Esta tabla se ve muy pinchona

Más que hablar de necesidad y prioridad, creo que podrías abordarlo de que es una obllgación / requerimiento de COFEPRIS https://www.gob.mx/cms/uploads/attachment/file/158493/Gu_a_de_buenas_pr_cticas_de_higiene_en_establecimientos_de_servicios_de_alimentos_y_bebidas.pdf

Author response:

Reviewer #1:

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

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

Major Comments 

(1) Dynamic localization of CAMSAP2 during Golgi reorientation

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

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

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

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

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

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

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

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

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

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

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

(2) MARK2 localization to the Golgi apparatus

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

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

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

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

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

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

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

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

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

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

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

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

References：

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

(3) Phosphorylation of CAMSAP2 by MARK2

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

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

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

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

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

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

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

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

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

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

(4) Identification of CAMSAP2 interaction partners

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

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

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

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

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

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

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

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

References：

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

Minor Comments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2: 

Summary  

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

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

Major comments

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

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

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

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

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

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

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

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

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

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

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

Minor comments

Sup-fig1  

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

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

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

Sup-fig2  

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

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

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

Figure 1  

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

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

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

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

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

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

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

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

Figure 2  

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

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

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

F: The time point of fixation is missing  

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

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

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

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

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

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

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

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

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

Sup-fig3  

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

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

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

Figure 3  

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

F: Presence of a white dot on the HT1080 plot  

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

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

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

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

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

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

Figure 4  

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

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

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

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

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

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

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

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

Figure 6  

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

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

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

- Figure 6A (revised Supplementary Figure 6E), we have added arrows to indicate the wound position, providing clearer guidance for interpreting the image.

- Figure 6B (revised Supplementary Figure 6F), we revised our quantification method based on the approach used in literature (Wu et al., 2016). Specifically, we analyzed the relationship between microtubules and the Golgi apparatus in cells at the leading edge of the wound. The x-axis represents the distance from the Golgi center, while the y-axis shows the normalized radial fluorescence intensity of microtubules and the Golgi apparatus.

Additionally, we revised the accompanying text for clarity and accuracy. The original description:

“In cells located before the wound edge, the Golgi apparatus maintained a ribbon-like shape, with a higher density of microtubules. In contrast, at the trailing edge of the wounding, the Golgi apparatus appeared more as stacks around the nucleus, with fewer microtubules”  was replaced with:

“Finally, to comprehensively understand the dynamics between non-centrosomal microtubules and the Golgi apparatus during Golgi reorientation, we conducted cell wound-healing experiments (Supplementary Figure 6 E-F). Our observations revealed notable changes in the Golgi apparatus and microtubule network distribution in relation to the wounding. These findings corroborate our earlier results and suggest a highly dynamic interaction between the Golgi apparatus and microtubules during Golgi reorientation” (Revised manuscript page 11 lines 3-10).

We hope these changes address the reviewer’s concerns and improve the clarity and robustness of our study. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the presentation and interpretation of our data. References：

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

Reviewer #3:  

Summary  

In this study, Xu et al. analyzed the wound healing process of HT1080 cells to elucidate the molecular mechanisms by which the Golgi apparatus exhibits transient dispersion before reorienting to the wound edge in the compact assembly structure. They focused on the role of the microtubule minus-end binding protein CAMSAP2, which mediates the linkage between microtubules and the Golgi membrane. At first, they noticed that CAMSAP2 transiently lost Golgi colocalization during the initial phase of the wound healing process. They further found that the cell polarity-regulating kinase MARK2 binds and phosphorylates S835 of CAMSAP2, thereby enhancing the interaction between CAMSAP2 and the Golgi protein Uso1. Together with the phenotypes of CAMSAP2, MARK2, and Uso1 KO cells, these authors argue that the MARK2dependent phosphorylation of CAMSAP2 plays an important role in the reassembly and reorientation of the Golgi apparatus after a transient dispersion observed during the wound healing process.

We sincerely thank the reviewer for their thoughtful summary of our study and constructive feedback. Your comments have been invaluable in refining our research and enhancing the clarity and impact of our manuscript.

Major comments

(1) The premise of this study was that during the wound healing process, the Golgi apparatus exhibits transient dispersion before reorientation to the front of the nucleus.  

In the first place, this claim has not been well established in previous studies or this paper. Therefore, the authors should present a proof of this claim in a clearer manner.  

To introduce this cellular event, the authors cite several papers in the introduction (page 4) and the results (page 6) sections. However, many papers cited are review articles, and some of them do not describe this change in the Golgi assembly structure before reorientation. Only two original articles discussed this phenomenon (Bisel et al. 2008 and Wu et al. 2016), and direct evidence was provided by only one paper (Wu et al. 2016) in which changes in the Golgi apparatus in wound-healing RPE1 cells were recorded by live imaging (Fig.7A in Wu et al. 2016).

Furthermore, it should be noted that this previous paper demonstrated that depletion of CAMSAP2 inhibits Golgi dispersion. Obviously, this conclusion is inconsistent with their statement to introduce this study (page4) that ‟This emphasizes CAMSAP2's role in sustaining Golgi integrity during critical cellular events like migration." In addition, it also contradicts the authors' model of the present paper (Fig. 6E), which argued that disruption of the Golgi association of CAMSAP2 facilitates the Golgi dispersion.  

We sincerely thank the reviewer for their detailed comments and for providing us with the opportunity to clarify the premise and conclusions of our study. Below, we address the main concerns raised:

First, to provide direct evidence of Golgi apparatus changes during the wound-healing process, we conducted live-cell imaging experiments. Our observations, presented in revised Supplementary Figure 2A, clearly demonstrate that the Golgi apparatus exhibits a transient dispersion state before reorienting toward the leading edge of the nucleus during migration.

Regarding the interpretation of previous studies, we acknowledge the reviewer’s concerns about the citation of review articles. To address this, we have revisited the literature and clarified that the phenomenon of Golgi dispersion during reorientation has been directly demonstrated in Wu et al (Wu et al., 2016), where live imaging of wound-healing RPE1 cells showed this dynamic behavior. Furthermore, we note that in Wu et al paper explicitly demonstrates that CAMSAP2 depletion promotes Golgi dispersion, contrary to the reviewer’s interpretation that "depletion of CAMSAP2 inhibits Golgi dispersion."

Our model focuses on the role of CAMSAP2 in restoring the Golgi from a transiently dispersed structure back to an intact ribbon-like structure during reorientation. Specifically, we propose that during this process, the disruption of CAMSAP2’s association with the Golgi affects this restoration, rather than directly promoting Golgi dispersion as suggested by the reviewer. We believe this distinction aligns with our data and the existing literature.

To strengthen the background of our study, we have revised the introduction and results sections (page 6, lines 6-13 and page 7, lines 1-17) to minimize reliance on review articles and have provided more explicit citations to original research papers. We hope this addresses the reviewer’s concern about the sufficiency of the cited literature.

We trust these clarifications and revisions resolve the reviewer’s concerns and enhance the robustness of our study. Once again, we are grateful for the reviewer’s constructive feedback, which has greatly helped refine our manuscript. References：

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

The authors did not provide experimental data for this temporal change in the Golgi assembly structures during the wound-healing process of HT1080 that they analyzed. They only provide an illustration of wound-healing cells (Fig.1F), in which cells are qualitatively discriminated and colored based on the Golgi states, without indicating the experimental basis of the discrimination.

According to their ambiguous descriptions in the text (page7), the reader can speculate that Fig. 1F is illustrated based on the images in Supplementary Fig. 2C. However, because of the low quality and presentation style of these data, it is impossible to recognize the assembly structures of the Golgi apparatus in wound-edge cells.  

If the authors hope to establish this premise claim for the present paper, they should provide their own data corresponding to the present Supplementary Fig. 2C in more clarity and present qualitative data verifying this claim, as Wu et al. did in Fig. 7A in their paper.

We sincerely thank the reviewer for their constructive feedback and the opportunity to address the concern regarding the lack of experimental data supporting the temporal changes in Golgi assembly during the wound-healing process.

To establish this premise, we conducted live-cell imaging experiments to observe the dynamic changes in the Golgi apparatus during directed cell migration. Our data, now presented in Supplementary Figure 2A, clearly demonstrate that the Golgi apparatus undergoes a transient dispersed state before reorganizing into an intact structure. These findings provide direct experimental evidence supporting our claim.

In addition, we have revised the data originally presented in Supplementary Figure 2C and enhanced its quality and presentation style. This supplementary figure now includes clearer images and annotations to better illustrate the Golgi assembly structures in wound-edge cells. The improved data presentation aligns with the standards set by Wu et al reported (Wu et al., 2016) and provides qualitative support for our observations.

We hope these additions and revisions address the reviewer’s concerns and strengthen the scientific rigor and clarity of our manuscript. We are grateful for the reviewer’s valuable suggestions, which have significantly improved the quality of our study. References：

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

(2) In Fig.1A-D, the authors claim that CAMSAP2 dissociates from the Golgi apparatus in cells "that have not yet completed Golgi reorientation and exhibit a transitional Golgi structure, characterized by relative dispersion and loss of polarity (page7)." However, I these analyses, they do not analyze the initial stage (0.5h after wound addition) of cells facing the wound edge, as they do in Supplementary Fig. 2C. Instead, they analyze cells separated from the wound edge at 2 h after wound addition when the wound-edge cells complete their polarization. These data are highly misleading because there is no evidence that the cells separated from the wound edge are really in the transitional state before polarization.  

In this regard, Fig. 1E shows the analysis of the wound-edge cells at 0.5 and 2 h after the addition of wound, which provides suitable data to verify the authors' claim. However, the corresponding legend indicates that these statistical data are based on the illustration in Fig. 1F, which is probably based on highly ambiguous data in Supplementary Fig. 2C (see above).  

Taken together, I strongly recommend the authors to remove Fig.1A-D. Instead, they should include the improved figure corresponding to the present Supplementary Fig.2C and present its statistical analysis similar to the present Fig.1E for this claim.

We sincerely thank the reviewer for their constructive feedback and recommendations. Below, we address the concerns raised regarding Figure 1A-D and Supplementary Figure 2C.

To provide stronger evidence for the transitional state of the Golgi apparatus during reorientation and the dynamic regulation of CAMSAP2 localization, we conducted live-cell imaging experiments. These results, now presented in Supplementary Figure 2A, clearly demonstrate that the Golgi apparatus undergoes a transitional state characterized by dispersion before reorienting toward the leading edge.

Additionally, we analyzed fixed wound-edge cells at different time points during directed migration to observe CAMSAP2’s colocalization with the Golgi apparatus. The results, shown in Figures 1A and 1B, reveal dynamic changes in CAMSAP2 localization, confirm its regulation during Golgi reorientation, and include a corresponding statistical analysis (page 7, lines 1-17).

These updates ensure that our claims are supported by robust and unambiguous data.

We hope these revisions address the reviewer’s concerns and provide clear and reliable evidence for the transitional state of the Golgi apparatus and CAMSAP2’s dynamic regulation. We are grateful for the reviewer’s constructive suggestions, which have greatly improved the quality and focus of our manuscript.

(3) In Supplementary Fig. 5 and Fig. 4, the authors claim that MARK2 phosphorylates S835 of CAMSAP2.  

There are many issues to be addressed. Otherwise, the above claim cannot be assumed to be reliable.  

First, the descriptions (in the text and method sections) and figures (Supplementary Fig.5) concerning the in vitro kinase assay and subsequent phosphoproteomic analysis are too immature and contain many errors.  

Legend to Supplementary Fig. 5 is too immature for comprehension. It should be completely rewritten in a more comprehensive manner. The figure in Supplementary Fig. 5C is also too immature for understanding. They simply paste raw mass spectrometric data without any modification for presentation.  

We sincerely apologize for the lack of clarity and inaccuracies in the original descriptions and figure legends for the in vitro kinase assay and phosphoproteomic analysis. We greatly appreciate the reviewer’s detailed comments, which have allowed us to address these issues comprehensively.

To improve clarity and accuracy, we have rewritten the figure legend for the original Supplementary Figure 5 (now Supplementary Figure 4) as follows:

(A): CBB staining of a gel with GFP-CAMSAP2, GST, and GST-MARK2. GFP-CAMSAP2 was expressed in Sf9 cells and purified. GST and GST-MARK2 were expressed in E. coli and purified.

(B): Western blot analysis of an in vitro kinase assay. GST or GST-MARK2 was incubated with GFP-CAMSAP2 in kinase buffer (50 mM Tris-HCl pH 7.5, 12.5 mM MgCl2, 1 mM DTT, 400 μM ATP) at 30°C for 30 minutes. Reactions were stopped by boiling in the loading buffer.

(C): Detection of phosphorylation at S835 in CAMSAP2 by mass spectrometry. The observed mass increases in b4, b5, b6, b7, b8, b10, b11, and b12 fragments indicate phosphorylation at Ser835.

(D): Kinase assay samples analyzed using Phos-tag SDS-PAGE. HEK293 cells were cotransfected with the indicated plasmids. Band shifts of CAMSAP2 mutants were examined via western blot. Phos-tag was used in SDS-PAGE, and arrowheads indicate the shifted bands caused by phosphorylation.

To address the reviewer’s concern about Supplementary Figure 5C, we have reformatted the mass spectrometry data to improve readability and presentation quality. The revised figure includes clearer annotations and graphical representations of the mass spectrometric evidence for phosphorylation at S835.

We believe these updates enhance the comprehensibility and reliability of our data, providing robust support for our claim that MARK2 phosphorylates CAMSAP2 at S835. We hope these

revisions address the reviewer’s concerns and demonstrate our commitment to improving the quality of our manuscript.

The readers cannot understand how the authors purified GFP-CAMSAP2 for the kinase assay.

The method section incorrectly states that the product was purified using Ni-resin.  

We thank the reviewer for their comment regarding the purification of GFP-CAMSAP2 for the kinase assay. We would like to clarify that GFP-CAMSAP2 carries a His-tag, which allows for purification using Ni-resin, as described in the Methods section (page 23, Lines 32-40). Therefore, the description in the Methods section is correct.

To avoid any potential misunderstanding, we have revised the Methods section to provide more detailed and precise descriptions of the purification process. Specifically, GFP-CAMSAP2 was cloned into the pOCC6_pOEM1-N-HIS6-EGFP vector, which includes a His-tag, and was expressed in Sf9 cells. The His-GFP-CAMSAP2 protein was purified using Ni-resin chromatography. Relevant details have been added to the Methods section (page 21, Lines 34-36:

“CAMSAP2 was cloned into the pOCC6_pOEM1-N-HIS6-EGFP vector expressed in Sf9, purified as His-GFP-CAMSAP2.”; page 23, Lines 32-33: “His-GFP-CAMSAP2 was cotransfected with bacmids into Sf9 cells to generate the passage 1 (P1) virus.”).

We hope these clarifications and revisions address the reviewer’s concern and improve the comprehensibility of our experimental details. We appreciate the reviewer’s feedback, which has helped us refine the manuscript.

In this relation, GST and GST-MARK2 are described as having been purified from Sf9 insect cells in the text section (page9) and legend to Supplementary Fig. 5, but from E. coli in the method section. Which is correct?  

We thank the reviewer for pointing out the inconsistencies in the descriptions regarding the source of GST and GST-MARK2. To clarify, both GST and GST-MARK2 were purified from E. coli, as stated in the Methods section (page 23, Lines 26-31). We have corrected the erroneous descriptions in the main text (page 8, Lines 35-36) and the legend to Supplementary Figure 4 to ensure consistency.

Additionally, we have updated the legend for Supplementary Figure 4A to state the sources of each protein explicitly:

“GFP-CAMSAP2 were expressed in Sf9 cells and purified. GST and GST-MARK2 were expressed in E. coli and purified.” (page 38, Lines 2-3)

These revisions ensure that the experimental details are accurate and consistent across the manuscript, eliminating any potential confusion. We appreciate the reviewer’s careful review and constructive feedback, which have helped us improve the clarity and reliability of our study.

Because the phosphoproteomic data (Supplementary Fig. 5C) are not provided clearly, the experimental data for Fig.4A, in which possible CAMSAP2 phosphorylation sites are illustrated, are completely unknown. For me, it is highly strange that only the serine residues are listed in Fig. 4A.

We sincerely thank the reviewer for raising this important point regarding Figure 4A and the phosphoproteomic data in Supplementary Figure 5C.

- Phosphorylation Sites in Figure 4A

The phosphorylation sites illustrated in Figure 4A are derived from our analysis of the original mass spectrometry data. These sites were included based on their high confidence scores and data reliability. Importantly, only serine residues met the stringent criteria for inclusion, as no threonine or tyrosine residues had sufficient evidence for phosphorylation. To clarify this, we have updated the figure legend for Figure 4A (page 32, Lines3-7).

- Improvements to Supplementary Figure 5C (Supplementary Figure 4D in the revised manuscript)

To enhance transparency and clarity, we have reformatted Supplementary Figure 4D to include clearer annotations. The revised figure highlights the phosphopeptides used to identify the phosphorylation sites and provides a more comprehensive presentation of the mass spectrometry data. To clarify this, we have updated the figure legend for Supplementary Figure 4D (page 38, Lines 11-13).

- Data Availability

We will follow the journal’s guidelines by uploading the raw mass spectrometry data to the required public database upon manuscript acceptance. This ensures that the data are accessible and reproducible in compliance with journal standards.

We hope these clarifications and updates address the reviewer’s concerns and improve the reliability and comprehensibility of our data presentation. We greatly appreciate the reviewer’s constructive feedback, which has helped us enhance the rigor and clarity of our manuscript.

Considering the crude nature of the GST-MARK2 sample used for the in vitro kinase assay (Supplementary Fig. 5A), it is unclear whether MARK2 is responsible for all phosphorylation sites on CAMSAP2 detected in the phosphoproteomic analysis. Furthermore, if GFP-CAMSAP2 was purified from Sf9 insect cells, these sites might have been phosphorylated before incubation for the in vitro kinase assay. The authors should address these issues by including a negative control using the kinase-dead mutant of MARK2 in their in vitro kinase assay.

We sincerely thank the reviewer for raising these important points regarding the potential prephosphorylation of GFP-CAMSAP2 and the role of MARK2 in the phosphorylation sites detected in our analysis.

To address the possibility that GFP-CAMSAP2 may have been pre-phosphorylated during its expression in Sf9 insect cells, we conducted an in vitro comparison. Specifically, we compared the band shifts observed in GST-MARK2 + GFP-CAMSAP2 versus GST + GFP-CAMSAP2 under identical conditions. As shown in Supplementary Figure 4B, the GST-MARK2 + GFP-CAMSAP2 group exhibited a clear upward band shift compared to the GST + GFP-CAMSAP2 group, indicating additional phosphorylation events induced by MARK2.

Regarding the inclusion of a kinase-dead MARK2 mutant as a negative control, we acknowledge this as a valuable suggestion for further confirming the specificity of MARK2 in phosphorylating CAMSAP2. While this experiment is not currently included, we plan to conduct it in our future studies to strengthen our findings.

We hope this clarification and the provided evidence address the reviewer’s concerns. We are grateful for this constructive feedback, which has helped us critically evaluate and refine our experimental approach.

(4) In Supplementary Fig.6A-C and Fig.5A-B, the authors claim that the phosphorylation of CAMSAP2 S835 is required for restoring the reduced reorientation of the Golgi in wound-healing cells and the delay in wound closure observed in MARK2 KO cells.  

If the aforementioned claim is adequately supported by experimental data, it indicates that the defects in Golgi repolarization and wound closure in MARK2 KO cells can be mainly attributed to the reduced phosphorylation of S835 of CAMSAP2 in HT1080. Considering the presence of many well-known substrates of MARK2 for regulating cell polarity, this claim is highly striking.  

However, to strongly support this conclusion, the authors should first perform a rescue experiment using MARK2 KO cells exogenously expressing MARK2. This step is essential for determining whether the defects observed in MARK2 KO cells are caused by the loss of MARK2 expression, but not by other artificial effects that were accidentally raised during the generation of the present MARK2 KO clone.  

We sincerely thank the reviewer for their insightful suggestion regarding the rescue experiment to confirm that the defects observed in MARK2 KO cells are specifically caused by the loss of MARK2 expression.

To address this, we performed a rescue experiment in MARK2 KO HT1080 cells by exogenously expressing GFP-MARK2. Our results, presented in Supplementary Figures 3C-E, demonstrate that GFP-MARK2 expression successfully restores the localization of CAMSAP2 on the Golgi apparatus in MARK2 KO cells.

These findings strongly support the conclusion that the defects in Golgi architecture and CAMSAP2 Golgi localization are directly attributable to the loss of MARK2 expression, rather than any artificial effects potentially introduced during the generation of the MARK2 KO clone.

We hope these additional experimental results address the reviewer’s concerns and provide robust evidence for the role of MARK2 in regulating Golgi reorientation and wound closure. We are grateful for the reviewer’s constructive feedback, which has significantly improved the rigor and clarity of our study.

In addition, to evaluate the impact of the rescue effect of CAMSAP2, the authors should include the data of wild-type HT1080 and MARK2 KO cells in Fig. 5B to reliably demonstrate the aforementioned claim.  

We thank the reviewer for their valuable suggestion to include data from wild-type HT1080 and MARK2 KO cells in Figure 5A-C to better evaluate the rescue effects of CAMSAP2.

In response, we have incorporated data from wild-type HT1080 and MARK2 KO cells into Figure 5A-C. These additions provide a comprehensive comparison and further demonstrate the impact of CAMSAP2-S835A and CAMSAP2-S835D on Golgi reorientation relative to the wild-type and MARK2 KO conditions.

These changes are reflected in Figures 5A-C.

We hope these updates address the reviewer’s concerns and strengthen the reliability of our conclusions. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the robustness of our study.

Principally, before checking the rescue effects in MARK2 KO cells, the authors should examine the rescue activity of the CAMSAP2 S835 mutants in restoring the reduced reorientation of the Golgi in wound-healing cells and the delay in wound closure observed in CAMSAP2 KO cells (Supplementary Fig.1F-H and Supplementary Fig.2A, B). These experiments are more essential experiments to substantiate the authors' claim.

We thank the reviewer for their insightful suggestion to examine the rescue activity of CAMSAP2 S835 mutants in CAMSAP2 KO cells to further substantiate our claims.

In Figure 4D-F, we observed significant differences between CAMSAP2 S835 mutants in their ability to restore Golgi structure and localization, indicating functional differences between these mutants. To better reflect the regulatory role of MARK2-mediated phosphorylation of CAMSAP2, we performed scratch wound-healing experiments in MARK2 KO cells by establishing stable cell lines expressing CAMSAP2 S835 mutants. These experiments allowed us to assess Golgi reorientation during wound healing and are presented in Figure 5A-C.

We also attempted to generate stable cell lines expressing GFP-CAMSAP2 and its mutants in CAMSAP2 KO cells. Unfortunately, these cells consistently failed to survive, preventing successful construction of the cell lines.

We hope these experiments and explanations address the reviewer’s concerns. We are grateful for the reviewer’s constructive feedback, which has helped us refine and improve our study.

(5) The data presented in Fig. 6A and B are not sufficient to support the authors' notion that "our observation revealed notable changes in the Golgi apparatus and microtubule network distribution in relation to the wounding. (page 11)"  

Fig. 6A, which includes only a single-cell image in each panel, does not demonstrate the general state of microtubules and the Golgi in the wound-edge cells. The reader cannot even know the migration direction of each cell.  

Fig.6 B are not suitable to quantitatively support the authors' claim. The authors should find a way to quantitatively estimate the microtubule density around the Golgi and the shape and compactness of the Golgi in each cell facing the wound, not estimating the colocalization of microtubules and the Golgi, as in the present Fig. 6B.  

We sincerely apologize for the confusion caused by our unclear descriptions and presentation.

Here, we clarify the purpose and improvements made to address the reviewer’s concerns. In this study, we primarily aimed to observe the relationship between microtubules and the Golgi apparatus in cells at the leading edge of the wound during directed migration. In Figure 6A (now Supplementary Figure 6E), the images represent cells located at the wound edge at different time points. To improve clarity, we have added arrows indicating the migration direction and updated the figure legend to describe these details (page 40 lines 13-14).

To better quantify the relationship between microtubules and the Golgi apparatus, we revised our analysis by referring to the quantitative method used in Figure 3F of the paper Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Specifically, we performed a radial analysis of fluorescence intensity in cells at the wound edge, measuring the distance from the Golgi center (x-axis) and the normalized radial fluorescence intensity of microtubules and the Golgi (y-axis). These results are now presented in Supplementary Figure 6E and 6F.

We hope these improvements address the reviewer’s concerns and provide stronger evidence for the changes in the Golgi apparatus and microtubule network distribution in relation to wound healing. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the clarity and rigor of our study.

The legends to Fig. 6A and B indicate that they compared immunofluorescent staining of cells at the edge of the wound after 0.5h and 2 h of migration. However, the authors state in the text that they compared "the cells located before the wound" and "the cells at the trailing edge of the wounding (page 11)."Although this description is highly ambiguous and misleading, if they compared the wound-edge cells and the cells separated from the wound edge at 2 h after cell migration here, they should improve the experimental design as I pointed out in the 2nd major comment.  

We thank the reviewer for their detailed feedback regarding the experimental design and the need to clarify our descriptions. We have addressed these concerns as follows:

- Clarification of descriptions:

We recognize that the previous description in the text regarding "the cells located before the wound" and "the cells at the trailing edge of the wounding" was ambiguous and potentially misleading. We have revised this text to accurately describe the experimental design. Specifically, we compared cells at the leading edge of the wound at different time points (0.5h and 2h post-migration). These corrections are reflected in figure legends (Supplementary Figure 6E and 6F ) and the Results section (page 11,lines 3-8).

- Improved experimental design:

To better support our conclusions, we performed live-cell imaging to observe the dynamic changes in the Golgi apparatus during directed migration. As shown in Supplementary Figure 2A, our results confirm that the Golgi apparatus undergoes a transient dispersed state before reorganizing into an intact structure.

Additionally, we performed fixed-cell staining at different time points to analyze the colocalization of CAMSAP2 with the Golgi apparatus in cells at the leading edge of the wound. The colocalization analysis, presented in Figures 1A-C, further demonstrates the dynamic regulation of CAMSAP2 during Golgi reorientation.

We hope these updates address the reviewer’s concerns and provide a clearer and more robust foundation for our conclusions. We are grateful for the reviewer’s constructive feedback, which has greatly enhanced the clarity and rigor of our study.

Minor comments  

(1) In Fig. 2 and Supplementary Fig. 3, the authors claim that MARK2 is enriched around the Golgi. However, this claim was based on immunofluorescent images of single cells and single-line scans.  

It is better to present the statistical data for Pearson's coefficient as shown in Figs. 1D and E. To demonstrateMARK2 enrichment around Golgi, but not localization in Golgi, the authors should find a way to quantify the specific enrichment of MARK2 signals in the Golgi region.  

We thank the reviewer for raising this important point regarding the enrichment of MARK2 around the Golgi apparatus. Upon further consideration, we acknowledge that our current data do not provide sufficient evidence to fully elucidate the mechanism of MARK2 localization to the Golgi.

To maintain the scientific rigor of our study, we have removed this claim and the corresponding content from the manuscript, including original Figures 2 and Supplementary Figure 3 that specifically discuss MARK2 enrichment. These changes do not affect the primary conclusions of the study, which focus on the role of MARK2-mediated phosphorylation of CAMSAP2.

We hope this clarification addresses the reviewer’s concerns. In the future, we plan to investigate the precise mechanism of MARK2 localization using additional experimental approaches. We are grateful for the reviewer’s constructive feedback, which has helped us refine the scope and focus of our manuscript.

(2) In Fig. 3 and Supplementary Fig. 4, the authors report that CAMSAP2 localization on the Golgi is reduced in cells lacking MARK2.  

Essentially, the present results support this claim. However, the authors should analyze the Golgi localization of CAMASP2 with the same quantification parameter because they used Pearson's coefficient in Fig. 1D, E and Supplementary Fig.4D but Mander's coefficient in Fig. 3C and Fig.4F.  

We thank the reviewer for their insightful comment regarding the consistency of quantification parameters used in our analysis of CAMSAP2 localization on the Golgi apparatus.

To address this concern, we have revised Figure 3C to use Pearson’s coefficient for consistency with Figure 1D, 1E (Figure 1B and 1E in the revised manuscript), and Supplementary Figure 4D (Supplementary Figure 3I in the revised manuscript). This ensures uniformity in the quantification parameters across these analyses.

For Figure 4F, we have retained Mander’s coefficient, as it accounts for variability in expression levels due to overexpression in individual cells. We believe this approach provides a more accurate reflection of CAMSAP2 localization under the experimental conditions shown in Figure 4F.

We hope these adjustments clarify our analysis and address the reviewer’s concerns. We greatly appreciate the reviewer’s constructive feedback, which has helped improve the consistency and accuracy of our study.

(3) In Fig.4D-F, the authors claim that S835 phosphorylation of CAMSAP2 is essential for its localization to the Golgi apparatus and for restoring the Golgi dispersion induced by CAMASAP2 depletion.  

Fig.4E indicates that the S835A mutant of CAMSAP2 significantly restores the compact assembly of the Golgi apparatus, and the differences in the rescue activities of the wild type, S835A, and S835D are rather small. These data contradict the authors' conclusions regarding the pivotal role of MARK2-mediated phosphorylation at the S835 site of CAMSAP2 in maintaining the Golgi architecture (page 9). The authors should remove the phrase "MARK2-mediated" from the sentence unless addressing the aforementioned issues (see 3rd major comment) and describe the role of S835 phosphorylation in more subdued tone.  

We thank the reviewer for their constructive feedback regarding the conclusions drawn about the role of MARK2-mediated phosphorylation of CAMSAP2 at S835.

In response, we have revised the relevant sentence to reflect a more nuanced interpretation of the data. Specifically, the original statement:

“These observations indicate that the phosphorylation of serine 835 in CAMSAP2 is essential for its proper localization to the Golgi apparatus.”

has been updated to:

“These observations indicate that MARK2 phosphorylation of serine at position 835 of CAMSAP2 affects the localization of CAMSAP2 on the Golgi and regulates Golgi structure” (page 9, Lines 27-29).

We hope this modification addresses the reviewer’s concerns. We are grateful for the feedback, which has helped us refine our conclusions and enhance the clarity of our manuscript.

(4) In Figs. 5I, J and Supplementary Fig.7A-E, the authors claim that the S835 phosphorylationdependent interaction of CAMSAP2 with Uso1 is essential for its localization to the Golgi apparatus.  

This claim was made based on immunofluorescent images of single cells and single-line scans, and was not sufficiently verified (Supplementary Fig.7B, C). Because this is a crucial claim for the present paper, the authors should present statistical data for Pearson's coefficient, as shown in Fig. 1D and E, to quantitatively estimate the Golgi localization of CAMSAP2.  

We thank the reviewer for their suggestion to present statistical data using Pearson's coefficient for a more robust quantification of the Golgi localization of CAMSAP2.

In response, we have revised the statistical analysis for Supplementary Figures 7B-C (Revised Figures 6F and 6G) to use Pearson's coefficient. This change ensures consistency with the quantification methods used in Figures 1D and 1E (Revised Figures 1B and 1E), allowing for a more standardized evaluation of CAMSAP2’s localization to the Golgi apparatus.

We hope this modification addresses the reviewer’s concerns and strengthens the quantitative support for our claims. We are grateful for the reviewer’s constructive feedback, which has helped improve the rigor of our study.

(5) The signal intensities of the immunofluorescent data in Fig. 4D, Fig. 5A, Sup-Fig. 3C and E, and Sup-Fig. 7S are very weak for readers to clearly estimate the authors' claims. They should be improved appropriately.  

We thank the reviewer for highlighting the need to improve the clarity of the immunofluorescent data presented in several figures.

In response, we have enhanced the signal intensities in Figures 4D, 5A, and Supplementary Figure 7D (Revised Supplementary Figure 6A) to make the signals clearer for readers, while ensuring that the adjustments do not alter the integrity of the original data. Supplementary Figures 3C and 3E was remove from our manuscript.

Additionally, to improve consistency and readability across the manuscript, we have standardized the quantification methods for similar analyses:

For CAMSAP2 localization to the Golgi, Pearson's coefficient has been used throughout the manuscript. Figure 3C has been updated to use Pearson's coefficient for consistency.

For Golgi state analysis in wound-edge cells, we have used the Golgi position relative to the nucleus as a uniform metric. This has been applied to Supplementary Figures 1F and 1G, Figures 2D and 2E, and Figures 5A and 5B.

We hope these adjustments address the reviewer’s concerns and improve the clarity and consistency of our study. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the quality of our manuscript.

(6) As indicated above, the authors frequently change the parameters or methods for quantifying the same phenomena (for example, the localization of CAMSAP on the Golgi and Golgi state in wound edge cells) in each figure. This is highly confusing. They should unify them.  

We thank the reviewer for their valuable feedback regarding the inconsistency in quantification methods across the manuscript.

To address this concern, we have carefully reviewed the entire manuscript and standardized the methods used for quantifying similar phenomena:

- CAMSAP2 localization on the Golgi: 

Pearson's coefficient is now consistently used throughout the manuscript. For example, Figure 3C has been updated to use Pearson's coefficient to align with other figures, such as Figures 1B and 1E.

- Golgi state in wound-edge cells: 

The Golgi state is now uniformly measured based on the position of the Golgi relative to the nucleus. This method has been applied to Supplementary Figures 1F and 1G, Figures 2D and 2E, and Figures 5A and 5B.

We believe these changes significantly improve the clarity and consistency of the manuscript, ensuring that readers can easily interpret the data. We are grateful for the reviewer’s constructive feedback, which has greatly helped us enhance the quality and rigor of our study.

(7) The legends frequently fail to clearly indicate the number of independent experiments on which each statistical analysis was based.  

We thank the reviewer for highlighting the need to clearly indicate the number of independent experiments for each statistical analysis.

In response, we have carefully reviewed the entire manuscript and updated the figure legends to include the number of independent experiments for every statistical analysis. This ensures transparency and allows readers to better evaluate the reliability of the data.

We hope these updates address the reviewer’s concerns and improve the clarity and rigor of the manuscript. We appreciate the reviewer’s constructive feedback, which has helped us enhance the quality of our work.

(8) Supplemental Figs. 4E and 4F are not cited in the text.  

We thank the reviewer for pointing out that Supplemental Figures 4E and 4F were not cited in the text.

To address this, we have updated the manuscript to cite these figures (Revised Figures 2H and 2I) in the appropriate section (page 8, lines 1-5).

“the absence of MARK2 can also influence the orientation of the Golgi apparatus during cell wound healing and cause a delay in wound closure (Figure 2 D-I and Figure 3 D).”

We hope this revision resolves the reviewer’s concern and improves the clarity and completeness of the manuscript. We appreciate the reviewer’s feedback, which has helped us refine our work.

(9) The data in Fig. 3 analyzed MARK2 knockout cells (not knockdown cells). The caption should be corrected.  

We thank the reviewer for pointing out the incorrect use of "knockdown" in the caption of Figure 3.

To address this, we have revised the title of Figure 3 from:

“MARK2 knockdown reduces CAMSAP2 localization on the Golgi apparatus.”

to:

“MARK2 affects CAMSAP2 localization on the Golgi apparatus.”

This updated caption reflects the inclusion of both MARK2 knockout and knockdown cell lines analyzed in Figure 3.

We hope this correction resolves the reviewer’s concern and ensures the accuracy of our manuscript. We greatly appreciate the reviewer’s attention to detail, which has helped us improve the clarity and consistency of our work.

(10) The present caption in Fig. 6 disagrees with the content of the figure.  

We thank the reviewer for pointing out the inconsistency between the caption and the content of Figure 6.

To address this issue, we have revised the content of Figure 6 to ensure it aligns accurately with the caption. The updated figure now reflects the description provided in the caption, eliminating any discrepancies and improving clarity for the readers.

We appreciate the reviewer’s constructive feedback, which has helped us enhance the accuracy and presentation of our manuscript.

(11) What do "CS" indicate in Fig. 4B and Supplementary Fig. 5D? The style used to indicate point mutants of CAMSAP2 should be unified. 835A or S835A?  

We thank the reviewer for pointing out the inconsistency in the naming of CAMSAP2 mutants.

To address this, we have revised all relevant figures and text to use the consistent format "S835A" and "S589A" for CAMSAP2 mutants. Specifically, in Figure 4B and Supplementary Figure 5D (now Supplementary Figure 4C), we have replaced the abbreviation "CS2" with "CAMSAP2" and updated the mutant names from "835A" and "589A" to "S835A" and "S589A," respectively. We hope these updates resolve the reviewer’s concerns and ensure clarity and consistency throughout the manuscript. We are grateful for the reviewer’s attention to detail, which has helped us improve the quality of our work.

(12) Uso1 is not a Golgi matrix protein.  

We thank the reviewer for pointing out the incorrect description of Uso1 as a Golgi matrix protein.

In response, we have revised the manuscript to replace all references to “USO1 as a Golgi matrix protein” with “USO1 as a Golgi-associated protein.” This correction ensures that the terminology used in the manuscript is accurate and consistent with current scientific understanding.

We appreciate the reviewer’s attention to detail, which has helped us improve the accuracy and quality of our manuscript.

Author response:

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

Reviewer #1 (Public review):

Summary:

In this manuscript, De La Forest Divonne et al. build a repertory of hemocytes from adult Pacific oysters combining scRNAseq data with cytologic and biochemical analyses. Three categories of hemocytes were described previously in this species (i.e. blast, hyalinocyte, and granulocytes). Based on scRNAseq data, the authors identified 7 hemocyte clusters presenting distinct transcriptional signatures. Using Kegg pathway enrichment and RBGOA, the authors determined the main molecular features of the clusters. In parallel, using cytologic markers, the authors classified 7 populations of hemocytes (i.e. ML, H, BBL, ABL, SGC, BGC, and VC) presenting distinct sizes, nucleus sizes, acidophilic/basophilic, presence of pseudopods, cytoplasm/nucleus ratio and presence of granules. Then, the authors compared the phenotypic features with potential transcriptional signatures seen in the scRNAseq. The hemocytes were separated in a density gradient to enrich for specific subpopulations. The cell composition of each cell fraction was determined using cytologic markers and the cell fractions were analysed by quantitative PCR targeting major cluster markers (two per cluster). With this approach, the authors could assign cluster 7 to VC, cluster 2 to H, and cluster 3 to SGC. The other clusters did not show a clear association with this experimental approach. Using phagocytic assays, ROS, and copper monitoring, the authors showed that ML and SGC are phagocytic, ML produces ROS, and SGC and BGC accumulate copper. Then with the density gradient/qPCR approach, the authors identified the populations expressing anti-microbial peptides (ABL, BBL, and H). At last, the authors used Monocle to predict differentiation trajectories for each subgroup of hemocytes using cluster 4 as the progenitor subpopulation.

The manuscript provides a comprehensive characterisation of the diversity of circulating immune cells found in Pacific oysters.

Strengths:

The combination of the two approaches offers a more integrative view.

Hemocytes represent a very plastic cell population that has key roles in homeostatic and challenged conditions. Grasping the molecular features of these cells at the single-cell level will help understand their biology.

This type of study may help elucidate the diversification of immune cells in comparative studies and evolutionary immunology.

Weaknesses:

The study should be more cautious about the conclusions, include further analyses, and inscribe the work in a more general framework.

Reviewer #1 (Recommendations for the authors):

The manuscript provides a comprehensive characterisation of the diversity of circulating immune cells found in Pacific oysters.

Major comments:

(1) The introduction would benefit from a clear description of what is known about immune cell development and diversity in this model. The bibliography on the three subtypes origins and properties (i.e. blast, hyalinocyte, and granulocytes) should be described in the introduction.

We thank Reviewer #1 for their valuable comments, which have allowed us to further improve our manuscript. We have enriched the introduction with the following addition (line 79 to 82):

“Blast-like cells are considered as undifferentiated hemocyte types (20), hyalinocytes (21) seem to be more involved in wound repair, and granulocytes, more implicated in immune surveillance. The latter are considered as the main immunocompetent hemocyte types (22).”

(2) The authors mentioned a previous scRNAseq dataset produced in another oyster species. They should compare the two datasets to show the robustness of the molecular signatures determined in the present study. In addition, the authors do not mention markers identified in the literature that could be relevant to characterize the clusters (e.g. inflammatory pathway PMID: 29751033, proliferative markers PMID: 36591234/ PMID: 29317231, granulocyte markers PMID: 30633961 ... list not exhaustive). Overall, the comparison of this manuscript dataset and the available literature is too partial

We appreciate the reviewer’s suggestion to compare our dataset with previously published scRNAseq data and to integrate markers from the literature. Below, we address these points in detail.

The transcription factors involved in hematopoiesis, such as Tal1, Sox, Runx, and GATA, are highly conserved across metazoans. These markers were identified in our dataset, consistent with findings in other species (1–3), including the previously mentioned scRNA-seq dataset in C. hongkongensis (4). However, defining robust and specific markers for distinct hemocyte types remains an ambitious goal that requires validation across diverse biological contexts - work that is beyond the scope of the present study. Additionally, meaningful comparisons between datasets are constrained by differences in annotation frameworks and the absence of a standardized system for defining hemocyte subtypes. These limitations underscore the need for harmonization efforts to facilitate robust cross-study comparisons. Nonetheless, our dataset provides a strong foundation for future comparative analyses once such standardization is achieved.

In response to the reviewer’s comment, we have added a paragraph to the discussion (lines 747 - 760) detailing that we identified conserved transcription factor markers in C. gigas and C. hongkongensis.

(3) The authors sequenced 3000 cells without providing more comprehensive information/rationale on the analysed population. What is the number of hemocytes found in an adult? What proportion of the whole hemocyte population does this analysis represent? Does it include the tissue-interacting hemocytes? Also, what is the rationale for choosing that specific stage?

We thank the reviewer for their insightful questions regarding the analyzed hemocyte population.

Adult 18-month-old Crassostrea gigas contain approximately 1 million circulating hemocytes per mL of hemolymph, with an average of 1 mL of hemolymph per individual. Thus, this represents approximately 1 million circulating hemocytes per oyster. For our scRNA-seq analysis, we sampled 3,000 hemocytes, which corresponds to 0.3% of the total circulating hemocyte population.

The number of cells processed was optimized to minimize the occurrence of doublets during scRNAseq. Following 10x Genomics Chromium guidelines, we loaded 4,950 cells to successfully recover a target of 3,000 cells, with a doublet rate of 2.4%, well below the target threshold of 2.5%. This information has been added on line 125 of the document. The target was 3,000 cells, and as reported in Supplementary Table S1, the estimated number of cells after STAR-solo alignment was 2,937. This ensures the reliability and accuracy of single-cell transcriptomic data.

We selected 18-month-old oysters for two key reasons: (i) to facilitate hemolymph collection, as hemocyte counts are more stable and sufficient at this stage, enabling us to collect enough cells for all planned experiments, including functional and cytological analyses; and (ii) to use oysters that are not susceptible to OsHV-1 μVar herpesvirus, which predominantly affects younger animals. This ensured that the hemocyte populations analyzed were not influenced by viral infections or related immune responses.

Our study focused on circulating hemocytes collected from hemolymph, which does not include tissue-interacting hemocytes. While these cells may represent an additional population of interest, they fall outside the scope of our current investigation.

By carefully selecting the animal stage and optimizing cell sampling, we ensured that the scRNA-seq dataset provides a robust representation of circulating hemocyte diversity while maintaining high data quality.

(4) For the GO term enrichment analysis, the authors included all genes presenting a cluster enrichment above L2FC>0.25. This seems extremely low to find distinct functions for each cluster. The risk is to call "cluster specific GO term" GO terms for which the genes are poorly enriched in the cluster. For the most important GO term mentioned in the text, the authors should show the expression levels of the genes (with DotPlot similar to Fig1D) to illustrate the specificity of the GO term. At last, the GO enrichment scores were apparently calculated using the whole genome as background. The analysis, aiming at finding differences between hemocyte subgroups, should use the genes detected in the dataset as background.

We appreciate the reviewer's concerns regarding the threshold used for GO term enrichment analysis and the choice of background genes. Below, we provide clarification on these points.

For nuanced comparisons, such as those between activation states of the same cell type, lower thresholds for log2FC (e.g., ≥0.25) are commonly used to detect subtle regulatory shifts. In single-cell RNA sequencing (scRNA-seq) analyses, it is typical to use a log2FC threshold between 0.25 and 0.5 to ensure that biologically relevant, yet subtle, changes are captured. For our analysis, this threshold was chosen to maintain sensitivity to such shifts, particularly given the diversity and functional specialization of hemocyte clusters.

To address the reviewer's suggestion, we will include DotPlot representations (similar to Fig. 1D) for the most significant GO terms highlighted in the text. This will illustrate the expression levels of the associated genes across clusters and demonstrate their specificity to the identified GO terms.

Regarding the background used in the GO enrichment analysis, we employed the Rank Based Gene Ontology Analysis (RBGOA) approach, which explicitly states in its documentation: "It is important to have the latter two tables representing the whole genome (or transcriptome) — at least the portion that was measured — rather than some select group of genes since the test relies on comparing the behavior of individual GO categories to the whole." Our analysis was conducted in agreement with these initial recommendations, ensuring that the results are consistent with the methodology outlined for RBGOA.

(5) The authors reannotated the genes of C. gigas to reach 73.1% annotation. What are the levels of annotations found prior to the reannotation? What do the scores/scale bars from the RBGOA analysis mean in Figures 2B-D?

Thank you for your comment. The original annotation for C. gigas was based on the work of Penaloza et al. (5), which provided GO annotations for 18,750 out of 30,724 genes, corresponding to 61% annotation. Following our reannotation efforts, we were able to increase the annotation coverage to 73.1%, enhancing the resolution of downstream analyses. In response to the reviewer’s comment, we have updated the results section (line 211 and 216) to explicitly include the original annotation coverage of 61% from the work of Penaloza et al., followed by details on our newly achieved annotation percentage of 73.1%.

Thank you for pointing this out. We apologize for the oversight regarding the scale bar in Figures 2BD. The colors in the original figure correspond to a z-score calculated from the gene ratio, which was not clearly explained and may have caused confusion. In the revised version of the manuscript, we propose a new representation to facilitate understanding and improve the clarity of the data presentation (Figure 2B).

(6) The authors describe first the result of the Kegg enrichment analysis and then of the RBGOA. To gain fluidity, I would suggest merging the results of both Kegg and RBGOA for each cluster.

Thank you for the suggestion. To enhance the fluidity of the results section, we have redesigned the KEGG/RBGOA figure (see figure 2A and 2B) to present the results for each cluster in an integrated manner. This revised approach aims to provide a clearer and more cohesive representation of the findings.

(7) The authors make correlations between gradient fraction containing multiple hemocyte populations and qPCR expression levels of cluster-specific markers to associated cytologic features with specific clusters. If feasible, I would recommend validating the association of several markers with hemocyte subgroups using in situ hybridisation or immunolabelling.

Cytological identification of hemocytes in our study relies on MCDH staining, which provides detailed morphological and cytological information. Unfortunately, the fixation methods required for in situ hybridization (ISH) or immunolabeling are not compatible with those used for MCDH staining. We attempted to combine these approaches but found that the fixation protocols necessary for ISH or immunolabeling compromised the quality of the cytological features observed with MCDH staining. Consequently, such validation was not feasible within the constraints of our experimental setup.

(8) Anti-microbial peptides are mentioned as enriched in agranular cells based on the gradient/qPCR analysis (Figure 6). Are these AMPs regulated by inflammatory pathways? Are any inflammatory pathways enriched in any scRNAseq cluster? In addition, without validating the data by directly labelling AMP in the different populations, it seems hard to conclude that AMP are expressed only by agranular cells.

In oysters, two families of antimicrobial peptides/proteins appear to be transcriptionally regulated in hemocytes in response to an infection. The first is that of Cg-BigDefs (6). A 2020 article indicates that the expression of CgBigDef1 is regulated by CgRel, an ortholog of the NFkB transcription factor, which also control the expression of the proinflammatory cytokine CgIL17 (7). Cg-BPI is induced in response to infection but its regulatory pathways remain unknown (8). The last well characterized family of antimicrobial peptides is Cg-Defs. It exhibits constitutive expression in hemocytes.

In our scRNA-seq analysis, CgRel (G12420) shows an increased expression in cluster 5, with a log2FC of 0.4 (equivalent to a 1.32-fold change or 32% higher expression compared to other clusters). Cluster 5 corresponds to blast-like cells, which are transcriptionally distinct and predominantly found in fractions 1, 2, and 3. These same fractions exhibit the highest CgBigDef expression, as demonstrated by qPCR.

From our qPCR results, we see no expression of the three AMP families in cell-sorted granular cells while the cell-sorted agranular cells are positive for the three AMP families, even for inducible ones. Still, we agree that labelling of cell sorted hemocyte populations would reinforce our data. We now specify in the text that further staining would be necessary to confirm these transcriptomic results (Discussion, lines 695 to 296).

(9) The authors should play down some statements concerning cluster identity. In the absence of a true lineage tracing approach, it is possible that those clusters represent states rather than true cell subtypes. Immune cells are very plastic in nature and able to adapt to the environment, even in conditions that are considered homeostatic.

We appreciate the reviewer’s insightful comment regarding the plasticity of immune cells and the potential for clusters to represent states rather than distinct cell subtypes. We agree that, in the absence of a lineage tracing approach, definitive classification of clusters as fixed subtypes is challenging. Immune cells, including those in invertebrates, are known for their high degree of plasticity and adaptability to environmental cues.

In response to the reviewer’s comment, we have revised the Discussion section to include a statement clarifying that these clusters may represent dynamic states rather than fixed subtypes, thereby acknowledging the plasticity of immune cells (lines 766 to 770).

(10) Related to the above issue, there is no indication of stem cells being present in the cell population. Is there any possibility to look for proliferative or progenitor markers? In homeostatic and in challenged conditions (for example Zymosan treatment)? This would provide some hints into the cellular pathways involved in the response. Perhaps determining the number/fraction of phagocytic cells in challenged conditions would help as well, in the absence of time-lapse assays.

Thank you for highlighting the possibility of stem cells or progenitor markers in our hemocyte populations. In our current analysis, we did not detect any known stem cell or proliferative markers, nor evidence of a clearly defined hematopoiesis site in the hemolymph. Indeed, previous work suggests that oyster hematopoiesis may occur in tissues such as the gills, implying that stem or progenitor cells might not circulate in the hemolymph under homeostatic conditions. Consequently, it is plausible that our observation of no proliferative cell populations partly reflects their absence in hemolymph, especially in naïve (unstimulated) oysters. To conclusively identify potential progenitor cells and their proliferative activity, further approaches involving deliberate perturbation of hemocyte homeostasis - such as immunological challenge (e.g., Zymosan treatment) combined with lineagetracing or proliferation assays - would be necessary. These future investigations would not only clarify whether proliferative cells emerge in the hemolymph in response to environmental or pathological stimuli but also help elucidate the broader cellular pathways underlying oyster immune responses.

In response to the reviewer’s comment, we have revised the Discussion (lines 742 to 745) and added : “Nevertheless, we did not detect any canonical stem or progenitor cell populations in our dataset, underscoring the need for future investigations - potentially involving immunological challenges and lineage-tracing assays - to clarify whether proliferative cells circulate in the hemolymph or instead reside primarily in tissue compartments.”

(11) Could the authors discuss the phagocytic hemocytes in light of scavenger receptor expression?

We thank the reviewer for this insightful question. Our study identifies macrophage-like cells and small granule cells as the principal phagocytes in Crassostrea gigas, capable of robust pathogen engulfment. Transcriptomic data reveal that these cell types express markers associated with endocytosis and immune defense pathways, such as CLEC and LACC24, which are integral to their phagocytic functionality.

Interestingly, our single-cell RNA sequencing analysis indicates that cluster 3, corresponding to small granule cells, expresses the scavenger receptor cysteine-rich (SRCR) gene G3876, annotated as an Low-density lipoprotein receptor-related protein with a Log2 fold change (Log2FC) of 0.77. This finding directly links small granule cells to scavenger receptor-mediated functions, supporting their role as professional phagocytes. Scavenger receptors, including SRCR proteins, are known for their ability to bind and internalize diverse ligands, including pathogens, and their presence in small granule cells highlights a potential mechanism for pathogen recognition and clearance.

Additionally, scavenger receptors are significantly expanded in oysters, as shown in Wang et al. (9). These receptors exhibit dynamic upregulation in hemocytes upon pathogen exposure, particularly following stimulation with pathogen-associated molecular patterns (PAMPs) such as lipopolysaccharide (LPS). This evidence suggests that SRCR proteins, including the one identified in our study, play a pivotal role in the phagocytic activities of hemocytes by facilitating pathogen recognition and internalization.

We propose to add this paragraph (lines 610 to 618) in the Discussion : “Interestingly, our scRNA-seq analysis indicates that SGC (cluster 3) expresses the scavenger receptor cysteine-rich (SRCR) gene G3876, annotated as an Low-density lipoprotein receptor-related protein with a Log2 fold change (Log2FC) of 0.77 linking them to scavenger receptor-mediated pathogen recognition and clearance. This aligns with findings by Wang et al. (9), who demonstrated significant expansion and dynamic regulation of SRCR genes in response to pathogen-associated molecular patterns. “

(12) I am not convinced by the added value of the lineage analysis and the manuscript could stand without it. There is no experimental validation to substantiate the filiation between the clusters. In addition, rooting the lineage to cluster 4 is poorly justified (enrichment in the ribosomal transcript). Cluster 6 is also enriched in ribosomal transcripts and this enrichment can be caused by the low threshold used for the selection of cluster-specific genes (L2FC >0.25). At last, cluster 4 > VC and cluster 4 >SGC belong to the same lineage according to Figure 7 FH.

We thank the reviewer for their detailed comments regarding the lineage analysis. We acknowledge the limitations in experimentally validating the proposed filiation between clusters, as hemocytes in Crassostrea gigas cannot currently be cultivated ex-vivo, and we lack the ability to isolate cells specifically from cluster 4 for further functional assays. Consequently, our lineage analysis is based solely on transcriptomic data and pseudo-time trajectory analysis.

Hematopoietic stem cells (HSCs) are a population of stem cells that are largely cell-cycle-quiescent (G0 phase) with low biosynthetic activity. Upon stimulation and stress HScs undergo proliferation and differentiation and produce all lineages of hemocytes.

Ribosomal proteins play a multifaceted role in preserving the balance between stem cell quiescence and activation. By ensuring precise regulation of protein synthesis, they allow stem cells to maintain their undifferentiated state while remaining poised for activation when needed. Furthermore, ribosomal proteins contribute to the cellular stress response, safeguarding stem cells from oxidative damage and other stressors that could compromise their functionality. Importantly, ribosomal biogenesis and the dynamic assembly of ribosomes provide a regulatory mechanism that fine-tunes the transition from self-renewal to differentiation, a critical feature of hematopoietic stem cells (HSCs) and other stem cell types. These mechanisms collectively highlight the indispensable role of ribosomal proteins in stem cell biology, underscoring their relevance to our study's findings.

In vertebrate, the maintenance of hematopoietic stem cells (HSCs) and hematopoietic homeostasis is widely acknowledged to rely on the proper regulation of ribosome function and protein synthesis (10). This process necessitates the coordinated expression of numerous genes, including genes that encode ribosomal proteins (RP genes) and those involved in regulating ribosome biogenesis and protein translation. Disruptions or mutations in these critical genes are associated with the development of congenital disorders (11). Among these, Rpl22 (found in cluster 4 with a Log2FC of 1.59) has been shown to play a pivotal role in HSC maintenance by balancing ribosomal protein paralog activity, which is critical for the emergence and function of HSCs (12).

Regarding the justification for rooting the lineage to cluster 4, our decision was informed by the enrichment of ribosomal transcripts and functional annotations suggesting a role in translation and cell proliferation, consistent with a precursor-like state. The use of a log2 fold-change (L2FC) threshold of >0.25, while conservative, allowed us to include subtle but meaningful transcriptional shifts essential for resolving lineage transitions.

Finally, the lineage progression from cluster 4 to vesicular cells (VC), macrophage-like cells (ML), and ultimately small granule cells (SGC) is supported by trajectory analysis (Figure 7FH), which consistently places VC and ML as intermediates in the differentiation process toward SGC. Although experimental validation is currently not feasible, these findings provide a conceptual framework for future investigations when cell isolation and functional validation tools become available.

(13) The figures containing heatmaps (Figure 7, Figure 2, Figure S10) or too many subpanels (Figure S5) and Table S5 are hardly readable.

Thank you for highlighting the issues related to the clarity of the heatmaps (Figures 2, 7, and S10), the multi-panel figure (Figure S5), and Table S5. In response to your feedback, we have revised all of these elements to enhance readability and comprehension. Specifically, we increased font sizes, optimized color scales, and reorganized the layout of the subpanels to emphasize the key findings. We also updated Table S5 to ensure that the data are presented in a clear and easily interpretable format.

We trust that these modifications address the concerns raised and improve the overall clarity of the figures and table.

(14) A number of single-cell analyses are now available in different species and the authors allude to similar pathways/transcription factors being involved. Perhaps the authors could expand on this in the discussion section.

Transcription factors involved in hematopoiesis, such as Tal1, Runx and GATA, are highly conserved across metazoans. Consistent with findings in other species, our dataset identifies these markers, reinforcing the evolutionary conservation of these pathways. Furthermore, these markers are also reported in the previous scRNA-seq dataset for C. hongkongensis (4), supporting the robustness of our molecular signatures. However, defining specific and robust markers for distinct hemocyte types remains an ambitious task, requiring additional validation in diverse biological and experimental contexts. This validation is beyond the scope of the present study.

In addition, meaningful comparisons between scRNA-seq datasets are constrained by differences in annotation frameworks and the absence of standardized definitions for hemocyte subtypes. Harmonizing these datasets to enable robust cross-species comparisons is a critical challenge for future studies. Nonetheless, the insights provided by our dataset establish a strong foundation for such comparative analyses when these standardization efforts are realized.

In crayfish (1), 16 transcriptomic clusters were identified corresponding to three hemocyte types, with markers such as integrin prominently expressed in hyalinocytes, consistent with our identification of integrin-related genes in hemocytes. In shrimp (1), 11 transcriptomic clusters were described, with markers of hemocytes in immune-activated states, that we observed also in our dataset. For Anopheles gambiae (2), 8 transcriptomic clusters were identified, including clusters with high ribosomal activity, analogous to those we described in our study. Finally, in Bombyx mori (3), 20 transcriptomic clusters were reported, corresponding to five cytological hemocyte types. Transcription factors such as bHLH, myc, and runt were identified in granulocytes and oenocytoid, showing parallels with markers identified in our dataset.

Despite these similarities, cross-species comparisons are hindered by variability in genome availability and annotation quality, which complicates the precise identification and functional characterization of genes across datasets. Notably, we did not detect pro-phenoloxidase genes in our dataset, unlike shrimp and crayfish, suggesting potential species-specific differences in immune mechanisms.

Regarding the previously published C. hongkongensis scRNA-seq dataset (4), we observe overlap in markers such as runx and GATA. However, direct comparisons remain limited due to differences in dataset annotations and definitions of hemocyte subtypes. This underscores the need for standardized frameworks to facilitate cross-study comparisons. While we emphasize that robust cross-species validation was beyond the scope of this study, our findings contribute valuable insights into the molecular signatures of oyster hemocytes and provide a framework for future comparative research.

We have expanded our discussion to include comparisons with available scRNAseq data from other invertebrate specie (lines 747 to 760)

Minor comments:

(1) Figure 2A-D: to increase the readability of the figure, the authors should display only the GO terms mentioned in the text and keep the full list in supplementary data.

To enhance the fluidity of the results section, we have redesigned the KEGG/RBGOA figure to present the results for each cluster in an integrated manner (See figure 2A and 2B).

(2) Line 223: the authors mention that cluster 1 is characterized by its morphology without providing an explanation or evidence.

We have revised the description of Cluster 1 to remove references to morphology, ensuring consistency with the data presented at this stage of the manuscript (lines 227 to 229) : ”Cluster 1, comprising 27.6 % of cells, is characterized by GO-terms related to myosin complex, lamellipodium, membrane and actin cytoskeleton remodelling, as well as phosphotransferase activity.”

(3) Line 306: the authors mentioned expression levels and associated them with Log2FC, which represents an enrichment, not the level of expression.

Thank you for pointing this out. We agree that log2FC represents enrichment rather than absolute expression levels. We have revised the text in the manuscript to clarify this distinction (line 309). The corrected text now states that log2FC reflects the degree of enrichment or depletion of a gene in a specific cluster relative to others, rather than its absolute expression level.

(4) Figure 4B: the figure shows the distribution of all hemocytes subgroups for each fraction. To better appreciate the distribution of the subgroups in the different fractions, it would be good to have the number of cells of each subtype in the fractions.

We thank the reviewer for their suggestion to include the number of cells of each subtype in the fractions. While we do not have the exact total number of cells per fraction, we systematically performed hemocyte counts for each fraction as part of our methodology. These counts provide a robust estimation of hemocyte distributions across fractions.

Including these counts in the figure could be an alternative approach; however, we believe it would not significantly enhance the interpretability of the data, as the focus of this analysis is on the relative proportions of hemocyte subtypes rather than absolute numbers. The current representation provides a clear and concise overview of subtype distribution patterns, which aligns with the goals of the study.

Nevertheless, if the reviewer considers it essential, we are open to integrating the hemocyte counts into the figure or supplementing the information in the text or supplementary materials to provide additional context.

(5) Line 487-488: the authors mentioned that monocle 3 can deduce the differentiation pathway from the mRNA splice variant. I did not find this information in the publication associated with the statement.

Thank you for pointing this out. We acknowledge the inaccuracy in our statement regarding Monocle3's capabilities. Monocle3 does not deduce differentiation pathways based on mRNA splice variants, as was erroneously suggested in the manuscript. Instead, Monocle3 performs trajectory inference using gene expression profiles. It calculates distances between cells based on their transcriptomic profiles, where cells with similar profiles are positioned closer together, and those with distinct profiles are farther apart. This method enables the construction of potential differentiation trajectories by identifying paths between transcriptionally related cells.

We revise the text in the manuscript to accurately describe this process and remove the incorrect reference to mRNA splice variants (lines 495 to 497).

(6) Figures 6C-H display heatmaps with two columns representing the beginning and the end of the lineage predicted. It would be more talkative to show the whole path presented in Figure S10.

Thank you for pointing out that Figures 7C–H currently only show the beginning and end of the predicted lineage, limiting the clarity of the intermediate stages. In response to your suggestion, we have revised these figures to include the full trajectory as presented in Figure S10, ensuring that the intermediate transitions are more clearly visualized. We believe these modifications offer a more comprehensive overview of the entire lineage and enhance the interpretability of our results.

Bibliography:

(1) F. Xin, X. Zhang, Hallmarks of crustacean immune hemocytes at single-cell resolution. Front. Immunol. 14 (2023).

(2) H. Kwon, M. Mohammed, O. Franzén, J. Ankarklev, R. C. Smith, Single-cell analysis of mosquito hemocytes identifies signatures of immune cell subtypes and cell differentiation. eLife 10, e66192 (2021).

(3) M. Feng, L. Swevers, J. Sun, Hemocyte Clusters Defined by scRNA-Seq in Bombyx mori: In Silico Analysis of Predicted Marker Genes and Implications for Potential Functional Roles. Front. Immunol. 13 (2022).

(4) J. Meng, G. Zhang, W.-X. Wang, Functional heterogeneity of immune defenses in molluscan oysters Crassostrea hongkongensis revealed by high-throughput single-cell transcriptome. Fish & Shellfish Immunology 120, 202–213 (2022).

(5) C. Peñaloza, A. P. Gutierrez, L. Eöry, S. Wang, X. Guo, A. L. Archibald, T. P. Bean, R. D. Houston, A chromosome-level genome assembly for the Pacific oyster Crassostrea gigas. GigaScience 10, giab020 (2021).

(6) R. D. Rosa, A. Santini, J. Fievet, P. Bulet, D. Destoumieux-Garzón, E. Bachère, Big Defensins, a Diverse Family of Antimicrobial Peptides That Follows Different Patterns of Expression in Hemocytes of the Oyster Crassostrea gigas. PLOS ONE 6, e25594 (2011).

(7) Y. Li, J. Sun, Y. Zhang, M. Wang, L. Wang, L. Song, CgRel involved in antibacterial immunity by regulating the production of CgIL17s and CgBigDef1 in the Pacific oyster Crassostrea gigas. Fish & Shellfish Immunology 97, 474–482 (2020).

(8) Evidence of a bactericidal permeability increasing protein in an invertebrate, the Crassostrea gigas Cg-BPI | PNAS. https://www.pnas.org/doi/abs/10.1073/pnas.0702281104.

(9) L. Wang, H. Zhang, M. Wang, Z. Zhou, W. Wang, R. Liu, M. Huang, C. Yang, L. Qiu, L. Song, The transcriptomic expression of pattern recognition receptors: Insight into molecular recognition of various invading pathogens in Oyster Crassostrea gigas. Developmental & Comparative Immunology 91, 1–7 (2019).

(10) R. A. J. Signer, J. A. Magee, A. Salic, S. J. Morrison, Haematopoietic stem cells require a highly regulated protein synthesis rate. Nature 509, 49–54 (2014).

(11) A. Narla, B. L. Ebert, Ribosomopathies: human disorders of ribosome dysfunction. Blood 115, 3196–3205 (2010).

(12) Y. Zhang, A.-C. E. Duc, S. Rao, X.-L. Sun, A. N. Bilbee, M. Rhodes, Q. Li, D. J. Kappes, J. Rhodes, D. L. Wiest, Control of Hematopoietic Stem Cell Emergence by Antagonistic Functions of Ribosomal Protein Paralogs. Developmental Cell 24, 411–425 (2013).

Reviewer #2 (Public review):

Summary:

This work provides a comprehensive understanding of cellular immunity in bivalves. To precisely describe the hemocytes of the oyster C. gigas, the authors morphologically characterized seven distinct cell groups, which they then correlated with single-cell RNA sequencing analysis, also resulting in seven transcriptional profiles. They employed multiple strategies to establish relationships between each morphotype and the scRNAseq profile. The authors correlated the presence of marker genes from each cluster identified in scRNAseq with hemolymph fractions enriched for different hemocyte morphotypes. This approach allowed them to correlate three of the seven cell types, namely hyalinocytes (H), small granule cells (SGC), and vesicular cells (VC). A macrophage-like (ML) cell type was correlated through the expression of macrophage-specific genes and its capacity to produce reactive oxygen species. Three other cell types correspond to blast-like cells, including an immature blast cell type from which distinct hematopoietic lineages originate to give rise to H, SGC, VC, and ML cells. Additionally, ML cells and SGCs demonstrated phagocytic properties, with SGCs also involved in metal homeostasis. On the other hand, H cells, nongranular cells, and blast cells expressed antimicrobial peptides. This study thus provides a complete landscape of oyster hemocytes with functional validation linked to immune activities. This resource will be valuable for studying the impact of bacterial or viral infections in oysters.

Strengths:

The main strength of this study lies in its comprehensive and integrative approach, combining single-cell RNA sequencing, cytological analysis, cell fractionation, and functional assays to provide a robust characterization of hemocyte populations in Crassostrea gigas.

(1) The innovative use of marker genes, quantifying their expression within specific cell fractions, allows for precise annotation of different cellular clusters, bridging the gap between morphological observations and transcriptional profiles.

(2) The study provides detailed insights into the immune functions of different hemocyte types, including the identification of professional phagocytes, ROS-producing cells, and cells expressing antimicrobial peptides.

(3) The identification and analysis of transcription factors specific to different hemocyte types and lineages offer crucial insights into cell fate determination and differentiation processes in oyster immune cells.

(4) The authors significantly advance the understanding of oyster immune cell diversity by identifying and characterizing seven distinct hemocyte transcriptomic clusters and morphotypes.

These strengths collectively make this study a significant contribution to the field of invertebrate immunology, providing a comprehensive framework for understanding oyster hemocyte diversity and function.

Weaknesses:

(1) The authors performed scRNAseq/lineage analysis and cytological analysis on oysters from two different sources. The methodology of the study raises concerns about the consistency of the sample and the variability of the results. The specific post-processing of hemocytes for scRNAseq, such as cell filtering, might also affect cell populations or gene expression profiles. It's unclear if the seven hemocyte types and their proportions were consistent across both samples. This inconsistency may affect the correlation between morphological and transcriptomic data.

We thank the reviewer for highlighting the importance of sample consistency and potential variability, and we acknowledge the need for clarification regarding the use of oysters from two different sources.

Oysters from La Tremblade (known pathogen-free in standardized conditions) were used to establish the hemocyte transcriptomic atlas through scRNA-seq and for cytological analyses. Oysters from the Thau Lagoon (Bouzigues) were used for cytological, functional, and fractionation experiments. These oysters were sampled during non-epidemic periods and monitored under Ifremer’s microbiological surveillance to ensure pathogen free status.

The cytological results (hemocytograms) presented in Figure 3 and Supplementary Figure S3 were derived from Thau Lagoon oysters. To clarify, we updated The Table 3 in Figure 3 and Supplementary Figure S3 to explicitly display hemocyte counts for oysters from both La Tremblade and Thau Lagoon. These data confirm consistent proportions of hemocyte types across both sources, with no significant differences (p > 0.05).

Hemocyte isolation and filtering protocols were rigorously optimized to preserve cell viability and morphology during scRNA-seq library preparation. Viability assays and cytological evaluations confirmed that these procedures did not significantly alter hemocyte populations or their proportions. Sample processing times were minimized to ensure that the scRNA-seq results accurately reflect the native state of the hemolymph.

Taken together, our results confirm that variability between oyster sources or methodological processes did not compromise our findings. This ensures that the correlations between morphological and transcriptomic data are reliable and robust.

(2) The authors claim to use pathogen-free adult oysters (lines 95 and 119), but no supporting data is provided. It's unclear if the oysters were tested for bacterial and viral contaminations, particularly Vibrio and OsHV-1 μVar herpesvirus.

The oysters used in this study were sourced from two distinct origins. First, the animals (18 months old) utilized for scRNA-seq and cytological analyses were obtained from the Ifremer controlled farm located in La Tremblade, France (GPS coordinates: 45.7981624714465, -1.150171788447683). This facility exclusively produces standardized oysters bred in controlled conditions with filtered seawater, entirely isolated from environmental known pathogens. The oysters from this source are certified “pathogen-free” upon arrival at the laboratory, following Ifremer's stringent quality control protocols. We have replaced the term 'pathogen-free' with 'known pathogen-free’ (line 123) to accurately reflect the animals' true status.

Second, for the fractionation experiments and functional tests, oysters were either sourced from the aforementioned Ifremer farm or from a producer located in the Thau Lagoon, France (GPS coordinates: 43.44265228308842, 3.6359883059292057). The Thau Lagoon is subject to comprehensive environmental and microbiological surveillance by the Ifremer monitoring network and the regional veterinary laboratory. For these experiments, we specifically selected oysters aged 18 months - an age associated with reduced susceptibility to OsHV-1 μVar herpesvirus - and ensured that sampling occurred outside of any detected epidemic periods. Furthermore, prior to experimentation, hemocyte samples from all oysters were examined. Oysters showing signs of contamination or exhibiting abnormal hemocyte profiles were excluded from the study.

These measures ensured that the oysters used in this work were of high health status and minimized the likelihood of bacterial or viral contamination, including Vibrio and OsHV-1 μVar.

(3) The KEGG and Gene Ontology analyses, while informative, are very descriptive and lack interpretation. The use of heatmaps with dendrograms for grouping cell clusters and GO terms is not discussed in the results, missing an opportunity to explore cell-type relationships. The changing order of cell clusters across panels B, C, and D in Figure 2 makes it challenging to correlate with panel A and to compare across different GO term categories. The dendrograms suggest proximity between certain clusters (e.g., 4 and 1) across different GO term types, implying similarity in cell processes, but this is not discussed. Grouping GO terms as in Figure 2A, rather than by dendrogram, might provide a clearer visualization of main pathways. Lastly, a more integrated discussion linking GO term and KEGG pathway analyses could offer a more comprehensive view of cell type characteristics. The presentation of scRNAseq results lacks depth in interpretation, particularly regarding the potential roles of different cell types based on their transcriptional profiles and marker genes. Additionally, some figures (2B, C, D, and 7C to H) suffer from information overload and small size, further hampering readability and interpretation.

Thank you for your valuable suggestions regarding the presentation and interpretation of our KEGG and Gene Ontology (GO) analyses. In response, we revised Figure 2 to enhance clarity and provide deeper insights into cell-type relationships and biological processes.

The revised figure 2 reorganizes GO term analysis into a more intuitive layout, grouping related biological processes and pathways in a structured manner. This approach replaces the dendrogram organization and provides a clearer visualization of key pathways for each cell cluster.

(4) The pseudotime analysis presented in the study provides modest additional information to what is already manifest from the clustering and UMAP visualization. The central and intermediate transcriptomic profile of cluster 4 relative to other clusters is apparent from the UMAP and the expression of shared marker genes across clusters (as shown in Figure 1D). The statement by the authors that 'the two types of professional phagocytes belong to the same granular cell lineage' (lines 594-596) should be formulated with more caution. While the pseudotime trajectory links macrophage-like (ML) and small granule-like (SGC) cells, this doesn't definitively establish a direct lineage relationship. Such trajectories can result from similarities in gene expression induced by factors other than lineage relationships, such as responses to environmental stimuli or cell cycle states. To conclusively establish this lineage relationship, additional experiments like cell lineage tracing would be necessary, if such tools are available for C. gigas.

We appreciate the reviewer’s detailed feedback on the pseudotime analysis and its interpretation. While we acknowledge that the clustering and UMAP visualization provide valuable insights, the pseudotime analysis offers a complementary approach by highlighting significantly expressed genes, including key transcription factors, that might otherwise be overlooked in differential expression analysis based solely on Log2FC between clusters. In our study, the pseudotime analysis revealed transcription factors known to play crucial roles in hemocyte differentiation, providing additional depth to our understanding of hemocyte lineage relationships and functional specialization.

Regarding the statement on lines 594 - 596, we agree that the evidence provided by pseudotime trajectories does not definitively establish a direct lineage relationship between macrophage-like (ML) and small granule-like (SGC) cells. Instead, these trajectories suggest potential developmental connections that warrant further investigation. We propose the following revised sentence (lines 616 to 618) :

"The pseudotime trajectory linking macrophage-like (ML) and small granule-like (SGC) cells suggests a potential developmental relationship within the granular cell lineage; however, this hypothesis requires further validation."

We also concur with the reviewer that additional experiments, such as cell lineage tracing, would be necessary to definitively establish this relationship. Unfortunately, the long-term cultivation of hemocytes in C. gigas is currently not feasible. However, we are planning to develop FACS-based approaches to separate the seven hemocyte subtypes, which will allow us to refine their ontology and explore their potential lineage relationships more precisely.

(6) Given the mention of herpesvirus as a major oyster pathogen, the lack of discussion on genes associated with antiviral immunity is a notable omission. While KEGG pathway analysis associated herpesvirus with cluster 1, the specific genes involved are not elaborated upon.

Thank you for your valuable observation regarding the lack of discussion on genes associated with antiviral immunity, particularly in the context of herpes virus infection. The KEGG pathway analysis indeed identified a weak signature associated with herpesvirus in Cluster 1, primarily involving genes encoding beta integrins. In humans, beta integrins have been described as receptors facilitating herpesvirus entry (1). However, in the case of naive oysters used in this study, the KEGG signature was subtle, likely reflecting the absence of active viral infection. Additionally, beta integrins are multifunctional molecules that also play critical roles in processes such as cell adhesion, a function attributed to hyalinocytes, as highlighted in our results.

Given the naive status of the oysters and the weak antiviral signature observed, we chose not to discuss these findings in detail in this study. However, ongoing work in our laboratory aims to further investigate the specific hemocyte populations targeted by OsHV-1, which may shed light on the role of integrins in antiviral immunity in oysters.

We hope this clarifies our approach and the context of the KEGG findings. Thank you for bringing this important perspective to our attention.

(7) The discussion misses an opportunity for comparative analysis with related species. Specifically, a comparison of gene markers and cell populations with Crassostrea hongkongensis, could highlight similarities and differences across systems.

In response to the reviewer’s comment, we have added a comparative analysis between C. hongkongensis and C. gigas hemocyte populations, situating our findings within the broader context of invertebrate immune cell diversity and specialization (lines 747 to 760)

Reviewer #2 (Recommendations for the authors):

(1) Lines 92-93: The authors should add references associated with transcriptomic studies of C. gigas hemocytes.

Thank you for pointing this out. In the revised manuscript, we have added references to previous transcriptomic studies of C. gigas hemocytes (line 83).

(2) Line 121 and 127: The authors should clarify whether 3,000 represents the number of cells loaded or their target for analysis.

The number of cells processed was optimized to minimize the occurrence of doublets during scRNAseq. Following 10x Genomics Chromium guidelines, we loaded 4,950 cells to successfully recover a target of 3,000 cells, with a doublet rate of 2.4%, well below the target threshold of 2.5%. This information has been added on line 125 of the document. The target was 3,000 cells, and as reported in Supplementary Table S1, the estimated number of cells after STAR-solo alignment was 2,937. This ensures the reliability and accuracy of single-cell transcriptomic data.

(3) Line 129: "Supp. Table 1" in the text and "Supp. Table S1" in the figure title should be edited.

The inconsistency between "Supp. Table 1" in the text and "Supp. Table S1" in the figure title has been corrected for uniformity throughout the manuscript (line 134).

(4) Line 138-139: The authors should clarify that the heatmap displays the top 10 positively enriched marker genes for each cluster, as identified by Seurat's differential expression analysis. It is important to note that the analysis does not explicitly show under-represented transcripts, but rather highlights the contrast between cluster-specific overexpressed genes and their lower expression in other clusters.

We have clarified that the heatmap displays the top 10 positively enriched marker genes for each cluster, as identified by Seurat's differential expression analysis, and that the analysis highlights cluster-specific overexpressed genes rather than explicitly showing under-represented transcripts (lines 143 - 145).

(5) Figure 1: The authors should consider improving or potentially removing Figure 1C. The gene IDs are not readable due to their small size, which significantly reduces the informative value of the figure. In addition, the data presented in this heatmap is largely redundant with the more informative and readable dot plot in Figure 1D, which shows both expression levels and the percentage of cells expressing each gene.

Thank you for your suggestion regarding Figure 1C. In the revised manuscript, we have removed the original panel C from the main figure and transferred it to Supplementary Figure S1K, which improves readability while retaining the relevant data. We have also renumbered the remaining panels for clarity, with the former panel D now designated as panel C. We believe these adjustments address the reviewer’s concerns and streamline the presentation of the data.

(6) Table 1: The authors should clarify in the legend the statistical significance criteria (adjusted p-value) for the genes listed.

As requested, we have added the adjusted p-value threshold (adj. p-value < 0.05) to the legend of Table 1.

(7) Line 188: The authors should align the text description of the KEGG pathways in cluster 7 with Figure 2A, describing Wnt signaling pathway and clarifying the terminology "endosome pathway" to ensure consistency.

In the revised text, we have aligned our description with Figure 2A by explicitly mentioning the Wnt signaling pathway in cluster 7 (lines 193 to 194).

The endo-lysosomal pathway encompasses a series of membrane-bound compartments and trafficking events responsible for the uptake of macromolecules from the extracellular environment, their subsequent sorting in endosomes, and eventual degradation in lysosomes. This pathway is tightly regulated, ensuring not only the breakdown of macromolecules but also the recycling of membrane components and signaling receptors essential for maintaining cellular homeostasis (2). In our study, the KEGG signatures of cluster 7 highlight the involvement of the endo-lysosomal pathway.

(8) Line 223: The authors should revise the description of cluster 1, avoiding references to morphology at this point in the manuscript, as no morphological data has been presented yet.

We have revised the description of Cluster 1 to remove references to morphology, ensuring consistency with the data presented at this stage of the manuscript (lines 227 to 229) : ”Cluster 1, comprising 27.6 % of cells, is characterized by GO-terms related to myosin complex, lamellipodium, membrane and actin cytoskeleton remodelling, as well as phosphotransferase activity.”

(9) Figure 2: The authors should revise Figure 2 to improve the clarity. For Figure 2A, they should address the redundancy in the "Global and overview maps" category by removing overlapping pathways such as carbon metabolism and biosynthesis of amino acids, which are likely represented in more specific metabolic categories (glycolysis, pentose). They could consider grouping similar pathways together, such as combining "Amino acid metabolism" with "Metabolism of other amino acids," and separating metabolic pathways from cellular processes for easier interpretation. They should also address the surprising absence of certain expected pathways like lipid metabolism, nucleotide metabolism, and cofactor/vitamin metabolism, as well as cellular processes such as cell growth and chromatin modeling. Even if these pathways are not enriched in specific clusters, mentioning their absence could provide valuable context for the reader.

In the revised version of the manuscript, we propose a new representation to facilitate understanding and improve the clarity of the data presentation.

(10) For Figures 2B, C, and D, the authors should significantly increase the font size of text and numbers, ensuring readability at 100% scale in PDF format. They could also add labels directly on each graph to clearly indicate the type of GO terms represented, (Biological Process, Cellular Component, or Molecular Function).

In the revised version of the manuscript, we propose a new representation to facilitate understanding and improve the clarity of the data presentation.

(11) Line 247-250: The authors should revise their description of cell types to follow the same order as presented in Figure 3A.

We have revised the description of cell types in the manuscript to follow the same order as presented in Figure 3A, as requested.

(12) Line 265-266: The authors should develop the significance of the nucleo-cytoplasmic ratio in hemocyte morphology and identification.

We thank the editor for bringing this to our attention and apologize for the discrepancy between the terminology used in the text and the results presented in Figure 3. The text refers to the nuclear-tocytoplasmic ratio (N/C), while the figure mistakenly displays the inverse ratio, cytoplasmic-to-nuclear ratio (C/N). We recognize that this inversion may cause confusion and will ensure consistency between the text and the figure.

To address this, we propose correcting the figure legend and labels in Figure 3 to align with the terminology used in the text (N/C ratio). This will prevent confusion and maintain clarity throughout the manuscript.

The nuclear-to-cytoplasmic (N:C) ratio, also known as the nucleus:cytoplasm ratio or N/C ratio, is a well-established measurement in cell biology that reflects the relative size of the nucleus to the cytoplasm. This ratio is frequently used as a morphologic feature in the diagnosis of atypia and malignancy in human cells, underscoring its diagnostic value. In the context of our study, we use the N:C ratio to provide a more precise and quantitative description of hemocyte types in Crassostrea gigas. Specifically, the N:C ratio allows us to distinguish between different hemocyte morphotypes, such as blasts and granular cells, and to enrich the characterization of their functional specialization. This quantitative measure supports the morphological classification and enhances the reproducibility and clarity of hemocyte identification.

(13) Line 286-294: The authors should review and correct the legend for Figure 3. It seems that the description of results related to Figure 3C has been mistakenly inserted into the legend.

We thank the reviewer for pointing out this issue with the legend of Figure 3. The description of results related to Figure 3C has now been removed from the legend. The revised legend focuses solely on the figure elements, improving clarity and consistency. We believe this adjustment addresses the reviewer's comment effectively.

(14) Figure 3: The authors should revise the legend for Figure 3A to provide more detailed and explicit descriptions of the "Size, shape and particularities" of the ML, SGC, BGC, and VC hemocyte types.

We thank the reviewer for their insightful suggestion to provide more explicit descriptions in the legend for Figure 3A. We have revised the legend to include detailed explanations of the "Size, shape, and particularities" for the ML, SGC, BGC, and VC hemocyte types. Specifically, we have clarified that size refers to the average granule diameter, shape describes the morphology of the granules (e.g., spherical or elongated), and particularities highlight distinguishing features such as granule color or fluorescence properties observed under specific staining or imaging conditions. We believe this updated legend provides the level of detail requested and enhances the clarity of the figure (lines 294 - 297).

(15) Figure 4: The authors should clarify the method used for calculating relative gene expression in Figure 4A and Figure 6. They should explicitly state in the figure legend that the expression was normalized to the Cg-rps6 reference gene, as mentioned in line 835. The authors should also provide details on the calculation method used (e.g., 2-ΔCt method) and confirm whether the reference gene was expressed at similar levels across all clusters.

We thank the reviewer for pointing out the need for additional clarity regarding the calculation of relative gene expression in Figures 4A and 6. To address this, we have revised the legends for both figures to explicitly state that gene expression levels were normalized to the reference gene Cg-rps6 and calculated using the 2^-ΔCt method. We have also confirmed that Cg-rps6 was stably expressed across all hemocyte clusters and explicitly mentioned this in the revised legends. These changes ensure greater transparency and address the reviewer’s concerns (lines 342 to 346).

(16) The authors could consider removing or modifying Figure 4B, as it appears to be redundant with Figure 3C. Both figures show the average percentage of each hemocyte type in the seven Percoll gradient fractions.

We thank the reviewer for highlighting potential redundancy between Figures 3C and 4B. While both figures present the distribution of hemocyte types across Percoll gradient fractions, Figure 4B serves a distinct and critical purpose in the manuscript. Specifically, it provides the numerical data necessary to understand the correlations shown in Figure 4A, where we analyze the relationship between gene expression levels and the distribution of hemocyte types. These detailed percentages are essential for interpreting the statistical robustness and biological relevance of the correlation matrix, which could not be derived solely from the qualitative visualization in Figure 3C.

(17) Figure 5: The authors should address the redundancy between Figure S7B and Figure 5B, as they appear to present the same data. In Figure S7B, "SGC" is incorrectly abbreviated as "G".

In the revised version of the manuscript, we addressed the redundancy between the two figures and we corrected the incorrectly abbreviated SGC.

(18) Line 412: The authors should correct the typographical error, changing "Pecoll" to "Percoll".

In the revised version of the manuscript, we correct this typographical error (line 417).

(19) Line 417: The statement about the inhibitor apocynin likely refers to Figure 5D, not Figure 5C.

In the revised version of the manuscript, we have corrected this reference error to accurately refer to Figure 5D (line 422).

(20) Line 441-444: The authors should provide references to support their annotation of cluster 1 as macrophage-like cells based on macrophage-specific genes. These references should cite established literature on known macrophage gene markers, particularly in bivalves or related species if available. They need to clarify whether specific gene markers exist for each of the hemocyte morphotypes they have identified. If such markers are known from previous studies, they should be mentioned and referenced.

We propose to modify lines 446 to 449 to address the reviewer's concerns. Cluster 1, which we have termed "macrophage-like" due to its pronounced phagocytic activity and reactive oxygen species (ROS) production, is enriched in Angiopoietin-1 receptor expression (Table 1). Angiopoietin receptors belong to the Tie receptor family, which is expressed in a subset of macrophages known as Tie2-expressing monocytes (TEMs) in humans (3–5). While our analysis reveals a strong overexpression of the Angiopoietin-1 receptor, we acknowledge that this receptor is not an exclusive marker for macrophages.

In bivalves, including oysters, no definitive molecular markers have been established for macrophagelike cells as they are defined functionally in this study. Consequently, the identification of such cells relies on their functional characteristics rather than strict marker expression. To clarify, we propose the following revision to the sentence:

Furthermore, this cluster expresses macrophage-related genes, including the macrophage-expressed gene 1 protein (G30226) (Supp. Data S1), along with maturation factors for dual oxidase, an enzyme involved in peroxide formation (Supp. Fig. S8), supporting its designation as macrophage-like based on functional characteristics.

(21) Figure 7: For Figures 7C to 7H, the authors should increase the font size of gene names and descriptions to ensure legibility in both printed versions and digital formats. To simplify these figures, the authors could consider displaying less differentially expressed genes for each lineage, along with the top genes for each differentiation pathway. If detailed gene information is crucial, they could move the full list to a supplementary table and reference it in the figure legend. Regarding Figure 7I, the authors should reorder the transcription factor genes by cluster and specificity to improve visualization and interpretation, like in Figure 1D.

Thank you for these valuable suggestions regarding Figure 7. We have revised Figures 7C–H to ensure improved readability. Furthermore, we have simplified these panels by highlighting fewer differentially expressed genes for each lineage. In Figure 7I, we have reordered the transcription factor genes by cluster and specificity, following a layout similar to Figure 1D, to facilitate clearer visualization and interpretation of the data.

(22) Line 490: The authors should provide more precise references to the specific GO terms and figure panels they are discussing.

To address this comment, we have revised the sentence and provided additional information in the text to clearly indicate where the corresponding figure panels can be found in the manuscript (line 499)

(23) Line 510: The authors state that "5 cell lineages could be defined," but the subsequent text and Figure 7C to H actually present 6 distinct lineages.

We have corrected in the manuscript. 6 lineages could be defined (line 521).

(24) Line 534: The authors should consider further investigating the pluripotent potential of cluster 4 cells by exploring known or potential stem cell markers in their scRNAseq data.

Thank you for highlighting the possibility of pluripotent potential of cluster 4. In our current analysis, we did not detect any known stem cell or proliferative markers, nor evidence of a clearly defined hematopoiesis site in the hemolymph. Indeed, previous work suggests that oyster hematopoiesis may occur in tissues such as the gills, implying that stem or progenitor cells might not circulate in the hemolymph under homeostatic conditions. Consequently, it is plausible that our observation of no proliferative cell populations partly reflects their absence in hemolymph, especially in naïve (unstimulated) oysters. To conclusively identify potential progenitor cells and their proliferative activity, further approaches involving deliberate perturbation of hemocyte homeostasis - such as immunological challenge (e.g., Zymosan treatment) combined with lineage-tracing or proliferation assays - would be necessary. These future investigations would not only clarify whether proliferative cells emerge in the hemolymph in response to environmental or pathological stimuli but also help elucidate the broader cellular pathways underlying oyster immune responses.

In response to the reviewer’s comment, we have revised the Discussion (lines 695 to 696) and added : “Nevertheless, we did not detect any canonical stem or progenitor cell populations in our dataset, underscoring the need for future investigations - potentially involving immunological challenges and lineage-tracing assays - to clarify whether proliferative cells circulate in the hemolymph or instead reside primarily in tissue compartments.”

(25) Figure S10: The authors should significantly improve the readability of Figure S10 by increasing the font size. Currently, the small font size makes it impossible for readers to discern the information presented.

Thank you for highlighting the readability concerns regarding Figure S10. In response to your comment, we have increased the overall size and font of the figure, ensuring that all labels and legends are clearly legible in both printed and digital formats. We believe these adjustments will allow readers to more easily interpret the information presented.

(26) Line 896: The authors should correct the typographical error on line 896 by deleting the additional bracket.

In the revised version of the manuscript, we correct this typographical error.

(27) Figure S12: The authors should address the absence of any reference to Figure S12 in the main text of the manuscript.

The reference to Supp. Figure S12 has been corrected. It was a referencing error between Supp. Figure S11(in the discussion, line 670) and Supp. Figure S12.

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(2) J. P. Luzio, P. R. Pryor, N. A. Bright, Lysosomes: fusion and function. Nat Rev Mol Cell Biol 8, 622–632 (2007).

(3) A. S. Harney, E. N. Arwert, D. Entenberg, Y. Wang, P. Guo, B.-Z. Qian, M. H. Oktay, J. W. Pollard, J. G. Jones, J. S. Condeelis, Real-Time Imaging Reveals Local, Transient Vascular Permeability, and Tumor Cell Intravasation Stimulated by TIE2hi Macrophage-Derived VEGFA. Cancer Discov 5, 932–943 (2015).

(4) M. De Palma, R. Mazzieri, L. S. Politi, F. Pucci, E. Zonari, G. Sitia, S. Mazzoleni, D. Moi, M. A. Venneri, S. Indraccolo, A. Falini, L. G. Guidotti, R. Galli, L. Naldini, Tumor-targeted interferon-alpha delivery by Tie2-expressing monocytes inhibits tumor growth and metastasis. Cancer Cell 14, 299–311 (2008).

(5) M. De Palma, M. A. Venneri, R. Galli, L. Sergi Sergi, L. S. Politi, M. Sampaolesi, L. Naldini, Tie2 identifies a hematopoietic lineage of proangiogenic monocytes required for tumor vessel formation and a mesenchymal population of pericyte progenitors. Cancer Cell 8, 211–226 (2005).

Reviewer #3 (Public review):

The paper addresses pivotal questions concerning the multifaceted functions of oyster hemocytes by integrating single-cell RNA sequencing (scRNA-seq) data with analyses of cell morphology, transcriptional profiles, and immune functions. In addition to investigating granulocyte cells, the study delves into the potential roles of blast and hyalinocyte cells. A key discovery highlighted in this research is the identification of cell types engaged in antimicrobial activities, encompassing processes such as phagocytosis, intracellular copper accumulation, oxidative bursts, and antimicrobial peptide synthesis.

A particularly intriguing aspect of the study lies in the exploration of hemocyte lineages, warranting further investigation, such as employing scRNA-seq on embryos at various developmental stages.

In the opinion of this reviewer, the discussion should compare and contrast the transcriptome characteristics of hemocytes, particularly granule cells, across the three species of bivalves, aligning with the published scRNA-seq studies in this field to elucidate the uniformities and variances in bivalve hemocytes.

Reviewer #3 (Recommendations for the authors):

Minor Concerns:

(1) In the context of C. gigas, the notable expansion of stress and immune-related genes in its genome stands out. It is anticipated that the article will discuss the expression patterns of classical immune-related genes like TLR and RLR across different cell clusters.

We appreciate the reviewer's interest in the expression patterns of classical immune-related genes, such as Toll-like receptors (TLRs) and RIG-I-like receptors (RLRs), across different cell clusters in Crassostrea gigas. In our single-cell RNA sequencing (scRNA-seq) analysis, we did not detect significant expression of TLR or RLR genes. This absence can be attributed to several factors. First, technical limitations of scRNA-seq: The droplet-based scRNA-seq technology employed in our study captures only a fraction of the transcripts present in each cell approximately 10–20% (https://kb.10xgenomics.com/hc/en-us/articles/360001539051-What-fraction-of-mRNA-transcriptsare-captured-per-cell). This inherent limitation often results in the underrepresentation of genes with low expression levels. Consequently, TLRs and RLRs, which may be expressed at low levels in certain hemocytes, could be undetected due to this capture inefficiency. TLRs are typically expressed at low basal levels under resting conditions and are upregulated in response to specific stimuli or pathogenic challenges (1, 2). Given that our study analyzed hemocytes in their basal state, the expression levels of these receptors may have been below the detection threshold of the scRNA-seq platform. Furthermore, as highlighted by De Lorgeril et al. (3) the expression of these immune receptors varies depending on the resistance of the oyster. This variability further underscores the dynamic and context-dependent nature of TLR and RLR expression

To comprehensively assess the expression patterns of TLRs and RLRs across different hemocyte clusters, future studies could incorporate targeted enrichment strategies, such as bulk RNA-seq or single-cell technologies with higher capture efficiencies. Additionally, analyzing hemocytes under stimulated conditions or comparing oysters with varying levels of resistance could provide insights into the inducible and context-specific expression of these immune receptors.

(2) Clarification is needed in lines 265-266 regarding the nucleo-cytoplasmic ratio (N/C) terminology to prevent confusion, considering the discrepancy with the results presented in Figure 3.

We thank the editor for bringing this to our attention and apologize for the discrepancy between the terminology used in the text and the results presented in Figure 3. The text refers to the nuclear-tocytoplasmic ratio (N/C), while the figure mistakenly displays the inverse ratio, cytoplasmic-to-nuclear ratio (C/N). We recognize that this inversion may cause confusion and will ensure consistency between the text and the figure.

To address this, we propose correcting the figure legend and labels in Figure 3 to align with the terminology used in the text (N/C ratio). This will prevent confusion and maintain clarity throughout the manuscript.

(3) The selection of cluster 4 as the root for pseudotime analysis based on high ribosomal protein expression raises questions. It would be beneficial to elaborate on the inclusion of other genes, such as cell cycle or mitotic-related genes, to validate the pseudotime analysis outcomes.

We appreciate the reviewer’s insightful comment on the significance of ribosomal proteins in stem cell maintenance.

Hematopoietic stem cells (HSCs) are a population of stem cells that are largely cell-cycle-quiescent (G0 phase) with low biosynthetic activity. Upon stimulation and stress HScs undergo proliferation and differentiation and produce all lineages of hemocytes.

Ribosomal proteins play a multifaceted role in preserving the balance between stem cell quiescence and activation. By ensuring precise regulation of protein synthesis, they allow stem cells to maintain their undifferentiated state while remaining poised for activation when needed. Furthermore, ribosomal proteins contribute to the cellular stress response, safeguarding stem cells from oxidative damage and other stressors that could compromise their functionality. Importantly, ribosomal biogenesis and the dynamic assembly of ribosomes provide a regulatory mechanism that fine-tunes the transition from self-renewal to differentiation, a critical feature of hematopoietic stem cells (HSCs) and other stem cell types. These mechanisms collectively highlight the indispensable role of ribosomal proteins in stem cell biology, underscoring their relevance to our study's findings.

In vertebrate, the maintenance of hematopoietic stem cells (HSCs) and hematopoietic homeostasis is widely acknowledged to rely on the proper regulation of ribosome function and protein synthesis (4). This process necessitates the coordinated expression of numerous genes, including genes that encode ribosomal proteins (RP genes) and those involved in regulating ribosome biogenesis and protein translation. Disruptions or mutations in these critical genes are associated with the development of congenital disorders (5). Among these, Rpl22 (found in cluster 4 with a Log2FC of 1.59) has been shown to play a pivotal role in HSC maintenance by balancing ribosomal protein paralog activity, which is critical for the emergence and function of HSCs (6).

(4) What is the resolution of the cell clustering employed in the study? Given that cluster 1 potentially encompasses two distinct cell types, Macrophage-Like and Big Granule cells, further sub-clustering efforts and correlation analyses between cluster markers and cell morphologies could aid in their differentiation.

Thank you for your inquiry regarding the resolution of our cell clustering. As described in the Materials and Methods section, we used the Seurat FindClusters function with a resolution parameter of r = 0.1 for the scRNA-seq dataset. We performed sub-clustering within Cluster 1, resulting in four distinct subclusters. However, despite analyzing various specific markers, we did not identify any marker uniquely associated with the Big Granule Cell (BGC) morphology. Notably, LACC24 specifically marks a subset of cells within Cluster 1, as shown in Supplementary Figure S8, although this gene alone was insufficient to definitively distinguish a distinct BGC population.

(5) Line 78's statement regarding the primary identification of three hemocyte cell types in C. gigas-blast, hyalinocyte, and granulocyte cells would benefit from including references to substantiate this claim.

We thank Reviewer #1 for their valuable comments, which have allowed us to further improve our manuscript. We have enriched the introduction with the following addition (lines 79 to 82):

“Blast-like cells are considered undifferentiated hemocyte types (Donaghy et al., 2010), hyalinocytes appear to play a key role in wound repair (de la Ballina et al., 2020), and granulocytes are primarily involved in immune surveillance. Among these, granulocytes are regarded as the main immunocompetent hemocyte type (Wang et al., 2017).”

Conclusion:

The authors largely achieved their primary objective of providing a comprehensive characterization of oyster immune cells. They successfully integrated multiple approaches to identify and describe distinct hemocyte types. The correlation of these cell types with specific immune functions represents a significant advancement in understanding oyster immunity. However, certain aspects of their objectives have not been fully achieved. The lineage relationships proposed on the basis of pseudotime analysis, while interesting, require further experimental validation. The potential of antiviral defense mechanisms, an important aspect of oyster immunity, has not been discussed in depth.

This study is likely to have a significant impact on the field of invertebrate immunology, particularly in bivalve research. It provides a new standard for comprehensive immune cell characterization in invertebrates. The identification of specific markers for different hemocyte types will facilitate future research on oyster immunity. The proposed model of hemocyte lineages, while requiring further validation, offers a framework for studying hematopoiesis in bivalves.

Bibliography:

(1) J. Chen, J. Lin, F. Yu, Z. Zhong, Q. Liang, H. Pang, S. Wu, Transcriptome analysis reveals the function of TLR4-MyD88 pathway in immune response of Crassostrea hongkongensis against Vibrio Parahemolyticus. Aquaculture Reports 25, 101253 (2022).

(2) Y. Zhang, X. He, F. Yu, Z. Xiang, J. Li, K. L. Thorpe, Z. Yu, Characteristic and Functional Analysis of Toll-like Receptors (TLRs) in the lophotrocozoan, Crassostrea gigas, Reveals Ancient Origin of TLR-Mediated Innate Immunity. PLOS ONE 8, e76464 (2013).

(3) J. de Lorgeril, B. Petton, A. Lucasson, V. Perez, P.-L. Stenger, L. Dégremont, C. Montagnani, J.M. Escoubas, P. Haffner, J.-F. Allienne, M. Leroy, F. Lagarde, J. Vidal-Dupiol, Y. Gueguen, G.

Mitta, Differential basal expression of immune genes confers Crassostrea gigas resistance to Pacific oyster mortality syndrome. BMC Genomics 21, 63 (2020).

(4) R. A. J. Signer, J. A. Magee, A. Salic, S. J. Morrison, Haematopoietic stem cells require a highly regulated protein synthesis rate. Nature 509, 49–54 (2014).

(5) A. Narla, B. L. Ebert, Ribosomopathies: human disorders of ribosome dysfunction. Blood 115, 3196–3205 (2010).

(6) Y. Zhang, A.-C. E. Duc, S. Rao, X.-L. Sun, A. N. Bilbee, M. Rhodes, Q. Li, D. J. Kappes, J. Rhodes, D. L. Wiest, Control of Hematopoietic Stem Cell Emergence by Antagonistic Functions of Ribosomal Protein Paralogs. Developmental Cell 24, 411–425 (2013).

El cuerpo tiene sistemas especiales (como las hormonas) que detectan cuando la glucosa está baja (hipoglucemia) y actúan para subirla y mantenerla en el nivel adecuado, porque el cerebro necesita glucosa para funcionar correctamente.

Significa que, durante el ejercicio, tu cuerpo está quemando entre un 40% y un 50% más de energía que la que usaría si estuvieras en reposo (sin hacer nada).

Si varia la oxidación, pierde electrones, libera energía para que el cuerpo pueda funcionar.

CONSTRUIR y DESTRUIR, dependiendo de lo que tu cuerpo necesite.

Preguntas de discusión: 1. ¿Qué medidas podrían tomar el gobierno Mexicano y los productores agrícolas para mitigar el impacto del cambio climático en la producción y los precios de los productos? 2. ¿Qué acciones internacionales/globales podrían ayudar a reducir los efectos en la inflación y la economía de México? 3. ¿De qué manera el aumento en los precios de los productos agrícolas afecta a diferentes sectores de la población y que se puede implementar para proteger estos sectores?

This measure seemed almost pointless. There seemed to be limited effect with there being more individual members than managers in the Associated Press. This is why there limited results because individuals continued to sell news to local stations resulting in them having to have further prohibition in there next meeting. This made me wonder if there would have been more effectiveness if the AP association carried out the further prohibition rules to begin with?

It's every interesting how something so simple can turn very complex by just adding or taking things away such as the two pictures we were shown.

2012 was a while ago, especially in technology terms, but the majority of the content in this position statement is still relevant and applicable to current day. Some technologies are named or referred to as "cutting edge" that are now just standard technology. That's the biggest marker of age. Otherwise, it's still good information.

La columna de la izquierda son inhibidores y la de la derecha, inductores

referencia evangelio

DOI: 10.1038/s44321-025-00200-y

Resource: RRID:Addgene_12259

Curator: @olekpark

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.1038/s44321-025-00200-y

Resource: RRID:Addgene_12260

Curator: @olekpark

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.1038/s44318-024-00330-y

Resource: None

Curator: @olekpark

SciCrunch record: RRID:Addgene_56261

What is this?

DOI: 10.1038/s44318-024-00330-y

Resource: RRID:Addgene_55124

Curator: @olekpark

SciCrunch record: RRID:Addgene_55124

What is this?

DOI: 10.1038/s44318-024-00330-y

Resource: RRID:Addgene_55911

Curator: @olekpark

SciCrunch record: RRID:Addgene_55911

What is this?

DOI: 10.1038/s44318-024-00330-y

Resource: RRID:Addgene_128744

Curator: @olekpark

SciCrunch record: RRID:Addgene_128744

What is this?

DOI: 10.1038/s42003-025-07918-y

Resource: Addgene (RRID:SCR_002037)

Curator: @olekpark

SciCrunch record: RRID:SCR_002037

What is this?

DOI: 10.1038/s42003-025-07899-y

Resource: None

Curator: @olekpark

SciCrunch record: RRID:Addgene_22575

What is this?

DOI: 10.1038/s42003-025-07899-y

Resource: RRID:Addgene_27080

Curator: @olekpark

SciCrunch record: RRID:Addgene_27080

What is this?

DOI: 10.1038/s42003-025-07899-y

Resource: RRID:Addgene_21162

Curator: @olekpark

SciCrunch record: RRID:Addgene_21162

What is this?

DOI: 10.1038/s42003-025-07899-y

Resource: (RRID:CVCL_9771)

Curator: @scibot

SciCrunch record: RRID:CVCL_9771

What is this?

DOI: 10.1038/s42003-025-07899-y

Resource: RRID:Addgene_27077

Curator: @scibot

SciCrunch record: RRID:Addgene_27077

What is this?

DOI: 10.1038/s42003-025-07899-y

Resource: RRID:Addgene_21164

Curator: @scibot

SciCrunch record: RRID:Addgene_21164

What is this?

DOI: 10.1038/s42003-025-07899-y

Resource: RRID:Addgene_27078

Curator: @scibot

SciCrunch record: RRID:Addgene_27078

What is this?

DOI: 10.1038/s41598-025-93222-y

Resource: None

Curator: @olekpark

SciCrunch record: RRID:Addgene_26844

What is this?

DOI: 10.1038/s41598-025-93222-y

Resource: RRID:Addgene_1736

Curator: @scibot

SciCrunch record: RRID:Addgene_1736

What is this?

DOI: 10.1038/s41467-025-57614-y

Resource: RRID:Addgene_28289

Curator: @olekpark

SciCrunch record: RRID:Addgene_28289

What is this?

DOI: 10.1038/s41467-025-57614-y

Resource: RRID:Addgene_12259

Curator: @olekpark

SciCrunch record: RRID:Addgene_12259

What is this?

Normalmente la transición del feudalismo al capitalismo se piensa a partir de la imprenta y la colonización de América. Me pregunto si a partir de lo digital y la colonización de Marte no estaremos entrando en algo distinto al tecnofascismo. Lo de tecnofeudalismo no lo comento porque no tiene sentido - el feudalismo era descentralizado; el gobierno actual es extremadamente centralizado, gracias a lo digital.

Padre, hijo y espíritu santo.

Briefing Document : "Balado « Prendre soin de soi »" Source : Excerpts from the podcast "Prendre soin de soi"

Date : Information not explicitly provided in the text.

Participants :

• Animatrice : Arianne Fiset, collaboratrice auprès du Comité Québécois pour les jeunes en difficulté de comportement.
• Invitées :Nancy Goyette, professeure titulaire en sciences de l'éducation à l'Université du Québec à Trois-Rivières.
• Marie-Hélène Veronau, professeure titulaire au département de psychologie de l'Université du Québec à Montréal et titulaire de la Chaire UQAM sur la promotion du bien-être durant les transitions scolaires et post-scolaires.
• Florence Lacroix, coordonnatrice du service en milieu éducatif de l'Institut Pacifique.

• Public Cible : Jeunes du troisième cycle du primaire et du secondaire.

• Objectif du Balado : Sensibiliser à l'importance de prendre soin de soi et d'expliquer ce qu'est la santé mentale positive.

Principaux Thèmes et Idées Clés :

• Définition de "Prendre soin de soi" et de la Santé Mentale Positive :
• Prendre soin de soi ne se limite pas au bien-être physique ("sentir bien dans son corps") mais inclut également le bien-être psychologique et émotionnel ("sentir bien dans sa tête et dans son cœur"), ce qui constitue la santé mentale.
• La santé mentale positive, selon l'Organisation mondiale de la santé, est "vraiment un état de bien-être dans lequel la personne elle peut se réaliser donc on surmonte là vraiment les petits défis de la vie quotidienne ça nous aide aussi à accomplir notre travail tu sais à l'école puis aussi ça nous aide à à contribuer à la collectivité tu sais dans la vie communautaire si on veut quand on est avec les amis".
• Les manifestations de la santé mentale positive incluent l'optimisme, la gratitude, l'empathie, la joie de vivre, la connaissance de ses forces et talents, et la capacité à identifier et utiliser des ressources en cas de difficulté.
• Importance de Cultiver le Bien-être :
• Des statistiques révèlent que parmi les élèves du secondaire, une proportion significative rapporte des difficultés de santé mentale, soulignant l'importance pour chacun de cultiver son bien-être, quel que soit son état actuel.
• "Peu importe notre état de santé mentale c'est important de cultiver notre bien-être puis c'est un des moyens en fait pour faire ça c'est de prendre soin de soi."
• La Normalité des Émotions Désagréables :
• Il est normal d'éprouver des émotions désagréables de temps en temps, et cela ne signifie pas nécessairement que notre santé mentale va mal si c'est occasionnel.
• Les émotions, qu'elles soient agréables ou désagréables, sont des indicateurs de notre position par rapport à nos objectifs et nous aident à mieux nous connaître et à choisir des actions alignées avec nos valeurs.
• "Toutes les émotions qu'elles soient agréables ou désagréables sont importantes c'est des outils pour nous aider à mieux nous connaître et à choisir des actions dans notre vie qui nous amènent vers des la réussite de nos buts qui sont les plus importants pour nous."

Facteurs Influant sur la Santé Mentale :

• Des difficultés relationnelles (famille, école), la violence, les injustices, et les défis majeurs de la vie (maladie, handicap) peuvent augmenter le risque de problèmes de santé mentale.
• Même l'inquiétude face au changement climatique peut avoir un impact sur la santé mentale des jeunes.
• Stratégies pour Prendre Soin de sa Santé Mentale :
• Gestion des Émotions : Il est préférable de faire face aux émotions désagréables plutôt que de les éviter. Il est conseillé de prendre le temps de les calmer, de les vivre, et de réfléchir à leurs causes. Ne pas hésiter à en parler à des adultes de confiance ou à des professionnels.
• Développement d'un Discours Intérieur Positif : Être plus tolérant envers soi-même, comme on le serait avec un ami. Se poser la question de ce que l'on dirait à un ami dans une situation similaire et appliquer ce discours à soi-même.
• Prendre du Recul : Face à une situation difficile, évaluer si c'est momentané et si cela impacte notre bien-être. Se demander s'il y a d'autres aspects de notre vie qui vont bien.
• Construire des Relations Positives et une Communication Bienveillante : Être conscient des limites de la communication écrite (manque d'indices non verbaux, difficulté à saisir le sarcasme) et de l'impact potentiel de nos paroles sur les autres.

• Connaissance de ses Forces et Qualités et celles des Autres : S'appuyer sur le soutien de son entourage.

• Bénéfices de Prendre Soin de Soi :

• Augmentation de la qualité de vie et capacité à vivre des expériences joyeuses et enrichissantes.
• Bienfaits physiques (activité sportive, bonne alimentation) et mentaux/émotionnels (détente, repos, meilleure concentration, disposition à l'apprentissage).
• Développement de la confiance en soi, meilleure gestion des défis, réalisation du potentiel.
• Amélioration des relations sociales et interpersonnelles.

• Contribuer au bien-être collectif : "si on se sent bien ben on va être capable de de refléter un état d'esprit des attitudes qui vont favoriser le bien-être de la collectivité des amis de notre famille".

• Le Bien-être est Collectif :

• Prendre soin de soi est important pour soi-même, mais a également un impact positif sur le bien-être des autres (famille, amis, entourage).

Conseils Pratiques et Ressources :

• Être à l'écoute de ses besoins et de ceux des autres.
• Développer la confiance en soi pour s'épanouir.
• Adopter des stratégies de bien-être comme se reposer, faire du sport, bien manger, agir pour améliorer le monde autour de soi (rendre service, gestes écologiques, prendre des nouvelles de ses proches).
• Une liste de ressources pour en apprendre davantage est disponible dans la description de l'épisode.

Citations Clés :

• "prendre soin de soi c'est pas seulement une question de sentir bien dans son corps [...] mais c'est aussi de sentir bien dans sa tête et dans son cœur" - Arianne Fiset
• "la santé mentale positive si on se fit là à l'Organisation mondiale de la santé c'est vraiment un état de bien-être dans lequel la personne elle peut se réaliser donc on surmonte là vraiment les petits défis de la vie quotidienne ça nous aide aussi à accomplir notre travail tu sais à l'école puis aussi ça nous aide à à contribuer à la collectivité" - Nancy Goyette
• "c'est normal vraiment d'avoir des émotions désagréables quelquefois ça arrive à tout le monde aux enfants aux adultes alors ça veut pas nécessairement dire que notre santé mentale va pas bien si c'est occasionnel" - Marie-Hélène Veronau
• "on a plus de risque d'avoir des problèmes de santé mentale quand on vit des difficultés dans nos relations [...] ou encore quand plus globalement on est dans un environnement où il y a la violence ou des injustices ou encore quand on doit faire face à des grands défis dans la vie" - Marie-Hélène Veronau
• "quand on vit des émotions il faut le prendre le temps peut-être de les calmer si on les trouve trop intenses prendre des grandes respirations trouver un endroit tranquille pour bien les vivre puis réfléchir à pourquoi on vit ces émotions" - Marie-Hélène Veronau
• "avoir le filtre du ou de la meilleure amie et de se poser la question par exemple si mon ami vivait ça en ce moment qu'est-ce que je lui dirai dans le fond" - Florence Lacroix
• "prendre soin de soi ça nous aide à s'épanouir comme personne à ressentir du bien-être malgré les difficultés malgré les expériences un petit peu moins agréables et ça fait en sorte aussi de cultiver une santé mentale positive" - Nancy Goyette
• "prendre soin de nos relations avec les adultes et les autres jeunes de notre entourage c'est aussi une façon de prendre soin de soi" - Arianne Fiset (conclusion)

Conclusion :

Le balado "Prendre soin de soi" aborde de manière accessible et informative l'importance de la santé mentale positive chez les jeunes. Il démystifie le concept, souligne la normalité des émotions et propose des stratégies concrètes pour cultiver le bien-être individuel et collectif.

L'accent est mis sur la connaissance de soi, la gestion des émotions, la qualité des relations et l'adoption d'un discours intérieur bienveillant.

Les interventions des expertes apportent une perspective scientifique et pratique, encourageant les jeunes à prendre activement soin de leur santé mentale et de celle de leur entourage.

E- It took me some time to learn how to use this. I'm very glad that this course is using online material. Helps my wallet.

por que el resultado es 1?.... pensaba que debia ser 2

¿Qué implica poblar variables de los valores, más allá de colocar cosas dentro de la caja?

se me dificulta un poco entender en esta parte cual es el mensaje y los objectos

Me sale el error "read-only"

Es decir, que son dos operaciones similares?

Creo que hoy no diría esto, pero bueno, son cosas que pensaba en ese tiempo y momento.

Précédemment, l'autrice a démontré que le contexte de discussion avait des conséquences dans la diffusion de fausses informations. A partir de là, (avec l’adverbe désormais) elle acte que chacun peut s'exprimer, échanger librement sur n'importe quel sujet que ce soit ; ce constat permet de démocratiser la parole jusqu'à mélanger les informations formelles avec les échanges informels. L'autrice qualifie les discussions de joueuses et moqueuses Elle semble généraliser son constat. Elle devrait nuancer ses propos car cela semble réducteur. En effet, cela n'est représentatif que d'une partie des échanges en réseau. Il y a certains internautes qui adoptent ce style de ton, et d'autres qui dispensent des échanges/informations sérieuses.

L'autrice diffuse des témoignages pour confirmer que le ton est relâché lorsque qu'on partage des fake news. Cela étant est-ce que cela justifie t-il les motivations des personnes optant pour ce comportement, pas sûr. Par ailleurs, en lisant les témoignages, nous pouvons nous demander si le partage de fausses informations ne seraient pas renforçateurs de liens sociaux contrairement à l'échange d'informations exactes?

On retrouve un deuxième argument . Sa critique nous amène à privilégier les éventuelles conséquences dans la vie réelle plutôt qu'aux traces laissées par le numérique. Sa question est intéressante parce qu'elle nous invite à nous demander si nos croyances pour les fake news dépendent de la quantité de diffusion ou de nos facteurs individuels? En suivant elle soulève l'utilité potentielle de certains fake news pour se moquer ou révéler une tromperie. Cela a pour effet de proposer de fait, une réflexion nuancée sur la réception, interprétation, manipulations des informations transmises. Toutefois, l'autrice pourrait contextualiser les raisons qui poussent à se moquer ou fausser l'information, préciser si ce procédé est courant ou isolé, analyser les motivations pour y recourir. https://www.lemonde.fr/les-decodeurs/article/2017/12/20/derriere-les-fausses-informations-il-y-a-souvent-des-motivations-financieres_5232487_4355770.html

Author response:

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

Overview:

We appreciate all the constructive comments from the reviewer and the reviewing editor, as their suggestions have significantly improved our manuscript. In response to their comments, we have made several key revisions: First, we have performed new colocalization analyses between the active zone marker UNC-10::GFP and all UNC-13L variants (UNC13L, UNC-13L^HK, UNC-13L^D1-5N, and UNC-13L^HK+D1-5N, all tagged with mApple). These results confirm that the mutations do not affect synaptic localization. Second, we have provided a clearer explanation of the “gain-of-function” term used in this study, emphasizing that it reflects an increased SV release due to C1-C2B module dysfunction rather than a single mechanistic state. Third, we have expanded the discussion on the physiological implications of the C1-C2B model, particularly its role in regulating synaptic transmission under varying neuronal activity conditions. Finally, to improve clarity and focus, we have removed unnecessary speculative discussions, ensuring that the revised manuscript centers on the most relevant findings.

We have reorganized the manuscript to incorporate these new results into the figures and text. Full responses to all reviewer comments are provided below. We hope that the reviewer and the editor find these revisions satisfactory and that our manuscript is now suitable for publication in eLife.

Joint Public Review:

Summary:

In this manuscript, the authors investigate how different domains of the presynaptic protein UNC-13 regulate synaptic vesicle release in the nematode C. elegans. By generating numerous point mutations and domain deletions, they propose that two membrane-binding domains (C1 and C2B) can exhibit "mutual inhibition," enabling either domain to enhance or restrain transmission depending on its conformation. The authors also explore additional Nterminal regions, suggesting that these domains may modulate both miniature and evoked synaptic responses. From their electrophysiological data, they present a "functional switch" model in which UNC-13 potentially toggles between a basal state and a gain-of-function state, though the physiological basis for this switch remains partly speculative.

Strengths:

(1) The authors conduct a thorough exploration of how mutations in the C1, C2B, and other regulatory domains affect synaptic transmission. This includes single, double, and triple mutations, as well as domain truncations, yielding a large, informative dataset.

(2) The study includes systematically measuring both spontaneous and evoked synaptic currents at neuromuscular junctions, under various experimental conditions (e.g., different Ca²⁺ levels), which strengthens the reliability of their functional conclusions.

(3) Findings that different domain disruptions produce distinct effects on mEPSCs, mIPSCs, and evoked EPSCs suggest UNC-13 may adopt an elevated functional state to regulate synaptic transmission.

Weaknesses:

It remains unclear whether the various domain alterations truly converge on a single "gain-offunction" state or instead represent multiple pathways for enhancing UNC-13 activity. Different mutations selectively affect spontaneous or evoked release, suggesting that each variant may not share the same underlying mechanism. Moreover, many conclusions rely on combining domain deletions or point mutations, yet the electrophysiological data show distinct outcomes across EPSCs, IPSCs, mini, and evoked responses. This raises questions about whether these manipulations all act on the same pathway and whether their observed additivity or suppression genuinely reflects a single mechanistic process. A unifying model-or at least a clearer explanation of why the authors infer one mechanistic state across different domain manipulations would strengthen the paper's conclusions.

We appreciate the comment and understand the potential confusion regarding the use of the term "gain-of-function" in the manuscript. To clarify, the gain-of-function state described in this study does not refer to a single specific mechanistic change in UNC-13 but rather to a high synaptic vesicle (SV) release state achieved by disrupting the C1-C2B module - either through dysfunction of the C1 domain or the C2B domain (as seen with the HK and DN mutations).

Our findings support a "seesaw" model in which the C1 and C2B domains maintain a dynamic balance in their interaction with the plasma membrane, binding to DAG and PIP2. This balance may increase the energy barrier for SV release, preventing excessive neurotransmitter release under basal conditions. However, the C1-C2B toggle may be disrupted by high neuronal activity and act in an unbalanced state, thereby enhancing synaptic transmission (i.e., the gain-of-function state). To address these concerns, we have provided a clearer explanation of this functional switch in the revised version of the manuscript (page 27).

Regarding the differences between spontaneous and evoked neurotransmitter release, our previous studies have revealed that these two forms of release do not always respond similarly to various unc-13 mutations. This is a common phenomenon observed in other synaptic protein mutants, including synaptotagmin, tomosyn, and complexin, which indicates distinct yet partially overlapping regulatory mechanisms. Our model is well supported by most of the electrophysiological results from HK, DN, and HK+DN mutations across different unc-13 isoforms (UNC-13L, UNC-13S, UNC-13R, UNC-13ΔC2A, UNC-13ΔX). The main exception is that in UNC-13ΔX^HK+DN mutants, the changes in mEPSCs and mIPSCs differ from those observed in evoked EPSCs. This suggests that the mechanisms regulating the functional switch of unc-13 may differ slightly between spontaneous and evoked release. Since the X region of unc-13 and Munc13 remains largely uncharacterized, our findings provide intriguing insights into its potential functional role.

The manuscript proposes that UNC-13 toggles from a basal to a "gain-of-function" state under normal synaptic activity. However, it does not address when or how this switch might occur in vivo, since it is demonstrated principally via artificial mutations. Providing direct evidence or additional discussion of such switching under physiological conditions would be particularly informative.

What is the physiological significance of the proposed gain-of-function state? The data suggest that certain mutants (e.g., HK+D1-5N) lacking the gain-of-function state can still support synaptic transmission at wild-type levels. How do the authors reconcile this with the idea that the gain-of-function state plays a critical role at the synapse?

We appreciate these comments. While our model is mainly based on the dysfunction of the C1-C2B module (through HK and DN mutations), it provides a potential physiological framework for understanding how the structural balance of C1-C2B relates to the variability of synaptic transmission in the nervous system. In the CNS, synaptic transmission is highly variable, and the temporal pattern of the presynaptic activity may require dynamic switching of the fusion machinery, including UNC-13, between different functional modes, thereby triggering synaptic transmission at various levels. Our model suggests that under conditions of high neuronal activity, the C1-C2B module may transition from a balanced to an unbalanced state (gain-of-function state), thereby enhancing synaptic transmission.

Regarding the physiological significance of the gain-of-function state, we acknowledge that certain mutants (e.g., HK+D1-5N) lacking this state can still support wild-type levels of synaptic transmission. This observation suggests that the gain-of-function state may not be strictly required for baseline synaptic function but rather plays a modulatory role under specific conditions, such as heightened neuronal activity or synaptic plasticity. Further investigations will be needed to determine the precise in vivo triggers and functional consequences of this switch under physiological conditions. Moreover, we will focus on several linker regions (between C1 and C2B, C2B and MUN) to investigate their potential roles in regulating synaptic transmission and their broader functional significance in UNC-13 dynamics.

The authors determined the fluorescence intensity of mApple-tagged UNC-13 variants (Figure 1J-K and Figure 7J-K), finding no significant changes compared to the wild-type. However, a more detailed analysis of the density or distribution of fluorescent puncta in axons could clarify whether certain mutations alter the localization of UNC-13 at synapses. Demonstrating colocalization with wild-type UNC-13 (or another presynaptic marker) would help rule out mislocalization effects.

We appreciate the comment. In response, we have included a more detailed analysis of the synaptic localization of both wild-type and mutated UNC-13L in the revised manuscript. Our data show that in all scenarios, UNC-13 proteins exhibit strong colocalization with the active zone marker UNC-10::GFP (Figure 1L). Along with the fluorescence intensity data in Figure 1J, our findings indicate that the C1 and C2B mutations do not affect the expression level or the localization of UNC-13 at synapses. These results have been incorporated into the revised manuscript (page 8) and in Figure 1L.

The study mainly relies on extrachromosomal transgenes, which can show variable copy numbers and expression levels among individual worm strains. This variability might complicate interpretation, as differences in expression could mask or exaggerate certain phenotypes.

We agree that the expression levels of synaptic proteins can influence synaptic transmission levels. However, given the large number of mutations and truncations employed in this study, generating single-copy rescue lines for all transgenic strains would be a significant undertaking. On average, we need to microinject 50-100 worms to obtain one single-copy line, whereas injecting only 5-10 worms allows us to generate at least three independent extrachromosomal arrays. Based on our previous work, we found that the synaptic transmission levels are comparable between various extrachromosomal rescue arrays of unc13 and their single-copy rescue lines (e.g., UNC-13L, UNC-13S, UNC-13R, UNC-13ΔC2A, UNC-13ΔC2B, etc.). In future studies, we aim to use single-copy expression or CRISPRbased methods to induce deletions or mutations in various synaptic proteins.

Finally, the discussion is somewhat diffused. Streamlining the text to focus on the most direct connections would help readers pinpoint the key conclusions and open questions.

We appreciate the comment. As suggested, we have refined the discussion section. Specifically, we have removed the last part of the discussion (Functional roles of the linkers in UNC-13).  

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Clarify the "Gain-of-Function" State. Provide stronger justification or explicit discussion of whether all manipulations that enhance SV release truly correspond to the same mechanistic state or if multiple conformational states might be at play.

The “gain-of-function” state in this manuscript refers to a specific conformational status of UNC-13 that enhances synaptic vesicle (SV) release probability (both spontaneous and evoked) as a result of mutations (HK and DN) in the C1 and C2B domains. This effect is observed across multiple UNC-13 isoforms, including UNC-13L, UNC-13S, and UNC-13R. Prior studies from our group and others have demonstrated that C1 and C2B exhibit conserved functions in regulating synaptic transmission (Li et al., 2019, Cell Reports; Liu et al., 2021, Cell Reports; Michelassi et al., 2017, Neuron), supporting the idea that these domains share a common mechanism for modulating SV release. Given that C1 and C2B act as a functional unit (Michelassi et al., 2017, Neuron; and this study), we define all synaptic states induced by the dysfunction of these two domains as the "gain-of-function" mode.

However, it is important to note that this classification does not apply to high-release probability states induced by mutations in other domains.

The concept of a gain-of-function state due to C1 and C2B dysfunction has been previously proposed in studies of Munc13. Basu et al. (2007, Journal of Neuroscience) demonstrated that the H567K mutation in Munc13-1 C1 increases both spontaneous and evoked release probability, leading to a gain-of-function mode. Similarly, work from the Südhof group showed that KW and DN mutations in Munc13-1 C2B also enhance release probability, thereby inducing a gain-of-function state (Shin et al., 2010, Nature Structural & Molecular Biology). Our recent findings further support this idea, showing that UNC-13 C2B D3,4N (Li et al., 2019, Cell Reports; Liu et al., 2021, Cell Reports; Michelassi et al., 2017, Neuron) and the newly identified D1-5N mutation (this study) significantly elevate SV release, consistent with the D1,2N mutations reported by Shin et al.

Overall, our study integrates and extends previous findings, providing strong evidence that the C1 and C2B domains function as a regulatory switch between a basal physiological mode, a gain-of-function mode (enhanced release), and a loss-of-function mode (impaired release). This framework advances our understanding of how C1 and C2B dysfunction affects synaptic transmission and plasticity.

(2) Add comparisons to wild-type UNC-13L: When presenting data for deletions/mutants as "controls," include a visual reference (e.g., dashed line in figures) showing wild-type UNC13L levels. This will help readers see whether each construct is above or below the normal activity baseline.

As suggested, a dashed line showing the level of UNC-13L has been added to the bar graphs of all evoked EPSCs. The functional switch model is well supported by the results of the evoked EPSCs.

(3) Mutant and wild-type UNC-13 colocalization analysis: Demonstrating whether each mutant localizes robustly to synapses, in comparison to wild-type UNC-13, would bolster the interpretation of electrophysiological changes. If the authors have these data, adding them would address the possibility of mislocalization.

We agree with the reviewer that there would be value to address the possibility of mislocalization. However, in our experience working with UNC-13 mutant colocalization, we have found that neither deleting the X, C1 and C2B domains in UNC-13L  nor deleting C1 and C2B domain in UNC-13MR or UNC-13R altered the synaptic colocalization with the active zone protein UNC-10/RIM (Li 2019, Liu 2021), suggesting that C1 and C2B domains in UNC-13 are not involved in the regulation of protein localization. Thus, the mutations in the C1 and C2B domains are unlikely leading to protein mislocalization in the synaptic region.

(4) If possible, adding analysis using single-copy transgenes to confirm that extrachromosomal array expression variability does not qualitatively change the conclusions.

We strongly agree with the reviewer that single-copy transgenes would provide more stable protein expression levels and further consolidate our conclusions. However, several factors give us confidence that the extrachromosomal array rescue approach does not introduce significant variability in our results: First, our prior research has shown that SV release levels are generally comparable between extrachromosomal arrays carrying various unc13 transgenes and their corresponding single-copy rescue lines (e.g., UNC-13L, UNC-13S, UNC-13R, UNC-13ΔC2A, and UNC-13ΔC2B). Second, the major conclusions in this study are drawn from highly consistent and robust changes in SV release between different rescue lines (e.g., UNC-13L^HK+DN vs UNC-13L^DN; UNC-13S^HK+DN vs UNC-13S^HK or UNC-13S^DN ). Third, our imaging data indicate that the protein levels are indistinguishable between different unc-13 rescue arrays carrying C1 and C2B mutations, further supporting the validity of our findings.

Additionally, due to our recent relocation to a new institute, we are still in the process of setting up our microinjection system. Generating single-copy transgenes for all the extrachromosomal arrays used in this study would require significant time. We appreciate the reviewer’s understanding of our current situation. For our future studies regarding unc-13 and other synaptic proteins, we will prefer to use single-copy expression rather than extrachromosomal arrays.

(5) Reduce the length and speculation in the Discussion. A concise discussion that focuses on the most direct implications of the present findings will help improve the readability of this paper.

We appreciate the comment. As suggested, we have refined the discussion section.

Specifically, the last part of the discussion (Functional roles of the linkers in UNC-13) was removed.

(6) Minor formatting detail: In Figure 5C (left panel), adjust the y-axis label to ensure it aligns properly and improves clarity.

We appreciate the reviewer’s suggestion and have adjusted the y-axis label accordingly in the revised version (see revised Figure 5).

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

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Reply to the reviewers

Dear Review Commons editorial team,

Thank you for coordinating the thorough and careful review of our manuscript. We are especially grateful to the four anonymous reviewers for recognizing the value of our work and for their constructive suggestions on how to improve it.

We are encouraged by the positive reception of our main conclusions on the robustness of adaptation to DNA replication stress and its relevance to multiple fields. All reviewers provided insightful comments, with reviewers #2 and #4 emphasizing that further experimental validation of the hypothesized role of reduced dNTPs in alleviating fitness during constitutive DNA replication stress would strengthen the paper. While the precise molecular mechanisms underlying this suppression are not the primary focus of this manuscript, we are eager to perform additional experiments based on the reviewers’ suggestions.

Below, we present a detailed revision plan in the form of a point-by-point response to their comments.

Reviewer #1 (Evidence, reproducibility and clarity):

This study investigates the compensatory evolutionary response of Saccharomyces cerevisiae to DNA replication stress, focusing on the influence of genotype-environment interactions (GXE). The authors used a range of experimental conditions with varying nutrient levels to assess evolutionary outcomes under replication stress. Their genomic analysis reveals that while glucose levels affect initial adaptation rates, the genetics of adaptation remain robust across all nutritional environments. The research offers new insights into the adaptability of S. cerevisiae, emphasizing the role of the nutritional environment in evolutionary processes related to DNA replication stress. It identifies recurrent advantageous mutations under different macronutrient availabilities and uncovers a novel role for the RNA polymerase II mediator complex in adaptation to replication stress. Overall, this well-designed study adds to the growing recognition of the complexity and robustness of evolutionary responses to environmental stressors. It provides strong evidence that compensatory evolution to replication stress is robust across varying nutritional conditions. It both challenges and reinforces previous findings regarding the resilience of the yeast genetic interaction network to environmental perturbations. The detailed analysis of specific compensatory mutations and their fitness impacts across different conditions offers valuable insights into adaptive dynamics over 1000 generations, contributing a clear empirical framework for understanding how replication-associated stress shapes evolutionary outcomes in diverse environments.

Based on the analysis:

1) The conclusions are generally well-supported by the presented data. The evolution experiments and genomic analyses are robust and provide convincing evidence for the study's main claims. The authors took steps to eliminate bias, such as maintaining an adequate Ne, which, if not done, could have compromised their conclusions by affecting genetic drift and limiting the population's access to beneficial mutations.

2) The figures are well-designed and easy to understand.

3) The methodology is well-described and appears reproducible. The authors provide sufficient details on experimental procedures. Experimental replication is adequate, with multiple evolutionary lines.

4) They also made efforts to validate their observations, such as the validation of mutations, the prediction of interactions in the Med14 structure, and its potential implication in gene regulation, as well as the analysis of the cumulative fitness benefit and the reconstruction of the quadruple mutant.

There are, however, a few results that would benefit from further clarification:

1) The experimental design is strong, offering a diverse range of conditions. However, the high glucose condition (8%) stands out as significantly different from the neutral 2% condition, both in range and margin, compared to the low glucose conditions (0.25-0.5%). While this mainly affects growth profiles and evolvability in the early generations, a brief explanation in the discussion would strengthen the conclusions. Specifically, addressing:

1. a) The rationale behind selecting these particular glucose concentrations.

2. b) How other glucose concentrations might influence the outcomes. Providing this additional context would enhance the reader's understanding of the experimental setup and its potential implications, while also offering insights into the broader applicability of the findings and possible directions for future research.

We thank the reviewer for pointing out the need to clarify the rationale behind the glucose concentrations used in our study, an aspect we agree should have been better explained. In response, we have added the following text detailing the chosen conditions and their established effects on cellular metabolism.

Line 67: “Glucose is the most abundant monosaccharide in nature, and represents the preferred source of energy for most cells.”

Line 110: “...we grew WT and ctf4Δ cells in varying glucose concentrations to induce distinct physiological states. Low glucose levels (0.25% and 0.5%) induce caloric restriction and ultimately glucose starvation (Lin et al 2000, Smith et al. 2009). These conditions elicit increased respiration (Lin et al., 2002), sirtuins expression (Guarente, 2013), autophagy (Bagherniya et al. 2018), DNA repair (Heydari et al., 2007), and reduced recombination at the ribosomal DNA locus (Riesen and Morgan, 2009) ultimately extending lifespan in several organisms (Kapahi et al., 2016). In contrast, standard laboratory conditions typically use 2% glucose, promoting a rapid proliferation environment to which strains have been adapted since laboratory domestication (Lindergren, 1949). Finally, elevated glucose concentrations (such as 8%) result in higher ethanol production (Lin et al., 2012) and reactive oxygen species (ROS) levels (Maslanka et al., 2017).

2) In the discussion section, a more explicit comparison with similar studies in other model organisms would help contextualize the findings within the broader field of evolutionary biology. While the results appear robust, it would be beneficial to explore how they align with or contrast to previous studies on DNA damage, particularly in bacteria or highly complex eukaryotes.

We appreciate this suggestion to better contextualize our findings within the broader literature, as it provides an opportunity to highlight the unique aspects of our work. While many studies have explored how environmental factors shape fitness landscapes and influence evolutionary strategies, to our knowledge, only a few have addressed this in the context of compensatory evolution, where cells must recover fitness lost due to intracellular perturbations. To address this point, we have added a discussion of additional examples involving other model organisms, highlighting their difference with the question asked in this work.

Line 34: “Genotype-by-environment (GxE) interactions are well-documented. For example, several studies on E. coli have demonstrated how different environments influence fitness and epistatic interactions among adaptive mutations in the Lenski Long-Term Evolution Experiment (Ostrowski et al., 2005, 2008; Flynn et al., 2012; Hall et al., 2019). Adaptive mutations in viral genomes similarly exhibit variable fitness effects across different hosts (Lalic and Elena, 2012; Cervera, 2016). Furthermore, interactions between mutations in the Plasmodium falciparum dihydrofolate reductase gene have been shown to predict distinct patterns of resistance to antimalarial drugs (Ogbunugafor et al., 2016). However, the role of environmental factors in shaping evolution within the context of compensatory adaptation, when fitness defects primarily arise from intracellular perturbations, remains much less explored.”

However, if the reviewer have particular additional studies in mind, we welcome further suggestions to include in the final manuscript.

Minor comments:

1) The presentation of data in the figures is clear and informative. However, some figure legends could benefit from more detailed explanations. For example, although the statistical tests used are mentioned in the methods section, it would be helpful to also include them in the figure legends, such as in legend 1acde, as well as in all other figures.

We are now reporting the statistical test used for each comparison also in figure legends.

2) In terms of broader conclusions, here are a few suggestions, though they are, of course, optional:

a) The study could benefit from exploring the potential trade-offs of adaptive mutations in the hypothetical return to environments without replication stress, at least theoretically. This would provide a more comprehensive understanding of the evolutionary constraints.

We thank the reviewer for the suggestion, we had performed the measurements but did not comment on them explicitly. We are now commenting on them as follows:

Line 310: “In the WT background, all mutations were nearly neutral, with only minimal deleterious or advantageous effects on fitness depending on glucose concentrations (Fig S4A).”

Line 468: “The nearly neutral effects on fitness of the core adaptive mutations in WT suggest that they are likely to persist even after the initial replication stress is resolved.”

b) A brief discussion of the potential limitations of using lab strains versus wild isolates of S. cerevisiae would offer valuable context for the generalizability of the findings.

This is an excellent point. While addressing it fully would warrant a separate manuscript, we provide our comments here, along with similar observations raised by this and other reviewers, as follows:

Line 450: “How generalizable are our conclusions about the reproducibility of evolutionary repair to DNA replication stress across other organisms, species, or replication challenges? While dedicated future studies are needed to fully address these important questions, several lines of evidence are encouraging. A recent report demonstrated that the identity of suppressor mutations of lethal alleles was conserved when introduced into highly divergent wild yeast isolates (Paltenghi and van Leeuwen, 2024). Similarly, earlier work showed that even ploidy, which significantly alters the target size for loss- and gain-of-function mutations, affected only the identity of the genes targeted by selection, while the broader cellular modules involved remained consistent (Fumasoni and Murray, 2021). Moreover, divergent organisms experiencing different types of DNA replication stress exhibit some of the adaptive responses described here. For example, the yeast genus Hanseniaspora, which lacks the Pol32 subunit of the replisome, has also been reported to have lost the DNA damage checkpoint (Steenwyk et al., 2019). Human Ewing sarcoma cells carrying the fusion oncogene EWS-FLI1 frequently exhibit adaptive amplification of the cohesin subunit RAD21 (Su et al., 2021). Together, these findings suggest that while the specific details of DNA replication perturbations and the genomic features of organisms may shape the precise targets of compensatory evolution, the overarching principles and cellular modules affected are broadly conserved.”

Furthermore, we plan to search a recently published database of variants found in natural isolates of S. cerevisiae to assess whether similar evolutionary processes to those described in this study may have occurred in wild strains.

c) It would be valuable to present the differences in ploidy in the context of other studies, such as the nutrient-limitation hypothesis (e.g., 'The Evolutionary Advantage of Haploid Versus Diploid Microbes in Nutrient-Poor Environments' by Bessho, 2015), since, as previously demonstrated by the authors of this article that is being reviewed, ploidy may influence the evolutionary trajectories of DNA repair.

d) Interrelating these three terms: nutrient-limitation, ploidy, and DNA repair could be an interesting avenue to explore in the discussion.

In response to comments c and d, we have now commented on the intersection between ploidy and other types of DNA perturbation in the paragraph starting in line 491 (see response above)

3) Specific details:

a) Line 116: To improve clarity, it would be beneficial to refer to the figure right after the statement: 'However, their relative fitness improved compared to the WT reference as the initial glucose levels (Figure X).'

b) Line 404: The statement about antibiotics and cancer progression is somewhat brief here; it might be helpful to provide more context on why this mechanism influences these processes (here or before).

c) Line 418: "were re-suspended in water containing zymolyase (Zymo Research, Irvine, CA, US, 0.025 μ/μL), incubated at". Something is missing in the units.

d) Line 459: "and G2 phases for each genotype was estimated by deriving the the relative cell distribution". The article "the" is repeated.

e) 1a: The x-axis ticks appear misaligned, which makes it difficult to interpret the boxplots. For example, at 0.25, the tick is closer to the orange boxplot than to the black one. In contrast, at 2%, the tick seems well-centered."

f) Figure 3 could benefit from a general legend at the top regarding the colors, as finding it in 2c was not intuitively easy.

The typos and suggestions raised in points 3a-f have now been corrected in the manuscript.

g) I didn't review the code on GitHub.

Reviewer #1 (Significance):

The main strength of the study is that it shows robustness of compensatory evolution across varying nutrient conditions. The study adds to the growing body of literature on DNA replication stress and evolutionary adaptation by showing that compensatory evolution can occur regardless of nutrient availability. This fundamental finding challenges prior assumptions that nutrient conditions significantly alter evolutionary outcomes, contributing to a more nuanced understanding of how cells respond to stress. Furthermore, the discovery of the RNA polymerase II mediator complex's role in this process is particularly novel and opens new lines of investigation.

Advance in the field: The results advance our understanding of evolutionary biology, particularly in the context of DNA replication stress and compensatory evolution. The study demonstrates that evolutionary repair mechanisms are predictable, even under variable environmental conditions, which has key implications for evolutionary biology and therapeutic applications.

Audience:

This paper will be of interest to a specialized audience in evolutionary biology, genomics, and cell biology, particularly those interested in DNA replication stress and adaptive evolution. Researchers studying stress responses in model organisms, such as S. cerevisiae, will find the findings valuable, as will those working in applied fields where stress adaptation is a critical factor (e.g., industrial yeast fermentation, drug development, disease resistance, cancer research, or aging studies).

Expertise:

Evolutionary biology, genomic analysis, and cellular stress responses, with a particular focus on experimental evolution under DNA damage stress in Saccharomyces cerevisiae. Recently graduated and beginner reviewer.

Reviewer #2 (Evidence, reproducibility and clarity):

The paper addresses the effect of sugar availability in shaping compensatory evolution. The first observation of the paper is that cell physiology changes by modulating glucose availability also in strains that come with defective DNA replication (ctf4-null previously studied by the authors). An intriguing result is that ctf4-null grows comparatively better in low concentrations of glucose. This is hypothesized to be a consequence of both the decrease in dNTPs in low glucose, which causes slow down of fork progression, and/or reduced fork collapse at rDNA locus. Hence, wild types and ctf4-null show an opposite trend: in the mutant, the lowest concentration of glucose is the least affected by the mutation; in wild type, the highest concentration is the least affected. Adaptation rate is inversely related with the initial fitness. The effect on physiology and adaptation rate is a starting point for asking the key question: are evolutionary trajectories influnced by the growth conditions? The answer is negative: evolution experiments show the very same core of genetic changes at all sugar concentrations. The result is apparently at odds with previous publications, and the authors conclude that, in this particular setting, availability of carbon sources plays a minor role compared to impaired DNA replication. The different rates of adaptation in WT and mutant is rather explained by the initial fitness at the different glucose concentrations, which, as mentioned, is opposite in WT and ctf4-null mutants. The paper also reports a new mutation in MED14, component of the transcription mediator complex, which rescues the lack of Ctf4 activity. The study is interesting and asks a relevant question. The experiments are well executed and convincing, but the paper can be strengthened by testing some of the hypotheses which are put forward.

Main points

1- The raw data for evolutionary dynamics (Figure S2C) are fitted with the power law suggested by Wiser and Lenski, and return different values of the parameter 'b'. The authors say that the result depends greatly on the initial conditions ("due to the varying initial fitness of ctf4Δ cells across different glucose environments, they display an opposite trend to WT"). Around the initial values, however, the curves are non-monotonic, especially for low glucose availability. Both for WT and ctf4-null there is an initial drop in fitness, after which fitness increases. If one would neglect this initial dynamics, the value of the parameter 'b' would likely be different.

The non-monotonic trend in fitness highlighted by the reviewer is likely due to technical factors: Fitness at Generation 0 was measured with high precision in a low-throughput manner early in the project. In contrast, fitness from Generation 100 to 1000 was measured later in the study in a high-throughput fashion, necessitated by the large number of competitions conducted (96 wells × 4 time points × 6 replicates = 2304 assays). This difference in methodologies may have introduced a slight offset when the datasets were combined at Generation 100. Following the reviewer’s suggestion, we have excluded the data point at Generation 100 responsible for this non-monotonic behavior and re-fitted the curves. While this adjustment has caused minor changes in the parameter ‘b’, the qualitative trends, particularly the opposing trends between WT and ctf4Δ as glucose increases, remain consistent (Figure_rev_only 1). To ensure transparency, we have retained all recorded fitness values in the original figure for reference.

In general, one can question whether curves with this shape are best fitted by the power law proposed by Wiser and Lenski. For example, for the WT 0.25% glucose the linear fit gives a better R2 (why do the authors show the linear fit anyway?). This impression is further reinforced by the observation that Wiser and Lenski fit dynamics that last 50.000 generation, here the curves last 1/50th of it. In conclusion, I would question whether the parameter 'b' is a solid measurement of 'rate of adaptation'. Also, normalizations makes it difficult to appreciate the result shown in Figure 2B. I think the authors should look for a different way to show the different trend in adaptation dynamics for different glucose concentrations between wild types and mutants. For example, they could move Figure S2C in the main text to stress the result shown in Figure 2C, which already shows the difference between WT and mutant. This is especially true if what Figure 2C shows is (evo-anc)/evo. This is not fully clear to me: in the legend it refers to the delta, in the label of the y-axis I read that this is a percentage.

We thank the reviewer for prompting us to clarify our methods for reporting fitness changes over time. The fitness values are reported, throughout the paper, as a percentage change relative to the reference WT strain. The gain in fitness during evolution (reported as Δ) represents the difference between the evolved strain (evo%) and the ancestral strain (anc%), calculated as Δ = evo% - anc%. This represents the absolute gain, rather than the relative gain. This value is still reported as a percentage as it’s the same scale and unit as the two values being subtracted. We have included additional details to clarify this aspect in the figure legend.

“(C) Absolute fitness gains (Δ) at generation 1000 for evolved WT (upper panel, black) and ctf4Δ (lower panel, orange) populations. Box plots show median, IQR, and whiskers extending to 1.5×IQR, with individual data points beyond whiskers considered outliers. Absolute fitness gains were calculated by subtracting the ancestral relative fitness from the relative fitness of the evolved (Δ = evo% - anc%), both calculated as percentages relative to the same reference strain in the same glucose concentration.”

To conclude: the data show a different trend between wild types and mutants, which is interesting. Fitting it with the power law seems to be neither required nor appropriate. I suggest the authors to show the WT vs mutant pattern differently.

We followed the reviewer’s suggestion and moved Figure S2C, which depicts the detailed fitness trajectories over time, into the main manuscript as Figure 2D. We agree that presenting these trajectories alongside the absolute fitness gains (now in Figure S2C) provides a more intuitive and effective depiction of the evolutionary dynamics of WT and ctf4Δ strains without relying solely on the power-law fit. Additionally, we quantified the mean adaptation rate, calculated as the absolute fitness gain (Δ) divided by the total number of generations (now Figure 2B). While no individual method definitively captures the adaptation rates across the experiment, these complementary analyses consistently highlight the same trends noted by the reviewer. We have re-written the main text as follows:

Line 171: “By generation 1000, both WT and ctf4Δ evolved lines achieved, on average, slightly higher fitness in low glucose compared to high glucose conditions (Fig S2B). However, due to the varying initial fitness of ctf4Δ cells across different glucose environments, they recovered the same extent of the original defect (Fig S2C). ctf4Δ lines displayed an opposite trend to WT, with increasing absolute fitness throughout the experiment as glucose concentration rose (Fig S2B vs S2D). The differint absolute fitness gains over the same number of generations highlight distinct mean adaptation rates (Fig 2B). These differences are evident when examining the evolutionary dynamics of the evolved lines over time (Fig 2C). Additionally, we approximated the fitness trajectories using the power law function (Fig 2C, dashed purple lines), previously proposed to describe long-term evolutionary dynamics in constant environments (Wiser et al., 2013). The parameter b in this formula determines the curve's steepness, and can be used to quantify the global adaptation rate over generations (Fig S2E). Collectively, these analyses demonstrate that, unlike WT cells, ctf4Δ lines adapt faster in the presence of high glucose. This evidence aligns with the declining adaptability observed in other studies (Moore et al., 2000; Kryazhimskiy et al., 2014; Couce & Tenaillon, 2015), where low-fitness strains consistently adapt faster than their more fit counterparts (Fig S2F).”

Overall, these results demonstrate that cells can recover from fitness defects caused by constitutive DNA replication stress regardless of the glucose environment. However, adaptation rates under DNA replication stress exhibit opposing trends compared to WT cells, with faster adaptation yielding greater fitness gains in higher glucose conditions.”

2- In Figure S2C, the individual trajectories for WT at 2% glucose are strangely variable. In this case, plotting the average does not make too much sense. This result is strange, since this is the default condition, where cells are grown without any change of sugar concentration. Can the authors give any rationale? Are there other available results to replace those published in Figure S2C?

We agree with the reviewer that the individual trajectories for WT at 2% glucose are intriguing. However, we do not find these results necessarily “strange” as they could be explained by the following rationale: WT cells have been cultivated in 2% glucose since the 1950s, likely fixing most beneficial mutations for this condition. When many isogenic strains are evolved in parallel, (a) some lines show no improvement due to the scarcity of available beneficial mutations, (b) others exhibit slight decreases in fitness due to genetic drift fixing deleterious mutations, and (c) a few lines discover rare beneficial mutations, leading to fitness increases. In contrast, other conditions represent “newer” environments with larger mutational target sizes, resulting in more consistent outcomes.

Prompted by the reviewer’s comment, we look for other studies reporting detailed fitness measurements of evolved WT strains in standard laboratory media. We downloaded and plotted the fitness data from Johnson et al. 2021, where authors studied the evolution of WT strains over 10.000 generations. Interestingly, we see that in the early phase of the evolution (generations 500-1400) evolved lines show similar levels of variability in fitness as the one reported in our study (Figure_rev_only 2). Of note is that in Johnson et al. 2021 most of the adaptive mutations alleviate the toxicity of the ade2-1 allele. In our WT strain the gene was preemptively restored, furter reducing the target size for adaptation in YPD.

We believe it is important to report these measurements and decided to leave the original data, with the appropriate quantifications of variability, in Figure 2.

3- The molecular explanation given for the rescue of ctf4-null proposes a very relevant role for dNTPs downregulation. Particularly, both for Irx1 and med14-H919P, the authors propose that this happens via Rnr1 downregulation. At this stage, this is only a hypothesis. The molecular verification of the central role of Rnr1 downregulation would make the conclusion much stronger. For example, a preliminary test would imply that duplicating RNR1 in ctf4-null irx1-null and/or ctf4-null med14-H919P would revert the rescue. Any other experiment addressing this point would be useful to improve the paper.

We agree that the experiment suggested by the reviewer, or similar tests, would substantiate our hypotheses and strengthen the paper. Specifically, we plan to perturb dNTP production in both ctf4Δ ixr1Δ and ctf4Δ med14-H919P mutants through genetic manipulation of known factors involved in dNTP synthesis. We will then compare the resulting fitness to the expectations based on our hypotheses: reduced fitness benefits of the double mutants upon increasing dNTP levels and/or increased fitness in ctf4Δ mutants by decreasing dNTP levels through alternative mechanisms.

4- The authors propose from Figure S4B that the rescue of ixr1-null is less evident at low sugar concentration since both conditions trigger a reduction of dNTPs. I think this is interesting, since it would provide a link between glucose concentration and evolutionary trajectories to adaptation, which is what the authors wanted to study. In particular, one would predict that 0.25% glucose would see less ixr1-null than the other glucose conditions. I could not (was not able to) confute this hypothesis from the data shown in the paper. Likewise, for med14-H919P. If the authors have not tested it, it would be worth trying.

We had reported the appearance and frequency of all ‘core adaptive mutations’ (Figure S6C) but did not explicitly test the likelihood of their appearance under different glucose conditions. Following the reviewer’s suggestion, we have now performed χ2 tests (on the presence or absence of mutations) and ANOVA tests (on their mean frequency) to determine whether any mutation is particularly enriched or depleted in a given glucose environment. At first glance, the results do not support the hypothesis proposed by the reviewer. However, we note that although ixr1 mutants are less beneficial in low glucose than in high glucose, they still confer an 8% fitness advantage, which is likely sufficient to drive clones to fixation. We believe the reviewer’s reasoning is correct but is potentially masked by the still elevated fitness advantage of ixr1 in low glucose.

To better convey the results of this analysis, we have included a visual representation of the presence and frequency of the mutations in Figure 6A, and the results of the χ2 and ANOVA tests in Supplementary File 5. We also comment on the analysis as follows:

Line 314: “Similarly, we did not detect differences in the frequency of occurrence (χ2 tests) or average fractions (ANOVA test) achieved by the mutations in the populations evolved under different glucose environments (Fig 6A, Fig S4C and Supplementary File 5. The presence of all mutations in the final evolved lines correlated with their fitness benefits, suggesting how their selection in all glucose conditions was mostly dictated by their relative fitness benefits, rather than the environment (Fig 6A).”

5- The combination of the four genetic adaptation (Fig 6B) would benefit from an experimental verification to show that the different solutions are not mutually exclusive. This is not obvious: if more than one solution acts by reducing dNTPs, maybe their combined effect is less strong than what measured theoretically. The authors could derive some clones at the end of the experiment and Sanger sequencing some of the four genes, to confirm the co-presence of some of them in the same cell.

The co-occurrence of nearly every combination of the four core adaptive mutations we identified can be inferred from their relative frequencies, as revealed by deep whole-genome sequencing of the evolved populations (Fig. S4C). In these data, we observe populations carrying each pairwise combination of mutations at frequencies exceeding 50%, implying their coexistence. Moreover, many combinations of mutations approach or reach fixation. A particularly striking example is ctf4Δ Population 11, evolved in 8% glucose, where all core adaptive mutations are present at 100% frequency. These findings provide robust evidence that the different adaptive solutions are not mutually exclusive and can coexist within the same genetic background.

Nevertheless, we agree that experimentally verifying the compatibility and fitness of the four genetic adaptations described in Figure 6B (now Fig 6C) would further strengthen our conclusions. To this end, we plan to reconstruct all combinations of mutations observed at high frequency in the final evolved populations. We will then measure their fitness and compare it to that of the evolved populations, as well as to the theoretical expectations based on additivity currently presented in Figure 6C.

Minor points

Figures

• S4B: in the legend it should be explained that it is compared to ctf4D

We now report how the values were obtained in the figure legend:

(D = |anc%|-|reconstraucted%|)

-2A: the color code is not fully clear to me: what does green and blue indicate? higher and lower than 2%?

We apogise for not having included an explicit description of the color code in Figure 2A. Throughout the paper blue refers to glucose starvation (light blue for 0,25%, dark blue for 0,5%), while green refers to glucose abundance (light blue for 2%, dark blue for 8%). We now include a detailed description of the color code when it first appears (Fig 1B) and make sure is properly reported in all figure legends.

• S3A: the authors should show the statistical difference between WT and ctf4-null, which is mentioned as non-existent in p.6

The p value is now represented in Fig S3A

Text

• RNR1 is not really the gene with the highest score in Figure 5D, not even close: can you give a rationale for pin-pointing it (see also main point 3)?

The reviewer is correct. Perturbations of the mediator complex, which regulate the expression of most of RNA PolII transcripts, is expected to result in changes in the expression of a large set of genes. However, our focus on dNTPs and RNR1 is based on the following rationale:

1. Gene Ontology Enrichment Analysis: The downregulated genes in our dataset are enriched for the 'nucleotide metabolism' term, which includes pathways critical for dNTP production and directly linked to DNA replication and repair.

2. Role of RNR1: Among the downregulated genes, RNR1 stands out as it encodes the major subunit of ribonucleotide reductase, the rate-limiting enzyme in dNTP synthesis. This enzyme is essential for DNA replication, and cells experiencing constitutive DNA replication stress, as in our system, are particularly sensitive to changes in dNTP levels.

To make this rationale more explicit to the reader, we are adding the following sentence in the discussion:

Line 404: “Nucleotide metabolism, particularly ribonucleotide reductase, is essential for dNTP production. Given the role of dNTPs in regulating DNA replication and repair, the advantage of med14-H919P mutants in the ctf4Δ background may stem from reduced dNTP levels caused by the perturbed TID domain."

In addition, following the reviewers’ suggestions, we are conducting additional experiments to investigate the role of med14-H919P mutants in enhancing fitness under conditions of constitutive DNA replication stress (See response to reviewer #4). We anticipate that the final revised manuscript will offer further insights into the role of dNTPs or present alternative explanations for the observed phenomena.

• The med14-H919P mutation is observed in 22/48 wells. I guess the authors checked already: are some of these wells close to each other in the plate?

Correct. We took significant precautions in our experimental design to prevent cross-contamination, as outlined in the Materials and Methods section. Specifically, rows of ctf4Δ samples were alternated with rows of WT samples. Daily dilutions were then performed row by row using a 12 channels pipette. This approach ensured that any potential carry-over of cells would result in them being placed in wells containing a different genotype, where they would be eliminated by the consistent use of genotype-specific drugs.

As a result of these measures, we do not observe any distinct pattern of core genetic adaptation corresponding to the plate layout (Figure_rev_only 3). The only exception are mutations in IXR1, which appear in all ctf4Δ strains (albeit with different alleles, see supplementary File 3). Moreover, we reasoned that if a highly fit strain had invaded other wells, all the pre-existing mutations from its lineage would have been detected in those wells. However, apart from the recurrent ixr1 and rad9 mutations, which are also strongly adaptive, we find no evidence of shared mutations in wells carrying the med14-H919P allele (Figure_rev_only 4).

• Compensatory evolution of ctf4-null in 2% glucose is the experiment published by Fumasoni and Murray in eLife. In that paper, there is no trace of mutations in MED14. I think the authors should comment on this (different method for detecting putative compensatory mutations?).

We also noticed the absence of MED14 mutations in the eLife study by Fumasoni and Murray and find this discrepancy intriguing. One possible explanation lies in methodological differences. Our current study employed an improved version of the mutational analysis pipeline. However, we have not yet reanalyzed the original data from the previous study to determine whether MED14 mutations were present but undetected.

Interestingly, in the current study, we observed that in 2% glucose, MED14 mutations arose in only 3 out of 12 populations, a frequency lower than in other glucose conditions (Figure S6C). Assuming a similar frequency occurred in the 8 populations evolved in 2% glucose by Fumasoni and Murray (2020), one would expect only 2 populations to carry the mutation. This number falls below the threshold required for our algorithm to detect statistically significant parallelism.

Additionally, two significant experimental differences may also contribute to the observed discrepancy. First, the culture volumes and vessels differed: 10 mL cultures in tubes were used previously, whereas 1.5 mL cultures in 96-well plates were used in the current study.

• I may be mistaken, but Szamecz et al do not actually investigate whether different conditions result in different evolutionary trajectories (i.e., different genetics), and so their results may not be at odds with those presented here.

The reviewer is correct that Szamecz et al. do not explicitly test whether different conditions result in different evolutionary trajectories. However, in the section titled “Compensatory Evolution Generates Diverse Growth Phenotypes across Environments,” they examine how lines evolved in 2% YPD perform across various environments. They report how in roughly 50% of the cases tested, evolved lines showed either no improvement or even some lower fitness than the ancestor (Figure 5A).

While this could be explained by the accumulation of detrimental non-adaptive mutations in specific contexts, it likely implies that the adaptive strategies compensating for the original mutation in one environment do not confer similar benefits in other environments. This observation contrasts with our findings in Figure 6D, where we demonstrate that the main adaptive strategies provide a consistent benefit across diverse environments, including those with glucose, nitrogen, or phosphate abundance or starvation.

We have now modified the introduction, results and discussion to avoid misleading interpretations:

Line 42: “Szamecz and colleagues examined the evolutionary trajectories of 180 haploid yeast gene deletions over 400 generations (Szamecz et al., 2014). They found that, while fitness recovery occurred in the environment where evolution took place, the evolved lines often showed no improvement over their ancestors in other environments. This suggests that compensatory mutations beneficial in one environment often fail to restore fitness in others.”

Line 327: “A previous study in yeast showed how evolved lines which compensate for detrimental defects of gene deletions in standard laboratory conditions often failed to show fitness benefits compared to their ancestor when tested in other environments (Szamecz et al., 2014). We thus investigated the extent to which the core genetic adaptation to DNA replication stress was beneficial under alternative nutrient conditions.”

Line 422: “What could explain the discrepancies between our results, and previous studies on evolutionary repair highlighting the role of the environment in shaping evolutionary trajectories (Filteau et al., 2015), and the heterogeneous behavior of evolved lines in various environments (Szamecz et al., 2014)?”

typos

p.18, line 564 preformed -> performed

1. 6 line 189 with a strongly skew -> with a strong skew ?

Typos are now corrected in the main text

Reviewer #2 (Significance):

This is a well-done paper that could be of interest for the community of evolutionary biologists, scientists working on metabolism and cell division. It addresses an interesting problem, how metabolism affects compensatory evolution. Among the strengths: experiments are well done, the results are novel, the cross-talk between metabolism and evolutionary repair is intriguing. Among the weaknesses, the fact that the molecular explanations for the observations are only hypothesized and not tested experimentally. This is where the authors could improve the manuscript.

Reviewer #3 (Evidence, reproducibility and clarity):

This paper combines phenotypic and genomic data from an experimental evolution study in yeast to assess how repeatable evolution is in response to DNA replication stress. Importantly, the authors ask whether genotype by environment interactions influence repeatability of their evolved lines. To this end, the authors have constructed an elegant highly-replicated experiment in which two yeast genotypes (WT and CTF4 KO) were evolved under a variety of glucose levels for 1,000 generations. Recurrent mutations are found across many replicates, suggesting that repeatability is robust to GxE interactions. Of course, the authors correctly identify that these results are dependent on many particulars, as is always the case in biology, but provide a comprehensive discussion to accompany their results. I do not have any major comments to give, but simply some suggestions and points of clarification.

Major comments: N/A

Minor comments:

L19: I found the definition for compensatory evolution/mutations to be somewhat vague in the introduction (and subsequently throughout the text). It's clear that this was written for a more medical/physiological audience, but without a more explicit explanation of compensatory evolution/mutations, it became difficult to properly weigh some claims/discussions made by the authors later on. Do you define compensatory mutations as those which completely recover WT function/fitness, or are simply of opposite effect to the altered genotype? Others define "compensatory evolution" as simply any epistastically interacting amino acid substitutions (Ivankov et al, 2014). It would be nice to see more explicitly defined.

We thank the reviewer for highlighting the need for a precise definition of compensatory evolution and compensatory mutations. We recognize that the literature encompasses multiple definitions, including the one cited by the reviewer, which emphasizes compensatory mutations within the context of structural biology. This particular definition, prevalent in molecular evolution, was introduced by Kimura (Kimura, 1985) and is frequently used to explain the co-occurrence of amino acid mutations within a protein. These mutations offset each other’s defects, restoring or maintaining protein function. Here, however, we are using an older and broader definition of compensatory mutation, first introduced by Wright (Wright, 1964, 1977, 1982) and frequently used in evolutionary genomics (e.g., Moore et al., 2000; Szamecz et al., 2014; Rajon and Mazel, 2013; Eckartt et al., 2024). This definition includes any mutation in the rest of the genome that compensates (fully or partially) for another mutation's detrimental effects on fitness.

We have now included this definition in the introduction:

Line 19: “Compensatory evolution is a process by which cells mitigate the negative fitness effects of persistent perturbations in cellular processes across generations. This adaptation occurs through spontaneously arising compensatory mutations anywhere in the genome (Wright, 1964, 1977, 1982) that partially or fully alleviate the negative fitness effects of perturbations (Moore et al., 2000). The successive accumulation of compensatory mutations over evolutionary timescales progressively repair the cellular defects, ultimately restoring fitness.”

Line 361: “Our findings demonstrate that while glucose availability significantly affects the physiology and adaptation speed of cells under replication stress, it does not alter the fundamental genome-wide compensatory mutations that drive fitness recovery and evolutionary repair.”

Along these lines, I would have liked to see a more direct comparison/discussion of the degree to which deletion lines recovered. I can see from Fig 2E and Fig S2B that fitness increased quite a bit; would it not be possible to include a figure on the degree of compensation (basically relative fitness of evolved deletion lines - relative fitness of ancestral deletion lines)?

If the reviewer is suggesting calculating the difference between the evolved and ancestor fitness, the data is already in Figure S2B and S2D, defined as ‘Absolute fitness gains Δ’ and calculated as Δ = evo% - anc%.

If instead is suggesting to plot the fitness of evolved deletion lines (Y axis) against the relative fitness of ancestral deletion lines (X axis), we have now produced the plot is Figure S2F.

To better understand the extent of the fitness recovery in Ctf4 strains, we have also calculated and plotted the ‘relative fitness gain’ calculated as |evo%| / |anc%| *100 (Figure S2C)

We are now commenting on these comparisons in the following paragraph:

Line 171: “By generation 1000, both WT and ctf4Δ evolved lines achieved, on average, slightly higher fitness in low glucose compared to high glucose conditions (Fig S2B). However, due to the varying initial fitness of ctf4Δ cells across different glucose environments, they recovered the same extenct of the original defect (Fig S2C), displaying an opposite trend to WT, with increasing absolute fitness throughout the experiment as glucose concentration rose (Fig S2B vs S2D). The differint absolute fitness gains over the same number of generations highlight distinct mean adaptation rates (Fig 2B). These differences are evident when examining the evolutionary dynamics of the evolved lines over time (Fig 2C). Additionally, we approximated the fitness trajectories using the power law function (Fig 2C, dashed purple lines), previously proposed to describe long-term evolutionary dynamics in constant environments (Wiser et al., 2013). The parameter b in this formula determines the curve's steepness, and can be used to quantify the global fitness change over generations (Fig S2E). Collectively, these analyses demonstrate that, unlike WT cells, ctf4Δ lines adapt faster in the presence of high glucose. This evidence aligns with the declining adaptability observed in other studies (Moore et al., 2000; Kryazhimskiy et al., 2014; Couce & Tenaillon, 2015), where low-fitness strains consistently adapt faster than their more fit counterparts (Fig S2F).”

L57: Another minor nitpick that just comes down to semantics. When discussing "96 parallel populations", it invokes a higher sense of replication than is actually present in the study. I would rephrase this to something along the lines of "12 replicate populations across 8 treatments under conditions of [...]".

We changed the sentence as follows:

Line 66: “We evolved 96 parallel populations of budding yeast, organized into 12 replicate lines, across four conditions of glucose availability (from starvation to abundance) with or without replication stress.”

L185-187: The wording here needs to be clarified. Be explicit in that are examine the ratio (or count) of synonymous to non-synonymous mutations here, otherwise the interpretations appears to be direct contradiction to the (as written) results. Only after viewing the supplemental figure was I able to figure out what exactly was meant here.

We changed the sentence as follows:

Line 212: “We found no significant differences in the numbers of synonymous mutations detected in evolved populations in WT and ctf4∆ populations (Fig. S3A). These results support the hypothesis that replication stress in ctf4∆ lines favors the retention of beneficial mutations, rather than simply increasing the overall mutation rate.”

L349-350: The authors observe higher rates of adaptation in deletion lines than WT lines, and discuss this in adequate detail. Although not explicitly mentioned, this is consistent with a diminishing returns epistasis model (that could be beneficial to discuss, but is not necessary), which has been implicated in modulating the degree of repeatability observed along evolutionary trajectories (Wünsche et al. 2017). Although definitely not required for this already very nice manuscript, I think it would be very rewarding if the authors were to eventually analyze fine-scale dynamics of phenotypic and genomic adaptation to mine for these putative interactions and their influence on repeatability.

We agree with the reviewer on how our results align with a model of diminishing returns epistasis. This pattern is apparent not only between ctf4Δ and WT lines but also among ctf4Δ lines evolved in different glucose conditions. This phenomenon likely arises from the interaction of various adaptive mutations, which we aim to explore further in a dedicated manuscript. However, until we do so, we prefer to refer generally to a pattern of declining adaptability. To explicit this trend we have now included Fig S2F and commented on it in the manuscript:

Line 181: “This evidence aligns with the declining adaptability observed in other studies (Moore et al., 2000; Kryazhimskiy et al., 2014; Couce & Tenaillon, 2015), where low-fitness strains consistently adapt faster than their more fit counterparts (Fig S2F).”

Line 388: "Our results are consistent with declining adaptability, as evidenced by the reduced rates of adaptation observed both between ctf4Δ and WT lines and among ctf4Δ lines evolved in different glucose conditions (Fig S2F)"

Reviewer #3 (Significance):

It is clear to me that a great deal of time and care has been put into this study and the preparation of this manuscript. The science and analyses are appropriate to answer the questions at hand, and it bodes well that whenever I had a question pop up while reading, they were typically answered immediately after. I think that this manuscript will be broadly relevant to both biologists both evolutionary and clinical, and was written in a way to be accessible to both.

As someone with an expertise in repeatable evolution, I felt most excited by the observation of so many parallel substitutions at a single amino acid across deletion lines. As the authors rightfully point out in the results and discussion, it's likely that this degree of robustness is highly dependent on the particular mechanism of disruption that cells experience. The authors then go above and beyond to functionally validate the putative molecular mechanisms of (repeatable) adaptation in this system. While it may not always be possible to accomplish in non-model organisms, such multi-modal approaches will be crucial to advance the field of repeatable evolution.

Reviewer #4 (Evidence, reproducibility and clarity):

The authors investigated the effects of DNA replication stress on adaptation in different nutrient availabilities by passaging wild-type and ctf4Δ Saccharomyces cerevisiae in media with varying levels of glucose over ~1000 generations. The ctf4Δ strain experiences increased DNA replication stress due to the deletion of a non-essential replication fork protein. The authors found differences in evolution between wild-type and ctf4Δ yeast, which held across different growth media. This study identified a compensatory single amino acid variant in Med14, a protein in the mediator complex of RNA polymerase II, that was specifically selected in ctf4Δ strains. The authors conclude that while environmental nutrient availability has implications for cell fitness and physiology, adaptation is largely independent and instead dependent on genetic background. The data provide excellent support for the key aspects of the models, although some details are (to me) overstated.

Major comments:

• A ctf4Δ mutant strain was used to investigate the effects of replication stress. Why was this mutant chosen instead of other deletions that cause different types of replication stress?

We appreciate the opportunity to clarify our rationale for choosing the ctf4Δ mutant. The following are the main reasons why we believe ctf4Δ strains represent an ideal tool to study a global perturbation of the DNA replication program over evolutionary timescales:

1. General replication stress: The absence of Ctf4 perturbs replication fork progression, leading to a spectrum of replication stress-related phenotypes, including DNA damage sensitivity, single-stranded DNA gaps, reversed forks (Abe et al., 2018; Fumasoni et al., 2015), checkpoint activation (Poli et al., 2012), cell cycle delays (Miles and Formosa, 1992), increased recombination (Alvaro et al., 2007), and chromosome instability (Kouprina et al., 1992). This broad disruption makes it an excellent model for observing global perturbations in replication processes. In contrast, other mutants typically affect specific enzymatic (e.g., POL32 and RRM3) or signaling (e.g., MRC1) functions, making them better suited to address specific questions.
2. Constitutive stress: Unlike drug-induced stress (e.g., Hydroxyurea; Krakoff et al., 1968) or conditional depletion systems (e.g., GAL1-POLε; Zhang et al., 2022), which cells can easily circumvent through single mutations, ctf4Δ enforces persistent replication stress. Its deletion cannot be complemented by a single mutation, ensuring a robust and consistent stress environment for evolutionary studies.

We have now modified the main text to convey these advantages in a concise form:

Line 91: “In the absence of Ctf4, cells exhibit multiple defects commonly associated with DNA replication stress, such as single-stranded DNA gaps and altered replication forks (Fumasoni et al., 2015), leading to basal cell cycle checkpoint activation (Poli et al., 2012). These defects result in severe and persistent growth impairments, cell cycle delays, elevated nucleotides pools and chromosome instability (Miles and Formosa, 1992; Kouprina et al., 1992; Poli at al., 2012), making ctf4Δ mutants an ideal model for studying the cellular consequences of general and constitutive replication stress over evolutionary time.”

It's not clear from the study that the effects are generalizable to other forms of replication stress.

As with any method to induce DNA replication stress (including commonly used drugs like HU) each approach inevitably affects replication in a specific manner. Testing the broader applicability of our conclusions would require evolving additional strains with different replisome perturbations. For instance, mutations in ELG1 and CTF18 (affecting the alternative Replication Factor C), POL30 (affecting the sliding clamp PCNA), POL32 (affecting Polε), RRM3 (protective helicase) and (MRC1 (coordinating leading strand activities and signalling to the checkpoint) would have to be taken into account. Furthermore, specific mutant alleles of Ctf4 that disrupt interactions with particular binding partners (Such as ctf4–4E and ctf4–3E, perturbing the interaction with the CMG helicase and accessory factors respectively) will be highly informative on which specific aspects of the replication stress generated by the lack of Ctf4 each adaptive mutation alleviate.

However, accommodating such extensive variability would inflate the sample size to an extent that will become unfeasible within the experimental design focused on capturing parallel evolution over a nutrient gradient (the primary focus of this study). We agree that this is an important question and intend to address it comprehensively in a dedicated future study.

• The authors could be clearer that a (the?) cause of the ctf4∆ fitness defect is spurious upregulation of RNR1. I don't think it is mentioned until the Discussion, but it is highly relevant to Fig 4, and to the adaptations one would expect from ctf4∆.

We thank the reviewer for the opportunity to clarify this aspect. We do not think that the fitness defects of ctf4∆ cells stem solely from the spurious upregulation of RNR1. However, we believe that a major aspect of the evolutionary adaptation is aimed at decreasing dNTP levels, potentially through different mechanisms. We are now mentionig increased dNTPs as major phenotype of ctf4∆ and commenting on the hypothesis more clearly in the discussion.

Line 93: “These defects result in severe and persistent growth impairments, cell cycle delays, elevated nucleotides pools and chromosome instability (Miles and Formosa, 1992; Kouprina et al., 1992; Poli at al., 2012)”

Line 409: “This condition will, in turn, be detrimental when proliferation rates are high (as in WT in high glucose) but beneficial under constitutive DNA replication stress (ctf4Δ), where cells experience spurious upregulation of dNTP production (Poli et al., 2012; Davidson et al., 2012).

• In Figure 1E, there is a very large spread in the relative fitness at 2% and 8% glucose, but this was not commented on. Is this heteroscedasticity expected?

The observed heteroscedasticity is expected. Our competition assays tend to exhibit increased variability when a strain approaches very low fitness levels. Specifically, as one strain nears extinction by the third day of competition, its abundance is estimated based on a much smaller number of events in the flow cytometer. Furthermore, we noticed a small number of reference cells carrying pACT1-yCerulean not showing strong fluorescence in 8% glucose. The nature of this effect is uncertain, and possibly linked to metabolism-linked changes in the cytoplasm. The combination of these two phenomena amplifies the impact of noise inherent to the methodology, leading to increased variability across replicates.

Nontheless, the overall decreasing fitness trend across glucose conditions, combined with the statistical significance observed between high and low glucose levels, collectively convey a roboust phenotype

• The med14-H919P mutant was highly selected in ctf4Δ strains, independent of glucose availability. Is this variant found in any natural yeast strains (i.e., are there environments that select for this variant)? Also, if this variant is found in natural strains, does it co-occur with other mutations that could affect DNA replication?

We agree that this is an intriguing question. To address it, we plan to explore existing databases of variants identified in S. cerevisiae natural isolates. Specifically, we will investigate whether the med14-H919P mutation is present in these strains, identify any potential environmental factors that may select for it, and assess whether it co-occurs with other mutations that could influence DNA replication processes.

• The statement on lines 271-273 is not particularly well-supported. The analysis of the Warfield data suggest that reduced expression of RNR1 could be causal, but the data don't go as far as showing how the med14 mutation is advantageous in ctf4∆. Further experimentation would be necessary to support the possibilities that the authors discuss.

The sentence the reviewer refers to is: “Overall, these results show how an amino acid substitution in the Med14 subunit of the mediator complex, putatively affecting transcription, is strongly selected, and advantageous, in the presence of constitutive DNA replication stress.” We are unsure which aspect of the statement is seen as unsupported. The mutation's strong selection in ctf4∆ is demonstrated in Figures 5A, 6A, and S4C, while its advantageous nature is supported by Figures 5B and S4B. Regarding the mechanism, we have been cautious with our phrasing, describing its effect on transcription as "putative" (Line 272) and suggesting that our observations “are compatible with” reduced dNTP availability in med14-H919P cells due to RNR1 downregulation (Line 361).

The main focus of this study is to explore how nutrient availability influences evolutionary dynamics and compensatory adaptation in cells lacking Ctf4. We believe the identification of a novel selected allele (Fig. 5A) and confirmation of its benefit across glucose conditions (Fig. 5B) serves as an excellent complement to the primary conclusions (present in the title). We invite the reviewer to consider that the molecular basis of such a phenotype is not mentioned in our abstract, as we believe that its precise characterization would require a dedicated study on Med14.

Nonetheless, we are encouraged by the reviewer’s interest in this newly identified compensatory mutant (also noted by Reviewer #2), and we are eager to perform further experiments to better understand the biological processes affected by this mutation. We plan to extend our work as follows:

Based on known phenotypes associated with perturbations of Med14, we propose the following novel hypotheses regarding the mechanism by which med14-H919P alleviates ctf4Δ defects:

1. Decreased replication-transcription conflicts: Conflicts between the transcription machinery and replication forks are known to cause fragile sites, leading to increased chromosome breaks and genomic instability (Garcia-Muse and Aguilera, 2016). A general reduction in PolII transcription during replication, resulting from perturbations of the mediator complex, could reduce these conflicts and mitigate the fitness defects observed in ctf4Δ cells.
2. Increased cohesin loading: We have demonstrated that amplification of the cohesin loader SCC2 is beneficial in the absence of Ctf4. Recent findings (Mattingly et al., 2022) indicate that the mediator complex recruits SCC2 to PolII-transcribed genes. The med14-H919P mutation may enhance the fitness of ctf4Δ cells by facilitating cohesin loading during DNA replication.
3. Decreased dNTP levels: As discussed in the manuscript, perturbations of Med14 subunits in the mediator complex reduce the expression of genes, including those associated with nucleotide metabolism. Notably, these include RNR1, the major subunit of ribonucleotide reductase. The med14-H919P mutation could benefit the ctf4Δ background by counteracting the reported spurious increase in dNTPs, which affects replication fork speed (Poli et al., 2012).

We plan to distinguish between these hypotheses using the following approaches. First, the proposed mechanisms underlying Hypotheses 1 and 3 suggest that med14-H919P is a loss-of-function mutation, while Hypothesis 2 implies a gain-of-function effect. Testing the impact of a heterozygous med14-H919P allele in a homozygous ctf4Δ strain will allow us to differentiate between these two categories of mechanisms. Additionally, we aim to investigate the molecular process affected by the med14-H919P allele by analyzing its genetic interactions with genes involved in replication-transcription conflicts, cohesin loading, and dNTP production (See also response to reviewer #2).

We believe that the results of these experiments will provide further insights on the mechanism of suppression exerted by med14-H919P in the presence of constitutive DNA replication stress, without diverting the reader from the main message of the paper.

• The authors comment that the med14-H919P mutant could have implications for the stability of Med14, based on computational modelling. Verifying the stability of the med14-H919P in vivo would strengthen this discussion.

We believe that in vivo and in vitro structural studies investigating the effect of this mutation on the stability and function of the Mediator complex are beyond the scope of this manuscript. These investigations would be more appropriately addressed in future, dedicated studies focused on these specific aspects.

• In the discussion, the authors propose that the context of the perturbation may influence the robustness of adaptation. A more detailed explanation of this point (including a discussion of the findings of other similar studies investigating different conditions) would be helpful to further bolster this section.

We are now supporting this concept more explicitly by commenting on other studies as follows:

Line 429: “Third, the environment’s influence on compensatory evolution may depend on the specific cellular module perturbed and its genetic interactions with other modules that are significantly influenced by environmental conditions. For example, the actin cytoskeleton, which must rapidly respond to extracellular stimuli, is likely to be more directly influenced by environmental factors (Filateau et al., 2015) compared to the DNA replication machinery, which operates within the nucleus and is relatively insulated from such changes. Supporting this idea, a study examining mutants’ fitness across diverse environments found that conditions such as different carbon sources or TOR inhibition, similar to those used in this study, primarily affected genes involved in vesicle trafficking, transcription, protein metabolism, and cell polarity. In contrast, genes associated with genome maintenance, as well as their epistatic interactions, were largely unaffected (Costanzo et al., 2021)”.

In addition, to further substantiate this hypothesis, we plan to re-analyze published datasets on fitness and epistatic interactions among genes in various environments, testing whether specific cellular modules are more prone to changes following shifts in nutrient conditions.

Minor comments: - Competitions were performed between ctf4Δ strains and a constructed strain with yCerulean integrated at ACT1. Is the fitness of the fluorescent strain comparable to the ancestral wild-type strain (i.e., in a competition between the ancestral WT and the fluorescent strain, does either have an advantage)?

We noticed a slight disadvantage of the reference strain compare to WT, likely due to the costs of the extra fluorescence reporter. However, the disadvantage is minimal, ranging from -0.5 to -2.5 depending on the glucose environment (raw measurments are reported supplementary file 1, sheet 5). To take this into account, all fitness reported in figures are normalized for the WT value measured in the same environment line 613: “Relative fitness of the ancestral WT strain was used to normalize fitness across conditions.​​”

• In Figure 3, the legends for panels B and C appear to be swapped. Discussion of Figure 3 on pages 6 and 7 appear to reference the wrong panels.

We are unsure about this typo. Main text and figure legend seem to refer to the appropriate panels, 3B for mutation fractions and 3C for mutation counts. Perhaps the organization of the panels with B being under A instead of on its right confounds the reader?

• In Figure 4A and B, having the same colour scale between both heatmaps is misleading, as the scales are different. Consider having the same scale across both heatmaps so that enrichments are visually comparable.

Following the reviewer’s suggestion we have have chosen a uniform heatmap to visually represent GO terms enrichment in WT and ctf4∆ genetic backgrounds.

• In Figure 4C, having a legend in the figure for node size would be helpful to understand the actual number of populations with mutations in each gene.

A legend for node size has now being added next to Figure 4C.

Reviewer #4 (Significance):

In this study, a high-throughput evolution experiment uncovered the effects of genetic background on the development of adaptive mutations. The authors were able to identify a single amino acid variant of Med14 (med14-H919P) that was positively selected in ctf4Δ. Furthermore, they demonstrated the causality of med14-H919P in conferring a fitness advantage in ctf4Δ. The novelty of this mechanistic finding opens future avenues of investigation regarding the interaction network of the mediator complex in conditions of DNA replication stress. A limitation of the study is that only one mechanism of replication stress was assessed (ctf4Δ). Other gene mutations that cause replication stress would be interesting to assess and would provide a more thorough investigation of the effects of DNA replication factors on evolvability. This work will be of interest to researchers in the population genetics and genotype-by-environment fields, as it suggests the robustness of evolvability to environmental factors in the specific condition of DNA replication stress. As discussed by the authors, this finding differs from other works that have linked environmental conditions to adaptive evolution to different conditions, and is concordant with work that indicates the robustness of genetic interactions to environmental stresses. Furthermore, the identification of the highly-selected med14-H919P variant will be of interest to the DNA replication field. There is the potential for future work investigating the role of Med14 in mediating the response to DNA replication stress in both yeast and mammalian cell contexts, since the authors note that there are links between altered mediator complex regulation and cancers. Although I suspect that the very different regulation of RNR in mammalian cells makes it unlikely that the kind of upregulation of dNTP pools seen in ctf4∆ would be induced by replication stress in mammalian cells.

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Referee #2

Evidence, reproducibility and clarity

Review of "Compensatory evolution to DNA replication stress is robust to nutrient availability" from Natalino and Fumasoni.

The paper addresses the effect of sugar availability in shaping compensatory evolution. The first observation of the paper is that cell physiology changes by modulating glucose availability also in strains that come with defective DNA replication (ctf4-null previously studied by the authors). An intriguing result is that ctf4-null grows comparatively better in low concentrations of glucose. This is hypothesized to be a consequence of both the decrease in dNTPs in low glucose, which causes slow down of fork progression, and/or reduced fork collapse at rDNA locus. Hence, wild types and ctf4-null show an opposite trend: in the mutant, the lowest concentration of glucose is the least affected by the mutation; in wild type, the highest concentration is the least affected. Adaptation rate is inversely related with the initial fitness.

The effect on physiology and adaptation rate is a starting point for asking the key question: are evolutionary trajectories influnced by the growth conditions? The answer is negative: evolution experiments show the very same core of genetic changes at all sugar concentrations. The result is apparently at odds with previous publications, and the authors conclude that, in this particular setting, availability of carbon sources plays a minor role compared to impaired DNA replication. The different rates of adaptation in WT and mutant is rather explained by the initial fitness at the different glucose concentrations, which, as mentioned, is opposite in WT and ctf4-null mutants.

The paper also reports a new mutation in MED14, component of the transcription mediator complex, which rescues the lack of Ctf4 activity. The study is interesting and asks a relevant question. The experiments are well executed and convincing, but the paper can be strengthened by testing some of the hypotheses which are put forward.

Main points

1. The raw data for evolutionary dynamics (Figure S2C) are fitted with the power law suggested by Wiser and Lenski, and return different values of the parameter 'b'. The authors say that the result depends greatly on the initial conditions ("due to the varying initial fitness of ctf4Δ cells across different glucose environments, they display an opposite trend to WT"). Around the initial values, however, the curves are non-monotonic, especially for low glucose availability. Both for WT and ctf4-null there is an initial drop in fitness, after which fitness increases. If one would neglect this initial dynamics, the value of the parameter 'b' would likely be different. In general, one can question whether curves with this shape are best fitted by<br /> the power law proposed by Wiser and Lenski. For example, for the WT 0.25% glucose the linear fit gives a better R2 (why do theauthors show the linear fit anyway?). This impression is further reinforced by the observation that Wiser and Lenski fit dynamics that last 50.000 generation, here the curves last 1/50th of it. In conclusion, I would question whether the parameter 'b' is a solidmeasurement of 'rate of adaptation'. Also, normalizations makes it difficult to appreciate the result shown in Figure 2B.

I think the authors should look for a different way to show the different trend in adaptation dynamics for different glucose concentrations between wild types and mutants. For example, they could move Figure S2C in the main text to stress the result shown in Figure 2C, which already shows the difference between WT and mutant. This is especially true if what Figure 2C shows is (evo-anc)/evo. This is not fully clear to me: in the legend it refers to the delta, in the label of the y-axis I read that this is a percentage.

To conclude: the data show a different trend between wild types and mutants, which is interesting. Fitting it with the power law seems to be neither required nor appropriate. I suggest the authors to show the WT vs mutant pattern differently.<br /> 2. In Figure S2C, the individual trajectories for WT at 2% glucose are strangely variable. In this case, plotting the average does not make too much sense. This result is strange, since this is the default condition, where cells are grown without any change of sugar concentration. Can the authors give any rationale? Are there other available results to replace those published in Figure S2C?<br /> 3. The molecular explanation given for the rescue of ctf4-null proposes a very relevant role for dNTPs downregulation. Particularly, both for Irx1 and med14-H919P, the authors propose that this happens via Rnr1 downregulation.

At this stage, this is only a hypothesis. The molecular verification of the central role of Rnr1 downregulation would make the conclusion much stronger. For example, a preliminary test would imply that duplicating RNR1 in ctf4-null irx1-null and/or ctf4-null med14-H919P would revert the rescue. Any other experiment addressing this point would be useful to improve the paper.<br /> 4. The authors propose from Figure S4B that the rescue of ixr1-null is less evident at low sugar concentration since both conditions trigger a reduction of dNTPs. I think this is interesting, since it would provide a link between glucose concentration and evolutionary trajectories to adaptation, which is what the authors wanted to study.

In particular, one would predict that 0.25% glucose would see less ixr1-null than the other glucose conditions. I could not (was not able to) confute this hypothesis from the data shown in the paper. Likewise, for med14-H919P. If the authors have not tested it, it would be worth trying.<br /> 5. The combination of the four genetic adaptation (Fig 6B) would benefit from an experimental verification to show that the different solutions are not mutually exclusive. This is not obvious: if more than one solution acts by reducing dNTPs, maybe their combined effect is less strong than what measured theoretically. The authors could derive some clones at the end of the experiment and Sanger sequencing some of the four genes, to confirm the co-presence of some of them in the same cell.

Minor points

Figures

• S4B: in the legend it should be explained that it is compared to ctf4D .
• 2A: the color code is not fully clear to me: what does green and blue indicate? higher and lower than 2%?
• S3A: the authors should show the statistical difference between WT and ctf4-null, which is mentioned as non-existent in p.6

Text

• RNR1 is not really the gene with the highest score in Figure 5D, not even close: can you give a rationale for pin-pointing it (see also main point 3)?
• The med14-H919P mutation is observed in 22/48 wells. I guess the authors checked already: are somee of these wells<br /> close to each other in the plate?
• Compensatory evolution of ctf4-null in 2% glucose is the experiment published by Fumasoni and Murray in eLife. In that paper,<br /> there is no trace of mutations in MED14. I think the authors should comment on this (different method for detecting<br /> putative compensatory mutations?).
• I may be mistaken, but Szamecz et al do not actually investigate whether different conditions result in different<br /> evolutionary trajectories (i.e., different genetics), and so their results may not be at odds with those presented here.

typos

p.18, line 564 preformed -> performed

p. 6 line 189 with a strongly skew -> with a strong skew ?

Significance

This is a well-done paper that could be of interest for the community of evolutionary biologists, scientists working on metabolism and cell division. It addresses an interesting problem, how metabolism affects compensatory evolution. Among the strengths: experiments are well done, the results are novel, the cross-talk between metabolism and evolutionary repair is intriguing. Among the weaknesses, the fact that the molecular explanations for the observations are only hypothesized and not tested experimentally. This is where the authors could improve the manuscript.

Author response:

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

Reviewer #1 (Public Review):

Summary: 

The authors investigated causal inference in the visual domain through a set of carefully designed experiments, and sound statistical analysis. They suggest the early visual system has a crucial contribution to computations supporting causal inference. 

Strengths: 

I believe the authors target an important problem (causal inference) with carefully chosen tools and methods. Their analysis rightly implies the specialization of visual routines for causal inference and the crucial contribution of early visual systems to perform this computation. I believe this is a novel contribution and their data and analysis are in the right direction. 

Weaknesses: 

In my humble opinion, a few aspects deserve more attention: 

(1) Causal inference (or causal detection) in the brain should be quite fundamental and quite important for human cognition/perception. Thus, the underlying computation and neural substrate might not be limited to the visual system (I don't mean the authors did claim that). In fact, to the best of my knowledge, multisensory integration is one of the best-studied perceptual phenomena that has been conceptualized as a causal inference problem.

Assuming the causal inference in those studies (Shams 2012; Shams and Beierholm 2022;

Kording et al. 2007; Aller and Noppeney 2018; Cao et al. 2019) (and many more e.g., by Shams and colleagues), and the current study might share some attributes, one expects some findings in those domains are transferable (at least to some degree) here as well. Most importantly, underlying neural correlates that have been suggested based on animal studies and invasive recording that has been already studied, might be relevant here as well.

Perhaps the most relevant one is the recent work from the Harris group on mice (Coen et al. 2021). I should emphasize, that I don't claim they are necessarily relevant, but they can be relevant given their common roots in the problem of causal inference in the brain. This is a critical topic that the authors may want to discuss in their manuscript. 

We thank the reviewer. We addressed this point of the public review in our reply to the reviewer’s suggestions (and add it here again for convenience). The literature on the role of occipital, parietal and frontal brain areas in causal inference is also addressed in the response to point 3 of the public review.

“We used visual adaptation to carve out a bottom-up visual routine for detecting causal interactions in form of launching events. However, we know that more complex behaviors of perceiving causal relations can result from integrating information across space (e.g., in causal capture; Scholl & Nakayama, 2002), across time (postdictive influence; Choi & Scholl, 2006), and across sensory modalities (Sekuler, Sekuler, & Lau, 1997). Bayesian causal inference has been particularly successful as a normative framework to account for multisensory integration (Körding et al., 2007; Shams & Beierholm, 2022). In that framework, the evidence for a common-cause hypothesis is competing with the evidence for an independent-causes hypothesis (Shams & Beierholm, 2022). The task in our experiments could be similarly formulated as two competing hypotheses for the second disc’s movement (i.e., the movement was caused by the first disc vs. the movement occurred autonomously). This framework also emphasizes the distributed nature of the neural implementation for solving such inferences, showing the contributions of parietal and frontal areas in addition to sensory processing (for review see Shams & Beierholm, 2022). Moreover, even visual adaptation to contrast in mouse primary visual cortex is influenced by top-down factors such as behavioral relevance— suggesting a complex implementation of the observed adaptation results (Keller et al. 2017). The present experiments, however, presented purely visual events that do not require an integration across processing domains. Thus, the outcome of our suggested visual routine can provide initial evidence from within the visual system for a causal relation in the environment that may then be integrated with signals from other domains (e.g., auditory signals). Determining exactly how the perception of causality relates to mechanisms of causal inference and the neural implementation thereof is an exciting avenue for future research. Note, however, that perceived causality can be distinguished from judged causality: Even when participants are aware that a third variable (e.g., a color change) is the best predictor of the movement of the second disc in launching events, they still perceive the first disc as causing the movement of the second disc (Schlottmann & Shanks, 1992).”

(2) If I understood correctly, the authors are arguing pro a mere bottom-up contribution of early sensory areas for causal inference (for instance, when they wrote "the specialization of visual routines for the perception of causality at the level of individual motion directions raises the possibility that this function is located surprisingly early in the visual system *as opposed to a higher-level visual computation*."). Certainly, as the authors suggested, early sensory areas have a crucial contribution, however, it may not be limited to that. Recent studies progressively suggest perception as an active process that also weighs in strongly, the topdown cognitive contributions. For instance, the most simple cases of perception have been conceptualized along this line (Martin, Solms, and Sterzer 2021) and even some visual illusion (Safavi and Dayan 2022), and other extensions (Kay et al. 2023). Thus, I believe it would be helpful to extend the discussion on the top-down and cognitive contributions of causal inference (of course that can also be hinted at, based on recent developments). Even adaptation, which is central in this study can be influenced by top-down factors (Keller et al. 2017). I believe, based on other work of Rolfs and colleagues, this is also aligned with their overall perspective on vision.  

Indeed, we assessed bottom-up contributions to the perception of a causal relation. We agree with the reviewer that in more complex situations, for instance, in the presence of contextual influences or additional auditory signals, the perception of a causal relation may not be limited to bottom-up vision. While we had acknowledged this in the original manuscript (see excerpts below), we now make it even more explicit:

“[…] we know that more complex behaviors of perceiving causal relations can result from integrating information across space (e.g., in causal capture; Scholl & Nakayama, 2002), across time (postdictive influence; Choi & Scholl, 2006), and across sensory modalities (Sekuler, Sekuler, & Lau, 1997).”

“[…] Neurophysiological studies support the view of distributed neural processing underlying sensory causal interactions with the visual system playing a major role.”

“[…] Interestingly, single cell recordings in area F5 of the primate brain revealed that motor areas are contributing to the perception of causality (Caggiano et al., 2016; Rolfs, 2016), emphasizing the distributed nature of the computations underlying causal interactions. This finding also stresses that the detection, and the prediction, of causality is essential for processes outside sensory systems (e.g., for understanding other’s actions, for navigating, and for avoiding collisions). The neurophysiology subserving causal inference further extend the candidate cortical areas that might contibute to the detection of causal relations, emphasizing the role of the frontal cortex for the flexible integration of multisensory representations (Cao et al., 2019; Coen et al., 2023).”

However, there is also ample evidence that the perception of a simple causal relation—as we studied it in our experiments—escapes top-down cognitive influences. The perception of causality in launching events is described as automatic and irresistible, meaning that participants have the spontaneous impression of a causal relation, and participants typically do not voluntarily switch between a causal and a noncausal percept. This irresistibility has led several authors to discuss a modular organization underlying the detection of such events (Michotte, 1963; Scholl & Tremoulet, 2000). This view is further supported by a study that experimentally manipulated the contingencies between the movement of the two discs (Schlottmann & Shanks, 1992). In one condition the authors created a launching event where the second disc’s movement was perfectly correlated with a color change, but only sometimes coincided with the first disc’s movement offset. Nevertheless, participants reported seeing that the first disc caused the movement of second disc (regardless of the stronger statistical relationship with the color change). However, when asked to make conscious causal judgments, participants were aware of the color change as the true cause of the second disc’s motion—therefore recognizing its more reliable correlation. This study strongly suggests that perceived and judged causality (i.e., cognitive causal inference) can be dissociated (Schlottmann & Shanks, 1992). We have added this reference in the revised manuscript. Overall, we argue that our study focused on a visual routine that could be implemented in a simple bottom-up fashion, but we acknowledge throughout the manuscript, that in a more complex situation (e.g., integrating information from other sensory domains) the implementation could be realized in a more distributed fashion including top-down influences as in multisensory integration. However, it is important to stress that these potential top-down influences would be automatic and should not be confused with voluntary cognitive influences.

“Note, however, that perceived causality can be distinguished from judged causality (Schlottmann & Shanks, 1992). Even when participants are aware that a third variable (e.g., a color change) is the best predictor of the movement of the second disc in launching events, they still perceive the first disc as causing the movement of the second disc (Schlottmann & Shanks, 1992).”

(3) The authors rightly implicate the neural substrate of causal inference in the early sensory system. Given their study is pure psychophysics, a more elaborate discussion based on other studies that used brain measurements is needed (in my opinion) to put into perspective this conclusion. In particular, as I mentioned in the first point, the authors mainly discuss the potential neural substrate of early vision, however much has been done about the role of higher-tier cortical areas in causal inference e.g., see (Cao et al. 2019; Coen et al. 2021). 

In the revised manuscript, we addressed the limitations of a purely psychophysical approach and acknowledged alternative implementations in the Discussion section.

“Note that, while the present findings demonstrate direction-selectivity, it remains unclear where exactly that visual routine is located. As pointed out, it is also possible that the visual routine is located higher up in the visual system (or distributed across multiple levels) and is only using a directional-selective population response as input.”

Moreover, we cite also the two suggested papers when referring to the role of cortical areas in causal inference (Cao et al, 2019; Coen et al., 2023):

“Neurophysiological studies support the view of distributed neural processing underlying sensory causal interactions with the visual system playing a major role. Imaging studies in particular revealed a network for the perception of causality that is also involved in action observation (Blakemore et al., 2003; Fonlupt, 2003; Fugelsang et al., 2005; Roser et al., 2005). The fact that visual adaptation of causality occurs in a retinotopic reference frame emphazises the role of retinotopically organized areas within that network (e.g., V5 and the superior temporal sulcus). Interestingly, single cell recordings in area F5 of the primate brain revealed that motor areas are contributing to the perception of causality (Caggiano et al., 2016; Rolfs, 2016), emphasizing the distributed nature of the computations underlying causal interactions, and also stressing that the detection, and the prediction, of causality is essential for processes outside purely sensory systems (e.g., for understanding other’s actions, for navigating, and for avoiding collisions). The neurophysiological underpinnings in causal inference further extend the candidate cortical areas that might contibute to the detection of causal relations, emphasizing the role of the frontal cortex for the flexible integration of multisensory representations (Cao et al., 2019; Coen et al., 2023).”

There were many areas in this manuscript that I liked: clever questions, experimental design, and statistical analysis.

Thank you so much.

Reviewer #1 (Recommendations for the authors):

I congratulate the authors again on their manuscript and hope they will find my review helpful. Most of my notes are suggestions to the authors, and I hope will help them to improve the manuscript. None are intended to devalue their (interesting) work. 

We would like to thank the reviewer for their thoughtful and encouraging comments.

In the following, I use pX-lY template to refer to a particular page number, say page number X (pX), and line number, say line number Y (lY). 

Major concerns and suggestions 

- I would suggest simplifying the abstract and significance statement or putting more background in it. It's hard (at least for me) to understand if one is not familiar with the task used in this study. 

We followed the reviewer’s suggestion and added more background in the beginning of the abstract. 

We made the following changes:

“Detecting causal relations structures our perception of events in the world. Here, we determined for visual interactions whether generalized (i.e., feature-invariant) or specialized (i.e., feature-selective) visual routines underlie the perception of causality. To this end, we applied a visual adaptation protocol to assess the adaptability of specific features in classical launching events of simple geometric shapes. We asked observers to report whether they observed a launch or a pass in ambiguous test events (i.e., the overlap between two discs varied from trial to trial). After prolonged exposure to causal launch events (the adaptor) defined by a particular set of features (i.e., a particular motion direction, motion speed, or feature conjunction), observers were less likely to see causal launches in subsequent ambiguous test events than before adaptation. Crucially, adaptation was contingent on the causal impression in launches as demonstrated by a lack of adaptation in non-causal control events. We assessed whether this negative aftereffect transfers to test events with a new set of feature values that were not presented during adaptation. Processing in specialized (as opposed to generalized) visual routines predicts that the transfer of visual adaptation depends on the feature-similarity of the adaptor and the test event. We show that negative aftereffects do not transfer to unadapted launch directions but do transfer to launch events of different speed. Finally, we used colored discs to assign distinct feature-based identities to the launching and the launched stimulus. We found that the adaptation transferred across colors if the test event had the same motion direction as the adaptor. In summary, visual adaptation allowed us to carve out a visual feature space underlying the perception of causality and revealed specialized visual routines that are tuned to a launch’s motion direction.”

- The authors highlight the importance of studying causal inference and understanding the underlying mechanisms by probing adaptation, however, their introduction justifying that is, in my humble opinion, quite short. Perhaps in the cited paper, this is discussed extensively, but I'd suggest providing some elaboration in the manuscript. Otherwise, the study would be very specific to certain visual phenomena, rather than general mechanisms.  

We have carefully considered the reviewer’s set of comments and concerns (e.g., the role of top-down influences, the contributions of the frontal cortex, and illustration of the computational level). They all appear to share the theme that the reviewer looks at our study from the perspective of Bayesian inference. We conducted the current study in the tradition of classical phenomena in the field of the perception of causality (in the tradition of Michotte, 1963 and as reviewed in Scholl & Tremoulet, 2000) which aims to uncover the relevant visual parameters and rules for detecting causal relations in the visual domain. Indeed, we think that a causal inference perspective promises a lot of new insights into the mechanisms underlying the classical phenomena described for the perception of causality. In the revised manuscript, we discuss therefore causal inference and how it relates to the current study. We now emphasize that in our study, a) we used visual adaptation to reveal the bottom-up processes that allow for the detection of a causal interaction in the visual domain, b) that the perception of causality also integrates signals from other domains (which we do not study here), and c) that the neural substrates underlying the perception of causality might be best described by a distributed network. By discussing Bayesian causal inference, we point out promising avenues for future research that may bridge the fields of the perception of causality and Bayesian causal inference. However, we also emphasize that perceived causality and judged causality can be dissociated (Schlottmann & Shanks, 1992).

We added the following discussion:

“We used visual adaptation to carve out a bottom-up visual routine for detecting causal interactions in form of launching events. However, we know that more complex behaviors of perceiving causal relations can result from integrating information across space (e.g., in causal capture; Scholl & Nakayama, 2002), across time (postdictive influence; Choi & Scholl, 2006), and across sensory modalities (Sekuler, Sekuler, & Lau, 1997). Bayesian causal inference has been particularly successful as a normative framework to account for multisensory integration (Körding et al., 2007; Shams & Beierholm, 2022). In that framework, the evidence for a common-cause hypothesis is competing with the evidence for an independent-causes hypothesis (Shams & Beierholm, 2022). The task in our experiments could be similarly formulated as two competing hypotheses for the second disc’s movement (i.e., the movement was caused by the first disc vs. the second disc did not move). This framework also emphasizes the distributed nature of the neural implementation for solving such inferences, showing the contributions of parietal and frontal areas in addition to sensory processing (for review see Shams & Beierholm, 2022). Moreover, even visual adaptation to contrast in mouse primary visual cortex is influenced by top-down factors such as behavioral relevance— suggesting a complex implementation of the observed adaptation results (Keller et al. 2017). The present experiments, however, presented purely visual events that do not require an integration across processing domains. Thus, the outcome of our suggested visual routine can provide initial evidence from within the visual system for a causal relation in the environment that may then be integrated with signals from other domains (e.g., auditory signals). Determining exactly how the perception of causality relates to mechanisms of causal inference and the neural implementation thereof is an exciting avenue for future research. Note, however, that perceived causality can be distinguished from judged causality: Even when participants are aware that a third variable (e.g., a color change) is the best predictor of the movement of the second disc in launching events, they still perceive the first disc as causing the movement of the second disc (Schlottmann & Shanks, 1992).”

- I'd suggest, at the outset, already set the context, that your study of causal inference in the brain is specifically targeting the visual domain, if you like, in the discussion connect it  better to general ideas about causal inference in the brain (like the works by Ladan Shams and colleagues). 

We would like to thank the reviewer for this comment. We followed the reviewer’s suggestion and made clear from the beginning that this paper is about the detection of causal relations in the visual domain. In the revised manuscript we write:

“Here, we will study the mechanisms underlying the computations of causal interactions in the visual domain by capitalizing on visual adaptation of causality (Kominsky & Scholl, 2020; Rolfs et al., 2013). Adaptation is a powerful behavioral tool for discovering and dissecting a visual mechanism (Kohn, 2007; Webster, 2015) that provides an intriguing testing ground for the perceptual roots of causality.”

As described in our reply to the previous comment, we now also discussed the ideas about causal inference.

- To better illustrate the implication of your study on the computational level, I'd suggest putting it in the context of recent approaches to perception (point 2 of my public review). I think this is also aligned with the comment of Reviewer#3 on your line 32 (recommendation for authors).  

In the revised manuscript, we now discuss the role of top-down influences in causal inference when addressing point 2 of the reviewer’s public review.

Minor concerns and suggestions 

- On p2-l3, I'd suggest providing a few examples for generalized and or specialized visual routines (given the importance of the abstract). I only got it halfway through the introduction. 

We thank the reviewer for highlighting the need to better introduce the concept of a visual routine. We have chosen the term visual routine to emphasize that we locate the part of the mechanism that is affected by the adaptation in our experiments in the visual system. At the same time, the concept leaves space with respect to the extent to which the mechanism further involves mid- and higher-level processes. In the revised manuscript, we now refer to Ullman (1987) who introduced the concept of a visual routine—the idea of a modular operation that sequentially processes spatial and feature information. Moreover, we refer to the concept of attentional sprites (Cavanagh, Labianca, & Thornton, 2001)—attention-based visual routines that allow the visual system to semi-independently handle complex visual tasks (e.g., identifying biological motion).

We add the following footnote to the introduction:

“We use the term visual routine here to highlight that our adaptation experiments can reveal a causality detection mechanism that resides in the visual system. At the same time, calling it a routine emphasizes similarities with a local, semi-independent operation (e.g., the recognition of familiar motion patterns; see also Ullman, 1987; Cavanagh, Labianca, & Thornton, 2001) that can engage mid- and higher-level processes (e.g., during causal capture, Scholl & Nakayama, 2002; or multisensory integration, Körding et al., 2007).”

In the abstract we now write:

“Here, we determined for visual interactions whether generalized (i.e., feature-invariant) or specialized (i.e., feature-selective) visual routines underlie the perception of causality.”

- On p4-l31, I'd suggest mentioning the Matlab version. I have experienced differences across different versions of Matlab (minor but still ...). 

We added the Matlab Version.

- On p6-l46 OSF-link is missing (that contains data and code). 

Thank you. We made the OSF repository public and added the link to the revised manuscript.

We added the following information to the revised manuscript.

“The data analysis code has been deposited at the Open Science Framework and is publicly available https://osf.io/x947m/.”

Reviewer #2 (Public Review):

This paper seeks to determine whether the human visual system's sensitivity to causal interactions is tuned to specific parameters of a causal launching event, using visual adaptation methods. The three parameters the authors investigate in this paper are the direction of motion in the event, the speed of the objects in the event, and the surface features or identity of the objects in the event (in particular, having two objects of different colors). The key method, visual adaptation to causal launching, has now been demonstrated by at least three separate groups and seems to be a robust phenomenon. Adaptation is a strong indicator of a visual process that is tuned to a specific feature of the environment, in this case launching interactions. Whereas other studies have focused on retinotopically specific adaptation (i.e., whether the adaptation effect is restricted to the same test location on the retina as the adaptation stream was presented to), this one focuses on feature specificity. 

The first experiment replicates the adaptation effect for launching events as well as the lack of adaptation event for a minimally different non-causal 'slip' event. However, it also finds that the adaptation effect does not work for launching events that do not have a direction of motion more than 30 degrees from the direction of the test event. The interpretation is that the system that is being adapted is sensitive to the direction of this event, which is an interesting and somewhat puzzling result given the methods used in previous studies, which have used random directions of motion for both adaptation and test events. 

The obvious interpretation would be that past studies have simply adapted to launching in every direction, but that in itself says something about the nature of this direction-specificity: it is not working through opposed detectors. For example, in something like the waterfall illusion adaptation effect, where extended exposure to downward motion leads to illusory upward motion on neutral-motion stimuli, the effect simply doesn't work if motion in two opposed directions is shown (i.e., you don't see illusory motion in both directions, you just see nothing). The fact that adaptation to launching in multiple directions doesn't seem to cancel out the adaptation effect in past work raises interesting questions about how directionality is being coded in the underlying process. 

We would like to thank the reviewer for that thoughtful comment. We added the described implication to the manuscript:

“While the present study demonstrates direction-selectivity for the detection of launches, previous adaptation protocols demonstrated successful adaptation using adaptors with random motion direction (Rolfs et al., 2013; Kominsky & Scholl, 2020). These results therefore suggest independent direction-specific routines, in which adaptation to launches in one direction does not counteract an adaptation to launches in the opposite direction (as for example in opponent color coding).”

In addition, one limitation of the current method is that it's not clear whether the motion direction-specificity is also itself retinotopically-specific, that is, if one retinotopic location were adapted to launching in one direction and a different retinotopic location adapted to launching in the opposite direction, would each test location show the adaptation effect only for events in the direction presented at that location? 

This is an interesting idea! Because previous adaptation studies consistently showed retinotopic adaptation of causality, we would not expect to find transfer of directional tuning for launches to other locations. We agree that the suggested experiment on testing the reference frame of directional specificity constitutes an interesting future test of our findings.

The second experiment tests whether the adaptation effect is similarly sensitive to differences in speed. The short answer is no; adaptation events at one speed affect test events at another. Furthermore, this is not surprising given that Kominsky & Scholl (2020) showed adaptation transfer between events with differences in speeds of the individual objects in the event (whereas all events in this experiment used symmetrical speeds). This experiment is still novel and it establishes that the speed-insensitivity of these adaptation effects is fairly general, but I would certainly have been surprised if it had turned out any other way. 

We thank the reviewer for highlighting the link to an experiment reported in Kominsky & Scholl (2020). We report the finding of that experiment now in the revised manuscript.

We added the following paragraph in the discussion:

“For instance, we demonstrated a transfer of adaptation across speed for symmetrical speed ratios. This result complements a previous finding that reported that the adaptation to triggering events (with an asymmetric speed ratio of 1:3) resulted in significant retinotopic adaptation of ambiguous (launching) test events of different speed ratios (i.e., test events with a speed ratio of 1:1 and of 1:3; Kominsky & Scholl, 2020).”

The third experiment tests color (as a marker of object identity), and pits it against motion direction. The results demonstrate that adaptation to red-launching-green generates an adaptation effect for green-launching-red, provided they are moving in roughly the same direction, which provides a nice internal replication of Experiment 1 in addition to showing that the adaptation effect is not sensitive to object identity. This result forms an interesting contrast with the infant causal perception literature. Multiple papers (starting with Leslie & Keeble, 1987) have found that 6-8-month-old infants are sensitive to reversals in causal roles exactly like the ones used in this experiment. The success of adaptation transfer suggests, very clearly, that this sensitivity is not based only on perceptual processing, or at least not on the same processing that we access with this adaptation procedure. It implies that infants may be going beyond the underlying perceptual processes and inferring genuine causal content. This is also not the first time the adaptation paradigm has diverged from infant findings: Kominsky & Scholl (2020) found a divergence with the object speed differences as well, as infants categorize these events based on whether the speed ratio (agent:patient) is physically plausible (Kominsky et al., 2017), while the adaptation effect transfers from physically implausible events to physically plausible ones. This only goes to show that these adaptation effects don't exhaustively capture the mechanisms of early-emerging causal event representation. 

We would like to thank the reviewer for highlighting the similarities (and differences) to the seminal study by Leslie and Keeble (1987). We included a discussion with respect to that paper in the revised manuscript. Indeed, that study showed a recovery from habituation to launches after reversal of the launching events. In their study, the reversal condition resulted in a change of two aspects, 1) motion direction and 2) a change of what color is linked to either cause (i.e., agent) or effect (i.e, patient). Our study, based on visual adaptation in adults, suggests that switching the two colors is not necessary for a recovery from the habituation, provided the motion direction is reversed. Importantly, the reversal of the motion direction only affected the perception of causality after adapting to launches (but not to slip events), which is consistent with Leslie and Keeble’s (1987) finding that the effect of a reversal is contingent on habituation/adaptation to a causal relationship (and is not observed for non-causal delayed launches). Based on our findings, we predict that switching colors without changing the event’s motion direction would not result in a recovery from habituation. Obviously, for infants, color may play a more important role for establishing an object identity than it does for adults, which could explain potential differences. We also agree with the reviewer’s point that the adaptation protocol might tap into different mechanisms than revealed by habituation studies in infants (e.g, Kominsky et al., 2017 vs. Kominsky & Scholl, 2020). 

We revised the manuscript accordingly when discussing the role of direction selectivity in our study:

“Habituation studies in six-months-old infants also demonstrated that the reversal of a launch resulted in a recovery from habituation to launches (while a non-causal control condition of delayed-launches did not; Leslie & Keeble, 1987). In their study, the reversal of motion direction was accompanied by a reversal of the color assignment to the cause-effectrelationship. In contrast, our findings suggest, that in adults color does not play a major role in the detection of a launch. Future studies should further delineate similarities and differences obtained from adaptation studies in adults and habituation studies in children (e.g., Kominsky et al., 2017; Kominsky & Scholl, 2020).”

One overarching point about the analyses to take into consideration: The authors use a Bayesian psychometric curve-fitting approach to estimate a point of subjective equality (PSE) in different blocks for each individual participant based on a model with strong priors about the shape of the function and its asymptotic endpoints, and this PSE is the primary DV across all of the studies. As discussed in Kominsky & Scholl (2020), this approach has certain limitations, notably that it can generate nonsensical PSEs when confronted with relatively extreme response patterns. The authors mentioned that this happened once in Experiment 3 and that a participant had to be replaced. An alternate approach is simply to measure the proportion of 'pass' reports overall to determine if there is an adaptation effect. I don't think this alternate analysis strategy would greatly change the results of this particular experiment, but it is robust against this kind of self-selection for effects that fit in the bounds specified by the model, and may therefore be worth including in a supplemental section or as part of the repository to better capture the individual variability in this effect. 

We largely agree with these points. Indeed, we adopted the non-parametric analysis for a recent series of experiments in which the psychometric curves were more variable (Ohl & Rolfs, Vision Sciences Society Meeting 2024). In the present study, however, the model fits were very convincing. In Figures S1, S2 and S3 we show the model fits for each individual observer and condition on top of the mean proportion of launch reports. The inferential statistics based on the points of subjective equality, therefore, allowed us to report our findings very concisely.

In general, this paper adds further evidence for something like a 'launching' detector in the visual system, but beyond that, it specifies some interesting questions for future work about how exactly such a detector might function. 

We thank the reviewer for this positive overall assessment.

Reviewer #2 (Recommendations for the authors):

Generally, the paper is great. The questions I raised in the public review don't need to be answered at this time, but they're exciting directions for future work. 

We would like to thank the reviewer for the encouraging comments and thoughtful ideas on how to improve the manuscript.

I would have liked to see a little more description of the model parameters in the text of the paper itself just so readers know what assumptions are going into the PSE estimation. 

We followed the reviewer’s suggestion and added more information regarding the parameter space (i.e., ranges of possible parameters of the logistic model) that we used for obtaining the model fits. 

Specifically, we added the following information in the manuscript:

“For model fitting, we constrained the range of possible estimates for each parameter of the logistic model. The lower asymptote for the proportion of reported launches was constrained to be in the range 0–0.75, and the upper asymptote in the range 0.25–1. The intercept of the logistic model was constrained to be in the range 1–15, and the slope was constrained to be in the range –20 to –1.”

The models provided very good fits as can be appreciated by the fits per individual and experimental condition which we provide in response to the public comments. Please note, that all data and analysis scripts are available at the Open Science Framework (https://osf.io/x947m/).

I also have a recommendation about Figure 1b: Color-code "Feature A", "Feature B", and "Feature C" and match those colors with the object identity/speed/direction text. I get what the figure is trying to convey but to a naive reader there's a lot going on and it's hard to interpret. 

We followed the reviewer’s suggestion and revised the visualization accordingly.

If you have space, figures showing the adaptation and corresponding test events for each experimental manipulation would also be great, particularly since the naming scheme of the conditions is (necessarily) not entirely consistent across experiments. It would be a lot of little figures, I know, but to people who haven't spent as long staring at these displays as we have, they're hard to envision based on description alone. 

We followed the reviewer’s recommendation and added a visualization of the adaptor and the test events for the different experiments in Figure 2.

Reviewer #3 (Public Review):

We thank the reviewer for their thoughtful comments, which we carefully addressed to improve the revised manuscript. 

Summary: 

This paper presents evidence from three behavioral experiments that causal impressions of "launching events", in which one object is perceived to cause another object to move, depending on motion direction-selective processing. Specifically, the work uses an adaptation paradigm (Rolfs et al., 2013), presenting repetitive patterns of events matching certain features to a single retinal location, then measuring subsequent perceptual reports of a test display in which the degree of overlap between two discs was varied, and participants could respond "launch" or "pass". The three experiments report results of adapting to motion direction, motion speed, and "object identity", and examine how the psychometric curves for causal reports shift in these conditions depending on the similarity of the adapter and test. While causality reports in the test display were selective for motion direction (Experiment 1), they were not selective for adapter-test speed differences (Experiment 2) nor for changes in object identity induced via color swap (Experiment 3). These results support the notion that causal perception is computed (in part) at relatively early stages of sensory processing, possibly even independently of or prior to computations of object identity. 

Strengths: 

The setup of the research question and hypotheses is exceptional. The experiments are carefully performed (appropriate equipment, and careful control of eye movements). The slip adaptor is a really nice control condition and effectively mitigates the need to control motion direction with a drifting grating or similar. Participants were measured with sufficient precision, and a power curve analysis was conducted to determine the sample size. Data analysis and statistical quantification are appropriate. Data and analysis code are shared on publication, in keeping with open science principles. The paper is concise and well-written. 

Weaknesses: 

The biggest uncertainty I have in interpreting the results is the relationship between the task and the assumption that the results tell us about causality impressions. The experimental logic assumes that "pass" reports are always non-causal impressions and "launch" reports are always causal impressions. This logic is inherited from Rolfs et al (2013) and Kominsky & Scholl (2020), who assert rather than measure this. However, other evidence suggests that this assumption might not be solid (Bechlivanidis et al., 2019). Specifically, "[our experiments] reveal strong causal impressions upon first encounter with collision-like sequences that the literature typically labels "non-causal"" (Bechlivanidis et al., 2019) -- including a condition that is similar to the current "pass". It is therefore possible that participants' "pass" reports could also involve causal experiences. 

We agree with the reviewer that our study assumes that the launch-pass dichotomy can be mapped onto a dimension of causal to non-causal impressions. Please note that the choice for this launch-pass task format was intentional. We consider it an advantage that subjects do not have to report causal vs non-causal impressions directly, as it allows us to avoid the oftencriticized decision biases that come with asking participants about their causal impression (Joynson, 1971; for a discussion see Choi & Scholl, 2006). This comes obviously at the cost that participants did not directly report their causal impression in our experiments. There is however evidence that increasing overlap between the discs monotonically decreases the causal impression when directly asking participants to report their causal impression (Scholl & Nakayama, 2004). We believe, therefore, that the assumption of mapping between launchesto-passes and causal-to-noncausal is well-justified. At the same time, the expressed concern emphasizes the need to develop further, possibly implicit measure for causal impressions (see Völter & Huber, 2021).

However, as pointed out by the reviewer, a recent paper demonstrated that on first encounter participants can have impressions in response to a pass event that are different from clearly non-causal impressions (Bechlivanidis et al., 2019). As demonstrated in the same paper, displaying a canonical launch decreased the impression of causality when seeing pass events in subsequent trials. In our study, participants completed an entire training session before running the main experiments. It is therefore reasonable to expect that participants observed passes as non-causal events given the presence of clear causal references. Nevertheless, we now acknowledge this concern directly in the revised manuscript.

We added the following paragraph to the discussion:

“In our study, we assessed causal perception by asking observers to report whether they observed a launch or a pass in events of varying ambiguity. This method assumes that launches and passes can be mapped onto a dimension that ranges from causal to non-causal impressions. It has been questioned whether pass events are a natural representative of noncausal events: Observers often report high impressions of causality upon first exposure to pass events, which then decreased after seeing a canonical launch (Bechlivanidis, Schlottmann, & Lagnado, 2019). In our study, therefore, participants completed a separate session that included canonical launches before starting the main experiment.”

Furthermore, since the only report options are "launch" or "pass", it is also possible that "launch" reports are not indications of "I experienced a causal event" but rather "I did not experience a pass event". It seems possible to me that different adaptation transfer effects (e.g. selectivity to motion direction, speed, or color-swapping) change the way that participants interpret the task, or the uncertainty of their impression. For example, it could be that adaptation increases the likelihood of experiencing a "pass" event in a direction-selective manner, without changing causal impressions. Increases of "pass" impressions (or at least, uncertainty around what was experienced) would produce a leftward shift in the PSE as reported in Experiment 1, but this does not necessarily mean that experiences of causal events changed. Thus, changes in the PSEs between the conditions in the different experiments may not directly reflect changes in causal impressions. I would like the authors to clarify the extent to which these concerns call their conclusions into question. 

Indeed, PSE shifts are subject to cognitive influences and can even be voluntarily shifted (Morgan et al., 2012). We believe that decision biases (e.g., reporting the presence of launch before adaptation vs. reporting the absence of a pass after the adaptation) are unlikely to explain the high specificity of aftereffects observed in the current study. While such aftereffects are very typical of visual processing (Webster, 2015), it is unclear how a mechanism that increase the likelihood of perceiving a pass could account for the retinotopy of adaptation to launches (Rolfs et al., 2013) or the recently reported selective transfer of adaptation for only some causal categories (Kominsky et al., 2020). The latter authors revealed a transfer of adaptation from triggering to launching, but not from entraining events to launching. Based on these arguments, we decided to not include this point in the revised manuscript.

Leaving these concerns aside, I am also left wondering about the functional significance of these specialised mechanisms. Why would direction matter but speed and object identity not? Surely object identity, in particular, should be relevant to real-world interpretations and inputs of these visual routines? Is color simply too weak an identity? 

We agree that it would be beneficial to have mechanisms in place that are specific for certain object identities. Overall, our results fit very well to established claims that only spatiotemporal parameters mediate the perception of causality (Michotte, 1963; Leslie, 1984; Scholl & Tremoulet, 2000). We have now explicitly listed these references again in the revised manuscript. It is important to note, that an understanding of a causal relation could suffice to track identity information based purely on spatiotemporal contingencies, neglecting distinguishing surface features.

We revised the manuscript and state:

“Our findings therefore provide additional support for the claim that an event’s spatiotemporal parameters mediate the perception of causality (Michotte, 1963; Leslie, 1984; Scholl & Tremoulet, 2000).”

Moreover, we think our findings of directional selectivity have functional relevance. First, direction-selective detection of collisions allows for an adaptation that occurs separately for each direction. That means that the visual system can calibrate these visual routines for detecting causal interactions in response to real-world statistics that reflect differences in directions. For instance, due to gravity, objects will simply fall to the ground. Causal relation such as launches are likely to be more frequent in horizontal directions, along a stable ground. Second, we think that causal visual events are action-relevant, that is, acting on (potentially) causal events promises an advantage (e.g., avoiding a collision, or quickly catching an object that has been pushed away). The faster we can detect such causal interactions, the faster we can react to them. Direction-selective motion signals are available in the first stages of visual processing. Visual routines that are based on these direction-selective motion signals promise to enable such fast computations. Please note, however, that while our present findings demonstrate direction-selectivity, they do not pinpoint where exactly that visual routine is located. It is quite possible that the visual routine is located higher up in the visual system, relying on a direction-selective population response as input.

We added these points to the discussion of the functional relevance: 

“We suggest that at least two functional benefits result from a specialized visual routine for detecting causality. First, a direction-selective detection of launches allows adaptation to occur separately for each direction. That means that the visual system can automatically calibrate the sensitivity of these visual routines in response to real-world statistics. For instance, while falling objects drop vertically towards the ground, causal relations such as launches are common in horizontal directions moving along a stable ground. Second, we think that causal visual events are action-relevant, and the faster we can detect such causal interactions, the faster we can react to them. Direction-selective motion signals are available very early on in the visual system. Visual routines that are based on these direction-selective motion signals may enable faster detection. While our present findings demonstrate direction-selectivity, they do not pinpoint where exactly that visual routine is located. It is possible that the visual routine is located higher up in the visual system (or distributed across multiple levels), relying on a direction-selective population response as input.”

Reviewer #3 (Recommendations for the authors):

- The concept of "visual routines" is used without introduction; for a general-interest audience it might be good to include a definition and reference(s) (e.g. Ullman.). 

Thank you very much for highlighting that point. We have chosen the term visual routine to emphasize that we locate the part of the mechanism that is affected by the adaptation in our experiments in the visual system, but at the same time it leaves space regarding the extent to which the mechanism further involves mid- and higher-level processes. The term thus has a clear reference to a visual routine by Ullman (1987). We have now addressed what we mean by visual routine, and we also included the reference in the revised manuscript.

We add the following footnote to the introduction:

“We use the term visual routine here to highlight that our adaptation experiments can reveal a causality detection mechanism that resides in the visual system. At the same time, calling it a routine emphasizes similarities with a local, semi-independent operation (e.g., the recognition of familiar motion patterns; see also Ullman, 1987; Cavanagh, Labianca, & Thornton, 2001) that can engage mid- and higher-level processes (e.g., during causal capture, Scholl & Nakayama, 2002; or multisensory integration, Körding et al., 2007).”

- I would appreciate slightly more description of the phenomenology of the WW adaptors: is this Michotte's "entraining" event? Does it look like one disc shunts the other?  

The stimulus differs from Michotte's entrainment event in both spatiotemporal parameters and phenomenology. We added videos for the launch, pass and slip events as Supplementary Material.

Moreover, we described the slip event in the methods section:

“In two additional sessions, we presented slip events as adaptors to control that the adaptation was specific for the impression of causality in the launching events. Slip events are designed to match the launching events in as many physical properties as possible while producing a very different, non-causal phenomenology. In slip events, the first peripheral disc also moves towards a stationary disc. In contrast to launching events, however, the first disc passes the stationary disc and stops only when it is adjacent to the opposite edge of the stationary disc. While slip events do not elicit a causal impression, they have the same number of objects and motion onsets, the same motion direction and speed, as well as the same spatial area of the event as launches.”

In the revised manuscript, we added also more information on the slip event in the beginning of the results section. Importantly, the stimulus typically produces the impression of two independent movements and thus serves as a non-causal control condition in our study. Only anecdotally, some observers (not involved in this study) who saw the stimulus spontaneously described their phenomenology of seeing a slip event as a double step or a discus throw.

We added the following description to the results section:

“Moreover, we compared the visual adaptation to launches to a (non-causal) control condition in which we presented slip events as adaptor. In a slip event, the initially moving disc passes completely over the stationary disc, stops immediately on the other side, and then the initially stationary disc begins to move in the same direction without delay. Thus, the two movements are presented consecutively without a temporal gap. This stimulus typically produces the impression of two independent (non-causal) movements.”

- In general more illustrations of the different conditions (similar to Figure 1c but for the different experimental conditions and adaptors) might be helpful for skim readers.  

We followed the reviewer’s recommendation and added a visualization of the adaptor and the test events for the different experiments in Figure 2.

- Were the luminances of the red and green balls in experiment 3 matched? Were participants checked for color anomalous vision?  

Yes, we checked for color anomalous vision using the color test Tafeln zur Prüfung des Farbensinnes/Farbensehens (Kuchenbecker & Broschmann, 2016). We added that information to the manuscript. The red and green discs were not matched for luminance. We measured the luminance after the experiment (21 cd/m² for the green disc and 6 cd/m² for the red disc). Please note, that the differences in luminance should not pose a problem for the interpretation of the results, as we see a transfer of the adaptation across the two different colors.

We added the following information to the manuscript:

“The red and green discs were not matched for luminance. Measurements obtained after the experiments yielded a luminance of 21 cd/m² for the green disc and 6 cd/m² for the red disc.”

“All observers had normal or corrected-to-normal vision and color vision as assessed using the color test Tafeln zur Prüfung des Farbensinnes/Farbensehens (Kuchenbecker & Broschmann, 2016).”

- Relationship of this work to the paper by Arnold et al., (2015). That paper suggested that some effects of adaptation of launching events could be explained by an adaptation of object shape, not by causality per se. It is superficially difficult to see how one could explain the present results from the perspective of object "squishiness" -- why would this be direction selective? In other words, the present results taken at face value call the "squishiness" explanation into question. The authors could consider an explanation to reconcile these findings in their discussion. 

Indeed, the paper by Arnold and colleagues (2014) suggested that a contact-launch adaptor could lead to a squishiness aftereffect—arguing that the object elasticity changed in response to the adaptation.  Importantly, the same study found an object-centered adaptation effect rather than a retinotopic adaptation effect. However, the retinotopic nature of the negative aftereffect as used in our study has been repeatedly replicated (for instance Kominsky & Scholl, 2020). Thus, the divergent results of Arnold and colleagues may have resulted from differences in the task (i.e., observers had to judge whether they perceived a soft vs. hard bounce), or the stimuli (i.e., bounces of a disc and a wedge, and the discs moving on a circular trajectory). It would be important to replicate these results first and then determine whether their squishiness effect would be direction-selective as well. We now acknowledge the study by Arnold and colleagues in the discussion:

“The adaptation of causality is spatially specific to the retinotopic coordinates of the adapting stimulus (Kominsky & Scholl, 2020; Rolfs et al., 2013; for an object-centered elasiticity aftereffect using a related stimulus on a circular motion path, see Arnold et al., 2015), suggesting that the detection of causal interactions is implemented locally in visual space.”

- Line 32: "showing that a specialized visual routine for launching events exists even within separate motion direction channels". This doesn't necessarily mean the routine is within each separate direction channel, only that the output of the mechanism depends on the population response over motion direction. The critical motion computation could be quite high level -- e.g. global pattern motion in MST. Please clarify the claim. 

We agree with the reviewer, that it is also possible that critical parts of the visual routine could simply use the aggregated population response over motion direction at higher-levels of processing. We acknowledge this possibility in the discussion of the functional relevance of the proposed mechanism and when suggesting that a distributed brain network may contribute to the perception of causality.

We would like to highlight the following two revised paragraphs.

“[…] Second, we think that causal visual events are action-relevant, and the faster we can detect such causal interactions, the faster we can react to them. Direction-selective motion signals are available very early on in the visual system. Visual routines that are based on these direction-selective motion signals may enable faster detection. While our present findings demonstrate direction-selectivity, they do not pinpoint where exactly that visual routine is located. It is possible that the visual routine is located higher up in the visual system (or distributed across multiple levels), relying on a direction-selective population response as input.”

Moreover, when discussing the neurophysiological literature we write:

“Interestingly, single cell recordings in area F5 of the primate brain revealed that motor areas are contributing to the perception of causality (Caggiano et al., 2016; Rolfs, 2016), emphasizing the distributed nature of the computations underlying causal interactions. This finding also stresses that the detection, and the prediction, of causality is essential for processes outside purely sensory systems (e.g., for understanding other’s actions, for navigating, and for avoiding collisions).”

-  p. 10 line 30: typo "particual".  

Done.

-  p. 10 line 37: "This findings rules out (...)" should be singular "This finding rules out (...)". 

Done.

-  Spelling error throughout: "underly" should be "underlie". 

Done.

-  p.11 line 29: "emerges fast and automatic" should be "automatically". 

Done.

Dans l'exercice fournit, il n'y a pas de section "paysage". Mais je suppose qu'il s'agit de "Voyages" dans notre code HTML... En revanche, il y a bien les images "paysage".

Reviewer #2 (Public review):

Summary:

This manuscript investigated the effect of olfactory cues on caterpillar performance and parasitoid avoidance in Pieris brassicae. The authors knocked out Orco to produce caterpillars with significantly reduced olfactory perception. These caterpillars showed reduced performance and increased susceptibility to a parasitoid wasp.

Strengths:

This is an impressive piece of work and a well-written manuscript. The authors have used multiple techniques to investigate not only the effect of the loss of olfactory cues on host-parasitoid interactions, but also the mechanisms underlying this.

Weaknesses:

I do have one major query regarding this manuscript - I agree that the results of the caterpillar choice tests in a y-maze give weight to the idea that olfactory cues may help them avoid areas with higher numbers of parasitoids. However, the experiments with parasitoids were carried out on a single plant. Given that caterpillars in these experiments were very limited in their potential movement and source of food - how likely is it that avoidance played a role in the results seen from these experiments, as opposed to simply the slower growth of the KO caterpillars extending their period of susceptibility? While the two mechanisms may well both take place in nature - only one suggests a direct role of olfaction in enemy avoidance at this life stage, while the other is an indirect effect, hence the distinction is important.

My other issue was determining sample sizes used from the text was sometimes a bit confusing. (This was much clearer from the figures).

I also couldn't find the test statistics for any of the statistical methods in the main text, or in the supplementary materials.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript focuses on the olfactory system of Pieris brassicae larvae and the importance of olfactory information in their interactions with the host plant Brassica oleracea and the major parasitic wasp Cotesia glomerata. The authors used CRISPR/Cas9 to knockout odorant receptor co-receptors (Orco), and conducted a comparative study on the behavior and olfactory system of the mutant and wild-type larvae. The study found that Orco-expressing olfactory sensory neurons in antennae and maxillary palps of Orco knockout (KO) larvae disappeared, and the number of glomeruli in the brain decreased, which impairs the olfactory detection and primary processing in the brain. Orco KO caterpillars show weight loss and loss of preference for optimal food plants; KO larvae also lost weight when attacked by parasitoids with the ovipositor removed, and mortality increased when attacked by untreated parasitoids. On this basis, the authors further studied the responses of caterpillars to volatiles from plants attacked by the larvae of the same species and volatiles from plants on which the caterpillars were themselves attacked by parasitic wasps. Lack of OR-mediated olfactory inputs prevents caterpillars from finding suitable food sources and from choosing spaces free of enemies.

Strengths:

The findings help to understand the important role of olfaction in caterpillar feeding and predator avoidance, highlighting the importance of odorant receptor genes in shaping ecological interactions.

Weaknesses:

There are the following major concerns:

(1) Possible non-targeted effects of Orco knockout using CRISPR/Cas9 should be analyzed and evaluated in Materials and Methods and Results.

Thank you for your suggestion. In the Materials and Methods, we mention how we selected the target region and evaluated potential off-target sites by Exonerate and CHOPCHOP. Neither of these methods found potential off-target sites with a more-than-17-nt alignment identity. Therefore, we assumed no off-target effect in our Orco KO. Furthermore, we did not find any developmental differences between WT and KO caterpillars when these were reared on leaf discs in Petri dishes (Fig S4). We will further highlight this information on the off-target evaluation in the Results section of our revised manuscript.

(2) Figure 1E: Only one olfactory receptor neuron was marked in WT. There are at least three olfactory sensilla at the top of the maxillary palp. Therefore, to explain the loss of Orco-expressing neurons in the mutant (Figure 1F), a more rigorous explanation of the photo is required.

Thank you for pointing this out. The figure shows only a qualitative comparison between WT and KO and we did not aim to determine the total number of Orco positive neurons in the maxillary palps or antennae of WT and KO caterpillars, but please see our previous work for the neuron numbers in the caterpillar antennae (Wang et al., 2023). We did indeed find more than one neuron in the maxillary palps, but as these were in very different image planes it was not possible to visualize them together. However, we will add a few sentences in the Results and Discussion section to explain the results of the maxillary palp Orco staining.

(3) In Figure 1G, H, the four glomeruli are circled by dotted lines: their corresponding relationship between the two figures needs to be further clarified.

Thank you for pointing this out. The four glomeruli in Figure 1G and 1H are not strictly corresponding. We circled these glomeruli to highlight them, as they are the best visualized and clearly shown in this view. In this study, we only counted the number of glomeruli in both WT and KO, however, we did not clarify which glomeruli are missing in the KO caterpillar brain. We will further explain this in the figure legend.

(4) Line 130: Since the main topic in this study is the olfactory system of larvae, the experimental results of this part are all about antennal electrophysiological responses, mating frequency, and egg production of female and male adults of wild type and Orco KO mutant, it may be considered to include this part in the supplementary files. It is better to include some data about the olfactory responses of larvae.

Thank you for your suggestion. We do agree with your suggestion, and we will consider moving this part to the supplementary information. Regarding larval olfactory response, we unfortunately failed to record any spikes using single sensillum recordings due to the difficult nature of the preparation; however, we do believe that this would be an interesting avenue for further research.

(5) Line 166: The sentences in the text are about the choice test between " healthy plant vs. infested plant", while in Fig 3C, it is "infested plant vs. no plant". The content in the text does not match the figure.

Thank you for pointing this out. The sentence is “We compared the behaviors of both WT and Orco KO caterpillars in response to clean air, a healthy plant and a caterpillar-infested plant”. We tested these three stimuli in two comparisons: healthy plant vs no plant, infested plant vs no plant. The two comparisons are shown in Figure 3C separately. We will aim to describe this more clearly in the revised version of the manuscript.

(6) Lines 174-178: Figure 3A showed that the body weight of Orco KO larvae in the absence of parasitic wasps also decreased compared with that of WT. Therefore, in the experiments of Figure 3A and E, the difference in the body weight of Orco KO larvae in the presence or absence of parasitic wasps without ovipositors should also be compared. The current data cannot determine the reduced weight of KO mutant is due to the Orco knockout or the presence of parasitic wasps.

Thank you for pointing this out. We did not make a comparison between the data of Figures 3A and 3E since the two experiments were not conducted at the same time due to the limited space in our BioSafety Ⅲ greenhouse. We do agree that the weight decrease in Figure 3E is partly due to the reduced caterpillar growth shown in Figure 3A. However, we are confident that the additional decrease in caterpillar weight shown in Figure 3E is mainly driven by the presence of disarmed parasitoids. To be specific, the average weight in Figure 3A is 0.4544 g for WT and 0.4230 g for KO, KO weight is 93.1% of WT caterpillars. While in Figure 3E, the average weight is 0.4273 g for WT and 0.3637 g for KO, KO weight is 85.1% of WT caterpillars. We will discuss this interaction between caterpillar growth and the effect of the parasitoid attacks more extensively in the revised version of the manuscript.

(7) Lines 179-181: Figure 3F shows that the survival rate of larvae of Orco KO mutant decreased in the presence of parasitic wasps, and the difference in survival rate of larvae of WT and Orco KO mutant in the absence of parasitic wasps should also be compared. The current data cannot determine whether the reduced survival of the KO mutant is due to the Orco knockout or the presence of parasitic wasps.

We are happy that you highlight this point. When conducting these experiments, we selected groups of caterpillars and carefully placed them on a leaf with minimal disturbance of the caterpillars, which minimized hurting and mortality. We did test the survival of caterpillars in the absence of parasitoid wasps from the experiment presented in Figure 3A, although this was missing from the manuscript. There is no significant difference in the survival rate of caterpillars between the two genotypes in the absence of wasps (average mortality WT = 8.8 %, average mortality KO = 2.9 %; P = 0.088, Wilcoxon test), so the decreased survival rate is most likely due to the attack of the wasps. We will add this information to the revised version of the manuscript.

(8) In Figure 4B, why do the compounds tested have no volatiles derived from plants? Cruciferous plants have the well-known mustard bomb. In the behavioral experiments, the larvae responses to ITC compounds were not included, which is suggested to be explained in the discussion section.

Thank you for the suggestion. We assume you mean Figure 4D/4E instead of Figure 4B. In Figure 4B, many of the identified chemical compounds are essentially plant volatiles, especially those from caterpillar frass and caterpillar spit. In Figure 4D/4E, most of the tested chemicals are derived from plants. We did include several ITCs in the butterfly EAG tests shown in figure 2A/B, however because the butterfly antennae did not respond strongly to ITCs, we did not include ITCs in the subsequent larval behavioural tests. Instead, the tested chemicals in Figure 4D/4E either elicit high EAG responses of butterflies or have been identified as significant by VIP scores in the chemical analyses. We will add this explanation to the revised version of our manuscript.

(9) The custom-made setup and the relevant behavioral experiments in Figure 4C need to be described in detail (Line 545).

We will add more detailed descriptions for the setup and method in the Materials and Methods.

(10) Materials and Methods Line 448: 10 μL paraffin oil should be used for negative control.

Thank you for pointing this out. We used both clean filter paper and clean filter paper with 10 μL paraffin oil as negative controls, but we did not find a significant difference between the two controls. Therefore, in the EAG results of Figure 2A/2B, we presented paraffin oil as one of the tested chemicals. We will re-run our statistical tests with paraffin oil as negative control, although we do not expect any major differences to the previous tests.

Reviewer #2 (Public review):

Summary:

This manuscript investigated the effect of olfactory cues on caterpillar performance and parasitoid avoidance in Pieris brassicae. The authors knocked out Orco to produce caterpillars with significantly reduced olfactory perception. These caterpillars showed reduced performance and increased susceptibility to a parasitoid wasp.

Strengths:

This is an impressive piece of work and a well-written manuscript. The authors have used multiple techniques to investigate not only the effect of the loss of olfactory cues on host-parasitoid interactions, but also the mechanisms underlying this.

Weaknesses:

(1) I do have one major query regarding this manuscript - I agree that the results of the caterpillar choice tests in a y-maze give weight to the idea that olfactory cues may help them avoid areas with higher numbers of parasitoids. However, the experiments with parasitoids were carried out on a single plant. Given that caterpillars in these experiments were very limited in their potential movement and source of food - how likely is it that avoidance played a role in the results seen from these experiments, as opposed to simply the slower growth of the KO caterpillars extending their period of susceptibility? While the two mechanisms may well both take place in nature - only one suggests a direct role of olfaction in enemy avoidance at this life stage, while the other is an indirect effect, hence the distinction is important.

We do agree with your comment that both mechanisms may be at work in nature, and we do address this in the Discussion section. In our study, we did find that wildtype caterpillars were more efficient in locating their food source and did grow faster on full plants than knockout caterpillars. This faster growth will enable wildtype caterpillars to more quickly outgrow the life-stages most vulnerable to the parasitoids (L1 and L2). The olfactory system therefore supports the escape from parasitoids indirectly by enhancing feeding efficiency directly.

In addition, we show in our Y-tube experiments that WT caterpillars were able to avoid plant where conspecifics are under the attack by parasitiods (Figure 3D). Therefore, we speculate that WT caterpillars make use of volatiles from the plant or from conspecifics via their spit or faeces to avoid plants or leaves potentially attracting natural enemies. Knockout caterpillars are unable to use these volatile danger cues and therefore do not avoid plants or leaves that are most attractive to their natural enemies, making KO caterpillars more susceptible and leading to more natural enemy harassment. Through this, olfaction also directly impacts the ability of a caterpillar to find an enemy-free feeding site.

We think that olfaction supports the enemy avoidance of caterpillars via both these mechanisms, although at different time scales. Unfortunately, our analysis was not detailed enough to discern the relative importance of the two mechanisms we found. However, we feel that this would be an interesting avenue for further research. Moreover, we will sharpen our discussion on the potential importance of the two different mechanisms in the revised version of this manuscript.

(2) My other issue was determining sample sizes used from the text was sometimes a bit confusing. (This was much clearer from the figures).

We will revise the sample size in the text to make it clearer.

(3) I also couldn't find the test statistics for any of the statistical methods in the main text, or in the supplementary materials.

Thank you for pointing this out. We will provide more detailed test statistics in the main text and in the supplementary materials of the revised version of the manuscript.

Y los sujetos y las sujetas?

Estaría lindo que en otro momento estudiáramos el aporte de Adele Goldberg en esta historia de masculinidades.

¿por qué se retoma objeto y no mensaje?

De acuerdo, así todo sería más claro y con más sentido. Podríamos empezar a nombrarla así.

me gusta ésta metáfora porque puede ser graficable, es decir, volviendo sobre la discusión de cómo descomplejizar el lenguaje especializado, y acercar a más personas a este tipo de tecnologías.

Considero que acá hay una idea con mucha potencia en relación a la noción ecosistémica de la célula y de lo vivo en general, como lo concibe Margulis. Acá un video que me gusta mucho en términos visuales para aportar otro lenguaje al texto:

https://www.youtube.com/watch?v=SfMwNBwIzQo

Aquí sería también importante una gráfica, así solo con texto me cuesta imaginarlo.

Me gusta la relación de la lectura con el hipervínculo, pero me parecería muy interesante la posibilidad de la marginalia como herramienta que aclara con múltiples posibilidades y le da otra estructura no lineal, ni arborea a los textos. Quizá una posibilidad para cardumem.

Esta página la he reescrito varias veces en la medida en que distintos talleres avanzan y luego de estar en la variante de Fossil de otro proyecto, llamado Holónica, finalmente está en esta última iteración de la Grafoscopedia.

Intenta dar una mirada conceptual de alto nivel, mientras prepara el terreno para el lugar donde explicaremos la sintaxis más en detalle. La idea es llegar a que podamos hacer ejercicios introductorios de para ver cómo estos conceptos o semántica encarnan en dicha sintaxis.

democratización de la historia

Ejemplo de como mentalizarme para magnetizar mi mente

Este es un compromiso a seguir fielmente para convertirse en un mejor ser humano, consta de 5 puntos interesantes.

Работы, в которых разделяется взгляд на то, что в этот период Маркс был идеалистом и гегельянцем

yws but b#ware that the present moment is the continual manifestation of future posibilities at ever expanding scale as fas as your heart mind can itself reach in momemnt of y/our intention guided persevering attention to matters that matter to y.our mind/s

y/our situatedness particular complexes

DOI: 10.1128/JVI.00254-18

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bdscstockkeepers

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1016/S0002-9440(10)65450-8

Resource: (NIH Nonhuman Primate Reagent Resource Cat# PR-0817, RRID:AB_2716320)

Curator: @giovanni.decastro

SciCrunch record: RRID:AB_2716320

What is this?

Briefing Document : Accessibilité de la méthodologie de recherche pour les élèves ayant des troubles des fonctions cognitives

Source : Excerpts de la présentation "Session parallèle 5 : "Expérimenter les marges"..."

Présentatrice : J., Maîtresse de conférences à l'Université de Caen, membre du CNEF et co-directrice du Laboratoire International pour l'Inclusion Scolaire.

Thème principal : Réflexion sur la manière de rendre accessible la méthodologie de recherche, notamment à travers l'entretien biographique, pour permettre aux élèves ayant des troubles des fonctions cognitives (TFC) d'exprimer leur vision du monde et de se réapproprier leur parcours.

Introduction et Contexte :

• La présentation s'inscrit dans le prolongement du travail doctoral de J., soutenu en 2020, portant sur les dispositifs ULIS dans le second degré et l'accueil des élèves estampillés TFC. Cette catégorie est définie comme large et hétérogène, incluant des jeunes avec des troubles autistiques, des troubles multiples, une déficience intellectuelle, des troubles du comportement, et des situations où le diagnostic est moins clair. Le choix du second degré se justifie par la phase charnière entre la poursuite scolaire et l'insertion professionnelle. La préoccupation centrale est de donner la parole à ces jeunes pour qu'ils partagent leur propre perspective.

• J. s'appuie sur l'approche des récits biographiques, en écho aux travaux de Lorim Mon Berger, comme moyen de recueillir leur parcours de vie et de formation. Elle qualifie ce public comme un "public à la communication entre", soulignant la nécessité d'une approche spécifique pour faciliter leur expression.

Position et Posture de la Chercheuse :

• L'expérience professionnelle antérieure de J. en tant qu'enseignante spécialisée, notamment dans le premier dispositif lycée professionnel accompagnant les jeunes de 18 à 22 ans, puis comme formatrice pour les enseignants spécialisés, influence sa posture actuelle de chercheuse. Cette trajectoire lui a permis d'endosser différentes postures (enseignante, conseillère, accompagnatrice), ce qui se ressent dans sa manière d'aborder la recherche et la prise en compte de la parole des jeunes.

Préoccupations Centrales de la Recherche :

J. expose trois préoccupations imbriquées :

• Ne pas s'engager dans la "voix de la prise en charge de la parole du narrateur" : Il s'agit d'éviter un recueil de données superficiel où le jeune se contente de répéter ce qu'il pense devoir dire. L'objectif est d'aller au-delà et de permettre aux jeunes de retrouver une place dans le processus de recherche.
• "moi ce que j'aimerais bien c'est vraiment me questionner sur comment on leur permettre de de retrouver une place"
• Penser l'environnement de la rencontre et de l'entretien biographique : À l'image d'un enseignant qui aménage un environnement d'apprentissage, le chercheur doit concevoir un cadre propice à la relation et à l'échange dans le cadre de l'entretien biographique. Celui-ci vise à ce que le jeune mette en mots et en sens son parcours de vie pour envisager l'avenir.
• "l'enseignant doit penser l'environnement d'apprentissage moi en tant que chercheur en fait je dois penser l'environnement de la rencontre en fait et particulièrement vis-à-vis de l'entretien biographique"
• L'objectif n'est pas de vérifier la véracité des propos, mais de comprendre la perspective du jeune : "moi ce qui m'intéresse c'est voilà comment lui voit les choses comment il les interprète comment il les comprend et comment il me les rapporte c'est-à-dire qu'on est vraiment vraiment pas sur un jugement de valeur moi je prends les choses en fait telles qu'elles me sont tel qu'elles me sont"
• Conséquences de l'engagement du chercheur dans l'accessibilisation de la relation et de l'espace d'échange : Cela conduit à la nécessité de définir un "environnement capacitant" (ou affordant), qui soit non délétère, prenne en compte les différences interindividuelles (anthropométrie, sexe, âge, culture, compensation des déficiences), et permette le développement de nouvelles connaissances et savoirs.
• "quand je vais penser mes temps d'entretien il me faut que je puisse créer un environnement dit non délétaire pour l'individu avec cette idée que ça puisse le le préserver ses capacités d'action"
• Cela implique de gérer le lieu, le stress, le timing, et d'adapter l'approche aux besoins spécifiques des jeunes ayant des difficultés de mémorisation, d'expression verbale, ou de passage de l'abstrait au concret.

L'Importance de la Coconstruction :

• La démarche prône une coconstruction de la recherche, allant au-delà d'une simple consultation. Il s'agit d'un cheminement où le chercheur se place aux côtés du jeune, se laissant guider et questionner, reconnaissant l'apport unique de sa perspective.

Entraves à la Communication dans le Contexte de l'Entretien :

• J. identifie plusieurs types d'entraves spécifiques à la situation d'entretien avec ces jeunes :

• Aspects émotionnels : Stress, timidité, potentielle signification du vouvoiement initial comme marque d'importance.

• Obstacles situationnels : Nature particulière du face-à-face avec un chercheur perçu comme sachant, habitude d'être systématiquement accompagné par un tiers (éducatif, familial, scolaire) entraînant des difficultés à se projeter et à parler en "je", manque d'habitude d'être regardé directement.
• Aspects culturels et sociologiques : Manque de connaissance des droits et des processus de recherche impliquant les jeunes.
• Conséquences des troubles ou du handicap : Difficultés à formuler, mémoriser, raconter, pouvant se traduire par un langage limité à des mots-clés.

Questions Cruciales Concernant les Modalités d'Entretien :

• Distance et proximité dans la relation : Nécessité d'une flexibilité méthodologique, pouvant aller de l'entretien directif au semi-directif, voire biographique, en fonction de la relation et des contraintes.
• Distance et proximité du lieu de la rencontre : Importance du choix du lieu (établissement scolaire, domicile, etc.) en fonction du sentiment de sécurité et de confort du jeune.
• Distance et proximité par rapport à la compréhension de leur propre parcours de vie : Adaptation à la manière dont le jeune perçoit son parcours (principalement scolaire ou plus large).
• Distance et proximité des supports et outils : Adaptation de l'utilisation de supports (visuels, matériels, etc.) en fonction des besoins et des préférences du jeune.
• "il y a une dose de flexibilité qui est qui est importante en fait pour pouvoir euh avoir on va dire un un retour un récit qui soit au plus proche de la réalité de la réalité du jeunne"

Trois Exemples Illustratifs :

• Nathanaël ou comment se réapproprier son parcours de vie : Un jeune initialement décrit comme peu loquace s'engage pleinement dans l'entretien réalisé à son domicile (choix du lieu et de l'heure, tenue soignée). Il valide ensuite la transcription en occultant les passages qu'il ne souhaite pas voir utilisés. Cet entretien a eu un "effet collatéral" inattendu : Nathanaël a pris la parole pour la première fois lors de son équipe de suivi de scolarisation, s'appropriant son parcours à travers le document créé et exprimant son ras-le-bol d'entendre les autres parler pour lui.
• Fleur ou la première fois où elle va parler en "je" (majuscule) : Une jeune qui initialement déclare n'avoir rien à dire se révèle grâce à la mise à disposition de matériel (post-it, feutres). L'utilisation de ces supports déclenche le récit. À la fin de l'entretien, elle demande à ce qu'on utilise le prénom "Fleur" pour la désigner, marquant une distinction entre son image publique et sa véritable identité, révélant des choses jamais dites auparavant.
• John et son père ou comment faire face au récit de son parcours sentiè protecteur : John exprime son agacement face à l'attitude surprotectrice des adultes qui l'accompagnent ("J'en ai pas besoin, je vais bien, faut arrêter de me prendre pour un gosse"). Son récit révèle une conscience de son potentiel et un désir d'autonomie qui n'étaient pas forcément perçus par son entourage. Le père de John, lors d'un entretien séparé, exprime également un sentiment de ne pas avoir été entendu concernant le parcours de son fils, soulignant une libération de la parole favorisée par le contexte de la recherche.

Responsabilité du Chercheur (Conclusion et Points de Discussion) :

Ces exemples soulignent la responsabilité du chercheur face aux effets parfois inattendus de la recherche. Il s'agit de prendre en considération la singularité des participants et d'accessibiliser l'environnement d'échange pour que leur parole puisse s'exprimer. Cela implique :

Une méthodologie "en situation" qui s'ajuste à la qualité de l'échange. Une réflexion sur les "dommages collatéraux" ou "effets secondaires" de l'entretien (prise de parole nouvelle, remise en question du vécu, etc.).

La question de la limite de la responsabilité du chercheur face aux conséquences de la prise de parole des participants.

La présentatrice conclut en ouvrant la discussion et en sollicitant des pistes de réflexion et des questions sur ces enjeux importants de la recherche avec des publics dits vulnérables.

Elle souligne la nécessité d'une approche flexible et attentive, reconnaissant que son expérience professionnelle antérieure est un atout fondamental dans cette démarche.

$$ (x + y)^+ = x^+ + y^+ $$ Link

Author response:

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

We thank the editor and reviewers for their supportive comments about our modeling approach and conclusions, and for raising several valid concerns; we address them briefly below. In addition, a detailed, point-by-point response to the reviewers’ comments are below, along with additions and edits we have made to the revised manuscript. 

Concerns about model’s biological realism and impact on interpretations

The goal of this paper was to use an interpretable and modular model to investigate the impact of varying sensorimotor delays. Aspects of the model (e.g. layered architecture, modularity) are inspired by biology; at the same time, necessary abstractions and simplifications (e.g. using an optimal controller) are made for interpretability and generalizability, and they reflect common approaches from past work. The hypothesized effects of certain simplifying assumptions are discussed in detail in Section 3.5. Furthermore, the modularity of our model allows us to readily incorporate additional biological realism (e.g. biomechanics, connectomics, and neural dynamics) in future work. In the revision, we have added citations and edits to the text to clarify these points.

Concerns that the model is overly complex

To investigate the impact of sensorimotor delays on locomotion, we built a closed-loop model that recapitulates the complex joint trajectories of fly walking. We agree that locomotion models face a tradeoff between simplicity/interpretability and realism — therefore, we developed a model that was as simple and interpretable as possible, while still reasonably recapitulating joint trajectories and generalizing to novel simulation scenarios. Along these lines, we also did not select a model that primarily recreates empirical data, as this would hinder generalizability and add unnecessary complexity to the model. We do not think these design choices are significant weaknesses of this model; in fact, few comparable models account for all joints involved in locomotion, and fewer explicitly compare model kinematics with kinematics from data. We have add citations and edits to the text to clarify these points in the revision. 

Concerns about the validity of the Kinematic Similarity (KS) metric to evaluate walking

We chose to incorporate only the first two PCA modes dimensions in the KS metric because the kernel density estimator performs poorly for high dimensional data. Our primary use of this metric was to indicate whether the simulated fly continues walking in the presence of perturbations. For technical reasons, it is not feasible to perform equivalent experiments on real walking flies, which is one of the reasons we explore this phenomenon with the model. We note the dramatic shift from walking to nonwalking as delay increases (Figure 5). To be thorough, in the revision, we have investigated the effect of incorporating additional PCA modes, and whether this affects the interpretation of our results. We have additionally added to the discussion and presentation of the KS metric to clarify its purpose in this study. We agree with the reviewers that the KS metric is too coarse to reflect fine details of joint kinematics; indeed, in the unperturbed case, we evaluate our model’s performance using other metrics based on comparisons with empirical data (Figures 2, 7, 8). 

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work, the authors present a novel, multi-layer computational model of motor control to produce realistic walking behaviour of a Drosophila model in the presence of external perturbations and under sensory and motor delays. The novelty of their model of motor control is that it is modular, with divisions inspired by the fly nervous system, with one component based on deep learning while the rest are based on control theory. They show that their model can produce realistic walking trajectories. Given the mostly reasonable assumptions of their model, they convincingly show that the sensory and motor delays present in the fly nervous system are the maximum allowable for robustness to unexpected perturbations.

Their fly model outputs torque at each joint in the leg, and their dynamics model translates these into movements, resulting in time-series trajectories of joint angles. Inspired by the anatomy of the fly nervous system, their fly model is a modular architecture that separates motor control at three levels of abstraction:

(1) oscillator-based model of coupling of phase angles between legs,

(2) generation of future joint-angle trajectories based on the current state and inputs for each leg (the trajectory generator), and

(3) closed-loop control of the joint-angles using torques applied at every joint in the model (control and dynamics).

These three levels of abstraction ensure coordination between the legs, future predictions of desired joint angles, and corrections to deviations from desired joint-angle trajectories. The parameters of the model are tuned in the absence of external perturbations using experimental data of joint angles of a tethered fly. A notable disconnect from reality is that the dynamics model used does not model the movement of the body and ground contacts as is the case in natural walking, nor the movement of a ball for a tethered fly, but instead something like legs moving in the air for a tethered fly.

n order to validate the realism of the generated simulated walking trajectories, the authors compare various attributes of simulated to real tethered fly trajectories and show qualitative and quantitative similarities, including using a novel metric coined as Kinematic Similarity (KS). The KS score of a trajectory is a measure of the likelihood that the trajectory belongs to the distribution of real trajectories estimated from the experimental data. While such a metric is a useful tool to validate the quality of simulated data, there is some room for improvement in the actual computation of this score. For instance, the KS score is computed for any given time-window of walking simulation using a fraction of information from the joint-angle trajectories. It is unclear if the remaining information in joint-angle trajectories that are not used in the computation of the KS score can be ignored in the context of validating the realism of simulated walking trajectories.

The authors validate simulated walking trajectories generated by the trained model under a range of sensorimotor delays and external perturbations. The trained model is shown to generate realistic jointangle trajectories in the presence of external perturbations as long as the sensorimotor delays are constrained within a certain range. This range of sensorimotor delays is shown to be comparable to experimental measurements of sensorimotor delays, leading to the conclusion that the fly nervous system is just fast enough to be robust to perturbations.

Strengths:

This work presents a novel framework to simulate Drosophila walking in the presence of external perturbations and sensorimotor delay. Although the model makes some simplifying assumptions, it has sufficient complexity to generate new, testable hypotheses regarding motor control in Drosophila. The authors provide evidence for realistic simulated walking trajectories by comparing simulated trajectories generated by their trained model with experimental data using a novel metric proposed by the authors. The model proposes a crucial role in future predictions to ensure robust walking trajectories against external perturbations and motor delay. Realistic simulations under a range of prediction intervals, perturbations, and motor delays generating realistic walking trajectories support this claim. The modular architecture of the framework provides opportunities to make testable predictions regarding motor control in Drosophila. The work can be of interest to the Drosophila community interested in digitally simulating realistic models of Drosophila locomotion behaviors, as well as to experimentalists in generating testable hypotheses for novel discoveries regarding neural control of locomotion in Drosophila. Moreover, the work can be of broad interest to neuroethologists, serving as a benchmark in modelling animal locomotion in general.

We thank the reviewer for their positive comments.

Weaknesses:

As the authors acknowledge in their work, the control and dynamics model makes some simplifying assumptions about Drosophila physics/physiology in the context of walking. For instance, the model does not incorporate ground contact forces and inertial effects of the fly's body. It is not clear how these simplifying assumptions would affect some of the quantitative results derived by the authors. The range of tolerable values of sensorimotor delays that generate realistic walking trajectories is shown to be comparable with sensorimotor delays inferred from physiological measurements. It is unclear if this comparison is meaningful in the context of the model's simplifying assumptions.

We now discuss how some of these assumptions affect the quantitative results in the section “Towards biomechanical and neural realism”. We reproduce the relevant sentences below:

“The inclusion of explicit leg-ground contact interactions would also make it harder for the model to recover when perturbed, because perturbations during walking often occur upon contact with the ground (e.g. the ground is slippery or bumpy).”

“We anticipate that the increased sensory resolution from more detailed proprioceptor models and the stability from mechanical compliance of limbs in a more detailed biomechanical model would make the system easier to control and increase the allowable range of delay parameters. Conversely, we expect that modeling the nonlinearity and noise inherent to biological sensors and actuators may decrease the allowable range of delay parameters.”

The authors propose a novel metric coined as Kinematic Similarity (KS) to distinguish realistic walking trajectories from unrealistic walking trajectories. Defining such an objective metric to evaluate the model's predictions is a useful exercise, and could potentially be applied to benchmark other computational animal models that are proposed in the future. However, the KS score proposed in this work is calculated using only the first two PCA modes that cumulatively account for less than 50% of the variance in the joint angles. It is not obvious that the information in the remaining PCA modes may not change the log-likelihood that occurs in the real walking data.

The primary reason we designed the KS metric was to determine whether the simulated fly continues walking in the presence of perturbations. We initially limited the analysis of the KS to the first 2 principal components. For completeness, we now investigate the additional principal components in Appendix 9 and the effect of evaluating KS with different numbers of components in Appendix 10. 

Overall, the results look similar when including additional components for impulse perturbations. For stochastic perturbations, the range of similar walking decreases as we increase the number of components used to evaluate walking kinematics. Comparing this with Appendix 9, which shows that higher components represent higher frequencies of the walking cycle, we conclude that at the edge of stability for delays (where sum of sensory and actuation delays are about 40ms), flies can continue walking but with impaired higher frequencies (relative to no perturbations) during and after perturbation. 

We added the following text in the methods:

“We chose 2 dimensions for PCA for two key reasons. First, these 2 dimensions alone accounted for a large portion of the variance in the data (52.7% total, with 42.1% for first component and 10.6% for second component). There was a big drop in variance explained from the first to the second component, but no sudden drop in the next 10 components (see Appendix 9). Second, the KDE procedure only works effectively in low-dimensional spaces, and the minimal number of dimensions needed to obtain circular dynamics for walking is 2. We investigate the effect of varying the number of dimensions of PCA in Appendix 10.”

(Note that we have corrected the percentage of variance accounted for by the principal components, as these numbers were from an older analysis prior to the first draft.)

We also reference Appendix 10 in the results:

“We observed that robust walking was not contingent on the specific values of motor and sensory delay, but rather the sum of these two values (Fig. 5E). Furthermore, as delay increases, higher frequencies of walking are impacted first before walking collapses entirely (Appendix 10).”

Reviewer #2 (Public Review):

Summary:

In this study, Karashchuk et al. develop a hierarchical control system to control the legs of a dynamic model of the fly. They intend to demonstrate that temporal delays in sensorimotor processing can destabilize walking and that the fly's nervous system may be operating with as long of delays as could possibly be corrected for.

Strengths:

Overall, the approach the authors take is impressive. Their model is trained using a huge dataset of animal data, which is a strength. Their model was not trained to reproduce animal responses to perturbations, but it successfully rejects small perturbations and continues to operate stably. Their results are consistent with the literature, that sensorimotor delays destabilize movements.

Weaknesses:

The model is sophisticated and interesting, but the reviewer has great concerns regarding this manuscript's contributions, as laid out in the abstract:

(1) Much simpler models can be used to show that delays in sensorimotor systems destabilize behavior (e.g., Bingham, Choi, and Ting 2011; Ashtiani, Sarvestani, and Badri-Sproewitz 2021), so why create this extremely complex system to test this idea? The complexity of the system obscures the results and leaves the reviewer wondering if the instability is due to the many, many moving parts within the model. The reviewer understands (and appreciates) that the authors tested the impact of the delay in a controlled way, which supports their conclusion. However, the reviewer thinks the authors did not use the most parsimonious model possible, and as such, leave many possible sources for other causes of instability.

We thank the reviewer for this observation — we agree that we did not make the goal of the work quite clear. The goal of this paper was to build an interpretable and generalizable model of fly walking, which was then used to investigate varying sensorimotor delays in the context of locomotion. To this end, we used a modular model to recreate walking kinematics, and then investigated the effect of delays on locomotion. Locomotion in itself is a complex phenomenon — thus, we have chosen a model that is complex enough to reasonably recapitulate joint trajectories, while remaining interpretable.

We have clarified this in the text near the end of the introduction:

“Here, we develop a new, interpretable, and generalizable model of fly walking, which we use to investigate the impact of varying sensorimotor delays in Drosophila locomotion.”

We also emphasize the investigation of sensorimotor delays in the context of locomotion in the beginning of the “Effect of sensory and motor delays on walking” section:

“... we used our model to investigate how changing sensory and motor delays affects locomotor robustness.”

We also remark that while they are very relevant papers for our work, neither of the prior papers focus on locomotion: the first involves a 2D balance model of a biped, and the second involves drop landings of quadrupeds.

Lastly, we note that the investigation of delay is not the only use for this model —  in the future, this model can also be used to study other aspects of locomotion such as the role of proprioceptive feedback (see “Role of proprioceptive feedback in fly walking” section). The layered framework of the model can also be extended to other animals and locomotor strategies (see “Layered model produces robust walking and facilitates local control” section”).

(2) In a related way, the reviewer is not sure that the elements the authors introduced reflect the structure or function of the fly's nervous system. For example, optimal control is an active field of research and is behind the success of many-legged robots, but the reviewer is not sure what evidence exists that suggests the fly ventral nerve cord functions as an optimal controller. If this were bolstered with additional references, the reviewer would be less concerned.

We thank the reviewer for the comment — we have now further clarified how our model elements reflect the fly’s nervous system. The elements we introduce are plausible but only loosely analogous to the fly’s nervous system. While we draw parallels from these elements to anatomy (e.g. in Fig 1A-B, and in the first paragraph of the Results section), we do not mean to suggest that these functional elements directly correspond to specific structures in the fly’s nervous system. A substantial portion of the suggested future work (see “Towards biomechanical and neural realism”) aims to bridge the gap between these functional elements and fly physiology, which is beyond the scope of this work. 

We have added clarifying text to the Results section:

“While the model is inspired by neuroanatomy, its components do not strictly correspond to components of the nervous system --- the construction of a neuroanatomically accurate model is deferred to future work (see Discussion).”

In the specific case of optimal control — optimal control is a theoretical model that predicts various aspects of motor control in humans, there is evidence that optimal control is implemented by the human nervous system (Todorov and Jordan, 2002; Scott, 2004; Berret et al., 2011). Based on this, we make the assumption that optimal control is a reasonable model for motor control in flies implemented by the fly nervous system as well. Fly movement makes use of proprioceptive feedback signals (Mendes et al., 2013; Pratt et al., 2024; Berendes et al., 2016), and optimal control is a plausible mechanism that incorporates feedback signals into movement.

We have added the following clarifying text in the Results section: 

“The optimal controller layer maintains walking kinematics in the presence of sensori motor delays and helps compensate for external perturbations. This design was inspired by optimal control-based models of movements in humans (Todorov and Jordan, 2002; Scott, 2004; Berret et al., 2011)”

(3) "The model generates realistic simulated walking that matches real fly walking kinematics...". The reviewer appreciates the difficulty in conducting this type of work, but the reviewer cannot conclude that the kinematics "match real fly walking kinematics". The range of motion of several joints is 30% too small compared to the animal (Figure 2B) and the reviewer finds the video comparisons unpersuasive. The reviewer would understand if there were additional constraints, e.g., the authors had designed a robot that physically could not complete the prescribed motions. However the reviewer cannot think of a reason why this simulation could not replicate the animal kinematics with arbitrary precision, if that is the goal.

We agree with the reviewer that the model-generated kinematics are not perfectly indistinguishable from real walking kinematics, and now clarify this in the text. We also agree with the reviewer that one could build a model that precisely replicates real kinematics, but as they intuit, that was not our goal. Our goal was to build a model that both replicates animal kinematics, and is interpretable and generalizable (which allows us to investigate what happens when perturbations and varying sensorimotor delays are introduced). There is a trade-off between realism and generalizability — a simulation that fully recreates empirical data would require a model that is completely fit to data, which is likely to be more complex (in terms of parameters required) and less generalizable to novel scenarios. We have made design choices that result in a model that balances these trade-offs. We do not consider this to be a weakness of the model; in fact, few comparable models account for all joints involved in locomotion, and fewer explicitly compare model kinematics with kinematics from data.

We have tempered the language in the abstract:

“The model generates realistic simulated walking that resembles real fly walking kinematics”

The tempered statement, we believe, is a fair characterization of the walking — it resembles but does not perfectly match real kinematics.

We have also introduced clarifying text in the introduction:

“Overall, existing walking models focus on either kinematic or physiological accuracy, but few achieve both, and none consider the effect of varying sensorimotor delays. Here, we develop a new, interpretable, and generalizable model of fly walking, which we use to investigate the impact of varying sensorimotor delays in Drosophila locomotion.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Potential typo on page 5:

2.1.2 Joint kinematics trajectory generator

Paragraph 4, last line: Original text - ".....it also estimates the current phase". Suggested correction - "...it also estimates the current phase velocity"

Done

Potential typo on page 8:

2.3 Model maintains walking under unpredictable external perturbations.

Paragraph 3, line 2: Original text - "...brief, unexpected force (e.g. legs slipping on an unstable surface)".

Consider replacing force with motion, or providing an example of a force as opposed to displacement (slipping).

Done

Potential typo on page 8:

2.3 Model maintains walking under unpredictable external perturbations.

Paragraph 3, line 4: Original text - "The magnitude of this velocity is drawn from a normal distribution...".

Is this really magnitude? If so, please discuss how the sign (+/-) is assigned to velocity, and how the normal distribution is centred so as to sample only positive values representing magnitude.

Indeed the magnitude of the velocity is drawn from a normal distribution. A positive or negative sign is then assigned with equal odds. We have added text to clarify this:

“The sign of the velocity was drawn separately so that there is equal likelihood for negative or positive perturbation velocities.”

Page 8:

2.3 Model maintains walking under unpredictable external perturbations.

In Paragraph 5: Why is the data reduced to only 2 dimensions? Could higher order PCA modes (cumulatively accounting for more than 50% variance in the data) not have distinguishing information between realistic and unrealistic walking trajectories?

We provide a longer response for this in the public review above.

Page 11:

Why wouldn't a system trained in the presence of external perturbations perform better? What is the motivation to remove external perturbations during training?

We agree that a system trained in the presence of external perturbations would probably perform better — however, we do not have data that contains walking with external perturbations. Nothing was removed — all the data used in this study involve a fly walking without perturbations.

We have added a clarification:

“our model maintains realistic walking in the presence of external dynamic perturbations, despite being trained only on data of walking without perturbations (no perturbation data was available).”

Page 16:

4.1 Tracking joint angles of D. melanogaster walking in 3D.

Paragraph 1: Readers who wish to collect similar data might benefit from specifying the exposure time, animal size in pixels (or camera sensor format and field of view), in addition to the frame rate. Alternatively, consider mentioning the camera and lens part numbers provided by the manufacturer.

This is a good point. We have updated the text to include these specifications:

“We obtained fruit fly D. melanogaster walking kinematics data following the procedure previously described in (Karashchuk et al, 2021). Briefly, a fly was tethered to a tungsten wire and positioned on a frictionless spherical treadmill ball suspended on compressed air. Six cameras (Basler acA800-510um with Computar zoom lens MLM3X-MP) captured the movement of all of the fly's legs at 300 Hz. The fly size in pixels ranges from about 300x300 up to 700x500 pixels across the 6 cameras. Using Anipose, we tracked 30 keypoints on the fly, which are the following 5 points on each of the 6 legs: body-coxa, coxa-femur, femur-tibia, and tibia-tarsus joints, as well as the tip of the tarsus.”

Potential typos on page 18:

4.3.3 Training procedure

Paragraph 2, line 1: Original text - "..(, p)"

Do the authors mean "...(, )"

Paragraph 2, line 2: Original text - "... (,, v, p)" Do the authors mean "... (,, v, )"?

Paragraph 3, line 3: Original text - "... (,, v, p)" Do the authors mean "... (,, v, )"?

Thank you for pointing out this issue. We have now fixed the phase p to be \phi to be consistent with the rest of the text.

Paragraph 3, line 3: Original text - "...()"

Do the authors mean "(d)"? If not, please discuss the difference between and d.

Thank you for pointing this out. \hat \theta and \theta_d were used interchangeably which is confusing. We have standardized our reference to the desired trajectory as \theta_d throughout the text.

Page 19:

Typo after eqn. (6):

Original text: "where x := q - q, ... A and B are Jacobians with respect to...."

Correction: "where x := q - q, ... Ac and Bc are Jacobians with respect to...."

Similar corrections in eqn. 7 and eqn. 8: A and B should be replaced with Ac and Bc. Done

Page 19, eqn. (10b):

Should the last term be qd(t+T) as opposed to qd(t+1)?

No: in fact (10a) contains the typo: it should be y(t+1) as opposed to y(t+T). This has been fixed.

Page 19

The authors' detailed description of the initial steps leading up to the dynamics model, involving the construction of the ODE, linearizing the system about the fixed point makes the text broadly accessible to the general reader. Similarly, adding some more description of the predictive model (eqn. 11 - 15) could improve the text's accessibility and the reader's appreciation for the model. This is especially relevant since the effects of sensorimotor delay and external perturbations, which are incorporated in the control and dynamics model, form a major contribution to this work. What do the matrices F, G, L, H, and K look like for the Drosophila model? Are there any differences between the model in Stenberg et al. (referenced in the paper) and the authors' model for predictive control? Are there any differences in the assumptions made in Stenberg et al. compared to the model presented in this work? The readers would likely also benefit from a figure showing the information flow in the model, and describing all the variables used in the predictive control model in eqn. 11 through eqn. 15 (analogous to Figure 1 in Stenberg et al. (2022)). Such a detailed description of the control and dynamics model would help the reader easily appreciate the assumptions made in modelling the effects of sensorimotor delay and external perturbations.

Done

Page 20:

Eqn. 12: Should z(t+1) be z(t+T) instead?

Similar comment for eqn. 14

No: we made a mistake in (10a); there should be no (t+T) terms; all terms should be (t+1) terms to reflect a standard discrete-time difference equation.

Eqn. 13: r(t) can be defined explicitly

Done

4.5 Generate joint trajectories of the complete model with perturbations Paragraph 2, line 2: Please read the previous comment

\hat \theta and \theta_d were previously used interchangeably which is confusing. We have standardized our reference to the desired trajectory as \theta_d throughout the text.

Original text - "Every 8 timesteps, we set :=...."

Does this mean dis set to? If so, the motivation for this is not clear.

We mean that \theta_d is set to be equal to \theta. We have replaced “:=” with “=” for clarity.

General comments for the authors:

Could the authors discuss the assumptions regarding Drosophila physiology implied in the control model?

The control model is primarily included as a plausible functional element of the fly’s nervous system, and as such implies minimal assumptions on physiology itself. The main assumption, which is evident from the description of the model components, is that the fly uses proprioceptive feedback information to inform future movements.

We have added clarifying text to the Results section:

“While the model is inspired by neuroanatomy, its components do not strictly correspond to components of the nervous system --- the construction of a neuroanatomically accurate model is deferred to future work (see Discussion).”

The authors acknowledge the absence of ground contact forces in the model. It is probably worth discussing how this simplification may affect inferences regarding the acceptable range of sensorimotor delay in generating realistic walking trajectories.

We agree, and discuss how some of these assumptions affect the quantitative results in the section “Towards biomechanical and neural realism”. We replicate the relevant sentences below:

“The inclusion of explicit leg-ground contact interactions would also make it harder for the model to recover when perturbed, because perturbations during walking often occur upon contact with the ground (e.g. the ground is slippery or bumpy).”

The effects of other simplifications are also mentioned in the same section.

Can the authors provide an insight into why the use of a second derivative of joint angles as the output of the trajectory generator () leads to more realistic trajectories (4.3.1 Model formulation, paragraph 1)?

Does the use of a second-order derivative of joint angles lead to drift error because of integration?

Could the distribution of θd produced be out of the domain due to drift errors? Could this affect the performance of the neural network model approximating the trajectory generator?

We are not sure why the second derivative works better than the first derivative. It is possible that modeling the system as a second order differential equation gives the network more ability to produce complex dynamics. 

As can be seen in the example time series in Figures 2 and 3 and supplemental videos, there is no drift error from integration, so it is unlikely to affect the performance of the neural network.

What does the model's failure (quantified by a low KS score) look like in the context of fly dynamics? What do the joint angles look like for low values of KS score? Does the fly fall down, for example?

Since the model primarily considers kinematics, a low KS score means that kinematics are unrealistic, e.g. the legs attain unnatural angles or configurations. Examples of this can be seen in videos 4-7 (linked from Appendix 1 of the paper), as well as in the bottom row of Fig. 5, panel A. Here, at 40ms of motor delay, L2 femur rotation is seen to attain values that far exceed the normal ranges. 

We have added a small clarification in the caption of Fig.5 panel A:

“low KS indicates that the perturbed walking deviates from data and results in unnatural angles

(as seen at 40ms motor delay)” 

We remark that since our simulations do not incorporate contact forces (as the reviewer remarks above, we simulate something like legs moving in the air for a tethered fly), the fly cannot “fall down” per se. However, if forces were incorporated then yes, these unrealistic kinematics would correspond to a fly that falls down or is no longer walking.

Reviewer #2 (Recommendations For The Authors):

L49: "Computational models of locomotion do not typically include delay as a tunable parameter, and most existing models of walking cannot sustain locomotion in the presence of delays and external perturbations". This remark confuses the reviewer.

(1) If models do not "typically" include delay as a tunable parameter, this suggests that atypical models do. Which models do? Please provide references.

Our initial phrasing was confusing. We meant to say that most models do not include delay, and some models do include delay as a fixed value (rather than a tunable value). We clarify in the updated text, which is replicated below:

“Computational models of locomotion typically have not included delays as a tunable parameter, although some models have included them as fixed values (Geyer and Herr, 2010; Geijtenbeek et al., 2013).”

(2) Has the statement that most existing models cannot sustain locomotion with delays been tested? If so, provide references. If not, please remove this statement or temper the language.

Since most models don’t include delays, they cannot be run in scenarios with delays. We clarify in the updated text, which is replicated below:

“Computational models of locomotion have not typically included delays. Some have included delay as a fixed value rather than a tunable parameter (Geyer and Herr, 2010; Geijtenbeek et al., 2013). However, in general, the impact of sensorimotor delays on locomotor control and robustness remains an underexplored topic in computational neuroscience.”

L57: "two of six legs lift off the ground at a time" - Two legs are off the ground at any time, but they do not "lift off" simultaneously in the fruit fly. To lift off simultaneously, contralateral leg pairs would need to be 33% out of phase with one another, but they are almost always 50% out of phase.

Thank you for pointing out this oversight. We have updated the text accordingly:

“Flies walk rhythmically with a continuum of stepping patterns that range from tetrapod (where two of six legs are off the ground at a time) to tripod (where three of six legs are off the ground at a time)"

L88: "a new model of fly walking" - The intention of the authors is to produce a model from which to learn about walking in the fly, is that correct? The reviewer has read the paper several times now and wants to be sure that this is the authors' goal, not to engineer a control system for an animation or a robot.

Indeed, this is our goal. We were previously unclear about this, and have made text edits to clarify this — we provide a longer response for this in the public review above (see (1)).

L126: "These desired phases are synchronized across pairs of legs to maintain a tripod coordination pattern, even when subject to unpredictable perturbations." - Does the animal maintain tripod coordination even when perturbed? In the reviewer's experience, flies vary their interleg coordination all the time. The reviewer would also expect that if perturbed strongly (as the supplemental videos show), the animal would adapt its interleg coordination in response. The author finds this assumption to be a weak point in the paper for the use of this disturbance exploring animal locomotion.

We do not know exactly how flies may react to our mechanical perturbations. However, we may hypothesize based on past papers. 

Couzin-Fuchs et al (2015) apply a mechanical perturbation to walking cockroaches. They find that that tripod is temporarily broken immediately after the perturbation but the cockroach recovers to a full tripod within one step cycle. 

DeAngelis et al (2019) apply optogenetic perturbations to fly moonwalker neurons that drive backward walking. Flies slow down following perturbation, but then recover after 200ms (about 2-3 steps) to their original speed (on average). 

Thus, we think it is reasonable to model a fly’s internal phase coupling to maintain tripod and for its intended speed to remain the same even after a perturbation. 

We do agree with the reviewer that it is plausible a fly might also slow down or even stop after a perturbation and we do not model such cases. We have added some text to the discussion on future work:

“Future work may also model how higher-level planning of fly behavior interacts with the lowerlevel coordination of joint angles and legs. Walking flies continuously change their direction and speed as they navigate the environment (Katsov et al, 2017; Iwasaki et al 2024). Past work shows that flies tend to recover and walk at similar speeds following perturbations (DeAngelis et al, 2019), but individual flies might still change walking speed, phase coupling, or even transition to other behaviors, such as grooming. Modeling these higher-level changes in behavior would involve combining our sensorimotor model with models for navigation (Fisher 2022) or behavioral transitions (Berman et al, 2016).”

L136: "...to output joint torques to the physical model of each leg" - Is this the ultimate output of the nervous system? Muscles are certainly not idealized torque generators. There are dynamics related to activation and mechanics. The reviewer is skeptical that this is a model of neural control in the animal, because the computation of the nervous system would be tuned to account for all these additional dynamics.

We agree with the reviewer that joint torques are not the ultimate output of the nervous system. We use a torque controller because it is parsimonious, and serves our purpose of creating an interpretable and modular locomotion model.

We also agree that muscles are an important consideration — we make mention of them later on in the paper under the section “Toward biomechanical and neural realism”, where we state “Another step toward biological realism is the incorporation of explicit dynamical models of proprioceptors, muscles, tendons, and other biomechanical aspects of the exoskeleton.”

Our goal is not to directly model neural control of the animal. We have introduced text clarifications to emphasize this — we provide a longer response for this in the public review above (see (2)).

L143: "To train the network from data, we used joint kinematics of flies walking on a spherical treadmill..." This is an impressive approach, but then the reviewer is confused about why the kinematics of the model are so different from those of the animal. The animal takes longer strides at a lower frequency than the model. If the model were trained with data, why aren't they identical? This kind of mismatch makes the reviewer think the approach in this paper is too complicated to address the main problem.

The design of our trajectory generator model is one of the simplest for reproducing the output of a dynamical system. It consists of a multilayer perceptron model that models the phase velocity and joint angle accelerations at each timestep. All of its inputs are observable and interpretable: the current joint angles, joint angle derivatives, desired walking speed, and phase angle. 

We chose this model for ease of interpretability, integration with the optimal controller, and to allow for generalization across perturbations. Given all of these constraints, this is the best model of desired kinematics we could obtain. We note that the simulated kinematics do match real fly kinematics qualitatively (Figure 2A and supplemental videos) and are close quantitatively (Figure 2B and C). We speculate that matching the animals’ strides at all walking frequencies may require explicitly modeling differences across individual flies. We leave the design and training of more accurate (but more complex) walking models for future work.

We add some further discussion about fitting kinematics in the discussion:

“Although we believe our model matches the fly walking sufficiently for this investigation, we do note that our model still underfits the joint angle oscillations in the walking cycle of the fly (see Figure 2 and Appendix 3). More precise fitting of the joint angle kinematics may come from increasing the complexity of the neural network architecture, improving the training procedure based on advances in imitation learning (Hussein et al., 2018), or explicitly accounting for individual differences in kinematics across flies (Deangelis et al., 2019; Pratt et al., 2024).”

Figure 2: The reviewer thinks the violin plots in Figure 2C are misleading. Joint angles could be greater or less than 0, correct? If so, why not keep the sign (pos/neg) in the data? Taking the absolute value of the errors and "folding over" the distribution results in some strange statistics. Furthermore, the absolute value would shroud any systematic bias in the model, e.g., joint angles are always too small. The reviewer suggests the authors plot the un-rectified data and simply include 2 dashed lines, one at 5.56 degrees and one at -5.56 degrees.

These violin plots are averages of errors over all phases within each speed. We chose to do this to summarize the errors across all phase angle plots, which are shown in detail in Appendix 3 and 4.

For the reviewer, we have added a plot of the raw errors across all phase angle plots in Appendix 5, E.

L156: Should "\phi\dot" be "\phi"?

We originally had a typo: we said “phase” when we meant “phase velocity”. This has been fixed. \phi\dot is correct.

L160: "This control is possible because the controller operates at a higher temporal frequency than the trajectory generator...". This statement concerns the reviewer. To the reviewer, this sounds like the higher-level control system communicates with the "muscles" at a higher frequency than the low-level control system, which conflicts with the hierarchical timescales at which the nervous system operates. Or do the authors mean that the optimal controller can perform many iterations in between updates from the trajectory generator level? If so, please clarify.

We mean that the optimal controller can perform many iterations in between updates from the trajectory generator level. The text has been clarified:

“This control is possible because the controller operates at a higher temporal frequency than the trajectory generator in the model. The controller can perform many iterations (and reject disturbances) in between updates to and from the trajectory generator.”

L225: "We considered two types of perturbations: impulse and persistent stochastic". Are these realistic perturbations? Realistic perturbations such as a single leg slipping, or the body movement being altered would produce highly correlated joint velocities.

These perturbations are not quite realistic — nonetheless, we illustrate their analogousness to real perturbations in the subsequent text in the paper, and restrict our simulations to ranges that would be biologically plausible (see Appendix 7). We agree that realistic perturbations would produce highly correlated joint accelerations and velocities, whereas our perturbations produce random joint accelerations. 

L265: "...but they are difficult to manipulate experimentally..." This is true, but it can and has been done. The authors should cite:

Bässler, U. (1993). The femur-tibia control system of stick insects-A model system for the study of the neural basis of joint control. Brain Research Reviews, 18(2), 207-226. 

Thank you for the suggestion, we have incorporated it into the text at the end of the referenced sentence.

L274: "...since the controller can effectively compensate for large delays by using predictions of joint angles in the future". But can the nervous system do this? Or, is there a reason to think that the nervous system can? The reviewer thinks the authors need stronger justification from the literature for their optimal control layer.

To clarify, this sentence describes a feature of the model’s behavior when no external perturbations are present. This is not directly relevant to the nervous system, since organisms do not typically exist in an environment free of perturbations — we are not suggesting that the nervous system does this.

In response to the question of whether the nervous system can compensate for delays using predictions: we know that delays are present in the nervous system, perturbations exist in the environment, and that flies manage to walk in spite of them. Thus, some type of compensation must exist to offset the effects of delays (the reviewer themself has provided some excellent citations that study the effects of delays). In our model, we use prediction as the compensation mechanism — this is one of our central hypotheses. We further discuss this in the section “Predictive control is critical for responding to perturbations due to motor delay”.

L319: "The formulation of a modular, multi-layered model for locomotor control makes new experimentally-testable hypotheses about fly motor control...". What testable hypotheses are these? The authors should explicitly state them. They are not clear to the reviewer, especially given the nonphysiological nature of the control system and the mechanics.

A number of testable hypotheses are mentioned throughout the Discussion section:

“Our model predicts that at the same perturbation magnitude, walking robustness decreases as delays increase. This could be experimentally tested by altering conduction velocities in the fly, for example by increasing or decreasing the ambient temperature (Banerjee et al, 2021).  If a warmer ambient temperature decreases delays in the fly, but fly walking robustness remains the same in response to a fixed perturbation, this would indicate a stronger role for central control in walking than our modeling results suggest.”

“In our model, robust locomotion was constrained by the cumulative sensorimotor delay. This result could be experimentally validated by comparing how animals with different ratios of sensory to motor delays respond to perturbations. Alternatively, it may be possible to manipulate sensory vs. motor delays in a single animal, perhaps by altering the development of specific neurons or ensheathing glia (Kottmeier et al., 2020). If sensory and motor delays have significantly different effects on walking quality, then additional compensatory mechanisms for delays could play a larger role than we expect, such as prediction through sensory integration, mechanical feedback, or compensation through central control.”

“we hypothesize that removing proprioceptive feedback would impair an insect's ability to sustain locomotion following external perturbations.”

“We propose that fly motor circuits may encode predictions of future joint positions, so the fly may generate motor commands that account for motor neuron and muscle delays.”

L323: "...and biomechanical interactions between the limb and the environment". In the reviewer's experience, the primary determinant of delay tolerance is the mechanical parameters of the limb: inertia, damping, and parallel elasticity. For example, in Ashtiani et al. 2021, equation 5 shows exactly how this comes about: the delay changes the roots and poles of the control system. This is why the reviewer is confused by the complexity of the model in this submission; a simpler model would explain why delays cannot be tolerated in certain circumstances.

We were previously unclear about the goal of the model, and have made text edits to clarify this — we provide a longer response for this in the public review above (see (1)).

L362: Another highly relevant reference here would be Sutton et al. 2023.

Done

L366: Szczecinski et al. 2018 is hardly a "model"; it is mostly a description of experimental data. How about Goldsmith, Szczecinski, and Quinn 2020 in B&B? Their model of fly walking has patterngenerating elements that are coordinated through sensory feedback. In their model, motor activation is also altered by sensory feedback. The reviewer thinks the statement "Models of fly walking have ignored the role of feedback" is inaccurate and their description of these references should be refined.

Thank you for the suggestion; we have tempered the language and revised this section to include more references, including the suggested one — text is replicated below. 

“Many models of fly walking ignore the role of feedback, relying instead on central pattern generators (Lobato-Rios et al., 2022; Szczecinski et al., 2018; Aminzare et al., 2018) or metachondral waves (Deangelis et al., 2019) to model kinematics. Some models incorporate proprioceptive feedback, primarily as a mechanism that alters timing of movements in inter-leg coordination (Goldsmith et al., 2020; Wang-Chen et al., 2023).”

We remark that Szczecinski et al does include a model that replicates data without using sensory feedback, so we think it is fair to include.  

L371: "...highly dependent on proprioceptive feedback for leg coordination during walking." What about Berendes et al. 2016, which showed that eliminating CS feedback from one leg greatly diminished its ability to coordinate with the other legs? This suggests that even flies depend on sensory feedback for proper coordination, at least in some sense.

Interesting suggestion – we have integrated it into the text a little further down, where it better fits:

“Silencing mechanosensory chordotonal neurons alters step kinematics in walking Drosophila (Mendes et al., 2013; Pratt et al., 2024). Additionally, removing proprioceptive signals via amputation interferes with inter-leg coordination in flies at low walking speeds (Berendes et al., 2016)”

L426: "The layered model approach also has potential applications for bio-mimetic robotic locomotion.". How fast can this model be computed? Can it run faster than real-time? This would be an important prerequisite for use as a robot control system.

The model should be able to be run quite fast, as it involves only

(1) Addition, subtraction, matrix multiplication, and sinusoidal computation on scalars (for the phase coordinator and optimal controller)

(2) Neural network inference with a relatively small network (for the trajectory generator) Whether this can run in real-time depends on the hardware capabilities of the specific robot and the frequency requirements — it is possible to run this on a desktop or smaller embedded device.

We do note that the model needs to first be set up and trained before it can be run, which takes some time (see panel D of Figure 1).

L432: "...which is a popular technique in robotics.". Please cite references supporting this statement.

We have added citations: the text and relevant citations are reproduced below:

“... which is a popular technique in robotics (Hua et al., 2021; Johns, 2021)

Hua J, Zeng L, Li G, Ju Z. Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning. Sensors. 2021; 21(4):1278

Johns E. Coarse-to-fine imitation learning: Robot manipulation from a single demonstration. In:

2021 IEEE international conference on robotics and automation (ICRA) IEEE; 2021. p. 4613–4619

L509: "We find that the phase offset across legs is not modulated across walking speeds in our dataset". This is a surprising result to the reviewer. Looking at Figure 6C, the reviewer understands that there are no drastic changes in coordinate with speed, but there are certainly some changes, e.g., L1-R3, L3-R1. In the reviewer's experience, even very small changes in interleg phasing can change the visual classification of walking from "tripod" to "tetrapod" or "metachronal". Furthermore, several leg pairs do not reside exactly at 0 or \pi radians apart, e.g., L1-L3, L2-L3, R1-R3, R2-R3. In conclusion, the reviewer thinks that setting the interleg coordination to tripod in all cases is a large assumption that requires stronger justification (or, should be eliminated altogether).

We made a simplifying assumption of a tripod coordination across all speeds. The change in relative phase coordination across speeds is indeed relatively small and additionally we see little change in our results across forward speeds (see Figures 4B, 5C and 5D). 

We have added text to clarify this assumption and what could be changed for future studies in the methods:

“We estimate $\bar \phi_{ij}$ from the walking data by taking the circular mean over phase differences of pairs the legs during walking bouts. We find that the phase offset across legs is not strongly modulated across walking speeds in our dataset (see Appendix 2) so we model $\bar \phi_{ij}$ as a single constant independent of speed. In future studies, this could be a function of forward and rotation speeds to account for fine phase modulation differences.”

L581: "of dimension...". Should the asterisk be replaced by \times? The asterisk makes the reviewer think of convolution. This change should be made throughout this paragraph.

Good point, done.

Figure 6: Rotational velocities in all 3 sections are reported in mm/s, but these units do not make sense. Rotational velocities must be reported in rad/s or deg/s.

The rotation velocity of mm/s corresponded to the tangential velocity of the ball the fly walked on. We agree that this does not easily generalize across setups, so we have updated the figure rotation velocities in rad/s. 

L619: The reviewer is unconvinced by using only 2 principal components of the data to compare the model and animal kinematics. The authors state on line 626 that the 2 principal components do not capture 56.9% of the variation in the data, which seems like a lot to the reviewer. This is even more extreme considering that the model has 20 joints, and the authors are reducing this to 2 variables; the reviewer can't see how any of the original waveforms, aside from the most fundamental frequencies, could possibly be represented in the PCA dataset. If the walking fly models looked similar to each other, the reviewer could accept that this method works. But the fact that this method says the kinematics are similar, but the motion is clearly different, leads the reviewer to suspect this method was used so the authors could state that the data was a good match.

Our primary use of the KS metric was to indicate whether the simulated fly continues walking in the presence of perturbations, hence we limited the analysis of the KS to the first 2 principal components. 

For completeness, we investigate the principal components in Appendix 9 and the effect of evaluating KS with different numbers of components in Appendix 10. 

The results look similar across components for impulse perturbations. For stochastic perturbations, the range of similar walking decreases as we increase the number of components used to evaluate walking kinematics. Comparing this with Appendix 9 showing that higher components represent higher frequencies of the walking cycle, we conclude that at the edge of stability for delays (where sum of sensory and actuation delays are about 40ms), flies can continue walking but with impaired higher frequencies (relative to no perturbations) during and after perturbation. 

We add text in the methods:

“We chose 2 dimensions for PCA for two key reasons. First, these 2 dimensions alone accounted for a large portion of the variance in the data (52.7% total, with 42.1% for first component and 10.6% for second component)). There was a big drop in variance explained from the first to the second component, but no sudden drop in the next 10 components (see Appendix 9). Second, the KDE procedure only works effectively in low-dimensional spaces, and the minimal number of dimensions needed to obtain circular dynamics for walking is 2. We investigate the effect of varying the number of dimensions of PCA in Appendix 10.”

(Note that we have corrected the percentage of variance accounted for by the principal components, as these numbers were from an older analysis prior to the first draft.)

We also reference Appendix 10 in the results:

“We observed that robust walking was not contingent on the specific values of motor and sensory delay, but rather the sum of these two values (Fig. 5E). Furthermore, as delay increases, higher frequencies of walking are impacted first before walking collapses entirely (Appendix 10).”

Briefing Document : Analyse du Plan Ministériel pour la Santé Mentale des Élèves (mars 2025)

Source : Excerpts from "Santé mentale des élèves : les insuffisances du plan ministériel.pdf", Café Pédagogique, 18 mars 2025.

Thème Principal : Le document analyse de manière critique le plan d'action ministériel pour la santé mentale des élèves, soulignant ses insuffisances au regard de la gravité de la situation et du manque de moyens structurels.

Idées et Faits Clés :

Reconnaissance Ministérielle et Insuffisances Soulignées : * Le ministère de l'Éducation nationale affiche sa mobilisation face à la dégradation préoccupante de la santé mentale des enfants et des jeunes, mettant en place un protocole-cadre pour la santé mentale allant "du repérage à la prise en charge". * Cependant, l'article met en évidence que ce plan, malgré des intentions louables, présente un certain nombre d'insuffisances. Selon Edmond Porra, secrétaire adjoint du Snupden-FSU, il s'inscrit dans le contexte d'"une santé scolaire dégradée".

Carence Dramatique de Ressources Humaines :

• L'article insiste sur la situation alarmante des personnels de santé scolaire :
• "Un infirmier scolaire pour 1 600 élèves, un psychologue EN pour 1 500 élèves, un assistant de service social pour 4000 élèves, un médecin scolaire pour 13 000 élèves".
• À cette carence s'ajoute un manque d'attractivité de ces métiers, avec une perspective de "quasi-disparition des médecins scolaires".
• Privatisation Progressive de la Médecine Scolaire :
• L'effondrement des ressources conduit à une "évolution progressive vers une privatisation de la médecine scolaire", notamment par la délégation aux médecins libéraux des prescriptions et des notifications d'aménagements d'examens.

Solution des "Secouristes en Santé Mentale" Jugée Insuffisante :

• Pour pallier le manque de professionnels, le ministère promeut la mise en place d'équipes de "secouristes en santé mentale" dans les établissements.
• L'article souligne que ces secouristes, à l'image des secouristes classiques, ne sont pas des professionnels de santé médicale ou mentale. Leurs interventions sont limitées à des "gestes de premier secours en santé mentale" et ne relèvent pas de la psychiatrie ou de la psychologie.
• Ces secouristes sont chargés de relayer les situations aux "équipes ressources" constituées par les personnels médico-sociaux, dont l'article questionne également la capacité d'action face au manque de moyens.

Risque de Départementalisation et d'Interchangeabilité des Missions :

• L'article craint que le dispositif actuel ne soit compatible avec un projet de "service départementalisé de la santé scolaire" regroupant les différents corps professionnels (médecins, infirmiers, psy-EN et AS).
• S'appuyant sur les préconisations de la Cour des Comptes, il est envisagé que ces professionnels, via une "formation complémentaire" et un "élargissement de leurs compétences", puissent s'engager dans des parcours plus diversifiés, avec une possible "interchangeabilité des missions" au sein d'un service fonctionnant "à la sollicitation".
• L'auteur y voit un risque de "remplacer le suivi au long cours des élèves par un système d’interventions au ticket auprès d’un guichet de services à la demande ou de brigades territorialisées", institutionnalisant ainsi la gestion de la pénurie.

Déplacement de la Question des Moyens vers l'Organisation :

• L'article perçoit une stratégie visant à masquer le manque de moyens en mettant l'accent sur l'organisation des moyens existants. Le protocole de santé mentale est vu comme une pièce complémentaire à cette "révolution copernicienne du pilotage de la santé scolaire en mode décroissant".

Nécessité de Moyens pour des Professionnels Qualifiés :

• L'auteur affirme qu'il ne faut pas se satisfaire d'une formation aux premiers secours en santé mentale pour les enseignants et personnels de vie scolaire en guise de compensation du manque de ressources.
• "La meilleure garantie pour la sécurité psychique des enfants que nous accueillons, c’est d’abord d’avoir les moyens d’un travail d’équipes de professionnels spécialisés et qualifiés auprès des établissements, plus que de faire peser ces missions sur d’autres personnels."

Critique de l'Usage du Concept de Santé Mentale Hors Contexte :

• L'article met en garde contre un usage non-critique du concept de santé mentale, comparant cela aux critiques formulées sur la thématique des risques psycho-sociaux dans le monde du travail.
• Le risque est de proposer des réponses abstraites, visant à adapter les individus aux conditions dégradées plutôt qu'à transformer ces conditions. "Le concept de santé mentale, qui tend désormais à suppléer celui de bien-être, court le risque de reproduire le même type de réponse abstraite et potentiellement contre-productive en prétendant agir sur un phénomène isolé de son contexte et de ses conditions d’apparition."

Ignorance des Facteurs Éducatifs et Sociaux :

• L'article déplore l'absence de réflexion sur les questions éducatives et sociales dans le diagnostic et les solutions proposées.
• Il souligne que "une politique éducative qui génère une anxiété de la performance et qui renforce la pression scolaire ne peut que produire un terrain favorable à la phobie scolaire."
• De même, les effets de la "brutalité du climat social" et de "l'insécurité des familles en difficulté sociale" sur la santé mentale des élèves sont largement ignorés par une politique qui se complaît dans la dénonciation de l'assistanat et la dégradation des protections sociales.

Détérioration des Structures de Soin Existantes :

• L'auteur questionne la pertinence de se préoccuper de la santé mentale des élèves tout en laissant à l'abandon les structures de soin publiques, telles que les centres médico-psychologiques (CMP).

Critique du "Marketing Protocolaire" :

• La présentation du plan est jugée axée sur une "forme protocolaire" qui induit des attentes potentiellement trompeuses auprès des familles, qui pourraient croire à une prise en charge médicale structurée.

• "La communication par le renvoi dans les établissements à l’existence du « protocole » comme solution entretient l’ambiguïté dans un contexte de saturation des dispositifs déjà surchargés dans les secteurs de la pédopsychiatrie et des soins psychologiques." Cela alimente des attentes irréalistes face au manque de ressources.

Impact Négatif sur les Personnels :

• Le plan ne prend pas en compte la santé mentale des personnels et risque de favoriser les ruptures dans un contexte déjà dégradé, notamment à travers un "fonctionnement managérial qui met à mal leur professionnalité" et un "management coercitif" basé sur un excès de protocolisation et une défiance envers les professionnels.

Manque d'Ambition et Solutions de Fortune :

• L'article conclut que malgré l'annonce de la santé mentale comme grande cause nationale, le plan d'action est loin d'être à la hauteur des enjeux, privilégiant "les solutions de fortune et les stratégies communicantes".

Il exprime une crainte quant aux effets réels du plan, anticipant plus de problèmes que de solutions.

Quote Significative :

"La meilleure garantie pour la sécurité psychique des enfants que nous accueillons, c’est d’abord d’avoir les moyens d’un travail d’équipes de professionnels spécialisés et qualifiés auprès des établissements, plus que de faire peser ces missions sur d’autres personnels."

Conclusion :

Le document critique sévèrement le plan ministériel pour la santé mentale des élèves, le considérant comme une réponse organisationnelle superficielle à un problème profond de manque de moyens structurels et de dégradation des services publics de santé et d'éducation.

L'accent mis sur la formation de "secouristes" et sur un protocole est perçu comme un cache-misère qui ne s'attaque pas aux racines du problème, telles que le manque de personnels spécialisés, la privatisation rampante de la médecine scolaire, et l'ignorance des facteurs éducatifs et sociaux contribuant à la détresse des élèves.

L'article exprime une forte inquiétude quant à l'efficacité réelle de ce plan et à son impact sur les élèves et les personnels.

Briefing webinaire fcpe sur l'École Inclusive

Date : 8 mai 2024

Source : Excerpts from "Texte collé" (Transcription d'une intervention orale)

Objet : Analyse des principaux thèmes, idées importantes et faits marquants concernant l'école inclusive en France, présentés lors d'une intervention.

Introduction :

Ce document synthétise les principaux points abordés dans l'extrait d'une intervention orale portant sur l'école inclusive.

L'intervenant, qui semble être un Inspecteur Conseil Technique en charge de l'école inclusive, partage sa vision, les évolutions récentes, les défis persistants et les moyens mis en œuvre pour favoriser une école pour tous en France.

L'intervention se situe dans le contexte du 20ème anniversaire de la loi du 11 février 2005, une législation marquante pour les droits des personnes en situation de handicap.

Thèmes Principaux et Idées Clés :

• Définition et Philosophie de l'École Inclusive :
• L'intervenant souligne la nuance entre "école pour tous" et "école inclusive", cette dernière étant perçue comme une extension nécessaire pour atteindre l'objectif d'une école unique pour tous les enfants, sans distinction. Il remet en question la nécessité d'ajouter un adjectif à "école" si l'objectif est réellement l'inclusion de tous.
• L'école inclusive ne doit pas être vue comme une simple "accumulation d'enfants à profil et avec une accumulation de structure ou de réponse". Il s'agit d'un changement de paradigme dans la perception de l'école.

• L'intervenant insiste sur l'importance de considérer chaque enfant d'abord comme un élève, avec une identité propre et des besoins éducatifs particuliers, plutôt que de se focaliser sur des étiquettes liées à un potentiel ou un handicap. "D'abord, je reconnais, c'est qu'il a des besoins, tu besoins éducatifs particuliers et vous voyez que essayer de sortir de cette idée de l'étiquette et de la et de l'identification, ça nous oblige nous aussi dans l'école à se demander d'abord quelle est l'identité de cet enfant?" (00:08:30-00:08:48).

• Il rappelle les textes fondateurs comme la Déclaration de Salamanque (1994) qui pose les principes que "chaque enfant a des caractéristiques, des intérêts, des aptitudes et des besoins d'apprentissage qui lui sont propres" (00:11:29-00:11:38) et que "les différences humaines sont normales et par conséquent, l'apprentissage doit être adapté aux besoins de chaque femme" (00:11:49-00:11:55).

La Notion de Difficulté et l'Écart à la Norme :

• La difficulté est définie comme un "écart entre les compétences et les performances de l'élève et ce qui est attendu de lui par rapport à son âge et aux attentes du système éducatif" (00:13:27-00:13:53).

• L'intervenant perçoit ces enfants en difficulté non pas comme un problème, mais comme une "force" (00:14:43) qui oblige les professionnels à interroger, adapter et répondre aux projets de l'école.

• L'École Inclusive comme Projet de Société :

• L'école inclusive n'est pas uniquement l'affaire de l'école, mais engage la société dans son ensemble pour "faire une place dans la société à tous" (00:15:50-00:15:58).

• L'enjeu est de passer d'une logique d'accessibilité ("je rends les choses accessibles") à une logique de compensation ("mesures complémentaires qui vont être décidées") et idéalement vers une société où l'environnement est adapté à chacun, sans nécessité de privilèges ou d'exclusion. Selon Charles Gardou, une société inclusive est une "société sans privilège ni exclusivité ni exclusion ni exclusion, chacun dans sa place et dans la société ni exclusivité" (00:21:36-00:22:29).

Évolution de la Loi de 2005 et Nécessité d'Interrogation :

• La loi de 2005 a "bouleversé, elle a modifié, elle a engagé énormément les personnels, les moyens, mais aussi les partenariats" (00:03:25-00:03:37).

• Après 20 ans, il est nécessaire d'interroger cette loi et de la regarder avec un œil nouveau en se projetant sur les prochaines années, notamment en ce qui concerne l'application du "droit commun".

• Moyens et Leviers pour l'École Inclusive :
• L'intervenant détaille différents moyens humains et dispositifs mis en place :
• Services d'écoute et d'information pour les familles : Numéro vert national, services dans chaque département sous l'autorité des DAEN (Directeurs Académiques des Services de l'Éducation Nationale).
• Équipes Mobiles d'Appui à la Scolarité (EMAS) : Professionnels du médico-social intervenant dans les écoles pour observer, étayer et former, préfigurateurs des futurs pôles d'appui à la scolarité.

• Enseignants référents : Experts académiques (une centaine dans l'académie de Montpellier) qui suivent les enfants et les familles.

• Accompagnants d'Élèves en Situation de Handicap (AESH) : Distinction entre différentes missions (aide aux actes de la vie quotidienne, aide à la scolarisation, aide à l'autonomie). La MDPH doit nommer clairement le type de mission.

• Dispositifs spécifiques en établissement ordinaire : ULIS (Unités Localisées pour l'Inclusion Scolaire) pour différents types de besoins (TFC, TED, TSA, TSL, Troubles moteurs, Déficience visuelle, Déficience auditive, Troubles des fonctions cognitives).

• Dispositifs pour les Troubles du Neuro-Développement (TND) : Stratégie nationale, plateformes de diagnostic et d'orientation (PCO), dispositifs d'accompagnement médico-social (CAMSP, CMPP, SESSAD).

• Établissements Médico-Sociaux (EMS) : IME, ITEP, etc., indispensables pour les enfants avec des handicaps majorés nécessitant une prise en charge pluriprofessionnelle. Un identifiant national est donné à tous les élèves, y compris ceux scolarisés en EMS, soulignant leur statut d'élève avant tout.

• Adaptation pédagogique : Nécessité d'interroger la pédagogie pour répondre aux besoins de tous.

• Formation des professionnels : Essentielle pour une meilleure compréhension et prise en charge des besoins spécifiques.

Défis et Points de Vigilance :

• Équité territoriale : Bien qu'une équité nationale dans la répartition des moyens soit affirmée, des manques criants de places dans certains dispositifs sont signalés, notamment en Île-de-France.
• Articulation entre les différents acteurs : Difficultés d'articulation entre l'éducation nationale, le médico-social et les familles.
• Refus scolaire anxieux (RSA) : Souligné comme un "fléau" nécessitant une réponse adaptée et globale (médicale, scolaire, familiale). L'idée que "l'enfant n'est pas à sa place" est rejetée.
• Aménagements d'examens : Question complexe avec une tension entre la nécessité de compensation et le risque de créer des aménagements trop spécifiques et difficiles à structurer. L'objectif reste pédagogique, visant l'autonomie de l'élève.
• Rased (Réseaux d'Aides Spécialisées aux Élèves en Difficulté) : Inégale répartition sur le territoire, fonctionnement par circonscription et non par école.
• Importance du Partenariat :
• La collaboration avec les familles et les associations (comme la FCPE) est essentielle pour faire remonter les besoins et les difficultés.
• Le médico-social est de plus en plus envisagé comme une "fonction ressource, un appui de l'école" (00:29:51-00:30:01).

Conclusion :

L'intervention met en lumière une volonté affirmée de faire progresser l'école inclusive en France, en s'appuyant sur un cadre législatif existant et des moyens humains et financiers en développement.

L'accent est mis sur une approche centrée sur les besoins individuels de chaque enfant, au-delà des étiquettes, et sur une vision de l'inclusion comme un projet sociétal.

Cependant, des défis importants subsistent en termes d'équité d'accès aux dispositifs, d'articulation entre les acteurs et de réponse à des problématiques spécifiques comme le refus scolaire anxieux.

La formation des professionnels et le partenariat avec les familles sont identifiés comme des leviers essentiels pour une école véritablement pour tous.

Citation Pertinente pour la Diffusion :

"Ce n'est pas juste de la du vocabulaire que j'utilise... on voit bien que c'est un paradigme qui modifie absolument tout dans la perception que l'on doit voir de ce qu'est l'école, pas une accumulation d'enfants à profil et avec une accumulation de structure ou de réponse..." (00:05:07-00:06:31)

Voici un sommaire de la présentation avec les horodatages correspondants :

• 00:00:03 - 00:00:13 : Introduction du webinaire intitulé "une école inclusive pour tous, présentation des dispositifs et des structures".
• 00:00:14 - 00:00:37 : Présentation des intervenants, notamment la secrétaire général adjointe et la vice-présidente de la FCPE, ainsi que Valérie Bareau, Isabelle Thomas Pinatel et Philippe Potre.
• 00:02:28 - 00:02:42 : Présentation de Monsieur Laffitte qui déroulera la présentation.
• 00:04:01 - 00:04:14 : Présentation du logo "national école inclusive" et du poste de Monsieur Laffitte en tant qu'inspecteur conseil technique.
• 00:05:52 - 00:07:00 : Le concept d'école inclusive où tout le monde se retrouve dans une même organisation, visant à terme une "école tout court". L'importance de considérer tous les enfants d'âge scolaire comme des élèves.
• 00:10:56 - 00:12:47 : Les textes fondateurs de l'école pour tous, notamment la déclaration de Salamanque en 1994 et la question du besoin, du respect de l'identité.
• 00:12:47 - 00:14:00 : La notion de difficulté dans le système éducatif. La présentation est organisée pour les familles.
• 00:19:15 - 00:21:21 : Adaptations et aménagements dans la classe, comme les pictogrammes pour les enfants avec autisme. L'inclusion se fait en donnant les moyens de grandir avec des aménagements. Une vision philosophique et un engagement envers un projet de société.
• 00:23:20 - 00:26:32 : Moyens et leviers pour l'école inclusive. Les informations sont disponibles sur des sites spécialisés.
• 00:26:33 - 00:27:04 : Coopération étroite avec le médico-social comme avenir. Travail avec l'agence régionale de santé.
• 00:28:46 - 00:30:15 : Explication des acronymes (avec une annexe dans le guide) et présentation des équipes mobiles d'appui à la scolarisation (EMAS) financées par l'agence régionale de santé et mises à disposition de l'école. Elles sont accessibles aux familles et saisies par les établissements.
• 00:30:35 - 00:32:29 : Moyens du droit commun qui ne nécessitent pas d'étiquette MDPH. Les élèves sont scolarisés dans le secteur mais aussi dans les IME et ITEP.
• 00:35:06 - 00:37:29 : Le rôle des AESH (Accompagnants d'Élèves en Situation de Handicap) et les trois missions définies par la MDPH : actes de la vie quotidienne, accès aux activités d'apprentissage, et activités de la vie sociale et relationnelle.
• 00:37:29 - 00:40:48 : Dispositifs et professionnels présents dans les écoles et établissements, notamment les ULIS (Unités Localisées pour l'Inclusion Scolaire).
• 00:40:49 - 00:43:18 : Plateformes de repérage et de diagnostic précoce des troubles du neurodéveloppement, notamment l'autisme.
• 00:43:19 - 00:45:20 : Les UEMA (Unités d'Enseignement Maternelle Autisme), les UEEA (Unités d'Enseignement Élémentaire Autisme) et les DSA (Dispositifs d'Autorégulation). Augmentation de ces dispositifs.
• 00:46:00 - 00:47:39 : Autres dispositifs spécifiques, comme ceux pour les enfants sourds et les SESSAD (Services d'Éducation Spécialisée et de Soins à Domicile). Présentation d'un outil en ligne pour trouver les établissements médico-sociaux.
• 00:48:40 - 00:52:00 : Développement des SESSAD, services intervenant sur notification MDPH à l'école ou dans la famille, et évolution vers des SESSAD plus généralistes. Présentation des ESE (Établissements Spécialisés pour Enfants) et des ITEP (Instituts Thérapeutiques, Éducatifs et Pédagogiques) pour les handicaps majorés et les troubles du comportement.
• 00:52:00 - 00:53:11 : Rapprochement entre les ITEP et les établissements ordinaires.
• 00:53:12 - 00:55:46 : Moyens dans le premier degré pour les parents. Présentation des IME (Instituts Médico-Éducatifs) et des IEM (Instituts d'Éducation Motrice).
• 00:55:46 - 00:57:07 : Les EA (Établissements Adaptés) avec des classes à effectif réduit et des enseignements adaptés. Les LEA (Lycées d'Enseignement Adapté).
• 00:57:07 - 00:59:09 : Leviers pour l'école inclusive, notamment la Conférence Nationale du Handicap d'avril 2023. Déploiement de matériel pédagogique adapté. Augmentation des professionnels du médico-social dans l'école.
• 00:59:10 - 01:02:41 : Importance de la pédagogie et adaptations nécessaires. Le levier de la formation initiale et continue des personnels, y compris les AESH et les personnels d'encadrement.
• 01:02:41 - 01:03:20 : Ressources nationales et sites d'information accessibles à tous.
• 01:05:13 - 01:07:11 : Présentation de l'outil Capco Inclusive, un espace de ressources pour observer l'enfant, ses besoins, trouver des aménagements pédagogiques et s'informer.
• 01:07:11 - 01:08:25 : Conclusion partielle sur l'importance de regarder ce qui fonctionne et exemple d'inauguration d'un dispositif d'autorégulation.
• 01:08:25 - 01:12:24 : Questions et réponses, notamment sur les dispositifs d'autorégulation (DSA) et le nombre de places.
• 01:12:24 - 01:14:00 : Question sur les LEA et le cumul avec une situation sociale nécessitant un internat.
• 01:14:00 - 01:18:36 : Question sur les aménagements des examens, explication des évolutions et des difficultés liées à l'accumulation des mesures de compensation. Un guide sur les aménagements des examens sera partagé. Nécessité d'une anticipation importante. Exemples d'aménagements parfois aberrants.
• 01:18:36 - 01:29:45 : Suite des questions et réponses. Question sur l'orientation et la réponse "il n'est pas à sa place". L'idée de lien entre les murs des établissements spécialisés et ordinaires. Des enfants en IME pourraient être en milieu ordinaire. Question sur la formation initiale des enseignants. Importance de casser les étiquettes.
• 01:29:45 - 01:32:32 : Question sur les RASED (Réseaux d'Aides Spécialisées aux Élèves en Difficulté) dans le premier degré, leur manque de présence partout et la mauvaise information aux familles. La FCP dénonce ce problème.
• 01:32:32 - 01:39:20 : Réponse concernant les RASED et confirmation du problème. Réflexion sur la formation des personnels avec des séminaires et des formations spécifiques sur l'école inclusive. Importance de la formation, de l'accompagnement et de l'information.
• 01:39:20 - 01:44:12 : Suite des questions et réponses, notamment sur la communication apaisée entre les familles et l'école. Le travail avec les familles est stratégique.
• 01:44:12 - 01:49:31 : Information sur d'autres accompagnements possibles pour les enfants en décrochage scolaire et sur des sites internet dédiés. Les liens vers les ressources seront partagés.
• 01:49:31 - 01:53:21 : Dernière question sur un film traitant de l'école inclusive (la réponse sera donnée ultérieurement). Conclusion du webinaire par Florence de la FCP, remerciements et rappel de la vision d'une école inclusive comme projet de société. Importance du partenariat avec les familles. Rôle des FCPE au niveau départemental et national. Appel au partage des informations.
• 01:53:21 - 01:53:21 : Fin du webinaire.

This section depicts the root of Melville's disdain for publishers and critics. As we have discussed, he was very particular about his style and hard on his own writing making it clear how any changes could be viewed as abominable. Furthermore, when it is his writing being criticized, it would be frustrating to know it had been altered in his own name. This idea is reflected in Pierre's struggles to find his footing as a writer when everyone in the world wants something different but he simply wants to write for himself and share that with the world.

The vertical bar indicates that the keyframed property is vector-valued, rather than a scalar value. So you'll get it linking x,y,z components of a Vec3f for example, but wouldn't see it for floats. You won't see it for multiple keyframes at different relative frametimes.

This is a short-term solution for not being able to key the x,y,z individually. I'm hoping I'll get to add this before the v1 release, but assume not and I'll let you know if I get time.

Author response:

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

We appreciate that both reviewers found our findings significant and recognized the strength of the presented data in demonstrating the potential value of ASO-mediated Emc10 expression modulation for treating 22q11.2DS. We are grateful for the reviewers' valuable input and constructive suggestions, which we believe have significantly strengthened our manuscript. Below, we address the main points and concerns, followed by our point-by-point responses:

Evaluation of ASO-Mediated Emc10 Reduction: We appreciate the feedback and the opportunity to clarify this point. While we agree that ASO-mediated reduction of Emc10 should ideally be evaluated at both the mRNA and protein levels, we would like to emphasize that this was indeed performed in our study. Specifically, we conducted both qRT-PCR and Western Blot (WB) assays on the same animal cohort, focusing on the left and right hippocampus (rather than the PFC) following ASO injection (see Figure S11C and D). We prioritized the hippocampus for the WB assay because our primary behavioral assays and observed phenotypes in this study are strongly hippocampus-centric. This approach reflects our aim to investigate Emc10's role in the brain regions most relevant to the observed phenotypes. We hope this clarification addresses the reviewer’s concerns. While protein-level analysis would ideally complement RNA measurements, the Emc10 antibodies available were suboptimal in specificity and sensitivity, requiring substantial optimization. Additionally, challenges in obtaining sufficient high-quality protein from small regions like the hippocampus limited the use of protein detection as a standalone method. We plan to refine antibody protocols or explore alternative methods in future work. Notably, in all instances where we performed parallel protein and RNA measurements in both, mouse brain tissue and human cell lines, there was excellent concordance between the datasets, strongly suggesting that mRNA levels are a reliable indicator of Emc10 protein levels in our model.

ASO Neuronal Uptake: While ASO uptake by neurons in the brain can vary considerably depending on factors such as ASO chemistry, delivery method, target brain region, and cell type, our targeted delivery approach, ASO design optimization, and ASO screening strategy were specifically tailored to achieve uniform and efficient uptake across hippocampal and cortical regions, in both neurons and glia. The figures included in our manuscript at both low and high magnification (see Figure S14A) clearly display the extensive (over 97%) overlap of ASO-positive cells (green signal) with cells expressing the neuronal marker NeuN (red signal). While quantifying ASO-positive cells in different brain regions could add value, the robust diffusion of ASO into neurons and glia is effectively demonstrated in the current figures and indirectly supported by the robust downregulation of Emc10 in ASO-treated animals as shown by qRT-PCR assays of hippocampal and cortical brain regions.

Transcriptomic Data in Mutant EMC10 NGN2-iNs: Reduction in EMC10 levels is not expected to directly affect transcription or to broadly reorganize the differential gene expression profile of the Q6/Q5 patient/control NGN2-iN lines. Accordingly, our transcriptional profiling was not designed to assess the direct impact of EMC10 deficiency on gene expression but rather to serve as an indirect measure of cellular pathways affected by the reduction in EMC10 levels in the patient Q6 line. We aimed to identify genes and related functional pathways differentially expressed between the Q6/Q5 patient/control lines, where these expression differences are either abolished or significantly attenuated in Q6/EMC10^HET or Q6/EMC10^HOM NGN2-iNs.

Statistical Analysis: We have meticulously reviewed all statistical analyses in the manuscript to ensure their appropriateness and adherence to established practices. For Figure S2, we acknowledge that the statistical details were not fully specified in the figure legend, though they are provided for each miRNA in Supplemental Table S2. In the revised manuscript, we ensured that the statistical methods and corresponding values are clearly indicated for each comparison.

We are confident that the revisions outlined above, along with the point-by-point responses provided below, will significantly strengthen our manuscript and address all the concerns raised by the reviewers. We would like to express our sincere thanks to the reviewers for their valuable feedback and constructive suggestions.

Reviewer #1 (Recommendations For The Authors):

My comments here are generally limited to minor comments that reflect possible small additions or edits to the manuscript:

(1) Panel 1A is very small. Please consider making that bigger as space permits.

We have increased the panel size of Figure 1A in the revised manuscript to improve its visibility and clarity.

(2) Are you able to identify the dot that represents EMC10 in panel 1C? I understand that EMC10 is represented in Supplementary Figure 4A.

We appreciate the reviewer's observation. In Figure 1C, the volcano plot depicts differentially expressed miRNAs in the Q5/Q6 neuronal samples, as identified through miRNA-sequencing. Since EMC10, as a protein-coding gene and a downstream target of miRNA dysregulation, is not included in this analysis. However, as the reviewer correctly notes, EMC10 gene expression is represented in the volcano plot in Supplementary Figure 4A, which displays differentially expressed genes identified through bulk RNA-seq analysis of the same neuronal samples. To avoid any confusion, we have clarified the title of Figure 1C to emphasize that it represents miRNA expression changes.

(3) With regard to studies using iPSC. Some of the studies are executed across multiple distinct pairs and some are only done in a single pair. Overall, while results are coherent and often complimentary, would it be valuable for the authors to comment on experiments where studies in multiple pairs seemed particularly important, or others wherein it was less important?

We thank the reviewer for this insightful question regarding our use of multiple versus single hiPSC pairs. Our investigation began with the Q5/Q6 sibling (dizygotic twin) pair, which shares the most similar genetic background. This minimized the impact of confounding genetic factors and provided a robust foundation for testing our hypothesis that EMC10 upregulation, driven by miRNA dysregulation, is a key consequence of the 22q11.2 deletion in human neurons, thus validating our previous findings from the Df(16)A^+/- mouse model (Stark et al., 2008; Xu et al., 2013). To ensure the generalizability of our findings, we incorporated additional hiPSC lines from another sibling pair as well as a case/control pair, demonstrating that EMC10 upregulation is a consistent feature of 22q11.2DS. Subsequently, we focused on the well-matched Q5/Q6 pair for detailed morphological, functional, and genetic rescue experiments. This approach allowed us to perform in-depth studies while controlling for potential genetic confounders. By using both multiple and single hiPSC pairs, we balanced the need for generalizable findings with the practical considerations of conducting technically complex and resource-intensive experiments. This strategy enabled us to provide both broad and detailed insights into the mechanisms underlying 22q11.2DS. We have modified the introductory paragraph of the Results section to better highlight this issue.

(4) While the majority of the experiments seem sufficiently powered to test the hypothesis in question in the iPSC studies, Figure 2B raises the question if the study replicates here were underpowered, and perhaps the authors might consider mentioning this, although this is a very minor comment.

We thank the reviewer for raising this point. We acknowledge that the statistical power to detect a significant difference in pre-miR-485 levels in Figure 2B may be limited due to the relatively small sample size and the inherent variability in hiPSC-derived neuronal cultures. However, it is important to emphasize that the functional impact of miRNAs is primarily mediated by their mature transcript forms. Our miRNA-seq data (Supplementary Table 2 and Figure S2) did not show significant alterations in the levels of mature miR-485-5p or miR-485-3p. This finding aligns with the reported expression pattern of miR-485 in hiPSC-derived neurons, where relatively low levels are observed in early neuronal development, with increased expression occurring in older, more mature neurons (Soutschek et al. 2023; https://ethz-ins.org/igNeuronsTimeCourse/ database from the Institute of Neurogenomics, ETH Zurich). This database provides a valuable resource for examining gene expression dynamics during human neuronal differentiation. Given that our hiPSC-derived neurons were analyzed at a relatively early developmental stage (DIV8 for these experiments), it is likely that miR-485 expression had not yet reached levels sufficient to reveal significant differences. While we acknowledge the potential limitation in statistical power for detecting subtle changes in pre-miR-485 levels, the combined evidence suggests that miR-485 may not be a significant contributor to the observed phenotypes at this developmental stage.

A paragraph has been added in the corresponding Results section to address this issue.

(5) There are a few situations where the authors could help out the reader a little bit by providing more labels on the figures directly. For example: in Figure 2, there are expression levels, over-expression, and inhibition of miRNA but the X-axis is named with similar labels for the miRNAs in question for each of these distinct experiments. If the authors want to help the reader, they may consider labeling these panels with a descriptive title to reflect the experiment being done or use more descriptive terms in the X-axis panels. Again, this is minor. Similarly, in Figure 5, it might be helpful for the authors to help out the reader again with more labels on the panels, such as in Figures 5B, 5C, and 5D. Would they consider labeling these panels, HPC, PFC, SSC with the brain location as they did in Figure 4?

We thank the reviewer for these helpful suggestions to improve the clarity of our figures. We have implemented the proposed changes. In Figure 2C-E, we have added specific titles to the panels to clearly distinguish between the different experimental conditions such as miRNA overexpression and inhibition. Similarly, in Figure 5, we labeled panels 5B, 5C, and 5D with the brain regions analyzed (HPC, PFC, SSC) to match the labeling used in Figure 4. We believe these revisions enhance the readability and overall interpretability of the figures, making it easier for readers to follow the experiments and results.

(6) Figure 3: There is some overshoot of the data in EMC10 homozygous null, in panel 3E, and also, overshoot of the het in panel 3H. Would there be value in the authors commenting on the potential basis for this in the discussion? Some issues are minor, such as the lack of electrophysiological analysis of circuits in vivo or in ex vivo slices that may further support the proposed rescue.

The reviewer correctly highlights the observation in Figures 3E and 3H, where the number of branch points in the Q6/EMC10^HOM line exceeds wildtype levels and the calcium response in the Q6/EMC10^HET and Q6/EMC10^HOM lines surpasses that of the control. This overshoot is indeed intriguing and warrants discussion. EMC10 is part of the ER Membrane Complex (EMC), which plays a critical role in the proper folding and localization of various membrane proteins, including neurotransmitter receptors and ion channels such as voltage-gated calcium channels (Chitwood et al., 2018; Shurtleff et al., 2018; Chitwood and Hegde, 2019). In the context of the 22q11.2 deletion, EMC10 dysregulation may disrupt the proper localization of these proteins at the synapse, affecting both dendritic morphology and calcium signaling. The precise basis of this overshoot remains unclear. The overshoot may result from a dosage-sensitive inhibitory effect of Emc10, where both reduced and increased expression alter normal neuronal processes, with excessive responses potentially triggered upon gene restoration by the mutant system’s adaptation to dysfunction, leading to altered receptor sensitivity or signaling dynamics. This underscores the critical importance of precise Emc10 expression for proper neuronal development and function, in line with previous findings suggesting that EMC10 plays an auxiliary or modulatory role in EMC function. A short comment on the potential basis for this overshoot has been added in the corresponding Results section of the manuscript. Regardless of the underlying mechanisms, these findings emphasize the importance of precise titration of ASO constructs, rigorous gene dosage controls, and thorough analysis of context-specific responses to ensure both efficacy and safety in clinical applications.

We also agree with the reviewer that electrophysiological studies, particularly in the 22q11.2 deletion mouse model, would provide valuable insights into the impact of EMC10 modulation by ASOs on neuronal activity and circuit function at the in vivo and ex vivo levels. Incorporating such experiments into future studies will allow us to assess synaptic transmission and plasticity, contributing to a more comprehensive understanding of the therapeutic potential of ASO-mediated EMC10 modulation in 22q11.2DS.

(7) Did the authors take out the behavior studies further than 9 weeks? Would the authors consider commenting on what they speculate might be the duration of the treatment effect? For both mice and definitely humans.

We thank the reviewer for raising the important question regarding the duration of the ASO treatment effect, which is crucial for translating our findings into clinically relevant therapies. While behavioral studies beyond 9 weeks were not conducted in this study, our in vivo experiments and findings from prior publications (detailed below) enable an informed speculative assessment.

We utilized 2'-O-methoxyethyl (2'-MOE) modified ASOs, known for their enhanced binding affinity, nuclease resistance, and increased metabolic stability. In our in vivo post-injection screening of ASOs (Figure S13C), we predicted that Emc10 expression levels return to normal WT levels (~T100%) approximately 26 weeks post-treatment in Emc10^ASO (#1466182) treated mice. This prediction is supported by our Emc10 expression profiles across various brain regions, which demonstrate robust repression of Emc10 lasting up to 10 weeks post-administration (Figure 6D-F). While these findings suggest that the treatment effect in our model could extend significantly beyond 10 weeks following a single ASO injection, further empirical validation is required through extended follow-up studies. Encouragingly, long-term effects of 2'-MOE ASOs have been observed in other neurological disorders (Kordasiewicz et al., 2012; Scoles et al., 2017; Finkel et al., 2017; Darras et al., 2019). However, factors such as ASO distribution, target cell turnover, and disease-specific pathophysiology could influence the duration of the effect. To address these uncertainties, we have added a paragraph in the Discussion section emphasizing the need for additional studies, including extended follow-up periods and eventual clinical trials, to determine the specific duration of effect for our Emc10^ASO constructs in treating 22q11.2DS.

Reviewer #2 (Recommendations For The Authors):

(1) It is acknowledged that the iPSC-derived cells in Figure 1 are no longer progenitors, but differentiation markers for astrocytes and glia are also needed in Figure 1b to establish that equal rates of differentiation have occurred across genotypes.

We thank the reviewer for raising this important point about ensuring equal rates of differentiation across genotypes. As the reviewer notes, we employed a well-established protocol for directed differentiation of hiPSCs into cortical neurons using a combination of small molecule inhibitors, as previously described by Qi et al. (2017). This protocol has been extensively validated and is known to robustly generate cortical neurons while actively suppressing glial differentiation, as evidenced by the lack of upregulation of glial markers such as GFAP, AQP4, or OLIG2 in the original study. Given the established neuronal specificity of this protocol and our focus on neuronal phenotypes, we prioritized the confirmation of successful neuronal differentiation using the established neuronal markers TUJ1 and TBR1. Therefore, additional markers for astrocytes and glia are not included in this figure, as we did not expect significant glial differentiation under these conditions. A sentence has been added in the corresponding Results section to address this issue.

(2) For the RNA-seq experiments outlined in Figures 3J and K, a more comprehensive analysis is needed of the genes disrupted in the parental Q6 line relative to the het and homo lines. What percent are rescued, unaffected, vs uniquely disrupted?

Reduction in EMC10 levels is not expected to directly affect transcription or broadly reorganize the gene expression profile of the Q6/Q5 NGN2-iN lines. Our transcriptional profiling was not designed to assess the direct impact of EMC10 deficiency on gene expression but rather to measure the cellular pathways affected by reduced EMC10 in the patient Q6 line. We identified genes differentially expressed between the Q6 (patient) and Q5 (control) lines, whose expression differences were either abolished or significantly attenuated ("rescued") in the Q6/EMC10^HET or Q6/EMC10^HOM lines. In the Q6/EMC10^HET line, 237 DEGs (6%) were rescued, while in the Q6/EMC10^HOM line, 382 DEGs (11%) were rescued. Importantly, further analysis revealed 103 shared rescued DEGs in these lines, which was statistically significant (enrichment factor = 1.7; p < 0.0001, based on a hypergeometric test). We added a new figure panel (Figure 3L) to visualize the significant overlap of rescued DEGs from the Q6/EMC10^HET and Q6/EMC10^HOM lines. This overlap suggests these genes play a critical role in biological pathways impacted by EMC10 levels, particularly in nervous system development, as indicated by our functional annotation analysis. We also performed protein-protein interaction (PPI) network analysis to explore the functional relationships among these 103 shared DEGs (Figure S8). Future studies will further investigate these gene sets to gain deeper insights into the molecular mechanisms underlying 22q11.2DS and the role of EMC10.

(3) The authors claim that 50% EMC10 loss in adult mice is safe and should be toned down. EMC10 knockout mice have motor, anxiety, and social phenotypes. It would be unique amongst highly dosage-sensitive genes (MeCP2, CDKL5, TCF4, FMR1, etc.) for there to only be a neurodevelopmental component. In all those cases, and others, the effects of over and under-expression are reversible into adulthood. Establishing the range in adults is critical to establishing therapeutic utility. Absent a detailed examination of non-cognitive phenotypes, this claim cannot be made.

The reviewer raises an important point about the potential effects of EMC10 reduction in adult mice and the need to establish a safe therapeutic window by evaluating both cognitive and non-cognitive phenotypes. We agree that such a comprehensive evaluation is critical for assessing the safety and translational potential of Emc10-targeting therapies. While the International Mouse Genotyping Consortium reported motor and anxiety phenotypes in homozygous Emc10 knockout mice, these data are unpublished and based on a relatively small number of animals. Furthermore, in our previous work (Diamantopoulou et al., 2017), we demonstrated that complete Emc10 loss does not impair cognition or social behavior, as assessed by prepulse inhibition (PPI), working memory (WM), and social memory (SM) assays (see Figure 3A-D; Diamantopoulou et al., 2017). Additionally, heterozygous Emc10 mice, which exhibit a ~50% reduction in Emc10 expression similar to that achieved with our ASO treatment, showed no evidence of motor deficits or anxiety-like behavior. Specifically, Emc10^+/- mice displayed locomotor activity comparable to WT mice in the open field (OF) test (Figure S4A, Diamantopoulou et al., 2017). Moreover, genetic normalization of Emc10 expression in Df(16)A^+/- mice demonstrated no signs of anxiety-like behavior, as assessed by the OF test (Figure S4A) and elevated plus maze (EPM) (Figure S4B; Diamantopoulou et al., 2017). To further support these findings, we have added new data to the current manuscript (see Figure S10J) showing that TAM treatment-mediated restoration of Emc10 levels in the brain of adult Df(16)A^+/- mice did not affect the time that mutant mice spent in the center area of the OF (Fig. S10J), suggesting that Emc10 reduction does not influence anxiety-related behavior. These results suggest that a 50% reduction in EMC10 expression is unlikely to result in motor or anxiety-like phenotypes in adult mice. Finally, as noted in the manuscript, in addition to prior findings from animal models, a substantial number of relatively rare LoF variants or potentially damaging missense variants have been identified in the human EMC10 gene among likely healthy individuals in gnomAD, a database largely devoid of individuals known to be affected by severe neurodevelopmental disorders (NDDs).

Nevertheless, the Discussion has been revised to underscore the importance of establishing a more detailed safety profile, including non-cognitive phenotypes, to fully validate the therapeutic potential of Emc10-targeting approaches. It also highlights the need for future studies to expand on these evaluations, addressing this critical aspect and laying a stronger foundation for advancing these findings into clinical drug development

(4) Supplemental Figure 10: The protein validation of Emc10 knockout following tamoxifen injection needs to be validated in all brain regions, not just the PFC. This is particularly important as the rest of the paper focuses on HPC-mediated phenotypes.

First, we want to emphasize that we conducted both qRT-PCR and WB assays on the same animal cohort, specifically examining the left and right hippocampus following ASO injection (see Figure S11C and D). This approach is crucial, given the central role of hippocampus in the phenotypes investigated in our ASO-mediated Emc10 knockdown experiments.

The reviewer raises an important point regarding the validation of EMC10 reduction at the protein level across all relevant brain regions using the Emc10 conditional knockout strain. We agree that such validation would ideally confirm the efficacy of our tamoxifen-induced knockout model comprehensively. However, we hope the reviewer appreciates that obtaining sufficient high-quality protein for WB analysis from smaller brain regions like the hippocampus poses a significant technical challenge. This difficulty is further compounded by the need to reserve the same samples for qRT-PCR to ensure consistency between mRNA and protein measurements. Importantly, our data from ASO-mediated Emc10 knockdown experiments (Figures S11C-D) demonstrate a clear and consistent correlation between reductions in Emc10 mRNA and protein levels in both the left and right hippocampus. Furthermore, in our constitutive Emc10-knockout mouse model (Diamantopoulou et al., 2017; see Figure S1A-B), we observed a strong agreement between mRNA and protein levels, supporting the reliability of mRNA data as a proxy for EMC10 protein levels in our experiments. Importantly, in all instances where we performed parallel protein and RNA measurements in human cell lines, there was excellent concordance between the datasets. Thus, while we acknowledge the limitations of relying primarily on mRNA data, we are confident that the Emc10 mRNA expression data in Figure S10 accurately reflect protein-level changes across brain regions in our conditional knockout model. To address this concern more fully in the future, we are working to refine antibody detection and optimize our protein extraction protocols to enable more routine and precise protein-level validation across smaller brain regions. We appreciate the reviewer’s feedback and will continue to refine our methodologies to strengthen the robustness of our findings.

(5) Figure 3: 1 way ANOVA would be more appropriate to analyze the data in B-G than t-tests.

We appreciate the suggestion of the reviewer. As mentioned above, we carefully selected statistical tests appropriate for each analysis. For Figure 3B-G, we chose to use pairwise t-tests to address specific hypotheses regarding the disease phenotype and rescue effects. This approach is consistent with prior experimental studies in the field, including our own (e.g., Xu et al., 2013; Figure 7H-I). Importantly, most of our t-tests yielded highly significant results (p < 0.001 or p < 0.01), reinforcing the robustness of our findings.

(6) Figure 5-6: Protein data is needed to complement the mRNA knockdown data.

We agree with the reviewer on the importance of protein-level validation to complement the mRNA knockdown data. As mentioned in our response to Reviewer’s Comment (4), in all instances where we performed parallel protein and RNA measurements, either in mouse brain or human cell lines, we observed excellent concordance between the datasets. This supports the reliability of our mRNA data as a proxy for protein changes. Nevertheless, we acknowledge the value of including protein validation in future experiments and will consider incorporating it to further strengthen our findings.

(7) The use of additional phenotypic measures is applauded in Figure 6, however, to appropriately interpret the data more is needed. Shao et al 2021 (Figure S9) show data from the International Mouse Genotyping Consortium claiming EMC10 KO mice have gait, activity, and anxiety phenotypes. All of these parameters could impact the SM assay and the y-maze assay. Changes in SM interaction time could be linked to anxiety or motor impairments, but interpreted as cognitive deficits because these symptoms were not assessed. At a minimum, discussion is needed about this limitation, as well as the inclusion of distance explored in the SM and Y-maze assays.

We thank the reviewer for their insightful comment regarding the potential influence of locomotor, gait, or anxiety phenotypes on the observed deficits in the SM and Y-maze assays. The behavioral phenotypes reported for Emc10 knockout mice by the International Mouse Genotyping Consortium (https://www.mousephenotype.org/data/genes/MGI:1916933) were limited to homozygous female mice and based on a small sample size (4–6 females) compared to a larger WT control group. Moreover, these data are unpublished and thus challenging to evaluate fully. Importantly, no abnormal behaviors were reported for Emc10 heterozygous knockout mice in these datasets. Additionally, the claim by Shao et al. (2021) regarding cognitive impairments in Emc10 knockout mice based on our previous work (Diamantopoulou et al., 2017) is inaccurate.

Our analysis of both the constitutive Emc10 knockout model (Diamantopoulou et al., 2017) and the current conditional Emc10 heterozygous knockout model consistently demonstrates that Emc10 reduction does not affect locomotor activity or anxiety-like behavior. In our earlier characterization of constitutive heterozygous Emc10 knockout mice (Emc10^+/-), we observed no signs of anxiety-like behavior or motor impairments in OF assays (see Figure 2A-B and Figure S4A, Diamantopoulou et al., 2017). Similarly, results from Df(16)A^+/- mice with genetically normalized Emc10 expression [Df(16)A^+/-; Emc10^+/-] also showed no indications of anxiety-like behavior or locomotor changes in the OF and EPM assays (see Figure S4A-B, Diamantopoulou et al., 2017). Consistent with these findings, our current data from Df(16)A^+/- mice with conditional Emc10 reduction in the brain show no significant differences in locomotor activity and anxiety-related measures as assessed by OF assays (Figure S10J). Furthermore, total arm entries in Y-maze assays conducted in Df(16)A^+/- mice treated with Emc10 ASOs were comparable to controls (Figures S14C and G-H), providing additional support for the conclusion that locomotor activity remains unaffected in these models.

We further appreciate the reviewer’s suggestion that changes in social interaction time during the SM assay could be influenced by anxiety or motor impairments. However, we consider this scenario unlikely in our model. Interaction times during the first trial of the SM assay, which measures general social interest, are comparable between Df(16)A^+/- mice with reduced Emc10 expression (either genetically or through ASO treatment) and WT controls (see Figures 4E, 5E, and S10G). These findings indicate that our mouse models do not exhibit inherent difficulties in initiating social interaction, as might be expected if motor impairments or heightened anxiety were present. Reduced social interaction is commonly used as a behavioral marker for anxiety in rodent studies (reviewed by Bailey and Crawley, Anxiety-Related Behaviors in Mice, 2009). “Anxious” mice typically exhibit decreased social interaction, spending less time engaging with other mice compared to non-anxious counterparts. However, the specific deficit we observe in the second trial of the SM assay—when mice are reintroduced to a familiar juvenile—is indicative of impaired social recognition memory, as previously documented for Df(16)A^+/- mice (Piskorowski et al., 2016; Donegan et al., 2020). This deficit is distinct from the general social avoidance typically associated with heightened anxiety.

Based on our comprehensive assessment of locomotor activity, anxiety-related behaviors, and social interaction, we conclude that the observed rescue of social memory and spatial memory deficits in mice with reduced Emc10 expression is most likely due to improved cognitive function rather than alterations in motor or anxiety-related domains.

(8) For ASO optimization experiments, it is not sufficient to claim robust uptake. A quantitative measure is needed using the PO antibody showing what percentage of cells were positive for the ASO. Since the contention is that only Emc10 in excitatory neurons is important, it would be helpful if this also included a breakdown of ASO uptake in excitatory and inhibitory neurons and astrocytes.

We thank the reviewer for highlighting the importance of quantifying ASO uptake and assessing cell-type specificity. To address this, we have added new data to the panel, as shown in the high-magnification images in Figure S14A. These images provide evidence that a large majority of NeuN-positive neurons exhibit a strong ASO signal. Specifically, we observed widespread ASO uptake (green) that extensively colocalized with the neuronal marker NeuN (red) in both the hippocampus and prefrontal cortex. Quantitative analysis of this overlap indicates that over 97% of NeuN-positive neurons were ASO-positive, demonstrating efficient neuronal uptake. This robust neuronal uptake aligns with the significant normalization of Emc10 levels and the behavioral improvements observed in ASO-treated Df(16)A^+/- mice, further supporting the functional efficacy of our approach in modulating Emc10 expression within the relevant neuronal populations. Overall, the observed ASO uptake in neurons, as demonstrated by IHC, combined with RNA assays and the behavioral improvements in treated mice, strongly supports the efficacy of our approach in targeting Emc10 expression in the intended neuronal populations.

(9) An interpretation is needed in Figure S3 as to why ~50% of the pathways increased are also present on the decreased list. Ie. G1/transition, viral reproductive process, pos regulator of cell stress, etc. 4/10 GO terms are present in both increased and decreased groups in A and 7/10 in B.

We thank the reviewer for pointing out the overlap between pathways enriched in both the upregulated and downregulated miRNA groups in Figure S3. This overlap likely reflects the complex nature of miRNA regulation, where individual miRNAs can target multiple genes within a pathway, and single genes can be regulated by multiple miRNAs, sometimes with opposing effects (reviewed in Bartel, 2009; Bartel, 2018). For example, in the “G1/S transition” pathway, upregulated miRNAs such as miR-92a-3p, miR-92b-3p, and miR-34a-5p may promote the transition by targeting cell cycle regulators like FBXW7, CDKN1C, and CDK6 (Zhou et al., 2015; Zhao et al., 2021; Oda et al., 2024). Conversely, downregulated miRNAs such as miR-143-3p and miR-200b are known to suppress the transition by targeting genes such as HK2 and GATA-4 (Zhou et al., 2015; Yao et al., 2013). Our analysis identified overlapping predicted target genes for both upregulated and downregulated miRNAs, supporting the notion that many genes are subject to complex regulation by multiple miRNAs with potentially synergistic or antagonistic effects. Thus, the enrichment of certain GO terms in both groups likely reflects this intricate interplay of miRNA-mediated gene regulation. Future investigations focusing on specific miRNA-target interactions within these pathways will be critical to fully elucidate the underlying mechanisms and better understand the functional consequences of these opposing regulatory effects.

Minor Concerns:

(1) Define SM before using it.

We have defined the SM assay in the main text upon its first mention, where we describe the assay and its relevance to cognitive function (see page 11 of the revised manuscript).

(2) Statistics have been run in Figure S2, but not presented. The text only states that the differences between groups are significant. Please add in.

We have revised the legend of Figure S2 to include the specific statistical test used (students t-tests) and the corresponding p-values.

(3) The switch from ASO1 to ASO2 between Figures 5 and 6 needs more discussion. Why were new ASOs generated when ASO1 worked?

We thank the reviewer for their question regarding the transition from Emc10^ASO1 to Emc10^ASO2 between Figure 4 and Figures 5-6. Emc10^ASO1 served as our initial proof-of-concept ASO construct, successfully demonstrating the feasibility of inhibiting Emc10 mRNA expression and providing evidence for behavioral rescue in our mouse model. As outlined in the manuscript, Emc10^ASO2 targets a different region of the Emc10 transcript (intron 1, Figure 5A) compared to Emc10^ASO1 (intron 2, Figure 4A). This distinction provides an additional layer of validation for our targeting strategy and ensures specificity in modulating Emc10 expression. In addition, Emc10^ASO1 exhibited limited distribution in the brain, primarily targeting the hippocampus with weaker inhibition of Emc10 in other regions such as the cortex (Figure 4C, right panel). Emc10^ASO2 overcame this limitation and achieve broader brain distribution, as demonstrated by the qRT-PCR data in Figure 5C. Given that 22q11.2DS can affect multiple brain regions and cognitive domains beyond the hippocampus, achieving broader distribution of the ASO is critical for a more comprehensive assessment of therapeutic potential.

(4) Page 3: Define "LoF"

We have defined Loss-of-Function (LoF) in the main text where it is first mentioned in the Introduction, where we discuss the potential of using LoF mutations to devise therapeutic interventions (see page 3 of the revised manuscript).

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Reply to the reviewers

Manuscript number: RC-2024-0284z

Corresponding author(s): Bérénice, Benayoun A

1. General Statements [optional]

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2. Point-by-point description of the revisions

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

This paper by McGill and colleagues explores sex differences in murine macrophages from different niches. They use a combination of publicly available, and newly developed datasets, and combine these using meta-analysis approaches. They explore DEGs between sexes - both common across niches, and specific to certain niches - and use enrichment analyses to identify pathways linked to these genes. Their overall conclusions are that gene expression changes in females are more consistent across niches, than for males, and are enriched in extracellular matrix-related genes. The paper is easy to follow and very well written.

Major Comments:

1. I would suggest Figure 1 be moved to a supplemental figure. We agree that the Xist and Ddx3y is QC and can be removed. However, we believe that the separation of macrophage transcriptomes based on sex in the Multidimensional Scaling plot is an important result. Thus, we have revised Figure 1 to only include the MDS plots and have moved the Xist/Ddx3y plots to the supplement (new Supplemental Figure S1) in line with the reviewer’s suggestion.

Line 106 - It should be clarified why 50 DEGs was selected as the cut off for exclusion.

We apologize that our cut off criteria was not explained clearly enough. Because these are publicly available datasets, every lab used different numbers of biological replicates, methods, and sequencing depths, impacting the power of the assay to detect differences in gene expression robustly. Since we were interested in functions that were sex-dimorphic, and that requires running functional enrichment analysis, we needed to have a minimum gene set size to be able to run these analyses, which, in the field, is usually accepted to be 50 genes for robustness. Thus, we used 50 DEGs and have updated the methods to explain our reasoning: “Applying a cutoff for the number of differentially expressed genes (DEGs) helps ensure data consistency and comparability across datasets with varying methodologies and sequencing depths. This prevents datasets with excessively low DEG counts from disproportionately influencing downstream analyses. A cutoff also reduces noise from spurious findings, prioritizing datasets with robust transcriptional changes that are more likely to be biologically meaningful. The excluded microglia dataset contained only 11 DEGs (whereas all other microglia datasets had hundreds of DEGs), the pleural macrophage dataset had 37 (whereas all other lung-related macrophage datasets had above 50), and the spleen macrophage dataset had only 30.” (page 12, lines 381-388).

Optional - would suggest sex chromosome-linked genes are excluded and the analysis redone to see if there are other autosomal genes that are statistically shadowed by the X and Y linked genes.

We thank the reviewer for this great suggestion, and we now added this point to the discussion (page 9, lines 260-268). However, we think that genes on the X and Y chromosomes will impact overall function of the macrophages and that they are necessary to understand how macrophages from males and females may support differences in immune function throughout life. We now add this in the discussion as a potential future direction: “We find that a majority of genes similarly differential across sexes among the macrophage niches are sex chromosome linked. X-linked genes like Tlr7, Cxcr3, and Kdm6a enhance immune responses in female macrophages, potentially increasing inflammation with age (Feng et al., 2024). Meanwhile, Y-linked genes such as Uty and Sry influence transcriptional regulation and inflammatory signaling in male macrophages, which may contribute to chronic low-grade inflammation (Lusis, 2019). These genetic differences affect macrophage activity, tissue-specific immune responses, and susceptibility to age-related diseases, highlighting the importance of sex-specific factors in immune research. Future research should also explore how non-sex chromosome-linked genes interact with these sex-specific mechanisms to further shape macrophage and immune function.” (page 9, lines 260-268).

More metadata about the included studies should be included eg mouse ages, strains, experimental manipulations etc. I can't seem to access all of the Supplemental tables so this may already be included in Table S1.

We agree that this information is important to take into consideration and have now included this information in Supplemental Table S1A, along with the accession numbers to each dataset. All mice were aged between 2 to 24 weeks and all on variations of the C57BL/6 background.

How relevant the findings in mice are for humans should be explained further in the discussion.

We agree that our discussion needs to better explain broader implications. Our findings are relevant for human health because macrophages play key roles in immunity, inflammation, and tissue homeostasis, and their functions are known to differ between sexes. Understanding these sex-specific transcriptional differences in mice can provide insights into how male and female immune systems respond differently to infections, autoimmune diseases, and aging in humans. Since macrophage phenotypes are influenced by both systemic factors (e.g., hormones) and tissue-specific environments, studying multiple macrophage subtypes from different organs helps identify conserved and context-dependent sex differences. Indeed, our findings suggest the ECM may be a potential mechanism underlying sex-biased diseases, such as higher autoimmune prevalence in females or increased susceptibility to certain infections in males. We have added this detail to the discussion (page 10, lines 269-275).

Minor Comments:

1. Lines 63-66 - need references here. This mirrors Reviewer 2’s major point #2. We agree with the reviewer that references are needed and now cite PMID: 31541153, PMID: 29533975, PMID: 37863894, PMID: 33415105, and PMID: 37491279 (page 4 line 68-69).

Line 61 and 69 - repeated.

We thank the reviewer for catching this oversight and have deleted the first instance of the sentence.

Reviewer #1 (Significance (Required)):

Although this study is primarily descriptive, it adds to the current knowledge about sex differences in macrophages, an important and relatively understudied area. Those interested in sex differences and in the innate immune system generally, plus those who study macrophages in any context, should be interested in this work.

We thank the reviewer for their interest in our work and their helpful suggestions.

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

Summary: The study investigates sex-specific differences in macrophage gene expression across various tissue niches by analyzing both newly generated and publicly available datasets of varying quality. The key finding is the identification of three consistently differentially expressed genes (DEGs) across all macrophage niches: the Y-chromosome-encoded genes Ddx3y and Eif2s3y, and the X-chromosome-specific gene Xist. However, the number of sex-dimorphic DEGs varied significantly between macrophage niches, with female-biased genes showing more consistency across datasets. To further explore these sex-specific differences, the authors performed an overrepresentation analysis of the DEGs across datasets. They found enriched gene sets associated with specific biological terms in female-biased macrophages from peritoneal macrophages, bone marrow-derived macrophages (BMDMs), and osteoclast progenitors (OCPs), while male-biased enrichment was observed in microglia, exudate macrophages, OCPs, and BMDMs. Notably, extracellular matrix (ECM)-related genes were specifically enriched in female peritoneal macrophages and OCPs, whereas the term "nucleic acid binding" was more prominent in male samples from microglia, BMDMs, and OCPs, driven by the Y-chromosome genes Uty and Kdm5d. A gene set enrichment analysis (GSEA) using Gene Ontology (GO) and Reactome databases further confirmed the enrichment of sex-biased pathways. Based on these findings, the authors conclude that three sex chromosome-associated genes are consistently differentially expressed across all datasets and that female-associated gene expression appears to be more stable, particularly in relation to ECM-associated processes.

Major Comments:

Are the key conclusions convincing?

1. The study provides valuable insights into sex-dimorphic gene expression in macrophages across different niches. However, some conclusions appear overinterpreted due to the limited number of differentially expressed genes (DEGs) driving specific terms in the overrepresentation analysis. The reliance on only a few recurring genes (e.g., Kdm5d, Eif2s3y, Uty, and Ddx3y) raises concerns about the biological significance of some enriched terms. A clearer discussion on the limitations of such findings is necessary. We apologize for the confusion. Although the Venn Diagram may give the impression that our comparisons are limited to those few genes, we only highlight them with bold text because they are a good quality control mechanism for our analyses.

Importantly, methods like gene set enrichment analysis [GSEA] use whole-transcriptome ranking, which means the results we obtain are driven by the entire transcriptome and not just a few genes (GSEA results are reported in Figure 5). We agree that further explanation of these methodologies would improve interpretation of our findings for readers unfamiliar with these analytical techniques. To address this, we have now added the following to the methods: “GSEA relies on whole-transcriptome ranking, ensuring that the results reflect global transcriptomic patterns rather than being influenced by only a few genes.” (page 13, lines 415-417).

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?Some claims, particularly those regarding the role of macrophages in diseases such as AD, histiocytosis, and osteoporosis, lack relevant references.

This mirrors minor point #1 from Reviewer #1. We apologize for not originally including references for this statement and have now updated the introduction and discussion with appropriate references: “Excessive macrophage activation is associated with numerous conditions, including neurodegeneration, atherosclerosis, osteoporosis, and cancer, many of which exhibit sex-biased tendencies (Chen et al., 2020; Hou et al., 2023; Li et al., 2023; Mammana et al., 2018)” (page 4, lines 67-69) and “Thus, investigating female and male-biased processes in macrophages, including the contribution of the ECM, will be an important step in developing treatments for diseases including, but not limited to, AD, histiocytosis, and osteoporosis(Chen et al., 2020; Cox et al., 2021; Hou et al., 2023; Li et al., 2023; Mammana et al., 2018)” (page 10, lines 285-288).

Would additional experiments be essential to support the claims of the paper? While additional wet-lab experiments are not strictly necessary, a deconvolution analysis of the datasets could be highly beneficial. This would allow the identification of enriched macrophage subtypes and help assess whether differences between datasets are driven by specific macrophage populations rather than global sex differences. Since peritoneal macrophage origin is influenced by age and inflammation status, deconvolution could also clarify dataset comparability.

The reviewer makes an interesting point. We apologize for the confusion regarding the purity and origin of these datasets. All the datasets we curated from public repositories for our analysis are from purified populations of macrophages. To clarify this, we now include a column with the purification method used for each of the datasets based on the original manuscript in revised Supplemental Table S1A.

Since all the used datasets were derived from pure macrophage populations, deconvolution (which is used to identify cellular proportions in heterogeneous contexts) would not accomplish much, predicting that all the cells in the data are macrophages. While some people have argued that deconvolution may be used to identify different cell states, this is very controversial, especially since the “pure” reference and the heterogeneous query are subject to batch effects (i.e. either from differences in bench processing, sex of provenance for target/query datasets, transcriptional impact of sorting methods, differences in transcriptomic quantification methods, etc.) which overshadow most differences beyond cell types. Thus, due to the known batch sensitivity of deconvolution methods and the fact that we only selected pure macrophage transcriptomic profiling datasets, using deconvolution to identify macrophage subtypes would not be informative/feasible. Importantly, we focused our analyses on datasets derived only from young, healthy, naïve animals (2 to 24 weeks), without any interference from age-related inflammation.

To make this caveat clearer, we have added sentences to the results section indicating the age range of the animals (page 6, lines 100-101), as well as in the discussion to discuss how inflammation states and age may change some of our findings (page 10, lines 295-299).

Are the suggested experiments realistic in terms of time and resources? Performing cell-type deconvolution using established computational tools (e.g., CIBERSORT, BisqueRNA, or single-cell deconvolution methods) would be a realistic approach within a few weeks and would significantly strengthen the study. This analysis would not require additional experimental work but could refine the interpretation of the dataset. Additionally, a PCA of all datasets could help identify potential similarities among macrophages from different niches and between sexes.

As explained in our response to point #4, the use of only datasets from purified macrophages from young animals (before any influence of age or disease) makes deconvolution analysis meaningless, especially due to batching concerns. Specifically, it would require us to generate paired single-cell and bulk datasets on all macrophage subtypes in house to remove batch-inducing experimental biases, which we believe is outside of the scope of this small bioinformatics study.

To the second point, doing a PCA of all the datasets together would not provide much new information beyond cell type of origin due to batching concerns that could not be corrected, which are a known problem in transcriptomics analyses (PMID:20838408, PMID:28351613). Since datasets come from different labs, using different isolation methods, RNA capture choices, library construction kits and sequencing platforms, the main separating effects overall will be batch/dataset, not biology (PMID:20838408, PMID:28351613). Indeed, this is what we observe (Reviewer Figure 1), with broad separation of datasets by tissue of origin, then dataset of origin. Additionally, the top 10 loadings for PC1 and PC2 are primarily associated to autosomal genes (i.e. not on the sex chromosomes; Reviewer Table 1).

Reviewer Figure 1. (A) PCA of all samples across datasets. Read counts were processed together through R package sva v.3.46.0 for surrogate variable estimation, and surrogate variables were removed using the removeBatchEffect function from ‘limma’ v.3.54.2. DESeq2 normalized counts were used to make the PCA. (B) Zoomed in PCA excluding three outlier sample to enable easier visual discrimination of samples.

Principal Component – Gene

Loading

Chromosome

PC1- Srcin1

0.013601

11

PC1- Cacna1c

0.013593

6

PC1- Pclo

0.01357

5

PC1- Tro

0.013547

X

PC1- Ppp4r4

0.013541

12

PC1- Ppp1r1a

0.01354

15

PC1- Homer2

0.013538

7

PC1- Caskin1

0.013535

17

PC1- Arhgef9

0.013527

X

PC1- Slc4a3

0.013499

1

PC2- Gm15446

0.017978

5

PC2- 1810034E14Rik

0.017897

13

PC2- Gm19557

0.017871

19

PC2- Pkd1l2

0.017792

8

PC2- H60b

0.017274

10

PC2- Appbp2os

0.01723

11

PC2- Mir7050

0.017221

7

PC2- Nkapl

0.017166

13

PC2- Tmem51os1

0.017083

4

PC2- Dpep3

0.016962

8

Reviewer Table 1. Top 10 loadings for principal component 1 and principal component 2 with their respective chromosomal location.

Thus, since batch effects can only be accounted for rigorously when they are not confounded by biology (and in our case since each dataset only looks at one type of macrophage), this cannot be corrected in a rigorous manner to yield the desired results.

We have added a sentence to the discussion to highlight how future work where macrophages from diverse niches would be profiled in parallel may give greater insights into niche-specific sex-dimorphic effects (page 10, line 295-296).

Are the data and the methods presented in such a way that they can be reproduced? Some methodological details are missing, particularly regarding:

The isolation of mouse peritoneal macrophages (details on injection and harvesting procedure needed). Quality control of isolated macrophages (How were contaminating cells excluded? Was additional validation performed beyond using the kit?)

The age of mice used for bone marrow-derived macrophages (BMDMs) is not provided, which is important given that immune responses can be age-dependent.

We appreciate the reviewer’s request for additional methodological details. We apologize for not being clear with our details and have updated the methods to be clearer (page 11, lines 320-346), as well as added this information in revised Supplemental Table S1A (e.g. age of animals and purification method as described in the original papers). For all our in house datasets, mice were 4-months old, and the text is now updated to reflect this: “Long bones (tibia and femur) of young (4-months-old) from both sexes were collected and bone marrow was flushed into 1.5mL Eppendorf tubes via centrifugation (30 seconds, 10,000g) (Amend et al., 2016)” (page 11, lines 334-336).

While we couldn’t check the purity post hoc for published datasets we identified for meta-analysis, we performed a purity check on our isolated peritoneal macrophages using Cd11b-F4/80 staining by flow cytometry and have now included this data (including gating strategy) in Supplemental Figure S4. For BMDMs, no purity check was performed, as there is extensive literature on the efficiency of this differentiation protocol which consistently yields > 90% of macrophages. This has been added to the methods: “We used a protocol that is expected to yield ~90% Cd11b+ F4/80+ cells (Mendoza et al., 2022; Toda et al., 2021)” (page 11, lines 336-337).

Are the experiments adequately replicated and statistical analysis adequate? The statistical analysis appears generally appropriate, but there are concerns about dataset inconsistencies that should be addressed. Some datasets were not used across all analyses, which is not clearly indicated in figures or text. This should be explicitly mentioned to avoid misleading interpretations.

We appreciate the reviewer’s careful evaluation of our statistical analysis and the concern regarding dataset inconsistencies.

We believe that the reviewer is referring to the omission of the exudate dataset from the Venn Diagram analysis (Figure 2C), as this is the only time that we did not report the results from all datasets. We originally chose not to include the exudate dataset in the shared differentially expressed gene (DEG) analysis, because it contained over 1,300 DEGs, whereas all other datasets had between 4–30 DEGs, resulting in an unreadable figure.

However, we agree that it is important to include for the readers, and while we have decided to still exclude the exudate dataset from Figure 1C for readability purposes, we now include the overlap analyses for all datasets in Supplemental Figure S2 using an upset plot (an alternative visualization method) showing all 6 niches, as well as a table panel that lists the shared genes across niches “Three genes were found to be differentially expressed across all six niches: Xist, Ddx3y, and Eif2s3y (Figure 2C, Supplemental Figure 2A,B)” (page 6, lines 124-126). We thank the reviewer for drawing our attention to this and making our analysis clearer for future readers.

Minor Comments

1. Figures are included twice in the manuscript. We apologize for this, and figures are now only included once.

The use of stereotypic colors in figures (e.g., blue for male, pink for female) could be reconsidered for better readability and to avoid reinforcing gender stereotypes.

While we understand that this color choice might feel gender normative, we respectfully disagree with the reviewer, as we believe that for the expediency of scientific communication it is important to choose a color palette that is easily understandable without confusion without even needing to consult a legend.

Importantly, we have been using the same color palette in all publications from the lab on sex-differences for consistency (Lu et al, Nat aging 2021 PMID: 34514433; McGill et al, PLoS ONE, 2023 PMID: 38032907; Kang et al, J Neuroinflammation, 2024 PMID: 38840206; McGill et al, STAR Protocols, 2021 PMID: 34820637), which is crucial for scientific rigor and communication consistency.

Results - Section 1

Line 92: The word 'identified' may not be the most appropriate choice here, as it implies discovery rather than selection. Consider rephrasing to 'compiled' or 'gathered' to more accurately reflect the process of assembling the datasets. Additionally, the sentence structure could be refined for clarity, such as specifying that the datasets include both newly generated and publicly available data.

We have changed two instances of using the word identified to “collected” and “gathered” (page 4, line 83 and page 6, line 98). We also adjusted the sentence to say, “Although we initially collected 21 datasets, both newly generated and publicly available, for our study, only 18 datasets were retained after various quality filtering steps for downstream analysis” (page 4, lines 83-85).

Line 95: Specify the source of exudate-derived macrophage data.

We have updated Supplemental Table S1A to make sure it was comprehensively describing the datasets we used in our analysis and double checked that it was complete (including for the exudate data). We have updated the text to reflect this: “All accession numbers and corresponding manuscripts are found in Supplemental Table S1A” (page 6, lines 103-104).

Figure 1/2A: The scheme overview lacks clarity-its purpose is unclear. The two identical boxes are redundant and do not provide additional insight. Consider illustrating the origins of different macrophage subtypes instead. The cutoff of >50 DEGs should be included in the schematic to improve clarity. Overrepresentation and GSEA analysis should not be illustrated multiple times across different figures-it is redundant.

In Figure 1A, we included the identical boxes to indicate that no datasets were excluded for incorrect labeling of males/females. However, we agree that this is unnecessary and have removed the second box as suggested.

In Figure 2A, we agree the identical boxes are unneeded as the Xist/Ddx3y quality control step was listed in Figure 1A, and we have modified the figure accordingly.

We also agree that including the DEG cutoff and removing the GSEA mention will streamline the figures and have updated them accordingly as well.

Line 100: The mention of R software should be moved to the Methods section instead of appearing in the Results section.

We have now updated the text to say, “Expression levels of male-specific Ddx3y and female-specific Xist genes across all samples were examined to ensure proper sex labeling of samples (Supplemental Figure 1A-U)” (page 6, lines 111-112).

Figure 1B-V: The current figure layout is visually cluttered. Consider plotting male and female datasets together in a single graph with different point shapes instead of separate panels for each specific niche.

This seems to echo the above request for a global PCA in Reviewer 2’s Major Point #4, which unfortunately cannot be included due to the disproportionate impact of batch effects that has been well documented in the literature (Reviewer Figure 1; PMID:20838408, PMID:28351613). However, to make the figure clearer and less cluttered, and to address related Reviewer 1’s Major Point #1, we have moved the Xist/Ddx3y plots to Supplemental Figure S1 and only include the Multidimensional Scaling plots in Figure 1 to showcase the sex separation in each dataset.

Text-Figure alignment: The text describes male/female-specific gene expression levels first, while the figure starts with MDS analysis. The order should be consistent.

We agree and have adjusted the text accordingly (lines 109-112).

Figure 2C: Exudate data is missing-explain why.

This point echoes major point #6. As explained above, we have clarified this and included new data panels for clarity (New Supplemental Figure S2).

Results - Section 2

Line 151: Use consistent terminology-either "DEGs" or "DE genes", not both.

We replaced all instances of “DE genes” with DEGs (lines 132, 137, 141, 147, 149, 163, and 397).

Figure 3A: The text suggests not all datasets were included in this analysis-this should be explicitly indicated in the figure.

We apologize for the confusion. All datasets were included in this analysis; however, some niches did not have any GO terms passing the FDR

Show the number of DEGs used for analysis.

We apologize for the confusion. For the ORA analyses (Figures 3 and 4), we indicate the number of DEGs used for analysis in the panel header. For the GSEA analysis (Figure 5, Supplemental Figure S3), all expressed genes are ranked based on effect size without any prior filter (see response to major point #1), so DEGs are irrelevant for these analyses.

Figure 3B: Smaller pale dots in the bubble plot are difficult to distinguish-consider using a darker outline.

We have now added outlines to all the bubbles in the plots to help improve visibility.

Line 158: The term "phagocytosis" appears inconsistent with the figure, where it is labeled "phagocytosis, recognition".

We have updated the text accordingly (page 7, line 170).

Figure 4B, D, E: The overrepresentation analysis is based on very few genes (often only 1-2 genes per term), which may lead to overinterpretation.

We apologize for the lack of clarity of our previous manuscript. The number of genes used for DEG analysis is in the panel titles of Figure 3 and 4. While the overlap is small, this is unlikely to be spurious since all of the pathways we discuss show significant enrichment with FDR

Consider explicitly naming these genes and discussing their biological role instead of assigning terms based on minimal evidence.

We now discuss these genes in the results: “Male-biased GO terms for microglia, OCPs, and BMDMs derived from four genes: Kdm5d, Uty, Ddx3y, and Eif2s3y. All of these are Y-linked genes and play crucial roles in regulating innate and adaptive immune responses (Meester et al., 2020). Kdm5d and Uty influence adaptive immunity through chromatin remodeling and histone modification, while Ddx3y and Eif2s3y shape innate immune responses by modulating macrophage activation and cytokine production via translation initiation and RNA processing (Bloomer et al., 2013; Hamlin et al., 2024; Meester et al., 2020) “(page 8, lines 195-200).

Figures S3G and S3H seem to be switched.

We are puzzled by this comment, as our original manuscript did not include a Supplemental Figure S3. Out of an abundance of caution, however, we checked that Supplemental Table S3G and H were correctly labelled, and independently confirmed that they are not switched.

Results - Section 3

Figure 5A does not add significant new insights. Consider refining its content to highlight key findings more effectively.

We respectfully disagree and believe that schematic overviews help readers understand what is accomplished in any specific figure and have thus decided to keep it.

Number of genes included in the analysis is not provided-this is important to assess significance and should be stated in methods and figure legends.

We apologize for the lack of clarity. As explained above, GSEA uses all the genes in rank order (PMID: 16199517), we now explain GSEA more explicitly in the text “GSEA relies on whole-transcriptome ranking, ensuring that the results reflect global transcriptomic patterns rather than being influenced by only a few genes” (page 13, lines 415-417).

Discussion 20. Line 201-203: Missing reference.

We have now updated the text with the proper reference: “Tissue-resident macrophages are crucial to proper immune system function (Guilliams et al., 2020). While all macrophages share the responsibility of clearing cellular debris and foreign bodies, tissue-resident macrophages also have unique responsibilities that facilitate homeostasis throughout the body (Guilliams et al., 2020; Varol et al., 2015)” (page 9, lines 227-230).

Reference 23 (1999) is outdated. Newer literature should be cited to reflect modern insights into sex differences in macrophages.

We have now updated the text with an updated reference for two outdated references: (i) “Sex differences have previously been reported in macrophages, with female macrophages having higher phagocytic activity than males (Scotland et al., 2011)” (page 9, lines 232-233) and (ii) “Dysfunctional OCPs are associated with development of osteoporosis, a disease that is four times more prevalent in women (Alswat, 2017)” (page 10, lines 284-285).

Peritoneal macrophages and OCPs originate from monocytes. Would deconvolution help identify enriched subtypes and assess dataset comparability?

As noted in Reviewer 2’s Major Points #3 and #4, deconvolution analysis is not meaningful for subtype analysis without paired isolated/bulk datasets, which are outside of the scope of this study to generate.

The 'more consistent' pathways found for female datasets are not discussed.

We now discuss pathways found among the female datasets: “In addition, GSEA analysis of REACTOME gene sets showed male-biased expression for cell cycle related pathways (average set size 499), and female-biased expression for G protein-coupled receptor (GPCR) signaling (average set size 122) and extracellular matrix organization (average set size 127) (Figure 5C, Supplemental Table S4S-AJ; consistent with our ECM observation, Supplemental Figure S3A). Macrophages express a wide variety of GPCRs that allow them to respond to different stimuli. The expression of specific GPCRs influences macrophage polarization toward either a pro-inflammatory or anti-inflammatory state (Wang et al., 2019). A manual review of the genes contributing to this GPCR enrichment reveals the presence of several chemokine-related genes (such as Ccl4, Ccr4, Cxcl1, and others) (Supplemental Table S4). This suggests that females may have an increased abundance of chemokine GPCRs, potentially contributing to heightened autoimmune activity, among other factors.” (page 8, lines 212-222).

Methods - Peritoneal macrophage isolation:

Details on injection and harvesting are missing.

We apologize for not being clear with our details and have modified the methods to be clearer (page 11, lines 320-331).

How was contamination from other cell types assessed? F4/80 selection may not be fully macrophage-specific, and contamination could occur due to insufficient washing or the presence of non-macrophage F4/80+ cells.

For the peritoneal macrophage datasets we generated, the macrophages were checked for purity through flow cytometry using Cd11b and F4/80 antibodies. We considered double positive Cd11b+ F4/80+ cells to be macrophages, which represents >95% of cells using our methodology (Supplemental Figure S4), without a difference between sexes.

For the BMDMs, we utilize a protocol that is expected to yield ~90% Cd11b+ F4/80+ cells (PMID: 35212988 and PMID: 33458708).

Finally, we now include the purification method for all publicly available datasets according to their original manuscript in Supplemental Table S1A and explicitly discuss the information for our in-house datasets in the methods (page 11, lines 321-346).

• Bone marrow macrophages:

Mouse age is not provided in the results part.

We now provide this information in the methods (page 11, line 334). All ages for all datasets are now included in Supplemental Table S1A.

Figure Legends

Figure 2: Peritoneal macrophages are abbreviated as PeriMac-consider using this abbreviation consistently in the text.

We respectfully disagree with the reviewer and choose to keep Peritoneal Macrophages spelled out in the text for clarity. We use the shorthand “PeriMac” in Figure 2 and Figure 5 solely for spacing purposes, but these are explained in the figure legend.

Reviewer #2 (Significance (Required)):

The study's strengths include the integration of multiple datasets, the use of both overrepresentation and GSEA, and the exploration of tissue-specific macrophage niches. These findings have relevance for diverse communities, including immunologists, sex-difference researchers, and those studying macrophage-driven diseases such as osteoporosis, neurodegeneration, and chronic inflammation. The work provides a foundation for further studies on sex-specific macrophage biology and may have implications for sex-specific therapeutic strategies. However, the study has limitations. The conclusions regarding enriched pathways rely heavily on a small number of DEGs, raising concerns about overinterpretation. Additionally, dataset variability and missing data for some analyses (e.g., exudate macrophages) could affect the robustness of the results.

Despite these limitations, the study makes a meaningful but incremental advance by highlighting stable sex-dimorphic patterns in macrophage biology. It provides insights for both fundamental and translational research, particularly for audiences focused on immune regulation, sex-specific gene expression, and tissue-specific macrophage function.

We thank the reviewer for understanding the importance of our work.

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

Summary: McGill et al. explore sex-based differences in macrophage gene expression across various tissues. Using a meta-analysis of publicly available and newly generated datasets, they identify conserved and divergent sex-dimorphic genes and pathways between tissues. Overall, the report is easy to follow and guides the reader through the analysis. The authors highlight the relevance of the report by noting sex differences in immune responses to infection, autoimmunity, and chronic diseases. The inclusion of 17 independent transcriptomic datasets provides a robust and extensive analysis of sex-based transcriptional differences. The authors explore potential biological implications of sex-based transcriptional differences using pathway analysis. Despite the overall strengths, there are some points for which further clarification and analysis would improve the manuscript. Detailed comments are listed below.

Major comments:

1. A comparison of the overall transcriptomic profiles of macrophages regardless of sex would be additive. Knowing the degree of similarities and differences among macrophages from different niches would help the reader determine what genetic programs vary by compartment. If macrophages are very different by niche, it is not surprising that they share few sex-dimorphic patterns. This mirrors Reviewer 2’s Major Point #4. While this approach may seem valuable, it would only be feasible if all datasets were generated simultaneously by the same lab using identical sequencing and library preparation protocols to avoid batch effects. In this case, biology and batch effects are confounded, making any global analysis misleading. Although the reviewer may find the limited overlap unsurprising, given that macrophages are generally considered to be the same cell type, our goal was to explore the extent of shared versus distinct features across datasets, which we believe to be an invaluable question for the field.

Although it would not be possible to do this rigorously with the data we curated, the question of niche specific gene regulation of macrophages has been studied, showing extensive niche-specific regulation: “While the question of niche-specific gene regulation has been studied, showing extensive niche-specific regulation (Gosselin et al., 2014; Lavin et al., 2014), a comprehensive and systematic study of sex-differences across macrophage subtypes has not yet been performed” (page 4, lines 78-81).

It is unclear what age and strain the mice were and the number of samples that were included (n) for each dataset. This information should be included in S1A. If different ages or strains were used, how might this impact findings?

This mirrors Reviewer 1’s Major Point #4. We agree that this information is important to take into consideration and have now included this information in Supplemental Table 1A, along with the accession numbers to each dataset. Because there is no aging effect (all mice are aged between 2 to 24 weeks) and all mice are on a variation of the C57BL/6 background, we don’t expect this to be a major problem impacting our findings.

The authors used a Jaccard index to examine similarities in sex-based differences across tissue compartments. They claim that there are more similarities in females. However, the male are female graphs (Fig. 1E,D) do not look that different. Is there a better way to display this?

We apologize for the lack of clarity. We clustered the Jaccard matrices using hierarchical clustering to determine patterns of sharing. Thus, in these figures, the samples cluster based on the degree of similarity in sex-biased genes. In the females, there is clear separation by macrophage origin (yolk sac or circulating monocytes); whereas males have some separation but also have some mixing (e.g. Peritoneal Macrophage 2 clustering with the yolk-sac derived macrophage datasets). Additionally, four microglia datasets are together in the females with only one separate, whereas in the males they are split into three. We included colored bars by the dataset names to help highlight clear separation by niche of origin.

We have added this detail to the text to better explain the similarities: “Our results indicate that female-biased genes were more consistent among the cell types compared to male-biased genes (Figures 2D,E). In females, there is clear separation by macrophage origin (yolk sac or circulating monocytes), with all the peritoneal macrophages clustering together, followed by bone-related macrophages, then microglia and lung macrophages. In the males, the five microglia datasets are split into three groups, and Peritoneal Macrophage 2 clusters with the yolk-sac derived macrophage datasets” (page 7, lines 155-160).

In the Gene Ontology analysis, it is unclear what type of GO pathways were included (biological process, cellular component, molecular function). Also, some of the GO analyses were done with very few genes (as little as 4).

This echoes Reviewer #2’s Major Comment #1. For the Overrepresentation analysis (ORA) using Gene Ontology, we use the “ALL” option to include biological process, cellular component, and molecular function terms. We used ORA to look at shared DEGs across datasets of the same niche which is why some have very low input. For this reason, we also performed Gene Set Enrichment Analysis that uses all genes, not just those differentially expressed at FDR 5%, to examine gene changes at a broader level. In the methods we have added this information: “The differentially expressed genes shared within each niche were divided into up and down-regulated based on the sign of the DEseq2 log2 fold change. These gene lists were used as the shared genes and all expressed genes across datasets in that specific niche were used as the universe for the clusterProfiler function ‘enrichGO’, using the “ALL” option to include biological process, cellular component, and molecular function terms” (page 13, lines 405-410) and “GSEA relies on whole-transcriptome ranking, ensuring that the results reflect global transcriptomic patterns rather than being influenced by only a few genes.” (page 13, lines 415-417)”.

Is it possible to combine datasets by tissue to remove potential batch effects before downstream analyses? At the very least, PCA on combined data may help determine if some biological (e.g., age, strain) or technical (batch) differences are contributing to identifying few common sex differences.

This mirrors Reviewer #2’s Major Point #4. Unfortunately, since every dataset only examined a single niche, biology and batches are confounded, and thus performing a PCA on all datasets together will be driven by technical rather than biological drivers. Batch effects are a well-documented issue in genomics (PMID:20838408, PMID:28351613) Indeed, this is largely observed when we attempt this analysis, with datasets clustering by batch (Reviewer Figure 1). Due to the issue of uncorrectable batch effects, we do not believe this analysis meets the rigor required to be included in the revised manuscript and have chosen to not include it.

Validation of key results would further strengthen the manuscript.

We agree that future validation is important but is beyond the scope of this purely bioinformatic analysis. We have included text in the revision to highlight the importance of future validation studies: “Thus, investigating female- and male-biased processes in macrophages, including the contribution of the ECM, will be an important step in developing treatments for diseases including, but not limited to, AD, histiocytosis, and osteoporosis, and future research will be essential to validate these findings and further refine therapeutic strategies (Chen et al., 2020; Cox et al., 2021; Hou et al., 2023; Li et al., 2023; Mammana et al., 2018)” (page 10, lines 285-289).

Further contextualization of key results would enhance the discussion. For example, ECM-related differences in female macrophages could have broader roles in wound healing, fibrosis, and migration.

We agree with the reviewers and have added this detail to the discussion: “ECM components are emerging as key regulators of innate immune responses (García-García & Martin, 2019). Macrophages contribute to ECM remodeling by producing and degrading collagens (Sutherland et al., 2023), and ECM-related differences in female macrophages may impact wound healing, fibrosis, and migration. In lung and kidney tissues, macrophages recruit and activate fibroblasts, influencing fibrosis through direct interactions and ECM-degrading enzymes (Nikolic-Paterson et al., 2014). The balance between ECM deposition and degradation is crucial for tissue homeostasis, as excessive fibrosis leads to pathology (Nikolic-Paterson et al., 2014; Ran et al., 2025). Mechanical properties of the ECM, such as stiffness and collagen crosslinking, enhance macrophage adhesion, migration, and inflammatory activation (Hsieh et al., 2019). These ECM cues direct macrophage behavior during injury response, influencing their ability to reach inflammation sites and promote repair. Thus, female-biased expression of ECM-related genes may contribute to phenotypes such as enhanced wound healing or even fibrosis(Balakrishnan et al., 2021; Harness-Brumley et al., 2014; Rønø et al., 2013) “ (page 9, lines 248-259).

Minor comments:

1. Line 51: In the introduction, the authors state that macrophages produce chemokines. There are other signaling molecules produced by macrophages (e.g., cytokines) that also contribute to immune responses. We apologize for this and have updated the text to say: “Macrophages are a key component of the mammalian immune system and are responsible for producing a diverse array of signaling molecules including (but not limited to) cytokines, chemokines, and interferons that activate the rest of the immune system to combat infection (Shapouri-Moghaddam et al., 2018)” (page 4, lines 49-52).

Line 53: The authors state that after birth the primary source of new macrophages come from differentiation of monocytes. However, some tissue resident macrophages are self-renewing.

We apologize for this oversight and have adjusted the text to say: “After birth, the primary source of new macrophages comes from the differentiation of monocytes, which can be recruited to tissues throughout life. However, some tissue resident macrophages can self-renew, including those from the pleural and peritoneal cavities (Röszer, 2018)” (page 4, lines 53-56).

Line 123: "spermatogenial" should be "spermatogonial"

We have updated the text accordingly (page 6, line 130).

Reviewer #3 (Significance (Required)):

Significance: • General assessment: The study provides a novel and comprehensive analysis of sex-dimorphic gene expression in macrophages, with key findings that emphasize the importance of ECM remodeling in female macrophages. The strengths include the broad dataset inclusion, rigorous quality control, and methodological rigor. However, consideration of potential confounding variables (e.g., age, strain) should be included and validation of key results would strengthen the manuscript. • Advance: This study advances knowledge by analyzing sex differences across multiple macrophage niches rather than focusing on a single tissue type. It extends findings from previous immune studies. • Audience: This report would be of interest to immunologists and researchers studying sex differences. Expertise: Immunology, sex differences in disease, macrophage biology, transcriptomics, and inflammation research.

We thank the reviewer for their positive comments on the impact of our work and for their useful feedback.

__ __

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Gosselin, D., Link, V. M., Romanoski, C. E., Fonseca, G. J., Eichenfield, D. Z., Spann, N. J., Stender, J. D., Chun, H. B., Garner, H., Geissmann, F., & Glass, C. K. (2014). Environment drives selection and function of enhancers controlling tissue-specific macrophage identities. Cell, 159(6), 1327-1340. https://doi.org/10.1016/j.cell.2014.11.023

Hamlin, R. E., Pienkos, S. M., Chan, L., Stabile, M. A., Pinedo, K., Rao, M., Grant, P., Bonilla, H., Holubar, M., Singh, U., Jacobson, K. B., Jagannathan, P., Maldonado, Y., Holmes, S. P., Subramanian, A., & Blish, C. A. (2024). Sex differences and immune correlates of Long Covid development, symptom persistence, and resolution. Sci Transl Med, 16(773), eadr1032. https://doi.org/10.1126/scitranslmed.adr1032

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Hou, P., Fang, J., Liu, Z., Shi, Y., Agostini, M., Bernassola, F., Bove, P., Candi, E., Rovella, V., Sica, G., Sun, Q., Wang, Y., Scimeca, M., Federici, M., Mauriello, A., & Melino, G. (2023). Macrophage polarization and metabolism in atherosclerosis. Cell Death Dis, 14(10), 691. https://doi.org/10.1038/s41419-023-06206-z

Lavin, Y., Winter, D., Blecher-Gonen, R., David, E., Keren-Shaul, H., Merad, M., Jung, S., & Amit, I. (2014). Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell, 159(6), 1312-1326. https://doi.org/10.1016/j.cell.2014.11.018

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Shapouri-Moghaddam, A., Mohammadian, S., Vazini, H., Taghadosi, M., Esmaeili, S. A., Mardani, F., Seifi, B., Mohammadi, A., Afshari, J. T., & Sahebkar, A. (2018). Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol, 233(9), 6425-6440. https://doi.org/10.1002/jcp.26429

Wang, X., Iyer, A., Lyons, A. B., Körner, H., & Wei, W. (2019). Emerging Roles for G-protein Coupled Receptors in Development and Activation of Macrophages. Front Immunol, 10, 2031. https://doi.org/10.3389/fimmu.2019.02031

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Referee #2

Evidence, reproducibility and clarity

Summary:

The study investigates sex-specific differences in macrophage gene expression across various tissue niches by analyzing both newly generated and publicly available datasets of varying quality. The key finding is the identification of three consistently differentially expressed genes (DEGs) across all macrophage niches: the Y-chromosome-encoded genes Ddx3y and Eif2s3y, and the X-chromosome-specific gene Xist. However, the number of sex-dimorphic DEGs varied significantly between macrophage niches, with female-biased genes showing more consistency across datasets. To further explore these sex-specific differences, the authors performed an overrepresentation analysis of the DEGs across datasets. They found enriched gene sets associated with specific biological terms in female-biased macrophages from peritoneal macrophages, bone marrow-derived macrophages (BMDMs), and osteoclast progenitors (OCPs), while male-biased enrichment was observed in microglia, exudate macrophages, OCPs, and BMDMs. Notably, extracellular matrix (ECM)-related genes were specifically enriched in female peritoneal macrophages and OCPs, whereas the term "nucleic acid binding" was more prominent in male samples from microglia, BMDMs, and OCPs, driven by the Y-chromosome genes Uty and Kdm5d. A gene set enrichment analysis (GSEA) using Gene Ontology (GO) and Reactome databases further confirmed the enrichment of sex-biased pathways. Based on these findings, the authors conclude that three sex chromosome-associated genes are consistently differentially expressed across all datasets and that female-associated gene expression appears to be more stable, particularly in relation to ECM-associated processes.

Major Comments:

Are the key conclusions convincing?

The study provides valuable insights into sex-dimorphic gene expression in macrophages across different niches. However, some conclusions appear overinterpreted due to the limited number of differentially expressed genes (DEGs) driving specific terms in the overrepresentation analysis. The reliance on only a few recurring genes (e.g., Kdm5d, Eif2s3y, Uty, and Ddx3y) raises concerns about the biological significance of some enriched terms. A clearer discussion on the limitations of such findings is necessary.

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Some claims, particularly those regarding the role of macrophages in diseases such as AD, histiocytosis, and osteoporosis, lack relevant references.

Would additional experiments be essential to support the claims of the paper?

While additional wet-lab experiments are not strictly necessary, a deconvolution analysis of the datasets could be highly beneficial. This would allow the identification of enriched macrophage subtypes and help assess whether differences between datasets are driven by specific macrophage populations rather than global sex differences. Since peritoneal macrophage origin is influenced by age and inflammation status, deconvolution could also clarify dataset comparability.

Are the suggested experiments realistic in terms of time and resources?

Performing cell-type deconvolution using established computational tools (e.g., CIBERSORT, BisqueRNA, or single-cell deconvolution methods) would be a realistic approach within a few weeks and would significantly strengthen the study. This analysis would not require additional experimental work but could refine the interpretation of the dataset. Additionally, a PCA of all datasets could help identify potential similarities among macrophages from different niches and between sexes.

Are the data and the methods presented in such a way that they can be reproduced?

Some methodological details are missing, particularly regarding: The isolation of mouse peritoneal macrophages (details on injection and harvesting procedure needed). Quality control of isolated macrophages (How were contaminating cells excluded? Was additional validation performed beyond using the kit?) The age of mice used for bone marrow-derived macrophages (BMDMs) is not provided, which is important given that immune responses can be age-dependent.

Are the experiments adequately replicated and statistical analysis adequate?

The statistical analysis appears generally appropriate, but there are concerns about dataset inconsistencies that should be addressed. Some datasets were not used across all analyses, which is not clearly indicated in figures or text. This should be explicitly mentioned to avoid misleading interpretations.

Minor Comments

Figures are included twice in the manuscript. The use of stereotypic colors in figures (e.g., blue for male, pink for female) could be reconsidered for better readability and to avoid reinforcing gender stereotypes.

Results - Section 1

• Line 92: The word 'identified' may not be the most appropriate choice here, as it implies discovery rather than selection. Consider rephrasing to 'compiled' or 'gathered' to more accurately reflect the process of assembling the datasets. Additionally, the sentence structure could be refined for clarity, such as specifying that the datasets include both newly generated and publicly available data.
• Line 95: Specify the source of exudate-derived macrophage data.
• Figure 1/2A: The scheme overview lacks clarity-its purpose is unclear. The two identical boxes are redundant and do not provide additional insight. Consider illustrating the origins of different macrophage subtypes instead. The cutoff of >50 DEGs should be included in the schematic to improve clarity. Overrepresentation and GSEA analysis should not be illustrated multiple times across different figures-it is redundant.
• Line 100: The mention of R software should be moved to the Methods section instead of appearing in the Results section.
• Figure 1B-V: The current figure layout is visually cluttered. Consider plotting male and female datasets together in a single graph with different point shapes instead of separate panels for each specific niche.
• Text-Figure alignment: The text describes male/female-specific gene expression levels first, while the figure starts with MDS analysis. The order should be consistent.
• Figure 2C: Exudate data is missing-explain why.

Results - Section 2

• Line 151: Use consistent terminology-either "DEGs" or "DE genes", not both.
• Figure 3A: The text suggests not all datasets were included in this analysis-this should be explicitly indicated in the figure.
  • Show the number of DEGs used for analysis.
• Figure 3B: Smaller pale dots in the bubble plot are difficult to distinguish-consider using a darker outline.
• Line 158: The term "phagocytosis" appears inconsistent with the figure, where it is labeled "phagocytosis, recognition".
• Figure 4B, D, E: The overrepresentation analysis is based on very few genes (often only 1-2 genes per term), which may lead to overinterpretation.
• Consider explicitly naming these genes and discussing their biological role instead of assigning terms based on minimal evidence.
• Figures S3G and S3H seem to be switched.

Results - Section 3

• Figure 5A does not add significant new insights. Consider refining its content to highlight key findings more effectively.
  • Number of genes included in the analysis is not provided-this is important to assess significance and should be stated in methods and figure legends.

Discussion

• Line 201-203: Missing reference.
• Reference 23 (1999) is outdated. Newer literature should be cited to reflect modern insights into sex differences in macrophages.
• Peritoneal macrophages and OCPs originate from monocytes. Would deconvolution help identify enriched subtypes and assess dataset comparability?
• The 'more consistent' pathways found for female datasets are not discussed.

Methods

• Peritoneal macrophage isolation:
  • Details on injection and harvesting are missing.
  • How was contamination from other cell types assessed? F4/80 selection may not be fully macrophage-specific, and contamination could occur due to insufficient washing or the presence of non-macrophage F4/80+ cells.
• Bone marrow macrophages:
  • Mouse age is not provided in the results part.

Figure Legends

• Figure 2: Peritoneal macrophages are abbreviated as PeriMac-consider using this abbreviation consistently in the text.

Significance

The study's strengths include the integration of multiple datasets, the use of both overrepresentation and GSEA, and the exploration of tissue-specific macrophage niches. These findings have relevance for diverse communities, including immunologists, sex-difference researchers, and those studying macrophage-driven diseases such as osteoporosis, neurodegeneration, and chronic inflammation. The work provides a foundation for further studies on sex-specific macrophage biology and may have implications for sex-specific therapeutic strategies.

However, the study has limitations. The conclusions regarding enriched pathways rely heavily on a small number of DEGs, raising concerns about overinterpretation. Additionally, dataset variability and missing data for some analyses (e.g., exudate macrophages) could affect the robustness of the results.

Despite these limitations, the study makes a meaningful but incremental advance by highlighting stable sex-dimorphic patterns in macrophage biology. It provides insights for both fundamental and translational research, particularly for audiences focused on immune regulation, sex-specific gene expression, and tissue-specific macrophage function.

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

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Referee #1

Evidence, reproducibility and clarity

This paper by McGill and colleagues explores sex differences in murine macrophages from different niches. They use a combination of publicly available, and newly developed datasets, and combine these using meta-analysis approaches. They explore DEGs between sexes - both common across niches, and specific to certain niches - and use enrichment analyses to identify pathways linked to these genes. Their overall conclusions are that gene expression changes in females are more consistent across niches, than for males, and are enriched in extracellular matrix-related genes. The paper is easy to follow and very well written.

Major Comments:

1. I would suggest Figure 1 be moved to a supplemental figure.
2. Line 106 - It should be clarified why 50 DEGs was selected as the cut off for exclusion.
3. Optional - would suggest sex chromosome-linked genes are excluded and the analysis redone to see if there are other autosomal genes that are statistically shadowed by the X and Y linked genes.
4. More metadata about the included studies should be included eg mouse ages, strains, experimental manipulations etc. I can't seem to access all of the supplementary tables so this may already be included in Table S1.
5. How relevant the findings in mice are for humans should be explained further in the discussion.

Minor Comments:

1. Lines 63-66 - need references here.
2. Line 61 and 69 - repeated.

Significance

Although this study is primarily descriptive, it adds to the current knowledge about sex differences in macrophages, an important and relatively understudied area. Those interested in sex differences and in the innate immune system generally, plus those who study macrophages in any context, should be interested in this work.

Cette page web provient du site Larousse dans la partie encyclopédie. Elle a pour sujet l'histoire de la médecine depuis ces débuts et l'évolution qu'il y a eu à travers le temps en partant de l'antiquité en passant par la période du moyen age où la médecine à cesser d'évoluer positivement puis la renaissance jusqu'au temps moderne.

Me parece potente la construcción que implica la narrativa de datos como una alternativa que representa esa forma flexible del pensamiento y las herramientas.

Etapa de prueba...

Será??

Reviewer #1 (Public review):

Summary:

This study aims to provide imaging methods for users of the field of human layer-fMRI. This is an emerging field with 240 papers published so far. Different than implied in the manuscript, 3T is well represented among those papers. E.g. see the papers below that are not cited in the manuscript. Thus, the claim on the impact of developing 3T methodology for wider dissemination is not justified. Specifically, because some of the previous papers perform whole brain layer-fMRI (also at 3T) in more efficient, and more established procedures.

The authors implemented a sequence with lots of nice features. Including their own SMS EPI, diffusion bipolar pulses, eye-saturation bands, and they built their own reconstruction around it. This is not trivial. Only a few labs around the world have this level of engineering expertise. I applaud this technical achievement. However, I doubt that any of this is the right tool for layer-fMRI, nor does it represent an advancement for the field. In the thermal noise dominated regime of sub-millimeter fMRI (especially at 3T) it is established to use 3D readouts over 2D (SMS) readouts. While it is not trivial to implement SMS, the vendor implementations (as well as the CMRR and MGH implementations) are most widely applied across the majority of current fMRI studies already. The author's work on this does not serve any previous shortcomings in the field.

The mechanism to use bi-polar gradients to increase the localization specificity is doubtful to me. In my understanding, killing the intra-vascular BOLD should make it less specific. Also, the empirical data do not suggest a higher localization specificity to me.

Embedding this work in the literature of previous methods is incomplete. Recent trends of vessel signal manipulation with ABC or VAPER are not mentioned. Comparisons with VASO are outdated and incorrect.

The reproducibility of the methods and the result is doubtful (see below).

I don't think that this manuscript is in the top 50% of the 240 layer-fmri papers out there.

3T layer-fMRI papers that are not cited:

Taso, M., Munsch, F., Zhao, L., Alsop, D.C., 2021. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL). Journal of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678X20982382

Wu, P.Y., Chu, Y.H., Lin, J.F.L., Kuo, W.J., Lin, F.H., 2018. Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex. Scientific Reports 8, 1-14. https://doi.org/10.1038/s41598-018-31292-x

Lifshits, S., Tomer, O., Shamir, I., Barazany, D., Tsarfaty, G., Rosset, S., Assaf, Y., 2018. Resolution considerations in imaging of the cortical layers. NeuroImage 164, 112-120. https://doi.org/10.1016/j.neuroimage.2017.02.086

Puckett, A.M., Aquino, K.M., Robinson, P.A., Breakspear, M., Schira, M.M., 2016. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage 139, 240-248. https://doi.org/10.1016/j.neuroimage.2016.06.019

Olman, C.A., Inati, S., Heeger, D.J., 2007. The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring. NeuroImage 34, 1126-1135. https://doi.org/10.1016/j.neuroimage.2006.08.045

Ress, D., Glover, G.H., Liu, J., Wandell, B., 2007. Laminar profiles of functional activity in the human brain. NeuroImage 34, 74-84. https://doi.org/10.1016/j.neuroimage.2006.08.020

Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stocker, T., Fernández-Cabello, S., Poser, B.A., Kronbichler, M., 2023. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 3. https://doi.org/10.52294/001c.85117

Scheeringa, R., Bonnefond, M., van Mourik, T., Jensen, O., Norris, D.G., Koopmans, P.J., 2022. Relating neural oscillations to laminar fMRI connectivity in visual cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhac154

Strengths:

See above. The authors developed their own SMS sequence with many features. This is important to the field. And does not leave sequence development work to view isolated monopoly labs. This work democratises SMS.<br /> The questions addressed here are of high relevance to the field: getting tools with good sensitivity, user-friendly applicability, and locally specific brain activity mapping is an important topic in the field of layer-fMRI.

Weaknesses:

(1) I feel the authors need to justify why flow-crushing helps localization specificity. There is an entire family of recent papers that aims to achieve higher localization specificity by doing the exact opposite. Namely, MT or ABC fRMRI aims to increase the localization specificity by highlighting the intravascular BOLD by means of suppressing non-flowing tissue. To name a few:

Priovoulos, N., de Oliveira, I.A.F., Poser, B.A., Norris, D.G., van der Zwaag, W., 2023. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Human Brain Mapping hbm.26227. https://doi.org/10.1002/hbm.26227.

Pfaffenrot, V., Koopmans, P.J., 2022. Magnetization Transfer weighted laminar fMRI with multi-echo FLASH. NeuroImage 119725. https://doi.org/10.1016/j.neuroimage.2022.119725

Schulz, J., Fazal, Z., Metere, R., Marques, J.P., Norris, D.G., 2020. Arterial blood contrast ( ABC ) enabled by magnetization transfer ( MT ): a novel MRI technique for enhancing the measurement of brain activation changes. bioRxiv. https://doi.org/10.1101/2020.05.20.106666

Based on this literature, it seems that the proposed method will make the vein problem worse, not better. The authors could make it clearer how they reason that making GE-BOLD signals more extra-vascular weighted should help to reduce large vein effects.

The empirical evidence for the claim that flow crushing helps with the localization specificity should be made clearer. The response magnitude with and without flow crushing looks pretty much identical to me (see Fig, 6d).<br /> It's unclear to me what to look for in Fig. 5. I cannot discern any layer patterns in these maps. It's too noisy. The two maps of TE=43ms look like identical copies from each other. Maybe an editorial error?

The authors discuss bipolar crushing with respect to SE-BOLD where it has been previously applied. For SE-BOLD at UHF, a substantial portion of the vein signal comes from the intravascular compartment. So I agree that for SE-BOLD, it makes sense to crush the intravascular signal. For GE-BOLD however, this reasoning does not hold. For GE-BOLD (even at 3T), most of the vein signal comes from extravascular dephasing around large unspecific veins and the bipolar crushing is not expected to help with this.

(2) The bipolar crushing is limited to one single direction of flow. This introduces a lot of artificial variance across the cortical folding pattern. This is not mentioned in the manuscript. There is an entire family of papers that perform layer-fmri with black-blood imaging that solves this with a 3D contrast preparation (VAPER) that is applied across a longer time period, thus killing the blood signal while it flows across all directions of the vascular tree. Here, the signal cruising is happening with a 2D readout as a "snap-shot" crushing. This does not allow the blood to flow in multiple directions.<br /> VAPER also accounts for BOLD contaminations of larger draining veins by means of a tag-control sampling. The proposed approach here does not account for this contamination.

Chai, Y., Li, L., Huber, L., Poser, B.A., Bandettini, P.A., 2020. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage 207, 116358. https://doi.org/10.1016/j.neuroimage.2019.116358

Chai, Y., Liu, T.T., Marrett, S., Li, L., Khojandi, A., Handwerker, D.A., Alink, A., Muckli, L., Bandettini, P.A., 2021. Topographical and laminar distribution of audiovisual processing within human planum temporale. Progress in Neurobiology 102121. https://doi.org/10.1016/j.pneurobio.2021.102121

If I would recommend anyone to perform layer-fMRI with blood crushing, it seems that VAPER is the superior approach. The authors could make it clearer why users might want to use the unidirectional crushing instead.

(3) The comparison with VASO is misleading.<br /> The authors claim that previous VASO approaches were limited by TRs of 8.2s. The authors might be advised to check the latest literature of the last years.<br /> Koiso et al. has performed whole brain layer-fMRI VASO at 0.8mm at 3.9 seconds (with reliable activation) and 2.7 seconds (with unconvincing activation pattern, though), and 2.3 (without activation).<br /> Also, whole brain layer-fMRI BOLD at 0.5mm and 0.7mm has been previously performed by the Juelich group at TRs of 3.5s (their TR definition is 'fishy' though).

Koiso, K., Müller, A.K., Akamatsu, K., Dresbach, S., Gulban, O.F., Goebel, R., Miyawaki, Y., Poser, B.A., Huber, L., 2023. Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset. Aperture Neuro 34. https://doi.org/10.1101/2022.08.19.504502

Yun, S.D., Pais‐Roldán, P., Palomero‐Gallagher, N., Shah, N.J., 2022. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Human Brain Mapping. https://doi.org/10.1002/hbm.25855

Pais-Roldan, P., Yun, S.D., Palomero-Gallagher, N., Shah, N.J., 2023. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front. Neurosci. 17, 1151544. https://doi.org/10.3389/fnins.2023.1151544

The authors are correct that VASO is not advised as a turn-key method for lower brain areas, incl. Hippocampus and subcortex. However, the authors use this word of caution that is intended for inexperienced "users" as a statement that this cannot be performed. This statement is taken out of context. This statement is not from the academic literature. It's advice for the 40+ user base that want to perform layer-fMRI as a plug-and-play routine tool in neuroscience usage. In fact, sub-millimeter VASO is routinely being performed by MRI-physicists across all brain areas (including deep brain structures, hippocampus etc). E.g. see Koiso et al. and an overview lecture from a layer-fMRI workshop that I had recently attended: https://youtu.be/kzh-nWXd54s?si=hoIJjLLIxFUJ4g20&t=2401

Thus, the authors could embed this phrasing into the context of their own method that they are proposing in the manuscript. E.g. the authors could state whether they think that their sequence has the potential to be disseminated across sites, considering that it requires slow offline reconstruction in Matlab?<br /> Do the authors think that the results shown in Fig. 6c are suggesting turn-key acquisition of a routine mapping tool? In my humble opinion it looks like random noise, with most of the activation outside the ROI (in white matter).

(4) The repeatability of the results is questionable.<br /> The authors perform experiments about the robustness of the method (line 620). The corresponding results are not suggesting any robustness to me. In fact the layer profiles in Fig. 4c vs. Fig 4d are completely opposite. Location of peaks turn into locations of dips and vice versa.<br /> The methods are not described in enough detail to reproduce these results.<br /> The authors mention that their image reconstruction is done "using in-house MATLAB code" (line 634). They do not post a link to github, nor do they say if they share this code.

It is not trivial to get good phase data for fMRI. The authors do not mention how they perform the respective coil-combination.<br /> No data are shared for reproduction of the analysis.

(5) The application of NODRIC is not validated.<br /> Previous applications of NORDIC at 3T layer-fMRI have resulted in mixed success. When not adjusted for the right SNR regime it can result in artifactual reductions of beta scores, depending on the SNR across layers. The authors could validate their application of NORDIC and confirm that the average layer-profiles are unaffected by the application of NORDIC. Also, the NORDIC version should be explicitly mentioned in the manuscript.

Akbari, A., Gati, J.S., Zeman, P., Liem, B., Menon, R.S., 2023. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD (preprint). Neuroscience. https://doi.org/10.1101/2023.04.06.535924

Knudsen, L., Guo, F., Huang, J., Blicher, J.U., Lund, T.E., Zhou, Y., Zhang, P., Yang, Y., 2023. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv. https://doi.org/10.1101/2023.10.29.564658

Comments on revisions:

Among all the concerns mentioned above, I think there is only one of the specific issues that was sufficiently addressed.<br /> The authors implemented a combination of three consecutive-dimensional flow crushers. Other concerns were not sufficiently addressed to change my confidence level of the study.<br /> - While the abstract is still focusing on the utility of using 3T, they do not give credit to early 3T layer-fMRI papers leading the way to larger coverage and connectivity applications.<br /> - While the author's choice of using custom SMS 2D readout is justified for them. I do not think that this very method will utilize widespread 3T whole brain connectivity experiments across the global 3T community. This lowers the impact of the paper.<br /> - The images in Fig. 5 are still suspiciously similar. To the level that the noise pattern outside the brain is identical across large parts of the maps with and without PR.<br /> - Maybe it's my ignorance, but I still do not agree why flow crushing focuses the local BOLD responses to small vessels.<br /> - While my feel of a misleading representation of the literature had been accompanied by explicit references, the authors claim that they cannot find them?!? Or claim that they are about something else (which they are not, in my viewpoint).<br /> Data and software are still not shared (not even example data, or nii data).

Author response:

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

General responses:

The authors sincerely thank all the reviewers for their valuable and constructive comments. We also apologize for the long delay in providing this rebuttal due to logistical and funding challenges. In this revision, we modified the bipolar gradients from one single direction to all three directions. Additionally, in response to the concerns regarding data reliability, we conducted a thorough examination of each step in our data processing pipeline. In the original processing workflow, the projection-onto-convex-set (POCS) method was used for partial Fourier reconstruction. Upon examination, we found that applying the POCS method after parallel image reconstruction significantly altered the signal and resulted in considerable loss of functional feature. Futhermore, the original scan protocol employed a TE of 46 ms, which is notably longer than the typical TE of 33 ms. A prolonged TE can increase the ratio of extravascular to intravascular contributions. Importantly, the impact of TE on the efficacy of phase regression remains unclear, introducing potential confounding effects. To address these issues, we revised the protocol by shortening the TE from 46 ms to 39 ms. This adjustment was achieved by modifying the SMS factor to 3 and the in-plane acceleration rate to 3, thereby minimizing the confounding effects associated with an extended TE.

Following these changes, we recollected task-based fMRI data (N=4) and resting-state fMRI data (N=14) under the updated protocol. Using the revised dataset, we validated layer-specific functional connectivity (FC) through seed-based analyses. These analyses revealed distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with statistically significant inter-layer differences. Furthermore, additional analyses with a seed in the primary sensory cortex (S1) corroborated the robustness and reliability of the revised methodology. We also changed the ‘directed’ functional connectivity in the title to ‘layer-specific’ functional connectivity, as drawing conclusions about directionality requires auxiliary evidence beyond the scope of this study.

We provide detailed responses to the reviewers’ comments below.

Reviewer #1 (Public Review):

Summary:

(1)   This study aims to provide imaging methods for users of the field of human layer-fMRI. This is an emerging field with 240 papers published so far. Different than implied in the manuscript, 3T is well represented among those papers. E.g. see the papers below that are not cited in the manuscript. Thus, the claim on the impact of developing 3T methodology for wider dissemination is not justified. Specifically, because some of the previous papers perform whole brain layer-fMRI (also at 3T) in more efficient, and more established procedures.

3T layer-fMRI papers that are not cited:

Taso, M., Munsch, F., Zhao, L., Alsop, D.C., 2021. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL). Journal of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678X20982382

Wu, P.Y., Chu, Y.H., Lin, J.F.L., Kuo, W.J., Lin, F.H., 2018. Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex. Scientific Reports 8, 1-14. https://doi.org/10.1038/s41598-018-31292-x

Lifshits, S., Tomer, O., Shamir, I., Barazany, D., Tsarfaty, G., Rosset, S., Assaf, Y., 2018. Resolution considerations in imaging of the cortical layers. NeuroImage 164, 112-120. https://doi.org/10.1016/j.neuroimage.2017.02.086

Puckett, A.M., Aquino, K.M., Robinson, P.A., Breakspear, M., Schira, M.M., 2016. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage 139, 240-248. https://doi.org/10.1016/j.neuroimage.2016.06.019

Olman, C.A., Inati, S., Heeger, D.J., 2007. The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring. NeuroImage 34, 1126-1135. https://doi.org/10.1016/j.neuroimage.2006.08.045

Ress, D., Glover, G.H., Liu, J., Wandell, B., 2007. Laminar profiles of functional activity in the human brain. NeuroImage 34, 74-84. https://doi.org/10.1016/j.neuroimage.2006.08.020

Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stocker, T., Fernández-Cabello, S., Poser, B.A., Kronbichler, M., 2023. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 3. https://doi.org/10.52294/001c.85117

Scheeringa, R., Bonnefond, M., van Mourik, T., Jensen, O., Norris, D.G., Koopmans, P.J., 2022. Relating neural oscillations to laminar fMRI connectivity in visual cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhac154

We thank the reviewer for listing out 8 papers related to 3T layer-fMRI papers. The primary goal of our work is to develop a methodology for brain-wide, layer-dependent resting-state functional connectivity at 3T. Upon review of the cited papers, we found that:

(1) One study (Lifshits et al.) was not an fMRI study.

(2) One study (Olman et al.) was conducted at 7T, not 3T.

(3) Two studies (Taso et al. and Wu et al.) employed relatively large voxel sizes (1.6 × 2.3 × 5 mm³ and 1.5 mm isotropic, respectively), which limits layer specificity.

(4) Only one of the listed studies (Huber et al., Aperture Neuro 2023) provides coverage of more than half of the brain.

While each of these studies offers valuable insights, the VASO study by Huber et al. is the most relevant to our work, given its brain-wide coverage. However, the VASO method employs a relatively long TR (14.137 s), which may not be optimal for resting-state functional connectivity analyses.

To address these limitations, our proposed method achieves submillimeter resolution, layer specificity, brain-wide coverage, and a significantly shorter TR (<5 s) altogether. We believe this advancement provides a meaningful contribution to the field, enabling broader applicability of layer-fMRI at 3T.

(2) The authors implemented a sequence with lots of nice features. Including their own SMS EPI, diffusion bipolar pulses, eye-saturation bands, and they built their own reconstruction around it. This is not trivial. Only a few labs around the world have this level of engineering expertise. I applaud this technical achievement. However, I doubt that any of this is the right tool for layer-fMRI, nor does it represent an advancement for the field. In the thermal noise dominated regime of sub-millimeter fMRI (especially at 3T), it is established to use 3D readouts over 2D (SMS) readouts. While it is not trivial to implement SMS, the vendor implementations (as well as the CMRR and MGH implementations) are most widely applied across the majority of current fMRI studies already. The author's work on this does not serve any previous shortcomings in the field.

We would like to thank the reviewer for their comments and the recognition of the technical efforts in implementing our sequence. We would like to address the points raised:

(1) We completely agree that in-house implementation of existing techniques does not constitute an advancement for the field. We did not claim otherwise in the manuscript. Our focus was on the development of a method for brain-wide, layer-dependent resting-state functional connectivity at 3T, as mentioned in the response above.

(2) The reviewer stated that "it is established to use 3D readouts over 2D (SMS) readouts". This is a strong claim, and we believe it requires robust evidence to support it. While it is true that 3D readouts can achieve higher tSNR in certain regions, such as the central brain, as shown in the study by Vizioli et al. (ISMRM 2020 abstract; https://cds.ismrm.org/protected/20MProceedings/PDFfiles/3825.html?utm_source=chatgpt.com ), higher tSNR does not necessarily equate to improved detection power in fMRI studies. For instance, Le Ster et al. (PLOS ONE, 2019; https://doi.org/10.1371/journal.pone.0225286 ). demonstrated that while 3D EPI had higher tSNR in the central brain, SMS EPI produced higher t-scores in activation maps.

(3) When choosing between SMS EPI and 3D EPI, multiple factors should be taken into account, not just tSNR. For example, SMS EPI and 3D EPI differ in their sensitivity to motion and the complexity of motion correction. The choice between them depends on the specific research goals and practical constraints.

(4) We are open to different readout strategies, provided they can be demonstrated suitable to the research goals. In this study, we opted for 2D SMS primarily due to logistical considerations. This choice does not preclude the potential use of 3D readouts in the future if they are deemed more appropriate for the project objectives.

The mechanism to use bi-polar gradients to increase the localization specificity is doubtful to me. In my understanding, killing the intra-vascular BOLD should make it less specific. Also, the empirical data do not suggest a higher localization specificity to me.

We will elaborate the mechanism and reasoning in the later responses.

Embedding this work in the literature of previous methods is incomplete. Recent trends of vessel signal manipulation with ABC or VAPER are not mentioned. Comparisons with VASO are outdated and incorrect.

The reproducibility of the methods and the result is doubtful (see below).

In this revision, we updated the scan protocol and recollected the imaging data. Detailed explanations and revised results are provided in the later responses.

I don't think that this manuscript is in the top 50% of the 240 layer-fmri papers out there.

We respect the reviewer’s personal opinion. However, we can only address scientific comments or critiques.

Strengths:

See above. The authors developed their own SMS sequence with many features. This is important to the field. And does not leave sequence development work to view isolated monopoly labs. This work democratises SMS.

The questions addressed here are of high relevance to the field: getting tools with good sensitivity, user-friendly applicability, and locally specific brain activity mapping is an important topic in the field of layer-fMRI.

Weaknesses:

(1) I feel the authors need to justify why flow-crushing helps localization specificity. There is an entire family of recent papers that aim to achieve higher localization specificity by doing the exact opposite. Namely, MT or ABC fRMRI aims to increase the localization specificity by highlighting the intravascular BOLD by means of suppressing non-flowing tissue. To name a few:

Priovoulos, N., de Oliveira, I.A.F., Poser, B.A., Norris, D.G., van der Zwaag, W., 2023. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Human Brain Mapping hbm.26227. https://doi.org/10.1002/hbm.26227.

Pfaffenrot, V., Koopmans, P.J., 2022. Magnetization Transfer weighted laminar fMRI with multi-echo FLASH. NeuroImage 119725. https://doi.org/10.1016/j.neuroimage.2022.119725

Schulz, J., Fazal, Z., Metere, R., Marques, J.P., Norris, D.G., 2020. Arterial blood contrast ( ABC ) enabled by magnetization transfer ( MT ): a novel MRI technique for enhancing the measurement of brain activation changes. bioRxiv. https://doi.org/10.1101/2020.05.20.106666

Based on this literature, it seems that the proposed method will make the vein problem worse, not better. The authors could make it clearer how they reason that making GE-BOLD signals more extra-vascular weighted should help to reduce large vein effects.

The proposed VN fMRI method employs VN gradients to selectively suppress signals from fast-flowing blood in large vessels. Although this approach may initially appear to diverge from the principles of CBV-based techniques (Chai et al., 2020; Huber et al., 2017a; Pfaffenrot and Koopmans, 2022; Priovoulos et al., 2023), which enhance sensitivity to vascular changes in arterioles, capillaries, and venules while attenuating signals from static tissue and large veins, it aligns with the fundamental objective of all layer-specific fMRI methods. Specifically, these approaches aim to maximize spatial specificity by preserving signals proximal to neural activation sites and minimizing contributions from distal sources, irrespective of whether the signals are intra- or extra-vascular in origin. In the context of intravascular signals, CBV-based methods preferentially enhance sensitivity to functional changes in small vessels (proximal components) while demonstrating reduced sensitivity to functional changes in large vessels (distal components). For extravascular signals, functional changes are a mixture of proximal and distal influences. While tissue oxygenation near neural activation sites represents a proximal contribution, extravascular signal contamination from large pial veins reflects distal effects that are spatially remote from the site of neuronal activity. CBV-based techniques mitigate this challenge by unselectively suppressing signals from static tissues, thereby highlighting contributions from small vessels. In contrast, the VN fMRI method employs a targeted suppression strategy, selectively attenuating signals from large vessels (distal components) while preserving those from small vessels (proximal components). Furthermore, the use of a 3T scanner and the inclusion of phase regression in the VN approach mitigates contamination from large pial veins (distal components) while preserving signals reflecting local tissue oxygenation (proximal components). By integrating these mechanisms, VN fMRI improves spatial specificity, minimizing both intravascular and extravascular contributions that are distal to neuronal activation sites. We have incorporated the responses into Discussion section.

The empirical evidence for the claim that flow crushing helps with the localization specificity should be made clearer. The response magnitude with and without flow crushing looks pretty much identical to me (see Fig, 6d).

In the new results in Figure 4, the application of VN gradients attenuated the bias towards pial surface. Consistent with the results in Figure 4, Figure 5 also demonstrated the suppression of macrovascular signal by VN gradients.

It's unclear to me what to look for in Fig. 5. I cannot discern any layer patterns in these maps. It's too noisy. The two maps of TE=43ms look like identical copies from each other. Maybe an editorial error?

In this revision, the original Figure 5 has been removed. However, we would like to clarify that the two maps with TE = 43 ms in the original Figure 5 were not identical. This can be observed in the difference map provided in the right panel of the figure.

The authors discuss bipolar crushing with respect to SE-BOLD where it has been previously applied. For SE-BOLD at UHF, a substantial portion of the vein signal comes from the intravascular compartment. So I agree that for SE-BOLD, it makes sense to crush the intravascular signal. For GE-BOLD however, this reasoning does not hold. For GE-BOLD (even at 3T), most of the vein signal comes from extravascular dephasing around large unspecific veins, and the bipolar crushing is not expected to help with this.

The reviewer’s statement that "most of the vein signal comes from extravascular dephasing around large unspecific veins" may hold true for 7T. However, at 3T, the susceptibility-induced Larmor frequency shift is reduced by 57%, and the extravascular contribution decreases by more than 35%, as shown by Uludağ et al. 2009 ( DOI: 10.1016/j.neuroimage.2009.05.051 ).

Additionally, according to the biophysical models (Ogawa et al., 1993; doi: 10.1016/S0006-3495(93)81441-3 ), the extravascular contamination from the pial surface is inversely proportional to the square of the distance from vessel. For a vessel diameter of 0.3 mm and an isotropic voxel size of 0.9 mm, the induced frequency shift is reduced by at least 36-fold at the next voxel. Notably, a vessel diameter of 0.3 mm is larger than most pial vessels. Theoretically, the extravascular effect contributes minimally to inter-layer dependency, particularly at 3T compared to 7T due to weaker susceptibility-related effects at lower field strengths. Empirically, as shown in Figure 7c, the results at M1 demonstrated that layer specificity can be achieved statistically with the application of VN gradients. We have incorporated this explanation into the Introduction and Discussion sections of the manuscript.

(2) The bipolar crushing is limited to one single direction of flow. This introduces a lot of artificial variance across the cortical folding pattern. This is not mentioned in the manuscript. There is an entire family of papers that perform layer-fmri with black-blood imaging that solves this with a 3D contrast preparation (VAPER) that is applied across a longer time period, thus killing the blood signal while it flows across all directions of the vascular tree. Here, the signal cruising is happening with a 2D readout as a "snap-shot" crushing. This does not allow the blood to flow in multiple directions.

VAPER also accounts for BOLD contaminations of larger draining veins by means of a tag-control sampling. The proposed approach here does not account for this contamination.

Chai, Y., Li, L., Huber, L., Poser, B.A., Bandettini, P.A., 2020. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage 207, 116358. https://doi.org/10.1016/j.neuroimage.2019.116358

Chai, Y., Liu, T.T., Marrett, S., Li, L., Khojandi, A., Handwerker, D.A., Alink, A., Muckli, L., Bandettini, P.A., 2021. Topographical and laminar distribution of audiovisual processing within human planum temporale. Progress in Neurobiology 102121. https://doi.org/10.1016/j.pneurobio.2021.102121

If I would recommend anyone to perform layer-fMRI with blood crushing, it seems that VAPER is the superior approach. The authors could make it clearer why users might want to use the unidirectional crushing instead.

We understand the reviewer’s concern regarding the directional limitation of bipolar crushing. As noted in the responses above, we have updated the bipolar gradient to include three orthogonal directions instead of a single direction. Furthermore, flow-related signal suppression does not necessarily require a longer time period. Bipolar diffusion gradients have been effectively used to nullify signals from fast-flowing blood, as demonstrated by Boxerman et al. (1995; DOI: 10.1002/mrm.1910340103). Their study showed that vessels with flow velocities producing phase changes greater than p radians due to bipolar gradients experience significant signal attenuation. The critical velocity for such attenuation can be calculated using the formula: 1/(2gGDd) where g is the gyromagnetic ratio, G is the gradient strength, d is the gradient pulse width and D is the time between the two bipolar gradient pulses. In the framework of Boxerman et al. at 1.5T, the critical velocity for b value of 10 s/mm² is ~8 mm/s, resulting in a ~30% reduction in functional signal. In our 3T study, b values of 6, 7, and 8 s/mm² correspond to critical velocities of 16.8, 15.2, and 13.9 mm/s, respectively. The flow velocities in capillaries and most venules remain well below these thresholds. Notably, in our VN fMRI sequences, bipolar gradients were applied in all three orthogonal directions, whereas in Boxerman et al.'s study, the gradients were applied only in the z-direction. Given the voxel dimensions of 3 × 3 × 7 mm³ in the 1.5T study, vessels within a large voxel are likely oriented in multiple directions, meaning that only a subset of fast-flowing signals would be attenuated. Therefore, our approach is expected to induce greater signal reduction, even at the same b values as those used in Boxerman et al.'s study. We have incorporated this text into the Discussion section of the manuscript.

(3) The comparison with VASO is misleading.

The authors claim that previous VASO approaches were limited by TRs of 8.2s. The authors might be advised to check the latest literature of the last years.

Koiso et al. performed whole brain layer-fMRI VASO at 0.8mm at 3.9 seconds (with reliable activation), 2.7 seconds (with unconvincing activation pattern, though), and 2.3 (without activation).

Also, whole brain layer-fMRI BOLD at 0.5mm and 0.7mm has been previously performed by the Juelich group at TRs of 3.5s (their TR definition is 'fishy' though).

Koiso, K., Müller, A.K., Akamatsu, K., Dresbach, S., Gulban, O.F., Goebel, R., Miyawaki, Y., Poser, B.A., Huber, L., 2023. Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset. Aperture Neuro 34. https://doi.org/10.1101/2022.08.19.504502

Yun, S.D., Pais‐Roldán, P., Palomero‐Gallagher, N., Shah, N.J., 2022. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Human Brain Mapping. https://doi.org/10.1002/hbm.25855

Pais-Roldan, P., Yun, S.D., Palomero-Gallagher, N., Shah, N.J., 2023. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front. Neurosci. 17, 1151544. https://doi.org/10.3389/fnins.2023.1151544

We thank the reviewer for providing these references. While the protocol with a TR of 3.9 seconds in Koiso’s work demonstrated reasonable activation patterns, it was not tested for layer specificity. Given that higher acceleration factors (AF) can cause spatial blurring, a protocol should only be eligible for comparison if layer specificity is demonstrated.

Secondly, the TRs reported in Koiso’s study pertain only to either the VASO or BOLD acquisition, not the combined CBV-based contrast. To generate CBV-based images, both VASO and BOLD data are required, effectively doubling the TR. For instance, if the protocol with a TR of 3.9 seconds is used, the effective TR becomes approximately 8 seconds. The stable protocol used by Koiso et al. to acquire whole-brain data (94.08 mm along the z-axis) required 5.2 seconds for VASO and 5.1 seconds for BOLD, resulting in an effective TR of 10.3 seconds. The spatial resolution achieved was 0.84 mm isotropic.

Unfortunately, we could not find the Juelich paper mentioned by the reviewer.

To have a more comprehensive comparison, we collated relevant literature on brain-wide layer-specific fMRI. We defined brain-wide acquisition as imaging protocols that cover more than half of the human brain, specifically exceeding 55 mm along the superior-inferior axis. We identified five studies and summarized their scan parameters, including effective TR, coverage, and spatial resolution, in Table 1.

The authors are correct that VASO is not advised as a turn-key method for lower brain areas, incl. Hippocampus and subcortex. However, the authors use this word of caution that is intended for inexperienced "users" as a statement that this cannot be performed. This statement is taken out of context. This statement is not from the academic literature. It's advice for the 40+ user base that wants to perform layer-fMRI as a plug-and-play routine tool in neuroscience usage. In fact, sub-millimeter VASO is routinely being performed by MRI-physicists across all brain areas (including deep brain structures, hippocampus etc). E.g. see Koiso et al. and an overview lecture from a layer-fMRI workshop that I had recently attended: https://youtu.be/kzh-nWXd54s?si=hoIJjLLIxFUJ4g20&t=2401

In this revision, we decided to focus on cortico-cortical functional connectivity and have removed the LGN-related content. Consequently, the text mentioned by the reviewer was also removed. Nevertheless, we apologize if our original description gave the impression that functional mapping of deep brain regions using VASO is not feasible. The word of caution we used is based on the layer-fMRI blog ( https://layerfmri.com/2021/02/22/vaso_ve/ ) and reflects the challenges associated with this technique, as outlined by experts like Dr. Huber and Dr. Strinberg.

According to the information provided, including the video, functional mapping of the hippocampus and amygdala using VASO is indeed possible but remains technically challenging. The short arterial arrival times in these deep brain regions can complicate the acquisition, requiring RF inversion pulses to cover a wider area at the base of the brain. For example, as of 2023, four or more research groups were attempting to implement layer-fMRI VASO in the hippocampus. One such study at 3T required multiple inversion times to account for inflow effects, highlighting the technical complexity of these applications. This is the context in which we used the word of caution. We are not sure whether recent advancements like MAGEC VASO have improved its applicability. As of 2024, we have not identified any published VASO studies specifically targeting deep brain structures such as the hippocampus or amygdala. Therefore, it is difficult to conclude that “sub-millimeter VASO is routinely being performed by MRI physicists on deep brain structures such as the hippocampus.”

Thus, the authors could embed this phrasing into the context of their own method that they are proposing in the manuscript. E.g. the authors could state whether they think that their sequence has the potential to be disseminated across sites, considering that it requires slow offline reconstruction in Matlab?

We are enthusiastic about sharing our imaging sequence, provided its usefulness is conclusively established. However, it's important to note that without an online reconstruction capability, such as the ICE, the practical utility of the sequence may be limited. Unfortunately, we currently don’t have the manpower to implement the online reconstruction. Nevertheless, we are more than willing to share the offline reconstruction codes upon request.

Do the authors think that the results shown in Fig. 6c are suggesting turn-key acquisition of a routine mapping tool? In my humble opinion, it looks like random noise, with most of the activation outside the ROI (in white matter).

As we mentioned in the ‘general response’ in the beginning of the rebuttal, the POCS method for partial Fourier reconstruction caused the loss of functional feature, potentially accounting for the activation in white matter. In this revision, we have modified the pulse sequence, scan protocol and processing pipelines.

According to the results in Figure 4, stable activation in M1 was observed at the single-subject level across most scan protocols. Yet, the layer-dependent activation profiles in M1 were spatially unstable, irrespective of the application of VN gradients. This spatial instability is not entirely unexpected, as T2*-based contrast is inherently sensitive to various factors that perturb the magnetic field, such as eye movements, respiration, and macrovascular signal fluctuations. Furthermore, ICA-based artifact removal was intentionally omitted in Figure 4 to ensure fair comparisons between protocols, leaving residual artifacts unaddressed. Inconsistency in performing the button-pressing task across sessions may also have contributed to the observed variability. These results suggest that submillimeter-resolution fMRI may not yet be suitable for reliable individual-level layer-dependent functional mapping, unless group-level statistics are incorporated to enhance robustness. We have incorporated this text into the Limitation section of the manuscript.

(4) The repeatability of the results is questionable.

The authors perform experiments about the robustness of the method (line 620). The corresponding results are not suggesting any robustness to me. In fact, the layer profiles in Fig. 4c vs. Fig 4d are completely opposite. The location of peaks turns into locations of dips and vice versa.

The methods are not described in enough detail to reproduce these results.

The authors mention that their image reconstruction is done "using in-house MATLAB code" (line 634). They do not post a link to github, nor do they say if they share this code.

We thank the reviewer for the comments regarding reproducibility and data sharing. In response, we have revised the Methods section and elaborated on the technical details to improve clarity and reproducibility.

Regarding code sharing, we acknowledge that the current in-house MATLAB reconstruction code requires further refinement to improve its readability and usability. Due to limited manpower, we have not yet been able to complete this task. However, we are committed to making the code publicly available and will upload it to GitHub as soon as the necessary resources are available.

For data sharing, we face logistical challenges due to the large size of the dataset, which spans tens of terabytes. Platforms like OpenNeuro, for example, typically support datasets up to 10TB, making it difficult to share the data in its entirety. Despite this limitation, we are more than willing to share offline reconstruction codes and raw data upon request to facilitate reproducibility.

Regarding data robustness, we kindly refer the reviewer to our response to the previous comment, where we addressed these concerns in greater detail.

It is not trivial to get good phase data for fMRI. The authors do not mention how they perform the respective coil-combination.

No data are shared for reproduction of the analysis.

Obtaining phase data is relatively straightforward when the images are retrieved directly from raw data. For coil combination, we employed the adaptive coil combination approach described by (Walsh et al.; DOI: 10.1002/(sici)1522-2594(200005)43:5<682::aid-mrm10>3.0.co;2-g ) The MATLAB code for this implementation was developed by Dr. Diego Hernando and is publicly available at https://github.com/welton0411/matlab .

(5) The application of NODRIC is not validated.

Previous applications of NORDIC at 3T layer-fMRI have resulted in mixed success. When not adjusted for the right SNR regime it can result in artifactual reductions of beta scores, depending on the SNR across layers. The authors could validate their application of NORDIC and confirm that the average layer-profiles are unaffected by the application of NORDIC. Also, the NORDIC version should be explicitly mentioned in the manuscript.

Akbari, A., Gati, J.S., Zeman, P., Liem, B., Menon, R.S., 2023. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD (preprint). Neuroscience. https://doi.org/10.1101/2023.04.06.535924

Knudsen, L., Guo, F., Huang, J., Blicher, J.U., Lund, T.E., Zhou, Y., Zhang, P., Yang, Y., 2023. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv. https://doi.org/10.1101/2023.10.29.564658

We appreciate the reviewer’s suggestion. To validate the application of NORDIC denoising in our study, we compared the BOLD activation maps before and after denoising in the visual and motor cortices, as well as the depth-dependent activation profiles in M1. These results are presented in Figure 3. The activation patterns in the denoised maps were consistent with those in the non-denoised maps but exhibited higher statistical significance. Notably, BOLD activation within M1 was only observed after NORDIC denoising, underscoring the necessity of this approach. Figure 3c shows the depth-dependent activation profiles in M1, highlighted by the green contours in Figure 3b. Both denoised and non-denoised profiles followed similar trends; however, as expected, the non-denoised profile exhibited larger confidence intervals compared to the NORDIC-denoised profile. These results confirm that NORDIC denoising enhances sensitivity without introducing distortions in the functional signal. The corresponding text has been incorporated into the Results section.

Regarding the implementation details of NORDIC denoising, the reconstructed images were denoised using a g-factor map (function name: NIFTI_NORDIC). The g-factor map was estimated from the image time series, and the input images were complex-valued. The width of the smoothing filter for the phase was set to 10, while all other hyperparameters were retained at their default values. This information has been integrated into the Methods section for clarity and reproducibility.

Reviewer #2 (Public Review):

This study developed a setup for laminar fMRI at 3T that aimed to get the best from all worlds in terms of brain coverage, temporal resolution, sensitivity to detect functional responses, and spatial specificity. They used a gradient-echo EPI readout to facilitate sensitivity, brain coverage and temporal resolution. The former was additionally boosted by NORDIC denoising and the latter two were further supported by parallel-imaging acceleration both in-plane and across slices. The authors evaluated whether the implementation of velocity-nulling (VN) gradients could mitigate macrovascular bias, known to hamper the laminar specificity of gradient-echo BOLD.

The setup allows for 0.9 mm isotropic acquisitions with large coverage at a reasonable TR (at least for block designs) and the fMRI results presented here were acquired within practical scan-times of 12-18 minutes. Also, in terms of the availability of the method, it is favorable that it benefits from lower field strength (additional time for VN-gradient implementation, afforded by longer gray matter T2*).

The well-known double peak feature in M1 during finger tapping was used as a test-bed to evaluate the spatial specificity. They were indeed able to demonstrate two distinct peaks in group-level laminar profiles extracted from M1 during finger tapping, which was largely free from superficial bias. This is rather intriguing as, even at 7T, clear peaks are usually only seen with spatially specific non-BOLD sequences. This is in line with their simple simulations, which nicely illustrated that, in theory, intravascular macrovascular signals should be suppressible with only minimal suppression of microvasculature when small b-values of the VN gradients are employed. However, the authors do not state how ROIs were defined making the validity of this finding unclear; were they defined from independent criteria or were they selected based on the region mostly expressing the double peak, which would clearly be circular? In any case, results are based on a very small sub-region of M1 in a single slice - it would be useful to see the generalizability of superficial-bias-free BOLD responses across a larger portion of M1.

We appreciate and understand the reviewer’s concerns. Given the small size of the hand knob region within M1 and its intersubject variability in location, defining this region automatically remains challenging. However, we applied specific criteria to minimize bias during the delineation of M1: 1) the hand knob region was required to be anatomically located in the precentral sulcus or gyrus; 2) it needed to exhibit consistent BOLD activation across the majority of testing conditions; and 3) the region was expected to show BOLD activation in the deep cortical layers under the condition of b = 0 and TE = 30 ms. Once the boundaries across cortical depth were defined, the gray matter boundaries of hand knob region were delineated based on the T1-weighted anatomical image and the cortical ribbon mask but excluded the BOLD activation map to minimize potential bias in manual delineation. Based on the new criteria, the resulting depth-dependent profiles, as shown in Figure 4, are no longer superficial-bias-free.

As repeatedly mentioned by the authors, a laminar fMRI setup must demonstrate adequate functional sensitivity to detect (in this case) BOLD responses. The sensitivity evaluation is unfortunately quite weak. It is mainly based on the argument that significant activation was found in a challenging sub-cortical region (LGN). However, it was a single participant, the activation map was not very convincing, and the demonstration of significant activation after considerable voxel-averaging is inadequate evidence to claim sufficient BOLD sensitivity. How well sensitivity is retained in the presence of VN gradients, high acceleration factors, etc., is therefore unclear. The ability of the setup to obtain meaningful functional connectivity results is reassuring, yet, more elaborate comparison with e.g., the conventional BOLD setup (no VN gradients) is warranted, for example by comparison of tSNR, quantification and comparison of CNR, illustration of unmasked-full-slice activation maps to compare noise-levels, comparison of the across-trial variance in each subject, etc. Furthermore, as NORDIC appears to be a cornerstone to enable submillimeter resolution in this setup at 3T, it is critical to evaluate its impact on the data through comparison with non-denoised data, which is currently lacking.

We appreciate the reviewer’s comments and acknowledge that the LGN results from a single participant were not sufficiently convincing. In this revision, we have removed the LGN-related results and focused on cortico-cortical FC. To evaluate data quality, we opted to present BOLD activation maps rather than tSNR, as high tSNR does not necessarily translate to high functional significance. In Figure 3, we illustrate the effect of NORDIC denoising, including activation maps and depth-dependent profiles. Figure 4 presents activation maps acquired under different TE and b values, demonstrating that VN gradients effectively reduce the bias toward the pial surface without altering the overall activation patterns. The results in Figure 4 and Figure 5 provide evidence that VN gradients retain sensitivity while reducing superficial bias. The ability of the setup to obtain meaningful FC results was validated through seed-based analyses, identifying distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with significant inter-layer differences (see Figure 7). Further analyses with a seed in the primary sensory cortex (S1) demonstrated the reliability of the method (see Figure 8). For further details on the results, including the impact of VN gradients and NORDIC denoising, please refer to Figures 3 to 8 in the Results section.

Additionally, we acknowledge the limitations of our current protocol for submillimeter-resolution fMRI at the individual level. We found that robust layer-dependent functional mapping often requires group-level statistics to enhance reliability. This issue has been discussed in detail in the Limitations section.

The proposed setup might potentially be valuable to the field, which is continuously searching for techniques to achieve laminar specificity in gradient echo EPI acquisitions. Nonetheless, the above considerations need to be tackled to make a convincing case.

Reviewer #3 (Public Review):

Summary:

The authors are looking for a spatially specific functional brain response to visualise non-invasively with 3T (clinical field strength) MRI. They propose a velocity-nulled weighting to remove the signal from draining veins in a submillimeter multiband acquisition.

Strengths:

- This manuscript addresses a real need in the cognitive neuroscience community interested in imaging responses in cortical layers in-vivo in humans.

- An additional benefit is the proposed implementation at 3T, a widely available field strength.

Weaknesses:

- Although the VASO acquisition is discussed in the introduction section, the VN-sequence seems closer to diffusion-weighted functional MRI. The authors should make it more clear to the reader what the differences are, and how results are expected to differ. Generally, it is not so clear why the introduction is so focused on the VASO acquisition (which, curiously, lacks a reference to Lu et al 2013). There are many more alternatives to BOLD-weighted imaging for fMRI. CBF-weighted ASL and GRASE have been around for a while, ABC and double-SE have been proposed more recently.

The major distinction between diffusion-weighted fMRI (DW-fMRI) and our methodology lies in the b-value employed. DW-fMRI typically measures cellular swelling using b-values greater than 1000 s/mm² (e.g., 1800 s/mm(sup>2</sup>). In contrast, our VN-fMRI approach measures hemodynamic responses by employing smaller b-values specifically designed to suppress signals from fast-flowing draining veins rather than detecting microstructural changes.

Regarding other functional contrasts, we agree that more layer-dependent fMRI approaches should be mentioned. In this revision, we have expanded the Introduction section to include discussions of the double spin-echo approach and CBV-based methods, such as MT-weighted fMRI, VAPER, ABC, and CBF-based method ASL. Additionally, the reference to Lu et al. (2013) has been cited in the revised manuscript. The corresponding text has been incorporated into the Introduction section to provide a more comprehensive overview of alternative functional imaging techniques.

- The comparison in Figure 2 for different b-values shows % signal changes. However, as the baseline signal changes dramatically with added diffusion weighting, this is rather uninformative. A plot of t-values against cortical depth would be much more insightful.

- Surprisingly, the %-signal change for a b-value of 0 is not significantly different from 0 in the gray matter. This raises some doubts about the task or ROI definition. A finger-tapping task should reliably engage the primary motor cortex, even at 3T, and even in a single participant.

- The BOLD weighted images in Figure 3 show a very clear double-peak pattern. This contradicts the results in Figure 2 and is unexpected given the existing literature on BOLD responses as a function of cortical depth.

- Given that data from Figures 2, 3, and 4 are derived from a single participant each, order and attention affects might have dramatically affected the observed patterns. Especially for Figure 4, neither BOLD nor VN profiles are really different from 0, and without statistical values or inter-subject averaging, these cannot be used to draw conclusions from.

We appreciate the reviewer’s suggestions. In this revision, we have made significant updates to the participant recruitment, scan protocol, data processing, and M1 delineation. Please refer to the "General Responses" at the beginning of the rebuttal and the first response to Reviewer #2 for more details.

Previously, the variation in depth-dependent profiles was calculated across upscaled voxels within a specific layer. However, due to the small size of the hand knob region, the number of within-layer voxels was limited, resulting in inaccurate estimations of signal variation. In the revised manuscript, the signal was averaged within each layer before performing the GLM analysis, and signal variation was calculated using the temporal residuals. The technical details of these changes are described in the "Materials and Methods" section. Furthermore, while the initial submission used percentage signal change for the profiles of M1, the dramatic baseline fluctuations observed previously are no longer an issue after the modifications. For this reason, we retained the use of percentage signal change to present the depth-dependent profiles. After these adjustments, the profiles exhibited a bias toward the pial surface, particularly in the absence of VN gradients.

- In Figure 5, a phase regression is added to the data presented in Figure 4. However, for a phase regression to work, there has to be a (macrovascular) response to start with. As none of the responses in Figure 4 are significant for the single participant dataset, phase regression should probably not have been undertaken. In this case, the functional 'responses' appear to increase with phase regression, which is contra-intuitive and deserves an explanation.

We agreed with reviewer’s argument. In the revised results, the issues mentioned by the reviewer are largely diminished. The updated analyses demonstrate that phase regression effectively reduces superficial bias, as shown in Figures 4 and 5.

- Consistency of responses is indeed expected to increase by a removal of the more variable vascular component. However, the microvascular component is always expected to be smaller than the combination of microvascular + macrovascular responses. Note that the use of %signal changes may obscure this effect somewhat because of the modified baseline. Another expected feature of BOLD profiles containing both micro- and microvasculature is the draining towards the cortical surface. In the profiles shown in Figure 7, this is completely absent. In the group data, no significant responses to the task are shown anywhere in the cortical ribbon.

We agreed with reviewer’s comments. In the revised manuscript, the results have been substantially updated to addressing the concerns raised. The original Figure 7 is no longer relevant and has been removed.

- Although I'd like to applaud the authors for their ambition with the connectivity analysis, I feel that acquisitions that are so SNR starved as to fail to show a significant response to a motor task should not be used for brain wide directed connectivity analysis.

We appreciate the reviewer’s comments and share the concern about SNR limitations. In the updated results presented in Figure 5, the activation patterns in the visual cortex were consistent across TEs and b values. At the motor cortex, stable activation in M1 was observed at the single-subject level across most scan protocols. However, the layer-dependent activation profiles in M1 exhibited spatial instability, irrespective of the application of VN gradients. This spatial instability is not entirely unexpected, as T2*-based contrast is inherently sensitive to factors that perturb the magnetic field, such as eye movements, respiration, and macrovascular signal fluctuations. Additionally, ICA-based artifact removal was intentionally omitted in Figure 4 to ensure fair comparisons across protocols, leaving some residual artifacts unaddressed. Variability in task performance during button-pressing sessions may have further contributed to the observed inconsistencies.

Although these findings suggest that submillimeter-resolution fMRI may not yet be reliable for individual-level layer-dependent functional mapping, the group-level FC analyses can still yield robust results. In Figure 7, group-level statistics revealed distinct functional connectivity (FC) patterns associated with superficial and deep layers in M1. These FC maps exhibited significant differences between layers, demonstrating that VN fMRI enhances inter-layer independence. Additional FC analyses with a seed placed in S1 further validated these findings (see Figure 8).

The claim of specificity is supported by the observation of the double-peak pattern in the motor cortex, previously shown in multiple non-BOLD studies. However, this same pattern is shown in some of the BOLD weighted data, which seems to suggest that the double-peak pattern is not solely due to the added velocity nulling gradients. In addition, the well-known draining towards the cortical surface is not replicated for the BOLD-weighted data in Figures 3, 4, or 7. This puts some doubt about the data actually having the SNR to draw conclusions about the observed patterns.

We appreciate the reviewer’s comments. In the updated results, the efficacy of the VN gradients is evident near the pial surface, as shown in Figures 4 and 5. In Figure 4, comparing the second and third columns (b = 0 and b = 6 s/mm², respectively, at TE = 38 ms), the percentage signal change in the superficial layers is generally lower with b = 6 s/mm² than with b = 0. This indicates that VN gradient-induced signal suppression is more pronounced in the superficial layers. Additionally, in Figure 5, the VN gradients effectively suppressed macrovascular signals as highlighted by the blue circles. These observations support the role of VN gradients in enhancing specificity by reducing superficial bias and macrovascular contamination. Furthermore, bias towards cortical surface was observed in the updated results in Figure 4.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

(1) L141: "depth dependent" is slightly misleading here. It could be misunderstood to suggest that the authors are assessing how spatial specificity varies as a function of depth. Rather, they are assessing spatial specificity based on depth-dependent responses (double peak feature). Perhaps "layer-dependent spatial specificity" could be substituted with laminar specificity?

We thank the reviewer for the suggestion. The term “depth dependent” has been replaced by “layer dependent” in the revised manuscript.

(2) L146-149: these do not validate spatial specificity.

The original text is removed.

(3) L180: Maybe helpful to describe what the b-value is to assist unfamiliar readers.

We have clarified the b-value as “the strength of the bipolar diffusion gradients” where it is first mentioned in the manuscript.

(4) Figure 1B: I think it would be appropriate with a sentence of how the authors define micro/macrovasculature. Figure 1B seems to suggest that large ascending veins are considered microvascular which I believe is a bit unconventional. Nevertheless, as long as it is clearly stated, it should be fine.

In our context, macrovasculature refers to vessels that are distal to neural activation sites and contribute to extravascular contamination. These vessels are typically larger in size (e.g., > 0.1 mm in diameter) and exhibit faster flow rates (e.g., > 10 mm/s).

(5) I think the authors could be more upfront with the point about non-suppressed extravascular effects from macrovasculature, which was briefly mentioned in the discussion. It could already be highlighted in the introduction or theory section.

We thank the reviewer’s suggestions. We have expanded the discussion of extravascular effects from macrovasculature in both the Introduction (5th paragraph) and Discussion (3rd paragraph) sections.

(6) The phase regression figure feels a bit misplaced to me. If the authors agree: rather than showing the TE-dependency of the effect of phase regression, it may be more relevant for the present study to compare the conventional setup with phase regression, with the VN setup without phase regression. I.e., to show how the proposed setup compares to existing 3T laminar fMRI studies.

In this revision, both the TE-dependent and VN-dependent effects of phase regression were investigated. The results in Figure 4 and Figure 5 demonstrated that phase regression effectively suppresses macrovascular contributions primarily near the gray matter/CSF boundary, irrespective of TE or the presence of VN gradients.

(7) L520: It might be beneficial to also cite the large body of other laminar studies showing the double peak feature to underscore that it is highly robust, which increases its relevance as a test-bed to assess spatial specificity.

We agreed. More literatures have been cited (Chai et al., 2020; Huber et al., 2017a; Knudsen et al., 2023; Priovoulos et al., 2023).

(8) L557: The argument that only one participant was assessed to reduce inter-subject variability is hard to buy. If significant variability exists across subjects, this would be highly relevant to the authors and something they would want to capture.

We thank the reviewer for the suggestions. In this revision, we have increased the number of participants to 4 for protocol development and 14 for resting-state functional connectivity analysis, allowing us to better assess and account for inter-subject variability.

(9) L637: add download link and version number.

The download link has been added as requested. The version number is not applicable.

(10) L638: How was the phase data coil-combined?

The reconstructed multi-channel data, which were of complex values, were combined using the adaptive combination method (Walsh et al.; DOI: 10.1002/(sici)1522-2594(200005)43:5<682::aid-mrm10>3.0.co;2-g). The MATLAB code for this implementation was developed by Dr. Diego Hernando and is publicly available at https://github.com/welton0411/matlab . The phase data were then extracted using the MATLAB function ‘angle’.

(11) L639: Why was the smoothing filter parameter changed (other parameters were default)?

The smoothing filter parameter was set based on the suggestion provided in the help comments of the NIFTI_NORDIC function:

function  NIFTI_NORDIC(fn_magn_in,fn_phase_in,fn_out,ARG)

% fMRI

%

%  ARG.phase_filter_width=10;

In other words, we simply followed the recommendation outlined in the NIFTI_NORDIC function’s documentation.

(12) I assume the phase data was motion corrected after transforming to real and imaginary components and using parameters estimated from magnitude data? Maybe add a few sentences about this.

Prior to phase regression, the time series of real and imaginary components were subjected to motion correction, followed by phase unwrapping. The phase regression was incorporated early in the data processing pipeline to minimize the discrepancy in data processing between magnitude and phase images (Stanley et al., 2021).

(13) Was phase regression applied with e.g., a deming model, which accounts for noise on both the x and y variable? In my experience, this makes a huge difference compared with regular OLS.

We appreciate the reviewer’s insightful comment. We are aware that the noise present in both magnitude and phase data therefore linear Deming regression would be a good fit to phase regression (Stanley et al., 2021). To perform Deming regression, however, the ratio of magnitude error variance to phase error variance must be predefined. In our initial tests, we found that the regression results were sensitive to this ratio. To avoid potential confounding, we opted to use OLS regression for the current analysis. However, we agreed Deming model could enhance the efficacy of phase regression if the ratio could be determined objectively and properly.

(14) Figure 2: What is error bar reflecting? I don't think the across-voxel error, as also used in Figure 4, is super meaningful as it assumes the same response of all voxels within a layer (might be alright for such a small ROI). Would it be better to e.g. estimate single-trial response magnitude (percent signal change) and assess variability across? Also, it is not obvious to me why b=30 was chosen. The authors argue that larger values may kill signal, but based on this Figure in isolation, b=48 did not have smaller response magnitudes (larger if anything).

We agreed with the reviewer’s opinion on the across-voxel error. In the revised manuscript, the signal was averaged within each layer before performing the GLM analysis, and signal variation was calculated using the temporal residuals. The technical details of these changes are described in the "Materials and Methods" section.

Additionally, the bipolar diffusion gradients were modified from a single direction to three orthogonal directions. As a result, the questions and results related to b=30 or b=48 are no longer applicable.

(15) Figure 5: would be informative to quantify the effect of phase regression over a large ROI and evaluate reduction in macrovascular influence from superficial bias in laminar profiles.

We appreciate the reviewer’s suggestion. In the revised manuscript, the reduction in macrovascular influence from superficial bias across a large ROI is displayed in Figure 5. Additionally, the impact on laminar profiles is demonstrated in Figure 4.

(16) L406-408: What kind of robustness?

We acknowledge that describing the protocol as “robust” was an overstatement. The updated results indicate that the current protocol for submillimeter fMRI may not yet be suitable for reliable individual-level layer-dependent functional mapping. However, group-level functional connectivity (FC) analyses demonstrated clear layer-specific distinctions with VN fMRI, which were not evident in conventional fMRI. These findings highlight the enhanced layer specificity achievable with VN fMRI.

(17) Figure 8: I think C) needs pointers to superficial, middle, and deep layers? Why is it not in the same format as in Figure 9C? The discussion of the FC results could benefit from more references supporting that these observations are in line with the literature.

In the revised results, the layer pooling shown in Figure 9c has been removed, making the question regarding format alignment no longer applicable. Additionally, references supporting the FC results have been added to the revised Discussion section (7th paragraph).

(18) L456-457: But correlation coefficients may also be biased by different CNR across layers.

That is correct. In the updated FC results in Figure 7 to 9, we used group-level statistics rather than correlation coefficients.

Reviewer #3 (Recommendations For The Authors):

The results in Figure 2-6 should be repeated over, or averaged over, a (small) group of participants. N=6 is usual in this field. I would seriously reconsider the multiband acceleration - the acquisition seemingly cannot support the SNR hit.

A few more specific points are given below:

(1) Abstract: The sentence about LGN in the abstract came for me out of the blue - why would LGN be important here, it's not even a motor network node? Perhaps the aims of the study should be made more clear - if it's about networks as suggested earlier then a network analysis result would be expected too. Expanding the directed FC findings would improve the logical flow of the abstract. Given the many concerns, removing the connectivity analysis altogether would also be an option.

We thank the reviewer for the suggestions. The LGN-related results indeed diluted the focus of this study and have been completely removed in this revision.

(2) Line 105: in addition to the VASO method, ..

The corresponding text has been revised, and as a result, the reviewer’s suggestion is no longer applicable.

(3) If out of the set MB 4 / 5 / 6 MB4 was best, why did the authors not continue with a comparison including MB3 and MB2? It seems to me unlikely that the MB4 acquisition is actually optimal.

Results: We appreciate the reviewer’s suggestions. In this revision, we decreased the MB factor to 3, as it allowed us to increase the in-plane acceleration rate to 3, thereby shortening the TE. The resulting sensitivity for both individual and group-level results is detailed in earlier responses, such as the response to Q16 for Reviewer #2.

(4) The formatting of the references is occasionally flawed, including first names and/or initials. Please consider using a reliable reference manager.

We used Zotero as our reference manager in this revision to ensure consistency and accuracy. The references have been formatted according to the APA style.

(5) In the caption of Figure 5, corrected and uncorrected p values are identical. What multiple comparisons correction was made here? A multiple comparisions over voxels (as is standard) would usually lead to a cut-off ~z=3.2. That would remove most of the 'responses' shown in figure 5.

We appreciate the reviewer’s comment. The original results presented in Figure 5 have been removed in the revised manuscript, making this comment no longer applicable.

for age and height (tal vez poner age y height en negrita)

Quizás incluir head() y tail()

Ça veut dire que quand les avocat·es sont seul·es, les Jaf parlent moins. Il y a 25% de ces audiences où personne d'autre ne parle cf 3e quartile. Quand les J sont seul·es, iels parlent plus mais ce n'est que 50% du temps

Avec Linda, les F parlent moins que les H quand pas d'avocat·e. NON, voir distribution Avec Thierry, elles parlent plus en présence d'avocat·e alors que c'est généralement moins quand il y en a.

Ce sont des courbes de niveau. Plus la couleur est claire, plus il y a une concentration d'individus.

Les NA sont plus souvent concerné·es par des dossiers avec violences et parlent moins parce qu'il y a des avocat·es dans ces dossiers-là. Creuser pourquoi plus concerné·es par dossiers avec violences.

Les dossiers à configuration mixte prennent le plus de temps. Peut-être est-ce dû à la complexité du dossier car les A accompagnent plus les J dans ces cas là. Peut-être est-ce dû au fait qu'il y a plus de personnes dans ces configurations.

Ne pas raisonner au niveau quanti sur les conciliés parce qu'il y en a trop peu. Les justiciables sont bcp plus nombreux en proportion à venir seul·es quand accord total.

Chez les Jaf qui font uniquement des Jafad, il y a de vraies différences entre Linda et Patrice, d'un côté, et Eugénie et Sandra, de l'autre (les premier·es sont magistrat·es de formation, les secondes, sont avocates de formation initiale). Comment expliquer cet écart ?

Thierry a la plus grande amplitude. Linda ne descend généralement pas sous les 3h30. C'est lié à l'audience qui dure seulement 1h52 et au fait qu'il y a peu d'audience par Jaf. Faire le lien avec le temps de parole des justiciables et par statut pour analyser ces différences. Il y a un effet pro qui existe sur les durées.

Là c'est l'inverse donc les avocat·es parlent plus quand il y a des problèmes et moins quand il y a des accords. Les Jaf et justiciables parlent plus en cas d'accord. Ça illustre le rôle des différent·es acteurices à l'audience. Les avocat·es sont plus impliqué·es dans les dossiers à problèmes et le sont moins dans les accords EN AUDIENCE.

Quand il y a des violences, les avocat·es parlent plus et les autres parlent moins

Les dossiers "à problèmes" sont donc entendus plus longuement que les standards. Les plus long sont ceux où il y a des violences et/ou des mesures d'investigation. Ce sont des dossiers qui peuvent être considérés comme conflictuels par les Jaf.

La configuration A+J est supérieure quand il y a des violences. Les justiciables et les avocat·es viennent bcp moins seul·es.

for age and height (tal vez poner age y height en negrita)

Quizás incluir head() y tail()

DOI: 10.1016/j.molcel.2018.05.013

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bdscstockkeepers

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/gigascience/giaf005

Resource: Mikado (RRID:SCR_016159)

Curator: @scibot

SciCrunch record: RRID:SCR_016159

What is this?

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

Learn more at Review Commons

Reply to the reviewers

General Statement:

We appreciate the reviewers for acknowledging the impact of our work to the field of neurodegeneration and motor neuron diseases as well as for the understanding of the biology and function of VAPB itself; “the idea of assaying the function of ALS-causing VAPB mutants in iPSC-derived neurons is great and would be a great asset to the field” (Reviewer 1) “The new iPSC-derived system to study VAPB mutations in human motor neurons is significant and has led the authors to discover new functions for VAPB (i.e., mitochondria-ER contacts).” (Reviewer 2). The main concern raised by both reviewers is that the doxycycline inducible VAPB iPSC lines may not fully recapitulate the physiological environment found in patients, as patients are heterozygous for the VAPB P56S mutation, and our lines had VAPB under the control of an exogenous doxycycline inducible promoter. While we maintain that the doxycycline inducible lines do provide their own substantial benefits to the interrogation of the ALS pathogenesis, namely the opportunity to identify mutant VAPB interactors compared to wild type VAPB interactors through proteomics, as well as to identify pathogenesis associated to mutant VAPB without the confounding effects of wild type VAPB, we do agree with both reviewers that the inclusion of heterozygous patient iPSC lines would increase the significance of our study. Thus, in this revised manuscript we have included iPSC patient lines harboring the VAPB P56S mutation which we reprogrammed in our lab and to uphold the highest standards in the stem cell field we also performed CRISPR mediated genomic editing to generate the isogenic corrected pair. In our assessment of the ALS patient iPSC-derived motor neurons, we have already observed the same mitochondria and translation dysfunction previously described in our work with the doxycycline-inducible VAPB P56S mutant iPSC lines. Most importantly, these phenotypes were also similarly rescued by the integrated stress response inhibitor (ISRIB). Collectively, these findings suggest that the proposed mechanism initially identified in doxycycline-inducible iPSC-derived motor neurons is preserved in ALS patient iPSC-derived motor neurons.

Reviewer #1 Major Point 1. The method of knocking out and selecting an inducible line in problematic. VAPB is an essential gene-patients with P56S are always heterozygotes, since nonfunctional VAPB is embryonic lethal. Selecting a knockout cell line is already choosing a parent that is very far from physiological, and the reexpression of P56S VAPB as the sole form also is not a good a model for understanding the contributions of P56S to disease. This approach is unusual, as it seems to overlook the advantages of working with iPSCs and patient-derived neurons. Unfortunately, the value of this amazing and rare system is diminished by the design of the selection method.

*Reviewer #2 Major Point 1. Why did the authors decide to make VAPB knockouts and then introduce the WT or P56S VAPB constructs on a lentivirus instead of generating the point mutations (or correcting them) directly in the endogenous locus? Data in Extended Fig. 1c and Extended Fig. 2a indicate significant differences in either the kinetics of WT vs. P56S VAPB expression (1c) or levels (2a). It seems important to be able to compare comparable levels of WT and mutant proteins, especially for the interpretation of the subsequent IP-MS experiments to identify PTP151. The authors may wish to consider generating (or obtaining) isogenic lines harboring the mutations at the endogenous locus so that equal levels of expression of WT and mutant VAPB can be assessed. *

Carried Out Revisions

The development of the inducible system for VAPB was specifically designed to enable a systematic investigation of the effects of mutant VAPB (VAPB P56S) on cellular homeostasis while minimizing confounding influences from the wild-type (WT) protein. Additionally, this system allowed us to assess VAPB P56S binding partners and compare them to those of VAPB WT, which would not have been feasible in the context of heterozygous ALS8 patient cells.

In response to Reviewer 2’s concern regarding differences in VAPB WT and VAPB P56S expression levels, we utilized ALS8 patient cells and familial controls to calibrate the doxycycline dose response. This approach allowed us to precisely adjust VAPB protein levels in the inducible system to match those observed in ALS8 patient and familial control iPSCs. As a result, the inducible VAPB P56S iPSCs recapitulate the VAPB expression levels found in ALS8 patient iPSCs, whereas the inducible VAPB WT iPSCs mimic the levels present in familial control iPSCs. Furthermore, the differential expression of VAPB between ALS8 patient and control cells is well documented in the literature (Mitne-Neto, et al., 2011)

Nonetheless, we acknowledge the significance of studying ALS patient-derived iPSCs. To address this, we obtained fibroblasts from an ALS8 patient carrying the heterozygous VAPB P56S mutation, originating from a genetic background distinct from the cells used in our inducible system. These fibroblasts were reprogrammed into iPSCs in our laboratory, followed by CRISPR/Cas9-mediated genome editing to generate isogenic corrected iPSCs as controls.

The resulting iPSC isogenic pair was differentiated into motor neurons following the protocol described in our manuscript. Notably, ALS8 patient iPSC-derived motor neurons exhibited reduced mRNA translation, as assessed by the SUnSET assay (Fig. 6A), along with a decrease in mitochondrial membrane potential, as determined using the JC-1 assay (Fig. 6B). These findings confirm that the hypotranslation and mitochondrial dysfunction initially identified in VAPB P56S doxycycline-inducible iPSC-derived motor neurons were successfully recapitulated in ALS8 patient iPSC-derived motor neurons. Furthermore, ISRIB treatment effectively rescued these phenotypic defects.

Overall, these results demonstrate that the molecular and cellular abnormalities identified in the original inducible system can be reliably reproduced in an ALS patient-derived model with a different genetic background, thereby reinforcing the significance and broader applicability of our findings.

Currently, we are investigating the electrophysiological properties of ALS8 patient iPSC-derived motor neurons compared to the isogenic control using the multi-electrode array (MEA) system. If a reduction in electrophysiological activity is observed, consistent with our initial findings in doxycycline-inducible VAPB P56S iPSC-derived motor neurons, we plan to treat the heterozygous patient-derived cultures with ISRIB on day 45 of differentiation. This will allow us to determine whether neuronal firing deficits in the heterozygous patient-derived motor neurons can be rescued.

All other concerns have been addressed in this revision.

Citation:

1. Mitne-Neto M, Machado-Costa M, Marchetto MC, Bengtson MH, Joazeiro CA, Tsuda H, Bellen HJ, Silva HC, Oliveira AS, Lazar M et al (2011) Downregulation of VAPB expression in motor neurons derived from induced pluripotent stem cells of ALS8 patients. Hum Mol Genet 20: 3642-3652 Reviewer #1 Major Point 2. The interactome analysis is not controlled properly to interpret. It is not the total amount of VAPB that needs to be used as the normalization control, since it is already known 90+% of the P56S VAPB is in cytoplasmic aggregates. The authors need to normalize to the amount of VAPB that made it to the contact sites-a much smaller amount in the cells expressing the diseased form. For example, the fact that the authors can still pull down detectable PTPIP51 in Fig. 2e actually argues for the opposite conclusion than what the authors have stated-if the authors have actually expressed just P56S in a true knock out condition, this means that P56S CAN still bind to PTPIP51 (and possibly even better than WT, as several previous papers have suggested-since there is ~100-fold less available for binding). Without an appropriate normalization, the authors cannot make any conclusion about how to interpret the results of this part of the paper.

Carried Out Revisions

We sincerely thank Reviewer 1 for highlighting this critical point. Previous studies have demonstrated that the VAPB P56S mutation increases its binding affinity for PTPIP51; however, it has been proposed that the overall reduction in VAPB levels in cells harboring the P56S mutation leads to a decrease in ER-mitochondrial contacts despite the enhanced affinity (De Vos et al., 2012).

To address this, we have repeated the co-immunoprecipitation experiment and normalized the data to VAPB levels. Consistent with Reviewer 1’s hypothesis, when normalized to soluble VAPB, we observe an increased affinity of VAPB P56S for PTPIP51. However, the total amount of PTPIP51 co-immunoprecipitated with VAPB remains significantly lower in the mutant compared to WT, likely due to the overall reduced levels of soluble VAPB P56S. This finding aligns with both Reviewer 1’s comment and the previous observations reported by De Vos et al. (2012).

Figure 2E has been updated to reflect the normalized co-immunoprecipitation data.

Citation:

1. De Vos, K. J. et al. VAPB interacts with the mitochondrial protein PTPIP51 to regulate calcium homeostasis. Hum Mol Genet 21, 1299-1311, doi:10.1093/hmg/ddr559 (2012). *Reviewer #1 Major Point 3. The electron microscopy data is not interpretable in this form. The authors have provided no data at all on how analysis was performed, how contact sites were defined, how samples were collected and ensured to be representative, blinding that was performed, how sources of bias were accounted for, etc. It is clear even from what little is shown that the authors are not focused on what matters to address their own questions. For example, apart from the P56S Day 35 example, none of the "contact sites" selected for the figure are even possible to be mediated by VAPB, since the distance between the ER and the mitochondria is too far for the maximum tethering distance of VAPB-PTPIP51. Since the authors have neglected to include scale bars in their zooms, the reader cannot be sure of the distance, but it is clearly in excess of 50 nm since there are obviously visible ribosomes between the two organelles. Additionally, the authors provide no information on what "% mitochondria in contact with ER" means (By organelle? By unit surface area? Is the data grouped by cell or all comes from a single cell? How do you account for contact sites vs. proximity by crowding? Etc.). *

2. *

Carried Out Revisions

We thank Reviewer 1 for their insightful comments on the analysis of the electron microscopy (EM) data and recognize the need for greater clarity in describing our quantification approach. To address this, we have revised the Electron Microscopy section of the Methods to explicitly detail our methodology for quantifying ER-mitochondria-associated membranes (ER-MAMs), as follows:

"A series of images at various magnifications were provided, and data were collected from unique images at the highest magnification for each condition: D35 WT (13 unique images), D35 P56S (21 unique images), D60 WT (13 unique images), and D60 P56S (18 unique images). All images for a given condition originated from a single well of a 12 mm Snapwell™ Insert with 0.4 µm Pore Polyester Membranes (Corning). No information on cell grouping or sampling strategy was supplied with the images; therefore, we treated the dataset as a random sampling of the culture. Images were blinded, and quantification was performed using FIJI. Mitochondria were identified based on the presence of cristae and a double membrane. The mitochondrial perimeter was traced using the free-draw tool, and the length of ER membranes within 50 nm of this perimeter was quantified. The final measurement represents the percentage of each mitochondrion’s perimeter in contact with the ER, aggregating all visually distinct ER-MAMs, as continuity beyond the imaging plane cannot be determined (Cosson et al., 2012; Csordás et al., 2010; Stoica et al., 2014). Each data point on the graph corresponds to a single mitochondrion, with data collected from multiple cells across the unique images provided by the Core, originating from a single biological replicate."

Regarding the quantification of ER-MAM distances, VAPB has not been definitively localized exclusively to either the rough or smooth ER. To ensure comprehensive analysis, we quantified ER-MAMs without restricting our assessment to a specific ER subdomain. We adopted a 50 nm threshold as the maximum distance for defining ER-MAMs, a well-established criterion that Reviewer 1 also referenced.

Furthermore, we disagree with Reviewer 1’s assertion that the presence of ribosomes should justify extending the ER-MAM threshold beyond 50 nm. Ribosomes in human cells have a well-documented diameter of 20–30 nm (Anger et al., 2013), which does not support the claim that an observed ribosome within the contact site necessitates a redefinition of the ER-MAM boundary.

We stand by our methodological approach and have updated the manuscript to ensure precision and clarity in our EM data analysis.

Citations:

1. Cosson, P., Marchetti, A., Ravazzola, M. & Orci, L. Mitofusin-2 independent juxtaposition of endoplasmic reticulum and mitochondria: an ultrastructural study. PLoS One 7, e46293 (2012).
2. Csordás, G. et al. Imaging interorganelle contacts and local calcium dynamics at the ER-mitochondrial interface. Mol Cell 39, 121-132 (2010).
3. Stoica, R. et al. ER–mitochondria associations are regulated by the VAPB–PTPIP51 interaction and are disrupted by ALS/FTD-associated TDP-43. Nat Commun 5, 3996 (2014).
4. Anger AM, Armache JP, Berninghausen O, Habeck M, Subklewe M, Wilson DN, Beckmann R. Structures of the human and Drosophila 80S ribosome. Nature. 2013 May 2;497(7447):80-5. doi: 10.1038/nature12104. PMID: 23636399. We would like to thank the Editor of Review Commons for clarifying Reviewer #1’s Major Point 4 and will be responding to the Editor’s interpretations as detailed in the Editorial Note.

Reviewer #1 Major Point 4. The strange pooling of data without explanation, unusual sample sizes, and lack of clarity about statistical testing, false hypothesis testing, and really any clear rigor in statistics of any kind make it impossible for a reader to have any confidence in the results presented here. The fact that every experiment in the paper has just enough n to trigger statistical significance as determined by the authors raises some concerns, suggesting potential biases. The reliability of these conclusions is questionable, especially if the authors were blinded to the identity of their own samples. This is particularly relevant for the EM data, where the determination of contact sites appears to have been made subjectively.

Reviewer #1: "The strange pooling of data without explanation"

• *

- When looking into the figures and their captions in more detail, we could also not understand the nature of the replicates and how the data was aggregated or “pooled”. In Figure 1, the stated number of replicates is "N=8 separate wells”. It is unclear whether these are 8 wells from a single dissociation/replating procedure (the procedure is described in Materials & Methods as follows: "Motor neurons were dissociated on day 25 of differentiation and re-plated onto 48-well MEA plate") or whether the eight are sampled across multiple plates across cultures obtained from independent dissociations procedures.

• We apologize for the lack of clarity and specificity. We have updated the Multi-Electrode Array Recordings portion of the Methods Section with the following: “iPSC-derived MNs from a single well of a 6-well plate thawed as day 15 MNP were dissociated and plated across 8 wells of the MEA plate. Each point on the graph is an average of the weighted mean firing rate of those 8 wells, normalized for cell count across genotypes, obtained after all firings were recorded by dissociating 2 wells per line, counting and averaging the cell numbers, and then normalizing all firings by the ratio of cell number between WT and P56S. Wells with no firing detected were excluded from quantification.”

- In Figure 3, the number of replicates is "N=13-21 images”. Here, it is unclear whether these images come from the same or independent samples, how many quantifications were performed per image, and how many images per sample were used.

• We have updated the Electron Microscopy Methods Section with the following: “We were provided with a series of images and magnifications and were able to gather data from unique images at the highest magnification level for each of the following categories: D35 WT: 13 unique images, D35 P56S: 21 unique images, D60 WT 13 unique images, D60 P56S: 18 unique images. All images for a given line come from a single well of a 12 mm Snapwell™ Insert with 0.4 µm Pore Polyester Membranes (Corning). No indication of cell grouping or sampling techniques was provided with the images, therefore the images were quantified as a random sampling of the culture. *Images were then blinded, and FIJI was used to quantify.” *

We are happy to make all images publicly available.

*- We also note that replicates are not mentioned in the proteomics analysis. *

• We apologize for missing this and thank the editor for mentioning it. The Proteomics portion of the methods section has been updated with the following: “The identification of VAPB binding partners via mass spectrometry was performed with one biological sample, while the validation of VAPB-PTPIP51 binding via co-immunoprecipitation and Western Blot was performed with three separate biological replicates.”

Reviewer #1: “unusual sample sizes”:

• *

- The wording is indeed not very explicit, but we believe it is reasonable to assume that this point refers to "N=13-21 images” and that it is not clear how the data were pooled. The reviewer makes the related point: "Is the data grouped by cell or all comes from a single cell?", which provides further context to this point.

• We thank the editor for this clarification, our response to Reviewer #1 Major Point 3 details the updates to Electron Microscopy section of the Methods and covers this. All images were provided to us by the Case Western Reserve University Electron Microscopy Core based on the number of quality images their team were able to obtain from our samples. Reviewer #1: “lack of clarity about statistical testing”:

• *

- We agree that without a clear description of the nature of the replicates, the statistical analysis is unclear.

• We hope with the updated clarity on the description of the nature of the replicates as detailed above, the nature of the statistical analysis is clearer. In addition, we have added a Statistical Analysis subsection in the Methods Section. Reviewer #1: "The reliability of these conclusions is questionable, especially if the authors were blinded to the identity of their own samples.”:

• *

- This is a typo; the word “not” is missing. It should read: "if the authors were NOT blinded to the identity…” and refers to concerns raised by the reviewers about evaluating the EM images.

• We apologize for this omission, each unique image was blinded after we received them from the core, and then quantification was performed on the blinded images. The Electron Microscopy portion of the methods section has been updated to include: “We were provided with a series of images and magnifications and were able to gather data from unique images at the highest magnification level for each of the following categories: D35 WT: 13 unique images, D35 P56S: 21 unique images, D60 WT 13 unique images, D60 P56S: 18 unique images. All images for a given line come from a single well of a 12 mm Snapwell™ Insert with 0.4 µm Pore Polyester Membranes (Corning). No indication of cell grouping or sampling techniques was provided with the images, therefore the images were quantified as a random sampling of the culture. Images were then blinded, and FIJI was used to quantify.”

Reviewer #1: “The figures suggest a lack of appropriate blinding, with cherry-picking evident even in the ‘representative’ images'”

• *

- We agree the wording is somewhat problematic. However, we also feel that there is a discrepancy between the differences highlighted between the EM images shown in Fig 3A and a rather modest change of the median by only a few percent, as shown in the respective violin plots. We agree with the reviewer that the images of Fig 3A might, therefore, not be “representative” of the quantified changes.

• We appreciate the editor's clarification and have selected images that more accurately represent the subtle changes in ER-MAMs observed in our quantification. These images have been included in Figure EV6 and referenced accordingly in the manuscript to ensure a balanced depiction of our findings. Additionally, we are prepared to make all images publicly available. We would like to again thank the editor for their clarification on Reviewer #1’s Major Point 4 as well as their agreement on the inappropriate nature of some of Reviewer #1’s comments.

*Reviewer#1 Minor points: 1. It is not accurate to describe Day 60 neurons as "aged" in the context of P56S-induced disease or imply they are a model for human aging. I could be mistaking, as I am not an iPSC expert, but I believe the field uses these terms in the context of iPSC-derived neurons to mean something more akin to "mature". The authors try to invoke this to argue for the relevance of their results to patient disease, unless the authors know this is somehow actually representative of neurons from older patients, I think this is misleading. *

Carried Out Revisions

We apologize for any confusion. Our use of the term "aged" was intended solely as a relative descriptor, indicating that day 60 motor neurons had been maintained in culture for a longer duration than day 35 motor neurons. It was not meant to suggest that these neurons represent a specific age or disease state, but rather that they had been cultured for an extended period.

Furthermore, we use the term "mature" specifically in the context of motor neuron differentiation to indicate the expression of motor neuron-specific markers, which occurs by day 25 of differentiation. To avoid ambiguity, we have revised the manuscript to use the term "culture time" instead, ensuring clarity in our terminology.

*Reviewer #1 Minor Point 2. The JC-1 experiment is not being appropriately controlled. These results are predicted by increased cell or mitochondrial death even if the membrane potentials are identical. The authors need to control for apoptotic signaling if they want to make this conclusion. There is an accepted standard in the mitochondrial field for assaying mitochondrial membrane potential (generally using TMRE or TMRM, but JC-1 can be used with proper controls), but it requires lots of careful controls not performed here (normalization to oligomycin- and FCCP-treated cells as a bare minimum. *

Carried Out Revisions

We would like to thank Reviewer 1 for this comment. We apologize for the omission, and we did treat the cells with CCCP provided in the JC-1 kit as a positive control. The JC-1 subsection of the methods has been updated to reflect this with the following: “A separate aliquot of cell suspension was also incubated with 1 uL of the supplied 50mM CCCP for 15 min prior to JC-1 dye addition, to act as a positive control and ensure the JC-1 dye was correctly detecting low MMP populations.”

• The flow cytometry experiments are problematic in general since the authors state that part of their incentive for studying mitochondria in this model is due to effects at synapses, and the sample preparation for the cytometer involved dissociating the cells (i.e.-removing all of the processes where synapses mostly reside). *

Carried Out Revisions