean that Dilp5 secretion is increased.

1. Also, the authors should state in the main text that it is Dilp5, not just any Dilp. Thanks for this suggestion and we have fixed this and referred to Dilp5 specifically throughout the text in the results section.

3) Data presentation: a. Sometimes the data are normalized to NF (Fig 4B-C), sometimes not (ex. Fig 4A, S4C). Unless there is a specific rationale for the data transformation, it would be more appropriate to show untransformed data (ex. Fig 4A, S4C), especially as the authors use two-way ANOVA to determine significance. Only showing the differences implies comparison against a hypothetical mean (i.e. μ0=0), not between two group means.

We thank the reviewers for bringing this issue to our attention. We updated all the figures to show untransformed data in the revised manuscript.

1. Some figures show both individual data points and summary statistics (mean, SD, ... ex. Fig 2A)-which I believe is ideal-but some show only one or the other (ex. Fig 2B, no summary statistics; Fig. 3, no data points. The manuscript would read more convincing if data visualization is consistent across figures. We thank the reviewers for their feedback. We have made changes to all the figures in the revised manuscript to improve visual consistency.

Minor comments: 1) High sugar diet: what is the actual sugar concentration in the NF v. HSD diets? The authors write that the HSD diet contains "30% more sugar" than the NF, but providing the final sugar concentrations-sucrose or others-would be informative for other scientists studying the effect of high sugar diets.

We thank the reviewer for their suggestion and now we have updated the methods to include this sentence. “After 7 days, flies were either maintained on normal diet or moved to a high sugar diet (HSD), composed of the same composition as normal diet but with an additional 300g of sucrose per liter”.

1. Additionally, the definition of HSD is inconsistent. Main text (Page 5, line 17) states that their HSD is "60% more sugar than normal media," whereas the figure legend (Fig 1) and the Methods state that the HSD contains "30% more sugar." We apologize for this egregious typo in the figure legend! We have now fixed this to say 30% HSD. Only 30% HSD was used throughout this study.

2) Starvation medium: please provide justification for why the authors used 1% sucrose/agar for starvation medium, instead of plain agar/water that most labs use. At least clarify and provide a reference for the claim that the 1% sucrose/agar "is a minimal food media to elicit a starvation response."

We are very grateful for this reviewer identifying this this methods description error and bring it to our attention. We used 0% sucrose agar for overnight starvation in this study as most labs do. The error occurred because we were using another manuscript from the lab to help draft the methods section (PMID: 29017032). In that study, where we assayed the effect of chronic starvation our lab used: “1% sucrose agar for 5 days at 25C”. However, in this current study, because we are testing acute effects of overnight starvation, we are using 0% sucrose agar.

3) Pect mRNA level is higher with HSD. This is surprising because not only, as authors mention, is increased PC32.2 with HSD suggests lower Pect activity, but also because Pect RNAi phenocopies long-term HSD in HDF behavior, lipid morphology, FOXO accumulation in fat body. The authors speculate that the data "likely shown an upregulation in an attempt to mediate the Pect dysregulation occurring at the protein level." If that were true, a western blot may be informative. Zhao and Wang (2020, PLoS Genetics) generated a Pect antibody that seems compatible with western blot applications. That being said, I don't think such data is critical for the manuscript. I mention this simply as a suggestion for the authors. a. page 8, line 22-23, did you mean to write "Given how PC32.2 is elevated after 14 days of exposure to HSD, we assumed that Pect levels would be low for flies under HSD," not "high?" Otherwise the subsequent 2 sentences don't make sense.

We agree that the most confusing aspect of the study was that Pect mRNA levels being very high on Day 14 HSD, but nonetheless the effects of Pect-KD phenocopied HSD. To resolve this, we have now performed lipidomic analyses on whole adult flies, when Pect is knocked-down (KD) by RNAi in the fat tissue. We now present a new dataset in Figure 6. Two striking changes occur. They are:

1. Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3).
2. Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding increase in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). In contrast, PE 36.2 trends upwards on 14 day HSD (Figure S7C) though not significantly. On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

We agree that a western blot would be informative as well, but we were unable to obtain the reagent from Dr. Wang’s group, precluding us from performing this request. See email snapshot.

To ensure that we appropriately discuss and clarify this issue, we have now included a section in the discussion - Page 14 Lines 26-34- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

“Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9) , but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

Reviewer #1 (Significance (Required)):

The work is potentially novel and interesting, but at this stage it's difficult to interpret what the phenotype signifies. Although the manuscript could be revised simply by modifying the text, experimentally addressing the concerns would significantly improve the work.

In sum, we hope we have addressed the key concern for Reviewer #1 as to whether the behavior we report here is indeed a dampening of starvation-induced feeding, or an effect of increase in baseline feeding. We hope that by reviewing our non-normalized data, they can appreciate that it is the former. Also, we hope that Reviewer #1 appreciates that we have strived to address the concerns by additional experiments, to clarify our findings and improve the impact of the work.

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

This intriguing manuscript by Kelly and colleagues uses the fruit fly Drosophila melanogaster as a model to understand how diet-induced obesity alters the feeding response over time. In particular, the authors findings indicate that chronic exposure to a high-sugar diet significantly alters the starvation-induced feeding response. These behavioral studies are complemented by a lipidomics approach that reveals how a chronic high sugar affects many lipid species, including phospholipids. The authors then pursue mechanistic studies that indicate phospholipid metabolism within the fat body appears to remotely affect insulin secretion from the insulin producing cells. Moreover, the changes in phospholipid abundance are associated with changes in insulin-signaling, including increased insulin secretion from the IPCs and elevated levels of FOXO within the nucleus.

I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The conclusions are mostly convincing, but a few follow-up experiments are required:

We are grateful for the reviewers constructive, detail-oriented, and balanced feedback, and their recognition of the value of this study. Now, we have performed additional experiments to address the key concerns raised by all reviewers. We hope that on reading the revised version of our study, that the reviewer continues to feel positive about the message of this study and its potential impact.

1. The key conclusions from the manuscript assume that manipulation of Pect expression levels alters phosphatidylethanolamine (PE) levels. However, the authors make no attempt to verify that the genetic experiments described herein actually affect PE levels. At a minimum, changes in PE levels should be verified for the Pect knockdown and overexpression lines. Similarly, there is no evidence that manipulation of either EAS or Pcyt2 induces the expected metabolic effects. I'm not asking that the longitudinal feeding experiments be repeated, simply that the authors measure the relevant lipid species, preferably with a targeted LC-MS approach.

Prompted by this reviewer, we performed targeted LC-MS on whole adult flies, on normal diet, to assess lipid levels for fat-specific Pect-KD and overexpression. We decided to focus on Pect, as its knock-down even on normal diet causes a dampened hunger-driven feeding behavior (Figure 7A) and phenocopied a 14-day HSD feeding phenotype.

We now present a new dataset in Figure 6. Two striking changes occur:

They are:

Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding decrease in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). It is to be noted that though overall levels of all PE species trend downwards, like the Clandinin lab study on Pect (PMID: 30737130), we did not find a significant change in the overall PC and PE levels.

• Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3). On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

Finally, fat-specific Pect-OE did not cause significant changes to lipid species (Figure S9). This could either be due to the fact that in fat-specific Pect-OE flies under normal food and that we were assaying whole body lipid levels and not fat-specific lipid changes. But to counter that, even a 60% reduction in Pect mRNA levels (Figure S6A), was sufficient to produce an effect on whole body phospholipid balance (Figure 6). Hence, we speculate that by maintaining a basally higher (7-fold higher Pect mRNA level Figure S6A), might allow 14-day HSD-fed flies to buffer the negative effects of HSD and we predict that it might take longer to disrupt the phospholipid balance and HDF response.

We have now included a section in the discussion - Page 14 Lines 26-34- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

“Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9), but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

A central hypothesis in the study is that the HSD over a period of 14 days results in insulin resistant and that these changes are leading to changes in hunger dependent feeding. I would encourage the authors to determine if Foxo mutants are resistant to these HSD-induced effects on HFD.

We thank the reviewers for this suggestion. However, given that dFOXO nuclear localization rather than expression levels regulate insulin sensitivity, we feel that disrupting dFOXO levels via mutation or knockdown will produce a plethora of indirect effects including developmental abnormalities (PMID: 24778227, PMID: 16179433, PMID: 29180716, PMID: 12893776). Our data suggest that chronic HSD treatment and Pect affect insulin sensitivity in fat tissue. However, we feel that investigating whether insulin sensitivity/FOXO signaling in fat tissue regulates feeding behavior is outside the scope of our work.

1. In lines 25-30, the authors draw the conclusion that an increase in unsaturated fatty acid species is associated with the HSD and that these changes results in a more fluid lipid environment. While I agree with the model, the manuscript contains no evidence to support such a model. Either test the hypothesis or move the last line of the section to the discussion.

We thank the reviewer for this important and insightful comment. We agree that the data we presented and discussed in the original version is at the moment speculative. Addressing the hypothesis that increase in unsaturated fatty acid species result in a more fluid lipid environment will require us to build tools and expertise. Hence, this hypothesis is better suited for exploration in a future study. Given this, we have moved this out of the results section into the Discussion section titled “HSD and fat-specific PECT-KD causes changes to phospholipid profile” (See excerpt below from page 13, lines 24-35).

“In addition to changes in phospholipid classes, we found that HSD caused an increase in the concentration of PE and PC species with double bonds (Figure S4C and S4D). Double bonds create kinks in the lipid bilayer, leading to increased lipid membrane fluidity which impacts vesicle budding, endocytosis, and molecular transport14,92. Hence it is possible that a mechanism by which HSD induces changes to signaling is by altering the membrane biophysical properties, such as by increased fluidity, which would have a significant impact on numerous biological processes including synaptic firing and inter-organ vesicle transport.”

Also, as per the reviewer’s guidance, given that we are speculating here, we have also shifted this dataset from Main figure 4 to supplement S4C and S4D.

In addition, lines 25-30 state that FFAs are increased after 14 days of a HSD. Figure 3A shows the exact opposite - FFAs are significantly decreased in 14 day fed animals despite being elevated in the 7 day fed animals. This is an interesting result that warrants discussion. Moreover, I would encourage to examine the lipidomic data more carefully to ensure that the text accurately portrays the lipid profiles.

We apologize for misstating that FFAs are decreased on 14-day HSD in the lines 25-30. It was an error and we have corrected this. We agree with the reviewer that the reduction of FFA on Day 14-HSD is an intriguing and unexpected observation that needs to be emphasized and further discussed. To this end, we have added figure S4B, wherein we have provided the difference in FFA concentration (by species) after days 7 and 14.

Furthermore, we have discussed what the potential meaning of reduced FFA at Day 14 implies in page 12, lines 19-27 of the Discussion section titled “HSD and fat-specific PECT-KD causes changes to phospholipid profile”. We have stated the following-

“We speculate that this reduction in FFA maybe due to their involvement in TAG biogenesis (PMID: 13843753). We were interested to see if the decrease in FFA correlated to a particular lipid species, as PE and PC are made from DAGs with specific fatty acid chains. However, further analysis of FFAs at the species level did not reveal any distinct patterns. The majority of FFA chains decreased in HSD, including 12.0, 16.0, 16.1, 18.0, 18.1, and 18.2 (Figure S4B). This data was more suggestive of a global decrease in FFA, likely being converted to TAG and DAG, rather than a specific fatty acid chain being depleted.”

The processed lipidomics data should also be included as supplementary data table so that they can be independently analyzed by the reader.

We thank the reviewer for this suggestion. As per the reviewers request, we have included the raw data as an attachment in our supplementary material (Supplementary Files 1-3.), so that interested readers can use the datasets generated in this study for future work and further analysis.

Beyond these experimental suggestions, the manuscript needs significant editing for clarity. While I won't provide a comprehensive list, the authors need to provide accurate descriptions and annotation of genotypes (including w[1118], which is written as W1118), typos, and formatting. I've listed a few examples below:

1. Page 3, Line 1 and 2: "...have been shown to impact feeding behavior and metabolism that leads to..." This is an awkward and grammatically incorrect sentence.
2. Page 3, Lines 7-32 is one very large paragraph but contains concepts that should be broken down over at least three paragraphs.
3. Page 3, Line 25: A description of the reaction catalyzed by Pect would be helpful for a manuscript focused on Pecte activity.
4. Page 4, Line 10: "previously characterized method of eliciting diet induced feeding behavior." As stated in the text, the method is previously described yet the manuscript characterizing the method isn't cited.
5. Figure legend 3 contains a random assortment of capitalized lipid species. Also, the names of lipid species are inappropriately broken into multiple names. Please use correct nomenclature throughout the manuscript.

The list above is nowhere near comprehensive. The manuscript requires significant editing.

We are grateful to the reviewer for drawing our attention to these errors. We have made significant edits to the revised manuscript to address the above-mentioned concerns, as well as made additional textual changes throughout and copyedited it. We hope that the reviewer will find the manuscript reads better and the clarity and preciseness is significantly improved.

Reviewer #2 (Significance (Required)):

I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The findings will significantly advance our understanding of how lipid metabolism links dietary nutrition with feeding behavior.

Once again, we are grateful for this reviewer’s thoughtful critique and encouraging words regarding our work and its potential impact.

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

Summary: This manuscript uses Drosophila to investigate how diet-induced obesity and the changes in the lipid metabolism of the fat boy modulate hunger-driven feeding (HDF) response. The authors first demonstrate that chronic exposure (14 days) of high sugar diet (HSD) suppresses HDF response. Through lipidome analysis, the authors identify a specific class of lipids to be elevated upon chronic HSD feeding. This coincided with the changes in expression of Pect, an enzyme that regulates the biosynthesis of these lipids. Modulating the expression of Pect specifically in the fat body affected HDF response.

We thank this reviewer for their rigorous and thoughtful critique and for identifying a key issue with our original study pertaining to a gap in how Pect mRNA levels on 14-day HSD are elevated but the Pect-KD phenocopies the HDF. Now by performing whole-body adult fly lipidomic on fat-specific Pect-KD we have resolved this issue and provided clarity on role of Pect in maintaining phospholipid homeostasis and thus subsequently impacts hunger-driven feeding. We hope the reviewer finds that the revised manuscript provides further clarity to the functional link between Pect’s role in fat-body and hunger-driven feeding.

Major comments: The author claim that the HDF response in HSD is distinct between early (5d, 7d) and chronic (day 14) HSD feeding. However, the data seem to indicate that HDF response is significantly decreased at all time points in HSD. For example, at day 5 HDF response was increased only 3-fold in HSD (Figure 1C) compared to around 50-fold increase in NF (Figure 1B). The scale of the Y-axis in Figure 1B and 1C is an order of magnitude different. Including the starved data (NFstv and HSDstv) in Figure S1, normalized to NF fed group, would better visualize the overall trends. Related to this, having the source data for the actual number of feeding events would be useful (e.g., to see the baseline changes in feeding in different time points in Figure 1 and the effect of genetic manipulations in Figure 7).

As per the reviewers request, we now have modified our graphs to show source data (Figure S1) and show the raw feeding events.

Then in the non-normalized graphs we plot, over a longitudinal time course, baseline and hunger-driven feeding events (Figure 1B-D). We also show that HSD fed flies do not display increased baseline feeding (Figure 1D) suggesting that the effect we see on HDF are no clouded by increased baseline feeding.

Yes, the reviewer makes an important point that HDF response on HSD fed flies is of a lower magnitude than NF fed flies. We think that is a biologically meaningful observation, as it suggests that flies have a remarkably fine-tuned ability to coordinate food-intake with nutrient store levels.

­­Now we have included a paragraph in the Discussion, Page 11 Lines 23-27, that say the following to ensure the readers appreciate this salient point raised by this reviewer.

*It is to be noted that the HDF response of HSD-fed flies (Figure 1C, Days 3-10) is of lower order of magnitude than the NF-fed flies. This suggests that that in addition to sensing an energy deficit and mobilizing fat stores (Figure 1F, 1G, S1), HSD fed flies calibrate their starvation-induced feeding to compensate only for the lost amount of fat. Overall, this suggests that flies have a remarkably fine-tuned ability to coordinate food-intake with nutrient store levels. *

The association between fat body Pect level and phospholipid levels is not clear. Day 14 of HSD feeding shows high expression of Pect in the fat body and elevated levels of PC32.0 and PC32.2. The authors assume the high expression of Pect in the fat body is due to the compensatory response, but there are no data indicating downregulation of Pect levels at the earlier time points of HSD feeding. A previous study demonstrated that Pect mutant flies have lower levels of PC32.0 but higher PC32.2 (PMID: 30737130).

We agree that one puzzling aspect of the original version of this study was that Pect mRNA levels being very high on Day 14 HSD, but nonetheless the effects of Pect-KD phenocopied HSD. To resolve this, prompted by Reviewer #2 and #3 concerns, for this revised version we have now performed lipidomic analyses on whole adult flies, when Pect is knocked down (KD) by RNAi in the fat tissue. We now present a new dataset in Figure 6. Two striking changes occu. They are:

1. Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3).
2. Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding increase in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). In contrast, PE 36.2 trends upwards on 14 day HSD (Figure S7C) though not significantly. On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

On day 14, HDF response was increased 70-fold in w1118 flies in NF (Figure 1B; w1118), but only 2.5-fold in lpp>LucRNAi control flies in NF (Figure 7A). This suggests that lpp-gal4 driver lines have a significant effect on HDF response. Using a different fat-body specific Gal4 line would be necessary to validate conclusions.

Regards reduced HDF magnitude, in our experience using UAS-Gal4 reduces HDF response magnitude consistently and cannot be compared to w1118 which is more robust. To account for background differences, we use Uas-Gal4 with control RNAi. It clearly shows differences in HDF response on starvation, but Pect and Pisd RNAi does not (Figure 7A). Hence, given that this experiment internally controls for any changes in HDF response for UAS-Gal4>RNAi, we conclude that HDF response in disrupted in Pect and PISD KD (Figure 7).

We only presented the Lpp-driver in our study, as this driver is the only fat-specific driver that has no leaky expression in other tissues, and is specific to fat as apolpp promoter used to generate this Gal4 line is only expressed in fat tissue (Eaton and colleagues, PMID: 22844248). Other widely used fat-specific drivers, including the pumpless-Gal4 (ppl-Gal4) driver has leaky expression in gut or other tissues (See Table 2 of this detailed study by Dr. Drummond- Barbosa https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7642949/). If the reviewer is aware of a fat-specific Gal4 line, other than Lpp-Gal4, which has a highly specific expression in the fat tissue without leaky expression in other tissues, then we are happy to take onboard the reviewer’s suggestion and try that fat-specific Gal4 that they suggest.

HSD feeding promotes Pect expression (Figure S3C) and global changes in phospholipid levels (Figure 3, 4). Therefore, shouldn't Pect overexpression (not Pect RNAi) in a normal diet mimic HSD feeding state and promote loss of HDF response? Conversely shouldn't knockdown of Pect in HSD rescue loss of HDF response?

We agree that a puzzling aspect is that Pect mRNA levels are significantly elevated in HSD Day-14, but Pect-KD showed displays the inappropriate HDF response. As we have described in our response to this reviewer on Page 19, we believe that Pect-KD and HSD disrupt PE and LPE balance overall but in different ways. Whereas Pect-OE using cDNA expression in fat body does not cause a significant change to any lipid class (Figure S9), and our results suggest that basally higher level of PECT is likely to be protective on HSD with respect to HDF(Figure 7B).

To ensure that we appropriately discuss and clarify this issue, we have now included a section in the discussion - Page 14 Lines 26-33- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

“Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9) , but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

We would have liked to test Pect protein expression on HSD, but since we were unable to access antibodies for Pect published in a prior study (PMID: 33064773) from Dr. Wang’s lab (see Page 10-11, of response to Reviewer #1). Hence, we were unable to test how the proteins levels of Pect correlate with the 250-fold increase mRNA expression.

In conclusion, we hope the reviewer appreciates that our results regarding Pect function are consistent with the main conclusion that achieving the right phospholipid balance between PE and LPE, is critical for an organism to display an appropriate HDF response.

Minor comments: All graphs should plot individual data points and showed as box and whisker plot as much as possible.

Thanks for this suggestion, we have added individual data points to the vast majority of figures in the paper. We have made exceptions to graphs such as seen in figure 1 and FigureS4B-D where we find individual data points add an unnecessary layer of complexity. We hope these changes provide additional clarity and strength to the claims made in this manuscript.

Data for day 14 missing in Figure S4A and S4B.

We have provided Day 14 for the PC composition and PE composition, due to changes in Figures, they are now S7A and S7B.

Reviewer #3 (Significance (Required)):

The interactions between diet-induced obesity, peripheral tissue homeostasis and feeding behavior is an interesting topic that can be addressed using Drosophila. This manuscript demonstrates how fat body Pect levels affect HSD induced changes in hunger-driven feeding response. However, at this point, the functional association between fat body Pect level, global phospholipid level, and loss of hunger-driven feeding response in chronic HSD feeding is not clear.

We hope the revised data, and discussion of the paper, provides well-substantiated functional association on the importance of maintaining phospholipid balance, driven by Pect enzyme, as a critical regulator of hunger-driven feeding behavior. As stated in the revised discussion, the key take home message of our manuscript is that on prolonged HSD exposure PC, PE and LPE levels are dysregulated, the loss of phospholipid homeostasis coincided with a loss of hunger-driven feeding. Following this lead on phospholipid imbalance, we then uncovered a critical requirement for the activity of the rate-limiting PE enzyme PECT within the fat tissue in controlling hunger-driven feeding.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Kelly et al. show that the difference between the feeding behavior of fed and starved flies (hunger-driven feeding; HDF) is absent in animals fed a high-sugar diet (HSD) for two weeks or more. The disappearance of HDF with HSD coincides with changes in phospholipid profiles caused by HSD. Furthermore, RNAi-mediated downregulation of PECT in the fat body-a key enzyme in the PE biosynthesis pathway-phenocopies physiological effects of HSD. Moreover, downregulation or overexpression in the fat body abolishes or induces HDF, respectively, abolishes or induces HDF, respectively, independent of HSD treatment.

Overall, the manuscript is well-written and the phenotypes are clear. However, I have major concerns regarding the authors' interpretation of the data and their conclusion. Most importantly, while it is clear that the authors' high-sugar dietary treatment affects feeding behavior and physiology, I am not convinced that the changes can be considered "hunger-driven"-which is central to the main point of the manuscript. Therefore, it is my recommendation that the authors substantially revise the manuscript by either showing additional/re-analyzed data that rule out alternative hypotheses, or rewriting the manuscript keeping alternative interpretations in mind.

Major comments:

1. The data do not sufficiently show that the long-term HSD regime disrupts "hunger-sensing." The manuscript should address alternative hypotheses by showing raw instead of normalized data, rewriting the manuscript with a new central conclusion, or running additional experiments that actually show a defect in hunger-driven response.
   • a. The main results that the authors rely on for the argument is that the ratio of feeding events that the starved and non-starved flies eat is different between the groups fed normal or HSD. However, because the authors only show normalized data (normalized to non-starved flies; Fig. 1), it is difficult to tell whether the change is due to a chronically increased feeding in non-starved HSD flies-maybe in perpetual hunger-like allostasis-or dampened starvation response. Indeed, the data shown in Fig S1 show that flies fed HSD for as short as 5 days show more frequent feeding events compared to age-matched controls fed normal food. It is possible that because the HSD-fed flies eat more than NF-fed flies, even without being starved, the ratio of starved/non-starved feeding is lower in the HSD-fed group-due to changes in the denominator, rather than the numerator.
   • b. It is also possible that the HSD-fed flies are simply not in as big an energy deficit physiologically, due to the increased fat deposits they've accumulated (as the authors show later in the manuscript). It may take longer for the fat HSD flies to reach substantial energy deficiency than the NF flies, but they still may eventually be able to appropriately respond to hunger, just like NF flies. In such case, it would be a misnomer to call this behavioral change a 'defect in hunger-driven feeding behavior.' Maybe an experiment with a dose-response curve of "hunger driven feeding response" as a function of duration of starvation would help?
2. How can you be sure that lower Dilp5 immunofluorescence is indicative of increased Dilp5 secretion? Wouldn't decreased production of dilp5 also have the same results?
   • a. Also, the authors should state in the main text that it is Dilp5, not just any Dilp.
3. Data presentation:
   • a. Sometimes the data are normalized to NF (Fig 4B-C), sometimes not (ex. Fig 4A, S4C). Unless there is a specific rationale for the data transformation, it would be more appropriate to show untransformed data (ex. Fig 4A, S4C), especially as the authors use two-way ANOVA to determine significance. Only showing the differences implies comparison against a hypothetical mean (i.e. μ0=0), not between two group means.
   • b. Some figures show both individual data points and summary statistics (mean, SD, ... ex. Fig 2A)-which I believe is ideal-but some show only one or the other (ex. Fig 2B, no summary statistics; Fig. 3, no data points. The manuscript would read more convincing if data visualization is consistent across figures.

Minor comments:

1. High sugar diet: what is the actual sugar concentration in the NF v. HSD diets? The authors write that the HSD diet contains "30% more sugar" than the NF, but providing the final sugar concentrations-sucrose or others-would be informative for other scientists studying the effect of high sugar diets.
   • a. Additionally, the definition of HSD is inconsistent. Main text (Page 5, line 17) states that their HSD is "60% more sugar than normal media," whereas the figure legend (Fig 1) and the Methods state that the HSD contains "30% more sugar."
2. Starvation medium: please provide justification for why the authors used 1% sucrose/agar for starvation medium, instead of plain agar/water that most labs use. At least clarify and provide a reference for the claim that the 1% sucrose/agar "is a minimal food media to elicit a starvation response."
3. PECT mRNA level is higher with HSD. This is surprising because not only, as authors mention, is increased PC32.2 with HSD suggests lower PECT activity, but also because PECT RNAi phenocopies long-term HSD in HDF behavior, lipid morphology, FOXO accumulation in fat body. The authors speculate that the data "likely shown an upregulation in an attempt to mediate the PECT dysregulation occurring at the protein level." If that were true, a western blot may be informative. Zhao and Wang (2020, PLoS Genetics) generated a PECT antibody that seems compatible with western blot applications. That being said, I don't think such data is critical for the manuscript. I mention this simply as a suggestion for the authors.
   • a. page 8, line 22-23, did you mean to write "Given how PC32.2 is elevated after 14 days of exposure to HSD, we assumed that PECT levels would be low for flies under HSD," not "high?" Otherwise the subsequent 2 sentences don't make sense.

Significance

The work is potentially novel and interesting, but at this stage it's difficult to interpret what the phenotype signifies. Although the manuscript could be revised simply by modifying the text, experimentally addressing the concerns would significantly improve the work.

The co-reviewer and I have expertise in Drosophila neurobiology and behavior.

Referees cross-commenting

Hi all, although the reviews hit upon some overlapping, but mostly different points, I agree with all of the concerns raised. There's some really interesting stuff here but some of the results, as presented, don't make sense. It's possible this will be clarified by revising the text, although I suspect it's more likely that the authors will have to add a number of the experimental suggestions made by the reviewers.

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Reply to the reviewers

*Reviewers comments in italics *

We thank all reviewers for their positive and encouraging comments and criticisms to improve our work. Here we present a reviewed version of the manuscript according to the comments risen.

• Reviewer #1 (Evidence, reproducibility and clarity (Required)): This is an interesting paper that identifies Tns3 as a potential effector of oligodendrocytes differentiation based on an ingenious strategy comparing regulatory binding sites of known master regulators of differentiation, and then shows using in vivo genetics that this role is indeed correct. Next, a potential mechanism is identified by showing co-localization with beta 1 integrin, known to regulate apoptosis of newly-formed oligodendrocytes. The results are well illustrated and the experiments performed with appropriate power using a broad range of techniques that combine in silico, in vitro and in vivo work to great effect.

I think this represents an important contribution that will be of significant interest to neuroscientists - the mechanisms regulating oligodendrocytes generation remain poorly understood and the evidence that this contributes to adult learning (adaptive myelination) and CNS regeneration makes this a key question. I would suggest that the following are considered before publication: We thank the reviewer for this positive comments and critics to improve the manuscript. The work describing the KO mice that were not used as they proved unsuitable need not be described - it breaks the logical flow.*

In agreement with the reviewer comment, we have reduced this part to a sort paragraph indicating that our analyses of several Tns3 constitutive KO lines showed developmental lethality and possible genetic compensation in Tns3 expression, leading us to conclude them inappropriate tools to study Tns3 function in oligodendrogenesis. We have summarized the data in Fig. S7 and the description in the method section.

It would be useful to compare the extent of cell death in the Tns3 cKO mice with that described in the alpha6 integrin KO and the integrin beta1 cKO (the Colognato and Benninger papers). Do they match? If not (and I suspect the Tns3 cKO death is greater) could other mechanisms be downstream of the Tns3?

In agreement with the reviewer comment, we have added the following paragraph to the discussion:

‘Knockout mice for integrin-a6 present a 50% reduction in brainstem MBP+ OLs at E18.5, just before they die at birth, accompanied by an increase in TUNEL+ dying OLs (Colognato et al, 2002). Similarly, conditional deletion of integrin-b1 in immature OLs by Cnp-Cre also leads to a 50% reduction in cerebellar OLs at P5, with a parallel increase in TUNEL+ dying OLs (Benninger et al., 2006). Therefore, given that Tns3-induced deletion in postnatal OPCs also leads to 40-50% reduction in OLs in both grey and white matter regions of the postnatal telencephalon (this study), paralleled by similar increase in TUNEL+ apoptotic oligodendroglia, we suggest that Tns3 is required for integrin-b1 mediated survival signal in immature oligodendrocytes.’

I'm not sure why the authors argue that the activation of beta 1 would not be informative experiment? This will regulate actin dynamics just as it regulates other integrin signaling pathways. Indeed, I would argue that an integrin activation experiments would be a neat way to prove mechanism (as it would be predicted to rescue the Tns3 cKO phenotype).

In agreement with the reviewer comment, we have removed this sentence: ‘If so, exogenous activation of integrin a6b1 in cultured OPCs by Mn2+ (Colognato et al., 2004) would not be expected to increase oligodendrogenesis in Tns3-iKO oligodendroglia.’

In an effort, to understand Tns3 function by acute Tns3-deletion in postnatal OPCs, we have compared the transcriptome of Tns3-iKO oligodendroglia compared to control cells, and we present these results in figure 7 pinpointing deregulated genes leading to reduced oligodendroglial differentiation, integrin dysregulation, increase apoptosis, and conflicting cell cycle signaling, and leaving for further studies the full characterization how the loss of Tns3 leads to the deregulation of these processes.

Can the authors provide any data on GM oligos and their OPCs? Is the requirement for Tns3 the same, and if so what might the implications be in the adult where new oligodendrocytes are being generated throughout life?

Indeed, in our analyses of Tns3-iKO mice, we provide quantifications of the cortex as a grey matter territory, showing a similar 40-50% reduction in OLs as in white matter areas (corpus callosum and fimbria, and mixed regions such as the striatum.

I note in S13 that integrin beta1 is not highly expressed in human oligos at the time in question. Does this call into question the relevance for human disease?

We realize that scRNAseq plots are never easy to interpret but it is important to note that the levels of expression are coded by the intensity of the color scale, while the surface of the dot plots indicate the experimental sensitivity to detect transcript expression in a larger or smaller proportion of the cells in a given cluster/cell type (due to the drop out limitation of current single cell RNA-seq technologies). Considering this, please note that beyond a stronger expression in neural progenitor cells (NPCs, blue color), integrin-b1 (Itgb1) transcripts are expressed at medium to high levels (green to blue) in human immature OLs (Fig. S13B), similar to their pattern of expression in mouse oligodendroglia (Fig. S13A).

Reviewer #1 (Significance (Required)): See above

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

*In this article, the authors identify and characterise Tensin3 (Tns3) as a target of key oligodendroglial transcription factors driving differentiation in the mouse. They use multiple transgenic models to describe loss of function, and suggest Tns3's action through integrin B1 signalling, with the key function being oligodendroglial survival.

There is extensive and impressive work here, including identification of Tns3 by ChIPseq, expression of Tns3 in brain development, analysis of human (ES-derived) and mouse scRNAseq to infer timing of expression in the differentiation pathway, generation of V5-tagged Tns3-KI mice to overcome antibody limitations, identification of its expression in mouse remyelination, generation of a new Tns3KO mouse, in vivo Crispr Tns3KO in development, generation of a conditional KO, for deletion in adulthood, and finally some culture work to investigate potential mechanisms of actions. The bottom line is that Tns3 is required for survival of OPCs and immature oligodendrocytes in development/remyelination in mouse at least, and loss leads to apoptosis (through p53 increase and loss of integrin-B1 signalling), leading to a failure of proper differentiation.

The experiments are carefully done, convincing and the tools generated impressive. There is clearly more to be done on clarifying the mechanism of action of Tns3, but I do not think further experiments on this topic are needed for this paper - they can wait for the next.*

We thank the reviewer for the positive and encouraging reviewing comments. In an effort, to understand Tns3 function by acute Tns3-deletion in postnatal OPCs, we have compared the transcriptome of Tns3-iKO oligodendroglia compared to control cells, and we present these results in figure 7 pinpointing deregulated genes leading to reduced oligodendroglial differentiation, integrin dysregulation, increase apoptosis, and conflicting cell cycle signaling, and leaving for further studies the full characterization how the loss of Tns3 leads to the deregulation of these processes.

My only query is whether the expression of Tns3 is also in immature OLs in human brain (rather than human ES-derived OLs). This should be easily checked with interrogation of online Shiny apps from already published snRNAseq from various groups on human post mortem adult brain, but if not present then in also baby/fetal brain. This would be interesting and may well be different from the ES_derived cells which tend to be very immature and would add interest to the possible translational impact.

According to the suggestion of the reviewer, we analyzed 69,174 snRNAseq GW9-GW22 from fetal cerebellum,; Aldinger & Miller, 2021; https://doi-org.proxy.insermbiblio.inist.fr/10.1038/s41593-021-00872-y), which we present now in Figure S3, finding a cluster of cells expressing iOL markers, including NKX2-2, TNS3, ITPR2, and BCAS1, similar to the hiPSCs-derived iOL1/iOL2 clusters and mouse iOL1/iOL2 clusters shown in Fig. S2.

We also analyzed other datasets without finding iOLs given their age or numbers, including:

• Immunopanned PDGFRA+ cells from human cortex GW20-GW24 (2690 cells, Huang and Kriegstein, Cell 2020) finding OPCs but not iOLs.

-The recently published dataset from GW8-GW10 human forebrain oligodendroglia (van Brugen & Castelo-Branco, Dev Cell 2022; https://doi.org/10.1016/j.devcel.2022.04.016) containing OPCs but not iOLs.

-The GW17 to GW18 human cortex (40,000 cells, Polioudakis & Geschwind, 2019, https://doi.org/10.1016/j.neuron.2019.06.011) containing OPCs but not iOLs.

Reviewer #2 (Significance (Required)): This work extends our knowledge of oligodendroglial differentiation, links it to the ECM and provides interest in manipulating this in diseases including glioma. My expertise: myelin, oligodendroglia, remyelination, human neuropathology

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

see below Reviewer #3 (Significance (Required)): Using purified oligodendrocytes target genes of key regulators of oligodendrocyte differentiation were analyzed, which led to the identification of Tensin-3. The authors performed a detail characterization of Tensin-3 expression. They found that Tensin-3 is highly expressed in immature mouse and human oligodendrocytes. Interestingly, Tensin-3 is selectively enriched in immature oligodendrocytes, and not present at detectable levels in OPCs and mature oligodendrocytes. Subsequently, the authors characterized Tensin-3 function by a series of knockdown approaches in vitro and in vivo. These series of experiments revealed an essential function of Tensin-3 in supporting oligodendrocytes survival. In the absence of Tensin-3 a large fraction of oligodendrocytes undergo apoptosis while differentiating to mature oligodendrocytes. This is a remarkable study applying an impressive array of methods that led to an important discovery in the field of oligodendrocyte biology. The main advances for the field are: 1) identification of a novel marker for premyelinating oligodendrocytes, 2) elucidation of Tensin-3 as a pro-survival factor in oligodendrocytes differentiation, 3) evidence of link of Tensin-3-integrin signal in survival of oligodendrocytes. The data is well presented and organized, and the paper well written. I recommend publication with only minor suggestions for a revision:

• *

We thank the reviewer for this positive comments and critics to improve the manuscript.

In Figure 2, only images are shown, and the data is referred to as highly expressed or strong co-localization. Even if the data looks clear, the authors should provide some quantification of the data in the figure.

We thank the reviewer for his comment and we have now provided a quantification of the fraction of Tns3+ cells expressing different markers of oligodendrocyte lineage progression/stages, and the percentage of each stage expressing Tns3.

Figure 3 is given too much weight in the manuscript text. I would recommend to shorten the text in the result section, and to move this figure to the supplement as it does not advance the story. It mainly shows that the KO mice still express transcripts in the brain. Were the transcripts lost in peripheral tissue?

• *

As mentioned above, in agreement with the reviewers #1 and #3 comments, we have reduced this part to a sort paragraph indicating that our analyses of several Tns3 constitutive KO lines showed developmental lethality and possible genetic compensation in Tns3 expression, leading us to conclude them inappropriate tools to study Tns3 function in oligodendrogenesis. We have summarized the data in Fig. S7 and the description in the method section.

Page 11: the authors describe in the text how the floxed allele was generated. This should be shifted to the supplement.

According to reviewers suggestion, we have moved the description of Tns3 floxed allele generation to the Methods section. Page 16: the authors refer to Bcas1 as a problematic marker for immature oligodendrocytes, because the transcript is also expressed in mature oligodendrocytes. The authors are correct that the transcript is expressed in mature oligodendrocytes. However, the proteins changes its localization when oligodendrocytes mature. On protein level, it is valuable and a selective marker, as antibodies only label pre-myelinating and actively myelinating cells. In mature oligodendrocytes, antibodies against Bcas1 do not label the cell, only myelin. The text is misleading and needs to be corrected.

In agreement with reviewers comment we have modified the text as follows: ‘An optimized protocol for immunodetection using Bcas1-recognizing antibodies has been shown to label iOLs (Fard et al., 2017).’

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

The manuscript by Tran et al. describes the mechanism by which IFNa treatment prevents the development of liver CRC metastasis in several mouse models. They show how continuous administration of IFNa strength liver vascular barrier by a direct effect on endothelial cells and avoids the trans-sinusoidal migration of tumour cells.

Major points:

1. Authors use an elegant orthotopic model of liver metastasis to confirm the effect of continuous IFNa on hepatic colonization (Fig.3). Although they extensively characterize the metastatic lesions, they do not show data on the potential impact of IFNa treatment in the primary caecum tumour. Authors should clarify if the described effects are taken place in the liver or/and in the caecum. It would be interesting to show if IFNa affects the primary tumour size, the extravasation of cancer cells and the immune infiltration since all these factors could have an impact in the number of liver lesions.

We thank the reviewer for acknowledging the importance of our results particularly in the context of the orthotopic mouse model we developed. We agree that displaying the results of continuous IFNα therapy on primary intracecal tumors, as well as the results pertaining to the few mice that develop microscopic or macroscopic liver metastasis, is important for the interpretation of our work. Thus, we evaluated the dimension of primary intracecal CRC lesions (Fig 3D,E) and we performed additional IHC characterization of the primary tumors (Fig S4A,B). The analysis showed that the dimension of the primary lesions and the markers we analyzed were non significantly modified by continuous IFNα therapy (Fig 3D,E and Fig S4A,B). These results favor the hypothesis that IFNα therapy does not modify the number of cells that spread from the primary tumors and seed into the liver, but it rather impinges on the intravascular containment of CRC cells circulating within the liver (Fig 3F). As said earlier, the data also highlight the possibility that CRC tumors may become refractory to IFNα or that the dose and schedule we adopted does not significantly affect the growth of established liver CRCs at late time points. The data are also consistent with results obtained with MC38Ifnar1_KO CRC cells indicating that continuous IFNα therapy does not require Ifnar1 expression by tumor cells to exert its antimetastatic function (Fig 4A,C-D). This is also in line with the high IFNα concentrations required to activate the "tunable" direct antiproliferative functions of this cytokine that exceed those achieved in our system (Catarinella et al, 2016; Schreiber, 2017). Text has been added in the revised manuscript at lines 175-197 and in the discussion lines 425-431.

1. Figure 3f right shows liver images without any obvious metastatic lesion. Since authors are analysing the effect of IFNa treatment in proliferation, vascularization and immune composition in liver tumours, they may show and quantify images with metastatic lesions and restrict the analysis to the tumour area.

Since the main finding of our manuscript regards the prevention of hepatic colonization by continuous IFNα therapy, we think that the original data presented in Fig 3G,H are representative of the overall efficacy of our strategy that confers protection in up to 60% of the mice carrying intramesenteric tumors of increasing dimensions (Fig 3H). We have thus maintained our original results, adding the quantification of all IHC data on groups of Sham control livers (n=6), as suggested. In any case, we also included the same IHC characterization of the few and small intrahepatic lesions that have bypassed the intravascular antimetastatic barrier (Fig S4C,D). Indeed, in agreement with the results observed in primary intracecal lesions, these metastatic lesions that developed in IFNαtreated mice showed similar markers of cell proliferation, neoangiogenesis, F4/80 macrophages and CD3+ T cells, as control lesions detected in NaCl-treated mice. Once again, the results highlight the possibility that CRC tumors, once established as micro/macroscopic metastases, may become refractory and resistant to IFNα therapy by downregulating the Ifnar1 in various components of the tumor microenvironment (Boukhaled et al., 2021; Katlinski et al., 2017). Text has been added in the revised manuscript at lines 175-197 and in the discussion lines 496-515.

1. Authors analyse the recombination efficiency of different mouse CRE lines by non-quantitative methods (PCR of hepatic genomic DNA and GFP expression by immunofluorescence in healthy liver). Since PDGFRβ-Cre/ERT2 and CD11c-Cre lines are used to exclude a role of IFNa on the targeted cells, authors should provide stronger evidences to support this. They may consider studding the ablation of Ifnar1 in FACS sorted fibroblasts and myeloid cells. Moreover, it would be important showing the proportion of GFP+ cells in the sorted populations to understand how broadly these stromal populations are targeted.

We thank the referee for raising this important issue, which is related to the relative efficiency of Ifnar1 recombination in each of the Cre-expressing mouse models we have used in the study. To this regard, we newly performed an extensive colocalization analysis quantifying the percentage of GFP+ cells that colocalize with cell specific markers (i.e., PDGFRβ, CD11c, F4/80 and CD31) of the various mouse models (PDGFRβCreERT2, CD11cCre and VeCadCreERT2, respectively) crossed with RosaZsGreen reporter mice. Colocalization analysis of GFP in the different systems was performed using the ImageJ “colocalization” algorithm developed by Pierre Bourdoncle (Institut Jacques Monod, Service Imagerie, Paris; 2003–2004). The method allows the generation of unsupervised profiles of co-localized pixels between two channels. This methodology has been included in the section Methods and Protocols, line 806-809. Of note, we observed an almost complete recombination in liver fibroblast (GFP+/PDGFRβ+), with about 98.2 ± 0.72% hepatic stellate cells that co-expressed GFP+ and PDGFRβ+ signals (see the new Fig S5E). Similarly, hepatic DCs (GFP+/CD11c+) had 94.17 ± 2.16% colocalization, while F4/80+ KCs or LCMs (GFP+/F4/80+) colocalized in 78.14 ± 5.03% (see the new Fig S5E). Finally, HECs, including LSECs, (GFP+/CD31+) showed 85.3 ± 5.03% colocalization (see the new Fig S5E,F), with no expression of GFP signals in cells other than CD31+. Note that these values indicate an almost complete colocalization of the Cre recombinase in the target cell types analyzed (see representative IF shown in Fig S5E). Text has been added in the revised manuscript at lines 225-233. Moreover, DEGs analysis between NaCl-treated VeCadIfnar1_KO and Ifnar1fl/fl HECs showed a significant downregulation of Ifnar1 expression in CD31+ VeCadIfnar1_KO cells, with a log2 fold-change of -0.387 and an adjusted p-value of 0.033, further confirming Cre recombination in HECs isolated from VeCadIfnar1_KO mice (as depicted in the heatmap of Fig 6B; the 12th gene of the Type I IFN response is Ifnar1). We have prepared all source images at higher dimension to better appreciate the colocalization within liver microvasculature. In addition, we performed several flow cytometry analyses to identify liver cell populations of Cre-recombinant mice that express Ifnar1. Unfortunately, the predicted low cellular surface expression of this molecule coupled with the experimental conditions needed to extract viable non-parenchymal cells from the liver have prevented us from obtaining informative results.

1. Ifnar1 ablation in VeCad+ cells prevents the effect of IFNa on tumour growth (Fig. 4d), suggesting the existence of anti-tumour mechanisms beyond the effects on hepatic colonization. Authors may consider checking proliferation, vascularization and immune infiltration in these tumours to enhance their conclusion.

We fully agree with the referee’s concern and as above mentioned, we have followed his/her suggestion and examined the existence of antitumor mechanisms beyond the effects on hepatic colonization in VeCadIfnar1_KO mice treated with NaCl or IFNα. To this end, 4 NaCl-Ifnar1fl/fl, 7 IFNα-Ifnar1fl/fl, 4 NaCl-VeCadIfnar1_KO and 4 IFNα-VeCadIfnar1_KO mice were intrasplenically injected with MC38 CRC cells (Fig S7A,B). Twenty-one days after injection, mice were euthanized and their livers analyzed for tumor size, proliferation, signs of angiogenesis (as denoted by CD34 staining) and immune infiltration (F4/80+ macrophages and CD3+ T cells). Consistent with data presented in Fig 4D, histological analysis showed that Ifnar1fl/fl mice did not develop liver metastases in IFNα-treated mice. Furthermore, metastatic lesions detected in VeCadIfnar1_KO mice treated or not with IFNα did not show significant differences in Ki67 positivity, CD34 staining or the amount of F4/80+ resident macrophages and CD3+ T cells. This further supports that the antimetastatic potential of IFNα therapy may be primarily depend on the inhibition of hepatic trans-sinusoidal migration, a limiting step in the metastatic cascade that could secondarily influence colonization and outgrowth (Chambers et al, 2002). Corresponding text has been added at lines 248-252.

1. Immune properties of LSECs are analysed in vivo by using a mouse CRE line that targets all endothelial cells, including those ones located in lymphoid organs, and evaluating T cell composition in the spleen. I found difficult to conclude that these properties are exerted directly by LSECs and not by other endothelial cells in vivo. To clarify the local effect of LSECs in modulating anti-tumour immunity, T cell composition and activation should be checked in tumours shortly after tamoxifen administration.

We thank the reviewer for pointing out this issue, which cannot not be tested directly because - as also mentioned by reviewer 2 - LSEC-specific Cre-recombinant driver mice do not exist . As also indicated in the cited literature, central memory T cells accumulate after peripheral priming in secondary lymphoid organs such as the spleen (Sallusto et al, 2004; Stone et al, 2009; Yu et al, 2019). To this end, the generation and regulation of antitumor immunity is a highly orchestrated multistep process involving the uptake of tumor-associated antigens by professional APCs, their time-consuming migration to draining lymph nodes and the generation of protective T cells. Unlike other APCs, HECs/LSECs do not need to migrate to draining lymph nodes to activate effector T cells, leading to a rapid intrahepatic CD8+ T cell activation. In this context, LSECs must not only efficiently uptake, process and present CRC-derived antigens coming from intravascularly contained tumor cells, but they also require the attraction and retention within the liver micro-vasculature of T cell populations necessary for the generation of effective antitumor immune responses, where chemokines play an important role (Lalor et al, 2002). As shown in Fig 6A-C, two prominent chemokines (Cxcl10 and Cxcl9) required for T cell recruitment to the liver are specifically upregulated only in HECs/LSECs from IFNα-treated Ifnar1fl/fl mice, whereas HECs from VeCadIfnar1_KO mice maintained low expression of these chemoattractants in both NaCl- and IFNα-treated mice. These data are also consistent with the in vitro cross-priming results (see Fig 7A,B) showing that in the absence of IFNα, HECs have a low capacity to prime naïve T cells (Katz et al, 2004), indicating that LSEC-primed by tumor-derived antigens coming from apoptotic intravascular CRC metastatic cells play an important role in inducing tolerance (Berg et al, 2006; Katz et al., 2004), especially when CRC cells quickly extravasate and position within the space of Disse, likely becoming less accessible to intravascular patrolling by naïve and effector T cells (Benechet et al, 2019; Guidotti et al, 2015). On the contrary, in IFNα-treated Ifnar1fl/fl mice, CRC cells are rapidly contained in the liver microvasculature (Fig 5A,B) with CRC-derived antigens that could be immediately taken up by LSECs due to their anatomical proximity and efficient endocytosis capacity, which is among the highest of all cell types in the body (Sorensen, 2020). Here, the continuous sensing of IFNα by LSECs upregulates several genes related to antigen processing and presentation pathways (Fig. 6B,D), leading to efficient cross-priming of tumor-specific CD8+ T cells to the same extent as professional APCs, such as splenic DCs (Fig 7B). Text has been added in the revised manuscript at lines 496-515. Finally, regarding the suggestion to analyze the role of HECs/LSECs in inducing antitumor T cell immunity shortly after tamoxifen administration, while we agree that it would be interesting to analyze HEC/LSEC-mediated T cell activation by treating NaCl- and IFNαtreated Ifnar1fl/fl and VeCadIfnar1_KO mice with tamoxifen after CRC cell injection, we would like to point out that tamoxifen treatment will not only induce Cre recombination and Ifnar1 loss on endothelial cells but it may also induce several “off-target” effects complicating the interpretation of the results. Indeed, tamoxifen is known to i) inhibit the in vitro proliferation of several CRC cell lines (Ziv et al, 1994), ii) impair the growth of CRC liver metastases in vivo (Kuruppu et al, 1998) and iii) modify matrix stiffness to reduce tumor cell survival (Cortes et al, 2019). Further, as IFNα modifies the hepatic vascular barrier and the accessibility of antigens by LSECs, the specific timing of tamoxifen treatment could also affect the immunological consequences of Ifnar1 deletion making these experiment impractical. For these reasons, we’d like not to perform the suggested experiment with tamoxifen.

Reviewer #1 (Significance):

The conclusions of this study are consistent with previously published literature and the biological insights are potentially useful to the cancer biology community.

Reviewer #2 (Evidence, reproducibility and clarity):

In this study Dr. Sitia's group investigated the effect of IFNα1 as perioperative agent preventing liver metastasis formation of colorectal carcinoma (CRC). To this end, various mouse models were used such as liver colonization models, i.e. intrasplenic and mesenterial injections of MC38 and CT26 CRC cell lines. Besides, spontaneous metastasis of CRC was analyzed by orthotopic injection of MC38 into the cecum. To study the influence of IFNα1 in these settings mini-osmotic pumps releasing IFNα1 were used. Moreover, conditional mouse models with a cell-type specific deficiency of Ifnar1 were compared. Altogether, the application of IFNα1 led to a reduction in liver colonization of CRC in all models studied. This was ascribed to decreased trans-sinusoidal migration of CRC and increased cross-priming by LSEC entailing in T cell activation.

Major comments:

Overall the study is well performed and the major conclusions seem to be drawn well. However, there are certain points I like to address:

• First, the authors started their experiments with MC38 and CT26 CRC cell lines. At the end they just applied MC38. The rational behind this should be clearly stated. Second, as in their previous publication (Catarinella et al, 2016) F1 hybrids of C57BL/6 x BALB/c mice were used for the experiments. However, I believe that the genetic heterogeneity might be strongly increased by this approach which might lead to difficult reproducibility of the results.

We thank the referee for raising this important issue; additional text describing the reason of our choice has been introduced at lines: 203-205. We respectfully disagree with the comment that CB6F1 hybrids may increase genetic heterogeneity and impair reproducibility of our results. Each CB6F1 hybrid individual is genetically identical to its littermates, sharing 50% of genes of each parental mouse line and being tolerant to reciprocal MHC-I genes (thus permitting the correct engraftment of both cell lines). We agree that the use of mismatched backcrosses after the F1 generation would increase genetic heterogeneity and thus may affect outcome. This is also the reason why we could not perform experiments with CT26 in the Ifnar1fl/fl conditional lines that are in C57BL/6 background and would have needed at least 10 generations of backcrossing in the BALB/c background before being suitable to such experiments. Finally, all experiments described in Fig 4, 5, 6 and 7 were performed in C57BL/6 mice using MC38 CRC cells with results that reproduced those obtained in CB6F1 hybrids, and very similarly to what we have previously reported with MC38 in C57BL/6 mice (see Fig 5 (Catarinella et al., 2016)).

• At page 16 the authors conclude that "patients suffering from chronic liver fibrotic disease... display lower incidence of hepatic metastases". In the community there is contradictory data (see Kondo et al, BJC, 2016, https://www.nature.com/articles/bjc2016155). This should be precisely discussed, otherwise this claim should be removed.

We thank the referee for raising this issue and modified the discussion accordingly. Text has been added in the revised manuscript at lines 455-457.

• In the discussion section the interplay of other cell types within the hepatic niche should be stated. For example, in Toyoshima's study a direct anti-tumoral effect of dendritic cells releasing IFNα1 was demonstrated (see Toyoshima et al, Cancer Immunol Res, 2019, https://aacrjournals.org/cancerimmunolres/article/7/12/1944/469540/IL6-Modulates-the-Immune-Status-of-the-Tumor). This further strengthens your data.

We agree with the reviewer's suggestion and added new text to recognized the interplay between different cell types such as dendritic cells within the hepatic niche (see new text at lines 505-515).

• Last, multiple times the authors write about data that is "not shown". Please either include these data in the manuscript or delete corresponding phrases because it is not possible for the reader to scrutinize it.

We fully agree with the referee’s concern and displayed all “not shown results” in Fig S1E and Fig S9C-I.

• Besides, I suggest additional experiments further substantiating the study:
• To see if this effect of IFNα1 is cell type-specific liver metastasis of other solid tumors such as breast cancer or melanoma should be investigated.

We agree with the reviewer's suggestion, as also indicated in our original discussion. We believe that additional experiments with other solid tumor cell lines would be important to generalize the potential of perioperative IFNα therapy. In particular, we believe that pancreatic ductal adenocarcinoma (PDAC), a highly lethal disease that most commonly metastasizes to the liver (Lambert et al, 2017), may benefit from our approach. It should be noted, however, that the pleotropic nature of IFNα allows this cytokine to inhibit tumor growth by several mechanisms. Above all, the ability of IFNα therapy to directly reduce tumor growth depends on the relative surface expression of Ifnar1 on each tumor cell and the ability to maintain such expression in the harsh tumor microenvironment during IFNα therapy. As the degradation of Ifnar1 by CRC tumors has been well described (Katlinski et al., 2017), it is possible that CRC tumors thus escaping the antitumor properties of endogenous type I interferons may respond less efficiently to therapeutic IFNα regimens such as those herein described. This notion is consistent with our data on primary orthotopic tumors (Fig. 3D,E), which are no longer responsive to continuous IFNα therapy as early as 7 days after implantation of CT26LM3 cells. In addition, the definition of the HEC/LSEC antimetastatic barrier has been possible only because CRC cells are not directly susceptible to the IFNα antiproliferative activity, which we observed in vitro at extremely high IFNα dosages (Catarinella et al., 2016) but not in vivo (as formally demonstrated by using MC38Ifnar_ko cells, Fig 4A). At any rate, we followed the reviewer’s suggestion and performed an additional experiment in which we intramesenterically injected the PDAC cell line Panc02 (H-2b, C57BL/6-derived) (Soares et al, 2014) into C57BL/6 mice 7 days after of NaCl or IFNα therapy initiation. As shown below, MRI analysis at day 21 showed that none of the IFNα-treated Panc02 challenged mice developed metastatic lesions, while NaCl controls displayed a high metastatic burden that required euthanization for ethical reasons of about 67% of these mice shortly after MRI analysis. These data indicate that perioperative IFNα therapy completely curbs metastatic development in IFNα-treated PDAC animals. The notion that these cells may be more IFNα-susceptible than CRCs may well depend on the relative capacity of the former cells to maintain Ifnar1 expression, as suggested by others (Zhu et al, 2014). Properly addressing the reviewer’s comment would thus require extensive investigations involving the establishment of new mouse models of metastases from other solid tumors, starting from the in vitro and in vivo regulation of surface Ifnar1 expression in each tumor cell. We strongly believe that this work has merit but we think that it should be reported separately.

• The authors applied a broad range of cell type-specific mice. However, a thorough characterization of the deletion of Ifnar1 in the corresponding cell types is missing. This is crucial for the manuscript.

We fully agree with the referee’s concern and as previously mentioned, we have improved the characterization of Ifnar1 deletion (see response to the same critique received from reviewer 1, comment 3).

• The capillarization of the hepatic vascular niche is a crucial point in this story. I believe that the hepatic endothelium should be further characterized by additional vascular markers.

In response to the reviewer’s suggestion, we have included in our analysis the characterization of Lyve-1, a marker of hepatic capillarization (Pandey et al, 2020; Wohlfeil et al, 2019). Indeed, IFNα treatment of Ifnar1fl/fl mice significantly increased the expression of Lyve-1, whereas IFNα treatment of VeCadIfnar1_KO mice showed no effect (Fig S9A,B), further corroborating our findings. Text has been added in the revised manuscript at lines 291-294. To better aid readers, we have prepared high-resolution images for each IF channel and have provided these data as source date for Fig S9A.

• Last, the data and methods appear adequately presented and experiments seem to be reproducible. Just in Figure 4 the exact number of mice and replicates are not clearly presented. Otherwise, everything is fine.

We thank the reviewer for raising this issue, which apparently was not properly described in our original submission. We have now included the exact number of mice in each experimental group in the figure legend to Fig 4.

Minor comments:

Overall the text and figures are accurately presented. However, I would like to add further minor comments:

• In Fig. 1 you present the IFNα dosing regimen. How do you explain the decrease in serum IFNα after day 2? Besides, the data points at day 0 should be excluded since measuring startet from day 2! Why did you decide to treat for seven days until the start of the experiment? One could think 2 days might already be enough.

We thank the reviewer for raising these important points. Regarding the pharmacokineticpharmacodynamic (PK-PD) behavior of our approach, we do not believe that MOP reduced its pumping efficacy after day 2 (Theeuwes & Yum, 1976), nor that counterregulatory mechanisms, such as the induction of anti-IFNα blocking antibodies, occurred in such a short time frame (Wang et al, 2001). It is neither feasible that IFNα treatment significantly downregulated Ifnar1 in the liver (as demonstrated by pSTAT1 activation after MOP treatment in Fig S1E). Rather, our results reflect the PK-PD behavior of other long-lasting formulations of IFNα, which depend on intrinsic pharmacological properties of IFNα already described in (Jeon et al, 2013). Text has been added in the revised manuscript at lines 110-112. We also corrected the figures in which we quantified serum IFNα. Indeed, blood was drawn one day before MOP implantation rather than on the same day of surgery to avoid additional blood loss, which could be a source of unnecessary stress for the animals. Therefore, we corrected the results section and Fig S1A-C and Fig 1A,B. The decision to start treatment 7 days rather than 2 days before seeding was made for several reasons: i) this study follows our previous gene/cell therapy approach, in which the time interval between reconstitution of the transduced bone marrow with Tie2-IFNα and tumor challenge was at least 7-8 weeks. We therefore thought that 7 days might be a sufficient/necessary time period to induce similar phenotypes in the liver after continuous IFNα administration; ii) 7 days is a time frame compatible with the perioperative period in humans (Horowitz et al, 2015). Furthermore, the side effects that patients may experience after IFNα therapy are generally limited to the first few days after administration, allowing patients to benefit from IFNα-induced vascular antimetastatic barriers at the time of surgery without potential side effects of IFNα. Because oncologic guidelines recommend starting adjuvant chemotherapy at least 4 weeks after surgery in stage 2-3 CRC patients at risk of later developing liver metastases (Engstrand et al, 2019; van Gestel et al, 2014), our proposed perioperative time frame does not even conflict with these indications (Van Cutsem et al, 2016). We have included additional text in the lines 131-132 to motivate the timing of our regimens.

• Fig. 2: Did you check for metastases in other organs than the liver at the timepoint of euthanization, e.g. lungs. In the discussion section you talk about a potential influence of IFNα1 on other organs. Therefore, I think that the mice should be thoroughly analyzed and the data presented. The manuscript will benefit from it.

We thank the reviewer for this valuable comment. Indeed, we always check for dissemination of CRC metastases on MRI analysis and necroscopy. As stated at lines 146-147 and 158 CRC tumors seeded in the liver vasculature after colonizing the liver do not spread to other organs such as the lungs. Indeed, CRC cells intravascularly seeded in the portal circulation, are trapped at the beginning of hepatic sinusoids because their diameter is bigger than that of liver sinusoids (Fig S8A,B). These micro-anatomic peculiarities are also thought to impede the spreading of tumor cells from periportal to centrilobular areas and to the general circulation (Catarinella et al., 2016; Vidal-Vanaclocha, 2008), and this is consistent with studies showing that in CRC patients undergoing surgery the majority of CRC-derived circulating tumor cells are found in the portal vein (Deneve et al, 2013).

• Overall, MRI pictures and pictures of IHC or IF are sometimes too small to see. Please provide pictures with larger magnification or enlarge the images.

We thank you for this suggestion and we have indeed increased the size of all MRI, IHC, and IF images to the maximum that will fit within the figure. In addition, we presented the images at the highest magnification available, without making digital enlargements that would significantly reduce resolution.

• Fig. 3 F, G: immune cell infiltration in the liver was analyzed. Please compare it to untreated, tumor-free wildtype liver tissue.

We appreciated the reviewer's suggestion and included the results of six Sham mice per each marker in our analysis. The text was added on the figure legends to Fig 3H and Fig S4B,D.

• Fig. 6: the graphs are too small to be read, especially the volcano plot and the gene names of the heatmap.

We increased the font size of genes in the volcano plots and heatmap in Fig 6A,B, as suggested.

• Fig. S6: Pictures of co-immunofluorescences are presented. For the reader it is really hard to distinguish the stainings and to identify colocalized areas. Please provide pictures with one channel to better compare the marker expression.

We thank the reviewer for pointing this out and we have tried to make each panel as large as possible to fit into a two-column figure. We have also prepared high magnification images of each channel for all immunofluorescence images, which we provide as source data. We hope that this is sufficient to help readers to interpret our results without increasing the number of main or supplementary figures.

• From page 8 onwards (section about transgenic mice) LSEC was used as kind of synonym for hepatic endothelial cells. Since there is still no LSEC-specific driver mouse, it should be stated "hepatic endothelial cells" instead.

We agree with this suggestion and thus have indicated that the results refer to HECs but include a large majority of LSECs. Indeed, LSECs make up the majority (~89%) of the total HEC population (Su et al, 2021). In addition, some SEM and TEM analyses were performed only on LSECs, as well as the IF analyses. Therefore, we believe that LSECs play an important role in this process. Although not specifically suggested, we have also changed the title of our manuscript to reflect the reviewer's suggestion. Thus, we propose "Continuous sensing of IFNα by hepatic endothelial cells shapes a vascular antimetastatic barrier" as new title.

• P. 11: there is a typo: Fig. Fig. S6G,H

We corrected this typo.

• P. 13: the authors describe Gata4 as inhibitor of subendothelial matrix deposition. This should be precisely written, since Gata4 originally is described as master-regulator of liver sinusoidal differentiation which leads to liver fibrosis development upon loss of Gata4.<br /> Besides, I came across a study of the same group that investigated the role of Notch signaling in hepatic CRC and melanoma metastasis (Wohlfeil et al, Cancer Res, 2019, https://aacrjournals.org/cancerres/article/79/3/598/638600/Hepatic-Endothelial-Notch-Activation-Protects). Similar to your study they tie the reduction in hepatic metastasis to capillarization of the hepatic microvasculature.

We agree with this suggestion and modified text accordingly. We are also glad that our results agree with previous reported literature that has now been correctly cited at lines 351-356 and in the discussion lines 474-476.

• The discussion reads like paraphrasing the results section. The manuscript would clearly benefit if the discussion section had been rewritten short and concisely.

We agree with this suggestion, and we have modified discussion accordingly. We are also willing to shorten the discussion by removing the schematic model that could possibly be used as a graphical abstract.

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Reviewer #2 (Significance):

• Since liver metastases of various tumor are tremendously hard to treat and mediates therapy resistance, the authors focus on a very important field of research - prevention of liver metastasis formation.
• This study adds insights into the mechanisms of action of IFNα1 in the hepatic microenvironment. It extends previous findings of Toyoshima who described anti-tumoral effects of IFNα1 released by dendritic cells in the liver.
• The study is well designed and will be of great interest for the scientific community. Besides, it will be appreciated by physicians, However, as mentioned in the discussion, further clinical studies by physicians are needed to translate its findings into the clinic.
• The author of this review works as physician and often deals with liver metastasis. It is one field of focus of her/his research.

Author response

• A comment on the overall organization of the paper. Figure 2 has a major location in the paper, but it seems that its main takeaway is that these MAPs aren't really involved in the main process this paper is probing. While these are important findings, it might be more satisfying to move some of the central results earlier.

We agree that this figure displays mostly negative results. However, most work on anaphase B microtubule dynamics from our group and others has focused on the effect that motors and MAPs may have on microtubule dynamics (EB1 and kinesin-8 in budding yeast, klp9 in fission yeast). Therefore, we consider it is important to clearly show that previously proposed candidates are not required for the observed decrease in microtubule growth speed, prior to introducing the unexpected effect of the membrane.

*A model schematic might drive home the main finding of the paper, and be particularly useful for readers who are not experts in microtubule or spindle dynamics. That said, the Discussion does an excellent job of summarizing the findings and explaining the takeaway message(s), even for the non-expert.

We have added a model schematic and we have referred to it in the main text.

Specific comments

• ‘In higher eukaryotes’ - Suggest avoiding the terms higher and lower when describing organisms, and instead, directly defining which organisms, for instance in animals/metazoans that would be a better description.

We have removed this terminology.

• Figure 1 E-F - It is hard to see the difference in the distribution, maybe a different color could be used instead of stars.

We have used a different color.

• Figure 1 Data shown in pink in G comes from 832 midzone length measurements during anaphase, from 60 cells in 10 independent experiments - The pink here does not correspond to the pink coding in D, consider colour choice for clarity across panels.

We have changed this.

• Finally, yeasts undergo closed mitosis - How does this relate to the findings in the Dey paper (cited here) which shows it was somewhat semi-closed or semi-open. According to the Dey paper, the membrane disassembles locally twice, at the SPB and the bridge.

Membrane disassembly at the nuclear membrane bridge occurs at late anaphase, and leads to the disassembly of the spindle, presumably by the action of cytoplasmic factors (Dey et al. 2020). We do not believe the membrane disassembly itself has a role in spindle elongation or microtubule dynamics, as when it happens the spindle is then disassembled. However, the fact that les1D reduces the decrease in microtubule growth speed associated with internalisation of microtubules in the nuclear membrane bridge suggest that the organisation of the nuclear membrane bridge required for its local disassembly at late anaphase might affect microtubule growth (see section “Formation of Les1 stalks […]”).

• ‘vertical comets in kymographs (Fig. 1C) do not correspond to non-growing microtubules, but rather microtubules that grow at a speed matching the sliding speed’- For clarity, it might be nice to add: "(as the SPB moves away from the plus end in the kymograph)".

We have included this useful clarification.

• ‘significantly shorter than in interphase, where growth events last more than 120 seconds on average [42, 43]. Microtubule shrinking speed did not change during anaphase either (Fig. 1-Supplement 1D), and was on average 3.56±1.75 μm/min, also lower than in interphase (~8 min/μm)’ - This comment concerns the comparison of growth and shrinking rate as well as growth duration. The authors did not measure microtubule dynamics in interphase in this manuscript but compared their numbers to literature values. The comparison raises some questions for three reasons: 1) the microscopy method used is different in this paper and the two references provided, 2) the sample is mounted differently compared to the two references provided - 1) and 2) combined could lead to different levels of stress on the cells which could affect MT dynamics-, 3) (probably the most important caveat) the experiments are done at different temperatures: 27C in this paper versus 25C in the references provided. Microtubule dynamics are sensitive to temperature so this could explain part of the differences observed. Also, there are multiple values published for MT dynamics in interphase depending on the strain used and the microscopy method used. Suggest that the authors measure microtubule dynamics in interphase cells at 27C in SIM to ensure that the differences are not due to the technical parameters employed. Small item - should ‘8 min/μm’ read “8 μm/min"?

We have measured microtubule growth speed and growth event duration using GFP-Mal3 during interphase and anaphase B in the same conditions as proposed (see Figure 1 – Supplement 2). Unfortunately, shrinkage speed cannot be measured using GFP-Mal3, so we cannot confirm that the difference between our measurements and the literature values would be observed.

• ‘we observed two populations of microtubules (fast and slow growing)’ - Does this statement about thistle fast and slow growing populations refer to the data in Fig. 1C and 2A?

Yes, we have added reference to this figures in the next sentence (mentioned below).

• ‘In some cells, all microtubules seemed to switch to the slow growing phase simultaneously (Fig. 1C), while in others fast and slow growing microtubules co-existed (Fig. 2A)’ - This is a very interesting observation, could we know how many cells (%) were detected in each case? Is it that in 90% of the cells the switch is simultaneous, and hence the microtubule growth is somehow synchronized? Or is it more random, e.g. around 50%?

This was just to point the reader to two kymographs and show that a clear point where all microtubules change speed is not present in all kymographs, as one may think from Fig. 1C. Later in the paper, we show that the change in growth depends on whether the microtubule rescue occurs inside or outside the nuclear membrane bridge, so it is a matter of where microtubules are rescued once the dumbbell transition occurs, which is a stochastic process. We have added another sentence pointing the reader to examples in the kymograph (see line 152, This representation captures…).

• ‘On such a plot, the data points visibly cluster in two separate clouds and the variation of growth speeds can be fitted by an error function (Fig. 1F)’ - It is unclear that there are two distinct clusters, maybe the assertion should be toned down, or some sort of cluster analysis provided.

We acknowledge that the data is widely spread across the y axis, and given that the magnitude “distance to the closest pole at rescue” is continuous the transition is not a clear cut. However, we consider the fact that the averaged curve closely matches the error function fit to be sufficient evidence for the existence of two populations of microtubule growth. Additionally, R2 of the fit is ~0.5 indicating that half of the variance is explained by this model. In any case, we show later that these two populations do exist (Fig. 3D), and why plotting microtubule growth against distance to the closest pole at rescue is a good way to segregate them (Fig. 3E).

• ‘speed of interphase microtubules (~2.3 μm/min)’ - It would be interesting to see the dynamics in a les1 mutant (Dey Nature 2020) paper. Just as a control for presence/absence of the bridge?

We thank the reviewers for kindly suggesting this interesting experiment. We have included it after the ase1 section. Les1 forms stalks at the edges of the nuclear membrane bridge that restrict nuclear membrane disassembly to the center of the bridge at the end of mitosis (Dey at al. 2020). While les1 deletion does not prevent the formation of the nuclear membrane bridge, it has been proposed that Les1 stalks may constitute sites of close interaction between the nuclear membrane and the spindle. Therefore, these sites may influence microtuble growth. Indeed, we have found that removing these Les1 stalks by either deleting les1 or nem1 leads to a smaller decrease in microtubule growth speed when plus ends enter the nuclear membrane bridge (see section “Formation of Les1 stalks […]”)

*‘Figure 2, Transition from fast to slow microtubule growth occurs in the absence of known anaphase MAPs’ - It looks like the overlap zone is larger on the mal3 kymograph. Is the size of the midzone changed in some of the mutants? It could be important to report. Related to it, is the spindle length changed in some of the mutants? (It does not look like it from the kymographs displayed).

The midzone is indeed longer in mal3D strains, now this can be seen in Fig. 2 – Supp. 2 and it is mentioned in the main text in line 272. As for the spindle length, diverse kinds of alterations in spindle length have been previously reported for the mutants that we used in this study. For instance, ase1D /cls1off cells have shorter spindles at anaphase onset (Loiodice et al. 2005 and data not shown), and klp5Dklp6D have longer spindles at anaphase onset (Syrivatkina et al. 2013). klp9D / clp1D / dis1D cells have lower spindle elongation velocity and may not reach the wild-type spindle length by the end of anaphase (Kruger et al. 2019). Despite these differences, the decrease in microtubule growth as a function of distance to the closest pole has a similar tendency across conditions, suggesting that the mentioned differences in spindle length are unlikely to have an important effect.

• Additionally, adding the data about rescue localization in the mutant (equivalent of Fig 1 G) would be interesting to better describe the role of these different proteins. Figure 2, Panel G to L - Could the authors indicate the value for the average +/- error in each bin for the WT and the mutants? Also, it is hard to say from the plots, but it looks like the WT average speed in the first bin is different in every panel, that would be good to know to have an idea of the reproducibility/variability.

We have added a figure with the rescue distribution (see Fig. 2 – Supp. 2). This apparent difference in the wt speed in different experiments might have come from looking at normalised data. The new way of representing the data in fig. 2H and J shows that the microtubule growth velocity in the wild-type is very consistent across experiments. We have added a table with microtubule growth velocity values (Table 1), and the source data is available.

• The dots making up the "thick lines" are centered on 1.5/2.5/etc.. in some panels (G and K) and centered on 1/2/3/etc.. the others (I,J,L). Could the authors provide some clarification?

We have fixed this inconsistency across the paper.

• Figure 3 - Can the authors indicate the average values +/- error for each of the distributions in Fig. 3D? Maybe on the plot itself, in the legend or as a table. This would make them easily available without having to infer them from the Y axis. This comment is also valid for Fig 4I and 4J.

We have added tables with average values and confidence intervals in the appendix.

• Figure 3E ‘Distance from the plus-end to the nuclear membrane bridge edge at rescue as a function of distance from the plus-end to the closest pole at rescue’ - The Y axis reads as "distance to the bridge edge" but it shows negative values, could this be "position to the bridge edge" instead? (same item throughout the text).

We have fixed this.

• Figure 3 ‘Number of events: 442 (30 cells) wt, 260 (27 cells) klp9OE, 401 (35 cells) cdc25-22, from 3 independent experiments’ - P values this small raise a concern. Presumably the number of degrees of freedom in the regression analysis should not exceed the number of independent experiments. Instead, the DoF listed under "error" in the analysis output is hundreds or thousands instead of 3. To address this, the regression analysis should use either the "Error" function in R or a linear mixed-effects model to account for the nesting of the repeated measurements within each independent experiment. Alternatively, it is also possible to just calculate summary means for each independent experiment, and calculate p values based on that N=3. See: Lazic. Experimental Design for Laboratory Biologists. p. 157. and the supplemental file of: https://doi.org/10.1371/journal.pbio.2005282 and the additional file 1 of: https://doi.org/10.1186/s12868-015-0228-5 and this for an alternative plotting approach: https://doi.org/10.1083/jcb.202001064 Recommend either recalculating the p values by one of the methods above or removing the reported p values from the paper. The large effects observed in many cases are self-evident without a significance metric, so eliminating the p values would be acceptable here. (This comment applies to other figures through the paper that report p values based on number of cells or number of measurements instead of number of independent samples/experiments.)

We thank the reviewers for suggesting the improvements to the statistical analysis, as well as for pointing us to useful resources that described the statistical methods and their implementation in detail. We have followed Aarts et al. 2015 and used a linear mixed effects model (see Methods>Statistical Analysis)

Due to the change in statistical analysis method, to show that some of the differences we had reported previously were significant, we included more cells in the analysis from our existing data. We did this for klp5Dklp6D kymographs (Fig. 2I and Fig.2 – Supp. 1). Spindle dynamics in ase1D (Fig. 5D and Fig. 5 – Supp. 1) and klp9D (Fig. 2 – Supp. 3 A, C). Cell length (Fig. 3 – Supp. 1A).

For the same reason, we measured anaphase spindle elongation velocity (Fig. 3 – Supp. 1C) from kymographs instead of measuring them from the 1 minute interval movies that we had used previously (from Fig. 3 – Supp 1B). We have reflected this in the methods (see added text in line 800 and deleted text in line 809 in the document with changes highlighted).

None of these changes has altered our conclusions.

• Figure 4 - Nice experiment. It brings the question of how cell-shape affects all these dynamics (probably out of the scope of this work). But a for3 mutant for example?

This is an interesting suggestion, to be tested in the future. Furthermore, we believe that nuclear shape should also have an important effect, since the spindle is confined inside the nuclear membrane. We would expect that mutants that perturb nuclear shape might have effects on microtubule growth. We have observed that the decrease in growth speed associated with internalisation of microtubules in the nuclear membrane bridge is reduced upon nem1 deletion, which increases nuclear membrane surface, and produces membrane ruffling (Fig. 4-Supplement 2). However, nem1 deletion also removes les1 stalks from the nuclear bridge (Dey et al. 2020). It would be interesting to find a perturbation of the nuclear membrane that does not remove the les1 stalks.

• ‘Ase1 is required for microtubule growth speed to decrease during anaphase B, this is unlikely to be a direct effect’ - If it is unlikely to be a direct Ase1 effect is the title of the section accurate? "Ase1 is required for normal rescue distribution and for microtubule growth speed to decrease in anaphase B"

Ase1 recruits multiple proteins to the spindle midzone, so the fact that ase1 deletion produces a given phenotype does not necessarily mean that this phenotype results from the absence of Ase1 protein activity. For instance, deleting ase1 perturbs rescue distribution, but it does not mean that Ase1 acts as a rescue factor itself, or at least to a relevant extent, given that deletion of cls1 completely prevents rescue, but ase1 deletion does not. In the discussion we propose some indirect effects of ase1 deletion that may produce this effect. In any case, upon more careful analysis we have found that ase1 deletion does not prevent the decrease in microtubule growth speed during anaphase B, but rather makes it smaller (see section “The decrease in growth speed associated with internalisation of microtubules in the nuclear membrane bridge is reduced upon ase1 deletion”).

• Figure 5 - What about an ase1 lem1 double mutant?

We suppose that the intended gene is les1. We have studied the effects of les1 deletion in the new version of the manuscript. However, we do not see the information we would obtain from a double deletion ase1D les1D.

• ‘In summary, Ase1 is required for rescue organisation and for microtubule growth speed to decrease during anaphase B ‘- In this context it could make sense to discuss the observations from this paper (doi:10.1371/journal.pone.0056808) about the role of Ase1 ortholog's MAP65-1 in coordinating MT dynamics within bundles.

In the mentioned paper, the authors showed that the presence of PRC1 (ase1 orthologue) in bundles increases microtubule rescue rate, and that it slightly reduces microtubule growth speed.

We observe a small increase in microtubule growth speed throughout anaphase upon ase1 deletion (Fig. 5), which is consistent with the in vitro observation that PRC1 decreases microtubule growth. However, once more this might not be a direct effect of Ase1, since less Cls1 is recruited if ase1 is deleted, and Cls1 reduces microtubule growth speed (Fig. 2). In addition, this can also be a result of higher concentration of tubulin / MAPs resulting from less polymerised tubulin in ase1 deleted cells, which have less spindle microtubules on average.

Regarding the increase in rescue rate produced by PRC1 in vitro, it is possible that Ase1 contributes to microtubule rescue in the spindle. However, given that no rescues occur upon inactivation of cls1 (Bratman et al. 2007), we believe Cls1 is the dominant factor, and Ase1 contribution is likely negligible.

• ‘We initially set the microtubule growth velocity to 1.6 μm/min (early anaphase speed, Fig. 1F), and aimed to reproduce the experimental distribution of positions of rescue and catastrophe at early anaphase (spindle length < 6 μm’ - Kudos to the authors for detailing the model and its parameters in a way that even non-modelling experts can understand.

Discussion - ‘Our data suggests that microtubule growth speed is mainly governed by spatial cues’ - Is it right to assume that in the cases where fast and slow growing microtubules were simultaneously observed, the fast microtubules were not/had not yet reached the midzone?

Our data suggests that it’s not about being inside the midzone, but rather inside the nuclear membrane bridge formed after the dumbbell transition. We have elaborated more on this in the main text, pointing the reader to examples in the kymograph, and giving a quantitative argument for distance to the closest pole being a better predictor than anaphase progression or position with respect to the center (which is equivalent to distance to the midzone), see line 152.

• Methods - ‘PIFOC module (perfect image focus), and sCMOS camera’ - Is this Nikon's "Perfect Focus" autofocus, or some other manufacturer's system? And back-thinned sCMOS.

We have clarified this in the Methods section.

imitations and constraints, leaving you happily unoptimized and free to roam, to wonder, to wander toward whatever makes you feel alive here and now in each moment.

Some may categorize handbooks on note taking within the productivity space as "self-help" or "self-improvement", but still view it as something that happens outside of ones' self. Doesn't improving one's environment as a means of improving things for oneself count as self-improvement?

Marie Kondo's minimalism techniques are all external to the body, but are wholly geared towards creating internal happiness.

Because your external circumstances are important to your internal mental state, external environment and decoration can be considered self-improvement.

Could note taking be considered exbodied cognition? Vannevar Bush framed the Memex as a means of showing associative trails. (Let's be honest, As We May Think used the word trail far too much.)

How does this relate to orality vs. literacy?

Orality requires the immediate mental work for storage while literacy removes some of the work by making the effort external and potentially giving it additional longevity.

Author Response

Reviewer #1 (Public Review):

1) In terms of the prior hypothesis here I think the authors justify a prior with respect to striatum and I think the most principled analysis of their hypothesis would be based on volumes of interest in striatum. Figure 1 does show difference in MTsat in striatum between neurotypicals and DLDs but the changes are all in the caudate I think- I cannot see anything in putamen. The authors actually describe changes in only one part of anterior caudate. The authors do describe a number of previous conflicting studies that examine caudate structural changes but that is not their hypothesis. The discussion goes into developmental changes affecting striatum at different times that might be relevant and would require a longitudinal study for a definitive study - as the authors acknowledge.

The reviewer is correct that at this statistical threshold we only observe MTsat differences in the caudate nucleus. Changes in the putamen did not survive this threshold. Lowering the threshold for MTsat (our maps are openly available on Neurovault), or an ROI analysis (see (https://osf.io/2ba57/)) does not reveal significant statistical differences in the putamen. As we noted in the paper, there are differences in the putamen in R1 (these are also observed in the ROI analysis).

2) There is a lot of overlap between the caudate signal in the two groups - although the correlation of individual differences is reasonable. The caudate signal would not allow group classification.

Yes, it is clear that these differences would not be sufficient to allow for group classification of DLD. We have discussed this overlap in the discussion.

3) Outside of the caudate they do show changes in left IFG and auditory cortex that are hypothesised. But there is a lot else going on - I was struck by occipital changes in figure 1 which are only mentioned once in the manuscript.

We now discuss these differences in the discussion. Note that we did not have any a priori hypotheses about these regions; to our knowledge, they have not been previously described and are not predicted by any theoretical accounts of DLD.

4) Should I be concerned by i) apparent signal changes in right anterior lateral ventricle from group comparison in figure 1 ii) signal change correlation in right anterior lateral ventricle in figure 4 (slice 22) and iii) signal change outside the pial surface of the occipital lobe in figure 1?

No – these may be accounted for by smoothing during analyses. Note, these changes at tissue boundaries are fairly commonly seen in statistical maps following smoothing but are not evident when data are projected onto a 3D surface.

Reviewer #2 (Public Review):

This work demonstrates the value that multiparameter mapping imaging protocols can have in uncovering microstructural neural differences in populations with atypical development. Previous studies looking at differences in brain structure have typically used voxel based morphometry (VBM) approaches where differences in volumes can be hard to interpret due to complex tissue compositions. The imaging protocol outlined in this paper can specifically index different tissue properties e.g. myelin, giving a much more sensitive and interpretable measure of structural brain differences. This paper applies this methodology to a population of adolescents with developmental language disorder (DLD). Previous evidence of structural brain differences in DLD is very inconsistent and, indeed, using traditional VBM the authors do not find a difference between children with DLD and those with typical language development. However, they provide convincing evidence that despite no macrostructural differences, children with DLD show clear differences in levels of myelin in the dorsal striatum and in brain regions in the wider speech and language network. This can help to reconcile previous inconsistent findings and provide a useful springboard for both theoretical and empirical work uncovering the nature of the brain bases of language disorders.

We are grateful for these comments, and to the reviewer for pointing out some key strengths of this work.

Strengths:

The imaging protocol is robust and is explained very clearly by the authors. It has been used before in other populations so is an established method but has not been applied to populations of children with DLD before, yielding novel and very interesting results. The authors demonstrate that this is a methodology which could have great value in other populations that display atypical development, increasing the impact of these findings.

The sample size is large for research in this area which increases confidence in the results and the conclusions.

Rather than relying solely on group differences in brain microstructure to draw conclusions about neural bases of language development, the authors correlated brain microstructural measures with performance on standardised language tests, allowing stronger inferences to be drawn about the relationships between structure and function. This is often an important omission from developmental neuroimaging work. It gave increased confidence in the finding that alterations in striatal myelin are linked to language difficulties.

Weaknesses:

The authors rightly use the CATALISE definition of developmental language disorder, which differs from much of the previous literature by not requiring that children with language difficulties have nonverbal ability that is in the normal range. As can be common when using this definition of DLD, the group with DLD have significantly weaker nonverbal ability than the typically developing group. The authors show that brain microstructural differences correlate with language ability but they don't rule out a correlation with nonverbal or wider cognitive skills. Given the widespread differences in myelination across areas of the brain, including those that weren't predicted e.g. medial temporal lobe, it is plausible that perhaps some of the brain microstructural differences are not linked directly to language impairment but a broader constellation of difficulties. Some of the arguments in the paper would be strengthened if this interpretation could be ruled out.

To rule out the effect of nonverbal IQ or wider cognitive differences, we have conducted stepwise regression analyses on the quantitative data extracted from the statistical cluster covering the caudate nuclei, assessing the influence of factors such as language proficiency, verbal memory and IQ. We find that language status accounts for the most variance, rather than nonverbal IQ or verbal memory (details are included in the paper).

We also discuss this point in the discussion, pointing to the presence of co-occurring differences in DLD and how these might account for some of the broader group differences we observe.

The authors acknowledge in the limitations section that their data cannot speak to whether brain differences are a cause or consequence of language impairment. However, there are some implied assumptions throughout the discussion of the results that brain differences in myelination have functional consequences for language learning. A correlation between structure and function does not indicate this level of causality, particularly in an adolescent population - function could just as easily have had structural consequences or environmental differences could have influenced both structure and function. In my view, the speculations about functional consequences of myelin differences are not fully supported by the data collected.

The reviewer is correct in saying that the myelin deficit could be either a cause or a consequence of DLD or even that both are caused by a third factor. We specifically address this in the discussion section, and note a longitudinal analysis would be the best way to address this question. Indeed, R3 notes about our paper, “…it does a very good job of avoiding the common trope of assuming neural differences play a causal role in DLD (when in fact, reduced atypical development could cause neural differences)”.

The data suggest that there is much greater variability in left caudate nucleus MTsat values for the DLD group than the other two groups. The impact this may have on the results is not discussed in the interpretation and it is unclear whether this greater variability occurs throughout all of the key MPM measures for the DLD group.

Thank you for raising this important issue. In figure 1, we only plot the MTsat values from the caudate nucleus for visualisation, and as you note, there we is a considerable degree of variability within the DLD group. However, and crucially, this difference would not influence statistical interpretation of our results. The whole-brain analysis used involves permutation testing, and is robust to a difference in group variability. However, the issue of variability within DLD is important and we now highlight this in our discussion, noting that not every child with DLD will have reduced striatal myelin. Indeed, this variability is even more evident in figure 4. An important challenge for future studies is to understand the link between striatal myelination and the spectrum of language variability.

Reviewer #3 (Public Review):

Developmental Language Disorder (DLD) is observed in children who struggle to learn and use oral language despite no obvious cause. It is extremely wide-spread affecting 7-10% of children, and extremely consequential as it persists throughout life and has downstream effects on reading, academic outcomes, and career success. A large number of prior studies have attempted to identify the structural neural differences that are associated with DLD. These have generally shown mixed results, but support a number of candidate regions including left hemisphere language areas (particularly the inferior frontal gyrus), and striatal regions that are possibly linked to learning. However, these studies have suffered from small sample sizes and conflicting results. Part of this may be their reliance on traditional voxel-based-morphometric techniques which estimate cortical thickness and gray matter density. The authors argue that these measures are biologically imprecise; gray matter can be thinner for example, due to synaptic pruning or increased mylenation.

The authors of this study offer a powerful new tool for understanding these differences. Multi-Parameter Mapping (MPM) is based on standard MRI techniques but offers several measures with much greater biological precision that can be tied specifically to myelination, a key marker of efficient neural transmission. The test a very large number of children (>150) with and without DLD using MPM and show strong evidence for fundamental biological differences in these children.

This study features a number of key strengths. First, at the level of neuro-imaging, the MPM technique is new in this population and offers fundamental insight that cannot be obtained by other measures. Indeed, the authors wisely use a traditional gray matter approach (voxel based morphometry) and find few if any differences between children with DLD and typical development. This offers a powerful proof of the sensitivity of this approach. Moreover, the authors analyze their data comprehensively, looking at two measures of myelin (MTsat and R1) and their convergence.

However, at the most important level, I think structural approaches (like MPM, diffusion weighted imaging and so forth) offer tremendous promise for dealing with this as they avoid the ambiguity associated with interpreting functional MRI. Are children showing reduced BOLD because they are less good at language processing? Or do the differences in brain function cause poorer language processing? Structural approaches - and MPM in particular - offer tremendous promise as they unambiguously assess the fundamental neuro-biology.

Beyond the neuro-imaging this study is also strong in their sample and the measurements of language. The sample size is very large and an order of magnitude larger than existing studies. It is well characterized, and the authors use a large set of well-motivated measures that capture the relevant dimensionality of language. Moreover, the authors treat language both as a clinical category and a continuous measure which is consistent with current thinking on the nature of DLD as potentially the low end of a continuous scale rather than a discrete disorder.

Finally, the discussion of this paper for the most part does a good job of fitting these neurobiological findings into our broader understanding of DLD. It does an excellent job of mapping the observed brain differences onto functional differences in the child. Importantly, in doing this it does a very good job of avoiding the common trope of assuming neural differences play a causal role in DLD (when in fact, reduced atypical development could cause neural differences).

We are very grateful for the reviewer for taking the time to read our work so closely and pointing out these strengths in the work.

Despite these strengths, I have a number of substantive concerns that if addressed will improve the overall impact of this paper.

First, as the authors are aware, there is a long running and active debate in DLD as to whether DLD is the tail end of continuous distribution of children or a unique disorder (Leonard, 1987, 1991; Tomblin, 2011; Tomblin & Zhang, 1999). The results here offer great promise for informing that debate. And in that vein the authors quite appropriately analyze their data in two ways: once using DLD as a categorical variable and once using continuous measures of language. However, they don't really attempt to wrestle with the differences between the model.

We have now included a section on the implications of our results for DLD in the discussion.

Second, I was a little surprised to see the authors highlight left IFG in the discussion to the degree they did. While there was clear evidence for reduced myelin there in the MTsat analysis, this did not hold up in R1 analysis, and even in the MTsat, IFG was clearly not the primary locus. Rather the areas of differences seemed to be centered at Pre- and Post-Central gyrus and extending ventrally (to IFG) and posteriorly from there. Given debate on the role of IFG in language specific processing in general (Diachek, Blank, Siegelman, Affourtit, & Fedorenko, 2020; Fedorenko, Duncan, & Kanwisher, 2013), it was not immediately clear to me why that area was important to highlight. For example, some of the posterior temporal areas (and motor areas) that were found were equally important for perceptual, lexical and phonological processing that are important for other theories of DLD.

We do see group differences in left IFG in the R1 analysis (see Figure 2) and they were more extensive than those seen in the MTsat analysis with which they overlapped. The reviewer is correct that the differences were limited to the opercular part of the IFG in both analyses whereas they extended more dorsally in the R1 analysis. They also extended ventrally to the anterior insular cortex. We respectfully disagree with the reviewer about the importance of highlighting these differences, given the importance of this region for language processing, and our previous hypotheses about this region. Even so, we agree that the posterior temporal and motor areas are of equal importance and have highlighted these in the discussion.

The authors rightly point to their differences in the striatum as supporting theories of DLD centered around differences learning. However, as they discuss, there are also large differences throughout the brain in both perceptual, motor and language areas. These would seem to support theories of DLD centered around processing and representation. In particular, the differences in myelination likely are linked to differences in the efficiency of neural coding. This would seem to favor two theoretical views that might be worth mentioning - speed of processing (Miller, Kail, Leonard, & Tomblin, 2001), and approaches based on lexical processing (McMurray, Klein-Packard, & Tomblin, 2019; McMurray, Samelson, Lee, & Tomblin, 2010; Nation, 2014). I was surprised these were not mentioned, given the clear link to the timecourse of processing. Does then suggest that these theories might complement each other? It would be useful to see some more discussion of the implications of these findings for broader theories.

We have now incorporated mention of these theories in the discussion and discuss implications. We agree with the reviewer that it would be interesting to see whether the different theories could be reconciled.

Reviewer #2 (Public Review):

Suvorov and colleagues present a well-supported genome-scale phylogeny for 149 Drosophila species based on thousands of single-copy-orthologs. They then use several approaches to estimate the extent of introgression across the phylogeny, and report that it is common both recently and deeper in the past.

The main strength of this paper is that it uses a scale of sequencing that allows an assessment of genus-wide trends with reasonably good power. It also presents two new analysis approaches, but these represent fairly minor modifications of existing techniques to suit multiple gene alignments, and unfortunately their reliability is not evaluated in this paper. Nevertheless, the main finding that introgression is common appears to be well supported. This finding echoes those of similar recent studies on taxa such as cichlid fishes and Heliconius butterflies. The different approaches used, and different levels of sampling in these different studies do not allow for quantitative comparisons, leaving us with the somewhat vague conclusion that introgression is 'common' in all of these taxa. Perhaps most critically, the present paper does not delve any deeper into the evolutionary impacts of introgression, nor the factors at the species or genomic level that might determine its frequency. Below I describe some areas of concern in more detail.

1. Extent of introgression

Perhaps equally as interesting as the frequency of introgression per species across the phylogeny is the proportion of the genome of each species that is affected. Without such estimates, the full extent of introgression is difficult to assess.

2. Sampling effects

Since this paper is attempting to make an (admittedly crude) estimate of the extent of introgression in the entire genus, some discussion is needed to address the possible consequences of the fact that only around 10% of species in the genus are represented. For example, if sampling is very even, perhaps most ancient events would be detectable, but more recent events may tend to be missed simply because the species involved are not sampled.

3. Ancestral structure

The reasoning provided for dismissing the possible effect of ancestral population structure is unconvincing. First, the authors argue that it "seems less likely" that non-sister taxa would have bred more frequently in the ancestral population. However, this is the entire basis of the problem: it might be unlikely, but it can happen. Eriksson and Manica (2012 https://doi.org/10.1073/pnas.1200567109) provided a very reasonable scenario in which colonisation of a new region can lead to this pattern.

Second, the authors argue that QuIBL "should not be impacted by ancestral structure because this method searches for evidence of a mixture of coalescence times: one older time consistent with ILS and one time that is more recent than the split in the true species tree and that therefore cannot be explained by ancestral structure." This argument needs clarification. My understanding is that the split in the "true species tree" would also be inflated if there was ancestral structure.

My view is that ancestral structure leading to discordance between gene trees and species trees is itself an interesting phenomenon. In some ways, it is not conceptually distinct from introgression occurring soon "after" speciation if we consider ancestral structure as the beginning of a continuous speciation process, so I don't think it would weaken the paper to accept this as a possible contributing process.

4. Discordant count test

The statistical analysis in the DCT accounts for multiple testing of many triplets for introgression, but there is no mention of the fact that these triplets are non-independent. It is not clear to me whether this makes the correction used more or less conservative.

If there are any cases where the internal branch is long and the number of ILS gene trees is very small or zero, use of a chi-squared test may not be appropriate.

5. Branch length test

The authors acknowledge that the BLT is "conceptually similar" to that of Hahn and Hibbins 2019 https://doi.org/10.1093/molbev/msz178, but to me it seems that the only material difference is the statistical procedure for testing for an significant difference between branch lengths.

An important consideration that appears to have been ignored is whether selection can impact the distribution of branch lengths, especially since many of the the BUSCO genes used here will be under strong selective constraint.

6. Intra-locus recombination

The paper needs to address the possible impact of intra-locus recombination on all of the introgression tests. For the DCT, I imagine that counts would be biased toward the species tree topology if the inferred trees span multiple distinct genealogies (see for example simulations by Martin and Van Belleghem 2017 https://doi.org/10.1534/genetics.116.194720 Figure S7). This might reduce test sensitivity.

Similarly, for the BLT, I would expect that true introgression would be more difficult to detect in the presence of recombination. It is possible that the block jackknife procedure of Hahn and Hibbins (2019, https://doi.org/10.1093/molbev/msz178) may be more suitable than the comparison of distributions of point estimates for genes used here.

Reviewer #3 (Public Review):

The authors compiled a collection of published and newly sequenced genomes to assemble the largest collection of Drosophila genomes to date. Using this dataset they extracted a set of single copy orthologs to use for phylogenomic analyses, with a focus on estimating a time-calibrated phylogeny and introgression.

This new dataset is a valuable resource that will serve the broader community of Drosophila researchers opening many new avenues for future phylogenomics research. The workflow of focusing on BUSCO genes for all comparative analyses is simple in a good way -- it is easy to understand how the data were collected and it should be easily reproducible -- which makes it easy to read past the genomics details and focus on the analyses of these data.

However, I feel this is an important aspect of the paper that should receive more details, perhaps in the supplement. I may have missed it, but I could not find statistics about this ortholog data set. On average, how long is each locus, how many variable sites are there, how many taxa are missing data for any given locus due to paralogy? Do the BUSCO genes include both introns and exons? It is also unclear from the description exactly how the BUSCO genes were extracted from genomes. Are they extracted from the final assembled genomes, or do you perform variant calling after identifying them to call heterozygous site? If heterozygosity is excluded, how might this impact metrics such as the branch length tests, especially among close relatives? It likely impacts node age estimates as well?

The authors use this dataset to infer phylogenetic relationships among taxa using both ML concatenation (IQtree) and a two-step MSC approach (Astral) which yielded quite similar topologies, and they examined the impact of filtering loci with treeshrink, which had minimal impact. This new topology represents a substantial step forward for understanding the relationships among major Drosophila clades.

One of the main results of this study is a new set of node age estimates on the tree. For this they estimated branch lengths in mcmctree from a concatenated matrix of 1000 loci in the presence of fossil calibrations. The fossil calibration scheme selected as the best option includes three fossils, one dating the divergence at the split from mosquitos (uniform 195-230Ma) and two ingroup calibrations (U(43,64) and U(15,43)). To me, the credible intervals on node ages seem incredibly narrow. The authors mention this as an improvement compared to earlier studies, but they also mention later that the total amount of sequence data does not greatly impact node dating. So I'm a bit confused why the node ages are expected to be more accurate here. It seems to me that time calibrations should be most accurate when the greatest number of fossils are available, and when very appropriate Bayesian priors on set on the analysis. The effect of sequence variation is then relatively small. But here there are very few fossils, one of which is hugely distant, and so I would not expect highly precise age estimates. So I guess my question to the authors is, what do you think is going on here? Perhaps further description in the supplement of how the mcmctree method implemented here differs from traditional node dating done in a program like BEAST would help to clarify.

Considering that this paper aims to infer the new best time calibrated tree for the Drosophila community, I think that the current description of fossil calibration schemes, which primarily refers to other publication names in the supplement, is insufficient. Which fossils are used in those studies, are you using those fossils as calibrations here, or are you implementing secondary calibrations based on their phylogenetic results? The reader should not have to read every one of those papers to understand the basis of the calibrations in this paper.

Fig.1 shows nodal age posterior probabilities. Are these 95% confidence intervals? The taxon labels are too small in this figure, both on the large tree and especially in the inset figure. The legend refers to fossil taxon names used for calibrations, but because it is still unclear to me where the fossils are placed on the tree. Are the calibrations indicated somewhere in the figure?

The authors demonstrate evidence of introgression by showing mostly overlapping evidence from two different types of tests. Together, these tests show that most major clades contain significant imbalanced discordance in gene tree counts or branch lengths. The taxon labels in Figure 2 are unfortunately quite unreadable, especially the matrix labels, which makes it difficult to interpret.

I do not see a reason for presenting new names and acronyms for the introgression tests used in this study. The "DCT" is described as being similar to a suite of existing tests which are also based on comparison of rooted-triplet gene tree frequencies. These methods have been presented in many frameworks (BUCKy, D-stat, f4, etc.) and the only difference here seems to be the precise method used to determine significance. Similarly "the BLT is conceptually similar to the D3 test" could be replaced by just saying we implemented the D3 test which we refer to here as a 'branch length test (BLT)' to clarify that you have not in fact created a new test (e.g., you say "The first method we developed was the discordant-count test...")

I am not very satisfied with the estimates of the "upper bounds" of introgression used here. It seems that there could possibly be many ways in which admixture edges could be drawn on the tree to explain the matrix of significant test results, and it is better to let formal network inference methods (e.g., SNAQ, Phylonet) infer these edges rather than guess at their placement. The current approach of "placing introgression events between pairs of branches for which most descendant extant taxa show evidence of introgression" leaves significant room for subjectivity.

The authors did implement phylonet, but not very exhaustively. Why only fit a single edge on the tree instead of multiple? The authors state "networks with more reticulation events would most likely exhibit a better fit to observed patterns of introgression but the biological interpretation of complex networks with multiple reticulations is more challenging". I don't think this type of result is any more complicated to understand than the current approach used by the authors of drawing edges manually. And it is much less subjective. The authors say that it is computationally intractable, and this may be true for clades above ~15 tips, but testing on smaller trees by subsampling 10-12 tips seems feasible. From my experience network inference using pseudo-likelihood methods in SNAQ or phylonet takes a few minutes to fit 1 edge, and a few hours to fit 2-3 edges.

Currently the two major results of the paper seem disjointed. The authors infer a time-calibrated tree, and they infer introgression events, but there is not much connection between the two. I applaud the authors on one hand for being cautious in interpreting their "upper bounds" of introgression to say too much about when they think introgression has occurred in the context of the time-calibrated tree. I think there is insufficient confidence in the introgression timing estimates to do that. But, what about the inverse relationships? Does this extent of introgression across the tree impact your confidence in the estimated timing of divergence events? One expectation would be that it is biasing all of the divergence times to appear younger. See my suggestions for addressing this.

Overall, this study presents an impressive new dataset and important new results that greatly impact our understanding of the evolutionary history of Drosophila. Although the estimates of node ages and introgression events may be imperfect, they are clearly a step forward. It is clear from these results that introgression has occurred throughout the history of Drosophila, and this study paves the way for further investigation of these patterns, as the authors propose in their conclusions.

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Reply to the reviewers

1. General Statements

We thank the reviewers for their careful and constructive analysis of our work. Our manuscript aims to exemplify the use of cryo-soft-X-ray tomography (cryoSXT) as a technique to study the dynamic changes to host-cell morphology that accompanies virus infection. This emerging method has several strengths when compared to other ultrastructural analysis techniques. Specifically, cryoSXT does not require the addition of contrast agents and therefore samples can be prepared via plunge cryopreservation alone, allowing us to capture them in a near-native state. Furthermore, the penetrating power of soft X rays and large field of view in cryoSXT allow rapid data acquisition, facilitating quantitative analysis of 10s to 100s of individual cells. We combined high-throughput cryoSXT data collection with semi-automated tomogram segmentation and fluorescence cryo-microscopy to study a recombinant herpes simplex virus (HSV)-1 that produces a pattern of fluorescence indicative of the stage of the infection in a single cell (‘timestamp’ HSV-1) and quantitatively monitored changes in lipid droplet, vesicle and mitochondrial morphology as HSV-1 infection progresses. In response to the reviewers’ comments, we have expanded our analysis of lipid droplet morphology, identifying a transient increase in the size of lipid droplets at early stages of HSV-1 infection, and completed additional fluorescence microscopy analysis to support our statements about the changes to microtubule, mitochondrial and Golgi morphology that accompany infection. Furthermore, we have included additional discussion on the relative merits of cryoSXT versus other ultrastructural analysis techniques like transmission electron microscopy, electron cryo-microscopy and electron cryotomography. We believe that our study serves as a powerful example of how cryoSXT can be used for quantitative cell biology and will be of broad interest to an audience of cell biologists and colleagues who study infection processes.

1. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity):

Summary

The authors have performed an explorative study, investigating morphological changes that occur in cells upon infection with Herpes Simplex Virus 1 (HSV-1) by the use of cryo soft X-ray tomography (cryoSXT). cryoSXT is an emerging technique for imaging of biological material, that allows for 3D imaging of significant volumes of cells under near-native conditions, without the need for sectioning or sample preparation other than rapid freezing. Reference (Groen et al. 2019) provides a nice list of examples from various biological samples. By the use of cryoSXT, the authors confirm findings that they have previously published by use of light and expansion microscopy (ref 16 from manuscript), namely an enrichment of small vesicles close to the nucleus and elongation and branching of mitochondria into interconnected networks in infected cells.

Infection experiments were done in two different cell types in this study (HFF and U2OS), and a timestamp reporter virus that allows to distinguish between early and late stages of infection was used to provide more context to the observed morphological changes in the cells.

Major comments

It is a bit difficult to follow the main message throughout the manuscript, as the topics brought up in the introduction, results and discussion sections are not very coherent. The introduction gives some background on the virus and the timestamp reporter system, and further focuses on cryoSXT as a method and how this can overcome sample preparation artefacts that might be introduced by chemical fixation and sample processing. The results do not contain any direct comparisons between cryoSXT and other methods or sample preparations (light microscopy or EM-based), and the discussion only to a small extent comes back to the advantages brought by cryoSXT compared to other methods. Rather the discussion largely revolves around the possible involvement of microtubules in generating the observed morphological changes, and the possible meaning of elongated mitochondria in infected cells. Both of these topics are barely introduced, and not at all experimentally interrogated in the case of microtubules. There is also some discussion about Golgi fragmentation, although this is also not directly interrogated by cryoSXT in the current manuscript.

We thank the reviewer for these comments. We have: - Updated the introduction to enunciate more clearly the aims of our study - Included a substantial comparison of the relative merits of cryoSXT versus other ultrastructural analysis techniques (TEM, cryoEM and cryoET) in the discussion - Updated the introduction to introduce the concepts of microtubule and mitochondrial morphology changes during infection that are covered in depth in the discussion - Included additional microscopy experiments, including super-resolution structured illumination microscopy (SIM), to demonstrate the changes in Golgi (Figures 6 and 7), microtubule (Figure 8) and mitochondrial (Suppl. Figure 4) morphology that accompany HSV-1 infection. These additional experiments support the hypotheses presented in the submitted manuscript, namely that microtubule organising centres are disrupted, Golgi membranes dispersed, and mitochondria redistributed as HSV-1 infection progresses.

The authors perform imaging with a 40nm or a 25nm zone plate, where the 25nm zone plate provides improved resolution of a smaller volume compared to the 40nm zone plate. The authors do not really make use of the improved resolution offered by the 25nm zone plate in the results, so the motivation for turning to this (and therefor also changing cell line) is a bit unclear. The reason for the U2OS cell line to better preserved during X ray imaging is also not discussed, maybe it has to do with the thickness of the cells (as the U2OS cells are very flat). Furthermore, images from the 25 nm zone plate are not compared side by side to neither the 40nm zone plate nor standard TEM, which makes it hard to judge what the increased resolution really brings.

Only one zone plate can be installed at any one time in the microscope and altering the zone plates requires extensive hardware changes that are outside the control of beamline users. We agree that this was not clearly discussed in the text. We have included additional text in the results (lines 207–208) and methods (lines 633–638) explaining this operational limitation and clarifying which zone plate was used for which experiment. In this study we observed that tomograms acquired with the 25 nm zone plate did not provide significantly more biological information than with the 40 nm zone plate, and thus both are suitable for characterisation of overarching cellular ultrastructural changes that accompany infection. We have added a sentence to this effect to the discussion (lines 410–412). Like U2OS cells, HFF-hTERT cells are also very flat. They appear more robust compared to HFFs when used for protracted exposures to soft X-rays and less likely to suffer from heat deposition after an extensive data collection round. We can speculate at this point that this could conceivably be due to the particular chemical composition of the intracellular environment in different cell lineages but it is impossible to offer anything other than speculation and therefore we have refrained from commenting further on this in the manuscript.

The switch from a 40 to a 25nm zone plate required a switch in the model system, as mentioned above. The chosen cell types are not linked to biological relevance however (neurons and epithelial cells are mentioned as relevant cell types in the introduction), and it is therefor a bit unclear what the relevance is of keeping results from both cell types and comparing the two, rather than sticking to the one that works with cryoSXT. The results from the U2OS cells could still be compared by LM to the HFF cells if this contributes to the aim of the study.

U2OS cells were chosen because they have been used previously for studies of HSV-1 infection (references 55–56) and are known to be well suited to cryoSXT analysis (references 32–33). We have added a sentence to this effect to the results (lines 208–211).

The distribution of the viral proteins of the timestamp reporter virus is used to categorize infected HFF cells into 4 infection stages. In the U2OS cells the protein distribution is a bit different, which only allows them to be categorized into early (stage 1+2) and late (stage 3+4) stage of infection. Although this is what the authors state in the text, all 4 stages are included in Fig.2 for the U2OS cells, so it is not clear how this subdivision is performed and it does not seem like an accurate representation of the data. Furthermore, the uninfected population is not included in the timecourse, and there is not really a gradual change in infection states over the different timepoints as one could have expected. Therefor it is a bit hard to see the relevance of the timecourse. In the paper where the reporter virus is published (ref 16), shorter infection times were used, which leads to a more gradual change in infection stages.

We thank the reviewer for pointing out these omissions. We have updated Figure 2A to only show the categories early (stage 1+2) and late (stage 3+4) for the U2OS cells. Furthermore, we have repeated the infection time course experiment, quantitating uninfected cells in addition to infected cells and including additional time points (2-, 4- and 6-hours post-infection). This new data (Figure 2B) demonstrates that the temporal profiles of infection progression are similar in HFF-hTERT and U2OS cells. Furthermore, it supports our choice of 9 hours post-infection as a suitable time point for plunge freezing of samples in order to obtain a mixture of cells at early and late stages of infection.

There is a lot of importance given to the morphological changes of mitochondrial networks in infected cells. However, the quantification represented in Fig.5B is a bit unclear. The mitochondria are classified into different groups, but there is no specific description of the definition and cutoff values of each group. The name of some groups is also confusing, such as "short and long" mitochondria. Furthermore, there are large differences between replicates (suppl. fig. 2). The authors state that some mitochondria are swollen, which they interpret as a sign of apoptosis. They find these swollen mitochondria in 75% of the tomograms of uninfected cells in replicate number 3. If this is indeed cell death this replicate is not healthy.

We apologise that the categorisation of mitochondria was not sufficiently clear in the submitted manuscript. The categories were percentage of tomograms that had the different mitochondrial morphologies present, not percentages of mitochondria. Thus, tomograms with both short and long mitochondria were classified as “short and long”. We have re-generated Figure 5C and Suppl. Figure 2C as a Venn diagram to illustrate this point more clearly. We have also updated the legend of Figure 5C (lines 845–850) to state clearly that the diagram shows percentage of tomograms with the relevant mitochondrial morphologies. The categorisation was performed manually and we have included examples of each category in Figure 5A. Manual classification can be subjective but, given the large number of tomograms analysed and the clear distinction between morphology in uninfected vs early- and late-stage infected cells, we are confident that our results are robust. We note that we have deposited all of the source tomograms in the Apollo repository at the University of Cambridge (https://doi.org/10.17863/CAM.78593); the data we used for this analysis are thus freely available for inspection and re-analysis by interested colleagues. We note that the swollen mitochondria were observed in multiple samples of uninfected and infected cells. This suggests that, regardless of infection, this is a common phenotype of U2OS cells. Others have observed this morphology by EM in the context of apoptosis and suggest it may represent porous mitochondria (reference 61). Although the proportion of tomograms containing these swollen mitochondria were higher in the uninfected sample of replicate 3, the other 25% contained typical mitochondrial morphologies that we could include in our analysis. The presence of inter-cell morphological variability such as this highlights the importance of imaging multiple cells within a population and performing several distinct biological replicates, as we have done in this study, to ensure project-relevant information is captured and delineated from the background structural variability inherent within a cell population. Previous cryoSXT studies had observed (but did not specifically comment on) a similar swollen mitochondrial morphology (reference 59). However, out of an abundance of caution we excluded all tomograms with swollen mitochondria from our analysis of mitochondrial branching (Figure 5C). Moreover, Tukey tests were performed per replicate for each pair of conditions in Figure 5C and statistical significance was reported only if it was observed independently in all three replicates. We are thus confident that any sampling error in replicate 3 that may arise from excluding tomograms will not have meaningfully altered our conclusions.

Minor comments

Results section 1, line 115-117: Where the authors state that it is unclear whether "naked" HSV-1 capsids would be visible by cryoSXT, it would be useful to refer to literature where these are observed by TEM, or to compare to TEM in their own experiments.

We have included references to previous TEM studies in the results (lines 128–129), as requested. However, we note that TEM and cryoSXT are fundamentally different as TEM uses contrast agents whereas contrast in cryoSXT arises from differential elemental densities (in particular the density of oxygen versus carbon or phosphorous). We have updated the results (lines 129–131) to clarify this point.

Results line 143: The authors state that it's hard to observe the perinuclear viruses with TEM, but there are several examples of this in the literature that could be referenced, e.g. (Skepper et al. 2001; Leuzinger et al. 2005; Baines et al. 2007; Johnson and Baines 2011), although this does not mean that they are not hard to find or that 3D is not advantegous.

We thank the reviewer for these references and we have added them to the manuscript.

Fig.4: It is unclear why all the vesicles are open-ended

This is due to the differential path-length of carbon rich (and thus high contrast) membrane traversed by the X-rays for the membranes normal or parallel to the incident X-ray beam. We have clarified this point in the results (lines 290–301).

Some places in the manuscript PFU per cell is used, other places MOI

Thank you for pointing this out. For consistency, we have changed all instances of PFU per cell to MOI.

If some specific adjustments to the methods had to be implemented for bio safely reasons (virus work), this should be stated in the methods.

We have added a section on biosafety measures to the methods (lines 562–568).

Access to the synchrotron should also be described

We have expanded the synchrotron access attribution the Acknowledgments section (lines 737– 738).

Discussion line 320: "consistent with previous research" - there is a reference missing.

Thank you for spotting this. We have now added the reference.

The quantifications are based on a limited number of tomograms, but there is no statement as to how the specific tomograms were selected. With a variability between replicates and tomograms, a random selection is important.

We included all tomograms collected for the relevant experimental condition in all our analyses unless otherwise stated. For the vesicle segmentation we chose four reconstructed tomograms from each condition at random (lines 690–691). For lipid droplet volume analysis and mitochondrial branching analysis we included all tomograms that matched our quality-control criteria. We have added a few sentences to the Segmentation and Graphs and Statistics sections of the methods (lines 691–694 and 724–733) describing our selection criteria for the lipid droplet, vesicle and mitochondrial branching analysis, respectively.

If gold fiducials are visible in the tomograms it could be useful to indicate, as they can look similar to lipid droplets to a non-expert reader.

We have indicated gold fiducials Figure 1 H, the only figure in which they are visible, with a gold star as requested.

Suppl. Fig.2: For clarity it would be good not to use the same color arrows to indicate different things in A and B.

Suppl. Figure 2B has been removed in response to another reviewer request.

Reviewer #1 (Significance):

The authors of this study demonstrate that cells infected by HSV-1 virus can be investigated by the use of cryoSXT, and use this to show that infected cells have more elongated and interconnected mitochondria, and an enrichment of small vesicles close to the nucleus. They thereby also show that cryoSXT offers a nice resolution for characterizing morphological changes in significant volumes of near native-state cells, and that the method offers a promising throughput for screening of large amounts of cells. However, the study does not really present new biological or technical advances compared to previously published literature, see e.g. Müller et.al. 2012, Duke et.al 2014, Perez Berna et.al. 2016, Groen et.al. 2019, Weinhardt et.al. 2020, Loconte et.al. 2021 (not cryo but demonstrates the advantage of capillaries), Kounatidis et.al. 2020, Scherer 2021 (ref 16 from paper), some of which are also referenced in the current study. The study could thus have profited from a more defined focus and possibly further experiments (live-cell imaging, CLEM, TEM, microtubules or more mechanistically focused) depending on the main interest of the authors. The advantage with the current broad focus (assuming that the main concerns are addressed) is that the study could interest a larger audience, ranging from virology, cell biology and immunology to microscopy and methods development.

We thank the reviewer for recognising the broad audience that will be interested in our manuscript. We believe that our analysis highlights the broad applicability of cryoSXT for analysing cell ultrastructure and changes that occur in response to infection. Furthermore, we think that our use of robust numerical analysis to quantitate the phenotypes we observe highlights the strength of cryoSXT as a high throughput technique for ultrastructural analysis. Our study is the first to investigate HSV-1 infection using cryoSXT and, in addition to confirming previous ultrastructural changes observed using other methods, we present new biological insight in organelle architecture and distribution such as that lipid droplets undergo a transient size increase during early stages of infection. We believe that we have demonstrated the robust utility of cryoSXT as a tool to study ultrastructural changes in response to insults, such as infection by intracellular pathogens, and hope that our manuscript will act as inspiration for others seeking to use cryoSXT to image cellular ultrastructure.

Reviewer #2 (Evidence, reproducibility and clarity):

The authors use soft X-ray tomography to examine cell structure following infection by herpes simplex virus-1 (HSV-1). This imaging method can provide 3D images of cryo-preserved intact cells without chemical fixation or staining. The authors find several morphological differences between uninfected and infected cells, including changes in the number and size of vesicles and in the size and shape of mitochondria.

This is a well-done study with careful and extensive analysis that in general produces convincing images to support the authors' conclusions. The procedures are clearly described and reproducible, and the authors have examined an impressive number of images and have performed appropriate statistical analyses.

We thank the reviewer for their positive comments.

I had two comments / suggestions regarding the findings about changes in morphology after infection. First, in the Discussion, the authors consider the possibility of Golgi fragmentation. Can the authors test this by counting Golgi before and after fragmentation?

We did not frequently observe well-defined Golgi apparatuses in our tomograms, consistent with previous cryoSXT studies (reference 61). We therefore performed new experiments using SIM microscopy to demonstrate the disruption of Golgi apparatus and trans-Golgi network in fixed U2OS cells stained with the markers GM130 and TGN46, respectively. These new results are presented in Figures 6 and 7 and in the results (lines 342–355).

Second, in the Results the authors report that they did not observe a change in lipid droplets after infection. However, the late-stage image in Fig. 5A seems to show such a change, with the lipid droplets becoming larger and darker relative to the early stage or uninfected cells. Maybe this is just the particular image that was selected, but perhaps it is worth looking at more images by eye just in case the segmentation procedure somehow missed this change.

We thank the reviewer for suggesting we re-visit the properties of lipid droplets. Based on this suggestion we segmented the lipid droplets from 94 tomograms and found a robust change in the median volume of lipid droplets at early stages of infection. We have included this new data in Figure 4C, Suppl Figure 2 and the text of the results (lines 302–312). The observation that lipid droplet volumes change is particularly interesting as another group recently observed similar changes in lipid droplets in response to HSV-1 infection of astrocytes and they postulate that this may modulate the cellular immune response (reference 85). Our data support and extend their conclusions, as described in the discussion (lines 476–494).

Minor comments:

Line 127 - As I understand it, the alignment by fiducial markers corrects primarily for small inaccuracies in tilting of the stage. Hopefully there are not significant vibrations in the microscope because this would also lead to loss of resolution during the exposure of each tilt angle.

Thank you, we have corrected “vibrations” to “small inaccuracies in tilting of the microscope stage”.

Line 145 - "electron light" Is this common usage? To me it seems more accurate to just say electrons because light to me means photons.

Thank you, we have corrected “electron light” to “electrons”.

Line 390 - detection OF ("of" is missing)

Thank you, we have made the correction.

Line 564 - Fig. 2 legend. "partial retention in the nucleus of U2OS cells". I am not sure where the nucleus is in the images. To me, it looks like there is almost no stain for ICP0 in hTERT at stage 1 and stage 3, and then cytoplasmic stain at stage 2 and stage 4. In contrast, for U2OS, the stain looks mostly nuclear until stage 4 when it is partially cytoplasmic. This all needs to be better explained, and perhaps arrows added to the images such that the reader does not have to guess.

We agree and have added a silhouette around each nuclei in Figure 2 to make this clearer. We have also added arrows to indicate the gC-mCherry enriched juxtanuclear compartment in cells at stage 3 (HFF-hTERT) or a late stage (U2OS) of infection.

Line 585 - The authors could consider rotating the images by 180{degree sign} in panel A (late) in order to maintain the same orientation of nucleus and cytoplasm. This would make it easier for readers to see the point.

Done as requested.

Line 614 - I could not find the length of the scale bar in the legend.

We apologise for omitting this – is has now been added.

Reviewer #2 (Significance):

The significance of the study is two-fold. First, it is a nice technical demonstration of what can be accomplished using soft X-ray tomography. I am qualified to evaluate this, since my expertise is in biological applications of this technique. The second significant aspect of the study is the demonstration of morphological changes in mitochondria and vesicles. I am not a virologist, so I do not know the literature on this point with regard to virus infection, but I find it interesting that the authors were able to detect such changes.

We thank the reviewer for their positive assessment of our work.

I believe the authors should cite a couple of papers:

10.1016/j.cell.2015.11.029 which looks at HSV infection and reports viral particles between the inner and outer nuclear membrane.

We have included a citation to this work as requested (lines 162–165).

10.1016/j.jsb.2011.11.025 which also reports nuclear membrane separations or bulges by soft X-ray tomography.

We have elaborated on this section and incorporated the reference as requested (lines 265– 276).

Regarding these nuclear membrane bulges, there are a number of papers that show they can also arise from mutations in nuclear-lamin associated proteins like nesprin and SUN (see for example https://doi.org/10.1093/hmg/ddm338). This is perhaps something interesting for the authors to think about, but not necessary for the current manuscript.

Thank you for this comment. We did consider studying the breakdown of the nuclear lamina during HSV-1 infection, as this has been shown in previous studies [e.g. 10.1101/2021.06.02.446771]. However, we could not robustly resolve the nuclear lamina from the nuclear envelope in uninfected cells. The nuclear lamina is quite thin (30–100 nm in width) and this may have confounded its identification.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary:

The manuscript by Nahas et al. describes the structural studies performed in U2OS cells infected with a recombinant HSV-1 virus that enables tracing the stage of the infection using fluorescent markers. This system was used to determine major structural changes in HSV-1 infected cells using cryo-soft X ray tomography (cryo-SXT) on near native-state samples. The data presented complement previous studies (particularly ref.16) using similar reagents but different microscopy techniques. While the data are generally well presented and discussed, they do not provide any substantially novel information on the structural changes in HSV-1. Nevetheless, they constitute an interesting technical achievement.

We thank the reviewer for supporting the technical quality of the analysis. In response to the comments of another reviewer we have extended our analysis and documented new biological information for this system relating to lipid droplet re-shaping and distribution in response to HSV-1 infection; all our new findings are included in the updated manuscript.

Major comments:

There are no major concerns on the data, although some of the statements could be revised for a more realistic interpretation of the results.

• In Figure 1F and lines 152-156 it is stated that a bulging of the nuclear envelope occurs around some of the putative particles, while in lines 243-244 and lines 625-628, it is stated that bulging occurs both in mock and infected cells. This should be clarified to avoid confusion. It is possible that authors differentiate both situations and this should be more clearly stated.

Many thanks for identifying a possible area of confusion. We have updated the results to clearly distinguish the expansion of the perinuclear space that accompanies virus nuclear egress (lines 160–175) from the bulges of the nuclear envelope that are observed in uninfected and infected cells (lines 265–276).

• The statistical tests are different for different hypothesis testing throughout the manuscript. The authors should justify in the methods section the use of one or another test. This will contribute to clarity in the hypothesis that is being test and will clarify the reason for the selected test.

We have significantly expanded the Graphs and Statistics section of the methods (lines 703– 734) to further justify the statistical tests used throughout our study.

• Sentence: "Our observation..." in lines 349-352. Even though the sentence is in the Discussion it is wildly speculative. The authors could use different approaches to tackle experimentally the question of whether active fusion or faulty fission is involved, but this is not the main subject the manuscript. Please revise the sentence or address experimentally, this would provide new insight into the impact of HSV-1 infection on mitochondrial network morphology. This sentence could be qualified as "speculative".

We agree that this section of the discussion strayed into speculative territory and have removed it from the updated manuscript.

• Although ref.16 provides evidence supporting Golgi fragmentation and mitochondrial elongation after HSV-1_timestamp virus infection in HFF cells, it would be important to show confocal microscopy data in U2OS cells, which were used for cryo-SXT, particularly since the authors refer differential virus kinetics and subcellular distribution of viral antigens in these cells. These would greatly contribute to support the statements regarding these two phenomena. It is very likely that the authors already have the data and could easily show them.

We have included new microscopy experiments to demonstrate changes in mitochondrial (Suppl. Figure 4) and Golgi (Figures 6 and 7) morphology that accompany HSV-1 infection, and these new experiments are now included in the results (lines 335–310 and 342–355).

-Line 269: Apposition of lipid droplets and mitochondria is not thoroughly described. This statement requires quantitation. Optimally, confocal imaging using Mitotracker and bodipy493/503 or superresolution imaging using specific antibodies may also contribute to strengthen the statement.

We agree with the reviewer that we do not at this stage have adequate data to support this assertion and have therefore removed it from the manuscript.

• It would be of great interest to document the budding events observed by cryo-SXT using higher resolution techniques and the kinetic resolution provided by the fluorescent infection fiducials. This would confirm the nature of the particles (using immunogold) and would demonstrate the the usefulness of the cryo-SXT data. This by itself would justify the use of cryo-SXT to temporally locate events that are difficult to visualize otherwise (as stated by the authors).

We agree with the reviewer that a correlative imaging strategy involving cryoSXT and fluorescence microscopy could aid in identifying features of infection, and have highlighted this interesting future direction in the discussion (line 406–409). However, performing such analysis will be a substantial experimental commitment in its own and is outside the scope of our current manuscript.

Minor comments:

• Given that the software used for segmentation (Contour) is not published, a minimal comparative description between manual and semi-automated segmentation may be shown in the supplementary, to illustrate the robustness of the new method and the reliability of the measurements.

We have now published a preprint (recently accepted in the journal Biological Imaging) that describes Contour in detail, which we have referenced in the updated manuscript: Nahas, K. L., Ferreira Fernandes, J., Crump, C., Graham, S. C. & Harkiolaki, M. (2021) Contour, a semi-automated segmentation and quantitation tool for cryo-soft-X-ray tomography. http://biorxiv.org/lookup/doi/10.1101/2021.12.03.470962

• Lines 278-280: statistical test and p value are not shown.

We have updated the text to include details of the statistical test and p value as requested (lines 326–330 of the updated manuscript).

• After line 376: It would be interesting to mention that transient elongation of mitochondria is observed during dengue virus infection (https://doi.org/10.1016/j.chom.2016.07.008) and that this has also consequences for innate immunity against viruses.

We thank the reviewer for this suggestion, which we have incorporated into the discussion (lines 522–523).

• Given that HSV-1 is a BSL-2 level virus and that a recombinant version (GMO) has been used in the study, the authors should describe the biosafety measures taken to image non-inactivated infectious samples by cryo-SXT. The authors should state that a biosafety committee has reviewed these activities.

We have included a Biosafety Measures section to the methods (lines 562–568) that details the biosafety measures used and their approval by the relevant committees.

Reviewer #3 (Significance):

This study constitutes an incremental technical advance in the study of HSV-1 infection. The broad context and the quasi-native structure of the cells enables documenting events that are difficult to observe thin sections for TEM.

This study is one of the few examples of the use of cryo-SXT for infected cell imaging. Other examples of the literature are cited as well as previous structural studies performed with higher resolution techniques.

The manuscript may be suitable for HSV-1 specialists and cell biologists interested in using near-native samples for gross cellular imaging and documentation of low-resolution maps revealing alterations in large subcellular structures.

We thank the reviewer for highlighting that ours is one of only a few comprehensive studies using cryoSXT, illustrating how it can be used to image cellular processes that are hard to ‘catch’ using techniques that require ultra-thin sectioning, and as such that it will be of interest to cell biologists studying infection processes in cellulo.

Reviewer #3 (Public Review):

The manuscript presents data that high expression of Protein Phosphatase 1 inhibitor in triple-negative breast cancer contributes to the poor outcome by downregulation of an important kinase, GSK3β. If substantiated, this would enhance our understanding of the pathophysiology of this important disease and might suggest new treatment options. Indeed, changes in PPP1R14C expression alter the behaviour of TNBC in cells and in mouse models, but the mechanistic links to GSK3 are not robustly established.

Fig 1-2 identified the PPP1R14C as upregulated in TNBC and with a significant correlation with worse outcome. Fig 3 and 4 show in vitro and in vivo effects of changes in PP1R14C consistent with increased proliferation, migration and metastasis in vivo. These studies look very solid and appear to identify a role for this phosphatase regulator in TNBC.

The weaker part of the manuscript is the mechanistic link to GSK3 regulation. Over-expression and knockdown of PPP1R14C have effects on GSK3β phosphorylation and downstream targets, but the direct connection is unclear and made challenging by a number of complex experimental issues.

The big questions -<br /> 1. Is GSK3 directly ubiquitylated by TRIM25 on K183? I don't think the data are strong here, for reasons elaborated on below.

2. Is GSK3 really the important target of PPP1R14C/PP1 complex? The biological data are correlative and the direct experiment, does GSK3β (S9A/K183R) rescue PPP1R14C over-expression, would need to be done. But since I suspect K183R is kinase-dead, this may fail.

3. The studies with C2 are confounded by the broad effects (including on PP2A) of treating cells with ceramide. Calling C2 a specific PP1 activator is I think unwarranted.

Specific comments:<br /> Why is there a band in Fig 5D lane 2, the Flag-PPP1R14C lane, in the absence of Flag-PPP1R14C?

Why in Fig 5E, F, G are there two bands in the pGSK3bS9 blot?<br /> The authors would need to show the total GSK3 coming down here too, and the total GSK3 present in Fig 5H as well.

I have trouble understanding the result in Fig 5H. According to this, global PP1 phosphatase activity increases 3 fold when PPP1R14C is knocked down. First, there is no method noted for this assay. How do we know this is specific to PP1? Second, PPP1R14C is only one of many PP1 interactors. How can its knockdown change cellular PP1 activity 3-fold? I note the knockout mouse for PPP1R14C had a 15% increase in thalamus PP1 activity (see fig 3, https://doi.org/10.1016/j.neuroscience.2009.10.007). This experiment needs much more in the way of controls.

Fig 6 evaluates the role of PPP1R14C in GSK3 protein stability. There is a fundamental weakness here - How do the authors know the ubiquitylated smear in the various Fig 6 assays is GSK3 versus a ubiquitylated protein that interacts with active GSK3? GSK3 phosphorylation directs many proteins (famously β-catenin and Myc) for ubiquitylation and degradation, so the co-IP of ubiquitylated proteins with GSK3 is to be expected if the IP stringency is not very very high. This is consistent with inactive pSER9 GSK3 not bringing down ubiquitylated proteins. An IP after for example boiling in SDS to break up large complexes would be needed to test if GSK3 itself, rather than associated substrates, is directly ubiquitylated.

Is TRIM25 specific for GSK3? It's identified by mass spectrometry. However, when I plug TRIM25 into the CRAPome database (https://reprint-apms.org) I find it comes down in 136/716 (19%) of all MS IP studies, making it a very common contaminant in IP. Thus the bar is high to show this is specific. Here the interaction is validated with over-expression of various truncation mutants.

Line 235: "K183 of GSK3β has been recognized as the ubiquitylation site". First, what is the reference for this statement? I found one paper (https://doi.org/10.1074/jbc.M116.771667 that claims this residue is important for FBXO17 K48 modification, not the K63 linkage associated with TRIM25). In the crystal structure of GSK3β, that K183 appears to coordinate the phosphates of ATP, so the effect of the K183R mutation may be to make the kinase inactive, which would confound their results. So an important experiment is, does K183R retain wildtype kinase activity? Or is it inactive, and so act like the phosphorylated S9 GSK3?

The reference for ceramide as a PP1 activator is not a primary reference, it is to a paper in the Journal of Endodontics, which uses it. It would be important to cite primary literature for this usage of C2. I note that many papers cite C2 ceramide as a PP2A activator. It is unclear what the rationale is for using it as a specific PP1 activator?

This is a "free write" that we did in class recently to think on how we want our experiences in this class to play out during the rest of the semester. As you can see from the first few phrases, I explained how I wanted to learn as much as possible to help me in the future. I made it very obvious that "it wasn't about the work" and that it goes far deeper than that.

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Reply to the reviewers

1. General Statements [optional]

We would like to thank the reviewers for their helpful and constructive comments.

2. Point-by-point description of the revisions

Reviewer #1

This reviewer thought our findings would be of interest to a broad range of scientists from both the centrosome and mitosis fields, but noted some important aspects for improvements.

Additional Experiments (we number these points for ease of discussion).

1. • Figure 3. The reviewer points out that because our analysis of Ana2-∆CC and Ana2-∆STAN mutant proteins was conducted in the presence of endogenous WT protein, we should be more cautious in our interpretation.* We agree and apologise for overstating these findings. We have now rewritten the title and text of this section to be more cautious (p11, para.2)

2. Figure 5A. The reviewer wonders whether the reduced recruitment of Sas-6 in the presence of Ana2(12A) is due to reduced binding, and they request we test this biochemically. This is our favoured interpretation, but we have been unable to test this biochemically for two reasons. First, although we have successfully purified several recombinant Sas-6 and/or Ana2 fragments (Cottee et al., eLife, 2015), the full-length proteins are poorly behaved (tending to precipitate, likely due to their inherent ability to self-oligomerise). Thus, we have been unable to reconstitute their interaction in vitro*. Second, as we show here, the proteins are normally expressed in embryos at surprisingly low concentrations (~5-20nM), and we can detect no interaction between them in coimmunoprecipitation experiments from embryo extracts (not shown). Indeed, this concentration is so low that Sas-6 does not even appear to form a homo-dimer in the embryo, even though Sas-6 clearly functions as a homo-dimer in centriole assembly (new Figure S4A). We now explain these points, and state that our favoured hypothesis that Ana2(12A) has reduced affinity for Sas-6 (or other core duplication proteins) remains to be tested (p22, para.2).

3. The Reviewer wonders if all 12 of the potential Cdk1 phosphorylation sites that we mutate in Ana2(12A) are important in vivo, and whether we have tested whether mutating fewer sites (e.g. the two sites [S284/T301] that we show are phosphorylated by Cdk1/Cyclin B in vitro) might be sufficient to recapitulate the Ana2(12A) phenotype. *We have now tested this by mutating just the S284/T301 sites to Alanine [Ana2(2A)], but the results were not very informative (Reviewer Figure 1 [RF1]). Whereas Ana2(12A) is recruited to centrioles for a longer period and to higher levels than WT Ana2 (Figure 4A), Ana2(2A) is recruited to centrioles for a normal period but to lower levels (RF1A,B). The interpretation of this result is complicated because western blots show that Ana2(2A) is also present at lower-levels than normal (RF1B). Thus, it is clear that Ana2(2A) does not recapitulate well the behaviour of Ana2(12A). We have decided not to present this data as it is difficult to interpret and it does not change any of our conclusions.

4. Figure 6. The reviewer asks whether the 12A mutations impair the interaction with Plk4, influence Plk4’s kinase activity or the ability of Plk4 to phosphorylate Ana2. These are excellent questions but, for the same reasons described in point 2 above, we cannot address them biochemically as we cannot purify well-behaved recombinant full-length Ana2 or active Plk4 in vitro, and both proteins are present at such low levels in the embryo that we cannot detect any interaction between them in embryo extracts. We are working hard to reconstitute in vitro* systems to probe these important points, but it may be sometime before we are able to do so.

5. Figure 7. The reviewer suggests that the 12D/E phosphomimetic substitutions introduce more negative charge than the putative phosphorylation of Ser/Thr residues and they ask if the Ana2(2D/E) [stated as Ana2(3D/E)] is, like the Ana2(12D/E) mutant, not efficiently recruited to centrioles.* This is a fair comment, but we have not analysed an Ana2(2D/E) mutant because, as described in point 3 above, the Ana2(2A) mutant did not recapitulate well the Ana2(12A) phenotype.

Minor comments

1. • Figure S1. The reviewer requests that we show that the mNG tag on its own is not recruited to centrioles.* We do not show this (as it would create a lot of white space in this Figure), but now state that mNG and dNG do not detectably localise to centrioles (p7, para.1).
2. • Figure S4C.* We have included the missing error bars (now Figure S4B).
3. • Figure S5A. The reviewer asks about the expression levels of the Ana2(12A) mutant, which are not shown in this Figure. They also state that the expression levels of the transgenes shown in Figure 5A are not similar.* The expression level of Ana2(12A) is shown in Figure S9, as this data was analysed independently of the other mutant proteins shown in Figure S5. We agree that it was overly simplifying the situation to state that the expression levels of WT Ana2-mNG, eAna2(∆CC)-mNG and eAna2(∆STAN)-mNG were “similar” (Figure S5), and we now specifically mention the differences between them (p11, para.3). Reviewer #2

This reviewer found this a rigorous study that advances our understanding of the regulation of centriole duplication, but raised some minor points.

Minor Points

The reviewer requests that we mention the literature describing how Ana2/STIL can influence the abundance and centriolar localisation of Plk4. We apologise for this omission, and have amended our description of this literature in the Introduction to include this point (p3, para.2).

The reviewer notes that we interpret the ability of the Ana2(12A) mutant to keep incorporating into the centrioles for a longer period as being consistent with our idea that rising levels of Cdk activity during S-phase normally reduce the ability of WT Ana2 to bind to the centriole. They ask us to show how Cdk activity increases over this time-course, and to test whether dampening Cdk has the same effect on Ana2 recruitment (i.e. allows Ana2 to be recruited for a longer period). The time-course of Cdk activation in these embryos has been reported previously (Deneke et al., Dev. Cell, 2016; we present the relevant data from this paper in RF#2A [black line]). This reveals how Cdk activity rises throughout S-phase, which is crucial for our model. To assess the effect of dampening Cdk activity in these embryos we have now analysed the effect of halving the genetic dose of Cyclin B (RF#2B). This perturbation extends S-phase length, but has a complicated effect on the recruitment dynamics of Ana2 (RF#2B). As we would predict, Ana2 is recruited to centrioles for a longer period in these embryos, but it is also recruited more slowly (so it accumulates to lower levels). This is consistent with our hypothesis that Cdk1 activity might first stimulate and then ultimately inhibit the centriolar recruitment of Ana2. The interpretation of this experiment is not straightforward, however, as dampening Cdk1 activity alters Ana2 recruitment dynamics (and many other processes in the embryo) in complicated ways, so we have decided not to include it in the manuscript.

The reviewer suggests that it would be valuable to show that all 12 of the potential Cdk1 phosphorylation sites in Ana2 can be phosphorylated by Cdk1 in vitro. We think this would not be particularly informative as our hypothesis does not rely on all 12 sites being phosphorylated to generate the Ana2(12A) phenotype. We simply mutate all 12 sites because we don’t know which, if any, are relevant. Thus, showing that some/all of the 12 sites can/cannot be phosphorylated in vitro does not test any hypothesis and would not change any of our conclusions. We now explain our thinking on this in more detail (p12, para.2)

Other points

Figure 3. We have corrected the amino-acid numbering mistakes.

Figure 5Aii. We have changed the x-axis (time) labelling in this and all other Figures.

Figure Legends. We have tried to eliminate the typos from the Figure legends, and apologise that these errors made it through to the final submitted version of our manuscript.

Reviewer #3

This reviewer thought our manuscript would be of great interest to not only the centrosome field but also to cell biologists more generally. Although they had no major concerns, they made a number of suggestions for improvements.

1. As the reviewer suggests, we now explicitly state that although the Ana2(12A) mutant appears to be largely functional, the overall conformation of the protein may be altered, changing its function in ways we do not appreciate (p21, para.2).

2. The reviewer suggests we include a multiple sequence alignment of Ana2/STIL proteins to provide more context about the distribution and conservation of the 12 S/T-P sites mutated in Ana2(12A).* This is an excellent idea, and we now include this in a new Figure S6, where we also provide more information about which of these sites have been shown to be phosphorylated in embryo or S2-cell extracts

3. The reviewer is confused as to why the 12A and 12D/E mutants rescue the ana2-/- mutant flies so well, which suggests that the mechanism we propose here cannot be essential for centriole duplication. We understand this confusion and we now make this point more clearly and explain why we think this occurs in more detail (e.g. p22, para.1). We propose that Cdk normally phosphorylates Ana2 to inhibit its ability to promote centriole duplication, but this phosphorylation does not entirely block this function. So, if all other elements of the system are functional, Ana2(12A) is recruited to centrioles for longer than normal, but this does not dramatically perturb centriole duplication because the many other factors that regulate centriole duplication (such as the pulse of Plk4 recruitment to centrioles [Aydogan et al., Cell, 2020]) still occur normally and are sufficient to ensure that centrioles still duplicate normally. When Ana2 phosphorylation is mimicked [Ana2(12D/E)], the ability of Ana2 to promote centriole duplication is perturbed (but not abolished). This perturbation is lethal in the early embryo—where the centrioles must duplicate in just a few minutes to keep pace with the rapid nuclear divisions. In somatic cells S-phase is much longer, so these cells can still duplicate their centrioles (as we observe) even though Ana2(12D/E) does not function efficiently. As we now explain, this phenotype (being lethal in the early embryo, but not in somatic cells) is a common feature of mutations that influence the efficiency* of centriole and centrosome assembly (p17, para.2).

4A. The reviewer asks us to comment in more detail on why centrioles do not seem to be elongated in the Ana2(12A) mutant wing disc cells (now Figure S8C), even though we show that Ana2(12A) (Figure 4A), and also Sas-6 (Figure 5), are recruited to centrioles for an abnormally long period. This is an excellent question and, although we do not know the answer, we now discuss this interesting point in more detail (p16, para.1). We think this is likely due to the “homeostatic” nature of centriole growth: in our hands, almost any perturbation that makes centrioles grow for a longer/shorter period, also makes them grow more slowly/quickly, so that they tend to grow to a similar size (Aydogan et al., JCB, 2018; Cell, 2020). This is fascinating, but poorly understood. When we perturb the system by expressing Ana2(12A), both Ana2(12A) and Sas-6 incorporate into centrioles for a longer period, as we predict (Figure 4A and 5A). Unexpectedly, however, Sas-6 is also recruited to centrioles much more slowly. Thus, as so often happens, when we perturb the system so the centrioles grow for a longer time, the centrioles “adapt” by growing more slowly. We do not currently understand why this occurs (although we speculate that Ana2 may also be regulated by Cdk/Cyclins to help recruit Sas-6 to centrioles in early S-phase). In the embryo, where S-phase is very short, this homeostatic compensation is not perfect, and the centrioles appear to actually be shorter than normal. In somatic wing-disc cells, where S-phase is much longer, we suspect that there is more scope for homeostatic compensation and so the centrioles grow to the correct size.

4B. In this point (also labelled [4] by the reviewer, so we have retained this numbering but labelled the points A and B) the reviewer asks why levels of Ana2(12A) eventually decline at centrioles once the embryos actually enter mitosis. The reviewer notes our rheostat theory, but suggests a discussion of other mechanisms might be interesting. This is a good point, and we agree that the observation that Ana2(12A) levels ultimately still decline at centrioles during mitosis is likely to be important in explaining why centriole duplication is not more dramatically perturbed by Ana2(12A). We now expand our discussion of this point, highlighting that other mechanisms must help to ensure that Ana2 is not recruited to centrioles during M-phase, and discussing the possibility that the receptors that recruit Ana2 to centrioles are themselves inactivated during mitosis by high levels of Cdk activity (p15, para.1). In such a model, the rapid drop in WT Ana2 centriolar levels is due to a combination of switching off Ana2’s ability to bind to centrioles (as we propose here) and switching off the ability of the centrioles to recruit Ana2. For Ana2(12A), only the latter mechanism would operate, so Ana2(12A) levels would start to drop later in the cycle (as the inflexion point at which Ana2 recruitment and loss balances out would be moved to later in the cycle), and these levels would drop more slowly—as we observe.

• The reviewer is confused to how the Ana2(12D/E) mutant can rescue the mutant phenotype when it is recruited to centrioles so poorly. Ana2(12D/E) is indeed recruited very poorly to centrioles in the experiment shown in Figure 7. However, this experiment had to be conducted in the presence of WT untagged Ana2—as the embryos do not develop in the presence of only Ana2(12D/E). We would predict that WT Ana2 would bind more efficiently to centrioles than Ana2(12D/E) (which appears to behave as if it has been phosphorylated by Cdk/Cyclins, and so cannot be recruited to centrioles efficiently). Thus, in the experiment we show in Figure 7, the Ana2(12D/E) protein is probably being “outcompeted” for binding to the centriole by the WT protein. In somatic cells expressing only* Ana2(12D/E) presumably sufficient mutant protein can be recruited to centrioles to support normal centriole duplication (as it no longer has to compete with the WT protein). We now explain our thinking on this point (p18, para.1).

• The reviewer wonders whether Ana2(12D/E) may be unable to homo-oligomerize, and this may explain why the protein is not recruited to centrioles efficiently even in the presence of WT protein. This is indeed a possibility, but we think it unlikely as it is widely believed that Ana2/STIL proteins must multimerize to be functional (Arquint et al., eLife, 2015; Cottee et al., eLife, 2015; Rogala et al., eLife, 2015; David et al., Sci. Rep., 2016). As Ana2(12D/E) strongly restores centriole duplication in ana2-/-* mutant somatic cells, it seems unlikely that it cannot multimerize. Nevertheless, we now specifically highlight that the 12D/E (and 12A) mutations might alter the ability of Ana2 to multimerise (p21, para.2).

We thank the reviewers again for their thoughtful and constructive comments. We hope they will agree that the revised manuscript is now improved and would be appropriate for publication in The Journal of Cell Biology.

With best wishes,

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Point-by-point description of the revisions

Black: Comments from reviewers

Green: Answers

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

Yamamoto and colleagues have investigated the interplay between microtubules (MTs) and actin in positioning the MTOC at "the cell centre". They have developed a novel experimental setup akin to a synthetic cell to study this question. Essentially a cell-sized (15 µm) microwell that is coated in lipid and then tubulin/actin added and the positioning of a MTOC proxy is studied by microscopy. This is a well executed study. These complicated biochemical reconstitutions are the hallmark of Blanchoin and Théry's group, but even so, it's clear that the exact conditions (e.g. tubulin concentration) are fiddly and critical for these experiments to work. The data are clear, well analysed and presented. In brief, the conditions for centring a cytoskeletal network and decentring/polarising it are recapitulated. This is a short, straightforward paper and I found the results to be clear and the authors' interpretation to be well supported by the data.

Two questions occurred to me as I read the paper: 1. While the setup is reminiscent of a cell, I suspect that the edge/wall of the microwell is much stiffer than the plasma membrane. So a MT that encounters the wall may behave differently in the cell. This would affect the non-actin conditions but possible also the conditions where an actin mesh is present. Maybe my intuition is not even correct, but I think this issue should be discussed in the paper as a potential limitation of the system.

Author response: We thank the reviewer for this wise comment. Indeed, the deformation of the container may impact the organization of the MT network, the force balance and the final position of the MTOC. We commented this limitation in the revised discussion (page 10 line 31). However, it should be noted that in the presence of a cortical actin network, MTs are much less capable of deforming the cell than in a vesicle or a in cell treated with actin drugs, so our conditions with a cortical actin network are physiologically relevant although the container can not be deformed.

1. The graphs in 3C and 4G (lesser extent Fig 1) show nicely that the aMTOC position has apparently rested at a steady state. Some representative trajectories are shown in some figures, but not mentioned much in the text. How does the pathlength (cumulative distance) over time compare to the "distance to centre" measurement? Is there more or less travel under the different conditions? From the supplementary videos it looks like there is a difference. An apparent resting position may still represent significant motion, e.g. circling the centre. What does an analysis of tracklength tell us, if anything?

Author response: We appreciated reviewer’s comment and followed his/her advice. We measured the pathlength (cumulative distance moved) based on the data shown in Figure 3C and 4G. The analysis confirmed that the MTOC was static in the presence of bulk actin network (shown in the new Supplementary Figure 6B). Interestingly, it also showed that the final position adopted by the MTOC in conditions where it could move more freely was also static, as revealed by the saturation of the pathlength after 1 hour. These analyses are shown in the new Supplementary Figure 6B for the centering in the absence of cortical actin, for the non-centering with long microtubules in Supplementary Figure 7E and for the centering with long MTs and a cortical actin network in Supplementary Figure 7E.

Very minor clerical point: - the first two sentences of the abstract could be clearer. "The position of centrosome, the main microtubule-organizing center (MTOC), is instrumental in the definition of cell polarity. It is defined by the balance of tension and pressure forces in the network of microtubules (MTs)." In the second sentence, "it" and "defined" are confusing. Are you talking about the position of the centrosome or cell polarity?

Author response: We thank the reviewer for this comment. As the reviewer suggested, this was a confusing description. Accordingly, we corrected the sentence in the abstract for :

The orientation of cell polarity depends on the position of the centrosome, the main microtubule-organizing center (MTOC). It is determined by the balance of tension and pressure forces in the network of microtubules (MTs).

Reviewer #1 (Significance (Required)):

As I see it, the main advance here is in novel experimental setup which has real potential in the field. Existing methods such as MTs inside lipid bubbles are limited, whereas as the microwell method with fabrication methods allows the shape of the "synthetic cell" to be carefully modulated. Tying the results together with cytosim simulations is also a powerful combination. There is a lot of interest in bottom-up reconstitution of cell biological phenomena, especially those that underlie specialised cell processes, e.g. polarity. My expertise: microtubules in a cellular context with limited experience of MT reconstitution assays.

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

Summary: This manuscript describes the use of an elegant in vitro reconstitution system to study the effect of variations in the organization of the actin network on the positioning of a microtubule organizing center (MTOC) within the cell. By using a reconstituted system the authors are able to specifically study the contribution of the "pushing" forces generated by microtubule (MT) growth, without the confounding influence of other factors, like pulling forces from MT motors. The authors find that a bulk actin networks at sufficient density can impair MTOC displacement, likely a result of the large viscous drag of the MTOC. Next they show that MTOC centering more resilient to changes in microtubule length. Finally they show that an asymmetric actin network can cause asymmetric positioning of the MTOC.

Major comments: 1) The model the authors put forth is that the growth of long MTs leads to decentering as a result of the MTs slipping along the well edge. The presence of a cortical actin mesh prevents this slipping. Their argument would be strengthened with and analysis of the MT behaviors in the various conditions. For example when discussing MTOC in well without actin...

"As they grew, they first ensured a proper centering but after an hour, MT elongation and slippage along microwell edges broke the network symmetry and MTs pushed aMTOC away from the center (Figure 1I, J and Supplementary Movie 2)"

In this movie I don't see evidence of MTs hitting the cortex and sliding on the "short" side of the well relative to the MTOC. An analysis of the behavior of MTs in various circumstances would help link the behavior of MTs to the movement of the MTOC for all of their conditions. What fraction of MTs hit the cortex and remain relatively motionless, what fraction slide, what fraction catastrophe, what fraction turn and follow the curve of the well? And how does this behavior change for microtubules that end up on the short side vs. the long side of the MTOC? This type of analysis would solidify their model for how centering/decentering occurs in the various conditions they test.

Author response: This is a fair criticism. The possibility to perform fine analysis of MT dynamics is technically limited by the fluorescent background due to free tubulin dimers. It is the reason why classical in vitro assays are monitored in TIRF microscopy, which is not possible here since MTOCs move in 3D in the microwells. In addition, working with higher laser power to increase the signal to noise ratio generates severe photodamages on MTs. Nevertheless, we could visualize MT dynamics and displacements near the edge of the microwells and describe their behavior more precisely than in the previous version of our manuscript. New images and tracking of MT behavior are now reported in the new Figure 4E, 4F and 5G, as well as the new supplementary Figure 4C, 4D, 7B, and 7C. We also replaced the supplementary movie 2 and Figure 1I in order to show more clearly MTs hitting and slipping along the well boundary. In addition, we also characterized the pivoting of MTs around the MTOC and near the edge of the microwell in order to better characterize the effect of cortical actin. This is now shown in the new Figure 4G and 4H as well as in the new Supplementary Figure 7C-D). We found that the changes in MT orientation and position, at the centrosome and at the contact with the microwell, were clearly prevented by the presence of cortical actin.

2) The authors use simulations to support their in vitro findings. However, their simulations have many more microtubules emanating from the MTOC than their experiment (Looks like about 50 in the cytosim and they state they are aiming for 15-20 in the aMTOCs). Do the simulations still reproduce the behavior of the in vitro system with a similar number of MTs?

Author response: This is another fair criticism. We addressed this point by performing simulations with 10~30 microtubules (the number of MTs is variable because of MT dynamics) which are more similar to the number of MTs that we obtained in our experimental conditions. Results were consistent with previous simulations with higher number of MTs and are now shown in the new supplementary figures 6E-F, 7G and 8I).

3) When the actin networks are asymmetric, the authors see decentering of the MTOC towards the side with less actin. However there is still actin on the side where the MTOC will move to and in some of their images it looks pretty think. Is the actin on that side not dense enough to prevent MT sliding along the "cortex"? If so, can they generate less dense, but uniform actin networks on the "cortex", where MTs can slide. Again descriptions of MT behaviors would be useful in understanding what is happening.

Author response: We thank the reviewer for asking this important question. We followed reviewer’s advice and generated homogeneous and less dense cortex by working at lower concentration of actin (0.5 mM). In such conditions, we could not see the centering effect that was observed with dense cortex. These new data are now shown in the new Supplementary Figure 7I. This effect was also tested with numerical simulations (new Supplementary Figure 7J) which were consistent with the key role played by actin network density for MT network positioning by cortical friction.

Minor Comments: 1)Title - the current title implies that actin is balancing the forces generated by the MTs. I'm not sure this is a good description of what is shown in the paper.

Author response: We thank the reviewer for pointing at this issue. We revised the title to:

Reconstitution of centrosome positioning by the production of pushing forces in microtubules growing against the actin network.

2)The discussion would benefit from more explanation about how the results of this paper relate to the classic examples of MTOC positioning they cite. How do they envision the actin and MTs interacting in these systems and what new insight have we gained from the experiments in this manuscript.

Author response: This is a good suggestion. We added some comments in our discussion about the actin network asymmetry in several classical examples of cell polarization and explained how our observations suggest some new interpretation on the role of this asymmetry in the reorganization of forces in the MT network and on the consequential peripheral positioning of the MTOC.

Reviewer #2 (Significance (Required)):

Overall, this work is a significant advance in our understanding of the potential mechanisms of MTOC movement in cells via pushing by MT growth. The experimental system they have developed is powerful advance, allowing meaningful MTOC reconstitution experiments to be performed in chambers of approximately cellular size. This is an important contribution to understanding the interaction between microtubule pushing and the actin cortex.

Reviewer expertise: Cell biology of MTOC assembly and positioning. I do not have the expertise to assess the parameters used to generate their cytosim models.

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

Review of "The architecture of the actin network can balance the pushing forces produced by growing microtubules" by Yamamoto et al.

The means by which cells maintain their characteristic cytoskeletal architectures is not well understood. This is in part because there is considerable variation in such architectures with, for example, fibroblasts, neurons, and epithelial cells. It is also in part because the microtubule, actin and intermediate filaments engage in a wide range of mechanical and signaling crosstalk mediated by a wealth of proteins and signaling networks, which further complicates the picture.

In the current study, Yamamoto take the welcome step of developing a simplified system for assessing the mutual contributions of microtubules and F-actin for general cytoskeletal organization in vitro (specifically, in lipid-lined microwells). This allows them to define basic principles of microtubule-F-actin interactions in the absence of the various confounding factors alluded to above. Using their model, they show that artificial MTOCs (aMTOCs) alone will center but as a complex function of microtubule length (controlled by varying tubulin concentrations). That is, the aMTOCs are randomly positioned with short microtubules, stably centered with intermediate length microtubules, and randomly oriented with very long microtubules (following symmetry breaking).

They then assess the contributions of F-actin to the centering process. In low concentrations of "bulk" F-actin (ie F-actin distributed throughout the droplet) there is no effect on centering whereas at higher concentrations of bulk F-actin, centering is impaired as is the translocation of the aMTOCs. In the presence of uniform peripheral F-actin, in contrast, aMTOC centering is enhanced, and rendered less sensitive to variations in microtubule length. Finally, when the authors contrive a situation in which the peripheral F-actin is non-uniform (by lowering the concentration of actin and adding alpha-actinin, which creates a peripheral ring of F-actin with (I think) relatively less F-actin within the ring), the aMTOCs position themselves within the ring.

Finally, the authors extend their results with simulations that indicate that the various behaviors can be explained by a combination of friction, pushing and slippage.

This study is fascinating and will be of general interest to anyone who seeks to understand the contributions of mechanical forces to cytoskeletal organization in a minimal system. I have only minor concerns; these are listed below.

1. Some of the terminology was a little confusing. The authors introduce the term "inner zone" (pg. 8) without defining it. From the context, it seems like they are talking about the approximate center of the ring of peripheral F-actin. If so, why not just do away with the term "inner zone" and refer to the ring center. If it isn't the ring center, then more explanation is needed as to what the inner zone actually is.

Author response: We apologize for this confusion and appreciate reviewer’s comment. We coined earlier the term “actin inner zone” to define the central cytoplasmic region in cells that is devoid of actin filament (Jimenez et al., Current Biology, 2021). Because it was a confusing point, we clarified this in the revised version of the manuscript (Page 8, Line 20). What we would like to call the “inner zone” is the region inside of the actin cortex. The definition of this zone and of its geometrical reference points were also pictured more precisely in the new Supplementary Figure 9B.

1. It is not clear from the text or the images if the region within the F-actin ring has less F-actin, more F-actin, or the same amount of F-actin as the region outside the F-actin ring. This point should be clarified, as it makes a big difference in the interpretation of the findings.

Author response: We apologize for this lack of clarity. In the revised version of our manuscript, we plotted a line scan intensity profile of the actin fluorescence (new Supplementary Figure 9B). It showed that the region within the actin inner zone contained much less actin than in the cortex. This is consistent with our interpretation of a region-selective pattern of friction acting on microtubules.

1. Ideally, the authors would include manipulations in which the high concentration of peripheral F-actin is combined with alpha-actinin because, as currently presented, the authors are drawing conclusions from changing two variables at once (ie going from a high concentration of peripheral F-actin to a lower concentration with added alpha-actinin). Thus, the authors cannot cleanly distinguish between effects that arise from F-actin asymmetry versus the presence of an F-actin crosslinker. Since the crosslinking is likely to change the mechanical properties of the peripheral F-actin network, this point should at least be addressed in the text, if not by experiments.

Author response: We are not sure to fully understand the reviewer’s point. We don’t understand how the crosslinking of a symmetric actin network could break the symmetry of the MT network and force its off-centering. The opposite is clearer to us. A homogeneous and loose actin network can allow MT gliding and MTOC off-centering (like in in Supplementary Figure 7J). The mechanical reinforcement of this network by crosslinkers could indeed resist gliding. But the consequence of this resistance would be similar to the consequence of a dense network: a more robust centering (like in Figure 4). So we don’t understand how the crosslinking by alpha-actinin, rather than the asymmetry of the actin network, could be at the origin of the off-centering we observed. In addition the off-centering of the MTOC was systematically aligned with the asymmetry of the actin network, so both parameters were clearly connected.

Reviewer #3 (Significance (Required)):

This is an elegant, well-designed study that provides a clear description of how basic mechanical forces can contribute to cytoskeletal organization in a simplified model system.

The Logos, Ethos, and Pathos of IndieWeb

Where is the IndieWeb?

Logos

One might consider the IndieWeb's indieweb.org wiki-based website and chat the "logos" of IndieWeb. There is a small group of about a hundred actove tp very active participants who hang out in these spaces on a regular basis, but there are also many who dip in and out over time as they tinker and build, ask advice, get some help, or just to show up and say hello. Because there are concrete places online as well as off (events) for them to congregate, meet, and interact, it's the most obvious place to find these ideas and people.

Ethos

Beyond this there is an even larger group of people online who represent the "ethos" of IndieWeb. Some may have heard the word before, some have a passing knowledge of it, but an even larger number have not. They all act and operate in a way that either seemed natural to them because they grew up in the period of the open web, or because they never felt accepted by the thundering herds in the corporate social enclosures. Many are not necessarily easily found or discovered because they're not surfaced or highlighted by the sinister algorithms of corporate social media, but through slow and steady work (much like the in person social space) they find each other and interact in various traditional web spaces. Many of them can be found in spaces like Tilde Club or NeoCities, or through movements like A Domain of One's Own, some can be found through a variety of webrings, via blogrolls, or just following someone's website and slowly seeing the community of people who stop by and comment. Yes, these discovery methods may involve a little more work, but shouldn't health human interactions require work and care?

Pathos

The final group of people, and likely the largest within the community, are those that represent the "pathos" of IndieWeb. The word IndieWeb has not registered with any of them and they suffer with grief in the long shadow of corporate social media wishing they had better user interfaces, better features, different interaction, more meaningful interaction, healthier and kinder interaction. Some may have even been so steeped in big social for so long that they don't realize that there is another way of being or knowing.

These people may be found searching for the IndieWeb promised land on silo platforms like Blogger, Tumblr or Medium where they have the shadow on the wall of a home on the web where they can place their identities and thoughts. Here they're a bit more safe from the acceleration of algorithmically fed content and ills of mainstream social. Others are trapped within massive content farms run by multi-billion dollar extractive companies who quietly but steadily exploit their interactions with friends and family.

The Conversation

All three of these parts of the IndieWeb, the logos, the ethos, and the pathos comprise the community of humanity. They are the sum of the real conversation online.

Venture capital backed corporate social media has cleverly inserted themselves between us and our interactions with each other. They privilege some voices not only over others, but often at the expense of others and only to their benefit. We have been developing a new vocabulary for these actions with phrases like "surveillance capitalism", "data mining", and analogizing human data as the new "oil" of the 21st century. The IndieWeb is attempting to remove these barriers, many of them complicated, but not insurmountable, technical ones, so that we can have a healthier set of direct interactions with one another that more closely mirrors our in person interactions. By having choice and the ability to move between a larger number of service providers there is an increasing pressure to provide service rather than the growing levels of continued abuse and monopoly we've become accustomed to.

None of these subdivisions---logos, ethos, or pathos---is better or worse than the others, they just are. There is no hierarchy between or among them just as there should be no hierarchy between fellow humans. But by existing, I think one could argue that through their humanity they are all slowly, but surely making the web a healthier, happier, fun, and more humanized and humanizing place to be.

Author Response

Reviewer #2 (Public Review):

Schumacher and Carlson present volumetric data on the brain and main brain areas in several linages of fish that have independently evolved electroreceptors and electrogenesis. The main question is if the evolution of this novel sensory system has led to similar changes in the brain. Previously, the same authors (Sukhum et al 2018) have shown an increase in the relative size of the cerebellum and hindbrain in mormyrid fishes, one group of electrogenic fish. Here they have collected data on South American weakly electric fishes (Gymnotiformes) and weakly electric catfishes (Synodontis spp.) as well as some outgroups. (22 additionally species). I think the question is very interesting, and the inclusion of electrogenic catfishes is particularly interesting as they are a largely understudied group. I do have some concerns about how the data has been analysed and presented.

1) A first conclusion is that gymnotiform and siluriform brains are not as enlarged as mormyrid brains, and that this suggests that an increase in brain size is not directly tied to an electrosensory system evolution. I think the story here is more complicated than that. From the data presented, it seems that mormyrids have a different body size-brain volume slope than other groups, but is unclear if this was tested in the PGLS model for brain vs body size, although mormirids show different slopes than other groups in the scaling of the cerebellum to brian volume. This difference in slope for body brain allometry has been confirmed by a manuscript published after the submission of this manuscript (Tsuboi 2021 BBE) with a large data set (~ 850 species, 21 of Osteoglossiformes). This steep slope close to one means that mormyrids with large body size have very large relative brain sizes but smaller mormyrids don't (this can be seen in figure 2). I think this needs to be addressed more carefully. First testing in the PGLS for body size vs brain size if mormyrids have a different slope and then in the discussion. Why mormyrids but not other electrogenic fish have evolved such a unique brain scaling?

We thank the reviewer for this suggestion. We combined our data with the data from Tsuboi 2021 and assessed how the brain-body allometry has changed across 870 actinopterygians. We identified 3 shifts in lineages with at least 3 descendants and 7 shifts total that were supported by both the OUrjMCMC and PGLS analyses. One of these identified shifts was along the branch leading to osteoglossiforms, with a secondary decrease in one lineage within mormyoids. A second identified shift was along the branch leading to Synodontis multipunctatus. However, we find no shifts along the branches leading to other electrosensory lineages. This suggests that although mormyroids do have a different brain-body allometry compared with other electrogenic fishes, this shift predates the origin of mormyrids as it is found in all osteoglossiforms and thus is unlikely to be related to the evolution of electrosensory systems. These changes are reflected in lines 778-826, 110-153, 513-528, 530-538, 569-575 and figure 3 and associated source data files. See also our detailed response to essential revision 1.

2) I think the number of outgroups species used are too few and spread among several different linages of teleosts. I think this unfortunately tampers some of the conclusions. Particularly seems to leave unanswered the question if other electrogenic fish have brain larger than non electrosensory or electrogenic fish. A large data set of brain and body size data for teleost has been published (Tsuboi et al 2018; 2021). Adding this data should allow to test for changes in body-brain size relationships in the each electrogenic clades. The addition of the additional data should allow to accurately test for difference in relative brain size between and within electrogenic clades and make it possible to test when exactly in the phylogeny of teleost have grade shits in the body-brain allometry have happened.

We thank the reviewer for this suggestion. We explicitly addressed this question by fixing shifts along the branches that evolved our three electrosensory phenotypes: evolution of electrogenesis, tuberous electroreceptors, and ampullary electroreceptors. After comparing these models to the unfixed shift model, a model where only osteoglossiforms have a shifted allometry (following the finding of Tsuboi 2021), a model where only intercept can shift, and a model with one shared allometry across all actinopterygians, we found that the unfixed shift model has a better fit than any of the electrosensory phenotype associated models. This further supports the conclusion that a shifted allometry/ large brain size is not necessary to evolve an electrosensory system. These additions are reflected in lines 778-826, 110-153, 513-528, 530-538, 569-575 and figure 3 and associated source data files. See also our detailed response to essential revision 1.

3) Next, the authors use a principal component analysis and phylogenetic linear models to test how much of brain variation is explained by concerted evolution vs mosaic and where the mosaic change have happened. Here, despite the few non electrogenic/ electrocereptive species, the differences are more clear. I do think that in the case of the linear models, the use brain volume as the independent variable is unnecessary. By regressing the total brain volume, the authors are regressing each structure partially against the same value, and not surprisingly, this generates tight linear correlations. Further, this makes grade shifts (i.e. changes in relative size) less apparent. I think only brain volume -the structure should be used and shown in all figures. This has been the standard in the field when testing for grade shifts.

We thank the reviewer for this comment. There is much debate in the field regarding whether to use brain volume or brain volume – region of interest as the independent variable, and both are commonly used. Originally, we had looked at both and found qualitatively similar results, but only presented the ‘region x brain volume’ results in the main text for brevity. We have revised this to include the results of statistical analyses for ‘region x brain volume – region’ and the accompanying figures in the main text for both the electrosensory phenotype comparisons and the within electrosensory phenotype comparisons (broadly distributed throughout the results and figure 5—figure supplement 1, figure 5—source data 4-6, figure 7—figure supplement 1, figure 7—source data 2). All of the major findings of relative mosaic shifts between tuberous receptor taxa and non-electric taxa, between electrogenic + ampullary only and non-electric taxa for cerebellum and torus, and no mosaic shifts with electrosensory phenotype in telencephalon hold regardless of the method, and we only find minor differences between the analyses for comparisons that had p values near 0.05. These discrepancies do not change any major conclusions. However, we have kept the reporting of ‘region x total brain volume’ analyses in the main text figures to be consistent with other large comparative studies in the field and our group’s previous work (Yopak et al 2010, Sukhum et al 2018).

4) Related to the previous point, the authors report significant decreases electrogenic clades in the size of the olfactory bulb, rest of the brain and optic tectum. I think this is and artifact that results from including the cerebellum and other enlarged areas (TS and hindbrain) in the dependent variable. Similarly, the authors state that they found no increase in the size of the telencephalon in electrogenic clades and that non-electric osteoglossiforms have a mosaic increase in telencephalon relative to non-electric otophysans. Again, I think this suffers from the same problem. Figure 4-figure supplement 2 actually provides some insight in this respect. When plotted against the rest of the brain, no apparent differences are found in the size of the optic tectum. In the case of the olfactory bulb only two of the out-group species seem to have larger OB than all other species. Regarding the telencephalon, when plotted against RoB, all osteoglossiform seem to have similar telencephalon size. These conclusions need to be carefully evaluated.

We thank the reviewer for identifying this miscommunication. We have moved previous figure 4—figure supplement 2 to the main text (now figure 6) and have added the statistical analyses and discussion of this point to both the results and discussion. We have also clarified the distinction between relative and absolute shifts in region sizes throughout but see in particular lines 261-295, 307-317, 330-331, 473-499. See also our detailed response to essential revision 3.

Reviewer #3 (Public Review):

The authors use micro-CT scanning and sophisticated statistical techniques to compare the sizes of various major brain regions across a sample of 32 fish species, including lineages that have independently evolved passive electroreception and, in a smaller subset, the ability to generate and sense weakly electric fields. They found that most of the variation in brain region sizes is linked to variation in total brain size, indicating concerted evolution. However, the analysis also reveals that the electrogenic lineages/species have selectively enlarged the cerebellum, the midbrain torus semicircularis, and the hindbrain. These findings are interesting and usefully extend the last author's prior work on a subset of these species.

A significant strength of the work is that it includes a relatively large number of species, makes a good attempt to understand how these species are related to one another (though the authors admit that the phylogeny is tentative), and that the analytical methods are quantitative and relatively sophisticated. It is also true that other researchers have long argued about the relative frequency and importance of concerted versus mosaic evolution. The present study is a valiant attempt to address this issue.

However, some key results must be viewed cautiously. Most important is that the dramatic increase in the cerebellum (and torus semicircularis and hindbrain), relative to the rest of the brain, must necessarily lead to some other brain regions appearing to have decreased in size. Therefore, their absolute size may well have stayed the same or even increased in evolution; it's just that the enlarged brain regions decrease the proportions of at least some other regions. The authors mentioned this caveat in their previous paper on mormyroids (Sukhum et al., 2018), but not in the present manuscript. As a result of the problem, it is difficult to interpret the documented variation in olfactory bulb, optic tectum, or telencephalon size; is that variation "real" or just artifacts of major changes in the size of other brain regions (mainly cerebellum, torus, and hindbrain). The best way to address this problem would have been to repeat the analysis using a "reference" brain region that is thought not to vary dramatically in size across the species of interest (e.g., "rest of brain"). However, I acknowledge that this approach also has limitations. Still, the problem should be addressed somehow.

We thank the reviewer for identifying this miscommunication. We have moved previous figure 4—figure supplement 2 to the main text (now figure 6) and have added the statistical analyses and discussion of this point to both the results and discussion. We have also clarified the distinction between relative and absolute shifts in region sizes throughout but see in particular lines 261-295, 307-317, 330-331, 473-499. See also our detailed response to essential revision 3.

One strength of the manuscript is that it provides information about y-intercepts and slopes. Many other studies simply note increases or decreases in average volume (before or after correcting for absolute brain size). I like knowing which changes in relative brain region size are grade shifts (changes in intercept) versus changes in slope. However, the authors don't really do anything with those results. What do they mean? Are there different kinds of evo-devo mechanisms that underlie the two types of changes (slope versus intercept)?

We thank the reviewer for this suggestion. We have added some discussion on potential mechanisms for evolutionary changes in intercept and slope (lines 543-559). Unfortunately, this topic is not well studied in fishes, which have extensive adult neurogenesis.

On a related note, do the major brain regions vary in allometric slope within a given lineage? The realization that such differences do exist (at least in mammals and cartilaginous fishes) contributed much to the excitement around the concept of concerted evolution, since it means that evolutionary changes in absolute brain size can lead to major shifts in brain region proportions, but the authors seemingly ignore this point.

We thank the reviewer for this suggestion. We do find variability in slope for different regions of each lineage. We reported these values (figure 5—source data 1, figure 7—source data 1) and add discussion of this point (lines 539-542).

Finally, I must confess that some of the study's findings didn't surprise me. It is well known among fish neurobiologists that mormyrids have a dramatically enlarged cerebellum and that all electrogenic gymnotoids and mormyroids have a very large torus semicircularis and dorsal/alar hindbrain. One didn't need the fancy analytical techniques to confirm this. To be fair, however, it had not been clear whether the cerebellum is enlarged in gymnotoid electric fish and their non-electrogenic relatives (the authors report that it is). Nor was it known that the weakly electric catfishes have a larger cerebellum (not so much for the torus) than their non-electric relatives. This is new information that raises interesting questions about how the electric catfishes are using their electrosensory system (I would have liked to see some discussion of this).

We thank the reviewer for this comment. We too agree that electric catfishes warrant further study into which species are electrogenic, whether their discharges are sporadic versus continuous, and how they are using their electrosensory systems. We have added further discussion on electric catfishes (lines 411-416, 425-437).

On balance, I appreciate that the authors have provided a large and useful data set , which they used to address an interesting set of questions about how brain evolution "works." I'm just disappointed that, for me, there are relatively few significant, novel insights. For example, the notion that "selection can impact structural brain composition to favor specific regions involved in novel behaviors" (last sentence of the abstract) is one that I've accepted for a long time. Maybe the conclusion can be made more interesting by focusing more explicitly on changes in the size of major brain regions versus smaller cell groups (where mosaic evolution is widely accepted).

We thank the reviewer for this suggestion. We agree that mosaic evolution is more readily detected in smaller subregions/ nuclei/ circuits and is found less so at the scale of major brain regions. We have adjusted the text throughout to further highlight this distinction, but see in particular lines 42-48, 500-528.

Reviewer #4 (Public Review):

The authors present a detailed and thorough comparative analysis of brain composition across 3 different lineages of weakly electric fish, and several non-electric fishes. The goal of this comparison was to determine whether the evolution of electrosensory systems is associated with common changes in brain composition across the three lineages. Several aspects of this research are highly novel, such as the use of m-CT imaging and phylogeny-informed multivariate statistics. Overall, the authors show that cerebellar enlargement is key to the evolution of electrosensory systems of all three groups and the enlargement of the hindbrain and torus semicircularis varies depending on the types of electroreceptors and electrical signals produced. This is one of very few examples in evolutionary neuroscience of convergent evolution of brain anatomy and behaviour and sets the stage for future research on other sensory specialists and clades.

Strengths

The comprehensive analysis provided by Schumacher and Carlson has several strengths. First, the use of m-CT scans to derive neuroanatomical measurements in fish is relatively novel and the detailed descriptions of brain region borders were greatly appreciated. Few papers that focus on comparative neuroanatomy put this degree of effort into describing how regions were differentiated and defined, but the level of detail provided here will allow other researchers to acquire data in an identical method and is therefore an important resource.

Second, the statistical analysis is phylogeny-informed and uses an array of approaches. Too many neurobiology papers either avoid phylogeny-informed statistics or execute them poorly. This paper is neither of those and should serve as a template for future studies in the field.

Third, the inclusion of some recording data for Synodontis is an important contribution. I am not an expert on weakly electric fish, but I do know that the catfish are understudied compared with gymnotiforms and mormyroids. Hopefully, this will result in some well-deserved attention to the diversity of catfishes.

Fourth, I found the manuscript as a whole well written and presented. In particular, the authors provided a novel way of incorporating additional statistical information into Figures 3 and 4.

Last, the supplemental video was great addition to the data presented.

Weaknesses

First, the Introduction was a bit brief for readers unfamiliar with weakly electric fishes. It would be helpful to provide a bit more information to a general audience. Including a figure depicting the phylogenetic relationships among some (not all) bony fish clade to illustrate the independent evolution of electrosensory systems across the three clades would be particularly helpful in this regard.

We thank the reviewer for this comment. We have included more background on the evolution of electrosensory systems in actinopterygians and included a figure showing this (lines 76-83, figure 1).

Second, I think it is important to determine if the principal component analysis changes if the volumetric data is scaled. One issue that can affect multivariate analyses is including variables that differ greatly in scale. For example, if one brain region varies between 0.5-1.2 mm3, but another varies from 10-50 mm3 across species, that difference in scale can sometimes affect the PCA. I suggest checking that the analyses are broadly the same if the volumetric data is scaled (e.g., converting to z-scores).

We thank the reviewer for this suggestion. We z-score normalized the regions and repeated the pPCA and found nearly identical results (lines 175-177, figure 4—figure supplement 1).

Third is there any information regarding malapteurid catfish? Are they similar enough to Synodontis or could they exhibit yet another brain type from that discussed in this study? The reason I ask is that the authors raise the issue of Torpedo, but do not discuss other strongly electric fish like Malapteurus (which is a siluriform related to Synodontis).

We thank the reviewer for this comment. We too agree that they would be worthwhile species to add. Unfortunately, there is no data available on malapteurid catfish, and we were unable to sample any. We have added discussion of this point to lines 411-416.

Last, some of the graphs in the supplemental material are too small with datapoints too crowded to effectively read them. Larger graphs would enable a more effective evaluation of how the various clades differ from one another.

We thank the reviewer for this comment. We enlarged the region x region plots and plotted species means instead to make it easier to visualize these data (Figure 6, figure 7—figure supplement 2-4).

the companies that have asked Clearview to delete these images, has Clearview done so?

• Didn't delete anything
• He thinks they are collecting data like other searching engine
• He believes the purpose of collecting data is favor by the social
• Don't want the big tech company control the publiced data, that they could just send a cease and desist, and control eveythign

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Reply to the reviewers

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

Unlike other cell organelles, mitochondria contain a small fraction of their genetic information. However, most of the genetic information about mitochondrial proteins is still in the cell's nucleus and the localization of the respective proteins to mitochondria is facilitated by localized translation of their mRNAs. In turn, the mRNA localization to the mitochondria is partly due to the co-translational association, via the mitochondrial target sequence (MTS) of the nascent peptide.

The manuscript "Mitochondrial mRNA localization is governed by translation kinetics and spatial transport" investigates the mechanisms of mRNA transport and attachment to mitochondria. Concerning mitochondria-localized mRNAs, two types of mRNAs have been distinguished before: mRNAs that are always attached to the mitochondrium (called "constitutively binding" by the authors) and mRNAs that become "sticky" only under certain conditions (called "conditionally binding" by the authors). Modeling the corresponding cellular processes biophysically, the authors infer that yeast cells exercise control over the localization of mRNA (and consequently over their metabolism) in two ways: via varying the mitochondrial volume fraction, and via varying the speed of translation elongation. Data from previously published genome-wide measurements of mRNAs that localize constitutively and conditionally via their MTS in budding yeast S. cerevisiae were used to investigate these mechanisms.

The manuscript is very well written and the analysis is of high quality. It starts with an introduction that thoroughly reviews many facets around the conducted research and briefly, but self-consistently, summarizes the current knowledge regarding mitochondrial localization of mRNAs. Next, the consequences of the modeling work (presented in the "methods"-section) are explored in the "Results"-section, which contains meaningful and instructive figures and explanations. The manuscript concludes with a comprehensive evaluation of the consequences of the conducted research. All in all, there are only very few minor changes that could be considered.

Content-wise, we suggest:

The modeling of translation kinetics is pretty coarse-grained, using only an average elongation rate per amino acid. Much work in this field was done using totally antisymmetric exclusion principle (TASEP)-based models (e.g. MacDonald, J.H. Gibbs, A.C. Pipkin: Kinetics of biopolymerization on nucleic acid templates; Duc, Saleem, Song: Theoretical analysis of the distribution of isolated particles in totally asymmetric exclusion processes: Application to mRNA translation rate estimation). Perhaps this work can be mentioned, and furthermore, the consequences of inhomogeneity of elongation rate for different codons and amino acids could be explored or at least discussed. In particular, this could shed light into the question if ribosome interference and tRNA charging times have any impact on mitochondrial mRNA localization.

Thank you to the reviewer for pointing us to these relevant papers. As suggested, we have added a paragraph to our Discussion that mentions this work and discusses the possible implications of inhomogeneous elongation along mRNA sequences. We find this suggestion (and the similar one made by the other reviewer) to explore inhomogeneous elongation particularly encouraging, because we are in the early stages of actively pursuing such work. We feel that beyond discussion, exploring the consequences of inhomogeneous elongation is beyond the scope of this work because significant further experimental work would be needed to quantify the impact of specific sequences on translation progress.

To our Discussion, we have added the following paragraph.

"In this work our quantitative model assumed uniform ribosome elongation rates along mRNA transcripts. In the presence of ribosome interactions, such dynamics can lead to both uniform and non-uniform ribosome densities and effective elongation rates along the transcript (MacDonald et al., 1968; Duc et al., 2018). With these uniform ribosome elongation rates, previous theoretical results suggest that collisions will be rare (Duc et al., 2018). However, elongation may not be homogeneous along an mRNA transcript, due to factors such as tRNA availability (Varenne et al., 1984), boundaries between protein regions (Thanaraj and Argos, 1996), amino acid charge (Charneski and Hurst, 2013), and short peptide sequences related to ribosome stalling (Sabi and Tuller, 2017). We have found that slow (homogeneous) elongation facilitates mitochondrial mRNA localization, by providing time for MTS maturation, diffusive search, and to maintain binding-competent MTS-mediated mRNA binding to mitochondria. We expect that inhomogeneities in elongation rate along mRNA could either enhance or reduce mitochondrial mRNA localization, controlled by whether slower elongation is in regions that favor longer MTS exposure. For example, a ribosome stall site following full MTS translation could provide more time for MTS maturation and facilitate mitochondrial localization. Future experimental work could identify such stalling sequences and point towards how modeling can improve understanding of sequence impact on localization."

Ribosome occupancy data from Arava used to infer translation parameters. But there are more recent data sets based on ribosome profiling. Any reason for not using the more recent data?

We thank the reviewer for bringing up this important point. Our text describing the origin of data for ribosome occupancy in the inset of Figure 2A lacked a citation to the dataset used, and we agree that more recent ribosome occupancy datasets are more appropriate. For the cumulative distributions of ribosome occupancy shown in the inset of Figure 2A, we used the ribosome occupancy data from Zid and O'Shea from 2014. The Arava data from 2003 was used for the cumulative distributions of Figure S1, to show that the similarity between conditional and constitutive genes in the inset of Figure 2A was present in more than a single dataset.

We have clarified the origin of the ribosome occupancy data in the text.

In the text description of the inset of Figure 2A, we now include a direct citation of Zid and O'Shea from 2014.

"These measurements (Zid and O'Shea, 2014) indicate that conditional and constitutive genes have similar distributions of ribosome occupancy (Fig. 2A, inset; see Fig. S1 for similar distributions of conditional and constitutive gene ribosome occupancy derived from (Arava et al., 2003))."

We also added a citation of Zid and O'Shea to the caption describing the inset of Figure 2A.

"Inset is cumulative distribution of ribosome occupancy (Zid and O’Shea, 2014), showing ribosome occupancy and β have similar distributions. "

To determine the translation parameters in our quantitative model, we applied the datasets of Couvillion et al from 2016 for relative protein per mRNA measurements and Zid and O'Shea from 2014 for ribosome occupancy measurements, combined with individual measurements from Morgenstern et al from 2016 and Riba et al from 2019. How these datasets and measurements are used is described in the Methods subsection “Calculation of translation rates”. In addition to the citations in the methods, we have added citations to the briefer description in the Results section.

"Using protein per mRNA and ribosome occupancy data (Couvillion et al., 2016; Morgenstern et al., 2017; Zid and O’Shea, 2014; Riba et al., 2019), we estimated the gene specific initiation rate kinit and elongation rate kelong for 52 conditional and 70 constitutive genes (see Methods)."

The effect of the mitochondrial volume fraction on mRNA localization is investigated with a diffusive model. However, the authors make a two dimensional Ansatz for the cell and mitochondrion while it would seem more natural to assume diffusion in three spatial dimensions, as the cell and mitochondria are both three dimensional objects and diffusion strongly depends on the number of dimensions it occurs in. Why was that Ansatz made and why is it justified?

Our diffusion model is in fact three-dimensional, rather than two dimensional. Specifically, we treat the search process as occurring in a three-dimensional cylinder, whose cross-section is shown in Figure 1D. We have added to Figure 1D to further describe how three-dimensional cylinders represent the mitochondrial proximity in the cell.

In the Results, we now write:

“Specifically, we treat the geometry as a sequence of concentric three-dimensional cylinders, each representing an effective region surrounding a tubule of the mitochondrial network. Figure 1D shows a two-dimensional cross-sectional view of these cylinders. The innermost cylinder represents a mitochondrial tubule…”

We have also clarified the caption of Figure 1D to include:

"Schematic of mRNA diffusion in spatial model, shown in cross-section. The cytoplasmic space is treated as a cylinder centered on a mitochondrial cylinder: the three dimensional volume extends along the cylinder axis (not shown)."

The range of variability in the localized fraction +/- CHX is smaller in the experiment compared to the model (Fig. 4B, C). What could be the rationale?

We agree that the variability in localized fraction from applying CHX is smaller in the experiment (Figure 4C) in comparison to the model (Figure 4B). Our model uses translation parameters (initiation and elongation rates) that are derived from experimental measurements that are expected to be quite noisy. We expect that this noise in the model parameters will expand the range of localization changes predicted by the model for CHX application.

In l. 417, the authors remark that "constitutively localized mRNAs are on average longer [...] than conditionally localized mRNAs." Yet constitutively localized mRNAs seem to have higher localized fraction than conditionally localized mRNAs. This is somewhat surprising. While it's clear that a higher diffusivity would be compatible with a faster response time of shorter, conditionally-localized mRNAs, it is not clear how the longer, less diffusive mRNAs would have a higher localization fraction. Perhaps the authors can clarify this point.

The reviewer is correct that experimental measurements show that constitutively-localized genes are, on average, longer than conditionally-localized genes. In our quantitative model, we assume the mRNA of all genes have the same diffusivity. We have used the same diffusivity for different genes because experimental measurements suggest that mRNA length and the number of translating ribosomes on an mRNA do not substantially impact mRNA diffusivity. In our Methods section, we have added citations to papers indicating lack of dependence of mRNA diffusivity on mRNA length.

"Simulated mRNA have a diffusivity of 0.1 𝜇m2/s. This diffusivity remains constant across genes and mRNA states, consistent with experimental measurements showing little dependence of mRNA diffusivity on mRNA length (Calderwood et al., 2016) or number of translating ribosomes (Wang et al., 2016)."

We have additionally clarified the part of our Discussion where we explain the distinction of our results from proposals based on differential mRNA diffusion speed.

"Lower occupancy was proposed to drive mRNA localization through increased mRNA mobility of a poorly loaded mRNA (Poulsen et al., 2019), as more mobile mRNA could more quickly find mitochondria when binding competent, increasing the localization of these mRNA. By contrast, our results imply an alternate prediction – that translational kinetics lead to enhanced localization of longer mRNAs, due to the increased number of loaded ribosomes bearing a binding-competent MTS. Indeed, constitutively localized mRNAs are on average longer than conditionally localized mRNAs."

Minor formal changes would be:

Setting the expressions of the fraction in the binding-competent state in l. 118 and the faction of the mRNA-accessible volume in l. 123 in normal math-environments instead of the inline-environment since they are of key importance to the following discussion.

These two equations (now equations (1) and (2)) are set as distinct equations that are now referred to by their equation numbers later in the manuscript.

l. 414 contains the verb "vary" twice

Thank you to the reviewer for pointing out this redundancy, the sentence now reads

"Translation kinetics can widely vary between genes ... "

l. 438 lacks an "h" in the word mitochondria

Thank you to the reviewer for pointing this out, this spelling error has been corrected. The sentence now reads "all mRNA transcripts studied would be highly localized to mitochondria in all conditions."

Reviewer #1 (Significance (Required)):

All in all, this is a strong manuscript that contains solid, simple but meaningful and by no means oversimplified models with impactful consequences on the understanding of mitochondrial mRNA localization. Furthermore, it is likely that the approach applies to other cellular compartments like the ER. The research is explained in a remarkably clear and focussed style which makes it easy to follow and meanwhile succeeds in not omitting any details.

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

Summary:

Arceo et al. have developed a stochastic, quantitative model of mitochondrial targeting sequence (MTS)-mediated mRNA localization to mitochondria in yeast. They use this model to investigate the role of translation- and diffusion kinetics in controlling mitochondrial mRNA localization of conditional as well as constitutional genes.

Most importantly, they find that neither mRNA diffusivity nor ribosome density alone are sufficient to account for the differences in localization that were experimentally observed for the two types of genes. Therefore, they implement an MTS maturation time into their model and find that they can now predict gene specific localization rates. Based on these observations, the authors conclude that yeast cells can regulate the localization of mRNAs to mitochondria through (controlling mitochondrial volume fractions and) differences in translation kinetics, which adjust the exposure time and numbers of mature MTSs that are presented on the mRNP and convey binding-competence.

Major comments:

Overall, the manuscript is well written and the conclusions are convincing. The underlying assumptions of the model make sense, but I have no background in modelling and can therefore only comment on the RNA biology aspects and general comprehensibility of the work.

• The authors calculate gene-specific translation initiation and elongation rates to model localization on different transcript classes. In this context,

(i) They use a single decay rate to estimate trajectory lifetime and this decay rate is such (1 nt / 600 s) that it would take the average yeast mRNA (~ 1400 nt; Smith et al., JCB, 2015) 10 days to be turned over. This is not consistent with physiological decay rates and as a consequence, they are essentially not accounting for mRNA turnover. This should be explained in the Methods.

The reviewer has highlighted a lack of clarity in our model description. The mRNA decay rate in the model is (1/600) inverse seconds per entire mRNA molecule, rather than (1/600) inverse seconds per nucleotide. This leads the typical mRNA lifetime to be 600 seconds. The sentence in the Methods section describing the decay timescale now reads "The mRNA decay rate is set to kdecay = 0.0017 s-1 per mRNA molecule, such that the typical decay time for an mRNA molecule is 600 s. This decay time is consistent with measured average yeast mRNA decay times ranging from 4.8 minutes (Chan et al., 2018) to 22 minutes (Chia and McLaughlin, 1979)."

(ii) Translation and decay are intrinsically linked and translation machinery also recruits decay enzymes. What is more, decay rates differ greatly for different mRNA transcripts. I cannot judge how feasible this is, but it might benefit the model if variable decay rates (i.e. modelled based on translation efficiency?) could be included.

We appreciate this suggestion from the reviewer. We have added a supplemental figure (Figure S4) to explore how mRNA decay rate can impact mitochondrial localization of mRNA. While longer decay rates have little impact on localization, if the decay rate is sufficiently high, the mRNA will have limited opportunity for translation to initiate and a binding-competent MTS to develop, substantially reducing localization. This analysis does not consider how the mRNA lifetime might be coupled with translational effects (such as ribosome stalling). Accounting for the impact of such more complex decay mechanisms would require substantial expansion of the model and extensive additional experiments to parameterize the coupling effects; we believe this extension would be beyond the scope of this manuscript.

To our Discussion, we have added

"While we have focused on how variation in translational kinetics between genes can impact mitochondrial mRNA localization, there is also significant variation in mRNA decay timescales (Chia and McLaughlin, 1979; Chan et al., 2018). Our model suggests (see Fig. S4) that the mRNA decay timescale has a limited effect on mitochondrial mRNA localization, unless the decay time is sufficiently short to compete with the timescale for a newly-synthesized mRNA to first gain binding competence. We leave specific factors thought to modulate mRNA decay, such as ribosome stalling (Mishima et al., 2022), as a topic of future study."

(iii) Along the same lines: Rare codons as well as specific stalling sequences, are known to slow down translation elongation on many transcripts (and will effectively increase MTS exposure time). Can the authors identify transcripts with such signal sequences (on a global scale, apart from TIM50) and incorporate in their model?

We find this suggestion (and the similar one made by the other reviewer) to explore stalling sequences particularly encouraging, because we are in the early stages of actively pursuing such work. We feel that beyond discussion, exploring the consequences of inhomogeneous elongation is beyond the scope of this work because significant further experimental work would be needed to quantify the impact of specific sequences on translation progress.

To our Discussion, we have added the following paragraph.

"In this work our quantitative model has applied uniform ribosome elongation rates along mRNA transcripts, which with ribosome interactions can lead to both uniform and non-uniform ribosome densities and effective elongation rates along the transcript (MacDonald et al., 1968; Duc et al., 2018). With these uniform ribosome elongation rates, previous theoretical results suggest that collisions will be rare (Duc et al., 2018). However, elongation may not be homogeneous along an mRNA transcript, due to factors such as tRNA availability (Varenne et al., 1984), boundaries between protein regions (Thanaraj and Argos, 1996), amino acid charge (Charneski and Hurst, 2013), and short peptide sequences related to ribosome stalling (Sabi and Tuller, 2017). We have found that slow (homogeneous) elongation facilitates mitochondrial mRNA localization, by providing time for MTS maturation, diffusive search, and maintains a binding-competent MTS-mediated mRNA binding to mitochondria. We expect that inhomogeneities in elongation rate along mRNA could either enhance or reduce mitochondrial mRNA localization, controlled by whether slower elongation is in regions that favor longer MTS exposure. For example, a ribosome stall site after the MTS is fully translated could provide more time for MTS maturation and facilitate mitochondrial localization. Future experimental work could identify such stalling sequences and point towards how modeling can improve understanding of sequence impact on localization."

• Reduced mature MTS exposure time is presented as one of the determining factors that regulate mitochondrial localization of conditionally localized transcripts. For my background, the underlying mechanisms that determine MTS maturation are insufficiently explained. I understand how chaperone recruitment can contribute to MTS maturation. However, it is not obvious to me how receptor binding would account for such long maturation times as the 40 s used here (Fig. 3, 4). I would appreciate if the authors could elaborate and possibly point to directions that their model could be used to study those.

We agree with the reviewer that the diffusive search time for a chaperone to find a newly-synthesized MTS would be very short (a small fraction of the proposed 40-second MTS maturation time), and we expect that this maturation period is largely controlled by chaperone and co-chaperone interaction timescales. There is a wide range of timescales for newly-synthesized (or misfolded) proteins to productively interact with a chaperone, and the literature provides examples of timescales comparable to 40 seconds, which we now cite.

To our Discussion, we have added

"While the diffusive search for a newly-synthesized MTS by chaperones is expected be very fast ( 100 seconds for human chaperone-mediated folding (Wu et al., 2020)."

We feel that modeling chaperone facilitation of MTS folding, to determine the timescale of this process, is very distinct from the topics covered in our manuscript, and thus beyond the scope of this work.

• One of the two main conclusions (at least according to the abstract) from the work is that yeast cells modulate mitochondrial volume fractions to regulate mRNA localization to mitochondria. This is a fact, not a novel finding. The other main conclusion, which is that cells use different translation dynamics to control mRNA localization, is intriguing and deserves more attention. It would be great if the authors could suggest/discuss an experimental approach (i.e. a single mRNA imaging experiment quantifying mitochondrial co-localization and translation kinetics of different reporter constructs) to test this hypothesis.

We appreciate the reviewer raising the point that yeast cells modulate mitochondrial volume fraction to regulate mitochondrial mRNA localization. While we previously showed this relationship between mitochondrial volume fraction and localization, we used experimental techniques (mutations, nutrient sources) that changed many other factors beyond mitochondrial volume fraction. In this work we have used a quantitative model, lacking those extraneous factors, to demonstrate that a change to mitochondrial volume fraction alone can lead to a change in mitochondrial mRNA localization. This work supports our interpretation of those previous experimental results.

To our Discussion we have added the sentence

"Previous experimental work suggested that changing mitochondrial volume fraction could control mitochondrial mRNA localization (Tsuboi et al., 2020) --- our quantitative modeling work provides further support for this mechanism of regulating mRNA localization."

The reviewer also requests a discussion of an experimental approach to test how cells use translational dynamics to control mRNA localization. With the advent of combined mRNA imaging and live translational imaging it would be interesting to directly measure translation in live cells to correlate localization with a time delay. Unfortunately there are currently no published live translational imaging studies in yeast, and thus such a measurement would require the development of the technique in yeast.

To our Discussion, we have added

"Experimentally testing our proposal for translation-controlled localization would involve using combined mRNA and live translational imaging (as yet undeveloped in yeast), to directly measure translation and correlate localization with a time delay, presenting a fruitful pathway for future study."

Minor comments:

• Figure 1: X axis labels between panel E and F are not consistent. Inset in panel F is mainly and first discussed in text. Please do not show data as tiny inset but as separate panel.

We have changed the axis label of Figure 1E to match the axis label of Figure 1G (previously Figure 1F). The inset of the old Figure 1F is now the new Figure 1F, and the old Figure 1F is now the new Figure 1G. We have adjusted the Figure 1 caption and the text description of Figure 1 to match these changes.

Elongation rates of 250 aa per second are not physiological. In mammalian cells elongation has been quantified to proceed between 1 and app. 20 aa per second (Wang et al, 2016; Wu et al., 2016; Yan et al., 2016; Morisaki et al., 2016).

The reviewer is correct that the elongation rates of 50/s and 250/s too large to be physiological. These large values have been deliberately selected to probe the nonequilibrium behavior of the quantitative model to test the prediction of the simpler four-state model, rather than represent physiological behavior.

To the text in the Results section discussing Figure 1F, we have added the following sentence.

"We include unphysiologically high elongation rates to compare to the expected behavior from the 4-state model."

Panel E: elongation rate range does not match Fig 1F nor median in Fig 3A.

The reviewer is correct that the elongation rate parameter range of Figure 1E does not match the elongation rates of Figure 1F or the median in Figure 3A. In Figure 1E, we aimed to show that the physiological range of translation parameters can produce a wide range of both MTSs per mRNA and mRNA binding competence for mitochondria.

We have expanded the description of Figure 1E in the text.

"By exploring the physiological range of translation parameters, many orders of magnitude of the mean number of translated MTSs per mRNA (β, see Eq. 5) are covered, which also covers the full range of mRNA binding competence (Fig.1E). We find that, for any set of physiological translation parameters, the number of binding-competent MTS sequences (β) is predictive of the fraction of time (fs) that each mRNA spends in the binding competent state (Fig.1E)."

• Figure 2A and S1: Please explain how ribosome occupancy is defined here and why it is so different between figures

We have inserted a citation for Zid 2014, to distinguish that the ribosome occupancy measurements in Figure 2A (Zid and O’Shea) and Figure S1 (Arava et al) come from two different techniques. Zid and O’Shea used ribosome profiling to obtain a relative, rather than absolute measurement. While Arava used a technique where they fractioned mRNAs based on the absolute number of ribosomes loaded across 14 fractions of a sucrose gradient, and measured the relative amount of mRNA in each fraction by microarray. So while ribosome occupancy in each paper was calculated in a very distinct manner, the comparison between conditional and constitutively localized mRNAs shows a very similar trend without significant differences in ribosome occupancy between these two classes of mRNAs with either measurement of ribosome occupancy.

To the caption of Figure S1, we have added

"These ribosome occupancy values cover a distinct range, in comparison to those of Fig. 2A, due to distinct experimental measurement techniques."

• Figure 2C: please show experimental data along with model prediction (in the same graph) so that conclusion becomes immediately apparent from figure not just main text. Label clearly (in figure) when experimental and when model data is shown (maybe by using consistent color scheme?)

We have added experimental data to Figure 2C. Throughout the manuscript, we have kept a consistent color scheme for data for mitochondrial localization for ATP3, TIM50, conditional, and constitutive mRNA, whether from model or experimental data. We have applied distinct line types (e.g. solid for model vs. dot-dashed with circles for experimental).

• Figure 4B and C: clearly indicate in figure which are experimental and which are modelled data

In Figures 4B and 4C, we have clarified which data is experimental and which is modeled by adding to the labels for each violin plot. Violin plot labels for model data now read "Model Conditional" or "Model Constitutive" and labels for experimental data now read "Expt Conditional" or "Expt Constitutive".

• Figure 4D: show experimental vs. model data in same graph (at same axis scaling) for comparability

We have added the experimental data, previously in the inset of Figure 4D, to the main part of Figure 4D.

• Line 305: "constitutive" mRNA

Thank you to the reviewer for pointing out this redundancy, the sentence now reads

"Figure 3C shows how the localization for the prototypical conditional and constitutive mRNA varies with the maturation time."

• Line 334: "other changes, such as diffusivity, are unable to separate the two gene groups" - what other changes? The authors only show diffusivity (Fig S3).

Thank you to the reviewer for pointing this out. We have revised this sentence to only refer to diffusivity changes.

"While introduction of this maturation time distinguishes the mitochondrial localization of conditional and constitutive gene groups (Fig. 4A vs Fig. 2B), changes to diffusivity are unable to separate the two gene groups (Fig. S3)."

• Line 403-405: maybe useful to argue against lower ribosome occupancies as drivers of nascent chain complex mobilities: Wang at el, Cell, 2016; single translation site imaging experiments indicating that ribosome occupancy is not the main determinant of mRNP mobility.

We thank the reviewer for the direction to this paper, which indeed indicates that ribosome occupancy has limited impact on mRNA diffusivity.

We now cite this paper in our Methods section.

"Simulated mRNA have a diffusivity of 0.1𝜇m2/s. This diffusivity remains constant across genes and mRNA states, consistent with experimental measurements showing little dependence of mRNA diffusivity on mRNA length (Calderwood et al., 2016) or number of translating ribosomes (Wang et al., 2016)."

• Line 601-607: include experimental references to explain how measures (25 nm vs 250 nm) were determined/selected.

The reviewer raises a valuable point, as it is important to motivate these lengthscales used in the model.

Microscopy with visible light has a lateral resolution limit of approximately 250 nm, often known as the Abbe limit. Accordingly, we assume that mRNA within 250 nm of mitochondria will be measured as adjacent to mitochondria. To the Methods section, we now include a short explanation and a citation.

Unlike the 250-nm diffraction limit, there is no widely-used reaction range for mRNA binding to intracellular substrates, nor a measurement of the required proximity for an MTS-bearing mRNA to bind to mitochondria. We estimate the 25-nm distance for mRNA binding to mitochondria from the following contributions:

• The yeast ribosome is 25 - 28 nm in diameter, or 13 - 14 nm in radius.
• Yeast MTSs have a length of up to 70 amino acids, with 20 estimated yeast MTS lengths having a mean of 31 amino acids. The MTS forms an amphipathic helix (an alpha helix), which has a pitch of 0.54 nm and 3.6 amino acids per turn, so the 31 amino acids will be approximately 5 nm long
• The MTS will be attached to the ribosome/mRNA by other peptide regions, expected to typically be a few nanometers in length So overall we estimate a 25 nm range for an MTS-bearing mRNA to bind to mitochondria.

To our methods, we have added this reasoning and accompanying citations.

"We estimate the 25-nm binding distance by combining several contributions. The yeast ribosome has a radius of 13 - 14 nm (Verschoor et al, 1998). The MTS region, up to 70 amino acids long, forms an amphipathic helix (Bacman et al., 2020) a form of alpha helix. With an alpha helical pitch of 0.54 nm and 3.6 amino acids per turn, a 31 amino acid MTS (the mean of 20 yeast MTS lengths (Dong et al., 2021)) is approximately 5 nm in length. An additional few nanometers of other peptide regions bridging the MTS to the ribosome provides an estimate of 25 nm for the range of an MTS-bearing mRNA to bind mitochondria. The 250-nm imaging distance is based on the Abbe limit to resolution with visible light (Georgiades et al., 2016)."

Reviewer #2 (Significance (Required)):

My field of expertise is the development of single mRNA imaging methods to quantify translation/decay dynamics in living mammalians systems. Thus, I cannot judge the significance of this work with respect to the modelling that is presented here.

However, I do appreciate that one of the main conclusions of this work, which is that cells might use different translation dynamics to control mRNA localization, is truly exciting and could be applied to other types of transcripts (this is exactly what SRP does for ER-targeted mRNAs) as well. Because mechanisms that regulate translation in a transcript-specific manner and in different subcellular localizations have only been described for a handful of cases, I think that this observation is worth following up on and should be appreciated by a broad scientific audience.

Reviewer #3 (Public Review):

In their study, the authors set up to challenge the long-held claim that cortical remapping in the somatosensory cortex in hand deprived cortical territories follows somatotopic proximity (the hand region gets invaded by cortical neighbors) as classically assumed. In contrast to this claim, the authors suggest that remapping may not follow cortical proximity but instead functional rules as to how the effector is used. Their data indeed suggest that the deprived hand area is not invaded by the forefront which is the cortical neighbor but instead by the lips which may compensate for hand loss in manipulating objects. Interestingly the authors suggest this is mostly the case for one-handers but not in amputees for who the reorganization seems more limited in general (but see my comments below on this last point).

This is a remarkably ambitious study that has been skilfully executed on a strong number of participants in each group. The complementarity of state-of-the-art uni- and multi-variate analyses are in the service of the research question, and the paper is clearly written. The main contribution of this paper, relative to previous studies including those of the same group, resides in the mapping of multiple face parts all at once in the three groups.

In the winner takes all approach, the authors only include 3 face parts but exclude from the analyses the nose and the thumb. I am not fully convinced by the rationale for not including nose in univariate analyses - because it does not trigger reliable activity - while keeping it for representational similarity analyses. I think it would be better to include the nose in all analyses or demonstrate this condition is indeed "noisy" and then remove it from all the analyses. Indeed, if the activity triggered by nose movement is unreliable, it should also affect multivariate.

The rationale for not including the hand is maybe more convincing as it seems to induce activity in both controls and amputees but not in one-handers. First, it would be great to visualize this effect, at least as supplemental material to support the decision. Then, this brings the interesting possibility that enhanced invasion of hand territory by lips in one-handers might link to the possibility to observe hand-related activity in the presupposed hand region in this population. Maybe the authors may consider linking these.

The use of the geodesic distance between the center of gravity in the Winner Take All (WTA) maps between each movement and a predefined cortical anchor is clever. More details about how the Center Of Gravity (COG) was computed on spatially disparate regions might deserve more explanations, however. Moreover, imagine that for some reason the forefront region extends both dorsally and ventrally in a specific population (eg amputees), the COG would stay unaffected but the overlap between hand and forefront would increase. The analyses on the surface area within hand ROI for lips and forehead nicely complement the WTA analyses and suggest higher overlap for lips and lower overlap for forehead but none of the maps or graphs presented clearly show those results - maybe the authors could consider adding a figure clearly highlighting that there is indeed more lip activity IN the hand region.<br /> In addition to overlap analyses between hand and other body parts, the authors may also want to consider doing some Jaccard similarity analyses between the maps of the 3 groups to support the idea that amputees are more alike controls than one-handers in their topographic activity, which again does not appear clear from the figures.

This brings to another concern I have related to the claim that the change in the cortical organization they observe is mostly observed in one-handers. It seems that most of this conclusion relies on the fact that some effects are observed in one-handers but not in amputees when compared to controls, however, no direct comparisons are done between amputees and one-handers so we may be in an erroneous inference about the interaction when this is actually not tested (Nieuwenhuis, 11). For instance, the shift away from the hand/face border of the forehead is also (mildly) significant in amputees (as observed more strongly in one-handers) so the conclusion (eg from the subtitle of the results section) that it is specific to one-hander might not fully be supported by the data. Similar to the invasion of the hand territory from the lips which is significant in amputees in terms of surface area. All together this calls for toning down the idea that plasticity is restricted to congenital deprivation (eg last sentence of the abstract). Even if numerically stronger, if I am not wrong, there are no stats showing remapping is indeed stronger in one-handers than in amputees and actually, amputees show significant effects when compared to controls along the lines as those shown (even if more strongly) in one-handers. Also, maybe the authors could explore whether there is actually a link between the number of years without hand and the remapping effects.

One hypothesis generated by the data is that lips remap in the deprived hand area because lips serve compensatory functions. Actually, also in controls, lips and hands can be used to manipulate objects, in contrast to the forehead. One may thus wonder if the preferential presence of lips in the hand region is not latent even in controls as they both link in functions?

Most people born since the advent of the filing cabinet and the computer have spent a lifetime using a hierarchical folder-based mental model for their knowledge. For greater value and efficiency one needs to get away from this model and move toward linking individual ideas together in ways that they can more easily be re-used.

To accomplish this many people use an index-based method that uses topical or subject headings which can be useful. However after even a few years of utilizing a generic tag (science for example) it may become overwhelmed and generally useless in a broad search. Even switching to narrower sub-headings (physics, biology, chemistry) may show the same effect. As a result one will increasingly need to spend time and effort to maintain and work at this sort of taxonomical system.

The better option is to directly link related ideas to each other. Each atomic idea will have a much more limited set of links to other ideas which will create a much more valuable set of interlinks for later use. Limiting your links at this level will be incredibly more useful over time.

One of the biggest benefits of the physical system used by Niklas Luhmann was that each card was required to be placed next to at least one card in a branching tree of knowledge (or a whole new branch had to be created.) Though he often noted links to other atomic ideas there was at least a minimum link of one on every idea in the system.

For those who have difficulty deciding where to place a new idea within their system, it can certainly be helpful to add a few broad keywords of the type one might put into an index. This may help you in linking your individual ideas as you can do a search of one or more of your keywords to narrow down the existing ones within your collection. This may help you link your new idea to one or more of those already in your system. This method may be even more useful and helpful for those who are starting out and have fewer than 500-1000 notes in their system and have even less to link their new atomic ideas to.

For those who have graphical systems, it may be helpful to look for one or two individual "tags" in a graph structure to visually see the number of first degree notes that link to them as a means of creating links between atomic ideas.

To have a better idea of a hierarchy of value within these ideas, it may help to have some names and delineate this hierarchy of potential links. Perhaps we might borrow some well ideas from library and information science to guide us? There's a system in library science that uses a hierarchical set up using the phrases: "broader terms", "narrower terms", "related terms", and "used for" (think alias or also known as) for cataloging books and related materials.

We might try using tags or index-like links in each of these levels to become more specific, but let's append "connected atomic ideas" to the bottom of the list.

Here's an example:

• broader terms (BT): [[physics]]
• narrower terms (NT): [[mechanics]], [[dynamics]]
• related terms (RT): [[acceleration]], [[velocity]]
• used for (UF) or aliases:
• connected atomic ideas: [[force = mass * acceleration]], [[$$v^2=v_0^2​+2aΔx$$]]

Chances are that within a particular text, one's notes may connect and interrelate to each other quite easily, but it's important to also link those ideas to other ideas that are already in your pre-existing body of knowledge.

See also: Thesaurus for Graphic Materials I: Subject Terms (TGM I) https://www.loc.gov/rr/print/tgm1/ic.html

Author Response

Reviewer #1 (Public Review):

Kwon, Huxlin and Mitchell compared motion perception and oculomotor responses in eight patients with post-stroke lesions in the primary visual cortex (V1). Motion perception was measured as peripheral motion discrimination thresholds (NDR) separately in the affected and the intact visual field. Due to restoration training, the NDR thresholds were below chance even in the affected visual field, indicating that some residual motion discrimination was possible. Oculomotor responses were measured as the gain of eye drifts (PFR) after saccades to dot patterns that are coherently drifting inside peripheral, stationary apertures. The authors distinguish between a predictive, open loop component up to 100 ms after the saccade that is entirely based on presaccadic motion processing in the peripheral visual field and a visually-driven component from 100 ms after the saccade that is based on postsaccadic motion processing in the fovea. While the PFR gain of patients in the intactfield was comparable to the data of healthy control subjects from a previous study (Kwon et al., 2019), the predictive, open-loop PFR gain of patients in the affected field was close to zero. This was not the case for the visually-driven PFR. The authors interpret their findings in terms of a dissociation between residual motion perception and absent predictive oculomotor control in patients with V1 lesions.

Strengths:<br /> The study contains a rare and valuable set of perceptual and oculomotor data from eight patients with lesions in V1, who underwent restoration training. The direct comparison between peripheral motion discrimination and predictive oculomotor responses is interesting and innovative. Also, the distinction between the predictive, open-loop and the closed-loop component of PFR is important. A potential dissociation between motion perception and oculomotor control would be very relevant for the understanding of different pathways of motion processing for perception and oculomotor control and also for the understanding of the effects of restoration trainings after lesions of V1.

Weaknesses:<br /> The dissociation between perception and oculomotor control in the affected field is primarily based on two results: First, the combination of low PFR gain (Figure 4A) on the one hand and low to medium NDR thresholds (Table 1) on the other hand. Second, the absence of a correlation between NDR thresholds and PFR gain (Figure 4B). However, the data are not as clear-cut. The regression of PRF gain on NDR thresholds in the intact-field predicts that there should be a substantial PRF gain only at NDR thresholds below about 0.3. For the affected field this applies only to three data points of which one shows a substantial PFR and is fully compatible with the data in the intact-field. Hence, the evidence of a dissociation between motion perception and oculomotor control is based on a very small number of data points. This also allows for a different interpretation: instead of assuming separate pathways for motion perception and oculomotor control in patients, the results might also be explained by a different read-out of the same motion signal for perception and oculomotor control, where oculomotor control applies a more conservative threshold and requires a higher internal signal strength than the motion perception.

The comparison of the patients' data to the data in the previous study (Kwon et al., 2019) is not very informative. First, the patients were considerably older than the participants in the previous study, and an age-matched control group would be favourable. That being said, the fact that the PFR gain was comparable for the intact-field of the patients and the previous study renders age-effects rather unlikely.

Second, there is no control data for the motion discrimination task, so we don't know what the NDR thresholds and even more importantly what the relationship between NDR thresholds and PFR gain in healthy observers would be.

We thank the reviewer for their evaluation. We have attempted to address concerns about sufficient sampling from blind-fields with recovery that reached the normal range by collecting additional data, doubling our sample size within that range. This is discussed above in “Essential revisions”, along with the alternative interpretation that perception and oculomotor control might rely on a different threshold in readout. The role of age differences was considered in the original manuscript, but this remains an unlikely factor, as the reviewer notes. With regard to normative NDR threshold data, surprisingly, this has not been published in visually-intact controls in a manner that is identical to that in the present study. However, prior work has established that performance in CB patients’ intact visual fields is normal across a wide range of behavioral measures that include luminance contrast sensitivity, processing of form, color and motion, as well as spatial and temporal frequencies (e.g. Barbur et al., 1980; Morland et al., 1999; Sahraie et al., 2006; Huxlin et al., 2009; Das et al., 2014; Levi et al., 2015). In the present study, we have thus used the intact-field as an internal control for blind-field performance in the same participant, as is standard in the field, expecting that intact-field NDR thresholds should be within the normal range. Verifying this is outside the scope of the present paper, but is now planned for our subsequent studies. Other detailed responses appear below to point by point for the reviewer’s “Recommendations for authors”.

Reviewer #2 (Public Review):

This study addresses the oculomotor behaviour of cortically-blind patients (with lesions in V1) who are instructed to perform a saccade toward a cued target placed either in their intact or in the blind visual field. The saccadic target consists in an aperture containing random-dot motion at 75% direction discrimination threshold ("NDR"), and is presented with iso-eccentric similar distractor apertures: with this kind of stimulus, the gaze of normally-sighted participants drifts smoothly in the direction of the target random dot motion immediately after the end of the saccade. Importantly, for some patients, a perceptual training had led to a good recovery of perceptual performance in the blind-field, as documented by the reduction of motion direction discrimination threshold to levels similar to the control healthy participants. Cortically-blind (CB) patients are shown to perform very similarly to control participants in terms of saccade accuracy, but they have longer latency. As for the postsaccadic ocular following response ("PFR"), the eye velocity component projected on the random-dot motion direction Is comparable to controls when the saccade was directed to the intactfield, but the mean PFR is significantly lower for saccades directed toward the blind-field. The authors conclude that V1 lesions result in a previously ignored selective impairment of the automatic transaccadic transmission of visual information that drive the ocular following response. In the supplementary information, it is also shown and the shift of saccadic landing position which is induced by the presaccadic target motion is strongly reduced (yet different from zero) for saccades to the blind-field locations in CB patients.

The manuscript is very well written and illustrated, and the addressed question is novel and highly interesting. The inclusion in the experiment of locations of the patients' blind-field for which some perceptual abilities had been recovered is particularly interesting. However some major weaknesses fragilize part of the results and undermine the interpretation of results (see below). I also list a series of other minor issues to be clarified or improved.

Main weaknesses:<br /> 1) Unfortunately, the present data do not allow to strongly support the conclusion that the reduced PFR gain in patients is decorrelated from the motion discrimination performance. As a matter of fact, in Figure 4B the function describing the relation between PFR gain and NDR is reasonably linear in a very limited interval of NDR values (say <0.3), and it should rather be described as a decreasing exponential, or similar, approaching 0 already for NDR~0.3. On the other hand, it is presumably hard to appropriately fit a similar exponential function to the blind-field datapoints, as the majority of the latter lay in the range of NDR threshold (say > 0.4) where the PFR gain would in any case be flat and close to 0. In other terms, in my view there aren't enough blind-field datapoints with low NDR threshold to assess a quantitative difference in the relation between PFR and NDR between CB patients and Control participants.

Finally, and probably just a misunderstanding of mine, shouldn't the empty circles in Figure 4A and 4B have the same y-coordinate (the PFR gain value)? It does not seem so when looking at these figures.

2) A second weak point, in my opinion, concerns the interpretation of the results and in particular the exclusion of a role for presaccadic attentional mechanisms. The authors claim (lines 356-358): "That the FEF and its projections to area MT are intact in V1-stroke patients suggests preservation of presaccadic planning and attention selection for the saccade target even when visual input is weak or abnormal in a blind-field" and this is definitely a valuable point. However a number of other physiological mechanisms involving V1 could play a role in the spatially-selective processing of motion and the argument that (lines 368 and ff) "other aspects of saccade pre-planning related to perceptual shifts in the position of motion targets, remain in the blind-field" is not very robust here, considering that the reduction in the angular deviation is very strong in the blind-field (Supplementary Figure 2).

Here is a speculative alternative interpretation: V1-lesioned patients suffer among others of a specific impairment for spatially-selective motion processing. Unfortunately, the training in peripheral motion discrimination does not test this particular possibility, if I understand correctly, as there was no other distractor aperture containing distracting motion information (see Fig 2A). In contrast, in the main experiment, a lack of spatial selectivity for motion integration may have strongly affected the presaccadic motion discrimination (being more global than local) as well as PFR and postsaccadic landing position shift (although the latter was partly spared). According to this possibility, a simple prediction is that depending on the (randomly determined) motion direction in the distracting apertures, the PFR (the true eye movement, not the projection according to the stimulus motion axis) should be deviated in different directions, coherent with a global integration of motion. Do the available data allow to verify this possibility? In general, I think that it would be interesting to analyse post-saccadic smooth eye velocity beyond the "projected" velocity.

We thank the reviewer for their evaluation, several parts of which overlap with Reviewers 1 and 3. In particular, the concerns about sufficient sampling from blind-fields that recover motion integration (NDR < 0.35) have been addressed by collecting additional data and performing new analyses, and we have also addressed possible impairments to spatial attention (see above in “Essential revisions”). The discrepancy noted in the y-ordinate between 4A and B is related to those analyses being by subject (4A) versus by visual field location (4B), which we already addressed above, in response to Reviewer 1. Other detailed responses appear below.

Reviewer #3 (Public Review):

The human visual system comprises a tangle of neural pathways that subserve different perceptual, cognitive, and motor functions. Unfortunate cases of brain damage can reveal surprising dissociations between the functions of damaged and spared tissue. Perhaps the most famous example is blindsight, when damage to visual regions of occipital cortex leads to subjective blindness in parts of the visual field while sparing some visually-guided actions. Kwon, Huxlin and Mitchell had a rare opportunity to study eight individuals with that type of cortical blindness due to stroke, and put them through a carefully designed regimen of visual training and oculomotor testing.

The main focus was a particular oculomotor behavior that they term the "post-saccadic following response": when a neurotypical person makes a saccade to an object moving in the periphery, their eyes immediately begin smoothly following the stimulus motion, due to an oculomotor plan made before the saccade began. In this case, the stroke patients were able to regain their ability to discriminate stimulus motion in the "blind" parts of the visual field, but upon saccading to those stimuli they did not show the immediate post-saccadic following response. This surprising result shows yet another splintering dissociation between perception and action, demonstrating that the effects of stroke can be very specific to certain motor actions.

Strengths:<br /> - The authors masterfully combined several techniques in a rare and carefully chosen sample of participants: neuropsychiatric evaluations, rehabilitation training, psychophysics and eye-movement analyses.<br /> - The analyses that link all those measures together, while complicated and precise, and elegantly and clearly presented.<br /> The study provides a twist on blindsight that is interesting philosophically, while also constraining our models of neural circuitry and informing approaches to rehabilitation after stroke.

Weakness:<br /> - The unique nature of this study is a strength but also potentially limits its impact: the authors studied one particular type of eye movement with a complicated, unnatural stimulus arrangement. For example, the stimuli were groups of random moving dots windowed through static apertures. These stimuli, which move but also don't, are quite different from real moving objects that people track with their eyes (flying birds, for example). A related issue, which the authors briefly acknowledge, is that the training was specifically directed towards explicit perceptual reports. We therefore don't know if the oculomotor behavior (the PFR) could also be trained.<br /> - The authors rely on traditional null-hypothesis tests (t-tests and correlations) to make binary judgements of whether each effect or difference is "significant" (p<0.05). Some of the conclusions would be more convincing if supplemented with power analyses, bootstrapped confidence intervals, and Bayes factors to evaluate the strength of evidence.

We thank Reviewer 3 for their evaluation. The choice of stimuli/task and their “naturalness” is addressed in our point by point responses to the “Recommendations for authors” below. We have also revised the manuscript to include boot-strapped confidence intervals, along with other statistics suggested by other reviewers, as noted under “Essential revisions for authors”. Other detailed responses appear below point by point.

Author Response

Reviewer #3 (Public Review):

Phillips and colleagues present results obtained by generating loss-of-function mutations in the YAP/TAZ ortholog of the unicellular holozoan Capsaspora owczarzaki. In previous work published collaboratively by the Pan and Ruiz-Trillo labs, the authors had shown that Capsaspora has orthologs of yorkie (yki) and hippo (hpo) and that when these genes were expressed in Drosophila they functioned in a way that was consistent with the well-characterized function of the Hippo pathway in regulating cell proliferation.

Characterizing the role of the pathway in Capsaspora required the ability to manipulate gene expression in that organism. In this manuscript, the authors describe remarkable progress in that area. They generate lines that stably express fluorescent proteins. Excitingly, they are able to use CRISPR/Cas9 and generate loss-of-function alleles using a donor-template strategy. These accomplishments pave the way for the study of Capsaspora using molecular tools.

The authors then use these technologies to generate biallelic loss of function mutations in Capsaspora. They find no evidence of defects in cell proliferation either when these cells are cultured by themselves or when they are mixed with wild-type cells. However, they do find evidence of abnormalities in the cytoskeleton. They find that the cells themselves, and the multicellular aggregates that they form are more irregular in shape. The cells appear to adhere to substrates better than wild-type cells. They show surface blebbing that changes in the cell cortex with evidence for altered actin dynamics.

From these experiments, the authors conclude that the ancestral function of the Hippo pathway is to regulate the cytoskeleton and that its ability to regulate cell proliferation was acquired more recently in evolution.

The technical achievements are impressive, the experiments are well designed and executed, and are presented clearly. I have no issues with them. However, I feel that two of the main conclusions that the authors make are not justified by the results.

1) The authors seem convinced that CoYki functions as a transcriptional regulator. They seem to suggest that it is primarily a regulator of cytoskeletal genes. There is a body of work from the Fehon laboratory that Yki has a function at the cell cortex in Drosophila that is independent of its function as a transcriptional regulator. See the work by Xu et al. 2018; PMID30032991 (not cited in this paper). In the absence of data that shows the localization of CoYki, I don't see how the authors can tell where it is working (in the nucleus or at the cell cortex) to regulate the cytoskeleton.

To provide support for asserting that coYki is transcriptional regulator, we have done the following:

• We have cited previous results showing that coYki and its binding partner coSd can, when expressed together in the Drosophila eye, induce transcription of Hippo pathway genes, indicating a role for coYki in transcriptional regulation

• We have examined the localization fluorescent fusions of coYki and a coYki (coYki 4SA) mutant predicted to be nonphosphorylatable by upstream Hippo pathway kinases. Enrichment of coYki at the cell cortex was not detected. However, the 4SA mutant showed increased localization in the nucleus relative to the WT coYki protein, arguing for a nuclear function of coYki.

These data are therefore consistent with the prevailing view of Yki/YAP/TAZ as a transcriptional regulator in other species. Nevertheless, we cannot formally exclude the possibility that coYki may also affect the cytoskeleton through a non-transcriptional manner as described by Xu et al., which we have now stated in the Results section of our manuscript.

2) Capsaspora and animals such as ourselves are equally separated by time from our last common ancestor. There is no reason to think that the function of signaling pathways in the Capsaspora lineage has been frozen in time while ours have evolved. Indeed, the amazing diversity of protists is consistent with lots of evolution in every lineage. One could easily argue from the same data that the ancestral function of the Hippo pathway was to regulate cell proliferation and that this was lost in the lineage that led to Capsaspora. As we learn more about the function of the Hippo pathway in diverse organisms, we will be in a better position to guess what the ancestral function was.

We agree that the function of signaling pathways in modern protists and their ancestors may not necessarily be identical, and that studies of Hippo signaling in other organisms, especially unicellular holozoans, may clarify which functions may have been ancestral, as we make a point to state at the end of our discussion. However, given that in animals Hippo signaling regulates the cytoskeleton and proliferation, and we find that in Capsaspora coYki affects the cytoskeleton but apparently not proliferation, it seems reasonable to us to suggest a model where cytoskeletal regulation was an ancient function, and the pathway was later co-opted for regulation of proliferation. We have added a section in the Discussion pointing out that we cannot, from our results, definitively conclude an ancestral Hippo pathway function.

In summary, this manuscript describes technological innovations that will have a big impact on those who want to study this organism. They also provide convincing data to show that the Capsaspora Yorkie ortholog regulates cytoskeletal dynamics and not cell proliferation. However, as described above, the authors would need to tone down some of their conclusions.

impacts

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Reply to the reviewers

We thank the referees for their valuable suggestions. We have revised the text accordingly and already conducted most of the requested experiments.

Reviewer #1

• 1. The authors state that addition of mannan increases length of Birbeck granules however, no data are presented. It would make this more convincing when the length is compared between conditions with and without mannan (as shown in Fig 4, where the condition without mannan is lacking).

Reply: Thank you for pointing out the missing data. We added an EM image of Birbeck granules and quantification of Birbeck granules formation in the absence of mannan (Figure 4A-D).

• Supp, fig 1B perhaps as a panel in main figure as this is an important control to show that Birbeck granules are isolated.

Reply: We moved the supplemental figure 1B to main figure 1D.

• 1. Only the(total) length of Birbeck granules is taken into account, but not the number of Birbeck granules. Is it possible to quantify the number of Birbeck granules.

Reply: We added Figure 4D to show the number of Birbeck granules. Note that the difference in the number of Birbeck granules was less significant than that of total length because there were numerous short fragments in the mutant specimen.

• Fig 5. Only the condition (ARGK) where there is virtually no Birbeck granules formation is included, however, is virus still internalized in the other conditions (MRGD or MRGK) as Birbeck granule formation was less effective but still present? It would be interesting to include those mutants. A more specific quantification would be by p24 ELISA. Is there a reason why immunoblotting has been chosen? In the supernatant condition, explain why the virus p24 seems less in the control condition whereas one would expect max concentration in that condition.

Reply: Thank you for suggesting the use of ELISA. We chose immunoblotting because of its higher sensitivity and lower cost. But ELISA is advantageous when it comes to comparing large number of samples. We performed p24 ELISA and quantified the virus internalization in all the mutants available (Figure 5C). As you pointed out, the transfer efficiency of the immunoblot in Figure 5A was not uniform across the membrane; Pr55 bands became denser toward the right, while p24 bands had a gradient in the opposite direction. The immunoblots and ELISA showed that about ~1% of the viruses were attached or internalized and ~99% did not interact with the cells. Thus, the attached/internalized viruses did not affect the amount of viruses in the supernatant. Results of ELISA also showed the amount of viruses in the supernatant were nearly equal among the samples (Figure S3B).

• Abstract First sentence: not mucosal tissue but mucosal epithelium Last sentence: Virual should be viral

Reply: We corrected the typo. Thank you.

• Discussion The last section comparing DC-SIGN and langerin is not clear and some overstatements are made. "Considering that DC-SIGN serves as an attachment receptor for viruses but not as an entry receptor, the possible structural coupling of lateral ligand binding and internalization implies that langerin functions as a more efficient entry receptor for viruses than DC-SIGN or other C-type lectins." It is not correct that langerin but not DC-SIGN can function as an entry receptor. DC-SIGN has been shown to facilitate infection of different viruses such DENV and ZIKV. In contrast, langerin can restrict viruses such as HIV-1 but also facilitate infection for example Influenza A and DENV. So attachment or entry is more likely a consequence of the internalization and dependence on pH changes for fusion as some viruses such as DENV fuse in acidic vesicles. This needs to be discussed more clearly.

Reply: Thank you for pointing out our wrong statement. We replaced the statement with weakened one as below:

Page 13, line 213: “The difference in the ligand-binding manner between langerin and DC-SIGN may contribute to their different carbohydrate recognition preferences (Valverde et al., 2020; Takahara et al., 2004).“

Reviewer #2 1) Langerin can exist on the cell surface and in Birbeck granules. They should examine langerin cell surface expression in the 3 states, wildtype, mutated and lectin - . Do the mutations change cell surface expression?

Reply: We performed surface labeling experiments and showed that those mutations did not affect surface expression of langerin (Figure S3A).

2) Birbeck granules are present in the absence of mannan and pathogens (see Pena-Cruz JCI 2018, PMID: 29723162). Thus, this suggests that Birbeck granules are present even without langerin clathrin coated pit internalization from the cell surface. How does their model account for this observation?

Reply: We think there are two possibilities:

1. Birbeck granules were shown to stem from the endoplasmic reticulum (Valladeau et al Immunity 2000; Lenormand et al PlosONE 2013). Since the rER is the site of glycosylation, langerin is likely to capture the oligo-mannose-glycosylated proteins within the rER and form Birbeck granules.
2. Blood plasma proteins such as immunoglobulin D, immunoglobulin E, and apolipoprotein B-100 are reported to carry high-mannose glycans (Clerc et al Glycoconj J. 2016). Those glycoproteins in the cell culture media can induce Birbeck granule formation.

   3) Different cell types can have varied Langerin levels (see Pena-Cruz JCI 2018, PMID: 29723162). Is Birbeck granule formation depend on certain level of langerin expression? Do Birbeck granules form when Langerin is present at low as compared to high levels?

Reply: In the course of the experiments, we isolated a cell line stably expressing langerin. However, langerin expressing cells were extremely slow in proliferation and the expression levels were low. To answer this question, we recovered this “failed” stable cell line and found that the low langerin-expressing cells can form Birbeck granules, but with lower efficiency (Figure S3C-E).

4) Authors use immunoblots to show that HIV is present in intra-cellular Langerin structures. It would be ideal to visualize HIV with presumably internal Birbeck granules using imaging techniques such as cryo-electron micrography or another form of high resolution imaging.

Reply: We are currently working on ultra-thin section electron microscopy of HIV-infected langerin-expressing cells. Visualization of HIV-containing Birbeck granules using cryo-electron microscopy is highly challenging because the current precision of cryo-FIB-SEM milling technique is too low to target a specific intracellular structure. We believe conventional electron microscopy will provide sufficiently convincing evidence that HIV is present within Birbeck granules.

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Reply to the reviewers

We are grateful for the referees' rigorous review of our manuscript and for their overall positive reception of our work. We have pasted below the entirety of the reviewers’ comments, interleaved with our responses.

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

In this manuscript, Gama et al. use a biophysical assay DAmFRET, structural analysis, and optogenetic tools to uncover the nucleation mechanism of CBM signalosome. They performed experiments first in yeast cells that lack death folds or related signaling networks, then confirmed their discoveries in human cells. The results presented here are clear and convincing. The paper is very well presented and clearly written.

They found it is the CARD domain of BCL10 that acts as a molecular switch that drives all-or-none activation of NF-kB. Monomeric BCL10 possesses an unfavorable conformation and serves as a nucleation barrier, keeping BCL10 in a supersaturated inactive state that allows for binary activation upon stimulation.

They also characterized CARD9 CARD domain and a coiled-coil region. They reasoned that CARD9CARD functions as a polymer seed to nucleate BCL10, and that the coiled-coil region has multimerization ability to facilitate nucleation. Furthermore, they characterized that MALT1 activation doesn't depend on BCL10 polymers but its own proximity. And MALT1 induces graded NF-kB activation, thus further demonstrating the binary activation is conferred by BCL10.

Major comments:

1. Fig S1D and E, the authors used TNF-a to activate NF-kB independent of CBM signalosome and found the activation in each cell increased with dose. In contrast, CBM activation led to bimodal cell activation. The authors claim that this is evidence that positive feedback upstream of NF-kB. We do not believe this claim can be made from this comparative experiment alone. We agree that positive feedback is important for activating an NF-kB response, but the comparison between CBM and TNFa is inaccurate and glosses over published data. Specifically, there is published data that TNF-a does activate a 'switch-like' or digital response, as defined by the translocation of p65 (see (Tay et al. 2010) among other studies that have examined p65 translocation at the single-cell level). The difference in T-sapphire expression between CBM and TNF activation is most likely due to TNFa induced oscillations of p65 translocation (although this is speculation on our part). Therefore we suggest to the authors that the TNF-a data (Fig S1D and E) should be omitted, as the claim of switch or not-switch as pertains to TNF signaling is more complex and nuanced than presented here. We believe omitting this data will strengthen the manuscript and avoid confusion in the field. The bimodal expression of the T-sapphire NF-kB reporter driven by the CBM signalosome activation is sufficient to claim an all-or-none response.

We thank the reviewer for this suggestion. We acknowledge that the activation of NF-κB by TNF-ɑ is more complex than we had presented, and agree that the differences in T-Sapphire reporter output could be attributed to p65 oscillations. We had not previously considered this interesting possibility -- which is not addressed by the present data -- believe it is worth future investigation. As suggested by the reviewer, we have now omitted the TNF-a data, and agree that this change does not impact the overall claims of the paper.

Fig 3B, the authors introduced CARD9CARD-µNS as a stable condensed seed for BLC10. However, considering CARD9CARD can form polymers at high concentration (Fig 3B and S3D), are these high expression levels of CARD9CARD able to induce BCL10-mEos3.1 assembly (as measured by DamFRET in yeast cells)? Can the authors examine BCL10 FRET at these high expression level of CARD9CARD? We assume that BCL10 will be assembled in these cells. This would provide a valuable control experiment and support the author's conclusions.

Indeed, this question is amenable to DAmFRET. Accordingly, we have now performed DAmFRET of yeast cells expressing Bc10-mEos3.1 in the presence of either CARD9CARD-mCardinal or mCardinal itself (see new Fig S6A and B, and associated results section). We confirmed that cells with high CARD9CARD-mCardinal expression had higher FRET on average than cells with low expression. Importantly, cells expressing high or low levels of mCardinal itself had the same FRET level (Fig S6).

Fig 3C, the text said "Whereas WT CARD9CARD assembled into polymers at high concentration, the pathogenic mutants R18W, R35Q, R57H, and G72S failed to do so (Fig 3C and S7B,C), explaining why they cannot nucleate BCL10". This claim that these mutants can not nucleate BCL10 does not have a figure call out or a reference. The authors then show the results in Fig 3E which supports this claim. Even though they were done in the context of full-length CARD, all proteins contain the I107E mutation that releases autoinhibition. For clarity, the authors should consider rearranging the text to avoid explaining a phenomenon and making conclusions before showing the results.

We have now rearranged this section to match the figures and claims.

Fig 4D, E and Video 1, the authors showed the nucleation of BCL10 into puncta within live cells is followed by p65 translocation to the nucleus. The authors claim that 'this result suggests that BCL10 is indeed supersaturated prior to stimulation' (paragraph 2 section titled BCL10 is endogenously supersaturated'). We fail to understand how this live-cell experiment leads to the conclusion BCL10 is supersaturated before stimulation. We think this text should be deleted from the text, or put into context with the DAmFRET data that lead the authors to make this claim. It would be interesting for the authors to define in discussion what are the golden criteria to claim a protein exists in a supersaturated state with live cells (by microscopy or other methods)? Adaptor protein assembly into puncta and the subsequent nuclear translocation of transcription factors is a common phenomenon across signalling pathways. Not all these pathways rely on signaling adaptors existing in a supersaturated state. The field of cell signaling (and cell biology in general) would benefit from a detailed definition of how these physical-chemical definitions of proteins are supported by experimental data. We believe that this paper will become a seminal paper in the field, and future work will benefit from a clear definition of how a claim of supersaturation is derived from the data.

We appreciate that the concept of supersaturation will be foreign to many biologists, and welcome this opportunity to elaborate. We have now rephrased the corresponding results section for figure 4D, E, and have added new evidence to support our claim that BCL10 is supersaturated, as had been requested by reviewer 2 (see below in response to point 1). Supersaturation, as we (correctly) use the term, occurs when the concentration of a protein in solution exceeds its equilibrium solubility for the given conditions. The term is also sometimes used to describe __global __protein “concentrations” in excess of the solubility limit, even if a dense phase has already formed and potentially depleted the effective concentration (in solution) to the solubility limit. This is a key distinction, as only the former implies a high-energy out-of-equilibrium scenario that predetermines a future change -- release of the excess energy via phase separation.

How does one experimentally determine if a protein is supersaturated? In theory, one may conclude that a protein is supersaturated if its assembly causes a net loss of energy from the system (i.e. exothermic). Unfortunately, it is likely not yet possible to perform such measurements with sufficient sensitivity inside a living cell. However, it is possible to infer that a protein is supersaturated if assembly can be shown to occur without a net input of energy to the system, i.e. without any change in thermodynamic control parameters such as temperature, pH, post-translational modifications, concentration of the protein, or concentration of any interacting factor. To do this, one introduces a substoichiometric amount of pre-assembled protein to the system. This manipulation will trigger assembly if the protein is supersaturated. If the protein is instead subsaturated, assembly will not occur and the exogenously added assemblies will simply dissolve. This phenomenon, known as “seeding” in the prion field, is considered a golden criterion sufficient to conclude that a protein has prion behavior. However, because bona fide prions additionally require a means for dissemination between cells, seeding analyzed at the cellular rather than population level is more appropriately considered a sufficient criterion for supersaturation (which is a prerequisite for classical prion behavior (Khan et al. 2018)). Our CARD9CARD-Cry2 experiment was designed to test this criterion. Specifically, it allowed us to introduce a seed independently of receptor activation, thereby precluding any orthogonal cellular response that might lower Bcl10 solubility through e.g. a post-translational change. That the seeds were substoichiometric is evidenced by the fact that Bcl10 polymerized homotypically following stimulation (i.e. it didn’t just bind to the CARD9CARD puncta, but went on to deposit onto itself).

How does assembly under this scenario differ in principle from the many examples of puncta formed by other signaling proteins that occur upon stimulation of their respective pathways? Puncta formation that is induced by a thermodynamic change in the cell cannot be said to have resulted from pre-existing supersaturation. Rather, the stimulus may have caused some change that either increases the effective concentration of the protein (e.g. upregulates its expression, induces a post-translational change that activates it, or releases an inhibitory factor) or reduces solvent activity (e.g. change in pH).

An additional requirement (necessary but not sufficient) is that the assembly must be regular with respect to some order parameter. That is to say, it must be a bona fide “phase”. At a minimum, this implies a uniform density. Additionally, for supersaturation to persist over biological timescales under physiological conditions and confinement volumes, the assembly (once formed) must also have structural repetition in at least two dimensions, i.e. crystallinity (Rodríguez Gama et al. 2021; Zhang and Schmit 2016). We know this to be true for Bcl10.

Rodríguez Gama A, Miller T, Halfmann R. 2021. Mechanics of a molecular mousetrap-nucleation-limited innate immune signaling. Biophys J 120:1150–1160. doi:10.1016/j.bpj.2021.01.007

Khan, T., Kandola, T.S., Wu, J., Venkatesan, S., Ketter, E., Lange, J.J., Rodríguez Gama, A., Box, A., Unruh, J.R., Cook, M., et al. (2018). Quantifying nucleation in vivo reveals the physical basis of prion-like phase behavior. Mol. Cell 71, 155-168.e7.

Zhang L, Schmit JD. 2016. Pseudo-one-dimensional nucleation in dilute polymer solutions. Phys Rev E 93:060401. doi:10.1103/PhysRevE.93.060401

Regarding the supersaturated state of BCL10, the authors convincingly use optogenetics to show how transient assemblies of CARD-Cry2 can template BCL10 assembly. This is a convincing experiment that shows templated nucleation of BCL10. To strengthen the claim that BCL10 is supersaturated endogenously we suggest the author quantify the expression of BCL10-mScarlet and CARD-Cry2 and ideally show that this phenomenon can be observed at expression levels equivalent to endogenous.

As stated above, that BCL10-mScarlet formed polymers that we observed to elongate homotypically off of the CARD9CARD seeds indicates that the protein was supersaturated under the conditions of the experiment. The concentration of CARD9 is not a relevant parameter in this case. We had already compared the expression of BCL10-mScarlet to endogenous BCL10 in 293T, THP-1, and human fibroblast cells by quantitative immunodetection (Fig. S10D), revealing that the expression level of our BCL10-mScarlet constructs matched that of endogenous BCL10, which was approximately the same in all cell lines. We also compared the distribution of expression levels of BCL10-mScarlet versus that of endogenous BCL10 using antibody staining followed by flow cytometry, which confirmed that the range of expression levels of BCL10-mScarlet falls within that of endogenous BCL10 in 293T cells (Fig. S10F). Hence, we believe our data suffice to conclude that Bcl10 is supersaturated at endogenous levels of expression.

Minor comments:

1. Special character "delta" is not displayed in the text (instead only a space).

This error occurred upon exporting the manuscript from our text editor to a PDF. We now have made sure all special characters are present in the PDF version.

Several cell lines including mouse, human, and yeast lines were used across this manuscript. It would be clearer and more helpful if the exact cell type of the line could be indicated. Such as, "BCL10-mEos3.1 yeast cells" instead of "BCL10-mEos3.1 cells", "BCL10-mScarlet HEK293T cells" instead of "BCL10-mScarlet cells".

We have now modified all instances to indicate the origin of the cell lines tested.

Fig 5B, the authors indicated that BCL10 colocalized with CARD9CARD, then please show the merged image as well.

We have now included the merged image to indicate colocalization in the inset images.

Fig 6E, authors claimed that cells were stimulated with blue light for the indicated durations. The longest duration is 12 hours. Please specify if it was continuous exposure or several rounds of exposure in the indicated durations.

We have now specified in the figure legends, text, and methods section, that this specific experiment used a continuous exposure of blue light.

Reviewer #1 (Significance (Required)):

This work used a combination of FRET and optogenetic tools to engineer CBM signaling and visualize the effects. They incorporated knowledge from structure biology, together with their results from mutations and truncations, dissected the significance of each protein in CBM signalosome, and demonstrated in detail how higher-order assemblies make all-or-none cellular decisions. We believe this paper will be a seminal paper in the field of cell signalling and cytoplasmic organization. It defines a new paradigm of macromolecules assembly of signalling complexes as being dependent on protein existing in a supersaturated state. Importantly this paper opens up new questions regarding macromolecular signaling complexes (found in many innate immune signaling pathways): How is protein supersaturation maintained and used throughout evolution to construct biochemical signalling switches?

This paper will be of particular interest to scientists working on immunity and cell signalling, especially in the field of higher-order assemblies. However, we feel the impact of this paper goes beyond these fields, and we believe this manuscript will be of broad interest to the cell biology and biophysics communities. For reference, our expertise is in innate immunity and cell biology.

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

In their manuscript entitled "A nucleation barrier springloads..." Rodriguez-Gama et al. dissect the assembly mechanism of the signalosome, composed of the proteins CARD9, BCL10 and MALT1, using a novel in-cell biophysical approach (DAmFRET). They first overexpressed fluorescently tagged versions of the proteins to promote their assembly in yeast and mammalian cells, finding that CARD9 forms higher order assemblies across a wide range of concentrations with no discontinuity in the DAmFRET profile. In contrast, the DAmFRET profile of BCL10 showed a clear separation between monomers and higher order assemblies, which started to form spontaneously only at higher BCL10 concentrations. Furthermore, at the two states of the proteins co-exist at all concentrations. These observations imply that there is a nucleation barrier to forming BCL10 assemblies. MALT1 showed no change in FRET regardless of its expression level. These observations, alongside fluorescence microscopy of the assemblies, and previous structural studies, suggest that BCL10 forms self-templating polymers that act as a switch for an all-or-nothing immune response, assayed in this case by monitoring the nuclear translocation of the NF-kB subunit p65. The authors also assessed the effects of known disease-causing mutations on the nucleation barrier, showing that changes in the strength of the nucleation barrier can have major effects on signalosome function. Finally, they used optogenetic methods to trigger assembly of individual signalosome components, providing insight into the minimal components/conditions required for signalosomes to work.

Major comments

Overall, the experiments by Rodriguez-Gama et al. offer convincing evidence that there is a nucleation barrier to BCL10 polymerisation, and that a CARD9 template is sufficient to overcome the barrier. Although the existence of a nucleation barrier had already been postulated, based on structural and other studies (referenced by the authors), it had lacked a rigorous demonstration. This work provides that demonstration, which is important for the signalosome field and more broadly applicable to researchers studying cellular decision making. The study further demonstrates that DaMFRET is an excellent to study protein assembly processes in their native environment, allowing the authors to tackle a question that would have been technically very difficult to address otherwise. The optogenetic experiments are a nice sufficiency test for their ideas.

We feel there are a few key points to address before publication.

1) One of the main conclusions is that spring-loading the nucleation barrier with high super-saturating BCL10 concentrations allows a decisive response. Although much of the data strongly imply this conclusion, the dependence of the immune response on BCL10 concentration was not tested directly. A key prediction of the nucleation barrier is that at concentrations below saturation, BCL10 should not be able to induce an all-or-nothing response when stimulated. At saturated/super-saturated concentrations BCL10 should be able to induce a response. At deeply super-saturated concentrations the response should start to be activated spontaneously in the absence of an external stimulus. These predictions could be tested using the doxycycline-inducible BCL10 system (Figure S2D), without establishing major new experimental avenues. We feel that such an experiment would strengthen the main conclusion. It might also help to shed light on whether being highly supersaturated enables a more decisive response than being just saturated.

This is a great idea. As the reviewer suggested, our Doxycycline-inducible BCL10 system enables us to induce and track the state of BCL10 over time. We have now performed the requested experiments (Fig. S9D, E) and incorporated the results into the relevant section of the text. In short, our new analyses show that BCL10 indeed has a concentration threshold for activation by stimulation, and that it can also nucleate spontaneously when overexpressed. Note that our original analyses in Fig. 4B and C also demonstrate spontaneous BCL10 activation at high concentrations. With this new evidence and the orthogonal approaches used in Fig. 5, we believe our data definitively support our conclusion that BCL10 is supersaturated.

2) Intuitively, readers might expect that if BCL10 is supersaturated then, once nucleated, it would rapidly assemble at the nucleation sites. In Figure 5B, CARD9CARD-miRFP670nano-Cry2 assemblies are optically induced throughout the cell. However, BCL10 appears to nucleate at just a few sites with a few minutes delay. More widespread nucleation and growth of BCL10 polymers seems to take longer (20-40 minutes, Figures 5B and 5C), after CARD9CARD-miRFP670nano-Cry2 has disassembled. Furthermore, in Figures 4D and 4E, very few BCL10 assemblies are visible/quantifiable after 70 minutes PMA exposure, but p65 has clearly entered the nucleus. It looks like BCL10 assembly slightly lags behind p65 nuclear entry. Can the authors provide a more detailed explanation of these kinetics?

We do note that the number of CARD9CARD clusters formed upon opto-stimulation exceeds the apparent number of BCL10 nucleation sites. We believe this is consistent with nucleation-limited kinetics, where the clustering of CARD9-CARD increases the local probability of nucleation. As nuclei form and grow, they lower the probability of subsequent nucleation elsewhere in the cell. Additionally, it is possible that our artificial seeds do not perfectly mimic the native CARD9 seeds that form upon natural stimulation (e.g. due to potential steric interference from the fluorophore and Cry2). We also acknowledge that there is a slight delay in the visible appearance of BCL10 polymers relative to p65 nuclear translocation. We expect that MALT1 activates already when the polymers are still too small to see (sub-resolution), whereas the polymers only become microscopically visible once they’ve grown quite a bit more.

3) Related to point 2 above, in Figure 5D, the leftmost cell in the field of view clearly contains CARD9CARD assemblies but there are no BCL10 assemblies and p65 is not imported into the nucleus (in contrast to the central cell in the field of view). How often does CARD9CARD optogenetic assembly lead to BCL10 assembly? In other words, can the authors quantify the cell-to-cell variability in this experiment?

Throughout our experiments, whether analyzing BCL10 puncta formation, NF-kB transcriptional activity, or p65 translocation, we observed a persistent nonresponsive fraction of cells even at saturating levels of stimulation. Specifically, approximately 30% of THP-1 cells failed to acquire T-Sapphire fluorescence or form BCL10-mEos3.2 puncta when stimulated with high levels of β-glucan (Fig 1B and E, respectively), and approximately 25% of 293T cells failed to acquire T-Sapphire fluorescence or exhibit p65 nuclear translocation when stimulated with high levels of PMA (Fig 1C and Fig 4E, respectively). Because these numbers did not depend on whether BCL10 was endogenously or exogenously expressed, we know that the underlying cell-to-cell heterogeneity involves factors upstream of BCL10. Indeed, the fraction of recalcitrant cells drops to 10% in our optogenetic experiments that bypass upstream factors (Fig S11E). Possible sources of heterogeneity include different physiological states of the cells or fluctuations in the expression levels of any upstream factor in the signaling pathway. We believe that this phenomenon is not unique to the CBM signalosome, as we (unpublished) and others (Fernandes-Alnemri T et al, 2009, Dick M et al, 2016) have similarly observed a fraction of non-responding cells upon activation of the inflammasome, which involves nucleation-limited polymerization of the adaptor protein ASC. While this phenomenon is interesting and may be important to our understanding of the full complexity of signalosomes in vivo, we believe that identifying the source of heterogeneity would be outside the scope of the present manuscript. We now describe this phenomenon in the final paragraph of the “Endogenous BCL10 is constitutively supersaturated” section.

Fernandes-Alnemri, T., Yu, JW., Datta, P. et al. AIM2 activates the inflammasome and cell death in response to cytoplasmic DNA. Nature 458, 509–513 (2009). https://doi.org/10.1038/nature07710

Dick, M., Sborgi, L., Rühl, S. et al. ASC filament formation serves as a signal amplification mechanism for inflammasomes. Nat Commun 7, 11929 (2016). https://doi.org/10.1038/ncomms11929

Minor comments

While the work is scientifically well done, the text reads as though it is meant for experts rather than a broad audience. This is a pity because it risks alienating readers. We suggest that some adjustments to the text (mainly additional explanations and not ruling out alternative interpretations of the data) would widen the audience and increase the impact of this important study. Below are some suggestions that might help.

1. In the first results section, the authors write: 'This suggests that Bcl10 but not CARD9 assembly occurs in a highly cooperative fashion that could, in principle (Koch, 2020), underlie the feed forward mechanism.' It isn't obvious how Figure 1 leads to this statement. Could the authors give a more detailed explanation?

We have now revised the text to elaborate on this interpretation.

One limitation of DAmFRET is that it can only detect a nucleation barrier where there is a difference in FRET between the monomer and the assembled form of the protein. However, it can't necessarily detect when there is not a nucleation barrier i.e. if there's no difference in FRET. The text seems to suggest that CARD9 and MALT1 don't have nucleation barriers to their assembly. While this might not be intentional, it would be helpful to explicitly state that CARD9 and MALT1 could also possess such barriers that are not detectable by this method. This wouldn't detract from the finding that BCL10 has a barrier that plays an important function.

The reviewer is correct that DAmFRET would not be able to detect a nucleation barrier if the assembled phase does not condense the fluorophore to a sufficiently high density for FRET to occur. In our experience, this is only a concern for very large proteins whose bulk “dilutes” the fluorophores within the assembly. Death domains, on the other hand, are only ~ 3 nm in diameter, and FRET occurs within a range of ~10 nm; hence we think it very unlikely that the death domains could be forming cryptic polymers that escape our detection. In any case, when assembly does produce a change in FRET, we can with confidence determine how strongly that form of assembly is governed by concentration. Hence, for CARD9, which does produce a FRET signal upon assembly, we can say that assembly has a smaller intrinsic nucleation barrier than that of BCL10. We further eliminated the possibility of multi-step nucleation (which would reduce the apparent nucleation barrier relative to the one-step ideal case) for CARD9 by showing that artificial condensates of the protein expressed in trans do not influence the concentration-dependence of FRET (Fig. 4 B). Finally, under all conditions where CARD9 lacked FRET, it also lacked signaling activity, suggesting there is not a cryptic functional assembly that evades our assay. Likewise MALT1, which lacked FRET at all concentrations, was entirely unable to activate NF-kB upon overexpression (Fig. S8 A and B), suggesting that it too is not forming a cryptic functional assembly that evades our assay. We therefore feel confident in our conclusion that CARD9 and MALT1 lack nucleation barriers of a magnitude comparable to that of BCL10. Note that our claim is not that they entirely lack a nucleation barrier (CARD9 after all does form a multi-dimensionally ordered polymer), but rather that we fail to observe a nucleation barrier and hence any barrier that may exist is insufficient to manifest at the cellular level.

In the final results section, the idea that MALT1 activation doesn't depend on BCL10 polymer structure doesn't necessarily follow from the data. An alternative interpretation is that optogenetic clustering of MALT1 causes it to recruit BCL10 and form BCL10-MALT1 filaments (structure solved by Schlauderer et al., 2018). Also, the optogenetic clustering of MALT1 may mimic some structure found in the BCL10 cluster. Therefore, we are neither convinced that the data unambiguously show that MALT1 activation strictly depends on multi-valency rather than an ordered structure of BCL10 polymers nor that this conclusion is truly necessary for the paper.

We agree that the reviewer’s alternative interpretation of this experiment is possible. However, we consider it unlikely because we performed the experiment with MALT1 lacking its Death Domain (residues 126-824), which mediates its interaction with BCL10 (Schlauderer et al., 2018). Our experiments then suggest that MALT1 clustering is sufficient for activation independent of any structuring mediated by BCL10. Nevertheless, we have now performed an additional control in which we treated these cells with PMA to induce BCL10 polymerization. As expected, the NF-kB transcriptional reporter utterly failed to activate in this condition, indicating that MALT1 does not interact with BCL10 polymers when it lacks its death domain. This aspect has been further elaborated in our response to reviewer 3 point 5.

What optical density do the yeast cells reach during the 16h induction in galactose? If they are in stationary phase, this could affect the assembly status of the proteins being expressed, as the cytoplasm becomes glassy when cells are starved, and this coincides with widespread protein aggregation/assembly (Joyner et al., 2016; Munder et al., 2016).

In our DAmFRET strategy, we first dilute an overnight culture and regrow the cells to log phase prior to resuspending them in galactose media. Our strain is engineered to undergo cell cycle arrest upon protein induction, hence exponential growth is prevented and the cells do not deplete galactose during the 16 hr induction. We have also performed many time courses of DAmFRET following induction and generally find no qualitative difference between early and late times (unpublished). Early time points simply have lower expression and correspondingly fewer cells in the high FRET state. Importantly, all comparisons between proteins are made with the same 16 hr induction.

Although these experiments show that thermodynamically lowering the BCL10 nucleation barrier (e.g. by post-translational modifications or protein expression levels) isn't required for a response, they don't rule it out. It would be good to state this in the discussion, as cells may have multiple mechanisms of switching on the signalosome.

We thank the reviewer for this suggestion and have now explicitly stated in the discussion that our experiments do not argue against possible thermodynamic tuning of the nucleation barrier.

The discussion compares signalosomes with condensates formed by liquid-liquid phase separation. This is an interesting comparison but it suggests that disordered assemblies would not be capable of performing signalosome-like functions. This needs to be explained more clearly. For example, non-amyloid prions seem to form gel-like assemblies with a high nucleation barrier that are capable of driving heritable traits, likely through self-templating (Chakravarty et al., 2020). Such examples could represent disordered assemblies with signalosome switch-like behaviour. Furthermore, there are examples of condensates that are induced by environmental changes e.g. Pab1 and Ded1 condensates (Riback et al., 2017; Iserman et al., 2020). This potentially allows the proteins to reach high concentrations and remain un-condensed until a change in heat or pH overcomes a nucleation barrier required for condensate formation. Although the condensates aren't self-templating, they seem to require energy for their disassembly. Combined, this also allows switch-like behaviour, where the switch is flipped back to the uncondensed off state once conditions return to normal. In general, crossing a phase boundary can represent a switch-like response. Finally, recent electron-tomography experiments show that ASC puncta comprise clusters of filaments (Liu et al., 2021, biorxiv). CARD9/BCL10 assemblies may have similar ultrastructures and liquid-liquid phase separation may well play a role in their assembly.

Indeed, we explicitly maintain that liquid phases cannot themselves perform signalosome-like functions. Chakravarty et al. 2020 did not observe amyloids associated with their phenomena, but the relevant experiments were not designed to exhaustively exclude an underlying ordered phase. To the extent that gelation is involved, their observations are fully consistent with ours. IUPAC defines a “gel” as a colloidal network involving a solid phase and a dispersed phase. The existence of a solid phase necessarily implies an underlying disorder-to-order transition, even if limited to small length scales. In the case of gelation associated with liquid-liquid phase separation, nucleation of the ordered phase simply occurs in two steps (first condensation, then ordering). Note also that a liquid phase could in principle give rise to a heritable phenotype if it activates a positive feedback in a molecular biological process involving the protein of interest (e.g. upregulation of its expression or a change in interacting factors). Chakravarty et al. did not exclude such phenomena (it would be very difficult to do so); hence it cannot be concluded that phase separation is responsible for the sustained phenotypic changes.

We do not fully follow the reviewer’s logic concerning the relevance of Pab1 and Ded1 condensates. These proteins only condense when their respective phase boundaries fall below the endogenous protein concentration, as upon thermal stress. The proteins are not supersaturated in the absence of such conditions (for example, they cannot be seeded), and it is incorrect to characterize the change in heat or pH as overcoming a pre-existing nucleation barrier. The concept of a nucleation barrier only applies under conditions where a phase is thermodynamically favored. It is also misleading to state that the Ded1 and Pab1 condensates require energy for disassembly. Rather, they require energy to disassemble rapidly. Unless the assemblies have accessed a more ordered phase as described above (two step nucleation), involving a lower phase boundary, they will inevitably dissolve after the conditions return to normal.

We have much prior experience with ASC. Although it has not been explicitly shown, that it forms ordered polymers and can behave as a prionoid in vivo suggests that it very likely operates the same way as BCL10 (i.e. is physiologically supersaturated). That full-length ASC forms clusters of filaments is not relevant (in our view) to the mechanism shown here, which only requires that filaments are indeed formed. Formally, the size of the relevant nucleus determines the minimum length scale at which ordering must manifest in our mechanism. Based on the structure of death domain filaments, this could be as small as tetramers or hexamers (a minimal but structurally complete “polymer”).

As stated above, and now elaborated in the discussion, our data do not exclude a role of thermodynamic regulation, as could lead to liquid-liquid phase separation, in tuning the nucleation barrier of Bcl10. What they do exclude is that such changes are required for Bcl10 to activate in the first place.

Can the authors comment on the loss of BCL10 in Echinodermata, Anthropoda, Nematoda? Is there another protein that plays a similar role? Could a CARD or PCASP protein possess self-templating properties? Could other methods of control be at play e.g. protein expression?

This is a very interesting question! We think the reviewer’s suggested explanations for the loss of BCL10 in those lineages are valid and worthy of future exploration. Nematodes such as C. elegans have lost multiple components of innate immunity. They have very few pathogen recognition receptors and also lack NF-kB! They do, however, have other adaptor proteins that the literature and our unpublished data suggest may have self-templating ability, such as TIR-1. Drosophila also encodes multiple TIR-containing proteins that are essential for innate immunity. In short, it is possible that other proteins have acquired the hypothetically essential role of supersaturation and nucleation-limited signaling in these organisms.

Figures 1B/1C: Can the authors comment on why the active cells plateau at about 70-75%? This is a striking feature of the plots, but the explanation may not be obvious to readers.

See our response to major point 3, above.

Figures 1D/1E: What was the concentration of B-glucan used in this experiment? This could be included in the figure legend. If greater than 1ug/ml this means that the % of active cells in Figure 1B matches the % of cells with BCL10 assemblies in Figures 1D/1E, which is potentially an important point.

We thank the reviewer for bringing this point to our attention. We have now indicated in the figure legend the concentration of B-glucan used in this experiment (10 μg/ml). That the percentage of active cells in Fig. 1B matches that of cells containing BCL10 polymers in Fig. 1D and E indeed strengthens the stated relationship between BCL10 assembly and NF-kB activation in THP-1 cells subjected to a relatively physiological stimulus. Additionally, we have performed experiments to measure the levels of p65 translocation in THP-1 cells treated with B-glucan that express BCL10-mEos3.2. This data is shown in Figs. S1D and E in response to reviewer 3.

Use of both 'BCL10' and 'Bcl10' when referring to the protein.

We have now replaced all instances where Bcl10 was used to follow guidelines for gene and protein name conventions.

Bruford EA, Braschi B, Denny P, Jones TEM, Seal RL, Tweedie S. Guidelines for human gene nomenclature. Nat Genet. 2020;52(8):754-758. doi:10.1038/s41588-020-0669-3

In the supplementary figures there are some formatting problems/missing words in the figure legends. In Figure S11 there is a black box covering the lower part of the figure.

We have now fixed these instances.

References used in this review

Chakravarty, A.K. et al. (2020) "A Non-amyloid Prion Particle that Activates a Heritable Gene Expression Program," Molecular Cell, 77(2), pp. 251-265.e9. doi:10.1016/j.molcel.2019.10.028.

Iserman, C. et al. (2020) "Condensation of Ded1p Promotes a Translational Switch from Housekeeping to Stress Protein Production," Cell, 181, pp. 818-831.e19. doi:10.1016/j.cell.2020.04.009.

Joyner, R.P. et al. (2016) "A glucose-starvation response regulates the diffusion of macromolecules," eLife, 5. doi:10.7554/eLife.09376.

Munder, M.C. et al. (2016) "A pH-driven transition of the cytoplasm from a fluid- to a solid-like state promotes entry into dormancy," eLife, 5(MARCH2016). doi:10.7554/ELIFE.09347.

Riback, J.A. et al. (2017) "Stress-Triggered Phase Separation Is an Adaptive, Evolutionarily Tuned Response," Cell, 168(6), pp. 1028-1040.e19. doi:10.1016/j.cell.2017.02.027.

Schlauderer, F. et al. (2018) "Molecular architecture and regulation of BCL10-MALT1 filaments," Nature Communications 2018 9:1, 9(1), pp. 1-12. doi:10.1038/s41467-018-06573-8.

Reviewer #2 (Significance (Required)):

The existence of a nucleation barrier had already been postulated, based on structural and other studies (referenced by the authors), it had lacked a rigorous demonstration. This work provides that demonstration, which is important for the signalosome field and more broadly applicable to researchers studying cellular decision making. The study further demonstrates that DaMFRET is an excellent to study protein assembly processes in their native environment, allowing the authors to tackle a question that would have been technically very difficult to address otherwise.

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

The study by Rodriguez Gama et al. addresses the molecular function of CBM complex-forming proteins CARD9, BCL10 and MALT1 in the activation of myeloid cells, using optogenetic tools, transcriptional reporters and biochemical approaches. It is known from previous studies that Bcl10 oligomerizes into filamentous oligomeric structures incorporating Malt1, and that these structures are nucleated by receptor-induced activation of CARD proteins such as CARD11 (in lymphocytes) or CARD9 (in myeloid cells), but the mechanism underlying the assembly of the resulting CBM complexes remain incompletely understood.

The authors develop beautiful optogenetic tools to address this question, and convincingly demonstrate that CARD9-mediated nucleation of BCL10 triggers a binary cellular NF-kB response in a spring-load-like fashion, and identify mutants of BCL10 and CARD9 that impact this capacity. Unfortunately, however, the authors do not do a good job to simplify this complex problem so it can be easily understood. In particular, the choices of mutants, models and experiments are not consistent between figures, and some data seem to be arbitrarily added or omitted. Complex hybrid constructs are also used, without assessing whether these are indeed functional in the corresponding ko cells. The paper would therefore benefit from a major overhaul. We also noticed that the literature is often not cited adequately and have included a (non-exhaustive) list of examples of wrong, incomplete, or erroneous citations below.

1. The initial observations of binary signaling are derived from a reporter system. Although there are controls to show that the reporter used does not function intrinsically cooperatively, it would be nice to see additional data to show that cooperativity occurs also at the level of endogenous response systems, for instance by qPCR-based assessment of a natural NF-kB target gene (induced for example by TNFa versus B-glucan in THP-1 cells, and by TNFa versus PMA in 293T cells).

As detailed in the introduction, NF-kB has been shown by multiple labs to activate in a binary fashion. Our manuscript shows that NF-kB activation occurs in a binary fashion both at the level of transcription and at the level of nuclear translocation (upstream of any transcriptional output). While we do agree that additional data could further illustrate the biological significance of our findings, we do not feel it is necessary for our conclusions. Note also that because NF-kB activation occurs in a binary fashion per cell, a simple qPCR experiment would not suffice to extend our findings to the broader Nf-kB regulon. Instead, one would have to use e.g. RNA-FISH or single cell RNA-seq, nontrivial experiments that would take months to complete.

The cell lines in Figures 1D-E (and also some of the BCL10 mutants used later on) would have been better run in the assays in the early parts of Figure 1. The final conclusion prior to the section The adaptor protein BCL10 is a nucleation-mediated switch is otherwise not justified. This is a central tenet of the paper, that is referred to again, with some other ancillary data to support it. These mutants reappear later in the paper, but it would have been better, and easier to make rescue lines of BCL10 KO in Figure 1, otherwise the logic is lost, and the models seem chosen arbitrarily.

The choice of experiments in different panels of Fig. 1 resulted from a chronological progression of reagent construction as the project evolved. We do appreciate that switching between the assays may lead readers to doubt one or the other. Therefore, we have now immunostained for endogenous p65 in the same experiment as for Fig. 1D and confirmed that p65 translocated to the nucleus only in THP-1 BCL10-KO cells that have been reconstituted with WT BCL10-mEos3.2, but not E53R. We think this additional evidence along with our orthogonal measurements in other reporter systems confirms our findings that BCL10 nucleation determines NF-kB activity.

Expression with microNS is not well controlled and gives little real evidence for what is occurring. It is unclear what the concentration of the protein expressed was, but certainly the relative expression of the CARD9(CARD) and the microNS version should be assessed.

We believe these concerns result from a misunderstanding. We assume the reviewer is referring to the experiment in Fig. 3B. Expression of muNS on its own has no effect on the DAmFRET of other proteins, and we have previously used it in exactly the same way as here (Holliday M et al. 2019 and Kandola T et al. 2021). Please note that muNS fusion proteins in our experiment have an orthogonal fluorescent protein whose spectra do not significantly overlap with those of mEos3.1. The experiment evaluates a protein’s ability, when condensed via its fusion to muNS, to nucleate an mEos3.1-fused protein that is expressed in trans. Fusion of proteins to muNS does not affect their expression levels, as we now show for CARD9CARD-muNS-mCardinal versus CARD9CARD-mCardinal (Fig. S6D).

Also, the AmFRET profile of CARD9CARD looks very weird, it cannot be compared to BCL10.

We are unsure in what way the AmFRET profile of CARD9CARD is “weird”. It is fully consistent with expectations and has been thoroughly explained in the text. We suspect the reviewer was bothered by the sharp acquisition of FRET at approximately 100 uM. As explained in the text, this represents the phase boundary, also known as the solubility line, for CARD9CARD polymers, which we previously showed in vitro (Holliday M et al. 2019). Above this concentration, the protein self-assembles without a nucleation barrier, hence the sharp but continuous change in FRET. BCL10 plots, in contrast, show a discontinuous acquisition of FRET, which indicates a nucleation barrier. In order to highlight that the CARD9CARD transition is understood and expected, we have also now added a line to the plot to demarcate the phase boundary.

We are not convinced of the usefulness of the introduction of a slew of disease-causing CARD9 mutations that may or may not be relevant to the authors' point. The fact that they do or do not function in a specific sub portion of an assay that may or may not be relevant to biological activity seems to be of interest but without biochemical understanding, little is clear.

While several reports have shown the clinical importance of these CARD9 mutations on susceptibility to fungal infections, little was known about the molecular mechanism underlying their effects. The inclusion of the disease-causing mutants to this paper is justified for the following reasons. First, they demonstrate the relevance of our work to disease. Second, they build off our findings to provide an otherwise unknown molecular mechanism of these mutants. We showed using independent methods that CARD9CARD mutations disrupt the ability to nucleate BCL10, via two different mechanisms. Finally, validating the disease-causing mutations allowed us to use them as controls for subsequent experiments demonstrating that BCL10 is supersaturated.

The Optogenetic experiments are interesting, but difficult to interpret without evidence that these MALT1 constructs are indeed still functional when expressed in MALT1-deficient THP-1 cells. We do not therefore think that this experiment shows a necessity for clustering to signal, just a sufficiency, and in a highly artificial construct.

We welcome the opportunity to elaborate on the optogenetic experiments. Since BCL10 and MALT1 are expressed ubiquitously across cell types, the validity of our findings should not depend on the cell type used. Indeed, much of what we already know about innate immunity signalosomes comes from work in HEK293T cells. Our optogenetic experiments using MALT1 were performed in 293T MALT1-KO cells in Figures 6E and F, and employed two distinct functional assays (p65 nuclear translocation and a transcriptional reporter). While our approach employs light to control clustering, similar approaches using (no less-artificial) chemically induced dimerization domains have been used to study caspase activation (Oberst A et al, 2010, Boucher D et al, 2018). Our use of light affords higher specificity, reversibility, and spatial and temporal control over MALT1 assembly than does chemically induced dimerization.

To demonstrate the necessity of clustering, we have now performed an experiment with MALT1(126-824)-miRFP670-Cry2 expressed in 293T MALT1 KO cells that contain a transcriptional reporter of NF-kB ,as in figures 6E and F. We added PMA to the cells and found that it failed to activate NF-kB (Fig. 6), confirming that the interaction of MALT1 (via its death domain) with polymerized BCL10 is required for activation. Note that MALT1 and BCL10 exist as a soluble heterodimer prior to BCL10 polymerization; hence it is polymerization, rather than the interaction itself, that activates MALT1. That artificial clustering rescues this defect strongly suggests that the effect of polymerization can be attributed to increased proximity rather than some allosteric effect communicated from BCL10 polymers through the MALT1 DD to its caspase-like domain.

Oberst, A., Pop, C., Tremblay, A.G., Blais, V., Denault, J.-B., Salvesen, G.S., and Green, D.R. (2010). Inducible dimerization and inducible cleavage reveal a requirement for both processes in caspase-8 activation. J. Biol. Chem. 285, 16632–16642.

Boucher, D., Monteleone, M., Coll, R.C., Chen, K.W., Ross, C.M., Teo, J.L., Gomez, G.A., Holley, C.L., Bierschenk, D., Stacey, K.J., et al. (2018). Caspase-1 self-cleavage is an intrinsic mechanism to terminate inflammasome activity. J. Exp. Med. 215, 827–840.

In the introduction and other parts of the paper, there are numerous instances where the previous literature in the field is not adequately cited. Examples include:

• In the introduction, it is weird to cite one original paper (a MALT1 ko study by Ruland et al., 2001; there are several other studies of ko papers for CBM components that would merit being citated along with this study) together with two reviews on that topic (Ruland and Hartjes 2019 and Gehring et al. 2018)

• In the introduction, the original study by Wang et al., 2002 should be cited together with Rebeaud et al., 2002; the two studies on the same topic were published back-to-back

• In the introduction, the statement "CARD10 and CARD14 are expressed in nonhematopoietic cells including intestinal and skin epithelia, respectively" should be supported by citations.

• Still in the introduction, the 2 references for the statement "... CARD14 gain of function mutations cause psoriasis (Howes et al., 2016; Jordan et al., 2012)" are not appropriate. There are several reports of patients with CARD14 mutations (the study by Jordan et al is only one of them) and several CARD14 mouse models that provoke a psoriasis-like phenotype, which would merit being cited.

• In the following sentence: "Point mutations and translocations involving BCL10 and MALT1 cause immunodeficiencies (Ruland and Hartjes, 2019), testicular cancer (Kuper-Hommel et al., 2013), and lymphomas (Zhang et al., 1999).", the citation style also seems completely random, combining the citation of a single original paper for lymphomas (Zhang et al. 1999) (there are several other important original studies on that topic or recent reviews that could be cited instead), together with a review on immunodeficiencies (Ruland and Hartjes, 2019) and then another single example for a role of BCL10 and MALT1 in carcinoma (the study by Kuper-Hommel et al. is one, but several other original publications exist on the latter topic, showing for example a role in breast carcinoma or glioblastoma).

• In the first section of the results, the reference cited for endogenous CARD10 expression in 293T cells (Ruland et al., 2001) is wrong, no endogenous CARD10 expression was assessed in that study

We have now revised the citations mentioned above and other instances to ensure adequate citations in each case.

Reviewer #3 (Significance (Required)):

The paper deals with a complex question, namely how the CBM signalosome assembles and functions to stimulate NF-kB signaling. This question is important to the understanding of pro-inflammatory immune responses and basic life sciences in general. As the focal point of the paper is complex, and tools to study such phenomena are at the limit of technical capabilities, this further increases the potential impact of the work.

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

The characterization of open-ended signalosomes in a number of innate-immunity and cell-death pathways, in particular formed by domains from the death-fold family, has led to the suggestions that these complexes allow a switch-like signalling response suitable for these pathways. It appears that this has been widely accepted. However, these suggestions are based largely on indirect observations and speculation.

Rodriguez-Gama and coworkers have decided to test these suggestions more directly. Their results confirm the suggestions. Based on my own experience, papers that validate widely adopted suggestions are often not considered seriously by top journals, who are looking for hot topics/paradigm-changing/surprising type results. I would urge the editors to consider seriously work such as in this paper, which directly tests important suggestions and does so at a technically high standard. The authors use a range of ingenious approaches, both with recombinant proteins and in cells, and including proteins from organisms from different parts of the evolutionary tree, to support their interpretations, so it is an extensive and high-quality study. I am impressed that so many different fusion proteins with fluorescent tags continued to function as expected, but I guess the authors controlled for this as much as they could.

Having said all this, I do get the feeling the authors are "over-selling" the nucleation barrier aspect of these signalling mechanisms. It is clearly an important and critical aspect of signalling in many systems, but then it is not the only important aspect; a number of other regulatory inputs play a role in different systems. So the statement "Our findings introduce a novel structure-function paradigm" in my view is overstretching things somewhat. Further in the Discussion section, the authors state "Existing explanations for the preponderance of ordered polymers in immune cell signalosomes have centered on the functions of multivalency at steady state, such as scaffolding and sensitivity enhancement resulting from the cooperativity of homo-oligomerization". They cite a small (and non-exhaustive) number of papers discussing this topic; all these include "seeding" or "nucleation" as an important part of the proposed mechanism. So I suggest the authors provide a more balanced discussion of this aspect. Different pathways appear to display a different level of switch-like behaviour, and one thing that the current version of the manuscript is missing is more discussion of other death fold-based systems and how the results on the CBM signalosome apply to these, and also other systems such as TIR domain-based ones, which currently get no mention whatsoever. In the CBM system, there seems to be one main nucleation barrier; can there be more than one in others?

We appreciate the reviewer’s perspective and have now acknowledged in the introduction and discussion additional prior literature that has paved the way for our study. Nevertheless, we maintain -- as now stated in the abstract -- that “our results defy the usual protein structure/function paradigm, and demonstrate that protein structure can evolve via selection for energetic maxima in addition to minima”. We have elaborated in the introduction and discussion how immune signaling provides the functional context in which such a paradigm can evolve, and how our findings uniquely support the paradigm.

One other aspect I need to express some criticism about is attention to detail - especially with a paper focusing on the physics behind biological processes, I would expect a higher standard of getting the terminology and units correct - see specific examples below. This can obviously be fixed easily.

Specific points are listed below. No page or line numbers are provided so I have done my best to make it clear what the comments refer to.

1. Abstract line 6 and throughout: in "NF-kB", the "k" is supposed to be "kappa" (Greek letter) - it stands for "nuclear factor kappa-light-chain-enhancer of activated B cells", not fully defined in the manuscript as far as I can see. Occasionally, small k is also used instead of the small cap K or whatever the authors used most of the time, but I don't think any of them use the Greek letter.

We had indeed used a version of the small “kappa” κ. We have now fixed the cases where we mistakenly used k instead of κ.

Page 2 (Introduction) paragraph 2 line 9: period missing at the end of sentence. Same Page 4 (Results: Assembly) paragraph 4 line 3.

This is now fixed.

Page 2 (Introduction) paragraph 2 line 15 and throughout: in long sentences, more commas can help help readability, for example before "leading" here. Similar page 15 paragraph 2 line 3 after "Additionally", paragraph 4 line 2 before "which".

We have now included more commas and tried to improve readability throughout.

Page 4 (Results: Assembly) paragraph 2 line 2: is "positive feedback" different from "cooperativity"? Is it a broader term that includes cooperativity, nucleation and other mechanisms? It may be useful to introduce some of these terms to avoid confusion by the readers.

“Positive feedback” is the broadest term as it is agnostic to mechanism. “Nucleation” refers to the initiation of a first order phase transition, which is one mechanism of positive feedback. Nucleation involves “cooperativity”, in that a higher order species is more stable than smaller species. However, cooperativity can occur for oligomers of finite size, whereas nucleation is reserved for phase transitions to species of infinite size. We appreciate that the use of so many related terms may have created more confusion than necessary. Hence, we have now revised the text to omit the more general terms -- “positive feedback” and “cooperativity” where possible.

Page 4 (Results: Assembly) paragraph 2 line 3: please define "TNF".

We have now fixed this and other acronyms.

Page 4 (Results: Assembly) paragraph 3 line 2: the use of size-exclusion chromatography to follow the size of complexes would assume that they are irreversible or very stable. It appears this may be the case here, but some discussion may be warranted.

We have now explained that SEC is appropriate for this experiment because large nucleation barriers generally imply stable assemblies.

Page 4 (Results: Assembly) paragraph 3 line 4 and throughout: the symbol for "kilodalton" is "kDa".

We have now fixed this mistake.

Page 4 (Results: Assembly) paragraph 3: I am not sure how the results discussed in this paragraph demonstrate that assembly occurs in cooperative fashion - just that there is a change in oligomeric states upon stimulation.

Cooperativity is implied by the absence of oligomer sizes between monomer and the large assembly. Nevertheless, we realized this can only be concluded in the case of homotypic assembly, which we cannot yet assume at this point in the paper. Therefore, we have revised this paragraph to say that the distribution is “consistent with” an underlying phase transition (which we then go on to prove).

Page 4 (Results: Assembly) paragraph 4 line 2: "WT" is not defined. Wild-type what? I presume "protein"?

We refer here to the wild-type protein. We have now fixed this mistake.

Page 4 (Results: Assembly) paragraph 4: it may be worth pointing out here the wild-type and mutant proteins expressed at similar levels; clearly the outcomes will depend on protein concentration in the cell. I believe the supplementary figure shows this to a large extent.

Indeed, our supplementary figure shows that the WT and mutant protein express to comparable levels. We have now pointed this out in the text.

Page 4 (Results: The adaptor) paragraph 1 line 4: "CARD domain" would stand for "caspase activation and recruitment domain domain". Please check throughout (including Supplementary Material).

We have fixed this mistake.

Page 4 (Results: The adaptor) paragraph 1 line 9: "expressed over a range of concentrations in cells" - this would imply the authors controlled expression - please rephrase to explain what exactly was done.

We have now rephrased this sentence to indicate that the range of expression results from the use of a genetic construct with cell-to-cell variation in copy number.

Page 5 (Results: The adaptor) paragraph 2 line 3 and throughout (including Supplementary Material): please use the Greek letter rather that "u" for micro.

We have now fixed this mistake.

Page 5 (Results: The adaptor) paragraph 3: this analysis is rather simplistic, it is not just the RMSD value, it is the nature of conformational change that is important? Please elaborate, I would think the papers presenting structural work have already discussed this to some extent?

The reviewer is correct; it is the nature of the conformational change that is most important. We are unsure how to accurately estimate the energy barrier separating the two conformations for each protein. However, we have now undertaken a collaboration to attempt to do so via FAST molecular simulations (Zimmerman and Bowman 2015). In lieu of the results of these ongoing studies, we have modified the text to acknowledge that RMSD does not necessarily relate to nucleation barriers.

Maxwell I. Zimmerman and Gregory R. Bowman. Journal of Chemical Theory and Computation, 2015, 11 (12), 5747-5757 DOI: 10.1021/acs.jctc.5b00737

Page 5 (Results: The adaptor) paragraph 4 line 5 and further in this section: some symbol(s) do not show in the pdf - before "(delta)", next page line 3-5 after "higher" and "both".

We have fixed this issue that resulted from exporting to a PDF file from our text editor.

Page 6 (Results: The adaptor) paragraph 4: interface IIa and IIIb are not introduced, and there is not even any reference provided here.

We have now added a reference for these mutations and elaborated on the interfaces IIa and IIIb.

Page 6 (Results: Pathogenic) paragraph 1 line 12: "FL" is not introduced.

We have now fixed this mistake.

Page 8 (Results: Pathogenic) paragraph 7: the text "absent the pathogenic mutations" is missing something.

We have now reworded this section.

Page 10 (Results: BCL10) paragraph 3: why does CARD9 CARD clustering peak and then disassemble (I guess "clustering" doesn't disassemble, please rewrite as well).

We have now fixed this mistake.

Page 11 (Results: MALT1) paragraph 1: I presume dimerization doesn't achieve the same level of proximity as higher-order multimerization?

Our interpretation here is that for MALT1, activation requires close proximity of more than two molecules. Although our dimerization module did not activate the caspase-like domain of MALT1, we know that it achieves close enough proximity to activate the caspase domain of CASP8. Hence we believe the MALT1 mechanism has a stoichiometry requirement in addition to a proximity requirement. This is, of course, consistent with the fact that activation normally occurs in the context of polymers rather than dimers.

Page 11 (Results: Ancient) paragraph 1 line 4: is this AlphaFold2?

That is correct, we used AlphaFold2. We have added that detail.

Page 12 (Discussion) paragraph 4: not sure if "molecular examples of evolutionary spandrels" will be clear to most readers.

We have now explained what evolutionary spandrels are, and elaborated on the relationship to our findings.

Page 14 (Materials: Plasmid) line 2 and throughout: "Golden Gate" is usually capitalized. Similar for "Gibson" further in the paragraph. The English in this paragraph is not up to standard in general; for example "Then placing..." is not a complete sentence, and a number of sentences ending with "via gibson" need to be rewritten.

We have now rewritten this paragraph.

Page 16 (Materials: Cell) line 4 and throughout: "2" in "CO2" should be subscripted.

This is now fixed.

Page 16 (Materials: Transient) line 6 and throughout (including Supplementary Material): please use a space between number and unit ("35 mm").

This is now fixed.

Page 16 (Materials: Generation) line 4 and throughout: to distinguish from "gram", please italicize "g" and/or use "x g".

We have now fixed this.

Page 17 (Materials: Yeast) line 3: please specify which table is "table X".

We have now fixed this mistake.

Page 17 (Materials: Mammalian) line 1: please provide full reference. Same next paragraph line 2.

We have now fixed this.

Page 17 (Materials: DAmFRET) line 3: "SSC" and "FSC" are not defined.

We have now fixed this.

Page 18 (Materials: Fluorescence) line 10: "Coefficient" does not have to be capitalized. It does not have to be defined again in the next paragraph.

We have now fixed this.

Page 19 (Materials: Optogenetic) line 1: "performed" rather than "made"?

We have now fixed this.

Page 19 (Materials: Protein) line 12: the Compass software doesn't have a reference?

We have now added the reference to the software.

References: please make format consistent: articles titles in sentence or title case.

We have now formatted all references to be consistent.

Legend to Fig. 1: I suggest "Schematic diagram"; and "h" rather than "hrs"; please check throughout (including Supplementary Material).

We agree with this suggestion.

Legend to Fig. S1: is "TNF-a" supposed to be "TNF-alpha"?

We have fixed this.

Legend to Fig. S7: please capitalize "Figure 2H".

We have fixed this.

Legend to Fig. S10F: please move "Dox" behind the concentration.

We have fixed this.

Fig. S14B: the colours in the superposition make it difficult to see the differences.

We have used a different color now.

Legend to Fig. S14: I suggest "structure...predicted by AlphaFold" (2?) and include the reference.

We agree with this suggestion.

Reviewer #4 (Significance (Required)):

As argued above, the significance of this paper is that it tests directly important hypotheses proposed or assumed previously, and does so at a technically high standard. No published report has done so to a similar extent.

The paper should be of interest to a broad audience from cell biologists and immunologists to biochemists, biophysicists and structural biologists.

My expertise is in structural biology or systems similar to the one studied here.

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Referee #3

Evidence, reproducibility and clarity

The study by Rodriguez Gama et al. addresses the molecular function of CBM complex-forming proteins CARD9, BCL10 and MALT1 in the activation of myeloid cells, using optogenetic tools, transcriptional reporters and biochemical approaches. It is known from previous studies that Bcl10 oligomerizes into filamentous oligomeric structures incorporating Malt1, and that these structures are nucleated by receptor-induced activation of CARD proteins such as CARD11 (in lymphocytes) or CARD9 (in myeloid cells), but the mechanism underlying the assembly of the resulting CBM complexes remain incompletely understood.

The authors develop beautiful optogenetic tools to address this question, and convincingly demonstrate that CARD9-mediated nucleation of BCL10 triggers a binary cellular NF-kB response in a spring-load-like fashion, and identify mutants of BCL10 and CARD9 that impact this capacity. Unfortunately, however, the authors do not do a good job to simplify this complex problem so it can be easily understood. In particular, the choices of mutants, models and experiments are not consistent between figures, and some data seem to be arbitrarily added or omitted. Complex hybrid constructs are also used, without assessing whether these are indeed functional in the corresponding ko cells. The paper would therefore benefit from a major overhaul. We also noticed that the literature is often not cited adequately and have included a (non-exhaustive) list of examples of wrong, incomplete, or erroneous citations below.

1) The initial observations of binary signaling are derived from a reporter system. Although there are controls to show that the reporter used does not function intrinsically cooperatively, it would be nice to see additional data to show that cooperativity occurs also at the level of endogenous response systems, for instance by qPCR-based assessment of a natural NF-kB target gene (induced for example by TNFa versus B-glucan in THP-1 cells, and by TNFa versus PMA in 293T cells).

2) The cell lines in Figures 1D-E (and also some of the BCL10 mutants used later on) would have been better run in the assays in the early parts of Figure 1. The final conclusion prior to the section The adaptor protein BCL10 is a nucleation-mediated switch is otherwise not justified. This is a central tenet of the paper, that is referred to again, with some other ancillary data to support it. These mutants reappear later in the paper, but it would have been better, and easier to make rescue lines of BCL10 KO in Figure 1, otherwise the logic is lost, and the models seem chosen arbitrarily.

3) Expression with microNS is not well controlled and gives little real evidence for what is occurring. It is unclear what the concentration of the protein expressed was, but certainly the relative expression of the CARD9(CARD) and the microNS version should be assessed. Also, the AmFRET profile of CARD9CARD looks very weird, it cannot be compared to BCL10.

4) We are not convinced of the usefulness of the introduction of a slew of disease-causing CARD9 mutations that may or may not be relevant to the authors' point. The fact that they do or do not function in a specific sub portion of an assay that may or may not be relevant to biological activity seems to be of interest but without biochemical understanding, little is clear.

5) The Optogenetic experiments are interesting, but difficult to interpret without evidence that these MALT1 constructs are indeed still functional when expressed in MALT1-deficient THP-1 cells. We do not therefore think that this experiment shows a necessity for clustering to signal, just a sufficiency, and in a highly artificial construct.

6) In the introduction and other parts of the paper, there are numerous instances where the previous literature in the field is not adequately cited. Examples include:

• In the introduction, it is weird to cite one original paper (a MALT1 ko study by Ruland et al., 2001; there are several other studies of ko papers for CBM components that would merit being citated along with this study) together with two reviews on that topic (Ruland and Hartjes 2019 and Gehring et al. 2018)
• In the introduction, the original study by Wang et al., 2002 should be cited together with Rebeaud et al., 2002; the two studies on the same topic were published back-to-back
• In the introduction, the statement "CARD10 and CARD14 are expressed in nonhematopoietic cells including intestinal and skin epithelia, respectively" should be supported by citations.
• Still in the introduction, the 2 references for the statement "... CARD14 gain of function mutations cause psoriasis (Howes et al., 2016; Jordan et al., 2012)" are not appropriate. There are several reports of patients with CARD14 mutations (the study by Jordan et al is only one of them) and several CARD14 mouse models that provoke a psoriasis-like phenotype, which would merit being cited.
• In the following sentence: "Point mutations and translocations involving BCL10 and MALT1 cause immunodeficiencies (Ruland and Hartjes, 2019), testicular cancer (Kuper-Hommel et al., 2013), and lymphomas (Zhang et al., 1999).", the citation style also seems completely random, combining the citation of a single original paper for lymphomas (Zhang et al. 1999) (there are several other important original studies on that topic or recent reviews that could be cited instead), together with a review on immunodeficiencies (Ruland and Hartjes, 2019) and then another single example for a role of BCL10 and MALT1 in carcinoma (the study by Kuper-Hommel et al. is one, but several other original publications exist on the latter topic, showing for example a role in breast carcinoma or glioblastoma).
• In the first section of the results, the reference cited for endogenous CARD10 expression in 293T cells (Ruland et al., 2001) is wrong, no endogenous CARD10 expression was assessed in that study

Significance

The paper deals with a complex question, namely how the CBM signalosome assembles and functions to stimulate NF-kB signaling. This question is important to the understanding of pro-inflammatory immune responses and basic life sciences in general. As the focal point of the paper is complex, and tools to study such phenomena are at the limit of technical capabilities, this further increases the potential impact of the work.

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Reply to the reviewers

Two reviewers commented on the smeared appearance of Tae1 bands in our Western blot analyses (Figure 4F and 5B) and asked us to improve their technical quality.

-We agree and will repeat these experiments with more careful attention to lysate preparation, using a higher percentage SDS gel for better separation of low molecular weight proteins as suggested.

Reviewer 2 requested that we assess how Tae1 variants impact interbacterial competition outcomes.

-We agree that this would be interesting to take a look at. While this will not be feasible for every variant we examine in the paper, we can conduct comparative interbacterial assays between P. aeruginosa and E. coli using P. aeruginosa strains with a tae1 point mutation for c110s. Given that our biochemical experiments show that this hyperactive variant evades inhibition by the cognate immunity protein, we expect that this may decrease P. aeruginosa fitness, even in the context of competition.

More generally, we think that examining Tae1 variants in the context of interbacterial competitions would be a critical orthogonal approach in order to validate that the DMS results have any bearing on competition outcomes. However, we feel that major focus of this paper is on the more molecular and biophysical insights that our approach can offer. Our study tests our assumptions about the kinds of features and surfaces that are important for proteins that engage with non-canonical complex substrates. It is, of course, interesting to think about the implications of this for physiological phenotypes and the drivers of toxin evolution. It is also exciting to imagine how this kind of information could be used to one day engineer certain interbacterial outcomes. We hope that others in the field will push our efforts into these directions, but we do not feel that these directions are essential for our conclusions. However, our conclusions on the molecular and biophysical aspects have helped generate interesting hypotheses in microbial ecology that could be largely followed up on by others.

In order to conduct well-controlled P. aeruginosa:E. coli competition assays for more Tae1 variants, we would need to generate a significant number of new P. aeruginosa strains encoding point mutations for each of our variants across several genetic backgrounds. The competitions themselves also require a considerable amount of work to optimize and quantify. We are able to do this for one of the variants as previously mentioned (C110S). It’s important to note that the first author of this paper, who was the primary driver of this work, is no longer in my lab or in academia. As for myself, I am also in the middle of a transition out of academia and am actively ramping down my lab at UCSF. I no longer have the space or appropriate set-up to support this longer-term effort.

Reviewer 2 asked that we examine Tae1 (WT and C110S) expression levels in vivo to more precisely examine whether increased self-intoxication by Tae1C110S in P. aeruginosa was due to differences in toxin activity or toxin levels.

We agree with this suggestion and will look at toxin protein levels by Western blot analysis in the context of P. aeruginosa cells grown 1) alone on solid media and 2) together with E. coli on solid media during interbacterial competition using conditions that match our other competition assays.

All 3 reviewers asked us to provide more experimental evidence addressing the hypothesis that differential peptidoglycan (PG) affinity across Tae1 variants could explain variation in toxic activity.

-We agree that this is an interesting point to follow up on further. To be clear, we also do not know whether this hypothesis is true at this stage, and the answer is not necessarily critical for our central advance, but we would like to give it a try! We have devised an approach to ask the question experimentally across a subset of our deep mutational scanning (DMS) variants.

Reviewer 1 suggested that we quantify in vitro binding affinities for PG using isothermal titration calorimetry (ITC). However, given that ITC requires high concentrations of well-defined homogeneous substrates, which we are not able to generate for more complex higher order structures of cell wall PG, we propose a pull-down based approach.

Briefly, we plan to conduct pull-downs using insoluble, purified cell wall sacculi from our two E. coli grown under the two conditions as bait for recombinant Tae1 proteins. Given that intact sacculi or inherently insoluble, we can simply collect bound Tae1 through centrifugation of sacculi pellets and examine the amount of Tae1 associated by Western blot analysis. These analyses will need to be conducted across a titration of Tae1 concentrations and also with catalytic activity inhibited to avoid solubilization of sacculi. We will block Tae1 hydrolysis by carrying out pull-downs in the presence of a general commercially-available cysteine hydrolase inhibitor, E64. If there is indeed differential affinity for PG underlying lytic differences across Tae1 variants, we would expect to see greater relative association of Tae1 variants with the type of cell wall sacculi that they more effectively lyse in our DMS screen. We would expect the reverse trend to also be true (lower affinity for less active variants).

Reviewer 1 would like to know if we have done lysis experiments with any E. coli mutants that only impact PG density but not PG polymer structure? If they haven’t tested any E. coli mutants, have we done lysis experiments using drugs that have a similar impact on PG? Even if we don’t include these data in the paper, the reviewer would like us to comment on the trends we have observed.

We have not done experiments in any mutants or chemical backgrounds known to only impact PG density but not polymer structure. We think this would be a very interesting angle! But unfortunately this is outside the scope of this study. It would require that we first experimentally confirm that the restrictive effect on only density is clearly demonstrated using a variety of techniques, including microscopy, chemical analyses, and biophysical probing of sacculi.

Reviewer 1 asked for additional DMS screens in more conditions

We love this idea! In fact, we hope that others are motivated to adopt our workflow to run many more DMS screens for T6S toxins, as we believe these screens provide a lot of useful and sometimes surprising insights that could be of great interest to others. However, we believe that the primary goal of this paper is to establish this methodology as a compelling approach for studying toxins and, more generally, proteins with complex cellular substrates. It does not necessarily fall within the scope of this paper to fully assess the mechanistic implications of cell wall diversity across a wide range of conditions.

In our experience, rigorously conducting DMS screens requires a significant amount of effort and resources to establish consistent experimental conditions. Also, a non-trivial number of costly sequencing-based experiments are required across control and variables for the results to be statistically sound and meaningful. Furthermore, experimental validation of results are ultimately important for our ability to confidently generate hypotheses stemming from these datasets. As stated above, the first author of this paper, who was the primary driver of this work, is no longer in my lab or in academia. As for myself, I am in the middle of a transition out of academia and am actively ramping down my lab at UCSF. I no longer have the space or appropriate set-up to support this longer-term effort.

Some great design principles and wisdom.

Author Response

Reviewer #1 (Public Review):

The authors sought to investigate the diet of the early fossil bird Jeholornis and its implications for bird-plant interactions in early bird evolution.

Major strengths were: 1) an exquisite near-complete cranial reconstruction of the early fossil bird Jeholornis from the Early Cretaceous of China, 2) a large sample of extant bird skulls (160) for the geometric morphometric analysis, and, 3) qualitative description of alimentary contents of extant birds.

Major weaknesses were: 1) restriction of diet consideration to only granivory and frugivory, 2) under-detailed comparisons between the extant and extinct alimentary contents, 3) unclear explanation of the connection between early fossil birds and seed dispersal.

Thanks for the summary of our work! To briefly reply to the weaknesses mentioned here (more details are provided in the following reply to the reviewer’s comments and suggestions):

1) We have added supplementary analyses according to the reviewer’s suggestions, so this should have been addressed now. Our morphometric analyses attempt to explain the presence of seeds in the gut contents of some individuals of Jeholornis. We believe there are only two possible explanations of the presence of these seeds: granivory or frugivory. Therefore, we were initially motivated by the need to rigorously rule-out a granivorous explanation of the present of seeds in the gut of Jeholornis, which then would demonstrate the partially frugivorous diet of Jeholornis - it doesn’t have to be a specialist frugivore and its supplementary diet components don’t influence the inference that the presence of seeds results from fruit-consumption. Fruit-consumption is the key mechanism that we provide evidence of for the first time in early birds, and is central to the potential for mutualisms between plants and early birds. However, our supplementary geometric morphometric analyses do indicate some clues about its supplementary diets that are useful. In particular, they rule out some other diets e.g. piscivory or a probing diet.

2) Our work is the first work we know to provide comparative data on the seed-containing gut contents of extant birds, as a tool to interpret fossil gut contents. For granivores and frugivores, we have done detailed 3D comparisons among several species. We think this is important, and we have done our best to document them clearly. However, for now, we have further clarified the images that we have presented, in response to a comment by referee 3 (see below). We hope that this also addresses the concerns of referee 1 here.

3) By providing direct evidence of fruit-consumption in early birds, we provided evidence of the mechanism for potential bird-plant co-evolutionary mutualism during the Early Cretaceous. We are not showing the direct evidence of the mutualism, although note that plants invest energy in fruit production specifically to attract fruit-eating animals to act as seed dispersers. Therefore, the inference of mutualism is not far-fetched and is very likely, even if direct evidence is almost impossible to preserve in fossils - so that we tend to tone down this statement rather than making it too strong. More detailed analyses based on more new fossil discoveries in the future are expected to further explore the role of birds the Cretaceous Terrestrial Revolution. However, our study is the first step to evidence and discuss this ecological topic and the furthest we could go based on the current fossil discoveries. Nevertheless, this seems important and will be the base of future studies.

The authors did not yet achieve their full aims because their methods limited the scope of their conclusions. Specifically, a third hypothesis that Jeholornis was neither granivorous nor frugivorous was not addressed in the study. This is especially poignant as the PCA data show overlap between the granivory and frugivory data points and the 'other diet' data points. If it is assumed that Jeholornis must be a granivore or a frugivore, then the results support frugivory over granivory for Jeholornis. However, as explained above, this assumption is not supported by the data provided so the third hypothesis needs to be tested.

Thank you very much for stating the concern of our study. It seems that there is some misunderstanding here about our study. Our analyses attempt to explain how seeds entered the gut content of Jeholornis, not to predict diet in the absence of evidence from gut content. That is why we tested between just two alternative explanations of the gut contents in our original analyses: (1) That seeds entered the gut through granivory (seed-consumption); and (2) That seeds entered the gut through frugivory (fruit-consumption). Based on this combined evidence of seeds in the gut, comparative study of the gut contents of extant birds, plus morphometrics of the skull and mandible, we claimed partial (possibly seasonal) frugivory - a form of facultative frugivory for this lineage. Therefore, we are not claiming specialised frugivory in Jeholornis as the reviewer might think. However, we acknowledge that the word 'frugivorous' might be misleading to some readers, who could interpret it as meaning 'specialised frugivorous'. To avoid this misunderstanding, we did consistently use adjectives such as 'partial', 'seasonal' and 'opportunistic' in our initial submission. And we have tried to reinforce this in our revised manuscript. For example, we converted some instances of ‘frugivory’ to ‘fruit-consumption’ to indicate the act of consuming fruit rather than a perceived idea of specialised frugivory.

We may also need to emphasize here that, the seed dispersal and frugivore ecology studies of the modern taxa show that, for most frugivores, fleshy fruits are a non-exclusive food resource, which is supplemented with other foods like animal prey and plants (Howe, 1986; Corlett, 1998; Jordano, 2000; Wilman et al., 2014). In addition, plants usually bear fruits only in certain seasons rather than being available throughout the year, which makes strictly specialized frugivore very rare. Therefore, avian frugivores occupy a wide range of diet space that is highly overlapping with some other diets. However, to reply to the comment from the reviewer and also make this clearer to some other readers, we conducted supplemental analyses by dividing 'other diets' further to test what diets Jeholornis possibly/impossibly had as supplements of frugivory. The results of them were shown in Figure 2 - figure supplements 3, Figure 2 - figure supplements 4 and Figure 2 - figure supplements 5 now. We revised and added these texts into the manuscript to describe the added supplemental analyses:

“Our main analysis is intended to test why seeds entered the gut of Jeholornis by distinguishing between two hypotheses, either (i) fruit consumption or (ii) seed consumption (Figure 2, Figure 2 - figure supplements 2).”

“Our supplemental analysis includes a further split of “Other diets”, separating the “Other diets” category into: (1) Probing for invertebrates; (2) Grabbing/pecking for invertebrates (Figure 2 - figure supplements 3); (3) Piscivores; (4) Animal-dominated omnivores; (5) Carnivores (Figure 2 - figure supplements 4); (6) Nectarivores; (7) Omnivores; (8) Plant-dominated omnivores (Figure 2 - figure supplements 5). Our prior expectation is that these analyses will not provide an unambiguous classification of the diet of Jeholornis on their own, because craniomandibular shape data does not completely differentiate among diets in birds (Navalon et al., 2019), but that they may be capable of ruling out the occurrence of some diets.”

The results of these supplemental analyses are as the descriptions we added in the manuscript:

“Our supplemental analyses exclude Jeholornis from possessing a probing diet, which occupy negative PC1 values (Figure 2 - figure supplements 3), as well as being a piscivore, which occupy positive PC2 values (Figure 2 - figure supplements 4). However, it cannot be distinguished from other diets such as the grabbing/pecking for invertebrates and omnivory (Figure 2 - figure supplements 3, 4, 5). Euclidean distances in the full multivariate shape space suggest that the mandible of Jeholornis is relatively similar to those of various omnivorous (e.g. Podica), seed-grinding (e.g. Calandrella), frugivorous (e.g. Crax), and invertebrate pecking (e.g. Picus) birds (Figure 2 - Source data 3).

“Similar to the results of the mandible analyses, the results of the supplemental analyses of cranial shape also exclude Jeholornis from possessing a probing diet, which occupy negative PC1 values (Figure 2 - figure supplements 3), as well as being a piscivore, which occupy positive PC2 values (Figure 2 - figure supplements 4).The other diets are also undistinguishable in the supplemental analyses of cranial shape (Figure 2 - figure supplements 3, 4, 5). Euclidean distances in the multivariate shape space, excluding PC3 (which describes the large-scale differences between stem- and crown-group birds) suggest that the cranium of Jeholornis is relatively similar to those of various frugivorous (e.g. Manucodia), seed-grinding (e.g. Pedionomus) and invertebrate pecking (e.g. Hymenops) birds (Figure 2 - Source data 4).”

These results are briefly merged into the discussion part:

“Mandibular and cranial shape excludes Jeholornis from being having a probing/piscivorous diet, and is consistent with omnivory, grabbing/pecking for invertebrates, or processing foliage (using the gastric mill).”

The existed main morphometric analyses show that a seed-cracking diet can be ruled out as an explanation of the presence of seeds in the gut of Jeholornis, which is its primary goal. In addition, our intention of this study is to show evidence for at least seasonal fruit consumption in some of the earliest birds (not specialised frugivorous), which all three reviewers seem to agree is a well-founded conclusion, and the bigger picture insights of our paper arise from that. Here with the new supplementary analyses inspired by the reviewer, the diet of Jeholornis is more detailed in our study, which may interest more readers concerning about the diet components of early birds.

The cranial reconstruction of Jeholornis and the alimentary content data for extant birds would be invaluable to the community. The geometric morphometric data are presented in a way that obscures how much overlap there is between dietary categories (non-frugivore and non-granivore diets are grouped as 'other diets'), so the utility of these data is unclear. This aspect has hampered the ability of the authors to reconstruct diet in Jeholornis and, thus, the bigger picture insights that can be drawn from these results, limiting the likely impact of the work.

Thank you very much for the positive comments about our cranial reconstruction of Jeholornis and the alimentary content data for extant birds.

It was not our intention to obscure the overlaps between the mandible/cranial shape of frugivorous birds, and those of other birds. In fact, we believed that this was clear from the plots, and from the way we described results in the text that various birds with ‘other diets’ could have similar mandible/cranial shape to Jeholornis. This degree of overlap is also expected based on recent studies that found evidence for only quite diffuse relationships between cranial form and diet in birds (Navalón et al., 2019). However, we also see the point that some readers might be curious about the nature of particular datapoints and it would be useful to clarify this. We therefore added supplementary analyses according to the reviewer’s comment/suggestion by dividing the 'other diets' category into several much more detailed categories, so the concern of the reviewer here that “the non-frugivore and non-granivore diets are grouped as 'other diets' is expected to have been addressed here.

Jeholornis is one of the earliest fossil birds, so understanding its diet and ecological role is important for understanding Mesozoic ecosystems and the emergence of modern ones.

Thank you very much for this good explanation of the importance of this study, and it also is what we believed when we wrote the manuscript. We hope that the referee will be satisfied with the efforts we made to address their initial comments that that our paper on the ecology and morphology of Jeholornis can be published in an appropriate venue.

Reviewer #3 (Public Review):

Hu et al. reported on a new specimen of the early bird Jeholornis, including a nearly complete skull. Using geometric morphometrics data collected from 3D and 2D retro-deformed reconstructions of its skull, the authors convincingly dismiss a seed-cracking feeding strategy for the taxon. They then use comparisons of 3D reconstructions of ingested seeds to extant birds with known feeding strategies to convincingly argue that Jeholornis was likely at least partially frugivorous. As such, this study provides the strongest evidence yet that early birds such as Jeholornis may have played a role in bird-mediated seed dispersal strategies in the Mesozoic.

Generally, the data presented in this paper support the authors' interpretations. The specimen at the core of this study is truly spectacular, and the authors' retro-deformation of its skull is skilled. The results of the authors' geometric morphometric analyses support their inference that Jeholornis was likely not a seed-cracker. Their comparisons of ingested seed shapes also convincingly supported a partially frugivorous diet. I especially applaud the authors' detailed description of their process of retro-deformation of the fossil skull (an example many should follow, including myself) as well as making both their raw data and their reconstructed surfaces available online.

Thank you very much for the summary of our work!

However, there are a few major and several minor issues that I believe need to be addressed.

1. The implications for possible bird-mediated seed dispersal are clear in this study, but they are not conclusive. Rather, the authors (convincingly) demonstrate that Jeholornis was at least partially frugivorous -- a necessary component of such a mutualistic interaction. The authors do not demonstrate that such an interaction actually occurs. These results are nonetheless exciting and important, but I think certain statements in the paper are too strong. A notable example is the title - "Earliest evidence for frugivory and seed dispersal by birds." I would strongly urge the addition of a single word to better reflect the data presented: "Earliest evidence for frugivory and possible seed dispersal by birds." Similarly, in lines 328-329 -- "Strong indications for at least seasonal frugivory in Jeholornis provides direct evidence of [specialised seed-dispersal by animals during the Early Cretaceous] for the first time" -- is not true. This paper does not provide direct evidence for this, but does provide a mechanism consistent with this. There are a handful of other statements in the paper that I think should be toned down to account for this.

Thanks for the helpful suggestions! We have revised the title to be “Earliest evidence for frugivory and potential seed dispersal by birds”, and revised this sentence to be “Evidence for at least seasonal frugivory in Jeholornis provides direct evidence of fruit-consumption by early birds, long before the origin of the bird crown-group. This provides an important indication of the likelihood that birds were recruited by plants for seed-dispersal very early in their evolutionary history, during the Early Cretaceous” now. We also revised through the manuscript to tone down some similar statements about the seed dispersal, such as “…indicating that birds may have been recruited for seed dispersal during the earliest stages of the avian radiation.”.

1. Much more information should be given about the new Jeholornis specimen. In the supplement, the authors state that "a few post cranial elements" (p. 17, line 352) are preserved along with the skull -- which elements? They should be figured and briefly described in the supplement. This is of relevance to the core assumption of the paper, namely that this individual belonged to Jeholornis -- the taxonomic assignment is based partially on the tail morphology -- which I assume means that, minimally, a complete tail is preserved. The authors also mention the pelvic morphology of the new specimen, so I assume at least some part of the pelvis is preserved. These should all be figured. Most anatomical discussion is limited to the skull (and especially the palate), which is understandable, given the focus of the paper. However, with that in mind, more attention should be paid to the retro-deformation of the skull. Figure 1 is quite attractive, but I'm confused by the differences in depicted preservation between the 3D (Fig. 1C, D) and 2D (Fig. 1E, F) reconstructions. For example, the braincase is not shown in panel C but is in panel E -- why? Is its shape inferred from other specimens for panel E? Again, I very much appreciate the inclusion of near step-by-step description of how the rostrum was retro-deformed. Minimally, a few comments on what isn't preserved would be useful.

1) We added the photograph of the whole slab of Jeholornis STM 3-8 as Figure 1 - figure supplements 1 here (the eLife format for supplementary figures), and revised this sentence to be “…and a few postcranial elements including the vertebral column, the pelvic girdle and fragmentary hindlimbs.” now. As you could see from the photograph, there are very few valid information could be extracted from the incompletely preserved postcranial elements. Considering this paper is focusing on the skull, we only mentioned the relatively better-preserved tail and pelvis in the taxonomic part.

2) We added “Dashed-lines indicate the elements not preserved but suspected to exist.” in the legend of Figure 1, and added the details of reconstructions of unpreserved elements in the end of CT scans and digital reconstructions in Materials and Methods part: “However, since the braincase is too flattened to be used as the reference for 3D retrodeformation, it was omitted in Figure1C and reconstructed according to its common shape in early birds in Figure 1E. The ectopterygoid is not preserved but suspected to exist as discussed in the Cranial Anatomy part, therefore it was reconstructed according to the shape of this element among other stem birds e.g. Archaeopteryx and Sapeornis (Elzanowski and Wellnhofer, 1996; Hu et al., 2019).”

1. The figures are visually attractive but I found some of them confusing or unclear. See my comments above regarding Figure 1. Despite the red arrows in Figure 4 and the supplemental figure, I was hard pressed to understand precisely what set the indicated seeds apart from the rest. In some cases I could see slight "dents" where one or two of the arrows indicated, but it was hard for me to see, even when I zoomed in on my screen. I think inset panels featuring zoom-ins on the indicated regions would be very useful in making the point the authors intend. Also, I don't know if the supplemental image naming/number scheme was imposed by the journal or is a choice by the authors, but I found it baffling. Something more traditional (like "Fig. S1" or "Supplemental Figure 1") would be much more efficient.

1) We have clarified the confusions in Figure 1 as suggested. For Figure 4 and related supplementary figures, the 3D reconstructed seeds are pretty clear, such as the broken ones in Figure 4B. The broken seeds in the scanning slices are more difficult to observe as the reviewer said, since the seed husks are very thin so that they are only slightly brighter, and that’s why we put the red arrows indicating the breakages there. To help readers observe them easier, we added some zoom-in panels and line drawings for the representative ones (not all of them since otherwise it would be too many) now as suggested by the reviewer;

2) The supplementary image naming/number scheme was imposed by the journal, and it would be more clear when the paper is digitally published, since these supplementary images will be connected to links in the legends of the main figures.

Author Response:

Reviewer #2 (Public Review):

This is an interesting and scientifically rigorous report documenting atypical, dendritic locations for the emerging axon of pyramidal neurons. This is not an entirely new observation (the authors cite relevant publications, including Kole and Brette, 2018 and Mendizabal-Zubiaga et al., 2007), but still important, as a relatively overlooked fact with functional implications. A main feature of the present report is an exceptionally thorough cross-species survey, from which the authors conclude that, as compared with non-primates, the macaque and human brains have a lower proportion of neocortical pyramidal neurons with axon carrying dendrites. The results might be further supported by additional experiments, especially ultrastructural data, or by including more extensive developmental data. There is a section on Development, but there is hardly any Discussion. However, these matters are raised and adequately treated by reference to the existing literature.

We cannot do EM with frozen material or DEPEX-cleared sections. The developmental aspects have been more extensivel discussed now, but we refrained from speculating too much, since we do not have physiological data.

Reviewer #3 (Public Review):

The authors used neuroanatomical techniques to study neocortical pyramidal neurons from several different mammalian species. Their message is that primate neocortex differs from that of other mammals in having substantially fewer cells with axons emanating from dendrites, rather than the canonical route from the soma. The authors employed a range of standard methods, ranging from tracer injection to Golgi impregnation to immunocytochemistry. The feature the authors report is undeniable; there clearly are axons that emanate from dendrites of neocortical pyramidal neurons. Prior studies have reported that these axons are more excitable, thus leading to the intriguing possibility of a fundamental architectural (and thus presumably functional) feature in how primate neocortex operates.

This is a provocative narrative, that leads to a number of interesting questions. However, I have reservations that the authors must address before I believe the claim that primates are really fundamentally different from other mammals in this respect. A strength but also a central limitation of this study is that different species were compared using different methods, and different areas were studied in different species. The authors make the implicit assumption that the prominence of this feature does not differ among cortical areas.

We initially considered it a strength of the study – looking into many area with many methods in many species. However, it seemed a bit like cherry-picking, and we now enlarged the data sets for a more systematic analysis. Please note, we assessed archived material. We are bound to what we have available. We now delivered areal comparisions, and I am afraid, the answer is NO, no remakable differences in the areas that we assessed in monkey and cat.

However, it is entirely plausible that the proportion of neurons with axon-carrying dendrites does differ among cortical areas. The authors also group neurons into 2 large populations: infra- and supragranular. But again, layers 2 and 3 differ from one another (as do layers 5 and 6) in the specific populations of pyramidal cells they contain (morphological and neurochemical types, inputs and outputs, etc.). Certainly many studies do group neurons into these broad populations, but for this kind of comparison relevant differences or similarities could have been lost. Comparisons among species ideally would have all been in the same layer and area.

As said, we are bound to what we have available. And this is more than what has ever been published on these question so far. The graph and the Tables to Figure 3B allow to compare species across the layers.

We are aware that pyramidal cells in the layers can differ. Looking into RNA seq papers, up to 19 types exist in mouse. How many could potentially then exist in human? There is no way of pulverizing our kind of analysis down to the level of 19 pyramidal cell types differing by some unexplained RNA signatures which so far exist only for mouse. The SMI-32 staining already “selects” for one subtype in that it stains preferentially so-called type 1 pyramidal cells (Molnar et al., 2006).

Another limitation is that the same method was not employed in different species. The reader needs to know that different methods reveal the same proportion of axon-carrying dendrites in a given area of a certain species. This should have been stated more clearly and earlier in the text; it took examination of the data tables to see this. The tables show that measurements were made in several different cortical areas. Can the authors provide any evidence that the proportion of neurons with axon-carrying dendrites does not differ in any one species among cortical areas?

We now provide areal comparisons for 5 fields in monkey (new Figure 4A) and visual fields in cat (new Fig. 4B), both with the same methods. We can even provide a within-individual comparison of brain areas and of methods. Another three areal values for the infant macaque have been plotted in Figure 3B.

Figure 3 description and/or legend needs to state clearly that different species' neocortex was studied in different areas (and if all Fig3 samples shown are from same layers).

Figure 3A is total cortex, Figure 3 B is by layers. Counting strategies are now described in detail in methods.

Supplementary Excel file suggests that for humans Golgi-Kopsch reveals fewer infragranular AcD-cells than Golgi-Cox (4.43 vs 1.39), while for adult macaques Golgi-Kopsch revealed fewer than biocytin injection or SMI-32/BetaIV-spectrin immunofluorescence (13.34 vs 7.98 vs 6.29). Since the human data relies on Golgi methods, the authors must reassure the readers that the comparison of species is validated by direct comparison of different methods.

The message that primates have fewer cells with axon-carrying dendrites than other mammals might therefore certainly be interesting but far less compelling. The message might be that primate neocortex is not qualitatively different from that of other species; instead they simply have somewhat fewer AcD-bearing neurons than other mammalian species. But even that more modest conclusion is suggested but not fully proven by the data here.

The referee was right at this point. Having doubled our data sets with more human data we now aggree: the Golgi method underestimates the AcD neurons simply because of optical limitations. We now extensively discuss the issue and we no longer do statistical analysis on human. The issue needs further investigation with more methods.

I was puzzled by Fig 4 not including primate tissue. If the message is that spine density does not differ in dendrites with and without axons, surely it would be important to include primate tissue in this comparison; the comparison between primates and on-primates is after all the core message of this study. I also do not think the values for each species for non-AcD and shared root should be connected by a line; I suggest instead there should simply be a scatter of values for each group with a large symbol indicating mean or median value of each group. This would facilitate comparison.

First to the graph on spines, now Figure 6. You have to connect the individual neurons by line, otherwise the major point can no longer be seen: the dendrites differ in spine counts, sometimes the AcD is higher than the other basals of the very same neuron, in the next cell the AcD had a lower count. Statistics did not even suggest a trend. We aggree that things may differ in immature neurons. Possibly, during early development the AcD gains advantages by means of its higher excitability.

Please read the methods part to this point, elegible neurons had to fullfil a number of criteria. We fully exploited the available material of rat and ferret; no more elegible neurons. We indeed tried the same in macaque. Section thickness 50 µm. We found exactly two neurons which fullfilled the criteria. We had no chance with this material given the enormous dimension of the pyramidal cell dendritic trees in monkey. They were simply cut. For this type of classical tracing studies, non-alternating section series were prepared and submitted to different types of staining. Section spacing was several hundred µm in each individual. No chance to “reconstruct” dendrites from adjacent sections, since there were no adjacent sections.

The core message of the study is still valid, also without the spine analysis in monkey.

People have evolved as social animals and this extends to thinking and interacting. We think better when we think socially (in groups) as opposed to thinking alone.

This in part may be why solo reading and annotating improves one's thinking because it is a form of social annotation between the lone annotator and the author. Actual social annotation amongst groups may add additonal power to this method.

I personally annotate alone, though I typically do so in a publicly discoverable fashion within Hypothes.is. While the audience of my annotations may be exceedingly low, there is at least a perceived public for my output. Thus my thinking, though done alone, is accelerated and improved by the potential social context in which it's done. (Hello, dear reader! 🥰) I can artificially take advantage of the social learning effects even if the social circle may mathematically approach the limit of an audience of one (me).

Is this responsible for some of the "group think" seen in the Republican party and the political right? Imitation of bad or counter-intuitive actions outweights scientifically proven better actions? Examples: anti-vaxxers and coronavirus no-masker behaviors? (Some of this may also be about or even entangled with George Lakoff's (?) tribal identity theories relating to "people like me".

Explore this area more deeply.

Another contributing factor for this effect may be the small-town effect as most Republican party members are in the countryside (as opposed to the larger cities which tend to be more Democratic). City dwellers are more likely to be more insular in their interpersonal relations whereas country dwellers may have more social ties to other people and groups and therefor make them more tribal in their social interrelationships. Can I find data to back up this claim?

How does link to the thesis put forward by Joseph Henrich in The WEIRDest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous? Does Henrich have data about city dwellers to back up my claim above?

What does this tension have to do with the increasing (and potentially evolutionary) propensity of humans to live in ever-increasingly larger and more dense cities versus maintaining their smaller historic numbers prior to the pre-agricultural timeperiod?

What are the biological effects on human evolution as a result of these cultural pressures? Certainly our cultural evolution is effecting our biological evolution?

What about the effects of communication media on our cultural and biological evolution? Memes, orality versus literacy, film, radio, television, etc.? Can we tease out these effects within the socio-politico-cultural sphere on the greater span of humanity? Can we find breaks, signs, or symptoms at the border of mass agriculture?

total aside, though related to evolution: link hypercycles to evolution spirals?

Author Response

Reviewer #1 (Public Review):

In the present study, the authors first analyzed simultaneously recorded human EEG-fMRI data and found the fMRI signatures of burst-suppression. Then, they reported such burst-suppression fMRI signatures in the other three species examined: macaques, marmosets, and rats. Interestingly, their results indicated an inter-species difference: the entire neocortex engaged in burst-suppression in rats, whereas most of the sensory cortices were excluded in primates. The fMRI signatures of burst-suppression were confirmed in several species, suggesting that such signature is a robust phenomenon across animals. These findings warrant further investigation into its neural mechanisms and functional implications.

Major Issues

1) One of the major findings is that burst-suppression in primates appeared to largely spare sensory cortices, especially V1. However, as seen in the tSNR map for macaques and marmosets (Figure 3 &4 -figure supplement 4), the tSNR around the primary visual cortex was much weaker than other cortices. Moreover, in marmosets, the EPI slices did not cover the entire brain and actually left most of the V1 uncovered as seen in Figure 4. If so, the authors should draw their conclusions very carefully when talking about the differences in V1 across species. It would be better to analyze and discuss how the tSNR differences affect their findings. For example, the author may consider including the tSNR as covariance in their map analysis.

The tSNR in the occipital cortex—especially in the macaque V1—is indeed lower than in more anterior parts of the brain. The higher noise in V1 may have obscured the burst-suppression signal and hindered its detection. That said, we think that burst-suppression would still be detectable at such low tSNR values. We base this claim on our analysis of another macaque brain region—area TE of the inferior temporal cortex (see our additions to Figure 3–figure supplement 4). The tSNR in areas TE and V1 is comparably low, and yet TE is significantly correlated with asymmetric PCs while V1 is not. Therefore, if the burst-suppression fluctuation was present in V1 we should have still detected it.

Regarding the marmoset data, part of V1 was indeed left out of our field of view, as explicitly shown in our figures (Figure 4 and Figure 4–figure supplement 3). Though we cannot exclude the possibility that the omitted posterior V1 engages in burst-suppression, we think that it is unlikely to behave any differently to more anterior visual areas. We sought more support for this view by obtaining full-brain fMRI data in one additional marmoset. We present this analysis in a new paragraph of the relevant Results section and in the new Figure 4–figure supplement 5. The asymmetric PC map in this individual showed widespread correlation across the neocortex, extending slightly further caudally compared with the group map presented in Figure 4. However, nearly all of V1—including the occipital pole—was still uncorrelated. Considering both the new full-brain marmoset data and the results from area TE in macaques, we think that our conclusion about the uncoupling of primate V1 during burst-suppression is still justified. That said, we have now explicitly included the relevant concerns in the manuscript text.

2) To confirm their findings, it would be great to look into the EEG signals around the sensory cortex (e.g., V1) to see whether the findings in fMRI could be also confirmed with EEG.

EEG signals around V1 were already examined during the previous analysis of the human dataset (Golkowski et al., 2017). As reported there, the EEG signal of the occipital electrodes did contain bursts, which could not be differentiated from bursts detected by more anterior electrodes in terms of onset timing, duration, or spectral content. This might mean that the BOLD signal in VI is truly uncoupled from electrical activity. However, we should also consider that EEG may lack the spatial resolution to detect a different activity originating from V1. As seen in the human map (Figure 3), the external cortical surface is almost exclusively covered with areas engaging in burst-suppression, whereas the ‘uncoupled’ V1 represents a small patch by comparison. Therefore, EEG cannot safely determine the nature of electrical activity in V1. We have added the above arguments to the last section of Results. We expect a conclusive answer to come from future electrophysiological recordings in nonhuman primates. The larger proportional size of visual areas in macaques and marmosets as well as the possibility of invasive intra-cranial recordings make these animals attractive models for addressing this question.

3) As seen in Figure 2-figure supplement 2, there was a significant anticorrelation with burst-suppression at the ventricular borders. It is unclear whether the authors have done physiological or white matter/CSF/global nuisance regression as most of the rest-fMRI studies did. Please make it clear. If not, please explain why and discuss whether it would affect their results.

We chose to analyze the data without CSF or global signal regression. CSF regression typically requires extracting the signal of a few voxels within the ventricles. Accurately placing such voxels is feasible in the human brain but challenging in small animal brains, especially in rodents. Rodent ventricles are very thin, making it difficult to place a CSF voxel that will not overlap with surrounding brain tissue. Since we had prioritized making the analysis as similar as possible across species, we decided to also forgo CSF regression in humans. While this was our original motivation for omitting CSF regression, we later came across an even more important concern. As we show in Figure 2–figure supplement 2, the CSF signal is not ‘noise’; rather, it is directly related to burst-suppression, and most likely caused by it. Regressing it out would remove much of the variance explained by burst suppression. The coherence between neural, hemodynamic, and CSF oscillations that we see in burst-suppression likely also occurs in other states characterized by global synchrony, as has been shown for non-rapid eye movement sleep (Fultz et al., 2019).

We think that global signal regression makes no sense in our case, given that our goal was to study a nearly global signal fluctuation. Global signal regression relies on the assumption that neuronal activity is variable across brain regions while many non-neuronal sources contribute globally to the brain signal (Murphy and Fox, 2017). This assumption does not hold true in cases where the neuronal activity itself is global.

4) Three different concentrations of the anesthetic sevoflurane were chosen for human participants. The authors found that the high concentration (3.9-4.6%) induced burst-suppression much better than the other two lower concentrations as expected. However, in rats, almost all asymmetric PCs were found at an intermediate concentration (2%) of isoflurane less at the low (1.5%) or high (2.5%) concentration in Rat 1. At the same time, all fMRI runs from Rat 2 with a 1.3% concentration of isoflurane had a prominent asymmetric PC. That is, it seems that only the high concentration of isoflurane could not induce burst-suppression well in rats, which was opposite to those findings in humans. The authors may explain what reasons may cause such differences and whether such differences may affect the major findings in differences between primates and rodents.

The three sevoflurane concentrations (‘high’, ‘intermediate’, ‘low’) used in humans do not necessarily correspond to the three isoflurane concentrations used in rats (2.5%, 2.0%, 1.5%). Comparing anesthetic concentrations across our datasets is challenging, since anesthetic potency is expected to vary depending on the drug (sevoflurane or isoflurane), animal species, age, and the co-administration of other drugs. Nevertheless, we may estimate equivalent concentrations across species by expressing them as multiples of the minimum alveolar concentration (MAC), i.e. the concentration that produces immobility in 50% of subjects undergoing a standard surgical stimulus.

For humans, we can use available age-related MAC charts (Nickalls and Mapleson, 2003) to express the three sevoflurane levels as follows: ~1 MAC (2%), 1.5 MAC (3%), 2.2–2.3 MAC (3.9–4.6%). For rats, we can rely on the previously reported isoflurane MAC value of 1.35% (Criado et al., 2000) to derive the following levels: 1.2 MAC (1.5%), 1.6 MAC (2%), 1.9 MAC (2.5 %), and ~1 MAC (1.3%, Rat 2 dataset). According to these conversions, fMRI-detectable burst-suppression occurred in humans at ~2 MAC (with some cases at 1.5 MAC), in the Rat 1 dataset at 1.2–1.6 MAC, and in the Rat 2 dataset at 1 MAC. There seems to be a difference between rats and humans as well as a discrepancy between the two rat datasets. The latter discrepancy could have arisen from differences in the calibration of isoflurane vaporizers at the two research sites (direct measurements of end-tidal anesthetic concentration were not obtained in rats).

In order to better interpret the observed human-rat difference we tried to also compute the multiples of MAC values for our nonhuman primate data, but this proved to be hard. For common marmosets, we are not aware of any published isoflurane MAC values. For long-tailed macaques, a value of 1.28% has been reported (Tinker et al., 1977), which gives a range of 0.7 – 1.2 MAC for our macaque dataset. However, that probably underestimates the actual depth of anesthesia in our experiments, since many of our macaques were old and MAC is known to decrease with age (Nickalls and Mapleson, 2003). Moreover, the administration of medetomidine during anesthesia induction may have further reduced the MAC (Ewing et al., 1993). Consequently, we cannot provide good MAC estimates for the nonhuman primate data and thus have no reference for comparison with other species.

Even if we knew the correct MAC value in all cases, it may be an inappropriate means of standardizing anesthetic concentrations for burst-suppression. The endpoint measured by MAC—immobility—is mainly mediated by anesthetic effects on the spinal cord and my not be a good predictor for effects on the brain (Rampil et al., 1993). In fact, burst-suppression itself has been proposed as a more appropriate endpoint for measuring anesthetic potency. The proposed metric (MACBS) is defined as the concentration that produces suppressions longer than 1 s in 50% of subjects and is not linearly related to MAC (Pilge et al., 2014).

In conclusion, if we reference anesthetic concentrations against the MAC, humans and rats indeed seem to exhibit burst-suppression at different concentration ranges. We are unable to perform the same referencing for non-human primates, due to lack of accurate MAC values. Moreover, it is unclear whether MAC is the appropriate reference to begin with. Discussing all these nuances would make the manuscript too long. That said, we have now added a new paragraph to the Discussion section, drawing attention to the fact that anesthetic concentrations are not standardized across species.

Reviewer #2 (Public Review):

The strong point in their manuscript is the originality of their results. Using the fMRI's spatial resolution, they can successfully reveal that not all brain areas are synchronized during the burst suppression. Furthermore, they can find that the difference is the most obvious when comparing primates with rats, which makes sense considering the distance on the phylogenetic tree. As far as I know, this manuscript first reports these points.

On the other hand, there is a weak point in their method. As they've already discussed this point, they needed to use arbitrary thresholds to evaluate whether there is burst suppression or not. Furthermore, this study cannot reject the possibility of spatial inhomogeneity and/or anesthesia-specific modulation in hemodynamic response. If there is such a mechanism, one can find different results from those obtained through electrical measurements.

1) The authors found that some sensory areas in primates are excluded from those highly synchronized during the burst suppression. While it is true, I wonder if each voxel in such areas shows burst suppression-like activity that is not synchronized with others. If this is the case, burst suppression can still be a global phenomenon. Though authors seem to investigate this point, they used in-ROI averaged time-series so that it cannot reject the possibility that each voxel inside the ROI is not synchronized but shows burst suppression in its manner. I recommend the authors look into each voxel if this is the case or not.

The reviewer raises an interesting point by proposing that it is possible for sub-regions within the excluded areas—e.g. within V1—to exhibit burst-suppression out-of-phase with each other, thus cancelling out in the mean V1 BOLD signal. We do not think this is the case, for several reasons. Firstly, we can exclude the possibility that any part of V1 exhibits bust-suppression in-phase with the rest of the cortex. The original first-level GLM analysis was a voxel-based univariate analysis. If any voxels within V1 were correlated with the global burst-suppression pattern, we would have seen it on the maps. We saw no such effect, except for some subjects in which a subset of V1 voxels was anti-correlated with the asymmetric PC (the effect was not significant in our group analysis). This anticorrelation was mostly located close to the ventral horns of the two lateral ventricles, and thus could have arisen by the same cycle of ventricular shrinkage-expansion that we describe in Figure 2–figure supplement 2. Secondly, no large clusters of V1 voxels exhibited burst-suppression out-of-phase with the dominant asymmetric PC. If this was the case, we would have seen a phase-shifted version of the fluctuation on the carpet plots. This still leaves the theoretical possibility that individual V1 voxels (or a few at a time) exhibit transitions between burst and suppression epochs out-of-phase with each other. In our response to the next point, we will explain why there is no way of detecting this with fMRI and we discuss whether such a possibility would even fit the label of burst-suppression.

2) The other but similar point is about their way to detect burst suppression. Why did they use the principal component? By definition, burst suppression should be defined by the existence of burst and suppressed periods. I cannot understand why they did not simply use this definition to check whether each voxel shows such an intermittent activity to evaluate whether it is a global phenomenon or not.

Burst-suppression on EEG is characterized by quasi-periodic suppressions of activity, during which the EEG signal drops close to being isoelectric. We cannot apply the same definition to fMRI, because the BOLD signal only represents relative changes and thus has no natural baseline equivalent to isoelectricity. Hence there is no way of telling whether a BOLD signal decrease corresponds to a complete activity cessation (suppression) or simply a relative decline. Therefore, we instead decided to rely on another defining feature of burst-suppression—synchrony. We knew that burst-suppression appears simultaneously across EEG electrodes, which means that large parts of the cortex (the major contributor to EEG signal) would have to be synchronized. Moreover, we knew that transitions between burst and suppression epochs occur on a very slow timescale and would be resolvable at a TR of 2 s. PCA allowed us to isolate the large slow synchronous component in the cortical BOLD signal, though this is hardly the only approach that would work. We chose PCA because it is a simple, deterministic, and easily interpretable algorithm.

On a related note, even if we could identify complete cessation of activity in the BOLD signal of a single voxel, it is unclear whether that would qualify as burst-suppression per the EEG definition. EEG electrodes pick up activity from areas much larger than a voxel, and thus the very presence of an EEG fluctuation presupposes synchrony on a larger spatial scale. If individual voxel-sized brain areas engaged in burst-suppression out-of-phase, that would probably not register as burst-suppression on an EEG electrode.

3) Why is there no synchronization during the slow-wave states under light anesthesia? During the slow-wave sleep, it is shown that the entire cortical network is decomposed into a modular-like network structure. Is there synchronization inside each module while no synchrony between modules?

We do not claim that there is no synchrony in the slow-wave state. We simply state that this state lacks the nearly global cortex-wide fluctuation that is produced by the abrupt transitions between burst and suppression epochs. In fact, the very presence of slow waves on EEG requires synchrony. However, this slow-wave synchrony occurs at a timescale too fast for fMRI to capture, and thus would not directly translate into a global BOLD fluctuation, as burst-suppression does.

Though the slow-wave state lacks global synchrony on fMRI, it may well exhibit within-module synchrony, as the reviewer suggests. Modules resembling the resting-state networks of wakefulness and sleep have been detected during isoflurane anesthesia in primates (Hori et al., 2020; Hutchison et al., 2011). These experiments were presumably conducted during the slow-wave state: burst-suppression would generate a global network, while the isoelectric state would erase any modular structure. We suspect that functional networks during the anesthetized slow-wave state resemble those present in slow-wave sleep. However, we have not assessed that in our study, since our primary goal was to map burst-suppression.

Reviewer #3 (Public Review):

The authors present a multicenter, multimodal rs-fMRI study of the spatial signature of burst suppression in the brain of humans, non-human primates and rats. They have used EEG to identify burst suppression activity in human data from simultaneous EEG-rs-fMRI measurements of subjects under servoflurane anesthesia. After having identified a (neurovascular) rs-fMRI representation of burst activity, the authors show that bursts can equally be identified from MR data alone. After a principal component analysis, bursts and their spatial signature were identified by an asymmetry of the correlation coefficients. Across species the authors identified similar spatial signatures, which were conserved for all (investigated) primates, but differed for rats. While rats showed a pan-cortical involvement, signatures in primates were more complex, e.g., not including the visual cortex.

In this study, the authors have presented a novel purely MR-based method to identify burst suppression and its spatial signature. Their method may be used to readily identify burst suppression in fMRI data. However, no general threshold for the median of the cortex-wide correlation could be identified. The authors also establish a conserved signature of burst suppression for primates and reveal subtle but important differences to rodents. Both achievements are novel and represent a major advance in the field of neuroimaging.

The study was well designed, including important control data to rule out artefacts as source of the observed burst suppression patterns. The particular strengths of this study are: (1) including multicentre data (although only rats were scanned at two different sites); and (2) including four species from humans to rats.

The manuscript was very carefully and well written (I did not even notice a single typo) and the figures were carefully devised, comprehensively illustrating the large amount of data. The authors further provide a comprehensive account of the relevant literature. Towards the end of their discussion they also clarify the difference in terminology used for burst suppression in some recent rodent studies.

The only (and in my opinion notable) weakness, is the lack of a general threshold for the asymmetry of the median of the cortex-wide correlation coefficients. With such a threshold, rs-fMRI could be readily used to automatically detect burst suppression across species. However, the authors clearly state this shortcoming and openly discuss its implications. I do not think that an altered experimental design or additional data could provide further remedy.

To conclude: This very comprehensive study was very well designed, extremely carefully performed, presents a novel tool for identification of burst suppression, and provides insight across species. It has clearly translational potential, which however, is limited by the lack of a general threshold for burst suppression detection.

I congratulate the authors for this very nice piece of work, and the most typo-free manuscript I have ever read.

We thank the reviewer for the positive and detailed feedback.

Author Response

Reviewer #1 (Public Review):

When theta phase precession was discovered (O'Keefe & Recce, 1993; place cell firing shifting from late to early theta phases as the rat moves through the firing field, averaged over many runs), it was realized that, correspondingly, firing moves from cells with firing fields that have been run through (early phase) to those whose fields are being entered (late phase), with the consequence that a broader range of cells will be firing at this late phase (Skaggs et al., 1996; Burgess et al., 1993; see also Chadwick et al., 2015). Thus, these sweeps could represent the distribution of possible future trajectories, with the broadening distribution representing greater uncertainty in the future trajectory.

Using data from Pfeiffer and Foster (2013), they examine how neurons could encode the distribution of future locations, including its breadth (i.e. uncertainty), testing a couple of proposed methods and suggesting one of their own. The results show that decoded location has increasing variability at later phases (corresponding to locations further ahead), and greater deviation from the actual trajectory. Further results (when testing the models below) include that population firing rate increased from early to late phases; decoding uncertainty does not change within-cycle, and the cycle-by-cycle variability (CCV) increases from early to late phases more rapidly than the trajectory encoding error (TEE).

They then use synthetic data to test ideas about neural coding of the location probability distribution, i.e. that: a) place cell firing corresponds to the tuning functions on the mean future trajectory (w/o uncertainty); b) the distribution is represented in the immediate population firing as the product of the tuning functions of active cells or c) (DDC) the distribution is represented by its overlap with the tuning curves of individual neurons; d) (their suggestion) that different possible trajectories are sampled from the target distribution in different theta cycles.

The product scheme has decreasing uncertainty with population firing rate, so would have to have maximal firing at early phases (corresponding to locations behind the rat), contradicting what was observed in the data, so this scheme is discarded.

The DDC scheme has an increased diversity of cells firing as the target distribution gets wider within each cycle, whereas the mean and sampling schemes do not have increasing variance within-cycle (representing a single trajectory throughout). The decoding uncertainty in the data did not vary within-cycle, so the DDC scheme was discarded.

The mean and sampling schemes are distinguished by the increase in CCV vs TEE with phase, which is consistent with the sampling scheme.

The analyses are well done and the results with synthetic data (assuming future trajectories are randomly sampled from the average distribution) and real data match nicely, although there is excess variability in the real data. Overall, this paper provides the most thorough analyses so far of place cell theta sweeps in open fields.

We thank the Reviewer for the accurate summary and the encouragement.

I found the framing of the paper confusing in a way that made it harder to understand the actual contribution made here. As noted in the discussion, the field has moved on from the 1990s and cycle-by-cycle decoding of theta sweeps has consistently shown that they correspond to specific trajectories moving from the current trajectory to potential future trajectories, consistent with continuous attractor-based models (in which the width of the activity bump cannot change, e.g. Hopfield, 2010). Thus it seems odd to use theta sweeps to test models of encoding uncertainty - since Johnson & Reddish (2007) we know that they seem to encode specific trajectories (e.g. either going one way or the other at a choice point) rather than an average direction with variance covering the possible alternatives.

We thank the reviewer for emphasising the connections to earlier work on theta sweeps during decision making, which suggests that alternative options before a decision point are assessed individually by hippocampal neuron populations in a simple maze. However, as also noted by the reviewer below, previous analysis of theta sweeps in the hippocampus were limited to discrete decisions in a linear maze, which only permits a limited exploration of the alternative hypotheses an animal might experience in a planning situation.

In particular, the dominant source of future uncertainty in a binary decision task is the chosen option (left or right) providing a distinctly bimodal predictive distribution. Bimodal distributions can not be easily approximated by variational methods (that includes the DDC or product schemes) but can be efficiently approximated by sampling. In contrast, in an open field the available options (changes in direction and speed) are not restricted by the geometry of the environment and the predictive distribution is relatively similar to a Gaussian distribution which can be efficiently approximated by all of the investigated encoding schemes.

Moreover, it has been widely reported that the hippocampal spatial code has somewhat different properties in linear tracks, where the physical movement of the animal is restricted by the geometry of the environment, than in open field navigation. Specifically, in linear tracks most neurons develop unidirectional place fields and the hippocampal population uses different maps to represent the two opposite running directions, whereas a single map and omnidirectional place fields are used in open fields (Buzsaki, 2005). In terms of representing future alternatives, it remains to be an open question if the scheme that is compatible with planning in a 1D environment generalises to two 2D environments. Our detailed comparison of the alternative encoding schemes provides an opportunity to demonstrate that a sampling scheme can be applied as a general computational algorithm to represent quantities necessary for probabilistic planning, while also demonstrating that alternative schemes are incompatible with it.

Moreover, these previous studies did not rule out the possibility that, in addition to alternating between discrete options, specific features of the population activity might also represent uncertainty (conditional to the chosen option) instantaneously as in the product or the DDC schemes.

We added a new paragraph (lines 74-88) to the introduction to clarify that one of the novel contributions of the paper is the generalisation of previous intuitions, largely based on work on binary decision tasks in mazes, to unrestricted open field environments.

The point that schemes that assume varying-width activity distribution might be unfit for modelling hippocampal theta activity is an interesting insight. Let us note that new results have pointed out that the fixed width activity bump is not a necesssary feature of attractor networks. It has recently been shown that in continuous attractors (modelling head direction cells in the fly) the amplitude of the bump can change and the changes can be consistent with the represented uncertainty (Kutschireiter et al., 2021 Biorxiv; https://doi.org/ 10.1101/2021.12.17.473253). We believe that similar principles also apply to higher-dimensional continuous attractor networks and therefore it is entirely possible to represent uncertainty via the amplitude of the bump (equivalent to the population gain) in the hippocampus.

Thus, the main outcomes of the simulations could reasonably be predicted in advance, and the possibility of alternative neural models of uncertainty explaining firing data remains: in situations where it is more reasonable to believe that the brain is in fact encoding uncertainty as the breadth of a distribution.

Having said that, most previous examples of trajectory decoding of theta sweeps have not been for navigation in open fields, and the analysis of Pfeiffer and Foster (2013; in open fields) was restricted to sequential 'replay' during sharp-wave ripples rather than theta sweeps. This paper provides the nicest decoding analyses so far of place cell theta sweeps in open field data. However, there are already examples of theta sweeps in entorhinal cortex in open fields (Gardner et al., 2019) showing the same alternating left/right sweeps as seen on mazes (Kay et al., 2020). Such alternation could explain the additional cycle-by-cycle variability observed (cf random sampling).

We thank the reviewer for encouraging us to more directly test the idea that alternating left right sweeps could explain the increased cycle-to-cylce variability in the data. We thoroughly analysed the data (see our answer to essential revisions 1.) and found that trajectories at subsequent theta cycles are strongly anticorrelated (Fig. 7, Fig. S11, lines 375-415)

Reviewer #2 (Public Review):

This study investigates how uncertainty about spatial position is represented in hippocampal theta sequences. Understanding the neural coding of uncertainty is important issue in general, because computational and theoretical work clearly demonstrates the advantages of tracking uncertainty to support decision-making, behavioural work in many domains shows that animals and humans are sensitive to it in myriad ways, and signatures of the neural representations of uncertainty have been demonstrated in many different systems/ circuits.

We thank the reviewer for the comment.

However, studies of whether and how uncertainty is signalled in the hippocampus has remained understudied. The question of how spatial uncertainty is represented is already interesting but recent interest in interpreting hippocampal sequences as important for planning and decision-making provide additional motivation.

A variety of experimental paradigms such as recordings in light vs. darkness, dual rotation experiments in which different cues are placed in conflict with another, "morph" and "teleportation" experiments and so on, all speak to this issue in some sense (and as I note below, could nicely complement the present study); and a number of computational models of the hippocampus have included some representation of uncertainty (e.g. Penny et al. PLoS Comp Biol 2013, Barron et al. Prog Neurobiol 2020). However, the present study fills an important gap in that it connects a theory-driven approach of when and how uncertainty could be represented in principle, with experimental data to determine which is the most likely scheme.

The analyses rely on the fundamental insight that states/positions further into the future are associated with higher uncertainty than those closer to the present. In support of this idea, the authors first show that in the data (navigation in a square environment, using the wonderful data from Pfeiffer & Foster 2013), decoding error increases within a theta sequence, even after correcting for the optimal time shift.

The authors then lay out the leading theoretical proposals of how uncertainty can be represented in principle in populations of neurons, and apply them to hippocampal place cells. They show that for all of these schemes, the same overall pattern results. The key advance of the paper seems to be enabled by a sophisticated generative model that produces realistic probability distributions to be encoded (that take into account the animal's uncertainty about its own position). Using this model, the authors show that each uncertainty coding scheme is associated with distinct neural signatures that they then test against the data. They find that the intuitive and commonly employed "product" and "DDC" schemes are not consistent with the data, but the "sampling" scheme is.

The final conclusion that the sampling scheme is most consistent with the data is perhaps not surprising, because similar conclusions have been reached from showing alternating representation of left and right at choice points cited by the authors (Johnson and Redish 2007; Kay et al. 2020; Tang et al. 2021) and "flickering" from one theta cycle to the next (Jezek et al. 2011). So, the most novel parts of the work to me are the rigorous ruling out of the alternative "product" and "DDC" schemes.

We thank the reviewer for helping us to clarify the main novelty of our work compared to previous studies. We have updated the introduction (lines ~74–88) to state more clearly how our analysis extends previous work largely restricted to binary decision tasks in mazes and not explicitly considering alternative probabilistic representations.

Overall I am very enthusiastic about this work. It addresses an important open question, and the structure of the paper is very satisfying, moving from principles of uncertainty encoding to simulated data to identifying signatures in actual data. In this structure, the generative model that produces the synthetic data is clearly playing an important role, and intuitively, it seems the conclusions of the paper depend on how well this testbed maps onto the actual data. I think this model is a real strength of the paper and moves the field forward in both its conceptual sophistication (taking into account the agent's uncertainty) and in how carefully it is compared to the actual data (Figures S2, S3).

We thank the reviewer for the encouraging words.

I have two overall concerns that can be addressed with further analyses.

First, I think the authors should test which of the components of this model are necessary for their results. For instance, if the authors simply took the successor representation (distribution of expected future state occupancy given current location) and compressed it into theta timescale, and took that as the probability distribution to be encoded under the various schemes, would the same predictions result? Figuring out which elements of the model are necessary for the schemes to become distinguishable seems important for future empirical work inspired by this paper.

The crucial part of our generative model is its probabilistic nature. Explicit formulation of the generative model under different coding schemes enables us to quantitatively account for the different factors contributing to the variability in the data. Specifically, when we compared sampling and mean codes, we partitioned variability of the represented locations across theta cycles into specific factors related to 1) decoding error; 2) difference between the true position of the animal and its own location estimate; 3) the animal’s own uncertainty about its spatial location; 4) updating this estimate in each theta cycle. This enabled us to derive quantities (CCV, TEE and EVindex) that can discriminate between sampling and mean schemes, and that could be directly measured experimentally. This would not be possible in a simpler model lacking an explicit representation of the animal’s internal uncertainty.

We believe that the assumptions of the model are rather general and those do not limit the scope of the model. Here we list the specific features of the model for clarity (Fig S1a):

1) Planned position (Fig S1a, left): the planned position is required to guide movements in the model. The specific way we generated the planned position was not essential for the simulations but we tuned the movement parameters to generate trajectories matching the real movement of the animal. It is defined as a random walk process for velocity which is the simplest model for smooth trajectories.

2) The inference part (Fig S1a, middle) is crucial for the model since we believe that hippocampal population activity is driven by the animal’s own beliefs about its position, which tells our approach apart from earlier studies (see paragraph around line 466). If the animal represents its predictions optimally then the predictions should be consistent with its movement within the environment. Thus, the consistency of the inference is a critical statistical property of the model, which can be guaranteed if the predictions are generated by the same model that is used for inferring the animal’s position. The simplest model that can be used for inference and predictions is the Kalman filter, which we opted for in our simulations.

3) The assumptions of the encoding model (Fig S1a, right and Fig 1b) are solely determined by the representational scheme being tested. All of the schemes rely on encoding the result of inference in population activity during theta cycles and the scheme determines how this encoding happens. This part of the model is clearly necessary for the analysis.

Alternatively, we could use the above mentioned successor representation (SR) framework (Dayan 1993) to represent possible trajectories and their associated uncertainty in our models of hippocampal population activity. However, this option introduces extra challenges: First, in the SR framework (Stachenfeld et al., 2017) neuronal firing rates are proportional to the discounted expected future number of times a particular location is going to be visited given the current policy and position. Thus, the SR does sum over all possible future visits and does not specify when exactly a particular state might be reached in the future which is inconsistent with the idea that trajectories are represented during theta sequences. Second, the SR represents the probability of occupying all future states in parallel without providing possible trajectories defining specific combinations of future state visits. This property is consistent with the product and the DDC encoding schemes but not with the other two. These two properties of the SR implies that this framework per se does not provide a fine-scale temporal description of how expected future state probabilities are related to the dynamics of the hippocampal population activity during theta oscillation.

Taken together, implementing theta time-scale dynamics using the SR framework would also require several additional model choices to generate consistent temporal trajectories from the expected future state occupancies, and even in this case the subjective uncertainty of the animal would not be consistently represented in the simulated data. Representing the animal’s subjective uncertainty in our model was an important component in contributing to the EV-index and had profound implications on the signatures of generative cycling in a two dimensional arena.

We have to note that on a slower time scale (calculating the average firing rate over multiple theta cycles) all of our encoding schemes are consistent with the SR framework (line 548).

Second, the analyses are generally very carefully and rigorously performed, and I particularly appreciated how the authors addressed bias resulting from noisy estimation of tuning curves (Figure S7). However, the conclusion that the "sampling" scheme is correct relies on there being additional variance in the spiking data. This is reminiscent of the discussions about overdispersion and how "multiple maps" account for it (Jackson & Redish Hippocampus 2007, Kelemen & Fenton PLoS Biol 2010), and the authors should test if this kind of explanation is also consistent with their data. In particular, the task has two distinct behavioral contexts, when animals are searching for the (not yet known) "away" location compared to returning to the known home location, which extrapolating from Jackson & Redish, could be associated with distinct (rate) maps leading to excess variance.

We thank the reviewer for this constructive comment. We note that the signature of the sampling scheme is variability in the decoded trajectory across subsequent theta cycles while overdispersion is usually defined as the supra-Poisson variability in the spiking of individual neurons evaluated across multiple runs or trials. Nevertheless, we tested the existence of multiple maps corresponding to the two distinct task phases and found that the maps representing the two task phases are very similar (Fig S11).

Such an analysis could also potentially speak to an overall limitation of the work (not a criticism, more of a question of scope) which is that there are no experimental manipulations/conditions of different amounts of uncertainty that are analyzed. Comparing random search (high uncertainty, I assume) to planning a path to a known goal (low uncertainty) could be one way to address this and further bolster the authors' conclusions.

We agree with the reviewer that the proposed framework provides additional insights into the way the population activity should change with specific experimental manipulations and can therefore inspire further experiments. In particular, a hallmark of probabilistic computations is that experimental manipulations that control the uncertainty of the animal should be reflected in population responses. In the visual processing such manipulations are indeed reflected in changing response variability, as predicted by sampling (Orban et al, Neuron 2016). In the current experimental paradigm there was no direct manipulation of uncertainty (we discuss this around lines 573-576). While one might argue that there are differences in the planning strategy in trials where the animal was heading for away reward and in those heading for home, this is not a very explicit test of the question. Still, to check if we can find traces of changes in uncertainty in the two conditions, we analysed the EV-index separately on home and away trials (Fig. S11e). We did not find systematic differences in the EV-index across these trial types.

Reviewer #3 (Public Review):

Summary of the goals:

The authors set out to test the hypothesis that neural activity in hippocampus reflects probabilistic computations during navigation and planning. They did so by assuming that neural activity during theta waves represents the animal's location, and that uncertainty about this location should grow along the path from the recent past to the future. They next generated empirical signatures for each of the main four proposals for how probabilities may be encoded in neural responses (PPC, DDC, Sampling) and contrasted them with each other and a non-probabilistic representation (scalar estimate of location). Finally, the authors compared their predictions to previously published neural activity and concluded that a sampling-based representation best explained neural activity.

Impact & Significance: This manuscript can make a significant impact on many fields in neuroscience from hippocampal research studying the functions and neural coding in hippocampus, through theoretical works linking the representation of uncertainty to neural codes, to modeling experimental paradigms using navigation tasks. The manuscript provides the following novel contribution to cognitive neuroscience:

• It exploits the inherent change in uncertainty about a parsimonious internal variable over time during planning to test hypotheses about probabilistic computations.
• A full model comparison of competing hypotheses for the neural implementation of probabilistic beliefs. This is a topic of wide interest and direct comparisons using data have been elusive.
• The study presents substantial empirical evidence for a sampling-based neural representation of the probability distribution over trajectories in the hippocampus, a finding with potential implications for other parts of neural processing. Strengths:
• Creative exploitation of a naturally occurring change in uncertainty over a parsimonious latent variable (location).
• Derivation of three empirical signatures using a combination of analytical and numerical work.
• Novel computational modelling & linking it to neural coding using 4 existing implementational models
• Comprehensive and rigorous data analysis of a large and high-quality neural dataset, with supplemental analyses of a second dataset
• Mostly very clear and high quality presentation We thank the Reviewer for the summary and for the positive feedback on the manuscript. Weaknesses:
• It is unclear to what degree the "signatures" depend on the details of the numerical simulation used by the authors to generate them. At least two of them (gain for the product scheme and excess variability for the sampling scheme) appear very general, but the degree of robustness should be discussed for all three signatures.

The generality of the signatures follows from the fact that we derived them from the fundamental properties of the encoding schemes. We tested their robustness using both idealised test data (Fig S6c-d, Fig S7b) and our simulated hippocampal model (Fig. 4c, Fig5b-c, Fig6b-g).

The reviewer is right that the sensitivity and robustness is a potential issue. These schemes have been originally proposed to encode static distributions ie., the neuronal activity was supposed to encode a specific probability distribution for an extended period of time. Therefore, when we test the signatures we make the simplifying assumption that a static distribution is encoded in the three separate phases of the theta cycle. It is currently unknown whether during theta sequences the trajectories are represented via discrete jumps in positions or as continuously changing locations. Therefore we used our numerical simulations to test whether the proposed signatures are sufficiently sensitive to discriminate the encoding schemes using the limited amount of data available and in the face of biological noise but also robust to the parameter choices and modelling assumptions.

Regarding the product code, the inverse relationship between the gain and the variance has been previously derived analytically for special cases (Ma et al., 2006). In the manuscript we show numerically that the same relationship holds for general tuning curve shapes (Fig. S6d). Finally we demonstrate that the gain is a robust signature that changes systematically along the theta cycles in the case of a product coding scheme.

Second, in the case of the DDC code we used the decoded variance of the posterior as the signature. Since DDC code relies on the overlap between the target distribution and the neuronal basis functions, potentially the most important source of error is if we overestimate the size of the encoding basis functions. To control for this factor, we first explored this effect in an idealised setting (in fig S7) and found that the decoded variance correlates with the encoded uncertainty both if we used the estimated basis functions or the empirical tuning curves for decoding. Next we performed the analysis in our simulated dataset in 4 different ways - either using empirical tuning curves (Fig 5c-d) or the estimated basis functions (Fig S8a-b), focusing on high spike count theta cycles or including all theta cycles. The fact that all these analyses led to similar results confirms the robustness of this signature.

Our third measure, the EV-index measures the variability of the encoded trajectories across theta cycles. The cycle-to-cycle variability is also affected by factors independent of whether a randomly sampled trajectory or the posterior mean is encoded. In particular, the encoded trajectory can start at different distances in the past and can be played at different speeds in different theta cycles. These factors are probably present in the data and all inflate the CCV. Another factor is the start and end time of the trajectories, which we may not be able to accurately find in the real data and confusing the end of a previous trajectory with the start of a new one can also inflate CCV. In our simulations we tested how these potential errors influence our analysis, and found that the EV index is surprisingly robust to such changes (Fig 6fg). An additional factor that the EV-index is sensitive to is the specific sampling algorithm used to sample the posterior: an algorithm that produces correlated samples is hard to distinguish from the MAP scheme. Our newly introduced analysis (Fig 7b) demonstrates this and explores the level of correlation between subsequent trajectories, providing evidence that trajectories decoded during exploration reflect the properties of anticorrelated samples, also a signature of efficient inference.

• The claims about "efficiency" lack a definition of what exactly is meant by that, and empirical support.

We thank the reviewer for pointing out this inconsistency in our terminology. What we generally meant by efficiency was a claim that pertains the computational level, according to Marr’s classification, i.e.that computations are probabilistic, that is, representation in the hippocampus takes into account uncertainty by representing a full posterior distribution. We performed an additional test, which concerns the algorithmic-level efficiency of the computations. We explored the efficiency of the sampling process by assessinga signature of efficientsampling, the expected number of sampled trajectories required to represent the distribution of possible future locations. We found that subsequent samples tended to be anti-correlated which is a signature of efficient sampling algorithms (Fig 7). In the revised manuscript we thus use the word efficient solely when we refer to the anticorrelated samples.

Author Response:

Reviewer #2:

The authors investigated changes in the unstressed and stressed oligomeric states of the mammalian endoplasmic reticulum (ER) stress sensor, IRE1a. Previous biochemical and microscopy studies in mammalian cells and studies of the related protein Ire1 in yeast, describe an increase in oligomerization of the stress sensor upon treatment of cells with chemical agents that impair the ER protein folding environment. The general view has been that IRE1 in unstressed cells is a monomer and varying degrees of misfolded protein stress stimulate dimerization, activation, and higher order oligomerization. Distinguishing between monomers and dimers, as well as tetramers or other small oligomers is technically challenging, especially for integral membrane proteins. To address this challenge, the authors turned to single particle tracking fluorescence microscopy of Halo-tagged endogenous IRE1. Using a clever combination of random labeling with two fluorescent dyes and oblique angle illumination to visualize single molecules, as well as dimers, the authors surprisingly find that their endogenous IRE1 reporter appears to be dimeric in homeostatic cells. This observation challenges the predominant model in which IRE1 is monomeric in unstressed cells and that even dimerization represents a switch into an active state. The authors claim to detect evidence for higher order oligomers following treatment with stressors. The authors then use a series of IRE1 mutants to identify how oligomerization is regulated and present a new model to reconcile the different models of IRE1 activation in the literature.

The authors have extensively characterized their novel experimental system in terms of protein expression levels, functionality, and ability to distinguish monomers and dimers. The data are well presented and the authors are clearly familiar with the arguments that have surrounded the IRE1 oligomer question. That the authors observe the characteristic XBP1 mRNA splicing activity in the absence of visible large IRE1 clusters may suggest that the large clusters reported by others may have distinct roles, perhaps in more permissive mRNA cleavage.

The present study is undermined by two major weaknesses. First, while the authors persuasively demonstrate that they can detect IRE1a dimers, a major claim of the manuscript rests upon detection of tetramers and possibly higher order oligomers. Unfortunately, the authors provide no independent controls to show what tetramer or higher order oligomer data would look like. Thus, the authors can only infer that higher order oligomers are detected, based on modest shifts in the percent of correlated particle trajectories observed in some cells. More robust evidence is needed to make claims of oligomerization. Tools have been developed by others that can induce reversible oligomerization of proteins. Application of these tools would provide powerful controls for tetramers or even higher order oligomers in this study.

The second, deeper concern, is the discrepancy between the Halo Tag clustering results in this study and studies by this lab and several other labs that report a distinct stress phenotype. In mammalian cells and yeast, IRE1 and Ire1, tagged with different fluorescent proteins or even a small HA peptide epitope tag, undergo quantitative visible formation of puncta or clusters upon treatment with stressors. The small number of bright clusters that form effectively deplete the rest of the ER of IRE1 signal. In the present study, the authors observe no visible change in IRE1-Halo localization in stress cells. The authors do not investigate the cause of this difference. While one might argue that the presence of stress-inducible IRE1 activity is sufficient to argue that the reporter in this study is functional, IRE1 reporters (that do cluster) described in previous studies by the Walter lab and other groups are also demonstrably functional. Does IRE1 normally cluster? Is it cell-type dependent? Tag-dependent? Notably, the Pincus et al. PLoS Biology paper from the Walter lab used two different fluorescent protein tags that do not heterozygously dimerize. Robust colocalization and FRET signals were detected upon treatment of cells with stressors and clustering was subsequently observed. A 2007 Journal of Cell Biology study from Kimata et al. reported clustering in yeast with an Ire1 tagged with an HA epitope peptide. The HA peptide seems unlikely to be prone to any oligomerization propensities that GFP tagged reporters might experience. Importantly, a 2020 PNAS paper from the Walter lab (Belyy et al.) studied clustering of a robustly monomeric mNeonGreen-tagged IRE1 in U2-OS cells and mouse embryonic fibroblasts and this construct readily clustered following stress induction.

When evaluated against the backdrop of the extensive literature describing the visual behavior of IRE1a in live cells, the absence of stress-induced clustering is both puzzling and disconcerting. Given the focus of this study is to use visual techniques to study IRE1a interactions, the burden of proof is on the authors to resolve this significant discrepancy with the rest of the IRE1a literature. One can easily imagine that incorporation of the majority of the pool of IRE1a into 10-100 clusters could produce very different correlated trajectory behavior. Until the authors can determine why their reporters behave differently from other IRE1a reporters and establish which version accurately reflects physiologic IRE1a behavior, the potential impact of the findings of this manuscript are of unknown value.

We thank the reviewer for this detailed assessment of our work. We agree that the question of apparent discrepancy in the formation of observable IRE1 clusters between this manuscript and earlier work is important. We have now addressed this issue both in the revised version of the manuscript and in specific point-by-point responses to reviewers’ comments. As a brief summary, we addressed the reviewer’s first concern (lack of controls larger than dimers) by cloning and validating a tetrameric HaloTag construct, the measurements from which were entirely consistent with the model we presented in the original version of the manuscript. To address the reviewer’s second concern, we present several lines of evidence showing that the discrepancy between the formation of microscopically visible IRE1 clusters in earlier studies and the absence of such clusters in the present work almost certainly results from differences in expression levels. First, our IRE1-HaloTag construct is perfectly capable of forming stress- induced clusters, as we show in the new Figure 1 – Figure Supplement 3. Second, we point to a parallel study by Gómez-Puerta et al., who demonstrate that a more “conventional” IRE1-GFP construct does not form visible stress-dependent puncta when it is expressed at a low level comparable to that of untagged IRE1 in HeLa cells, despite being fully active. Third, our earlier work in the 2020 PNAS paper referenced by the reviewer actually showed that even in the overexpression context, IRE1-mNeonGreen only forms visible puncta in just over half of all cells, despite the fact that XBP1 processing is nearly 100% effective in bulk assays. Furthermore, in the same paper we show that, rather than all IRE1 molecules being sequestered in clusters, only a small fraction (~5%) of IRE1-mNeonGreen assembles into large puncta while the remaining 95% of IRE1 stays uniformly distributed throughout the ER. Taken together, we believe that IRE1 does have the propensity to assemble into larger clusters when its expression levels are high (regardless of the tag used), but that these clusters are not strictly required for its activation. We have made significant changes to the discussion section of the manuscript to clarify the above points and directly address the apparent discrepancy between the present work and earlier studies.

Reviewer #3:

In this paper, the authors' aim was to test how IRE1's oligomerization state relates to its activation status without relying on ectopic overexpression. The principle underlying the work is a rather simple one, which is that, if the population of IRE1 can be labeled stochastically with either of two different fluorescent probes, then if the protein dimerizes, presuming single molecules can be visualized, correlated migration of a spot of each fluorophore should be observed for some of those dimers. Any correlated migration, maintained for long enough, will by necessity by some sort of dimer or multimer. In principle, if my math is right, the correlation should be 50% of spots of each color, assuming all the molecules are in a dimer, all molecules are labeled with one fluorophore or the other, and the koff of the fluorophores is very low. In practice, the correlation appears closer to 10%, which the authors establish using a control molecule that should not dimerize except by chance, and another for which pseudo-dimerization is enforced due to the two HALO domains used to bind the fluorophores being conjugated to the same molecule in cis. Much of the paper is devoted to establishing the fundamentals of the system. For these experiments, the authors replaced endogenous IRE1 with the HALO-tagged version to generate near-normal expression and show that the IRE1-HALO behaves similarly to endogenous. They also show that correlated migration is observed in the dimer control to a much greater extent than in the monomer.

Using these findings, they demonstrate, in my mind quite conclusively, that IRE1 exists as a dimer even in the unstimulated state. During ER stress, the authors observe a state that is more highly ordered. Mathematical modeling suggests a transition from predominantly dimers to a mix of dimers and something more highly ordered, with tetramers being the simplest explanation. Satisfyingly, a mutation that breaks the known dimer interface causes the protein to exist solely in monomers, as does deletion of the IRE1 lumenal domain, while disrupting the oligomerization interface keeps the protein as dimers. Mutation or deletion of the kinase and RNase domains does not affect higher order status, suggesting that activation of these domains is not a prerequisite for assembly. It is clear from this that the central claims of the paper, which is that IRE1 exists in a dimer in the basal state and transitions to a higher ordered structure in the activated state, are supported. Moreover, the general approach is likely to be appealing to the study of other molecules activated by multimerization.

We thank the reviewer for this thoughtful and helpful analysis of our work.

The principal advance of the paper is the technological approach for tracking IRE1 (and, presumably, other molecules whose activity is regulated by dimerization). The approach is quite elegant for that purpose. Its impact in terms of conclusions about IRE1 is perhaps less clear. The authors rationalize their endogenous-replacement approach by describing how their previous efforts and those of others relied on ectopic overexpression of GFP-tagged IRE1. The authors take great pains to claim that the observed multimerization status of the IRE1-HALO constructs is not a function of expression level, which would imply then that expression level alone is not responsible for the previously observed IRE1 oligomeric puncta. It is not clear why exactly the authors' results differ from this group's previous studies on the topic nor where the truth lies, including whether something inherent to the GFP-tagged overexpression approach favors non-physiologic structures, whether the difference is fundamentally one of cell type, or whether multimerization and activation are correlated but not causally related, with multimer-breaking mutations killing IRE1 by some other mechanism.

The question of reconciling our present data with earlier work (including work from our group) is clearly and understandably a central question for all three reviewers. As we detailed above in our responses to reviewers 1 and 2, we are convinced that the formation of large IRE1 clusters is largely dependent on expression level rather than the differences between fluorescent protein tags and the HaloTag. We added new supplementary figures and substantially revised the text of the manuscript to address this question directly.

Interpreting the data is also complicated by the fact that, while the authors point out that the percent of correlated trajectories (i.e., the measurement of multimerization state) does not itself correlate with expression level (using trajectories-per-movie as a proxy), the proper conclusion from that lack of correlation is not that variance in expression level does not account for the changes in apparent multimerization status, but instead that it cannot be the only factor. In some sense, the authors are attempting to play the argument both ways, by arguing that expression level matters for IRE1 activation (from previous studies) and that it doesn't (from this study). I think to address this the authors will need to better account, one way or another, for why the findings presented here differ from their previous findings and why these are the more salient (if in fact they are).

This is a very important point, and we thank the reviewer for raising it. We are not arguing that expression levels do not matter for the formation of oligomers; quite the contrary, as detailed above and in the revised version of the text, we believe that the formation of massive IRE1 oligomers observed in previous studies and in the new Figure 1 – Figure Supplement 3 is mainly a function of elevated concentration. What we do claim is that our approach can reliably pick out oligomeric differences within the relatively narrow range of concentrations used for single-particle tracking experiments in this paper. We are using the very weak truncated CMVd3 promoter in all transient transfection experiments, and we are only analyzing data from cells that have a comparable density of single-molecule spots to the density we observe in endogenously tagged IRE1-HaloTag cells. In fact, the metric of “trajectories per movie” used as a proxy for expression levels in Figure 5 – Figure Supplement 1 is an overestimation of the true variability of expression levels, since each movie only covers a small fraction of each cell’s area and the number of observed molecules varies depending on cell morphology. Practically speaking, all cells that we image have expression levels that are clustered together rather narrowly, roughly within differences of no more than a factor of 3. These levels, in turn, are significantly lower than the expression levels used in earlier papers by our group and others.

The other somewhat substantial issue is that there is no control for what higher order structures look like. The authors give no sense for the dynamic range of the multimerization assay. I would presume that tetramers would show a higher percentage of correlated trajectories than dimers, and octamers higher still, and that the mathematical model accounts for this theoretical possibility in calculating an average protomer number of 2.7 in the stress condition, but it would be better to see that in practice; at first glance it would seem that engineering a tetrameric and/or higher order control and validating it would be straightforward.

This is another great point raised by all reviewers. In the revised version of the manuscript, we engineered a new tetrameric control construct (See Figure 2 – Figure Supplement 1), the results from which agree remarkably well with the mathematical model we developed in the original version of the manuscript (see Figure 2 – Figure Supplement 3)

Lastly, the data analysis lacks statistical justification for its conclusions. I presume given the high number of readings that the observed changes are all statistically significant, but that should be indicated, as in most cases the 95% confidence intervals shown are overlapping.

This is another excellent point. The reviewer is correct that all relevant conclusions are statistically supported by the data, and our analysis code immediately calculates pairwise p- values for every plot using one of several relevant tests. Our preferred test is the permutation test, since it makes no assumptions about the underlying distributions being compared. To avoid cluttering the main plots, we have included tables of pairwise p-values for each plot in the revised version of the manuscript.

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Reply to the reviewers

Responses to reviewers’ comments are in blue text, original reviewers’ comments in black text.

Response to Reviewer 1.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): In this manuscript Neiro et al. aim to expand our knowledge on the regulation of gene expression in stem cells of the planarian model organism. As a first step the authors used published available data to expand the repertoire of the planaria transcriptome. By combining 183 RNAseq datasets the authors were able to identify thousands of new coding and non-coding transcripts. They then screened for TF motifs in the new annotations, identifying 551 putative TFs, of which 248 were already described in the planarian literature. The most substantial contribution of this work to the field of stem cells and planaria biology is the characterization of new putative enhancers that were identified by performing H3K27ac ChIP-seq and ATAC-seq and combining these data with previously published H3K4me1 ChIPseq dataset.

We thank the reviewer for their careful assessment of our work, we agree that the identification of likely enhancers genome wide is a substantial contribution. Equally the improved annotation of all genes, including transcription factors we choose to focus on here, is a substantial step forward for the planarian research community.

By overlapping H3K27ac and H3K4me1the authors find 5,529 new enhancers, for which they report a higher chromatin accessibility than random points in the genome as assessed by ATAC-seq. By using ATAC-footprints Neiro et al. refined the subset of TFs that have binding motifs in the predicted enhancer-like regions and present a list of 22,489 such factors. The manuscript is well written and organized and overall, the reported data will provide an important resource to study gene expression regulation in planaria's stem cells. However, this manuscript would greatly benefit from some functional validation to support the predicted gene regulatory networks. One option would be to use a CRISPR-dCas9-KRAB system to silence the putative enhancers identified in the manuscript and check by qPCR the expression of nearby genes.

Currently mis-expression technologies, in order too directly test enhancer elements in driving expression, are still not available in planarians. This also preempts us using the suggested silencing system used in mammals and other animals with robust mis-expression tools.

If this type of experiment is not feasible in planaria (I am not an expert in this model organism) another simple but key experiment would be to perform a knockdown of one (or more) putative enhancer-bound TFs identified in this study followed by RNA-seq. This would allow the authors to verify what are the target genes of the putative enhancer-bound TFs and if they correspond to the predicted gene networks they identified. Simultaneously, this experiment would allow the authors to verify if there are any changes in the expression of differentiation/pluripotency markers as a result of the knockdown of the putative enhancer-bound TF.

These experiments are possible, but this would be the work of many labs in the future expert in studying those TFs and their roles in planarian stem cells and regeneration. However, what we can do is analyze existing RNA-seq data further. There are a number of studies where TF have been studied and RNA-seq performed after RNAi. Although these studies are performed in specific experimental regenerative contexts, and not specifically in stem cells, it will be possible to look at expression changes of genes with predicted enhancers bound by these TFs. We propose to execute this analysis and add it to the manuscript, rather than perform further TF RNAi experiments. This analysis is feasible within a 3-month revision time. We would add that currently their no genes are implicated in controlling pluripotency in the same way we might consider, for example, OSKM in mammals. Our identification of the TFs enriched in stem cell expression and implicated in binding predicted enhancers suggests future candidates.

Minor revision: • The authors have mostly focused on the identification of enhancer-bound TFs. However, it would be interesting to look at differential enrichment of TFs in promoters versus enhancers and identify if there are specific factors that are enriched specifically at the planarian newly identified enhancer regions.

We have not looked at potential TF binding sites near promoters/transcriptional start sites. We will try to add an analysis that considers this in our revision.

• All tornado plots are missing a colorbar (Fig3 and FigS2)

We will fix this error

• There is a typo in the discussion: "the combined use of chip-seq data, RNAi of a histone methyltransferase combines with chip-seq" should be changed to "combined".

We will fix this and other typographical errors.

Reviewer #1 (Significance (Required)):

The manuscript is well written and organized and overall the reported data will provide an important resource to study gene expression regulation in planaria's stem cells.

We thank the reviewer for their appreciation of our work

**Referees cross-commenting**

I agree with the other reviewers that additional functional data should be added to support the author's claims (such as knock down of potential TFs that are identified by computational analyses and assessing the impact on gene expression).

See response above, with regard to adding further analysis for testing this possibility.

In addition, as noticed by the third reviewer, all data should be made publicly available to the scientific community.

We have made all data publicly available and will submit all relevant data to public database repositories in advance of final publication after final peer review.

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

Summary:

This manuscript aims at identifying enhancers in the planarian Schmidtea mediterranea. The authors start with the integration of transcriptome with genome sequencing data to more precisely annotate the genome of the planarian Schmidtea mediterranea. The second part of the manuscript actually then deals with the identification of potentially active enhancer elements in adult stem cells of this regenerating organism using genomic techniques like ATAC-seq and ChIP-seq of histone marks combined with motif searches and in silico footprint analysis. Using these data, the authors predict regulatory interactions potentially critical for pluripotency and regeneration in planarian adult stem cells.

MAJOR COMMENTS:

• Are the key conclusions convincing? 1) The authors claim (already in the abstract) that their study identifies enhancers regulating adult stem cells and regenerative mechanisms. This is an over-statement found throughout the manuscript, as none of these enhancers are functionally tested nor is it shown that target gene expression changes when transcription factors predicted to interact with such enhancers are knocked down.

We agree and it was not our intention to overstate our results, this is why we have tried to refer to putative enhancers, enhancer-like elements etc in manuscript from the title onwards. Only once we have demonstrated a set of elements with key conserved and widely supported characteristics do we suggest we have a set of higher confidence enhancers to study. However, we will adjust the manuscript to reflect that our claims await direct testing as is the case for all enhancers implicated with the approaches used here.

Another example is at the end of paragraph 1 of section 2.4. Here the authors claim that identifying many fate-specific transcription factor genes in the vicinity of potential enhancers is a further proof that the identified regions represent "real enhancers". It strongly supports this hypothesis, but no evidence for real enhancer activity.

We agree the total body of evidence strongly supports that we have identified enhancer elements, but as above will adjust the language to suggest further directed functional work will follow from many groups.

Thus, although the authors state that the regulatory interactions and networks they predict from their data can be studied now in future, they should be more careful with their wording and correct these over-statements. Therefore, the key conclusion is that they identified by various techniques potential enhancers, which are close to genes controlling adult stem cells and potentially controlling these genes, which has to be shown by further analyses.

We agree

Thus, also the title needs to be changed.

We propose changing ‘enhancer-like’ to “predicted enhancers” in the title, and "defines" to "predicts" as well as broadly adjusting the text to caveat that further work will clarify their functions and roles.

The authors have no proof that the networks are active in planarian adult stem cells, as they do not show that the predicted networks are active in the presented way.

We agree, see comments above. It was not our attention to claim we are showing pathways that were definitely active, rather predicted by our experiments and analyses of the data from these experiments.

2) Similarly, the identification of TF motifs within these potential motifs strongly suggests but not shows that these factors are binding, even when these sites were found to be bound by a protein using the ATAC-seq footprinting analysis. Thus, the authors need to be careful with their wording. One example is in the second paragraph of section 2.5, where the authors write that "We found that numerous FSTFs were binding to putative intronic enhancers ... ". The motif suggests that these factors bind, however, they have no experimental confirmation that these sequences are indeed bound by the planarian TFs.

We agree. We will clarify that ATAC foot printing is the only data suggestive of these motifs being bound and that further experiments will be required for more evidence. We will state this in the section of results and add this explicitly to the discussion

In sum, this manuscript uses existing genomic tools to define potential enhancer regions in the planarian Schmidtea mediterranea. The manuscript is informative yet descriptive, as tit presents no functional evidence for any of the predictions. If further toned down, the key conclusions are valid.

Future functional experiments to test the roles of all TFs and enhancers is now possible due to our work.The combination of data and analyses provides strong support of enhancer elements activity in stem cells across the genome.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The experiments performed are well designed and in line with what is known in the field about enhancer architecture. However, as this model system is not very well characterized on that level and the authors do not provide real experimental evidence that any of the identified regions has really enhancer activity and that any of the identified motifs binds indeed the predicted TF, the authors need to be very careful with their statements. The authors should maybe emphasize even stronger that all the GRNs predicted under section 2.6 are really preliminary and need to be validated.

Yes, we are happy to be even clearer about this as the reviewer suggests

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. One experiment that could provide more evidence for their predicted regulatory interactions is to knock-down one of the FSTFs for which motifs have been identified in potential enhancer regions and to study expression of associated genes (to confirm that the enhancers potentilla bound by these TFs control the expression of associated genes) or by analyzing the chromatin status of selected chromatin regions (by Q-PCR). These experiments would strongly support the claims of the authors. However, it also depends strongly on the journal whether I would consider these experiments essential or "nice to have".

This suggestion of possible extra experiments is very similar to that of Reviewer 1. We are copying our earlier comment as this also addresses this point.

“These experiments are possible, but this would be the work of many labs in the future expert in studying those TFs and their roles in planarian stem cells and regeneration. However, what we can do is analyze existing RNA-seq data further. There are a number of studies where TF have been studied and RNA-seq performed after RNAi. Although these studies are performed in specific experimental regenerative contexts, and not specifically stem cells, it will be possible to look at expression changes of genes with predicted enhancers bound by these TFs. We propose to execute this analysis and add it to the manuscript, rather than perform further TF RNAi experiments. This analysis is feasible within a 3-month revision time. We would add that currently their no genes implicated in controlling pluripotency in the same way we might consider OSKM in mammals. Our identification of the TFs enriched in stem cell expression and implicated in binding predicted enhancers suggests future candidates.”

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. This reviewer is not an expert in Schmidtea mediterranea, thus it is hard to judge how time consuming these experiments would be. Cost-wise they should be feasible, as it would include primarily Q-PCR experiments. And some functional back-up of their claims would be very helpful.

See previous comment regarding additional analysis.

• Are the data and the methods presented in such a way that they can be reproduced? For the parts I can judge, yes.

• Are the experiments adequately replicated and statistical analysis adequate? It is not clear from the manuscript how many replicates of the ChIP-seq experiments were done.

Chip-Seq replicate data description will be explicitly added to the methods

MINOR COMMENTS:

• Specific experimental issues that are easily addressable.

• Are prior studies referenced appropriately? For the literature I can judge, yes.

• Are the text and figures clear and accurate? The figures are clear, the text (besides over-statements) is clear. However, the writing can be improved. A few examples: section 2.2 paragraph 1: "... we found 248 to be described in the planarian literature in some way." In which way described?; same paragraph: "... but significantly we could identify new homologs of ..." what does significantly mean? Which test etc? section 2.2, last paragraph: "Most TFs assigned to the X1 and Xins compartments and the least to the X2 compartment", "Very few TFs had expression in X1s and Xins to the exclusion of X2 expression as would be expected by overall lineage relationships"; what do these sentences mean?

We thank the reviewer for paying careful attention to the language in our manuscript throughout. We will provide clearer explanation of the sentences indicated. We will better explain terms specific to the planarian model system that are obviously not intuitive

. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? No over-statements.

See previous comments agreeing with the need to carefully adjust our language to avoid this

Reviewer #2 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. This manuscript identifies genome-wide potential enhancers in adult planarian stem cells, and thus represents a very valuable resource for the community to study these enhancers and the gene regulatory networks they control in the future.

• Place the work in the context of the existing literature (provide references, where appropriate). As I am not a planarian scientist, it is hard to judge this part.

• State what audience might be interested in and influenced by the reported findings. In my opinion, this work will be primarily interesting for people working with planarian. When functional data exist, this might be also interesting for researchers working generally on regeneration.

Given the nature of our data we also think all groups working on animal stem cells would be interested in our data and analyses

• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. My field of expertise is transcriptional regulation using genomic techniques, however I am not familiar with the model Schmidtea mediterranea.

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

Neiro et al. capitalize on existing genomic data for the planarian Schmidtea mediterranea and new ChIP-seq and ATAC-seq data to use computational approaches to identify putative enhancers in the planarian genome. They integrate analysis of enhancers with transcription factor binding sites to generate testable hypotheses for the regulatory function of transcription factors active in stem cells or control of cell lineage trajectories. Their work creates an excellent resource for future work to resolve the regulatory logic underpinning stem cell biology and tissue regeneration in planarians.

We are glad the reviewer likes our research.

Major: Overall, the work in this manuscript and methodology are well executed and presented. However, the authors should consider the following comments to improve the clarity and accessibility of the data and interpretations.

1) The new transcriptome does not appear to be publically accessible. The links to Github resources are broken, and there is nothing on Neiro's Github page. Will the new transcriptome be integrated with Planmine?

The new annotation has been available for over a year as we wished the community to have access to it ASAP (see Garcia Castro, 2021, Genome Biology https://doi.org/10.1186/s13059-021-02302-5). We tested the links in the paper before depositing our preprint and after review and they seemed to work for us both within and outside our institutional network. We can only apologize if they were broken or have not worked for the reviewer. We are unclear if this new annotation will be included in Planmine, but we will ask the colleagues maintaining this database to consider including it.

2) Figure 1: Ternary plot in 1F. The legend is not clear or could be explained better. What is the metric? It could be my misunderstanding, but I didn't consider the ternary plots as insightful or unnecessary. Perhaps the authors can expand on what they are showing.

These plots are important in demonstrating the distribution of mRNA expression of all genes across cell sorted compartments. Given the broad lineage relationship between sorted cell compartments This analysis allows us to identify genes expressed predominantly in one cell compartment or another, or across a specific transition. For example, genes enriched in X2 cells and Xins, but not X1 are likely to be enriched in post-mitotic differentiating progeny and differentiated cells. In contrast to single cell data where expression data can be sparse this analysis with bulk data allows identification and assignation of low expressed genes, like transcription factors. We will provide further explanation of this in the revised text.

1I is a map of exons, not alternative splicing. So, it isn't clear what the authors intend t show. Are the specific exons that are more likely to be spliced? Is the figure necessary?

We wish to demonstrate the power of annotation approach and the richness of the annotation for looking at alternate splicing. We propose to a more informative figure that indicates the variety of splice forms. We apologize for this oversight.

3) Figure 2: 2A labels Xins as irradiation responsive. Is this the case (just making sure)?

The reviewer is correct, this is wrong! This should read “irresponsive” or “irradiation resistant” In Figure 1A. We thank the reviewer for spotting this error. We will fix this.

2F-G: Ternary plot in F seems redundant with G, but that could be my lack of understanding. In 2G, what is represented on the plots on the right of the hierarchical clusters?

The ternary plot (2F) and heatmap of hierarchical clustering (2G) are complementary ways to visualize the proportional expression values of transcription factors. The ternary plot (2F) allows an overview of all the proportional expression values, while the heatmap (2G) shows how the proportional values may be grouped into clusters of similar expression profiles and displays the relative size of these clusters. For example, the heatmap shows that the clusters of X1 and Xins are more prominent than X2, suggesting that there are realtivey a few X2-specific transcription factors. We will add text to better to explain this difference.

4) Figure 3: The heat maps need a legend (i.e., please define the colors). In addition, labeling the figures could help the reader. For example, in G-J, a header about the different experiments above each map, such as "enhancers" and "random," etc., would make the figure more accessible.

We agree we label the figures to be more easily interpretable and provide an independent scale and legend for the heatmaps.

5) Figure 5: Although it is in the figure legend, the authors could label the 6th track as "RNA-seq in X1."

We will add this to the figure.

6) Section 2.6 second page last sentence of the first paragraph "GRN of asexual reproduction is not active in neoblasts" data in the supplement? Is it not shown?

We apologize for this poorly written sentence. In line with Reviewer 2s comments this statement needs to be toned down and clarified. The raw information is included in the general table of enhancers (Supplementary Table 2), but the genomic tracks visually highlighting the motifs at the promoters of lox5b and post2b were not included. We will add these to the Supplementary information and clarify Supplementary Table 2.

7) Discussion: The discussion about pluripotency factors in planarians could be expanded. The authors could contrast the study's findings with Önal et al. 2012.

We agree we will expand our discussion to compare with previous studies and also summarize what is available from other animals with pluripotent adult stem cells

Minor: The manuscript has no page numbers or line numbers, so I'll provide a general location of the potential issues.

1) Section 2 - newly identified isoforms are shorter (1656 vs. 1618). Is the order of the median length reversed?

Yes, we will correct this.

2) No mention of Figure S1B in the text.

It is mentioned in the paragraph regarding splicing, but perhaps not in a useful context. We will add a correct reference to this figure in the presentation of transcript diversity.

3) Figure 1H should be 1I in the text?

Yes, we will correct this

4) The discussion contains some minor typos and grammatical errors.

We will address with careful rereading.

We thank the reviewer for spotting these errors and we will fix them in revision.

Reviewer #3 (Significance (Required)):

Neiro et al. provide an excellent resource for the planarian community. The paper is generally very well written and easy to read. The new transcriptome described, which improves the annotation of the planarian genome, should be made readily available. It would be excellent if the transcriptome could be incorporated in Planmine.

We will ask Planmine and the Rink lab to consider this. The annotation (without broad analysis) has been available since the pre-print for Garcia Castro, 2021, Genome Biology was deposited in BioRxiv.

Furthermore, the authors provide a comprehensive list of transcription factors in the planarian Schmidtea mediterranea. Their work provides insight into which factors are highly expressed in the stem cell compartment. Their computational identification of transcription factors and putative enhancers will be helpful to the growing community of researchers studying stem cell and regenerative biology using planarians. In addition, the large dataset generated in this study could inform studies in the evolution of regulatory sequences and transcription factor function.

**Referees cross-commenting**

The data presented are well supported by previous studies. As noted by the authors, it is not possible to make transgenic planarians, and thus the field needs to rely on indirect methods. The authors focus on using the stem cell population, which can be isolated from the animals. Overall, I don't think additional experiments are necessary. Additional RNAi experiments combined with RNA-seq (using the stem cells) could take 6-12 months to complete. I believe this is a solid contribution that should be framed as a resource paper. The authors should pay close attention to Reviewer #2's suggestions and edit the paper accordingly.

I have 20 years of experience in the field. It would be unreasonable to ask the authors to do more experiments, especially in this post-pandemic environment. I hope this helps.

We thank the reviewer for the comments.

Reviewer #3 (Public Review): 

In this paper, Troendle et al investigate changes in alpha oscillation across childhood and adolescence. The main goal of this investigation is to examine how alpha oscillations change across these age ranges, by investigating a large open dataset and adopting new methods that should help to address methodological limitations of many previous analyses. In particular, a key goal is to examine changes in periodic alpha power, and control for potential confounds due to changes in peak frequency and/or aperiodic activity. To do so, they employ a novel spectral parametrization method, and systematically compare measures of isolated periodic alpha activity to conventional measures. Overall, they find that they can replicate the age-related decrease of total alpha power when using conventional methods. However, when explicitly measuring and controlling for aperiodic activity, they find that periodic alpha activity actually increases with age. They suggest this discrepancy can be explained by changes in aperiodic activity, as the aperiodic slope and intercept are found to systematically change across age, in a way that likely drives the finding decrease of total alpha power, while the periodic alpha power actually increases. There are also some follow up analyses, including relating alpha power to anatomical measures of the thalamus, and to performance on an attention task. 

Strengths of this investigation include that it analyzes multiple, large datasets with well motivated methods. I think the goal of this paper addresses an important question, in terms of seeking to clarify some basic patterns of oscillation changes across development, and doing so in a rigorous way, both in terms of employing methods that are robust to estimating different features of the data, and in terms of using multiple, large datasets, including an internal replication of the main findings. I find the main goal and analysis compelling in terms of examining how alpha activity changes across this age range. 

I also find some limitations to some aspects of this paper and analysis that could be improved, as they do not always clearly describe the context or support the claims that are made for some of the follow-up analyses, as described in the following. 

1. Framing and prior literature 

I find some limitations in the organizing of this paper and it's relationship to prior work that could be improved, as I find that the paper could do better situating the analyses here with prior work, in particular in relation to the methodological issues it is addressing, and prior work on aperiodic activity. 

For example, in the abstract it is stated that "simulations in this study show that conventional measures of alpha power are confounded". Despite this statement, simulations are not a core feature of this study. There are a couple simulated examples in the supplement, which are referred to in lines 89-95, however it's worth nothing noting that while this section does not include any citations, the described issues, and related simulations, are very similar to points that have been made previously in the literature, that seem like they should be cited here: <br /> - Donoghue, T., Dominguez, J., & Voytek, B. (2020). Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity. ENeuro, 7(6), ENEURO.0192-20.2020. https://doi.org/10.1523/ENEURO.0192-20.2020 <br /> - Donoghue, T., Schaworonkow, N., & Voytek, B. (2021). Methodological considerations for studying neural oscillations. European Journal of Neuroscience, ejn.15361. https://doi.org/10.1111/ejn.15361 

The paper also understates previous work on aperiodic activity, and the degree to which it is known to vary with age, in line 116-117 stating "there is insufficient evidence for the reported significant association between age and aperiodic signal components". This seems to ignore the large number of studies that have replicated this finding, including (some non-exhaustive examples): <br /> - Thuwal, K., Banerjee, A., & Roy, D. (2021). Aperiodic and Periodic Components of Ongoing Oscillatory Brain Dynamics Link Distinct Functional Aspects of Cognition across Adult Lifespan. Eneuro, 8(5), ENEURO.0224-21.2021. https://doi.org/10.1523/ENEURO.0224-21.2021 <br /> - Voytek, B., Kramer, M. A., Case, J., Lepage, K. Q., Tempesta, Z. R., Knight, R. T., & Gazzaley, A. (2015). Age-Related Changes in 1/f Neural Electrophysiological Noise. Journal of Neuroscience, 35(38), 13257-13265. https://doi.org/10.1523/JNEUROSCI.2332-14.2015 <br /> Perhaps this claim is supposed to more specifically reflect the age-range analyzed here, in which case recent studies examining this (in relatively large datasets) are also not mentioned here, including, for example: <br /> - Donoghue, T., Dominguez, J., & Voytek, B. (2020). Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity. ENeuro, 7(6), ENEURO.0192-20.2020. https://doi.org/10.1523/ENEURO.0192-20.2020 <br /> - Hill, A. T., Clark, G. M., Bigelow, F. J., Lum, J. A. G., & Enticott, P. G. (2022). Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood. Developmental Cognitive Neuroscience, 54, 101076. https://doi.org/10.1016/j.dcn.2022.101076 

The notes above do not undermine the utility of examining alpha oscillations in detail, but I think the specific contribution of this work could be better contextualized in terms of other existing work. In the introduction, for example, the following review is an important piece of work that could be cited when introducing aperiodic activity: <br /> - He, B. J. (2014). Scale-free brain activity: Past, present, and future. Trends in Cognitive Sciences, 18(9), 480-487. https://doi.org/10.1016/j.tics.2014.04.003 

2. Model quality control 

A limitation to the methods employed in this study is a lack of description of if and how model fit quality was evaluated. For the method of parametrizing neural power spectra that is employed, it is important to validate that models fit the data well, otherwise the estimated parameters may be unreliable. This is especially important in developmental and clinical data, as analyzed here, as this data can be quite noisy, and differences in levels of noise across ages or between clinical groups could plausibly lead to differences in model fit quality. Useful quality checks for this kind of analysis would be to report the average r-squared (or model error) for the parametrized data, and to examine whether model fit quality is significantly related to age, or clinical status. 

Note that there is also a detailed guide for how best to apply spectral parametrization to developmental datasets, including notes on quality control, that may be useful: <br /> - Ostlund, B., Donoghue, T., Anaya, B., Gunther, K. E., Karalunas, S. L., Voytek, B., & Pérez-Edgar, K. E. (2022). Spectral parameterization for studying neurodevelopment: How and why. Developmental Cognitive Neuroscience, 54, 101073. https://doi.org/10.1016/j.dcn.2022.101073 

Not reporting any quality control metrics of the model fits also deviates from the analysis of the validation dataset as described in the pre-registered analysis (https://osf.io/7uwy2), which includes the note that the plan is for data to be excluded from the analysis if there is a bad model fit (R-squared < 0.9). It is unclear from the manuscript if this was done at all - and if so, why it was not described, and if not, why this deviates from the pre-registration. Note that though examining and reporting model fit quality is important, it is unclear where the value of 0.9 in the pre-registration came from, and it is unclear if this is an appropriate threshold for these specific datasets. 

3. The analysis of the relationship between the aperiodic intercept and aperiodic exponent 

There is an analysis in this paper that attempts to evaluate whether the change in aperiodic intercept that is observed is more than expected due to the measured change in aperiodic exponent. The approach taken for this analysis is ill-posed, and the interpretations made of this analysis are not supported. The issue is that the degree to which the intercept changes due to a change in exponent depend on the rotation frequency, which is not acknowledged or addressed in the analysis employed here. 

For example, for spectra rotated at 0 Hz, there is no measured change in offset from a change in exponent, whereas for a rotation at 100 Hz, there is a large influence of exponent on the change in offset, with different degrees of impact in between. The results of this analysis are therefore heavily influenced by the rotation frequency that is used. The analysis by the authors uses a rotation frequency of 19 Hz, however, there is no justification provided for this value. It is noted as being the middle point of the analyzed range, however, this itself is unrelated to whether it is an appropriate rotation frequency (since which frequency the spectrum rotates at is unrelated to the experimenter's decision of which frequency range to analyze). 

In real data, we don't a priori know what the rotation frequency point is, and in general it need not be a single, consistent point, and between subjects, is difficult to measure. To get a sense of what it might be, anecdotally, we can see in Figure 2C that in this particular subset, the rotation point is not at 19 Hz, and appears to be at a higher frequency. If the rotation point is actually higher than 19 Hz, then the analysis employed will systematically under-estimate the impact of the measured exponent change - leading to the conclusion that intercept is changing over and above the influence of the exponent. However, this conclusion is only valid if the rotation point of 19 Hz is accurate, and we would likely arrive at a different conclusion by picking a different rotation point. This analysis, by itself, is therefore invalid. Such an analysis would require a clear motivation of having measured the correct rotation frequency to be interpretable. 

4. Flanker Analysis 

Also relating to organization (similar to point 1) it is unclear why the analysis of the Flanker task, which is alluded to in the abstract, is only mentioned in the Discussion section. Given that this appears to be a key analysis, it is unclear why it is not presented in detail in the Results. The Flanker task and analysis is also not described in much detail in the methods. An issue with the Flanker analysis only being mentioned in the Discussion, with a link to supplemental table, is that the details of the results are somewhat obfuscated from the reader. When looking at these results, two key features seem notable - the first that though it is significant effect of aperiodic-adjusted alpha power, the beta value is very small (many times smaller than the coefficients for age and gender), and second, that although it doesn't quite pass significance, the estimated beta value for the total alpha power has the same magnitude as for the individualized alpha power. Between these two features, it is not clear if the relationship between aperiodic-adjusted alpha power and the Flanker performance is of sufficient magnitude to interpret that alpha power is related to attentional performance, and it's not clear that aperiodic-adjusted alpha power is more related to attentional performance than total alpha power (since a difference in significance does not necessarily imply a significant difference in the parameters). I think this analyses, as presented, therefore does not clearly support the claim made in the abstract that alpha power is found to relate to improved attentional performance.

Discussion, revision and decision

Author response

To: Adam Marcus, co-founder Retraction Watch & Alison Abritis, PhD, researcher at Retraction Watch

Major Problems: I found serious deficits in both for this article, and thus I have serious concerns as to the usefulness of this article. Therefore, I have not proceeded in a line-by-line, as I consider the overall problems to be grave enough to require attention and revision before getting to lesser items of clarity.

I would like to point out that the authors show a marvelous attention to their work, and they have much to contribute to the field of retraction studies, and I do honestly look forward to their future work. However, in order for the field to move ahead with accuracy and validity, we must no longer just rely on superficial number crunching, and must start including the complexities of publishing in our analyses, as difficult and labor-intensive as it might be.

We do not consider that our article presents serious problems nor that it would be useless.

It is possible that a different view on the subject, some tendency to forbearance (understandable) for the difficult life of the publishing industry, along with some difficulties in understanding the ideas presented in the article, may have led to a series of points of view that we would like to comment on below.

We would first like to thank the reviewers for their comments, some of which will allow us to improve and nuance, using objective elements, the analysis of this bumpy field represented by the ecosystem of retracted publications. Because we have based our study on data from freely accessible sources of information, we will not insist too much on commenting on this issue.

The authors stated that they used the search protocol (and therefore presumably the same dataset) as described in Toma & Padureanu, 2021, and do not indicate any process to compensate for its weaknesses. In the referenced study, the authors (same as for this article) utilized a PubMed search using only “Retracted Publication” in Publication Type. This search method is immediately insufficient, as some retracted articles are not bannered or indexed as retracted in PubMed. This issue is well-understood among scholars who search databases for retractions, and by now one would expect that these searches would strive to be more comprehensive.

A better method, if one insists on restricting the search to PubMed, would have been to use Publication Type to search for “retracted publication,” and then to search for “retraction of publication,” and to compare the output to eliminate duplications. There are even more comprehensive ways to search PubMed, especially since some articles are retitled as “Withdrawn” – Elsevier, for example, uses the term instead of “Retracted” for papers removed within a year of their publication date – but do not come in searches for either publication type. Even better would have been to use databases with more comprehensive indexing of retractions.

In an ideal world, if any effort were to be made, it would be aimed at better indexing and managing existing databases, not at generating query strategies to make up for their shortcomings.

Thank you very much for the suggestions on the search strategy. We do not consider that the use of "Retracted Publication [PT]" should be compensated in any way but, if it should be compensated, we wouldn't want to add "Retraction of publication". We consider that using a search protocol more specific to systematic reviews is not very useful in our case: data are added/updated continuously (sometimes late), incorrect indexing can be corrected, the number of retracted articles increases from month to month; the same strategy can give different results at different times regardless of its complexity. Putting extra effort into detecting problematic articles without knowing the benefit but expecting it only highlights issues that can be improved at the publisher/editor(content delivery) and database level(indexing).

The dataset analyzed is a snapshot of a particular time interval and nothing more. Even during the analysis we found, in the case of one publisher, the addition of details to the initially incomplete retraction notes. Hence the need for follow-up studies. Therefore in the case of retractions, unlike the reviewer, we prefer an approach based on simple and easily reproducible strategies, widely accessible sources of information, and several steps. The first step in this strategy is the "number crunching" stage which includes this article.

1. The authors are using the time from publication to retraction based on the notice dates and using them to indicate efficacy of oversight by publishers. However, this approach is seriously problematic. It takes no notice of when the publisher was first informed that the article was potentially compromised. Publishers who respond rapidly to information that affects years/decades old publications will inevitably show worse scores than those who are advised upon an article’s faults immediately upon its publication, but who drag their heels a few months in dealing with the problem.

Indeed, the article uses the time between publication and retraction(exposure time – ET) as one of the SDTP score components for assessing editorial/publisher performance. Data on when a publisher or editor has been informed of problems with an article, apart from being relatively rare, is not a substitute for a retraction note. Moreover, the use of such information may induce a risk of bias.

We mention in the article the need to use reporting standards for retraction notes, and one element that might be useful is, indeed, the date on which the publisher or editor was informed of problems with an article. Unfortunately, as the author of this review knows very well, information precedes investigation; the retraction note contains (or should contain) much more data than the initial information about the quality problems of an article.

Our article aims to suggest a score for measuring publication performance in the context of retracted articles that would also allow an assessment of the dynamics of the activity of correcting the scientific record and, more importantly, how publishers engage in post-publication quality control. ET is only one component of this score.

It is quite clear from the data presented in the article that a publisher/journal that emphasizes systematic back-checking will have an increasingly longer average lifespan of retracted articles, logically higher than one that does not do this type of checking. We don't see precisely where the reviewer thinks there is a problem: once the checking is done, the ET will decrease, and a publisher that takes concrete steps to correct the literature will ultimately have a better reputation. This does not mean that a higher ET is laudable, it suggests that there is a post-publication quality control but also that the peer review process has let problematic articles through and that the control of these articles has been carried out late. This is an argument for more active involvement of publishers (as potential generators of editorial policies) in post-publication control.

Second, there is little consistency in dealing with retractions between publishers, within the same publishers or even within the same journal. Under the same publisher, one journal editor may be highly responsive during their term, while the next editor may not be. Most problems with articles quite often are first addressed by contacting the authors and/or journal editors, and publishers – especially those with hundreds of journals – may not have any idea of the ensuing problem for weeks or months, if at all. Therefore, the larger publishers would be far more likely to show worse scores than publishers with few journals to manage oversight.

It is exactly this inconsistency that we highlight in the article. Differing policies, attitudes, and responsiveness does not mean that a publisher cannot/should not ask questions about the effectiveness of internal processes and resources used for post-publication quality control or the implementation of uniform measures across journals in its portfolio.

Third, the dates on retraction notices are not always representative of when an article was watermarked or otherwise indicated as retracted. Elsevier journals often overwrite the html page of the original article with the retraction notice, leaving the original article’s date of publication alone. A separate retraction notice may not be published until days, weeks or even years after the article has been retracted. Springer and Sage have done this as well, as have other publishers – though not to the same extent (yet).

Historically, The Journal of Biological Chemistry would publish a retraction notice and link it immediately to the original article, but a check of the article’s PDF would show it having been retracted days to weeks earlier. They have recently been acquired by Elsevier, so it is unknown how this trend will play out. And keep in mind, in some ways this is in itself not a bad thing – as it gives the user quicker notice that an article is unsuitable for citation, even while the notice itself is still undergoing revisions. It just makes tracking the time of publication to retraction especially difficult.

We used the same date for all articles in our study (the one listed in PubMed), thus ensuring a uniform criterion for all publishers. If this date was not in PubMed we used the date from the retraction notes on the journal website but this was for a small number of articles. How different publishers handle retraction processes or the delay with which these are published is primarily related to internal editorial procedures, and these delays are reflected in the ET. In our experience, most articles retracted by Elsevier are available online, supplemented, and not replaced by retraction notes, which we think is an excellent policy.

1. As best as can be determined, the authors are taking the notices at face value, and that has been repeatedly shown to be flawed. Many notices are written as a cooperative effort between the authors and journal, regardless of who initiated the retraction and under the looming specter of potential litigation.

Shown to be flawed by who? Indeed, in our study, we refer to the retraction notes published by the journals. The fact that they are incomplete or formulated under the threat of litigation only supports our view that publishers and editors need to make a more significant effort to correct the biomedical literature, including avoiding litigation when the retraction note clearly describes the reasons for retraction. The way the retraction note is worded should be an editorial prerogative and should primarily aim at correcting scientific literature, not at appeasing egos, careers, or financial interests.

Trying to establish who initiated a retraction process strictly by analyzing the notice language is destined to produce faulty conclusions. Looking just at PubPeer comments, questions about the data quality may be raised days/month/years before a retraction, with indications of having contacted the journal or publisher. And yet, an ensuing notice may be that the authors requested the retraction because of concerns about the data/image – where the backstory clearly shows that impetus for the retraction was prompted by a journal’s investigation of outside complaints. As an example, the recent glut of retractions of papers coming from paper mills often suggest the authors are requesting the retraction. This interpretation would be false, however, as those familiar with the backstory are aware that the driving force for many of these retractions were independent investigators contacting the journals/publishers for retraction of these manuscripts.

Once again, the author of this review does not seem to fully understand our study, apparently favouring information published on third-party websites over that the journals officially assumed. The retraction notes represent the material available to a researcher doing documentation on a particular topic. The clarity and information contained in the note is the editor's or publisher’s responsibility, reflecting their performance and concern for the integrity of the science. Interpretation of a retraction note/analyzing an article occurs in this context. Not everyone has time for further investigation or to search third-party sites for information that is, with a notable exception, the result of a selection bias.

Assigning the reason for retraction from only the text of the notice will absolutely skew results. As already stated, in many cases, journal editors and authors work together to produce the language. Thus, the notice may convey an innocuous but unquestionable cause (e.g., results not reproducible) because the fundamental reason (e.g., data/image was fabricated or falsified) is too difficult to prove to a reasonable degree. Even the use of the word “plagiarism” is triggering for authors’ reputations – and notices have been crafted to avoid any suggestion of such, with euphemisms that steer well clear of the “p” word. Furthermore, it has been well-documented that some retractions required by institutional findings of misconduct have used language in the notice indicating simple error or other innocuous reasons as the definitive cause.

We understand your point of view and the situations presented may be accurate. However, from our point of view, the only valid reference remains the retraction note published on the journal's website. The existence of wording difficulties and various other problems that may arise are more likely to do with a tendency of the reviewer to make excuses for journals reluctant to indicate precisely what the reasons for retracting the article are. There are plenty of retraction notes in which the images with problems (including whether they were plagiarized, reused, manipulated, fabricated, etc.) are indicated with great precision, there are equally plenty of notes in which the word plagiarism is used without hesitation, indicating the sources, how they were informed, what was plagiarized. No matter how many hesitant publishers/editors there are, it should not be forgotten that there are many journals/publishers who take their role seriously, acknowledge and learn from their mistakes, thus providing a real service to the scientific community.

The authors also discuss changes in the quality of notices increasing or decreasing in publishers – but without knowing the backstory. Having more words in a notice or giving one or two specific causes cannot in itself be an indicator of the quality (i.e., accuracy) of said notice.

"Knowing the backstory" is not part of our objectives, and neither is assessing the quality of the retraction notes. This is also very difficult to do due to the lack of an accepted standard format. We are trying to propose a score composed of several parameters resulting from existing (or non-existing) data in the retraction notes so that we can have a picture of retractions at publisher level. Knowing the backstory is not relevant, reading and interpreting the official retraction note is relevant.

1. The authors tend to infer that the lack of a retraction in a journal implies a degree of superiority over journals with retractions. Although they qualify it a bit ( “Are over 90% of journals without a retracted article perfect? It is a question that is quite difficult to answer at this time, but we believe that the opinion that, in reality, there are many more articles that should be retracted (Oransky et al. 2021) is justified and covered by the actual figures.”), the inference is naive. First, they have not looked at the number of corrections within these journals. Even ignoring that these corrections may be disproportionate within different journals and require responsive editorial staff, some journals have gone through what can only be called great contortions to issue corrections rather than retractions.

We believe that this is a case of reviewer confusion generated either by the insufficiently precise wording of the text or a lack of understanding of our study objectives. We are trying to point out that more than 90% of the journals in the NLM catalogue-PubMed subset have not retracted a single article. We are not trying to say that journals without retracted articles are superior to the others. As explained in the article, we referred to retraction notes, not corrections.

Second, the lack of retractions in a journal speaks nothing to the quality of the articles therein. Predatory journals generally avoid issuing retractions, even when presented with outright proof of data fabrication or plagiarism. Meanwhile, high-quality journals are likely to have more, and possibly more astute, readers, who could be more adept at spotting errors that require retraction.

Of course, the quality level of articles in a journal is not determined by the number of articles removed.

Third, smaller publishers/journals may not have the fiscal resources to deal with the issues that come with a retraction. As an example, even though there was an institutional investigation finding data fabrication, at least one journal declined to issue a retraction for an article by Joachim Boldt (who has more than 160 retractions for misconduct) after his attorneys made threats of litigation.

Threats of lawsuits are instead a failure of a publisher/journal to adapt to the realities of the publishing business or to the risk of misconduct. This is something that needs to change.

Simply put, the presence or lack of a retraction in a journal is no longer a reasonable speculation about the quality of the manuscripts or the efficiency of the editorial process.

We have not attempted to suggest this, we have only analyzed the retracted articles and their associated retraction notes. On the other hand, the way a journal/publisher handles the retraction of problematic articles still reflects, to some extent, the quality/performance of the editorial processes.

1. I am concerned that the authors appear to have made significant errors in their analysis of publishers. For example, they claim that neither PLOS nor Elsevier retracted papers in 2020 for problematic images. That assertion is demonstrably false.

This is wrong. In our dataset, there are eleven PLOS articles related to human health with the publication year 2019 and 2020. None of these have images as retraction reasons.

Regarding the 21 Elsevier articles published in 2020, there is nothing in the retraction notes to indicate that the article was retracted because of the images. In 2 retraction notes there is mention of the comments made by Dr. Bik (The Tadpole Paper Mill - Science Integrity Digest) but the text of these (retraction notes) stops at the authors' inability to provide the raw data underlying the article.

Our study is based only on the content of the retraction notes published and assumed by the journal, not on opinions/comments appearing on other sites, which, for unknown/unmentioned reasons, are not officially assumed in the retraction note. Therefore, we consider the statement in the review to be questionable at best, as the use of material other than the retraction notes has severe implications for the internal and external validity of the study and the suggestion to use such methods is, in our opinion, wrong. We would also like to draw attention to the fact that many retraction notes are explicitely mentioning the request to provide raw images and the authors' inability to provide them.

Anyway, as far as images are concerned, our article suggested that there are publishers which seem to adopt image analysis technologies faster than others. The numbers are not really relevant in this case but the trend is: it describes the publishing activity complexity better than the numbers.

Reviewer response

We appreciate the authors’ zeal in standing by their work.

In regard to the deficits in the search process, the author states, “We do not consider that the use of ‘Retracted Publication [PT]’ should be compensated in any way but, if it should be compensated, we wouldn't want to add ‘Retraction of publication’”

There is a lack of appreciation for the complexities of indexing retracted materials in an indexing site such as PubMed. To have a comprehensive search, one should not be choosing to use either “Retracted Publication [PT]” OR “Retraction of Publication [PT].” One would use both, and then filter out the duplicates, because some retractions are indexed by retraction notices, some only have “Retracted” added to the indexed title and the publication type changed to “Retracted Publication.” Use of only one or the other guarantees that the search is far less comprehensive than it should be.

The authors state, “In an ideal world, if any effort were to be made, it would be aimed at better indexing and managing existing databases, not at generating query strategies to make up for their shortcomings.”

There is at least one database (http://retractiondatabase.org) that has a far more comprehensive indexing of retractions and is publicly available for use.

In Item 3, where it is pointed out that retraction notices themselves are inaccurate and cannot be taken at face value as to the reason behind the retraction, the authors responded, “Shown to be flawed by who?” — By an article cited in the manuscript:

Fang, Ferric C.; Steen, R. Grant; Casadevall, Arturo (2012): Misconduct accounts for the majority of retracted scientific publications. In Proceedings of the National Academy of Sciences of the United States of America 109 (42), pp. 17028–17033. DOI: 10.1073/pnas.1212247109.

“To understand the reasons for retraction, we consulted reports from the Office of Research Integrity and other published resources (7, 8), in addition to the retraction announcements in scientific journals. Use of these additional sources of information resulted in the reclassification of 118 of 742 (15.9%) retractions in an earlier study (4) from error to fraud.” Followed by “These factors have contributed to the systematic underestimation of the role of misconduct and the overestimation of the role of error in retractions (3, 4), and speak to the need for uniform standards regarding retraction notices (5).”

The authors then choose to state that it is the “editorial prerogative” – and that when notices “are incomplete or formulated under the threat of litigation [it] only supports our view that publishers and editors need to make a more significant effort to correct the biomedical literature, including avoiding litigation when the retraction note clearly describes the reasons for retraction.”

Following our attempt to explain why understanding the real reason behind a retraction is important to study the publication of notices, the authors respond: “Once again, the author of this review does not seem to fully understand our study, apparently favouring information published on third-party websites over that the journals officially assumed.”

First, yes, we do understand the study. We read a lot of these. Second, the “third-party websites” we prefer include the Office of Research Integrity and the Retraction Watch blog, where background investigations into the causes of retraction notices are described. If the authors are challenging the reference to PubPeer, keep in mind that journals initiate investigations based on comments on that website, and have taken to citing the website in their notices.

Had the authors not chosen to categorize the reasons for retraction, their reasoning may have had more support – but they did, and in doing so, by just using the notice with no further review, their findings address only the notice itself, with no context.

We recommend that the manuscript be substantially revised with strong attention to the comments we made in our original review.

Discussion, revision and decision

Author response

To: Adam Marcus, co-founder Retraction Watch & Alison Abritis, PhD, researcher at Retraction Watch

Major Problems: I found serious deficits in both for this article, and thus I have serious concerns as to the usefulness of this article. Therefore, I have not proceeded in a line-by-line, as I consider the overall problems to be grave enough to require attention and revision before getting to lesser items of clarity.

I would like to point out that the authors show a marvelous attention to their work, and they have much to contribute to the field of retraction studies, and I do honestly look forward to their future work. However, in order for the field to move ahead with accuracy and validity, we must no longer just rely on superficial number crunching, and must start including the complexities of publishing in our analyses, as difficult and labor-intensive as it might be.

We do not consider that our article presents serious problems nor that it would be useless.

It is possible that a different view on the subject, some tendency to forbearance (understandable) for the difficult life of the publishing industry, along with some difficulties in understanding the ideas presented in the article, may have led to a series of points of view that we would like to comment on below.

We would first like to thank the reviewers for their comments, some of which will allow us to improve and nuance, using objective elements, the analysis of this bumpy field represented by the ecosystem of retracted publications. Because we have based our study on data from freely accessible sources of information, we will not insist too much on commenting on this issue.

The authors stated that they used the search protocol (and therefore presumably the same dataset) as described in Toma & Padureanu, 2021, and do not indicate any process to compensate for its weaknesses. In the referenced study, the authors (same as for this article) utilized a PubMed search using only “Retracted Publication” in Publication Type. This search method is immediately insufficient, as some retracted articles are not bannered or indexed as retracted in PubMed. This issue is well-understood among scholars who search databases for retractions, and by now one would expect that these searches would strive to be more comprehensive.

A better method, if one insists on restricting the search to PubMed, would have been to use Publication Type to search for “retracted publication,” and then to search for “retraction of publication,” and to compare the output to eliminate duplications. There are even more comprehensive ways to search PubMed, especially since some articles are retitled as “Withdrawn” – Elsevier, for example, uses the term instead of “Retracted” for papers removed within a year of their publication date – but do not come in searches for either publication type. Even better would have been to use databases with more comprehensive indexing of retractions.

In an ideal world, if any effort were to be made, it would be aimed at better indexing and managing existing databases, not at generating query strategies to make up for their shortcomings.

Thank you very much for the suggestions on the search strategy. We do not consider that the use of "Retracted Publication [PT]" should be compensated in any way but, if it should be compensated, we wouldn't want to add "Retraction of publication". We consider that using a search protocol more specific to systematic reviews is not very useful in our case: data are added/updated continuously (sometimes late), incorrect indexing can be corrected, the number of retracted articles increases from month to month; the same strategy can give different results at different times regardless of its complexity. Putting extra effort into detecting problematic articles without knowing the benefit but expecting it only highlights issues that can be improved at the publisher/editor(content delivery) and database level(indexing).

The dataset analyzed is a snapshot of a particular time interval and nothing more. Even during the analysis we found, in the case of one publisher, the addition of details to the initially incomplete retraction notes. Hence the need for follow-up studies. Therefore in the case of retractions, unlike the reviewer, we prefer an approach based on simple and easily reproducible strategies, widely accessible sources of information, and several steps. The first step in this strategy is the "number crunching" stage which includes this article.

1. The authors are using the time from publication to retraction based on the notice dates and using them to indicate efficacy of oversight by publishers. However, this approach is seriously problematic. It takes no notice of when the publisher was first informed that the article was potentially compromised. Publishers who respond rapidly to information that affects years/decades old publications will inevitably show worse scores than those who are advised upon an article’s faults immediately upon its publication, but who drag their heels a few months in dealing with the problem.

Indeed, the article uses the time between publication and retraction(exposure time – ET) as one of the SDTP score components for assessing editorial/publisher performance. Data on when a publisher or editor has been informed of problems with an article, apart from being relatively rare, is not a substitute for a retraction note. Moreover, the use of such information may induce a risk of bias.

We mention in the article the need to use reporting standards for retraction notes, and one element that might be useful is, indeed, the date on which the publisher or editor was informed of problems with an article. Unfortunately, as the author of this review knows very well, information precedes investigation; the retraction note contains (or should contain) much more data than the initial information about the quality problems of an article.

Our article aims to suggest a score for measuring publication performance in the context of retracted articles that would also allow an assessment of the dynamics of the activity of correcting the scientific record and, more importantly, how publishers engage in post-publication quality control. ET is only one component of this score.

It is quite clear from the data presented in the article that a publisher/journal that emphasizes systematic back-checking will have an increasingly longer average lifespan of retracted articles, logically higher than one that does not do this type of checking. We don't see precisely where the reviewer thinks there is a problem: once the checking is done, the ET will decrease, and a publisher that takes concrete steps to correct the literature will ultimately have a better reputation. This does not mean that a higher ET is laudable, it suggests that there is a post-publication quality control but also that the peer review process has let problematic articles through and that the control of these articles has been carried out late. This is an argument for more active involvement of publishers (as potential generators of editorial policies) in post-publication control.

Second, there is little consistency in dealing with retractions between publishers, within the same publishers or even within the same journal. Under the same publisher, one journal editor may be highly responsive during their term, while the next editor may not be. Most problems with articles quite often are first addressed by contacting the authors and/or journal editors, and publishers – especially those with hundreds of journals – may not have any idea of the ensuing problem for weeks or months, if at all. Therefore, the larger publishers would be far more likely to show worse scores than publishers with few journals to manage oversight.

It is exactly this inconsistency that we highlight in the article. Differing policies, attitudes, and responsiveness does not mean that a publisher cannot/should not ask questions about the effectiveness of internal processes and resources used for post-publication quality control or the implementation of uniform measures across journals in its portfolio.

Third, the dates on retraction notices are not always representative of when an article was watermarked or otherwise indicated as retracted. Elsevier journals often overwrite the html page of the original article with the retraction notice, leaving the original article’s date of publication alone. A separate retraction notice may not be published until days, weeks or even years after the article has been retracted. Springer and Sage have done this as well, as have other publishers – though not to the same extent (yet).

Historically, The Journal of Biological Chemistry would publish a retraction notice and link it immediately to the original article, but a check of the article’s PDF would show it having been retracted days to weeks earlier. They have recently been acquired by Elsevier, so it is unknown how this trend will play out. And keep in mind, in some ways this is in itself not a bad thing – as it gives the user quicker notice that an article is unsuitable for citation, even while the notice itself is still undergoing revisions. It just makes tracking the time of publication to retraction especially difficult.

We used the same date for all articles in our study (the one listed in PubMed), thus ensuring a uniform criterion for all publishers. If this date was not in PubMed we used the date from the retraction notes on the journal website but this was for a small number of articles. How different publishers handle retraction processes or the delay with which these are published is primarily related to internal editorial procedures, and these delays are reflected in the ET. In our experience, most articles retracted by Elsevier are available online, supplemented, and not replaced by retraction notes, which we think is an excellent policy.

1. As best as can be determined, the authors are taking the notices at face value, and that has been repeatedly shown to be flawed. Many notices are written as a cooperative effort between the authors and journal, regardless of who initiated the retraction and under the looming specter of potential litigation.

Shown to be flawed by who? Indeed, in our study, we refer to the retraction notes published by the journals. The fact that they are incomplete or formulated under the threat of litigation only supports our view that publishers and editors need to make a more significant effort to correct the biomedical literature, including avoiding litigation when the retraction note clearly describes the reasons for retraction. The way the retraction note is worded should be an editorial prerogative and should primarily aim at correcting scientific literature, not at appeasing egos, careers, or financial interests.

Trying to establish who initiated a retraction process strictly by analyzing the notice language is destined to produce faulty conclusions. Looking just at PubPeer comments, questions about the data quality may be raised days/month/years before a retraction, with indications of having contacted the journal or publisher. And yet, an ensuing notice may be that the authors requested the retraction because of concerns about the data/image – where the backstory clearly shows that impetus for the retraction was prompted by a journal’s investigation of outside complaints. As an example, the recent glut of retractions of papers coming from paper mills often suggest the authors are requesting the retraction. This interpretation would be false, however, as those familiar with the backstory are aware that the driving force for many of these retractions were independent investigators contacting the journals/publishers for retraction of these manuscripts.

Once again, the author of this review does not seem to fully understand our study, apparently favouring information published on third-party websites over that the journals officially assumed. The retraction notes represent the material available to a researcher doing documentation on a particular topic. The clarity and information contained in the note is the editor's or publisher’s responsibility, reflecting their performance and concern for the integrity of the science. Interpretation of a retraction note/analyzing an article occurs in this context. Not everyone has time for further investigation or to search third-party sites for information that is, with a notable exception, the result of a selection bias.

Assigning the reason for retraction from only the text of the notice will absolutely skew results. As already stated, in many cases, journal editors and authors work together to produce the language. Thus, the notice may convey an innocuous but unquestionable cause (e.g., results not reproducible) because the fundamental reason (e.g., data/image was fabricated or falsified) is too difficult to prove to a reasonable degree. Even the use of the word “plagiarism” is triggering for authors’ reputations – and notices have been crafted to avoid any suggestion of such, with euphemisms that steer well clear of the “p” word. Furthermore, it has been well-documented that some retractions required by institutional findings of misconduct have used language in the notice indicating simple error or other innocuous reasons as the definitive cause.

We understand your point of view and the situations presented may be accurate. However, from our point of view, the only valid reference remains the retraction note published on the journal's website. The existence of wording difficulties and various other problems that may arise are more likely to do with a tendency of the reviewer to make excuses for journals reluctant to indicate precisely what the reasons for retracting the article are. There are plenty of retraction notes in which the images with problems (including whether they were plagiarized, reused, manipulated, fabricated, etc.) are indicated with great precision, there are equally plenty of notes in which the word plagiarism is used without hesitation, indicating the sources, how they were informed, what was plagiarized. No matter how many hesitant publishers/editors there are, it should not be forgotten that there are many journals/publishers who take their role seriously, acknowledge and learn from their mistakes, thus providing a real service to the scientific community.

The authors also discuss changes in the quality of notices increasing or decreasing in publishers – but without knowing the backstory. Having more words in a notice or giving one or two specific causes cannot in itself be an indicator of the quality (i.e., accuracy) of said notice.

"Knowing the backstory" is not part of our objectives, and neither is assessing the quality of the retraction notes. This is also very difficult to do due to the lack of an accepted standard format. We are trying to propose a score composed of several parameters resulting from existing (or non-existing) data in the retraction notes so that we can have a picture of retractions at publisher level. Knowing the backstory is not relevant, reading and interpreting the official retraction note is relevant.

1. The authors tend to infer that the lack of a retraction in a journal implies a degree of superiority over journals with retractions. Although they qualify it a bit ( “Are over 90% of journals without a retracted article perfect? It is a question that is quite difficult to answer at this time, but we believe that the opinion that, in reality, there are many more articles that should be retracted (Oransky et al. 2021) is justified and covered by the actual figures.”), the inference is naive. First, they have not looked at the number of corrections within these journals. Even ignoring that these corrections may be disproportionate within different journals and require responsive editorial staff, some journals have gone through what can only be called great contortions to issue corrections rather than retractions.

We believe that this is a case of reviewer confusion generated either by the insufficiently precise wording of the text or a lack of understanding of our study objectives. We are trying to point out that more than 90% of the journals in the NLM catalogue-PubMed subset have not retracted a single article. We are not trying to say that journals without retracted articles are superior to the others. As explained in the article, we referred to retraction notes, not corrections.

Second, the lack of retractions in a journal speaks nothing to the quality of the articles therein. Predatory journals generally avoid issuing retractions, even when presented with outright proof of data fabrication or plagiarism. Meanwhile, high-quality journals are likely to have more, and possibly more astute, readers, who could be more adept at spotting errors that require retraction.

Of course, the quality level of articles in a journal is not determined by the number of articles removed.

Third, smaller publishers/journals may not have the fiscal resources to deal with the issues that come with a retraction. As an example, even though there was an institutional investigation finding data fabrication, at least one journal declined to issue a retraction for an article by Joachim Boldt (who has more than 160 retractions for misconduct) after his attorneys made threats of litigation.

Threats of lawsuits are instead a failure of a publisher/journal to adapt to the realities of the publishing business or to the risk of misconduct. This is something that needs to change.

Simply put, the presence or lack of a retraction in a journal is no longer a reasonable speculation about the quality of the manuscripts or the efficiency of the editorial process.

We have not attempted to suggest this, we have only analyzed the retracted articles and their associated retraction notes. On the other hand, the way a journal/publisher handles the retraction of problematic articles still reflects, to some extent, the quality/performance of the editorial processes.

1. I am concerned that the authors appear to have made significant errors in their analysis of publishers. For example, they claim that neither PLOS nor Elsevier retracted papers in 2020 for problematic images. That assertion is demonstrably false.

This is wrong. In our dataset, there are eleven PLOS articles related to human health with the publication year 2019 and 2020. None of these have images as retraction reasons.

Regarding the 21 Elsevier articles published in 2020, there is nothing in the retraction notes to indicate that the article was retracted because of the images. In 2 retraction notes there is mention of the comments made by Dr. Bik (The Tadpole Paper Mill - Science Integrity Digest) but the text of these (retraction notes) stops at the authors' inability to provide the raw data underlying the article.

Our study is based only on the content of the retraction notes published and assumed by the journal, not on opinions/comments appearing on other sites, which, for unknown/unmentioned reasons, are not officially assumed in the retraction note. Therefore, we consider the statement in the review to be questionable at best, as the use of material other than the retraction notes has severe implications for the internal and external validity of the study and the suggestion to use such methods is, in our opinion, wrong. We would also like to draw attention to the fact that many retraction notes are explicitely mentioning the request to provide raw images and the authors' inability to provide them.

Anyway, as far as images are concerned, our article suggested that there are publishers which seem to adopt image analysis technologies faster than others. The numbers are not really relevant in this case but the trend is: it describes the publishing activity complexity better than the numbers.

Reviewer response

We appreciate the authors’ zeal in standing by their work.

In regard to the deficits in the search process, the author states, “We do not consider that the use of ‘Retracted Publication [PT]’ should be compensated in any way but, if it should be compensated, we wouldn't want to add ‘Retraction of publication’”

There is a lack of appreciation for the complexities of indexing retracted materials in an indexing site such as PubMed. To have a comprehensive search, one should not be choosing to use either “Retracted Publication [PT]” OR “Retraction of Publication [PT].” One would use both, and then filter out the duplicates, because some retractions are indexed by retraction notices, some only have “Retracted” added to the indexed title and the publication type changed to “Retracted Publication.” Use of only one or the other guarantees that the search is far less comprehensive than it should be.

The authors state, “In an ideal world, if any effort were to be made, it would be aimed at better indexing and managing existing databases, not at generating query strategies to make up for their shortcomings.”

There is at least one database (http://retractiondatabase.org) that has a far more comprehensive indexing of retractions and is publicly available for use.

In Item 3, where it is pointed out that retraction notices themselves are inaccurate and cannot be taken at face value as to the reason behind the retraction, the authors responded, “Shown to be flawed by who?” — By an article cited in the manuscript:

Fang, Ferric C.; Steen, R. Grant; Casadevall, Arturo (2012): Misconduct accounts for the majority of retracted scientific publications. In Proceedings of the National Academy of Sciences of the United States of America 109 (42), pp. 17028–17033. DOI: 10.1073/pnas.1212247109.

“To understand the reasons for retraction, we consulted reports from the Office of Research Integrity and other published resources (7, 8), in addition to the retraction announcements in scientific journals. Use of these additional sources of information resulted in the reclassification of 118 of 742 (15.9%) retractions in an earlier study (4) from error to fraud.” Followed by “These factors have contributed to the systematic underestimation of the role of misconduct and the overestimation of the role of error in retractions (3, 4), and speak to the need for uniform standards regarding retraction notices (5).”

The authors then choose to state that it is the “editorial prerogative” – and that when notices “are incomplete or formulated under the threat of litigation [it] only supports our view that publishers and editors need to make a more significant effort to correct the biomedical literature, including avoiding litigation when the retraction note clearly describes the reasons for retraction.”

Following our attempt to explain why understanding the real reason behind a retraction is important to study the publication of notices, the authors respond: “Once again, the author of this review does not seem to fully understand our study, apparently favouring information published on third-party websites over that the journals officially assumed.”

First, yes, we do understand the study. We read a lot of these. Second, the “third-party websites” we prefer include the Office of Research Integrity and the Retraction Watch blog, where background investigations into the causes of retraction notices are described. If the authors are challenging the reference to PubPeer, keep in mind that journals initiate investigations based on comments on that website, and have taken to citing the website in their notices.

Had the authors not chosen to categorize the reasons for retraction, their reasoning may have had more support – but they did, and in doing so, by just using the notice with no further review, their findings address only the notice itself, with no context.

We recommend that the manuscript be substantially revised with strong attention to the comments we made in our original review.

Author Response

Reviewer #1 (Public Review):

Liu et al investigated the role of Wnt/β-catenin pathway in the genesis of thermogenic adipocytes. Their study shows that some adipocytes exhibited Wnt/β-catenin signaling ("Wnt+ adipocytes") in intrascapular brown adipose tissue (iBAT), inguinal white adipose tissue (iWAT), epidydimal WAT (eWAT), and bone marrow (BM). There was a different level of the possession of Wnt+ adipocytes between the different depots with iBAT expressing 17%, iWAT expressing 6.9%, and eWAT expressing the least at 1.3%. Expression of these adipocytes was noted on embryonic day 17.5 and was present in a higher percentage in female mice compared to male mice and in younger mice compared to older mice, which aligns with their observation that Wnt+ adipocytes are thermogenic.

The authors also noted that Wnt+ adipocytes can differentiate from human stromal cells. In regards to the pathway, Wnt/β-catenin adipocytes are distinct from classical brown adipocytes at molecular and genomic levels. It was noted that Tcf7L2 was largely expressed in Wnt+ adipocytes but other Tcf proteins (Tcf 1, Tcf 3, and Lef1) were not. Wnt- cells showed a reversible delay in maturation with LF3, however, no cell death was noted. Wnt/β-catenin adipocytes seem to depend on AKT/mTOR signaling. It was further shown that insulin is a key factor in mTOR signaling and Wnt+ adipocyte differentiation.

Upon cold exposure, UCP1+/Wnt- beige fat emerges largely surrounding Wnt+ adipocytes, implicating that Wnt+ adipocytes serve as a "beiging initiator" in a paracrine manner. Lastly, mice with implanted Wnt+ adipocytes had a significantly better glucose tolerance which suggests that Wnt+ adipocytes have a beneficial impact on whole-body metabolism. I found no major flaws in the method and data largely supports their conclusion that Wnt+ adipocytes have (at least some) a significant role in thermogenesis/metabolism, which I think is a very impressive and innovative finding.

Thanks so much for the outstanding summary of our manuscript. We feel sorry that we somehow did not make it clear in the original manuscript that the percentage of Wnt+ adipocytes is higher in male mice than that in females.

Reviewer #2 (Public Review):

Liu et al present evidence for the surprising finding of Tcf/Lef-active, "Wnt+" mature adipocytes. They report that Wnt+ adipocytes arise during embryogenesis and regulate cold-induced beiging in surrounding adipocytes. Tcf/Lef transcriptional activity in these cells is Wnt-ligand independent and instead appears to be stimulated by insulin-dependent AKT/mTOR signaling. Using a diphtheria toxin inducible depletion mouse model, the authors show that Wnt+ cells play an important role in glucose homeostasis.

As the authors have acknowledged, proper assignment of adipocyte nuclei is a notoriously difficult histological challenge. Mesenchymal cells sit directly adjacent to the adipocyte plasma membrane and their nuclei are often incorrectly assigned to the adipocyte both in vivo and in vitro. Pparg nuclear co-staining is helpful, however, Pparg is very highly expressed by endothelial cells and Col15a1+ committed preadipocytes, which are intercalated throughout the adipose. The authors have made an impressive attempt to address this concern by generating a Tcf/Lef-CreER mouse line to fluorescently label Wnt+ adipocytes, however, it is not entirely clear if the images presented support the conclusion that mature adipocytes are being labeled. Given that Wnt+ mature adipocytes are the core conclusion of this manuscript, and because this hypothesis runs counter to a large body of literature concluding that Wnt signaling inhibits adipogenesis, the authors have assumed a very high burden of proof that these are indeed Wnt+ mature adipocytes in vivo.

Thanks for the outstanding summary of our manuscript.

To address these concerns, the authors could utilize the specificity of in vivo single-nuclei RNA-Seq. Several data resources have been published (https://singlecell.broadinstitute.org/single_cell/study/SCP1376/a-single-cell-atlas-of-human-and-mouse-white-adipose-tissue), and the authors should re-analyze these data for subpopulations of mature adipocytes that express a transcriptional signature of active Tcf/Lef signaling. It is unfortunate that the authors were unable to successfully perform single-nuclei analysis of the Wnt+ adipocytes as this would significantly enhance this manuscript. The physiologic relevance of the single-cell analysis of immortalized, in-vitro differentiated clonal cell lines is questionable.

We took the advice by Reviewer 2 and intersected our scRNA-seq data on Wnt+ adipocytes with the published single-nucleus sequencing (sNuc-seq) dataset of mouse iWAT (Emont et al., 2022). Because the activation of Tcf/Lef signaling in the Wnt+ adipocytes is relied on AKT/mTOR signaling but not the conventional Wnt ligands and receptors, those traditional downstream markers of Wnt signaling such Axins were not found specifically enriched in the Wnt+ adipocytes. Therefore, the AKT/mTOR-dependent Wnt signaling in Wnt+ adipocytes appears to regulate expression of genes distinct from that controlled by the conventional Wnt signaling pathway. This conclusion is supported by our recent studies that inhibition of this AKT/mTOR-dependent Wnt signaling by LF3 in Wnt+ adipocytes negatively impact pathways implicated in “PI3K/Akt signaling”, “insulin signaling”, “thermogenesis”, and “fatty acid metabolism” et al (see below for details). However, we found that one cluster (mAd3) of sNuc-seq dataset, which is relatively enriched in Tcf7l2, expresses remarked high levels of Cyp2e1 as well as Cfd that encodes Adipsin. These genes, regarded as hallmark of mAd3 cluster, are also uniquely or highly expressed in Wnt+ adipocytes. Interestingly, the percentage of mAd3 among the total iWAT adipocytes in chow-fed male group is about 5%, which is very close to that of Wnt+ adipocytes in vivo (~7%). Thus, mAd3 possibly represents Wnt+ adipocytes in iWAT. These analyses are included in the revision.

Reviewer #3 (Public Review):

It is becoming increasingly clear that adipocytes are not homogenous, but rather comprise several distinct subtypes with specific physiological functions. The mechanisms that underlie the development and distinct roles of each adipocyte subtype are of great interest for understanding the biology of metabolic regulation and its impairments in metabolic disease. In this manuscript, the authors describe a previously unknown population of adipocytes in mice, which are characterized by a special form of beta-catenin signaling. They perform a comprehensive series of experiments in cultured cells, in mouse models of in-vivo lineage tracing, and transplantation experiments to define the origin and function of these adipocytes. They find that the formation of these Wnt+ adipocytes is dependent on insulin signaling, and find possible roles in thermogenic adipose tissue development. Overall, the conclusions of this study are very convincing in their identification of a subpopulation of adipocytes displaying non-canonical Wnt signaling. The proposed role of these adipocytes as regulators of thermogenesis is more ambiguous, and their physiological function remains unclear.

Thanks for the good comments. To distinguish this AKT/mTOR dependent intracellular Wnt signaling in Wnt+ adipocytes from the conventional non-canonical Wnt signaling, we feel that it would be appropriate to call this signaling as atypical Wnt signaling.

• The new adipocyte types are identified through expression of a reporter for TCF/Lef signaling. This reporter is classically activated by Wnt/beta-catenin and using both siRNA depletion of beta-catenin as well as an allele lacking its transcriptional activation domain, the authors confirm the reporter expression is dependent on the presence of beta-catenin and TCF7L2, but independent of canonical Wnt signaling.

• The involvement of TCF7L2 is also probed using a specific inhibitor of the beta-catenin/TCF7L2 interactions, LF3, which inhibited reporter expression. Inhibition of canonical Wnt signaling was without effect.

• The authors isolate clonal lines of precursor cells that give rise to Wnt+ or Wnt- adipocytes from mouse brown adipose tissue. They find that Wnt+ adipocytes are dependent on the Wnt pathway, as inhibition by LF3 induces cell death.

• To further probe the nature of Wnt+ and Wnt- adipocytes, the authors perform scRNASeq on cells after 7 days of adipose induction and find 2 distinctive cell populations. The finding of 2 distinct populations is expected, given the a priori separation of cells as a function of GFP expression. It is not clear why scRNASeq was chosen over RNASeq on the population, since the fat content of adipocytes may preclude full characterization of the most differentiated cells.

With scRNA-seq, it would be more convincing to identify specific subpopulation of cells, as adipocytes are well known to be heterogenous.

Overall, this experiment is less informative on the mechanisms by which Wnt+ adipocytes display Wnt signaling dependency for viability, and what their functional role might be.

Yes, these are major questions to be addressed in our future studies.

• The non-canonical nature of Wnt signaling in Wnt+ adipocytes prompted the authors to explore the role of the insulin/PI3K/AKT/MTOR pathway. They find enhanced basal activity of this pathway in Wnt+ adipocytes. It was not explored whether this enhanced activity persists under insulin stimulation; this is relevant as feedback mechanisms within the signaling pathway may result in lower signaling under stimulated conditions.

• To test the relevance of insulin signaling in-vivo on non-canonical Wnt signaling in adipocytes the authors use the Akita mouse, which lacks the insulin-2 gene and find a marked decrease in reporter activity, confirming the requirement for insulin signaling for expression of this non-canonical Wnt pathway.

• To determine the functional role of Wnt+ adipocytes, the authors explore their relationship to mitochondrial respiratory activity and thermogenesis. They perform experiments to monitor mitochondrial membrane potential and oxygen consumption rate and find higher overall O2 consumption, and lower membrane potential in adipocyte populations vicinal to Wnt+ adipocytes. Overall these results are not fully convincing: The traces are highly variable from cell to cell, and rigorous quantification of uncoupled respiration is limited by the small number of cell lines analyzed; only one cell line of Wnt- and two Wnt+ adipocytes are analyzed. In situ differences in membrane potential would be more convincing if performed on homogenous collections of Wnt- and Wnt+ adipocytes to better understand stochastic variance.

Thanks for the suggestions. Actually, the results of mitochondrial membrane potential assay on mixed adipocyte culture gave us the initial hint of the potential paracrine effect of Wnt+ adipocytes.

• To determine the role of Wnt+ adipocytes in-vivo thermogenesis, the authors expose mice to cold temperature and monitor the proportion of UCP1+ adipocytes in relation to Wnt signaling. They find a proportion of Wnt+ adipocytes expressing UCP1. Whether this proportion is higher or lower than that of Wnt- adipocytes is not quantified, so it is unclear whether Wnt+ adipocytes preferentially develop beige characteristics. The authors find that UCP1+, Wnt- adipocytes are topologically close to Wnt+ adipocytes, and hypothesize a paracrine signaling role. However, this correlation may be explained by known topological biases in inguinal fat pad beiging, where adipocytes closer to lymph node preferentially induce UCP1. The Wnt+ adipocyte population may coincidentally be present in this region.

As shown in Figure 5-figure supplement 1E, while all Wnt+ adipocytes were co-stained with UCP1, the percentage of Wnt+ adipocytes did not increase after cold challenge. As shown in Figure 5-figure supplement 1C, the initial beiging response is closely associated with Wnt+ adipocytes, but not topological bias.

• To functionally determine the role of Wnt+ adipocytes in thermogenesis, the authors ablate the Wnt+ lineage through expression of diphtheria toxin using a Fabp4-Flox-DTA mouse crossed to Tcf/Lef-CreERT2 mice. Less than 50% of these mice displayed impaired thermogenesis upon cold exposure. The authors interpret this finding to signify a partial role for Wnt+ adipocyte beiging in thermogenic regulation. This conclusion is not fully supported, as Fabp4 is expressed in many cells other than adipocytes, and therefore the phenotype of the affected mice is not unambiguously attributable to loss of Wnt+ adipocytes. An additional concern is that diphtheria toxin-induced cell death will lead to tissue inflammation, with potential functional effects on thermogenesis. The degree of cell death and inflammation should be measured and reported.

While Fabp4 is expressed in some SVFs, the Fabp4-Flox-DTA allele is not activated by Tcf/Lef-CreERT2 allele, as T/L-GFP reporter is not seen in freshly isolated SVFs of iWAT (Figure 2-figure supplement 1A). To avoid potential side effects of DTA-induced cell death on adipose tissues, we compounded the Tcf/Lef-rtTA allele with TRE-Cre and floxed Pparg alleles (PpargF/F) to prevent the differentiation of Wnt+ adipocytes. These new results are included in the revision as supplemental results (Figure 5-figure supplement 2G).

• The finding that Akita mice lack Wnt+ adipocytes was used to determine whether these mice are susceptible to cold-induced challenges. The authors report a decrease in cold-induced UCP1 expression in these mice. This conclusion, derived from a single immunofluorescence image, is not fully convincing in the absence of additional metrics.

Additional analyses are included in the revision, as Figure 5-figure supplement 3.

• To further explore the role of Wnt+ adipocytes in systemic metabolism, the authors conduct implantation studies of Wnt+ adipocytes and measure effects on glucose tolerance. They show a significant difference in glucose excursions in mice harboring fat pads developed from Wnt+ adipocytes. These results are convincing, but the conclusion may be due to enhanced volume of additional functional fat developing from Wnt+ adipocytes.

In this experiment, unbiased mBaSVF adipocytes were used in parallel as control.

Author Response

Reviewer #2 (Public Review):

1. The manuscript seems to claim that the study shows that S4 is the voltage sensor and S4 moves in KCNQ2. This has been repeated in Abstract, Introduction and Results. However, by this time S4 movements as a voltage sensor are well accepted mechanisms. The importance of the work is actually that it defines parameters of the VSD movement in KCNQ2 such as the stretch of S4 in and out of the membrane, and the relationship between VSD activation and pore opening. These points should be brought out as the rationale and significance of this work, rather than the well-known S4 function.

We thank Reviewer# 2 for this important comment that was also brought up by Reviewer# 3. We apologize for over emphasizing that the 4th TM segment is the voltage sensor and that the S4 moves in KCNQ2 channels. This might be the result of the author’s past struggle to convince earlier reviewers that the fluorescence signals at a given position are not an experimental artifact, but S4 moving during channel opening. We are very happy to learn that this is now a well-accepted mechanism.

In the revised version, we now state:

Abstract: “Here, we define parameters of voltage sensor movements in wt-KCNQ2 and channels bearing epilepsy-causing mutations using cysteine accessibility and voltage clamp fluorometry (VCF).”

Introduction: “Similar to that seen in other Kv channels, the fourth transmembrane segment contains several highly conserved positively charged amino acid residues that move in response to changes in membrane voltages that functions as the voltage sensor(25-28)[…]Although these studies provided insight into S4 rearrangements, they did not define parameters of S4 movement, such as the dynamic relationship between S4 activation and pore opening during voltage-controlled gating of KCNQ2 channels.

Results: We deleted: “Collectively, these close correlations in time (Figure 3) and voltage dependence (Figure 2C) of fluorescence and current suggest that the environmental changes around labeled F192C at the outer end of S4 rendered fluorescence signals that seem to report on S4 motion associated with the opening and closing of the channel gate.”

And simply state: “The close correlations in time (Figure 3) and voltage dependences (Figure 2G) of S4 motion (fluorescence) and activation gate (ionic current) resemble those observed for homologous KCNQ1 (without KCNE1)(42) and KCNQ3 channels(41, 43)”

We also rewrote in its entirety the subsection: “Disease-causing mutations differentially affect S4 and gate domains” (Pages 10-11).

1. The closeness of fluorescence and current traces and FV and GV curves led to the conclusion that the movement of a single VSD could trigger channel opening. The rationale for connecting the experimental observations to this conclusion needs to be well explained when the conclusion is first made. References that have made similar arguments such as Osteen et al PNAS 2010; Westhoff et al PNAS 2019 should be cited. In addition, as the authors recognized in Discussion, the same observations can also lead to an alternative conclusion such that the movements of four VSDs highly cooperative to all activate and then open the pore. However, this alternative mechanism is not mentioned until at the end of the manuscript, while "the movement of a single VSD opening the pore" is firmly claimed in Abstract and Results. Some justifications need to be provided for this.

Thank you for this important observation, the wording we used was clumsy. Since we removed the kinetic model (Figure 6 in the original manuscript), we have also deleted any sentences that discuss concerted or independent S4 movement in the Abstract and Result sections. We only discussed that these alternatives, concerted or independent S4 movement, might explain our VCF data which shows that both the steady-state voltage dependence of S4 transitions and the kinetics closely follow those of ionic currents. Both references – Osteen et al PNAS 2010 and Westhoff et al PNAS 2019 have also been added – as recommended by the reviewer and apologize for overlooking these references in the original manuscript.

1. An explanation is needed for how same the covalent MTS modification of N190C at two voltages resulted in different GV relations (Fig 1E).

Thank you for pointing out this important point. We have spent a good deal of time since we received the reviews answering this important point that was also raised as a concern by Revewer# 1. To that end, we have included additional data that support the idea that N190C channels are accessible in both the open and closed states. This is now clearly addressed in Recommendations for the Authors, first Specific Suggestions from Reviewer #1. See above Response to the first Specific suggestions from Reviewer# 1 on Pages 2-5.

In the original submission, we only used the protocols shown old Figure 1. We applied MTSET only at +20-mV for the open state and – 80-mV for the closed state. We used – 100-mV and – 120 mV for the closed state of A193C and S199C, respectively, because compared to the wt channels, these cysteine mutants shifted the GV relationship to negative voltages.

In the revised version, to further strengthen our conclusions, we have used a new protocol: For each cysteine mutant, we have designed a protocol in which we first apply MTSET at hyperpolarized voltages (closed) before switching to depolarized voltages (open) on the same cell, in a pairwise manner.

This is now described in the Result subsection “State-dependent external S4 modifications consistent with S4 as voltage sensor”, Pages 6-8 of the revised manuscript and new Figure 1 and Figure 1-figures supplement 3 and 4.

We also apologize for the lack of clarity in citing reference 40 in the original submission. This reference is deleted in the revised version, in light of our new data on N190C (new Figure 1 and Figure 1-figures supplement 3 and 4), which strengthen our claims that N190C modification occurs in in both states (open and closed).

1. The model in Fig 6F raises several concerns including vertical transitions having the rates of VSD activation and detailed balance is violated.

The reviewer raises an important concern in our original Figure 6F (model). Based on the Editors and reviewers comments, we have removed Figure 6 from the original manuscript to eliminate any of potential misunderstanding about the data presented. In future studies, we will gather additional fluorescence and current data using different protocols and dimer constructs to provide a more in depth description of KCNQ2 gating.

1. Discussion. The argument of no intermediate open state based on K/Rb permeability ratio assumes that the pore properties such as ion selection and permeability of KCNQ2 are the same as that of KCNQ1. The evidence for this assumption is not provided or discussed. On the other hand, some evidence suggests that the VSD of KCNQ2 may activate in two steps. For instance, the time course of VSD activation can be fitted with two exponentials, and the fluorescence increases after a plateau at voltages > 0 mV in FV curves (Fig 2C). How these results affect the conclusion should be discussed.

We agree with the reviewer that the claim of a lack of an intermediate open state in KCNQ2 channels based on the Rb/K data provided in the original submission assumed that the pore properties of KCNQ2 are the same as those seen in KCNQ1 channels. Since we did not show sufficient experimental evidence to prove this point, we have removed Figure 6 (the model) from the revised manuscript. In the future, we will provide more evidence to build stronger support for the potential existence of intermediate and active open states in KCNQ2 channels. As such, we have removed the model shown in the original manuscript. Future studies will be performed to refine the KCNQ2 model, including the use of mutations that can lock the S4 in the intermediate or activated states in KCNQ2, as has been performed in the KCNQ1 channel by Zaydman et al; PMID: 25535795). These experiments will provide more conclusive results regarding the different S4 states.

We have now re-analyzed the data and concluded that while the time course of the fluorescence appeared to have multiple exponentials, our fluorescence data lacked sufficient resolution to reliably estimate the first (fast) component. This might be because of the low signal-to-noise ratio of our VCF or/and because the filtering might have limited the tau-on from the optical signal (shown to be 20 ms in Figure 3C of the original submission).

As suggested by reviewers # 3, we have removed the kinetics comparison of fluorescence and current in the revised version of Figure 3, and simply state: …” There is a close correlation between the time course of fluorescence signals and ionic currents at all the voltages tested (Figure 3B, D). The close correlations in time (Figure 3) and voltage dependences (Figure 2G) of S4 motion (fluorescence) and activation gate (ionic current) resemble those observed for homologous KCNQ1 (without KCNE1)(42) and KCNQ3 channels(41, 43).”

As for the last part of the reviewer comments, the apparent increase in fluorescence after a plateau at voltages > 0mV has now also been revised. We have attempted new VCF at voltages more positive than + 40 mV to probe if a putative second fluorescence component after the plateau phase develops or if it is just artifacts of the experimental system. To get reliably fluorescence signals, we need a huge expression of labeled KCNQ2* channels (often producing currents larger than 100uA). It is considerably more difficult to properly clamp these high expressing cells, especially at extreme voltages. This experimental limitation makes it challenging to draw conclusions about the occurrence of a second fluorescent component. It may be possible to perform the cut—open technique coupled with VCF in order to shed light on this issue, but these experiments would require significant upgrade of the set up that we currently do not have this in place.

Reviewer #3 (Public Review):

1. I am convinced that the fluorescence signals reflect the voltage sensor conformation in the system. The authors focus quite a lot of attention on demonstrating that the fluorescence signals are not an experimental artifact, which is fine.

We thank Reviewer# 3 for this observation. We apologize for over emphasizing that the fluorescence signals reflect the voltage sensor conformation in the system. As state above in response to a similar comment from Reviewer #1, this might be the result of the author’s past struggle to convince earlier reviewers that the fluorescence signals at a given position are not an experimental artifact, but S4 moving during channel opening. This has been amended in the revised version.

However, I feel the authors could be more cautious in terms of describing how the mutations or dye conjugation may alter some of the gating properties. A place where this may be very important is in the description or characterization of activation kinetics as lacking sigmoidicity, which is part of the argument that these channels may open with only a fraction of voltage sensors activated. This may be correct in the modified (dye-conjugated) channel recordings, but many other recordings of unmodified channels (Figure 1) or WT KCNQ2 or 3 channels exhibit some sigmoidicity. I wonder if this difference may arise because the dye labeling may prevent complete VSD deactivation or interfere with gating in some other way. I would also add that this comment isn't meant to diminish the importance of the findings, I just think it would be wise to qualify some of the description of data with these possible caveats.

We thank the reviewer for this suggestion, which we believe improves the flow and description of data considering all possible limitations. The reviewer is right. The mutation F192C on its own accelerates the kinetics of activation and causes a leftward shift in the GV curve of KCNQ2 channels. Moreover, labeling F192C with either fluorophore further shifts the GV towards negative potentials.

In the revised version, we have rewritten the Result subsection ‘Tracking S4 movement of KCNQ2 channels using voltage-clamp fluorometry (VCF)’ almost in its entirety. In this subsection, we now bring to the forefront the changes associated with the measurement of gating properties caused by the mutations or dye conjugation that we agree helps with data interpretation. We made a direct comparison of voltage dependence and kinetics between wt, unlabeled KCNQ2-F192C, and labeled-KCNQ2F192C channels (new Figures 2 and Figure 2-figure supplement 1).

These differences are also discussed on Pages 12-13 of the revised manuscript. See also below response to Recommendations for the authors:

1. A brief aside on this point is that a lack of sigmoidicity does not necessarily imply a single transition required for opening - it can also arise if there is a rate-limiting step during a sequence of pre-open transitions.

Thanks -good point-. We will keep this possibility in mind for future studies where the model will be developed.

1. The generation of a quantitative model is a useful application of the data. It was not clear to me whether there was a benefit to using multiple-exponential components to fit the fluorescence signals and generate a more complex model. This may add complexity where it may not be necessary, as it is not clear whether the fluorescence signals require multiple components for an adequate fit.

Thank you for your comment. We agree with the reviewer that our model is underdeveloped and needs additional VCF data to better describe KCNQ2 gating. Based on all three reviewers concerns and as suggested by the Reviewing editor in his summary, we removed the kinetic model from this manuscript and will work to refine this model in our future studies.

Author Response

Reviewer #1 (Public Review):

This paper address the "origins and drivers of Neotropical diversity." The Neotropics have high diversity of plants and animals relative to other global regions. There are also many hotspots of global biodiversity (species richness) within the Neotropics.

This paper aggregates 150 time-calibrated phylogenies from different groups of plants and animals that occur predominantly in the Neotropics. They analyze the diversification dynamics of these clades over time primarily using the method of Morlon et al. (2011; PNAS) as implemented in RPANDA (Morlon et al. 2016). The authors find that most clades have constant rates of speciation and extinction over time.

Thank you for having reviewed our study and for your feedback.

The strength of the paper is that it aggregates many previously published phylogenies of Neotropical organisms. However, it is unclear whether the method used gives meaningful inferences about diversification dynamics over time (e.g. Burin et al. 2019; Syst. Biol.). Therefore, the overall contribution of the study is somewhat questionable.

This is a legitimate comment, and we understand the skepticism on a study that relies on macroevolutionary models of questionable robustness (e.g. Kubo & Iwasa 1995 - Evolution; Rabosky & Lovette 2008 - Evolution; Crisp & Cook 2009 - Evolution; Quental & Marshall 2010 - TREE; Burin et al. 2019 - Syst. Biol.; Louca & Pennell 2020 - Nature; Pannetier et al. 2021 - Evolution).

The methodology used here has been thoroughly tested with both simulations (e.g. Morlon et al. 2011 - PNAS; Lewitus & Morlon 2018 - Syst. Biol.; Condamine et al. 2019 - Ecol. Lett.) and empirical cases (e.g. Lewitus et al. 2018 - Nat. Ecol. Evol.; Condamine et al. 2019 - Ecol. Lett.). We cannot deny that such a methodology is fully free from issues, which affect all birth-death models, and brings the question: are we able to reliably infer the diversification model and identify parameter values of this model (Louca & Pennell 2020 - Nature)? These concerns are not likely to be resolved in the short term. Although many studies are making progress in understanding the behavior of diversification rate functions, showing, for example, that equally likely diversification functions (i.e. the congruent parameter space of Louca & Pennell 2020 - Nature) can share common features, with diversification rate patterns being robust despite non-identifiability (Höhna et al., 2022 - bioRxiv; Morlon et al., 2022 - TREE).

Being aware of these concerns, we also relied on the recently developed Pulled Diversification Rates method (Louca & Pennell 2020 – Nature; Louca et al., 2018 - PNAS) that is supposed to correct for the identifiability issue raised by recent studies. Hence, applying both traditional and pulled birth-death models to all phylogenies, we have shown a good consistency in the inferred models, which suggests that our study can provide meaningful estimates of diversification. Our empirical study is also one of the first to perform such a large-scale methodological comparison in diversification analyses (pulled vs. traditional birth-death models) while addressing a key question in evolutionary biology. We have now emphasized this point in the conclusions of our study: “To the extent possible, these results are based on traditional diversification rates, and on the recently developed Pulled Diversification Rates method that is supposed to correct for the identifiability issue raised by recent studies associated with traditional diversification rates (71). Hence, applying both traditional and pulled birth-death models to all phylogenies, we have shown a good consistency in the inferred models, which suggests that our study can provide meaningful estimates of diversification”.

The design of the study is also somewhat problematic. There is no comparison to other regions outside the Neotropics, so the study cannot address why the Neotropics are so diverse relative to other continental regions. Similarly, within the Neotropics, the authors do not find significant differences in diversification rates or dynamics among regions. As far as I can tell, they do not attempt to relate patterns of diversification to patterns of species richness among regions within the Neotropics (and presumably they would find no significant patterns if they did).

We agree with this remark. We are sorry for this confusion. Our study does not aim at addressing why the Neotropics are more diverse than other regions in the world. We simply wanted to establish that the Neotropics are the richest region in the world based on previous studies, and that we are interested in understanding what are the patterns/drivers behind such a diversity. In the Introduction, we state that such diversity is not evenly distributed within the Neotropics, and that some regions are richer (e.g. Andes) than others (e.g. southern cone of South America). Diversity models, from Stebbins (1974), have long been proposed to explain this unbalanced diversity. Our study has then defined different bioregions within the Neotropics in which we have looked for differences in diversification patterns. In other words, we do “attempt to relate patterns of diversification to patterns of species richness among regions within the Neotropics”, although we were not able to explain the observed differences in species richness by differences in diversification dynamics (i.e. diversification dynamics are similar across regions). Please, see our response to the essential revision point 1 addressing this comment.

In the revised version, we have changed the title of the study as: “Diversification dynamics of plants and tetrapods in the Neotropics through time, clades and biogeographic regions”. We hope you will find this new title better fits the content of the article. In addition, to avoid any confusion in light of your comment, we have deleted the following sentence from the introduction: “But such an assessment is required to understand the origin of Neotropical diversity and why the Neotropics are more diverse than other regions in the world”.

The authors set up their study by claiming that most previous attempts to explain Neotropical diversity relied on two evolutionary models: cradles vs. museums of diversity. The justification cited for this thinking comes mostly from papers from the last century or before. I do not think that this represents the cutting edge of modern thinking about this topic. Many researchers moved on from this dichotomy long ago.

Thank you for this interesting comment. You are right. The cradle and museum models of diversity are indeed old definitions (Stebbins 1974 - Flowering Plants: Evolution Above the Species Level), but they were convenient to formulate clear and testable hypotheses on the processes underlying the observed patterns of diversity that Stebbins described. We agree that Stebbins’ view is likely outdated, and that is why we took advantage of these models to draw a series of hypotheses relying on evolutionary processes, which has been argued as a “cutting edge of modern thinking about this topic” (Vasconcelos et al. 2022 - Am. Nat.). In the revised version, we have extended the explanation for our rationale to rely on Stebbins’ models and propose process-based hypotheses to explain diversity patterns. We also cite Vasconcelos et al. (2022 - Am. Nat.). We have modified the introduction as follows: “Although the concepts of cradle and museum have contributed to stimulate numerous macroevolutionary studies, a major interest is now focused on the evolutionary processes at play rather than the diversity patterns themselves (23). Four alternative evolutionary trajectories of diversity dynamics could be hypothesized to explain the Neotropical diversity observed today: …”.

However, we will argue as well that some contemporary studies still rely on the cradle and museum framework to frame their studies, for example: McKenna et al. (2006 - PNAS), Couvreur et al. (2011 - BMC Biol.), Condamine et al. 2012 (BMC Evol. Biol.), Moreau & Bell (2013 - Evolution), Dornburg et al. (2017 - Nat. Ecol. Evol.). A search in Google Scholar with "Neotropic AND cradle AND diversif*" returns 1,700 results since 2010. That is why we would like to emphasize that this framework should be abandoned, because it does not rely on evolutionary processes and does not consider the full spectrum of hypotheses explaining Neotropical diversity. In the revised version, we have qualified our assertion that most studies are based on these models, which we agree is not entirely true. We have modified the corresponding paragraph as follows: “Attempts to explain Neotropical diversity traditionally relied on two evolutionary models. In the first, tropical regions are described as a “cradle of diversity”, [...] Although not mutually exclusive (15), the cradle vs. museum hypotheses primarily assume evolutionary scenarios in which diversity expands through time without limits (16). However, expanding diversity models may be limited in their ability to explain the entirety of the diversification phenomenon in the Neotropics. For example, expanding diversity models cannot explain the occurrence of ancient and species-poor lineages in the Neotropics (17–19) or the decline of diversity observed in the Neotropical fossil record (20–22). Although the concepts of cradle and museum have contributed to stimulate many macroevolutionary studies, the major interest is now focused on the evolutionary processes at play rather than the diversity pattern (23)”. We hope you will find this new paragraph better represents current thinking in the field.

There are potentially interesting differences in the diversification dynamics of plants and animals, but this depends on whether we can believe the inferences of the diversification dynamics or not.

Thank you for pointing this out. We understand the concern because of the general (not new) skepticism on macroevolutionary models (e.g. Kubo & Iwasa 1995 - Evolution; Rabosky & Lovette 2008 - Evolution; Burin et al. 2019 - Syst. Biol.; Louca & Pennell 2020 - Nature; Pannetier et al. 2021 - Evolution). Unfortunately, the study of PDR did not help to confirm/reject this particular conclusion.

We thus remain cautious with our results, and we have acknowledged several caveats that should be kept in mind when interpreting them. Here, the same methodological treatment has been applied to both animals and plants, and yet the results indeed indicate different diversification patterns. In addition, our results remained stable to AIC variations (Figure 5 - figure supplement 1), and regardless of the paleo-temperature curve considered for the analyses. Still, we do not “believe” the inferences made with birth-death models in general are accurate, but as long as these models are applied in a well-defined framework and thoroughly performed with a hypothesis-driven approach, recent studies have shown that one can interpret the results and draw conclusions (Helmstetter et al. 2021 - Syst. Biol.; Morlon et al. 2022 - TREE).

For this new version of the manuscript, and following the suggestions of reviewer 3, we have conducted new analyses to assess whether the contrasted diversification dynamics found here between plants and tetrapods could be explained by differences in their datasets (i.e. differences in tree size, crown age, or sampling fraction of the phylogenies). We found that the higher proportion of increasing dynamics observed in plants cannot be explained by significant differences in these factors, strengthening our conclusions.

Reviewer #2 (Public Review):

In this study, the authors explored the evolution dynamics of Neotropical biodiversity by analyzing a very large data set, 150 phylogenies of seed plants and tetrapods. Furthermore, they compared diversification models with environment-dependent diversification models to seek potential drivers. Lastly, they evaluated the evolutionary scenarios across biogeographic regions and taxonomic groups. They found that most of the clades were supported by the expansion model and fewer were supported by saturation and declining models. The diversity dynamics do not differ across regions but differ substantially across taxa. The data set they compared is impressive and comprehensive, and the analysis is rigorous. The results broadened our understanding of the evolutionary history of the Neotropical biodiversity which is the richest in the world. It will attract broad interest to evolutionary biologists as well as the public interested in biodiversity.

Thank you very much for your review and the positive input.

Reviewer #3 (Public Review):

This manuscript seeks to address a series of questions about lineage diversification in the Neotropics. The authors first fit a range of lineage diversification models to over 150 neotropical seed plant and tetrapod phylogenies to characterize diversification dynamics. Their work indicates that a constant diversification model was most frequently the best fit model, while time-, temperature- and Andean uplift-dependent models were far less frequently favored. The authors then attempted to determine whether distinct biogeographic clusters existed by using clade abundance patterns as a proxy for long-term diversification within regions. They found that while clades were widespread across ecoregions, regional assemblages could be binned into five clusters reflecting clade endemism. Finally, they asked whether diversification dynamics of individual lineages varied by parent clade, by environment (temperature through time, and Andean uplift) and by biogeographic region, finding that diversity trajectories best explained by environmental drivers and parent clade identity, while no significant association was detected with biogeographic region. I especially appreciated the detailed model-testing procedure, the inclusion of pulled rates, tests for phylogenetic signal in the results, and the acknowledgment of caveats. By using a massive dataset and, and a battery of cutting-edge analyses, the authors provide new insight into questions that have intrigued biologists for decades.

Thank you for reviewing our study and for your positive feedback.

1. The neotropics, as defined here, extends from Tierra del Fuego to Central Florida, rather than from the Tropic of Cancer-Capricorn. I was confused by this broad circumscription, and wondered whether the findings presented here could be biased by the inclusion of these exclusively or primarily extra-tropical regions (such as "elsewhere" and "Chaco+Temperate south America") and lineages.

Thank you for this comment, which is also in line with the second comment of Reviewer 1. We understand the confusion. The Neotropics, as originally defined by Alfred Wallace, represent a broad region including many types of ecosystems and biomes (not only tropical ones): i.e. the Neotropical realm. It also has a paleobiogeographic significance, as the whole South American continent was isolated for tens of millions of years (Simpson 1983). This definition is well accepted in the field of biogeography and evolutionary biology and we followed it to avoid adding a new definition. A Google Scholar search with keywords “Neotropic AND phylogen AND diversificat*” returns >24,000 hits. Our biogeo-regionalization and clustering results also corroborate the strong connection between South American temperate and tropical biotas: very few clades were restricted or exclusive to a single region, and in most cases, clades comprised species from tropical regions (Cerrado, Caatinga) together with species from the temperate South America zones (Chaco, Temperate South America; Figure 6, Source Data 1).

That being said, we did not find significant differences in diversification rates (or diversity dynamics) across temperate and tropical regions (indeed, between any region), even if temperate regions were analyzed separately (Figure-6-figure supplement 2), suggesting that our results would have been similar if we had confined the Neotropics to tropical latitudes, as in a more climatic circumscription. Although, if we would have circumscribed the Neotropics to the tropical latitudes, many of the 150 clades would have not been selected. Hence, our study would have less insights into our understanding of the diversification processes explaining the Neotropical biodiversity in the broad sense.

1. Model categories and clade diversification dynamics were also linked to the size and age of the phylogeny, such that small and young clades tended to exhibit constant diversification, while exponential and declining dynamics were linked to more diverse and older clades. As one of the main conclusions is that seed plant diversification is more frequently characterized by constant diversification (relative to that of tetrapods), I cannot help but wonder if seed plant phylogenies tend to also be younger and less diverse than those of tetrapods. Figure S1 shows distributions an overview of the distribution but lacks a formal, statistical comparison.

This is a very good point. We agree this comparison is relevant to support our conclusions, but it was missing from our results. We have now compared tree size, crown age and sampling fraction across taxonomic groups, and found that the higher proportion of increasing dynamics, characteristic of plants, cannot be explained by significant differences in these factors. As can be seen in new Figure-2-figure supplement 2 on the manuscript, tree size does not differ among plants, mammals, birds and squamates. Crown age does not differ among plants, mammals and birds. Groups do differ on sampling fraction: plant (p < 0.01) and squamate (p < 0) phylogenies are significantly worst sampled than the phylogenies of other groups. Yet plants show a higher frequency of increasing dynamics than squamates, and other tetrapods (Figure 4). Incomplete taxon sampling has the effect of flattening out lineages-through-time plots towards the present, and thus artificially increasing the detection of diversification slowdowns rather than diversification increases (Cusimano & Renner 2010 – Syst. Biol.).

We have included this important piece of information in the results “In our dataset, amphibian phylogenies are significantly larger than those of other clades (p < 0.05) (Figure 2 - figure supplement 2). Amphibian and squamate phylogenies are also significantly older (p < 0). Groups also differ in sampling fraction: plant (p < 0.01) and squamate (p < 0) phylogenies are significantly worst sampled than phylogenies of other groups.”; and in the discussion section: “Differences in the phylogenetic composition of the plant and tetrapod datasets do not explain this contrasted pattern. On average, plant phylogenies are not significantly younger or species-poorer than tetrapod phylogenies (Figure 2 - figure supplement 2). Yet, the proportion of clades experiencing increasing dynamics is significantly higher for plants (Figure 4). Plant phylogenies are significantly worst sampled than those of most other tetrapods, though, as explained above, incomplete taxon sampling has the opposite effect: flattening out lineages-through-time plots towards the present (83).”

1. I wondered whether it was possible to disentangle time-dependent decreasing diversification from decreasing temperature in young trees? I raise this because it appears that (generally speaking) most of the clades have diversified over periods in which temperature has generally been declining.

This is also a very good point. It is common to observe that two different models are equally likely or close in terms of statistical support. Previously, Condamine et al. (2019 - Ecol. Lett.) reported that the ΔAIC between the best and second-best diversification model was often below the threshold of 2, which is typically chosen to statistically distinguish models (see Fig. 3 and Fig. S5 in Condamine et al. 2019). Simulation analyses confirmed that it was not enough to distinguish the best and second-best models with confidence (see Fig. S6 in Condamine et al. 2019). This applies to any kind of clade.

However, in the case of time-dependent decreasing diversification and temperature-dependent decreasing diversification, one can further test the effect of past temperatures by smoothing more the temperature curve so that the features of ups and downs are removed. Previously, Condamine et al. (2019 - Ecol. Lett.) found that smoothing strongly decreased the support for temperature-dependent models (Fig. S13a) to the point where it was lost (Fig. S13b), showing that the support for temperature-dependent models was not simply due to a temporal trend in diversification rates potentially unlinked to temperature.

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Reply to the reviewers

Major comments:

Are the key conclusions convincing?

We discuss 4 key conclusions.

__# 1 __A PRC of the segmentation clock was constructed.

Although the authors have produced an interesting phase map, the regulation function F(\phi) of the circle map does not give the phase response curve (PRC) (Hoppensteadt & Keener 1982, Guevara & Glass 1982). This holds only when the system is stimulated with very short pulses (ideally Dirac delta), but the experimental pulses here are a quarter of the intrinsic period.

There are several definitions of the PRC (Dirac pulses PRCs, linear PRCs, etc.). We use the general definition from Izhikievich, 2007: “In contrast to the common folklore, the function PRC (θ) can be measured for an arbitrary stimulus, not necessarily weak or brief. The only caveat is that to measure the new phase of oscillation perturbed by a stimulus, we must wait long enough for transients to subside“.

The corresponding equation from Izhikievich (section 10.1.3) is

PRC(θ)= θ_new-θ

which is equivalent to our Equation 1.

Hence, the key assumption we make is that after perturbing the system, we are back on the limit cycle as pointed out by Izhikievich. We think this is a reasonable assumption, because the perturbation we impose is relatively weak, despite pulsing for almost one quarter of the intrinsic period. The concentrations of DAPT we used in this current study are just enough to elicit a measurable response, and further lowering the concentration does not result in entrainment within our experiment time (0.5uM, Figure S7B in submitted version of the manuscript). Additionally, we previously reported that periodic pulsing with 2uM DAPT did not result in change of the Notch signaling activity with respect to control samples (Sonnen et al., 2018). Along similar lines, the DAPT drug concentrations we used are much lower compared to what has been used in previous studies aiming to perturb signaling levels, e.g. 100uM and 50uM used in study of segmentation clock in zebrafish embryos (Özbudak and Lewis, 2008 and Liao et al., 2016, respectively), and 25uM used in study of the segmentation clock in mouse PSM cells (Hubaud et al., 2017). Combined, we reason that we apply weak perturbations that allow to extract the PRC of the segmentation clock during entrainment. Additional evidence that indeed we have revealed a meaningful PRC is provided below, please see our response to point #3.

__# 2 __Furthermore, in eq. 1 T_ext must be the winding number, and the modulus must be in units of

phase, either one or two pi, for the circle map to be correct. Thus, calling the measured response of the system a PRC is not convincing.

We thank the reviewer for pointing this out. We indeed rescaled everything to express the PRC in units of phase. We made this more explicit and updated equations throughout the text.

__# 3 __The system is being entrained. Technically, It would also be easier to get the stroboscopic maps

in the quasi-periodic regime since all the points in the circle will be sampled. Since no quasi-periodic response was demonstrated, the claim of entrainment is not convincing.

While, in principle, PRC can be indeed obtained from responses in the “quasi-periodic” regime, such an approach is, in practice, challenging due to the intrinsic noise. The closest approximation to this is the phase response after the first pulse, that we reproduce below and compare to our inferred PRC, where we indeed clearly see a high noise level. Nevertheless, also the PRC based on the 1st pulse is in agreement with the PRC we derived from the entrainment data.

In the entrained regime, one can get a much more reliable estimate of the phase response despite the noise. The level of noise in the stroboscopic map lowers as the samples approach entrainment (Figure S12), and the entrainment phase itself is a reliable statistical quantity that can be used to infer regions of the PRC as the detuning is varied.

In addition, and maybe even more importantly, we identify several key features characteristics of entrainment, such as the change of entrainment phase as a function of detuning (Figure 7, Figure S6-S7 in submitted version of the manuscript) and the dependency of the time to entrainment as a function of initial phase (Figure 6). While additional features can be linked, in theory, with entrainment, i.e. period-doubling, higher harmonics (Figure 5), quasi-periodicity, we do not agree with the reviewer that all of these need, or in fact, can be found in the experimental data, in particular because of the influence of the noise. Conversely the positive experimental evidence that we provide for the presence of entrainment, combined with the theoretical framework we develop, justifies, in our view, the conclusions we make.

__# 4 __The response of the system to external pulses is compatible with a SNIC. This is compatible, but

it is equally compatible with other explanations. Assuming that the PRC is the same as the regulation function F(\phi), the PRC in Kotani 2012 (PRL 2012 fig. 3C) would be a similar shape as that shown by the authors. Similar models to that in Kotani et al., have been studied, but a SNIC has not been found (an der Heiden & Mackey 1982). It is relatively straightforward to construct a phenomenological model with a SNIC, but having underlying biological insight is not guaranteed. No argument for choosing a SNIC is given, so this emphasis of the paper is not convincing.

It is true that the mapping of PRCs to oscillators is undetermined, in the sense that many systems could potentially give rise to similar PRCs. That said, there is value in parsimonious models, which often generalize very well despite their simplicity. This explains why in neuroscience, constant sign PRCs are generally associated with SNIC. There is a mathematical reason for this : 1-D oscillators with resetting (such as the quadratic fire-and-integrate model) are the simplest models displaying constant sign PRCs, and are the “normal” form for SNICs. In other words, SNIC bifurcations are among the simplest ones compatible with constant sign PRCs, and we think it is informative to point this out. In our manuscript, we go one step further by actually fitting the experimental PRC with a simple, analytical model that allows us to compute Arnold tongue for any values of the perturbation (contrary to more complex models).

Other models such as Kotani 2012 can display similar PRC shapes, but they are of mathematically higher complexity, and furthermore it is not clear how such systems might behave when entrained. For instance that model in particular uses delayed differential equations, and as such contains long term couplings, so that a perturbation might have effects over many cycles, which is not consistent with the hypothesis we here make of a relatively rapid return to the limit cycle. Furthermore, for more complex models, PRCs are analytical only in the linear regime, while our model is analytical for all perturbations. That said, we agree that other types of oscillators can be associated with constant sign PRCs, and we have given more details in this part, in particular we better emphasize the Class I vs Class II oscillators as a way to broaden our discussion on PRC, and emphasize the “infinite period” bifurcation category which is more intuitive and further includes saddle node homoclinic bifurcations.

__# 5 __The work demonstrates coarse graining of complex systems.

This conclusion is correct, but coarse graining theory-driven analysis and control of dynamical systems has been established for many years. What is new here is that it is applied specifically to the in vitro culture system of the mouse segmentation clock.

We agree it is new to successfully apply coarse-graining analysis and, importantly, control, to the in vitro culture system of the mouse segmentation clock. We also agree that such an approach has been pioneered and established for many years, especially in (theoretical) physics, but indeed, the key question is whether and how this can be applied to complex biological systems. Insights coming from theoretical considerations on idealized physical systems might not necessarily apply to biology, as already pointed out by Winfree.

There are still very few examples in biology with coarse graining similar to what we do here. We think there is immense value in demonstrating that quantitative insights, and control of the biological systems, can be obtained without precise knowledge of molecular details, which is still counter-intuitive to many biologists. In this sense, we think our report will be of interest to both colleagues within the field of the segmentation clock and also to anyone interested to in the question, how theory and physics guided approaches can enable novel insight into biological complexity.

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

Following on the points above, each of these needs to be corrected or re-done, and/or the conclusions need to be modified accordingly.

We have modified the manuscript in response to all those points.

# 6 Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. If the authors wish to make the strong claim of determining a true PRC, Dirac delta-like perturbation needs to be applied, or approximated by short time duration pulses compared to the intrinsic period.

Please refer to our response to point #1 and #3..

# 7 *Are the suggested experiments realistic in terms of time and resources? It would help if you could *

add an estimated cost and time investment for substantial experiments.

It's not clear to this reviewer if it is feasible to deliver a very short pulse and record a response. But this may not be relevant, see above.

Please refer to our response to point #1 and #3 .

Are the data and the methods presented in such a way that they can be reproduced?

Yes.

Are the experiments adequately replicated and statistical analysis adequate?

Yes.

Minor comments:

Specific experimental issues that are easily addressable.

No issues.

Are prior studies referenced appropriately?

Yes.

# 8 Are the text and figures clear and accurate?

Figure 1D illustrates how a PRC should be obtained, but doesn't show the experimental protocol applied in the paper.

Figure 1D is a general introduction on the phase description of oscillators and phase response. It demonstrates how a perturbation can change the phase and is not supposed to represent the experimental protocol. We describe how data are analyzed and how phases are extracted in Supplementary Note 1.

__# 9 __In Figure 5B, 10 uM DAPT, the traces are already synchronized before the pulse train starts,

which makes the subsequent behavior difficult to interpret.

It appears here that by chance, the samples were already almost synchronized. We notice however that the establishment of a stable rhythm with the pulses (which here is not a multiple of the natural period) supports entrainment, and is already evident when looking at the timeseries with respect to the perturbation. The temporal evolution of the instantaneous period further confirms this, showing a change in period close to ½ zeitgeber period (which is very different from the natural period of ~140 mins). This also relates to point #35, in reply to both comments we have further expanded this figure to better show the 2:1 entrainment, adding statistics on the measured period and period evolution for a zeitgeber period of 300 mins.

# 10 Do you have suggestions that would help the authors improve the presentation of their data and Conclusions? The text includes several paragraphs reviewing broad principles of coarse graining and making general conclusions. This is confusing, because, as mentioned above, there is no new general advance in this paper. The interesting contributions here are specific to the applications to the segmentation clock, and the text should be focused on this aspect.

As commented above for #3 , we respectfully disagree that there is no “new general advance” in this paper. It is far from obvious that a complex ensemble of coupled oscillators implicated in embryonic development would be amenable to such coarse-graining theory. Of note, we still do not have a full understanding of neither the core oscillators in individual cells, nor what slows these down and eventually stops the oscillations, and multiple recent works suggest that both phenomena are under transient nonlinear control (e.g. our own work in Lauschke 2013). It is remarkable that despite this lack of detailed mechanistic insight, general entrainment theory can be applied to the segmentation process at the tissue level. We further show that classical entrainment theory alone is not sufficient to account for the experimental findings. Specifically, we need to account for a period change that we interpret as an internal feedback, an insight that would be impossible without our coarse-graining approach. While the results might of course be specific to the segmentation process, we think our approach motivated by coarse-graining theory and leading to new insights into the process is of general interest. We tried to make these points explicit in our conclusion.

Reviewer #1 (Significance (Required)):

Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

Description of the complex mouse segmentation clock in terms of a simple model and its PRC is an interesting, original and non-trivial result. The proposal that the segmentation clock is close to a SNIC bifurcation provides a consistent dynamical explanation of slowing behavior that has been recognized for some time, but not fully understood. This proposal also raises a hypothesis about the behavior of the underlying molecular regulatory networks, which may be tested in the future. The increase or decrease of the intrinsic period due to the zeitgeber period is not expected from theory, pointing to structures in internal biochemical feedback loops, an idea which again may be tested in the future. Also surprising from a theoretical perspective, the spatial gradient of period in the system persisted after entrainment. Although the categorization of the generic behavior is interesting, by its nature there is little from this that might give a typical developmental biologist any conclusions about pathways or molecules. The successes and limits of the theoretical description do nevertheless focus future attention on interesting behaviors.

# 11 Place the work in the context of the existing literature (provide references, where appropriate).

Such an analysis of the segmentation clock is based strongly on the experimental system and results in Sonnen et al., 2018, and goes well beyond it in terms of the dynamical analysis. It provisionally categorizes the mouse segmentation clock as a Class I excitable system, allowing its dynamics at a coarse grained level to be compared to other oscillatory systems. In this aspect of simplification, it is similar to approach of Riedel-Kruse et al., 2007 who used a mean-field model of oscillator coupling to explain the synchrony dynamics observed in the zebrafish segmentation clock in response to blockade of coupling pathways, thereby allowing a high-level comparison to other synchronizing systems.

It is interesting the reviewer sees similarities with the work of Riedel-Kruse et al, which uses a mean-field variable Z that corresponds to a classical approach, as described in Pikovsky’s textbook, to quantify synchronization of oscillators. In our view, while of course we work in the same context of coupled oscillators in the PSM, our approach based on perturbing and monitoring the system’s PRC in real-time provides a novel strategy to gain insight. This is evidenced by the fact that our quantifications of synchronization and insight into the PRC is the basis to exert precise control of the pace and rhythm of segmentation.

State what audience might be interested in and influenced by the reported findings.

Developmental biologists, biophysicists

Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Developmental biology, somitogenesis, dynamical systems theory, biophysics, cell signaling

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

Summary: This is a beautifully elegant study that tests how previously published theoretical predictions about entraining nonlinear oscillators applies to a biological oscillator, the segmentation clock. The authors use a combination of state of the art experimental techniques, signal processing and analytical theory to reach a series of interesting and novel conclusions.

They show that the segmentation clock period can be entrained through Notch inhibitor (DAPT) pulses acting as an external clock (referred to as zeitgeber) using a previously developed and sophisticated microfluidic perfusion system. Pulsing DAPT every 120 to 180min can change the internal clock period while entrainment beyond this range leads to higher order coupling to the zeitgeber period, i.e. entrainment of every other pulse. They then perform entrainment experiments where the concentration of DAPT is changed to elicit a change in the strength of interaction between the internal clock and the external stimulus (referred to as zeitgeber strength); interestingly at low strength response to entrainment is more variable leading to entrainment occurring in some samples while others remain unaffected (Figure 4A); overall, higher concentration leads to faster entrainment (Figure 4C). The experimental data is then analysed using stroboscopic maps to reveal that a stable entrainment phase shift is achieved between the internal clock and the external zeitgeber. Phase response curve (PRC) analysis indicates that the system response is not sinusoidal but predominantly characterised by negative PRC, a behaviour consistent with saddle-node on invariant cycle (SNIC); it also reveals that the intrinsic period changes in a non-linear way and that this effect is reversible when external stimulation stops. Finally, a theoretical model is proposed to represent the segmentation clock as a dynamical system; this is based upon Radial Isochron Cycle with Acceleration (ERICA), an extension motivated by the PRC analysis results which are incompatible with a Radial Isochron Cycle (RIC); this model has predictive capability and could be used to design new control strategies for entrainment of the segmentation clock.

This study makes a series of key conclusions which are of particular importance in understanding the dynamic response of a biological oscillators. Firstly, given it's the characteristics of the dynamic response to entrainment, the segmentation clock is likely close to a SNIC bifurcation and this can explain the tendency for relaxation of the period over time. Secondly, the clock period was changed in a non-linear way in the direction of the zeitgeber period, a finding which is interpreted to indicate the presence of feedback of the segmentation clock onto itself, potentially via Wnt. This makes an excellent prediction that if tested experimentally would greatly improve the impact of the study. It is also noted that the entrainment of the segmentation clock does not abolish spatial periodicity and phase wave emergence suggesting that single cell oscillators can adjust to periodic perturbation while maintaining emergent properties. This is also a significant result that would need to be followed up with experiments and computation however would be best suited to a separate study.

Major comments:

__# 12 __The coarse graining is a major point that would need to be clarified since the rest of the analysis

and theoretical modelling in the paper flow from this. Firstly, the interpretation of the schematic in Figure 1A on experimental data collection is not immediately obvious to the reader, lacks a clear flow between the different panels or steps (which could be numbered for example) and does not have a legend to indicate the different colour mapping.

We are grateful to the reviewer for this comment. We have implemented in Figure 1A all the changes suggested by the reviewer: we numbered the different steps and have added a colour mapping. In addition we have rephrased the caption of Fig 1A to better connect the experimental steps.

__# 13 __Secondly, Figure 2A which explicitly addresses coarse graining is not clear enough. Is the

message here that by excluding the inner parts of the sample with a radial ROI, a similar dynamic response is observed over time?

Yes, indeed this is the point and we have adjusted the figure and text to explain this better. Our goal is to focus on the quantification of segmentation pace and rhythm. This is best captured by reporters such as LuVeLu, which has maximum intensity in regions where segment forms, and which dynamics is known to be strongly correlated to segmentation (Aulehla et al., 2007; Lauschke and Tsiairis et al., 20132). The global ROI is thus expected to precisely capture these segmentation and clock dynamics and we have now included more validation data and have also edited the text to make this very important point clearer:

“To perform a systematic analysis of entrainment dynamics, we first introduced a single oscillator description of the segmentation clock. We used the segmentation clock reporter LuVeLu, which shows highest signal levels in regions where segments form \cite{Aulehla_A_2007}. Hence, we reasoned that a global ROI quantification, averaging LuVeLu intensities over the entire sample, should faithfully report on the segmentation rate and rhythm, essentially quantifying 'wave arrival' and segment formation in the periphery of the sample.”

Figure 2A indeed shows that the dynamics (from the timeseries) is very similar when considering the entire field of view (global ROI) or when considering only the periphery of the 2D-assay (excluding central regions). We modified Figure 2A to clarify this point by indicating each measurement as either global ROI or global ROI minus the diameter of the excluded circular region (e.g. global ROI - 50px). We also emphasized in the caption that timeseries are obtained using global ROI, unless otherwise specified. We included a link (https://youtu.be/fRHsHYU_H2Q) in the caption to a movie of 2D-assay subjected to periodic pulses of DAPT (or DMSO) and corresponding timeseries from global ROI.

Since the inner part of the sample corresponds to the posterior side how do we interpret similarities and differences between signals with different ROIs?

As stated above, the global ROI measurements essentially capture the signal at the periphery where segments form and faithfully mirrors segmentation rate and rhythm. We have now included a comparison to the center ROI, also in response to reviewer’s comments, see our response #34.

The result shows that the period and PRC in the center matches the one found in the periphery, i.e. global ROI. We have shown previously that center and periphery differ in their oscillation phase by 2pi, i.e., one full cycle (Lauschke et al., 2013). We interpret these findings as confirmation of our analysis strategy, i.e. the global ROI allows a very reproducible, unbiased quantification that reports on segmentation clock and period.

__# 14 __A quantitative analysis of essential coarse-grained properties such as period and amplitude

should be performed for different ROIs and across multiple samples. As this effectively masks any spatial differences, limitations of this approach should be clearly stated in the Discussion. For example in lines 466-470 where it is difficult to interpret the slowing down tendency and relate back to single cell level.

As outlined in our response to comment #13 and also #34, we chose an analysis that allows to determine the segmentation pace and rhythm, i.e. segment formation, which is well captured by LuVeLu signal and a global ROI analysis. We agree that a spatially resolved analysis of dynamic behaviour is important (and indeed a gradient of amplitude might be relevant in such context), but we think this is beyond the scope of the current study focused on the system level segmentation clock behaviour. We have revised the discussion as suggested by the reviewer to make this point approach and the need for future studies clearer.

__# 15 __The functional characterisation of the sample using LFNG, AXIN2 and MESP2 is unclear. The

images included in Figure 2D representing expression observed when tissue explants are grown within the microfluidic chip are difficult to interpret and would require a more detailed description of anterior-posterior, pillars etc; it is also difficult to view the bright-field since it is presented as a merged image.

It is particularly difficult to see the somite boundaries for the same reason. In lines 113-117 the authors state that the global oscillation period matches the periodic boundary formation. How do we reach this conclusion from these images? What is the variability between samples?

If these two issues would be addressed it would increase confidence in the coarse graining argument and thus would strengthen the importance of the findings in the study.

We thank the reviewer for this feedback, and we have added more quantifications to address this point directly in the modified Figure 2. Importantly, we added the quantification of the rate of segmentation in multiple samples based on segment boundary formation (new Figure 2D) and compared this to the global ROI quantifications using the reporter lines LuVeLu. This data provides clear evidence that the quantification of global ROI reporter intensities closely matches the rate of morphological segment boundary formation. In addition, we show that segment formation and also Wnt-signaling oscillations (Axin2-Achilles) and the segmentation marker Mesp2 (Mesp2-GFP) are all entrained to the zeitgeber period. We have also revised the text to clarify this important validation of our quantitative approach.

In addition, we provide, in the revised Figure Suppl. 2, details of entrained samples, focusing on the segmenting regions. The brightfield and reporter channels were separated, emphasizing the segment boundaries and the expression pattern of the reporters. For ease of visualization, these samples were also re-oriented so that the tissue periphery (corresponding to anterior PSM) is at the top while the tissue center (corresponding to the posterior PSM) is at the bottom. This now additionally better shows the localization of the different reporters with respect to the segment boundary. We also included supplementary movies showing timelapse of samples expressing either Axin2-GSAGS-Achilles or Mesp2-GFP that were subjected to periodic DAPT pulses, with their respective controls.

Several minor points could be addressed to improve the manuscript and are listed below:

# 16 Figure 1 A the colormap and axes for the oscillatory traces should be defined

We thank the reviewer, and we have modified the figure accordingly (related to point # 12). A colormap and axes for the illustrated timeseries are now included.

# 17 Strength of zeitgeber is not defined and there is no analytical expression provided; how does it

relate to DAPT concentration? Is the fact that low DAPT concentration corresponds to weak strength expected or is it a result?

Zeitgeber strength generally refers to the magnitude of the perturbation periodically applied to an oscillator. With DAPT pulses, our expectation was that both the duration of the pulse and the drug concentration could influence the strength. Practically, the pulse duration was kept constant for all experiments and the concentration was varied. We thus expected that DAPT concentration would indeed be correlated to zeitgeber strength. We have discussed multiple evidence supporting this assumption in the main text, and this is indeed a result. In particular, as explained in the section “The pace of segmentation clock can be locked to a wide range of entrainment periods”, higher DAPT concentration gives rise to faster and better entrainment, as expected from classical theory. In the context of Arnold tongue, weaker zeitgeber strength corresponds to narrower entrainment region, which is experimentally observed (Fig 8F, showing regions where the clock is entrained).

From a modelling standpoint, Zeitgeber strength corresponds to parameter A which is the amplitude of the perturbation. Possible zeitgeber strength was inferred from the model by matching the experimental entrainment phase with that obtained from the model isophases. As explained in Supplementary Note 2, we tested four concentrations of DAPT (0.5, 1, 2, and 3 uM) respectively corresponding to A values of 0.13, 0.31,0.43, 0.55. As we can see, those A values are not linear in DAPT concentrations, which is expected since multiple effects (such as saturation) can occur.

__# 18 __In some figures it looks like the amplitude of oscillations may change with DAPT concentration

and hence zeitgeber strength? Is this expected?

We have not systematically analyzed the amplitude effect and have, intentionally, focused on the period and phase readout as most robust and faithful parameters to be quantified. Regarding the amplitude of LuVeLu reporter, we are cautious given that it is influenced, potentially, by the (artificial) degradation system that we included in LuVeLu, i.e. a PEST domain. This effect concerns the amplitude, but not the phase and period, explaining our strategy.

That said, we agree with the referee that DAPT concentrations might change the amplitude of oscillations. Such change could even play a role in the change of intrinsic period (in fact a similar mechanism drives overdrive suppression for cardiac oscillators, Kunysz et al., 1995). But since the change of period can be more easily measured and inferred, we prefer to directly model it instead of introducing a new hypothesis on amplitude/period coupling, at least for this first study of entrainment.

__# 19 __Figure 2A including the black area creates confusion and it is unclear which ROI is used in the

rest of the study; consider moving this to a supplementary figure perhaps

We thank the reviewer for this feedback (related to point #13), and we have modified the figure accordingly. As we responded to point # 13: We modified Figure 2A, by indicating each measurement as either global ROI or global ROI minus the diameter of the excluded circular region (e.g. global ROI - 50px). We also emphasized in the caption that timeseries are obtained using global ROI, unless otherwise specified.

__# 20 __What type of detrending is used in Figure 2 and throughout (include info in the figure legend)?

We used sinc-filter detrending, described and validated in detail previously (Mönke et al., 2020), as specified in Supplementary Note 1: Materials and methods > H. Data analysis > Monitoring period-locking and phase-locking: In this workflow, timeseries was first detrended using a sinc filter and then subjected to continuous wavelet transform. We thank the reviewer for pointing out that this detail is lacking in the figure captions, and we have modified the captions accordingly.

__# 21 __Figure 2D merged images are difficult to read/interpret (see major comments)

We thank the reviewer for this comment, and we have modified the figure accordingly (please see response to related point #15).

__# 22 __Kuramoto order parameter is used to quantify the level of synchrony across the different samples

however it is not defined in the text. Is it also possible to assess variability in each sample? For example how quickly does entrained occur in each sample? How faithfully the peaks of expression beyond 80min (to exclude initial unsynchronised state) match with zeitgeber time? This would help make the point that weak strength leads to a more variable response which is an interesting finding.

We have now added a mathematical definition of the Kuramoto parameter in Supplementary Note 1.

A high order parameter corresponds to coherence between samples, as also elaborated in respective figure captions (e.g. in the caption for polar plots in Figure 4D).

In terms of variability in response to entrainment, we thank the reviewer for the comments, which has prompted us to perform an additional analysis, now included as Figure S13 in the Supplement.

Briefly, we represent below figures showing how different samples get synchronized with the zeitgeber. To do this, we first represent the zeitgeber signal as a continuous uniformly increasing phase (“zeitgeber time”) with period : . The initial condition for is chosen so that the zeitgeber phase at the moment of last pulse is matching the experimental entrainment phase for each . We plot for each sample (dotted lines) and the zeitgeber phase (magenta line). To quantify how well each sample is following the zeitgeber time, we compute the Kuramoto parameter: . By the end of experiment most samples reach , indicating entrainment. Most samples need zeitgeber cycles to become entrained. For min the entrainment takes much longer (edge of the Arnold tongue). For min there is much variability, which can be explained by the horizontal region in the PRC around the entrainment phase. As suggested by the referee, synchronization is faster for higher DAPT concentration. So those dynamics are indeed consistent with the expectation from classical PRC theory.

# 23 Do samples change period to Tzeit in similar ways - i.e. patterns over time. It looks like the

kuramoto order parameter and period drop initially - why?

We do not have a direct answer as to why the Kuramoto first order parameter and the period drop for the condition the reviewer specified. It has to be noted though that because of how wavelet analysis is done (cross-correlation of the timeseries with wavelets), the period and phase determination at the boundaries of the time series are less reliable (edge effects, see Mönke et al., 2020). Because of this, we should take caution when considering data to and from the first and last pulses, respectively. This was explicitly stated in the generation of stroboscopic maps: “As wavelets only partially overlap the signal at the edges of the timeseries, resulting in deviations from true phase values (Mönke et al., 2020), the first and last pulse pairs were not considered in the generation of stroboscopic maps.”

# 24 In Figure 4C why is the Kuramoto order parameter already higher in the 2uM DAPT conditions at

the start of the experiment?

Samples can, by chance, start synchronously and this results in a high Kuramoto first order parameter. Because of this likelihood, it is thus important to interpret the entrainment behaviour of multiple samples using various readouts, in addition to a high Kuramoto first order parameter. We investigated entrainment of the samples based on several measures: multiple samples remaining (or becoming more) synchronous (because each sample actively synchronizes with the zeitgeber), period-locking (where the pace of the samples match the pace of the zeitgeber, which can be distinct from natural pace), and phase-locking (where there is an establishment of a stable phase relationship between the samples and the zeitgeber).

# 25 Figure 3C and Figure S2 require statistical testing between CTRL and DAPT in each condition

p-values were calculated for the specified conditions and were added in the caption of the figures. These values are enumerated here:

• Figure 3C
• 170-min 2uM DAPT (vs DMSO control): p
• Figure S2
• 120-min 2uM DAPT (vs DMSO control): p = 0.064
• 130-min 2uM DAPT (vs DMSO control): p = 0.003
• 140-min 2uM DAPT (vs DMSO control): p = 0.272
• 150-min 2uM DAPT (vs DMSO control): p = 0.001
• 160-min 2uM DAPT (vs DMSO control): p To calculate p-values, two-tailed test for absolute difference between medians was done via a randomization method (Goedhart, 2019). This confirms that the period of samples subjected to pulses of DAPT is not equal to the controls, except for the 140-min condition (where the zeitgeber period is equal to the natural period, i.e. 140 mins).

# 26 Figure 3A gray shaded area not clearly visible on the graph

We have decided to remove the interquartile range (IQR) in the specified figure as it does not serve a crucial purpose in this case. By removing it in Figure 3A, the timeseries of individual samples are now clearer.

# 27 Figure 6C colour maping of time progression is not clearly visible on the graph; the interpretation

of this observation is unclear in the text and the figure

We agree that the low quality of the image is unfortunate, and it seems that our file was greatly compressed upon submission. We have checked the proper quality of figures in the resubmitted version of the manuscript.

Regarding the interpretation of Figure 6C, we conclude that in our experiments the entrainment phase is an attractor or stable fixed point, in line with theory (Granada and Herzel, 2009; Granada et al., 2009),. We had elaborated this in the text (lines 248-252 of the submitted version of the manuscript): at the same zeitgeber strength and zeitgeber period, faster (or slower) convergence towards this fixed point (i.e. entrainment) was achieved when the initial phase of the endogenous oscillation (φinit) was closer or farther to φent.

# 28 Figure 7A circular spread not clearly visible on the graph

Similar to point #27, we have provided a high resolution graph for the re-submission and hopefully resolved this issue.

# 29 Figure S7A difficult to see the difference between colours

See point #28.

# 30 Is it possible to compare the PRC and the plots of period over time during entrainment? The PRC

is mainly negative (Fig 8A1,A2), in my understanding this means a delay, however the periods seem to decrease over time before entraining to the Tzeit (Fig 3B). Is this reflective of a decrease in Kuramoto parameter and potential de-synchronisation of single cells before re-synchronisation at Tzeit?

To address this question, we now plot the Phase response with colors indicating pulse number in new Supplementary Figure S13. While capturing the entire PRC as a function of time would require many more experiments (in particular to sample the phases far from entrainment phase), we still clearly see that the PRCs appear to translate vertically as the oscillator is being entrained, i.e. the latter time points are shifted up (down) for T_zeit = 120 (170) min, respectively.

# 31 Fig 8A What is the importance/meaning of the PRC being similar shape between different

entrainment periods? Does this reflect that the underlying gene network is the same?

If one single gene network is responsible for oscillations, we expect from dynamical systems theory that the PRC are not only of similar shape but actually the same, independent of the entrainment period. What is surprising is that the PRC for different entrainment periods do not overlap, and the simplest explanation for this is that the intrinsic period changes with entrainment, all things being kept equal (including the underlying gene networks). This relates to the previous point since we indeed observe that the PRC “translates” vertically with the pulse number for longer periods. The change of period might be due to a long-term regulation as detailed in the discussion.

# 32 The spatial period gradient and wave propagation under DAPT (Figure S8) should be included in

the results and not just the discussion.

We fully agree with the reviewer that both the establishment and the maintenance of a spatial phase gradient is of great interest. However, many more experiments would be required to fully quantify and understand the processes at play here, which we believe to be out of the scope of the current manuscript. To keep the focus of the paper on the global segmentation clock itself, we prefer to keep this figure in Supplement.

Reviewer #2 (Significance (Required)):

We currently do not have a detailed understanding of how biological oscillators integrate local signals from their neighbours as well global external signals to give rise to complex patterning that is important for embryonic development. Main bottlenecks that hinder our understanding are lack of real-time endogenous dynamic response together with known global inputs as well as comprehensive models that can explain emergent behaviour in a variety of tissues.

This study goes a long way in addressing these bottlenecks in the embryonic tissue responsible for somite formation, a dynamical and oscillatory system also known as the segmentation clock. Firstly, they rely on a state-of-the-art previously developed system to entrain endogenous response in live tissue explants using precise microfluidic control. They test the complete range of exogenous perturbation periods and use an existing live reporter (LuVeLu) to monitor endogenous response. They also identify higher order coupling relationships whereby every other LuVeLu peak is entrained through external stimulation.

As the stimulation system does not control but rather perturb the endogenous response, the observations from LuVeLu provide a unique opportunity in understanding input-output relationships and thus describing the dynamic response of the segmentation clock. Authors propose to study dynamic behaviour of the clock using coarse-graining and focus on describing the overall response over time while amalgamating spatial information. Appropriate coarse-graining is an important strategy in addressing complex problems and is widely used. They use sophisticated methodology such as phase response curves and Arnold tongue mapping to make several important observations. For example the nonlinear shortening and elongation of the period in response to stimulation is particularly interesting since this may indicates a feedback of the clock onto itself potentially via Wnt. Another key observation is that the spatial periodicity and phase wave activity persists in the perturbed conditions suggesting that individual single cell oscillators can adjust their behaviour to external input while retaining coordination with their neighbours. Finally, the authors go on to construct a general dynamical model of the segmentation clock and use this to conclude that the intrinsic period of the oscillator is altered and that the oscillator can be considered excitable.

This work sheds light onto mechanisms of coordination of Notch activity in assemblies of cells observed in living tissue, an area of research that is important not only for somitogenesis but also for understanding gene expression patterning in many other tissues where Notch plays a critical role, for example in the development of the neural system and organs. As a study of a real-world nonlinear oscillator this work is directly of interest to theoreticians and synthetic biology experts interested in understanding complex patterning and emergence.

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

In this manuscript, authors studied the system-level responses of the somite segmentation clock by the coarse-grained theoretical-experimental approach, applying the theory of entrainment to understanding the phase responses of mouse pre-somitic mesoderm (PSM) tissues in the presence of periodic perturbation of Notch inhibitor DAPT generated by micro-fluidics technique. It was demonstrated that the segmentation clock is responsive to diverse range of the perturbation-periods from 120 to 180 min, can be period- and phase-locked, and the efficiency is dependent of the DAPT concentration (input-strength). The authors also observed two cycles of the segmentation-clock ticking in single cycles of 300 or 350 min period-perturbation, suggesting that higher order (2:1 mode) entrainment. They also applied stroboscopic maps to analysis and found that entrainment-phases are dependent of period of DAPT pulses, which is recapitulating theoretical predictions. The estimation of the phase response curve (PRC) of the segmentation clock revealed that the inferred PRC is an asymmetrical and mainly negative function, which represents characteristic features in oscillators that emerge after saddle-node on invariant cycle (SNIC) bifurcation. These results also indicated that the the segmentation clock changed the intrinsic period during entrainment.

Major comments:

# 33 I have major concerns about the relevance of the global time-series analysis proposed in Fig.2

and conclusion about the changes of the intrinsic period during entrainment. The validity of the global time-series analysis should be carefully analyzed, because it could bring artifacts in estimated values of the intrinsic period. The authors concluded (page 3, line 172) that the period calculated by the global analysis represents similar values with the rate of segment formation, but there is no data about the quantification of the periods of segmentation, such as the frequency of Mesp2 reporter expression.

We thank the reviewer for this feedback. We have now added the quantification of the period of segment formation (new Figure 2E) and show its strong correspondence to the dynamics of reporters used (Lfng, Axin2, and Mesp2). Please see also our response to point #15 with additional comments regarding the validation of the global time-series analysis.

# 34 Another related issue is the presence of spatial period gradient as mentioned (page 13, line 524).

One possible approach to circumvent this issue would be "local" time-series analysis; for instance, just focusing on the "putative posterior" regions that are close to source-positions of waves. Authors can re-compute and estimate PRCs by using such a method.

We thank the reviewer for this suggestion and have accordingly now included the analysis of a localized ROI at the center (center ROI) of the 2D-assays (new Figures S5-S6). We also computed the PRC from center ROIs as shown below. We note strong correspondence between the global ROI and the center ROI.

# 35 I have another major concern about the evidence of higher order entrainment shown in Fig.5. If

the 1:2 entrainment is successful, we can expect that the values of observed period is close to the half of the period of pulses; However, the period shown in Fig.5B looks like 185 min longer than the half of 350 min. Is this gap due to the temporal accuracy of time-lapse movies?

We do not think the discrepancy comes from a problem of temporal accuracy as the temporal accuracy is the same for all movies and there is no reason why there would be a specific issue for this set of experiments. In addition, we have re-analyzed the data to calculate the period from the stroboscopic maps. Mathematically speaking, we take the stroboscopic map as (see PDF) and use this to estimate the period of oscillation in entrained samples , in particular inverting the formula for 1:2 entrainment we have : see PDF.

The advantage of this method is that it gives a more ``instantaneous” estimation of the period.

The results are as follows:

350 10uM: 187 +- 8 min (average across entrained samples from the last zeitgeber period)

350 5uM: 193 +- 13 min (average across entrained samples from the last zeitgeber period)

300 2uM: 148 +- 8 min (averaged across entrained samples and from two last periods)

This additional analysis is in agreement with the wavelet analysis.

The reviewer is right that for 350 minutes, entrained samples show an observed period that is higher than expected, also based on this new additional analysis. The reason for this is not known. One explanation is the relatively short observation time, especially considering for pulses separated by as much as 350-minutes, i.e. only 3 pulses are applied. [We notice that for 300 minutes pulses, the period converges to 150 mins between the 3rd and the 4th pulse]. We have adjusted the text in the results section to reflect that for 350min entrained samples, the observed period ‘approaches’ the predicted value, while for 300min entrained samples, the observed period is very close to it, i.e. 147mins In addition, we comment that the phase distribution narrows with time, another indication supporting higher order entrainment.

# 36 Also, authors showed the period evolution towards 1:2 locking with just one condition (350 min).

Authors can show the data for multiple conditions as in Fig. 3D, at least for 300 min and 325 min pulses and add the data about final entrained period with statistic analysis that supports the difference between the entrained period and the natural period (140 min).

We thank the reviewer for this feedback and have modified the figure accordingly. In particular, in Figure 5A, we have added the period evolution plot for samples subjected to 300-min periodic pulses of 2uM DAPT (or DMSO for control). Additionally, we have added Figure 5D, which plots the average period in the 300-min and 350-min conditions. We summarize the median average period here with computed p-values:

• 300-min pulses of 2uM DAPT (or DMSO for control): p-value = 0.191
• CTRL: 130.39 mins

• DAPT: 146.45 mins

• 350-min pulses of 5uM DAPT (or DMSO for control): p-value = 0.049

• CTRL: 127 mins

• DAPT: 174.86 mins

• 350-min pulses of 10uM DAPT (or DMSO for control): p-value = 0.016

• CTRL: 142.82 mins
• DAPT: 185.12 mins

Minor comments:

# 37 The authors can draw vertical lines indicating the T_zeit in Fig.3B, Fig.4B and Fig.5B in order to

help comparisons between T_zeit and patterns of period (solid lines).

We thank the reviewer for this comment. We have accordingly added a horizontal line indicating Tzeit in Figures 3B, 4B, S4A, and S5A (figure panel numbers based on the submitted version of the manuscript). We similarly added a horizontal line indicating 0.5Tzeit in the period evolution plots of 300-min and 350-min conditions in Figures 5A and 5B, respectively.

# 38 In Fig.5A, the authors can show period evolution in the case of 300 min DAPT-pulses as shown

in Fig.5B.

We thank the reviewer for this feedback (related to point #36), and we have modified the figure accordingly.

# 39 In Fig.6B DAPT panel, the authors can draw the points of phi_ent as shown in Fig.7A.

We thank the reviewer for this comment, and we have modified the figure accordingly.

# 40 In Fig. 8F, authors can put the information about DAPT concentration at the right y-axis.

This is a similar comment as point #17, see above. In brief, we do not know the precise relation between the strength of the perturbation in our model and DAPT concentration, zeitgeber strength was inferred from the model by matching the experimental entrainment phase with that obtained from the model isophases.

# 41 In Fig. 8G, the PRC in the panel "170 mins" does not have any fixed point (cross sections with

horizontal lines of "0" phase response). If entrainment is successful, there should be stable and unstable fixed points, but those are absent, although 170 min pulses succeeded in the entrainment as shown in Fig.3D. Authors can explain where the fixed points are.

The fixed points are indeed defined by the intersection with a horizontal line, but not with the ‘0’ line. They are found where the phase response compensates for the detuning/period mismatch, not at ‘0’ phase response. (See PDF for more details).

Note however on Fig 8G that we further observe a vertical shift of the PRC, which prompted us to propose a change of the intrinsic period with (as explained in the text when we introduce Figs 8A1-2).

Another way to visualize fixed points is offered in Fig 16 D-E, where we plot the inferred corrected PTC and the stroboscopic maps: there, fixed points correspond to intersections with the diagonal.

Reviewer #3 (Significance (Required)):

Although the phase-analysis has been widely applied to various biological systems, such as circadian clocks, cardiac tissues and neurons, this paper represents the first detailed experimental analysis of the segmentation clock based on the theory of phase dynamics. The major results are inline with theoretical predictions, whereas the suggestion about the SNIC bifurcation is attractive not only to the theoretical researchers but also to the experimental biologists; it has been believed that the segmentation clock consists of negative-feedback oscillator that emerge by Hopf bifurcation, whereas this paper proposes another possibility of the molecular network structure for the clockwork. This issue is related to recently proposed hypothesis about the excitable system in the segmentation clock based on the Yap signaling (Hubaud et al. Cell 171, 668 (2017)). However, unfortunately, discussion about detailed molecular networks are not abundant.

# 42 Thus, maybe the main readers are computational biologists and systems biologists.

We thank the reviewer for his/her significance comment. We have added comments on the bifurcation structure of the segmentation clock and on excitable systems in the discussion. While our focus is on coarse-graining so that we do not and cannot infer precise molecular details, we can still infer some properties of the underlying networks. In particular we now cite several papers explaining how systems with tunable periods/excitable are indicative of the interplay between positive and negative feedbacks. We think those considerations are of interest to a broad range of biologists interested in connecting experiments to theory.

Samson is extremely important to Don Quixote. At first glance we think of him as the antagonist however, as the story progresses we find that he is trying to help the Don. "The ir a key figure that fulfills a double function: to cheer up Don Quijote so that he may go out for the third time and also to induce him to return home." This makes him a pivotal part of this story.

Presence and Sense of Sanson Carrasco | Request PDF. https://www.researchgate.net/publication/298984686_Presence_and_sense_of_Sanson_Carrasco.

this seems unnecessary

I don't think it is as easy as she makes it sound. As we learned in last week's article, there are some kids who don't even understand the order of taking pre-algebra before algebra simply because they don't have people in their lives to explain that to them. It may seem easy to her because it is a path a lot of people in her family have taken or that her family holds strong values to school so she knows more about it.

I find this to be true in my experience. I have worked with some professors who baulk at the idea of spending time creating or searching for a course banner image. There's other examples related to this, but I personally think that something as simple as finding or creating a course banner image can excite students. Or, if it's a corporate training hosted through Rise or Storyline, this may just be little visual elements and images that add a little something to the visual experience.

Author Response

Reviewer #1 (Public Review):

This study provides data suggesting that tonic presynaptic a7 nicotinic receptor activity enhances corticostriatal input-mediated excitation of striatal medium spiny neurons; the data also suggest that tonic a4b2 nicotinic receptor activity on PV-fast spiking GABA interneurons inhibits striatal medium spiny neurons. These data advance our understanding about the complex cholinergic regulation of striatal neuronal circuits.

The presented data are generally clean and high quality; but there are some problems that require the authors' attention.

We thank the Reviewer for their insightful comments. We have addressed each point below with additional data and/or text. We believe these revisions have made the manuscript significantly stronger.

1. In this study, ADP is a key parameter manipulated by several pharmacological treatments. But it is not clearly defined. The authors indicate EPSP and ADP are distinct by stating "LED pulse of increasing intensity generates excitatory postsynaptic potentials (EPSPs), or an AP followed by an after depolarization (ADP)." But the data (e.g. Fig. 1B) indicates that much of the ADP is probably EPSP. Please clarify. If much of the ADP is indeed EPSP, how are the data interpretation and the overall conclusion affected?

We apologize for the oversight. The main focus of our study is on how tonic nAChR activation controls the timing of striatal output; our justification for including the ADP in our experimental analysis was simply corroborative, in that it represents an additional, easily measured parameter of the postsynaptic response to convergent cortical stimulation that 1) can be modulated by similar local inhibitory circuits that we show to mediate the effect of tonic nAChR activation and 2) is positioned (as opposed to EPSPs) to influence subsequent spiking, should the appropriate synaptic cues be present (which are deliberately omitted in our study). That said, under our experimental conditions EPSPs and ADPs were similar in both their kinetics and modulation by mecamylamine, suggesting that they represent mechanistically similar responses to cortical afferents. The defining difference (besides ADPs exhibiting larger amplitudes) is that they appear either in the absence of or following a spike. For these reasons we ultimately decided that reporting changes in both ADPs and EPSPs would be redundant, and limited our analyses to ADPs. Text has been added to the first paragraph of the results section to address these points.

In Fig. 1F, ADP is absent. Why? Please clarify.

Figure 1F shows an example of a SPN held at a mimicked ‘up-state’, achieved by injecting positive somatic current to produce a ‘resting’ membrane potential of -55-50mV. In this scenario, the ‘up-state’ membrane potential is higher than what would be reached during most ADPs evoked from Vrest, preventing the observation of ADPs in many trials. Text has been added to the end of the first paragraph in the results section to clarify this point.

If ADP is distinct from EPSP here in MSNs, has it been reported in the literature, and how is it generated?

Under our experimental conditions, we do not see any major differences between EPSPs and what we term ADPs (other than amplitude), at least in terms of kinetics and modulation by mecamylamine. That said, we have added text to the first paragraph of the results section that references previous work (Flores-Barrera et al.) describing suprathreshold depolarizations proceeding SPN spikes, which shaped our reasoning for including this measure in our study.

1. In Fig. 1F, the holding potential for mecamylamine is a few mV more negative than the control, but the spike latency is shorter under mecamylamine. This is hard to understand because membrane potential (current-injection-induced depolarization + EPSP) determines spike firing and latency. If the holding potential is the same, then it's easy to understand (larger EPSP under mycamylamine).

Thanks for pointing this out! We agree that this might seem counter-intuitive in terms of Vrest and EPSP amplitude only. Given that mecamylamine reduces GABAergic inputs to SPNs, the reduction in spike latency in this case is consistent with a reduction of GABA receptor mediated shunting. We have added this point to the text in the 3rd paragraph of the results section, which we think strengthens our justification to look at GINs as the potential mediators of mecamylamine’s effect on spike latency.

1. Data in Fig. 2D, E are weak. The spiking ability of whole-cell recorded neurons often declines over time (evidence: the AP duration for the red trace is longer); recovery/partial recovery from MLA is needed for the data to be reliable. Fig. 2E shows 8 cells: 6 had no response, 2 increased. Sample size needs to increase.

We appreciate this comment. Our initial justification for this experiment was from previous reports that alpha-7 nAChRs reduce corticostriatal glutamate release probability. We have now added additional data (Figure 2 supplemental data) showing that blockade of tonically activated alpha-7 nAChRs with the more specific antagonist MLA was not sufficient to change corticostriatal synaptic strength or release probability. In parallel, as we began increasing the sample size of the experiment testing the effect of MLA on spike latency, we noticed that the effect size became smaller than what we initially reported, which was already modest. Given the modest effect size of MLA on spike latency (with no presynaptic mechanism to offer), we reason that it would likely have minimal impact compared to the larger effect of mecamylamine. For this reason, we have backed off our conclusion that TONIC activation of presynaptic alpha-7 nAChRs on corticostriatal axon terminals will have a meaningful physiological impact on SPN spike timing. Accordingly, we removed previous figure 2D/E, but supplemented Figure 2A/B/C with new data (figure 2 supplement) demonstrating the lack of effect of tonic nAChR activation on corticostriatal synapse release probability. The title of the manuscript has been altered to reflect this.

1. Fig. 7: the data on DhbE increasing AP duration is not convincing: no effect in 4 neurons, increase in 4 other neurons, and decrease in other neurons. Data ismore important than p<0.05. How do you interpret DhbE increasing AP duration?

Point taken. We shouldn’t let a statistical calculation dominate the interpretation of a mostly mixed population result. Furthermore, upon revisiting this figure we realized that the main points pertinent to our conclusions (mecamylamine hyperpolarizes PV-FSI Vrest) were obscured by data that were of limited relevance. We have re-focused this figure to highlight data that are directly pertinent to our interpretation. This included removing the AP duration data set in question, which does not add to or inform our conclusions. We have further strengthened our conclusion that PV-FSIs are a primary mediator of the effect of tonic nAChR activation on spike latency by adding new data showing that pharmacologically blocking cortical activation of PV-FSIs occludes the effect of mecamylamine (new figure 8, see comments to Reviewer 2).

Fig. 7F shows AP duration for PV-FSI is around 1.75 ms (some are over 2 ms, recorded at 35 C). This is unusually long. Also, the AP rise time is around 1.4 ms, very long. 1.75 ms total rise time vs. 1.4 ms for just rise: they do not add up?

Please see our response to the above point.

Reviewer #2 (Public Review):

This manuscript examines one aspect of how acetylcholine influences striatal microcircuit function. While striatal cholinergic interneurons are known to be engaged in key events and tasks related to the basal ganglia in vivo, and pharmacological studies indicate cholinergic signaling is complex and critical to striatal function, the mechanistic details by which acetylcholine regulates individual cell types within the striatum, as well as how these integrate to shape striatal output, remain largely unknown. This work thus addresses an important problem in the basal ganglia field, with likely relevance to both normal function and disease-related dysfunction. The authors used a brain slice preparation in which a large number of excitatory cortical inputs to the striatum are activated, and they could measure the resulting activation of striatal projection neurons (SPNs). Their primary finding was that in this preparation, blocking nicotinic acetylcholine signaling resulted in more rapid activation of SPNs. They then explored some of the potential mechanisms for this phenomenon, and conclude that in their preparation, cholinergic interneurons are engaged both tonically and phasically, resulting in recruitment of local GABAergic interneurons that provide feedforward inhibition onto SPNs. They show that one striatal GABAergic interneuron subclass, PV-FSI, are modestly excited by tonic nicotinic signaling, and suggest this may be one contributor to their primary finding.

Strengths of the study include the focus on cholinergic signaling across multiple striatal cell types, careful and clearly displayed slice electrophysiology, good writing, and a methodical approach to pharmacology.

Weaknesses include reliance on the Thy1-ChR2 line to activate excitatory cortical inputs to the striatum (this line may be less specific to cortical pyramidal neurons than a specific Cre recombinase mouse line used with Cre-dependent ChR2, and thus have unintended influences on the results), and despite a strong start, a fairly weak mechanistic exploration of what GABAergic neuron subclasses might contribute to their original phenomenon.

We thank the Reviewer for their thoughtful and constructive comments. The Reviewer identified two weakness of our study, as presented. The first weakness was our reliance on a transgenic mouse line (Thy1-ChR2) to activate cortical inputs to the striatum. Specifically, how a potential lack of specificity/ectopic expression of ChR2 in non-glutamatergic cortical neurons may impact our interpretation of the data. The second is that we did not make an effort to identify the specific subclass(es) of GINs that contribute to the phenomenon we describe. We have addressed both of these comments with new experiments, which we will describe individually below.

1) Specificity of corticostriatal afferent activation in Thy1-ChR2 mice. As the Reviewer keenly points out, although Thy1-ChR2 mice are often used as a tool to specifically activate excitatory corticostriatal nerve terminals with optogenetic stimuli, there is concern that ChR2 expression is not exclusively limited to glutamatergic cortical neurons. If present, direct optogenetic activation of non-cortical striatal afferents would influence our results and impact our interpretation. We have addressed this issue experimentally by adding two new types of experiments (and related text, pages 7-8).

We have added new data using immunohistochemical staining to survey for ectopic expression of ChR2 in the cortex. Staining for GAD, to broadly identify GABAergic neurons, displayed no overlap with ChR2-expressing cortical neurons in Thy1-ChR2 mice. Since a population of GABAergic somatostatin-expressing cortical neurons (particularly in the auditory cortex), have been shown to directly innervate the striatum (Rock et al., 2016), we also show that we found no evidence for somatostatin-ChR2 colocalization in our mice. Furthermore, we report no evidence for somatic expression of ChR2 in the striatum. We do report somatic expression of ChR2 in a population of globus pallidus soma, and add text to describe the above data (figure 3 supplement ) as well as published data identifying ChR2 in axons of the substantia nigra. Together, these data suggest that cortical expression of ChR2 is limited to non-GABAergic neurons, though do not eliminate the possibility of a direct monosynaptic GABAergic input to the striatum form non-cortical (and extrastriatal) brain regions. We describe newly added experimental data below to address this possibility.

We have added new data to directly test if the optogenetic stimulation protocol used in this study induces a monosynaptic GABAergic current in SPNs (figure 3 supplement). We report that an optogenetically-evoked monosynaptic GABAergic current is indeed detected in SPNs, though it is unlikely to affect our results or interpretations for two reasons. First, based on the newly added histological data, the source of this GABAergic current is non-cortical and extrastriatal. Second, and more importantly, this input is insensitive to mecamylamine (new data, figure 3 supplement) and as such would not be modulated by the key manipulations presented in this study. Finally, experiments described below – instructed by a suggestion made by Reviewer 2 (see below) – show that blocking glutamatergic synaptic activation of a class of striatal GINs eliminates the effect of mecamylamine on SPN spike latency, ruling out the involvement of a monosynaptic GABAergic input in mediating the phenomenon.

2) Identification of the key GIN subclass that mediates the phenomenon. Our initial manuscript included data demonstrating the feasibility of PV-FSIs in participating in the phenomenon we described, but we agree with the Reviewer that we stopped well short of identifying the class of GINs that are actually involved. We have added two new data sets to the manuscript that now corroborate both the involvement and necessity of PV-FSIs in mediating this phenomenon. First, we have added data showing that striatal SOM+ interneurons respond to mecamylamine differently than PV-FSIs do: while mecamylamine hyperpolarizes PV-FSIs, it depolarizes the average membrane potential of SOM+ interneurons and has no effect on their spontaneous firing frequency, making them unlikely candidates to mediate the phenomenon we describe. Second, we have added data showing that pharmacologically preventing cortical activation of PV-FSIs both mimics and occludes the effect of mecamylamine on spike latency and ADP amplitude (new figure 8). This data also rules out the involvement of certain other classes of GINs, such as PLTS interneurons, as the pharmacological manipulation we performed (blockade of calcium-permeable GluA2-lacking AMPA receptors) does not affect their response to cortical inputs (Gittis et al., 2010).

Reviewer #3 (Public Review):

The manuscript by Matityahu et al., investigated the role of tonic activation of AChRs on the spike timing of striatal spiny projection neurons (SPNs) in acute striatal slices. By selectively activation of corticostrialal projections using optogenetic tools (ChR2), they find that pharmacological blockade of presynaptic α7 nAChRs delays SPN spikes, whereas blockade of α4β2 nAChRs on GABAergic interneurons advances SPN spikes. The work is carefully done with proper control experiments, and the main conclusions are mostly well supported by data.

Although they only constitute ~1% of the total striatal neurons in rodents and humans, cholinergic interneurons (ChINs) are gatekeepers of striatal circuitry because of their extensively arborized axons and varicosities which tonically release ACh. Whereas the role of muscarinic AChRs (mAChRs) in modulating striatal output has been well established, the role of nAChRs (especially the tonic activation) remains to be elucidated. The study is solid and the results are new and convincing. The data suggest that tonic activation of nAChRs may place a "brake" on SPN activity, and the lift of this brake during pauses of ChIN firing in response to salient stimuli may be critical for striatal information processing and learning. The findings from this study will enhance our understanding of the role of tonic nAChR activation in controlling SPNs and striatal output.

We thank the reviewer for their careful reading of our manuscript and for their kind words and helpful suggestions.

Unjustified Conclusions and Suggestions:

1) The change of the SPN spike timing by AChR modulation is on a few milliseconds time scale. To make the current study more significant, the authors should design and perform additional experiments to demonstrate the functional consequence in controlling striatal output and learning. For example, will activation or blockade of nAChRs have effects on striatal STDP?

We too would be thrilled to see the results of such experiments. Unfortunately our early attempts to perform such tests (e.g., crossing Thy1-ChR2 mice with ChAT-Cre mice to selectively express halorhodopsin in CINs, and combine cortical excitation with silencing of CINs) have been plagued by technical challenges, and would require time and resources that we feel are pragmatically beyond the scope of this study. That said, we’ve included new text (particularly, page 15) discussing how our results may fit with a newly published study on the role of CINs in corticostriatal LTP (Reynolds et al., 2022).

2) Modulation of striatal circuitry is complex. The addition of a diagram illustrating the hypothesis and key results would help.

Excellent suggestion. We have added a summary diagram, which is now figure 9.

• ABOUT property "Report broken translators"

I think that Barnes will not agree this characterization. Barnes's idea is simply that there is no single group corresponding to the term "woman". Instead, there are multiple groups that may be the semantic value of "woman". Some of them are much more gerrymandered. I think the idea does not imply that no one is really a woman. Instead, the upshot is simply that when we consider whether one is really a woman, we must attend to the meaning of "woman".

Author Response:

We largely agree with the assessment of the Reviewers. Indeed, as noted by Reviewer #2, under the urgent conditions of our experiment, the onset of the cue modulates competing saccade plans that are already ongoing. The reviewer is correct in considering that the initial motor plans are endogenously generated, as they favor one location or the other based simply on the subject's internal bias or preference. We would just note that the endogenous signal that we focus on refers to a later modulation which, based on the perceived cue location and the task rules, directs the motor plans to the correct target location. According to our findings, this endogenous modulation occurs after the exogenous response and acts in the opposite way, boosting the anti-saccade plan and curtailing the activity that would otherwise trigger an erroneous pro-saccade. Thus, three things may happen in each trial: (1) initial, uninformed motor plans are endogenously generated, (2) the cue onset exogenously reinforces the plan toward the cue, and (3) an informed endogenous signal suppresses the plan toward the cue and boosts the plan toward the anti location. We think the novelty here is in being able to characterize these distinct events, which unfold within a few tens of milliseconds of each other.

Reviewer #3 considered our conclusion that the exogenous response "is entirely insensitive to behavioral context" too strong, and that is a fair point. Conclusions apply to the degree that experimental conditions are valid in general, and furthermore, the deviations from the idealized predictions were small but not zero. However, we do not consider the assumption noted by the reviewer, that saccade-related neural activity ramps up before the saccade goal is known, as a weakness. We have, in fact, recorded such activity in several oculomotor areas using similar urgent-choice designs (Stanford et al., Nat Neurosci 13:379, 2010; Costello et al., J Neurosci 33:16394, 2013; Costello et al., J Neurophysiol 115:581, 2016; Scerra et al., Curr Biol 29:294, 2019; Seideman et al., bioRxiv, 2021, https://doi.org/10.1101/2021.02.16.431470), and the responses in the frontal eye field (FEF) in particular conform quite closely with those assumed by the model (Stanford et al., Nat Neurosci 13:379, 2010; Costello et al., 2013; Salinas et al., Front Comput Neurosci 4:153, 2010). Rather than a potential liability, we think the early ramping activity is a key constraint for any model of urgent choice performance.

Author Response

Reviewer #1 (Public Review):

In this article, Miettinen and colleagues exploit the suspended microchannel resonator developed in their lab and optimize the method to be able to record single live mammalian cells for very long periods of times, across several cell division cycles, while performing a double measure of their buoyant mass in media of different densities (H2O and D2O). Because water exchanges fast enough inside the cell, it allows them to define a dry mass and a dry volume, and thus a density of dry material for single cells along the entire cell division cycle. These measures lead them to confirm and clarify some points from previous studies from their lab and others, such as exponential growth also in dry mass and the fact that buoyant mass and this new dry mass are the same thing in interphase cells. They then find that this is not true during mitosis, mostly because dry mass density increases in early mitosis (dry mass decreases and dry volume decreases even more, suggesting that there is a loss of material of density lower then the average dry mass density). The authors rule out a number of potential mechanisms and give evidence for a role of exocytosis, more precisely exocytosis of lysosomal content. Blocking this phenomenon prevents the change in dry mass density but does not affect cell division. They propose some potential function for this phenomenon, including the interesting hypothesis that this helps cleaning the lysosomal content which might contain some toxic components, so that daughter cells are born with 'clean' lysosomes. Cool idea! It is also quite amazing that the precision of their method allows them to detect this event.

The main question I have concerns the definition of dry mass and dry volume. The authors should discuss in more details what it represents physically. Technically, this is defined by their equation 1, which relates their measure of buoyant mass to a dry mass and a volume of water as parameters to fit from the buoyant mass data. One gets to this equation by writing the definition of buoyant mass as the mass of the cell minus the mass of the equivalent volume of the surrounding medium. But then, to get what the authors find, one has to write that the cell mass is the sum of the dry mass and the mass of water contained in the cell (which makes the dry mass easy to understand) and then to write that the cell volume is the sum of a volume of water and of a volume of dry material. This then defines a dry volume, as the difference between the volume of the cell and the volume of the water contained in the cell (which is the parameter Vwater in the equation 1). At least this is how I got to this equation. The question I asked myself then is: what is this dry volume? Is it really the volume occupied by the dry mass in the cell? This is probably not the case, since dry mass is solvated in the cell. One can estimate this solvated volume using the van't Hoff/Ponder relation, which can be found changing the osmolarity of the external medium. It defines an excluded volume, which is the total volume excluded by macromolecules (like for a van der Waals gas) - it is usually between 25 and 30% of the cell volume. This volume contains the dry mass plus a certain fraction of the water, so it is not exactly the dry mass volume as defined here by the authors. I am worried that this dry mass volume, which is mathematically defined here and calculated from the fit of the equation, is not a standard physical quantity and so it is not easy to relate it to standard biophysical theories (e.g. equations of state), and its behavior could be very unintuitive even for simple systems. This makes the variation in this quantity not easy to interpret, and thus also the variation in dry mass density is not easy to interpret in physical terms.

That being said, it is still clear that whatever this is, it changes in early mitosis, and it seems to be related to exocytosis, so I am not saying that the authors are wrong here. They potentially indeed detect this increase of exocytosis. But they should discuss more what they think this quantity is, either in the methods or in the discussion of the article. In particular, the sentence at the bottom of page 5, line 104, is not clear ('We are not aware of any other single cell methods capable of quantifying this biophysical feature of a cell'), since this measure is not really clearly a biophysical feature of a cell, but is defined a bit artificially from the equation which defines the dry mass volume from the measures of buoyant mass.

Thank you for the detailed and very constructive feedback. As stated above in the Essential Revisions section, we have now clarified the terminology we use and made the terminology more consistent with existing literature. We have also better defined the concept behind our method. Our updated Measurement Method section now states (page 3) that: “In our approach, we consider the buoyant mass of a cell to be dependent on two distinct physical “sections” of the cell, the dry content and the water content. To measure the cell’s dry content independently of the water content, we measure the cell’s buoyant mass in H2O and D2O-based solutions. Under these conditions, the influence of the water content on buoyant mass can be excluded, because the intracellular water is exchanged with extracellular water, making the intracellular water content neutrally buoyant with extracellular solution. This allows us to detect the cell’s dry mass (i.e. total mass – water mass), dry volume (i.e. total volume – water volume) and dry mass density (i.e. dry mass / dry volume).”

The reviewer is also correct that our method measures a dry volume which is, by our model’s definition, the volume occupied by the dry mass independently of water. In other words, our method & measurement model assumes that the intracellular water exchange is 100% complete. The reviewer is correct that some water may be retained, and we cannot directly measure the amount of H2O left inside the cell after immersion in D2O-based media. However, our results indicate that our dry volume measurements are not limited by the water exchange time that the cell experiences (Figure 1–figure supplement 2). In other words, in our measurements, cells exchange all the water they can exchange, be that 100% or 98%. This is further supported by our new estimations of the time needed to transport all water in and out of the cell (see above, other comments section #1, and our updated manuscript page 5). Note that, as our method only exchanges H2O to D2O instead of removing all water from the cell, dry mass will always remain solvated in either H2O or D2O, which makes it plausible that 100% of the water content is exchanged.

As the reviewer keenly points out, our measured dry volume is biophysically distinct from the more classically measured excluded cell volume (or dehydrated cell volume), which still includes some water in the excluded cell volume quantifications. Consistently, our method measures dry volumes that are smaller (~15%) than what the excluded volumes typically are (~25-30%). We do not consider this a limitation of our method, but rather an opportunity for new measurements. That being said, we completely agree with the reviewer that this may cause confusion in the readers. To address this point, our Measurement Method section now states (page 4) that: “Importantly, our approach assumes that all water within the cell is exchangeable between H2O and D2O. Accordingly, our dry volume measurement is distinct from the excluded cell volume detected by measuring cell volume following strong hyperosmotic shocks, which does not remove all water from the intracellular space.”

Finally, we have also changed the sentence “We are not aware of any other single cell methods capable of quantifying this biophysical feature of a cell” (page 5) so that it only refers to a metric, which hasn’t been quantified before on a single-cell level. We believe that this minor change will avoid the suggestion that dry volume is of biophysical importance on its own.

Reviewer #2 (Public Review):

The new suspended microchannel resonator (SMR)-based method described in this paper enables high precision and high temporal resolution single-cell measurements of key physical properties: cell dry mass and the density of cell dry mass, which depends on the macromolecular composition of the cell. The validity of the method is rigorously tested with several convincing control experiments. This method will be useful for future studies investigating cell size and growth regulation and the coordination of mass, volume and density in animal cells.

Using their method, the authors report two important results. First, they confirm that buoyant mass measurement is a valid proxy for cell mass in interphase, an important finding given that SMR measurements have been one of the best and most productive approaches to investigating cell mass growth regulation. Second, they provide evidence that some cell types lose dry mass during metaphase by a mechanism that involves exocytosis, emphasizing how mass, volume, and density dynamics are more complex than during the rest of the cell cycle.

While this paper presents very interesting results, it would benefit significantly from two main improvements. First, the different physical variables studied here (dry mass, dry density, dry mass density, dry volume) should be better defined, and the terminology revised to provide a more straightforward and intuitive description of their biological meaning. Several sections of the paper (especially the introduction and the discussion of Fig. 2-4) should be re-written to help the reader understand the message. Second, some of the drug treatments require more replicates to provide more conclusive answers.

Thank you for this constructive feedback. As stated above in the Essential Revisions section, we have now changed our terminology to increase clarity. Our new density measurement in this manuscript (dry mass divided by dry volume) is now defined as ‘dry mass density’. This change has been applied throughout our manuscript, including our manuscript title. In addition, we have added clearer definitions of each term to our Introduction and Measurement Method sections. Furthermore, we have minimized the use of the term ‘dry composition’ throughout our manuscript, as we now realize this may cause confusion to some readers.

More specifically, our introduction (page 3) now states: “Here, we introduce a new approach for monitoring single cell’s dry mass (i.e. total mass – water mass), dry volume (i.e. total volume – water volume), and density of the dry mass (i.e. dry mass / dry volume), which we will refer to as dry mass density.” These definitions are also repeated in our Measurement Method section (page 4), as many readers may look for the definitions in that section. We have also done many other minor modifications to our main text throughout the manuscript to help the readers understand our message.

In addition, as detailed above in the Essential Revisions section 3, we have adjusted the writing of our manuscript to avoid overly strong claims where our replicate numbers are insufficient. More specifically, we now avoid conclusions where we claim that inhibition of cytokinesis has no influence on dry mass and dry mass density changes in mitosis.

Reviewer #3 (Public Review):

In this manuscript, the authors extend the Manalis lab's vibrating cantilever approach by adding the ability to rapidly exchange media with heavy water. This allows the authors to measure dry mass and its density in growth and proliferating cells. This resolves a previous discrepancy of the cantilever approach and quantitative phase imaging and shows that cells in early mitosis likely increase lysosomal exocytosis. This is an interesting piece of work.

The authors report that: "On average, the FUCCI L1210 cells lost ~4% of dry mass and increased dry density by ~2.5%, and these changes took place in approximately 15 minutes (Figure 3C). In extreme cases, cells lost ~8% of their dry mass while increasing dry density by ~4%". Although these changes may sound small, I believe they would require significant changes to the cell composition. I.e., to increase the overall dry mass density by 4% while losing 8% of the cell's dry mass, the cell would need to lose almost exclusively low-density components, which may not be typical for exocytosis. Moreover, even if all of those lost 8% of cell dry mass are exclusively lipids (or other low-density components), it is not intuitively obvious that such a loss would be sufficient to cause a 4% change to the dry density. To make this more convincing, the authors should provide a simple mathematical model that would roughly estimate how the cell composition (e.g., the contents of lipids vs proteins) needs to change and what the composition of the lost (secreted) components needs to be to provide the observed changes to the dry mass and density, given the existing information on average cell composition and the densities of different biomolecules (lipids, sugars, proteins, etc).

Thank you for this comment. The reviewer is correct that significant changes to the cell composition are needed to explain the phenotypes we observe. As stated above in the Essential Revisions section, we fully agree that such calculations could be very useful in interpreting our results. Our manuscript now contains a new paragraph (discussion section, page 13), where we state: “The magnitude of dry mass density increase in mitosis was large. We have previously observed similar magnitude changes in dry mass density when perturbing proliferation in mammalian cell (Feijo Delgado et al., 2013). To provide some rough estimates of what kind of compositional changes would be required to achieve the dry mass loss and dry mass density increase, we carried out a back-of-the-envelope calculations. Assuming a typical mammalian cell composition and typical macromolecule dry mass densities (Alberts, 2008; Feijo Delgado et al., 2013), we calculated the degree of lipid loss needed to increase dry mass density by 2.5%. This suggested that cells would have to secrete ~1/3 of their lipid content in early mitosis. This could be achieved via lysosomal exocytosis of lipids. Lipid droplets, the main lipid storages inside cells, are frequently trafficked into and degraded in lysosomes (Singh et al., 2009), and lipid droplets can also be secreted via lysosomal exocytosis (Minami et al., 2022). However, it seems likely that the mitotic dry mass density increase also involves secretion of other low dry mass density components (e.g. lipoproteins, specific metabolites) and/or a minor, transient increase in high dry mass density components (e.g. RNAs, specific proteins) in early mitosis. Indeed, CDK1 activity has been suggested to drive a transient increase in protein and RNA content in early mitosis (Asfaha et al., 2022; Clemm von Hohenberg et al., 2022; Miettinen et al., 2019; Shuda et al., 2015).”