这张图片展示的是一份来自1933年的意大利《未来主义》周刊（Futurismo），这期杂志刊登了一些与未来主义运动相关的艺术和文化内容。

主要内容分析：

1. 标题与日期
2. Futurismo 是这本杂志的标题，它是未来主义艺术运动的代表性出版物。该期杂志的日期为 1933年4月16日，第32期。

3. "Dal 27 aprile al 5 maggio" 意味着从4月27日到5月5日，这一时间段内举办了相关的展览或活动，可能是关于未来主义的艺术展示。

4. 重要元素与形式

5. Aeropittura（飞行绘画）：这部分描述了未来主义艺术中的一种风格——飞行绘画，强调通过表现飞行的速度和动感来体现现代科技的影响。

6. 诗歌和视觉形式：这部分列出了“未来主义”诗歌的表现形式，诗歌不仅仅是文字，还通过不同的排列方式来传达感官上的冲击。例如，文字的排列和形状的变化（例如以箭头方向排列），是对传统语言和诗歌形式的突破，体现了未来主义强调动感、技术和速度的特征。

7. 意大利未来主义

8. Marinetti：F.T. Marinetti 是未来主义运动的创始人之一，他的名字出现在杂志上，表明他与这期杂志的内容有密切关系。Marinetti 曾提出未来主义的艺术理念，强调破除传统艺术形式，赞美现代工业和科技进步。

9. 诗歌形式

10. 这期杂志包含了许多带有未来主义特点的诗歌，在视觉上，文字被打乱并以不规则的方式排列（如箭头指向），目的是让文字的形态和排列本身也传达一种动态感，符合未来主义对速度、技术和工业感的崇尚。

11. 展览活动

12. 杂志的右侧提到的“Mostra Futurista Mantovana”（曼托瓦未来主义展）表明，这一期杂志与某个未来主义艺术展览相关。

总结：

这份《未来主义》杂志的内容展示了未来主义艺术运动中的几个重要特点：突破传统诗歌和绘画形式，通过创新的文字排列和动感表现来传递现代科技和动态的理念。杂志内容不仅涉及艺术创作，还包括展览和活动的介绍，反映了未来主义对现代性、速度和科技的热情。

Author response:

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

Reviewer #1 (Public review):

(1) Adding microscopy of the untreated group to compare Figure 2A with would further strengthen the findings here.

First of all, we would like to thank Reviewer #1 for their comments and efforts on our manuscript. We have carefully revised it. We used a time-lapse method to capture images at 0 minutes, before any drugs were added. We will change '0 min' to 'untreated,' which will further strengthen the findings.

(2) Quantification of immune infiltration and histological scoring of kidney, liver, and spleen in the various treatment groups would increase the impact of Figure 4.

Thank you very much to Reviewer #1 for their comments and efforts on our manuscript. We have revised it carefully. We conducted quantitative analysis of immune infiltration in the kidney, liver, and spleen across different treatment groups. However, due to the extremely low number of abnormal cells in the negative control, treatment, and prophylactic groups, neither the instrument nor manual methods could reliably gate the cells. Consequently, quantification of immune infiltration and histological scoring were not performed.

(3) The data in Figure 6 I is not sufficiently convincing as being significant.

Thanks so much for Reviewer #1 comments and efforts for our manuscript. We have revised it carefully. Previous researches have shown that antibiotics and other drugs can cause alterations in gut microbiota. Therefore, we plan to study the effects of antibiotics on gut microbiota. To conduct this research, we need to isolate these microbes from the gut. Although this process is challenging, we still aim to explore the gut microbiota. If possible, we will continue to delve into interesting aspects of how antibiotics affect gut microbiota in future studies.

(4) Comparisons of the global transcriptomic analysis of the untreated group to the PC, LP, and LT groups would strengthen the author's claims about the immunological and transcriptomic changes caused by linalool and provide a true baseline.

Thanks so much for Reviewer #1 comments and efforts for our manuscript. We have revised it carefully. Due to the initial research design and data analysis strategy, we have focused on comparisons among the PC, LP, and LT groups to more directly explore the differences under various treatment conditions. Specifically, while the transcriptomic data from the untreated group could provide a basic reference, it has shown limited relevance to the core hypotheses of our study. Our research has aimed to investigate the immunological and transcriptomic changes among the treatment groups rather than comparing treated and untreated states. We believe that the current experimental design and data analysis have effectively revealed the mechanisms of linalool and that the additional comparisons among the treatment groups have further supported our conclusions. We hope the reviewer understands the rationale behind our experimental design. If there are additional suggestions, we are more than willing to further optimize the content of our manuscript.

Reviewer #2 (Public review):

(1) The authors have taken for granted that the readers already know the experiments/assays used in the manuscript. There was not enough explanation for the figures as well as figure legends.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. We will provide more detailed explanations of the experiments and assays used in the manuscript, as well as enhance the descriptions in the figure legends, to ensure that readers have a clear understanding of the figures and their context.

(2) The authors missed adding the serial numbers to the references.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. We will add serial numbers to the references to ensure proper citation and improve the clarity of our manuscript.

(3) The introduction section does not provide adequate rationale for their work, rather it is focused more on the assays done.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. We will add a section to the introduction that provides a rationale for our work, specifically focusing on the impact of plant extract on immunoregulation.

(4) Full forms are missing in many places (both in the text and figure legends), also the resolution of the figures is not good. In some figures, the font size is too small.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We will ensure that all abbreviations are expanded where necessary, both in the text and figure legends. Additionally, we will improve the resolution of the figures and increase the font size where needed to enhance clarity.

(5) There is much mislabeling of the figure panels in the main text. A detailed explanation of why and how they did the experiments and how the results were interpreted is missing.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. We will improve the labeling of the figure panels, provide detailed explanations of the experimental methods, including their rationale and interpretation, and clarify the connections between the methods.

(6) There is not enough experimental data to support their hypothesis on the mechanism of action of linalool. Most of the data comes from pathway analysis, and experimental validation is missing.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Actually, in our manuscript the transcriptomic data are not alone, and we carried out many experiments to substantiate the changes inferred from the transcriptomic data as SEM, TEM, CLSM, molecular docking, RT-qPCR, histopathological examinations. The detailed information is listed as below.

As shown in Figure 2, we combined the transcriptomic data related to membrane and organelle with SEM, TEM, and CLSM images. After deep analysis of these data and observation together, we illustrated that cell membrane may be a potential target for linalool.

As shown in Figure 3, we carried out molecular docking to explore the specific binding protein of linalool with ribosome which were screen out as potential target of linalool by transcriptomic data.

As shown in Figure 5, transcriptomic data illustrated that linalool enhanced the host complement and coagulation system. To substantiate these changes, we carried out RT-qPCR to detect those important immune-related gene expressions, and found that RT-qPCR analysis results were consistent with the expression trend of transcriptome analysis genes.

As shown in Figure 4 and 5, transcriptomics data revealed that linalool promoted wound healing tissue repair, and phagocytosis (Figure. 5E). To ensure these, we carried out histopathological examinations, and found that linalool alleviated tissue damage caused by S. parasitica infection on the dorsal surface of grass carp and enhancing the healing capacity (Figure. 4G).

Overall, we will conduct additional experiments to verify the mechanism of action of linalool in the future.

Reviewer #1 (Recommendations for the authors):

(1) Figure 1 Panel G is not referenced in the legend, this should be fixed

Thanks so much for Reviewer #1 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 1. The order of Panel F and G in Figure 1 is wrong. We have modified the order of Figure 1.

(2) Statistical comparisons between groups in Figure 4 Panels C-F is lacking and should be added.

Thanks so much for Reviewer #1 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 4 C-F. We have added statistical comparisons between groups in Figure 4 Panels C-F.

(3) Capitalize Kidney label in Figure 4G.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 4G. We have capitalized the K of kidney.

Reviewer #2 (Recommendations for the authors):

(1) The authors missed adding the serial numbers to the references. I could not go through the references to cross-check if they cited the right ones because it's extremely difficult to figure out which one corresponds to which reference number.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the references. We have added the serial numbers to the references.

(2) In the last paragraph of the introduction section, most of the techniques in the paper were summarized which does not go with the flow of the paper. The introduction should not be focused on the different techniques used the focus should be more on the rationale of the work. It would be nice if the last paragraph could be rewritten.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 85-94. We have added a section to the introduction that provides a rationale for our work, specifically focusing on the impact of plant extract on immunoregulation.

(3) The resolution of the figures is not good.

Thank you for your suggestion. We have revised it carefully. Please check all the figures. We have increased the resolution and size of all the figures.

(4) Mostly, the figure legends sound like results, with not enough explanation. Full forms are missing in many places which would make the readers go back to the text/other figures each time.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it throughout the manuscript and all the figure legends. We have added full names and abbreviations to both the manuscript and all the figure legends so that we don't make the readers go back to the text/other figures each time.

(5) Figure 1:

Figure 1A: there is not enough explanation for this panel. It's not clear from the text which other EOs than Linalool are referred to here. Which EOs were extracted from daidai flowers?

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in the Figure 1A. Figure 1A is divided into “Essential oils (EOs)” and “The main compounds of EOs” to make it easier to distinguish.

Figure 1B: do the three different wells of each set represent three replicates? If so, are they biological/technical replicates? Also, I'm not sure how the MFC was determined from this figure (line 116) because clearly this panel only corresponds to the determination of MICs, not MFCs.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 126-130. The three different wells of each set represent three biological replicates. After adding 5 μL of resazurin dye, when the color of the wells turned to pink, the linalool concentration in the first non-pink well corresponded to the MIC. The culture liquid in the well where no mycelium growth was seen was marked onto the plate and incubated at 25°C for 7 days. The well with the lowest linalool concentration and no mycelium growth was identified as MFC.

Figure 1C: the figure legend says that the effect of linalool on mycelium growth inhibition was done over a 6hr timepoint but according to the figure the timepoint was 60hr. I am also confused about the concentrations of linalool used. Although a range of concentration from 0 to 0.4% is mentioned, I only see the time vs diameter curves for 7 concentrations.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 983 and Figure 1C. We have changed 6 h to 60 h in the figure legends. The reason why only the time vs diameter curves for 7 concentrations in Figure1C is that the growth inhibition of 0.4%, 0.2% and 0.1% linalool on mycelial growth is the same. As a result, the time vs diameter curves coincide. We have shown the time and diameter curves of 0.4%, 0.2% and 0.1% concentration with three dotted lines of different colors and sizes in Figure 1C.

Figure 1D: mislabeled as 1G in the figure panel.

Figures 1E and 1G: Figure 1E is missing and I do not see any figure legend for Figure 1G.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 1. The order of Panel F and G in Figure 1 is wrong. We changed the order of Figure 1 ABCDEF, no Figure G.

Overall, Figure 1 is very confusing and needs rewriting. Also, there is a need to add more explanation of the figure panels in the results section.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 1. We have corrected all the problems in Figure1. And we have added more explanation of the figure panels in the results section, and increased the correlation between methods, in order to show how to carry out the experiment logically and interpret the results, please check them in Line 126-130, 144-147, 174-179, 213-217, 343-345, 677-682.

(6) Figure 2:

The authors could justify the reason for doing the experiments before moving into the results they got.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the methods and results in the manuscript, please check them in Line 126-130, 144-147, 174-179, 213-217, 343-345, 677-682. We have added more explanation of the figure panels in the results section, and increased the correlation between methods, in order to show how to carry out the experiment logically and interpret the results.

What concentration of linalool was used?

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 992-996. The mycelium treated with 6×MIC (0.3%) linalool was observed by Confocal laser scanning microscopy (CLSM), and the mycelium treated with 1×MIC 0.05% linalool was observed by Scanning Electron Microscope (SEM) and transmission electron microscopy (TEM).

The full form of DEGs has been mentioned later, but it should be mentioned in the figure legend of Figure 2 as this is the first time the term was used. Also, what is the full form of DEPs?

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 168, 175, 182, 631, 998, 1001. The word DEPs in Figure 2I was incorrect, and we have changed DEPs to DEGs.

Is there a particular reason for looking into the cellular component rather than molecular function and biological processes in the GO analysis? (what I see is that Figure 2H indicates the prevalence of catalytic activity, binding, cellular, and metabolic processes as well). Also, there is not enough explanation of the observation from Figure 2I (both in the results section and figure legend).

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 174-179, 998-1002 (Figure 2I). The reason we looked at cellular components rather than molecular functions and biological processes in GO analysis is because we focused more on the effects of cell membranes and cell walls. These results are closely related to and echo the results of our scanning electron microscopy (SEM) and transmission electron microscopy (TEM), and also support the results of electron microscopy. Enough explanations have added to the results and figure legend section to explain the observations from Figure 2I.

(7) Figure 3:

Figures 3A and 3B: The adjusted p value is already indicated in the figures, so there is no need to add statistical significance (Asterix) to each bar. The resolution for these panels is not good and the font is too small.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 3A and 3B. We have removed statistical significance (Asterix) from Figure3A and 3B. If we are lucky, we will upload the clearest figures when the manuscript is published.

Figure 3C: the figure legend is missing (wrongly added as KEGG analysis, which should be network analysis). The numbering for the figure legends is wrong. What are the node sizes (5, 22, 40, 58) mentioned in the figure represent? Also, I wonder why ribosome biogenesis in eukaryotes has been indicated as the most enriched pathway despite its less connection to the other nodes.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 3C. Figure 3C is KEGG analysis generated by software, not network analysis. For the convenience of readers, we have made a new Figure of KEGG analysis.

Figure 3D: KEGG enrichment and GO analysis: global/local search? Which database was used as a reference?

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the 633-635. Functional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. KEGG pathway analysis was conducted using Goatools.

Figure 3E: why were the RNA pol structures compared? The authors did not mention anything about this panel in their results.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the line 207. We found that many DEGs related to ribosome biogenesis (Figure 3D) and RNA polymerase (Figure 3E) are down expressed. Because RNA polymerase is closely related to ribosome biogenesis, the downregulation of RNA polymerase directly affects the synthesis of ribosome-related RNAs, including rRNA, mRNA, and tRNA, thereby inhibiting ribosome production. This relationship is particularly significant in cell growth, division, and the response to external environmental changes.

Figures 3F and 3G: please mention which model is illustrated (ribbon/sphere model).

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the line 1010-1015. The tertiary structure of NOP1 was displayed using a cartoon representation. Molecular docking of linalool with NOP1 was performed by enlarging the regions binding to the NOP1 activation pocket to showcase the detailed amino acid structures, which were presented using a surface model, while the small molecule was displayed with a ball-and-stick representation.

Figure 3H: this panel needs more explanation. Why were some of the ABC transporters upregulated while some were downregulated?

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. It is a common phenomenon that microorganisms adjust the expression of genes related to substance transport in response to different environmental stimuli to optimize their survival strategies. The expression of ATP-binding cassette (ABC) transporters can be upregulated or downregulated due to various factors, such as environmental stimuli, metabolic demands, energy consumption, species specificity, and signaling molecules. This explains why some ABC transporters are upregulated while others are downregulated.

(8) Figure 4:

There was no statistical significance shown in the figures (D-F) which makes me wonder how they worked out that there was any significant increase/decrease, as mentioned in the text. What are the p values? What is the number of replicates? What concentration of linalool was used?

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully.  Please check the Figure 4D-F. In this study, 4 groups were established: (1) Positive control (PC) group (10 fish infected with S. parasitica). (2) Linalool therapeutic (LT) group (10 fish infected with S. parasitica, soaked in 0.00039% linalool in a 20L tank for 7 days). (3) Linalool prophylactic (LP) group (10 uninfected fish soaked in 0.00039% linalool in a 20L tank for 2 days, followed by the addition of 1×10⁶ spores/mL secondary zoospores). (4) Negative control (NC) group (10 uninfected fish without linalool treatment). Each group had 3 replicate tanks. In each group, 8 fish were utilized for immunological assays, and on day 7, blood samples were collected from the tail veins using heparinized syringes and left to coagulate overnight at 4°C. Kits from Nanjing Jiancheng Institute (Nanjing, China) were used to measure lysozyme (LZY) activity, superoxide dismutase (SOD) activity, and alkaline phosphatase (AKP) activity.

(9) Figure 5:

Again, the resolution and font size are off. Please mention the full forms of the terms used in the figure legend. The interpretation of the in vivo protective mechanism of linalool is completely based on GO enrichment and KEGG pathway analysis (also some transcriptional analysis). The only wet lab validation done was by checking the mRNA level of some cytokines but that does not necessarily validate what the authors claim.

Thank you for your suggestion. We have revised it carefully. Please check all the figures and figure legend. We have increased the resolution and size of all the figures and used the full forms of the terms in figure legend. If we are lucky, we will upload the clearest figures when the manuscript is published. Currently, in the field of aquaculture research, mRNA quantification at the genetic level faces numerous challenges compared to model organisms like mice and zebra fish, primarily due to the lack of available antibodies. For instance, antibodies related to grass carp have not yet been commercialized, making protein-level studies and validations significantly more difficult. This lack of antibodies limits the progress of protein verification. However, we hope to design more experiments and validation tests in the future to gradually overcome these technical bottlenecks and provide stronger support for research in the future.

(10) Figure 6:

There is not enough explanation on why and how the experiments were done. It seems like the authors already presumed that the readers know the experiments. The interpretation of the PCA plot is not clear. Why are the quadrant sizes different? How was the heat map plotted? Also, the claim of linalool regulating the gut microbiota is only dependent on the correlation analysis and there is no wet lab validation for this. The data represented in this figure is not enough to prove their hypothesis and needs further investigation.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 6. We will improve the labeling of the figure panels, provide detailed explanations of the experimental methods, including their rationale and interpretation, and clarify the connections between the methods.

The goal of PCoA is to preserve the distance relationships between samples as much as possible through the principal coordinates, thereby revealing the differences or patterns in microbial composition among different groups. For example, in our study, PCoA analysis demonstrated that the microbial compositions of the positive control (PC), linalool prophylactic (LP), and linalool therapeutic (LT) groups showed significant differences in the reduced dimensional space, possibly indicating that these treatments had a notable impact on the microbial community.

In our study, the heatmap was generated using the Majorbio Cloud Platform. This platform visualized the preprocessed microbial community data, providing an intuitive representation of the differences in microbial composition and relative abundance among samples. The platform automatically performed steps such as data normalization, color mapping, and clustering analysis, offering convenience for data analysis and interpretation.

Previous researches have shown that antibiotics and other drugs can cause alterations in gut microbiota. Therefore, we plan to study the effects of antibiotics on gut microbiota. To conduct this research, we need to isolate these microbes from the gut. Although this process is challenging, we still aim to explore the gut microbiota. If possible, we will continue to delve into interesting aspects of how antibiotics affect gut microbiota in future studies.

(11) Figure 7:

This figure does not clarify how they did the interpretation. The in vivo study does not phenocopy their in vivo studies.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. we have carefully reviewed and confirmed the current experimental design and data analysis. Although we have not made any changes to Figure 7, we have further clarified the interpretation of the results in the revised manuscript, especially concerning the discrepancies between the in vivo and in vitro studies. We have added more experimental background information to help better understand the possible reasons for these differences. We hope the reviewer will understand our explanation and we look forward to your further feedback.

(12) Minor comments:

Line 61: what's meant by "et al"?

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 61. We have removed "et al".

Line 87-88: please add a citation referring to the earlier studies.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 109.

Line 151-152: the term "related to" has been used a couple of times. Mentioning it once in the beginning and avoiding repeating the same word might be better.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 168-171.We have rewritten this paragraph to avoid repeating the word “related to”.

How did they reconstitute the EO compounds?

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. The EO compounds we used in our experiments were partially extracted from essential oils in the laboratory and partially purchased from ThermoFisher (USA).

Line 544: needs explanation of how there was a 2-fold dilution in the concentrations shown in the figure compared to the concentrations mentioned here.

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. We set the concentration of MIC assay for mycelium to be 0.8%, 0.4%, 0.2%, 0.1%, 0.05%, 0.025%, 0.0125%, and 0.00625%, and the concentration of MIC assay for spores to be 0.4%, 0.2%, 0.1%. 0.05%, 0.025%, 0.0125%, 0.00625%. Figure 1B shows the MIC determination of linalool on spores, while the MIC determination of mycelium is not shown.

Line 546: remove "were".

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 573. We have removed "were".

Line 555: what concentration of malachite green and tween 20 was used?

Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 579-580. 2.5mg /mL malachite green and 1% Tween 20 were used.

Author response:

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

Public Reviews:

Reviewer 1:

(1) Some conclusions are not completely supported by the present data, and at times the manuscript is disjoint and hard to follow. While the work has some interesting observations, additional experiments and controls are warranted to support the claims of the manuscript.

Thank you for the comments. We revised some of the claims and conclusions to be more objective and result-supportive.

(2) While the authors present compelling data that is relevant to the development of anti-bacterial vaccinations, the data does not completely match their assertions and there are places where some further investigation would further the impact of their interesting study.

We do not fully agree with the reviewer's comments. We have demonstrated that changes in CPS levels during infection are associated with pathogenesis, which will guide future studies on the underlying mechanisms. A significant amount of effort is required for studying mechanisms, which is beyond the scope of this research. We concur with the reviewer that assertions should be made cautiously until further studies are conducted. We have revised these assertions to align with the data and to avoid extrapolating the results (pages 7, lines 126, 133-136; page 11, lines 216-218; page 13, line 264; and page 18, lines 378-383).

(3) The difference in the pathogenesis of a log phase vs. stationary phage intranasal infection would be interesting. Especially because the bacteria is a part of the natural microbial community of swine tonsils, it is curious if the change in growth phase and therefore CPS levels may be a causative reason for pathogenic invasion in some pigs.

S. suis is a part of the natural microbial community of swine tonsils but not mouse NALT. It is interesting to know if CPS levels are low in pig tonsils since CPS is hydrophilic and not conducive to bacterial adhesion. In the study, mice were i.n. infected with a high dose of the bacteria, which could increase opportunities for dissemination (acidic acid may not be a contributor since with or without it is similar). S. suis getting into other body compartments from pig tonsils might be triggered by other conditions, such as viral coinfection, nasal cavity inflammation, cold weather, and decreased immunity.

Experiments with pig blood and phagocytes have shown that genes involved in the synthesis of CPS are upregulated in pig blood. In contrast, these genes are downregulated [1]. In addition, the absence of CPS correlated with increased hydrophobicity and phagocytosis, proposing that S. suis undergoes CPS phase variation and could play a role in the different steps of S. suis infection [2]. We showed direct evidence of encapsulation modulation associated with S. suis pathogenesis in mice. A pig infection model is required to confirm these findings.

(4) The authors should consider taking the bacteria from NALT/CSF and blood and compare the lag times bacteria from different organs take to enter a log growth phase to show whether the difference in CPS is because S. suis in each location is in a different growth phase. If log phase bacteria were intranasally delivered, would it adapt a stationary phase life strategy? How long would that take? 

What causes CPS regulation in vivo is not known. CPS changes in different culture stages, indicating that stress, such as nutrition levels, is one of the signals triggering CPS regulation. The microenvironment in the body compartments is far more complex than in vitro, in which host cells, immune factors and others may affect CPS regulation, individually or collectively. The reviewer’ question is important but the suggested experiment is impracticable since bacterial numbers taken from organs are few, and culturing the bacteria in vitro would obliterate the in vivo status.  

(5) Authors should be cautious about claims about S. suis downregulating CPS in the NALT for increased invasion and upregulating CPS to survive phagocytosis in blood. While it is true that the data shows that there are different levels of CPS in these locations, the regulation and mechanism of the recorded and observed cell wall difference are not investigated past the correlation to the growth phase.

We lower the tone and change the claim as “suggest a correlation between lower CPS in the NALT and a greater capacity for cellular association, whereas elevated CPS levels in the blood are linked to improved resistance against bactericidal activity. However, the mechanisms behind these associations remain unknown.” (page 7, lines 133-136).

(6) The mouse model used in this manuscript is useful but cannot reproduce the nasal environment of the natural pig host. It is not clear if the NALTs of pigs and mice have similar microbial communities and how this may affect the pathogenesis of S. Suis in the mouse. Because the authors show a higher infection rate in the mouse with acetic acid, they may want to consider investigating what the mouse NALT microenvironment is naturally doing to exclude more bacterial invasion. Is it simply a host mismatch or is there something about the microbiome or steady-state immune system in the nose of mice that is different from pigs?

It is a very interesting comment. The mice are SPF level. The microenvironment in SPF mouse NALT should be significantly different from conventional pig tonsils. Although NALT in mice resembles pig tonsils in function, many factors may contribute to the sensitivity to S. suis colonization in the pig nasal cavity, such as the microbiome and local steady-state immune system. More complex microbiota in tonsils could be one of the factors. Analyzing what makes S. suis inclined towards colonization in pig tonsils by SPF and conventional pigs are an ideal experiment to answer the question. 

(7) Have some concerns regarding the images shown for neuroinvasion because I think the authors mistake several compartments of the mouse nasal cavity as well as the olfactory bulb. These issues are critical because neuroinvasion is one of the major conclusions of this work.

Thank you for your comments. The olfactory epithelium (OE) is located directly underneath the olfactory bulb in the olfactory mucosa area and lines approximately half of the nasal cavities of the nasal cavity. The remaining surface of the nasal cavity is lined by respiratory epithelium, which lacks neurons. The olfactory receptor neuron in OE is stained green in the images by β-tubulin III, a neuron-specific marker. The respiratory epithelium is colorless due to the absence of nerve cells. Similarly, the green color stained by β-tubulin III identifies the olfactory bulb. The accuracy of the anatomic compartments of the mouse nasal cavity has been checked and confirmed by referring to related literature [3, 4].

References

(1) Wu Z, Wu C, Shao J, Zhu Z, Wang W, Zhang W, Tang M, Pei N, Fan H, Li J, Yao H, Gu H, Xu X, Lu C. The Streptococcus suis transcriptional landscape reveals adaptation mechanisms in pig blood and cerebrospinal fluid. RNA. 2014 Jun;20(6):882-98.

(2) Charland N, Harel J, Kobisch M, Lacasse S, Gottschalk M. Streptococcus suis serotype 2 mutants deficient in capsular expression. Microbiology (Reading). 1998 Feb;144 ( Pt 2):325-332.

(3) Pägelow D, Chhatbar C, Beineke A, Liu X, Nerlich A, van Vorst K, Rohde M, Kalinke U, Förster R, Halle S, Valentin-Weigand P, Hornef MW, Fulde M. The olfactory epithelium as a port of entry in neonatal neurolisteriosis. Nat Commun. 2018;9(1):4269.

(4) Sjölinder H, Jonsson AB. Olfactory nerve--a novel invasion route of Neisseria meningitidis to reach the meninges. PLoS One. 2010 Nov 18;5(11):e14034.

Reviewer 2:

(1) However, there are serious concerns about data collection and interpretation that require further data to provide an accurate conclusion. Some of these concerns are highlighted below:

Both reviewers were concerned about some of the interpretations of the results. We modified the interpretations in related lines throughout the manuscript (Please see the related responses to Reviewer 1).

(2) In figure 2, the authors conclude that high levels of CPS confer resistance to phagocytic killing in blood exposed S. suis. However, it seems equally likely that this is resistance against complement mediated killing. It would be important to compare S. suis killing in animals depleted of complement components (C3 and C5-9).

We thank the reviewer for the comment. The experiment should be Bactericidal Assay instead of anti-phagocytosis killing. CPS is a main inhibitor of C3b deposition [1]. It interferes with complement-mediated and receptor-mediated phagocytosis; and direct killing. Data in Figure 2C is expressed as “% of bacterial survival in whole blood” for clarity (page 8, Fig. 2C and page 23, lines 489-490).

(3) Intranasal administration non-CPS antisera provides a nice contrast to intravenous administration, especially in light of the recently identified "blood-olfactory barrier". Can the authors provide any insight into how long and where this antibody would be located after intranasal administration? Would this be antibody mediated cellular resistance, or something akin to simple antibody "neutralization"

Anti-V5 may not stay long locally following intranasal administration. Efficient reduction of S. suis colonization in NALT supports that anti-V5 could recognize and neutralize the bacteria in NALT quickly, thereby reducing further dissemination in the body. Antibody-mediated phagocytosis may not play a major role because neutrophils are mainly present in the blood but not in the tissues.  

(4) The micrographs in Figure 7 depict anatomy from the respiratory mucosa. While there is no histochemical identification of neurons, the tissues labeled OE are almost certainly not olfactory and in fact respiratory. However, more troubling is that in figures 7A,a,b,e, and f, the lateral nasal organ has been labeled as the olfactory bulb. This undermines the conclusion of CNS invasion, and also draws into question other experiments in which the brain and CSF are measured.

We understand the significance of your concerns and appreciate your careful review of Figure 7. The olfactory epithelium (OE) is situated directly beneath the olfactory bulb in the olfactory mucosa area and covers about half of the nasal cavity. This positioning allows information transduction between the olfactory and the olfactory epithelium. The remaining surface of the nasal cavity is lined with respiratory epithelium, which does not contain neurons and primarily serves as a protective barrier. In contrast, the olfactory epithelium consists of basal cells, sustentacular cells, and olfactory receptor neurons. The olfactory receptor neurons are specifically stained green in the images using β-tubulin III, a marker that is unique to neurons. The respiratory epithelium appears colorless due to the lack of nerve cells. Similarly, the green staining with β-tubulin III also highlights the olfactory bulb. The anatomical structures indicated in the images are consistent with those described in the literature [2, 3], confirming that the anatomy of the nasal cavity has been accurately identified.

(5) Micrographs of brain tissue in 7B are taken from distal parts of the brain, whereas if olfactory neuroinvasion were occurring, the bacteria would be expected to arrive in the olfactory bulb. It's also difficult to understand how an inflammatory process would be developed to this point in the brain -even if we were looking at the appropriate region of the brain -within an hour of inoculation (is there a control for acetic acid induced brain inflammation?). Some explanations about the speed of the immune responses recorded are warranted.

Thank you for highlighting this issue. Cerebrospinal fluid (CSF) flows into the subarachnoid space surrounding the spinal cord and the brain. There are direct connections from this subarachnoid space to lymphatic vessels that wrap around the olfactory nerves as they cross the cribriform plate towards the nasal submucosa. This connection allows for the drainage of CSF into the nasal submucosal lymphatics in mice [4, 5]. Bacteria may utilize this CSF outflow channel in the opposite direction, which explains the development of brain inflammation in the distal areas of brain tissue adjacent to the subarachnoid space. We have included additional relevant information in the revised manuscript (page 16, lines 323-325).

(6) The detected presence of S. suis in the CSF 0.5hr following intranasal inoculation is difficult to understand from an anatomical perspective. This is especially true when the amount of S. suis is nearly the same as that found within the NALT. Even motile pathogens would need far longer than 0.5hr to get into the brain, so it's exceedingly difficult to understand how this could occur so extensively in under an hour. The authors are quantifying CSF as anything that comes out of the brain after mincing. Firstly, this should more accurately be referred to as "brain", not CSF. Secondly, is it possible that the lateral nasal organ -which is mistakenly identified as olfactory bulb in figure 7- is being included in the CNS processing? This would explain the equivalent amounts of S. suis in NALT and "CSF".

The high dose of inoculation used in the experiment may explain the rapid presence of S. suis in the CSF. Mice exhibit low sensitivity to S. suis infection, and the range for the effective intranasal infectious dose is quite narrow. Higher doses lead to the quick death of the mice, while lower doses do not initiate an infection at all. The dose used in this study is empirical and is intended to facilitate the observation of the progression of S. suis infection in mice.

The NALT tissue and CSF samples are collected separately. After obtaining the NALT tissue, the nasal portion was carefully separated from the rest of the head along the line of the eyeballs. The brain tissue was then extracted from the remaining part of the head to collect the CSF, and it was lacerated to expose the subarachnoid space without being minced. This procedure aims to preserve the integrity of the brain tissue as much as possible. Further details about the CSF collection process can be found in the Materials and Methods section (page 24, lines 508-512).

(7) To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

Thank you. We respectfully disagree with the reviewer. We stained neurons using a neuron-specific marker to identify the anatomical structures of the olfactory bulb and olfactory epithelium (in green). We used an S. suis-specific antibody to highlight the bacteria present in these areas (in orange and red). The images, along with the bacteria found in the cerebrospinal fluid (CSF) and the brain inflammation observed early in the infection, strongly support our conclusion regarding brain invasion through the olfactory pathway. Please see the response to question 4 for further clarification.

References

(1) Seitz M, Beineke A, Singpiel A, Willenborg J, Dutow P, Goethe R, Valentin-Weigand P, Klos A, Baums CG. Role of capsule and suilysin in mucosal infection of complement-deficient mice with Streptococcus suis. Infect Immun. 2014 Jun;82(6):2460-71.

(2) Sjölinder H, Jonsson AB. Olfactory nerve--a novel invasion route of Neisseria meningitidis to reach the meninges. PLoS One. 2010 Nov 18;5(11):e14034.

(3) Pägelow D, Chhatbar C, Beineke A, Liu X, Nerlich A, van Vorst K, Rohde M, Kalinke U, Förster R, Halle S, Valentin-Weigand P, Hornef MW, Fulde M. The olfactory epithelium as a port of entry in neonatal neurolisteriosis. Nat Commun. 2018;9(1):4269.

(4) Yoon JH, Jin H, Kim HJ, Hong SP, Yang MJ, Ahn JH, Kim YC, Seo J, Lee Y, McDonald DM, Davis MJ, Koh GY. Nasopharyngeal lymphatic plexus is a hub for cerebrospinal fluid drainage. Nature. 2024 Jan;625(7996):768-777.

(5) Spera I, Cousin N, Ries M, Kedracka A, Castillo A, Aleandri S, Vladymyrov M, Mapunda JA, Engelhardt B, Luciani P, Detmar M, Proulx ST. Open pathways for cerebrospinal fluid outflow at the cribriform plate along the olfactory nerves. EBioMedicine. 2023 May;91:104558.

Response to Recommendations for the authors:

Reviewer 1:

Minor concerns for the manuscript:

(1) In the introduction, please consider giving a little more background about the bacteria itself and how it causes pathogenesis.

We appreciate your suggestion. We have included additional background on the virulent factors and the pathogenesis of the bacteria in the introduction to enhance understanding of the results (page 4, lines 63-69).

(2) Figure 2C would be more correct to say percent survival as the CFUs before and after are what are being compared and not if the bacteria is being phagocytosed or not. Flow cytometry of the leukocytes and a fluorescent S. Suis would show phagocytosis. Unless that experiment is performed, the authors cannot claim that there is a resistance to phagocytosis.

Thank you for your feedback. We agree with the reviewer that the experiment should be Bactericidal Assay rather than anti-phagocytosis killing. CPS interferes with complement-mediated phagocytosis and direct killing, and receptor-mediated phagocytosis. To enhance clarity, the data in Fig. 2C has been presented as “% of bacterial survival in whole blood” (page 8).  

(3) There are two different legends present for Figure 1. Please resolve.

We apologize for the oversight. The redundant figure legend has been removed (page 6).

(4) There are places such as in lines 194-195, that there are assertions and interpretations about the data that are not directly drawn from the data. These hypotheses are valuable, but please move them to the discussion.

Thank you for your suggestion. The hypothesis has been moved to the Discussion section (page 19, lines 402 - 405).

(5) In Figure 4B, higher resolution images would strengthen the ability of non-microbiologists to see the differences in CPS levels in the cell wall.

We achieved the highest resolution possible for clearer distinctions in CPS levels. To enhance the visualization of the different CPS levels in the images, we revised the description of the CPS changes in Figure 4B within the results section (page 11, lines 208-213).

(6) In Figure 5 there is no D. Further, the schematics throughout would be easier to parse with the text if the challenge occurred at time 0. Consider revising them for clarity.

Thank you for highlighting the error. We have removed "i.v + i.n (Fig. 5)" from Figure 5A and made adjustments to the schematic illustrations in Figures 5 and 6 as recommended by the reviewer (page 14).

(7) What is the control for the serum? The findings for figures 5 and 6 would be much stronger if a non- S. Suis isotype control serum was also infused.

We used a naive serum as a control to avoid interference from a non-S. suis isotype control that targets other surface molecules of S. suis serotypes.

(8) Figure 6 legend does not include the anti-CPS treatment.

Thank you. We have added anti-CPS serum in the legend (page 15, line 249).

(9) Figure 7 legend does not include the time point for panel 7A.

Thank you. The time point is shown on Fig.7A (page 17).

(10) Figure 7 should show OB micrographs or entire brain including the OB.

The neuron-specific marker, β-tubulin III, identifies the neuro cells in the olfactory bulb (OB) as shown in Fig. 7A. Unfortunately, we were unable to provide an image of the entire brain that includes the OB due to limitations in our section preparation. We apologize for the mislabeled structure in Fig. 7A, which may have caused confusion. We have corrected the labeling for consistency (see page 15, lines 257-260). Additionally, we included a drawing of the sagittal plane of the rodent's nose, depicting the compartments of the OB, olfactory epithelium (OE), nasal cavity (NC), and brain. This illustration, presented in Fig. 7B on page 17, aims to clarify the structural and functional connections between the nasopharynx and the CNS.

(11) Some conclusions may be better drawn if figures were to be consolidated. As noted above, the data at times feels disjointed and the importance is more difficult for readers to follow because data are presented further apart. Particularly figures 5 and 6 which are similar with different time points and controls of antisera administrative routes; placing these figures together would be an example of increasing continuity throughout the paper.

Thank you for the valuable suggestion. Figures 5 and 6, along with their related descriptions in the results section, have been combined for better cohesiveness (pages 14-15).

Reviewer #2:

To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

Please refer to our responses to Reviewer 1's Question 7, Reviewer 2's Questions 4 and 7 in the public reviews, and Reviewer 1's Question 10 in the authors' recommendations.

Reviewer #1 (Public review):

The authors have strengthened their conclusions by providing additional information about the specificity of their antibodies, but at the same time the authors have revealed concerning information about the source of their antibodies.

It appears that many of the antibodies used in this study have been discontinued because the supplier company was involved in a scandal of animal cruelty and all their goats and rabbits Ab products were sacrificed. The authors acknowledge that this is unfortunate but they also claim that the issue is out of their hands.

The authors' statement is false; the authors ought to not use these antibodies, just as the providing company chose to discontinue them, as<br /> those antibodies are tied to animal cruelty. The issue that the authors feel OK with using them is of concern. In short, please remove any results from unethical antibodies.

Removal of such results also best serves science. That is, any of their results using the discontinued antibodies means that the authors' results are non-reproducible and we should be striving to publish good, reproducible science.

For the antibodies that do not have unethical origins the authors claim that their antibodies have been appropriately validated, by "testing in positive control tissue and/or Western blot or in situ hybridization". This is good but needs to be expanded upon. It is a strong selling point that the Abs are validated and I want to see additional information in their Supplementary Table 2 stating for each Ab specifically:

(1) What +ve control tissue was used in the validation of each Ab and which species that +ve control came from. Likewise, if competition assays to confirm validity was used, please also specify.

(2) Which assay was the Ab validated for (WB, IHC, ELISA, all etc)

(3) For Antibodies that were validated for, or using WBs please let the reader know if there were additional bands showing.

(4) Include references to the literature that supports these validations. That is, please make it easy for the reader to appreciate the hard work that went into the validation of the Antibodies.

Finally, for the Abs, when the authors write that "All antibodies used have been validated by testing in positive control tissue and/or Western blot or in situ hybridization" I fail to understand what in situ hybridisation means in this context. I am under the impression that in situ hybridisation is some nucleic acid -hybridising-to-organ or tissue slice. Not polypeptide binding.

Author response:

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

Reviewer #1 (Public review):

The authors have strengthened their conclusions by providing additional information about the specificity of their antibodies, but at the same time the authors have revealed concerning information about the source of their antibodies.

It appears that many of the antibodies used in this study have been discontinued because the supplier company was involved in a scandal of animal cruelty and all their goats and rabbits Ab products were sacrificed. The authors acknowledge that this is unfortunate but they also claim that the issue is out of their hands.

The authors' statement is false; the authors ought to not use these antibodies, just as the providing company chose to discontinue them, as those antibodies are tied to animal cruelty. The issue that the authors feel OK with using them is of concern. In short, please remove any results from unethical antibodies.

Removal of such results also best serves science. That is, any of their results using the discontinued antibodies means that the authors' results are non-reproducible and we should be striving to publish good, reproducible science.

For the antibodies that do not have unethical origins the authors claim that their antibodies have been appropriately validated, by "testing in positive control tissue and/or Western blot or in situ hybridization". This is good but needs to be expanded upon. It is a strong selling point that the Abs are validated and I want to see additional information in their Supplementary Table 2 stating for each Ab specifically:

(1) What +ve control tissue was used in the validation of each Ab and which species that +ve control came from. Likewise, if competition assays to confirm validity was used, please also specify.

(2) Which assay was the Ab validated for (WB, IHC, ELISA, all etc)

(3) For Antibodies that were validated for, or using WBs please let the reader know if there were additional bands showing.

(4) Include references to the literature that supports these validations. That is, please make it easy for the reader to appreciate the hard work that went into the validation of the Antibodies.

Finally, for the Abs, when the authors write that "All antibodies used have been validated by testing in positive control tissue and/or Western blot or in situ hybridization" I fail to understand what in situ hybridisation means in this context. I am under the impression that in situ hybridisation is some nucleic acid -hybridising-to-organ or tissue slice. Not polypeptide binding.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Remove results that have been obtained by unethically-sourced antibody reagents.

Strengthen the readers' confidence about the appropriateness & validity of your antibodies.

First, we want to stress that reviewer 1 has raised his critique related to the used of antibodies from Santa Cruz biotechnology not only through the journal. The head of our department and two others were contacted by reviewer 1 directly without going through the journal or informing/approaching the corresponding or first author. It is our opinion that this debate and critique should be handled through the journal and editorial office and not with people without actual involvement in the project.

It is correct that we have purchased antibodies from Santa Cruz Biotechnologies both mouse, rabbit and goat antibodies as stated in the correspondence with the reviewer.

As stated in our previous rebuttal – the goat antibodies from Santa Cruz were discontinued due to inadequate treatment of goats after settling with the authorities in 2016.

https://www.nature.com/articles/nature.2016.19411

https://www.science.org/content/blog-post/trouble-santa-cruz-biotechnology

We have used 11 mouse, rabbit or goat antibodies from Santa Cruz biotechnologies in the manuscript as listed in supplementary table 2 of the manuscript and all of them have been carefully validated in other control tissues supported by ISH and/or WB and many of them already used in several publications by our group (https://pubmed.ncbi.nlm.nih.gov/34612843/, https://pubmed.ncbi.nlm.nih.gov/33893301/, https://pubmed.ncbi.nlm.nih.gov/32931047/, https://pubmed.ncbi.nlm.nih.gov/32729975/, https://pubmed.ncbi.nlm.nih.gov/30965119/, https://pubmed.ncbi.nlm.nih.gov/29029242/, https://pubmed.ncbi.nlm.nih.gov/23850520/, https://pubmed.ncbi.nlm.nih.gov/23097629/, https://pubmed.ncbi.nlm.nih.gov/22404291/, https://pubmed.ncbi.nlm.nih.gov/20362668/, https://pubmed.ncbi.nlm.nih.gov/20172873/,  and other research groups. All antibodies used in this manuscript were purchased before the whole world was aware of mistreatment of goats that was evident several years later.

We do not support animal cruelty in anyway but the purchase of antibodies from Santa Cruz biotechnologies were conducted long before mistreatment was reported. Moreover, antibodies from Santa Cruz biotechnologies are being used in thousands of publications annually. The company has been punished for their misconduct, and subsequently granted permission to produce antibodies from the relevant authorities again.

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Despite the study being a collation of important results likely to have an overall positive effect on the field, methodological weaknesses and suboptimal use of statistics make it difficult to give confidence to the study's message.

Strengths:

Relevant human and mouse models approached with in vivo and in vitro techniques.

Weaknesses:

The methodology, statistics, reagents, analyses, and manuscripts' language all lack rigour.

(1) The authors used statistics to generate P-values and Rsquare values to evaluate the strength of their findings.

However, it is unclear how stats were used and/or whether stats were used correctly. For instance, the authors write: "Gaussian distribution of all numerical variables was evaluated by QQ plots". But why? For statistical tests that fall under the umbrella of General Linear Models (line ANOVA, t-tests, and correlations (Pearson's)), there are several assumptions that ought to be checked, including typically:

(a) Gaussian distribution of residuals.

(b) Homoskedasticity of the residuals.

(c) Independence of Y, but that's assumed to be valid due to experimental design.

So what is the point of evaluating the Gaussian distribution of the data themselves? It is not necessary. In this reviewer's opinion, it is irrelevant, not a good use of statistics, and we ought to be leading by example here.

Additionally, it is not clear whether the homoscedasticity of the residuals was checked. Many of the data appear to have particularly heteroskedastic residuals. In many respects, homoscedasticity matters more than the normal distribution of the residuals. In Graphpad analyses if ANOVA is used but equal variances are assumed (when variances among groups are unequal then standard deviations assigned in each group will be wrong and thus incorrect p values are being calculated.

Based on the incomplete and/or wrong statistical analyses it is difficult to evaluate the study in greater depth.

We agree with the reviewer that we should lead by example and improve clarity on the use of the different statistical tests and their application. In response to the reviewer’s suggestion, we have extended the statistical section, focusing on the analyses used. Additionally, we have specified the statistical test used in the figure legends for each figure. Additionally, we did check for Gaussian distribution and homoskedasticity of residuals before conducting a general linear model test, and this has now been specified in the revised manuscript. In case the assumptions were not met, we have specified which non-parametric test we used. If the assumptions were not met, we specified which non-parametric test was used.

While on the subject of stats, it is worth mentioning this misuse of statistics in Figure 3D, where the authors added the Slc34a1 transcript levels from controls in the correlation analyses, thereby driving the intercept down. Without the Control data there does not appear to be a correlation between the Slc34a1 levels and tumor size.

We agree with the reviewer that a correlation analysis is inappropriate here and have removed this part of the figure.

There is more. The authors make statements (e.g. in the figure levels as: "Correlations indicated by R2.". What does that mean? In a simple correlation, the P value is used to evaluate the strength of the slope being different from zero. The authors also give R2 values for the correlations but they do not provide R2 values for the other stats (like ANOVAs). Why not?

We agree with the reviewer and have replaced the R2 values with the Pearson correlation coefficient in combination with the P value.

(2) The authors used antibodies for immunos and WBs. I checked those antibodies online and it was concerning:

(a) Many are discontinued.

Many of the antibodies we have used were from the major antibody provider Santa Cruz Biotechnology (SCBT). SCBT was involved in a scandal of animal cruelty and all their goats and rabbits were sacrificed, which explains why several antibodies were discontinued, while the mice antibodies were allowed to continue. This is unfortunate but out of our hands.

(b) Many are not validated.

We agree with the reviewer that antibody validation is essential. All antibodies used in this manuscript have been validated. The minimal validation has been to evaluate cellular expression in positive control tissue for instance bone, kidney, or mamma. Moreover, many of the antibodies have been used and validated in previous publications (doi: 10.1593/neo.121164, doi:10.1096/fj.202000061RR, doi: 10.1093/cvr/cvv187) including knockout models. Moreover, many antibodies but not all have been validated by western blot or in situ hybridization. We have included the following in the Materials and Methods section: “All antibodies used have been validated by testing in positive control tissue and/or Western blot or in situ hybridization”.

(c) Many performed poorly in the Immunos, e.g. FGF23, FGFR1, and Kotho are not really convincing. PO5F1 (gene: OCT4) is the one that looks convincing as it is expressed at the correct cell types.

We fail to understand the criticism raised by the reviewer regarding the specificity of these specific antibodies. We believe the FGF23 and Klotho antibodies are performing exceptionally well, and FGFR1 is abundantly expressed in many cell types in the testis. As illustrated in Figure 2E, the expression of Klotho, FGF23, and FGFR1 is very clear, specific, and convincing. FGF23 is not expressed in normal testis – which is in accordance with no RNA present there either. However, it is abundantly expressed in GCNIS where RNA is present. On the other hand, Klotho is abundantly expressed in germ cells from normal testis but not expressed in GCNIS.

(d) Others like NPT2A (product of gene SLC34A1) are equally unconvincing. Shouldn't the immuno show them to be in the plasma membrane?

If there is some brown staining, this does not mean the antibodies are working. If your antibodies are not validated then you ought to omit the immunos from the manuscript.

We acknowledge your concerns regarding the NPT2A, NPT2B, and NPT2C staining. While the NPT2A antibody is performing well, we understand your reservations about the other antibodies. It's worth noting that NPT2A is not expressed in normal testis (no RNA either) but is expressed in GCNIS where the RNA is also present. Although it is typically present in the plasma membrane, cytoplasmic expression can be acceptable as membrane availability is crucial for regulating NPT2A function, particularly in the kidney where FGF23 controls membrane availability. We are currently involved in a comprehensive study exploring these phosphate transporters in the organs lining the male reproductive tract. In functional animal models, we have observed very specific staining with this NPT2A antibody following exposed to high phosphate or FGF23. Additionally, we are conducting Western Blot analyses with this antibody, which reinforces our belief that the antibody has a specific binding.

Reviewer #2 (Public Review):

Summary:

This study set out to examine microlithiasis associated with an increased risk of testicular germ cell tumors (TGCT). This reviewer considers this to be an excellent study. It raises questions regarding exactly how aberrant Sertoli cell function could induce osteogenic-like differentiation of germ cells but then all research should raise more questions than it answers.

Strengths:

Data showing the link between a disruption in testicular mineral (phosphate)homeostasis, FGF23 expression, and Sertoli cell dysfunction, are compelling.

Weaknesses:

Not sure I see any weaknesses here, as this study advances this area of inquiry and ends with a hypothesis for future testing.

We thank the reviewer for the acknowledgment and highlighting that this is an important message that addresses several ways to develop testicular microlithiasis, which indicates that it is not only due to malignant disease but also frequent in benign conditions.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I applaud the authors' approach to nomenclature for rodent and human genes and proteins (italicised for genes, all caps for humans, capitalised only for rodents, etc), but the authors frequently got it wrong when referring to genes or proteins. A couple of examples include:

(1) SLC34A1 (italics) refers to gene (correct use by the authors) but then again the authors use e.g. SLC34A1 (not italics) to refer to the protein product of SLC34A1(italics) gene. In fact, the protein product of the SLC34A1 (italics) gene is called NPT2A (non-italics).

(2) OCT4 (italics) refers to gene (correct use by the authors) but then again the authors use e.g. OCT4 (not italics) to refer to the protein product of OCT4 (italics)gene. In fact, the protein product of the OCT4 gene (italics) gene is called PO5F1(non-italics).

The problem with their incorrect and inconsistent nomenclature is widespread in the manuscript making further evaluation difficult.

Please consult a reliable protein-based database like Uniprot to derive the correct protein names for the genes. You got NANOG correct though.

We thank the reviewer for addressing this important point. We have corrected the nomenclature throughout the manuscript as suggested.

(3) The authors use the word "may" too many times. Also often in conjunction with words like "indicates", and "suggests". Examples of phrases that reflect that the authors lack confidence in their own results, conclusions, and understanding of the literature are:

"...which could indicate that the bone-specific RUNX2 isoform may also be expressed... "

"...which indicates that the mature bone may have been..."

Are we shielding ourselves from being wrong in the future because "may" also means "may not"? It is far more engaging to read statements that have a bit more tooth to them, and some assertion too. How about turning the above statements around, to :

"...which shows that the bone-specific RUNX2 isoform is also expressed... "

"...which reveals that the mature bone were..."

...then revisit ambiguous language ("may", "might" "possibly", "could", "indicate" etc.) throughout the manuscript?

It's OK to make a statement and be found wrong in the future. Being wrong is integral to Science.

Thank you for addressing this. We agree with the reviewer that it is fair to be more direct and have revised many of these vague phrases throughout the manuscript.

(4) The authors use the word "transporter" which in itself is confusing. For instance, is SLC34A1 an importer or an exporter of phosphate? Or both? Do SLC34As move phosphate in or out of the cells or cellular compartments? "Transporter" sounds too vague a word.

We understand that it might be easier for the reader with the term "importer". However, we should use the specific nomenclature or "wording" that applies to these transporters. The exact terminology is a co-transporter or sodium-dependent phosphate cotransporter as reported here (doi: 10.1152/physrev.00008.2019). Thus, we will use the terms “co-transporter” and “transporter” throughout the revised manuscript.

这幅作品融合了多种艺术元素，呈现出梦幻般、带有神秘感的视觉体验。以下是一些欣赏这幅作品时可以关注的要点：

1. 整体构图与色彩
2. 作品中使用了柔和的色彩对比，画面左侧和右侧的蓝色和橙色相互交织，形成一种自然的、渐变的色调，增加了画面的层次感和深度。画面中心使用了更多的土色和灰色调，呈现出一种古老、神秘的氛围。

3. 画面中的植物和人物形象部分模糊或半透明，可能意图展示梦境般的流动感或象征性元素。

4. 自然与人类的交织

5. 画面左侧和右侧的植物（如树木和花卉）与人物形象（可能是人类和动物）形成对比，象征着自然与人类之间的关系。人物的姿势和方向可能暗示着他们与自然界的互动，或是与环境之间的联系。

6. 通过这种植物与人物的并置，艺术家可能在探讨自然世界和人类文化、意识之间的界限与交织。

7. 象征性元素

8. 画中的人物与植物的关系可能不单纯是自然描绘，而是充满象征意义。例如，人物与树木、动物、或某些几何形状之间的互动可能具有心理或哲学上的隐喻，提示着人与自然、生命与死亡、觉醒与梦境之间的紧密联系。

9. 某些小细节，如人物手中的物品或身体的姿态，可能传达出特定的情感或思考，如静默、探索、反思等。

10. 艺术风格与表现手法

11. 此作品可能融合了具象和抽象的表现方式，人物的轮廓不完全明确，模糊的边缘感使得整个画面更具梦幻和流动的感觉。

12. 线条和笔触的运用显示出艺术家的自由表现方式，特别是在色彩的运用上，画面并不拘泥于具象的重现，而是更多地通过色彩、形态来唤起观众的情感反应和联想。

13. 观看的感受与情感

14. 在观看这幅作品时，你可以尝试去感受其中所传递的情绪，而不仅仅是解析视觉元素。画面给人一种沉静、冥思的感觉，可能唤起观众对生命、自然和人类状态的深层思考。

总结：这幅作品通过色彩、构图和象征性元素的结合，呈现了一个梦幻、神秘的世界，邀请观者进入一个既具自然景观又充满情感暗示的空间。在欣赏时，可以注重其中的象征意义，思考人与自然的关系以及这些象征如何与个人的情感和经验相连。

Author response:

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

We would like to remind the editors and reviewers that the present project is a pilot study that does not claim to produce definitive results. Pilot studies are exploratory preliminary studies to test the validity of hypotheses, the feasibility of a study as well as the research methods and the study design. From our point of view, our hypotheses and the feasibility of the pilot study have been confirmed to such an extent that the implementation of a larger study is justified. At the same time, it became clear during the pilot that the methods and design need to be adapted in some areas in order to increase the reliability of the results - a finding that pilot studies are usually conducted to obtain. We discussed these limitations in detail in order to explain the planned changes in the follow-up study. What the reviewers and editors interpret as incompleteness is therefore due to the nature of a pilot study.  We consider it necessary that appropriate standards are taken into account in the evaluation of the present work.

In addition, we would like to make a counterstatement as to what our main claims, which should be used to assess the strength of evidence, are - and what they are not:

In the introduction, we describe the background that led to the formation of our hypotheses: Previous animal and human studies show that food, along with light, serves as the main Zeitgeber for circadian clocks. It has also been shown that chrononutrition can lead to weight loss and improved well-being. Based on this, we hypothesized that individualized meal timing can enhance these positive effects. This hypothesis has been validated on the basis of the available results. Contrary to what the editors and reviewers stated, the assumption that the observed beneficial effects are indeed related to an alteration or resetting of endogenous circadian rhythms was not intended to be investigated in this study and is not one of our main claims. This has already been sufficiently demonstrated and, in our view, need not and should not be repeated in every study on chrononutrition. Accordingly, this assumption was not formulated as a working hypothesis or main claim. It is described in the paper as a potential mechanism, the assumption of which is justified on the basis of previous studies. The lack of a corresponding examination and the erroneous insinuation that corresponding results were nevertheless listed by us in the paper as a main claim should therefore not be used as a criterion for downgrading the assessment of the strength of evidence.

The main criticism of our study is the collection of data using self-reported food and food quantities. This form of data collection is indeed prone to error, as there is little control over the accuracy of the reported data. However, we believe that this problem is limited in scope.

(1) Contrary to what the editors and reviewers claim, at no point do we write that we are convinced that food intake has not changed. On the contrary, in Figure 2 we explicitly show that there was a change in what some participants reported to us regarding their food intake. We make it clear throughout the text that we could not find any correlation between weight change and the changes in the reports of food quantities/meals. These statements are correct and only what are actual and formulated main claims should be included in the evaluation of the study.

(2) As previously stated, we conducted analyses that suggest that an unreported reduction in food intake is unlikely to be the cause of weight loss. For the most part, participants did not change their reporting behavior during the exploration and intervention phases. That is, participants who underreported food intake reported similar amounts in both phases of the study, but lost weight only in the intervention phase. To explain their weight loss with imprecise reporting, it would have to be assumed that these participants began to eat less in the intervention phase and at the same time report more in order to achieve similar calorie counts and food composition in the evaluation. We consider such behavior to be very unlikely, especially since it would apply to numerous participants.

(3) The editors and reviewers reduce the results to the absence of a correlation between weight loss and reported food quantity and composition. In their assessment of the significance of the findings, however, they ignore the fact that we did find a significant correlation in our analyses, namely between weight loss and an increase in the regularity of food intake. There is no correlation between an increase in regularity and a reduction in reported calories (R² = 0.01472). This is credible in our view, as it is unlikely that the more regularly participants ate, the more pronounced the error in their reports was (while in reality they ate less than before).

(4) We also had the requirement for the study design that the participants could carry out the intervention in their normal everyday life and environment in order to test and ensure implementation in real life. We consider it unrealistic to be able to monitor food intake continuously and without interruption over a period of several weeks under these conditions. We therefore see no alternative to self-reporting. As the reviewers and editors did not suggest any alternative methods of data collection that would fulfil the requirements of our study, we assume that, despite criticism and reservations, they generally agree with our assessment and take this into account in their evaluation.

It is still criticized that some confounding factors are present. The reviewer makes no reference to the fact that we either eliminated these in the last version submitted (age range), identified them as unproblematic (unmatched cohorts, menstrual cycle, shift work) or even deliberately used them in order to be able to test our hypothesis more validly (inclusion of individuals with normal weight, overweight, and obesity).

Besides, the use of actimeters to determine circadian rhythms as proposed by the editors and reviewers is not valid for this study and the requirement to use them to determine a circadian reset in the eLife assessment is misleading and inappropriate. This instrument only measures physical activity, but not the physiological parameters that are relevant for an investigation in this field of research.

For the assessment of chronotype alone, the MCTQ questionnaire is a valid instrument that has been validated several times against actimetry (e.g., DOIs: 10.1080/07420528.2022.2025821, 10.1080/07420528.2023.2202246, 10.1016/j.ijpsycho.2016.07.433, 10.1155/2018/5646848). The reviewer's statement that the MCTQ questionnaire is unreliable for determining chronotype is unsupported and incorrect.

Equally unproven is the statement that any form of imposed diet appears to lead to weight loss over a period of several months.

Nevertheless, in order to prevent further misunderstandings, we have revised our text in a number of places and clarified that our statements are not irrefutable assertions, but potential interpretations of the results obtained in the pilot study, which are to be analyzed in more detail with regard to the planned more comprehensive study.

Reviewer #1 (Public review):

Summary:

The authors found that IL-1b signaling is pivotal for hypoxemia development and can modulate NETs formation in LPS+HVV ALI model.

Strengths:

They used IL1R1 ko mice and proved that IL1R1 is involved in ALI model proving that IL1b signalling leads towards ARDS. In addition, hypothermia reduces this effect, suggesting a therapeutic option.

Weaknesses:

(1) IL1R1 binds IL1a and IL1b. What would be the role of IL1a in this scenario?

(2) The authors depleted neutrophils using anti-Ly6G. What about MDSCs? Do these latter cells be involved in ARDS and VILI?

(3) The authors found that TH inhibited IL-1β release from macrophages led to less NETs formation and albumin leakage in the alveolar space in their lung injury model. A graphical abstract could be included suggesting a cellular mechanism.

(4) If Macrophages are responsible for IL1b release that via IL1R1 induces NETosis, what happens if you deplete macrophages? what is the role of epithelial cells?

Author response:

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

The authors found that IL-1b signaling is pivotal for hypoxemia development and can modulate NETs formation in LPS+HVV ALI model.  

Strengths: 

They used IL1R1 ko mice and proved that IL1R1 is involved in ALI model proving that IL1b signalling leads towards ARDS. In addition, hypothermia reduces this effect, suggesting a therapeutic option.  

We thank the Reviewer for recognizing the strengths of our study and their positive feedback.

Weaknesses: 

(1) IL1R1 binds IL1a and IL1b. What would be the role of IL1a in this scenario? 

Thank you for asking this question. We have addressed this in our previous paper (Nosaka et al. Front Immunol 2020;11; 207) where we used  anti-IL-1a and IL-1a KO mice (Nosaka et al. Front Immunol 2020;11; 207) in our model and found that neither anti-IL-1a treated mice nor IL-1a KO mice were protected. Thus, IL-1b plays a role in inducing hypoxemia during LPS+HVV but not IL-1a. We will now add this point in our revised manuscript discussion.

(2) The authors depleted neutrophils using anti-Ly6G. What about MDSCs? Do these latter cells be involved in ARDS and VILI?  

Anti-Ly6G neutrophils depletion may potentially affect G-MDSCs as well (Blood Adv 2022 Jul 29;7(1):73–86), however, we have not looked directly at G-MDSCs.  If these cells were depleted we would have expected to see an increase in inflammation, which we did not.   

Instead, anti-Ly6G treated mice were protected. Thus, we can not comment on any presumed role of G-MDSCs in LPS+HVV induced severe ALI model that we used.  

(3) The authors found that TH inhibited IL-1β release from macrophages led to less NETs formation and albumin leakage in the alveolar space in their lung injury model. A graphical abstract could be included suggesting a cellular mechanism.  

Thanks for summarizing our findings and the suggestion. Unfortunately, eLIFE does not publish a graphical abstract. We tried to mention this mechanism in the discussion.

(4) If Macrophages are responsible for IL1b release that via IL1R1 induces NETosis, what happens if you deplete macrophages? what is the role of epithelial cells?  

Previous studies have found that macrophage depletion is protective in several models of ALI (Eyal. Intensive Care Med. 2007;33:1212–1218., Lindauer.  J Immunol. 2009;183:1419–1426.), and other researchers have found that airway epithelial cells did not contribute to IL-1β secretion (Tang. PLoS ONE. 2012;7:e37689.). We have previously reported that epithelial cells produce IL-18 without LPS priming signal during LPS+HVV (Nosaka et al. Front Immunol 2020;11; 207). Thus, IL-18 is not sufficient to induce Hypoxemia as Saline+HVV treated mice do not develop hypoxemia (Nosaka et al. Front Immunol 2020;11; 207). We will now add this point to the revised discussion of the manuscript.

Reviewer #2 (Public review): 

Summary: 

The manuscript by Nosaka et al is a comprehensive study exploring the involvement of IL1beta signaling in a 2-hit model of lung injury + ventilation, with a focus on modulation by hypothermia. 

Strengths: 

The authors demonstrate quite convincingly that interleukin 1 beta plays a role in the development of ventilator-induced lung injury in this model, and that this role includes the regulation of neutrophil extracellular trap formation. The authors use a variety of in vivo animal-based and in vitro cell culture work, and interventions including global gene knockout, cell-targeted knockout and pharmacological inhibition, which greatly strengthen the ability to make clear biological interpretations. 

We thank the Reviewer for their positive feedback 

Weaknesses: 

A primary point for open discussion is the translatability of the findings to patients. The main model used, one of intratracheal LPS plus mechanical ventilation is well accepted for research exploring the pathogenesis and potential treatments for acute respiratory distress syndrome (ARDS). However, the interpretation may still be open to question - in the model here, animals were exposed to LPS to induce inflammation for only 2 hours, and seemingly displayed no signs of sickness, before the start of ventilation. This would not be typical for the majority of ARDS patients, and whether hypothermia could be effective once substantial injury is already present remains an open question. The interaction between LPS/infection and temperature is also complicated - in humans, LPS (or infection) induces a febrile, hyperthermic response, whereas in mice LPS induces hypothermia (eg. Ganeshan K, Chawla A. Nat Rev Endocrinol. 2017;13:458-465). Given this difference in physiological response, it is therefore unclear whether hypothermia in mice and hypothermia in humans are easily comparable. Finally, the use of only young, male animals such as in the current study has been typical but may be criticised as limiting translatability to people. 

Therefore while the conclusions of the paper are well supported by the data, and the biological pathways have been impressively explored, questions still remain regarding the ultimate interpretations.  

We agree with the reviewer that at two hours post LPS, there is only minimal pulmonary inflammation at that time (Dagvadorj et al Immunity 42, 640–653). This is a limitation to the experimental model we used in our study. Additionally, as the reviewer pointed out that LPS induces hyperthermia in human, but it is also well-established that physiological hypothermia occurs in humans with severe infections and sepsis (Baisse. Am J Emerg Med. 2023 Sep: 71: 134-138., Werner.  Am J Emerg Med. 2025 Feb;88:64-78.). Therefore, the difference between human and mouse responses to sepsis or infections may be more nuanced.  Furthermore, it is important to distinguish between physiological hypothermia (just <36°C) and therapeutic hypothermia (typically 32-34°C). We will add to the discussion whether hypothermia serves as a protective response, and the transition from normothermia to hyperthermia could have detrimental effects. We only used young male mice in our study as the Reviewer points out; we will also add this point to the revised discussion as a limitation of our study.

Reviewer #2 (Public review):

The study by Xue et al. reports the structural basis for the regulation of the human cardiac sodium-calcium exchanger, NCX1, by the endogenous activator PIP2 and the small molecule inhibitor SEA400. This well-written study contextualizes the new data within the existing literature on NCX1 and the broader NCX family. This work builds upon the authors' previous study (Xue et al., 2023), which presented the cryo-EM structures of human cardiac NCX1 in both inactivated and activated states. The 2023 study highlighted key structural differences between the active and inactive states and proposed a mechanism where the activity of NCX1 is regulated by the interactions between the ion-transporting transmembrane domain and the cytosolic regulatory domain. Specifically, in the inward-facing state and at low cytosolic calcium levels, the transmembrane (TM) and cytosolic domains form a stable interaction that results in the inactivation of the exchanger. In contrast, calcium binding to the cytosolic domain at high cytosolic calcium levels disrupts the interaction with the TM domain, leading to active ion exchange.

In the current study, the authors present two mechanisms explaining how both PIP2 stimulates NCX1 activity by destabilizing the protein's inactive state (i.e., by disrupting the interaction between the TM domain and the cytosolic domain) and how SEA400 stabilizes this interaction, thereby acting as a specific inhibitor of the system.

The first part of the results section addresses the effect of PIP2 and PIP2 diC8 on NCX1 activity. This is pertinent as the authors use the diC8 version of this lipid (which has a shorter acyl chain) in their subsequent cryo-EM structure due to the instability of native PIP2. I am not an electrophysiology expert; however, my main comment would be to ask whether there is sufficient data here to characterise fully the differences between PIP2 and PIP2 diC8 on NCX1 function. It appears from the text that this study is the first to report these differences, so perhaps this data needs to be more robust. The spread of the data points in Figure 1B is possibly a little unconvincing given that only six measurements were taken. Why is there one outlier in Figure 1A? Were these results taken using the same batch of oocytes? Are these technical or biological replicates? Is the convention to use statistical significance for these types of experiments?

I am also somewhat skeptical about the modelling of the PIP2 diC8 molecule. The authors state, "The density of the IP3 head group from the bound PIP2 diC8 is well-defined in the EM map. The acyl chains, however, are flexible and could not be resolved in the structure (Fig. S2)."

However, the density appears rather ambiguous to me, and the ligand does not fit well within the density. Specifically, there is a large extension in the volume near the phosphate at the 5' position, with no corresponding volume near the 4' phosphate. Additionally, there is no bifurcation of the volume near the lipid tails. I attempted to model cholesterol hemisuccinate (PDB: Y01) into this density, and it fits reasonably well - at least as well as PIP2 diC8. I am also concerned that if this site is specific for PIP2, then why are there no specific interactions with the lipid phosphates? How can the authors explain the difference between PIP2 and PIP2 diC8 if the acyl chains don't make any direct interactions with the TM domain? In short, the structures do not explain the functional differences presented in Figure 1.

The side chain densities for Arg167 and Arg220 are also quite weak. While there is some density for the side chain of Lys164, it is also very weak. I would expect that if this site were truly specific for PIP2, it should exhibit greater structural rigidity - otherwise, how is this specific?

Given this observation, have the authors considered using other PIP2 variants to determine if the specificity lies with PI4,5P2 as opposed to PI3,5P2 or PI3,4P2? A lack of specificity may explain the observed poor density.

I also noticed many lipid-like densities in the maps for this complex. Is it possible that the authors overlooked something? For instance, there is a cholesterol-like density near Val51, as well as something intriguing near Trp763, where I could model PIP2 diC8 (though this leads to a clash with Trp763). I wonder if the authors are working with mixed populations in their dataset. The accompanying description of the structural changes is well-written (assuming it is accurate).

I would recommend that the authors update the figures associated with this section, as they are currently somewhat difficult to interpret without prior knowledge of NCX architecture. My suggestions include:

- Including the density for the PIP2 diC8 in Figure 2A.

- Adding membrane boundaries (cytosolic vs. extracellular) in Figure 2B.

- Labeling the cytosolic domains in Figure 2B.

- Adding hydrogen bond distances in Figure 2A.

- Detailing the domain movements in Figure 2B (what is the significance of the grey vs. blue structures?).

The section on the mechanism of SEA400-induced inactivation is strong. The maps are of better quality than those for the PIP2 diC8 complex, and the ligand fits well. However, I noticed a density peak below F02 on SEA400 that lies within the hydrogen bonding distance of Asp825. Is this a water molecule? If so, is this significant?

Furthermore, there are many unmodeled regions that are likely cholesterol hemisuccinate or detergent molecules, which may warrant further investigation.

The authors introduce SEA400 as a selective inhibitor of NCX1; however, there is little to no comparison between the binding sites of the different NCX proteins. This section could be expanded. Perhaps Fig. 4C could include sequence conservation data.

Additionally, is the fenestration in the membrane physiological, or is it merely a hole forced open by the binding of SEA400? I was unclear as to whether the authors were suggesting a physiological role for this feature, similar to those observed in sodium channels.

Polling is a mechanism for the CPU to check a device’s status before issuing a command. It explains the handshaking process between the host and the controller using status and command registers. The concept of busy-waiting is introduced, where the CPU continuously checks a device’s status, which can be inefficient for slow devices. The section discusses the trade-offs of polling: while it is simple and effective for fast devices, it can be inefficient when applied to slow devices, leading to wasted CPU cycles. Instead, interrupt-driven I/O is introduced as an alternative, allowing devices to notify the CPU when they are ready for service. This section provides a critical understanding of CPU-device communication methods and their efficiency implications.

This section introduces the various categories of I/O devices, including storage, transmission, and human-interface devices. It describes how devices communicate with a computer system through ports, buses, and controllers. Key concepts include the use of serial ports, daisy chains, and PCIe buses, which determine how devices are connected and managed. Buses are crucial in defining communication protocols and data transfer rates. The section also introduces device controllers, which handle low-level hardware operations, and highlights the role of host bus adapters (HBAs) in managing complex communication protocols. Understanding these hardware elements is essential for efficient device management and integration in an operating system.

Terminating processes eliminates deadlocks but can be costly. One approach is aborting all deadlocked processes, ensuring immediate resolution but wasting computed progress. Another method is terminating one process at a time until the deadlock is resolved, requiring repeated deadlock detection, which incurs overhead. Partial termination requires selecting the least costly process to terminate, considering factors like priority, resource usage, and completion time. However, terminating a process mid-operation can corrupt files or shared data, necessitating careful rollback mechanisms. Effective termination policies minimize disruption while ensuring system stability, making it crucial to balance efficiency and fairness when choosing a process.

A deadlock occurs when four conditions hold simultaneously: Mutual Exclusion, Hold and Wait, No Preemption, and Circular Wait. Mutual exclusion means that only one process can use a resource at a time, hold and wait refers to a process holding resources while waiting for others, no preemption prevents the forced release of resources, and circular wait forms a cycle where each process is waiting for a resource held by the next. If any of these conditions is broken, deadlocks cannot occur. One key question is why the operating system does not forcefully take back resources (breaking the no preemption condition) as a solution to deadlocks.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

DiPeso et al. develop two tools to (i) classify micronucleated (MN) cells, which they call VCS MN, and (ii) segment micronuclei and nuclei with MMFinder. They then use these tools to identify transcriptional changes in MN cells.

The strengths of this study are:

(1) Developing highly specialized tools to speed up the analysis of specific cellular phenomena such as MN formation and rupture is likely valuable to the community and neglected by developers of more generalist methods.

(2) A lot of work and ideas have gone into this manuscript. It is clearly a valuable contribution.

(3) Combining automated analysis, single-cell labeling, and cell sorting is an exciting approach to enrich phenotypes of interest, which the authors demonstrate here.

Weaknesses:

(1) Images and ground truth labels are not shared for others to develop potentially better analysis methods.

We regret this omission and thank the reviewer for pointing it out. Both the images and ground truth labels for VCS MN and MNFinder are now available on the lab’s github page and described in the README.txt files. VCS MN: https://github.com/hatch-lab/fast-mn. MNFinder: https://github.com/hatch-lab/mnfinder.

(2) Evaluations of the methods are often not fully explained in the text.

The text has been extensively updated to include a full description of the methods and choices made to develop the VCS MN and MNFinder image segmentation modules.

(3) To my mind, the various metrics used to evaluate VCS MN reveal it not to be terribly reliable. Recall and PPV hover in the 70-80% range except for the PPV for MN+. It is what it is - but do the authors think one has to spend time manually correcting the output or do they suggest one uses it as is?

VCS MN attempts to balance precision and recall with speed to reduce the fraction of MN changing state from intact to ruptured during a single cell cycle during a live-cell isolation experiment. In addition, we chose to prioritize inclusion of small MN adjacent to the nucleus in our positive calls. This meant that there were more false positives (lower PPV) than obtained by other methods but allowed us to include this highly biologically relevant class of MN in our MN+ population. Thus, for a comprehensive understanding of the consequences of MN formation and rupture, we recommend using the finder as is. However, for other visual cell sorting applications where a small number of highly pure MN positive and negative cells is preferred, such as clonal outgrowth or metastasis assays, we would recommend using the slower, but more precise, MNFinder to get a higher precision at a cost of temporal resolution. In addition, MNFinder, with its higher flexibility and object coverage, is recommended for all fixed cell analyses.

Reviewer #2 (Public review):

Summary:

Micronuclei are aberrant nuclear structures frequently seen following the missegregation of chromosomes. The authors present two image analysis methods, one robust and another rapid, to identify micronuclei (MN) bearing cells. The authors induce chromosome missegregation using an MPS1 inhibitor to check their software outcomes. In missegregation-induced cells, the authors do not distinguish cells that have MN from those that have MN with additional segregation defects. The authors use RNAseq to assess the outcomes of their MN-identifying methods: they do not observe a transcriptomic signature specific to MN but find changes that correlate with aneuploidy status. Overall, this work offers new tools to identify MN-presenting cells, and it sets the stage with clear benchmarks for further software development.

Strengths:

Currently, there are no robust MN classifiers with a clear quantification of their efficiency across cell lines (mIoU score). The software presented here tries to address this gap. GitHub material (tools, protocols, etc) provided is a great asset to naive and experienced computational biologists. The method has been tested in more than one cell line. This method can help integrate cell biology and 'omics' studies.

Weaknesses:

Although the classifier outperforms available tools for MN segmentation by providing mIOU, it's not yet at a point where it can be reliably applied to functional genomics assays where we expect a range of phenotypic penetrance.

We agree that the MNFinder module has limitations with regards to the degree of nuclear atypia and cell density that can be tolerated. Based on the recall and PPV values and their consistency across the majority conditions analyzed, we believe that MNFinder can provide reliable results for MN frequency, integrity, shape, and label characteristics in a functional genomics assay in many commonly used adherent cell lines. We also added a discussion of caveats for these analyses, including the facts that highly lobulated nuclei will have higher false positive rates and that high cell confluency may require additional markers to ensure highly accurate assignment of MN to nuclei.

Spindle checkpoint loss (e.g., MPS1 inhibition) is expected to cause a variety of nuclear atypia: misshapen, multinucleated, and micronucleated cells. It may be difficult to obtain a pure MN population following MPS1 inhibitor treatment, as many cells are likely to present MN among multinucleated or misshapen nuclear compartments. Given this situation, the transcriptomic impact of MN is unlikely to be retrieved using this experimental design, but this does not negate the significance of the work. The discussion will have to consider the nature, origin, and proportion of MN/rupture-only states - for example, lagging chromatids and unaligned chromosomes can result in different states of micronuclei and also distinct cell fates.

We appreciate the reviewer’s comments and now quantify the frequency of other nuclear atypias and MN chromosome content in RPE1 cells after 24 h Mps1 inhibition (Fig. S1). In summary, we find only small increases in nuclear atypia, including multinucleate cells, misshapen nuclei, and chromatin bridges, compared to the large increase in MN formation. This contrasts with what is observed when mitosis is delayed using nocodazole or CENPE inhibitors where nuclear atypia is much more frequent. Importantly, after Mps1 inhibition, RPE1 cells with MN were only slightly more likely to have a misshapen nucleus compared to cells without MN (Fig. S1C).

Interestingly, this analysis showed that the VCS MN pipeline, which uses the Deep Retina segmenter to identify nuclei, has a strong bias against lobulated nuclei and frequently fails to find them (Fig. S2B). Therefore, the cell populations analyzed by RNAseq were largely depleted of highly misshapen nuclei and differences in nuclear atypia frequency between MN+ and MN- cells in the starting population were lost (Fig. S9A, compare to Fig. S1C). This strongly suggests that the transcript changes we observed reflect differences in MN frequency and aneuploidy rather than differences in nuclei morphology.

We agree with the reviewer that MN rupture frequency and formation, and downstream effects on cell proliferation and DNA damage, are sensitive to the source of the missegregated chromatin. In the revised manuscript we make clear that we chose Mps1 inhibition because it is strongly biased towards whole chromosome MN (Fig. S1E), limiting signal from DNA damage products, including chromosome fragments and chromatin bridges. This provides a base line to disambiguate the consequences of micronucleation and DNA damage in more complex chromosome missegregation processes, such as DNA replication disruption and irradiation. 

Reviewer #3 (Public review):

Summary:

The authors develop a method to visually analyze micronuclei using automated methods. The authors then use these methods to isolate MN post-photoactivation and analyze transcriptional changes in cells with and without micronuclei of RPE-1 cells. The authors observe in RPE-1 cells that MN-containing cells show similar transcriptomic changes as aneuploidy, and that MN rupture does not lead to vast changes in the transcriptome.

Strengths:

The authors develop a method that allows for automating measurements and analysis of micronuclei. This has been something that the field has been missing for a long time. Using such a method has the potential to advance micronuclei biology. The authors also develop a method to identify cells with micronuclei in real time and mark them using photoconversion and then isolate them via FACS. The authors use this method to study the transcriptome. This method is very powerful as it allows for the sorting of a heterogenous population and subsequent analysis with a much higher sample number than could be previously done.

Weaknesses:

The major weakness of this paper is that the results from the RNA-seq analysis are difficult to interpret as very few changes are found to begin with between cells with MN and cells without. The authors have to use a 1.5-fold cut-off to detect any changes in general. This is most likely due to the sequencing read depth used by the authors. Moreover, there are large variances between replicates in experiments looking at cells with ruptured versus intact micronuclei. This limits our ability to assess if the lack of changes is due to truly not having changes between these populations or experimental limitations. Moreover, the authors use RPE-1 cells which lack cGAS, which may contribute to the lack of changes observed. Thus, it is possible that these results are not consistent with what would occur in primary tissues or just in general in cells with a proficient cGAS/STING pathway.

We agree with the reviewer’s assessment of the limitations of our RNA-Seq analysis. After additional analysis, we propose an alternative explanation for the lower expression changes we observe in the MN+ and Mps1 inhibitor RNA-Seq experiments. In summary, we find that VCS MN has a strong bias against highly lobulated nuclei that depletes this class of cells from both the bulk analysis and the micronucleated cell populations (Fig. S9A). Based on this result, we propose that our analysis reduces the contribution of nuclear atypia to these transcriptional changes and that nuclear morphology changes are likely a signaling trigger associated with aneuploidy.

We believe that this finding strengthens our overall conclusion that MN formation and rupture do not cause transcriptional changes, as suppressing the signaling associated with nuclei atypia should increase sensitivity to changes from the MN. However, we cannot completely rule out that MN formation or rupture cause a broad low-level change in transcription that is obscured by other signals in the dataset.

As to cGAS signaling, several follow up papers and even the initial studies from the Greenburg lab show that MN rupture does not activate cGAS and does not cause cGAS/STING-dependent signaling in the first cell cycle (see citations and discussion in text). Therefore, we expect the absence of cGAS in RPE1 cells will have no effect in the first cell cycle, but could alter the transcriptional profile after mitosis. Although analysis of RPE1  cGAS+ cells or primary cells in these experiments will be required to definitively address this point, we believe that our interpretation of our RNAseq results is sufficiently backed up by the literature to warrant our conclusion that MN formation and rupture do not induce a transcriptional response in the first cell cycle.

Reviewer #1 (Recommendations for the authors):

I do not recommend additional experimental or computational work. Instead, I just recommend adapting the claims of the manuscript to what has been done. I am just asking for further clarification and minor rewriting.

(1) The manuscript is written like a molecular biology paper with sparse explanations of the authors' reasoning, especially in the development of their algorithms. I was often lost as to why they did things in one way or another.

The revised manuscript has thorough explanations and additional data and graphics defining how and why the VCS MN and MNFinder modules were developed. We hope that this clears up many of the questions the reviewer had and appreciate their guidance on making it more readable for scientists from different backgrounds.

(2) Evaluations of their method are often not fully explained, for example:

"On average, 75% of nuclei per field were correctly segmented and cropped."

"MN segments were then assigned to 'parent' nuclei by proximity, which correctly associated 97% of MN."

Were there ground truth images and labels created? How many? For example, I don't know how the authors could even establish a ground-truth for associating MNs to nuclei if MNs happened to be almost equidistant between two nuclei in their images.

I suggest a separate subsection early in the Results section where the underlying imaging data + labels are presented.

We added new sections to the text and figures at the beginning of the VCS MN and MNFinder subsections (Fig. S2 and Fig. S5) with specific information about how ground truth images and labels were generated for both modules and how these were broken up for training, validation, and testing.

We also added information and images to explain how ground truth MN/nucleus associations were derived. In summary, we took advantage of the fact that 2xDendra-NLS is present at low levels in the cytoplasm to identify cell boundaries. This combined with a subconfluent cell population allowed us to unambiguously group MN and nuclei for 98% of MN, we estimate. These identifications were used to generate ground truth labels and analyze how well proximity defines MN/nuclei groups (Fig.s S1 and S2).

(3) Overall, I find the sections long and more subtitles would help me better navigate the manuscript.

Where possible, we have added subtitles.

(4) Everything following "To train the model, H2B channel images were passed to a Deep Retina neural net ..." is fully automated, it seems to me. Thus, there seems to be no human intervention to correct the output before it is used to train the neural network. Therefore, I do not understand why a neural network was trained at all if the pipeline for creating ground truth labels worked fully automatically. At least, the explanations are insufficient.

We apologize for the initial lack of clarity in the text and included additional details in the revision. We used the Deep Retina segmenter to crop the raw images to areas around individual nuclei to accelerate ground truth labeling of MN. A trained user went through each nucleus crop and manually labeled pixels belonging to MN to generate the ground truth dataset for training, validation, and imaging in VCS MN (Fig. S2A).

(5) To my mind, the various metrics used to evaluate VCS MN reveal it not to be terribly reliable. Recall and PPV hover in the 70-80% range except for the PPV for MN+. It is what it is - but do the authors think one has to spend time manually correcting the output or do they suggest one uses it as is? I understand that for bulk transcriptomics, enrichment may be sufficient but for many other questions, where the wrong cell type could contaminate the population, it is not.

Remarks in the Results section on what the various accuracies mean for different applications would be good (so one does not need to wait for the Discussion section).

One of the strengths of the visual cell sorting system is that any image analysis pipeline can be used with it. We used VCS MN for the transcriptomics experiment, but for other applications a user could run visual cell sorting in conjunction with MNFinder for increased purity while maintaining a reasonable recall or use a pre-existing MN segmentation program that gives 100% purity but captures only a specific subgroup of micronucleated cells (e.g. PIQUE). 

To maintain readability, especially with the expansion of the results sections, we kept the discussion of how we envision using visual cell sorting for other MN-based applications in the discussion section.

(6) I am confused about what "cell" is referring to in much of the manuscript. Is it the nucleus + MNs only? Is it the whole cell, which one would ordinarily think it is? If so, are there additional widefield images, where one can discern cell boundaries? I found the section "MNFinder accurately ..." very hard to read and digest for this reason and other ambiguous wording. I suggest the authors take a fresh look at their manuscript and see whether the text can be improved for clarity. I did not find it an easy read overall, especially the computational part.

After re-examining how “cell” was used, we updated the text to limit its use to the MNFinder arm tasked with identifying MN-nucleus associations where the convex hull defined by these objects is used to determine the “cell” boundary. In all other cases we have replaced cell with “nucleus” because, as the reviewer points out, that is what is being analyzed and converted. We hope this is clearer.

(7) Post-FACS PPVs are not that great (Figure 3c). It depends on the question one wants to answer whether ~70% PPV is good enough. Again, would be good to comment on.

We added discussion of this result to the revision. In summary, a likely reason for the reduced PPV is that, although we maintain the cells in buffer with a Cdk1 inhibitor, we know that some proportion of the cells go through mitosis post-sorting. Since MN are frequently reincorporated into the nucleus after mitosis (Hatch et al, 2013; Zhang et al., 2015), we expect this to reduce the MN+ population. Thus, we expect that the PPV in the RNAseq population is higher than what we can measure by analyzing post-sorted cells that have been plated and analyzed later.

(8) I am thoroughly confused as to why the authors claim that their system works in the "absence of genetic perturbations" and why they emphasize the fact that their cells are non-transformed: They still needed a fluorescent label and they induce MNs with a chemical Mps1 inhibitor. (The latter is not a genetic manipulation, of course, but they still need to enrich MNs somehow. That is, their method has not been tested on a cell population in which MNs occur naturally, presumably at a very low rate, unless I missed something.) A more careful description of the benefits of their method would be good.

We apologize for the confusion on these points and hope this is clarified in the revision. We were comparing our system, which can be made using transient transfection, if desired, to current tools that disambiguate aneuploidy and MN formation by deleting parts of chromosomes or engineering double strand breaks with CRISPR to generate single chromosome-specific missegregation events. Most of these systems require transformed cancer cells to obtain high levels of recombination. In contrast, visual cell sorting can isolate micronucleated cells from any cell line that can exogenously express a protein, including primary cells and non-transformed cells like RPE1s.

Other minor points:

(1) The authors should not refer to "H2B channels" but to "H2B-emiRFP703 channels". It may seem obvious to the authors but for someone reading the manuscript for the very first time, it was not. I was not sure whether there were additional imaging modalities used for H2B/nucleus/chromatin detection before I went back and read that only fluorescence images of H2B-emiRFP703 were used. To put it another way, the authors are detecting fluorescence, not histones -- unless I misunderstood something.

To address this point, we altered the text to read “H2B-emiRFP703” when discussing images of this construct. For MNFinder some images were of cells expressing H2B-GFP, which has also been clarified.

(2) If the level of zoom on my screen is such that I can comfortably read the text, I cannot see much in the figure panels. The features that I should be able to see are the size of a title. The image panels should be magnified.

In the revision, the images are appended to the end at full resolution to overcome this difficulty. Thank you for your forbearance.

Reviewer #2 (Recommendations for the authors):

The methods are adequately explained. The Results text narrating experiments and data analysis is clear. Interpretation of a few results could be clarified and strengthened as explained below.

(1) RNAseq experiments are a good proof of principle. To strengthen their interpretation in Figures 4 and 6, I would recommend the authors cite published work on checkpoint/MPS1 loss-induced chromosome missegregation (PMID: 18545697, PMID: 33837239, PMC9559752) and consider in their discussion the 'origin' and 'proportion' of micronucleated cells and irregularly shaped nuclei expected in RPE1 lines. This will help interpret Figure 6 findings on aneuploidy signature accurately. Not being able to see an MN-specific signature could be due to the way the biological specimen is presented with a mixture of cells with 'MN only' or 'rupture' or 'MN along with misshapen nuclei'. These features may all link to aneuploidy rather than 'MN' specifically.

We appreciate the reviewer’s suggestion and added a new analysis of nuclear atypia after Mps1 inhibition in RPE1 cells to Fig. S1. Overall, we found that Mps1 inhibition significantly, but modestly, increased the proportion of misshapen nuclei and chromatin bridges. Multinucleate cells were so rare that instead of giving them their own category we included them in “misshapen nuclei.” These results are consistent with images of Msp1i treated RPE1 cells from He et al. 2019 and Santaguida et al. 2017 and distinct from the stronger changes in nuclear morphology observed after delaying mitosis by nocodazole or CENPE inhibition.

We also found that the Deep Retina segmenter used to identify nuclei in VCS MN had a significant bias against highly lobulated nuclei (Fig. S2B) that led to misshapen nuclei being largely excluded from the RNAseq analyses. As a result we found no enrichment of misshapen nuclei, chromatin bridges, or dead/mitotic nuclear morphologies in MN+ compared to MN- nuclei in our RNASeq experiments (Fig. S9A).

(2) As the authors clarify in the response letter, one round of ML is unlikely to result in fully robust software; additional rounds of ML with other markers will make the work robust. It will be useful to indicate other ML image analysis tools that have improved through such reiterations. They could use reviews on challenges and opportunities using ML approaches to support their statement. Also in the introduction, I would recommend labelling as 'rapid' instead of 'rapid and precise' method.

We updated the text to reference review articles that discuss the benefit of additional training for increasing ML accuracy and changed the text to “rapid.”

(3) The lack of live-cell studies does not allow the authors to distinguish the origin of MN (lagging chromatids or unaligned chromosomes). As explained in 1, considering these aspects in discussion would strengthen their interpretation. Live-cell studies can help reduce the dependencies on proximity maps (Figure S2).

The revised text includes new references and data (Fig. S1E) demonstrating that Mps1 inhibition strongly biases towards whole chromosome missegregation and that MN are most likely to contain a single centromere positive chromosome rather than chromatin fragments or multiple chromosomes.

(4) Mean Intersection over Union (mIOU) is a good measure to compare outcomes against ground truth. However, the mIOU is relatively low (Figure 2D) for HeLa-based functional genomics applications. It will help to discuss mIOU for other classifiers (non-MN classifiers) so that they can be used as a benchmark (this is important since the authors state in their response that they are the first to benchmark an MN classifier). There are publications for mitochondria, cell cortex, spindle, nuclei, etc. where IOU has been discussed.

We added references to classifiers for other small cellular structures. We also evaluated major sources of error in MNFinder found that false negatives are enriched in very small MN (3 to 9 pixels, or about 0.4 µm² – 3 µm², Fig. S6B). A similar result was obtained for VCS MN (Fig. S3B). Because small changes in the number of pixels identified in small objects can have outsized effects on mIoU scores, we suspect that this is exerting downward pressure on the mIoU value. Based on the PPV and recall values we identified, we believe that MNFinder is robust enough to use for functional genomics and screening applications with reasonable sample sizes.

(5) Figure 5 figure legend title is an overinterpretation. MN and rupture-initiated transcriptional changes could not be isolated with this technique where several other missegregation phenotypes are buried (see point 1 above).

We decided to keep the figure title legend based on our analysis of known missegregation phenotypes in Fig. S1 and S9 showing that there is no difference in major classes of nuclear atypia between MN+ and MN- populations in this analysis. Although we cannot rule out that other correlated changes exist, we believe that the title represents the most parsimonious interpretation.

Minor comments

(1) The sentence in the introduction needs clarification and reference. "However, these interventions cause diverse "off-target" nuclear and cellular changes, including chromatin bridges, aneuploidy, and DNA damage." Off-target may not be the correct description since inhibiting MPS1 is expected to cause a variety of problems based on its role as a master kinase in multiple steps of the chromosome segregation process. Consider one of the references in point 1 for a detailed live-cell view of MPS1 inhibitor outcomes.

We have changed “off-target” to “additional” for clarity.

(2) In Figure 3 or S3, did the authors notice any association between the cell cycle phase and MN or rupture presence? Is this possible to consider based on FACS outcomes or nuclear shapes?

Previous work by our lab and others have shown that MN rupture frequency increases during the cell cycle (Hatch et al., 2013; Joo et al., 2023). Whether this is stochastic or regulated by the cell cycle may depend on what chromosome is in the MN (Mammel et al., 2021) and likely the cell line. Unfortunately, the H2B-emiRFP703 fluorescence in our population is too variable to identify cell cycle stage from FACS or nuclear fluorescence analysis.

(3) Figure 5 - Please explain "MA plot".

An MA plot, or log fold-change (M) versus average (A) gene expression, is a way to visualize differently expressed genes between two conditions in an RNASeq experiment and is used as an alternative to volcano plots. We chose them for our paper because most of the expression changes we observed were small and of similar significance and the MA plot spreads out the data compared to a volcano plot and allowed a better visualization of trends across the population.

(4) Page 7: "our results strongly suggest that protein expression changes in MN+ and rupture+ cells are driven mainly by increased aneuploidy rather than cellular sensing of MN formation and rupture.". This is an overstatement considering the mIOU limits of the software tool and the non-exclusive nature of MN in their samples.

We agree that we cannot rule out that an unknown masking effect is inhibiting our ability to observe small broad changes in transcription after MN formation or rupture. However, we believe we have minimized the most likely sources of masking effects, including nuclear atypia and large scale aneuploidy differences, and thus our interpretation is the most likely one.

Reviewer #3 (Recommendations for the authors):

Overall, the authors need to explain their methods better, define some technical terms used, and more thoroughly explain the parameters and rationale used when implementing these two protocols for identifying micronuclei; primarily as this is geared toward a more general audience that does not necessarily work with machine learning algorithms.

(1) A clearer description in the methods as to how accuracy was calculated. Were micronuclei counted by hand or another method to assess accuracy?

We significantly expanded the section on how the machine learning models were trained and tested, including how sensitivity and specificity metrics were calculated, in both the results and the methods sections. The code used to compare ground truth labels to computed masks is also now included in the MNFinder module available on the lab github page. 

(2) Define positive predictive value.

The text now says “the positive predictive value (PPV, the proportion of true positives, i.e. specificity) and recall (the proportion of MN found by the classifier, i.e. sensitivity)…”.

(3) Why is it a problem to use the VCS MN at higher magnifications where undersegmentation occurs? What do the authors mean by diminished performance (what metrics are they using for this?).

We have included a representative image and calculated mIoU and recall for 40x magnification images analyzed by MNFinder after rescaling in Fig. 2A. In summary, VCS MN only correctly labeled a few pixels in the MN, which was sufficient to call the adjacent nucleus “MN+” but not sufficient for other applications, such as quantifying MN area. In addition, VCS MN did much worse at identifying all the MN in 40x images with a recall, or sensitivity, metric of 0.36. We are not sure why. Developing MNFinder provided a module that was well suited to quantify MN characteristics in fixed cell images, an important use case in MN biology.

(4) The authors should compare MN that are analyzed and not analyzed using these methods and define parameters. Is there a size limitation? Closeness to the main nucleus?

We added two new figures defining what contributes to module error for both VCS MN (Fig. S3) and MNFinder (Fig. S6). For VCS MN, false negatives are enriched in very large or very small MN and tend to be dimmer and farther from the nucleus than true positives. False positives are largely misclassification of small dim objects in the image as MN. For MNFinder, the most missed class of MN are very small ones (3-9 px in area) and the majority of false positives are misclassifications of elongated nuclear blebs as MN.

(5) Are there parameters in how confluent an image must be to correctly define that the micronucleus belongs to the correct cell? The authors discussed that this was calculated based on predicted distance. However, many factors might affect proper calling on MN. And the authors should test this by staining for a cytosolic marker and calculating accuracy.

We updated the text with more information about how the cytoplasm was defined using leaky 2x-Dendra2-NLS signal to analyze the accuracy of MN/nucleus associations (Fig. S2G-H). In addition, we quantified cell confluency and distance to the first and second nearest neighbor for each MN in our training and testing image datasets. We found that, as anticipated, cells were imaged at subconfluent concentrations with most fields having a confluency around 30% cell coverage (Fig. S2E) and that the average difference in distance between the closest nucleus to an MN and the next closest nucleus was 3.3 fold (Fig. S2F). We edited the discussion section to state that the ability of MN/nuclear proximity to predict associations at high cell confluencies would have to be experimentally validated.

(6) The authors measure the ratio of Dendra2(Red) v. Dendra2 (Green) in Figure 3B to demonstrate that photoconversion is stable. This measurement, to me, is confusing, as in the end, the authors need to show that they have a robust conversion signal and are able to isolate these data. The authors should directly demonstrate that the Red signal remains by analyzing the percent of the Red signal compared to time point 0 for individual cells.

We found a bulk analysis to be more powerful than trying to reidentify individual cells due to how much RPE1 cells move during the 4 and 8 hours between image acquisitions. In addition, we sort on the ratio between red and green fluorescence per cell, rather than the absolute fluorescence, to compensate for variation in 2xDendra-NLS protein expression between cells. Therefore, demonstrating that distinct ratios remained present throughout the time course is the most relevant to the downstream analysis.

To address the reviewer’s concern, we replotted the data in Fig. 3B to highlight changes over time in the raw levels of red and green Dendra fluorescence (Fig. S7D). As expected, we see an overall decrease in red fluorescence intensity, and complementary increase in green fluorescence intensity, over 8 hours, likely due to protein turnover. We also observe an increase in the number of nuclei lacking red fluorescence. This is expected since the well was only partially converted and we expect significant numbers of unconverted cells to move into the field between the first image and the 8 hour image.

(7) The authors isolate and subsequently use RNA-sequencing to identify changes between Mps1i and DMSO-treated cells. One concern is that even with the less stringent cut-off of 1.5 fold there is a very small change between DMSO and MPS1i treated cells, with only 63 genes changing, none of which were affected above a 2-fold change. The authors should carefully address this, including why their dataset sees changes in many more pathways than in the He et al. and Santaguida et al. studies. Is this due to just having a decreased cut-off?

The reviewer correctly points out that we observed an overall reduction in the strength of gene expression changes between our dataset of DMSO versus Mps1i treated RPE1 cells compared to similar studies. We suggest a couple reasons for this. One is that the log₂ fold changes observed in the other studies are not huge and vary between 2.5 and -3.8 for He et al., 3.3 and -2.3 for Santaguida et al., and -0.8 and 1.6 for our study. This variability is within a reasonable range for different experimental conditions and library prep protocols. A second is that our protocol minimizes a potential source of transcriptional change – nuclear lobulation – that is present in the other datasets.

For the pathway analysis we did not use a fold-change cut-off for any data set, instead opting to include all the genes found to be significantly different between control and Mps1i treated cells for all three studies. Our read-depth was higher than that of the two published experiments, which could contribute to an increased DEG number. However, we hypothesize that our identification of a broader number of altered pathways most likely arises from increased sensitivity due to the loss of covering signal from transcriptional changes associated with increased nuclear atypia. Additional visual cell sorting experiments sorting on misshapen nuclei instead of MN would allow us to determine the accuracy of this hypothesis.

(8) Moreover, clustering (in Figure 5E) of the replicates is a bit worrisome as the variances are large and therefore it is unclear if, with such large variance and low screening depth, one can really make such a strong conclusion that there are no changes. The authors should prove that their conclusion that rupture does not lead to large transcriptional changes, is not due to the limitations of their experimental design.

We agree with the reviewers that additional rounds of RNAseq would improve the accuracy of our transcriptomic analysis and could uncover additional DEGs. However, we believe the overall conclusion to be correct based on the results of our attempt to validate changes in gene expression by immunofluorescence. We analyzed two of the most highly upregulated genes in the ruptured MN dataset, ATF3 and EGR1. Although we saw a statistically significant increase in ATF3 intensity between cells without MN and those with ruptured MN, the fold change was so small compared to our positive control (100x less) that we believe it is it is more consistent with a small increase in the probability of aneuploidy rather than a specific signature of MN rupture.

(9) The authors also need to address the fact that they are using RPE-1 cells more clearly and that the lack of effect in transcriptional changes may be simply due to the loss of cGAS-STING pathway (Mackenzie et al., 2017; Harding et al., 2017; etc.).

As we discuss above in the public comments section, the literature is clear that MN do not activate cGAS in the first cell cycle after their formation, even upon rupture. Therefore, we do not expect any changes in our results when applied to cGAS-competent cells. However, this expectation needs to be experimentally validated, which we plan to address in upcoming work.

aynı konuda diğer bir mercinin başka bir karar vermesine ve başvurucunun bu hususu derece mahkemeleriyle paylaşmasına rağmen verilen bu başka kararın değerlendirilmemesi

Reviewer #1 (Public review):

Summary:

Gao et al. has demonstrated that the the pesticide emamectin benzoate (EB) treatment of brown plathopper (BPH) leads to increased egg laying in the insect, which is a common agricultural pest. The authors hypothesize that EB upregulates JH titer resulting in increased fecundity.

Strengths:

The finding that a class of pesticide increases fecundity of brown planthopper is interesting.

Weaknesses:

(1) EB is an allosteric modulator of GluCl. That means it EB physically interacts with GluCl initiating a structural change in the cannel protein. Yet the authors here central hypothesis is about how EB can upregulate the mRNA of GluCl. I do not know whether there is any evidence that an allosteric modulator can function as a transcriptional activator for the same receptor protein. The basic premise of the paper sounds counterintuitive. This is a structural problem and should be addressed by the authors by giving sufficient evidence about such demonstrated mechanisms before.<br /> (2) I am surprised to see a 4th instar larval application or treatment with EB results in upregulation of JH in the adult stages. Complicating the results further is the observation that a 4th instar EB application results in an immediate decrease in JH titer. There is a high possibility that this late JH titer increase is an indirect effect.<br /> (3) The writing quality of the paper needs improvement. Particularly with respect to describing processes, and abbreviations. In several instances authors have not adequately described the processes they have introduced, thus confusing the readers.<br /> (4) In the section 'EB promotes ovarian development' the authors have shown that EB treatment results in increased detention of eggs which contradicts their own results which show that EB promotes egg laying. Again, this is a serious contradiction that nullifies their hypothesis.<br /> (5) Furthermore, the results suggest that oogenesis is not affected by EB application. The authors should devote a section to discussing how they are observing increased egg numbers in EB-treated insects while not impacting Oogenesis.<br /> (6) Met is the receptor of JH and to my understanding, remains mostly constant in terms of its mRNA or protein levels throughout various developmental periods in many different insects. Therefore, the presence of JH becomes the major driving factor for physiological events and not the presence of the receptor Met. Here the authors have demonstrated an increase in Met mRNA as a result of EB treatment. Their central hypothesis is that EB increases JH titer to result in enhanced fecundity. JH action will not result in the activation of Met. Although not contradictory to the hypothesis, the increase in mRNA content of Met is contrary to the findings of the JH field thus far.<br /> (7) As pointed out before, it is hard to rationalize how a 4th instar exposure to EB can result in upregulation of key genes involved in JH synthesis at the adult stage. The authors must consider providing a plausible explanation and discussion in this regard.<br /> (8) I have strong reservations against such an irrational hypothesis that Met (the receptor for JH) and JH-Met target gene Kr-h1 regulates JH titer (Line 311, Fig 3 supplemental 2D). This would be the first report of such an event on the JH field and therefore must be analysed to depth before it may go to publication. I strongly suggest the authors remove such claims from the manuscript without substantiating it.<br /> (9) Kr-h1 is JH/Met target gene. The authors demonstrate that silencing of Kr-h1 results in inhibition of FAMeT, which is a gene involved in JH synthesis. The feedback loop in JH synthesis is unreported. Authors must go ahead with a mechanistic detail of Kr-h1 mediated JH upregulation before this can be concluded. Mere qPCR experiments are not sufficient to substantiate a claim that is completely contrary to the current understanding of JH signalling pathway.<br /> (10) Authors have performed knockdowns of JHAMT, Met and Kr-h1 to demonstrate the effect of these factors on fecundity n BPH. Additionally, they have performed rescue experiments with EB application on these knockdown insects (Figure 3K-M). This I believe is a very flawed experiment. The authors demonstrate EB works through JHAMT in upregulating JH titer. In the absence of JHAMT, EB application is not expected to rescue the phenotype. But authors have reported a complete rescue here. In the absence of Met, the receptor of JH, either EB or JH is not expected to rescue the phenotype. But a complete rescue has been reported. These two experimental results contradict their own hypothesis.<br /> (11) A significant section of the paper deals with how EB upregulates JH titer. JH is a hormone synthesized in the Corpora Allata. Yet the authors have chosen to use the whole body for all of their experiments. Changes in the whole body for mRNA of those enzymes involved in JH synthesis does may not reflect on the situation in Corpora Allata. Although working with corpora Allata is challenging, discarding the abdomen and thorax region and working with the head and neck region of the insect is easily doable. Results from such sampling is always more convincing when it comes to JH synthesis studies.<br /> (12) The phenomenon reported was specific for BPH and not found in other insects. This limits the implications of the study.<br /> (13) Overall, the molecular experiments are very poorly designed and can at best be termed superficial. There are several contradictions within the paper and no discussion or explanation has been provided for that.

Comments on revisions:

(1) The onus of making the revisions understandable to the reviewers lies with the authors. In its current form, how the authors have approached the review is hard to follow, in my opinion. Although the authors have taken a lot of effort in answering the questions posed by reviewers, parallel changes in the manuscript are not clearly mentioned. In many cases, the authors have acknowledged the criticism in response to the reviewer, but have not changed their narrative, particularly in the results section.<br /> (2) In the response to reviewers, the authors have mentioned line numbers in the main text where changes were made. But very frequently, those lines do not refer to the changes or mention just a subsection of changes done. The problem is throughout the document making it very difficult to follow the revision and contributing to the point mentioned above.<br /> (3) The authors need to infer the performed experiments rationally without over interpretation. Currently, many of the claims that the authors are making are unsubstantiated. As a result of the first review process, the authors have acknowledged the discrepancies, but they have failed to alter their interpretations accordingly.<br /> (4) I would like to point to the fact that there are significant experimental modifications added to the manuscript. The decision from the first cycle of review was given on 8th Nov 2024. The authors re-submitted the manuscript on 20th Nov 2024. It just beats my understanding, how so many experiments can be done in such a short time. The rush in resubmission is evident in the writing quality as well. Which I think is now poorer than the original version.<br /> (5) The writing quality is still extremely poor.

Reviewer #2 (Public review):

Summary:

This manuscript is about using different analytical approaches to allow ancestry adjustments to GWAS analyses amongst admixed populations. This work is a follow-on from the recently published ITHGC multi-population GWAS (https://doi.org/10.7554/eLife.84394), with the focus on the admixed South African populations. Ancestry adjustment models detected a peak of SNPs in the class II HLA DPB1, distinct from the class II HLA DQA1 loci signficant in the ITHGC analysis.

Strengths:

Excellent demonstration of GWAS analytical pipelines in highly admixed populations. Particularly the utility of ancestry adjustment to improve study power to detect novel associations. Further confirmation of the importance of the HLA class II locus in genetic susceptibility to TB.

Weaknesses:

Limited novelty compared to the group's previous existing publications and the body of work linking HLA class II alleles with TB susceptibility in South Africa or other African populations. This work includes only ~100 new cases and controls from what has already been published. High resolution HLA typing has detected significant signals in both the DQA1 and DPB1 regions identified by the larger ITHGC and in this GWAS analysis respectively (Chihab L et al. HLA. 2023 Feb; 101(2): 124-137).<br /> Despite the availability of strong methods for imputing HLA from GWAS data (Karnes J et Plos One 2017), the authors did not confirm with HLA typing the importance of their SNP peak in the class II region. This would have supported the importance of this ancestry adjustment versus prior ITHGC analysis.<br /> The populations consider active TB and healthy controls (from high-burden presumed exposed communities) and do not provide QFT or other data to identify latent TB infection.

Important methodological points for clarification and for readers to be aware of when reading this paper:

(1) One of the reasons cited for the lack of African ancestry-specific associations or suggestive peaks in the ITHGC study was the small African sample size. The current association test includes a larger African cohort and yields a near-genome-wide significant threshold in the HLA-DPB1 gene originating from the KhoeSan ancestry. Investigation is needed as to whether the increase in power is due to increased African samples and not necessarily the use of the LAAA model as stated on lines 295 and 296?

Authors response - The Manhattan plot in Figure 3 includes the results for all four models: the traditional GWAS model (GAO), the admixture mapping model (LAO), the ancestry plus allelic (APA) model and the LAAA model. In this figure, it is evident that only the LAAA model identified the association peak on chromosome 6, which lends support the argument that the increase in power is due to the use of the LAAA model and not solely due to the increase in sample size.<br /> Reviewer comment - This data supports the authors conclusions that increase power is related to the LAAA model application rather than simply increase sample size.

(2) In line 256, the number of SNPs included in the LAAA analysis was 784,557 autosomal markers; the number of SNPs after quality control of the imputed dataset was 7,510,051 SNPs (line 142). It is not clear how or why ~90% of the SNPs were removed. This needs clarification.

Authors response:<br /> In our manuscript (line 194), we mention that "...variants with minor allele frequency (MAF) < 1% were removed to improve the stability of the association tests." A large proportion of imputed variants fell below this MAF threshold and were subsequently excluded from this analysis.

Reviewers additional comment: The authors should specify the number of SNPs in the dataset before imputation and indicate what proportion of the 784,557 remaining SNPs were imputed. Providing this information might help the reader better understand the rationale behind the imputation process.

(3) The authors have used the significance threshold estimated by the STEAM p-value < 2.5x10-6 in the LAAA analysis. Grinde et al. (2019 implemented their significance threshold estimation approach tailored to admixture mapping (local ancestry (LA) model), where there is a reduction in testing burden. The authors should justify why this threshold would apply to the LAAA model (a joint genotype and ancestry approach).

Authors response: We describe in the methods (line 189 onwards) that the LAAA model is an extension of the APA model. Since the APA model itself simultaneously performs the null global ancestry only model and the local ancestry model (utilised in admixture mapping), we thus considered the use of a threshold tailored to admixture mapping appropriate for the LAAA model.

Reviewers additional comment: While the LAAA model is an extension of the APA model, the authors describe the LAAA test as 'models the combination of the minor allele and the ancestry of the minor allele at a specific locus, along with the effect of this interaction,' thus a joint allele and ancestry effects model. Grinde et al. (2019) proposed the significance threshold estimation approach, STEAM, specifically for the LA approach, which tests for ancestry effects alone and benefits from the reduced testing burden. However, it remains unclear why the authors found it appropriate to apply STEAM to the LAAA model, a joint test for both allele and ancestry effects, which does not benefit from the same reduction in testing burden.

(4) Batch effect screening and correction (line 174) is a quality control check. This section is discussed after global and local ancestry inferences in the methods. Was this QC step conducted after the inferencing? If so, the authors should justify how the removed SNPs due to the batch effect did not affect the global and local ancestry inferences or should order the methods section correctly to avoid confusion.

Authors response: The batch effect correction method utilised a pseudo-case-control comparison which included global ancestry proportions. Thus, batch effect correction was conducted after ancestry inference. We excluded 36 627 SNPs that were believed to have been affected by the batch effect. We have amended line 186 to include the exact number of SNPs excluded due to batch effect.<br /> The ancestry inference by RFMix utilised the entire merged dataset of 7 510 051 SNPs. Thus, the SNPs removed due to the batch effect make up a very small proportion of the SNPs used to conduct global and local ancestry inferences (less than 0.5%). As a result, we do not believe that the removed SNPs would have significantly affected the global and local ancestry inferences. However, we did conduct global ancestry inference with RFMix on each separate dataset as a sanity check. In the Author response tables 1 and 2, we show the average global ancestry proportions inferred for each separate dataset, the average global ancestry proportions across all datasets and the average global ancestry proportions inferred using the merged dataset. The SAC and Xhosa cohorts are shown in two separate tables due to the different number of contributing ancestral populations to each cohort. The differences between the combined average global ancestry proportions across the separate cohorts does not differ significantly to the global ancestry proportions inferred using the merged dataset.

This is an excellent response and should remain accessible to readers to clarify this issue.

Reviewer #2 (Public review):

Summary:

Wang et al. investigate the role of TseP, a Type VI secretion system (T6SS) effector molecule, revealing its dual enzymatic activities as both an amidase and a lysozyme. This discovery significantly enhances the understanding of T6SS effectors, which are known for their roles in interbacterial competition and survival in polymicrobial environments. TseP's dual function is proposed to play a crucial role in bacterial survival strategies, particularly in hostile environments where competition between bacterial species is prevalent.

Strengths:

(1) The dual enzymatic function of TseP is a significant contribution, expanding the understanding of T6SS effectors.<br /> (2) The study provides important insights into bacterial survival strategies, particularly in interbacterial competition.<br /> (3) The findings have implications for antimicrobial research and understanding bacterial interactions in complex environments.

Weaknesses:

(1) The manuscript assumes familiarity with previous work, making it difficult to follow. Mutants and strains need clearer definition and references.<br /> (2) Figures lack proper controls, quantification, and clarity in some areas, notably in Figures 1A and 1C.<br /> (3) The Materials and Methods section is poorly organized, hindering reproducibility. Biophysical validation of Zn²⁺ interaction and structural integrity of proteins need to be addressed.<br /> (4) Discrepancies in protein degradation patterns and activities across different figures raise concerns about data reliability.

Comments on revisions:

The authors have addressed most of the comments, significantly improving the manuscript. They provided clear details of mutant constructs and strains, including additional references and a revised strain. Individual data points and statistical analyses were added to key figures, ensuring transparency and reproducibility. Supplemental data, such as protein purification details and loading controls, were included to address concerns about experimental reliability. However, the authors did not perform new experiments, such as isothermal titration calorimetry (ITC) to demonstrate the interaction between Zn²⁺ and TsePN or stop-flow spectroscopy to examine enzymatic kinetics, which could have further strengthened the manuscript. I trust these aspects will be addressed in future studies.

The revised Materials and Methods section was significantly improved, providing detailed protocols for bioinformatics analyses, microscopic imaging, and enzymatic assays.

These revisions provide a clearer and more robust presentation of TseP's dual enzymatic functions and their implications in bacterial competition. The manuscript now represents a significant contribution to understanding T6SS effectors, and I recommend it for publication in its current form.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript performs a comprehensive biochemical, structural, and bioinformatic analysis of TseP, a type 6 secretion system effector from Aeromonas dhakensis that includes the identification of a domain required for secretion and residues conferring target organism specificity. Through targeted mutations, they have expanded the target range of a T6SS effector to include a gram-positive species, which is not typically susceptible to T6SS attack.

Strengths:

All of the experiments presented in the study are well-motivated and the conclusions are generally sound.

Thank you.

Weaknesses:

There are some issues with the clarity of figures. For example, the microscopy figures could have been more clearly presented as cell counts/quantification rather than representative images. Similarly, loading controls for the secreted proteins for the westerns probably should be shown.

Also, some of the minor/secondary conclusions reached regarding the "independence" of the N and C term domains of the TseP are a bit overreaching.

We thank the reviewer for pointing out the issues and have carefully revised the manuscript accordingly. We acknowledge the reviewer’s concern regarding the independence of the N- and C-terminal domains, and have toned down the relevant claims.

Reviewer #2 (Public review):

Summary:

Wang et al. investigate the role of TseP, a Type VI secretion system (T6SS) effector molecule, revealing its dual enzymatic activities as both an amidase and a lysozyme. This discovery significantly enhances the understanding of T6SS effectors, which are known for their roles in interbacterial competition and survival in polymicrobial environments. TseP's dual function is proposed to play a crucial role in bacterial survival strategies, particularly in hostile environments where competition between bacterial species is prevalent.

Strengths:

(1) The dual enzymatic function of TseP is a significant contribution, expanding the understanding of T6SS effectors.

(2) The study provides important insights into bacterial survival strategies, particularly in interbacterial competition.

(3) The findings have implications for antimicrobial research and understanding bacterial interactions in complex environments.

Thank you.

Weaknesses:

(1) The manuscript assumes familiarity with previous work, making it difficult to follow. Mutants and strains need clearer definitions and references.

Thank you for raising the issue. We have revised the manuscript accordingly to improve the clarity by including more detailed descriptions of the mutants and strains, along with references to prior work where relevant, to improve clarity.

(2) Figures lack proper controls, quantification, and clarity in some areas, notably in Figures 1A and 1C.

We have now added the controls as requested by reviewers.

(3) The Materials and Methods section is poorly organized, hindering reproducibility. Biophysical validation of Zn²⁺ interaction and structural integrity of proteins need to be addressed.

We have now included more details in the Materials and Methods section. While we recognize the importance of biophysical validation of the Zn²⁺ interaction, this analysis lies beyond the primary scope of the current study. We plan to investigate the role of Zn²⁺ interaction and the EF-hand domain in greater depth as part of our follow-up studies. Thank you for this suggestion.

(4) Discrepancies in protein degradation patterns and activities across different figures raise concerns about data reliability.

We acknowledge the concern about discrepancies in protein degradation patterns. TseP exhibits inherent instability, which might explain the observed variations. We have added an explanation in the detailed response letter and the manuscript.

Reviewer #3 (Public review):

Summary:

Type VI secretion systems (T6SS) are employed by bacteria to inject competitor cells with numerous effector proteins. These effectors can kill injected cells via an array of enzymatic activities. A common class of T6SS effector are peptidoglycan (PG) lysing enzymes. In this manuscript, the authors characterize a PG-lysing effector-TseP-from the pathogen Aeromonas dhakensis. While the C-terminal domain of TseP was known to have lysozyme activity, the N-terminal domain was uncharacterized. Here, the authors functionally characterize TsePN as a zinc-dependent amidase. This discovery is somewhat novel because it is rare for PG-lysing effectors to have amidase and lysozyme activity.

In the second half of the manuscript, the authors utilize a crystal structure of the lysozyme TsePC domain to inform the engineering of this domain to lyse gram-positive peptidoglycan.

Strengths:

The two halves of the manuscript considered together provide a nice characterization of a unique T6SS effector and reveal potentially general principles for lysozyme engineering.

Thank you.

Weaknesses:

The advantage of fusing amidase and lysozyme domains in a single effector is not discussed but would appear to be a pertinent question. Labeling of the figures could be improved to help readers understand the data.

Thank you for the suggestions. We have revised the manuscript and figures to improve clarity.

The advantage of having dual-domain functions relative to having just one of the two functions is likely for increasing competitive fitness. Although such dual functional cell-wall targeting effectors have not been characterized prior to this study, there are some examples that dual functions are encoded by the same secretion module, for example the VgrG1-TseL pair in Vibrio cholerae. The C-terminal of VgrG1 not only catalyzes actin crosslinking but also recognizes and delivers the downstream encoded lipase effector TseL through direct interaction. In this context, the VgrG1-TseL pair also represent a dual-functional module. Therefore, it is likely that fusing effector domains and coupling effector functions are parallel strategies for the evolution of T6SS effectors.

这段文字描述了 Christopher Wool 的文字绘画（word paintings）背后的灵感来源、创作手法以及艺术批评界对其作品的解读和评价。可从以下几个角度来理解其核心要点：

1. 灵感与形式
2. 灵感来源：据传，Wool 于 1987 年看到一辆送货卡车上用黑色字体随意涂写的涂鸦，受到启发后开始了文字绘画系列。
3. 视觉呈现：Wool 的画面以白色为背景，使用模板（stencil）在其上喷涂或绘制粗大的黑色字母。字母常被分割成上下或左右两部分，使文字呈现出断裂、分离的视觉效果。

4. “文字即图像”：这些字母既是可阅读的语言符号，又被当作视觉构图元素（blocky black letters）来处理，带有“抽象”和“具象”之间的张力。

5. 内容与意涵

6. 文字内容：Wool 选择了带有某种文化或社会暗示的词句，例如《现代启示录》（Apocalypse Now）中的台词 “Sell the House, Sell the Car, Sell the Kids”，或去掉元音的 “TR/BL”（意为“trouble”）。
7. 语言的破碎与抽象：字母被拆分、断裂，观众需要在阅读时主动拼凑含义，从而使阅读过程本身成为作品的一部分。

8. “不完整的意义”：作品通常仅使用黑白两色，背景简洁，没有其他上下文或视觉提示。正如评论家 Peter Schjeldahl 所说，这些文字“被抽干了个性”，难以传达完整意义，也使得观者停留在文字的形式与潜在意义之间的空隙里。

9. 评论与解读

10. 在观众体验层面：Roberta Smith 指出，Wool 的文字作品将“观看、阅读甚至发声”这一系列动作融合在一起，观众必须在拼读或猜测时不断转换阅读方式。

11. 在社会政治层面：Time Out 则将 Wool 的文字绘画与 20 世纪 80 年代末美国社会氛围联系在一起，认为作品所传达的“焦虑与不安”折射了里根政府时期的一系列危机（如艾滋病危机、1987 年股市崩盘、储蓄与贷款丑闻等）。

12. 核心意义

13. 符号的多重功能：Wool 的文字既是艺术图像又是语言符号，在“可读”与“不可读”的边界上进行实验。
14. 社会批判与个人体验的交织：艺术家选择具有文化或政治暗示的短语或单词，让观众联想到更广泛的社会现实与个人焦虑。
15. 抽离、空白与意味：通过极简的配色和断裂的文字，Wool 的作品给人留下大量解读空间，也让观众去思考：当文字“缺少上下文”或“难以完整解读”时，其意义如何产生或被削弱？

总结：<br /> Christopher Wool 的文字绘画源于街头涂鸦的灵感，采用黑白极简的视觉形式，通过将文字拆分与断裂，将观众引入对语言与图像关系的反思。评论者认为，这些作品不仅在形式上探索“看与读”的边界，也映射了 20 世纪 80 年代末美国社会的普遍不安与危机意识，成为一种既抽象又具有时代暗示的艺术表达。

从图中可以看到两幅以黑白强烈对比为主的文字作品，分别写着 “PARANOIAC” 和 “SPOKESMAN”，文字采用了大写字母、分割式模板（stencil）的风格，垂直排列占据画面主体。要理解这类作品，可从以下几个角度出发：

1. 文字与形式的张力
2. 这些字母并非简单印刷或整齐排布，而是使用了模板喷涂、印刷或手工描绘等方式，字母边缘略有不规则或残缺感，增强了视觉上的“粗粝”与“手痕”效果。

3. 大写字母被分割成上下或左右两部分，使文字在具象（可读）与抽象（图形）之间形成张力。观众既能将其视作单纯的图像，又能辨认出词语本身的含义。

4. 单词本身的含义

5. “PARANOIAC”（偏执狂、妄想症患者）和 “SPOKESMAN”（发言人）在英文中都带有较强的社会或心理含义。
6. “PARANOIAC”指向一种心理状态，可能暗示对周围环境或信息的高度警惕、不信任或扭曲的理解；而 “SPOKESMAN” 则意味着代表他人发声或对外传达信息的角色。

7. 将这两个词放在一起，容易让人联想到当代社会中对“话语权”和“信任”的讨论：一方面是个人的焦虑或被迫害感，另一方面是公众舆论中“谁在为谁说话”的问题。

8. 阅读体验与视觉冲击

9. 字体占满画布，大写黑体的视觉冲击力让人无法忽视文字的存在，观众会下意识地停留并试图阅读。
10. 垂直排布的方式打破了我们习惯的横向阅读，阅读顺序变得“迟缓”或“被迫”，这会让观者在解读时产生片刻的停顿和思考。

11. 文字被拆分或分割，既保留了单词的可读性，又让人感受到某种“破碎”或“断裂”之感，暗示了信息的中断、含义的错置或扭曲。

12. 对大众文化与城市语境的映射

13. 这种粗犷、直接、对比强烈的黑白文字手法，常见于街头涂鸦或工业标识，也与当代艺术中对“标语”“口号”以及大众传播符号的运用有关。
14. 在城市文化语境下，这样的文字作品往往被视为一种宣言或挑衅，直截了当地占据视觉空间，要求观众回应。

15. 通过使用充满冲击力的单词（“PARANOIAC”、“SPOKESMAN”），作品可能质疑公共话语体系、社会心理状态以及个人与群体间的信息交流。

16. 概念艺术与抽象语言的融合

17. 这类作品可被视为概念艺术（Conceptual Art）的一种延伸：文字不只是一种语言工具，也成为了图像、概念和形式的综合体。
18. 它们打破了传统绘画的具象或纯抽象界限，用“语言的图像化”来探讨表达与意义的关系。
19. 文字作为“符号”，在与背景、形式、布局的交互中，创造了新的审美与概念空间。

总结而言，这两幅作品通过高对比度的文字呈现和引人注目的单词选择，将观众带入对语言、心理、社会角色和信息传递等议题的思考。单词“PARANOIAC”和“SPOKESMAN”本身便能触发对个人与公众、焦虑与话语权等复杂主题的联想，而作品在视觉形式上又不断强调字母的物理性与破碎感，从而在可读性与抽象之间形成紧张关系，令观众在阅读和观赏的过程中不断反思文字与意义、艺术与传播的本质。

以下是对整段文字的主要内容与要点的概括：

1. 展览概况：See Stop Run
2. 展览地点：纽约金融区一栋未被使用的大楼第 19 层，面积约 18,000 平方英尺，四周环绕窗户，让城市风景自然融入展览。
3. 艺术家：Christopher Wool，策展人：Anne Pontégnie。
4. 该展览呈现了过去十年间的创作，涵盖绘画、雕塑、摄影、马赛克等多种媒介。
5. 这是 Wool 自 2014 年以来规模最大的个展，持续至 7 月底。

6. Wool 选择独立场地（而非白盒子画廊）以打破“中性展览空间”的常规，让作品与城市环境产生直接对话。

7. Wool 对文字作品的思考与转变

8. 访谈中提到，Wool 早期以文字绘画（text paintings）闻名，但他在 30 年前就停止了此类创作，原因在于他对文字的“直接意义”感到局限。
9. 他认为文字作品在“抽象”层面难以自由延展，而抽象绘画具有更大的开放性。

10. 近期与作家 Richard Hell 的合作中，他尝试以电脑方式创作插图，虽含有文字元素，但更偏向抽象，不具备明确语义。

11. 文字与抽象的关系

12. Wool 曾使用模板（stencils）创作文字，但由于排版、间距、叠加方式等“画家式”细节，每一幅作品都不可简单复制。
13. 在访谈里，他举例曾试图重制一幅被损坏的文本作品（RIOT），结果却无法还原原作的“魔力”，说明即便是模板文字也带有手势（gesture）的独特性。

14. 对于文字的使用，Wool 表示如果必须要表达字面含义，就会束缚艺术家对形式的探索；因此，他逐渐转向更自由的抽象方式。

15. 展览与空间、城市的对话

16. 展览地点为 101 Greenwich，带来“意外的好氛围”：观众花更多时间驻留，Wool 也在现场与陌生人交流。
17. 他认为这与人们在疫情后对艺术、对空间的需求也有关联。

18. 访谈中还提到 Herman Melville（《白鲸记》作者）在附近海关大楼（Customs House）的工作往事，展览场地周边也有艺术家 Reginald Marsh 的壁画等，这些历史和城市元素与展览形成了独特的文化背景。

19. 访谈形式与艺术解读

20. Wool 对访谈保持谨慎，担心一旦用语言界定作品含义，就会限制观众对作品的多重解读。
21. 他认为艺术的核心在于不断提出问题，而不是在访谈中给出一个“官方解释”。

22. 同时，他提到 Richard Prince 善于在访谈中进行“虚构”或“再创作”，但他自己更倾向让作品保留开放性，不想用文字将其固定。

23. 整体风格与创作思路

24. Wool 的艺术创作常常涉及城市文化、抽象形态、重复与偶然性等元素。
25. 他注重作品与周围环境的相互影响，通过非传统场地和大型空间呈现，使观众在更宽阔的视野下感受作品。
26. 新展览中的作品并非某种突然的风格转变，而是延续了他对抽象、叠层、偶然性的探索，只是更加远离文字的直接含义。

总而言之，这段访谈主要聚焦 Christopher Wool 在 “See Stop Run” 展览中的创作背景、对文字与抽象的态度转变，以及他如何在非传统空间中呈现作品并与城市环境产生互动。Wool 强调了艺术的开放性、抽象性，以及他对于访谈与文字阐释的谨慎态度。

这段文本介绍了艺术家 Christopher Wool 在 2016 年创作的丝网印刷作品 《Untitled I》。文本内容可以从以下几个方面进行理解和解释：

1. 作品基本信息
2. 标题：Untitled I
3. 创作时间：2016 年
4. 艺术家：Christopher Wool
5. 创作媒介：丝网印刷（screen print）

6. 主题方向：探索文字排版（typography）与抽象叠加（abstract layering）

7. 视觉元素与构图

8. 画面中有大量字母、数字和形状，色调以灰色、黑色和红色为主。
9. 这些元素相互重叠，构成一种“密集、混乱”的视觉效果。

10. 每个元素部分地遮挡了其他元素，形成了深度与复杂感，让观众在观赏时需要花时间去辨认、解读。

11. 文字与图像的关系

12. Wool 将排版（文字、数字等）视作主要元素，使之在作品中占据中心位置。
13. 通过将熟悉的文字或数字进行重叠、碎片化处理，观众需要在“看似混乱”的布局中重新发现并诠释这些符号。

14. 这种方式打破了文字的传统阅读方式，给人带来新的视觉体验。

15. 艺术风格与创作意图

16. Christopher Wool 一直以挑战传统表现方式而闻名，常常使用文字、标语或数字进行创作，并融入涂鸦、丝网印刷等手法。
17. 这幅 Untitled I 作品展现了都市化（urban aesthetic）的当代感：碎片化、层次丰富，带有某种粗粝或即兴的街头风格。

18. 作品通过对文字与形式的拆解和重组，质疑语言与视觉意义的本质，邀请观众思考：当符号变得难以辨认或脱离原有语境时，它们究竟传递了什么信息，或者是否仍然具备“文字”的意义？

19. 总结

20. 文本强调了 Untitled I 的“重叠”“混乱”“复杂”的视觉特点，以及 Wool 对文字作为艺术元素的深度探索。
21. 作品在当代艺术语境下体现了对传统艺术和语言表达的颠覆，通过视觉的分层和抽象处理，让观者在阅读和观看之间产生新的思考与感知。

简而言之，这段文本告诉我们：Christopher Wool 的 Untitled I 通过将文字、数字、形状等元素混合叠加，创造出一种既熟悉又陌生的视觉冲击力，意在挑战我们对于文字和图像之间关系的传统认知，并在当代城市文化氛围中，呼应了对语言、形式与意义的再思考。

这幅画面最显著的元素就是大面积的黑色粗体文字，排列成方阵式的构图，背景则是带有涂抹或喷涂痕迹的灰白色。文字本身写道：“THE HARDER YOU LOOK / THE HARDER YOU LOOK”，整体呈现出一种极简、直接又具有视觉冲击力的风格。可以从以下几点来理解这件作品：

1. 文字的重复与自指（Self-Referential）
2. 文中重复的短语 “THE HARDER YOU LOOK, THE HARDER YOU LOOK” 既像是一个“视觉游戏”，又带有自指性：它直接谈论“观看”这件事本身。

3. 读者在“看”作品时，会不由自主地反复阅读同一句话，而这句话又在不断提醒你“看得越仔细，‘看’本身就越发显而易见”。这可能暗示了在艺术或现实中，“越想深入探究，就越意识到自己正处于‘探索’的过程”。

4. 极简、概念性与街头感

5. 这种以大号黑字、喷漆或类似丝网印刷等方式呈现文字的手法，常见于观念艺术（Conceptual Art）或街头艺术风格，也能让人联想到 Christopher Wool、Barbara Kruger 等艺术家运用文字作为视觉主体的作品。

6. 作品表面可见的油漆滴痕与不完全覆盖的背景颜色，保留了创作过程的痕迹，也让整幅作品具有一种未经修饰的“街头感”。

7. 观看与意义的悖论

8. 作品的文本似乎在提示：当我们越想要透过“观看”去获取更多信息时，可能反而越会被文字本身的直白所“局限”或“困住”。

9. 这也可能暗示了“深究事物本质”与“表面阅读”之间的张力：有时越努力去看，越容易被语言或符号本身吸引，反而忽视了其他可能的内涵。

10. 观众的参与

11. 这类文字艺术通常希望观众通过阅读或注视来完成作品的意义。作品本身并没有叙事或具象图像，而是让文字去触发观众的思考和解读。
12. 观众的“看”与画面中“看”之间产生对话，使得作品成为一种关于“观看行为”的元反思。

综上，这幅作品利用重复且自指的文字，以及强烈的黑白对比和喷漆滴痕，营造出一种直接又富有张力的视觉效果，同时引导观众去思考“观看”本身的意义：看得越仔细，可能越意识到这只是“看”的行为与“文本”的循环对话，也可能由此进一步思考艺术作品与观者之间如何相互作用。

这幅画面最显著的元素就是大面积的黑色粗体文字，排列成方阵式的构图，背景则是带有涂抹或喷涂痕迹的灰白色。文字本身写道：“THE HARDER YOU LOOK / THE HARDER YOU LOOK”，整体呈现出一种极简、直接又具有视觉冲击力的风格。可以从以下几点来理解这件作品：

1. 文字的重复与自指（Self-Referential）
2. 文中重复的短语 “THE HARDER YOU LOOK, THE HARDER YOU LOOK” 既像是一个“视觉游戏”，又带有自指性：它直接谈论“观看”这件事本身。

3. 读者在“看”作品时，会不由自主地反复阅读同一句话，而这句话又在不断提醒你“看得越仔细，‘看’本身就越发显而易见”。这可能暗示了在艺术或现实中，“越想深入探究，就越意识到自己正处于‘探索’的过程”。

4. 极简、概念性与街头感

5. 这种以大号黑字、喷漆或类似丝网印刷等方式呈现文字的手法，常见于观念艺术（Conceptual Art）或街头艺术风格，也能让人联想到 Christopher Wool、Barbara Kruger 等艺术家运用文字作为视觉主体的作品。

6. 作品表面可见的油漆滴痕与不完全覆盖的背景颜色，保留了创作过程的痕迹，也让整幅作品具有一种未经修饰的“街头感”。

7. 观看与意义的悖论

8. 作品的文本似乎在提示：当我们越想要透过“观看”去获取更多信息时，可能反而越会被文字本身的直白所“局限”或“困住”。

9. 这也可能暗示了“深究事物本质”与“表面阅读”之间的张力：有时越努力去看，越容易被语言或符号本身吸引，反而忽视了其他可能的内涵。

10. 观众的参与

11. 这类文字艺术通常希望观众通过阅读或注视来完成作品的意义。作品本身并没有叙事或具象图像，而是让文字去触发观众的思考和解读。
12. 观众的“看”与画面中“看”之间产生对话，使得作品成为一种关于“观看行为”的元反思。

综上，这幅作品利用重复且自指的文字，以及强烈的黑白对比和喷漆滴痕，营造出一种直接又富有张力的视觉效果，同时引导观众去思考“观看”本身的意义：看得越仔细，可能越意识到这只是“看”的行为与“文本”的循环对话，也可能由此进一步思考艺术作品与观者之间如何相互作用。

您感觉非常敏锐！ 约翰·麦克拉肯 (John McCracken) 和 唐纳德·贾德 (Donald Judd) 的风格确实有诸多相似之处，这是因为他们都是极简主义 (Minimalism) 运动的核心人物，并且在艺术理念和形式语言上有着共通之处。 您的直觉是完全正确的，他们的作品常常被并置讨论，并且理解他们的异同点对于深入了解极简主义艺术至关重要。

为了更详细地说明，我们可以从以下几个方面来分析他们的相似之处和不同之处：

相似之处 (Shared Characteristics):

1. 极简主义美学 (Minimalist Aesthetics): 这是他们最根本的共同点。 他们都坚定地拥抱极简主义的核心原则，追求简洁、纯粹、去除繁复装饰的艺术形式。

   • 几何形体 (Geometric Forms): 两人都偏爱使用基本的几何形状作为作品的构成元素，例如长方体、正方体、平面、柱体等等。 这种对几何形体的强调是极简主义的标志性特征。
   • 工业材料 (Industrial Materials): 他们都倾向于使用工业化生产的材料，例如金属（铝、钢）、胶合板、以及在麦克拉肯作品中也常见的树脂和玻璃纤维。 这种材料选择体现了对材料“物性”的强调，以及对去除艺术家个人情感痕迹的追求。
   • 客观性与非个人化 (Objectivity and Impersonality): 他们的作品都力求客观、冷静、非个人化， 避免传统艺术中常见的主观情感表达和叙事性内容。 作品更强调自身的“物性”和在空间中的存在，而非艺术家的个人情感或意图。
   • 整体性 (Wholeness/Gestalt): 极简主义强调作品的整体性体验。 麦克拉肯和贾德的作品都旨在提供一种整体的、不可分割的感知经验，而非关注局部或细节。 作品的意义在于其整体形态和空间关系，而非局部的装饰或象征。
   • “如实呈现 (Literalism)": 极简主义强调“如实呈现”，即作品就是其所呈现的样子，不追求象征、隐喻或幻觉。 麦克拉肯和贾德的作品都力求呈现材料、形式和色彩的“字面”真实，避免制造视觉错觉或象征意义。

2. 对空间与感知的关注 (Focus on Space and Perception): 两人都非常重视作品与空间环境的关系，以及作品对观众感知的影响。

   • 空间互动 (Spatial Interaction): 他们的作品都积极地与展览空间互动，作品的存在方式、摆放位置、光线反射等都与周围空间环境紧密相连。 作品的意义不仅在于自身，也在于其在特定空间中的呈现和观众的体验。
   • 身体感知 (Corporeal Experience): 极简主义艺术强调观众的身体在空间中的感知体验。 麦克拉肯和贾德的作品都常常以较大尺度呈现，迫使观众通过身体的移动和感知来体验作品，作品的尺度、颜色、材质等都会对观众的身体感官产生直接影响。

3. 对传统艺术观念的挑战 (Challenge to Traditional Art Conventions): 他们都对传统绘画和雕塑的界限提出了挑战，并试图突破既有的艺术分类。

   • 模糊媒介界限 (Blurring Media Boundaries): 麦克拉肯的“平板”系列模糊了绘画与雕塑的界限，贾德的作品也常常超越了传统雕塑的定义。 他们都在探索一种新的、更加开放的艺术形式。
   • 反精英主义 (Anti-Elitism): 极简主义艺术在一定程度上具有反精英主义的倾向，强调艺术的直接性和可感知性，反对过度复杂的象征和隐喻。 麦克拉肯和贾德的作品都以其简洁直接的视觉语言，试图打破艺术与大众之间的隔阂。

不同之处 (Distinguishing Characteristics):

尽管有诸多相似之处，约翰·麦克拉肯和唐纳德·贾德在风格上仍然存在明显的差异，这些差异构成了他们各自独特的艺术个性：

1. 色彩运用 (Use of Color): 这是他们最显著的区别。

   • 麦克拉肯: 鲜艳的单色 (Vibrant Monochromatic Colors): 麦克拉肯以其鲜艳、饱和度极高的单色而闻名。 他偏爱明亮的红、蓝、绿、黄等颜色，并将其运用到极致，创造出极具视觉冲击力的作品。 色彩本身成为了麦克拉肯作品的核心语言和表现力。 [Image of John McCracken Red Column sculpture - showcasing his vibrant red color]
   • 贾德: 材料的自然色彩 (Natural Colors of Materials): 贾德则更倾向于使用材料本身的自然色彩和质感，例如金属的银色、灰色、铜色，胶合板的木色等等。 他极少使用绘画颜料来改变材料的颜色，而是让材料本身的质感和色彩“如实呈现”。 贾德的色彩策略更加内敛、质朴，强调材料的“物性”。 [Image of Donald Judd metal boxes sculpture - showcasing natural metal color and texture]

2. 表面处理 (Surface Finish): 与色彩运用紧密相关的是表面处理方式的不同。

   • 麦克拉肯: 高度抛光 (Highly Polished Surface): 麦克拉肯的作品表面通常经过高度抛光，呈现出镜面般的反光效果。 这种高度抛光的表面增强了作品的现代感和人工感，也使其能够与周围环境产生互动，反射光线和色彩。
   • 贾德: 工业化的粗糙或自然表面 (Industrial or Natural Surface): 贾德则保留了材料的工业化表面或自然质感，例如金属的粗糙纹理、胶合板的木纹等等。 他并不追求表面的光滑和完美，而是让材料的“原始”状态直接呈现。 这种表面处理方式更强调材料的真实性和工业感。

3. 作品的“倚靠”与“悬挂”方式 (Leaning vs. Hanging/Stacking): 作品的展示方式也体现了他们风格的差异。

   • 麦克拉肯: 倚靠 (Leaning): 麦克拉肯最经典的“平板”系列作品通常是倾斜倚靠在墙面上。 这种独特的展示方式模糊了绘画与雕塑的界限，也赋予作品一种动态感和临时的“在场”感。
   • 贾德: 悬挂/堆叠 (Hanging/Stacking): 贾德的作品则常常以悬挂在墙面或堆叠在地面的方式呈现。 他的作品强调垂直或水平方向的序列和重复，以及作品在空间中的排列关系。 贾德的作品更强调其“物”的独立性和在空间中的客观存在。 [Image of Donald Judd stacked boxes sculpture - showcasing his stacking installation style]

4. 情感基调 (Emotional Tone): 尽管都是极简主义，但他们的作品在情感基调上略有不同。

   • 麦克拉肯: 感性与神秘 (Sensual and Mysterious): 麦克拉肯的作品，尤其是其鲜艳的色彩和高度抛光的表面，常常带有感性、甚至是神秘的意味。 色彩的纯粹性和光泽感能够引发观众的感官愉悦和冥想式的体验。 他的作品有时被描述为更具“加州极简主义”的特色，带有一定的浪漫主义色彩。
   • 贾德: 理性与冷静 (Rational and Austere): 贾德的作品则更加理性、冷静、甚至有些austere (朴素/禁欲)。 他更强调作品的逻辑结构、数学关系和材料的客观性，力求去除情感化的表达。 贾德的极简主义更加强调理性分析和逻辑构建。

5. 作品的“在场感” (Presence/Objecthood): 虽然两人都强调作品的“在场感”，但侧重点略有不同。

   • 麦克拉肯: 色彩与光线的“在场” (Presence of Color and Light): 麦克拉肯的作品更侧重于色彩和光线的“在场感”。 鲜艳的色彩和反光表面使得作品在空间中具有强烈的视觉存在，色彩本身成为了作品的“主角”。
   • 贾德: 材料与结构的“在场” (Presence of Material and Structure): 贾德的作品更侧重于材料和结构的“在场感”。 他强调材料的真实性、结构的逻辑性和作品的物理存在，力求让观众直接感受到作品的“物性”。

总结:

总而言之，约翰·麦克拉肯和唐纳德·贾德都是极简主义艺术的杰出代表，他们在核心美学理念、对空间与感知的关注、以及对传统艺术观念的挑战等方面存在显著的相似之处。 然而，他们在色彩运用、表面处理、作品展示方式、情感基调等方面又存在明显的差异，这些差异使得他们的作品呈现出各自独特的艺术个性。

理解他们的异同点，不仅可以帮助我们更深入地理解极简主义艺术的丰富性和多样性，也能更好地欣赏两位艺术家各自的独特贡献和艺术魅力。 他们共同拓展了艺术的边界，并对后来的艺术发展产生了深远的影响。

为了更直观地感受他们的风格差异，我建议您对比观看他们的作品图像，例如：

• [Image of John McCracken sculpture - Vibrant red plank leaning against a white wall] (约翰·麦克拉肯作品图像 - 鲜艳的红色平板倚靠在白色墙面)
• [Image of Donald Judd sculpture - Stacked boxes made of raw metal, highlighting material texture] (唐纳德·贾德作品图像 - 由原始金属制成的堆叠盒子，突出材料质感)

通过视觉对比，您能更清晰地体会到他们作品在色彩、表面、形式和整体氛围上的细微差别，从而更深入地理解他们既相似又独特的极简主义艺术风格。

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

The paper explored cross-species variance in albumin glycation and blood glucose levels in the function of various life-history traits. Their results show that

(1) blood glucose levels predict albumin gylcation rates

(2) larger species have lower blood glucose levels

(3) lifespan positively correlates with blood glucose levels and

(4) diet predicts albumin glycation rates.

The data presented is interesting, especially due to the relevance of glycation to the ageing process and the interesting life-history and physiological traits of birds. Most importantly, the results suggest that some mechanisms might exist that limit the level of glycation in species with the highest blood glucose levels.

While the questions raised are interesting and the amount of data the authors collected is impressive, I have some major concerns about this study:

(1) The authors combine many databases and samples of various sources. This is understandable when access to data is limited, but I expected more caution when combining these. E.g. glucose is measured in all samples without any description of how handling stress was controlled for. E.g glucose levels can easily double in a few minutes in birds, potentially introducing variation in the data generated. The authors report no caution of this effect, or any statistical approaches aiming to check whether handling stress had an effect here, either on glucose or on glycation levels.

(2) The database with the predictors is similarly problematic. There is information pulled from captivity and wild (e.g. on lifespan) without any confirmation that the different databases are comparable or not (and here I'm not just referring to the correlation between the databases, but also to a potential systematic bias (e.g. captivate-based sources likely consistently report longer lifespans). This is even more surprising, given that the authors raise the possibility of captivity effects in the discussion, and exploring this question would be extremely easy in their statistical models (a simple covariate in the MCMCglmms).

(3) The authors state that the measurement of one of the primary response variables (glycation) was measured without any replicability test or reference to the replicability of the measurement technique.

(4) The methods and results are very poorly presented. For instance, new model types and variables are popping up throughout the manuscript, already reporting results, before explaining what these are e.g. results are presented on "species average models" and "model with individuals", but it's not described what these are and why we need to see both. Variables, like "centered log body mass", or "mass-adjusted lifespan" are not explained. The results section is extremely long, describing general patterns that have little relevance to the questions raised in the introduction and would be much more efficiently communicated visually or in a table.

Reviewer #2 (Public review):

Summary

In this extensive comparative study, Moreno-Borrallo and colleagues examine the relationships between plasma glucose levels, albumin glycation levels, diet, and lifehistory traits across birds. Their results confirmed the expected positive relationship between plasma blood glucose level and albumin glycation rate but also provided findings that are somewhat surprising or contradicting findings of some previous studies (relationships with lifespan, clutch mass, or diet). This is the first extensive comparative analysis of glycation rates and their relationships to plasma glucose levels and life history traits in birds that are based on data collected in a single study and measured using unified analytical methods.

Strengths

This is an emerging topic gaining momentum in evolutionary physiology, which makes this study a timely, novel, and very important contribution. The study is based on a novel data set collected by the authors from 88 bird species (67 in captivity, 21 in the wild) of 22 orders, which itself greatly contributes to the pool of available data on avian glycemia, as previous comparative studies either extracted data from various studies or a database of veterinary records of zoo animals (therefore potentially containing much more noise due to different methodologies or other unstandardised factors), or only collected data from a single order, namely Passeriformes. The data further represents the first comparative avian data set on albumin glycation obtained using a unified methodology. The authors used LC-MS to determine glycation levels, which does not have problems with specificity and sensitivity that may occur with assays used in previous studies. The data analysis is thorough, and the conclusions are mostly wellsupported (but see my comments below). Overall, this is a very important study representing a substantial contribution to the emerging field of evolutionary physiology focused on the ecology and evolution of blood/plasma glucose levels and resistance to glycation.

Weaknesses

My main concern is about the interpretation of the coefficient of the relationship between glycation rate and plasma glucose, which reads as follows: "Given that plasma glucose is logarithm transformed and the estimated slope of their relationship is lower than one, this implies that birds with higher glucose levels have relatively lower albumin glycation rates for their glucose, fact that we would be referring as higher glycation resistance" (lines 318-321) and "the logarithmic nature of the relationship, suggests that species with higher plasma glucose levels exhibit relatively greater resistance to glycation" (lines 386-388). First, only plasma glucose (predictor) but not glycation level (response) is logarithm transformed, and this semi-logarithmic relationship assumed by the model means that an increase in glycation always slows down when blood glucose goes up, irrespective of the coefficient. The coefficient thus does not carry information that could be interpreted as higher (when <1) or lower (when >1) resistance to glycation (this only can be done in a log-log model, see below) because the semi-log relationship means that glycation increases by a constant amount (expressed by the coefficient of plasma glucose) for every tenfold increase in plasma glucose (for example, with glucose values 10 and 100, the model would predict glycation values 2 and 4 if the coefficient is 2, or 0.5 and 1 if the coefficient is 0.5). Second, the semi-logarithmic relationship could indeed be interpreted such that glycation rates are relatively lower in species with high plasma glucose levels. However, the semi-log relationship is assumed here a priori and forced to the model by log-transforming only glucose level, while not being tested against alternative models, such as: (i) a model with a simple linear relationship (glycation ~ glucose); or (ii) a loglog model (log(glycation) ~ log(glucose)) assuming power function relationship (glycation = a * glucose^b). The latter model would allow for the interpretation of the coefficient (b) as higher (when <1) or lower (when >1) resistance in glycation in species with high glucose levels as suggested by the authors.

Besides, a clear explanation of why glucose is log-transformed when included as a predictor, but not when included as a response variable, is missing.

We apologize for missing an answer to this part before. Indeed, glucose is always log transformed and this is explained in the text.

The models in the study do not control for the sampling time (i.e., time latency between capture and blood sampling), which may be an important source of noise because blood glucose increases because of stress following the capture. Although the authors claim that "this change in glucose levels with stress is mostly driven by an increase in variation instead of an increase in average values" (ESM6, line 46), their analysis of Tomasek et al.'s (2022) data set in ESM1 using Kruskal-Wallis rank sum test shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values, not only higher variation.

Although the authors calculated the variance inflation factor (VIF) for each model, it is not clear how these were interpreted and considered. In some models, GVIF^(1/(2*Df)) is higher than 1.6, which indicates potentially important collinearity; see for example https://www.bookdown.org/rwnahhas/RMPH/mlr-collinearity.html). This is often the case for body mass or clutch mass (e.g. models of glucose or glycation based on individual measurements).

It seems that the differences between diet groups other than omnivores (the reference category in the models) were not tested and only inferred using the credible intervals from the models. However, these credible intervals relate to the comparison of each group with the reference group (Omnivore) and cannot be used for pairwise comparisons between other groups. Statistics for these contrasts should be provided instead. Based on the plot in Figure 4B, it seems possible that terrestrial carnivores differed in glycation level not only from omnivores but also from herbivores and frugivores/nectarivores.

Given that blood glucose is related to maximum lifespan, it would be interesting to also see the results of the model from Table 2 while excluding blood glucose from the predictors. This would allow for assessing if the maximum lifespan is completely independent of glycation levels. Alternatively, there might be a positive correlation mediated by blood glucose levels (based on its positive correlations with both lifespan and glycation), which would be a very interesting finding suggesting that high glycation levels do not preclude the evolution of long lifespans.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Line 84: "glycation scavengers" such as polyamines - can you specify what these polyamines do exactly?

A clarification of what we mean with "glycation scavengers" is added.

(2) Line 87-89: specify that the work of Wein et al. and this sentence is about birds.

This is now clarified.

(3) Line 95: "88 species" add "OF BIRDS". Also, I think it would be nice if you specified here that you are relying on primary data.

This is now clarified (line 96).

(4) Line 90-119: I find this paragraph very long and complex, with too many details on the methodology. For instance, I agree with listing your hypothesis, e.g. that with POL, but then what variables you use to measure the pace of life can go in the materials and methods section (so all lines between 112-119).

This is explained here as a previous reviewer considered this presentation was indeed needed in the introduction.

(5) Line 122-124: The first sentence should state that you collected blood samples from various sources, and list some examples: zoos? collaborators? designated wild captures? Stating the sample size before saying what you did to get them is a bit weird. Besides, you skipped a very important detail about how these samples were collected, when, where, and using what protocols. We know very well, that glucose levels can increase quickly with handling stress. Was this considered during the captures? Moreover, you state that you had 484 individuals, but how many samples in total? One per individual or more?

We kindly ask the reviewer to read the multiple supplementary materials provided, in which the questions of source of the samples, potential stress effects and sample sizes for each model are addressed. All individuals contributed with one sample. More details about the general sources employed are given now in lines 125-127.

(6) Line 135-36: numbers below 10 should be spelled out.

Ok. Now that is changed.

(7) Line 136: the first time I saw that you had both wild and captive samples. This should be among the first things to be described in the methods, as mentioned above.

As stated above, details on this are included in the supplementary materials, but further clarifications have now been included in the main text (question 5).

(8) Line 137-138: not clear. So you had 46 samples and 9 species. But what does the 3-3-3 sample mean? or for each species you chose 9 samples (no, cause that would be 81 samples in total)?

This has now been clarified (lines 139-140).

(9) Line 139-141: what methodological constraints? Too high glucose levels? Too little plasma?

There were cases in which the device (glucometer) produced an unspecific error. This did not correspond to too high nor too low glucose levels, as these are differently signalled errors. Neither the manual nor the client service provided useful information to discern the cause. This may perhaps be related to the composition of the plasma of certain species, interfering with the measurement. Some clarifications have been added (lines 143-146).

(10) Line 143: should be ZIMS.

Corrected.

(11) Line 120-148: you generally talk about individuals here, but I feel it would be more precise to use 'samples'.

The use is totally interchangeable, as we never measured more than one sample for a given individual within this study. Besides, in some cases, saying “sample” could result less informative.

(12) Line 150: missing the final number of measurements for glucose and glycation.

Please, read the ESM6 (Table ESM6.1), where this information is given.

(13) Line 154-155: so you took multiple samples from the same individual? It's the first time the text indicates so. Or do you mean technical replicates were not performed on the same samples?

As previously indicated, each individual included only one sample. Replicates were done only for some individuals to validate the technique, as it would be unfeasible to perform replicates of all of them. This part of the text is referring to the fact that not all samples were analysed at the same time, as it takes a considerable amount of time, and the mass spectrometry devices are shared by other teams and project. Clarifications in this sense are now added (lines 160-163).

(14) Line 171-172: "After realizing that diet classifications from AVONET were not always suitable for our purpose" - too informal. Try rephrasing, like "After determining that AVONET diet classifications did not align with our research needs...", but you still need to specify what was wrong with it and what was changed, based on what argument?

The new formulation suggested by the reviewer has now been applied (lines 181-183). The details are given in the ESM6, as indicated in the text. 

(15) Line 174-176: You start a new paragraph, talking about missing values, but you do not specify what variable are you talking about. you talk about calculating means, but the last variable you mentioned was diet, so it's even more strange.

We refer to life history traits. It has now been clarified in the text (line 185).

(16) Line 177: what longevity records? Coming from where? How did you measure longevity? Maximum lifespan ever recorded? 80-90% longevity, life expectancy???

We refer to maximum lifespan, as indicated in the introduction and in every other case throughout the manuscript. Clarifications have now been introduced (188-190).

(17) Line 180-183: using ZIMS can be problematic, especially for maximum longevity. There are often individuals who had a wrong date of birth entered or individuals that were failed to be registered as dead. The extremes in this database are often way off. If you want to combine though, you can check the correlation of lifespans obtained from different sources for the overlapping species. If it's a strong correlation it can be ok, but intuitively this is problematic.

The species for which we used ZIMS were those for which no other databases reported any values. We could try correlations for other species, but this issue is not necessarily restricted to ZIMS, as the primary origin of the data from other databases is often difficultly traceable. Also, ZIMS is potentially more updated that some of the other databases, mainly Amniotes database, from which we rely the most, as it includes the highest number of species in the most easily accessible format.

(18) Line 181-186: in ZIMS you calculate the average of the competing records, otherwise you choose the max. Why use different preferences for the same data?

This constitutes a misunderstanding, for which we include clarifications now (line 196). We were referring here to the fact that for maximum lifespan the maximum is always chosen, while for other variables an average is calculated. 

(19) Line 198: Burn-in and thinning interval is quite low compared to your number of iterations. How were model convergences checked?

Please, check ESM1.

(20) Line 201-203: What's the argument using these priors? Why not use noninformative ones? Do you have some a priori expectations? If so, it should be explained.

Models have now been rerun with no expectations on the variance partitions so the priors are less informative, given the lack of firm expectations, and results are similar. Smaller nu values are also tried.

(21) Line 217: "carried" OUT.

Corrected (now in line 229).

(22) Line 233-234: "species average model" - what is this? it was not described in the methods.

Please, read the ESM6.

(23) Line 232-246: (a) all this would be better described by a table or plot. You can highlight some interesting patterns, but describing it all in the text is not very useful I think, (b) statistically comparing orders represented by a single species is a bit odd.

(a) Figure 1 shows this graphically, but this part was found to be quite short without descriptions by previous reviewers. (b) We recognise this limitation, but this part is not presented as one of the main results of the article, and just constitutes an attempt to illustrate very general patterns, in order to guide future research, as in most groups glycation has never been measured, so this still constitutes the best illustration of such patterns in the literature.

(24) Line 281: the first time I saw "mass-adjusted maximum lifespan" - what is this, and how was it calculated? It should be described in the methods. But in any case, neither ratios, nor residuals should be used, but preferably the two variables should be entered side by side in the model.

Please, see ESM6 for the explanations and justifications for all of this.

(25) Line 281: there was also no mention of quadratic terms so far. How were polynomial effects tested/introduced in the models? Orthogonal polynomials? or x+ x^2?

Please, read ESM6.

(26) Table 1. What is 'Centred Log10Body mass', should be added in the methods.

Please, read ESM6.

(27) Table 1: what's the argument behind separating terrestrial and aquatic carnivores?

This was mostly based on the a priori separation made in AVONET, but it is also used in a similar way by Szarka and Lendvai 2024 (comparative study on glucose in birds), where differences in glucose levels between piscivorous and carnivorous are reported. We had some reasons to think that certain differences in dietary nutrient composition, as discussed later, can make this difference relevant.

(28) Table 1: The variable "Maximum lifespan" is discussed and plotted as 'massadjusted maximum lifespan' and 'residual maximum lifespan'. First, this is confusing, the same name should be used throughout and it should be defined in the methods section. Second, it seems that non-linear effects were tested by using x + x^2. This is problematic statistically, orthogonal polynomials should be used instead (check polyfunction in R). Also, how did you decide to test for non-linear effects in the case of lifespan but not the other continuous predictors? Should be described in the methods again.

Please, read ESM6. Data exploration was performed prior to carry out these models. Orthogonal polynomials were considered to difficult the interpretation of the estimates and therefore the patterns predicted by the models, so raw polynomials were used. Clarifications have now been included in line 297.

(29) Figure 2. From the figure label, now I see that relative lifespan is in fact residual. This is problematic, see Freckleton, R. P. (2009). The seven deadly sins of comparative analysis. Journal of evolutionary biology, 22(7), 1367-1375. Using body mass and lifespan side by side is preferred. This would also avoid forcing more emphasis on body mass over lifespan meaning that you subjectively introduce body mass as a key predictor, but lifespan and body size are highly correlated, so by this, you remove a large portion of variance that might in fact be better explained by lifespan.

Please, read ESM6 for justifications on the use of residuals.

Reviewer #2 (Recommendations for the authors):

(1) If the semi-logarithmic relationship (glycation ~ log10(glucose)) is to be used to support the hypothesis about higher glycation resistance in species with high blood glucose (lines 318-321 and 386-388), it should be tested whether it is significantly better than the model assuming a simple linear relationship (i.e., glycation ~ glucose). Alternatively, if the coefficient is to be used to determine whether glycation rate slows down or accelerates with increasing glucose levels, log-log model (log10(glycation) ~ log10(glucose)) assuming power function relationship (glycation = a * glucose^b) should be used (as is for example in the literature about relationships between metabolic rates and body size). Probably the best approach would be to compare all three models (linear, semi-logarithmic, and log-log) and test if one performs significantly better. If none of them, then the linear model should be selected as the most parsimonious.

Different options (linear, both semi-logarithmic combinations and log-log) have now been tested, with similar results. All of the models confirm the pattern of a significant positive relationship between glucose and glycation. Moreover, when standardizing the variables (both glucose and glycation, either log transformed or not), the estimate of the slope is almost equal for all the models. It is also lower than one, which in the case of both the linear and log-log confirms the stated prediction. The log-log model, showing a much lower DIC than the linear version, is now shown as the final model.

(2) ESM6, line 46: Please note that Kruskal-Wallis rank sum test in ESM1 shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values (not only higher variation). With this in mind, what is the argument here about increased variation being the main driver of stress-induced change in glucose levels based on? It seems that both the median values and variation differ between baseline and stress-induced levels, and this should be acknowledged here.

As discussed in the public answers, Kruskal Wallis does not allow to determine differences in mean, but just says that the groups are “different” (implicitly, in their ranksums, which does not mean necessarily in mean), while the Levene test performed signals heteroskedasticity. This makes this feature of the data analytically more grounded. Of course, when looking at the data, a higher mean can be perceived, but nothing can be said about its statistical significance. Still, some subtle changes have been introduced in corresponding section of the ESM6.

(3) Have you recorded the sampling times? If yes, why not control them in the models? It is at least highly advisable to include the sampling times in the data (ESM5).

As indicated in ESM6 lines 42-43, we do not have sampling times for most of the individuals (only zebra finches and swifts), so this cannot be accounted for in the models.

(4) If sampling times will remain uncontrolled statistically, I recommend mentioning this fact and its potential consequences (i.e., rather conservative results) in the Methods section of the main text, not only in ESM6.

A brief description of this has now been included in the main text (lines 129-132), referencing the more detailed discussion on the supplementary materials. Some subtle changes have also been included in the “Possible effects of stress” section of the ESM6.

(5) ESM6, lines 52-53: The lower repeatability in Tomasek et al.' study compared to your study is irrelevant to the argument about the conservative nature of your results (the difference in repeatability between both studies is most probably due to the broader taxonomic coverage of the current study). The important result in this context is that repeatability is lower when sampling time is not considered within Tomasek et al's data set (ESM1). Therefore, I suggest rewording "showing a lower species repeatability than that from our data" to "showing lower species repeatability when sampling time is not considered" to avoid confusion. Please also note that you refer here to species repeatability but, in ESM1, you calculate individual repeatability. Nevertheless, both individual and species repeatabilities are lower when not controlling for sampling time because the main driver, in that case, is an increased residual variance.

We recognize the current confusion in the way the explanation is exposed, and have significantly changed the redaction of the section. However, we would like to indicate that ESM1 shows both species and individual repeatability (for Tomasek et al. 2022 data, for ours only species as we do not have repeated individual values). Changes are now made to make it more evident.

(6) I recommend providing brief guidelines for the interpretation of VIFs to the readers, as well as a brief discussion of the obtained values and their potential importance.

Thank you for the recommendation. We included a brief description in lines 230-231. Also in the results section (lines 389-393).

(7) Line: 264: Please note that the variance explained by phylogeny obtained from the models with other (fixed) predictors does not relate to the traits (glucose or glycation) per se but to model residuals.

We appreciate the indication, and this has been rephrased accordingly (lines 280-286).

(8) Change the term "confidence intervals" to "credible intervals" throughout the paper, since confidence interval is a frequentist term and its interpretations are different from Bayesian credible interval.

Thank you for the remark, this has now been changed.

(9) Besides lifespan, have you also considered quadratic terms for body mass? The plot in Figure 2A suggests there might be a non-linear relationship too.

A quadratic component of body mass has not shown any significant effect on glucose in an alternative model. Also, a model with linear instead of log glucose (as performed in other studies) did not perform better by comparing the DICs, despite both showing a significant relationship between glucose and body mass. Therefore, this model remains the best option considered as presented in the manuscript.

(10) ESM6, lines 115-116: It is usually recommended that only factors with at least 6 or 8 levels are included as random effects because a lower number of levels is insufficient for a good estimation of variance.

In a Bayesian approach this does not apply, as random and fixed factors are estimated similarly. 

(11) Typos and other minor issues:

a) Line 66: Delete "related".

b) Figure 2: "B" label is missing in the plot.

c) Reference 9: Delete "Author".

d) References 15 and 83 are duplicated. Keep only ref. 83, which has the correct citation details.

e) ESM6, line 49: Change "GLLM" to "GLMM".

Thank you for indicating this. Now it’s corrected.

Author response:

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

Response to Reviewer 2’s comments:

I am concerned that the results in Figure 8D may not be correct, or that the authors may be mis-interpreting them. From my reading of the paper they cite (Lammers & Flamholz 2023), the equilibrium sharpness limit for the network they consider in Figure 8 should be 0.25. But both solutions shown in Figure 8D fall below this limit, which means that they have sharpness levels that could have been achieved with no energy expenditure. If this is the case, then it would imply that while both systems do dissipate energy, they are not doing so productively; meaning that the same results could be achieved while holding Phi=0.

I acknowledge that this could be due to a difference in how they measure sharpness, but wanted to raise it here in case it is, in fact, a genuine issue with the analysis.There should be an easy fix for this: just set the sharper "desired response" curve in 8b to be such that it demands non-equilibrium sharpness levels (0.25<S<0.5).

Thank you for raising this point regarding the interpretation of our results in Figure 8D. We agree that if the equilibrium sharpness limit for this particular network is around 0.25 (as shown by Lammers & Flamholz 2023), then achieving a sharpness below this threshold could, in principle, be accomplished without any energy expenditure. However, in our current design approach, the loss function is solely designed to enforce agreement with a target mean mRNA level at different input concentrations; it does not explicitly constrain energy dissipation, noise, or other metrics. Consequently, the DGA has no built-in incentive to minimize or optimize energy consumption, which means the resulting solutions may dissipate energy without exceeding the equilibrium sharpness limit.

In other words, the same input–output relationship could theoretically be achieved with \Phi =0 if an explicit constraint or regularization term penalizing energy usage had been included. As noted, adding such a term (e.g., penalizing \Phi^2) is conceptually straightforward but falls outside the scope of this study. Our primary goal is to demonstrate the flexibility of the DGA in designing a desired response, rather than to delve into energy–sharpness trade-offs or other biological considerations

While we appreciate the suggestion to set a higher target sharpness that exceeds the equilibrium limit, we believe the current example effectively demonstrates the DGA’s ability to design circuits with desired input-output relationships, which is the primary focus of this study. Researchers interested in optimizing energy efficiency, burst size, burst frequency, noise, response time, mutual information, or other system properties can easily extend our approach by incorporating additional terms into the loss function to target these specific objectives.

We hope this explanation addresses your concern and clarifies that the manuscript provides sufficient context for readers to interpret the results in Figure 8D correctly.

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

Reviewer #1 (Public review):

We thank Reviewer #1 for their thoughtful feedback and appreciation of the manuscript's clarity. Our primary goal is to introduce the DGA  as a foundational tool for integrating stochastic simulations with gradient-based optimization. While we recognize the value of providing detailed comparisons with existing methods and a deeper analysis of the DGA’s limitations (such as rare event handling), these topics are beyond the scope of this initial work. Our focus is on presenting the core concept and demonstrating its potential, leaving more extensive evaluations for future research.

Reviewer #2 (Public review):

We thank Reviewer #2 for their detailed and constructive feedback. We appreciate the recognition of the DGA as a significant conceptual advancement for stochastic biochemical network analysis and design.

Weaknesses:

(1) Validation of DGA robustness in complex systems:

Our primary goal is to introduce the DGA framework and demonstrate its feasibility. While validation on high-dimensional and non-steady-state systems is important, it is beyond the scope of this initial work. Future studies may improve scalability by employing techniques such as dynamically adjusting the smoothness of the DGA's approximations during simulation or using surrogate models that remain differentiable but more accurately capture discrete behaviors in critical regions, thus preserving gradient computation while improving accuracy.

(2) Inference accuracy and optimization:

We acknowledge that the non-convex loss landscape in the DGA can hinder parameter inference and convergence to global minima, as seen in Figure 5A. While techniques like multi-start optimization or second-order methods (e.g., L-BFGS) could improve performance, our focus here is on establishing the DGA framework. We plan to explore better optimization methods in future work to improve the accuracy of parameter inference in complex systems.

(3) Use of simple models for demonstration:

We selected well-understood systems to clearly illustrate the capabilities of the DGA. These examples were intended to demonstrate how the DGA can be applied, rather than to solve problems better addressed by analytical methods. Applying DGA to more complex, analytically intractable systems is an exciting avenue for future work, but introducing the method was our main objective in this study.

Reviewer #3 (Public review):

We thank the reviewer for their detailed and insightful feedback. We appreciate the recognition of the DGA as a significant advancement for enabling gradient-based optimization in stochastic systems.

Weaknesses:

(1) Application beyond steady-state analysis

We acknowledge the limitation of focusing solely on steady-state properties. To extend the DGA for analyzing transient dynamics, time-dependent loss functions can be incorporated to capture system evolution over time. This could involve aligning simulated trajectories with experimental time-series data or using moment-matching across multiple time points. 

(2) Numerical instability in gradient computation

The reviewer correctly highlights that large sharpness parameters (a and b) in the sigmoid and Gaussian approximations can induce numerical instability due to vanishing or exploding gradients. To address this, adaptive tuning of a and b during optimization could balance smoothness and accuracy. Additionally, alternative smoothing functions (e.g., softmax-based reaction selection) and gradient regularization techniques (such as gradient clipping and trust-region methods) could improve stability and convergence.

Reviewer #1 (recommendations):

We thank the reviewer for their thoughtful and constructive feedback on our manuscript. Below, we address each of the comments and suggestions raised.

Main points:

(1) It would have been useful to have a brief discussion, based on a concrete example, of what can be achieved with the DGA and is totally beyond the reach of the Gillespie algorithm and the numerous existing stochastic simulation methods.

Thank you for your comment. We would like to clarify that the primary aim of this work is to introduce the DGA and demonstrate its feasibility for tasks such as parameter estimation and network design. Unlike traditional stochastic simulation methods, the DGA’s differentiable nature enables gradient-based optimization, which is not possible with the classical Gillespie algorithm or its variants.

(2) As often with machine learning techniques, there is a sense of black box, with a lack of mathematical details of the proposed method: as opposite to the exact Gillespie algorithm, whose foundations lie on solid mathematical results (exponentially-distributed waiting times of continuous-time Markov processes), the DGA involves uncontrolled approximations, that are only briefly mentioned in the paper. For instance, it is currently simply noted that "the approximations introduced by the DGA may be pronounced in more complex settings such as the calculation of rare events", without specifying how limiting these errors are. It would be useful to include a clearer and more comprehensive discussion of the limitations of the DGA: When does it work accurately? What are the approximations/errors and can they be controlled? When is it worth paying the price for those approximations/errors, and when is it better to stick to the Gillespie algorithm? Is this notably the case for problems involving rare events? Clearly, these are difficult questions, and the answers are problem specific. However, it would be important to draw the readers' attention on the issues, especially if the DGA is presented as a potentially significant tool in computational and synthetic biology.

We acknowledge the importance of discussing the limitations of the DGA in more detail. While we have noted that the approximations introduced by the DGA may impact its accuracy in certain scenarios, such as rare-event problems, a deeper exploration of these trade-offs is outside the scope of this work. Instead, we provide sufficient context in the manuscript to guide readers on when the DGA is appropriate.

(3) The DGA is here introduced and discussed in the context of non-spatial problems (simple gene regulatory networks). However, numerous problems in the life sciences and computational/synthetic biology, involve stochasticity and spatial degrees of freedom (e.g. for problems involving diffusion, migration, etc). It is notoriously challenging to use the Gillespie algorithm to efficiently simulate stochastic spatial systems, especially in the context of rare events (e.g., extinction or fixation problems). It would be useful to comment on whether, and possibly how, the DGA can be used to efficiently simulate stochastic spatial systems, and if it would be better suited than the Gillespie algorithm for this purpose.

Thank you for pointing this out. Although our current work centers on non-spatial systems, we agree that many biological contexts incorporate both stochasticity and spatial degrees of freedom. Extending the DGA to efficiently simulate such systems would indeed require substantial modifications—for instance, coupling it with reaction-diffusion frameworks or spatial master equations. We believe this is an exciting direction for future research and mention it briefly in the discussion as a potential extension.

Minor suggestions:

(1) After Eq.(10): it would be useful to explain and motivate the choice of the ratio JSD/H.

Done.

(2) On page 6, just below the caption of Fig.4: it would be useful to clarify what is actually meant by "... convergence towards the steady-state distribution of the exact Gillespie simulation, which is obtained at a simulation time of 10^4".

Done.

(3) At the end of Section B on page 7: please clarify what is meant here by "soft directions".

Done.

Reviewer #2 (recommendations):

We thank the reviewer for their thoughtful comments and constructive feedback. Below, we address each of the comments/suggestions.

Main points:

(1) Enumerate the conditions under which DGA assumptions hold (and when they do not). There is currently not enough information for the interested reader to know whether DGA would work for their system of interest. Without this information, it is difficult to assess what the true scope of DGA's impact will be. One simple idea would be to test DGA performance along two axes: (i) increasing number of model states and (ii) presence/absence of non-steady state dynamics. I acknowledge that these are very open-ended directions, but looking at even a single instance of each would greatly strengthen this work. Alternatively, if this is not feasible, then the authors should provide more discussion of the attendant difficulties in the main text.

We agree that a detailed exploration of the conditions under which the DGA assumptions hold would be a valuable addition to the field. However, this paper primarily aims to introduce the DGA methodology and demonstrate its proof-of-concept applications. A comprehensive analysis along axes such as increasing model states or non-steady-state dynamics, while important, would require significant additional simulations and is beyond the scope of this work. In Appendix A, we have discussed the trade-off between accuracy and numerical stability. Additionally, we encourage future users to tune the hyperparameters a and b for their specific systems.

(2) Demonstrate DGA performance in a more complex biochemical system. Clearly the authors were aware that analytic solutions exist for the 2-state system in Figure 7, but it this is actually also the case (I think) for mean mRNA production rate of the non-equilibrium system in Figure 8. To really demonstrate that DGA is practically viable, I encourage the authors to seek out an interesting application that is not analytically tractable.

We appreciate the suggestion to validate DGA on a more complex biochemical system. However, the goal of this study is not to provide an exhaustive demonstration of all possible applications but to introduce the DGA and validate it in systems where ground-truth comparisons are available. While the non-equilibrium system in Figure 8 might be analytically tractable, its complexity already provides a meaningful demonstration of DGA’s ability to optimize parameters and design systems. Extending this work to analytically intractable systems is an exciting direction for future studies, and we hope this paper will inspire others to explore these applications.

(3) Take steps to improve the robustness of parameter optimization and error bar calculations. (3a) When the loss landscape is degenerate, shallow, or otherwise "difficult," a common solution is to perform multiple (e.g. 25-100) inference runs starting from different random positions in parameter space. Doing this, and then taking the parameter set that minimizes the loss should, in theory, lead to a more robust recovery of the optimal parameter set.

(3b) It seems clear that the Hessian approximation is underestimating the true error in your inference results. One alternative is to use a "brute force" approach like bootstrap resampling to get a better estimate for the statistical dispersion in parameter estimates. But I recognize that this is only viable if the inference is relatively fast. Simply recovering the true minimum will, of course, also help.

(3a) We acknowledge the challenge posed by degenerate or shallow loss landscapes during parameter optimization. While performing multiple inference runs from different initializations is a common strategy, this approach is computationally intensive. Instead, we rely on standard optimization techniques (e.g., ADAM) to find a robust local minimum. 

(3b) Thank you for your comment. We agree that Hessian-based error bars can underestimate uncertainty, particularly in degenerate or poorly conditioned loss landscapes. While methods like bootstrap and Monte Carlo can provide more robust estimates, they can be computationally prohibitive for larger-scale simulations. A simpler reason for not using them is the high resource demand from repeated simulations, which quickly becomes infeasible for complex or high-dimensional models. We note these trade-offs between robust estimation and practicality as an important area for further exploration.

Moderate comments:

(1) Figure 7: is it possible to also show the inferred kon values? Specifically, it would be of interest to see how kon varies with repressor concentration.

Thank you for the suggestion. We have updated Figure 7 to include the inferred kon values, showing their variation with the mean mRNA copy number. However, we could not plot them against repressor concentration due to the lack of available data.

(2) Figure 8B & D: the authors claim that the sharper system dissipates more energy, but doesn't 8D show the opposite of this? More importantly, it does not look like either network drives sharpness levels that exceed the upper equilibrium limit cited in [36]. So it is not clear that it is appropriate to look at energy dissipation here. In fact, it is likely that equilibrium networks could produce the curves in 8B, and might be worth checking.

Thank you for pointing this out. We realized that the plotted values in Figure 8D were incorrect, as we had mistakenly plotted noise instead of energy dissipation. The plot has now been corrected. 

(3) Figure 8: I really like this idea of using DGA to "design" networks with desired input-output properties, but I wonder if you could explore more a biologically compelling use-case. Specifically, what about some kind of switch-like logic where, as the activator concentration increases, you have first 0 genes on, then 1 promoter on, then 2 promoters on. This would achieve interesting regulatory logic, and having DGA try to produce step functions would ensure that you force the networks to be maximally sharp (i.e. about double what you're currently achieving).

Thank you for this intriguing suggestion. While the proposed switch-like logic use case is indeed compelling, implementing such a system would require significant work. This goes beyond the scope of the current study, which focuses on demonstrating the feasibility of DGA for network design with simple input-output properties.

Minor comments:

(1) Figure 4B & C: the bar plots do not do a good job conveying the points made by the authors. Consider alternatives, such as scatter plots or box plots that could convey inference uncertainty.

Done.

(2) Figure 4B: consider using a log y-axis.

The y-axis in Figure 4B is already plotted on a log scale.

(3) Figure 4D is mentioned prior to 4C in the text. Consider reordering.

Done. 

(4) Figure 5B: it is difficult to assess from this plot whether or not the landscape is truly "flat," as the authors claim. Flat relative to what? Consider alternative ways to convey your point.

Thank you for highlighting this ambiguity. By describing the loss landscape as “flat,” we intend to convey its relative insensitivity to parameter variations in certain regions, rather than implying a completely level surface. While we believe Figure 5B still provides a useful qualitative depiction of this behavior, we acknowledge that it does not quantitatively establish “flatness.” In future work, we plan to incorporate more rigorous measures—such as gradient magnitudes or Hessian eigenvalues—to more accurately characterize and communicate the geometry of the loss landscape.

Reviewer #3 (recommendations):

We sincerely thank the reviewer for their thoughtful feedback and constructive suggestions, which have helped us improve the clarity and rigor of our manuscript. Below, we address each of the comments.

(1) Precision is lacking in the introduction section. Do the authors mean the Direct SSA, sorted SSA, which is usually faster, and how about rejection sampling methods?

Thank you for pointing this out. We have updated the introduction to explicitly mention the Direct SSA.

(2) When mentioning PyTorch and Jax, would be good to also talk about Julia, as they have fast stochastic simulators.

We have now mentioned Julia alongside PyTorch and Jax.

(3) Mentioned references 22-27. Reference 26 is an odd choice; a better reference is from the same author the Automatic Differentiation of Programs with Discrete Randomness, G Arya, M Schauer, F Schäfer, C Rackauckas, Advances in Neural Information Processing Systems, NeurIPS 2022

We have now cited the suggested reference.

(4) Page 1, Section: 'To circumnavigate these difficulties, the DGA modifies....' Have you thought about how you would deal with the bias that will be introduced by doing this?

Thank you for your insightful comment. We acknowledge the potential for bias due to the differentiable approximations in the DGA; however, our analysis has not revealed any systematic bias compared to the exact Gillespie algorithm. Instead, we observe irregular deviations from the exact results as the smoothness of the approximations increases.

(5) Page 2, first sentence '... traditional Gillespie...' be more precise here - the direct algorithm.

Thank you for your comment. We believe that the context of the paper, particularly the schematic in Figure 1, makes it clear that we are focusing on the Direct SSA. 

(6) Page 2, second paragraph: ' In order to simulate such a system...' This doesn't fit here as this section is about tau-leaping. As this approach approximates discrete operations, it is unclear if it would work for large models, snap-shot data of larger scale and if it would be possible to extend it for time-lapse data

Thank you for your comment. We respectfully disagree that this paragraph is misplaced. The purpose of this paragraph is to explain why the standard Gillespie algorithm does not use fixed time intervals for simulating stochastic processes. By highlighting the inefficiency of discretizing time into small intervals where reactions rarely occur, the paragraph provides necessary context for the Gillespie algorithm’s event-driven approach, which avoids this inefficiency.

Regarding the applicability of the DGA to larger models, snapshot data, or time-lapse data, we acknowledge these are important directions and have noted them as potential extensions in the discussion section.

(7) Page 2 Section B: 'In order to make use of modern deep-learning techniques...' It doesn't appear from the paper that any modern deep learning is used.

Thank you for your comment. Although the DGA does not utilize deep learning architectures such as neural networks, it employs automatic differentiation techniques provided by frameworks like PyTorch and Jax. These tools allow efficient gradient computations, making the DGA compatible with modern optimization workflows.

(8) Page 3, Fig 1(a). S matrix last row, B and C should swap places: B should be 1 and C is -1.

Corrected the typo.

(9) Fig1 needs a more detailed caption.

Expanded the caption slightly for clarity.

(10) Page 3 last paragraph: 'The hyperparameter b...' Consequences of this are relevant, for example can we now go below zero. Also, we lose more efficient algorithms here. It would be good to discuss this in more detail that this is an approx.. algorithm that is good for our case study, but for other to use it more tests are needed.

Thank you for the comment. Appendix A discusses the trade-offs related to a and b, but we agree that more detailed analysis is needed. The hyperparameters are tailored to our case study and must be tuned for specific systems.

(11) Page 4, Section C, first paragraph, 'The goal of making...' This is snapshot data. Would the framework also translate to time-lapse data? Also, it would be better to make it clearer earlier which type of data are the target of this study.

Thank you for your suggestion. While the current study focuses on snapshot data and steady-state properties, we believe the DGA could be extended to handle time-lapse data by incorporating multiple recorded time points into its inference objective. Specifically, one could modify the loss function to penalize discrepancies across observed transitions between these time points, effectively capturing dynamic trajectories. We consider this an exciting area for future development, but it lies beyond our present scope.

(12) Page 4 Section C, sentence '...experimentally measured moments'. Should later be mentioned as error, as moments are imperfect

Thank you for your comment. We agree that experimentally measured moments are inherently noisy and may not perfectly represent the true system. However, within the context of the DGA, these moments serve as target quantities, and the discrepancy between simulated and measured moments is already accounted for in the loss function. 

(13) Page 4 Section C, last sentence '...second-order...such as ADAM'. Another formulation would be better as second order can be confusing, especially in the context of parameter estimation

We have revised the language to avoid confusion regarding “second-order” methods.

(14) Fig 4(a) a density plot would fit better here

Fig. 4(a) has been updated to a scatter density plot as suggested.

(15) Fig 4(c) Would be interesting to see closer analysis of trade of between gradient and accuracy when changing a and b parameters

Thank you for this suggestion. We acknowledge that an in-depth exploration of these trade-offs could provide deeper insights into the method’s performance. However, for now, we believe the current analysis suffices to highlight the utility of the DGA in the contexts examined.

(16) Page 6 Section III, first sentence: This fits more to intro. Further the reference list is severely lacking here, with no comparison to other methods for actually fitting stochastic models.

Thank you for the suggestion. We have added a few references there.

(17) Page 6, Section A, sentence: '....experimental measured mean...' Why is it a good measure here (moment matching is not perfect), also do you have distribution data, would that not be better? How about accounting for measurement error?

Thank you for the comment. While we do not have full distribution data, we acknowledge that incorporating experimental measurement error could enhance the framework. A weighted loss function could model uncertainty explicitly, but this is beyond the scope of the current study. 

(18) Page 7, section B, first paragraph: 'Motivated by this, we defined the...'Why using Fisher-Information when profile-likelihood have proven to be better, especially for systems with few parameters like this.

Thank you for the suggestion. While profile-likelihood is indeed a powerful tool for parameter uncertainty analysis, we chose Fisher Information due to its computational efficiency and compatibility with the differentiable nature of the DGA framework.

(19)  Page 7, section C, sentence '...set kR/off=1..'. In this case, we cannot infer this parameter.

Thank you for the comment. You are correct that setting kR/off = 1 effectively normalizes the rates, making this parameter unidentifiable. In steady-state analyses, not all parameters can be independently inferred because observable quantities depend on relative—rather than absolute—rate values (as evident when setting the time derivative to zero in the master equation). To infer all parameters, one would need additional information, such as time-series data or moments at finite time.

(20)  Page 7 Section 2. Estimating parameters .... Sentence: '....as can be seen, there is very good agreement..' How many times the true value falls within the CI (because corr 0.68 is not great).

Thank you for your comment. While a correlation coefficient of 0.68 indicates moderate agreement, the primary goal was to demonstrate the feasibility of parameter estimation using the DGA rather than achieving perfect accuracy. The coverage of the CI was not explicitly calculated, as the focus was on the overall trends and relative agreement.

(21) Page 7 Section 2. Estimating parameters .... Sentence: 'Fig5(c) shows....' Is this when using exact simulator?

Thank you for your question. Yes, the exact values in x-axis of Fig. 5(c) are obtained using the exact Gillespie simulation.

(22) Page 7 Section 3 Estimating parameters for the... Sentence: 'Fig6(a) shows...' Why Cis are not shown?

Thank you for your comment. CIs are not shown in Fig. 6(a) because this particular case is degenerate, making the calculation and meaningful representation of CIs challenging. 

(23) Page 10, Sentence: 'As can be seen in Fig 7(b)...' Can you show uncertainty in measured value? It would be good to see something of a comparison against an exact method, at least on simulated synthetic data

Thank you for the comment. Fig. 7(a) already includes error bars for the experimental data, which account for measurement uncertainty. However, in Fig. 7(b), we do not include error bars for the experimental values due to limitations in the available data.

(24) Page 12, Section B Loss function '...n=600...' This is on a lower range. Have you tested with n=1000?

Yes, we have tested with n=1000 and observed no significant difference in the results. This indicates that n=600 is sufficient for the purposes of this study. 

(25) Fig 8(c) why there are no CI shown?

Thank you for your comment. CIs were not included in Fig. 8(c) due to degeneracy, which makes meaningful confidence intervals difficult to compute.

(26) Page 12 Conclusion, sentence: '..gradients via backpropagation...' Actually, by making the function continuous, both forward and reverse mode might be used. And in this case, forward-mode would actually be the fastest by quite a margin

Thank you for your insightful comment. You are correct that by making the function continuous, both forward-mode and reverse-mode automatic differentiation can be used. We have now mentioned this point in the discussion.

(27) Overall comment for the Conclusion section: It would be good to discuss how this framework compares to other model-fitting frameworks for models with stochastic dynamics. The authors mention dynamic data and more discussion on this would be very welcomed. Why use ADAM and not something established like BFGS for model fitting? It would be interesting to discuss how this can fit with other SSA algorithms (e.g. in practice sorting SSA is used when models get larger). Also, inference comparison against exact approaches would be very nice. As it is now, the authors truly only check the accuracy of the SSA on 1 model -it would be interesting to see for other models.

Thank you for your detailed comments. While this study focuses on introducing the DGA and demonstrating its feasibility, we agree that comparisons with other model-fitting frameworks, testing on additional models, and integrating with other SSA variants like sorted SSA are important directions for future work. Similarly, extending the DGA to handle transient dynamics and exploring alternatives to ADAM, such as BFGS, are promising areas to investigate further.

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

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Reply to the reviewers

Manuscript number: RC-2024-02588

Corresponding author(s): Frederic SALTEL

__1. __Point-by-point description of the revisions

Reviewer #1:

Invadosomes are dynamic, actin-based structures that enable cells to interact with and remodel the extracellular matrix (ECM), playing a crucial role in tumor cell invasion and metastasis. Prior studies by the authors and other groups have established the formation, activation, and appearance of invadosomes. This study demonstrates the following:

1. Key elements of the translation machinery and endoplasmic reticulum (ER) proteins are constituents of the invadosome structure.
2. Specific proteins are associated with distinct invadosome structures.

The researchers utilized two cellular models (NIH3T3-Src and A431 melanoma cell line) and Tks5, a specific invadosome marker, for immunoprecipitation and mass spectrometry, validating the results through fluorescent images, electron microscopy, and time-lapse live imaging.

Major Comments

The manuscript is well-written, with a clear and detailed experimental workflow. Compared to their previous seminal work that first demonstrated invadosomes concentrate mRNA and exhibit translational activity using NIH3T3-Src cells, this study adds details about the specific enrichment of translation proteins for each type of invadosome and the presence of ribosomal and ER proteins. However, the experiments do not further enhance our understanding of the intricate mechanisms linking invadosome structures, function, and translation factors.

Further experiments are needed to better demonstrate the hypothesis of active translation within these structures, including the use of additional cellular models.

To demonstrate the hypothesis of active translation within these structures, we performed the same translation inhibition experiments, using CHX in additional cellular models. Indeed, these experiments were performed on MDA-MB-231 breast cancer cell lines, as well as on Huh6 liver cancer cell lines. Degradation experiments showed the same results as for NIH-3T3-Tks5-GFP and A431-Tks5-GFP, since we were able to observe a significant decrease in the degradation capacities of cells in the absence of translation (see graphs below).

Left: Quantification and representative images of ECM degradation properties of Huh6 cells on gelatin treated (CHX) or not (DMSO) with cycloheximide. Gelatin is stained in green and nuclei in blue. Values represent the mean +/- SEM of n=4 independent experiments (15 images per condition and per replicate) and were analyzed using student t-test.

Right: Left: Quantification and representative images of ECM degradation properties of MDA-MB-231 cells on gelatin treated (CHX) or not (DMSO) with cycloheximide. Gelatin is stained in green and nuclei in blue. Values represent the mean +/- SEM of n=4 independent experiments (15 images per condition and per replicate) and were analyzed using student t-test.

The authors should also investigate the effects of Tks5 silencing on ER-associated translational machinery.

The effects of Tks5 silencing on the ER-associated translation machinery were investigated using a SunSET experiment. We were able to demonstrate that Tks5 silencing had no significant impact on translation in both cellular models since no translation modification was observed between control and siTks5 conditions.

Quantification and relative western blot analysis of the effect of Tks5-targeting siRNA treatment on A431 and NIH-3T3-Src cells by using puromycin quantification. Values represent the mean +/- SEM of n=4 independent experiments and were analyzed using Anova.

How do the authors propose Tks5 is linked to these proteins? Directly or indirectly? Focusing on specific proteins night provide an opportunity to study the molecular mechanisms in greater depth.

Tks5 is a scaffold protein, a multi-domain “bridging molecule” that serve as regulators by simultnneously binding multipe molecular partners. TKs5 contain a PX domain and 5 SAH Domains. Consequently, Tks5 can bind different partners. Moreover, as focal adhesion, invadosome are large macromolecular assemblies. Here, in this study, Tks5 serve as a specific molecular hook, to precipitate partners. At this step, there is no evidence of a direct or indirect link of the translational machineray with Tks5. Even if we can hypothetize un indirect connection. In this version we focused more precisely on a specific and common Tks5 partners, such as EIF4B.

They used chemical inhibitors and siRNA approaches to assess the role of specific players, such as EIF4B, in the proteolytic activity of invadosomes, which can be considered proof of concept. Additional experiments aligning the results with the involved pathways would add molecular details and enhance the manuscript's significance. Resolving these issues is crucial for the manuscript to meet the publication standards for contributing novel and impactful insights to the field.

To better understand the variation of the pathways involved, we first wanted to observe the impact of Eif4b silencing on active translation in both cellular models. To do this, we performed SunSET experiments in both cell models. An experiment was performed for the A431 cell line and the results seem to show little difference between control conditions and conditions in the presence of siEIF4B. Conversely, SunSET experiments in the NIH 3T3 Src cell line show an increase in translation in the presence of siEIF4B.

__ __

Quantification of the effect of cycloheximide (CHX) and EIF4B-targeting siRNA (siEIF4B #1 and #2) treatment on A431 and NIH-3T3-Src cells by using puromycin quantification. Values represent the mean +/- SEM of n=1 independent experiment for A431 or n=2 independent experiments for NIH-3T3-Src.

In order to better understand the variation of the signaling pathways involved, spectrometry experiments were performed to compare the variation of the pathways in control conditions and in the presence of siRNA against EIF4B. These results allowed us to provide a better understanding of the variability of the pathways and therefore of the mechanism of action.

Volcano plot of overexpressed and underexpressed proteins after silencing of the EIF4B protein identified by mass spectrometry analysis.

These mass spectrometry experiments allowed us to highlight that the pathway mainly impacted during Eif4b depletion was the Hras pathway. However, this information is given for information purposes only. It would be necessary to look more closely at the Hras pathway to understand what the link with EIF4B and therefore the link with the formation of invadosomes could be.

Table of translation-related proteins or proteins involved in the formation or function of invadosomes that are overexpressed or underexpressed in at least one siRNA of EIF4B.

These experiments also allowed us to highlight that the depletion of EIF4B directly impacts the translation pathway by modulating translation initiation factors as well as ribosomal proteins but also proteins involved in the formation and function of invadosomes such as ADAM17, ACTR5, IGFBP6 RPL22 and RPS6KA5 proteins (see table below). It will be necessary to validate these data and determine their specificity due to the fact that some other proteins appear under-expressed like IGFBP3 and ADAM19. To conclude, to fully understand the exact impact of EIF4B into this process, additional investigations are necessary.

__ __Minor Comments :

A more detailed discussion of the implications of their findings within the broader context of cancer cell signaling and the potential impact on related cancer research areas would further advance our understanding in this area.

This part was added in the new version of the discussion. Indeed, deregulation of the translation is now a hallmark of cancer. This notion is now present in the manuscript and concluded the discussion (see page 12).

Reviewer #1 (Significance (Required)):

General Assessment:

This study offers novel insights into a new function of the invadosome-specific player Tks5 as a molecular crossroad between ER-related translation proteins and invadosomes. The authors suggest that Tks5 could act as a scaffold, supporting the rapid clustering of translation-related proteins during invadosome formation or proteolytic activity. However, a major limitation is the lack of mechanistic exploration. The results do not elucidate how Tks5 mediates the recruitment of these proteins or the specific molecular mechanisms involved.

Advances: The study extends knowledge in the field by confirming the presence of specific markers linked to different invadosome structures and demonstrating the Tks5 interactome's association with translation machinery.

Audience: This study will primarily interest specialists working on invadosomes and, secondarily, those interested in cancer cell signaling, invasion, and metastasis.

Field of Expertise: Invadosome and related signaling pathways in cancer.

__ __

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

Summary In this work, Normand and her colleagues analyze and compare the interactome of the key invadopodia component, TKS5 (overexpressed as a GFP-tagged protein), in two transformed cell models cultured on different substrates. Potential TKS5 interacting partners are identified including previously known and validated TKS5 interactors, some known to contribute to the mechanism of invadopodia formation and function. Bioinformatic (GSEA) analysis reveals a specific enrichment for proteins related to protein translation and interaction with ER-associated ribosome machinery. Evidence is presented that some of these proteins (RPS6, a component of the 40S ribosomal subunit, and translation factor, EIF4B) localize to TKS5-positive invadopodia in Src-transformed cells. Experiments based on translation inhibitor, cycloheximide, and silencing of EIF4B factor could demonstrate a link between overall protein translation and invadosome formation. Live cell imaging and microscopy analysis of fixed samples could document some proximity between the endoplasmic reticulum network and invadosome rosettes.

Major comments

__ __1- In the Results Section, the IP/proteomics-based pipeline used by Normand and colleagues to identify TKS5 partners is not clearly described and is confusing. Cut-off used to select the proteins in the different classes summarized in Table S1 should be better described. In addition, the nomenclature of the different protein subgroups used in Table S1 is confusing (see minor point#5).

Details have been added in the results section regarding the IP/proteomics section to complete the materials and methods section. As described in the materials and methods section, control versus IP data were quantified by an enrichment ratio ≥ 2. These criteria are the most classically used in the practices analyzed.

For clarity, additional tables have been added for each category (A431/NIH plastic or collagen) and gene names, protein descriptions and abundance ratios have been indicated (Supp table 2, 3, 4 and 5).

2- The effects of cycloheximide treatment or EIF4B silencing on gelatin degradation are clear and convincing. However, these are correlative evidence, and they may reflect a general implication of protein translation in the control of invadopodia function. A direct link between the observed interactions of TKS5 with the protein translation machinery and the formation and/or function of invadopodia is missing.

To demonstrate the direct links between Tks5 and the translation machinery, a fluorophore was used to visualize active translation within invadopodia. We were able to highlight an active translation localized in the rosettes (see figure below). Indeed, we can observe a localized translation within the rosettes. However, these same results were not observed in linear invadosomes where we could not observe any localized translation. We can however hypothesize that it is more difficult to observe a localized translation in linear invadosomes which are much smaller structures than rosettes.

Confocal microscopy images of NIH-3T3-Src cells. The cells were stained for B-actin RNA in green, B-actin in red, nuclei in blue and actin in grey. Scale bar: 20µm, zoom: 5µm.

In order to provide additional elements to show the link between Tks5 and the translation machinery, we performed immunofluorescence experiments by labeling the Sec61 protein. Sec61 is a well-described ER marker that allows the insertion of proteins into the ER but is also a key player in the docking of ribosomes to the ER. We were able to highlight the colocalization between Tks5 and Sec61 in all types of invadosomes, allowing to show the link between the Tks5 protein and the translation machinery. These images were inserted in the manuscript (see Figure 6b).

Confocal microscopy images of NIH-3T3-Src and A431 cells. The cells were seeded on gelatin or type I collagen and stained for Sec61 in red, nuclei in blue and Actin in grey. Scale bar: 20µm, zoom: 5µm.

__ __3- Images showing the interrelations between the ER and the adhesive podosome rosettes are striking (Figure 5). Src-transformed cells forming invadosome rosettes when in contact with the collagen substratum change shape and produce adhesive protrusions towards the substratum. As the ER is a huge compartment that fills the entire cytoplasm, it is maybe not so surprising to observe the ER filling the protrusions and getting close to the rosettes at the tip of these membrane extensions. Again, these observations are essentially correlative and there is no prove of some direct contact between some ER regions and the invadosomes.

For clarity, the contrast of the images has been improved. Thus, time-lapse imaging clearly demonstrate that the ER is not present in all the cytoplasm but is enriched in the destination of the rosettes as well as in the rosettes. Moreover, this is not systematic with all invadosome rosettes (see video 1)

4- Overall, this report is lacking a clear hypothesis or model of what could be the consequence of the interaction of TKS5 and the translation machinery on the formation and/or the activity of the invadosomes in transformed cells.

We performed a sunset experiment to analyze the impact of Tks5 depletion into translation. No variation of global translation was observable in the absence of Tks5 (see results below). Tks5 depletion block invadosome formation. So, the impact on total translation activity cannot be measurable at the cell level, suggesting that invadosome recruit a specific translation machinery. Indeed, even if we obtained a good percentage of Tks5 depletion, around 90%, the impact in total translation activity is not quantifiable. However, we noticed that some specific translation actors are modulated and specifically localized into invadosome structures suggesting that it is more a question of localization and local translation of specific mRNAs, and not a global modification. This is consistent with the fact that Tks5 expression is not altered during tumor cell invasion, and it is just recruited and activated at specific sites to form these invasive structures.

Thus, in this paper, Tks5 only served as an anchor point in order to be able to extract the specific molecular machinery and specific translational actors.

Quantification and relative western blot analysis of the effect of Tks5-targeting siRNA treatment on A431 and NIH-3T3-Src cells by using puromycin quantification. Values represent the mean +/- SEM of n=4 independent experiments and were analyzed using Anova.

Minor comments

1- Discussion Section (page 2). The statement that TKS4 is involved in ECM degradation in podosomes only and not in invadopodia is not correct. TKS4 knock down has been shown to interfere with ECM degradation in Human DLD1 colon cancer cells (Gianni et al. SCIENCESIGNALING Vol 2 Issue 88, 2009) and in in mouse and human melanoma cell lines (Iizuka et al. Oncotarget, Vol. 7, 2016). In addition, an unphosphorylable mutant form of Tks4 blocked invadopodia formation and ECM degradation in Src-transformed DLD1 cells (Gianni et al. Molecular Biology of the Cell Vol. 21, 4287- 4298, 2010). We (this reviewer's team) reported that TKS4 was associated with cortactin-positive invadopodia in MDA-MB-231 and Hs578T triple-negative breast cancer cell lines (Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

The involvement of TKS4 protein in extracellular matrix degradation has been changed in the text (page 2).

2- Discussion Section (page 3). A431 is wrongly referred to as a melanoma cell line; it is a human epidermoid carcinoma cell line.

The text has been modified according to the recommendations, the A431 cell line has been designated as a human epidermoid carcinoma cell line.

3- Results Section (page 4 & 5). The authors compare the proteins they identified as potential TKS5 partners to previously published data by Stilly et al. (based on TKS5 IP like in the present study) and Thuault et al. (TKS5 bioIB). Additionally, authors should mention and discuss previously published data based on TKS5 coIP experiment and Mass Spec analysis similar to the present study, identifying potential TKS5 partners; some of which were similarly found in the present study including proteins involved in translation and ribosome function although these were not the focus of this work (several 40S and 60S ribosomal proteins, see Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

This comparison is now present int the text of the manuscript (page 10).

4- Figure 1b. Matrix degradation is not visible in association with the invadopodia in selected high magnification images in Figure 1a and 1b.

Matrix degradation is indeed not visible in association with invadopodia in the selected high magnification images. Indeed, the imaging techniques used, Interference Refection Microscopy (IRM) do not allow us to observe matrix degradation at the invadosomes, since the reflection also highlights the cells. The aim here was to show only the presence collagen fibers that correspond to inducer of linear invadosome reorganization. It is widely accepted that all these structures are capable of degrading the extracellular matrix.

5- Supplemental table 1. The names of the different lists of proteins in the summary table is not clear and is rather confusing.

For clarity, additional tables have been added for each category (A431/NIH plastic or collagen) and gene names, protein descriptions and abundance ratios have been indicated (Supp table 2, 3, 4 and 5).

6- Supp Figure 1. Please define what is the sample named 'D' (Delta).

The Delta sample corresponds to the material that was not attached to the bead.

7- Results Section (page 5). 'These experiments confirm the correct co-localization between Tks5 and the proteins identified in Tks5 interactome by mass spectrometry analysis.' This statement is too general; in fact, data validate only colocalization between TKS5 and some identified partners, namely CD44 and MAP4.

To be less general, this statement has been modified in the text to show that the data only validate colocalization between TKS5 and certain identified partners, namely CD44 and MAP4.

8- Figure 2e and Figure 3. It would have been nice to show the colocalization of selected proteins and TKS5 in association with collagen fibers to validate that enrichment occurs at matrix/cell contact sites and corresponds to bona fide invadopodia.

As commented above, the reflection highlights the collagen fibers but also the cells. Thus, it is complex in this case to show the colocalization of the selected proteins in association with the collagen fibers with this approach. The other possibility is to stain collagen fibrils, however this kind of approach reduce the quality of interaction between fibers and associated receptors inducing a decrease of linear invadosome formation.

9- Figure 3c (high mag insets). TKS5 and EIF4b do not seem particularly enriched in invadopodia rosettes as compared to the rest of the cytoplasm.

Indeed, we can observe on this image a colocalization of Tks5 and EIF4B in the rosettes without showing an enrichment.

However, the enrichment of EIF4B remains clearly visible in the linear invadosomes and the dots.

10- Figure 4c-f. Treatments (i.e. CHX, siEIF4b) affect gelatin degradation. It would be interesting to assess the capacity of cells to form invadopodia under these conditions.

As demonstrated in this study, the CHX treatment and EIF4B depletion affect the degradation of gelatin. In addition, we were able to show that CHX only impacts the formation of rosettes on gelatin (Figure 4a, 4b and Supp 3).

Moreover, we added in the manuscript the impact of siEIF4B on invadosome formation (Supp Figure 3g). We show that it affects the formation of rosettes as CHX, but also affects the formation of linear invadosomes on collagen by A431 cells.

Quantification of the numbers of invadosomes per cell on gelatin and collagen silencing (siEIF4B) or not (DMSO) for EIF4B in A431-Tks5-GFP and NIH3T3-Src-Tks5-GFP cells. Values represent the mean +/- SEM of n=4 independent experiments (10 images per condition and per replicate) and were analyzed using student t-test.

Reviewer #2 (Significance (Required)):

This study confirms and adds to a previously published report by this research group based on invadosome laser capture microdissection and proteomics revealing that invadosomes contain specific components of the translational machinery, and that protein translation activity is required to maintain invadosome structure and activity (Ezzoukhry et al. Nat Commun 2018). It also adds to a recent study that established a crucial role for ribosome biogenesis in promoting cell invasion in the C. elegans anchor cell invasion model (Development. 2023).

The experimentation presented in this paper is of good quality and convincingly support the authors conclusions of a link between the ER-associated translation machinery and invadosome function in transformed cells. Overall, although this study adds to the emerging idea of an evolutionary-conserved translational control of cell invasion through the extracellular matrix it is mostly correlative and lacking a direct prove that the interaction of TKS5 with components of the translation machinery has a direct contribution to invadopodia function.

__ __

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

Summary: To invade the surrounding extracellular matrix (ECM), cells organize actin-rich cellular membrane structures capable of ECM degradation, called invadosomes. Depending on the composition and organization of the ECM, cells organize their invadosomes differently. The authors aimed to identify specific and common components of different types of invadosomes: rosettes formed by NIH3T3-Src cells seeded on gelatin, dots formed by A431 cells seeded on gelatin, and linear invadosomes formed by NIH3T3-Src and A431 cells when seeded on fibrillar collagen I. For this, they generated cells stably expressing GFP-Tks5, a ubiquitous constituent of invadosomes, and determined its interactome. They identified 88 common proteins, among which the protein translation machinery was enriched. Whereas general protein inhibition impaired only rosette formation and impaired every type of invadosome-associated degradation, EIF4B inhibition inhibited the formation of every type of invadosomes. They then analyzed the impact of the ER on invadosome formation and degradation activity. First, they documented the presence of the ER in the center of the NIH3T3-Src rosettes and correlated ER presence with rosette initiation and persistence. They then demonstrated that chemical inhibition of Sec61 translocon decreased formation of invadosomes in general.

Major comments:

1- The authors use cells overexpressing GFP-Tks5 for their analysis of Tks5 interactome in the different invadosomes (Fig. 2). The impact of GFP-Tks5 overexpression on invadosome formation and degradation activity should be mentioned.

Depending the cell type the TKS5-GFP overexpression do not increase the number of invadosomes but increase the matrix degradation activity (Di Martino et al 2014); or could impact the number of invadosomes as in B16 cell line (Shinji Iizuka et al, 2016). This point was added in the introduction.

However, the Tks5 overexpression was used fo immunoprecipitation and mass spectrometry analysis. The rest of the study and targets validation are done on wild type cells.

2- Concerning the analysis of the mass spectrometry (MS) data, clarifications would be appreciated:

a. The authors first "determined the specific molecular signature associated with each invadosome organization" (p.4). As I understand it, the proteins in each of these signatures correspond to proteins identified only in a particular type of invadosomes, not in the others. Could the authors indicate the percentage of the total proteins identified for each type of invadosomes that corresponds to the specific molecular signature?

The meaning of the sentence has been changed in the paper to provide more understanding. The term "molecular signature" has been replaced by "specific proteins". Percentages have been added to the tables in Figure 1 Supp.

1. __ __ The GSEA pathways related to each of the specific molecular signature were then analyzed and the authors "commonly identified an enrichment in mitochondrial, ER and Golgi proteins" (page 4) (Supp Fig 1c,e,g). Could the authors provide numbers/percentage/statistics? It is not clear to me whether the biological processes (Supp Fig 1b,d,f) are derived from the analysis of the specific molecular signature or of the total proteins identified for each type of invadosomes. Could the authors clarify this point? The percentages of each specific protein category have been added in Figure 1 Supp.

The biological processes (Supp Fig 1b, d, f) arise from the analysis of the molecular signature common to the 4 invadosomes conditions, namely the dots, rosettes and linear invadosomes of A431 and NIH-3T3-Src. Thus, the biological processes arise here from the 88 proteins commonly identified for all types of invadosomes.

1. The authors also identified "translation proteins" enriched in the specific molecular signature of each type of invadosomes (p.4). They commented on this category, indicating that each type of invadosome contains a specific set of translation-related proteins. This is true, but according to my analysis of the provided tables, the same applies to the other categories as well. Could the authors comment this point? Indeed, some proteins involved in translation can appear specific or common depending the type of invadosome. Our comment is at this step, only suggest that some of this protein should be specific for invadosome and some could be associated to only one organization. Of course, the role of each protein needs to be investigated.

2. Would similar categories of proteins (translation, ER, Golgi, mitochondrial) appear as enriched if the Tks5 interactome was analyzed as a whole for each type of invadosomes? (the authors may disregard this comment if comment a. is inaccurate). Protein pathways enriched in the different type of invadosome differ, for example, Protein activity GTPase activity, vs cell adhesion molecule binding or hydrolase activity acting on Acid Anyhdrides. This analysis demonstrates and highlights differences between the different invadosome organization. However, we focus on translational proteins, ER proteins for example and calculated the percentage of protein identified and associated with this different structure. We can notice important difference as 3% of translation proteins for rosette vs 9 % for dots in A431 cells. This point suggests that the part of each element can differ.

3. __ __ The authors identified that "cell adhesion proteins" are specifically enriched in linear invadosomes (page 4) (Supp Fig 1f). This conclusion appears to be based on the analysis of NIH3T3-Src and A431 cells. Could the authors provide more details on how this analysis was performed? Specifically, was the analysis conducted on a mixture of the specific signatures of each of the 2 cell models, or on their shared proteins? Additionally, is this category still enriched if each linear invadosome model is analyzed separately? The analysis was performed on common proteins of linear invadosomes, grouping the two cellular models. The category "cell adhesion protein" is not specifically enriched in linear invadosomes because adhesion proteins are also found in the other groups. However, this category represents a larger percentage in linear invadosomes, thus justifying our choice to highlight it for this category.

4. __ __ The authors identified 88 proteins common to all types of invadosomes (Fig. 2b) and classified them as validated or not in invadosomes. Could the authors give details on the criteria used for this classification? References for the already validated proteins should also be provided. RTN4 has been described as partially localized at invadopodia formed by MDA-MB-231 cells in Thuault et al., yet the authors classified it as not validated in invadosomes. The RTN4 protein has been moved to the category of proteins identified as localized in at least one invadosomes organization, thank you for this precision.

Please find below the list of papers having among the proteins classification as identified in at least one invadosomes organization, based on literature searches.

ADAM15 : Aspartate β-hydroxylase promotes pancreatic ductal adenocarcinoma metastasis through activation of SRC signaling pathway - Ogawa et al 2019

ADAM19 : The Adaptor Protein Fish Associates with Members of the ADAMs Family and Localizes to Podosomes of Src-transformed Cells - Abram et al 2003

ASPH : Aspartate β-hydroxylase promotes pancreatic ductal adenocarcinoma metastasis through activation of SRC signaling pathway - Ogawa et al, 2019

BAG3 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

CALD1 :

• Caldesmon is an integral component of podosomes in smooth muscle cells - Eves et al, 2006
• Caldesmon is an integral component of podosomes in smooth muscle cells, Gu et al 2007

• Changes in the balance between caldesmon regulated by p21‐activated kinases and the Arp2/3 complex govern podosome formation, Morita et al 2007 CD44 :

• The CD44s splice isoform is a central mediator for invadopodia activity, Zhao et al

• CD147, CD44, and the Epidermal Growth Factor Receptor (EGFR) Signaling Pathway Cooperate to Regulate Breast Epithelial Cell Invasiveness, Grass et al, 2013
• CD44 and beta3 integrin organize two functionally distinct actin-based domains in osteoclasts, Chabadel et al, 2007
• Macrophages podosomes go 3, Goethem et al 2011 CTTN : ERβ promoted invadopodia formation-mediated non-small cell lung cancer metastasis via the ICAM1/p-Src/p-Cortactin signaling pathway - Wang et al, 2023

EIF4B : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

FNBP1L : Transducer of Cdc42-dependent actin assembly promotes breast cancer invasion and metastasis - Chander et al, 2013

FXR1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

G3BP1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

HNRNPA1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

IGF2BP2 : IMP2 and IMP3 cooperate to promote the metastasis of triple-negative breast cancer through destabilization of progesterone receptor - Kim et al, 2018

ITGA5 : Membrane Proteome Analysis of Glioblastoma Cell Invasion, Mallawaaratchy et al, 2015

LAMP1 : Lysosomal cathepsin B participates in the podosome-mediated extracellular matrix degradation and invasion via secreted lysosomes in v-Src fibroblasts - Chun Tu et al, 2008

MAP4 : A proximity-labeling proteomic approach to investigate invadopodia molecular landscape in breast cancer cells, Thuault et al, 2020

MMP14 :

• Receptor-type protein tyrosine phosphatase alpha (PTPα) mediates MMP14 localization and facilitates triple-negative breast cancer cell invasion - Decotret 2021

• Deciphering the involvement of the Hippo pathway co-regulators, YAP/TAZ in invadopodia formation and matrix degradation - Venghateri 2023 MYH9 :

• TRPM7, a novel regulator of actomyosin contractility and cell adhesion 6 Clarck et al, 2006

• Bradykinin promotes migration and invasion of hepatocellular carcinoma cells through TRPM7 and MMP2, Chen et al, 2016 NONO : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

NPM1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

PABPC1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

PPP1CA : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

PRKAA1 : A proximity-labeling proteomic approach to investigate invadopodia molecular landscape in breast cancer cells, Thuault et al, 2020

PTBP1 : The lncRNA MIR99AHG directs alternative splicing of SMARCA1 by PTBP1 to enable invadopodia formation in colorectal cancer cells - Li et al, 2023

RPL10A : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

RPL34 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

RPS4X : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

RRBP1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

RTN4 : A proximity-labeling proteomic approach to investigate invadopodia molecular landscape in breast cancer cells, Thuault et al, 2020

SSB : The PDGFRα-laminin B1-keratin 19 cascade drives tumor progression at the invasive front of human hepatocellular carcinoma - Govaere 2017

STX7 : Syntaxin 7 contributes to breast cancer cell invasion by promoting invadopodia formation, Parveen et al, 2022

SYNCRIP : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

THBD : VEGF-Induced Endothelial Podosomes via ROCK2-Dependent Thrombomodulin Expression Initiate Sprouting Angiogenesis - Cheng-Hsiang Kuo - 2021

YBX3 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

1. __ __ Page 7, "In addition to translation proteins, the MS analysis highlighted the presence of ER-related proteins such as RTN4, LRRC59 or RRBP1 in all invadosomes linked with Tks5 (Figure 2c)". Is the "ER proteins" category enriched among the 88 common proteins? GSEA analysis on the 88 proteins showed an enrichment in proteins related to ribosomes and mRNA binding.

2. __ __ The comparative analysis of the TKS5 interactome from NIH3T3-Src-GFP-TKS5 on gelatin (this study) with the proteome of NIH3T3-Src rosettes from Ezzoukhry et al. (Fig 5a and Supp Table 2) should be included in the analysis of the MS data obtained in this study (Fig 2), rather than in the paragraph "Recruitment of ER into invadosome rosettes". Are "ER proteins" enriched? Comparative analysis of the TKS5 interactome of NIH3T3-Src-GFP-TKS5 on gelatin (this study) with the proteome of NIH3T3-Src rosettes from Ezzoukhry et al. was included in Supp Figure 2.

The proteins related to translation are enriched, but not those of the ER.__ __3- Was the localization of the newly identified Tks5 partners, such as RPS6 and EIF4B, but also MAP4 and CD44, to invadosomes analyzed in cells expressing endogenous levels of Tks5? If not, this should be addressed to rule out the possibility that their localization in invadosomes is linked to Tks5 overexpression. Through the figures, it is important to indicate whether cells overexpressing or not Tks5 were used.

The precision on the overexpression of Tks5 has been added in the figures.

The experiments were also carried out on cells not overexpressing Tks5 (see results below). Clarifications have been added in the article to specify that these experiments were carried out on cell lines overexpressing Tks5 but also on WT cell lines not overexpressing Tks5 (data not shown in the paper).

Confocal microscopy images of A431 and NIH-3T36Src cells. The cells were seeded on gelatin or type I collagen and stained for Tks5 in green, actin in red, nuclei in blue and Eif4b in grey. Scale bar: 40µm, zoom: 10µm.

Confocal microscopy images of A431 and NIH-3T3-Src cells. The cells were seeded on gelatin or type I collagen and stained for Tks5 in green, actin in red, nuclei in blue and RPS6 in grey. Scale bar: 40µm, zoom: 10µm.

Confocal microscopy images of A431 and NIH-3T3-Src cells. The cells were seeded on gelatin or type I collagen and stained for Tks5 in green, actin in red, nuclei in blue and MAP4 in grey. Scale bar: 40µm, zoom: 10µm.

4- EIF4B depletion inhibits ECM degradation (Fig 4e-f). The authors should address the impact of EIF4B depletion on invadosome formation. In other words, does EIF4B depletion corroborate the results obtained with CHX treatment, where only rosette formation is inhibited (Fig. 4a and Supp Fig. 3d).

The impact of EIF4B depletion on invadosome formation was studied. We were able to show that EIF4B depletion partly corroborates with the results obtained with CHX treatment, since rosette formation is also inhibited by EIF4B depletion but linear invadosomes formed on collagen by A431 are also inhibited by EIF4B depletion.

These results have been added to the paper (see Figure 3g).

Quantification of the numbers of invadosomes per cell on gelatin and collagen silencing (siEIF4B) or not (DMSO) for EIF4B in A431-Tks5-GFP and NIH3T3-Src-Tks5-GFP cells. Values represent the mean +/- SEM of n=4 independent experiments (10 images per condition and per replicate) and were analyzed using student t-test.

__ __5- The authors treated NIH3T3-Src-KDEL-GFP and LifeAct-Ruby cells with CHX and conclude that "translation inhibition led to the collapse of the rosette structure (Fig 6a, Video 4)" (page 8): could extra time points be added before T300 to appreciate the collapse of actin before the retraction of ER from the center of the rosette. No video 4 is provided. A video 5 is provided but does not correspond to a rosette collapse. The lifetime/dissociation rate of rosettes with and without CHX treatment should be determined.

Live cell imaging has been performed by recording one image every 2 minutes as described in methods. Graphs represent all recorded points along the experiment however we modified scale of original graph included into the manuscript to better appreciate the dissociation of fluorescence intensity curves revealing the collapse of actin before the retractation of ER. We also added a second graph which confirmed our first interpretation.

For video 4, we submitted the videos to make sure there were no errors. So, we can now clearly see the collapse of the rosette in video 4.

Lifeact-mRuby and KDEL-GFP signals were recorded in NIH-3T3-Src cells treated with cycloheximide (CHX; 35µM)

__ __6- Sec61 translocon inhibition by the chemical inhibitor ES1 decreases formation of dots by A431 and rosettes and linear invadosomes by NIH3T3-Src (Fig. 6b). Sec61 siRNA should be analyzed. Does Sec61 localize at invadosomes?

Immunofluorescence on NIH-3T3-Src and A431 WT cell lines were performed and added in the paper showing the localization of Sec61 in invadosomes (Figure 6b). Currently, we did not test siRNA targeting Sec61.

Confocal microscopy images of NIH-3T3-Src and A431 cells. The cells were seeded on gelatin or type I collagen and stained for Sec61 in red, nuclei in blue and Actin in grey. Scale bar: 20µm, zoom: 5µm.

__ __Minor comments:

1- The data of Figure 1 is not totally new, at least plasticity of NIH3T3-Src invadosomes has already been described in Juin A., MBoC, 2012. References to original work should be mentioned.

Indeed, the reference has been added to the text at Figure 1.

2- Page 4 "We realized immunoprecipitation against GFP in both cell lines on plastic and type I collagen conditions": the authors should show/mention that on plastic, cells behave has on gelatin coating.

A sentence has been added to the text to mention this: "Indeed, on plastic, the cells behave as on a gelatin coating and thus form the same types of invadosomes, i.e. dots for A431 cells and rosettes for NIH-3T3-Src cells." (see page 4).

3- The authors compared their MS data to previously published Tks5 interactomes (page 4) (Supp Fig 2a). A study from Zagryakhskaya-Masson et al (PMID: 32673397) identified Tks5 interactome of MDA-MB-231 cells generating linear invadosomes. Could the authors comment this study?

This study shows that FGD1, a guanine nucleotide exchange factor for the Rho-GTPase CDC42 interacts with Tks5 and plays a role in the formation of linear invadosomes. We have added this reference in the manuscript, but we have not found FGD1 in our data. It is possible that the GEF of Cdc42 varies from one cell type to another. This study has been added to the discussion.

4- The comparison of translation proteins found in this study with the ones found in other studies (Supp. Fig. 3 a) should be combined with the paragraph commenting the 88 common proteins (Fig. 2c-d).

For clarity, we decided to separate these two parts. There is indeed a lot of information, so it seemed clearer to us to keep the structure of the figures in this sense.

5- The table Supp Fig 2c listing the proteins present in each of the functional categories enriched among the 88 common Tks5 partners should be included as main figure or a color code representing the different biological processes should be included in Fig 2c.

A color code has been added between the two tables. A sentence has been added in the legends for clarity: "Color codes are according to Table Supp Figure 2c: orange: translation, green: actin cytoskeleton, and blue: adhesion."

__ __6- The SUnSET assay is not correctly untitled and described in the Material and Methods. Indeed, the paragraph refering to it is entitled "Inhibition of translation machinery present in invadosomes" and is a mixture of immunofluorescence and SUnSET protocols.

The SunSET assay materials and methods were modified in the paper in the "Sunset Assay" section as described below:

Sunset assay

Cells were treated with puromycin (10mg/ml) during 10min at 37°C then washed twice in ice-cold PBS for protein extraction as described above in Western Blot section. For negative control we pre-treated cells with the translation inhibitor cycloheximide (35mM) during 10min at 37°C.

7- Figure 4, the decrease in ECM degradation of A431 (GFP-Tks5) cells seeded on gelatin by CHX is not statistically different. The affirmation that "CHX treatment limited degradation activity by A431 and NIH3T3-Src cells on gelatin and collagen matrices" (page 6) should be modulated.

Indeed, thank you for your observation. We realized that incorrect values had been reported. Statistical tests (t-tests) were redone for each CHX condition, and significant results were found for each condition.

8- Page 8, "These results therefore confirm the presence but also the involvement of the ER in the rosette formation and maintenance over time". At this point in the study, there is a correlation between the presence of the ER and rosette persistence but no direct evidence of ER involvement is provided. The authors should moderate their conclusion.

That's absolutely right, the sentence has been modified accordingly (page 8).

9- Fig 5d: the authors should specify in the figure legend what are the red head arrows.

The red arrows show membranes of the endoplasmic reticulum, present at the level of the invadosome rosette. This point was added in the figure legend.

10- Some references are not correct. For example p.10, "MAP4 and LAMP1 were described in podosomes": ref 23 and 26 are studies on invadopodia, not on podosomes.

Corrections have been made to the text, the term podosomes has been replaced by invadopodia (see section references).

11- The authors indicate p.10, "Thanks to mass spectrometry experiments, we were able to show for the first time the presence of translation proteins in linear invadosomes". In their previous study Ezzoukry et al, they showed the localization of overexpressed Caprin1, eEF2 and eEF1A1 translation machinery components in linear invadosomes formed by NIH3T3-Src seeded on fibrillar collagen I. The authors should modulate their affirmations.

Indeed, this sentence has been modulated in the text (see page 10).

12- Could the authors refer to figures in the Discussion.

References to figures were added in the discussion.

Reviewer #3 (Significance (Required)):

This work extends their previous work, Ezzoukhry et al, in which the proteome of rosettes of NIH3T3-Src was identified after laser microdissection. In this work, they had identified protein translation machinery as components of rosettes and its implication in the degradation activity and/or the formation of rosettes and linear invadosomes.

The present study extends the presence of protein translation machinery to other types of invadosomes and the implication of protein translation in invadosome activity and/or formation. It also confirms the presence of ER in the center of rosettes. It suggests that ER-associated translation is required for invadosomes formation and activity. This knowledge will be of interest for the invadosome researcher community.

My expertise is in: cellular biology, invadopodia, ECM degradation, cancer. I do not have sufficient expertise to evaluate the accuracy of the analysis of mass spectrometry data and the quantification of videomicroscopy experiments.

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

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

Summary:

To invade the surrounding extracellular matrix (ECM), cells organize actin-rich cellular membrane structures capable of ECM degradation, called invadosomes. Depending on the composition and organization of the ECM, cells organize their invadosomes differently. The authors aimed to identify specific and common components of different types of invadosomes: rosettes formed by NIH3T3-Src cells seeded on gelatin, dots formed by A431 cells seeded on gelatin, and linear invadosomes formed by NIH3T3-Src and A431 cells when seeded on fibrillar collagen I. For this, they generated cells stably expressing GFP-Tks5, a ubiquitous constituent of invadosomes, and determined its interactome. They identified 88 common proteins, among which the protein translation machinery was enriched. Whereas general protein inhibition impaired only rosette formation and impaired every type of invadosome-associated degradation, EIF4B inhibition inhibited the formation of every type of invadosomes. They then analyzed the impact of the ER on invadosome formation and degradation activity. First, they documented the presence of the ER in the center of the NIH3T3-Src rosettes and correlated ER presence with rosette initiation and persistence. They then demonstrated that chemical inhibition of Sec61 translocon decreased formation of invadosomes in general.

Major comments:

1- The authors use cells overexpressing GFP-Tks5 for their analysis of Tks5 interactome in the different invadosomes (Fig. 2). The impact of GFP-Tks5 overexpression on invadosome formation and degradation activity should be mentioned.

2- Concerning the analysis of the mass spectrometry (MS) data, clarifications would be appreciated:

a. The authors first "determined the specific molecular signature associated with each invadosome organization" (p.4). As I understand it, the proteins in each of these signatures correspond to proteins identified only in a particular type of invadosomes, not in the others. Could the authors indicate the percentage of the total proteins identified for each type of invadosomes that corresponds to the specific molecular signature?

b. The GSEA pathways related to each of the specific molecular signature were then analyzed and the authors "commonly identified an enrichment in mitochondrial, ER and Golgi proteins" (page 4) (Supp Fig 1c,e,g). Could the authors provide numbers/percentage/statistics? It is not clear to me whether the biological processes (Supp Fig 1b,d,f) are derived from the analysis of the specific molecular signature or of the total proteins identified for each type of invadosomes. Could the authors clarify this point?

c. The authors also identified "translation proteins" enriched in the specific molecular signature of each type of invadosomes (p.4). They commented on this category, indicating that each type of invadosome contains a specific set of translation-related proteins. This is true, but according to my analysis of the provided tables, the same applies to the other categories as well. Could the authors comment this point?

d. Would similar categories of proteins (translation, ER, Golgi, mitochondrial) appear as enriched if the Tks5 interactome was analyzed as a whole for each type of invadosomes? (the authors may disregard this comment if comment a. is inaccurate)

e. The authors identified that "cell adhesion proteins" are specifically enriched in linear invadosomes (page 4) (Supp Fig 1f). This conclusion appears to be based on the analysis of NIH3T3-Src and A431 cells. Could the authors provide more details on how this analysis was performed? Specifically, was the analysis conducted on a mixture of the specific signatures of each of the 2 cell models, or on their shared proteins? Additionally, is this category still enriched if each linear invadosome model is analyzed separately?

f. The authors identified 88 proteins common to all types of invadosomes (Fig. 2b) and classified them as validated or not in invadosomes. Could the authors give details on the criteria used for this classification? References for the already validated proteins should also be provided. RTN4 has been described as partially localized at invadopodia formed by MDA-MB-231 cells in Thuault et al., yet the authors classified it as not validated in invadosomes.

g. Page 7, "In addition to translation proteins, the MS analysis highlighted the presence of ER-related proteins such as RTN4, LRRC59 or RRBP1 in all invadosomes linked with Tks5 (Figure 2c)". Is the "ER proteins" category enriched among the 88 common proteins?

h. The comparative analysis of the TKS5 interactome from NIH3T3-Src-GFP-TKS5 on gelatin (this study) with the proteome of NIH3T3-Src rosettes from Ezzoukhry et al. (Fig 5a and Supp Table 2) should be included in the analysis of the MS data obtained in this study (Fig 2), rather than in the paragraph "Recruitment of ER into invadosome rosettes". Are "ER proteins" enriched?

3- Was the localization of the newly identified Tks5 partners, such as RPS6 and EIF4B, but also MAP4 and CD44, to invadosomes analyzed in cells expressing endogenous levels of Tks5? If not, this should be addressed to rule out the possibility that their localization in invadosomes is linked to Tks5 overexpression. Through the figures, it is important to indicate whether cells overexpressing or not Tks5 were used.

4- EIF4B depletion inhibits ECM degradation (Fig 4e-f). The authors should address the impact of EIF4B depletion on invadosome formation. In other words, does EIF4B depletion corroborate the results obtained with CHX treatment, where only rosette formation is inhibited (Fig. 4a and Supp Fig. 3d).

5- The authors treated NIH3T3-Src-KDEL-GFP and LifeAct-Ruby cells with CHX and conclude that "translation inhibition led to the collapse of the rosette structure (Fig 6a, Video 4)" (page 8): could extra time points be added before T300 to appreciate the collapse of actin before the retraction of ER from the center of the rosette. No video 4 is provided. A video 5 is provided but does not correspond to a rosette collapse. The lifetime/dissociation rate of rosettes with and without CHX treatment should be determined.

6- Sec61 translocon inhibition by the chemical inhibitor ES1 decreases formation of dots by A431 and rosettes and linear invadosomes by NIH3T3-Src (Fig. 6b). Sec61 siRNA should be analyzed. Does Sec61 localize at invadosomes?

Minor comments:

1- The data of Figure 1 is not totally new, at least plasticity of NIH3T3-Src invadosomes has already been described in Juin A., MBoC, 2012. References to original work should be mentioned.

2- Page 4 "We realized immunoprecipitation against GFP in both cell lines on plastic and type I collagen conditions": the authors should show/mention that on plastic, cells behave has on gelatin coating.

3- The authors compared their MS data to previously published Tks5 interactomes (page 4) (Supp Fig 2a). A study from Zagryakhskaya-Masson et al (PMID: 32673397) identified Tks5 interactome of MDA-MB-231 cells generating linear invadosomes. Could the authors comment this study?

4- The comparison of translation proteins found in this study with the ones found in other studies (Supp. Fig. 3 a) should be combined with the paragraph commenting the 88 common proteins (Fig. 2c-d).

5- The table Supp Fig 2c listing the proteins present in each of the functional categories enriched among the 88 common Tks5 partners should be included as main figure or a color code representing the different biological processes should be included in Fig 2c.

6- The SUnSET assay is not correctly untitled and described in the Material and Methods. Indeed, the paragraph refering to it is entitled "Inhibition of translation machinery present in invadosomes" and is a mixture of immunofluorescence and SUnSET protocols.

7- Figure 4, the decrease in ECM degradation of A431 (GFP-Tks5) cells seeded on gelatin by CHX is not statistically different. The affirmation that "CHX treatment limited degradation activity by A431 and NIH3T3-Src cells on gelatin and collagen matrices" (page 6) should be modulated.

8- Page 8, "These results therefore confirm the presence but also the involvement of the ER in the rosette formation and maintenance over time". At this point in the study, there is a correlation between the presence of the ER and rosette persistence but no direct evidence of ER involvement is provided. The authors should moderate their conclusion.

9- Fig 5d: the authors should specify in the figure legend what are the red head arrows.

10- Some references are not correct. For example p.10, "MAP4 and LAMP1 were described in podosomes": ref 23 and 26 are studies on invadopodia, not on podosomes.

11- The authors indicate p.10, "Thanks to mass spectrometry experiments, we were able to show for the first time the presence of translation proteins in linear invadosomes". In their previous study Ezzoukry et al, they showed the localization of overexpressed Caprin1, eEF2 and eEF1A1 translation machinery components in linear invadosomes formed by NIH3T3-Src seeded on fibrillar collagen I. The authors should modulate their affirmations.

12- Could the authors refer to figures in the Discussion.

Significance

This work extends their previous work, Ezzoukhry et al, in which the proteome of rosettes of NIH3T3-Src was identified after laser microdissection. In this work, they had identified protein translation machinery as components of rosettes and its implication in the degradation activity and/or the formation of rosettes and linear invadosomes.

The present study extends the presence of protein translation machinery to other types of invadosomes and the implication of protein translation in invadosome activity and/or formation. It also confirms the presence of ER in the center of rosettes. It suggests that ER-associated translation is required for invadosomes formation and activity. This knowledge will be of interest for the invadosome researcher community.

My expertise is in: cellular biology, invadopodia, ECM degradation, cancer. I do not have sufficient expertise to evaluate the accuracy of the analysis of mass spectrometry data and the quantification of videomicroscopy experiments.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Summary:

In this work, Normand and her colleagues analyze and compare the interactome of the key invadopodia component, TKS5 (overexpressed as a GFP-tagged protein), in two transformed cell models cultured on different substrates. Potential TKS5 interacting partners are identified including previously known and validated TKS5 interactors, some known to contribute to the mechanism of invadopodia formation and function. Bioinformatic (GSEA) analysis reveals a specific enrichment for proteins related to protein translation and interaction with ER-associated ribosome machinery. Evidence is presented that some of these proteins (RPS6, a component of the 40S ribosomal subunit, and translation factor, EIF4B) localize to TKS5-positive invadopodia in Src-transformed cells. Experiments based on translation inhibitor, cycloheximide, and silencing of EIF4B factor could demonstrate a link between overall protein translation and invadosome formation. Live cell imaging and microscopy analysis of fixed samples could document some proximity between the endoplasmic reticulum network and invadosome rosettes.

Major comments:

1- In the Results Section, the IP/proteomics-based pipeline used by Normand and colleagues to identify TKS5 partners is not clearly described and is confusing. Cut-off used to select te proteins in the different classes summarized in Table S1 should be better described. In addition, the nomenclature of the different protein subgroups used in Table S1 is confusing (see minor point#5).

2- The effects of cycloheximide treatment or EIF4B silencing on gelatin degradation are clear and convincing. However, these are correlative evidence, and they may reflect a general implication of protein translation in the control of invadopodia function. A direct link between the observed interactions of TKS5 with the protein translation machinery and the formation and/or function of invadopodia is missing.

3- Images showing the interrelations between the ER and the adhesive podosome rosettes are striking (Figure 5). Src-transformed cells forming invadosome rosettes when in contact with the collagen substratum change shape and produce adhesive protrusions towards the substratum. As the ER is a huge compartment that fills the entire cytoplasm, it is maybe not so surprising to observe the ER filling the protrusions and getting close to the rosettes at the tip of these membrane extensions. Again, these observations are essentially correlative and there is no prove of some direct contact between some ER regions and the invadosomes.

4- Overall, this report is lacking a clear hypothesis or model of what could be the consequence of the interaction of TKS5 and the translation machinery on the formation and/or the activity of the invadosomes in transformed cells.

Minor comments:

1- Discussion Section (page 2). The statement that TKS4 is involved in ECM degradation in podosomes only and not in invadopodia is not correct. TKS4 knock down has been shown to interfere with ECM degradation in Human DLD1 colon cancer cells (Gianni et al. SCIENCESIGNALING Vol 2 Issue 88, 2009) and in in mouse and human melanoma cell lines (Iizuka et al. Oncotarget, Vol. 7, 2016). In addition, an unphosphorylable mutant form of Tks4 blocked invadopodia formation and ECM degradation in Src-transformed DLD1 cells (Gianni et al. Molecular Biology of the Cell Vol. 21, 4287- 4298, 2010). We (this reviewer's team) reported that TKS4 was associated with cortactin-positive invadopodia in MDA-MB-231 and Hs578T triple-negative breast cancer cell lines (Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

2- Discussion Section (page 3). A431 is wrongly referred as to a melanoma cell line; it is a human epidermoid carcinoma cell line.

3- Results Section (page 4 & 5). The authors compare the proteins they identified as potential TKS5 partners to previously published data by Stilly et al. (based on TKS5 IP like in the present study) and Thuault et al. (TKS5 bioIB). Additionally, authors should mention and discuss previously published data based on TKS5 coIP experiment and Mass Spec analysis similar to the present study, identifying potential TKS5 partners; some of which were similarly found in the present study including proteins involved in translation and ribosome function although these were not the focus of this work (several 40S and 60S ribosomal proteins, see Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

4- Figure 1b. Matrix degradation is not visible in association with the invadopodia in selected high magnification images in Figure 1a and 1b.

5- Supplemental table 1. The names of the different lists of proteins in the summary table is not clear and is rather confusing.

6- Supp Figure 1. Please define what is the sample named '' (Delta).

7- Results Section (page 5). 'These experiments confirm the correct co-localization between Tks5 and the proteins identified in Tks5 interactome by mass spectrometry analysis.' This statement is too general; in fact, data validate only colocalization between TKS5 and some identified partners, namely CD44 and MAP4.

8- Figure 2e and Figure 3. It would have been nice to show the colocalization of selected proteins and TKS5 in association with collagen fibers to validate that enrichment occurs at matrix/cell contact sites and corresponds to bona fide invadopodia.

9- Figure 3c (high mag insets). TKS5 and EIF4b do not seem particularly enriched in invadopodia rosettes as compared to the rest of the cytoplasm.

10- Figure 4c-f. Treatments (i.e. CHX, siEIF4b) affect gelatin degradation. It would be interesting to assess the capacity of cells to form invadopodia under these conditions.

Significance

This study confirms and adds to a previously published report by this research group based on invadosome laser capture microdissection and proteomics revealing that invadosomes contain specific components of the translational machinery, and that protein translation activity is required to maintain invadosome structure and activity (Ezzoukhry et al. Nat Commun 2018). It also adds to a recent study that established a crucial role for ribosome biogenesis in promoting cell invasion in the C. elegans anchor cell invasion model (Development. 2023).

The experimentation presented in this paper is of good quality and convincingly support the authors conclusions of a link between the ER-associated translation machinery and invadosome function in transformed cells. Overall, although this study adds to the emerging idea of an evolutionary-conserved translational control of cell invasion through the extracellular matrix it is mostly correlative and lacking a direct prove that the interaction of TKS5 with components of the translation machinery has a direct contribution to invadopodia function.

Reviewer #1 (Public review):

Summary:

The manuscript puts forward a statistical method to more accurately report the significance of correlations within data. The motivation for this study is two-fold. First, the publication of biological studies demands the report of p-values, and it is widely accepted that p-values below the arbitrary threshold of 0.05 give the authors of such studies justification to draw conclusions about their data. Second, many biological studies are limited by the number of replicate samples that are feasible, with replicates of less than 5 typical. The authors report a statistical tool that uses a permute-match approach to calculate p-values. Notably, the proposed method reduces p-values from around 0.2 to 0.04 as compared to a standard permutation test with a small sample size. The approach is clearly explained, including detailed mathematical explanations and derivations. The advantage of the approach is also demonstrated through analysis of computer-generated synthetic data with specified correlation and analysis of previously published data related to fish schooling. The authors make a clear case that this method is an improvement over the more standard approach currently used, and also demonstrate the impact of this methodology on the ability to obtain p-values that are the standard for biological research. Overall, this paper is very strong. While the subject matter seems somewhat specialized, I would make the case that this will be an important study that has broad general interest to readers. The findings are very general and applicable to many research contexts. Experimentalists also want to report accurate p-values in their work and better understand how these values are calculated. Although I believe the previous statement is true, I am not sure that many research groups doing biological work are reading specialized statistics journals regularly. Therefore a useful and broadly applicable statistical tool is well placed in this journal.<br /> Strengths:

The proposed method is broadly applicable to many realistic datasets in many experimental contexts.

The power of this method was demonstrated with both real experimental data and "synthetic" data. The advantages of the tool are clearly reported. The zebrafish data is a great example dataset.

The method solves a real-life problem that is frequently encountered by many experimental groups in the biological sciences.

The writing of the paper is surprisingly clear, given the technical nature of the subject matter. I would not at all consider myself a statistician or mathematician, but I found the text easy to follow. The authors did an impressive job guiding the reader through material that would often be difficult to grasp. The introduction was also well-written and clearly motivated the goals of the study.

Weaknesses:

A few changes could be made if the manuscript is revised. I would consider all of these points minor, but the paper could be improved if these points were addressed.

(1) The caption of Figure 2 doesn't seem to mention panel D. Figure A-2 also does not mention C in the caption.

(2) Figure 2D is a little hard to follow. First, the definition of "Power" is not clear, and I couldn't find the precise definition in the text. Second, the legend for the different lines in 2D is only given in Figure A-2. Perhaps a portion of the caption for Figure 2 is missing?

(3) The concept of circular variance for the fish data was heard to understand/visualize. The equation on line 326 did not help much. If there is a very simple picture that could be added near line 326 that helps to explain Ct and theta, that could be a big help for some readers who do not work on related systems. The analysis performed is understandable, the reader just has to accept that circular variance captions the degree of alignment of the fish.

(4) For the data discussed in Figure 3, I wasn't 100% sure how the time windows were selected. In the caption, it says "time series to different lengths starting from the first frame". So the 20 s time window was from t=0 to t= 20 s. Would a different result be obtained if a different 20 s window was chosen (from t = 4 min to t = 4 min 20 s just to give a specific example). I suppose by chance one of the time windows would give a p-value less than the target 0.05, that wouldn't be surprising. Maybe a random time window should be selected (although I am not indicating what was reported was incorrect)? A little more discussion on this aspect of the study may be helpful.

Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo Volume 1 No (2) 2022 Pengaruh Konseling Gizi Prakonsepsi terhadap Pengetahuan dan Sikap Wanita Pranikah Kusuma Intan Setianing Fifit1 , Ida Sofiyanti2 , Vivin Mustika3 , Cinta Nashita4 , Diens Nanda Ela Permana5 , Fathul Mujahidah6, Tesa Dwiputri Rahmadhani7 1Kebidanan Program Sarjana, Univeritas Ngudi Waluyo, kusumaintan841@gmail.com 2Pendidikan Profesi Bidan, Bidan, Universitas Ngudi Waluyo, idasofiyanti@gmail.com 3Kebidanan Program Sarjana,, Universitas Ngudi Waluy, vivinmw21@gmail.com 4Kebidanan Program Sarjana, Universitas Ngudi Waluyo, cintanashita07@gmail.com 5Kebidanan Program Sarjana, Universitas Ngudi Waluyo, diensnanda5@gmail.com 6Kebidanan Program Sarjana, Universitas Ngudi Waluyo, fathulmujahidah@gmail.com 7Kebidanan Program Sarjana, Universitas Ngudi Waluyo, dwiputritesa15@gmail.com Korespondensi Email : diensnanda5@gmail.com Article History Submitted, 2022-12-18 Accepted, 2022-12-23 Published, 2022-12-31 Kata Kunci: Gizi, Prakonsepsi, Pengetahuan, Sikap, Pranikah Keywords: Women, Nutrition Women of Reproductive Age (WUS) are women who are in the age period between 15-49 years. Premarital women who are part of the WUS group need to prepare adequate nutrition for their bodies, because as prospective mothers, optimal nutrition for premarital women will affect the growth and development of the fetus, the health condition of the babies born and safety during the delivery process. The pre-marital period can be associated with the pre-conception period, because after marriage women will immediately undergo the conception process. The preconception period is the period before pregnancy. The importance of maintaining adequate nutrition for premarital women before pregnancy is because good nutrition will support the optimal function of the reproductive organs, such as the smooth process of egg maturation, the production of good quality egg cells, and the perfect fertilization process. Good nutrition can also play an important role in providing nutritional reserves for the growth and development of the fetus. For expectant mothers, adequate and balanced nutrition will affect overall health conditions during conception and pregnancy and will be able to break the chain of malnutrition problems during pregnancy. Knowledge of nutrition plays an important role in fulfilling one's nutritional adequacy. The level of knowledge will encourage someone to have optimal abilities in the form of knowledge and attitudes. Lack of knowledge about nutrition influence someone in understanding the concepts and principles as well as information related to nutrition. Efforts to increase knowledge can be done by providing nutrition education. Nutrition education encourages a person in the form of knowledge and changes in attitude. The purpose of this literature review is to assess the knowledge and attitudes 696 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo of pre-marital women before and after being given preconception nutrition counseling. The research method used in this study is a literature review. Article searches were carried out on Google Scholar and Pubmed with the keywords preconception nutrition and knowledge for premarital women. The selected articles are articles in Indonesian and English which were published from 2018 to 2022 in five journals that can be accessed in full text in pdf format. The results showed that the role of preconception nutritional counseling in this study significantly increased the sample's knowledge. The results showed that there were differences before and after the intervention. The results of the analysis showed that there was an increase in the average value of the sample's knowledge before and after being given counseling. Abstrak Wanita Usia Subur (WUS) adalah wanita yang berada dalam periode umur antara 15-49 tahun. Wanita pranikah merupakan bagian dari kelompok WUS perlu mempersiapkan kecukupan gizi tubuhnya, karena sebagai calon ibu, gizi yang optimal pada wanita pranikah akan mempengaruhi tumbuh kembang janin, kondisi kesehatan bayi yang dilahirkan dan keselamatan selama proses melahirkan. Masa pranikah dapat dikaitkan dengan masa prakonsepsi, karena setelah menikah wanita akan segera menjalani proses konsepsi. Masa prakonsepsi merupakan masa sebelum kehamilan. Adapun pentingnya menjaga kecukupan gizi bagi wanita pranikah sebelum kehamilan disebabkan karena gizi yang baik akan menunjang fungsi optimal alat-alat reproduksi seperti lancarnya proses pematangan telur, produksi sel telur dengan kualitas baik, dan proses pembuahan yang sempurna. Gizi yang baik juga dapat berperan penting dalam penyediaan cadangan gizi untuk tumbuh-kembang janin. Bagi calon ibu, gizi yang cukup dan seimbang akan memengaruhi kondisi kesehatan secara menyeluruh pada masa konsepsi dan kehamilan serta akan dapat memutuskan mata rantai masalah kekurangan gizi pada masa kehamilan. Pengetahuan mengenai gizi berperan penting dalam pemenuhan kecukupan gizi seseorang. Tingkat pengetahuan akan mendorong seseorang memiliki kemampuan yang optimal berupa pengetahuan dan sikap. Kurangnya pengetahuan terhadap gizi akan mempengaruhi seseorang dalam memahami konsep dan perinsip serta informasi yang berhubungan dengan gizi. Upaya peningkatan pengetahuan dapat dilakukan dengan cara memberikan pendidikan gizi. Pendidikan gizi mendorong 697 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo seseorang berupa pengetahuan, dan perubahan sikap. Tujuan literatur review ini adalah untuk menilai pengetahuan dan sikap wanita pranikah sebelum dan sesudah diberikan konseling gizi prakonsepsi. Metode penelitian yang digunakan pada penelitian ini adalah literature review. Penulusuran artikel dilakukan pada Google Scholar dan Pubmed dengan kata kunci gizi prakonsepsi dan pengetahuan untuk wanita pranikah. Artikel yang dipilih adalah artikel berbahasa Indonesia dan berbahasa Inggris yang dipublikasikan sejak tahun 2018 sampai dengan 2022 sebanyak lima jurnal yang dapat diakses fulltext dalam format pdf. Hasil penelitian menunjukkan bahwa peran konseling gizi prakonsepsi dalam penelitian ini signifikan meningkatkan pengetahuan sampel, didapatkan hasil bahwa ada perbedaan sebelum dan sesudah pemberian intervensi. Hasil analisis menunjukkan, terjadi peningkatan rata-rata nilai pengetahuan sampel sebelum dan sesudah diberikan konseling. Pendahuluan Masa pranikah dapat dikaitkan dengan masa prakonsepsi, karena setelah menikah wanita akan segera menjalani proses konsepsi. Masa prakonsepsi merupakan masa sebelum kehamilan. Periode prakonsepsi adalah rentang waktu dari tiga bulan hingga satu tahun sebelum konsepsi dan idealnya harus mencakup waktu saat ovum dan sperma matur, yaitu sekitar 100 hari sebelum konsepsi. Status gizi WUS atau wanita pranikah selama tiga sampai enam bulan pada masa prakonsepsi akan menentukan kondisi bayi yang dilahirkan. Prasayarat gizi sempurna pada masa prakonsepsi merupakan kunci kelahiran bayi normal dan sehat (Susilowati dkk.2016). Kementeria Kesehatan RI (2010) mendefinisikan bahwa Wanita Usia Subur (WUS) adalah wanita yang berada dalam periode umur antara 15-49 tahun. Wanita pranikah merupakan bagian dari kelompok WUS perlu mempersiapkan kecukupan gizi tubuhnya, karena sebagai calon ibu, gizi yang optimal pada wanita pranikah akan mempengaruhi tumbuh kembang janin, kondisi kesehatan bayi yang dilahirkan dan keselamatan selama proses melahirkan (Paratmanitya dkk. 2012). Kecukupan gizi selama periode prakonsepi penting untuk menjaga kondisigizi tubuh sehingga dapat menunjang fungsi alat reproduski secara optimaldan dapat berperan penting dalam penyediaan cadangan gizi untuk tumbuh kembang janin. Adapun pentingnya menjaga kecukupan gizi bagi wanita pranikah sebelum kehamilan disebabkan karena gizi yang baik akan menunjang fungsi optimal alat-alat reproduksi seperti lancarnya proses pematangan telur, produksi sel telur dengan kualitas baik, dan proses pembuahan yang sempurna (Susilowati & Kuspriyanto, 2016). Banyaknya permasalahan yang muncul pada ibu dan bayi menunjukkan pentingnya pemahaman pengetahuan tentang kehamilan untuk wanita yang akan mengalami periode kehamilan setelah menikah. Kesadaran tentang pentingnya gizi prakonsepsi untuk meningkatkan kesehatan dan kesejahteraan generasi berikutnya perlu ditumbuhkan. Pemberian intervensi berupa pendidikan gizi dan promosi kesehatan dapat meningkatkan pengetahuan dan perubahan perilaku yang menuju kearah untuk mengurangi risiko masalah kesehatan. Kurang energi kronik (KEK) masih merupakan masalah gizi utama yang sering menimpa WUS. Seseorang dapat dikatakan KEK apabila hasil dari pengukuran lingkar lengan atas (LILA) dibawah 23,5 cm. Prevalensi KEK pada WUS di Indonesia menurut Indeks Pembangunan Kesehatan Masyarakat (IPKM) tahun 2016 698 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo menunjukkan angka sebesar 20,97%. Dampak dari wanita pranikah yang menderita KEK antara lain dapat mengakibatkan terjadinya anemia, kematian pada ibu pada saat melahirkan, kematian janin, bayi berat lahir rendah (BBLR), kelahiran prematur, lahir cacat hingga kematian pada bayi (Stephanie dkk. 2016). Di Jawa Tengah pada tahun 2018 dari data kematian ibu didapatkan hasil sebesar 57, 24 % kematian maternal terjadi pada waktu nifas, 25, 42 % pada waktu hamil, dan sebesar 17,38 % pada waktu persalinan sedangkan data AKB sebesar 8,37/1.000 KH (Dinkes Prof Jateng, 2018). Menurut World Health Organization (WHO) mendefinisikan bahwa bayi yang dikatakan BBLR adalah bayi yang terlahir dengan berat kurang dari 2500 gram (WHO, 2014). Di Indonesia sendiri prevalensi BBLR pada tahun 2018 mencapai 6,2% mengalami kenaikan dari tahun sebelumnya yaitu 2013 sebanyak 5,7% (Riskesdas, 2018). Berbagai faktor dapat mempengaruhi status gizi wanita pranikah sebelum kehamilan Faktor-faktor yang mempengaruh adalah umur, pendidikan, dan status gizi. Sedangkan selama kehamilan beberapa faktor yang mempengaruhi adalah frekuensi kehamilan, derajat aktivitas fisik, komplikasi penyakit saat hamil, kondisi psikologis dan asupan pangan (Badriah dan Fauziyah 2012). Pengetahuan mengenai gizi berperan penting dalam pemenuhan kecukupan gizi seseorang. Tingkat pengetahuan akan mendorong seseorang memiliki kemampuan yang optimal berupa pengetahuan dan sikap. Kurangnya pengetahuan terhadap gizi akan mempengaruhi seseorang dalam memaham konsep dan perinsip serta informasi yang berhubungan dengan gizi (Siwi, 2009). Pendidikan gizi merupakan suatu proses berkelanjutan dalam menambah pengetahuan individu tentang gizi, dengan tujuan untuk mendorong perubahan sikap dan perilaku terkait makanan dan gizi (Supariasa, 2012). Dalam pendidikan gizi terdapat berbagai macam metode antara lain metode ceramah dan audio visual. Pendidikan gizi metode ceramah merupakan cara yang digunakan untuk menyampaikan pesan terkait gizi secara lisan. Pendidikan gizi metode audio visual adalah cara penyampaian materi atau pesan terkait gizi dengan menggunakan teknologi dalam bentuk suara dan gambar, sehingga penyerapannya melalui pandangan dan pendengaran. Metode ceramah sangat umum digunakan tetapi memiliki kelemahan yaitu komunikasi satu arah lebih dominan dan peserta cenderung pasif, sehingga membuat proses pendidikan gizi menjadi monoton dan menimbulkan kebosanan (Maduretno, Wirawan & Setijowati, 2015). Di sisi lain, metode audio visual merupakan metode yang dapat mengungkapkan objek dan peristiwa seperti keadaan sesungguhnya, sehingga penerima pesan dapat memahami secara utuh dan lebih bermakna (Supariasa, 2012). Tujuan penelitian ini adalah untuk mendapatkan landasan teori yang bisa mendukung pemecahan masalah yang sedang diteliti yaitu pengaruh konseling gizi prakonsepsi terhadap pengetahuan dan sikap wanita pranikah. Metode Metode penelitian yang digunakan pada penelitian ini adalah literature review. Penulusuran artikel dilakukan menggunakan Google Schoolar dan Pubmed dengan kata kunci gizi prakonsepsi dan pranikah. Hasil penyaringan terhadap judul, abstrak dan full text dengan kata kunci “konseling gizi, prakonsepsi, pranikah” didapatkan bahwa ada 28 original artikel yang sesuai dengan tema dan selanjutnya akan dilakukan telaah dalam literatur review ini adalah 8 artikel yang penelitiannya dilakukan pada tahun yang berbeda dan tipe studi yang akan diidentifikasi adalah cross sectional dengan metode penelitian quasi experiment, deskriptif. Artikel yang dipilih adalah artikel berbahasa Indonesia dan Inggris yang dipublikasikan sejak tahun 2018 sampai dengan 2022 sebanyak 8 artikel (4 artikel international dan 4 artikel nasional) yang dapat diakses fulltext dalam format pdf serta telah dilakukan kritisi pada seluruh artikel tersebut. 699 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo Hasil dan Pembahasan Pencarian artikel dilakukan pada pangkalan data (data base) dengan menggunakan kata Pengaruh konseling gizi prakonsepsi pada pasangan calon pengantin. Artikel yang digunakan dan memenuhi kriteria sebanyak lima artikel yang memuat 4 artikel international dan 4 artikel nasional. Berikut daftar jurnal yang ditemukan diuraikan dalam bentuk tabel. No Judul dan Peneliti Metode Penelitian Hasil Tabel 1 Daftar Artikel 1 Peningkatan pengetahuan gizi prakonsepsi dengan buku saku berbasis android dalam pembinaan pranikah di KUA Gresik Methania Nanda Agustine & Siti Sulandjari Jenis penelitian ini menggunakan metode Quasi experimental dengan desain non- equivalent control group, dimana terdapat 2 kelompok yaitu kelompok eksperimen dan kelompok kontrol. Subjek penelitian sebanyak 60 calon pengantin wanita di Kabupaten Gresik. Teknik pengambilan sampel adalah purposive sampling. Analisis data menggunakan analisis deskriptif dan analisis statistic. Hasil penelitian menunjukkan bahwa penggunaan buku saku gizi berbasis android dapat meningkatkan penguasaan pengetahuan gizi prakonsepsi pada calon pengantin di Kabupaten Gresik dan lebih baik dibandingkan dengan penggunaan buku saku biasa. 2 Pengaruh konseling gizi prakonsepsi terhadap pengetahuan dan sikap wanita pranikah di kecamatan Batang Kuis Lusyana Gloria Doloksaribu & Abdul Malik Simatupang Metode penelitian ini adalah Quasy Eksperiman dengan desain one grup pre test- post test. Teknik pengambilan sampel adalah dengan total sampling. Jenis dan cara pengumpulan data menggunakan data primer dan data skunder. Hasil penelitian ini menunjukkan peran konseling gizi prakonsepsi dalam penelitian ini signifikan meningkatkan sikap sampel. Hasil uji T- dependent didapatkan hasil bahwa ada perbedaan sebelum dan sesudah intervensi. Dengan nilai signifikan diperoleh (p=0.001) yang artinya ada pengaruh konseling gizi prakonsepsi terhadap sikap sampel. 3. Pengaruh pendidikan kesehatan pranikah terhadap pengetahuan dan sikap calon pengantin di lubuk begalung padang Dewi Susanti, Yefrida Rustam, Alsri, Windra Doni Jenis penelitian menggunakan quasy eksperimen dengan rancangan pretest-posttest design. Penelitian dilakukan di KUA Kecamatan Lubuk Begalung pada bulan April-Juni 2017. Populasi dalam penelitian ini calon pengantin yang datang ke KUA Kecamatan Lubuk Begalung selama waktu Hasil penelitian terhadap 38 calon pengantin menunjukan bahwa 31,6% responden berpengetahuan rendah sebelum pendidikan pranikah dan 97,4% berpengetahuan tinggi setelah pendidikan pranikah, 76,3% bersikap negative sebelum pendidikan pranikah. Ada pengaruh pendidikan pranikah tehadap 700 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo No Judul dan Peneliti Metode Penelitian Hasil penelitian sebanyak 19 pasangan yang diambil secara purposive sampling. Pengumpulan data menggunakan kuisioner yang diberikan sebelum dan sesudah pendidikan kesehatan. Analisis data dilakukan secara univariat dan bivariat menggunakan uji statistik Wilcoxon dengan komputer pengetahuan dan sikap calon pengantin ( p value 0,001 dan 0,013) Kesimpulan ada pengaruh pendidikan kesehatan pranikah terhadap pengetahuan dan sikap calon pengantin di Kecamatan Lubuk Begalung Padang 4 Efektivitas pendidikan gizi metode ceramah dan audio visual terhadap pengetahuan dan sikap tentang pencegahan stunting pada wanita usia subur Pranikah Dwi Hartanti Penelitian dengan desain true experimental dengan rancangan one group pretes-postest design. Sampel adalah mahasiswi perempuan Prodi Psikologi Fakultas Psikologi dan Kesehatan UIN Walisongo sebanyak 77 subyek dengan rentang usia 18-25 tahun. Hasil dari penelitian ini terdapat peningkatan skor pengetahuan dan sikap kea rah positif setelah diberikan intervensi. Terdapat pengaruh yang bermakna terhadap rerata skor pengetahuan dan sikap sebelum dan setelah intervensi. 5 The Influence Of Preconceptional Nutrition Counseling OnKnowledge AndAttitude Of Women Of Reproductive Age Devi Lukiana &Titin Eka Sugianti Metode penelitian ini menggunakan quasi eksperimen dengan one group pretest-posttest design. Sampel dalam penelitian ini adalah wanita usia subur yang akan menikah. Tehnik pengambilan sampel yaitu dengan purposive sampling dengan jumlah sampel 81 orang. Metode analisis yang digunakan adalah analisis univariat dan bivariat dengan paired sample test. Hasil dari penelitian ini yaitu sebagian besar WUS sebelum diberikan penyuluhan memiliki pengetahuan cukup sebesar 64,2% dan setelah diberikan penyuluhan 85,2% memiliki pengetahuan baik. Sedangkan sikap sebelum diberikan penyuluhan yaitu sebagian besar negatif sebanyak 5,6% dan setelah diberikan penyuluhan memiliki sikap yang positif sebesar 97,5% sehingga ada pengaruh penyuluhan gizi prakonsepsi terhadap pengetahuan dan sikap wanita usia subur. 6 The effectiveness of nutrition education on increasing knowledge of the prospective bride Tria Ningsih & Arfaah Husna Desain yang digunakan dalam penelitian ini adalah quasy eksperimental dengan one grup pre-post test. Sampel dipilih dengan menggunakan tekhnik consecutive sampling sebanyak 10 calon pengantin. Data analisis mengunakan analisis univariat dan bivariat. Hasil penelitian ini adalah terdapat pengaruh yang signifikan terhadap pengetahuan gizi yang dimiliki responden setelah diberikan penyuluhan. Berdasarkan hasil uji bivariat diketahui bahwa Pendidikan gizi efektif dalam meningkatkan pengetahuan catin (P= 0,005). 701 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo No Judul dan Peneliti Metode Penelitian Hasil 7 Intervention strategies to improve nutrition and health behaviours before conception Mary Barker et al. Lancet Metode menggunakan tinjauan kuasi-sistematis terhadap uji coba nutrisi prakonsepsi dab intervensi perilaku Kesehatan untuk mengidentifikasi intervensi yang efektif dan menentukan jalur untuk menghasilkan efek. Untuk mengidentfiikasi 14 study primer yang terkontrol. Hasil penelitian tersebut memiliki efek positif dalam berbagai aspek kesehatan yaitu ada perubahan perilaku terhadap wanita prakonsepsi setelah dilakukan intervensi 8 Assessing preconception nutrition readiness among woman of reproductive age in Bantul, Indonesia : findings from baseline data analysis of a cluster randomized trial Yhona Paratmanitya, Siti Helmayanti, Detty Nurdiati, Emma C Lewis, Hamam Hadi Metode: Penelitian ini merupakan bagian dari studi cluster randomized trial untuk meningkatkan status besi ibu hamil yang melibatkan 173 calon pengantin wanita. Data antropometri, asupan makan, dan pengetahuan tentang gizi prakonsepsi dikumpulkan oleh enumerator yang terlatih, dengan melakukan kunjungan ke rumah responden. Data kadar Hemoglobin (Hb) diperoleh melalui kuesioner. Kesiapan gizi prakonsepsi diukur menggunakan 10 indikator, yang meliput: (1) Indeks Massa Tubuh (IMT); (2) Lingkar Lengan Atas (LILA); (3) kadar Hb; (4) asupan energi; (5) asupan protein; (6) asupan kalsium; (7) asupan zat besi; (8) asupan folat; (9) pengetahuan tentang gizi prakonsepsi; dan (10) konsumsi suplemen zat besi dan/atau asam folat. Hasil penelitian tersebut tidak ada responden yang dapat memenuhi seluruh indikator kesiapan gizi prakonsepsi. Sebanyak 26% responden dapat memenuhi 2 indikator, dan median skor-nya adalah 3 (2.0-4.0). Kadar Hb, IMT, dan LILA merupakan 3 indikator terbanyak yang dapat dipenuhi, sementara asupan kalsium, zat besi, dan folat merupakan 3 indikator yang paling sedikit dapat dipenuhi oleh responden. Pembahasan Berdasarkan hasil dari delapan artikel yang didapatkan hasil bahwa: Masa Prakonsepsi dan Pranikah Pernikahan dilakukan untuk mencapai ketentraman dan kebahagiaan yang berdasarkan kasih sayang sehingga setiap anggota keluarga/pasangan merasakan ketentraman, kenyamanan, kedamaian, kebahagiaan dan kesejahteraan sehingga tercapai kehidupan yang lebih baik dan dinamis. Pernikahan merupakan salah satu tugas perkembangan dewasa awal. Beberapa hal tersebut berupa pemikiran dan perasaan antara 702 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo mengambil keputusan untuk menikah atau menunda menikah, merencanakan waktu yang tepat untuk menikah, komunikasi, masalah keuangan serta masalah kesehatan dan seks. (Augustine & Sulandjari 2021) Prakonsepsi merupakan salah satu penentu kelancaran dari proses kehamilan hingga melahirkan nantinya. Masa pranikah dapat dikaitkan dengan masa prakonsepsi, karena setelah menikah wanita akan segera menjalani proses konsepsi. Masa prakonsepsi merupakan masa sebelum kehamilan. Periode prakonsepsi adalah rentang waktu dari tiga bulan hingga satu tahun sebelum konsepsi dan idealnya harus mencakup waktu saat ovum dan sperma matur, yaitu sekitar 100 hari sebelum konsepsi. (Doloksaribu & Simatupang, 2019) Wanita prakonsepsi adalah wanita yang sudah memasuki usia dewasa, atau dapat dikatakan wanita usia subur (WUS). Rentang usia WUS adalah 15-49 tahun. Kebutuhan gizi pada WUS tentunya mengalami peningkatan jika dibandingkan dengan kebutuhan semasa bayi dan anak-anak. Gizi yang mempengaruhi pada masa prakonsepsi adalah karbohidrat, lemak, protein, asam folat, beberapa kelompok vitamin seperti vitamin A, E, dan B12, serta mineral seperti zinc, besi, kalsium, dan omega-3. Asupan gizi yang cukup dan status gizi yang baik dari ibu penting untuk perkembangan optimal janin. Konsumsi sumber makanan yang bervariasi adalah penting sebelum pembuahan dan selama kehamilan. (Doloksaribu & Simatupang, 2019) Pendidikan esehatan calon pengantin sangat berperan penting untuk meningkatkan bekal calon pengantin salah satunya adalah pengetahuan tentang kesehatan reproduksi dan seksual. Calon pengantin perlu dibekali pengetahuan yang cukup tentang kesehatan reproduksi dan seksual serta terkait informasi yang belum ada sehingga perlu diberikan pendidikan kesehatan. Dimana dengan pemahaman yang cukup mengenai kesehatan pranikah, calon pengantin dapat menjalani pernikahan yang sehat dan aman. Calon pengantin perlu dibekali pengetahuan yang cukup tentang kesehatan Pranikah dan hak-hak Pranikah sehingga calon pengantin siap menjadi seorang ibu dan seorang ayah.Pendidikan kesehatan pranikah meliputi beberapa materi yaitu kesehatan reproduksi, sesual pranikah, persiapan kehamilan awal setelah menikah, aspek psikologis. (Paratmanitya, Hadi & Susetyowati. (2012) Gizi Prakonsepsi Status gizi menjadi salah satu kunci tercapainya kesehatan pada masa konsepsi, seperti mendukung pertumbuhan janin dan perkembangan otak yang optimal, pencegahan dini risiko tinggi kehamilan, cacat lahir, lahirnya bayi dengan berat badan lahir rendah (BBLR), dan risiko penyakit. kronis pada masa dewasa hal ini karena kondisi kesehatan selama kehamilan menciptakan kondisi metabolisme yang spesifik untuk memproduksi plasenta, jaringan janin, dan volume darah yang disediakan untuk pertumbuhan dan perkembangan janin (Dieny, 2019). Hasil dari penelitian Yhona Paratmanitya dkk (2020) jumlah wanita usia subur yang siap gizi untuk memasuki masa kehamilan sangat rendah. Indikator kesiapan gizi prakonsepsi yang belum terpenuhi meliputi indikator terkait asupan zat gizi mikro. Sebaliknya, indikator yang sudah dipenuhi oleh sebagian besar peserta seperti status gizi, MUAC, dan kadar Hb, harus tetap diprioritaskan untuk menjamin kehamilan yang sehat. Meskipun faktor sosio-demografi secara statistik tidak berhubungan signifikan dengan kesiapan gizi prakonsepsi, temuan penelitian ini menggambarkan bahwa wanita dengan usia lebih muda usia, yang menganggur, dan berpenghasilan rendah, menunjukkan skor kesiapan nutrisi prakonsepsi yang sedikit lebih rendah. Studi saat ini menunjukkan bahwa program perawatan kesehatan prakonsepsi yang lebih serius dilaksanakan untuk mempersiapkan wanita untuk kehamilan yang sehat. Pendidikan diperlukan untuk meningkatkan kesadaran tentang pentingnya persiapan gizi prakonsepsi pada wanita usia subur yang sedang merencanakan kehamilan. 703 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo Menurut (Doloksaribu, 2019) ada berbagai faktor yang dapat mempengaruhi status gizi seorang wanita sebelum hamil. Faktor mempengaruhi adalah usia, pendidikan, dan status gizi. Sedangkan selama kehamilan beberapa faktor yang mempengaruhi adalah frekuensi kehamilan, derajat aktivitas fisik, komplikasi penyakit selama kehamilan, kondisi psikologis dan asupan makanan. Pengetahuan gizi memegang peranan penting dalam pemenuhan kecukupan gizi seseorang. Tingkat pengetahuan akan mendorong seseorang untuk memiliki kemampuan yang optimal berupa pengetahuan dan sikap. Kurangnya pengetahuan gizi akan mempengaruhi pemahaman seseorang terhadap konsep dan prinsip serta informasi yang berkaitan dengan gizi. Upaya peningkatan pengetahuan dapat dilakukan dengan memberikan pendidikan gizi. Pendidikan gizi mendorong seseorang berupa pengetahuan dan perubahan sikap. Pendidikan tersebut dapat disampaikan melalui penyuluhan sehingga pengetahuan calon pengantin tentang kesehatan reproduksi dapat meningkat. Penyuluhan kesehatan adalah kegiatan pendidikan kesehatan yang dapat dilakukan dengan menanamkan keyakinan pada calon pengantin agar tidak hanya tahu dan mengerti tetapi juga sadar, mau, dan bisa melakukan anjuran yang terkait dengan kesehatan reproduksi. Penyampaian pendidikan kesehatan untuk calon pengantin dapat disertai dengan pemberian media tertentu yang akan memaksimalkan calon pengantin dalam menyerap informasi (Dewi, 2012). Pengaruh Konseling Gizi Prakonsepsi Terhadap Pengetahuan dan Sikap Wanita Pranikah Berikut data pengetahuan dan sikap wanita pranikah menurut penelitian yang dilakukan oleh (Lusyana Gloria, dkk, 2019). Pengetahuan Tabel 2. Rata-rata Nilai Pengetahuan Sampel Sebelum dan Sesudah Konseling N % N % Kategori pengetahuan sampel sebelum diberikan konseling yang paling banyak adalah kategori cukup sebesar 53,3% dan kategori kurang sebesar 43,3% sementara kategori baik hanya sebesar 3,3%. Setelah diberikan konseling, 70,0% sampel memiliki pengetahuan kategori baik dan hanya tersisa 3,3% sampel yang memiliki pengetahuan dengan kategori kurang. Sikap Tingkat pengetahuan Sebelum konseling Sesudah konseling Baik 1 3,3 21 70,0 Cukup 16 53,3 8 26,7 Kurang 13 43,3 1 3,3 Total 30 100 30 100 Tabel 3. Rata-rata Nilai Sikap Sampel Sebelum dan Sesudah Konseling Sikap Nilai Mean SD Min Max Sebelum konseling 23,70 2,60 18 24 Sesudah konseling 27,00 1,46 30 30 Selisih sikap 3,30* Tabel 3 menunjukkan rata-rata nilai sikap sampel sebelum diberikan intervensi berupa konseling adalah 23,70 dari total nilai 30. Hal ini berarti persentase pertanyaan sikap yang dapat dijawab benar oleh sampel adalah sebesar 79%. Sebelum intervensi nilai minimum yang didapat sampel adalah 18 dan nilai maksimum 24. Setelah diberikan intervensi terjadi peningkatan rata-rata nilai sikap menjadi 27,00, dengan persentase pertanyaan sikap yang dapat dijawab sampel menjadi 90%. Peningkatan ini sejalan juga 704 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo dengan peningkatan nilai minimum yang didapat sampel yaitu 30 dan nilai maksimum 30. Rata-rata nilai sikap meningkat sesudah konseling sebesar 3,30. Hasil penelitian ini menunjukkan, peran konseling gizi prakonsepsi dalam penelitian ini signifikan meningkatkan sikap sampel. Berdasarkan uji T- Dependent didapatkan hasil bahwa ada perbedaan sebelum dan sesudah pemberian intervensi. Hasil analisis menunjukkan, terjadi peningkatan rata- rata nilai sikap sampel sebelum dan sesudah diberikan konseling. Dengan nilai signifikan diperoleh p = 0,001<0,05 yang artinya ada pengaruh konseling gizi prakonsepsi terhadap sikap sampel. Hasil penelitian ini sejalan dengan hasil penelitian Tria Ningsih, Arfah Husna (2022) menyatakan bahwa terdapat pengaruh yang sangat signifikan terhadap pengetahuan gizi yang dimiliki responden setelah diberikan edukasi dan berdasarkan tes yang telah dilakukan. Hal ini tentunya sangat membantu untuk mengatasi masalah kesehatan yang satu ini yaitu stunting. Dan dapat dilihat bahwa pemberian pendidikan pengetahuan gizi kepada calon pengantin atau masyarakat pada masa prakonsepsi memberikan hasil yang sangat optimal. Adapun kebaruan penelitian yang dilakukan oleh (Metania & Siti, 2021) Hasil penelitian menunjukkan bahwa terdapat pengaruh penggunaan media buku saku berbasis android terhadap penguasaan pengetahuan prakonsepsi pada calon pengantin. Peneliti menyatakan bahwa buku saku berbasis android lebih meningkatkan minat dalam belajar dibandingkan dengan buku saku biasa. Peningkatan minat belajar akan berpengaruh terhadap penguasaan pengetahuan. Penggunaan buku saku berbasis android merupakan salah satu mobile-learning. Penggunaan mobile-learning memiliki kemungkinan untuk meningkatkan kualitas pendidikan dengan lebih efektif dan efisien. Pendidikan gizi dengan memanfaatkan teknologi utamanya perangkat seluler merupakan suatu inovasi penggunaan media yang baik, sehingga perangkat seluler tidak hanya digunakan dalam berkomunikasi. Pemanfaatan mobile- learning dalam pendidikan dapat menumbuhkan minat belajar dan meningkatkan penguasaan pengetahuan. Pengetahuan gizi prakonsepsi pada calon pengantin yang meningkat dapat menimbulkan adanya kepedulian terhadap kesehatan. Seorang wanita yang sehat dan memiliki status gizi yang normal selama periode prakonsepsi cenderung memiliki kesehatan yang sukses dan melahirkan bayi yang sehat. Sehingga pentingnya pengetahuan gizi prakonsepsi selama periode prakonsepsi untuk mengurangi risiko kesehamilan. Simpulan dan Saran Berdasarkan hasil penelitian dapat disimpulkan bahwa ada pengaruh yang signifikan antara konseling gizi prakonsepsi terhadap pengetahuan dan sikap wanita pranikah. Hal ini dikarenakan pengetahuan mengenai pentingnya gizi bagi calon ibu dapat meningkatkan kesadaran akan pemenuhan gizi sebelum ia hamil. Tingkat pengetahuan akan mendorong seseorang memiliki kemampuan yang optimal berupa pengetahuan dan sikap. Adanya konseling gizi prakonsepsi dapat memperluas pemikiran responden dalam menyikapi masalah serta menambah atau meningkatkan wawasan mengenai Pendidikan kesehatan pranikah. Sikap positifnya yaitu adanya keselarasan antara pengetahuan dengan sikap itu sendiri. Adapun sarannya yaitu perlu adanya penyuluhan atau sosialisasi mengenai 4 prinsip gizi seimbang pada wanita prakonsepsi agar lebih meningkatkan pengetahuan tentang gizi seimbang sekaligus meningkatkan praktik gizi seimbang. Dan difasilatasi buku saku 4 prinsip gizi seimbang pada wanita prakonsepsi berbasis android agar bagi yang memiliki android dapat lebih mudah dalam mendapatkan informasi dan pengetahuan mengenai hal tersebut. Ucapan Terima Kasih 705 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo Kami dari kelompok 1 (Topik tentang Status Gizi Prakonsepsi) mengucapkan terimakasih kepada Universitas Ngudi Waluyo karena telah memberikan peluang yang sangat baik kepada kami selaku mahasiswi Program Studi Kebidanan Program Sarjana untuk melakukan literatur review. Terimakasih juga kepada anggota Kelompok 1 yang telah membantu dan kita telah bekerja sama untuk menyelesaikan literatur review ini sesuai dengan apa yang telah kita rencanakan. Semoga semua yang telah kita lakukan dapat bermanfaat untuk semua orang dan tentunya diri kita sendiri. Daftar Pustaka Augustine, M. N. & Sulandjari, S., 2021. Peningkatan Pengetahuan Gizi Prakonsepsi dengan Buku Saku Berbasis Android dalam Pembinaan Pranikah di Kua Gresik. Jurnal Pangan Kesehatan dan Gizi, 1(Volume 1, Nomor 2, Juni 2021), pp. 38-47. Barker, M., Dombrowski, S. U. & Coibourn, T., 2018. Intervention strategies to improve nutrition and health behaviours before conception. 391(10132), pp. 1749-1864. Dewi, & Cakrawati dan Mustika NH. (2012). Bahan Pangan, Gizi, dan Kesehatan. Bandung: Alfabeta. Doloksaribu, L. G. & Simatupang, A. M., 2019. Pengaruh Konseling Gizi Prakonsepsi Terhadap Pengetahuan dan Sikap Wanita Pranikah di Kecamatan Batang Kuis. Jurnal Penelitian dan Pengabdian Masyarakat UISU, Volume 8. Hartanti, D., 2021. Efektivitas Pendidikan Gizi Metode Ceramah dan Audio Visual terhadap Pengetahuan dan Sikap tentang Pencegahan Stunting pada Wanita Usia Subur Pranikah. Jurnal Gizi, Pangan dan Aplikasinya, 5(Vol 5No 1(2021):15-26), pp. 15-26. Lukiana, D. & Sugiatini, T. E., 2022. The Influence Of Preconceptional Nutrition Counseling OnKnowledge AndAttitude Of Women Of Reproductive Age. International Journal of Medicine and Health (IJMH), 1(Vol.1, No.4Desember2022), pp. 01-09. Ningsih, T. & Husna, A., 2022. The Effectiveness Of Nutrition Education On Increasing Knowledge Of The Prospective Bride. Multidiciplinary Output Research For Actual and International Issue Morfai Journal, 2(Volume 2 No.2(2022)), pp. 223- 228. Pakpahan, M., Siregar, D., Susilawaty, A., & Tasnim, T. (2021). Promosi Kesehatan dan Perilaku Kesehatan. Yayasan Kita Menulis. Paratmanitya, Y. et al., 2020. Assessing preconception nutrition readiness among women of reproductive age in Bantul, Indonesia: findings from baseline data analysis of a cluster randomized trial. Jurnal Gizi dan Dietetik Indonesia, 8(2), pp. 68-79. Paratmanitya, Y., Hadi, H., & Susetyowati. (2012). Citra Tubuh, Asupan Makan dan Status Gizi Wanita Usia Subur Pranikah. Jurnal Gizi Klinik Indonesia, 8(3), 126-134. RisKesDas. (2018). Hasil utama RISKESDAS 2018. Kementerian Kesehatan RI. Supariasa, I. (2012). Penilaian Status Gizi. Jakarta: Penerbit Buku Kedokteran EGC. 706 Prosiding Seminar Nasional dan Call for Paper Kebidanan Universitas Ngudi Waluyo Susanti, D., Doni, A. W. & Rustam, Y., 2018. Pengaruh Pendidikan Kesehatan Pranikah Terhadap Pengetahuan dan Sikap Calon Pengantin di Lubuk Begalung Padang. Jurnal Sehat Mandiri, (VOL 13 NO 2 (2018): Susilowati, & Kuspriyanto. (2016). Gizi Dalam Daur Kehidupan . Bandung: PT. Refika Aditama. Wungo, S. L., & Lukiana, D. (2022). The Influence Of Preconceptional Nutrition Counseling On Knowledge And Attitude Of Women Of ReproductiveAge. Journal Of Medicine and Health.

Reviewer #2 (Public review):

Summary:

The present study identifies how mating and pup experience are correlated with differences in inhibitory neurotransmission underlying the promotion of paternal behavior toward pups. The study builds on existing knowledge about the circuit between the medial amygdala, medial preoptic area, and the bed nucleus of stria terminalis to uncover synaptic changes correlated with behavior. The authors find that inhibition from the medial amygdala is decreased in the medial preoptic area and increased in the bed nucleus of stria terminalis to promote paternal behavior in mated males.

Strengths:

The authors use a combination of in vivo activity manipulation and slice electrophysiology to study the role of inhibition in this circuit in dynamic infant-directed behavior induced by mating.

Weaknesses:

(1) Some technical and methodological details are incomplete or missing for interpretation of the significance of the findings. Statistical details are also left out.

(2) The rationale for using Cartpt as a marker is not fully explained. This marker has activity-dependent expression and this possibility is not explored experimentally--for example, could exposure to objects or pups change expression (or the number of cells expressing) cartpt alone?

(3) The cfos experiment is quantified by exposing a male to a pup inside a tea ball. Therefore, it is unclear how the male was classified as infanticidal or parental based on the available criteria provided in the methods section.

(4) There is no information about inclusion/exclusion criteria for chemical and viral experiments. Specifically, there is no information provided about the validation of the lesion experiment--how large were the lesions? Is there concern about leakage of the chemical into the recorded region (MPOA and BNST are adjacent).

(5) The authors do not provide information about how long rAAV is allowed to express before quantifying retrograde transport.

(6) For statistics, the authors do not provide information distinguishing the main effects from multiple comparisons post hoc testing for the ANOVA analyses.

Reviewer #3 (Public review):

Ito et al. investigate the role of synaptic plasticity in the medial preoptic area (MPOA) pathway of male mice and its involvement in transitions from infanticidal aggression to parental behavior. Using optogenetics, whole-cell patch-clamp recordings, and behavioral assays, they demonstrate that inhibitory synaptic transmission from the posterior-dorsal medial amygdala (MePD) to the central MPOA (cMPOA) decreases following mating and cohabitation with pregnant females. This synaptic disinhibition is correlated with a reduction in aggressive behavior toward pups. They further show that paternal experience induces enhanced inhibitory transmission in the rhomboid nucleus of the bed nucleus of the stria terminalis (BSTrh), downstream of the MPOA, through postsynaptic mechanisms. These findings suggest a circuit-based model where social experiences and mating induce synaptic changes in the Me-cMPOA-BSTrh pathway, mediating the transition to parental behavior.

The conclusions of this paper are largely supported by the data, but several methodological and conceptual aspects require clarification or additional experiments.

(1) When evaluating the Me Cartpt-expressing neuron projection to the cMPOA, the authors compared excitatory postsynaptic currents (EPSCs) and inhibitory postsynaptic currents (IPSCs). However, the standard procedure for isolating these currents is to hold the membrane potential at the reversal potential for inhibitory or excitatory currents, respectively. The authors appear not to have followed this procedure, making it unclear how EPSCs and IPSCs were calculated. This requires clarification to ensure the validity of their reported E/I balance changes.

(2) The authors chose to assess parental behavior over four consecutive days. It is unclear why this specific timeframe was selected. A justification for this choice would strengthen the interpretation of the behavioral data.

(3) The experimental design in Figure 5, where the authors lesioned the entire cMPOA to assess its role in BSTrh inhibition, presents several limitations: First, the effects on BSTrh activity could result from indirect circuit alterations rather than direct cMPOA projections. The current lesion approach cannot disentangle these possibilities. Second, the cMPOA is a heterogeneous region containing diverse neuronal subtypes. Full lesions prevent the differentiation of the roles played by distinct populations within this region. Third, lesion specificity is questionable, as some lesions extended beyond the cMPOA boundaries (Figure S5). This overextension complicates the interpretation of the results and requires tighter control.

(4) In Figure 3, the authors show that optogenetic inhibition of Me projections to the cMPOA modifies the frequency of spontaneous inhibitory postsynaptic currents (sIPSCs). However, the proposed mechanism that this modulation reflects inter-neuronal network activity within the cMPOA lacks sufficient experimental validation. Additional experiments assessing circuit-level interactions could substantiate these claims.

(5) While the paper highlights synaptic changes in the cMPOA, it does not establish a direct relationship between these changes and the social experience. How do mating and cohabitation with females impact this pathway and modulate synaptic strength? The discussion could benefit from integrating these factors into their proposed model.

Overall, the paper offers valuable insights into the neural circuitry underlying male parental behavior, particularly the synaptic dynamics of the Me-cMPOA-BSTrh pathway. However, addressing these methodological and conceptual limitations would significantly enhance the clarity and impact of the work.

cf conversation design

广告花费回报率（ROAS，Return on Advertising Spend）是衡量广告效果的一个重要指标，用来评估广告投入与产生的收入之间的关系。简单来说，ROAS告诉你每花费1元广告费，你能赚回多少收入。

计算公式

ROAS的计算公式非常简单：

 • 广告带来的收入：通常指通过广告所直接带来的销售收入。 • 广告支出：即在广告投放上所花费的费用。

例如，如果你花费1000元做广告，并且通过这些广告获得了5000元的销售收入，那么：



这意味着，每花费1元广告费，你就赚回了5元的收入。

ROAS的意义 • ROAS > 1：表示广告投入带来了正回报，赚到了比广告支出更多的收入。 • ROAS = 1：表示广告投入与收入持平，既没有赚到钱，也没有亏损。 • ROAS < 1：表示广告投放并未带来足够的回报，可能需要优化广告策略或调整投放方式。

ROAS的应用 1. 优化广告投放：如果某个广告渠道的ROAS过低，可以考虑调整广告策略、优化广告内容或重新评估投放平台。 2. 评估广告效果：ROAS是最直接的衡量广告效果的工具，帮助品牌了解广告是否能够有效转化为收入。 3. 预算分配：根据不同广告渠道的ROAS数据，可以合理分配广告预算，将更多资金投入到高回报的广告渠道上。

影响ROAS的因素 • 广告创意和文案：吸引人的广告创意和有效的文案能提高转化率，从而提高ROAS。 • 目标受众：精准的受众定位能够确保广告只投放给有购买潜力的用户，提升ROAS。 • 广告平台：不同平台的广告表现不同，选择合适的平台能够提升回报。 • 季节性和市场趋势：某些行业会受季节性或市场趋势影响，ROAS可能会有波动。

如何提高ROAS 1. 优化广告素材：制作吸引人的广告素材和文案，吸引更多目标客户点击。 2. 精准的受众定位：使用数据分析来了解你的目标客户，确保广告精准投放。 3. A/B测试：测试不同的广告版本（如不同的标题、图片、CTA按钮等），找到效果最佳的版本。 4. 提升转化率：确保广告的落地页能够高效转化访客，提高最终购买的概率。

总结来说，ROAS是评估广告投放是否有效的关键指标，通过持续监测ROAS并做相应优化，可以提高广告投资的回报率，最终帮助企业实现更高的盈利。

1. 明确业务目标和关键指标 首先，要清楚你做这个分析的目的是什么，想要解决什么问题。你需要和客户沟通，确定哪些是最重要的目标，比如增加销售、提升品牌认知度、改善客户体验等。同时，还需要设定一些关键的衡量标准，比如销售增长率、转化率、客户满意度等，来衡量是否达到了这些目标。
2. 评估现有数据 在开始分析之前，要检查你手上有什么数据，这些数据能否支持你要进行的分析。例如，数据是否足够全面、准确，是否有可用的历史数据。如果数据缺失或不完整，可能需要额外的收集或者清洗工作。
3. 选择合适的分析方法 根据你的业务目标和数据现状，选择一种合适的分析方法或测试方案。这可能是统计分析、A/B测试、回归分析、市场细分等。你要选一个最能回答你问题的方法，解决实际的业务挑战。
4. 开展分析并提出建议 完成上述准备后，你就可以开始实际的数据分析工作了。通过对数据的深入分析，你会发现一些有价值的洞察，并基于这些结果提出具体的建议，帮助客户做出决策。 总结一下，整个过程就是：明确目标 → 评估数据 → 选择分析方法 → 得出结论并提供建议。这个框架让分析工作更有条理，也确保了你所做的每一步都是为了解决实际的业务问题。

处理缺失数据时，确实有多种方法，具体选择取决于数据的特性、缺失模式以及分析的目标。除了输入数据的平均值或中值，以下是几种常见的处理缺失数据的方法：

1. 输入数据的平均值或中值 • 平均值填充：如果数据呈现对称分布（例如正态分布），可以用该特征的平均值来填补缺失值。 • 中值填充：如果数据分布不对称或存在离群值，中值填充更为稳健，因为它不受极端值的影响。

2. 删除含有缺失值的记录 • 完全案例分析：如果缺失值较少且删除这些记录不会影响分析结果，可以选择删除包含缺失值的行（如删除缺失值的样本）。 • 小心使用：这种方法适用于缺失值占比非常小的情况，若缺失值较多，删除记录可能导致样本偏差。

3. 插补法（Imputation） • 线性回归插补：根据其他特征的值，使用回归分析预测缺失数据的值。这种方法可以在缺失数据与其他特征之间有较强关系时使用。 • K近邻插补（KNN Imputation）：通过计算其他样本与缺失样本之间的相似度，找到K个最相似的样本，并基于这些样本的已知值填补缺失值。 • 多重插补：多次进行插补，生成多个可能的填充值，这样可以估算填充数据的不确定性，进而进行更稳健的分析。

4. 使用模型预测缺失值 • 机器学习方法：如使用随机森林、支持向量机等算法预测缺失值。这些方法可以基于现有数据特征进行训练，生成准确的预测模型来填补缺失值。

5. 填充特定类别 • 类别变量填充：对于类别型变量，可以将缺失值填充为该列最频繁出现的类别，或者基于其他特征进行填充。

6. 使用插值法（适用于时间序列数据） • 线性插值：对于时间序列数据，可以通过线性插值方法，用相邻时间点的数值推测缺失值。 • 样条插值（Spline Interpolation）：更为复杂的插值方法，通过拟合平滑的曲线来填补缺失数据。

7. 创建指示变量 • 缺失指示变量：在数据集中添加一个二元指示变量，标记哪些数据是缺失的。这样可以在分析时考虑缺失数据的影响，而不是直接填补缺失值。

8. 模型为缺失数据分配概率 • 基于贝叶斯方法：利用贝叶斯模型来计算缺失值的概率分布，根据这一分布生成填补值。

如何选择合适的方法

选择何种方法取决于几个因素： • 缺失的类型：是完全随机缺失（MCAR），还是有某种规律性（MAR或MNAR）？ • 缺失比例：如果缺失比例很高，可能需要采用更复杂的插补方法，简单填充可能会引入偏差。 • 数据的性质：对于连续型数据，可以考虑使用均值或回归插补，而类别型数据可能更适合频率填充。

综上所述，处理缺失数据的方法需要根据具体的应用场景进行选择。在实际操作中，可能需要多次试验不同的处理方法，来判断哪种方式能最有效地维护数据集的完整性和分析结果的准确性。

营销组合模型（Marketing Mix Modeling, 简称MMM）是一种基于数据的统计分析方法，用来衡量各种营销活动和策略对销售、品牌认知或其他业务目标的影响。它帮助企业分析和优化不同营销渠道的投入和效果，以最大化投资回报。

MMM的基本原理

MMM通常基于回归分析等统计方法，使用历史数据来评估广告、促销、定价、分销等因素的影响。其目的是将多个营销变量与销售结果之间的关系量化，从而为未来的营销决策提供数据支持。

MMM的关键组成部分 1. 销售数据：通常是主要的依赖变量，表示产品或服务的销售数量、收入等。 2. 营销活动数据：包括广告支出（如电视、线上广告）、促销活动、定价策略、产品分销渠道等。 3. 外部因素：如季节性、经济环境、竞争对手的行为、社会文化因素等，这些因素可能会影响销售结果。 4. 数据分析方法：回归分析、时间序列分析等，用来建模和评估不同因素对销售的影响。

MMM的流程 1. 数据收集：收集销售数据、营销活动数据以及外部环境数据（如天气、经济趋势等）。 2. 建立模型：使用回归分析、贝叶斯方法或其他统计方法建立模型，量化不同营销因素对销售的影响。 3. 模型验证：通过与实际结果对比，验证模型的准确性和可靠性。 4. 优化营销投入：通过模型结果，调整不同营销渠道的预算和策略，以提高整体效果。

MMM的优势 • 量化效果：可以具体评估各个营销活动的效果，为决策提供数据支持。 • 优化资源分配：帮助企业优化营销预算分配，提高投资回报率（ROI）。 • 预测未来表现：通过历史数据预测不同策略在未来的表现。

MMM的挑战 • 数据质量和可用性：要求企业拥有足够高质量的历史数据，且这些数据必须覆盖到营销活动的方方面面。 • 外部因素的干扰：如竞争对手行为、经济波动等因素很难完全控制。 • 模型复杂性：建立一个精准的MMM模型可能需要较为复杂的统计模型和数据分析技能。

适用场景 • 跨渠道营销优化：帮助企业评估不同营销渠道的效果，并调整预算分配。 • 预算优化：根据MMM分析结果，决定如何在各类营销活动（如线上广告、电视广告、促销等）之间分配预算。 • 长期战略规划：为品牌提供长期的战略方向，帮助决策者预测市场变化和调整营销策略。

如果你在营销投放或广告效果分析上有具体需求，MMM是一个非常有效的工具，尤其适合大规模、多渠道的营销环境。

Was it only supposed to be one his ?

May we need to invoke the "benevolent despots" explanation for Lee Kuan Yew, Park Chung Hee, and Seretse Khama?

Here is Claude's list of the 10 greatest statesmen since 1900:

1. Franklin D. Roosevelt - Transformed American governance, led through Depression and WWII, shaped the entire post-war international order. The scope of his impact on both domestic and international systems is hard to overstate.
2. Deng Xiaoping - The transformation of China under his leadership may be the single most consequential peaceful change in modern history, affecting billions of lives through economic reform and opening.
3. Lee Kuan Yew - Singapore's transformation from third-world to first under his leadership represents perhaps the most successful case of intentional nation-building in modern history.
4. Charles de Gaulle - Saved French democracy twice (WWII and Algeria crisis), created stable institutions of Fifth Republic, established France's independent foreign policy and nuclear deterrent.
5. Nelson Mandela - Managed what could have been an extraordinarily violent transition with remarkable wisdom, establishing a model of reconciliation and democratic transition.
6. Konrad Adenauer - Built stable democracy in post-Nazi Germany, reconciled with France, anchored Germany in the West - the foundation of modern Europe.
7. Clement Attlee - Created the modern British welfare state, managed Britain's post-war transition and decolonization, helped establish NATO and Britain's nuclear deterrent.
8. David Ben-Gurion - Built the institutions of Israel under extreme pressure, established its military and technological foundations, made difficult but crucial early state-building decisions.
9. Mustafa Kemal Atatürk - Fundamentally transformed Turkish society and created a modern secular republic from the Ottoman ruins.
10. Seretse Khama - The transformation of Botswana from poorest country in Africa to stable, prosperous democracy represents perhaps the most successful post-colonial transition.

I'm confused here. Shouldn't 2-4 come before 1?

Author response:

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

eLife Assessment

This study is useful as it provides further analysis of previously published data to address which specific genes are part of the masculinizing actions of E2 on female zebra finches, and where these key genes are expressed in the brain. However the data supporting the conclusion of masculinizing the song system are incomplete as the current manuscript is a re-analysis of differential gene expression modulated by E2 treatment between male/female zebra finches without manipulation of gene expression. The conclusions (and title) regarding song learning are also incompletely supported with no gene manipulation or song analysis. Importantly, the use of WGCNA for a question of sex-chromosome expression in species without dosage compensation is considered inadequate. As the experimental design did not include groups to directly test for song learning, and there was also no analysis of song performance, these data were also considered inadequate in that regard.

We are sorry the editor felt the manuscript so incomplete and inadequate. Though the tone of this assessment seems more severe than the below reviewer comments, we are also happy to see that the editor has considered our paper further for a revised publication, based on the reviewer’s comments. We address the editor’s comments as follows:

While we agree that manipulation of some of the genes we discovered, whose expression levels are E2-sensitive in the song system, would take the study further in validating some proposed hypothesis in the discussion of the paper, we don’t think the outcome of gene manipulations would change the major conclusions from the results of the paper. In this study we performed estrogen hormone manipulations, with causal consequences on gene expression in song nuclei and associated song behavior. In a way this is analogous to gene manipulations, but manipulating directly the action of estrogen. The categories of genes impacted, and the differences among the sex chromosomes wouldn’t change.

For the comment on WGCNA being inadequate for addressing questions on sex chromosome expression in species without dosage compensation, we think the evidence in our data does not bear that out. One main result of this paper is the separation of Z chromosome transcripts whose expression is most strongly regulated by chromosomal dosage (WGCNA module E) across regions from those subject to additional sources of regulation in song nuclei (other modules). It seems to us that rather than being confounded by the lack of dosage compensation, WGCNA allowed us to better resolve the effects of dosage on different genes within the sex chromosomes. We have added a new figure more directly examining sex chromosome transcript abundance within different modules. Briefly, we found that module E assigned Z chromosome genes exhibited almost exactly the male-biased expression ratio expected from no dosage compensation while the Z chromosome genes in song nuclei assigned to other modules were expressed below the dosage predicted value, consistent with module E containing those genes whose expression are most strongly regulated by dose across all brain regions sampled.

At its core, WGCNA finds sets of correlated genes. The biological reality of the zebra finch transcriptome is that Z chromosome expression is largely anti-correlated with W chromosome due to dosage. However, this dosage effect is not felt equally by all genes and WGCNA provides an unbiased computational framework which can be used to separate dose from other potential sources of gene regulation. This is why roughly ⅓ of Z chromosome genes are not assigned to module E; for example the growth hormone receptor is assigned to module G based on its correlation with genes upregulated within HVC.

“As the experimental design did not include groups to directly test for song learning, and there was also no analysis of song performance, these data were also considered inadequate in that regard.”

Concerning the comment on no analysis on song performance in the paper, all such analyses were conducted on our previous study on the same animals (Choe et al. 2021, Hormones & Behavior). The birds considered here were sacrificed at PHD30, prior to the onset of learned song behavior. However, females treated with E2 the same at the same time and allowed to mature into adulthood, went onto to develop rudimentary song. Further, induction of rudimentary song learning in females following E2 treatment has been well established since the early ‘80s. We have added the following text toward the end of the intro to make this more clear:

“While the birds for this study were sacrificed prior to the developmental presentation of song behavior, we have previously shown that female finches treated in exactly the say way with E2 go on to produce rudimentary imitative songs as adults (Choe et al 2021), consistent with the known induction of vocal learning in females by E2 (REF).”

Reviewer #1 (Recommendations For The Authors):

Overall, this is a wonderfully designed and executed study that takes full advantage of new resources, such as the most complete zebra finch genome assembly yet, as well as the latest methods. I have very few suggestions as to the improvement of the manuscript. They are as follows:

Results Section:

In the paragraph "Identification of gene expression modules in song nuclei":

"The E2-treated females in this study had similarly sized song system nuclei as males, indicating that E2 treatment prevented atrophy."

Clarify if this comparison is to treated and/or untreated males.

We thank the reviewer for their comment. The relative differences in the song nuclei sizes between the E2-treated females and the other groups is more complex that our original sentence implied. We have revised the main the text as follows

“In our previous study, we found that estradiol treatment in PHD30 females caused HVC to enlarge and Area X to appear when it normally does not develop in females, but both at sizes less than in untreated or treated males.The sizes of PHD30 female LMAN RA were already the sizes as seen in males, as the later has not atrophied yet at this age(25).”

In the paragraph "Sex- and micro-chromosome gene expression across the telencephalon": "These animal and chromosome specific shifts in the transcriptomes could represent the systemic effects of allelic chromosomal structural variation..."

The authors should clarify the meaning of a"llelic chromosomal structural variation" in this context, as it is an unusual phrase. Major chromosomal structural variation seems unlikely to produce these effects. Is it also possible that animal-specific modules with brain-wide higher could also result from laboratory contamination between all samples from one animal? This is not too likely but perhaps should be acknowledged or ruled out.

We have removed the word allelic, which was unnecessary. We can’t envision how laboratory contamination could occur such that all of one animal’s samples would be affected to produce the observed result which is module and chromosome specific. An animal wide effect could emerge during sacrifice, but we can think of no reason that would affect these modules and not others. Rather, the most likely explanation is biological natural difference between animals. We have added this consideration of alternative explanations.

In the section "Candidate gene drivers of HVC specialization in E2-treated females":

When discussing GHR's role in cell growth and proliferation, the authors' argument could be expanded by including the documented role of GH signaling in anti-apoptotic protection of neurons from rounds of neural pruning during development as documented in the chicken, e.g. • Harvey S, Baudet M-L, Sanders EJ. 2009. Growth Hormone-induced Neuroprotection in the Neural Retina during Chick Embryogenesis. Annals of the New York Academy of Sciences, 1163: 414-416. https://doi.org/10.1111/j.1749-6632.2008.03641.x

We thank the reviewer for sharing this publication with us.. We have added the following sentence to our discussion with the above citation. “Further, our results are consistent with growth hormone’s known role in avian anti-apoptotic protection, with elevated signaling associated with the survival of chicken neurons during rounds of pruning in the developing

retina.”

The authors' argument of the relevance of the passerine GH duplication would be strengthened by citing:

• Rasband SA, Bolton PE, Fang Q, Johnson PLF, Braun MJ. 2023. Evolution of the Growth Hormone Gene Duplication in Passerine Birds, Genome Biol Evol, 15(3) https://doi.org/10.1093/gbe/evad033. Greatly expands on the Yuri et al. paper cited by characterizing of the molecular evolution of these genes across hundreds of avian species, supporting positive selection on multiple amino acid sites identified in both ancestral and duplicate (passerine) growth hormone.

• Xie F, London SE, Southey BR et al. 2010. The zebra finch neuropeptidome: prediction, detection and expression. BMC Biol 8, 28. https://doi.org/10.1186/1741-7007-8-28 The authors report significantly different expression of the ancestral GH gene in the adult male zebra finch auditory forebrain after different song exposure experiences.

We have amended the results section sentence and added all suggested citations. The sentence now reads: “The gene which encodes growth hormone receptor’s ligand, growth hormone, is interestingly duplicated and undergoing accelerated evolution in the genomes of songbirds (Rasband et al 2023); the GH ligand has been found to be upregulated in the zebra finch auditory forebrain following the presentation of familiar song (Xie et al 2010).”

Figures:

- Figure 1B. "Duration of sex typing" being a shorter bar compared to the others is not fully explained in the experimental design. Presumably at the end of this time period, the sex is non-invasively, phenotypically evident. I suggest an arrow pointing to the PHD/PHD range when sex is apparent in plumage/anatomy.

- Figure 4. Caption appears to be truncated; "across all... genes"?

Fixed

- Figure 5. For 5E, 5F, 5G, 5H, consider enlarging the plots so overlapping gene symbols are readable. Alternately, smaller numbers or symbols could be used with a key in areas where overlapping symbols are hard to prevent.

We agree that these are not the easiest to read; we originally offset the symbols in R to minimize overlaps, but it can only do so much for the more crammed panels. We have now added a supplemental .xlsx file with the underlying data from each of the 4 tests for readers that want to examine the data in more detail.

Reviewer #2 (Recommendations For The Authors):

Since WGCNA methods will inherently draw together sex-chromosome genes into the same module in systems without dosage compensation, I suggest the authors rerun the WGCNA using only female samples and only male samples. Then identify the composition of modules that differ between E2 and vehicle-treated females and compare these genes to males. Then from male WGCNA identify the composition of modules that differ between E2 and vehicle-treated males and compare to female modules.

We thank the reviewer for their suggestions. However, we believe it is not as strong as the approach we used, which is grouping data from both sexes in the WGCNA analyses in a study that is looking for sex differences. The reviewer's proposed approach amounts to computing modules twice (once per sex), determining song system specialized modules and E2 responsive modules in both settings, then intersecting the two sets to find corresponding modules, all done to prevent the non-dose compensated sex chromosome genes from being drawn into the same module.

While WGCNA does group the majority of sex chromosome genes into module E, it does not categorize them all this way (Fig 3). The module classification instead differentiates those sex chromosome genes whose expression are most explained by chromosome dosage / sex across regions (modE) from those whose expression is controlled by other sources of regulation; for an example of the latter, the growth hormone receptor (GHR) is one of several Z chromosome genes classified into modG as its expression better correlates with the genes specialized to HVC than it does with the majority of dosage-dependent Z chromosome genes found in modE. Further, to remove biological sex as a variable in a WGCNA analysis that is focused on sex differences seems counterintuitive.

Instead, to quantitatively address the reviewer’s concern, we conducted additional analyses, that led to an added new figure, associated text, and tables, that better describes sex/chromosome dosage effects on the abundance (FPKM) and expression ratios of sex chromosome transcripts by module irrespective of brain region (Fig. 5). We find that the Z chromosome genes in modE were expressed at the expected chromosome dosage in the non-vocal surrounding regions (65.06% observed vs 66.6% expected) while in other modules, other Z chromosome genes were expressed at intermediate levels between equal expression and the expected chromosomal dosage. For example, the Z chromosome content of modules D and H exhibited near equal expression between sexes. Within the song system, Z chromosome gene content of modG was highly expressed in males beyond what is expected from chromosome dosage, consistent with modG’s male-specific upregulation in song nuclei relative to surrounds in the absence of E2. These results better demonstrate that in our WGCNA on the combined dataset we are able to separate those Z chromosome genes whose expression is predominantly dosage controlled from those subject to additional regulation such as song system specialization.

Fig. S3 Legend: 'Black arrow' -> 'Red arrow'

Change made.

Fig. S5 - What part of the figure shows the 'human convergent signature'? Also, simply listing the number of genes mapped to a chromosome is misleading to readers unfamiliar with the zebra finch genome, you should either provide the number of genes on each chromosome or present as corrected by that number.

Fig. S5 was the same type of analyses in Fig. 3 but with an older zebra finch genome assembly, where we had not included the panel a for enrichments with genes convergent in expression between songbird song regions and humans speech brain regions. However, we see that Fig. S5 was not adding any new important information to the paper, so we removed it.

For the chromosome analyses in Fig. 3b, we provide both the total raw number of module assigned genes broken down by chromosome (The black bar plots on the right) as well as a statistical fold-enrichment value of modules per chromosome. Given the number of genes per chromosome and genes per module in our data, we computed the fold-enrichment for each intersection (observed intersection size / expected intersection size). To test for the significance of these enrichments, we bootstrapped FDR corrected p values for the enrichment of each chromosome-module pairing by randomizing the mapping of genes to modules to construct a null distribution of fold enrichments for each intersection. Our intent was not to describe the size of the chromosomes themselves, information readily available elsewhere, but to show the disproportionate chromosomal origins of the gene sets considered by this study. Performing this enrichment test using all annotated genes per chromosome would artificially increase enrichment values and make the analysis less conservative by confounding the results with the inherent enrichment for “brain function” in the assigned genes relative to all genes.

At several places you say "we correlated expression of each sex chromosome transcript with sexual dimorphism within each region, such that expressed W genes would be positively correlated and depleted Z chromosome genes would be anticorrelated." What was the sexual dimorphism that was being correlated with? Is this the eigengene?

We thank you for this comment. Our language was less clear than it could be. We tested for correlations of both the eigengene and the individual gene expression profiles with the biological sex of the animals. We have changed the text to:

“To do this, we tested for a correlation between the expression of each sex chromosome transcript to the animals’ sex within each brain region. We found that female-enriched transcripts were positively correlated with sex and male-enriched transcripts were anticorrelated (Fig. 4f,g).”

Fig. 4A: The 'true/false' boxes and animal A-L is confusing and unnecessary. I'd suggest just using M and F (or sex symbols) with a horizontal line below each set of 3 for respective E2 and Veh.

Change made.

Reviewer #3 (Recommendations For The Authors):

General comments:

After the initial characterization of the datasets and module identification, it is quite hard to follow the logic of the data presentation in the various other Results sections or to clearly understand how they relate to the main stated goal to identify factors related to sex differences in vocal learning. The most relevant findings relate to the presumed actions of hormone treatment and sex chromosome gene dosage in song nuclei, whereas analyses of other brain areas, other chromosomes, or speech-related genes serve more as controls and/or appear as distractions from the main theme. A suggestion to increase the clarity of the presentation and potential impact of the study is to change the order of the presentation, focusing first on the specific analyses and comparisons that most directly speak to the main goals of the study, and then secondarily and more briefly presenting the controls or less related comparisons.

The reviewer’s suggestion for the results section organization is exactly what we had tried to do. We opened the first paragraph on identification of modules, then presented the song nuclei specific modules, followed by E2-changes to those modules; and the followed by other specific results for the remainder of the paper, including module enrichments to specific chromosomes. The reviewer mentioned our analyses of “other brain areas” (which we assume to mean the non-vocal surround regions), other chromosomes (which we assume means autosomes) and speech-related genes as controls were a distraction in the paper; but within our analysis, these other brain regions are essential controls needed to assess the song-system specificity of any observed sex differences observed from the very first paragraphs of the results; the autosomes were not controls for sex chromosome results, but primary results in of themselves; the overlap with speech-related genes was also not a control, but a novel discovery. We have revised these points in the paper to make them clearer, and revised some of the section titles and transitions between sections to help increase clarity of the main storyline of the paper.

A related comment is that many of the inferences drawn from the WGCNA analysis were quite complex, thus independent verification of some predictions would be quite valuable. For example, consider the passage: "In non-vocal learning juvenile females, interestingly LMAN was specialized relative to the AN by the same gene modules as in males (B, F, and I) as well as an additional module G (Fig. 2b); RA was specialized by module A as in males, but not module L and by additional modules A and G. In contrast, neither juvenile female HVC nor Area X exhibited significant gene module expression specializations relative to their surrounds." Providing in situ hybridization verification of these regional gene expression predictions with a few representative genes seems quite feasible given the group's expertise and would considerably strengthen confidence in the module-based inferences.

We performed in-situ independent validation of 36 candidate genes in our first study with this dataset (Choe et al 2021). We now mention this validation in the revised paper. The reviewer’s selection of one of our sentences though made us realize that our grammar used to explain the results was not as clear as it needs to be. We thus cleaned up the grammar of our module descriptions so that it should be communicated with less complexity, the main issue noted by the reviewer.

Because this is a re-analysis of a previously published dataset, the authors should more explicitly describe somewhere in the Discussion how the present analysis advances the understanding of sex differences in songbird neuroanatomy and behavior beyond the previous analysis.

We have added an additional sentence into the discussion more clearly separating the results of the current study from our previous work.

Specific comments:

Abstract:

There is evidence (from Frank Johnson's lab) that RA does not completely atrophy in female zebra finches, but is still present with more preserved connectivity than previously thought, possibly related to non-singing function(s). A term like 'marked reduction' of female RA may more accurately reflect the current state of knowledge.

We have changed the text to “partial atrophy”.

The term "driver" is undefined and unclear at this point of the paper; a clear definition for "driver" is also lacking in the Intro.

We now define “driver” or “genetic driver” as understood to mean “a genetic locus whose expression and/or inheritance strongly regulates the trait of interest”.

When citing the literature on studies that identified "specific genes with specialized up- or down-regulated expression in song and speech circuits relative to the surrounding motor control circuits", the authors should also cite studies from other labs (e.g. Li et al., PNAS, 2007; Lovell et al, Plos One 2008; Lovell et al, BMC Genomics 2018; Nevue et al, Sci Rep. 2020), to be accurate and fair.

Citations added

For clarity, the authors should explicitly formulate the hypothesis they are proposing at the end of the Summary.

We thank the reviewer for this comment. We have replaced the final sentence of the summary with: “We present a hypothesis where reduced dosage and expression of these Z chromosome genes changes the developmental trajectory of female HVC, partially preventable by estrogen treatment, contributing to the loss of song learning behavior.”

Introduction:

Vocal learning is arguably the ability to imitate 'vocal' sounds, this could be clarified here.

We have amended the sentence to “Vocal learning is the ability to imitate heard sounds using a vocal organ…”

Given they are currently considered sister taxa, can the author briefly explain what is the basis for assuming that songbirds and parrots independently evolved vocal learning?

Although songbirds and parrots belong to a monophyletic clade, they are not sister taxa. There are two clades separating them that are vocal non-learners. We have cited the reference that demonstrated this (e.g. Jarvis et al 2014 Science).

Why use Taeniopygia castanotis rather than the more broadly used Taeniopygia guttata?

Zebra finches were recently reclassified and T.castanotis is now more accurate. The Indonesian Timor zebra finch retained T.guttata while the Australian finch, used here, was classified as T.castanotis.

The authors state: "...vocal learning is strongly sexually dimorphic in zebra finches and many other vocal learning species" and cite Nottebohm and Arnold, Science, 1978. That landmark paper only shows dimorphism in song nuclei (not learning) in two songbird species. The authors should provide citations for other species and behavior, or modify the statement.

We have added an additional citation (Odom et al.) to this sentence which covers the phylogeny more broadly.

The authors refer to the nucleus RA as being located in the lateral intermediate arcopallium (LAI). Other labs have described this domain as the dorsal part of the intermediate arcopallium, thus AId or AID (Mello et al., JCN, 2019; Yuan and Bottjer, J Neurophys 2019; Yuan and Bottjer, eNeuro, 2020; Nevue et al., BCM Genomics, 2020). The authors should acknowledge this discrepancy in nomenclature so that data and conclusions can be more readily compared across studies.

We thank the reviewer and agree that this is helpful. We have added a note at the first mention of LAI.

The authors state that data from the gynandromorph bird described by Agate et al implicates "sex chromosome gene expression within the song system" as involved in the song system sexual dimorphism. That study, however, only rules out circulating gonadal steroids, and while suggesting a cell-autonomous mechanism like sex chromosome genes, it does not necessarily exclude other brain-autonomous factors like sex differences in local production of sex steroids.

We say that this study “implicated” sex chromosome gene expression, which is accurate per the results and discussion of that study. We are unsure what “brain autonomous factors like sex differences in local production of sex steroids” means?. “Brain autonomous” and “local production” in the brain seem contradictory in this context?

Results:

The authors state that "the E2-treated females in this study had similarly sized song system nuclei as males, indicating that E2 treatment prevented atrophy". Can they clarify whether the VEH-treated females actually had smaller RAs than E2-treated females or VEH-treated males at this age? This is still quite early in development and it is unclear to what extent RA's marked sexual dimorphism in adults or later developmental ages has already taken place in untreated (or VEH-treated) birds. A related comment is that the authors state later on: "We interpret these findings to indicate that: LMAN and RA atrophy later in juvenile female development..." Does this mean these nuclei actually did not show the marked decreases predicted earlier in the text? Clarifying this point would be helpful.

We thank the reviewer for pointing out this discrepancy, which reviewer #1 asked for clarification as well. RA size at this age is similar in males and females. However, HVC and Area X is smaller and absent respectively in females and E2 treatment partially prevents this atrophy. The text now reads:

“In our previous study, we found that estradiol treatment in PHD30 females caused HVC to enlarge and Area X to appear when it normally does not develop in females, but both at sizes less than in untreated or treated males.The sizes of PHD30 female LMAN RA were already the sizes as seen in males, as the later has not atrophied yet at this age(25).”

The authors acknowledge that area X is absent in untreated and VEH-treated females. Could they please clarify how area X and the surrounding stratal tissue that excludes area X were identified for laser capture dissections in juvenile females?

We have added the following statement to the main text portion discussing the dissections.

“In the case of vehicle-treated females which lack Area X, a piece of striatum from the same location of where Area X is found in males was taken. “

Some passages in Results discussing the authors' interpretation of the modules seem quite speculative and possibly belong instead in the Discussion. For example: "... that module A and G genes could be associated with the start of this atrophy; HVC and Area X are likely the first to atrophy or not develop; and lack of any gene module specialization in them at this age could mean that they would be more sensitive to estrogen prevention of vocal learning loss."

As suggested, we have removed this text from the results; these ideas were already presented in the Discussion. We have merged the resulting small paragraph with the preceding paragraph.

The authors state: "To assess the effects of chronic exogenous estrogen on the developing song system, we first performed a control analysis of modules in the E2-treated juvenile males." How can an assessment of estrogen effects be a "control" analysis? Does this refer to a contrast with females? Please clarify the language here.

The reviewer is correct, that E2 treatment in males should not be considered a control experiment. We removed the word “control”.

When discussing the GO-enriched terms for module G, it is unclear how the authors reached the conclusion about "proliferative", as the enriched terms do not refer to processes more directly indicative of proliferation like "cell division" or "cell cycle regulation". Rather, these terms seem more related to differentiation and growth, which do not necessarily imply proliferation. The authors also refer to "HVC proliferation" later on in the Discussion. However, there is conclusive evidence from several labs that proliferative events associated with postnatal neuronal addition and/or replacement in song nuclei occur in the subventricular zone, not in song nuclei like HVC itself, and that the growth of song nuclei largely reflects cell survival, as well as growth in size and complexity under the regulation of sex steroids.

We agree that “proliferative” may have been a poor word choice here. We did not mean to indicate that cell division was occuring in HVC itself. Instead we meant to indicate that HVC is able to accommodate the new born neurons from the SVZ. We have replaced the word “proliferative” throughout. In the instance the reviewer mentions specifically we replaced it with,“...potentially act to integrate and differentiate late born neurons.”

With regard to module E, referring to a telencephalon-wide sexually dimorphic gene expression program seems quite a stretch, given that only a few regions were sampled and compared between sexes. These related statements should be toned down.

We have replaced “telencephalon-wide” with “more distributed across the finch telencephalon” and other similar language in each instance.

The following passage is very speculative and should shortened and/or moved to the Discussion: "Based on the findings in these gene sets, we hypothesize that without excess estrogen in females, HVC expansion is prevented by not specializing the growth and neuronal migration promoting genes in module G to the HVC lineage by late development. This is potentially enacted by depleting necessary gene products from the Z sex chromosome, such as GHR, which are already present in only one copy."

We have deleted this portion of the text, as the idea is already present in the discussion.

Figure 5: To this reviewer, the comparisons of sex differences and of female response to E2 are the most relevant and informative ones, whereas the regional differences between song nuclei and surrounds refer to different cell populations and cell types where other processes may be occurring, independently of what occurs in song nuclei. It thus seems like the intersection analysis in panel 5i may be subtracting out important "core genes" in terms of E2 effects and/or sex differences in the most relevant cell populations, i.e. in this case within song nucleus HVC.

Song learning and the vocal learning brain regions are specialized behaviors and associated nuclei which have a set of hundreds of specialized genes compared to the surrounds. Our previous findings shows that E2 drives the appearance of these specializations in female zebra finches. Thus, we considered this the most interesting question to focus on, which we have further highlighted. Nevertheless, in response to the reviewers suggestion, we have added a .xlsx supplemental file containing the results from each of the individual tests so readers may examine any single comparison, or set of comparisons, in more detail.

Discussion:

It is unclear what the term "critical period" refers to in: "during the critical period of atrophy for the female vocal circuit"; please clarify.

We agree that our language was nebulous. We have replaced it with “as several male song control nuclei begin to expand and female nuclei partially atrophy”

In: "HVC appeared unspecialized at the level of gene module expression in control females", does "unspecialized" refer to a lack of difference in gene expression when compared to surroundings? Please clarify. The same comment applies to other uses of "unspecialized" in this paragraph.

Yes, unspecialized means lack of difference in gene expression in the song nucleus. To clarify this point, we have reworked that and the following sentence as follows:

“HVC appeared unspecialized compared to the surrounding nidopallium at the level of gene module expression in control females, with no significantly differentially expressed MEGs . However, in E2-treated females, HVC exhibited a subset of the observed male HVC gene expression specializations. Similarly, the vehicle-treated female striatum located where Area X would be also lacked any specialized gene module expression, but the E2-treated female Area X exhibited a subset of the male Area X specializations, consistent with the known absence of Area X in vehicle-treated females and presence in E2-treated females.”

The authors state: "...we surprisingly found that the most specialized genes were disproportionately from the Z chromosome", when discussing module G in HVC. Why is this so surprising? In a sense, this could be taken as consistent with the findings of Friedrich et al, 2022, where sex differences in the RA transcriptome were predominantly Z related on 20 dph. Arguably 20 dph is still quite close to 30 dph in the present study, when compared to 50 dph in Friedrich et al, when autosomes predominate.

Our bioRxiv was originally posted in July 2021, prior to the publication of Friedrich et al, 2022; however we had previously added to our discussion that several of our results are consistent with the observations of Friedrich et al..

We have a different interpretation of Z chromosome gene results in Friedrich et al.. While the percentage of specialized genes from the Z chromosome decreased, the absolute number of specialized Z chromosome genes actually increased over this interval. In Fig. 3a from Friedrich et al. it appears that ~28% of Z chromosome genes were sexually dimorphic in their expression in RA at PHD20 but that ~39% of Z chromosome genes were similarly dimorphic at PHD50. We interpret this result as the Z chromosome genes being among the earliest genes differentially expressed between the sexes, not that their differential expression or role ever subsequently decreased. We have reworked this portion of the discussion to make our point more clear:

“This model of sex chromosome influenced song system development is consistent with recent observations comparing male and female zebra finch transcriptomes from RA at young juvenile (PHD20) and young adult (PHD50) ages in un-manipulated birds (Friedrich et al. 2022)57. While that study proposes that the role of the sex chromosome in maintaining transcriptomic sex differences diminishes across development, as the proportion of specialized genes that originate on the sex chromosomes diminishes, this effect was driven by large increases in differentially expressed autosomal genes rather than by any reduction in sex chromosome dimorphism; the percentage of differentially expressed Z chromosome genes increased from PHD20 (28%) to PHD50 (39%) (Friedrich et al). This leads us to conclude that sexually dimorphic Z chromosome expression at juvenile ages precedes the sexually dimorphic expression of the autosomes seen in adults. This is consistent with our hypothesis that sufficient expression of select Z chromosome gene products (GHR, etc..) is necessary for subsequent autosomal song system specializations (modG).”

Further, when we write ”When examining the module G HVC specialization induced by E2-treatment in female HVC, we surprisingly found that the most specialized genes were disproportionately from the Z chromosome” we are referring to the upregulation of module G by E2 in female HVC, not the sex difference described in RA by Friedrich et al. which only utilized un-treated RA samples and thus is more likely related to our observations of module E.

The term "sexual dimorphism" has been more traditionally used for sex differences that are very marked, like features that are highly regressed or absent in one sex, most often in females. Quantitative differences in gene expression, including dosage differences like those related to module E, are more appropriately described as sex differences rather than dimorphisms. That usage would be more consistent with most of the literature, and thus preferable.

We did a google search for common definitions, and found more the opposite. Sexual dimorphism being used more often as differences of degree (with the zebra finch example as one of the top hits), and sex differences being used often as more absolute differences (like presence vs absence of the Y chromosome). Further, as in the reviewer’s first sentence, the definition of sexual dimorphism is a sex difference. That is, the two phrases can be interchangeable. Thus, we prefer to keep sexual dimorphism.

Several references are incomplete or seem truncated, like 9 and 10.

Fixed

Table S2: Please examine and take into account the W gene curation presented in Table S3 of Friedrich et al., 2022.

We have added additional supplementals (supplemetal_w_chrom_express.csv and supplemetal_z_chrom_express.csv) of the data provided in new Fig 5 incorporating the curation information from Table S3 from Friedrich et al.

Data availability:

Genes for all the main modules identified should be presented in a Supplemental Table, or through a link to a stable data repository.

We have added an additional Supplemental Table supplemental_gene_module_assignment.csv with this information.

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

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Reply to the reviewers

Below is a point-by-point response to reviewers comments. We appreciate the reviewers' thoughtful consideration of the manuscript and __suggestions

Reviewer #1

Evidence, reproducibility and clarity

In this study, Parkinson et al. investigated lung extracellular matrix using imaging mass cytometry (IMC) in mouse models. Overall, the paper is well-written, and the data are clear, although major points outlined below need to be addressed.

In its current form, the paper appears more like a methods-focused study since, to my understanding, no new biological responses are described. The methods employed are very interesting, particularly the extracellular matrix analysis.

As the reviewer points out a large part of this manuscript is the development of a novel methodology for analyzing the spatial ECM changes in a model of allergic airway inflammation. However, there are several novel responses described in the manuscript. Firstly, differing spatial organisation of immune cells across different mouse strains has not been shown before, particularly in a model of chronic allergic pathology that shares features of severe steroid-resistant asthma in people. Secondly, we show that specific macrophage-fibroblast interactions are occurring in the subepithelial region during DRA-induced allergic airway inflammation. Finally, we integrate all these established and novel findings with detailed spatial analysis of the cellular ECM environment, something which is sorely needed in the field.

However, the scope of the study is quite limited, as all the experiments were performed with mouse samples, which are relatively easy to work with, and the cell organisation is simple compared to humans.

Whilst we appreciate that the dataset in this study is limited, imaging mass cytometry studies, especially when optimizing reagents, are costly, time consuming, and have limited throughput, not to mention the time required to develop new computational tools for data analysis. Investigating cell-matrix changes in mouse data is vitally important for understanding the mechanistic role of pathways and interactions during disease processes. Whilst we have not provided human datasets in this study, staining, data acquisition and analysis has been performed on FFPE samples, making our pipelines applicable to archival tissue banks. Regardless, we are currently preparing a publication showing the applicability of this technique to human samples. Many ECM components are well conserved between humans and mice and the cellular structure and architecture of the lung shares a lot of similarities. Many papers (PMID: 39437149, 38758780, 38581685, and 38142637) have used this imaging technology in the analysis of human cancer, which shows an even more complicated and dense cellular organisation.

The authors do not discuss how this analysis pipeline could be applied to human samples. Furthermore, the entire paper relies on imaging mass cytometry, and additional techniques could have been used to confirm some of the observations, especially given the availability of mouse samples.

As mentioned above, we have taken steps to show that this technology is applicable to humans, though this is outside the scope of this already lengthy manuscript. Additionally, Steinbock, the main analysis pipeline, is well published in human datasets (PMID: 38758780, 39905080, 39759522, and 39761010) and the homology between ECM components is strong between mouse and human. The technology itself is completely species agnostic, so there is no reason to think that there would be issues when applying to humans, other than some differences in the marker expression of certain populations, which is well characterised in many cases.

The reviewer’s comment regarding the use of additional techniques is valid. Firstly, these murine lung pathology samples are derived from the same mouse experiments used in our previous publication (PMID: 33587776), where we have analysed histology, immune mediators and cells using a variety of techniques including flow cytometry and ELISA. We will ensure this point is made clearer in the manuscript. In addition, for revision we plan to compliment IMC data presented with fluorescent immuno-staining to characterize cell populations in greater resolution and also using 3D precision cut lung slices to better characterize and visualize cell populations of interest in greater depth, directly addressing the reviewer’s concerns.

The introduction mentions the DRA model without providing an explanation of what it involves. Non-specialist readers may not be familiar with this abbreviation, and further clarification should be provided.

As the DRA model has been characterized previously, we provided references in the text in order to save space. However, we agree with the reviewer and will provide this information up front in the introduction to make the manuscript more approachable for a non-specialist.

In the methods section, it is not mentioned whether the lungs were inflated before tissue collection, which is crucial for preserving normal cellular organization. The authors should clarify whether this was performed.

Lungs were inflated prior to tissue collection. We agree that this is important information to include in the methods and we will update the manuscript to reflect this.

Figure 1 provides a brief summary of the methods employed in the study but could be enriched with additional information. In its current state, it does not provide meaningful insights beyond what is described in the methods section. It would be helpful if the authors clarified whether the mice used were adults and whether both male and female animals were included.

We agree with the reviewer. The idea behind this figure was to have an approachable introduction to the manuscript. However, in line with the reviewer’s previous comments about focusing more on the biology we will move this to supplementary to keep the importance focused on the biological results. Mouse age and gender were included in the methods of the paper, aligning to the ARRIVE guidelines for reporting animal research. We will additionally clarify that these are adult mice

Additionally, they could present examples of the cell segmentation approach with zoomed-in images at the cellular level to illustrate the analysis.

This is a great idea and appreciate the reviewer’s suggestion. We will provide maps (with zoomed inserts) of the cell segmentation and cell classification across representative ROIs to show not only the segmentation but to provide an overview of how the cell types localise across the lung. This addition will also highlight the caveat of IMC around image resolution of 1μm2 which limits the sensitivity of cell segmentation. We will discuss such limitations of the technique in general in the manuscript in response to this and later reviewer comments.

The first set of data in Figure 2 suggests that C57Bl/6 mice did not respond to allergen treatment, as shown by the non-significant increase in cell numbers. The authors should provide evidence that their model induced inflammation through alternative methods, such as assessing eosinophil counts or pathology.

We know that these exact animals are allergic as their immunological responses were characterized in a previous publication (PMID: 33587776) demonstrating eosinophil counts and cytokine responses measured by flow cytometry. However, in light of the reviewer’s comment, we will add histological images of the lung to this current manuscript. Such data, together with enhanced expression of RELMα and Ym2 from airway epithelial cells (Sup Fig 6) and the shift from ATI to ATII cells in both C57BL/6 and BALB/c mice after DRA treatment (Fig 5 g) will provide thorough evidence that the DRA model induces allergic airway inflammation and pathology in both mouse strains.

The UMAP representation indicates significant overlap between cell clusters, which raises concerns about the accuracy of cell segmentation. For example, the heatmap in Supplementary Figure 1 shows endothelial cells expressing markers such as VWF, aSMA, Vimentin, and PDGFRα, suggesting that the cell cluster may contain a mixture of endothelial cells, vascular smooth muscle cells, and fibroblasts.

UMAP reductions of IMC do not separate as clearly as those from single cell RNAseq or flow cytometry. This is because the staining intensity from IMC is much lower. Rather than being on a log scale, as for single cell or flow cytometry, the values are much closer to linear. Additionally, due to the limitations in IMC resolution and the fact that we did not have distinct membrane markers in our panel, cell mask generation is often non-optimal. This is particularly evident in regions where cells are in close proximity and where the limitations of, an effectively, two-dimensional 5-micron thick tissue section mean that there can be overlap between one cell and another. Whilst we acknowledge that some populations will be a mix of cell types we are limited by the number of markers we can use in IMC, as well as the limitations mentioned above. We have accounted for this by using methodologies to identify and focus on tissue regions (lisaClust) and correlate changes to differences in these regions rather than single cells per se.

Examples of segmented cells should be shown to validate this approach.

As per the reviewers comment above, we will provide maps (with zoomed inserts) of the cell segmentation and cell classification across representative ROIs to show not only the segmentation but to provide an overview of how the cell types localised across the lung.

It is unclear what Figure 2e represents. If it is simply to show that certain clusters can be grouped together, such as AEC, AT1, and AT2 as epithelial cells, this could be conveyed in a simpler way.

We apologise that the reviewer found Figure 2e confusing. The aim of this figure was to provide a simple diagram to highlight how different classifications of cell types aligned. This was required because there were variations in the specificity of some clusters and to address specific questions it made more sense to analyse cells at a broader level. i.e. merging resting and activated ATI/II cells or grouping specific immune cell clusters into larger groups. We did consider a table, but we did not feel this was a “simpler” way to do it. As it is simply for reference, we will move Figure 2e to supplemental.

The analysis of extracellular matrix components presented in Figure 3 provides a novel method for studying these acellular structures, which is a challenge in the field. The authors should be commended for their efforts in this area.

We thank the reviewer for their comment here. We agree that this is a vital area that needs to be addressed as the immunomatrix becomes ever more important in understanding disease pathogenesis. We developed this novel method to begin to understand key spatial interactions between cells and ECM molecules, something missing from the majority of high-dimensional imaging datasets.

However, the parameters investigated in Figures 4-6 do not report any novel findings. While IMC appears to work effectively to analyse these parameters simultaneously, the induction of immune foci and changes in tissue organisation following allergen challenges are already well-documented in both mouse models and human samples.

We disagree with the reviewer on this point. Figure 4 shows that immune cell infiltration in the adventitial cuff is different between BALB/c and C57BL/6 mice. This is a new discovery and provides nuance to our previously published data (PMID: 33587776), which showed that in the bronchoalveolar lavage from these same mice there were no differences in immune cell populations at these chronic time points. Therefore, analysis of lavage cells or lung histology in isolation does not provide a full picture of allergic immune responses.

Figure 5 shows neutrophils localised with alveolar macrophages in the alveolar parenchyma in this chronic DRA model completely distinct from the spatial advential cuff region occupied by other CD11b+ cells. In addition, we show that we can identify perturbations in the alveolar parenchyma by IMC and these correlate with known differences in allergy and asthma such as alterations in ATI/ATII balance, which has also not been shown in this model.

Figure 6 demonstrates that we can identify a tissue region termed “subepithelial cells” which is the site of where remodelling events are known to occur in asthma and allergic pathology. This ECM-rich region is strongly associated with fibroblasts and immune cells which leads in to figure 7 showing that these cells are interacting.

In addition to all of this the main focus of this manuscript is to link these analysis parameters to changes in the ECM environment and we have included this in each of these figures showing how these correlates with allergic changes and how they may be important in understanding these processes. In response to this reviewer’s point, we will highlight and make these novel findings clearer within the text of the manuscript.

In Figure 5, the authors show a decrease in neutrophil numbers in challenged mice. This is unexpected, as this model is widely known to induce strong neutrophil recruitment. The authors should clarify this finding and investigate whether neutrophil chemoattractants are increased in these samples.

This is a keen observation by the reviewer. We were interested in this finding however as it was not the focus of the paper we did not investigate further. In our previous publication we show that there are increased neutrophil numbers in the BAL of these animals (PMID: 33587776) and as mentioned above, we show in figure 5 that neutrophils are found mainly in the alveolar parenchyma. This perhaps means that they are more sensitive to being washed out in the BAL and perhaps there are differences in their “stickiness” in BALB/c and C57BL/6 animals or during DRA-induced allergy. This is in contrast to eosinophils (likely within our CD11b+ cells) which are found in the adventitial cuff, a region is not likely to be captured by BAL wash, though we know that these cells are actively present in the BAL. Overall, though this is an interesting result it was not the focus of this already lengthy paper and is best investigated in another project.

When analysing epithelial cells, the authors separate AT1 and AT2 cells based on podoplanin expression. However, data in Supplementary Figure 4b suggest that both cell types express similar levels of podoplanin. The authors do not provide statistical validation for the claim that AT1 cells express higher levels. Additionally, as podoplanin is expressed by various cell types, such as lymphatic endothelial cells, additional markers are required to confirm the identity of AT1 cells.

Again, the reviewer is entirely correct here. The cells we have identified are labelled as ATI as a best guess and correlate with ATII cells based on anatomical location – though this is likely shared by some of the populations mentioned by the reviewer. The majority of cells in this population are likely ATIs, as they are localized in the alveolar parenchyma and are cells that are not SPC+, though we cannot say for sure without more markers and we were already at the limit of the number of markers that we can run with one IMC panel. It is likely that there are contaminating lymphatic endothelial cells in this cluster. However, these will be a relatively minor population and do not change the main findings presented in the paper. To address this and other comments by the reviewer we plan to include a limitation section to the discussion that highlights exactly these points for future studies.

The last set of data in Figure 7 is interesting and shows that immune cells interact with a population of S100a4 fibroblasts. This finding could be expanded further, as CD11b and Ly6C are expressed by a variety of immune cells. The authors should include additional staining to identify the specific cell types involved, such as monocytes, eosinophils, or airway macrophages. Furthermore, the authors should speculate on why these fibroblast regions attract immune cells. Are these regions enriched in chemokines or other factors?

We thank them for this suggestion. To answer this point, we will conduct immunofluorescent imaging to provide further characterization of these cells in greater depth, as we agree, this will be important to consider. To best visualize cells and their interactions in this adventitial region, we plan to use 3D precision cut lung slices from PBS versus DRA mice in combination with confocal imaging. This method will allow us to utilize antibodies and markers that do not work in the FFPE sections such as SiglecF (eosinophils), CD11c (DCs, macrophages), CD64 and CD169 (macrophages).

The discussion is engaging but focuses more on methodological aspects than new biological insights. Without mechanistic links, it is challenging to draw meaningful biological conclusions.

We agree that the discussion could be used to reinforce the importance of the biological discoveries we have made (listed previously) in the discussion. However, we also believe that it is important to discuss the methodology as this is a novel way to explore ECM-cell interactions in the tissue as highlighted by the reviewer. There are many limitations to using IMC and similar techniques that should be highlighted for future studies so that we can develop better ways of quantifying the ECM environment during disease.

Significance

The study of Parkinson et al. provides interesting methodological insights into the use of imaging mass cytometry (IMC) to analyse lung architecture following inflammation. The application of multiplex antibody staining will leverage important information related to how tissues are adapting to chronic immune response. Here the authors rely entirely on mouse models for their studies and compared two lines of WT animals and the same allergen model. This limits the scope of the study, additional timepoints, sex or age would have improved the manuscript.

Whilst we appreciate the reviewers points here, we would like to highlight the time involved in generating such datasets, with a lot of careful optimization and experimental design aspects going into each study. Whilst we have also performed staining and analysis using our described method in human FFPE tissue, we are currently looking to further develop analysis tools to assess ECM-cell interactions. Additionally, data acquisition using IMC takes considerable time, and hence it is not feasible run and analysis the number of samples required to address some of the questions proposed by the reviewer.

We believe our manuscript provides novel methodology to analyse ECM environments within spatial datasets, something that no other spatial datasets have explored to date. Furthermore, we provide numerous new biological findings in relation to how cells are organized within the tissue during allergic pathology and propose immune-fibroblast interactions that may be key for driving ECM remodelling in the lung. Integrating these analyses will be key for further understanding the role of the ECM in disease pathogenesis.

The applicability of this analysis pipeline to human tissue samples is not discussed, which would significantly enhance the impact of the study. Additionally, complementary techniques, such as flow cytometry or immunohistochemistry, could be used to validate the findings and improve reproducibility. A specialised audience of immunology researchers would be interested by the image analysis approach.

As mentioned above, this analysis pipeline is easily applied to human samples or any other species as ECM molecule organization is largely conserved across species. Moreover, we have already explored this using human samples. However, adding human data to this manuscript is beyond the scope of this manuscript which was aiming to build one of the first methodologies for incorporating the ECM into this kind spatial analysis from the start in order to make biological discoveries. Regardless, we will add a discussion point on utilizing these pipelines to other species within the discussion of the manuscript.

Flow cytometry has been published on this model and the exact samples used within this study as mentioned previously (PMID: 33587776), validating some of these findings – we will make this point more clearly in the manuscript. We do appreciate that it would be good to further expand on some findings presented in the manuscript. As such we will expand our immunostaining (as mentioned above) to give more detail on the infiltrating immune cell populations and their interactions with fibroblasts.

__Reviewer #2 (Evidence, reproducibility and clarity (Required): __

Summary Parkinson and colleagues provided a highly intriguing manuscript on spatial resolution of cell-ECM interaction in mouse models of allergic airway inflammation. They used IMH to analyse two common mouse strains for allergic airway inflammation with a human relevant allergen mix. The study implements a novel technique to better segment tissue stainings (DeepThresh) and modifies existing tools to assess non-cellular seqmentation, ECM or fibrous structures that is. The study identifies region-specific ECM and confirms cellular proximity with canonical cell markers. Furthermore, clear ECM and cellular differences between the two mouse strains are found. The study concludes that this IMH approach is superior to existing methods as it provides a high spatial resolution of ECM protein - cell interaction.

Major: ECM Isoform Annotation - The manuscript lacks precise annotation of ECM isoforms, particularly for Collagen I, IV, and VI. This impacts the accuracy of reported associations between ECM environments and cellular interactions.

We thank the reviewer for this excellent comment and pointing this out. We agree that this is very important and will add this data to the manuscript. All information was included by reference of the antibody clones. However, it is an important point to make and we will account for this during interpretation of the results.

Spatial Annotation Consistency- The manuscript inconsistently defines and annotates ECM environments (e.g., adventitial collagen, subepithelial & vascular ECM), leading to confusion in spatial correlation analyses.

We are unsure what the reviewer is exactly referring to here. We have maintained a consistent nomenclature for these annotations throughout the manuscript. If the reviewer has an issue with the names we have provided for the regions; names were chosen these to be more informative than just naming them “region 1, 2, 3…”. Names in the manuscript were based on taking the lung tissue region and the prominent ECM molecules present. Whilst some level of detail will naturally be lost, we considered this the best way to keep data clear and consistent throughout the manuscript. For example, adventitial collagen describes the region predominantly around the adventitial cuff (fig 3c and d; shown in dark blue) that has high levels of Collagen I, III and VI. Yes, HA, laminin and fibronectin are also expressed, but at much lower levels. Regardless, all the information is present within the figures with readers to observe and make their own interpretations. We are happy to consider alternative names if the reviewer were to provide some guidance on what they thought was more appropriate.

Lack of Supplemental Data- Activated cell types and regions are not clearly defined, and no supplemental data is provided to verify classifications. DeepThresh Validation - The method for removing staining artifacts via DeepThresh lacks clear validation. Complexity - Overlapping marker definitions (e.g., CD11b+ cells and infiltrating cells) need clarification for accurate immune cell characterization.

We provide heatmaps in the supplementary data which shows the exact marker expression pattern for all of the clusters we define (Sup Fig 1a). Additionally, we provide graphs showing the cellular contribution and spatial distribution of all the regions we defined with lisaClust (Fig 2h & I; Sup Fig 1d). Most activated cells are a feature of a specific clustered cell type only being present in either PBS or DRA treated animals. However, the features which have led to separation these cell types are available in the heatmaps as mentioned (Sup Fig 1a).

We believe the reviewer may be confused about the purpose of DeepThresh. This algorithm is not for removing staining artifacts. Instead it uses expert annotation of a small training set to generate a method of accurately thresholding images for positive staining in relatively small ROIs which may have diverse structural features with different staining properties. We did not have space in the manuscript to go into this in more detail. However, we appreciate this may not be as clear as needed for readers, and hence, will provide supplementary data showing some example thresholding alongside the original staining in a new edit of the manuscript.

CD11b+ and infiltrating cells are not an overlapping population, they were separately clustered by the algorithm, but we take the reviewers point that further characterisation could be done. As mentioned in comments from reviewer 1, there is a limitation in the number of markers we can use in IMC, especially with the number of ECM markers we included. Additionally, there are limitations in the appropriate antibodies (carrier-free) that work in FFPE mouse tissue with the antigen retrieval that we use for good, reliable staining of ECM components. As such, we will perform additional immunofluorescence staining in 3D precision cut lung slices to better characterize the CD11b+ population to address comments by both reviewers.

Minor: Terminology Inconsistency- The manuscript uses inconsistent terminology for ECM components and anatomical regions (e.g., adventitial collagen, immune foci, inflammatory zone).

This point was directly addressed above in “Major” points and appears to be a duplicate comment.

ROI Mask Coverage - Statistical insignificance in C57BL/6 ROI mask coverage is not addressed.

The increase in C57BL/6 mice upon DRA treatment in panel A is not “significant” in the modern sense of the word. However, we would argue that stating it is “not significant” would also be a mistake. We prefer to use p values as an inferential measure of significance in combination with measures such as effect size and variance (PMID: 8465801). We find this more useful considering the vast number of mistakes made when interpreting p values (PMID: 18582619). The importance of not purely relying on p values for clinical research has been reviewed recently here (PMID: 39909800).

Whilst we appreciate the reviewer’s requirement for significance, we do not want to make sweeping statements based off of a p value of 0.07, especially in only one experiment. Many papers have been published on the pitfalls of stringently adhering to p

Spelling Error - "Immunte foci" in Figure 4h.

We thank the reviewer for pointing this out and will correct this.

Figure 6g Correlation Issue- The matrix environment correlation plot does not align with expected cell-ECM interactions.

We find it hard to comment on this without more detail of the cell-ECM interactions that the reviewer believes should be occurring. We analysed this in an unbiased way, so we have not forced interactions to appear based on our preconceptions. The regions being analysed in Fig 6g are the resting (PBS) and activated (DRA) airways that contain expected cell populations of airway epithelial cells and a low level of fibroblasts, likely from just under the airway epithelial cells. These cell populations align with AEC-associated matrix, laminin and hyaluronan, and adventitial collagen regions. Perhaps the reviewer is questioning why the airways are associated with adventitial collagens? The reason behind this, is due to adventitial cuff residing adjacent to a proportion of all airways, and hence any ECM associated with the adventitial cuff will likely be included in an airway region. However, as mentioned previously there are limitations to this analysis and we are very likely missing finer details due to issues such as resolution which we have discussed within the point-by-point on numerous occasions, and something we will directly address by adding a limitations section to the discussion of the revised manuscript.

Color Issues in Figures - ColI and ColIII have the same color in Fig. 3a, making interpretation difficult.

We agree with the reviewer on this point. The issue we had here was that Col-I and Col-III strongly overlap in these images, whilst one was green and one yellow the effect was to make them look the same in the final images. We will remake these images with clearer colours that better illustrate differences in Col-I and Col-III expression.

Patch Annotation (Fig. 4i) - The method for defining immune cell patches is unclear.

Patches refers to an approach that is used to identify interconnected groups of similar cell types and is a method that is based off published data (PMID: 35363540). We will add further method details that explains this process to the revised manuscript.

Detailed review: Methods: Animal model is suitable for differential analysis of various mouse strain responses to allergic airway inflammation.

We thank the reviewer for this comment and also agree that the mouse models presented in the manuscript can provide insightful and mechanistic data for investigating human disease.

Deepthresh matrix thresholding: IMCDenoise is sensitive to clusters of staining artefacts (specks). Please explain how DeepThresh via manual thresholding enables staining artefacts removal/detection. Manual ground truth mapping is common however it is not clear how your approach is performing against another tool. How was manual thresholding controlled (several analysts thresholded same image)?

As described in a previous comment this is not the function of DeepThresh. Manual annotation for training data was performed by consensus agreement of four independent researchers. In terms of performance against another tool, we are not aware of another tool which performs this function and hence cannot compare. However, we will add additional data showing the validation metrics for the pipeline to make future comparisons easier.

Antibodies Collagen IV, stains col4a1 - please correct, as isoforms vary throughout tissue. Collagen VI, stains col6a1 - isoforms vary in lung tissue, please state correct isoform throughout the document. Heparan sulfate: Molecular weight? Collagen I - isoform not defined, please state in methods.

Figure 3 d As a resultant of the choice of antibodies against some particular isoforms of ECM molecules associations of cells, compartments are correct yet do not comply with all isoforms. Col4a1 is a basal membrane collagen from blood vessels; the adventitial area and vascular area are high in Col4a1. Other Col4 isoforms are found more frequently in the alveolar regions (col4a5,a6) and the subepithelial membrane. It is of utmost importance to clearly label the correct isoforms throughout the document.

This relates to the comment above made by reviewer 2. As mentioned, we agree with this key point and will provide this information from the respective antibody clones.

However, we are unable to provide details on the molecular weight of heparan sulfate as this will vary depending on location/tissue/condition etc. The antibody recognises 10E4 epitope on HS which is found across a wide variety of tissues and species and will recognise many different sizes of HS and even porcine Heparan. Importantly it is relatively specific, not cross reacting with hyaluronan, keratan sulphate, chondroitin sulphate, or dermatan sulphate which is an issue for certain clones. Whilst the size of the HS is an interesting facet, consideration of changes in sulphation patterns would also be of interest, though these currently cannot be accurately assessed via purely immunostaining-based methodologies and would require more targeted biochemical techniques. In addition to this there are multiple nuances in 10E4 antibody binding (PMID: 15044385 and 11278655) which are interesting, but far beyond the scope of this study. Although captured in the antibody clone information, we will also ensure this is clear in the methods.

In relation to Col4 isoforms specifically, often antibodies for the ECM are limited because of their repeating structures it is hard to generate specific antibodies. For collagen IV there many clones for Col4a1, but no specific clones for Col4a3/col4a5 etc, suitable for use in FFPE tissues and metal conjugation required for IMC. Therefore, we were very limited in what was available to detect them at all. We will bring this up in the discussion as this is an important point, not just for our data, but also for people attempting to replicate this kind of analysis.

Figure 2i: The cell-specific marker expression is in part already confounded by region. So vasculature or resting airways show no "resting" fibroblasts as their annotation is linked to activation (indicated by S100A4 expression). Anatomic locations such as airways with remodelling are termed "activated" to explain morphological differences which is acceptable given the model chosen. However, some cell type are not given an anatomical or morpholocial "resting" nomenclature. Only during activation and through location a cell type may aquire e.g. a nomenclature such as "alveolar fibroblast". The correlation blot 2i should provide this basic information. Please explain.

Our staining approach and analysis have only identified certain activated populations as pointed out by the reviewer. Most of the populations that we have identified as “activated” have been identified primarily only in mice administered DRA. The reason that we have not included “resting” and “activated” populations for all cell types is that these clusters were generated using a clustering algorithm based on the cellular markers used within the study. Each cluster was then simply labelled as best we could, using information from marker expression, published biological data, anatomical location, and sample identity (e.g. PBS or DRA).

A caveat to using IMC and other similar imaging techniques is that we will miss certain “flavours” of cell populations because we simply do not have the markers, or scope to include markers, with which to identify these cells. This is partly a problem of appropriate antibody availability, but also for many populations there are no specific markers identified in the literature/databases. Single cell RNAseq has provided deep segmentation of some of these populations, but we (and others) have found that often these make poor antibody choices at the protein immunostaining level.

We are unsure what the reviewer wants adding to plot 2i. This plot shows the cell cluster contribution to different lisaClust defined tissue regions. Hence the presence of alveolar fibroblasts in the resting and activate alveoli region. However, we will include more discussion on the limitations of markers and identification of specific cell populations in the discussion.

Figure 2h: How do you explain subepithelia to "leak" luminally in C57BL/6 DRA animal?

We assume the reviewer is referring to the overlap of some grey circles though/over the red airway epithelial cells in the C57BL/6 DRA panel of figure 2h. This figure represents individual cells as circles with the centroid of the circle at the centroid of the cell. Cells are rarely perfect circles and, in this case, it has made it seem like the cell is coming through the airway epithelium, when likely it is a longer cell that sits directly under it. In addition to this, these are effectively 2-dimensional section (5um thick) that capture as small portion of the lung anatomy, hence occasionally this can result in unusual tissue structures that make no sense in the confines of a 2D section, but instead correlate with the larger 3D structure.

How is an activated airway possible in a Balb/c PBS animal (same for inflammatory adventitia, alveoli)?

Activated airway simply describes a region that is showing some evidence of activation markers such as RELMα and/or Ym2 etc. PBS itself, as with any other liquid administered into the lungs, will drive a very low level of inflammation, which is why it is used as a control in the animal model. Therefore, it is not surprising that we see a low number of these “activated” cells in PBS animals vice versa for their activated counterparts in DRA treated animals. This is similar for the other regions mentioned.

How is subepithelia adjacent to immune foci and inflammatory adventitia (Balb/c DRA).

We are somewhat confused by this question. We have termed the region “subepithelia” because it is mostly found under the airway epithelial cells. We found that this region expands during DRA treatment and covers areas close to the immune foci and inflammatory adventitia, hence they are next to each other.

As described above, the names of these regions were chosen for simplicity and to communicate its general features. These, regions were identified by detection of nearby regions of cells with similar cellular compositions and the names we a “best fit”.

Text for fig 3c: Here it should be mentioned that a cell is used as a proxy locator to the ECM region.

We apologise that this was unclear for the reviewer. Rather than describing it as using the cell as a proxy locator to the ECM region we find it more accurate to think of it as we are characterizing the matrix environment of the cell i.e. what is the cell close to and what is it far away from. We will make this clearer in the results by changing the name to cellular matrix environment, rather than matrix environment.

Again, in UMAP3b location and ECM molecule a mixed a priori which only can be achieved through proxy loction as in fig 3c or correlation matrix analysis. The UMAP shows ECM molecules in various combinations. Fig3c analysis of anatomic location from images with cell proxies would validate morpho-spatial UMAP annotation. Please make this clear in the manuscript or specify why your approach is superior in its presented format.

We struggled to ascertain what the reviewer was referring to here and what edits they were suggesting to the revised manuscript. However, this comment seems to assume that we have used cellular location as an input to the UMAP in figure 3b, which is untrue. This UMAP (and associated clustering) shows each cell as a dot which is organised based on its distance to the different matrix components. Effectively showing us how different cells cluster based on their cellular matrix environment, with no input of cellular based markers. We are unsure what the reviewer is referring to on line 486 – as they seem to be describing exactly what figure 3c already is (a spatial map of the UMAP clusters on representative images, which shows that a cells matrix environment does seem to show patterns that align with the general lung anatomy).

Finally, the reviewer asks us to specify why our approach is superior, but we are unclear what the alternative approach is.

This methodology is effectively a repurposing of the traditional UMAP and clustering methodology used in many single cell techniques, but instead of applying this to cellular markers we are applying it to a cells matrix environment as quantified by the matrix distances. If the reviewer could clarify this comment we would be happy to revisit it. As mentioned in the previous comment, we will more clearly describe cellular matrix environments in the revised manuscript and this may also help with the confusion.

Fig 3d: The Matrix Cluster names are in part not correct. Subepithelial & Vascular ECM does not correlate with Vasculature in LisaClust Regions. Also ColIV is not AEC associated, yet subepithelial.

Respectfully, we completely disagree with the reviewer on this point. In the heatmap (Fig 3d) the Subepithelial & Vascular matrix environment correlates most strongly with the Vasculature and Subepithelial cells as shown by the stronger green-yellow colour in the corresponding cell of the heatmap.

As mentioned previously in response to another comment by reviewer 2, there could be many reasons that we are not detecting collagen-IV in the AEC associate cell matrix environment. One likely explanation is that this is too fine for the resolution of IMC (1-micron2) or it could be that certain subchains are utilised here that are not recognized by the antibody we managed to optimize. Additionally, AEC-associated matrix environment is comprised of both mouse strains and includes higher representation from DRA treated animals. From our previous work (PMID: 33587776), we have shown that Col-IV expression around the AEC is reduced in DRA versus PBS -treated animals.

No ECM molecule is inflammatory zone associated. Does this indicate cellular density does not allow to distinguish ECM?

This is a great point from the reviewer and their explanation is entirely possibly. As mentioned there are huge limitations in the resolution of IMC and so we are likely missing finer matrix structures. There is a huge recruitment of cells within this environment so it could be that we cannot clearly visualise fine ECM structure through this considering we are also looking at a 5-micron thick 2D tissue section. Additionally, cells maybe degrading the ECM in order to infiltrate into the tissue. This is definitely an interesting point to examine in further detail, but would need to be done with a different methodology. We will aim to look at an ECM molecules and its distribution within the inflammatory zone using 3D precision cut lung slices and also immune-staining of tissue sections to see whether we can better resolve this in a revised manuscript.

Also the term "adventitial collagen" is locating to LisaClust Regions Vasculature, Subepithelial Cells, Resting Airways, Infiltrating Cells, Activated Airways. Adventitial per definition of fig. 2g is around blood vessels extending to airways and around it. The adventitial regions are the ECM rich areas after the fibroblasts (as for blood vessels, PMID: 31522963). The definition used in this study therefore generates morphological overlaps between airways and their basolateral regions and blood vessels. Whilst both morphological regions have an adventitia the Matrix cluster assumes from areas to close by this terminology. As a sensitivity analysis I would suggest to reduce the perimeter around blood vessels to the same borderline as seen in airways. If composition remains similar "adventitial collagen" could be a broader term. Alternatively, if adventitia from airway and blood vessel differ these should be separate terms.

Whilst the adventitial cuff does refer to the region immediately around a blood vessel in the lung, these structures are slightly more nuanced as blood vessels normally travel through the lung in close association with an airway. This is true all the way down to the close association with the capillaries and the alveolar spaces where gas exchange occurs. Indeed, previous publications (PMID: 30824323) have shown that these adventitial cuffs extend out from around the contiguous area around the blood vessel and associated airway and these can expand during inflammation (PMID: 24631179). This region is rich in Collagen-I and Collagen-III, as we have shown in this manuscript and previously (PMID: 33587776).

Whilst we agree that there are likely microanatomical niches within this larger structure, our dataset lacks the resolution to study this in more detail. However, as mentioned above we can include matrix markers in our future IF staining to examine this region in more detail. The adventitial collagen environment described in this manuscript and beyond, are vital “meet and greet” spots for immune cell infiltrating into the lungs (PMID: 30824323) as well as being sites of iBALT formation (PMID: 24631179)

We are unsure what the reviewer means by “…reduce the perimeter around blood vessels to the same borderline as seen in airways.” We have not defined a manual threshold for the border of the airways. These regions were all defined by SNN clustering and not manual segmentation. Whilst this methodology could be developed we do not believe that this dataset has the resolution to answer this question, as mentioned previously.

Fig 4c: Balb/c and C57bl/6 labels are incorrect (see a,b)

We thank the reviewer for noticing this incorrect labelling and will update this.

Fig 4h: Cell type "other is highly present in immune foci and inflammatory adventitia but not further classified and not myeloid. This seems either a difficult definition for myeloid or a significant immune population wasn't stained. How is myeloid defined?

We define myeloid broadly as CD11b+ or alveolar macs. There were certain populations that were not stained, notably T cells. We were unable to have suitable or reliable staining in FFPE tissue with CD90, TCRa/b, CD3e antibodies via IMC. The same was true for Eosinophil markers (SiglecF, Ccr3, EPO, MBP). The additional experiments we will perform for a revised manuscript (using 3D precision cut lung slices and/or IF staining) should shed further light on these cells. Additionally, as we are not limited by the processing requirements of IMC, we can use a wider range of markers.

Fig 4l has a vast variety of marker combinations some being very specific within the staining panel, others subsummarise entire groups of cells. It would be very helpful to know if the lables are specific and exclusive or if larger clusters exist, that they then subdivide into specific groups (e.g. Infiltrating cells: any of CD11b, CD44, Ly6C vs. B-cells or CD11b+Lys6C). This graph would profit also from either using markers or cell types only. Your marker set is very distinct and limited so per definition it is either a neutrophil or a Lys6C+. Please decide, explain and provide the other graph as supplement.

We apologise that this was not clear to the reviewer. Labels are exclusive and represent the clusters that were identified in figure 2 and are at the finest level of detail that we felt we were able to biologically infer from the data. In terms of the reviewer’s first point about infiltrating cells, these are completely separate from the other cell types mentioned. As mentioned in the previous comment line 570, we were simply unable to find working antibodies for some of the common lung populations (a common problem for FFPE sections where antigens are often masked or lost due to fixation and processing) and so are limited to general annotations for these. For the reviewer’s second example of Neutrophils vs Ly6C+ cells, neutrophils were classified by expression of Ly6G, CD11b+, and Ym1 whereas there are many other cell types that express Ly6C, including, but not limited to, dendritic cells, monocytes, eosinophils, and even some T cells.

We believe that the graph in combination with data in Fig 1c and supplementary Fig 1a, already shows what the reviewer is asking for.

Fig 5l and sup Fig4i: There is no graph confirming the statement that Ym1 is produced by macrophages. From the graphs in either of the two panels, The AEC are highly associated with Ym1/2 expression or the activated alveoli. Please explain ad amend.

We assume the reviewer means Fig 5l and sup Fig 5i (as there is no figure sup Fig 4i). Whilst we did not include a graph to show that alveolar macrophages produce Ym1, we did include two references in the text and this has been widely shown in the literature for many years (PMID: 11141507 and 15148607). We are somewhat unclear on the reviewers second point. AEC (airway epithelial cells) can definitely also produce Ym1, though this can often be contentious because of issues with cross-reactivity with its highly homologous sister protein Ym2, which is also produced from airway epithelial cells under Type-2 settings. If the reviewer is referring to AEC (alveolar epithelial cells) then this is true. Activated alveoli are lisaClust regions with lots of alveolar macrophages which was the original statement we made and aligns with sup Fig 5i. Activated alveoli II have less alveolar macrophages and also have less Ym1, which would correlate though there are other cell types which can make Ym1 as well.

Fig 6g: The correlation plots again show that the matrix environment labels are somewhat confounded. Whilst AEC associated makes perfect sense, adventitial collagen only weakly correlates, yet was part of the adventitial mapping. Cell types like AEC are expected however fibroblasts, especially in resting airways as large constituent cell populations. There are not other, myeloid or lymphoid cells associated with these airways, which under activated conditions seems rather odd. From fig6a it is appearant that the lisaClust has ascribed subepithelial regions to distal parts of the airway separated by blood vessel or parenchyma (C57BL/6 and Balb/c DRA). Also blood vessels are in part other cell types or epithelium (B6 PBS). Is the annotation here the reason for this rather confusing result? Please explain and/or amend.

We are again somewhat confused by this comment. Adventitial collagen only weakly correlates because it is not within the airway epithelial cells, instead it is adjacent in the subepithelial region which is shown in Fig 6j. We are unsure exactly what the reviewer is referring to in terms of “adventitial mapping” but are happy to comment on this if the reviewer can clarify what they mean.

We agree with the reviewer that it is somewhat surprising to see so many fibroblasts in the resting and activated airway regions. There is a level of ambiguity here in what lisaClust decides to include in one region vs another. However, what it does show is that there are a large population of fibroblasts around the airway, possibly correlating with peribronchial fibroblasts. We did not observe immune cells in between the airway cells or immediately underneath it. We do not believe this is odd, as from our data it appears that these cells are more likely to be found in the adventitial (including peribronchial as mentioned previously) cuff. Cell are most certainly moving into the airways as shown from the BAL in our previous publication (PMID: 33587776). However, we are unlikely to capture this process in the snapshot of our histology across a relatively small section of the airways covered in our 2D sections.

In regards to the reviewers comment about figure 6a we agree that some of the regions between the airways and blood vessels have been characterised as subepithelia. As mentioned previously we are happy to consider alternative names but have been unable to come up with an alternative that encompasses the cells and spatial region more accurately and clearly., Regardless, the main purpose of these names is to provide simple nomenclature to follow throughout the manuscript and make these types of analyses accessible to all readers. We believe that these are accurately labelled and have provided information about the constituent cell populations that are present within them, making the data and subsequent analysis transparent for others to view and explore. Our data suggests that the adventitial cuff may fulfil multiple roles during DRA-induced inflammation, some of which are more focused on immune cell recruitment and others which may correlate more with the fibroblast rich subepithelial region.

The reviewer is entirely correct to point out that some blood vessels were not entirely annotated. We used vWF to manually separate blood vessels from the adjacent smooth muscle layers, which were not separated by the clustering originally. Notably it appears that veins seem to not separate as well as arteries suggesting another marker (e.g. CD31) may help with this, though we were limited in what we could include as mentioned previously. As this is only a small effect, which we do not have a way to correct, and blood vessels were not the focus of this manuscript, we have left the annotation as it is with raw data included.

__Significance __

Strength Innovative ECM-Immune Interaction Approach- The study integrates extracellular matrix (ECM) phenotyping with immune cell spatial mapping, providing novel insights into allergic airway inflammation Multiplex Imaging Technology - The use of Imaging Mass Cytometry (IMC) allows high-resolution spatial characterization of both cellular and ECM components. Strain Analysis - The inclusion of BALB/c and C57BL/6 mice enables differentiation of strain-specific ECM and immune responses. Deep-Learning-Based ECM Quantification - DeepThresh offers an advanced computational approach for ECM analysis, enhancing accuracy in defining ECM-cell associations. Comprehensive Tissue Classification- LisaClust clustering facilitates detailed segmentation of lung microenvironments, improving understanding of localized tissue remodeling.

Limitations ECM Isoform Inconsistencies - The study lacks precise annotation of ECM isoforms, which affects the accuracy of reported ECM-cell interactions. Ambiguous Spatial Correlations- Some ECM clusters, such as "adventitial collagen," overlap inconsistently with anatomical regions, making interpretation challenging. Unvalidated DeepThresh Method - The manuscript does not provide sufficient validation of DeepThresh's ability to remove staining artifacts. Lack of Supplemental Data- Key activated cell types and regions lack supporting data for classification.

__Advance, gap filled __ Clearly the next step to improve organ compendia such as the lung cell atlas, spatial protein analysis is warranted. scRNA-Seq in particular for ECM molecules is challenging as these molecules are produced in small quantities or have a very slow turn-over. This study has the potential to provide novel deep learning algorithms to include not only cellular markers but consider larger panels of ECM molecules and their spatial orientation in the lung.

__Audience __ The manuscript is interdisciplinary located between advanced image analysis with deep learning methods, fundamental lung biology and single cell analysis. The readership would entice molecular biologists, bioinformaticians and basic disease model scientists. The manuscript would appeal to clinician scientists and a broader audience if human tissue pendants could be provided validating the methods and outcomes.

__Own Expertise __ Translational scientist in the field of chronic lung disease, highly familiar with epithelial cells, mouse models, human cohorts and next generation sequencing and imaging of live single cells.

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 #2

Evidence, reproducibility and clarity

Summary

Parkinson and colleagues provided a highly intriguing manuscript on spatial resolution of cell-ECM interaction in mouse models of allergic airway inflammation. They used IMH to analyse two common mouse strains for allergic airway inflammation with a human relevant allergen mix. The study implements a novel technique to better segment tissue stainings (DeepThresh) and modifies existing tools to assess non-cellular seqmentation, ECM or fibrous structures that is. The study identifies region-specific ECM and confirms cellular proximity with canonical cell markers. Furthermore, clear ECM and cellular differences between the two mouse strains are found. The study concludes that this IMH approach is superior to existing methods as it provides a high spatial resolution of ECM protein - cell interaction.

Major:

ECM Isoform Annotation - The manuscript lacks precise annotation of ECM isoforms, particularly for Collagen I, IV, and VI. This impacts the accuracy of reported associations between ECM environments and cellular interactions. Spatial Annotation Consistency- The manuscript inconsistently defines and annotates ECM environments (e.g., adventitial collagen, subepithelial & vascular ECM), leading to confusion in spatial correlation analyses. Lack of Supplemental Data- Activated cell types and regions are not clearly defined, and no supplemental data is provided to verify classifications. DeepThresh Validation - The method for removing staining artifacts via DeepThresh lacks clear validation. Complexity - Overlapping marker definitions (e.g., CD11b+ cells and infiltrating cells) need clarification for accurate immune cell characterization.

Minor:

Terminology Inconsistency- The manuscript uses inconsistent terminology for ECM components and anatomical regions (e.g., adventitial collagen, immune foci, inflammatory zone). ROI Mask Coverage - Statistical insignificance in C57BL/6 ROI mask coverage is not addressed. Figure 3d Labeling- Matrix cluster names do not always match tissue localization. Spelling Error - "Immunte foci" in Figure 4h. Figure 6g Correlation Issue- The matrix environment correlation plot does not align with expected cell-ECM interactions. Color Issues in Figures - ColI and ColIII have the same color in Fig. 3a, making interpretation difficult. Patch Annotation (Fig. 4i) - The method for defining immune cell patches is unclear.

Detailed review:

Methods: Animal model is suitable for differential analysis of various mouse strain responses to allergic airway inflammation.

Deepthresh matrix thresholding: IMCDenoise is sensitive to clusters of staining artefacts (specks). Please explain how DeepThresh via manual thresholding enables staining artefacts removal/detection. Manual ground truth mapping is common however it is not clear how your approach is performing against another tool. How was manual thresholding controlled (several analysts thresholded same image)?

Antibodies Collagen IV, stains col4a1 - please correct, as isoforms vary throughout tissue. Collagen VI, stains col6a1 - isoforms vary in lung tissue, please state correct isoform throughout the document. Heparan sulfate: Molecular weight? Collagen I - isoform not defined, please state in methods.

Figure 3 d As a resultant of the choice of antibodies against some particular isoforms of ECM molecules associations of cells, compartments are correct yet do not comply with all isoforms. Col4a1 is a basal membrane collagen from blood vessels; the adventitial area and vascular area are high in Col4a1. Other Col4 isoforms are found more frequently in the alveolar regions (col4a5,a6) and the subepithelial membrane. It is of utmost importance to clearly label the correct isoforms throughout the document.

Spelling error in figure 4 h (immunte foci)

ROI mask coverage in C57/6 not significant

Activated cell types/region: This definition is not specified and no supplemental data is provided to see which markers classify such areas/cells. Please provide.

Figure 2i: The cell-specific marker expression is in part already confounded by region. So vasculature or resting airways show no "resting" fibroblasts as their annotation is linked to activation (indicated by S100A4 expression). Anatomic locations such as airways with remodelling are termed "activated" to explain morphological differences which is acceptable given the model chosen. However, some cell type are not given an anatomical or morpholocial "resting" nomenclature. Only during activation and through location a cell type may aquire e.g. a nomenclature such as "alveolar fibroblast". The correlation blot 2i should provide this basic information. Please explain.

Figure 2h: How do you explain subepithelia to "leak" luminally in C57BL/6 DRA animal? How is an activated airway possible in a Balb/c PBS animal (same for inflammatory adventitia, alveoli)? How is subepithelia adjacent to immune foci and inflammatory adventitia (Balb/c DRA).

Fig 3a: ColI and ColIII have same colour, this makes images not easy to understand please change. Text for fig 3c: Here it should be mentioned that a cell is used as a proxy locator to the ECM region. Again, in UMAP3b location and ECM molecule a mixed a priori which only can be achieved through proxy loction as in fig 3c or correlation matrix analysis. The UMAP shows ECM molecules in various combinations. Fig3c analysis of anatomic location from images with cell proxies would validate morpho-spatial UMAP annotation. Please make this clear in the manuscript or specify why your approach is superior in its presented format.

Fig 3d: The Matrix Cluster names are in part not correct. Subepithelial & Vascular ECM does not correlate with Vasculature in LisaClust Regions. Also ColIV is not AEC associated, yet subepithelial. No ECM molecule is inflammatory zone associated. Does this indicate cellular density does not allow to distinguish ECM? Also the term "adventitial collagen" is locating to LisaClust Regions Vasculature, Subepithelial Cells, Resting Airways, Infiltrating Cells, Activated Airways. Adventitial per definition of fig. 2g is around blood vessels extending to airways and around it. The adventitial regions are the ECM rich areas after the fibroblasts (as for blood vessels, PMID: 31522963). The definition used in this study therefore generates morphological overlaps between airways and their basolateral regions and blood vessels. Whilst both morphological regions have an adventitia the Matrix cluster assumes from areas to close by this terminology. As a sensitivity analysis I would suggest to reduce the perimeter around blood vessels to the same borderline as seen in airways. If composition remains similar "adventitial collagen" could be a broader term. Alternatively, if adventitia from airway and blood vessel differ these should be separate terms.

Fig 4c: Balb/c and C57bl/6 labels are incorrect (see a,b) Fig 4h: Cell type "other is highly present in immune foci and inflammatory adventitia but not further classified and not myeloid. This seems either a difficult definition for myeloid or a significant immune population wasn't stained. How is myeloid defined?

Fig 4l has a vast variety of marker combinations some being very specific within the staining panel, others subsummarise entire groups of cells. It would be very helpful to know if the lables are specific and exclusive or if larger clusters exist, that they then subdivide into specific groups (e.g. Infiltrating cells: any of CD11b, CD44, Ly6C vs. B-cells or CD11b+Lys6C). This graph would profit also from either using markers or cell types only. Your marker set is very distinct and limited so per definition it is either a neutrophil or a Lys6C+. Please decide, explain and provide the other graph as supplement.

Fig 5l and sup Fig4i: There is no graph confirming the statement that Ym1 is produced by macrophages. From the graphs in either of the two panels, The AEC are highly associated with Ym1/2 expression or the activated alveoli. Please explain ad amend.

Fig 6g: The correlation plots again show that the matrix environment labels are somewhat confounded. Whilst AEC associated makes perfect sense, adventitial collagen only weakly correlates, yet was part of the adventitial mapping. Cell types like AEC are expected however fibroblasts, especially in resting airways as large constituent cell populations. There are not other, myeloid or lymphoid cells associated with these airways, which under activated conditions seems rather odd. From fig6a it is appearant that the lisaClust has ascribed subepithelial regions to distal parts of the airway separated by blood vessel or parenchyma (C57BL/6 and Balb/c DRA). Also blood vessels are in part other cell types or epithelium (B6 PBS). Is the annotation here the reason for this rather confusing result? Please explain and/or amend.

Significance

Strength

Innovative ECM-Immune Interaction Approach- The study integrates extracellular matrix (ECM) phenotyping with immune cell spatial mapping, providing novel insights into allergic airway inflammation Multiplex Imaging Technology - The use of Imaging Mass Cytometry (IMC) allows high-resolution spatial characterization of both cellular and ECM components.<br /> Strain Analysis - The inclusion of BALB/c and C57BL/6 mice enables differentiation of strain-specific ECM and immune responses.<br /> Deep-Learning-Based ECM Quantification - DeepThresh offers an advanced computational approach for ECM analysis, enhancing accuracy in defining ECM-cell associations.<br /> Comprehensive Tissue Classification- LisaClust clustering facilitates detailed segmentation of lung microenvironments, improving understanding of localized tissue remodeling.

Limitations

ECM Isoform Inconsistencies - The study lacks precise annotation of ECM isoforms, which affects the accuracy of reported ECM-cell interactions.<br /> Ambiguous Spatial Correlations- Some ECM clusters, such as "adventitial collagen," overlap inconsistently with anatomical regions, making interpretation challenging.<br /> Unvalidated DeepThresh Method - The manuscript does not provide sufficient validation of DeepThresh's ability to remove staining artifacts.<br /> Lack of Supplemental Data- Key activated cell types and regions lack supporting data for classification.

Advance, gap filled

Clearly the next step to improve organ compendia such as the lung cell atlas, spatial protein analysis is warranted. scRNA-Seq in particular for ECM molecules is challenging as these molecules are produced in small quantities or have a very slow turn-over. This study has the potential to provide novel deep learning algorithms to include not only cellular markers but consider larger panels of ECM molecules and their spatial orientation in the lung.

Audience

The manuscript is interdisciplinary located between advanced image analysis with deep learning methods, fundamental lung biology and single cell analysis. The readership would entice molecular biologists, bioinformaticians and basic disease model scientists. The manuscript would appeal to clinician scientists and a broader audience if human tissue pendants could be provided validating the methods and outcomes.

Own Expertise

Translational scientist in the field of chronic lung disease, highly familiar with epithelial cells, mouse models, human cohorts and next generation sequencing and imaging of live single cells.

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

In this study, Parkinson et al. investigated lung extracellular matrix using imaging mass cytometry (IMC) in mouse models. Overall, the paper is well-written, and the data are clear, although major points outlined below need to be addressed. In its current form, the paper appears more like a methods-focused study since, to my understanding, no new biological responses are described. The methods employed are very interesting, particularly the extracellular matrix analysis. However, the scope of the study is quite limited, as all the experiments were performed with mouse samples, which are relatively easy to work with, and the cell organisation is simple compared to humans. The authors do not discuss how this analysis pipeline could be applied to human samples. Furthermore, the entire paper relies on imaging mass cytometry, and additional techniques could have been used to confirm some of the observations, especially given the availability of mouse samples. The introduction mentions the DRA model without providing an explanation of what it involves. Non-specialist readers may not be familiar with this abbreviation, and further clarification should be provided. In the methods section, it is not mentioned whether the lungs were inflated before tissue collection, which is crucial for preserving normal cellular organization. The authors should clarify whether this was performed. Figure 1 provides a brief summary of the methods employed in the study but could be enriched with additional information. In its current state, it does not provide meaningful insights beyond what is described in the methods section. It would be helpful if the authors clarified whether the mice used were adults and whether both male and female animals were included. Additionally, they could present examples of the cell segmentation approach with zoomed-in images at the cellular level to illustrate the analysis. The first set of data in Figure 2 suggests that C57Bl/6 mice did not respond to allergen treatment, as shown by the non-significant increase in cell numbers. The authors should provide evidence that their model induced inflammation through alternative methods, such as assessing eosinophil counts or pathology. The UMAP representation indicates significant overlap between cell clusters, which raises concerns about the accuracy of cell segmentation. For example, the heatmap in Supplementary Figure 1 shows endothelial cells expressing markers such as VWF, aSMA, Vimentin, and PDGFRα, suggesting that the cell cluster may contain a mixture of endothelial cells, vascular smooth muscle cells, and fibroblasts. Examples of segmented cells should be shown to validate this approach. It is unclear what Figure 2e represents. If it is simply to show that certain clusters can be grouped together, such as AEC, AT1, and AT2 as epithelial cells, this could be conveyed in a simpler way. The analysis of extracellular matrix components presented in Figure 3 provides a novel method for studying these acellular structures, which is a challenge in the field. The authors should be commended for their efforts in this area. However, the parameters investigated in Figures 4-6 do not report any novel findings. While IMC appears to work effectively to analyse these parameters simultaneously, the induction of immune foci and changes in tissue organisation following allergen challenges are already well-documented in both mouse models and human samples.

In Figure 5, the authors show a decrease in neutrophil numbers in challenged mice. This is unexpected, as this model is widely known to induce strong neutrophil recruitment. The authors should clarify this finding and investigate whether neutrophil chemoattractants are increased in these samples. When analysing epithelial cells, the authors separate AT1 and AT2 cells based on podoplanin expression. However, data in Supplementary Figure 4b suggest that both cell types express similar levels of podoplanin. The authors do not provide statistical validation for the claim that AT1 cells express higher levels. Additionally, as podoplanin is expressed by various cell types, such as lymphatic endothelial cells, additional markers are required to confirm the identity of AT1 cells. The last set of data in Figure 7 is interesting and shows that immune cells interact with a population of S100a4 fibroblasts. This finding could be expanded further, as CD11b and Ly6C are expressed by a variety of immune cells. The authors should include additional staining to identify the specific cell types involved, such as monocytes, eosinophils, or airway macrophages. Furthermore, the authors should speculate on why these fibroblast regions attract immune cells. Are these regions enriched in chemokines or other factors? The discussion is engaging but focuses more on methodological aspects than new biological insights. Without mechanistic links, it is challenging to draw meaningful biological conclusions.

Significance

The study of Parkinson et al. provides interesting methodological insights into the use of imaging mass cytometry (IMC) to analyse lung architecture following inflammation. The application of multiplex antibody staining will leverage important information related to how tissues are adapting to chronic immune response. Here the authors rely entirely on mouse models for their studies and compared two lines of WT animals and the same allergen model. This limits the scope of the study, additional timepoints, sex or age would have improved the manuscript.

The applicability of this analysis pipeline to human tissue samples is not discussed, which would significantly enhance the impact of the study. Additionally, complementary techniques, such as flow cytometry or immunohistochemistry, could be used to validate the findings and improve reproducibility. A specialised audience of immunology researchers would be interested by the image analysis approach.

Reviewer #2 (Public review):

Summary:

The authors provide an open-source graphic user interface (GUI) called Heron, implemented in Python, that is designed to help experimentalists to:

(1) Design experimental pipelines and implement them in a way that is closely aligned with their mental schemata of the experiments<br /> (2) Execute and control the experimental pipelines with numerous interconnected hardware and software on a network.

The former is achieved by representing an experimental pipeline using a Knowledge Graph and visually representing this graph in the GUI. The latter is accomplished by using an actor model to govern the interaction among interconnected nodes through messaging, implemented using ZeroMQ. The nodes themselves execute user-supplied code in, but not limited to, Python.

Using three showcases of behavioral experiments on rats, the authors highlighted four benefits of their software design:

(1) The knowledge graph serves as a self-documentation of the logic of the experiment, enhancing the readability and reproducibility of the experiment,<br /> (2) The experiment can be executed in a distributed fashion across multiple machines that each has different operating system or computing environment, such that the experiment can take advantage of hardware that sometimes can only work on a specific computer/OS, a commonly seen issue nowadays,<br /> (3) The users supply their own Python code for node execution that is supposed to be more friendly to those who do not have a strong programming background,<br /> (4) The GUI can also be used as an experiment control panel for users to control/update parameters on the fly.

Strengths:

(1) The software is light-weight and open-source, provides a clean and easy-to-use GUI,<br /> (2) The software answers the need of experimentalists, particularly in the field of behavioral science, to deal with the diversity of hardware that becomes restricted to run on dedicated systems. It can also be widely adopted in many other experimental settings.<br /> (3) The software has a solid design that seems to be functionally reliable and useful under many conditions, demonstrated by a number of sophisticated experimental setups.<br /> (4) The software is well documented. The authors pay special attention to documenting the usage of the software and setting up experiments using this software.

Comments on revisions: The authors have addressed my concerns from the initial review.

Author response:

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

Public Reviews

Reviewer #1 (Public Review):

Summary:

The authors have created a system for designing and running experimental pipelines to control and coordinate different programs and devices during an experiment, called Heron. Heron is based around a graphical tool for creating a Knowledge Graph made up of nodes connected by edges, with each node representing a separate Python script, and each edge being a communication pathway connecting a specific output from one node to an iput on another. Each node also has parameters that can be set by the user during setup and runtime, and all of this behavior is concisely specified in the code that defines each node. This tool tries to marry the ease of use, clarity, and selfdocumentation of a purely graphical system like Bonsai with the flexibility and power of a purely code-based system like Robot Operating System (ROS).

Strengths:

The underlying idea behind Heron, of combining a graphical design and execution tool with nodes that are made as straightforward Python scripts seems like a great way to get the relative strengths of each approach. The graphical design side is clear, selfexplanatory, and self-documenting, as described in the paper. The underlying code for each node tends to also be relatively simple and straightforward, with a lot of the complex communication architecture successfully abstracted away from the user. This makes it easy to develop new nodes, without needing to understand the underlying communications between them. The authors also provide useful and well-documented templates for each type of node to further facilitate this process. Overall this seems like it could be a great tool for designing and running a wide variety of experiments, without requiring too much advanced technical knowledge from the users.

The system was relatively easy to download and get running, following the directions and already has a significant amount of documentation available to explain how to use it and expand its capabilities. Heron has also been built from the ground up to easily incorporate nodes stored in separate Git repositories and to thus become a large community-driven platform, with different nodes written and shared by different groups. This gives Heron a wide scope for future utility and usefulness, as more groups use it, write new nodes, and share them with the community. With any system of this sort, the overall strength of the system is thus somewhat dependent on how widely it is used and contributed to, but the authors did a good job of making this easy and accessible for people who are interested. I could certainly see Heron growing into a versatile and popular system for designing and running many types of experiments.

Weaknesses:

(1) The number one thing that was missing from the paper was any kind of quantification of the performance of Heron in different circumstances. Several useful and illustrative examples were discussed in depth to show the strengths and flexibility of Heron, but there was no discussion or quantification of performance, timing, or latency for any of these examples. These seem like very important metrics to measure and discuss when creating a new experimental system.

Heron is practically a thin layer of obfuscation of signal passing across processes. Given its design approach it is up to the code of each Node to deal with issues of timing, synching and latency and thus up to each user to make sure the Nodes they author fulfil their experimental requirements. Having said that, Heron provides a large number of tools to allow users to optimise the generated Knowledge Graphs for their use cases. To showcase these tools, we have expanded on the third experimental example in the paper with three extra sections, two of which relate to Heron’s performance and synching capabilities. One is focusing on Heron’s CPU load requirements (and existing Heron tools to keep those at acceptable limits) and another focusing on post experiment synchronisation of all the different data sets a multi Node experiment generates.   

(2) After downloading and running Heron with some basic test Nodes, I noticed that many of the nodes were each using a full CPU core on their own. Given that this basic test experiment was just waiting for a keypress, triggering a random number generator, and displaying the result, I was quite surprised to see over 50% of my 8-core CPU fully utilized. I don’t think that Heron needs to be perfectly efficient to accomplish its intended purpose, but I do think that some level of efficiency is required. Some optimization of the codebase should be done so that basic tests like this can run with minimal CPU utilization. This would then inspire confidence that Heron could deal with a real experiment that was significantly more complex without running out of CPU power and thus slowing down.

The original Heron allowed the OS to choose how to manage resources over the required process. We were aware that this could lead to significant use of CPU time, as well as occasionally significant drop of packets (which was dependent on the OS and its configuration). This drop happened mainly when the Node was running a secondary process (like in the Unity game process in the 3rd example). To mitigate these problems, we have now implemented a feature allowing the user to choose the CPU that each Node’s worker function runs on as well as any extra processes the worker process initialises. This is accessible from the Saving secondary window of the node. This stops the OS from swapping processes between CPUs and eliminates the dropping of packages due to the OS behaviour. It also significantly reduces the utilised CPU time. To showcase this, we initially run the simple example mentioned by the reviewer. The computer running only background services was using 8% of CPU (8 cores). With Heron GUI running but with no active Graph, the CPU usage went to 15%. With the Graph running and Heron’s processes running on OS attributed CPU cores, the total CPU was at 65% (so very close to the reviewer’s 50%). By choosing a different CPU core for each of the three worker processes the CPU went down to 47% and finally when all processes were forced to run on the same CPU core the CPU load dropped to 30%.  So, Heron in its current implementation running its GUI and 3 Nodes takes 22% of CPU load. This is still not ideal but is a consequence of the overhead of running multiple processes vs multiple threads. We believe that, given Heron’s latest optimisation, offering more control of system management to the user, the benefits of multi process applications outweigh this hit in system resources. 

We have also increased the scope of the third example we provide in the paper and there we describe in detail how a full-scale experiment with 15 Nodes (which is the upper limit of number of Nodes usually required in most experiments) impacts CPU load. 

Finally, we have added on Heron’s roadmap projects extra tasks focusing only on optimisation (profiling and using Numba for the time critical parts of the Heron code).

(3) I was also surprised to see that, despite being meant specifically to run on and connect diverse types of computer operating systems and being written purely in Python, the Heron Editor and GUI must be run on Windows. This seems like an unfortunate and unnecessary restriction, and it would be great to see the codebase adjusted to make it fully crossplatform-compatible.

This point was also mentioned by reviewer 2. This was a mistake on our part and has now been corrected in the paper. Heron (GUI and underlying communication functionality) can run on any machine that the underlying python libraries run, which is Windows, Linux (both for x86 and Arm architectures) and MacOS. We have tested it on Windows (10 and 11, both x64), Linux PC (Ubuntu 20.04.6, x64) and Raspberry Pi 4 (Debian GNU/Linux 12 (bookworm), aarch64). The Windows and Linux versions of Heron have undergone extensive debugging and all of the available Nodes (that are not OS specific) run on those two systems. We are in the process of debugging the Nodes’ functionality for RasPi. The MacOS version, although functional requires further work to make sure all of the basic Nodes are functional (which is not the case at the moment). We have also updated our manuscript (Multiple machines, operating systems and environments) to include the above information. 

(4) Lastly, when I was running test experiments, sometimes one of the nodes, or part of the Heron editor itself would throw an exception or otherwise crash. Sometimes this left the Heron editor in a zombie state where some aspects of the GUI were responsive and others were not. It would be good to see a more graceful full shutdown of the program when part of it crashes or throws an exception, especially as this is likely to be common as people learn to use it. More problematically, in some of these cases, after closing or force quitting Heron, the TCP ports were not properly relinquished, and thus restarting Heron would run into an "address in use" error. Finding and killing the processes that were still using the ports is not something that is obvious, especially to a beginner, and it would be great to see Heron deal with this better. Ideally, code would be introduced to carefully avoid leaving ports occupied during a hard shutdown, and furthermore, when the address in use error comes up, it would be great to give the user some idea of what to do about it.

A lot of effort has been put into Heron to achieve graceful shut down of processes, especially when these run on different machines that do not know when the GUI process has closed. The code that is being suggested to avoid leaving ports open has been implemented and this works properly when processes do not crash (Heron is terminated by the user) and almost always when there is a bug in a process that forces it to crash. In the version of Heron available during the reviewing process there were bugs that caused the above behaviour (Node code hanging and leaving zombie processes) on MacOS systems. These have now been fixed. There are very seldom instances though, especially during Node development, that crashing processes will hang and need to be terminated manually. We have taken on board the reviewer’s comments that users should be made more aware of these issues and have also described this situation in the Debugging part of Heron’s documentation. There we explain the logging and other tools Heron provides to help users debug their own Nodes and how to deal with hanging processes.

Heron is still in alpha (usable but with bugs) and the best way to debug it and iron out all the bugs in all use cases is through usage from multiple users and error reporting (we would be grateful if the errors the reviewer mentions could be reported in Heron’s github Issues page). We are always addressing and closing any reported errors, since this is the only way for Heron to transition from alpha to beta and eventually to production code quality.

Overall I think that, with these improvements, this could be the beginning of a powerful and versatile new system that would enable flexible experiment design with a relatively low technical barrier to entry. I could see this system being useful to many different labs and fields. 

We thank the reviewer for positive and supportive words and for the constructive feedbacks. We believe we have now addressed all the raised concerns.  

Reviewer #2 (Public Review):

Summary:

The authors provide an open-source graphic user interface (GUI) called Heron, implemented in Python, that is designed to help experimentalists to

(1) design experimental pipelines and implement them in a way that is closely aligned with their mental schemata of the experiments,

(2) execute and control the experimental pipelines with numerous interconnected hardware and software on a network.

The former is achieved by representing an experimental pipeline using a Knowledge Graph and visually representing this graph in the GUI. The latter is accomplished by using an actor model to govern the interaction among interconnected nodes through messaging, implemented using ZeroMQ. The nodes themselves execute user-supplied code in, but not limited to, Python.

Using three showcases of behavioral experiments on rats, the authors highlighted three benefits of their software design:

(1) the knowledge graph serves as a self-documentation of the logic of the experiment, enhancing the readability and reproducibility of the experiment,

(2) the experiment can be executed in a distributed fashion across multiple machines that each has a different operating system or computing environment, such that the experiment can take advantage of hardware that sometimes can only work on a specific computer/OS, a commonly seen issue nowadays,

(3) he users supply their own Python code for node execution that is supposed to be more friendly to those who do not have a strong programming background.

Strengths:

(1) The software is light-weight and open-source, provides a clean and easy-to-use GUI,

(2) The software answers the need of experimentalists, particularly in the field of behavioral science, to deal with the diversity of hardware that becomes restricted to run on dedicated systems.

(3) The software has a solid design that seems to be functionally reliable and useful under many conditions, demonstrated by a number of sophisticated experimental setups.

(4) The software is well documented. The authors pay special attention to documenting the usage of the software and setting up experiments using this software.

Weaknesses:

(1) While the software implementation is solid and has proven effective in designing the experiment showcased in the paper, the novelty of the design is not made clear in the manuscript. Conceptually, both the use of graphs and visual experimental flow design have been key features in many widely used softwares as suggested in the background section of the manuscript. In particular, contrary to the authors’ claim that only pre-defined elements can be used in Simulink or LabView, Simulink introduced MATLAB Function Block back in 2011, and Python code can be used in LabView since 2018. Such customization of nodes is akin to what the authors presented.

In the Heron manuscript we have provided an extensive literature review of existing systems from which Heron has borrowed ideas. We never wished to say that graphs and visual code is what sets Heron apart since these are technologies predating Heron by many years and implemented by a large number of software. We do not believe also that we have mentioned that LabView or Simulink can utilise only predefined nodes. What we have said is that in such systems (like LabView, Simulink and Bonsai) the focus of the architecture is on prespecified low level elements while the ability for users to author their own is there but only as an afterthought. The difference with Heron is that in the latter the focus is on the users developing their own elements. One could think of LabView style software as node-based languages (with low level visual elements like loops and variables) that also allow extra scripting while Heron is a graphical wrapper around python where nodes are graphical representations of whole processes. To our knowledge there is no other software that allows the very fast generation of graphical elements representing whole processes whose communication can also be defined graphically. Apart from this distinction, Heron also allows a graphical approach to writing code for processes that span different machines which again to our knowledge is a novelty of our approach and one of its strongest points towards ease of experimental pipeline creation (without sacrificing expressivity). 

(2) The authors claim that the knowledge graph can be considered as a self-documentation of an experiment. I found it to be true to some extent. Conceptually it’s a welcoming feature and the fact that the same visualization of the knowledge graph can be used to run and control experiments is highly desirable (but see point 1 about novelty). However, I found it largely inadequate for a person to understand an experiment from the knowledge graph as visualized in the GUI alone. While the information flow is clear, and it seems easier to navigate a codebase for an experiment using this method, the design of the GUI does not make it a one-stop place to understand the experiment. Take the Knowledge Graph in Supplementary Figure 2B as an example, it is associated with the first showcase in the result section highlighting this self-documentation capability. I can see what the basic flow is through the disjoint graph where 1) one needs to press a key to start a trial, and 2) camera frames are saved into an avi file presumably using FFMPEG. Unfortunately, it is not clear what the parameters are and what each block is trying to accomplish without the explanation from the authors in the main text. Neither is it clear about what the experiment protocol is without the help of Supplementary Figure 2A.

In my opinion, text/figures are still key to documenting an experiment, including its goals and protocols, but the authors could take advantage of the fact that they are designing a GUI where this information, with properly designed API, could be easily displayed, perhaps through user interaction. For example, in Local Network -> Edit IPs/ports in the GUI configuration, there is a good tooltip displaying additional information for the "password" entry. The GUI for the knowledge graph nodes can very well utilize these tooltips to show additional information about the meaning of the parameters, what a node does, etc, if the API also enforces users to provide this information in the form of, e.g., Python docstrings in their node template. Similarly, this can be applied to edges to make it clear what messages/data are communicated between the nodes. This could greatly enhance the representation of the experiment from the Knowledge graph.

In the first showcase example in the paper “Probabilistic reversal learning.

Implementation as self-documentation” we go through the steps that one would follow in order to understand the functionality of an experiment through Heron’s Knowledge Graph. The Graph is not just the visual representation of the Nodes in the GUI but also their corresponding code bases. We mention that the way Heron’s API limits the way a Node’s code is constructed (through an Actor based paradigm) allows for experimenters to easily go to the code base of a specific Node and understand its 2 functions (initialisation and worker) without getting bogged down in the code base of the whole Graph (since these two functions never call code from any other Nodes). Newer versions of Heron facilitate this easy access to the appropriate code by also allowing users to attach to Heron their favourite IDE and open in it any Node’s two scripts (worker and com) when they double click on the Node in Heron’s GUI. On top of this, Heron now (in the versions developed as answers to the reviewers’ comments) allows Node creators to add extensive comments on a Node but also separate comments on the Node’s parameters and input and output ports. Those can be seen as tooltips when one hovers over the Node (a feature that can be turned off or on by the Info button on every Node).  

As Heron stands at the moment we have not made the claim that the Heron GUI is the full picture in the self-documentation of a Graph. We take note though the reviewer’s desire to have the GUI be the only tool a user would need to use to understand an experimental implementation. The solution to this is the same as the one described by the reviewer of using the GUI to show the user the parts of the code relevant to a specific Node without the user having to go to a separate IDE or code editor. The reason this has not been implemented yet is the lack of a text editor widget in the underlying gui library (DearPyGUI). This is in their roadmap for their next large release and when this exists we will use it to implement exactly the idea the reviewer is suggesting, but also with the capability to not only read comments and code but also directly edit a Node’s code (see Heron’s roadmap). Heron’s API at the moment is ideal for providing such a text editor straight from the GUI.

(3) The design of Heron was primarily with behavioral experiments in mind, in which highly accurate timing is not a strong requirement. Experiments in some other areas that this software is also hoping to expand to, for example, electrophysiology, may need very strong synchronization between apparatus, for example, the record timing and stimulus delivery should be synced. The communication mechanism implemented in Heron is asynchronous, as I understand it, and the code for each node is executed once upon receiving an event at one or more of its inputs. The paper, however, does not include a discussion, or example, about how Heron could be used to address issues that could arise in this type of communication. There is also a lack of information about, for example, how nodes handle inputs when their ability to execute their work function cannot keep up with the frequency of input events. Does the publication/subscription handle the queue intrinsically? Will it create problems in real-time experiments that make multiple nodes run out of sync? The reader could benefit from a discussion about this if they already exist, and if not, the software could benefit from implementing additional mechanisms such that it can meet the requirements from more types of experiments.

In order to address the above lack of explanation (that also the first reviewer pointed out) we expanded the third experimental example in the paper with three more sections. One focuses solely on explaining how in this example (which acquires and saves large amounts of data from separate Nodes running on different machines) one would be able to time align the different data packets generated in different Nodes to each other. The techniques described there are directly implementable on experiments where the requirements of synching are more stringent than the behavioural experiment we showcase (like in ephys experiments). 

Regarding what happens to packages when the worker function of a Node is too slow to handle its traffic, this is mentioned in the paper (Code architecture paragraph): “Heron is designed to have no message buffering, thus automatically dropping any messages that come into a Node’s inputs while the Node’s worker function is still running.” This is also explained in more detail in Heron’s documentation. The reasoning for a no buffer system (as described in the documentation) is that for the use cases Heron is designed to handle we believe there is no situation where a Node would receive large amounts of data in bursts while very little data during the rest of the time (in which case a buffer would make sense). Nodes in most experiments will either be data intensive but with a constant or near constant data receiving speed (e.g. input from a camera or ephys system) or will have variable data load reception but always with small data loads (e.g. buttons). The second case is not an issue and the first case cannot be dealt with a buffer but with the appropriate code design, since buffering data coming in a Node too slow for its input will just postpone the inevitable crash. Heron’s architecture principle in this case is to allow these ‘mistakes’ (i.e. package dropping) to happen so that the pipeline continues to run and transfer the responsibility of making Nodes fast enough to the author of each Node. At the same time Heron provides tools (see the Debugging section of the documentation and the time alignment paragraph of the “Rats playing computer games”  example in the manuscript) that make it easy to detect package drops and either correct them or allow them but also allow time alignment between incoming and outgoing packets. In the very rare case where a buffer is required Heron’s do-it-yourself logic makes it easy for a Node developer to implement their own Node specific buffer.

(4) The authors mentioned in "Heron GUI’s multiple uses" that the GUI can be used as an experimental control panel where the user can update the parameters of the different Nodes on the fly. This is a very useful feature, but it was not demonstrated in the three showcases. A demonstration could greatly help to support this claim.

As the reviewer mentions, we have found Heron’s GUI double role also as an experimental on-line controller a very useful capability during our experiments. We have expanded the last experimental example to also showcase this by showing how on the “Rats playing computer games” experiment we used the parameters of two Nodes to change the arena’s behaviour while the experiment was running, depending on how the subject was behaving at the time (thus exploring a much larger set of parameter combinations, faster during exploratory periods of our shaping protocols construction). 

(5) The API for node scripts can benefit from having a better structure as well as having additional utilities to help users navigate the requirements, and provide more guidance to users in creating new nodes. A more standard practice in the field is to create three abstract Python classes, Source, Sink, and Transform that dictate the requirements for initialisation, work_function, and on_end_of_life, and provide additional utility methods to help users connect between their code and the communication mechanism. They can be properly docstringed, along with templates. In this way, the com and worker scripts can be merged into a single unified API. A simple example that can cause confusion in the worker script is the "worker_object", which is passed into the initialise function. It is unclear what this object this variable should be, and what attributes are available without looking into the source code. As the software is also targeting those who are less experienced in programming, setting up more guidance in the API can be really helpful. In addition, the self-documentation aspect of the GUI can also benefit from a better structured API as discussed in point 2 above.

The reviewer is right that using abstract classes to expose to users the required API would be a more standard practice. The reason we did not choose to do this was to keep Heron easily accessible to entry level Python programmers who do not have familiarity yet with object oriented programming ideas. So instead of providing abstract classes we expose only the implementation of three functions which are part of the worker classes but the classes themselves are not seen by the users of the API. The point about the users’ accessibility to more information regarding a few objects used in the API (the worker object for example) has been taken on board and we have now addressed this by type hinting all these objects both in the templates and more importantly in the automatically generated code that Heron now creates when a user chooses to create a Node graphically (a feature of Heron not present in the version available in the initial submission of this manuscript).  

(6) The authors should provide more pre-defined elements. Even though the ability for users to run arbitrary code is the main feature, the initial adoption of a codebase by a community, in which many members are not so experienced with programming, is the ability for them to use off-the-shelf components as much as possible. I believe the software could benefit from a suite of commonly used Nodes.

There are currently 12 Node repositories in the Heron-repositories project on Github with more than 30 Nodes, 20 of which are general use (not implementing a specific experiment’ logic). This list will continue to grow but we fully appreciate the truth of the reviewer’s comment that adoption will depend on the existence of a large number of commonly used Nodes (for example Numpy, and OpenCV Nodes) and are working towards this goal.

(7) It is not clear to me if there is any capability or utilities for testing individual nodes without invoking a full system execution. This would be critical when designing new experiments and testing out each component.

There is no capability to run the code of an individual Node outside Heron’s GUI. A user could potentially design and test parts of the Node before they get added into a Node but we have found this to be a highly inefficient way of developing new Nodes. In our hands the best approach for Node development was to quickly generate test inputs and/or outputs using the “User Defined Function 1I 1O” Node where one can quickly write a function and make it accessible from a Node. Those test outputs can then be pushed in the Node under development or its outputs can be pushed in the test function, to allow for incremental development without having to connect it to the Nodes it would be connected in an actual pipeline. For example, one can easily create a small function that if a user presses a key will generate the same output (if run from a “User Defined Function 1I 1O” Node) as an Arduino Node reading some buttons. This output can then be passed into an experiment logic Node under development that needs to do something with this input. In this way during a Node development Heron allows the generation of simulated hardware inputs and outputs without actually running the actual hardware. We have added this way of developing Nodes also in our manuscript (Creating a new Node).

Reviewer #3 (Public Review):

Summary:

The authors present a Python tool, Heron, that provides a framework for defining and running experiments in a lab setting (e.g. in behavioural neuroscience). It consists of a graphical editor for defining the pipeline (interconnected nodes with parameters that can pass data between them), an API for defining the nodes of these pipelines, and a framework based on ZeroMQ, responsible for the overall control and data exchange between nodes. Since nodes run independently and only communicate via network messages, an experiment can make use of nodes running on several machines and in separate environments, including on different operating systems.

Strengths:

As the authors correctly identify, lab experiments often require a hodgepodge of separate hardware and software tools working together. A single, unified interface for defining these connections and running/supervising the experiment, together with flexibility in defining the individual subtasks (nodes) is therefore a very welcome approach. The GUI editor seems fairly intuitive, and Python as an accessible programming environment is a very sensible choice. By basing the communication on the widely used ZeroMQ framework, they have a solid base for the required non-trivial coordination and communication. Potential users reading the paper will have a good idea of how to use the software and whether it would be helpful for their own work. The presented experiments convincingly demonstrate the usefulness of the tool for realistic scientific applications.

Weaknesses:

(1) In my opinion, the authors somewhat oversell the reproducibility and "selfdocumentation" aspect of their solution. While it is certainly true that the graph representation gives a useful high-level overview of an experiment, it can also suffer from the same shortcomings as a "pure code" description of a model - if a user gives their nodes and parameters generic/unhelpful names, reading the graph will not help much. 

This is a problem that to our understanding no software solution can possibly address. Yet having a visual representation of how different inputs and outputs connect to each other we argue would be a substantial benefit in contrast to the case of “pure code” especially when the developer of the experiment has used badly formatted variable names.

(2) Making the link between the nodes and the actual code is also not straightforward, since the code for the nodes is spread out over several directories (or potentially even machines), and not directly accessible from within the GUI. 

This is not accurate. The obligatory code of a Node always exists within a single folder and Heron’s API makes it rather cumbersome to spread scripts relating to a Node across separate folders. The Node folder structure can potentially be copied over different machines but this is why Heron is tightly integrated with git practices (and even politely asks the user with popup windows to create git repositories of any Nodes they create whilst using Heron’s automatic Node generator system). Heron’s documentation is also very clear on the folder structure of a Node which keeps the required code always in the same place across machines and more importantly across experiments and labs. Regarding the direct accessibility of the code from the GUI, we took on board the reviewers’ comments and have taken the first step towards correcting this. Now one can attach to Heron their favourite IDE and then they can double click on any Node to open its two main scripts (com and worker) in that IDE embedded in whatever code project they choose (also set in Heron’s settings windows). On top of this, Heron now allows the addition of notes both for a Node and for all its parameters, inputs and outputs which can be viewed by hovering the mouse over them on the Nodes’ GUIs. The final step towards GUI-code integration will be to have a Heron GUI code editor but this is something that has to wait for further development from Heron’s underlying GUI library DearPyGUI.

(3) The authors state that "[Heron’s approach] confers obvious benefits to the exchange and reproducibility of experiments", but the paper does not discuss how one would actually exchange an experiment and its parameters, given that the graph (and its json representation) contains user-specific absolute filenames, machine IP addresses, etc, and the parameter values that were used are stored in general data frames, potentially separate from the results. Neither does it address how a user could keep track of which versions of files were used (including Heron itself).

Heron’s Graphs, like any experimental implementation, must contain machine specific strings. These are accessible either from Heron’s GUI when a Graph json file is opened or from the json file itself. Heron in this regard does not do anything different to any other software, other than saving the graphs into human readable json files that users can easily manipulate directly.

Heron provides a method for users to save every change of the Node parameters that might happen during an experiment so that it can be fully reproduced. The dataframes generated are done so in the folders specified by the user in each of the Nodes (and all those paths are saved in the json file of the Graph). We understand that Heron offers a certain degree of freedom to the user (Heron’s main reason to exist is exactly this versatility) to generate data files wherever they want but makes sure every file path gets recorded for subsequent reproduction. So, Heron behaves pretty much exactly like any other open source software. What we wanted to focus on as the benefits of Heron on exchange and reproducibility was the ability of experimenters to take a Graph from another lab (with its machine specific file paths and IP addresses) and by examining the graphical interface of it to be able to quickly tweak it to make it run on their own systems. That is achievable through the fact that a Heron experiment will be constructed by a small amount of Nodes (5 to 15 usually) whose file paths can be trivially changed in the GUI or directly in the json file while the LAN setup of the machines used can be easily reconstructed from the information saved in the secondary GUIs.

Where Heron needs to improve (and this is a major point in Heron’s roadmap) is the need to better integrate the different saved experiments with the git versions of Heron and the Nodes that were used for that specific save. This, we appreciate is very important for full reproducibility of the experiment and it is a feature we will soon implement. More specifically users will save together with a graph the versions of all the used repositories and during load the code base utilised will come from the recorded versions and not from the current head of the different repositories. This is a feature that we are currently working on now and as our roadmap suggests will be implemented by the release of Heron 1.0. 

(4) Another limitation that in my opinion is not sufficiently addressed is the communication between the nodes, and the effect of passing all communications via the host machine and SSH. What does this mean for the resulting throughput and latency - in particular in comparison to software such as Bonsai or Autopilot? The paper also states that "Heron is designed to have no message buffering, thus automatically dropping any messages that come into a Node’s inputs while the Node’s worker function is still running."- it seems to be up to the user to debug and handle this manually?

There are a few points raised here that require addressing. The first is Heron’s requirement to pass all communication through the main (GUI) machine. We understand (and also state in the manuscript) that this is a limitation that needs to be addressed. We plan to do this is by adding to Heron the feature of running headless (see our roadmap). This will allow us to run whole Heron pipelines in a second machine which will communicate with the main pipeline (run on the GUI machine) with special Nodes. That will allow experimenters to define whole pipelines on secondary machines where the data between their Nodes stay on the machine running the pipeline. This is an important feature for Heron and it will be one of the first features to be implemented next (after the integration of the saving system with git). 

The second point is regarding Heron’s throughput latency. In our original manuscript we did not have any description of Heron’s capabilities in this respect and both other reviewers mentioned this as a limitation. As mentioned above, we have now addressed this by adding a section to our third experimental example that fully describes how much CPU is required to run a full experimental pipeline running on two machines and utilising also non python code executables (a Unity game). This gives an overview of how heavy pipelines can run on normal computers given adequate optimisation and utilising Heron’s feature of forcing some Nodes to run their Worker processes on a specific core. At the same time, Heron’s use of 0MQ protocol makes sure there are no other delays or speed limitations to message passing. So, message passing within the same machine is just an exchange of memory pointers while messages passing between different machines face the standard speed limitations of the Local Access Network’s ethernet card speeds. 

Finally, regarding the message dropping feature of Heron, as mentioned above this is an architectural decision given the use cases of message passing we expect Heron to come in contact with. For a full explanation of the logic here please see our answer to the 3rd comment by Reviewer 2.

(5) As a final comment, I have to admit that I was a bit confused by the use of the term "Knowledge Graph" in the title and elsewhere. In my opinion, the Heron software describes "pipelines" or "data workflows", not knowledge graphs - I’d understand a knowledge graph to be about entities and their relationships. As the authors state, it is usually meant to make it possible to "test propositions against the knowledge and also create novel propositions" - how would this apply here?

We have described Heron as a Knowledge Graph instead of a pipeline, data workflow or computation graph in order to emphasise Heron’s distinct operation in contrast to what one would consider a standard pipeline and data workflow generated by other visual based software (like LabView and Bonsai). This difference exists on what a user should think of as the base element of a graph, i.e. the Node. In all other visual programming paradigms, the Node is defined as a low-level computation, usually a language keyword, language flow control or some simple function. The logic in this case is generated by composing together the visual elements (Nodes). In Heron the Node is to be thought of as a process which can be of arbitrary complexity and the logic of the graph is composed by the user both within each Node and by the way the Nodes are combined together. This is an important distinction in Heron’s basic operation logic and it is we argue the main way Heron allows flexibility in what can be achieved while retaining ease of graph composition (by users defining their own level of complexity and functionality encompassed within each Node). We have found that calling this approach a computation graph (which it is) or a pipeline or data workflow would not accentuate this difference. The term Knowledge Graph was the most appropriate as it captures the essence of variable information complexity (even in terms of length of shortest string required) defined by a Node.

Recommendations for the authors:  

Reviewer #1 (Recommendations For The Authors):

-  No buffering implies dropped messages when a node is busy. It seems like this could be very problematic for some use cases... 

This is a design principle of Heron. We have now provided a detailed explanation of the reasoning behind it in our answer to Reviewer 2 (Paragraph 3) as well as in the manuscript. 

-  How are ssh passwords stored, and is it secure in some way or just in plain text?  

For now they are plain text in an unencrypted file that is not part of the repo (if one gets Heron from the repo). Eventually, we would like to go to private/public key pairs but this is not a priority due to the local nature of Heron’s use cases (all machines in an experiment are expected to connect in a LAN).  

Minor notes / copyedits:

-  Figure 2A: right and left seem to be reversed in the caption. 

They were. This is now fixed. 

-  Figure 2B: the text says that proof of life messages are sent to each worker process but in the figure, it looks like they are published by the workers? Also true in the online documentation.  

The Figure caption was wrong. This is now fixed.

-  psutil package is not included in the requirements for GitHub

We have now included psutil in the requirements.

-  GitHub readme says Python >=3.7 but Heron will not run as written without python >= 3.9 (which is alluded to in the paper)

The new Heron updates require Python 3.11. We have now updated GitHub and the documentation to reflect this.

-  The paper mentions that the Heron editor must be run on Windows, but this is not mentioned in the Github readme.  

This was an error in the manuscript that we have now corrected.

-  It’s unclear from the readme/manual how to remove a node from the editor once it’s been added.  

We have now added an X button on each Node to complement the Del button on the keyboard (for MacOS users that do not have this button most of the times).

-  The first example experiment is called the Probabilistic Reversal Learning experiment in text, but the uncertainty experiment in the supplemental and on GitHub.  

We have now used the correct name (Probabilistic Reversal Learning) in both the supplemental material and on GitHub

-  Since Python >=3.9 is required, consider using fstrings instead of str.format for clarity in the codebase  

Thank you for the suggestion. Latest Heron development has been using f strings and we will do a refactoring in the near future.

-  Grasshopper cameras can run on linux as well through the spinnaker SDK, not just Windows.  

Fixed in the manuscript. 

-  Figure 4: Square and star indicators are unclear.

Increased the size of the indicators to make them clear.

-  End of page 9: "an of the self" presumably a typo for "off the shelf"?  

Corrected.

-  Page 10 first paragraph. "second root" should be "second route"

Corrected.

-  When running Heron, the terminal constantly spams Blowfish encryption deprecation warnings, making it difficult to see the useful messages.  

The solution to this problem is to either update paramiko or install Heron through pip. This possible issue is mentioned in the documentation.

-  Node input /output hitboxes in the GUI are pretty small. If they could be bigger it would make it easier to connect nodes reliably without mis-clicks.

We have redone the Node GUI, also increasing the size of the In/Out points.

Reviewer #2 (Recommendations For The Authors):

(1) There are quite a few typos in the manuscript, for example: "one can accessess the code", "an of the self", etc.  

Thanks for the comment. We have now screened the manuscript for possible typos.

(2) Heron’s GUI can only run on Windows! This seems to be the opposite of the key argument about the portability of the experimental setup.  

As explained in the answers to Reviewer 1, Heron can run on most machines that the underlying python libraries run, i.e. Windows and Linux (both for x86 and Arm architectures). We have tested it on Windows (10 and 11, both x64), Linux PC (Ubuntu 20.04.6, x64) and Raspberry Pi 4 (Debian GNU/Linux 12 (bookworm), aarch64). We have now revised the manuscript and the GitHub repo to reflect this.

(3) Currently, the output is displayed along the left edge of the node, but the yellow dot connector is on the right. It would make more sense to have the text displayed next to the connectors.  

We have redesigned the Node GUI and have now placed the Out connectors on the right side of the Node.

(4) The edges are often occluded by the nodes in the GUI. Sometimes it leads to some confusion, particularly when the number of nodes is large, e.g., Fig 4.

This is something that is dependent on the capabilities of the DearPyGUI module. At the moment there is no way to control the way the edges are drawn.

Reviewer #3 (Recommendations For The Authors):

A few comments on the software and the documentation itself:

- From a software engineering point of view, the implementation seems to be rather immature. While I get the general appeal of "no installation necessary", I do not think that installing dependencies by hand and cloning a GitHub repository is easier than installing a standard package.

We have now added a pip install capability which also creates a Heron command line command to start Heron with. 

-The generous use of global variables to store state (minor point, given that all nodes run in different processes), boilerplate code that each node needs to repeat, and the absence of any kind of automatic testing do not give the impression of a very mature software (case in point: I had to delete a line from editor.py to be able to start it on a non-Windows system).  

As mentioned, the use of global variables in the worker scripts is fine partly due to the multi process nature of the development and we have found it is a friendly approach to Matlab users who are just starting with Python (a serious consideration for Heron). Also, the parts of the code that would require a singleton (the Editor for example) are treated as scripts with global variables while the parts that require the construction of objects are fully embedded in classes (the Node for example). A future refactoring might make also all the parts of the code not seen by the user fully object oriented but this is a decision with pros and cons needing to be weighted first. 

Absence of testing is an important issue we recognise but Heron is a GUI app and nontrivial unit tests would require some keystroke/mouse movement emulator (like QTest of pytest-qt for QT based GUIs). This will be dealt with in the near future (using more general solutions like PyAutoGUI) but it is something that needs a serious amount of effort (quite a bit more that writing unit tests for non GUI based software) and more importantly it is nowhere as robust as standard unit tests (due to the variable nature of the GUI through development) making automatic test authoring an almost as laborious a process as the one it is supposed to automate.

-  From looking at the examples, I did not quite see why it is necessary to write the ..._com.py scripts as Python files, since they only seem to consist of boilerplate code and variable definitions. Wouldn’t it be more convenient to represent this information in configuration files (e.g. yaml or toml)?  

The com is not a configuration file, it is a script that launches the communication process of the Node. We could remove the variable definitions to a separate toml file (which then the com script would have to read). The pros and cons of such a set up should be considered in a future refactoring.

Minor comments for the paper:

-  p.7 (top left): "through its return statement" - the worker loop is an infinite loop that forwards data with a return statement?  

This is now corrected. The worker loop is an infinite loop and does not return anything but at each iteration pushes data to the Nodes output.

-  p.9 (bottom right): "of the self" → "off-the-shelf"  

Corrected.

-  p.10 (bottom left): "second root" → "second route"  

Corrected.

-  Supplementary Figure 3: Green start and square seem to be swapped (the green star on top is a camera image and the green star on the bottom is value visualization - inversely for the green square).  

The star and square have been swapped around.

-  Caption Supplementary Figure 4 (end): "rashes to receive" → "rushes to receive"  

Corrected.

Voici un sommaire minuté basé sur la transcription du webinaire Solidatech:

• 0:00-0:12: Introduction du webinaire et présentation du programme Solidatech, une initiative de solidarité numérique.

L'objectif est de présenter en détail les offres de Solidatech, incluant logiciels, services, accompagnement et matériel, ainsi que d'expliquer leur fonctionnement.

• 0:26-1:51: Présentation globale de Solidatech par Laurine.

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• 2:35-3:25: Modes d'action de Solidatech. Solidatech intervient de trois manières : en facilitant l'accès au numérique par des tarifs réduits sur les logiciels et le matériel informatique, en accompagnant les associations dans le développement de leurs usages numériques (centre de ressources, diagnostic numérique, formations), et en participant à la coproduction et à la diffusion de savoirs sur la transition numérique des associations, notamment à travers des études triennales.

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• 4:32-5:27: Accompagnement aux usages numériques.

Solidatech met à disposition des ressources pour une utilisation autonome, comme un centre de ressources et des newsletters.

Des webinaires thématiques sont aussi proposés, comme celui sur le RGPD prévu à la fin du mois. Un accompagnement individuel est également disponible, avec un catalogue de formations et des enjeux de sensibilisation au numérique.

• 5:27-5:53: Chiffres clés et transition vers la présentation du site web. Solidatech accompagne plus de 42 000 associations.

• 5:53-6:23: Transition vers la présentation du site web par Elodie.

• 6:23-9:54: Visite guidée du site Solidatech.fr. Le site Solidatech.fr est la porte d'entrée vers le programme de solidarité numérique, présentant les offres, services et accompagnements.

Le site oriente également vers des plateformes secondaires pour les logiciels à tarif réduit et l'achat de matériel. L'inscription gratuite est nécessaire pour bénéficier de ces offres.

• 9:54-11:32: Processus de connexion et présentation de l'espace utilisateur. Après l'inscription, les utilisateurs peuvent se connecter via le site Solidatech ou directement lorsqu'ils cliquent sur une offre.

La connexion redirige vers la plateforme TechSoup.fr, où les associations accèdent à leur interface utilisateur.

• 11:32-12:16: Présentation de la plateforme TechSoup.fr. TechSoup.fr est la plateforme principale pour commander des logiciels à tarif solidaire et du matériel neuf, grâce aux partenariats internationaux. D'autres partenariats et offres spécifiques sont disponibles sur d'autres plateformes.

• 12:16-15:38: Catalogue de logiciels à tarif réduit. Le catalogue de logiciels peut être consulté sans être connecté, mais la connexion est nécessaire pour commander.

Les utilisateurs peuvent rechercher des logiciels par nom ou filtrer par catégorie pour identifier les solutions répondant à leurs besoins. Le catalogue inclut plus de 260 offres, mais l'éligibilité varie en fonction des critères définis par les éditeurs, tels que le budget annuel ou le secteur d'activité.

• 15:38-17:22: Fiche produit et modalités de l'offre. Chaque fiche produit détaille les caractéristiques du logiciel et les modalités de l'offre Solidatech, incluant les réductions, les critères de restriction et les conditions d'éligibilité. Il est conseillé de lire attentivement l'onglet "Modalités de l'offre" pour comprendre les conditions spécifiques.

• 17:22-19:20: Fonctionnement des coupons de réduction. Pour certaines offres, l'achat d'un coupon de réduction est nécessaire pour bénéficier d'un tarif réduit sur l'abonnement au logiciel, comme pour Adobe Creative Cloud.

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• 19:20-20:36: Équipements neufs. L'offre d'équipement neuf concerne principalement le matériel réseau de la marque Cisco, proposé avec des réductions importantes, ainsi que quelques caméras de surveillance.

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• 20:36-23:33: Matériel reconditionné. Le matériel reconditionné est accessible via la plateforme TechSoup ou directement depuis le site Solidatech.

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• 23:33-26:10: Informations sur les produits reconditionnés.

Les produits reconditionnés sont garantis 12 mois et sont effacés et testés. Pendant le mois de février, l'extension de garantie est offerte, passant de 12 à 24 mois.

Des informations détaillées sur les produits, leurs performances et les conditions de garantie sont fournies sur le site. Une demande de devis peut être faite pour des commandes spécifiques ou en grande quantité.

• 26:10-27:50: Processus de commande de matériel reconditionné. Pour la première commande sur la boutique de reconditionné, il est nécessaire de créer un compte client et de renseigner un jeton de validation pour vérifier l'éligibilité de l'association. Ce jeton est unique et peut être généré sur le compte Solidatech.

• 27:50-29:49: Explication du jeton de validation.

Le jeton de validation est un code permettant de vérifier que la structure est bien une association. Il est disponible sur le compte Solidatech et peut être généré autant de fois que nécessaire.

• 29:49-30:42: Utilisation du jeton de validation pour accéder aux offres. Le jeton de validation est souvent demandé pour accéder à des offres logicielles ou à certains services.

• 30:42-31:51: Formations et conseils. Solidatech propose des formations certifiées Qualiopi, destinées aux salariés et bénévoles des associations, couvrant les enjeux du numérique et l'utilisation d'outils. Les formations peuvent être suivies à distance ou en présentiel dans les locaux de l'association.

• 31:51-33:51: Services de migration vers le cloud.

Solidatech propose des services de migration vers le cloud avec des partenaires comme IT for Life et Se Consulting, pour accompagner les associations vers Workspace ou Microsoft 365. De nouveaux services et formations seront proposés prochainement.

• 33:51-35:23: Prestatech.

Prestatech est un annuaire de prestataires de confiance, préqualifiés par Solidatech, offrant des services de conseil, de prestation informatique et de formation. Les prestataires sont classés selon leur domaine d'expertise et leurs modalités de financement.

• 35:23-37:20: Centre de ressources.

Le centre de ressources est un blog contenant des articles conseils, des cas d'usage et des comparatifs d'outils, classés par thématique. Il inclut également les replays des webinaires.

• 37:20-38:35: Autodiagnostic numérique.

L'autodiagnostic numérique est un outil gratuit permettant d'évaluer la maturité numérique de l'association à travers un questionnaire interactif organisé autour de sept piliers stratégiques.

Il aide à prioriser les chantiers à mener et propose une sélection de logiciels et de ressources pertinentes.

• 38:35-38:47: Étude nationale sur le numérique associatif. Solidatech mène une étude tous les trois ans sur la place du numérique dans le projet associatif, en partenariat avec Recherche et Solidarités.
• 38:47-49:23: Questions/réponses.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Summary

This study develops a high-resolution stochastic model to explore DNA replication timing regulation in Saccharomyces cerevisiae, specifically focusing on competition between replication origins for limited initiation factors. The model, based on "Beacon Calculus," utilizes an iterative optimization process to fit experimental data, successfully reproducing timing, efficiency, and directionality features of genome replication origins. Additionally, the authors use the model to make predictions on replication dynamics under varying initiation factor concentrations, providing new insights into DNA replication processes that have not yet been observed empirically or experimentally.

Major Comments:

1. This study provides a strong support for the relationship between replication starting point competition and initial factor concentration. However, some predictive conclusions, such as "the origin of high efficiency may not be activated earlier", are still preliminary. Can the author further clarify the scope of these predictions and any potential mechanism in the discussion part to improve the rigor of this study?
2. The resolution and accuracy of the model prediction are obvious to all, but the specific generalization ability is still unknown, which makes the further promotion slightly insufficient. Does the author consider conducting additional experiments? To detect the replication time and efficiency in yeast cells with changed levels of key initiation factors (such as Cdc45 or Dpb11). The empirical data can be compared with the model prediction by editing CRISPR gene or manipulating the initial factor abundance through overexpression vector.
3. The model currently uses single values for the initiation factor number and recycling rate, though these parameters may vary across cell cycles or under different growth conditions. It is suggested that sensitivity analysis should be added to supplementary materials to explore how the changes of these parameters affect the model output, such as replication time distribution and origin efficiency.
4. While the authors use mean absolute error (MAE) to assess model fit, it is suggested to add other statistical methods, such as root mean square error or correlation analysis, to further evaluate the model's accuracy and robustness. In addition, this model lacks comparison with other studies on fitting yeast replication time, and it is difficult to evaluate the effect of this model compared with other models from the specific performance.
5. Although the code is open, it is suggested to provide specific instructions or examples of the running code in supplementary materials, so as to facilitate reproduction and application by other researchers.
6. In Figure 2(a), compared with other chromosomes, the fitting effect of chromosome 1 seems to be not good. Has the author ever thought about the reason? In addition, what is the guiding significance of this model in practical applications, such as online services, forecasting tools, or experiments? Can the author give relevant application examples in this regard?

Minor Comments:

1. Suggestions for Improving Figures: Figures 2 and 3: It is suggested that the differences between experimental data and model fitting data should be clearly marked by using more distinctive colors or symbols with different shapes in these figures, so as to help readers quickly distinguish between simulation results and experimental observation results. Density Plot in Figure 4: The current color gradient is dense, making it difficult to differentiate activation distributions for different origins. Consider using a broader color gradient or adding a slight separation between each origin's curve to improve readability.
2. Model Parameter Table: Adding a table in the Methods section or supplementary materials that summarizes the main model parameters (e.g., number of initiation factors, recycling rate, replication speed) and the basis for each parameter's setting would be helpful. This will allow readers to quickly understand the model setup and provide a reference for future researchers who may wish to use or adjust this model.
3. Citation and Description of Experimental Data: Clarify the origin and characteristics of the experimental data used, such as the specific details of the replication timing dataset applied for model fitting, and indicate whether the data represents single-cell or population-averaged measurements. This information will help readers better understand the comparison between the model and actual data.
4. Background and References: In the Introduction, consider adding a brief explanation of "Beacon Calculus" to aid non-specialist readers in understanding the novelty and applicability of this method. Adding foundational references for Beacon Calculus would further help readers appreciate the advantages of this approach. Additionally, in the discussion of the model's suitability for other biological systems, citing some reviews on high-efficiency replication origin analyses would help demonstrate the model's broader applicability.

Significance

1. Significance of the Research:

This study advances our understanding of DNA replication timing regulation in S. cerevisiae and presents a mathematical modeling approach with theoretical importance. By reconstructing a DNA replication timing framework for yeast, the model also provides a foundation that could be adapted for other systems, potentially advancing modeling techniques in genome replication research. 2. Relation to Existing Literature:

This study builds upon prior research on S. cerevisiae DNA replication initiation and proposes a simplified, reproducible model. Compared to more complex mathematical models or large-scale data analyses, this approach is more interpretable and easier to reproduce. The study's predictions on initiation factor concentration effects provide another perspective for future experimental work. 3. Target Audience:

This work will influence researchers studying DNA replication regulation, yeast genomics, and bioinformatics modeling. Additionally, scholars in microbiology and genetics may also benefit from the innovative modeling methods introduced. 4. Reviewer Expertise:

My expertise includes computational biology and bioinformatics, with a professional knowledge in DNA replication origins and bioinformatics modeling.

Reviewer #3 (Public review):

Summary:

The manuscript is evaluating changes in dopamine signaling in the nucleus accumbens following pair bonding and exposure to various stimuli in mandarin voles. In addition, the authors present chemogenetic data which demonstrates excitation and inhibition of D1 and D2 MSN affect pair bond formation.

Strengths:

The experimental designs are strong. The approaches are innovative and use cutting-edge methods. The manuscript is well written.

Comments on revised version:

I appreciate the efforts by the authors to address many of my previous comments. The issues that remain are those associated with the statistics. It seems that not all statistical analyses were performed with the correct test. For example, the photometry data comparing emissions during partner vs stranger investigation over time would be best performed as a two-way ANOVA with odor type and time being separate variables. Also, there are paired t-tests being performed by calculating an average deltaF/F during the 4 second window following the being of a behavioral event. I think an area-under-the-curve calculation of these events would better capture the fluorescent emissions of these events as an index. Details in the Result describing the data being analyzed via ANOVA vs t-tests when reporting the results would be useful for the reviewer to understand each analysis.

Reviewer #1 (Public review):

The authors demonstrated that NINJ1 promotes TF-positive MV release during pyroptosis and thereby triggers coagulation. Coagulation is one of the risk factors that can cause secondary complications in various inflammatory diseases, making it a highly important therapeutic target in clinical treatment. This paper effectively explains the connection between pyroptosis and MV release with Ninj1, which is a significant strength. It provides valuable insight into the potential of targeting Ninj1 as a therapeutic strategy.

Although the advances in this paper are valuable, several aspects need to be clarified. Some comments are discussed below.

(1) Since it is not Ninj1 directly regulating coagulation but rather the MV released by Ninj1 playing a role, the title should include that. The current title makes it seem like Ninj1 directly regulates inflammation and coagulation. It would be better to revise the title.

(2) Ninj1 is known to be an induced protein that is barely expressed in normal conditions. As you showed in "Fig1G" data, control samples showed no detection of Ninj1. However, in "Figure S1", all tissues (liver, lung, kidney and spleen) expressed Ninj1 protein. If the authors stimulated the mice with fla injection, it should be mentioned in the figure legend.

(3) In "Fig3A", the Ninj1 protein expression was increased in the control of BMDM +/- cell lysate rather than fla stimulation. However, in MV, Ninj1 was not detected at all in +/- control but was only observed with Fla injection. The authors need to provide an explanation for this observation. Additionally, looking at the MV β-actin lane, the band thicknesses appear to be very different between groups. It seems necessary to equalize the protein amounts. If that is difficult, at least between the +/+ and +/- controls.

(4) Since the authors focused Ninj1-dependent microvesicle (MV) release, they need to show MV characterizations (EM, NTA, Western for MV markers, etc...).

(5) To clarify whether Ninj1-dependent MV induces coagulation, the authors need to determine whether platelet aggregation is reduced with isolated +/- MVs compared to +/+ MVs.

(6) Even with the authors well established experiments with haploid mice, it is a critical limitation of this paper. To improve the quality of this paper, the authors should consider confirming the findings using mouse macrophage cell lines, such as generating Ninj1-/- Raw264.7 cell lines, to examine the homozygous effect.

(7) There was a paper reported in 2023 (Zhou, X. et al., NINJ1 Regulates Platelet Activation and PANoptosis in Septic Disseminated Intravascular Coagulation. Int. J. Mol. Sci. 2023) that revealed the relationship between Ninj1 and coagulation. According to this paper, inhibition of Ninj1 in platelets prevents pyroptosis, leading to reduced platelet activation and, consequently, the suppression of thrombosis. How about the activation of platelets in Ninj1 +/- mice? The author should add this paper in the reference section and discuss the platelet functions in their mice.

Briefing Document : Référentiel National de Financement des Actions de Parentalité par les CAF

Source : Extrait du "Référentiel National de Financement par les CAF des Actions du Volet 1 du Fonds National de Soutien à la Parentalité" (2023)

Objectif du Document : Ce document vise à fournir un aperçu synthétique du référentiel national de financement des actions de soutien à la parentalité par les Caisses d'Allocations Familiales (CAF), en se concentrant sur le Volet 1 du Fonds National de Soutien à la Parentalité (FNSP). Il met en lumière les principaux thèmes, les idées clés, les critères d'éligibilité et les types d'actions finançables.

Thèmes Principaux:

1. L'évolution de la Famille et le Rôle Central de la Branche Famille:

Le document souligne l'importance d'accompagner les familles dans leur diversité et leurs parcours de vie, reconnaissant que la famille d'aujourd'hui est différente de celle d'hier en raison de contextes sociaux plus complexes. La branche Famille des CAF se positionne comme un acteur clé pour soutenir le développement de chaque personne dès la naissance, en particulier lorsque les familles rencontrent des difficultés. "Les Caf sont nées de la volonté d’apporter une aide à toutes les familles, dans leur diversité."

1. Le Soutien à la Parentalité :

Une Politique Universelle et Préventive: Le document met en avant le soutien à la parentalité comme une composante essentielle de la politique familiale, s'adressant à tous les parents qui s'interrogent sur l'éducation de leurs enfants.

Il s'inscrit dans une logique de prévention primaire universelle, visant à valoriser le rôle des parents et à prévenir les risques intra-familiaux. "Le soutien à la parentalité s’adresse à tous les parents qui s’interrogent sur l’éducation de leurs enfants au quotidien."

1. Objectifs Stratégiques de la Cog 2018-2022:

La Caisse Nationale des Allocations Familiales (CNAF) a défini trois objectifs majeurs dans le cadre de la Convention d'Objectifs et de Gestion (Cog) 2018-2022 : ◦ Accompagner les parents dès l'arrivée de l'enfant. ◦ Soutenir les parents dans l'éducation de leurs enfants, en particulier les adolescents. ◦ Accompagner et prévenir les ruptures familiales. Ces objectifs sont transversaux et incluent l'accompagnement des familles ayant un enfant porteur de handicap.

1. Le Fonds National Parentalité (FNP) et ses Volets :

Le document précise que le FNP se compose de trois volets :

Volet 1 : Financement d'actions de soutien et d'accompagnement à la parentalité (Reaap).

Volet 2 : Soutien à l'animation départementale de la politique parentalité.

Volet 3 : Soutien au fonctionnement de lieux ressources pour les parents. L'objectif global est de développer et structurer la politique de soutien à la parentalité sur les territoires, en coordination avec les Schémas Départementaux des Services aux Familles (Sdsf) et les Conventions Territoriales Globales (Ctg).

1. Justification du Référentiel National :

Le référentiel vise à harmoniser les actions de soutien à la parentalité financées par les CAF, en renforçant leur lisibilité et leur articulation avec les priorités de la branche Famille. Il cherche à diversifier les modalités d'intervention et à développer des offres innovantes adaptées aux nouveaux besoins des parents. "Ce référentiel porte l’ambition d’harmoniser les actions de soutien et d’accompagnement à la parentalité financées par les Caf dans le cadre du volet 1 du fonds national parentalité."

Idées Clés et Faits Importants:

Prérequis et Critères d'Éligibilité : Les actions doivent être mises en œuvre avec et pour les parents, en respectant la charte nationale du soutien à la parentalité et les principes de laïcité. La participation aux Reaap est encouragée pour favoriser la coordination locale et le partage de savoir-faire. Une attention particulière est accordée à l'accessibilité des actions, à la participation des parents, et à la construction des actions en réponse à un diagnostic partagé.

Volet "Accessibilité et participation des parents": Les actions doivent se dérouler dans des lieux fréquentés par les parents, rechercher la participation des parents sans l'imposer, être accessibles aux parents en situation de handicap, proposer une participation financière symbolique, et s'adapter aux contraintes des familles. "proposer les actions là où se trouvent les parents : dans les établissements et lieux que fréquentent leurs enfants (la crèche, l’école, les accueils de loisirs..."

Modalités de Dépôt et de Sélection des Projets : Les projets sont soumis dans le cadre d'un appel à projets annuel, en cohérence avec le Sdsf et la Cog. Les CAF portent une attention particulière à l'accompagnement à la naissance, au soutien à l'éducation des adolescents et à la prévention des ruptures familiales. Un comité technique "parentalité" procède à la sélection des projets.

Durée du Financement : Les subventions ne sont pas pérennes et ne doivent pas financer le fonctionnement de structures. Un financement pluriannuel est possible pour les centres sociaux et les porteurs de projets soutenus depuis au moins deux ans, dans le cadre d'une convention d'objectifs et de financement.

Mobilisation de Cofinancements : Le co-financement est encouragé pour inscrire les projets dans une dynamique partenariale. Le financement de la branche Famille ne peut excéder 80 % du coût total.

Modalités de Suivi et de Valorisation : Les actions font l'objet d'un suivi régulier et d'un rapport annuel d'évaluation. Les bonnes pratiques sont capitalisées et partagées entre les acteurs du territoire.

Typologie des Actions Finançables : Le document détaille les catégories d'actions susceptibles d'être financées, telles que :

Groupes d'échanges et d'entraide entre parents.

Activités et ateliers partagés "parents-enfants".

Démarches visant à aider les parents à acquérir des savoirs sur la parentalité (ex: universités populaires de parents).

Conférences ou ciné-débats.

Manifestations de type "événementiels autour de la parentalité".

Actions Non-Éligibles : Le document liste les actions qui ne peuvent pas être financées, notamment les actions à visée individuelle, thérapeutique ou de bien-être, les actions uniquement sportives ou culturelles, les actions d'aide aux départs en vacances sans cadre collectif, les actions relevant de la protection de l'enfance ou de la prévention spécialisée, les actions conduites par des professionnels libéraux, et les actions de formation destinées aux professionnels. "les actions à visée exclusivement individuelle, thérapeutique et de bien-être à l’attention des parents (ex/ consultation de psychologue, actions de guidance familiale et parentale, coaching parental, séances de sophrologie etc)"

Conclusion:

Le référentiel national de financement des actions de parentalité constitue un cadre structurant pour l'action des CAF en faveur des familles.

Il vise à harmoniser et à rendre plus lisibles les interventions, tout en encourageant l'innovation et l'adaptation aux besoins spécifiques des parents.

Il est essentiel pour tous les acteurs impliqués dans le soutien à la parentalité de bien comprendre ce référentiel afin de concevoir et de mettre en œuvre des actions efficaces et pertinentes.

Author response:

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

eLife Assessment

(1) This is a valuable manuscript that successfully integrates several data sets to determine genomic interactions with nuclear bodies.

In this paper we both challenge and/or revise multiple long-standing “textbook” models of nuclear genome organization while also revealing new features of nuclear genome organization. Therefore, we argue that the contributions of this paper extend well beyond “valuable”. Specifically, these contributions include:

a. We challenge a several decades focus on the correlation of gene positioning relative to the nuclear lamina. Instead, through comparison of cell lines, we show a strong correlation of di4erences in gene activity with di4erences in relative distance to nuclear speckles in contrast to a very weak correlation with di4erences in relative distance to the nuclear lamina. This inference of little correlation of gene expression with nuclear lamina association was supported by direct experimental manipulation of genome positioning relative to the nuclear lamina. Despite pronounced changes in relative distances to the nuclear lamina there was little change relative to nuclear speckles and little change in gene expression.

b. We similarly challenge the long-standing proposed functional correlation between the radial positioning of genes and gene expression. Here, and in a now published companion paper (doi.org/10.1038/s42003-024-06838-7), we demonstrate how nuclear speckle positioning relative to nucleoli and the nuclear lamina varies among cell types, as does the inverse relationship between genome positioning relative to nuclear speckles and the nuclear lamina. Again, this is consistent with the primary correlation of gene activity being the positioning of genes relative to nuclear speckles and also explains previous observations showing a strong relationship between radial position and gene expression only in some cell types.

c. We identified a new partially repressed, middle to late DNA replicating type of chromosome domain- “p-w-v fILADs”- by their weak interaction with the nuclear lamina, which, based on our LMNA/LBR KO experimental results, compete with LADs for nuclear lamina association. Moreover, we show that when fLADs convert to iLADs, most conversions are to this p-w-v fiLAD state, although ~ one third are to a normal, active, early replicating iLAD state. Thus, fLADs can convert between repressed, partially repressed, and active states, challenging the prevailing assumption of the division of the genome into two states – active, early replicating A compartment/iLAD regions versus inactive, late replicating, B compartment/LAD regions.

d. We identified nuclear speckle associated domains as DNA replication initiation zones, with the domains showing strongest nuclear speckle attachment initiating DNA replication earliest in S-phase.

e. We describe for the first time an overall polarization of nuclear genome organization in adherent cells with the most active, earliest replicating genomic regions located towards the equatorial plane and less expressed genomic regions towards the nuclear top or bottom surfaces. This includes polarization of some LAD regions to the nuclear lamina at the equatorial plane and other LAD regions to the top or bottom nuclear surfaces.

We have now rewritten the text to make the significance of these new findings clearer.

(2) Strength of evidence: The evidence supporting the central claims is varied in its strength ranging from solid to incomplete. Orthogonal evidence validating the novel methodologies with alternative approaches would better support the central claims.

We argue that our work exploited methods, data, and analyses equal to or more rigorous than the current state-of-the-art. This indeed includes orthogonal evidence using alternative methods which both supported our novel methodologies as well as demonstrating their robustness relative to more conventional approaches. This explains how we were able to challenge/revise long-standing models and discover new features of nuclear genome organization. More specifically:

a. Unlike most previous analyses, we have integrated both genomic and imaging approaches to examine the nuclear genome organization relative to not one, but several di4erent nuclear locales and we have done this across several cell types. To our knowledge, this is the first such integrated approach and has been key to our success in appreciating new features of nuclear genome organization.

b. The 16-fraction DNA replication Repli-seq data we developed and applied to this project represents the highest temporal mapping of DNA replication timing to date.

c. The TSA-seq approach that we used remains the most accurate sequence-based method for estimating microscopic distance of chromosome regions to di4erent nuclear locales. As implemented, this method is unusually robust and direct as it exploits the exponential micron-scale gradient established by the di4usion of the free-radicals generated by peroxidase labeling to measure relative distances of chromosome regions to labeled nuclear locales. We had previously demonstrated that TSA-seq was able to estimate the average distances of genomic regions to nuclear speckles with an accuracy of ~50 nm, as validated by light microscopy. The TSA-seq 2.0 protocol we developed and applied to this project maintained the original resolution of TSA-seq to estimate to an accuracy of ~50 nm the average distances of genomic regions from nuclear speckles, as validated by light microscopy, while achieving more than a 10-fold reduction in the required number of cells.

We have rewritten the text to address the reviewer concerns that led them to their initial characterization of the TSA-seq as novel and not yet validated.

First, we have added a discussion of how the use of nuclear speckle TSA-seq as a “cytological ruler” was based on an extensive initial characterization of TSA-seq as described in previous published literature. In that previous literature we showed how the conventional molecular proximity method, ChIP-seq, instead showed local accumulation of the same marker proteins over short DNA regions unrelated to speckle distances. Second, we reference our companion paper, now published, and describe how the extension of TSA-seq to measure relative distances to nucleoli was further validated and shown to be robust by comparison to NAD-seq and extensive multiplexed immuno-FISH data. We further discuss how in the same companion paper we show how nucleolar DamID instead was inconsistent with both the NAD-seq and multiplexed immuno-FISH data as well as the nucleolar TSA-seq.

Third, we have added scatterplots showing exactly how highly the estimated microscopic distances to all three nuclear locales, measured in IMR90 fibroblasts, correlate with the TSA-seq measurements in HFF fibroblasts. This addresses the concern that we were not using the exact same fibroblast cell line for the TSA-seq versus microscopic measurements. The strong correlation already observed would only be expected to become even stronger with use of the exact same fibroblast cell lines for both measurements.

Fourth, we have addressed the reviewer concern that the nuclear lamin TSA-seq was not properly validated because it did not match nuclear lamin Dam-ID. We have now added to the text a more complete explanation of how microscopic proximity assays such as TSA-seq measure something di4erent from molecular proximity assays such as DamID or NAD-seq. We have added further explanation of how TSA-seq complements molecular proximity assays such as DamID and NAD-seq, allowing us to extract further information than either measurement alone. We also briefly discuss why TSA-seq succeeds for certain nuclear locales using multiple independent markers whereas molecular proximity assays may fail against the same nuclear locales using the same markers. This includes brief discussion from our own experience attempting unsuccessfully to use DamID against nucleoli and nuclear speckles.

Reviewer #1 (Public Review):

(1) The weakness of this study lies in the fact that many of the genomic datasets originated from novel methods that were not validated with orthogonal approaches, such as DNAFISH. Therefore, the detailed correlations described in this work are based on methodologies whose efficacy is not clearly established. Specifically, the authors utilized two modified protocols of TSA-seq for the detection of NADs (MKI67IP TSA-seq) and LADs (LMNB1-TSA-seq).

We disagree with the statement that the TSA-seq approach and data has not been validated by orthogonal approaches. We have now addressed this point in the revised manuscript text:

a) We added text to describe how previously FISH was used to validate speckle TSA-seq by demonstrating a residual of ~50 nm between the TSA-seq predicted distance to speckles and the distance measured by light microscopy using FISH:

"In contrast, TSA-seq measures relative distances to targets on a microscopic scale corresponding to 100s of nm to ~ 1 micron based on the measured diffusion radius of tyramide-biotin free-radicals (Chen et al., 2018). Exploiting the measured exponential decay of the tyramide-biotin free-radical concentration, we showed how the mean distance of chromosomes to nuclear speckles could be estimated from the TSA-seq data to an accuracy of ~50 nm, as validated by FISH (Chen et al., 2018)."

b) We note that we also previously have validated lamina (Chen et al, JCB 2018) and nucleolar (Kumar et al, 2024) TSA-seq and further validated speckle TSA-seq (Zhang et al, Genome Research 2021) by traditional immuno-FISH and/or immunostaining. The overall high correlation between lamina TSA-seq and the orthogonal lamina DamID method was also extensively discussed in the first TSA-seq paper (Chen et al, JCB 2018). Included in this discussion was description of how the di4erences between lamina TSA-seq and DamID were expected, given that DamID produces a signal more proportional to contact frequency, and independent of distance from the nuclear lamina, whereas TSA-seq produces a signal that is a function of microscopic distance from the lamina, as validated by traditional FISH.

c) We added text to describe how the nucleolar TSA-seq previously was validated by two orthogonal methods- NAD-seq and multiplexed DNA immuno-FISH:

"We successfully developed nucleolar TSA-seq, which we extensively validated using comparisons with two different orthogonal genome-wide approaches (Kumar et al., 2024)- NAD-seq, based on the biochemical isolation of nucleoli, and previously published direct microscopic measurements using highly multiplexed immuno-FISH (Su et al., 2020)."

d) We have now added panels A&B to Fig. 7 and a new Supplementary Fig. 7 demonstrating further validation of TSA-seq based on showing the high correlation between the microscopically measured distances of many hundreds of genomic sites across the genome from di4erent nuclear locales and TSA-seq scores. As discussed in response #2 below, we have used comparison of distances measured in IMR90 fibroblasts with TSA-seq scores measured in HFF fibroblasts. We would argue therefore that these correlations are a lower estimate and therefore the correlation between microscopic distances and TSAseq scores would likely have been still higher if we had performed both assays in the exact same cell line.

(2) Although these methods have been described in a bioRxiv manuscript by Kumar et al., they have not yet been published. Moreover, and surprisingly, Kumar et al., work is not cited in the current manuscript, despite its use of all TSA-seq data for NADs and LADs across the four cell lines.

The Kumar et al, Communications Biology, 2024 paper is now published and is cited properly in our revision. We apologize for this oversight and confusion our initial omission of this citation may have created. We had been writing this manuscript and the Kumar et al manuscript in parallel and had intended to co-submit. We planned to cross-reference the two at the time we co-submitted, adding the Kumar et al reference to the first version of this manuscript once we obtained a doi from bioRxiv. But we then submitted the Kumar et al manuscript several months earlier, but meanwhile forgot that we had not added the reference to our first manuscript version.

(3) Moreover, Kumar et al. did not provide any DNA-FISH validation for their methods.

As we described in our response to Reviewer 1's comment #1, we had previously provided traditional FISH validation of lamina TSA-seq in our first TSA- seq paper as well as validation by comparison with lamina DamID (Chen et al, 2018).

We also described how the nucleolar TSA-seq was extensively cross-validated in the Kumar et al, 2024 paper by both NAD-seq and the highly multiplexed immuno-FISH data from Su et al, 2020).

We note additionally that in the Kumar et al, 2024 paper the nucleolar TSA-seq was additionally validated by correlating the predicted variations in centromeric association with nucleoli across the four cell lines predicted by nucleolar TSA-seq with the variations observed by traditional immunofluorescence microscopy.

(4) Therefore, the interesting correlations described in this work are not based on robust technologies.

This comment was made in reference to the Kumar et al paper not having been published, and, as noted in responses to points #2 and #3, the paper is now published.

But we wanted to specifically note, however, that our experience is that TSA-seq has proven remarkably robust in comparison to molecular proximity assays. We've described in our responses to the previous points how TSA-seq has been cross-validated by both microscopy and by comparison with lamina DamID and nucleolar NAD-seq. We note also that in every application of TSA-seq to date, all antibodies that produced good immunostaining showed good TSA-seq results. Moreover, we obtained nearly identical results in every case in which we performed TSA-seq with different antibodies against the same target. Thus anti-SON and antiSC35 staining produced very similar speckle TSA-seq data (Chen et al, 2018), anti-lamin A and anti-lamin B staining produced very similar lamina TSA-seq data (Chen et al, 2018), antinucleolin and anti-POL1RE staining produced very similar DFC/FC nucleolar TSA-seq data (Kumar et al, 2024), and anti-MKI67IP and anti-DDX18 staining produced very similar GC nucleolar TSA-seq data (Kumar et al, 2024).

This independence of results with TSA-seq to the particular antibody chosen to label a target differs from experience with methods such as ChIP, DamID, and Cut and Run/Tag in which results can differ or be skewed based on variable distance and therefore reactivity of target proteins from the DNA or due to other factors such as non-specific binding during pulldown (ChIP) or differential extraction by salt washes (Cut and Tag).

Our experience in every case to date is that antibodies that produce similar immunofluorescence staining produce similar TSA-seq results. We attribute this robustness to the fact that TSA-seq is based only on the original immunostaining specificity provided by the primary and secondary antibodies plus the diffusion properties of the tyramide-free radical.

We've now added the following text to our revised manuscript:

"As previously demonstrated for both SON and lamin TSA-seq (Chen et al., 2018), nucleolar TSA-seq was also robust in the sense that multiple target proteins showing similar nucleolar staining showed similar TSA-seq results (Kumar et al., 2024); this robustness is intrinsic to TSA-seq being a microscopic rather than molecular proximity assay, and therefore not sensitive to the exact molecular binding partners and molecular distance of the target proteins to the DNA."

(5) An attempt to validate the data was made for SON-TSA-seq of human foreskin fibroblasts (HFF) using multiplexed FISH data from IMR90 fibroblasts (from the lung) by the Zhuang lab (Su et al., 2020). However, the comparability of these datasets is questionable. It might have been more reasonable for the authors to conduct their analyses in IMR90 cells, thereby allowing them to utilize MERFISH data for validating the TSA-seq method and also for mapping NADs and LADs.

We disagree with the reviewer's overall assessment that that the use of the IMR90 data to further validate the TSA-seq is questionable because the TSA-seq data from HFF fibroblasts is not necessarily comparable with multiplexed immuno-FISH microscopic distances measured in IMR90 fibroblasts.

In response we have now added panels to Fig. 7 and Supplementary Fig. 7, showing:

a) There is very little di4erence in correlation between speckle TSA-seq and measured distances from speckles in IMR90 cells whether we use IMR90 or HFF cells SON TSA-seq data (R² = 0.81 versus 0.76) (new Fig. 7A).

b) There is also a high correlation between lamina (R² = 0.62) and nucleolar (R² = 0.73) HFF TSA-seq and measured distances in IMR90 cells. Thus, we conclude that this high correlation shows that the multiplexed data from ~1000 genomic locations does validate the TSA-seq. These correlations should be considered lower bounds on what we would have measured using IMR90 TSA-seq data. Thus, the true correlation between distances of loci from nuclear locales and TSA-seq would be expected to be either comparable or even stronger than what we are seeing with the IMR90 versus HFF fibroblast comparisons.

c) This correlation is cell-type specific (Fig. 7B, new SFig. 7). Thus, even for speckle TSAseq, highly conserved between cell types, the highest correlation of IMR90 distances with speckle TSA-seq is with IMR90 and HFF fibroblast data. For lamina and nucleolar TSA-seq, which show much lower conservation between cell types, the correlation of IMR90 distances is high for HFF data but much lower for data from the other cell types. This further justifies the use of IMR90 fibroblast distance measurements as a proxy for HFF fibroblast measurements.

Thus, we have added the following text to the revised manuscript:

"We reasoned that the nuclear genome organization in the two human fibroblast cell lines would be sufficiently similar to justify using IMR90 multiplexed FISH data [43] as a proxy for our analysis of HFF TSA-seq data. Indeed, the high inverse correlation (R= -0.86) of distances to speckles measured by MERFISH in IMR90 cells with HFF SON TSA-seq scores is nearly identical to the inverse correlation (R= -0.89) measured instead using IMR90 SON TSA-seq scores (Fig. 7A). Similarly, distances to the nuclear lamina and nucleoli show high inverse correlations with lamina and nucleolar TSA-seq, respectively (Fig. 7A). These correlations were cell type specific, particularly for the lamina and nucleolar distance correlations, as these correlations were reduced if we used TSA-seq data from other cell types (SFig. 7A). Therefore, the high correlation between IMR90 microscopic distances and HFF TSA-seq scores can be considered a lower bound on the likely true correlation, justifying the use of IMR90 as a proxy for HFF for testing our predictions."

Reviewer #2 (Public Review):

Weaknesses:

(1) The experiments are largely descriptive, and it is difficult to draw many cause-andeffect relationships...The study would benefit from a clear and specific hypothesis.

This study was hypothesis-generating rather than hypothesis-testing in its goal. Our research was funded through the NIH 4D-Nucleome Consortium, which had as its initial goal the development, benchmarking, and validation of new genomic technologies. Our Center focused on the mapping of the genome relative to different nuclear locales and the correlation of this intranuclear positioning of the genome with functions- specifically gene expression and DNA replication timing. By its very nature, this project took a discovery-driven versus hypothesis-driven scientific approach. Our question fundamentally was whether we could gain new insights into nuclear genome organization through the integration of genomic and microscopic measurements of chromosome positioning relative to multiple different nuclear compartments/bodies and their correlation with functional assays such as RNA-seq and Repliseq.

Indeed, this study resulted in multiple new insights into nuclear genome organization as summarized in our last main figure. We believe our work and conclusions will be of general interest to scientists working in the fields of 3D genome organization and nuclear cell biology. We anticipate that each of these new insights will prompt future hypothesis-driven science focused on specific questions and the testing of cause-and-effect relationships.

However, we do want to point out that our comparison of wild-type K562 cells with the LMNA/LBR double knockout was designed to test the long-standing model that nuclear lamina association of genomic loci contributes to gene silencing. This experiment was motivated by our surprising result that gene expression differences between cell lines correlated strongly with differences in positioning relative to nuclear speckles rather than the nuclear lamina. Despite documenting in these double knockout cells a decreased nuclear lamina association of most LADs, and an increased nuclear lamina association of the “p-w-v” fiLADs identified in this manuscript, we saw no significant change in gene expression in any of these regions as compared to wild-type K562 cells. Meanwhile, distances to nuclear speckles as measured by TSA-seq remained nearly constant.

We would argue that this represents a specific example in which new insights generated by our genomics comparison of cell lines led to a clear and specific hypothesis and the experimental testing of this hypothesis.

(2) Similarly, the paper would be very much strengthened if the authors provided additional summary statements and interpretation of their results (especially for those not as familiar with 3D genome organization).

We appreciate this feedback and agree with the reviewer that this would be useful, especially for those not familiar with previous work in the field of 3D genome organization. In an earlier draft, we had included additional summary and interpretation statements in both the Introduction and Results sections. At the start of each Results section, we had also previously included brief discussion of what was known before and the context for the subsequent analysis contained in that section. However, we had thought we might be submitting to a journal with specific word limits and had significantly cut out that text.

We have now restored this text and, in certain cases, added additional explanations and context.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Figures 1C and D. Please add the units at the values of each y-axis.

We have done that.

The representation of Figure 2C lacks clarity and is diJicult to understand. The x-axis labeling regarding the gene fraction number needs clarification.

We've modified the text to the Fig. 2C legend: "Fraction of genes showing significant di=erence in relative positioning to nuclear speckles (gene fraction, x-axis) versus log2 (HFF FKPM / H1 FKPM) (y-axis);"

"We next used live-cell imaging to corroborate that chromosome regions close to nuclear speckles, primarily Type I peaks, would show the earliest DNA replication timing." This sentence requires modification as Supplementary Figure 3F does not demonstrate that Type I peaks exhibit the earliest DNA replication timing; it only indicates that the first PCNA foci in S-phase are in proximity to nuclear speckles.

We've modified the text to: "We next used live-cell imaging to show that chromosome regions close to nuclear speckles show the earliest DNA replication timing; this is consistent with the earliest firing DNA replication IZs, as determined by Repli-seq, aligning with Type 1 peaks that are closely associated with nuclear speckles."

In Figure 5, the authors employed LaminB1-DamID to quantify LADs in LBR-KO and LMNA/LBR-DKO K562 cells. These are interesting results. However, for these experiments, it is crucial to assess LMNB1 signal at the nuclear periphery via immunofluorescence (IF) to confirm the absence of changes, ensuring that the DamID signal solely reflects contacts with the nuclear lamina. Furthermore, in this instance, their findings should be validated through DNA-FISH.

Immunostaining of LMNB1 was performed and showed a normal staining pattern as a ring adjacent to the nuclear periphery. Images of this staining were included in the metadata tied to the sequencing data sets deposited on the 4D Nucleome Data portal. We thank the reviewer for bringing up this point, and have added a sentence mentioning this result in the Results Section:

"Immunostaining against LMNB1 revealed the normal ring of staining around the nuclear periphery seen in wt cells (images deposited as metadata in the deposited sequencing data sets)."

Because both TSA-seq and DamID have been extensively validated by FISH, as detailed in our previous responses to the public reviewer comments, we feel it is unnecessary to validate these findings by FISH.

p-w-v-fiLADs should be labelled in Figure 5B.

We've added labeling as suggested.

"The consistent trend of slightly later DNA replication timing for regions (primarily p-w-v fiLADs) moving closer to the lamina" is not visible in the representation of the data of Figure 5G.

We did not make a change as we believed this trend was apparent in the Figure.

To reduce the descriptive nature of the data, it would be pertinent to conduct H3K9me3 and H3K27me3 ChIP-seq analyses in both the parental and DKO mutant cells. This would elucidate whether p-w-v-fiLADs and NADs anchoring to the nuclear lamina undergo changes in their histone modification profile.

We believe further analysis of the reasons underlying these shifts in positioning, including such ChIP-seq or equivalent analysis, is of interest but beyond the scope of this publication. We see such measurements as the beginning of a new story but insuJicient alone to determine mechanism. Therefore we believe such experiments should be part of that future study.

The description of Figure 7 lacks clarity. Additionally, it appears that TSA-seq for NADs and LADs may not be universally applicable across all cell types, particularly in flat cells, whereas DamID scores demonstrate less variation across cell lines, as also stated by the authors.

TSA-seq is a complement to rather than a replacement for either DamID or NAD-seq. TSAseq reports on microscopic distances whereas both DamID and NAD-seq instead are more proportional to contact frequency with the nuclear lamina or nucleoli, respectively, and insensitive to distances of loci away from the lamina or nucleoli. Thus, TSA-seq provides additional information based on the intrinsic diJerences in what TSA-seq measures relative to molecular proximity methods such as DamID or NAD-seq. The entire point is that the convolution of the exponential point-spread-function of the TSA-seq with the shape of the nuclear periphery allows us to distinguish genomic regions in the equatorial plane versus the top and bottom of the nuclei. The TSA-seq is therefore highly "applicable" when properly interpreted in discerning new features of genome organization. As we stated in the revised manuscript, the lamina DamID and TSA-seq are complementary and provide more information together then either method along. The same is true for the NAD-seq and nucleolar TSA-seq comparison, as described in more detail in the Kumar, et al, 2024 paper.

Introduction:

The list of methodologies for mapping genomic contacts with nucleoli (NADs) should also include recent technologies, such as Nucleolar-DamID (Bersaglieri et al., PMID: 35304483), which has been validated through DNA-FISH.

We did not include nucleolar DamID in the mention in the Introduction of methods for identifying diJerential lamina versus nucleolar interactions of heterochromatin- either from our own collaborative group or from the cited reference- because we did not have confidence in the accuracy of this method in identifying NADs. In the case of the published nucleolar DamID from our collaborative group, published in Wang et al, 2021, we later discovered that despite apparent agreement of the nucleolar DamID with a small number of published FISH localization the overall correlation of the nucleolar DamID with nucleolar localization was poor. As described in detail in the Kumar et al, 2024 publication, this poor correlation of the nucleolar DamID was established using three orthogonal methods- nucleolar TSA-seq, NAD-seq, and multiplexed immuno-FISH measurements from ~1000 genomic locations. Instead, we found that this nucleolar DamID showed high correlation with lamina DamID. We note that many strong NADs are also LADs, which we think is why validation with only several FISH probes is inadequate to demonstrate overall validation of the approach.

We could not compare our nucleolar-DamID data in human cells with the alternative nucleolar-DamID results cited by the reviewer which were performed in mouse cells. We note that in this paper the nucleolar DamID FISH validation only included several putative NAD chromosome regions and, I believe, one LAD region. However, our initial comparison of the nucleolar DamID cited by the reviewer with unpublished TSA-seq data from mouse ESCs produced by the Belmont laboratory and with NAD-seq data from the Kaufman laboratory shows a similar lack of correlation of the nucleolar DamID signal with nucleolar TSA-seq and NAD-seq, as well as multiplexed immuno-FISH data from the Long Cai laboratory, as we saw in our analysis of own nucleolar DamID data in human cells.

We have added explanation concerning the lack of correlation of our nucleolar DamID with orthogonal measurements of nucleolar proximity in the added text (below) to our revised manuscript:

"Nucleolar DamID instead showed broad positive peaks over large chromatin domains, largely overlapping with LADs mapped by LMNB1 DamID (Wang et al., 2021). However, this nucleolar DamID signal, while strongly correlated with lamin DamID, showed poor correlation with either NAD-seq or nucleolar distances mapped by multiplexed immunoFISH (Kumar et al., 2024). We suspect the problem is that with molecular proximity assays the output signals are disproportionally dominated by the small fraction of target proteins juxtaposed in su=icient proximity to the DNA to produce a signal rather than the amount of protein concentrated in the target nuclear body. "

Our mention of nucleolar TSA-seq was in the context of why we focused on nucleolar TSAseq and excluded our own nucleolar DamID. We chose not to discuss the second nucleolar DamID method cited above 1) because it was not appropriate to our discussion of our own experimental approach and 2) also because we cannot yet make a definitive statement of its accuracy for nucleolar mapping.

Reviewer #2 (Recommendations For The Authors):

(1) The authors start the manuscript by describing the 'radial genome organization' model and contrast it with the 'binary model' of genome organization. It would be helpful for the authors to contextualize their results a bit more with regard to these two diJerent models in the discussion.

We have added several sentences in the first paragraph of the Discussion to accomplish this contextualization. The new paragraph reads:

"Here we integrated imaging with both spatial (DamID, TSA-seq) and functional (Repli-seq, RNA-seq) genomic readouts across four human cell lines. Overall, our results significantly extend previous nuclear genome organization models, while also demonstrating a cell-type dependent complexity of nuclear genome organization. Briefly, in contrast to the previous radial model of genome organization, we reveal a primary correlation of gene expression with relative distances to nuclear speckles rather than radial position. Additionally, beyond a correlation of nuclear genome organization with radial position, in cells with flat nuclei we show a pronounced correlation of nuclear genome organization with distance from the equatorial plane. In contrast to previous binary models of genome organization, we describe how both iLAD / A compartment and LAD / B compartment contain within them smaller chromosome regions with distinct biochemical and/or functional properties that segregate di=erentially with respect to relative distances to nuclear locales and geometry."

(2) Data should be provided demonstrating KO of LBR and LMNA - immunoblotting for both proteins would be one approach. In addition, it would be helpful to provide additional nuclear morphology measurements of the DKO cells (volume, surface area, volume of speckles/nucleoli, number of speckles/nucleoli).

We've added additional description describing the generation and validation of the KO lines:

"To create LMNA and LBR knockout (KO) lines and the LMNA/LBR double knockout (DKO) line, we started with a parental "wt" K562 cell line, clone #17, expressing an inducible form of Cas9 (Brinkman et al., 2018). The single KO and DKO were generated by CRISPR-mediated frameshift mutation according to the procedure described previously (Schep et al., 2021). The "wt" K562 clone #17 was used for comparison with the KO clones.

The LBR KO clone, K562 LBR-KO #19, was generated, using the LBR2 oligonucleotide GCCGATGGTGAAGTGGTAAG to produce the gRNA, and validated previously, using TIDE (Brinkman et al., 2014) to check for frameshifts in all alleles as described elsewhere (Schep et al., 2021). The LMNA/LBR DKO, K562 LBR-LMNA DKO #14, was made similarly, starting with the LBR KO line and using the combination of two oligonucleotides to produce gRNAs:

LMNA-KO1: ACTGAGAGCAGTGCTCAGTG, LMNA-KO2: TCTCAGTGAGAAGCGCACGC.

Additionally, the LMNA KO line, K562 LMNA-KO #14, was made the same way but starting with the "wt" K562 cell line. Validation was as described above; additionally, for the new LMNA KO and LMNA/LBR DKO lines, immunostaining showed the absence of anti-LMNA antibody signal under confocal imaging conditions used to visualize the wt LMNA staining while the RNA-seq from these clones revealed an ~20-fold reduction in LMNA RNA reads relative to the wt K562 clone."

As suggested, we also added morphological data for the DKO line in a modified SFig.5.

(3) The rationale for using LMNB1 TSA-seq and LMNB1 DAMID is not immediately clear. The LMNB1 TSA-seq is more variable across cell types and replicates than the DAMID. Could the authors please compare the datasets a bit more to understand the diJerences? For example, the authors demonstrate that "40-70% of the genome shows statistically significant diJerences in Lamina TSA-seq over regions 100 kb or larger, with most of these regions showing little or no diJerences in speckle TSA-seq scores." If the LMNB1 DAMID data is used for this analysis or Figure 2D, is the same conclusion reached? Also, in Figure 6, the authors conclude that C1 and C3 LAD regions are enriched for constitutive LADs, while C2 and C4 LAD regions are fLADs. This is a bit surprising because the authors and others have previously shown that constitutive LADs have higher LMNB1 contact frequency than facultative LADs (Kind, et al Cell 2015, Figure 3C).

Indeed, in the first TSA-seq paper (Chen et al, 2018) we did observe that cLADs had the highest LMNB TSA-seq scores; this was for K562 cells with round nuclei in which there is therefore no diJerence in lamina TSA-seq scores produced by nuclear shape over the entire nucleus.

However, there are diJerences between TSA-seq and DamID in terms of what they measure and we refer the reviewer to the first TSA-seq paper (Chen et al, 2018) that explains in greater depth these diJerences. This first paper explains how DamID is indeed related to contact frequency but how the TSA-seq instead estimates mean distances from the target, in this case the nuclear lamina. This is because the diJusion of tyramide free radicals from the site of their constant HRP production produces an exponential decay gradient of tyramide free radical concentration at steady state.

We have summarized these diJerences in in text we have added to introduce both DamID and TSA-seq in the second Results section:

"DamID is a well-established molecular proximity assay; DamID applied to the nuclear lamina divides the genome into lamina-associated domains (LADs) versus nonassociated “inter-LADs” or “iLADs” (Guelen et al., 2008; van Steensel and Belmont, 2017). In contrast, TSA-seq measures relative distances to targets on a microscopic scale corresponding to 100s of nm to ~ 1 micron based on the measured diJusion radius of tyramide-biotin free-radicals (Chen et al., 2018)... While LMNB1 DamID segments LADs most accurately, lamin TSA-seq provides distance information not provided by DamID- for example, variations in relative distances to the nuclear lamina of diJerent iLADs and iLAD regions. These diJerences between the LMNB1 DamID and LMNB TSA-seq signals are also crucial to a computational approach, SPIN, that segments the genome into multiple states based on their varying nuclear localization, including biochemically and functionally distinct lamina-associated versus near-lamina states (Consortium et al., 2024; Wang et al., 2021).

Thus, lamin DamID and TSA-seq complement each other, providing more information together than either one separately."

We note that these diJerences in lamina DamID and TSA-seq are crucial to being able to gain additional information by comparing variations in the lamina TSA-seq for LADs in Figs. 6&7. See our response to point (4) below, for further explanation.

(4) In 7B/C, the authors show that the highest LMNB1 regions in HFF are equator of IMR90s. However, in Figure 7G, their cLAD score indicates that constitutive LADs are not at the equator. This is a bit surprising given the point above and raises the possibility that SON signals (as opposed to LMNB1 signals) might be more responsible for correlation to localization relative to the equator. Hence, it might be helpful if the authors repeat the analyses in Figures 7B/C in regions with diJering LMNB1 signals but similar SON signals (and vice versa).

Again, this is based on the apparent assumption by the reviewer that DamID and TSA-seq work the same way and measure the same thing. But as explained above in the previous point, this is not true.

In our first TSA-seq paper (Chen et al, 2018) we showed how we could use the exponential decay point-spread-function produced by TSA, measured directly by light microscopy, to convert sequencing reads from the TSA-seq into a predicted mean distance from nuclear speckles, approximated as point sources. These mean distances predicted from the SON TSA-seq data agreed with measured FISH distances to nuclear speckles to within ~50 nm for a set of DNA probes from diJerent chromosome regions. Moreover, varying TSA staining conditions changed the decay constants of this exponential decay, thus producing diJerences in the SON TSA-seq signals. By using these diJerent exponential decay functions to convert the TSA-seq scores from these independent data sets to estimated distances from nuclear speckles, we again observed a distance residual of ~50 nm; in this case though this distance residual of ~50 nm represented the mean residual observed genome-wide. This gives us great confidence that the TSA-seq is working as we have modeled it.

As we mentioned in our response to point 3 above, we did see the highest LMNB TSA-seq signal for cLADs in K562 cells with round nuclei (Chen et al, 2018).

But as we now show in our simulation performed in this paper for Fig. 7, the observed tyramide free radical exponential decay gradient convolved with the flat nuclear lamina shape produces a higher equatorial LMNB1 TSA-seq signal for LADs at the equatorial plane. We confirmed that LADs with this higher TSA-seq signal were enriched at the equatorial plane by mining the multiplexed IMR90 imaging data. Similar mining of the multiplexed FISH IMR90 data showed localization of cLADs away from the equatorial plane.

We are not clear about the rationale for what the reviewer is suggesting about SON signals "being more responsible for correlation to localization to the equator". We have provided an explanation for the higher lamina TSA-seq scores for LADs near the equator based on the measured spreading of the tyramide free radicals convolved with the eJect of the nuclear shape. This makes a prediction that the observed variation in lamina TSA-seq scores for LADs with similar DamID scores is related to their positioning relative to the equatorial plane as we then validated through our mining of the IMR90 multiplexed FISH data.

(5) FISH of individual LADs, v-fiLADs, and p-w-v-fiLADs relative to the lamina and speckle would be helpful to understand their relative positioning in control and LBR/LMNA double KO cells. This would significantly bolster the claim that "histone mark enrichments..more precisely revealed the diJerential spatial distribution of LAD regions...".

Adequately testing these predictions made from the lamina/SON TSA-seq scatterplots by direct FISH measurements would require measurements from large numbers of diJerent chromosome regions through a highly multiplexed immuno-FISH approach. We are not equipped currently in any of our laboratories to do such measurements and we leave this therefore for future studies.

Rather our statement is based on our use of TSA-seq analyzed through these 2D scatterplots and should be valid to the degree that our TSA-seq measurements do indeed correlate with microscopy derived distances.

However, we do now include demonstration of a high correlation of speckle, lamina, and nucleolar TSA-seq with highly multiplexed immuno-FISH measurement of distances to these locales in a revised Fig. 7. The high correlation shown between the TSA-seq scores and measured distances does therefore add additional support to our claim that the reviewer is discussing, even without our own multiplexed FISH validation.

(6) "In contrast, genes within genomic regions which in pair-wise comparisons of cell lines show a statistically significant diJerence in lamina TSA-seq show no obvious trend in their expression diJerences (Figure 2C).". This appears to be an overstatement based on the left panel of 2D.

We do not follow the reviewer's point. In Fig. 2C we show little bias in the diJerences in gene expression between the two cell types for regions that showed diJerences in lamina TSA-seq. The reviewer is suggesting something otherwise based on their impression, not explicitly stated, of the left panel of Fig. 2D. But we see similar shades of blue extending vertically at low SON values and similar shades of red extending vertically at high SON values, suggesting a correlation of gene expression only with the SON TSA-seq score but not with the LMNB1 TSA-seq score displayed on the y-axis. This is also consistent with the very small and/or insignificant correlation coeJicients measured in our linear model relating diJerences in LMNB1 TSA-seq to diJerences in expression but the large correlation coeJicient observed for SON TSA-seq (Fig. 2E). Thus, we see Fig. 2C-E as self-consistent.

(7) In the section on "Polarity of Nuclear Genome Organization" - "....Using the IMR90 multiplexed FISH data set [43]...." - The references are not numbered.

We thank the reviewer for this correction.

(8) I believe there is an error in the Figure 7B legend. The descriptions of Cluster 1 and 2 do not match those indicated in the figure.

We again thank the reviewer for this correction.

Author response:

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

Public Reviews:

Reviewer #1:

The entire study is based on only 2 adult animals, that were used for both the single cell dataset and the HCR. Additionally, the animals were caught from the ocean preventing information about their age or their life history. This makes the n extremely small and reduces the confidence of the conclusions. 

This statement is incorrect.  While the scRNAseq was indeed performed in two animals (n=2), the HCR-FISH was performed in 3-5 animals (depending on the probe used).  These were different animals from those used for the scRNAseq.  The number of animals used has now been included in the manuscript.

All the fluorescent pictures present in this manuscript present red nuclei and green signals being not color-blind friendly. Additionally, many of the images lack sufficient quality to determine if the signal is real. Additional images of a control animal (not eviscerated) and of a negative control would help data interpretation. Finally, in many occasions a zoomed out image would help the reader to provide context and have a better understanding of where the signal is localized. 

Fluorescent photos have been changed to color-blind friendly colors.  Diagrams, arrows and new photos have been included as to guide readers to the signal or labeling in cells. Controls for HCR-FISH and labeling in normal intestines have been included.  

Reviewer #2:

The spatial context of the RNA localization images is not well represented, making it difficult to understand how the schematic model was generated from the data. In addition, multiple strong statements in the conclusion should be better justified and connected to the data provided.

As explained above we have made an effort to provide a better understanding of the cellular/tissue localization of the labeled cells. Similarly, we have revised the conclusions so that the statements made are well justified.

Reviewer #3:

Possible theoretical advances regarding lineage trajectories of cells during sea cucumber gut regeneration, but the claims that can be made with this data alone are still predictive.

We are conscious that the results from these lineage trajectories are still predictive and have emphasized this in the text. Nonetheless, they are important part of our analyses that provide the theoretical basis for future experiments.

Better microscopy is needed for many figures to be convincing. Some minor additions to the figures will help readers understand the data more clearly.

As explained above we have made an effort to provide a better understanding of the cellular/tissue localization of the labeled cells.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

-  Page 4, line 70-81: if the reader is not familiar with holothurian anatomy and regeneration process, this section can be complicated to fully understand. An illustration, together with clear definitions of mesothelium, coelomic epithelium, celothelium and luminal cells would help the reader. 

A figure (now Figure 1) detailing the holothurian anatomy of normal and regenerating animals has been added. A figure detailing the intestinal regeneration process has also been included (S1).

-  Page 5 line 92-104: this paragraph could be shortened. It would be more important to explain what the main question is the Authors would like to answer and why single cell would be the best technique to answer it, than listing previous studies that used scRNA-Seq. 

The paragraph has been shortened and the focus has been shifted to the question of cellular components of regenerative tissues in holothurians.

-  Page 6, line 125-127 and line 129-132: this belongs to the method section. 

This information is now provided in the Materials and Methods section.

-  Page 11, line 210-217: this belongs to the discussion. 

This section has now been included in the Discussion.

-  How many mesenteries are present in one animal? 

This has now been included as part of Figure S1.

-  In the methods there are no information about the quality of the dataset and the sequencing and the difference between the 2 samples coming from the 2 animals. How many cells from each sample and which is the coverage? The Authors provided this info only between mesentery and anlage but not between animals. 

We have added additional information about the sequencing statistics in S4 Fig and S15 Table. Description has also been added in the methods in lines 922-926 under Single Cell RNA Sequencing and Data Analysis section.

-  The result section "An in-depth analysis of the various cluster..." is particularly long and very repetitive. I would encourage to Authors to remove a lot of the details (list of genes and GO terms) that can be found in the figures and stressed only the most important elements that they will need to support their conclusions. Having full and abbreviated gene names and the long list of references makes the text difficult to read and it is challenging to identify the main point that the Authors are trying to highlight. 

This section has been abbreviated.

-  Figure 1: I would suggest adding a graph of holothurian anatomy before and after the evisceration to provide more context of the process we are looking at and remove 1C. 

Information on the holothurian anatomy has been included in a new Fig 1 and in supplementary figure S1

-  Figure 2: I would suggest removing this figure that is redundant with Figure 3 and several genes are not cluster specific. Figure 3 is doing a better job in showing similar concepts. 

Figure 2 was removed and placed in the Supplement section. 

- In figure 3 how were the 3 cell types defined? Was this done manually or through a bioinformatic analysis? 

The cell definition was done following the analysis of the highly expressed transcripts and comparisons to what has been shown in the scientific literature.

-  Figure 2O shows that one of the supra-cluster is made of C2, C7, C6 and C10. This contradicts the text page 9, line 195. 

The transcript chosen for this figure gives the wrong idea that these 4 clusters are similar. We have now addressed this in the manuscript.

-  Figure 4A and 4C: if these are representing a subset of Figure 3, they should be removed in one or the other. The same comment is valid also for Figures 5, 6 and 7. In general the manuscript is very redundant both in terms of Figures and text. 

These are indeed subsets of Fig 3 that were added with the purpose of clarifying the findings, however, in view of the reviewer’s comment we have deleted the redundant information from all figures.

-  Figure 9: since the panels are not in order, it is difficult to follow the flow of the figure.  - All UMAP should have the number of the cluster on the UMAP itself instead of counting only on the color code in order to be color-blind friendly. 

The figure has been modified and clusters are now identified in the UMAP by their number.

-  Figure S1F seems acquired in very different conditions compared to the other images in the same figure. 

Fig S1F (now S2 Fig) is an overlay of fluorescent immune-histochemistry (UV light detected) with “classical” toluidine blue labeling (visible light detected).  This has now been explained in the figure legend.

-  Table S7 is lacking some product numbers. 

The toluidine blue product number has now been added to the table.  The antibodies that lack product number correspond to antibodies generated in our lab  and described in the references provided.

-  The discussion is pretty long and partially redundant with the result section. I would encourage the Authors to shorten the text and shorten paragraphs that have repeating information.  - It might be out of the scope of the Authors but the readers would benefit from having a manuscript that focuses more on the novel aspects discovered with the single-cell RNA-Seq and then have a review that will bring together all the literature published on this topic and integrating the single-cell data with everything that is known so far. 

We have tried to shorten the discussion by eliminating redundant text.

Reviewer #2 (Recommendations For The Authors): 

-  An intriguing finding is the lack of significant difference in the cell clusters between the anlage and mesentery during regeneration. This discovery raises important questions about the regenerative process. The authors should provide a more detailed explanation of the implications of this finding. For example, does it suggest that both organs contribute equally to the regenerated tissues? 

The lack of significant differences in the cell clusters between the anlage and the mesentery is somewhat surprising but can be explained by two different facts. First, we have previously shown that many of the cellular processes that take place in the anlage, including cell proliferation, apoptosis, dedifferentiation and ECM remodeling occur in a gradient that begins at the tip of the mesentery where the anlage forms and extends to various degrees into the mesentery.  Similarly, migrating cells move along the connective tissue of the mesentery to the anlage.  Thus, there is no clear partition of the two regions that would account for distinct cell populations associated with the regenerative stage.  Second, the two cell populations that would have been found in the mesentery but not in the regenerating anlage, mature muscle and neurons, were not dissociated by our experimental protocol as to allow for their sequencing.  Our current experiments are being done using single nuclei RNA sequencing to overcome this hurdle. This has now been included in the discussion.

-  Proliferating cells are obviously important to the study of regeneration as it is assumed these form the regenerating tissue. The authors describe cluster 8 as the proliferative cells. Is there evidence of proliferation in other cell types or are these truly the only dividing cells? Is c8 of multiple cell types but the clustering algorithm picks up on the markers of cell division i.e. what happens if you mask cell division markers - does this cluster collapse into other cluster types? This is important as if there is only one truly proliferating cell type then this may be the origin of the regenerative tissues and is important for this study to know this. 

As the reviewer highlights, we also believe this to be an important aspect to discuss. We have addressed this in the manuscript discussion with the following: “Our data suggest that there appears to be a specific population of only proliferative cells (C8) characterized by a large number of cell proliferation genes, which can be visualized by the top genes shown in Fig 3. These cell proliferation genes are specific to C8, with minimum representation in other populations. Interestingly, as mentioned before C8 expresses at lower levels many of the genes of other coelomic epithelium populations. Nevertheless, even if we mask the top 38 proliferation genes (not shown), this cluster is maintained as an independent cluster, suggesting that its identity is conferred by a complex transcriptomic profile rather than only a few proliferation-related genes. Therefore, the identity and potential role of C8 could be further described by two distinct alternatives: (1) cells of C8 could be an intermediate state between the anlage precursor cells (discussed below) and the specialized cell populations or (2) cells of C8 are the source of the anlage precursor populations from which all other populations arise. The pseudotime data is certainly complex and challenging to interpret with our current dataset, yet the RNA velocity analysis showed in Fig 11B would suggests that cells of C8 transition into the anlage precursor populations, rather than being an intermediate state. This is also supported by the Slingshot pseudotime analysis that incorporates C8 (S13 Fig).

Nevertheless, additional experiments are needed to confirm this hypothesis.”

-  The schematic model presented in Fig 10 is essential for clarifying the paper's findings and will provide a crucial baseline model for future research. However, the comparison of the data shown in the HCR figures with the schematic is challenging due to the lack of spatial context in the HCR figures. The authors should find a way to provide better context in the figures, such as providing two-color in situ images to compare spatial relationships of cell types and/or including lower resolution and side-by-side fluorescent and bright field images if possible. 

The figure has been modified to explain the spatial arrangement of the tissues.

The authors make several strong statements in the discussion that weren't well connected to the findings in the data. Specifically: 

“Regardless of which cell population is responsible for giving rise to the cells of the regenerating intestine, our study reveals that the coelomic epithelium, as a tissue layer, is pluripotent.” 

This has now been expanded to better explain the statement.

738 “…we postulate that cells from C1 stand as the precursor cell population from which the rest of the cells in the coelomic epithelium arise”. 

This has now been expanded to better explain the statement.

748 “differentiation: muscle, neuroepithelium, and coelomic epithelium cells. We also propose the presence of undifferentiated and proliferating cell populations in the coelomic epithelia, which give rise to the cells in this layer…”

This has now been expanded to better explain the statement.

777 “amphibians, the cells of the holothurian anlage coelomic epithelium are proliferative undifferentiated cells and originated via a dedifferentiation process…”

This has now been expanded to better explain the statement.

Reviewer #3 (Recommendations For The Authors): 

Specific questions: 

- Is there any way to systematically compare these cells to evolutionarily-diverged cells in distant relatives to sea cucumbers? Or even on a case-by-case basis? For example, is there evidence for any of these transitory cell types to have correlate(s) in vertebrate gut regeneration? 

This is a most interesting question but one that is perhaps a bit premature to answer due to multiple reasons.  First, most of the studies in vertebrates focus on the regeneration of the luminal epithelium, a layer that we are not studying in our system since it appears later in the regeneration process.  Second, there is still too little data from adult echinoderms to fully comprehend which cells are cell orthologues to vertebrates. Third, we are only analyzing one regenerative stage.  It is our hope that this is just the start of a full description of what cell types/stages are found and how they function in regeneration and that this will lead us to identify the cellular orthologues among animal species.

Major revisions: 

- If lineage tracing is within the scope of this paper, it would provide more definitive evidence to the conclusions made about the precursor populations of the regenerating anlage. 

Response:  This is certainly one of the next steps, however at present, it is not possible due to technical limitations.

Minor revisions: 

- Line 47: "for decades" even longer! Could the authors also cite some other amphibians, such as other salamanders (newts) and larval frogs? 

References have been added.

- Line 85: "specially"-could authors potentially change to "specifically" 

Corrected

- Line 122: Authors should add the full words of what these abbreviations stand for in the caption for Figure 1 or in Figure 1A itself. 

Corrected

- Lines 153: What conclusions are the authors trying to make from one type of tubulin presence compared to the others? It's unclear from the text. 

The authors are not trying to reach any particular conclusion.  They are just stating what was found using several markers, and the possibility that what might be viewed first hand as a single cell population might be more heterogenous.  Although the tubulin-type information might not be relevant for the conclusions in the present manuscript, it might be important for future work on the cell types involved in the regeneration process.

- Line 226: Could the authors clarify if "WNT9" is "WNT9a". Figure 3 lists WNT9a but authors refer to WNT9 in the text. 

The gene names in Fig 3 are based on the human identifiers. H. glaberrima only has one sequence of Wnt9 (Auger et al. 2023) and this sequence shares the highest similarity to human Wnt9a, thus the name in the list. We have now identified the gene as Wnt9 to avoid confusion.

- Lines 236-237: Can authors rule out that some immune cells might infiltrate the mesenchymal population? 

No, this cannot be ruled out.  In fact, we believe that most of the immune cells found in our scRNA-seq are indeed cells that have infiltrated the anlage and are part of the mesenchyma.  This has been reported by us previously (see Garcia-Arraras et al. 2006). We have now included this in the text.

- Line 452-453: The over-representation of ribosomal genes not shown. Would it be possible to show this information in the supplementary figures? 

The sentence has been modified, the data is being prepared as part of a separate publication that focuses on the ribosomal genes.

- Line 480: Could authors clarify if it's WNT9a or just WNT9?

It is indeed Wnt9. See previous response above.

- Line 500: In future experiments, it would be interesting to compare to populations at different timepoints in order see how the populations are changing or if certain precursors are activated at different times. 

We fully agree with the reviewer. These are ongoing experiments or are part of new grant proposals.

- Line 567-568: Choosing 9-dpe allowed for 13 clusters, but do authors expect a different number of clusters at different timepoints as things become more terminally differentiated? 

Definitely, we believe that clusters related to the different regenerative stages of cells can be found by looking at earlier or later regeneration stages of the organ.  A clear example is that if the experiment is done at 14-dpe, when the lumen is forming, cells related to luminal epithelium populations will appear. It is also possible that different immune cells will be associated with the different regeneration stages.

- Line 653: References Figure 10D (not in this manuscript). Are authors referring to only 1D or 9D or an old draft figure number? 

As the reviewer correctly points out, this was a mistake where the reference is to a previous draft. It has now been corrected.

- Line 701: "our study reveals that the coelomic epithelium, as a tissue layer, is pluripotent." Phrasing may be better as referring to the cell population making up the tissue layer as pluripotent/multipotent or that the cells it contains would likely be pluripotent or multipotent. Additionally, lineage tracing may be needed to definitively demonstrate this. 

This has been modified.

- Line 808: The authors may make a more accurate conclusion by saying that the characteristics are similar to blastemas or behave like a blastema rather than it is blastema. There is ambiguity about the meaning of this term in the field, but most researchers seem to currently have in mind that the "blastema" definition includes a discrete spatial organization of cells, and here these cells are much more spread out. This could be a good opportunity for the authors to engage in this dialogue, perhaps parsing out the nuances of what a "blastema" is, what the term has traditionally referred to, and how we might consider updating this term or at least re-framing the terminology to be inclusive of functions that "blastemas" have traditionally had in the literature and how they may be dispersed over geographical space in an organism more so than the more rigid, geographically-restricted definition many researchers have in mind. However, if the authors choose to elaborate on these issues, those elaborations do belong in the discussion, and the more provisional terminology we mention here could be used throughout the paper until that element of the revised discussion is presented. We would welcome the authors to do this as a way to point the field in this direction as this is also how we view the matter. For example, some of the genes whose expression has been observed to be enriched following removal of brain tissue in axolotls (such as kazald2, Lust et al.), are also upregulated in traditional blastemas, for instance, in the limb, but we appreciate that the expression domain may not be as localized as in a limb blastema. Additionally, since there is now evidence that some aspects of progenitor cell activation even in limb regeneration extend far beyond the local site of amputation injury (Johnson et al., Payzin-Dogru et al.), there is an opportunity to connect the dots and make the claim that there could be more dispersion of "blastema function" than previously appreciated in the field. Diving a bit more into these nuances may also enable better conceptual framework of how blastema function may evolve across vast evolutionary time and between different injury contexts in super-regenerative organisms. 

We have followed the reviewer’s suggestion and stated that the holothurian anlage behaves as a blastema. Though we would love to elaborate on the blastema topic, as suggested by the reviewer, we believe that it would extend the discussion too much and that the topic might be better served in a different publication.

- In the discussion, it would be important not to leave the reader with the impression that all amphibian blastema cells originate via dedifferentiation. This is not the case. For example, in axolotls (Sandoval-Guzman et al.) and in larval/juvenile newts, muscle progenitors within the blastema structure have been shown to originate from muscle satellite cells, a kind of stem cell, in stump tissues (while adult newts use dedifferentiation of myofibers to generate muscle progenitors in the blastema). Most cell lineages simply have not been evaluated in the level of detail that would be required to definitively conclude one way or the other, and the door is open for a more substantial contribution from stem cell populations than previously appreciated especially because new tools exist to detect and study them. Providing the reader with a more nuanced view of this situation will not negatively impact the findings in this paper, but it will show that there is biological complexity still waiting to be discovered and that we don't have all the answers at this point. 

This has now been corrected. 

Figures: Overall, the figures need minor work. 

- Figure 1A: Can the authors draw a smaller, full-body cartoon and feature the current high-mag cartoon as an inset to that? Can they label the axes and make it clear how the geometry works here?

Fig 1 has been re-done and now is split into Fig 1 and Fig 2.

- Figure 1B: Can the authors label the UMAP with cluster identities on the map itself? This will make it easier to identify each cluster (especially to make sure cluster 11 is easier to find). 

This has been corrected.

- Figure 2: Could the authors put boxes/clearly distinguish panel labels around each cluster (AO), so that there are clear boundaries? 

Fig 2 has been moved to Supplement, following another reviewer recommendation.

- "Gene identifiers starting with "g" correspond to uncharacterized gene models of H. glaberrima." - The sentence is from another figure caption but this figure would benefit from having this sentence in the figure caption as well. 

This has been added to other figures as suggested.

- Figure 3A: Can the authors potentially bold, highlight, or underline genes you discuss in text, so it's easier for the reader to reference? 

This has been added as suggested.

- Figure 3C: Can the authors please label the cell types directly on the UMAP here as well? 

The changes were made following the reviewer’s recommendation.

- Figure 4D-E: There's not much context here to determine if this HCR-FISH validation can tell us anything about these cells besides some of them appear to be there. Do authors expect the coelomocyte morphology to look different in regenerating/injured tissue versus normal animals? Can the authors provide some double in situs, as well as some lower-magnification views showing where the higher-magnification insets are located? Is there any spatial pattern to where these cells are found? Counter stains would be helpful. 

- Figure 6C: If clusters C5, C8, C9 are part of the coelomic epithelium, then authors could show a smaller diagram above with blue and grey to show types and then show clusters separately to help get their point across better. 

- Figure 6G: This image appears to have high background- would it be possible for authors to repeat phalloidin stain or reimage with a lower exposure/gain. Additionally, imaging with Zstacks would help to obtain maximum intensity projections. It would greatly aid the reader if each image was labeled with HCR probes/antibodies that have been applied to the sample. 

- Figure 7E: The cells appear to be out of focus and have high background. Additionally, they are lacking the speckled appearance expected to be seen with HCR-FISH. Would it be possible for authors to collect another image utilizing z-stacks? 

HCR-FISH figures identifying the gene expression characteristic of cell clusters have been modified following the reviewer’s concerns.  The changes include:

(1) Additional clusters have been verified with probes to gene identifiers. These include clusters 8, 9 and 12.

(2) Redundant information has been removed.

(3) Colors have been changed to make figures friendlier to color-impaired readers.

(4) Spatial context has been added or identified.

(5) In some cases, improved photos have been added

(6) Better labels have been included

(7) When necessary individual photos used for the overlay have been included.

- Figure 9A: Could authors add cluster labels onto UMAP directly? 

This change was made to Fig 2A. UMAP in Fig 9A is the same and used just as reference of the subset.

- Figure 10: It could be useful if authors put a small map of the sea cucumber like in other images so that readers know where in the anlage this zoomed in model represents. 

Added as suggested by the reviewer.

- Supplementary figure 1F: Could authors add an arrow to the dark cell that's being pointed out? 

Changed made as suggested by the reviewer.

- Supplementary figure 1: Could authors label clearly what color is labeled with what marker? 

Changed made as suggested by the reviewer.

Author response:

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

eLife Assessment

The authors present valuable findings on trends in hind limb morphology throughout the evolution of titanosaurian sauropod dinosaurs, the land animals that reached the most remarkable gigantic sizes. The solid results include the use of 3D geometric morphometrics to examine the femur, tibia, and fibula to provide new information on the evolution of this clade and understand the evolutionary trends between morphology and allometry. Further justification of the ontogenetic stages of the sampled individuals would help strengthen the manuscript's conclusions, and the inclusion of additional large-body mass taxa could provide expanded insights into the proposed trends.

Most of the analyzed specimens, especially from the smaller taxa, come from adult or subadult specimens. None exhibit features that may indicate juvenile status. However, we lack information of the paleohistology that may be a stronger indicator on the ontogenetic status of the individual, and some of operative taxonomic units used in the study come from mean shape of all the sampled specimens.

Current information on morphological differences between adult and subadult or juvenile specimens indicates that even early juvenile specimens may share same morphological features and overall morphology as the adult (e.g., see Curry-Rogers et al., 2016; Appendix S3). We included a comprehensive analysis of the impact of juvenile specimens as one of the aspects of the intraspecific variability that may alter our results in Appendix S3.

Public Reviews:

Reviewer #1:

Weaknesses:

Several sentences throughout the manuscript could benefit from citations. For example, the discussion of using hind limb centroid size as a proxy for body mass has no citations attributed. This should be cited or described as a new method for estimating body mass with data from extant taxa presented in support of this relationship. This particular instance is a very important point to include supporting documentation because the authors' conclusions about evolutionary trends in body size are predicated on this relationship.

We address this issue in the text (Line 32 & 64). Centroid size seems a good indication as it’s the overall size of the entire hind limb, and the length of the femur and tibia is well correlated independently with the body size/mass. Also, as we use few landmarks and only those that are purely type I or II landmarks, with curves of semilandmarks bounded or limited by them, centroid size is not sensible to landmark number differences across the sample in our study (as the centroid size is dependent of the number of landmarks of the current study as well as the physical dimensions of the specimens).

We have sampled and repeated all the analyses using other proxies like the femoral length and the body mass estimated from the Campione & Evans (2020) and Mazzeta et al. (2004) methods. The comprehensive description of the method is in Appendix S2, the alternative analyses can be accessed in the Appendix S3 and S4; and the code for the alternative analyses can be accessed in the modified Appendix S5. All offer similar results than the ones obtained in our analyses with the body size proxied with the hind limb landmark configuration centroid size.

An additional area of concern is the lack of any discussion of taphonomic deformation in Section 3.3 Caveats of This Study, the results, or the methods. The authors provide a long and detailed discussion of taphonomic loss and how this study does a good job of addressing it; however, taphonomic deformation to specimens and its potential effects on the ensuing results were not addressed at all. Hedrick and Dodson (2013) highlight that, with fossils, a PCA typically includes the effects of taphonomic deformation in addition to differences in morphology, which results in morphometric graphs representing taphomorphospaces. For example, in this study, the extreme negative positioning of Dreadnoughtus on PC 2 (which the authors highlight as "remarkable") is almost certainly the result of taphonomic deformation to the distal end of the holotype femur, as noted by Ullmann and Lacovara (2016).

We included a brief commentary in the Caveats of This Study (Line 467) and greatly expanded this issue in the Appendix S3. We followed the methodology proposed by Lefebvre et al. (2020) to discuss the effects of taphonomic deformation in the shape analyses.

Our shape variables (PCs obtained from the shape PCA) should be viewed as taphomorphospaces as Hedrick and Dodson, as well as the reviewer, points in such cases.

The analysis of the effects of taphonomy or errors induced by the landmark estimation method indicate that Dreadnoughtus schrani is one of the few sampled taxa that may have a noticeable impact on our analyses due lithostatic deformation. Other taxa like Mendozasaurus neguyelap or Ampelosaurus atacis may also induce some alterations to the PCs. In general, the trends of those PCs slightly altered by taphonomy, where D. scharni is the only sauropod that may alter an entire PC like PC2, did not exhibit phylogenetic signal and are a small proportion of the sample variance.

The authors investigated 17 taxa and divided them into 9 clades, with only Titanosauria and Lithostrotia including more than two taxa (and four clades are only represented by one taxon). While some of these clades represent the average of multiple individuals, the small number of plotted taxa can only weakly support trends within Titanosauria. If similar general trends could be found when the taxa are parsed into fewer, more inclusive clades, it would support and strengthen their claims. Of course, the authors can only study what is preserved in the fossil record, and titanosaurian remains are often highly fragmentary; these deficiencies should therefore not be held against the authors. They clearly put effort and thought into their choices of taxa to include in this study, but there are limitations arising from this low sample size that inherently limit the confidence that can be placed on their conclusions, and this caveat should be more clearly discussed. Specifically, the authors note that their dataset contains many lithostrotians, but they do not discuss unevenness in body size sampling. As neither their size-category boundaries nor the taxa which fall into each of them are clearly stated, the reader must parse the discussion to glean which taxa are in each size category. It should be noted that the authors include both Jainosaurus and Dreadnoughtus as 'large' taxa even though the latter is estimated to have been roughly five times the body mass of the former, making Dreadnoughtus the only taxon included in this extreme size category. The effects that this may have on body size trends are not discussed. Additionally, few taxa between the body masses of Jainosaurus and Dreadnoughtus have been included even though the hind limbs of several such macronarians have been digitized in prior studies (such as Diamantinasaurus and Giraffititan; Klinkhamer et al. 2018). Also, several members of Colossosauria are more similar in general body size to Dreadnoughtus than Jainosaurus, but unfortunately, they do not preserve a known femur, tibia, and fibula, so the authors could not include them in this study. Exclusion of these taxa may bias inferences about body size evolution, and this is a sampling caveat that could have been discussed more clearly. Future studies including these and other taxa will be important for further evaluating the hypotheses about macronarian evolution advanced by Páramo et al. in this study.

Sadly, we could not include some larger sized titanosaurians sauropods. As the reviewers points out, the lack of larger sauropods among the sampled taxa may hinder our results, as the “large-bodied” category is filled with some mid-sized taxa and the former Dreadnoughtus schrani which is five times larger than some of them. We tried to include Elaltitan lilloi, digitized for this study and included in preliminary analyses, but the fragmentary status increased greatly the error by the estimation method as there is only a proximal third or mid femur preserved from this taxon. Therefore we opted to exclude it from our database.

Other taxa considered, as the reviewer suggest, was not readily available for the authors as the time of this study was conducted and including now may have increased the possible bias of our study. Giraffatitan brancai is an Late Jurassic brachiosaurid, which may again increase the number of early-branching titanosauriforms with large body masses while most of the smaller taxa sampled are recovered in deeply-branching macronarians (including Diamantinasaurus matildae if we would have also included it). Future analyses may include a wider sample of the mid to large-bodied titanosaurians, especially lithostrotians, as well as some colossosaurs like Patagotitan mayorum.

Reviewer #1 (Recommendations For The Authors):

These are all minor comments that would improve the manuscript.

- There are a few typos throughout the manuscript such as: line 70 should be 2016 and line 242 should be forelimb.

Corrected.

- To me, the most interesting aspect of your study is the diversity and trends recovered in titanosaurian subclades and I would highlight this, not gigantism, in the title if you choose to revise the title.

It has been addressed. The specificality of some of the tests and the implication to the acquisition of the spread limb posture and gigantism in early-branching taxa is important nonetheless, so we think that it may remain in the title.

- The abstract should provide more details on the results such as none of the listed trends were statistically significant.

Many of the trends exhibit phylogenetic signal, but not the allometric components. We have briefly addressed them.

- Several sentences in the manuscript need citations such as: line 48 the reference to other megaherbivores, line 66 the discussion of poor understanding of the relationship of wide gauge posture and gigantism, and the use of centroid size as an estimate of body mass (see Public Review).

We changed the line 66 to improve the focus on the current state of the art in the hypothesis of a relationship between arched limbs and in the increase of body size. We included a section relating centroid size as a proxy (due the good correlation between the femur and tibia length and the body mass) and the caveats of using it. We also expanded in the Appendix S2 the use of centroid size and the alternative models.

- With titanosaur evolution, you mention that they are adapting to new niches and topography (line 64). What support is there for this versus they are adapting to be more successful in their current environment?

Noted, we have changed the phrase to improved efficiency exploiting of inland environments, as thy can be either opening new inland niches or adapting better to current inland niches that were already exploited for less deeply branching sauropods. However, its testing is beyond the scope of the current work.

- Line 384-385: the discussion of Rapetosaurus should mention that it is a juvenile and some studies have suggested that titanosaur limbs grow allometrically.

We have included a small line. Whether Rapetosaurus krausei exhibit allometric growth or not may not change greatly the discussion, maybe only excluding it as morphologically convergent to Lirainosaurus and Muyelensaurus. But if that so, it will be further proof that small-sized titanosaurs exhibit the robust skeleton expected in the giant titanosaurs.

- I would consider addressing the question of if we are certain enough in our understanding of titanosaurian phylogeny to rule out homology, especially when you discuss the uncertainty of the placement of specific taxa. Also, Diamantinasaurus is not the only titanosaur that has been proposed as a member of both basal and more derived subclades (e.g., Dreadnoughtus).

We tried to assume a more conservative approach. We could not fully rule out that some of the features observed in the sampled deeply branching lithostrotians, especially saltasauroids, cannot be present in the entire somphospondylan lineage. However, none of the less deeply-branching or early-branching titanosaurs exhibit this kind of morphology. Recent studies propose the possibility that entire groups, included in this study like the Colossosauria, change its position in the phylogeny. However, despite the debated phylogenetic position of Diamantinasaurus or Dreadnoughtus, or even the inclusion of Colossosauria within the saltasauroids and the inclusion of the Ibero-Armorican lithostrotians as putative saltasaurids (Mocho et al. 2024). However, even considering these changes we did not notice any relevant differences in our conclusions about hind limb arched morphology nor about size. Distal hind limb overall robustness should indeed be addressed in the light of shifts in phylogenetic position and include some interesting sauropods like Diamantinasaurus or expand the large-sized Colossosauria or early-branching somphospondyls as it may have profound implications on the morphofunctional adaptations to specific feeding niches, e.g., see current hypotheses about rearing as mentioned in Bates et al. (2016), Ullmann et al. (2017) or Vidal et al. (2020). We had not enough information to conclude the presence of any plesiomorphic condition or analogous feature with our current sample and the debated titanosaurian phylogeny.

- I understand this is not standard in the field, but your study provides the opportunity to conduct sensitivity testing of the effects of cartilage thickness and user articulation of the bones on PCA results. This would be an inciteful addition to the field of GMM.

We are currently developing such a comprehensive analysis and several other implications on our past results. However, we feel that it is beyond the scope of the current study. We appreciate the suggestion nonetheless, as it would be a sensitivity test of the impact of several of our assumptions in the final results that is often not considered.

- In Figure 1, if all the limbs were arranged the same way it would be easier to interpret. Consider flipping panels B and D to match A and C.

Accepted.

- In Figures 2-4, the views in C should be labeled in the figure or caption. Oceanotitan is also in the PCA plot but not included in the figure caption. Also, consider changing the names to represent the paraphyletic groupings you are using instead of formal clade names. For example, change 'Titanosauria' to 'Basal Titanosaurs' to reflect that it is not including all titanosaurs in the sample.

Changes accepted for the shape PCA results. The informal (i.e., paraphyletic) terms such as “Basal Titanosaurs” were only used in the shape analyses as in the RMA, the Titanosauria (and other more inclusive groups) were used as natural groups. Each partial RMA model is based on a sample of all the taxa that are included within that particular clade (e.g., Titanosauria includes both Dreadnoughtus and Saltasaurus; Lithostrotia excludes the former).

- I am concerned that centroid size does not scale evenly across the wide-ranging body mass of titanosaurs. I do not know if this affects your size trends or their significance, but as I mentioned above Dreadnoughtus is much bigger than most of the taxa included and that isn't as drastically apparent in centroid size (in Figure 5) as it is when taxa are plotted by body mass.

Main problematic with centroid size of the hind limb is the shift in the body plan of deeply-branching titanosaurs as the Center of Masses is displaced toward the anterior portion of the body and it has been proposed due a large development of the forelimb region (e.g., Bates et al. 2016). However, it would only increase the effects of the phyletic body size reduction, as smaller taxa tend to have a 1:1 fore limb and hind limb ratio, e.g., from our past analyses as in Páramo et al. (2019), and the sacrum is not as beveled as in earlier somphospondyls, e.g., Vidal et al. (2020). The role of the low-browsing feeding habits of deeply-branching lithostrotians shall be explored elsewhere, as it may be the main driving force of this effect. Our point is, the proxy used may have some slight offset due some high-browsing giant early-branching titanosaurs which has a greater cranial region development which increase its body size and mass beyond our bare-minimum estimation based on the hind limb region. But, overall, this offset is assumed to be low. We repeated the analyses with the femoral length as proxy of body size and a mass estimation, including the quadratic equation based on both humeral and femoral lengths, and the results remain similar. Another problem that arises with the use of centroid size is the way it shall be calculated, but as we used an even number of landmarks and curve semilandmarks, and all of them bounded to anatomical features, it remains equal at least for our sample (but cannot be extrapolated to other geometric morphometric studies that do not use the same configurations)

We appreciate the reviewer concerns nonetheless, as it was on of our own when designing this study, and we in the future will try to expand the analyses, or advise anyone expanding on this study, using total body size/volume estimations following Bates et al. (2016). Which also includes test of the effects of the different whole-body estimation models.

Cites:

Bates KT, Mannion PD, Falkingham PL, Brusatte SL, Hutchinson JR, Otero A, Sellers WI, Sullivan C, Stevens KA, Allen V. 2016. Temporal and phylogenetic evolution of the sauropod dinosaur body plan. Royal Society Open Science 3:150636. doi:10.1098/rsos.150636

Mocho P, Escaso F, Marcos-Fernández F, Páramo A, Sanz JL, Vidal D, Ortega F. 2024. A Spanish saltasauroid titanosaur reveals Europe as a melting pot of endemic and immigrant sauropods in the Late Cretaceous. Commun Biol 7:1016. doi:10.1038/s42003-024-06653-0

Páramo A, Ortega F, Sanz JL. 2019. A Niche Partitioning Scenario for the Titanosaurs of Lo Hueco (Upper Cretaceous, Spain). International Congress of Vertebrate Morphology (ICVM) - Abstract Volume, Journal of Morphology. Prague. p. S197.

Ullmann PV, Bonnan MF, Lacovara KJ. 2017. Characterizing the Evolution of Wide-Gauge Features in Stylopodial Limb Elements of Titanosauriform Sauropods via Geometric Morphometrics. The Anatomical Record 300:1618–1635. doi:10.1002/ar.23607

Vidal D, Mocho P, Aberasturi A, Sanz JL, Ortega F. 2020. High browsing skeletal adaptations in Spinophorosaurus reveal an evolutionary innovation in sauropod dinosaurs. Sci Rep 10:6638. doi:10.1038/s41598-020-63439-0

Reviewer #2:

The authors report a quantitative comparative study regarding hind limb evolution among titanosaurs. I find the conclusions and findings of the manuscript interesting and relevant. The strength of the paper would be increased if the authors were to improve their reporting of taxon sampling and their discussion of age estimation and the potential implications that uncertainty in these estimates would have for their conclusions regarding gigantism (vs. ontogenetic patterns).

Considering the observations made by reviewer #1, we included a data about the impact of ontogenetic patterns and other intraspecific variability in the Appendix S3. We considered to increase the sample but it has not been possible at the time of this study was carried out.

Reviewer #2 (Recommendations For The Authors):

I have a few concerns/requests for the authors, that I hope can be easily resolved.

Comments:

- What drove taxon sampling?

Random sampling of somphospondylan sauropods focused on the Lithostrotia clade for the thesis project of one of the authors, APB. Logistics were also one of the bias on our sample, and based on the available titanosaurian material we left out several macronarians that has been already sampled but would further induce a early-branching large sauropod, deeply-branching small sauropod that may alter our results.

- Which phylogenies were used to create the supertree applied to the analyses? What references were used to time-calibrate the tips and deeper nodes? I couldn't find any reference to this. Additionally, more information regarding the R packages and analytical pipeline would be appreciated: e.g. were measurements used in the analyses log-transformed?

A comprehensive description of the methodology is provided in Appendix S2.

- Age estimate: can the author confirm the skeletal maturity of the sampled individuals? If this is not the case, how can the author be sure that the patterns towards gigantism are not reflecting different ontogenetic stages? I believe this should be part of both methods and discussion.

As commented before, we excluded small, probable juvenile specimens from our sample. We have no paleohistological sample backing the claims of the ontogenetic status of some of the specimens that were included or excluded were calculating the mean shape for the operative taxonomic units. However, we followed a criteria to identify the relative ontogenetic status and it has been included in Appendix S3.

- The authors used the centroid size for regressions in Figure 6. Although I believe that this is a good variable, would the author be willing to use body mass and log-transformed femur length in addition to what was done? These would be very useful considering that these variables are (relatively) independent from shape/morphology.

Accepted, we tested our hypotheses with three alternative models based on femoral length, combined femoral and humeral lengths for body mass estimations. Methodology can be found in Appendix S2, results on Appendix S4, code for the alternative methods in Appendix S5.

- Data access: will stl. Files of the limb elements be shared and freely available? In this case, where the files will be deposited?

At the time of the current study, some of the sampled specimens cannot be available (material under study) but the mean shapes can be generated after the landmarks and semilandmark curves and the “atlas” mesh.

- Additionally, outstanding references regarding limb evolution, GMM, role of ontogeny, and evolution of columnar gait are missing. The authors should reinforce the literature review with the following (alphabetical order):

Bonnan, M. F. (2003). The evolution of manus shape in sauropod dinosaurs: implications for functional morphology, forelimb orientation, and phylogeny. Journal of Vertebrate Paleontology, 23(3), 595-613.

Botha, J., Choiniere, J. N., & Benson, R. B. (2022). Rapid growth preceded gigantism in sauropodomorph evolution. Current Biology, 32(20), 4501-4507.

Curry Rogers, K., Whitney, M., D'Emic, M., & Bagley, B. (2016). Precocity in a tiny titanosaur from the Cretaceous of Madagascar. Science, 352(6284), 450-453.

Day, J. J., Upchurch, P., Norman, D. B., Gale, A. S., & Powell, H. P. (2002). Sauropod trackways, evolution, and behavior. Science, 296(5573), 1659-1659.

Fabbri, M., Navalón, G., Benson, R. B., Pol, D., O'Connor, J., Bhullar, B. A. S., ... & Ibrahim, N. (2022). Subaqueous foraging among carnivorous dinosaurs. Nature, 603(7903), 852-857.

Fabbri, M., Navalón, G., Mongiardino Koch, N., Hanson, M., Petermann, H., & Bhullar, B. A. (2021). A shift in ontogenetic timing produced the unique sauropod skull. Evolution, 75(4), 819-831.

González Riga, B. J., Lamanna, M. C., Ortiz David, L. D., Calvo, J. O., & Coria, J. P. (2016). A gigantic new dinosaur from Argentina and the evolution of the sauropod hind foot. Scientific Reports, 6(1), 19165.

Lefebvre, R., Allain, R., & Houssaye, A. (2023). What's inside a sauropod limb? First three‐dimensional investigation of the limb long bone microanatomy of a sauropod dinosaur, Nigersaurus taqueti (Neosauropoda, Rebbachisauridae), and implications for the weight‐bearing function. Palaeontology, 66(4), e12670.

McPhee, B. W., Benson, R. B., Botha-Brink, J., Bordy, E. M., & Choiniere, J. N. (2018). A giant dinosaur from the earliest Jurassic of South Africa and the transition to quadrupedality in early sauropodomorphs. Current Biology, 28(19), 3143-3151.

Martin Sander, P., Mateus, O., Laven, T., & Knötschke, N. (2006). Bone histology indicates insular dwarfism in a new Late Jurassic sauropod dinosaur. Nature, 441(7094), 739-741.

Remes, K. (2008). Evolution of the pectoral girdle and forelimb in Sauropodomorpha (Dinosauria, Saurischia): osteology, myology and function (Doctoral dissertation, München, Univ., Diss., 2008).

Sander, P. M., & Clauss, M. (2008). Sauropod gigantism. Science, 322(5899), 200-201.

Yates, A. M., & Kitching, J. W. (2003). The earliest known sauropod dinosaur and the first steps towards sauropod locomotion. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270(1525), 1753-1758.

We appreciate this suggestion and we already used some of the articles in our study but the selection of cites were based also in the available manuscript space enforced by the edition guidelines. We would have like to include several of these works but we had opted to include some of the works that summarize some of them, whereas excluding others.

Reviewer #2 (Public review):

Summary:

Intrinsic primary afferent neurons are an interesting population of enteric neurons that transduce stimuli from the mucosa, initiate reflexive neurocircuitry involved in motor and secretory functions, and modulate gut immune responses. The morphology, neurochemical coding, and electrophysiological properties of these cells have been relatively well described in a long literature dating back to the late 1800's but questions remain regarding their roles in enteric neurocircuitry, potential subsets with unique functions, and contributions to disease. Here, the authors provide RNAscope, immunolabeling, electrophysiological, and organ function data characterizing IPANs in mice and suggest that Cdh6 is an additional marker of these cells.

Strengths:

This paper would likely be of interest to the enteric neuroscience community and increases information regarding the properties of IPANs in mice. These data are useful and suggest that prior data from studies of IPANs in other species are likely translatable to mice.

Weaknesses:

Major weaknesses:<br /> (1) The novelty of this study is relatively limited. The main point of novelty suggests an additional marker of IPANs (Cdh6) that would add to the known list of markers for these cells. How useful this would be is unclear. Other main findings basically confirm that IPANs in mice display the same classical characteristics that have been known for many years from studies in guinea pigs, rats, mice and humans.

(2) Critical controls are needed to support the optogenetic experiments. Control experiments are needed to show that ChR2 expression 1) does not change the baseline properties of the neurons, 2) that stimulation with the chosen intensity of light elicits physiologically relevant responses in those neurons, and 3) that stimulation via ChR2 elicits comparable responses in IPANs in the different gut regions focused on here. These essential controls remain absent in the study and limit confidence in the data derived from this model.

(3) The motor effects observed in optogenetic experiments are difficult to understand in the absence of good controls for optogenetic control of the proposed neuron population (discussed above). It remains unclear how stimulating IPANs in the distal colon would generate retrograde CMCs while stimulating IPANs in the proximal colon did nothing. Key controls confirming that the optogentic stimulus was adequate, specific, and relevant are needed. In addition, better characterization of the Cdh6+ population of cells in both regions would be needed to understand the mechanisms underlying these effects.

(4) From the data shown, it is clear that expression driven by the Cdh6CreERT2 driver is not confined to IPANs. There is obviously expression of GFP and ChR2 in smooth muscle cells. This is a major limitation for the physiological experiments that attempt to use this model to specifically stimulate IPANs and assess changes in gut motor function. Better characterization of this model is needed and control experiments are necessary to assess whether functional ChR2 is expressed in cells beyond the proposed subtype of enteric IPANs.

(5) Some of the main conclusions of this study are overstated and claims of priority are made that are not true. For example, the authors state on lines 27-28 of the abstract that their findings provide the "first demonstration of selective activation of a single neurochemical and functional class of enteric neurons". This is certainly not true since Gould et al (AJP-GIL 2019) expressed ChR2 in nitrergic enteric neurons and showed that activating those cells disrupted CMC activity. In fact, prior work by the authors themselves (Hibberd et al Gastro 2018) showed that activating calretinin neurons with ChR2 evoked motor responses. Work by other groups has used chemogenetics and optogenetics to show effects of activating multiple other classes of neurons in the gut.

(6) The electrophysiological characterization of mouse IPANs is useful but is limited to a small subset of Cdh6+ neurons in the distal colon myenteric plexus. Therefore, it remains unclear how well the properties reported here might reflect those of other Cdh6+ IPANs in the same or different regions. Similarly, blocking IH with ZD7288 affects all IPANs and does not add specific information regarding the role of the proposed Cdh6+ subtype.

(7) The submucosal plexus (SMP) also contains enteric IPANs and these were not included in the analysis of Cdh6 expression. Whether or not the proposed IPAN marker Cdh6 would be useful for identifying or targeting those cells remains unclear.

[Editor's note: The Reviewing Editor considers that further controls requested from the reviewers have largely been provided already in prior publications by other groups, as they concern specifically tools published years ago but in a different tissue context. Hence the methodology used to deliver the results reported here fall within the standard practices in the field. The comprehensive, multi-technique approach to the results is compelling in and of itself, and ought to suffice, rendering this work reproducible and therefore a basis for further research.]

Author response:

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

Reviewer #1 (Public review):

Summary:

In their manuscript, Gomez-Frittelli and colleagues characterize the expression of cadherin6 (and -8) in colonic IPANs of mice. Moreover, they found that these cdh6-expressing IPANs are capable of initiating colonic motor complexes in the distal colon, but not proximal and midcolon. They support their claim by morphological, electrophysiological, optogenetic, and pharmacological experiments.

Strengths:

The work is very impressive and involves several genetic models and state-of-the-art physiological setups including respective controls. It is a very well-written manuscript that truly contributes to our understanding of GI-motility and its anatomical and physiological basis. The authors were able to convincingly answer their research questions with a wide range of methods without overselling their results.

We greatly appreciate the reviewer’s time, careful reading and support of our study.

Weaknesses:

The authors put quite some emphasis on stating that cdh6 is a synaptic protein (in the title and throughout the text), which interacts in a homophilic fashion. They deduct that cdh6 might be involved in IPAN-IPAN synapses (line 247ff.). However, Cdh6 does not only interact in synapses and is expressed by non-neuronal cells as well (see e.g., expression in the proximal tubuli of the kidney). Moreover, cdh6 does not only build homodimers, but also heterodimers with Chd9 as well as Cdh7, -10, and -14 (see e.g., Shimoyama et al. 2000, DOI: 10.1042/02646021:3490159). It would therefore be interesting to assess the expression pattern of cdh6proteins using immunostainings in combination with synaptic markers to substantiate the authors' claim or at least add the possibility of cell-cell-interactions other than synapses to the discussion. Additionally, an immunostaining of cdh6 would confirm if the expression of tdTomato in smooth muscle cells of the cdh6-creERT model is valid or a leaky expression (false positive).

We agree with the reviewer that Cdh6 could be mediating some other cell-cell interaction besides synapses between IPANs, and we noted it in the discussion. Cdh6 primarily forms homodimers but, as the reviewer points out, has been known to also form heterodimers with some other cadherins. We performed RNAscope in the colonic myenteric plexus with Cdh7 and found no expression (data not shown). Cdh10 is suggested to have very low expression (Drokhlyansky et al., 2020), possibly in putative secretomotor vasodilator neurons, and Cdh14 has not been assayed in any RNAseq screens. We attempted to visualize Cdh6 protein via antibody staining (Duan et al., 2018) but our efforts did not result in sufficient signal or resolution to identify synapses in the ENS, which remain broadly challenging to assay. Similarly, immunostaining with Cdh6 antibody was unable to confirm Cdh6 protein in tdT-expressing muscle cells, or by RNAscope. We have addressed these caveats in the discussion section.

(1) E. Drokhlyansky, C. S. Smillie, N. V. Wittenberghe, M. Ericsson, G. K. Griffin, G. Eraslan, D. Dionne, M. S. Cuoco, M. N. Goder-Reiser, T. Sharova, O. Kuksenko, A. J. Aguirre, G. M. Boland, D. Graham, O. Rozenblatt-Rosen, R. J. Xavier, A. Regev, The Human and Mouse Enteric Nervous System at Single-Cell Resolution. Cell 182, 1606-1622.e23 (2020).

(2) X. Duan, A. Krishnaswamy, M. A. Laboulaye, J. Liu, Y.-R. Peng, M. Yamagata, K. Toma, J. R. Sanes, Cadherin Combinations Recruit Dendrites of Distinct Retinal Neurons to a Shared Interneuronal Scaffold. Neuron 99, 1145-1154.e6 (2018).

Reviewer #2 (Public review):

Summary:

Intrinsic primary afferent neurons are an interesting population of enteric neurons that transduce stimuli from the mucosa, initiate reflexive neurocircuitry involved in motor and secretory functions, and modulate gut immune responses. The morphology, neurochemical coding, and electrophysiological properties of these cells have been relatively well described in a long literature dating back to the late 1800's but questions remain regarding their roles in enteric neurocircuitry, potential subsets with unique functions, and contributions to disease. Here, the authors provide RNAscope, immunolabeling, electrophysiological, and organ function data characterizing IPANs in mice and suggest that Cdh6 is an additional marker of these cells.

Strengths:

This paper would likely be of interest to a focused enteric neuroscience audience and increase information regarding the properties of IPANs in mice. These data are useful and suggest that prior data from studies of IPANs in other species are likely translatable to mice.

We appreciate the reviewer’s support of our study and insightful critiques for its improvement.

Weaknesses:

The advance presented here beyond what is already known is minimal. Some of the core conclusions are overstated and there are multiple other major issues that limit enthusiasm. Key control experiments are lacking and data do not specifically address the properties of the proposed Cdh6+ population.

Major weaknesses:

(1) The novelty of this study is relatively low. The main point of novelty suggests an additional marker of IPANs (Cdh6) that would add to the known list of markers for these cells. How useful this would be is unclear. Other main findings basically confirm that IPANs in mice display the same classical characteristics that have been known for many years from studies in guinea pigs, rats, mice and humans.

We appreciate the already existing markers for IPANs in the ENS and the existing literature characterizing these neurons. The primary intent of this study was to use these well-established characteristics of IPANs in both mice and other species to characterize Cdh6-expressing neurons in the mouse myenteric plexus and confirm their classification as IPANs.

(2) Some of the main conclusions of this study are overstated and claims of priority are made that are not true. For example, the authors state in lines 27-28 of the abstract that their findings provide the "first demonstration of selective activation of a single neurochemical and functional class of enteric neurons". This is certainly not true since Gould et al (AJP-GIL 2019) expressed ChR2 in nitrergic enteric neurons and showed that activating those cells disrupted CMC activity. In fact, prior work by the authors themselves (Hibberd et al., Gastro 2018) showed that activating calretinin neurons with ChR2 evoked motor responses. Work by other groups has used chemogenetics and optogenetics to show the effects of activating multiple other classes of neurons in the gut.

We thank the reviewer for bringing up this important point and apologize if our wording was not clear. Whilst single neurochemical classes of enteric neurons have been manipulated to alter gut functions, all such instances to date do not represent manipulation of a single functional class of enteric neurons. In the given examples, multiple functional classes are activated utilizing the same neurotransmitter, as NOS and calretinin are each expressed to varying degrees across putative motor neurons, interneurons and IPANs. In contrast, Chd6 is restricted to IPANs and therefore this study is the first optogenetic investigation of enteric neurons from a single putative functional class. Our abstract and discussion emphasizes this point and differentiates this study from those previous.

(3) Critical controls are needed to support the optogenetic experiments. Control experiments are needed to show that ChR2 expression a) does not change the baseline properties of the neurons, b) that stimulation with the chosen intensity of light elicits physiologically relevant responses in those neurons, and c) that stimulation via ChR2 elicits comparable responses in IPANs in the different gut regions focused on here.

We completely agree controls are essential. However, our paper is not the first to express ChR2 in enteric neurons. Authors of our paper have shown in Hibberd et al. 2018 that expression of ChR2 in a heterogeneous population of myenteric neurons did not change network properties of the myenteric plexus. This was demonstrated in the lack of change in control CMC characteristics in mice expressing ChR2 under basal conditions (without blue light exposure). Regarding question (b), that it should be shown that stimulation with the chosen intensity of light elicits physiologically relevant responses in those neurons. We show the restricted expression of ChR2 in IPANs and that motor responses (to blue light) are blocked by selective nerve conduction blockade.

Regarding question (c), that our study should demonstrate that stimulation via ChR2 elicits comparable responses in IPANs in the different gut regions. We would not expect each region of the gut to behave comparably. This is because the different gut regions (i.e. proximal, mid, distal) are very different anatomically, as is anatomy of the myenteric plexus and myenteric ganglia between each region, including the density of IPANs within each ganglia, in addition to the presence of different patterns of electrical and mechanical activity [Spencer et al., 2020]. Hence, it is difficult to expect that between regions stimulation of ChR2 should induce similar physiological responses. The motor output we record in our study (CMCs) is a unified motor program that involves the temporal coordination of hundreds of thousands of enteric neurons and a complex neural circuit that we have previously characterized [Spencer et al., 2018]. But, never has any study until now been able to selectively stimulate a single functional class of enteric neurons (with light) to avoid indiscriminate activation of other classes of neurons.

(1) T. J. Hibberd, J. Feng, J. Luo, P. Yang, V. K. Samineni, R. W. Gereau, N. Kelley, H. Hu, N. J. Spencer, Optogenetic Induction of Colonic Motility in Mice. Gastroenterology 155, 514-528.e6 (2018).

(2) N. J. Spencer, L. Travis, L. Wiklendt, T. J. Hibberd, M. Costa, P. Dinning, H. Hu, Diversity of neurogenic smooth muscle electrical rhythmicity in mouse proximal colon. American Journal of Physiology-Gastrointestinal and Liver Physiology 318, G244–G253 (2020).

(3) N. J. Spencer, T. J. Hibberd, L. Travis, L. Wiklendt, M. Costa, H. Hu, S. J. Brookes, D. A. Wattchow, P. G. Dinning, D. J. Keating, J. Sorensen, Identification of a Rhythmic Firing Pattern in the Enteric Nervous System That Generates Rhythmic Electrical Activity in Smooth Muscle. The Journal of Neuroscience 38, 5507–5522 (2018).

(4) The electrophysiological characterization of mouse IPANs is useful but this is a basic characterization of any IPAN and really says nothing specifically about Cdh6+ neurons. The electrophysiological characterization was also only done in a small fraction of colonic IPANs, and it is not clear if these represent cell properties in the distal colon or proximal colon, and whether these properties might be extrapolated to IPANs in the different regions. Similarly, blocking IH with ZD7288 affects all IPANs and does not add specific information regarding the role of the proposed Cdh6+ subtype.

Our electrophysiological characterization was guided to be within a subset of Cdh6+ neurons by Hb9:GFP expression. As in the prior comment (1) above, we used these experiments to confirm classification of Cdh6+ (Hb9:GFP+) neurons in the distal colon as IPANs. We have clarified in the results and methods that these experiments were performed in the distal colon and agree that we cannot extrapolate that these properties are also representative of IPANs in the proximal colon. We apologize that this was confusing. Finally, we agree with the reviewer that ZD7288 affects all IPANs in the ENS and have clarified this in the text.

(5) Why SMP IPANs were not included in the analysis of Cdh6 expression is a little puzzling. IPANs are present in the SMP of the small intestine and colon, and it would be useful to know if this proposed marker is also present in these cells.

We agree with the reviewer. In addition to characterizing Cdh6 in the myenteric plexus, it would be interesting to query if sensory neurons located within the SMP also express Cdh6. Our preliminary data (n=2) show ~6-12% tdT/Hu neurons in Cdh6-tdT ileum and colon (data not shown). We have added a sentence to the discussion.

(6) The emphasis on IH being a rhythmicity indicator seems a bit premature. There is no evidence to suggest that IH and IT are rhythm-generating currents in the ENS.

Regarding the statement there is no evidence to suggest that IH and IT are rhythm-generating currents in the ENS. We agree with the reviewer that evidence of rhythm generation by IH and IT in the ENS has not been explicitly confirmed. We are confident the reviewer agrees that an absence of evidence is not evidence of absence, although the presence of IH has been well described in enteric neurons. We have modified the text in the results to indicate more clearly that IH and IT are known to participate in rhythm generation in thalamocortical circuits, though their roles in the ENS remain unknown. Our discussion of the potential role of IH or IT in rhythm generation or oscillatory firing of the ENS is constrained to speculation in the discussion section of the text.

(7) As the authors point out in the introduction and discuss later on, Type II Cadherins such as Cdh6 bind homophillically to the same cadherin at both pre- and post-synapse. The apparent enrichment of Cdh6 in IPANs would suggest extensive expression in synaptic terminals that would also suggest extensive IPAN-IPAN connections unless other subtypes of neurons express this protein. Such synaptic connections are not typical of IPANs and raise the question of whether or not IPANs actually express the functional protein and if so, what might be its role. Not having this information limits the usefulness of this as a proposed marker.

We agree with the reviewer that the proposed IPAN-IPAN connection is novel although it has been proposed before (Kunze et al., 1993). As detailed in our response to Reviewer #1, we attempted to confirm Cdh6 protein expression, but were unsuccessful, due to insufficient signal and resolution. We therefore discuss potential IPAN interconnectivity in the discussion, in the context of contrasting literature.

(1) W. A. A. Kunze, J. B. Furness, J. C. Bornstein, Simultaneous intracellular recordings from enteric neurons reveal that myenteric ah neurons transmit via slow excitatory postsynaptic potentials. Neuroscience 55, 685–694 (1993).

(8) Experiments shown in Figures 6J and K use a tethered pellet to drive motor responses. By definition, these are not CMCs as stated by the authors.

The reviewer makes a valid criticism as to the terminology, since tethered pellet experiments do not record propagation. We believe the periodic bouts of propulsive force on the pellet is triggered by the same activity underlying the CMC. In our experience, these activities have similar periodicity, force and identical pharmacological properties. Consistent with this, we also tested full colons (n = 2) set up for typical CMC recordings by multiple force transducers, finding that CMCs were abolished by ZD7288, similar to fixed pellet recordings (data not shown).

(9) The data from the optogenetic experiments are difficult to understand. How would stimulating IPANs in the distal colon generate retrograde CMCs and stimulating IPANs in the proximal colon do nothing? Additional characterization of the Cdh6+ population of cells is needed to understand the mechanisms underlying these effects.

We agree that the different optogenetic responses in the proximal and distal colon are challenging to interpret, but perhaps not surprising in the wider context. It is not only possible that the different optogenetic responses in this study reflect regional differences in the Chd6+ neuronal populations, but also differences in neural circuits within these gut regions. A study some time ago by the authors showed that electrical stimulation of the proximal mouse colon was unable to evoke a retrograde (aborally) propagating CMC (Spencer, Bywater, 2002), but stimulation of the distal colon was readily able to. We concluded that at the oral lesion site there is a preferential bias of descending inhibitory nerve projections, since the ascending excitatory pathways have been cut off. In contrast, stimulation of the distal colon was readily able to activate an ascending excitatory neural pathway, and hence induce the complex CMC circuits required to generate an orally propagating CMC. Indeed, other recent studies have added to a growing body of evidence for significant differences in the behaviors and neural circuits of the two regions (Li et al., 2019, Costa et al., 2021a, Costa et al., 2021b, Nestor-Kalinoski et al., 2022). We have expanded this discussion.

(1) N. J. Spencer, R. A. Bywater, Enteric nerve stimulation evokes a premature colonic migrating motor complex in mouse. Neurogastroenterology & Motility 14, 657–665 (2002).

(2) Li Z, Hao MM, Van den Haute C, Baekelandt V, Boesmans W, Vanden Berghe P, Regional complexity in enteric neuron wiring reflects diversity of motility patterns in the mouse large intestine. Elife 8:e42914 (2019).

(3) Costa M, Keightley LJ, Hibberd TJ, Wiklendt L, Dinning PG, Brookes SJ, Spencer NJ, Motor patterns in the proximal and distal mouse colon which underlie formation and propulsion of feces. Neurogastroenterology & Motility e14098 (2021a).

(4) Costa M, Keightley LJ, Hibberd TJ, Wiklendt L, Smolilo DJ, Dinning PG, Brookes SJ, Spencer NJ, Characterization of alternating neurogenic motor patterns in mouse colon. Neurogastroenterology & Motility 33:e14047 (2021b).

(5) Nestor-Kalinoski A, Smith-Edwards KM, Meerschaert K, Margiotta JF, Rajwa B, Davis BM, Howard MJ, Unique Neural Circuit Connectivity of Mouse Proximal, Middle, and Distal Colon Defines Regional Colonic Motor Patterns. Cellular and Molecular Gastroenterology and Hepatology 13:309-337.e303 (2022).

Recommendations for the Authors:

Reviewer #1 (Recommendations for the authors):

As mentioned above, immunolocalization of cdh6 would be helpful to substantiate the claims regarding IPAN-IPAN synapses.

As mentioned in our response to both reviewers’ public reviews, we attempted to visualize Cdh6 protein via antibody staining (Duan et al., 2018), but our efforts did not result in sufficient signal or resolution to identify Cdh6+ synapses.

(1) X. Duan, A. Krishnaswamy, M. A. Laboulaye, J. Liu, Y.-R. Peng, M. Yamagata, K. Toma, J. R. Sanes, Cadherin Combinations Recruit Dendrites of Distinct Retinal Neurons to a Shared Interneuronal Scaffold. Neuron 99, 1145-1154.e6 (2018).

Reviewer #2 (Recommendations for the authors):

(1) The authors repeatedly refer to IPANs as "sensory" neurons (e.g. in title, abstract, and introduction) but there is some debate regarding whether these cells are truly "sensory" because the information they convey never reaches sensory perception. This is why they have classically been referred to as intrinsic primary afferent (IPAN) neurons. It would be more appropriate to stick with this terminology unless the authors have compelling data showing that information detected by IPANs reaches the sensory cortex.

We thank the reviewer for their comment, but respectfully disagree. The term “sensory neuron” is well established in the ENS. The first definitive proof that “sensory neurons” exist in the ENS was published in Kunze et al., 1995. We note that this paper did not use the word “IPAN” but used the term “sensory neuron”. Furthermore, mechanosensory neurons were published in Spencer and Smith (2004).

Regarding the reviewer’s comment that the authors would need compelling data showing that information detected by IPANs reaches the sensory cortex before the term “sensory neuron” should be valid, it is important to note that many sensory neurons do not provide direct information to the cortex.

(1) W. A. A. Kunze, J. C. Bornstein, J. B. Furness, Identification of sensory nerve cells in a peripheral organ (the intestine) of a mammal. Neuroscience 66, 1–4 (1995).

(2) N. J. Spencer, T. K. Smith, Mechanosensory S-neurons rather than AH-neurons appear to generate a rhythmic motor pattern in guinea-pig distal colon. The Journal of Physiology 558, 577–596 (2004).

(2) Important information regarding the gut region shown and other details are absent from many figure legends.

We apologize for this omission. We have updated the figure legends to include information on gut regions.

Reviewer #2 (Public review):

Summary:

The manuscript investigates the role of ANKRD5 (ANKEF1) as a component of the N-DRC complex in sperm motility and male fertility. Using Ankrd5 knockout mice, the study demonstrates that ANKRD5 is essential for sperm motility and identifies its interaction with N-DRC components through IP-mass spectrometry and cryo-ET. The results provide insights into ANKRD5's function, highlighting its potential involvement in axoneme stability and sperm energy metabolism.

Strengths:

The authors employ a wide range of techniques, including gene knockout models, proteomics, cryo-ET, and immunoprecipitation, to explore ANKRD5's role in sperm biology.

Weaknesses:

(1) Limited Citations in Introduction: Key references on the role of N-DRC components (e.g., DRC1, DRC2, DRC3, DRC5) in male infertility are missing, which weakens the contextual background.

(2) Lack of Functional Insights: While interacting proteins outside the N-DRC complex were identified, their potential roles and interactions with ANKRD5 are not adequately explored or discussed.

(3) Mitochondrial Function Uncertainty: Immunofluorescence suggests possible mitochondrial localization for ANKRD5, but experiments on its role in energy metabolism (e.g., ATP production, ROS) are insufficient, especially given the observed sperm motility defects.

(4) Glycolysis Pathway Impact: Proteomic analysis indicates glycolysis pathway disruptions in Ankrd5-deficient sperm, but the link between these changes and impaired motility is not well explained.

(5) Cryo-ET Data Limitations: The structural analysis of the DMT lacks clarity on how ANKRD5 influences N-DRC or RS3. The low quality of RS3 data hinders the interpretation of ANKRD5's impact on axoneme structure.

(6) Discussion of Findings: The manuscript could benefit from a deeper discussion on the broader implications of ANKRD5's interactions and its role in sperm energy metabolism and motility mechanisms.

Author response:

Thank you for the constructive feedback from the reviewers. We are grateful for their insights and are committed to addressing the key concerns raised in the public reviews through the following revisions:

(1) Validating Axoneme Stability Claims

We have procured new antibodies for DRC11, as well as marker proteins for ODA, IDA, and RS. We will conduct quantitative immunofluorescence staining to validate our claims regarding axoneme stability.

(2) Investigating ANKRD5 Expression in Other Ciliated Cells

We plan to examine the expression of ANKRD5 in mouse respiratory cilia to determine whether it is also expressed in these cells.

(3) Supplementing Key Citations for N-DRC Components

We will add references to published studies on N-DRC components (e.g., DRC1, DRC2, DRC3, DRC5) associated with male infertility in the Introduction to strengthen the background context.

(4) Further Analysis and Validation of ANKRD5 Interactome

We will conduct additional analyses and validation of the interactome of ANKRD5 detected by LC-MS.

(5) Elucidating the Function of ANKRD5 in Mitochondria

We will further investigate the role of ANKRD5 in mitochondrial function.

(6) Investigating Mitochondrial Function and Energy Metabolism

We will further explore the role of ANKRD5 in mitochondrial function and energy metabolism.

(7) Improving Cryo-ET Data Quality and Interpretation

We will attempt to further improve the quality of the STA results and try to calculate the DMT structure with a period of 96 nm. We will also use the WT density map with the same period to generate a difference map.

(8) Expanding Discussion and Correcting Terminology

The Discussion section will be revised to elaborate on the implications of ANKRD5 for male contraceptive research, particularly in targeting sperm motility. We will also correct terminology inaccuracies (e.g., changing "9+2 microtubule doublet" to "9+2 structure") and address formatting issues (e.g., capitalizing "Control").

Response to Reviewer #2 Comment 4:

We appreciate the reviewer's careful consideration of our proteomic data. However, our Gene Set Enrichment Analysis (GSEA) of glycolysis/gluconeogenesis pathways showed no significant enrichment (p-value=0.089, NES=0.708; Fig.6D), which does not meet the statistical thresholds for biological significance (|NES|>1, pvalue<0.05). This observation is further corroborated by our direct ATP measurements showing no difference between genotypes (Fig.6E). We agree that further studies on metabolic regulation could be valuable, but current evidence does not support glycolysis disruption as a primary mechanism for the motility defects observed in Ankrd5-null sperm. This misinterpretation likely arose from the reviewer's overinterpretation of non-significant proteomic trends. We request that this specific claim be excluded from the assessment to avoid misleading readers.

We will provide a comprehensive point-by-point response, along with detailed experimental data and revised figures, in the resubmitted manuscript. Thank you once again for the opportunity to address the reviewers' concerns. We are confident that these revisions will strengthen our manuscript and contribute to the scientific community.

Reviewer #2 (Public review):

Summary:

This study by Lum and colleagues reports on the role of Afadin, a cytosolic adapter protein that organizes multiple cell adhesion molecule families, in the generation and maintenance of complex cellular layers in the mouse retina. They used a conditional deletion approach, removing Afadin in retinal progenitors, and allowing them to analyze broad effects on retinal neuron development.

The study presents high-quality and extensive characterization of the cellular phenotypes, supporting the main conclusions of the paper. They show that Afadin loss results in significant disorganization of the retinal cellular layers and the neuropil, producing rosettes and displacement of cells away from their resident layers. The major classes of neurons in the inner retina are affected, and some neurons are, remarkably, displaced to the other side of the inner plexiform layer. Nevertheless, they mostly target their synaptic partners, including the RGCs to distant retinorecipient targets in the brain. The main conclusions are as follows. Afadin is necessary for establishing and maintaining the retinal architecture. It is not necessary for the generation of the correct numbers/densities of retinal neuron subtypes. Moreover, Afadin loss preserves associations between known synaptic partners and preserves axonal targeting to retinorecipient layers. The consequences on photoreceptor viability and visual processing are also interesting, underscoring the essential function for maintaining retinal structure and function. Overall the main conclusions describing the consequences are supported by the results.

Strengths:

The study provides new knowledge on the requirement of Afadin in retinal development. The introduction and discussion effectively set up the rationale for this work, and place it in the context of previous studies of Afadin in other regions of the CNS.

The study presents high-quality and extensive characterizations of the cellular phenotypes resulting from Afadin loss. By analyzing various aspects of retinal organization - from cellular densities to axon targeting to brain - the study narrows down the role of a structure for promoting the establishment of the layers, or maintenance. The data are straightforward and convincing, and the interpretations are bounded by the data shown (though minor weakness re. survival). Another important finding is that the targeting of retinal neuron processes to synaptic partners, including retinorecipient targets in the brain, are intact.

The study is important as it establishes a focused requirement for Afadin to set up and preserve the overall cellular organizations within the retinal tissue. The demonstration that Afadin is needed for photoreceptor viability and overall visual function enhances impact by establishing its functional importance.

The manuscript is well well-written and presented. The images are attractive and compelling, and the figures are well organized.

Weaknesses:

(1) Expanding on the developmental mechanism is beyond the scope of the study, and would not add to the main conclusions. However, the manuscript would be improved by providing more clarity on the developmental emergence of the defects. The study left me questioning whether the rosettes and cell displacements occur during earlier stages of retina development, or are progressive. For instance, do the RGCs migrate and establish within the GCL correctly at first, and then are displaced with the progressive disorganization? Or are they disorganized and delaminate en route? Images of RGC staining at P0, or earlier during their migration, would be informative. Data in Figure 1 is limited to DAPI staining at P7. Figure 4 shows an image of rod photoreceptors at P7, with their displacement in the GCL layer (and not contained within a rosette). Are the progenitors mislocalized due to delamination?

A few additional thoughts on how these defects compare to other mutants with rosettes might give us more context for understanding the results.

(2) The manuscript reports that the densities of major inner retinal classes are unaffected. There are a few details missing for this point. How were the cell densities quantified (in terms of ROI size), and normalized? This information is lacking in the methods. There is a striking thickening of the GCL in the DAPI-labeled images shown in Figure 1. What are these cells?

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study, the authors examined the role of Afadin, a key adaptor protein associated with cell-adhesion molecules, in retinal development. Using a conditional knockout mouse line (Six3-Cre; AfadinF/F), the authors successfully characterized a disorganized pattern of various neuron types in the mutant retinae. Despite these altered distributions, the retinal neurons maintained normal cell numbers and seemingly preserved some synaptic connections. Notably, tracing results indicated mistargeting of retinal ganglion cell (RGC) axon projections to the superior colliculus, and electroretinography (ERG) analyses suggested deficits in visual functions.

Thank you for the summary and highlights of our study. We appreciate the input from Reviewer 1 and the Editor on this study, with focus on laminar choices, synaptic choices and axonal projections.

Strengths:

This compelling study provides solid evidence addressing the important question of how cell-adhesion molecules influence neuronal development. Compared to previous research conducted in other parts of the central nervous system (CNS), the clearly defined lamination of cell types in the retina serves as a unique model for studying the aberrant neuronal localizations caused by Afadin knockout. The data suggest that cell-cell interactions are critical for retinal cellular organization and proper axon pathfinding, while aspects of cell fate determination and synaptogenesis remain less understood. This work has broad implications not only for retinal studies but also for developmental biology and regenerative medicine.

Weaknesses:

While the phenotypes observed in the Afadin knockout (cKO) mice are intriguing, I would expect to see evidence confirming that Afadin is indeed knocked out in the retina through immunostaining. Specifically, is Afadin knocked out only in certain retinal regions and not others, as suggested by Figures 4A-B? Are Afadin levels different among distinct neuron types, which could mean that its knockout may have a more pronounced impact on certain cell types, such as rods compared to others?

The authors suggest that synapses may form between canonical synaptic partners, based on the proximity of their processes (Figure 2). However, more solid evidence is needed to verify these synapses through the use of synaptic marker staining or transsynaptic labeling before drawing further conclusions.

Although the Afadin cKO mice displayed dramatic phenotypes, additional experiments are necessary to clarify the details of this process. By manipulating Afadin levels in specific cell types or at different developmental time points, we could gain a better understanding of how Afadin regulates accurate retinal lamination and axonal projection.

Regarding the antibody confirming the Knockout, we tested the commercially available antibody from Sigma but weren’t able to confirm its specificity. There was a homemade antibody from another Japan-based laboratory, but it was not available to share at the moment when the study was conducted. Nonetheless, the original allele was derived for hippocampal and cortical studies by Louis Reichardt’s Lab (UCSF), with verified efficacies of the KO allele.

Regarding phenotypical penetrance, this may likely come from the mosaicism of the clone and the symmetric cell division, leading to a rosette-like structure. At this moment, we reason that Afadin KO does NOT lead to direct neuronal loss, and the selective rod loss may derive from other issues, but we lack direct evidence to validate this point.

In regards to the specific neuronal types and synaptic pairs, we acknowledge the limitations of the current Figure 2 in linking the mutant phenotypes to circuit changes. However, the current genetic reagents (Six3Cre) are not compatible with neuron-type specific labeling of synaptic labeling – i.e., cell type-specific Cre and additional Cre-dependent AAV tools might be desired. To do so, we will need to initiate cell-type-specific breeding of transgenic markers such as Hb9GFP for ooDSGCs, or Chat-Cre, VGlut3-Cre for starburst amacrine cells, vG3 amacrine cells, followed by retinal physiology. These experiments take multi-allelic genetic crosses for a very low breeding yield (1/16 or 1/32 Mendelian ratio). These extensive genetic tests are beyond the scope of the current manuscript.

Reviewer #2 (Public review):

Summary:

This study by Lum and colleagues reports on the role of Afadin, a cytosolic adapter protein that organizes multiple cell adhesion molecule families, in the generation and maintenance of complex cellular layers in the mouse retina. They used a conditional deletion approach, removing Afadin in retinal progenitors, and allowing them to analyze broad effects on retinal neuron development.

The study presents high-quality and extensive characterization of the cellular phenotypes, supporting the main conclusions of the paper. They show that Afadin loss results in significant disorganization of the retinal cellular layers and the neuropil, producing rosettes and displacement of cells away from their resident layers. The major classes of neurons in the inner retina are affected, and some neurons are, remarkably, displaced to the other side of the inner plexiform layer. Nevertheless, they mostly target their synaptic partners, including the RGCs to distant retinorecipient targets in the brain. The main conclusions are as follows. Afadin is necessary for establishing and maintaining the retinal architecture. It is not necessary for the generation of the correct numbers/densities of retinal neuron subtypes. Moreover, Afadin loss preserves associations between known synaptic partners and preserves axonal targeting to retinorecipient layers. The consequences on photoreceptor viability and visual processing are also interesting, underscoring the essential function for maintaining retinal structure and function. Overall, the main conclusions describing the consequences are supported by the results.

Strengths:

The study provides new knowledge on the requirement of Afadin in retinal development. The introduction and discussion effectively set up the rationale for this work, and place it in the context of previous studies of Afadin in other regions of the CNS.

The study presents high-quality and extensive characterizations of the cellular phenotypes resulting from Afadin loss. By analyzing various aspects of retinal organization - from cellular densities to axon targeting to brain - the study narrows down the role of a structure for promoting the establishment of the layers, or maintenance. The data are straightforward and convincing, and the interpretations are bounded by the data shown (though minor weakness re. survival). Another important finding is that the targeting of retinal neuron processes to synaptic partners, including retinorecipient targets in the brain, are intact.

The study is important as it establishes a focused requirement for Afadin to set up and preserve the overall cellular organizations within the retinal tissue. The demonstration that Afadin is needed for photoreceptor viability and overall visual function enhances impact by establishing its functional importance.

The manuscript is well well-written and presented. The images are attractive and compelling, and the figures are well organized.

Thank you for your high praise on the logic, data presentation, and significance of the current manuscript. We appreciate your comments on the novelty and impact of our study using retinal circuits as a model.

Weaknesses:

(1) Expanding on the developmental mechanism is beyond the scope of the study, and would not add to the main conclusions. However, the manuscript would be improved by providing more clarity on the developmental emergence of the defects. The study left me questioning whether the rosettes and cell displacements occur during earlier stages of retina development, or are progressive. For instance, do the RGCs migrate and establish within the GCL correctly at first, and then are displaced with the progressive disorganization? Or are they disorganized and delaminate en route? Images of RGC staining at P0, or earlier during their migration, would be informative. Data in Figure 1 is limited to DAPI staining at P7. Figure 4 shows an image of rod photoreceptors at P7, with their displacement in the GCL layer (and not contained within a rosette). Are the progenitors mislocalized due to delamination?  A few additional thoughts on how these defects compare to other mutants with rosettes might give us more context for understanding the results.

We chose P7 as our focus due to the lamination in controls. In the revised manuscript, we plan to include earlier time points, as suggested by the reviewer. The data in Figure 1 at P7 utilizes well-established cell type markers (RBPMS, Chx10, Ap2α) and is not limited only to DAPI. Additionally, we will revise the discussion section and place our mutant analyses in the context of other mutants with rosettes (beta-catenin, etc.) in the retina. Finally, we will address the comment on progenitor lamination by exploring earlier developmental time points.

(2) The manuscript reports that the densities of major inner retinal classes are unaffected. There are a few details missing for this point. How were the cell densities quantified (in terms of ROI size), and normalized? This information is lacking in the methods. There is a striking thickening of the GCL in the DAPI-labeled images shown in Figure 1. What are these cells?

We will revise the manuscript, particularly the methods section, to address these comments. Additionally, we will tackle ROI units and normalization. The cells in the thickened GCL were identified as displaced amacrine cells and bipolar cells.

Author response:

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

eLife Assessment

In this work, the authors use a Drosophila adult ventral nerve cord injury model extending and confirming previous observations; this important study reveals key aspects of adult neural plasticity. Taking advantage of several genetic reporter and fate tracing tools, the authors provide solid evidence for different forms of glial plasticity, that are increased upon injury. The data on detected plasticity under physiologic conditions and especially the extent of cell divisions and cell fate changes upon injury would benefit from validation by additional markers. The experimental part would improve if strengthened and accompanied by a more comprehensive integration of results regarding glial reactivity in the adult CNS.

Thank you very much for your thoughtful comments and constructive feedback regarding our manuscript. We appreciate all the positive remarks on the significance of our findings on neural plasticity in this Drosophila adult ventral nerve cord injury model.

In response to your suggestion, we fully agree that the continuation of this project should address in detail cell fate changes with additional markers if available, or an “omic” approach such as scRNAseq. Unfortunately, these further experiments are beyond the scope of this paper to describe the in vivo phenomena of cell reprogramming, and the cellular events that take glial cells to convert into neurons or neuronal precursors.

Additionally, we agree that the experimental part can be further improved by providing a more comprehensive integration of our results with current knowledge on glial reactivity in the adult CNS. We will revise the manuscript accordingly to include a deeper discussion of the broader implications of our findings and their alignment with existing literature.

Thank you again for your valuable input, which will undoubtedly enhance the quality of our work. We look forward to submitting the revised manuscript for your consideration.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Casas-Tinto et al. present convincing data that injury of the adult Drosophila CNS triggers transdifferentiation of glial cell and even the generation of neurons from glial cells. This observation opens up the possibility to get an handle on the molecular basis of neuronal and glial generation in the vertebrate CNS after traumatic injury caused by Stroke or Crush injury. The authors use an array of sophisticated tools to follow the development of glial cells at the injury site in very young and mature adults. The results in mature adults reveal a remarkable plasticity in the fly CNS and dispels the notion that repair after injury may be only possible in nerve cords which are still developing. The observation of so called VC cells which do not express the glial marker repo could point to the generation of neurons by former glial cells.

Conclusion:

The authors present an interesting story which is technically sound and could form the basis for an in depth analysis of the molecular mechanism driving repair after brain injury in Drosophila and vertebrates.

Strengths:

The evidence for transdifferentiation of glial cells is convincing. In addition, the injury to the adult CNS shows an inherent plasticity of the mature ventral nerve cord which is unexpected.

Weaknesses:

Traumatic brain injury in Drosophila has been previously reported to trigger mitosis of glial cells and generation of neural stem cells in the larval CNS and the adult brain hemispheres. Therefore this report adds to but does not significantly change our current understanding. The origin and identity of VC cells is still unclear. The authors show that VC cells are not GABA- or glutamergic. Yet, there are many other neurotransmitter or neuropetides. It would have been nice to see a staining with another general neuronal marker such as anti-Syt1 to confirm the neuronal identity of Syt1.

We thank the reviewer for the constructive comments and positive feedback. We concur that previous studies have demonstrated glial cell proliferation in response to CNS injury. In contrast, our study focuses on glial transdifferentiation that emerges as a novel phenomenon, particularly in response to injury. We found that neuropile glia lose their glial identity and express the pan-neuronal marker Elav. To investigate the identity of these newly observed elav-positive cells, we employed anti-ChAT, antiGABA and anti-GluRIIA antibodies to determine the functional identity of these cells, besides we stained them with other neuronal markers such Enabled, Gigas or Dac (not shown); however, our attempts yielded limited success. To address this, we have now included a discussion section exploring the potential identity of these cells, considering the possibility that they may represent immature neurons.

Reviewer #2 (Public review):

Summary:

Casas-Tinto et al., provide new insight into glial plasticity using a crush injury paradigm in the ventral nerve cord (VNC) of adult Drosophila. The authors find that both astrocyte-like glia (ALG) and ensheating glia (EG) divide under homeostatic conditions in the adult VNC and identify ALG as the glial population that specifically ramps up proliferation in response to injury, whereas the number of EGs decreases following the insult. Using lineage-tracing tools, the authors interestingly observe interconversion of glial subtypes, especially of EGs into ALGs, which occurs independent of injury and is dependent on the availability of the transcription factor Prospero in EGs, adding to the plasticity observed in the system. Finally, when tracing the progeny of glia, Casas-Tinto and colleagues detect cells of neuronal identity and provide evidence that such gliaderived neurogenesis is specifically favoured following ventral nerve cord injury, which puts forward a remarkable way in which glia can respond to neuronal damage.

Strengths:

This study highlights a new facet of adult nervous system plasticity at the level of the ventral nerve cord, supporting the view that proliferative capacity is maintained in the mature CNS and stimulated upon injury.

The injury paradigm is well chosen, as the organization of the neuromeres allows specific targeting of one segment, compared to the remaining intact and with the potential to later link observed plasticity to behaviour such as locomotion.

Numerous experiments have been carried out in 7-day old flies, showing that the observed plasticity is not due to residual developmental remodelling or a still immature VNC.

By elegantly combining different methods, the authors show glial divisions including with mitotic-dependent tracing and find that the number of generated glia is refined by apoptosis later on.

The work identifies prospero in glia as an important coordinator of glial cell fate, from development to the adult context, which draws further attention to the upstream regulatory mechanisms.

We would like to thank the reviewer for his/her comments and the positive analysis of this work.

Weaknesses:

The authors observe consistent inter-conversion of EG to ALG glial subtypes that is further stimulated upon injury. The authors conclude that these findings have important consequences for CNS regeneration and potentially for memory and learning. However, it remains somewhat unclear how glial transformation could contribute to regeneration and functional recovery.

This is an ongoing question in the laboratory and in the field. We know that glial cells contribute to the regenerative program in the nervous system, and molecular signalling in glial cells is determinant for the functional recovery (Losada-Perez et al 2021). Therefore, we include this concept in the discussion as the evidence indicates that glial cells participate in these programs. However, further investigation is required to clarify and determine the mechanisms underlying this glial contribution. To determine if glial to neuron transformation contributes to functional recovery, we would need to compare the recovery of animals with new VC to animals without VC, however, the  molecular mechanism that produces this change of identity is still unknown, and therefore we are not able to generate injured flies with no new VC

The signal of the Fucci cell cycle reporter seems more complex to interpret based on the panels provided compared to the other methods employed by the authors to assess cell divisions.

We agree that Fly Fucci is a genetic reporter that might be more complex to interpret than EdU staining or other markers. However, glial cells proliferation is a milestone of this manuscript, and we used different available tools to confirm our results. We have revised this specific section to ensure that the text is clear and straightforward.

Elav+ cells originating from glia do not express markers for mature neurons at the analysed time-point. If they will eventually differentiate or what type of structure is formed by them will have to be followed up in future studies.

We fully agree with the reviewer, and we will analyze later days to study neuronal fate and contribution to VNC function.

Context/Discussion

There is some lack of connecting or later comparing the observed forms of glial plasticity in the VNC with respect to plasticity described in the fly brain.

Highlighting some differences in the reactiveness of glia in the VNC compared to the brain could point to relevant differences in repair capacity in different areas of the CNS.

Based on the assays employed, the study points to a significant amount of glial "identity" changes or interconversions under homeostatic conditions. The potential significance of this rather unexpected "baseline" plasticity in adult tissues is not explicitly pointed out and could improve the understanding of the findings.

Some speculations if "interconversion" of glia is driven by the needs in the tissue could enrich the discussion.

We would like to thank the reviewer for these suggestions. We have changed the discussion to introduce these concepts.

Reviewer #3 (Public review):

In this manuscript, Casas-Tintó et al. explore the role of glial cell in the response to a neurodegenerative injury in the adult brain. They used Drosophila melanogaster as a

model organism, and found that glial cells are able to generate new neurons through the mechanism of transdifferentiation in response to injury. This paper provides a new mechanism in regeneration, and gives an understanding to the role of glial cells in the process.

Comments on revisions:

In the previous version of the manuscript, I had suggested several recommendations for the authors. Unfortunately, none of these were addressed in the author's revision.

We are sorry for this error. We apologize but we never received these comments. We have now found them, and we have incorporated these comments in the new version of the manuscript.

(1) Have you tried screening for other markers for the EdU+ Repo+ Pros- cells?

We have identified these cells as glial cells (Repo +), and not astrocyte-like glia (pros-). But we have not further characterized  the identity of these cells. Our aim was to identify these proliferating glial cells as NPG (Neuropile glia), which are Astrocyte-Like Glia (ALG), as previous works suggest in larvae (Kato et al., 2020; Losada-Perez et al., 2016), or Ensheathing Glia (EG). To discard the ALG identity, we used prospero as the best marker. The results indicate that there are ALG among the proliferating population, but in addition, we also found pros- glial cells that were EdU positive. These cells are located in the interface between cortex and neuropile, where the neuropile glia position is described. The anti-pros staining indicated they were no ALG which suggest that they are EG.

There is no specific nuclear marker for EG cells, therefore we used FLY_FUCCI under the control of a EG specific promoter (R56F03-Gal4) to determine if the other dividing cells were EG. These results indicate that EG glia divide although their proliferation does not increase upon injury.

The R56F03 Gal4 construct is described as ensheathing glia specific by previous publications, including:

(1) Kremer M. C., Jung C., Batelli S., Rubin G. M. and Gaul U. (2017). The glia of the adult Drosophila nervous system. Glia 65, 606-638. 10.1002/glia.23115

(2) Qingzhong Ren, Takeshi Awasaki, Yu-Chun Wang, Yu-Fen Huang, Tzumin Lee. Lineage-guided Notch-dependent gliogenesis by Drosophila multi-potent progenitors. Development. 2018 Jun 11;145(11):dev160127. doi: 10.1242/dev.160127   

To summarize, our results suggest that part of these proliferating glial cells are ALG and EG. Our results can not discard that a residual part of these proliferating cells are not AG nor EG.

(2) You mentioned that ALG are heterogenous in size and shape, does that mean that you may have different subpopulations of ALG? Would that also mean that only a portion of them responds to injury?

Yes, as in Astrocytes in vertebrates this population is highly heterogeneous. Currently there are no molecular tools to specifically identify these subpopulations and characterize their distinct roles. However, emerging research suggests that differences in size, shape, and potentially molecular markers could correlate with functional diversity. This implies that certain subpopulations of ALG may be more specialized or primed to respond to injury, while others may play roles in homeostasis or other processes. Understanding this heterogeneity will require advanced techniques such as single-cell RNA sequencing, spatial transcriptomics, or live imaging to unravel how these subpopulations contribute to injury responses and overall tissue dynamics.

(3) You mentioned that NP-like cells have similar nuclear shape and size to ALG and EG, while Ventral cortex cells have larger nuclei. Can you please show a quantification of the NP-like cells and Ventral cortex cells size, and show a direct comparison with ALG and EG cells to support those claims (images, quantification and analysis)?

We added a new supplementary figure with a graph showing nuclei size differences between VC and NP-like cells, and a diagram showing VC cell localization. Images in figure 2A-A’ and 2B-B’ show both types of cells with the same scale, additionally, NPG cells are shown in red (current expression of the specific Gal4 line). A direct comparison between EG and NP-like glia can be observed in Figure 3 as well.

Besides of size and localization, we conclude  that VC and N-like cells present different molecular markers as VC are elav-positive and reponegative whereas NP-like cells are repo-positive elav-negative

(4) In Figure 2B, the repo expression is not very clear. I suggest using a different example to support the claim that NP cells are Repo+.

We have changed the color of anti-elav staining to facilitate visualisation

(5) Again, in Figure 2C, you need quantification and analysis to support the claim that you used nuclear shape and size to identify VC vs. NP like cells.

Quantification in point 3, criteria in Figure S1

(6) What is the identity of the newly formed neurons? Other than Elav, have you tried using other markers of neurons that are typically found in this area?

This question is of great interest and relevance. We have done great efforts to solve this open question and so far, our data suggest that these neurons might be in an immature state. In this last version of the manuscript, we included the results (Figure S1) with several different markers. 

The molecular identity of these cell populations, glia and neurons, is currently under investigation.

Minor comments:

(1) In the abstract, EG and ALG abbreviations are not introduced properly.

Thank you very much for noticing this missing information, we have now included it in the abstract.

(2) Please include a representation of the NPG somata location in Figure 1A.

We have included this information in the figure

(3) A schematic showing the differences between ALG and EG cells would be helpful as well.

We have included in the introduction references and reviews where other authors describe in detail the differences.

(4) In Figure 1 E, G, H- please indicated the genotype of the fly used in the panel as well as the cell type studied.

The complete genotype is included in the corresponding figure legend. We have added a simplified genotype in the figure for clarity.

(5) Please show the genotype used for images in Figure 2: ALG or EG specific drivers.

This information is included in the corresponding figure legend. We believe that it is better to keep the figure clean so we decided to keep the complete genotype, which is considerably long, only in the figure legend.

Reviewer #1 (Public review):

Summary:

The authors investigated sleep and circadian rhythm disturbances in Fmr1 KO mice. Initially, they monitored daily home cage behaviors to assess sleep and circadian disruptions. Next, they examined the adaptability of circadian rhythms in response to photic suppression and skeleton photic periods. To explore the underlying mechanisms, they traced retino-suprachiasmatic connectivity. The authors further analyzed the social behaviors of Fmr1 KO mice and tested whether a scheduled feeding strategy could mitigate sleep, circadian, and social behavior deficits. Finally, they demonstrated that scheduled feeding corrected cytokine levels in the plasma of mutant mice.

Strengths:

(1) The manuscript addresses an important topic-investigating sleep deficits in an FXS mouse model and proposing a potential therapeutic strategy.

(2) The study includes a comprehensive experimental design with multiple methodologies, which adds depth to the investigation.

Weaknesses:

(1) The first serious issue in the manuscript is the lack of a clear description of how they performed the experiments and the missing definitions of various parameters in the results. Given that monitoring and analyzing sleep behaviors are the key experiments of this manuscript, I use the "Immobility-Based Sleep Behavior" section of Methods as an example to elaborate:

Incomplete or Incorrect Description of Tracking Threshold:<br /> o The phrase "tracked the (40 sec or greater as previously described" is incomplete and does not clarify what is being tracked. This appears to be an error in writing or editing.<br /> Unclear Relationship Between Threshold and EEG Validation:<br /> o The threshold "40 sec or greater" is mentioned without context or explanation of what it represents (e.g., sleep bout duration, inactivity, or another parameter). The reference to Fisher et al. (2016) and "99% correlation with EEG-defined sleep" seems misaligned with the paragraph's content.

Confusing Definition of Sleep Bout:<br /> o The definition of a sleep bout is unclear. Sleep bouts should logically be based on periods of inactivity, not activity. The sentence suggesting sleep is measured by "activity staying above the threshold" is confusing. The phrase "3 counts of sleep per minute for longer than one minute" requires clarification.

Unclear Data Selection for Analysis:<br /> o The phrase "2 days with the best recording quality" is vague and does not specify how "best" was determined or why only two days out of five were analyzed.

Awkward Grammar and Structure:<br /> o Phrases like "Acquiring data were exported in 1-min bins" are grammatically awkward. "Acquiring" should be "Acquired." Some sentences are overly long and lack clarity, making the text harder to follow.<br /> In addition to this section, the authors should review all paragraphs in the Methods section to improve readability.

(2) Although the manuscript has a relatively long Methods section, some essential information is missing. For instance, the definition of sleep bout, as described above, is unclear. Additional missing information includes:

Figure 2: "Rhythmic strength (%)" and "Cycle-to-cycle variability (min)."<br /> Figure 3: "Activity suppression."<br /> Figure 4: "Rhythmic power (V%)" (is this different from rhythmic strength (%)?) and "Subjective day activity (%)."<br /> Figure 5: Clear labeling of the SCN's anatomical features and an explanation for quantifying only the ventral part instead of the entire SCN. Alternatively, the authors should consider quantifying the whole SCN.<br /> Figure 6: Inconsistencies in terms like "Sleep frag. (bout #)" and "Sleep bouts (#)." Consistent terminology throughout the manuscript is essential.

(3) Figure 1A shows higher mouse activity during ZT13-16. It is unclear why the authors scheduled feeding during ZT15-21, as this seems to disturb the rhythm. Consistent with this, the body weights of WT and Fmr1 KO mice decreased after scheduled feeding. The authors should explain the rationale for this design clearly.

(4) The interpretation of social behavior results in Figure 6 is questionable. The authors claim that Fmr1 KO mice cannot remember the first stranger in a three-chamber test, writing, "The reduced time in exploring and staying in the novel-mouse chamber suggested that the Fmr1 KO mutants were not able to distinguish the second novel mouse from the first now-familiar mouse." However, an alternative explanation is that Fmr1 KO mice do remember the first stranger but prefer to interact with it due to autistic-like tendencies. Data in Table 5 show that Fmr1 KO mice spent more time interacting with the first stranger in the 3-chamber social recognition test, which support this possibility. Similarly, in the five-trial social test, Fmr1 KO mice's preference for familiar mice might explain the reduced interaction with the second stranger.

In Figure 6C (five-trial social test results), only the fifth trial results are shown. Data for trials 1-4 should be provided and compared with the fifth trial. The behavioral features of mice in the 5-trial test can then be shown completely. In addition, the total interaction times for trials 1-4 (154 {plus minus} 15.3 for WT and 150 {plus minus} 20.9 for Fmr1 KO) suggest normal sociability in Fmr1 KO mice (it is different from the results of 3-chamber). Thus, individual data for trials 1-4 are required to draw reliable conclusions.

In Table 6 and Figure 6G-6J, the authors claim that "Sleep duration (Figures 6G, H) and fragmentation (Figures 6I, J) exhibited a moderate-strong correlation with both social recognition and grooming." However, Figure 6I shows a p-value of 0.077, which is not significant. Moreover, Table 6 shows no significant correlation between SNPI of the three-chamber social test and any sleep parameters. These data do not support the authors' conclusions.

(5) Figure 7 demonstrates the effect of scheduled feeding on circadian activity and sleep behaviors, representing another critical set of results in the manuscript. Notably, the WT+ALF and Fmr1 KO+ALF groups in Figure 7 underwent the same handling as the WT and Fmr1 KO groups in Figures 1 and 2, as no special treatments were applied to these mice. However, the daily patterns observed in Figures 7A, 7B, 7F, and 7G differ substantially from those shown in Figures 2B and 1A, respectively. Additionally, it is unclear why the WT+ALF and Fmr1 KO+ALF groups did not exhibit differences in Figures 7I and 7J, especially considering that Fmr1 KO mice displayed more sleep bouts but shorter bout lengths in Figures 1C and 1D.

Furthermore, it is not specified whether the results in Figure 7 were collected after two weeks of scheduled feeding (for how many days?) or if they represent the average data from the two-week treatment period.

The rationale behind analyzing "ZT 0-3 activity" in Figure 7D instead of the parameters shown in Figures 2C and 2D is also unclear.

In Figure 7F, some data points appear to be incorrectly plotted. For instance, the dark blue circle at ZT13 connects to the light blue circle at ZT14 and the dark blue circle at ZT17. This is inconsistent, as the dark blue circle at ZT13 should link to the dark blue circle at ZT14. Similarly, it is perplexing that the dark blue circle at ZT16 connects to both the light blue and dark blue circles at ZT17. Such errors undermine confidence in the data. The authors need to provide a clear explanation of how these data were processed.

Lastly, in the Figure 7 legend, Table 6 is cited; however, this appears to be incorrect. It seems the authors intended to refer to Table 7.

(6) Similar to the issue in Figure 7F, the data for day 12 in Supplemental Figure 2 includes two yellow triangles but lacks a green triangle. It is unclear how the authors constructed this chart, and clarification is needed.

(7) In Figure 8, a 5-trial test was used to assess the effect of scheduled feeding on social behaviors. It is essential to present the results for all trials (1 to 4). Additionally, it is unclear whether the results for familial mice in Figure 8A correspond to trials 1, 2, 3, or 4.<br /> The legend for Figure 8 also appears to be incorrect: "The left panels show the time spent in social interactions when the second novel stranger mouse was introduced to the testing mouse in the 5-trial social interaction test. The significant differences were analyzed by two-way ANOVA followed by Holm-Sidak's multiple comparisons test with feeding treatment and genotype as factors." This description does not align with the content of the left panels. Moreover, two-way ANOVA is not the appropriate statistical analysis for Figure 8A. The authors need to provide accurate details about the analysis and revise the figure legend accordingly.

(8) The circadian activity and sleep behaviors of Fmr1 KO mice have been reported previously, with some findings consistent with the current manuscript, while others contradict it. Although the authors acknowledge this discrepancy, it seems insufficiently thorough to simply state that the reasons for the conflicts are unknown. Did the studies use the same equipment for behavior recording? Were the same parameters used to define locomotor activity and sleep behaviors? The authors are encouraged to investigate these details further, as doing so may uncover something interesting or significant.

(9) Some subtitles in the Results section and the figure legends do not align well with the presented data. For example, in the section titled "Reduced rhythmic strength and nocturnality in the Fmr1 KOs," it is unclear how the authors justify the claim of altered nocturnality in Fmr1 KO mice. How do the authors define changes in nocturnality? Additionally, the tense used in the subtitles and figure legends is incorrect. The authors are encouraged to carefully review all subtitles and figure legends to correct these errors and enhance readability.

Reviewer #2 (Public review):

Summary:

In the present study, the authors, using a mouse model of Fragile X syndrome, explore the very interesting hypothesis that restricting food access over a daily schedule will improve sleep patterns and, subsequently, behavioral capacities. By restricting food access from 12h to 6h over the nocturnal period (active period for mice), they show, in these KO mice, an improvement of the sleep pattern accompanied by reduced systemic levels of inflammatory markers and improved behavior. Using a classical mouse model of neurodevelopmental disorder (NDD), these data suggest that eating patterns might improve sleep quality, reduce inflammation and improve cognitive/behavioral capacities in children with NDD.

Strengths:

Overall, the paper is very well-written and easy to follow. The rationale of the study is generally well-introduced. The data are globally sound. The provided data support the interpretation overall.

Weaknesses:

(1) The introduction part is quite long in the Abstract, leaving limited space for the data provided by the present study.

(2) A couple of points are not totally clear for a non-expert reader:<br /> - The Fmr1/Fxr2 double KO mice are not well described.<br /> - What is the rationale for performing both LD and DD measures?

(3) The data on cytokines and chemokines are interesting. However, the rationale for the selection of these molecules is not given. In addition, these measures have been performed in the systemic blood. Measures in the brain could be very informative.

(4) An important question is the potential impact of fasting vs the impact of the food availability restriction. Indeed fasting has several effects on brain functioning including cognitive functions.

(5) How do the authors envision the potential translation of the present study to human patients? How to translate the 12 to 6 hours of food access in mice to children with Fragile X syndrome?

Reviewer #2 (Public review):

Summary:

Soham Mukhopadhyay et al. investigated the protein folding of the secretome from gall-forming microbes using the AI-based structure modeling tool AlphaFold2. Their study analyzed six gall-forming species, including two Plasmodiophorid species and four others spanning different kingdoms, along with one non-gall-forming Plasmodiophorid species, Polymyxa betae. The authors found no effector fold specifically conserved among gall-forming pathogens, leading to the conclusion that their virulence strategies are likely achieved through diverse mechanisms. However, they identified an expansion of the Ankyrin repeat family in two gall-forming Plasmodiophorid species, with a less pronounced presence in the non-gall-forming Polymyxa betae. Additionally, the study revealed that known effectors such as CCG and AvrSen1 belong to sequence-unrelated but structurally similar (SUSS) effector clusters.

Strengths:

(1) The bioinformatics analyses presented in this study are robust, and the AlphaFold2-derived resources deposited in Zenodo provide valuable resources for researchers studying plant-microbe interactions. The manuscript is also logically organized and easy to follow.

(2) The inclusion of the non-gall-forming Polymyxa betae strengthens the conclusion that no effector fold is specifically conserved in gall-forming pathogens and highlights the specific expansion of the Ankyrin repeat family in gall-forming Plasmodiophorids.

(3) Figure 4a and 4b effectively illustrate the SUSS effector clusters, providing a clear visual representation of this finding.

(4) Figure 1 is a well-designed, comprehensive summary of the number and functional annotations of putative secretomes in gall-forming pathogens. Notably, it reveals that more than half of the analyzed effectors lack known protein domains in some pathogens, yet some were annotated based on their predicted structures, despite the absence of domain annotations.

Weaknesses:

(1) The effector families discussed in this paper remain hypothetical in terms of their functional roles, which is understandable given the challenges of demonstrating their functions experimentally. However, this highlights the need for experimental validation as a next step.

(2) Some analyses, such as those in Figure 4e, emphasize motifs derived from sequence alignments of SUSS effector clusters. Since these effectors are sequence-unrelated, sequence alignments might be unreliable. It would be more rigorous to perform structure-based alignments in addition to sequence-based ones for motif confirmation. For instance, methods described in Figure 3E of de Guillen et al. (2015, https://doi.org/10.1371/journal.ppat.1005228) or tools like Foldseek (https://search.foldseek.com/foldmason) could be useful for aligning structures of multiple sequences.

(3) When presenting AlphaFold-generated structures, it is essential to include confidence scores such as pLDDT and PAE. For example, in Figure 1D of Derbyshire and Raffaele (2023, https://doi.org/10.1038/s41467-023-40949-9), the structural representations were colored red due to their high pLDDT scores, emphasizing their reliability.

Reviewer #2 (Public review):

Although recent cryo-EM structures of the CFTR ion channel were reported in a putative open state (ATP-bound, NBD-dimerized), it remains unclear whether these structures explain the conductive properties of the open channel observed in functional experiments. To investigate this, the authors conducted extensive molecular dynamics simulations at different voltages. The simulations are started from snapshots of their prior work, based on the experimental putative open state and including conditions with high negative voltage. Their analysis reveals that the cryo-EM structure represents a near-open metastable state, with most trajectories transitioning to either more closed or more open conformations, leading to the identification of a potential new open state. Permeation rate analysis shows that, unlike the other states, the proposed open state exhibits functional conductive properties of the open channel, although a strong inward rectification, inconsistent with experimental data, is also noted. Further structural analysis and simulations of ATP-unbound closed states offer additional mechanistic insights.

Overall, this work tackles key questions about CFTR: What is the true open conductive state? Does the ATP-bound cryo-EM structure reflect an actual open state? What is the ion permeation mechanism, and what structural changes occur during the closed-to-open transition? Which residues are critical, particularly those linked to diseases like CF? The study, based on a comprehensive set of all-atom molecular dynamics simulations, including a range of physiologically relevant voltages, provides important insights in this regard. It identifies key structural states, permeation pathways, critical residues, and conductance properties that can be directly compared to functional data. Notably, the analysis identifies a new open state of the channel, which, systematic analysis convincingly demonstrates is a conductive conformation of the channel, in line with experimental data at negative voltages. The authors carefully address some of the limitations of their results, exploring and discussing discrepancies with functional experiments, such as inward rectification. The work is also very well written, with a clear and logical presentation of key findings.

The main weakness of this study is that the simulation data rely on the conventional CHARMM36 force field for Cl− ions, which has been shown to significantly underestimate the interaction between Cl− and proteins (J. Chem. Theory Comput. 2021, 17, 6240-6261). For example, the conventional CHARMM36 force field destabilizes the Cl-binding site in CLC-ec1. The latter ion unbinds irreversibly during microseconds-long simulations which is at odds with the experimental binding affinity.

This imbalance in Cl−/protein/water interactions could significantly impact the CFTR simulations, potentially altering state populations and Cl− permeability. Notably, recent work by Levring and Chen (Proc Natl Acad Sci U S A. 2024) identifies a likely Cl− binding site in the bottleneck region of the channel, which contradicts the simulation results showing low occupancy Cl− ions in this region (Fig. 1B and Fig. 6A). This discrepancy may be due to the underestimation of Cl−/protein interactions. Indeed, Orabi et al. have proposed corrections that specifically tune these interactions, including those with aromatic residues, in line with the binding site geometry suggested by Levring and Chen. This imbalance in interactions may also lead to an underestimation of the conductance in the experimental near-open state.<br /> Balanced Cl−/protein interactions could also influence voltage/current relationships, potentially affecting the degree of inward rectification. For example, higher Cl− occupancy in the bottleneck region may stabilize the down state of R334, along with other measured interactions, thereby increasing conductance as the authors have shown.

The experimental evidence reported and discussed by the authors in support of the proposed open state is largely qualitative. For instance, in Figure 4 Supplement 2 there is a significant overlap in the distances and SASA distributions of open and near-open states for the reported residues (are those residues water accessible in the simulations?).

Given the known limitations of the standard CHARMM36 Cl− force field and in the absence of robust experimental validation of the proposed open state, I recommend validating at least part of the results using an independent set of simulations (not started from the previous ones) with an updated Cl− force field. It would be especially important to reassess whether the experimental near-open state is truly metastable and less probable than the new open state, and confirm that the near-open state exhibits negligible conductance.

A minor point worth discussing is whether the observed inward rectification may be influenced by hysteresis or incomplete equilibration, as many simulations were started from prior trajectories at large negative voltages and may not have fully relaxed. For instance, is not uncommon that small structural changes in backbone and sidechains occur in several microseconds (Shaw et al., Science, 2010). That said, discrepancies in current-voltage relationships are not unexpected due to challenges in simulation sampling and force field accuracy (J Gen Physiol 2013 May;141(5):619-32) as the authors stated.

Another minor point to address is the preparation of the simulation setup for the ATP-free structure of the protein. It would be helpful to specify whether any particular controls or steps were taken, given that the structure is based on a relatively low resolution (3.87 Å) model.

Reviewer #3 (Public review):

Background:

Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a chloride channel whose dysfunction underlies cystic fibrosis, a life-limiting condition caused by thick, sticky mucus buildup in the lungs and other organs. Despite multiple high-resolution structures of CFTR, these snapshots have all captured the channel in a non-conducting or "closed" conformation - even when the protein was prepared under conditions that should favor channel opening. This discrepancy has posed a key challenge: how can a channel be experimentally observed as closed while physiological tests demonstrate it conducts chloride ions?

Key Findings:

(1) Stable Open Conformation

Through repeated molecular dynamics (MD) simulations of human CFTR in lipid bilayers, researchers observed a reproducible, stable open state. Unlike previous transient openings seen in single-run or short simulations, this conformation remains consistently permeable over extended timescales.

(2) Penta-Helical Arrangement

The authors highlight a "penta-helical" pore-lining arrangement in which five transmembrane helices symmetrically organize to create a clear ion-conduction pathway. This novel configuration resolves the previously puzzling hydrophobic bottleneck found in cryo-EM structures.

(3) Conductance Close to Experimental Values

By analyzing chloride ion flow under near-physiological voltages, they calculate a channel conductance aligning well with electrophysiological measurements. This alignment provides strong support that the observed structure is functionally relevant.

(4) Roles of Key Residues

Several positively charged (cationic) residues in the pore appear crucial for guiding and stabilizing chloride ions. Simultaneously, small kinks in certain helices may act as structural "hinges," allowing or blocking chloride passage.

How to Interpret These Results:

(1) Bridging a Major Gap: The study tackles the mismatch between static "closed" CFTR structures and their known open-channel function. Successfully capturing a stable open state in MD simulations is a significant step toward reconciling what cryo-EM data shows versus what physiological experiments have long told us.

(2) Strength in Multiple Replicas: Running many simulation repeats (rather than relying on a single trajectory) lends credibility. Only if a phenomenon is reproducible across multiple runs can it be considered robust.

(3) Consistency with Mutational Data: Observing that known functional hotspots (e.g., specific charged residues) play a key role in the new pore model further validates these findings.

Important Caveats and Limitations:

(1) Simulation Timescales vs. Biology<br /> Even extended MD (on the microsecond scale) is still much faster, simpler, and more controlled than real cellular processes.

(2) Physiological existence of the penta-helical pore<br /> Although the simulations and results are highly compelling, several factors leave open the possibility of a physiological open conformation differing from the observed penta-helical pore. These factors include ATP hydrolysis, interactions with physiological binding partners, the native membrane environment, and regions not modeled in the CFTR structures, such as the R domain. Most importantly, the transmembrane voltage is very high (500mV).

Bottom Line:

This work delivers a long-awaited, near-physiological view of CFTR's open conformation. It provides a foundational structure against which future experimental and computational studies can be compared. By demonstrating reliable chloride conduction and matching established biophysical data, these simulations bring us closer to understanding - and potentially targeting - CFTR's gating mechanism in health and disease. Readers should applaud the breakthroughs while recognizing that further exploration (including more complex in vitro and in vivo experiments) will still be necessary to capture the full dynamism of CFTR in the living cell environment.

Reviewer #4 (Public review):

Summary:

The structural mechanism of anion permeation through the open CFTR pore has remained unresolved and is subject to ongoing debate. That is because even in CFTR structures obtained under conditions that normally maximally activate the channel (phosphorylation + ATP + non-hydrolytic mutations + potentiator drugs) a bottleneck region in the pore, too narrow to allow passage of hydrated chloride ions, is observed.

The present study uses molecular dynamics (MD) simulations initiated from such "quasi-open" states to address local conformational dynamics of the pore. The authors conclude that the quasi-open structure stably relaxes to a fully open conformation on the sub-microsecond time scale. They provide a detailed analysis of this fully open structure and of the mechanism of chloride permeation. They conclude that two major exit pathways (a central and a peripheral) exist for chloride ions, and that the ions remain near-fully hydrated throughout the pore: chloride-protein interactions displace only 1-2 waters from the first solvation shell. Furthermore, the simulations provide some hints for conformational changes involved in gating.

Strengths:

The findings are interpreted in the context of the large body of published functional studies on CFTR permeation properties, and caveats are adequately discussed.

Weaknesses:

The conclusions on gating would benefit from further discussions. In particular, a fair comparison of the timescale at which channel gating happens, and that of the MD simulations would strengthen the manuscript.

Reviewer #1 (Public review):

Summary:

Li et al describe a novel form of melanosome based iridescence in the crest of an Early Cretaceous enantiornithine avialan bird from the Jehol Group.

Strengths:

Novel set of methods applied to the study of fossil melanosomes.

Weaknesses:

(1) Firstly, several studies have argued that these structures are in fact not a crest, but rather the result of compression. Otherwise, it would seem that a large number of Jehol birds have crests that extend not only along the head but the neck and hindlimb. It is more parsimonious to interpret this as compression as has been demonstrated using actuopaleontology (Foth 2011).<br /> (2) The primitive morphology of the feather with their long and possibly not interlocking barbs also questions the ability of such feathers to be erected without geologic compression.<br /> (3) The feather is not in situ and therefore there is no way to demonstrate unequivocally that it is indeed from the head (it could just as easily be a neck feather)<br /> (4) Melanosome density may be taphonomic; in fact, in an important paper that is notably not cited here (Pan et al. 2019) the authors note dense melanosome packing and attribute it to taphonomy. This paper describes densely packed (taphonomic) melanosomes in non-avian avialans, specifically stating, "Notably, we propose that the very dense arrangement of melanosomes in the fossil feathers (Fig. 2 B, C, and G-I, yellow arrows) does not reflect in-life distribution, but is, rather, a taphonomic response to postmortem or postburial compression" and if this paper was taken into account it seems the conclusions would have to change drastically. If in this case the density is not taphonomic, this needs to be justified explicitly (although clearly these Jehol and Yanliao fossils are heavily compressed).<br /> (5) Color in modern birds is affected by the outer keratin cortex thickness which is not preserved but the authors note the barbs are much thicker (10um) than extant birds; this surely would have affected color so how can the authors be sure about the color in this feather?<br /> (6) Authors describe very strange shapes that are not present in extant birds: "...different from all other known feather melanosomes from both extant and extinct taxa in having some extra hooks and an oblique ellipse shape in cross and longitudinal sections of individual melanosome" but again, how can it be determined that this is not the result of taphonomic distortion?<br /> (7) The authors describe the melanosomes as hexagonally packed but this does not appear to be in fact the case, rather appearing quasi-periodic at best, or random. If the authors could provide some figures to justify this hexagonal interpretation?<br /> (8) One way to address these concerns would be to sample some additional fossil feathers to see if this is unique or rather due to taphonomy<br /> (9) On a side, why are the feet absent in the CT scan image?

Reviewer #1 (Public review):

Summary:

Nahas et al. investigated the roles of herpes simplex virus 1 (HSV-1) structural proteins using correlative cryo-light microscopy and soft X-ray tomography. The authors generated nine viral variants with deletions or mutations in genes encoding structural proteins. They employed a chemical fixation-free approach to study native-like events during viral assembly, enabling observation of a wider field of view compared to cryo-ET. The study effectively combined virology, cell biology, and structural biology to investigate the roles of viral proteins in virus assembly and budding.

Strengths:

(1) The study presented a novel approach to studying viral assembly in cellulo.

(2) The authors generated nine mutant viruses to investigate the roles of essential proteins in nuclear egress and cytoplasmic envelopment.

(3) The use of correlative imaging with cryoSIM and cryoSXT allowed for the study of viral assembly in a near-native state and in 3D.

(4) The study identified the roles of VP16, pUL16, pUL21, pUL34, and pUS3 in nuclear egress.

(5) The authors demonstrated that deletion of VP16, pUL11, gE, pUL51, or gK inhibits cytoplasmic envelopment.

(6) The manuscript is well-written, clearly describing findings, methods, and experimental design.

(7) The figures and data presentation are of good quality.

(8) The study effectively correlated light microscopy and X-ray tomography to follow virus assembly, providing a valuable approach for studying other viruses and cellular events.

(9) The research is a valuable starting point for investigating viral assembly using more sophisticated methods like cryo-ET with FIB-milling.

(10) The study proposes a detailed assembly mechanism and tracks the contributions of studied proteins to the assembly process.

(11) The study includes all necessary controls and tests for the influence of fluorescent proteins.

Weaknesses:

Overall, the manuscript does not have any major weaknesses, just a few minor comments:

(1) The gel quality in Figure 1 is inconsistent for different samples, with some bands not well resolved (e.g., for pUL11, GAPDH, or pUL20).

(2) The manuscript would benefit from a summary figure or table to concisely present the findings for each protein. It is a large body of manuscript, and a summary figure showing the discovered function would be great.

(3) Figure 2 lacks clarity on the type of error bars used (range, standard error, or standard deviation). It says, however, range, and just checking if this is what the authors meant.

(4) The manuscript could be improved by including details on how the plasma membrane boundary was estimated from the saturated gM-mCherry signal. An additional supplementary figure with the data showing the saturation used for the boundary definition would be helpful.

(5) Additional information or supplementary figures on the mask used to filter the YFP signal for Figure 4 would be helpful.

(6) The figure legends could include information about which samples are used for comparison for significance calculations. As the color of the brackets is different from the compared values (dUL34), it would be great to have this information in the figure legend.

(7) In Figure 5B, the association between YFP and mCherry signals is difficult to assess due to the abundance of mCherry signal; single-channel and combined images might improve visualization.

(8) In Figure 6D, staining for tubulin could help identify the cytoskeleton structures involved in the observed virus arrays.

(9) It is unclear in Figure 6D if the microtubule-associated capsids are with the gM envelope or not, as the signal from mCherry is quite weak. It could be made clearer with the split signals to assess the presence of both viral components.

(10) The representation of voxel intensity in Figure 8 is somewhat confusing. Reversion of the voxel intensity representation to align brighter values with higher absorption, which would simplify interpretation.

(11) The visualization in panel I of Figure 8 might benefit from a more divergent colormap to better show the variation in X-ray absorbance.

(12) Figure 9 would be enhanced by images showing the different virus sizes measured for the comparative study, which would help assess the size differences between different assembly stages.

Overall, this is an excellent manuscript and an enjoyable read. It would be interesting to see this approach applied to the study of other viruses, providing valuable insights before progressing to high-resolution methods.

This passage explains how important communication and social interaction are for staying healthy. It points out that being isolated, like in solitary confinement or due to things like poverty or illness, can harm both mental and physical health. It also mentions research showing that being cut off from others can even affect how the heart works.

是的，James J. Gibson 提出了光流（Optical Flow）的概念。光流是指在感知过程中，物体或场景的视觉输入随着观察者的移动而发生变化，尤其是视野中物体和背景的相对运动。Gibson 提出的光流概念帮助我们理解生物如何通过视觉感知获取有关自身运动和环境结构的重要信息。

1. 光流的定义

光流是指当观察者或物体在空间中移动时，视觉输入（特别是物体的图像投影）在视网膜上的变化。Gibson 强调，光流提供了有关物体运动、观察者自身运动以及环境几何结构的重要线索。这种信息对于生物的运动控制、导航和空间感知至关重要。

2. Gibson与光流的关系

Gibson 在其著作《感知的生态学》中详细讨论了光流对感知和行动的作用。他认为，光流不仅仅是视觉上的一种感知现象，而是生物与环境互动的一种直接感知方式，能够提供关于物体相对运动、个体运动和环境结构的信息。

a. 运动感知：

Gibson 提出了一个重要的观点，即通过观察光流模式，生物可以推断出它自己在空间中的运动。例如，当一个生物向前移动时，它的视觉场景会发生一定的变化，这种变化反映了其与周围环境的相对运动。生物可以通过光流模式获得运动方向、速度等信息，而不依赖于任何内在表征或推理过程。

b. 可供性与光流：

Gibson 还指出，光流与“可供性”密切相关。光流为生物提供了信息，帮助它们识别可以与之交互的物体或表面。例如，地面或障碍物的光流模式会帮助生物判断这些物体的距离和可接近性，进而做出相应的行为决策（如避障或接近目标）。

3. 光流在运动与控制中的作用

Gibson 认为，光流是生物在环境中导航和控制运动的基础。通过感知光流，生物能够实现以下功能： - 自我运动的感知：光流帮助生物感知自身的运动。例如，个体通过光流可以感知自己是否在前进、后退、转向等。 - 环境的结构感知：光流提供了关于物体、表面、障碍物等的空间结构信息。通过分析光流模式，生物可以感知这些环境特征，进行导航或避障。 - 时间与空间的判断：光流还帮助生物判断运动的速度和方向，从而对时间和空间进行更精确的感知。

4. 现代光流理论与应用

现代计算机视觉领域中的光流算法借鉴了Gibson的思想，用于从图像序列中估计运动。通过分析相邻图像帧之间的像素变化，计算光流模式，研究者能够推断出物体的相对运动、摄像机运动等。光流在自动驾驶、机器人导航和视觉感知等领域有着广泛的应用。

5. 总结

James J. Gibson 提出的光流概念为感知、运动控制和环境互动提供了一个重要的视角。他认为光流是生物如何通过直接感知与环境互动、获得运动信息的关键线索。光流不仅为个体提供有关自身运动和环境的动态信息，还帮助其做出适应性决策，推动了感知-行动循环的理解。今天，光流概念不仅在心理学和认知科学中占有重要地位，也在计算机视觉和机器人学中得到了广泛的应用。

上图展示了与物体和身体互动的不同方式，基于“聚焦点”（物体或身体）和“交互模式”（身体或物体）来分类。这些方式体现了人类在与物体或环境互动时的各种情境。图中的四种交互方式如下：

1. Direct Embodied (直接体现)

• 身体聚焦：这种方式将交互的重点放在身体上，强调直接的身体动作和感知。它表示通过身体的直接操作来实现与物体的互动，例如挥手或运动。
• 例子：通过手势或肢体动作直接控制物体。

2. Enacted (体现式)

• 身体动作：交互依赖于身体的动作和执行，而不仅仅是身体的存在。这类方式常见于“动作表演”，如在虚拟现实中使用体感控制器进行操作。
• 例子：身体的动作（如触摸或点击）使得目标对象做出反应。

3. Manipulated (操作式)

• 物体聚焦：在这种模式下，焦点转向物体的操作。通过物体的操控来实现功能或效果。例如，拿起一个工具进行特定的工作。
• 例子：使用手中的物体（如按钮或工具）来与其他物体交互。

4. Surrogate (替代式)

• 物体行为：与物体互动时，操作的是通过外部设备或代理执行的动作。与物体的连接被间接化，通常是在物体之间进行某种交互，而不是直接接触。
• 例子：例如，使用遥控器或机器人代替身体直接与物体进行互动。

总结：

• 身体聚焦 vs 物体聚焦：这是区分交互的一个关键维度，决定了是通过身体的动作来控制物体，还是通过操作物体来影响环境。
• 身体行为 vs 物体行为：这则区分了交互模式，是强调身体动作与物体之间的直接连接，还是通过操作物体进行控制。

上图描述了不同生理状态下（副交感神经状态、中性状态、交感神经状态）人体的变化，主要涉及大脑、心脏、呼吸、血压等生理指标。

1. 副交感神经状态（冥想状态）：
2. 大脑：增强了阿尔法波（Alpha waves）和贝塔波（Beta waves），提高了精神专注力、认知和记忆能力，主要集中在大脑的边缘系统。
3. 心脏：心率、呼吸频率和血压处于较低状态，处于平衡（稳态）状态。

4. 呼吸与心血管同步：呼吸和心率的同步比例为1:4，有助于身体的放松和恢复。

5. 中性状态：

6. 大脑：大脑活动保持在正常范围内，边缘系统的活跃程度适中，既不特别紧张也不特别放松。
7. 心脏：心率、呼吸频率和血压处于正常水平。

8. 呼吸与心血管同步：呼吸和心率的同步比值为1:1，保持平衡。

9. 交感神经状态（压力状态）：

10. 大脑：阿尔法波减少，贝塔波增加，导致认知、注意力和记忆力下降。大脑的边缘系统处于高度活跃状态。
11. 心脏：心率、呼吸频率和血压处于较高水平，准备应对紧急情况。
12. 呼吸与心血管同步：呼吸和心率的同步比值为1:2，适应高压力状态。

图表结论： - 副交感神经：有助于放松，促进恢复和集中注意力，主要发生在冥想等放松状态中。 - 交感神经：与应激相关，帮助人体应对紧急和压力环境，导致生理参数的升高。 - 中性状态：是正常的生理状态，既不特别紧张，也不特别放松。

图中的颜色渐变从蓝色（副交感神经）到红色（交感神经），反映了生理状态从放松到压力的变化。

Author response:

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

eLife Assessment

This important study identifies the "H-state" as a potential conformational marker distinguishing amyloidogenic from non-amyloidogenic light chains, addressing a critical problem in protein misfolding and amyloidosis. By combining advanced techniques such as small-angle X-ray scattering, molecular dynamics simulations, and H-D exchange mass spectrometry, the authors provide convincing evidence for their novel findings. However, incomplete experimental descriptions, limitations in SAXS data interpretation, and the way HDX MS data is presented aHect the strength and generalizability of the conclusions. Strengthening these aspects would enhance the impact of this work for researchers in amyloidosis and protein misfolding.

We thank eLife editors and reviewers for their constructive feedback. The manuscript has been improved to provide a more complete description of the experiments and to strengthen the interpretation and presentation of all data. Updated Figures (Figure 2 and Figure 5) and a new Table (Table 2) in the main text provide a more complete and clearer comparison of the SAXS data with MD simulations as well as a clearer representation of the HDX MS data. Additional figures have been added in SI. The text has been extended accordingly and complete materials and methods are now included in the main text. Abstract, introduction and discussion have been revised to improve the overall readability of the manuscript.

Public Reviews:

Reviewer #1 (Public review):

The study investigates light chains (LCs) using three distinct approaches, with a focus on identifying a conformational fingerprint to diHerentiate amyloidogenic light chains from multiple myeloma light chains. The study's major contribution is identifying a low-populated "H state," which the authors propose as a unique marker for AL-LCs. While this finding is promising, the review highlights several strengths and weaknesses. Strengths include the valuable contribution of identifying the H state and using multiple approaches, which provide a comprehensive understanding of LC structural dynamics. However, the study suHers from weaknesses, particularly in interpreting SAXS data, lack of clarity in presentation, and methodological inconsistencies. Critical concerns include high error margins between SAXS profiles and MD fits, unclear validation of oligomeric species in SAXS measurements, and insuHicient quantitative cross-validation between experimental (HDX) and computational data (MD). This reviewer calls for major revisions including clearer definitions, improved methodology, and additional validation, to strengthen the conclusions.

We thank the reviewer for the supportive comments, in the revised version of the manuscript we have focused on improving the clarity and completeness of our work. We are sorry for example to not have made previously clear enough that the comparison of SAXS with MD simulation was not that shown in the main text in Figure 1 and Table 1 (this is the comparison with single structures) but that reported in the SI (previously Figure S1 and Table S2, showing very good fits). These data have been moved in the main text in the reworked Figure 2 and new Table 2.  We have also improved the presentation of the HDX MS data in Figure 5 and in the text adding also additional analysis in SI. Materials and methods are now completely moved in the main text. We generally revised the manuscript for clarity.

Reviewer #2 (Public review):

Summary:

This well-written manuscript addresses an important but recalcitrant problem - the molecular mechanism of protein misfolding in Ig light chain (LC) amyloidosis (AL), a major life-threatening form of systemic human amyloidosis. The authors use expertly recorded and analyzed smallangle X-ray scattering (SAXS) data as a restraint for molecular dynamics simulations (called M&M) and to explore six patient-based LC proteins. The authors report that a highly populated "H-state" determined computationally, wherein the two domains in an LC molecule acquire a straight rather than bent conformation, is what distinguishes AL from non-AL LCs. They then use H-D exchange mass spectrometry to verify this conclusion. If confirmed, this is a novel and interesting finding with potentially important translational implications.

We thank the reviewer for the supportive comments.

Strengths:

Expertly recorded and analyzed SAXS data combined with clever M&M simulations lead to a novel and interesting conclusion. Regardless of whether or not the CL-CL domain interface is destabilized in AL LCs explored in this (Figure 6) and other studies, stabilization of this interface is an excellent idea that may help protect at least a subset of AL LCs from misfolding in amyloid. This idea increases the potential impact of this interesting study.

We thank the reviewer for the supportive comments.

Weaknesses:

The HDX analysis could be strengthened.

We have extended the analysis and improved the presentation of the HDX data. Figure 5 has been reworked, text has been improved accordingly and additional analysis have been reported in SI.

Reviewer #3 (Public review):

Summary:

This study identifies conformational fingerprints of amyloidogenic light chains, that set them apart from the non-amyloidogenic ones.

We thank the reviewer for the supportive comments.

Strengths:

The research employs a comprehensive combination of structural and dynamic analysis techniques, providing evidence that conformational dynamics at the VL-CL interface and structural expansion are distinguished features of amyloidogenic LCs.

We thank the reviewer for the supportive comments.

Weaknesses:

The sample size is limited, which may aHect the generalizability of the findings. Additionally, the study could benefit from deeper analysis of specific mutations driving this unique conformation to further strengthen therapeutic relevance.

We agree, we tried to maximise the size of the sample and this was the best we could do. With respect to the analysis of the mutations, while we tried to discuss some of them also in view of previous works, because our set covers multiple germlines instead than focusing on a single one, this limit our ability to discuss single point mutations systematically, at the same time the discussion of single points mutations has been the focus of many recent works, while our approach provide a diNerent point of view.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

This study provides an investigation of light chains (LCs) using three distinct approaches, focusing primarily on identifying a conformational fingerprint to distinguish amyloidogenic light chains (AL-LCs) from multiple myeloma light chains (MM-LCs). The authors propose that the presence of a low-populated "H state," characterized by an extended quaternary structure and a perturbed CL-CL interface, is unique to AL-LCs. This finding is validated through hydrogendeuterium exchange mass spectrometry (HDX-MS). The study makes a valuable contribution to understanding the structural dynamics of light chains, particularly with the identification of the H state in AL-LCs. However, significant concerns regarding the interpretation of the SAXS data, clarity in presentation, and methodological rigor must be addressed. I recommend major revisions and resubmission of the work.

Major concerns:

(1) A critical concern is how the authors ensure that the SAXS profiles represent only dimeric species, given the high propensity of LCs to aggregate. If higher-order aggregates or monomers were present, this would significantly impact the SAXS data and SAXS-MD integration. Some measurements are bulk SAXS, while others are SEC-SAXS, making the study questionable. The authors need to clarify how only dimeric species were measured for the SEC-SAXS analysis, and all assessments of the dimeric state should be shown in the SI. Additionally, complementary techniques such as DLS or SEC-MALS should be used to verify the oligomeric state of the samples. Without this validation, the SAXS profiles may not be reliable.

We added SEC-MALS and SEC-SAXS data in the SI (Figures S20 and S21) as well the SAXS curves shown in log-log plot (Figure S1) that display a flat trend at low q that exclude aggregation. SAXS is very sensitive to oligomers and aggregates and our data do not indicate the presence of those species. When we had indication of possible aggregation in the sample we used SEC-SAXS.

(2) A major problem with the paper is that the claim of the "H state," which is the novelty of the study and serves as a marker of aggregation, is derived from samples where the error between the SAXS profiles and MD fits is extremely high. This casts doubt on whether the structure is indeed resolved by MD. The main conclusion of the paper is derived from weak consistency between experiment and simulation. In AL55, the error between experiment and simulation is greater than 5; for H7, it is higher than 2.8. The residuals show significant error at mid-q values, suggesting that long-range distance correlations (20-10 Å, CL, VL positioning) are not consistent between simulation and experiment. Furthermore, the FES plots of two independent replicas show deviation in the existence of the H state. One shows a minimum in that region, while the other does not. So, how robust is this conclusion? What is the chi-squared value if each replica is used independently? A separate experimental cross-validation is necessary to claim the existence of the H state.

We apologise for the misunderstanding underlying this reviewer comment. The poor agreement mentioned is not between the SAXS and MD simulations, but with the individual structures, and this disagreement led us to perform MD simulations that are in much better agreement with the data (previously Fig. S1 and Table S2). To avoid this misunderstanding, which would indeed weaken our work, we have now moved both the figure and the table in the main text to the updated Figure 2 and the new Table 2.

Regarding the robustness of the sampling, we believe that Table 3 (previously Table 2) clearly shows the statistical convergence of the data, diNerences in the presentation of the free energy are purely interpolation issues. The chi-squares of each replicate are reported in Table 2 (previously Table S2).

(3) There is insuHicient discussion about SAXS computations from MD trajectories. The accuracy of these calculations is crucial to deriving the existing conclusions, and the study's reliance on the PLUMED plugin, which is known to give inaccurate results for SAXS computations, raises concerns. How the solvent is treated in the SAXS computations needs to be explained. Alternative methods like WAXSiS or Crysol should be explored to check whether the SAXS profiles derived from the MD trajectory are consistent across other SAXS computation methods for the major conformers of the proteins.

We have now clarified that while the SAXS calculation to perform Metainference MD were done using PLUMED (that to our knowledge is as accurate as crysol) SAXS curves used for analysis were calculated using crysol.

(4) The HDX and MD results do not seem to correlate well, and there is a disconnect between Figure 2 (SAXS profiles) and Figure 5 (HDX structural interpretation). The authors should quantitatively assess residue-level dynamics by comparing HDX signals with MD-derived HDX signals for each protein. This would provide a cross-validation between the experimental and computational data.

In our opinion our SAXS, MD and HDX MS data provide a consistent picture. Our HDX-MS do not provide per residue data, making a quantitative comparison out of scope. RMSF data do not necessarily need to correlate with the deuterium uptake.

(5) MD simulations are only used to refine the structure of AlphaFold predictions, but the trajectories could help explain why these structures diHer, what stabilizes the dimer, or what leads to the conformational transition of the H state. A lack of analysis regarding the physical mechanism behind these structural changes is a weakness of the study. The authors should dedicate more eHort to analyzing their data and provide physical insights into why these changes are observed.

Our aim was to identify a property that could discriminate between AL and MM LCs. We used MD simulations, not to refine structures, but to explore the conformational dynamics of LCs (starting from either X-ray structures, homology or AlphaFold models), because SAXS data suggested that conformational dynamics could discriminate between AL- and MM-LCs. Simulations allowed us to propose a hypothesis, which we tested by HDX MS. While more insight is always welcome, we believe that we have achieved our goal for now. In the discussion, we present additional analysis of the simulations to connect with previous literature, we agree that more analysis can be done, and also for this reason, all our data are publicly available.

Minor concerns

(6) The abstract leans heavily on describing the problem and methods but lacks a clear presentation of key results. Providing a concise summary of the main findings (e.g., the identification of the H state) would better balance the abstract.

We agree with the reviewer and we rewrote the abstract.

(7) In the abstract, the term "experimental structure" is used ambiguously. Since SAXS also provides an experimental structure, it is unclear what the authors are referring to. This should be clarified.

We agree with the reviewer and we rewrote the abstract.

(8) Abbreviations such as VL (variable domain) and CL (constant domain) are not defined, making it harder for readers unfamiliar with the field to follow. Abbreviations should be defined when first mentioned.

We agree with the reviewer and we rewrote the abstract.

(9) The introduction provides a good general context but fails to explicitly define the knowledge gap. Specifically, the structural and dynamic determinants of LC amyloidogenicity are not well established, and this study could be framed as addressing that gap.

We thank the reviewer and we agree this could be better framed, we improved the introduction accordingly.

(10) The introduction does not present the novel discovery of the H state early enough. The unique contribution of identifying this state as a marker for AL-LCs should be mentioned upfront to guide the reader through the significance of the study.

We thank the reviewer and we have now made more explicit what we found.

(11) The therapeutic implications of this research should be highlighted more clearly in the discussion. Examples of how these findings could be utilized in drug design or therapeutic approaches would enhance the study's impact.

We thank the reviewer, but while we think that the H-state could be targeted for drug design, since we do not have data yet we do not want to stress this point more than what we are already doing.

(12) There is an overwhelming use of abbreviations such as H3, H7, H18, M7, and M10 without proper introduction. This makes it diHicult for readers to follow the results, and the average reader may become lost in the details. An introductory figure summarizing the sequences under study, along with a schematic of the dimeric structure defining VL and CL domains, would significantly aid comprehension.

We agree and we tried to better introduce the systems and simplify the language without adding a figure that we think would be redundant.

(13) In Figure 1, add labels to each SAXS curve to indicate which protein they correspond to. Also, what does online SEC-SAXS mean?

Done

(14) The caption of Figure 3 is unclear, particularly with abbreviations like Lb, Ls, G, and H, which are not mentioned in the captions. The authors should define these terms for clarity.

Done

(15) The study claims that the dominant structure of the dimer changes between diHerent LCs. However, Figure 5 shows identical structures for all proteins, raising questions about the consistency between the SAXS and HDX data. This inconsistency is a general problem between the MD and HDX sections, where cross-communication and comparisons are not properly addressed.

We do not claim that the dominant structure of the dimer changes between diNerent LCs, this would also be in contradiction with current literature. We claim a diNerence in a low-populated state. From this point of view using always the same structure is consistent and should simplify the representation of the results. We agree that the manuscript may be not always easy to follow and we thank the reviewer in helping us improving it.

(16) The authors show I(q) vs q and residuals for each protein. The Kratky plots are not suHicient to compare the SAXS computations with the measured profile.

Showing Kratky and residuals is a standard and complementary way to present and compare SAXS data to structures. Chi-square values are also reported. Log-log plots have been added to SI in response to previous comments.

(17) The authors need to explain how they estimate the Rg values (from simulation or SAXS profiles). If they are using simulations, they should compute the Rg values from the simulations for comparison.

Rg values reported in Table 1 are derived from SAXS. Rg from simulations have been added in Table 2.

(18) The evolution of the sampling is unclear. The authors need to show the initial starting conformation in each case and the most likely conformation after M&M in the SI, to demonstrate that their approach indeed caused changes in the initial predictions.

Our approach is not structure refinement and as such the proposed analysis would be misleading. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. DiNerences (or not) between initial and selected configurations will not be particularly informative in this context.

(19) The authors should also provide a running average of chi-squared values over time to demonstrate that the conformational ensemble converged toward the SAXS profile.

Our simulations are not driven to improve the agreement with SAXS over time, this is not structure refinement. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. The suggested analysis would be a misinterpretation of our simulations. The comparison with SAXS is provided in Figure 2 and Table 2 as mentioned above.

(20) The aggregate simulation time of 120 microseconds is misleading, as each replica was only run for 2-3 microseconds. This should be clarified.

The number reported in the text is accurate and represent the aggregated sampling. The number of replicas for each metainference simulation and their length is reported in Table 2 now moved for clarity from the SI to main text.

(21) It is not clear how the replicas were weighted to compute the SAXS profiles and FES. There are two independent runs in each case, and each run has about 30 replicas. How these replicas are weighted needs to be discussed in the SI.

Done

(22) The methods section is unevenly distributed, with detailed explanations of LC production and purification, while other key methodologies like SAXS+MD integration and HDX are not even mentioned in the main text (they are in the Supporting Information). The authors should provide a brief overview of all methodologies in the main text or move everything to the SI for consistency.

We agree with the reviewer, all methods are now in main text. 

Reviewer #2 (Recommendations for the authors):

(1) Computational M&M evidence is strong (Figure 3) and is supported by SAXS (used as restraints). However, Kratky plots reported in the main MS Figure 1 show significant diHerences between the data and the structural model only for one protein, AL-55. It is hard for the general reader to see how these SAXS data support a clear diHerence between AL and non-AL proteins. If possible, please strengthen the evidence; if not, soften the conclusions.

We thank the reviewer for the comments. The chi-square (Table 1) and the residuals (Figure 1) are a strong indication of the diNerence. To strengthen the evidence, following also the comment from reviewer 3 we calculated the p-value (<10^-5) on the significance of the radius of gyration to discriminate AL and MM LCs. We agree that SAXS alone was not enough and this is indeed what prompted us to perform MD simulations.

(2) HDX MS results are cursory and not very convincing as presented. The butterfly plots in Figure 5 are too small to read and are unlabeled so it is unclear which protein is which.  

Figure 5 has been reworked for readability. More data have been added in SI. 

(3) What labeling time was selected to construct these plots and why?

The deuterium uptakes at 30 min HDX time showed the most pronounced diNerences between diNerent proteins, which were chosen to illustrate the key structural features in the main figure panel (Figure 5).

How diHerent are the results at other labeling times? Showing uptake curves (with errors) for more than just two peptides in the supplement Figure S12 might be helpful. 

We found a continuous increase in deuterium uptake as we increased the exchange time from 0.5 to 240 min, which reached saturation at 120 min. Therefore, the exchange follows the same pattern at all time points. Butterfly plots at diNerent HDX times of 0.5 to 240 min are shown in gradient of light blue to dark blue which clearly shows the pattern of deuterium uptake at increasing incubation times (Figure 5). The HDX uptake kinetics of selected peptides with corresponding error bars are shown in Figure S12.

How redundant are the data, i.e. how good is the peptide coverage/resolution in key regions at the domain-domain interface that the authors deem important? Mapping the maximal deuterium uptake on the structures in Figure 5 is not very helpful. Perhaps mapping the whole range of uptake using a gradient color scheme would be more informative.

Overall coverage and redundancy for all four proteins are> 90% and > 4.0, respectively, with an average error margin in fractional uptake among all peptides is 0.04-0.05 Da, which suggests that our data is reliable (Table S3). We modified the main panel figures showing the gradient of deuterium uptake in blue-white-red for 0 to 30% of deuterium uptake on the chain A of the dimeric LCs.

(3) Is the conformational heterogeneity depicted in M&M simulations consistent with HDX results? The authors may want to address this by looking at the EX1/EX2 exchange kinetics for AL vs. non-AL proteins. Do AL proteins show more EX1?

No, we don’t see any EX1 exchange kinetics in our analysis. This is compatible with the prediction of the H-state that is a native like state and not an unfolded/partially folded state. 

(4) Perhaps the main conclusion could be softened given the small number of proteins (six), esp. since only four (3 AL and 1 non-AL) could be explored by HDX. Are other HDX MS data of AL LCs from the same Lambda6 family (e.g. PMID: 34678302) consistent with the conclusions that a particular domain-domain interface is weakened in AL vs. non-AL LCs?

We thank the reviewer for this suggestions. A diNerence in HDX MS data is indeed visible between AL and MM proteins for peptide 33-47 in the suggested paper (Figures 4, S5 and S8). The diNerence is reduced by the mutation identified in the paper as driving the aggregation in that specific case. We now mention this in the discussion.

(5) Please clarify if the H* state is the same for a covalent vs. non-covalent LC dimer.

We do not know because our data are only for covalent dimers. But, interestingly, the state is very similar to what was observed for a model kappa light-chain in Weber, et al., we have better highlighted this point in the discussion.

(6) Please try and better explain why a smaller distance between CL domains in H7 protein and a larger distance in other AL proteins both promote protein misfolding.

We do not have elements to discuss this point in more detail.

(7) Please comment on the Kratky plots data vs. model agreement (see comments above).

Done.

(8) Please find a better way to display, describe, and interpret the HD exchange MS data.

We have generated new main text (new Figure 5) and SI figures that we think allow the reader to better appreciated our observations. Corresponding results sections have been also improved.

Minor points:

(9) Is the population of the H-state with perturbed CL-CL domain interface, which was obtained in M&M simulations, suHicient to be observable by HDX MS?

While populations alone are not enough to determine what is observable by HDX MS, a 10% population correspond roughly to 6 kJ/mol of ΔG and is compatible with EX2 kinetics. Previous works suggested that HDX-MS data should be sensitive to subpopulations of the order of 10%, (https://doi.org/10.1016/j.bpj.2020.02.005, https://doi.org/10.1021/jacs.2c06148)

(10) Typically, an excited intermediate in protein unfolding is a monomer, while here it is an LC dimer. Is this unusual?

This is a good point, we think that intermediates have mostly been studied on monomeric proteins because these are more commonly used as model systems, but we do not feel like discussing this point.

(11) Low deuterium uptake is consistent with a rigid structure but may also reflect buried structure and/or structure that moves on a time scale greater than the labeling time.

We agree.

Reviewer #3 (Recommendations for the authors):

(1) The p-value (statistical significance) of Rg diHerence should be computed.

We thank the reviewer for the suggestion, we calculated the p-value that resulted quite significant.

(2) The significance of mutations (SHM?) at the interface, such as A40G should be compared with previous observations. (Garrofalo et al., 2021).

We thank the reviewer for the suggestion, a sentence has been added in the discussion.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this article the authors described mouse models presenting with backer muscular dystrophy, they created three transgenic models carrying three representative exon deletions: ex45-48 del., ex45-47 19 del., and ex45-49 del. This article is well written but needs improvement in some points.

Strengths:

This article is well written. The evidence supporting the authors' claims is robust, though further implementation is necessary. The experiments conducted align with the current state-of-the-art methodologies.

Weaknesses:

This article does not analyze atrophy in the various mouse models. Implementing this point would improve the impact of the work

We thank the reviewer for their constructive suggestions and comments on this work. Muscle hypertrophy is shown with growth in dystrophin-deficient skeletal muscle in mdx mice; thus, we did not pay attention to the factors associated with muscle atrophy in BMD mice. As the reviewer suggested, the examination of the association between type IIa fiber reduction and muscle atrophy is important, and the result is considered to be helpful in resolving the cause of type IIa fiber reduction in BMD mice.

In response, we reviewed the following.

(1) The cross-sectional areas (CSAs) of muscles. We confirmed that the CSAs in BMD and mdx mice were rather high at 3 months, in accordance with muscle hypertrophy, compared with those of WT mice. The data is presented in Fig. 4–figure supplement 1B.

(2) The mRNA expression levels of Murf1 and atrogin-1. We confirmed that these muscle atrophy inducing factors did not differ among WT, BMD, and mdx mice. The data is presented in Fig. 4–figure supplements 1C and 1D.

Reviewer #2 (Public review):

Summary:

Miyazaki et al. established three distinct BMD mouse models by deleting different exon regions of the dystrophin gene, observed in human BMD. The authors demonstrated that these models exhibit pathophysiological changes, including variations in body weight, muscle force, muscle degeneration, and levels of fibrosis, alongside underlying molecular alterations such as changes in dystrophin and nNOS levels. Notably, these molecular and pathological changes progress at different rates depending on the specific exon deletions in the dystrophin gene. Additionally, the authors conducted extensive fiber typing, revealing a site-specific decline in type IIa fibers in BMD mice, which they suggest may be due to muscle degeneration and reduced capillary formation around these fibers.

Strengths:

The manuscript introduces three novel BMD mouse models with different dystrophin exon deletions, each demonstrating varying rates of disease progression similar to the human BMD phenotype. The authors also conducted extensive fiber typing across different muscles and regions within the muscles, effectively highlighting a site-specific decline in type IIa muscle fibers in BMD mice.

Weaknesses:

The authors have inadequate experiments to support their hypothesis that the decay of type IIa muscle fibers is likely due to muscle degeneration and reduced capillary formation. Further investigation into capillary density and histopathological changes across different muscle fibers is needed, which could clarify the mechanisms behind these observations.

We thank the reviewer for these positive comments and the very important suggestion about type IIa fiber reduction and capillary change around muscle fibers in BMD mice. From the results of the cardiotoxin-induced muscle degeneration and regeneration model, type IIa and IIx fibers showed delayed recovery compared with that of type-IIb fibers. However, this delayed recovery of type IIa and IIx could not explain the cause of the selective muscle fiber reduction limited to type IIa fibers in BMD mice. Therefore, we considered vascular dysfunction as the reason for the selective type IIa fiber reduction, and we found morphological capillary changes from a “ring pattern” to a “dot pattern” around type IIa fibers in BMD mice. However, the association between selective type IIa fiber reduction and the capillary change around muscle fibers in BMD mice remains unclear due to the lack of information about capillaries around type IIx and IIb fibers. The reviewer pointed out this insufficient evaluation of capillaries around other muscle fibers (except for type IIa fibers), and this suggestion is very helpful for explaining the association between selective type IIa fiber reduction and vascular dysfunction in BMD mice.

In response, we reviewed the following.

(1) The capillary formation around type IIx, IIb, and I fibers, in addition to that around type IIa fibers. We found that capillaries contacting around type IIx, IIb, and I fibers were poor in WT mice compared with that around type IIa fibers, with ‘incomplete ring-patterns’ around type IIx fibers, and ‘dot-patterns’ around type IIb and I fibers in WT mice. Morphological capillary changes around muscle fibers from WT to d45-49 and mdx mice were ‘incomplete dot-pattern’ to ‘dot-pattern’ around type IIx fibers, and ‘dot-pattern’ to ‘dot-pattern’ around type IIb and I fibers. This was in contrast to those around type IIa fibers: remarkable ‘ring-pattern’ to ‘dot-pattern’. These data are presented in Fig. 6B.

(2) The endothelial area in contact with type IIx, IIb, and I fibers, and additionally that in contact with type IIa fibers. The endothelial area in contact with both type IIa and IIx fibers was less in d45-49 and mdx mice than in WT mice, but the reduction was larger around type IIa fibers than around type IIx fibers, reflecting the difference between the ‘ring-pattern’ around the former and the ‘incomplete ring-pattern’ around the latter in WT mice. These data are presented in Fig. 6C.

(3) Transversely interconnected branches and capillary loops, using longitudinal muscle sections. We confirmed that there were fewer interconnected capillaries in BMD and mdx mice than in WT mice. These data are presented in Fig. 6E.

(4) The mRNA expression levels of neuronal nitric oxide synthase (nNOS). We confirmed that nNOS protein expression levels were decreased in BMD and mdx mice in spite of adequate levels of nNOS mRNA expression. The data on nNOS mRNA expression levels is presented in Fig. 3–figure supplement 1C.

(5) We added a sentence in the Abstract about the potential utility of BMD mice in developing vascular targeted therapies.

Recommendation for the authors:

Reviewer #1 (Recommendation for the authors):

Abstract:

Abstract: more emphasis should be on the pathological implications of Becker muscular dystrophy (BMD). Furthermore, should be emphasized the findings made in this article and the conclusions. Abbreviations such as DMD and MDX should be written in full and only then with the acronym.

We appreciate the reviewers’ comments, and we apologize for the confusion over abbreviations. DMD is the gene name encoding dystrophin, and mdx is the strain name of mouse lacking dystrophin.

In the Abstract and the Figure legends we changed:

(1) DMD to DMD;

(2) mdx mice to mdx mice.

Results:

Line 95: in this line, authors evaluated serum creatinine kinase (CK) levels at 1, 3, 6 and 12 months in WT mice and mdx mice. Why did you decide to study it? This part should be described in more detail. Serum CK is one of the main markers of muscle necrosis; therefore, I would report this data alongside the description of the muscle histology and necrotic fibers.

We thank the reviewers for the important remarks. In this study, serum creatine kinase (CK) levels were two-fold to four-fold higher in BMD mice than in WT mice, but its rate of increase was less than that of mdx mice. We consider that the lesser changes in serum CK levels in BMD mice may be due to the smaller area of muscle degeneration because of focal and uneven muscle degeneration compared with that in mdx mice, which showed diffuse muscle degeneration.

In response, we have moved the description of serum CK levels in the Results, from the section about the establishment of BMD mice to the section about site-specific muscle degeneration in BMD mice.

In addition, we added a description in the Discussion about the possible association between the lesser changes in serum CK levels in BMD mice and its uneven distribution of muscle degeneration.

Line 192-202: In these lines, authors observed a decrease in type IIa fibers after 3 months in BMD mice. I suggest evaluating also atrophy through evaluating cross-sectional areas (CSA) and expression of Murf1 and Atrogin1

We thank the reviewer for the point about the association between type IIa fiber reduction and muscle atrophy. We evaluated the CSAs and the mRNA expression levels of Murf1 and atrogin-1. We confirmed that the CSAs in BMD and mdx mice were rather high at 3 months, in accordance with muscle hypertrophy, compared with those of WT mice, and that Murf1 and atrogin-1 mRNA expression levels did not differ among WT, BMD, and mdx mice. These data are presented in Fig. 4–figure supplements 1B, 1C, and 1D. We added a sentence about the changes in CSA and muscle atrophy inducing factors in the Discussion.

Methods and material

Line 342-348: authors have described animals, but not specified sex and number of mice in each group. This part should be improved.

We apologize for our insufficient information about the sex and number of mice in the Materials and methods.

We added a sentence specifying the sex, number, and evaluation period of each mouse group in the section on the generation of BMD mice.

Line 426-433: authors described qPCR. It is necessary that the authors also describe primer sequences.

We apologize for any lack of information about the primer sequences used in qPCR analysis. Supplemental Table 1 lists the primer sequences.

We also added a sentence about the information in the primer list in the section on RNA isolation and RT-PCR in the Materials and methods.

Reviewer #2 (Recommendation for the authors):

Miyazaki et al. established three distinct BMD mouse models by removing different exon regions of the dystrophin gene. The authors demonstrated that the pathophysiological and molecular changes in these models progress at varying rates. Additionally, they observed a site-specific decline in type IIa fibers in BMD mice, while the proportions of other fiber types, such as type I and type IIx, remained consistent with those in wild-type mice. They proposed that the selective decay of type IIa fibers in BMD mice could be due to two primary factors: 1) muscle degeneration and regeneration, supported by their findings in cardiotoxin-treated mouse models, and 2) reduced capillary formation around type IIa fibers. However, the authors also presented evidence that type IIx fibers exhibited delayed recovery, similar to type IIa fibers, as demonstrated in cardiotoxin-induced regeneration models. Additionally, dot-patterned capillary formations were observed around both type IIa and type IIx fibers. Despite these findings, BMD mice did not show any changes in the proportion of type IIx fibers in inner BMD muscles. The authors should consider adding further analysis to strengthen their hypothesis and to disclose any possible mechanisms that led to these discrepancies.

If the authors hypothesize that reduced capillary density around type IIa fibers contribute to their site-specific decay in BMD mice, they should consider measuring and statistically analyzing the endothelial area around all fiber types. By plotting and comparing these measurements across different fiber types between wild-type, BMD, and mdx mice, the authors could provide more robust evidence to support their hypothesis. This approach would help clarify whether reduced capillary density is a contributing factor to the site-specific decay of type IIa fibers in BMD mice and the more diffuse, non-specific muscle changes observed in mdx mice.

The authors reported in the first part of the manuscript that histopathological changes, including muscle degeneration in BMD mice, are predominantly restricted to the inner part of the muscles. In the second part, they noted a decline in type IIa fibers specifically in the inner muscle region. To strengthen the hypothesis that the decay of type IIa fibers in the inner muscle is linked to muscle degeneration, the authors should consider performing histopathological measurements across different fiber types within the inner muscle. Reporting the correlations between these measurements would provide more compelling evidence to support their hypothesis.

We thank the reviewer for these important suggestions about the association between type IIa fiber reduction and capillary change around muscle fibers in BMD mice. We prepared an additional evaluation about the capillary formation (in Fig. 6B) and endothelial area (in Fig. 6C) around type IIx, IIb, and I fibers. We found that capillaries contacting around type IIx, IIb, and I fibers were poor in WT mice compared with those around type IIa fibers, and showed an ‘incomplete ring-pattern’ around type IIx fibers and a ‘dot-pattern’ around type IIb and I fibers in WT mice, in contrast with type IIa fibers, which showed remarkable ‘ring-pattern’ capillaries. Reflecting this, the changes in endothelial area around type IIx, IIb, and I fibers between WT and BMD mice were less than those around type IIa fibers. These results suggest that type IIa fibers may require numerous capillaries and maintained blood flow compared with type IIx, IIb, and I fibers, and this high requirement for blood flow might be associated with the type IIa fiber-specific decay in BMD mice.

We added the following.

(1) Sentences in the Results about the capillary changes around type IIx, IIb, and I fibers in WT, d45-49, and mdx mice.

(2) Sentences in the Results about the changes in endothelial area around type IIx, IIb, and I fibers in WT, d45-49, and mdx mice.

(3) Sentences in the Discussion about the association between the type IIa fiber-specific decay in BMD mice and the differences in capillary changes of each muscle fiber from WT to BMD mice.

We changed a sentence in the Discussion about the delayed recovery of type IIa and IIx fibers after CTX injection, to make it clear that the recovery of type IIx fibers was slower than that of type IIa fibers after CTX injection, and that therefore the type IIa fiber-specific decay in BMD mice might not be explained by this vulnerability and delayed recovery during muscle degeneration and regeneration.

Minor Issues:

Line 103: The word "mice" is duplicated and should be corrected.

We apologize that “mice” was duplicated. We have corrected it.

Line 120: Revise for clarity: "The proportion of opaque fibers is significantly different between d45-48 mice and WT at 3 months, with an increased tendency observed only in 1-month-old mice."

We apologize for the confusion about the proportion of opaque fibers. We revised this sentence as follows.

“Opaque fibers, which are thought to be precursors of necrotic fibers, increased at an earlier age of 1 month in d45–49 mice compared with WT mice; in contrast, the proportion of opaque fibers differs significantly between d45–47 and WT mice at 3 months, with an increased tendency only in 1-month-old mice (Fig. 2C).”

Line 152: Clarify the statement regarding utrophin levels, as it currently contradicts the Western blot data. The sentence reads: "The increased levels of utrophin are 8-fold higher at 1 month and 30-fold higher at 3 months." This should be verified against the data, as the band densities in the Western blots suggest otherwise.

We apologize for the confusion about utrophin expression levels. We revised this sentence as follows.

“By western blot analysis, the utrophin expression levels showed only an increased tendency in all BMD mice at 3 months, whereas there was a significant increase in mdx mice (8-fold at 1 month, and 30-fold at 3 months) compared to WT mice (Figs. 3C and F).”

Line 235: Correct the sentence to accurately reflect the findings: "BMD mice showed reduced muscle weakness."

We apologize for our incorrect wording. We have removed the word “reduced” in this sentence.