Reviewer #3 (Public review):

Summary:

The authors demonstrate that activation of TFEB facilitates cholesterol clearance in cell models of Niemann-Pick type C (NPC). This is done through a variety of approaches including activation of TFEB by sulforaphane (SFN), a naturally occurring small-molecule TFEB agonist. SFN induces TFEB nuclear translocation and promotes lysosomal exocytosis. In an NPC mouse model, SFN dephosphorylates/activates TFEB in the brain and rescues the loss of Purkinje cells.

Strengths:

NPC is a severe disease and there is little in the way of treatment. The manuscript points towards some treatment options. However, the title, the title "Small-molecule activation of TFEB Alleviates Niemann-Pick Disease..." is far too strong and should be changed.

Weaknesses:

(1) The manuscript is extremely hard to read due to the writing; it needs careful editing for grammar and English.

(2) There are a number of important technical issues that need to be addressed.

(3) The TFEB influence on filipin staining in Figure 1A is somewhat subtle. In the mCherry alone panels there is a transfected cell with no filipin staining and the mCherry-TFEBS211A cells still show some filipin staining.

(4) Figure 1C is impressive for the upregulation of filipin with U18666A treatment. However, SFN is used at 15 microM. This must be hitting multiple pathways. Vauzour et al (PMID: 20166144) use SFN at 10 nM to 1microM. Other manuscripts use it in the low microM range. The authors should repeat at least some key experiments using SFN at a range of concentrations from perhaps 100 nM to 5 microM. The use of 15 microM throughout is an overall concern.

Author Response:

Thank you for your interest in our paper. We would also like to thank the anonymous reviewers for their critical and constructive comments. Although the reviewers found our work interesting, they raised several important concerns about our study. To address these concerns, mostly we will perform new experiments as following.

1. Examine whether antioxidant-NAC can block SFN-induced TFEB-nuclear translocation in NPC cells;

2. Examine whether calcineurin inhibitor (FK506+CsA) or Ca 2+ inhibitor (Bapta-AM) can block SFN-induced TFEB-nuclear translocation in NPC cells.

3. Investigate whether cholesterol was cleared by activation of TFEB by SFN in vivo tissues.

4. Investigate whether SFN-evoked the lysosomal exocytosis is TFEB-dependent by using TFEB-KO cells.

5. Examine the effect of NPC1 deficiency on dextran trafficking by studying the localization of CF- dex and Lamp1.

6. Perform cytotoxicity experiments to examine whether SFN used in this study is cytotoxic in various cell lines

In addition, according to the reviewers’ suggestions, we will make clarifications and corrections wherever appropriate in the manuscript. Below please find our point-by-point responses and plans to the reviewers’ comments.

Reviewer #1 (Public review):

Summary:

The authors are trying to determine if SFN treatment results in dephosphorylation of TFEB, subsequent activation of autophagy-related genes, exocytosis of lysosomes, and reduction in lysosomal cholesterol levels in models of NPC disease.

Strengths:

(1) Clear evidence that SFN results in translocation of TFEB to the nucleus.

(2) In vivo data demonstrating that SFN can rescue Purkinje neuron number and weight in NPC1-/- animals.

Thank you for the support!

Weaknesses:

(1) Lack of molecular details regarding how SFN results in dephosphorylation of TFEB leading to activation of the aforementioned pathways. Currently, datasets represent correlations.

Thank you for this constructive comment. The reviewer is right that in this manuscript the molecular mechanism of SFN-activated TFEB has not been discussed in details. Because previously we have shown that SFN induces TFEB nuclear translocation via a Ca 2+ - dependent but MTOR (mechanistic target of rapamycin kinase)-independent mechanism through a moderate increase in reactive oxygen species (ROS). And calcineurin-mediated TFEB dephosphorylation underlies SFN-induced TFEB activation. These data have been published in 2021 autophagy (Li, Shao et al. 2021) . Therefore, in this study we did not mention this part. We will add the molecular mechanism of TFEB activation by SFN in the discussion part. And to further confirm this mechanism in NPC cells, we will also perform experiments including: 1) examine whether antioxidant-NAC can block SFN-induced TFEB-nuclear translocation in NPC cells; 2) examine whether calcineurin inhibitor (FK506+CsA) can block SFN-induced TFEB-nuclear translocation in NPC cells.

(2) Based on the manuscript narrative, discussion, and data it is unclear exactly how steady-state cholesterol would change in models of NPC disease following SFN treatment. Yes, there is good evidence that lysosomal flux to (and presumably across) the plasma membrane increases with SFN. However, lysosomal biogenesis genes also seem to be increasing. Given that NPC inhibition, NPC1 knockout, or NPC1 disease mutations are constitutively present and the cell models of NPC disease contain lysosomes (even with SFN) how could a simple increase in lysosomal flux decrease cholesterol levels? It would seem important to quantify the number of lysosomes per cell in each condition to begin to disentangle differences in steady state number of lysosomes, number of new lysosomes, and number of lysosomes being exocytosed.

Thank you for the suggestion. It is important to define the three states 1) original number of lysosomes, 2) number of new lysosomes, and 3) number of lysosomes being exocytosis. However, we have checked literature, so far it seems that there is no good method that could clearly differentiate the three states of lysosomes.

(3) Lack of evidence supporting the authors' premise that "SFN could be a good therapeutic candidate for neuropathology in NPC disease".

Suggestion was taken! We will investigate whether cholesterol was reduced by activation of TFEB by SFN in vivo to strength the point that SFN could be a potential therapeutic compound for NPC treatment. And to avoid confusion, we have removed this sentence.

Reviewer #2 (Public review):

Summary:

This study presents a valuable finding that the activation of TFEB by sulforaphane (SFN) could promote lysosomal exocytosis and biogenesis in NPC, suggesting a potential mechanism by SFN for the removal of cholesterol accumulation, which may contribute to the development of new therapeutic approaches for NPC treatment.

Strengths:

The cell-based assays are convincing, utilizing appropriate and validated methodologies to support the conclusion that SFN facilitates the removal of lysosomal cholesterol via TFEB activation.

Weaknesses:

(1) The in vivo experiments demonstrate the therapeutic potential of SFN for NPC. A clear dose-response analysis would further strengthen the proposed therapeutic mechanism of SFN. Additional data supporting the activation of TFEB by SFN for cholesterol clearance in vivo would strengthen the overall impact of the study

We understand the reviewer’s point. We examined two doses of SFN-30 and 50mg/kg. As shown in Fig.6, SFN (50mg/kg), but not 30mg/kg prevents a degree of Purkinje cell loss in the lobule IV/V of cerebellum, suggesting a dose-correlated preventive effect of SFN. In vivo experiments with higher concentrations of SFN and optimized dosage form of SFN were planned in the future study, but will not be included in this study.

We will investigate whether cholesterol was cleared by activation of TFEB by SFN in vivo.

(2) In Figure 4, the authors demonstrate increased lysosomal exocytosis and biogenesis by SFN in NPC cells. Including a TFEB-KO/KD in this assay would provide additional validation of whether these effects are TFEB-dependent.

Thank you for this valuable suggestion. We will investigate whether SFN-evoked the lysosomal exocytosis is TFEB-dependent by using TFEB-KO cells.

(3) For lysosomal pH measurement, the combination of pHrodo-dex and CF-dex enables ratiometric pH measurement. However, the pKa of pHrodo red-dex (according to Invitrogen) is ~6.8, while lysosomal pH is typically around 4.7. This discrepancy may account for the lack of observed lysosomal pH changes between WT and U18666A-treated cells. Notably, previous studies (PMID: 28742019) have reported an increase in lysosomal pH in U18666A-treated cells.

We understand the reviewer’s point. But we used pHrodo™ Green-Dextran (P35368, Invitrogen), but not pHrodo red-dex to measure the lysosomal luminal acidity. According to the product information from Invitrogen, pHrodo Green-dex conjugates are non-fluorescent at neural pH, but fluorescence bright green at acidic pH ranges 4-9, such as those in endosomes and lysosomes. Therefore, pHrodo Green-dex can be used to monitor the acidity of lysosome (Hu, Li et al. 2022) . We also used LysoTracker Red DND-99 (Thermo Scientific, L7528) to measure lysosomal pH (Fig. 4G, H), which is consistent with results of pHrodo Green/CF measurement. Overall, in our hands, we have not detected pH change of lysosomes in U18666A-treated NPC1 cell models.

(4) The authors are also encouraged to perform colocalization studies between CF-dex and a lysosomal marker, as some researchers may be concerned that NPC1 deficiency could reduce or block the trafficking of dextran along endocytosis.

Suggestion was taken! We will examine the effect of NPC1 deficiency on dextran trafficking by studying the localization of CF-dex and Lamp1.

(5) In vivo data supporting the activation of TFEB by SFN for cholesterol clearance would significantly enhance the impact of the study. For example, measuring whole-animal or brain cholesterol levels would provide stronger evidence of SFN's therapeutic potential.

We really appreciate the reviewer’s suggestions. We will investigate whether cholesterol was cleared by activation of TFEB by SFN in vivo.

Reviewer #3 (Public review):

Summary:

The authors demonstrate that activation of TFEB facilitates cholesterol clearance in cell models of Niemann-Pick type C (NPC). This is done through a variety of approaches including activation of TFEB by sulforaphane (SFN), a naturally occurring small-molecule TFEB agonist. SFN induces TFEB nuclear translocation and promotes lysosomal exocytosis. In an NPC mouse model, SFN dephosphorylates/activates TFEB in the brain and rescues the loss of Purkinje cells.

Strengths:

NPC is a severe disease and there is little in the way of treatment. The manuscript points towards some treatment options. However, the title, the title "Small-molecule activation of TFEB Alleviates Niemann-Pick Disease..." is far too strong and should be changed.

Weaknesses:

(1) The manuscript is extremely hard to read due to the writing; it needs careful editing for grammar and English.

We will thoroughly check grammar to improve the manuscript.

(2) There are a number of important technical issues that need to be addressed.

We will address the technical issues mentioned in the following.

(3) The TFEB influence on filipin staining in Figure 1A is somewhat subtle. In the mCherry alone panels there is a transfected cell with no filipin staining and the mCherry-TFEBS211A cells still show some filipin staining.

We understand the reviewer’s point. We will investigate whether cholesterol is cleared by activation of TFEB by SFN in vivo.

(4) Figure 1C is impressive for the upregulation of filipin with U18666A treatment. However, SFN is used at 15 microM. This must be hitting multiple pathways. Vauzour et al (PMID: 20166144) use SFN at 10 nM to 1microM. Other manuscripts use it in the low microM range. The authors should repeat at least some key experiments using SFN at a range of concentrations from perhaps 100 nM to 5 microM. The use of 15 microM throughout is an overall concern.

We understand the reviewer’s point. See RESPONSE #1, previously we have shown that SFN (10–15 μM, 2–9 h) induces robust TFEB nuclear translocation in a dose- and time-dependent manner in HeLa GFP-TFEB stable cells as well as in other human cell lines without cytotoxicity (Li, Shao et al. 2021) . According to previous results, in this study, we chose SFN (15 μM) to examine its effect on cholesterol clearance. We will add the information in the discussion part. In this study, we will perform dose-response TFEB nuclear translocation in NPC model cells as well as cytotoxicity experiments to examine whether the concentrations of SFN used in various cell lines are toxic.

References:

Hu, M. Q., P. Li, C. Wang, X. H. Feng, Q. Geng, W. Chen, M. Marthi, W. L. Zhang, C. L. Gao, W. Reid, J. Swanson, W. L. Du, R. Hume and H. X. Xu (2022). "Parkinson's disease-risk protein TMEM175 is a proton-activated proton channel in lysosomes.” Cell 185(13): 2292-+.

Li, D., R. Shao, N. Wang, N. Zhou, K. Du, J. Shi, Y. Wang, Z. Zhao, X. Ye, X. Zhang and H. Xu (2021). “Sulforaphane Activates a lysosome-dependent transcriptional program to mitigate oxidative stress.” Autophagy 17(4): 872-887.

A giant... and a dwarf... are brothers.

A bitch-slap so potent that even the rude giant sees the light.

What an interesting metaphor: the wounded giant as a heckled artist.

The vibes are impeccable.

Will the maids betray the sorcerer?

Gorgeous imagery.

The winter vibes are on point here: isolated, snowy, barren, windy, arboreal!

How poignant and symbolic of Ruslan and Ludmila.

Love the neutral evil/lawful neutral/chaotic neutral/true neutral Gothic antagonist.

Haunting, badass intro to Canto II.

????

Study this section

england

ορισμος για resilience & adaptation/ can be used in my concepts framework

the eu commision report, is focusing on the negative effects of cc for the european continent, stressing that the southern countries and in some cases the Balkans, are the ones that wil mainly be influenced by cc. Agriculture as we have seen in many previous readings today, will decline bc of drought and extreme weathwer events in countries such as greece italy and spain. The bad thing is, that since southern economies are very connected to agriculture, this deterioration, will have a negative effect in the whole country bc the infrasrructure is not good, and it cannot easily repair potential damages caused by CC.

:from:

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I started reading this paper with great interest, which flagged over time. As someone with extensive experience both publishing peer-reviewed research articles and working with publication data (Web of Science, Scopus, PubMed, PubMedCentral) I understand there are vagaries in the data because of how and when it was collected, and when certain policies and processes were implemented. For example, as an author starting in the late 1980s, we were instructed by the journal “guide to authors” to use only initials. My early papers were all only using initials. This changed in the mid-late 1990s. Another example, when working with NIH publications data, one knows dates like 1946 (how far back MedLine data go), 1996 (when PubMed was launched), and 2000 (when PubMedCentral was launched) and 2008 (when NIH Open Access policy enacted). There are also intermediate dates for changes in curation policy…. that underlie a transition from initials to full name in the biomedical literature.

I realize that the study covers all research disciplines, but still I am surprised that the authors of this paper don’t start with an examination of the policies underlying publications data, and only get to this at the end of a fairly torturous study.

As a reader, this reviewer felt pulled all over the place in this article and increasingly frustrated that this is a paper that explores the Dimensions database vagaries only and not really the core overall challenges of bibliometric data, irrespective of data source. Dimensions ingests data from multiple sources — so any analysis of its contents needs to examine those sources first.

A few specific comments:

• The “history of science” portion of the paper focuses on English learned societies in the 17th century. There were many other learned societies across Europe, and also “papers” (books, treatises) from long before the 17th century in Middle-eastern and Asian countries (e.g, see history of mathematics, engineering, governance and policy, etc.). These other histories were not acknowledged by the authors. Research didn’t just spring full-formed out of Zeus’ head.

• It is unclear throughout if the authors are referring to science, research, which disciplines are or are not included. The first chart on discipinary coverage is Fig 13 and goes back to 1940ish. Also, which languages are included in the analysis? For example, Figure 2 says “academic output” but from which academies? What countries? What languages? Disciplines? Also, in Figure 2, this reviewer would have like to see discussion about the variability in the noisiness of the data over time.

• The inclusion of gender in the paper misses the mark for this reviewer. When dealing with initials, how can one identify gender? And when working in times/societies where women had to hide their identity to be published…. how can a name-based analysis of gender be applied? If this paper remains a study of the “initial era”, this reviewer recommends removing the gender analysis.

• Reference needed for “It is just as important to see ourselves reflected in the outputs of the research careers…” (section B).

• Reference needed for “This period marked the emergence of “Big Science” (Section B). How do we know this is Big Science? What is the relationship with the nature of science careers? Here it would be useful perhaps to mention that postdocs were virtually unheard of before Sputnik.

• Fig 3. This would be more effective as a % total papers than absolute #.

• Gradual Evolution of the Scholarly Record. This reviewer would like to see proportion of papers without authors. A lot of history of science research is available for this period, and a few references here would be welcome, as well as a by-country analysis (or acknowledgement that the data are largely from Europe and/or English-speaking countries).

• Accelerated Changes in Recent Times. Again, this reviewer would like to see reference to scholarship on the history of science. One of the things happening in the post WW2 timeframe is the increase in government spending (in the US particularly) on R&D and academic research. So, is the academy changing or is it responding to “market forces”.

• Reflective richness of data. “Evolution of the research community” is not described in the text, not is collaborative networks.

• In the following paragraph, one could argue that evaluation was a driver of change, not a response to it. This reviewer would like to see references here.

• II. Methodology. (i) 2nd sentence missing “to” “… and full form to refer to an author name…”. (ii) 2nd para the authors talk about epochs, but the data could be (are) discontinuous because of (a) curation policy, (b) curation technology, (c) data sources (e.g., Medline rolled out in the 1960s and back-populated to 1946). (iii) 4th para referes to Figs 3 and 4 showing a marked change between 1940 and 1950, but Fig 3 goes back only to 1960, and Fig 4 is so compressed it is hard to see anything in that time range. (iv) Para 7. “the active publishing community is a reasonable proxy for the global research population”. We need a reference here and more analysis. Is this Europe? English language? Which disciplines? All academia? Dimensions data? (v) Para 12 “In exploring the issue of gender…” see comments above. Gender is an important consideration but is out of scope, in this reviewer’s opinion, for this paper focused on use of initials vs. full name.

• Listing 1. Is there a resolvable URL/DOI for this query?

• Figs 9-11, 14, 15. This reviewer would like to see a more fulsome examination / discussion of data discontinuities. Particularly around ~1985-2000.

Discussion

• The country-level discussion suggests the data (publications included) are only those that have been translated into English. Please clarify. Also, please add references in this section. There are a lot of bold statements, such as “A characteristic of these countries was the establishment of strong national academies.” Is this different from other places in the world? How? In the para before this statement, there is a phrase “picking out Slavonic stages” that is not clear to this reviewer.

• The authors seem to get ahead of themselves talking about “formal” and “informal” in relation to whether initials or full names are used. And then discuss the “Power Distance” and end up arguing that it isn’t formal/informal … but rather publisher policies and curation practices driving the initial era and its end.

• And then the authors come full circle on research articles being a technology, akin to a contract. Which is neat and useful. But all the intermediate data analysis is focused on the Dimensions data base and this reviewer would argue should be a part of the database documentation rather than a scholarly article.

• This reviewer would prefer this paper be focused much more tightly on how publishing technology can and has driven the sociology of science. Dig more into the E. Journal Analysis and F. Technological analysis. Stick with what you have deep data for, and provide us readers with a practical and useful paper that maybe, just maybe, publishers will read and be incentivized to up their game with respect to adoption of “new” technologies like ORCID, DOIs for data, etc. Because these papers are not just expositions on a disciplinary discourse, they are also a window into how science (research) works and is done.

The presented preprint is a well-researched study on a relevant topic that could be of interest to a broad audience. The study's strengths include a well-structured and clearly presented methodology. The code and data used in the research are openly available on Figshare, in line with best practices for transparency. Furthermore, the findings are presented in a clear and organized manner, with visualization that aid understanding.

At the same time, I would like to draw your attention to a few points that could potentially improve the work.

1. I think it would be beneficial to expand the annotation to approximately 250 words.

2. The introduction starts with a very broad context, but the connection between this context and the object of the research is not immediately clear. There are few references in this section, making it difficult to determine whether the authors are citing others or their own findings.

3. The transition to the main topic of the study is not well-defined, and there is no description of the gap in the literature regarding the object of study. Additionally, "bibliometric archaeology" appears at the end of the introduction but is only mentioned again later in the discussion, which may cause confusion for the reader.

4. It would be helpful to clearly state the purpose and objectives of the study both in the Introduction and in the abstract as well.

5. Besides, it is important to elaborate on the contribution of this study in the introduction section.

6. The same applies to the background - a very broad context, but the connection with the object of the research is not entirely clear.

7. Page 4 - as far as I understand, these are conclusions from a literature review, while point 3 (Reflective Richness of Data) does not follow from the previous analysis.

8. The overall impression of the introduction and background is that it is an interesting text, but it is not well related to the objectives of the study. I would recommend shortening these sections by making the introduction and literature review more pragmatic and structured. At the same time, this text could be published as a standalone contribution.

9. As I mentioned above, the methodology refers to the strengths of the study. However, in this section, it would be helpful to introduce and justify the structure of presenting the results.

10. In the methodology section, the authors could also provide a footnote with a link to the code and dataset (currently, it is only given at the end).

11. With regard to the discussion, I would like to encourage the authors to place their results more clearly in the academic context. Ideally, references from the introduction and/or literature review would reappear in this section to help clarify the research contribution.

12. Although Discussion C is an interesting read, it seems more related to the introduction than the results. Again, the text itself is rather interesting, but it would benefit from a more thorough justification.

Remarks on the images:

1. At least the data source for the images should be specified in the background, because it is not obvious to the reader before describing the methodology.

2. The color distinction between China and Russia in Figure 8 is not very clear.

3. The gray lines in Figures 9-11 make the figures difficult to read. Additionally, the meaning of these lines is not clearly indicated in the legends of Figures 10 and 11. These issues should be addressed. 

All comments and suggestions are intended to improve the article. Overall, I have a very positive impression of the work.

Sincere, 

Dmitry Kochetkov

Overview

This manuscript provides an in-depth examination of the use of initials versus full names in academic publications over time, identifying what the authors term the "Initial Era" (1945-1980) as a period during which initials were predominantly used. The authors contextualize this within broader technological, cultural, and societal changes, leveraging a large dataset from the Dimensions database. This study contributes to the understanding of how bibliographic metadata reflects shifts in research culture.

Strengths

+ Novel concept and historical depth

The paper introduces a unique angle on the evolution of scholarly communication by focusing on the use of initials in author names. The concept of the "Initial Era" is original and well- defined, adding a historical dimension to the study of metadata that is often overlooked. The manuscript provides a compelling narrative that connects technological changes with shifts in academic culture.

+ Comprehensive dataset

The use of the Dimensions database, which includes over 144 million publications, lends significant weight to the findings. The authors effectively utilize this resource to provide both anecdotal and statistical analyses, giving the paper a broad scope. The differentiation between the anecdotal and statistical epochs helps clarify the limitations of the dataset and strengthens the authors' conclusions.

+ Cross-disciplinary relevance

The study's insights into the sociology of research, particularly the implications of name usage for gender and cultural representation, are highly relevant across multiple disciplines. The paper touches on issues of diversity, bias, and the visibility of researchers from different backgrounds, making it an important contribution to ongoing discussions about equity in academia.

+ Technological impact

The authors successfully connect the decline of the "Initial Era" to the rise of digital publishing technologies, such as Crossref, PubMed, and ORCID. This link between technological infrastructure and shifts in scholarly norms is a critical insight, showing how the adoption of new tools has real-world implications for academic practices.

Weaknesses

-  Lack of clarity and readability

While the manuscript is rich in data and analysis, it can be dense and challenging to follow for readers not familiar with the technical details of bibliometric studies. The text occasionally delves into highly specific discussions that may be difficult for a broader audience to grasp while other concepts are introduced in cursory. Consider condensing the introduction section, removing unrelated historical accounts, and leading the audience to the key objectives of this research much earlier.

-  Missing empirical case studies

The manuscript remains largely theoretical, relying heavily on data analysis without providing concrete case studies or empirical examples of how the "Initial Era" affected individual disciplines or researchers. A more detailed exploration of specific instances where the use of initials had significant consequences would make the findings more tangible. Incorporating case studies or anecdotes from the history of science that illustrate the real-world impacts of the trends identified in the data would enrich the paper. These examples could help ground the analysis in practical outcomes and demonstrate the relevance of the "Initial Era" to contemporary debates.

-  Half-baked comparative analysis

Although the paper presents interesting data about different countries and disciplines, the comparative analysis between these groups could be further developed. For example, the reasons behind the differences in initial use between countries with different writing systems or academic cultures are not fully explored. A more in-depth comparative analysis that explains the cultural, linguistic, or institutional factors driving the observed differences in initial use would add nuance to the findings. This could involve a more detailed discussion of how non-Roman writing systems influence name formatting or how specific national academic policies shape author metadata.

-  Limited discussion of alternative explanations

While the authors link the decline of the "Initial Era" to technological advancements, other potential explanations, such as changing editorial policies (“technological harmonisation”), shifts in academic prestige, or the influence of global collaboration, are not fully explored. The paper could benefit from a broader discussion of these factors. Expanding the discussion to include alternative explanations for the decline of initial use, and how these might interact with technological changes, would provide a more comprehensive view. Engaging with literature on academic publishing practices, editorial decisions, and global research trends could help contextualize the findings within a wider framework.

Conclusion

This manuscript offers a novel and insightful analysis of the evolution of name usage in academic publications, providing valuable contributions to the fields of bibliometrics, science studies, and research culture. With improvements in clarity, comparative analysis, and the incorporation of case studies, this paper has the potential to make a significant impact on our understanding of how metadata reflects broader societal and technological changes in academia. The authors are encouraged to refine their discussion and expand on the implications of their findings to make the manuscript more accessible and applicable to a wider audience.

Aug 14, 2024

Nov 20, 2024

Nov 20, 2024

Authors:

• Simon Porter (Digital Science) s.porter@digital-science.com
• Daniel Hook (Digital Science) d.hook@digital-science.com

2

10.48550/arXiv.2404.06500

The Rise and Fall of the Initial Era

this paragrapgh is about solutions for cc impacts but since its mostly recommendations, im not sure it will be effective for my Lit Review/ Maybe ill wiat until my fieldwork?

resilience mesures, potentia solutions for what could be done to withstand cc schocks

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

Learn more at Review Commons

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Reply to the reviewers

Manuscript number: RC-2024-02546

Corresponding author: Woo Jae, Kim

1. General Statements

This is the second version of revision.

After thoroughly reviewing the comments provided by the EMBO Journal reviewers, we found their feedback to be highly constructive and valuable for enhancing our manuscript without the need for additional experiments. For example, Reviewer 1 acknowledged that our "data are intriguing and some of the experiments are quite convincing," but suggested that the manuscript contained excessive data that required simplification. This sentiment was echoed by Reviewer 2. In response, we have completely reformatted our manuscript to eliminate unnecessary imaging quantification data and CrzR-related screening data. The reviewers noted the density of our experimental data, which has led us to focus on the SIFa to Crz-CrzR circuit mechanisms related to heart function and interval timing in future projects.

Reviewer 2's comments were generally more moderate, and we successfully addressed all five of their points with detailed explanations and modifications to our manuscript. They positively remarked that "Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide and contributes to an understanding of how motivated behavior such as mating is orchestrated by modulatory peptides." Additionally, Reviewer 3 accepted our manuscript without any further comments.

In summary, we believe we have effectively addressed all concerns raised by Reviewers 1 and 2, resulting in a clearer manuscript that is more accessible to a broader audience.

2. Point-by-point description of the revisions

Reviewer #1

General Comments: In this revision of their manuscript, Zhang et al have attempted to address most of the points raised by the reviewers, however, they have not assuaged my most important concerns. The manuscript contains a ton of information, but at times this is to the detriment of the narrative flow. I had a lot of trouble following the rationale of each experiment, and the throughline from one experiment to the next is not always obvious. The data are intriguing, and some of the experiments are quite convincing, but other experiments are either superfluous or have methodological issues. I will summarize the most acute issues below.

• *Answer: Thank you for your thoughtful feedback and for acknowledging our efforts to address your previous comments. We appreciate your recognition of the intriguing nature of our data and the convincing aspects of our experiments. In this second revision, we have taken your concerns regarding the narrative flow and data overload to heart. We have completely reshaped our manuscript, significantly reducing unnecessary data, including the NP5270 data and overlapping quantification results that did not contribute meaningfully to the storytelling. Our goal was to streamline the presentation of our findings to enhance clarity and coherence, ensuring that each experiment clearly supports the overarching narrative. We believe these revisions will not only improve the readability of our manuscript but also allow readers to follow the rationale behind each experiment more easily. We are confident that this refined approach will make our contributions clearer and more impactful. Thank you once again for your constructive insights, which have been invaluable in guiding us toward a more focused and compelling presentation of our work.

Comment 1. *The authors argue that genetic controls are unnecessary because they have been conducted in previously published papers. I am concerned with this argument, as it is good practice to repeat controls with each experiment. However, I am overall convinced by the basic phenotype indicating that panneuronal SIFaR knockdown eliminates the changes in mating duration associated with previous experience. As for the more restricted 24F06-GAL4, the phenotype is odd-the flies do actually change their mating duration, just in the opposite direction of controls. Doesn't this imply that these flies are still capable of "interval timing", and of changing their mating strategy following exposure to rivals or following sexual experience? *

• *

__ Answer:__ We appreciate the reviewer's critical comments regarding genetic control and the intriguing phenotypes we observed in specific genetic combinations. We fully agree with the reviewer and have repeated all genetic control experiments for this revision, confirming that our genetic controls consistently demonstrate intact LMD and SMD behaviors, as previously reported. These genetic control experiments have been included in Supplementary Information 1-2. We are grateful to the reviewer for the opportunity to reaffirm that LMD and SMD represent stable behavioral phenotypes suitable for genetically studying interval timing, supported by reproducible data.

• *

We acknowledge the reviewer's insightful comments about the exciting phenotype observed when SIFaR is knockdown which shows both singly reared and sexually experienced male show lengthened mating duration in contrast to normal LMD and SMD behaviors. Actually, we have observed such phenotype when specific neural circuits are disrupted such as when sNPF peptidergic signaling is disrupted in restricted neuronal population [4]. We are now investigating such phenotype as hypothesis as disinhibition. We explained this phenotype and about disinhibition in main text as below.

In the spatial, the targeted reduction of SIFaR expression in the GAL424F06 neuronal subset resulted in a notable alteration of mating behavior. Both singly reared and sexually experienced flies exhibited an extended mating duration relative to naïve flies, contrary to the expected reduction. This observation indicates a deficit in the neural mechanism responsible for modulating mating duration, suggesting a disinhibition-like effect within the neural circuitry governing mating behavior. We have also previously observed a similar phenotype when sNPF peptidergic signaling is inhibited in specific neuronal circuits [62]. Disinhibition, characterized by the alleviation of inhibitory constraints, permits the activation of neural circuits that are ordinarily repressed. This process is instrumental in sculpting behavioral patterns and facilitating the sequential progression of behaviors. Through the orchestrated promotion of select neuronal activation and concurrent inhibition of competing neural routes, disinhibition empowers the brain with the ability to dynamically ascertain and preserve the requisite behavioral state, concurrently smoothing the transition to ensuing behavioral phases [63]. It is known that Drosophila neural circuits also exhibit disinhibition phenotypes in light preference and ethanol sensitization [64,65]. Further investigation is needed to uncover the underlying mechanisms of this disinhibition-like phenotype observed in LMD and SMD behaviors.

This reversed phenotype strongly suggests a disruption in interval timing, as one would expect that if interval timing were normal and intact, male flies would decrease their mating duration in response to appropriate environmental changes. For instance, research has shown that patients with Parkinson's disease exhibit heterogeneity in temporal processing, leading to disrupted interval timing phenotypes [5]. Therefore, if male flies subjected to social isolation or sexual experience do not show a reduction in mating duration compared to control conditions, it indicates a potential disruption in their interval timing mechanisms. We appreciate the reviewer's encouragement to further explore this intriguing disinhibition-like phenotype, and we plan to investigate this aspect in our future projects.

Comment 2. *I am glad the see the addition of data assessing the extent of SIFaR and CrzR RNAi knockdown; however, this has not completely addressed my concerns about interpretation of behavioral phenotypes. In both cases, the knockdown was assessed by qPCR using the very strong tub-GAL4 driver. mRNA levels are decreased but not nearly eliminated. Thus, when in line 177-178 the authors assert: "Consequently, we infer that the knockdown of SIFaR using the HMS00299 line nearly completely diminishes the levels of the SIFaR protein," the statement is not supported by the data. The qPCR results showed a knockdown at the mRNA level of ~50%. No assays were conducted to measure protein levels. The conclusions should be tempered to align with the data. Furthermore, it is not clear that knockdown is as successful with other drivers, which means that negative behavioral data must be interpreted with caution. For example, the lack of phenotype with repo-GAL4 driving SIFaR RNAi or elav-GAL4 driving CrzR RNAi could be due to a lack of efficient knockdown. This should be acknowledged. *

   __Answer:__ We appreciate the reviewer's critical observation regarding the efficiency of SIFaR knockdown. We fully agree that it is essential to confirm both for ourselves and our readers that the SIFaR knockdown phenotype is robust and convincing. At the outset of this project, we tested all available SIFaR-RNAi strains following established protocols within the fly community to ensure consistency in our findings. When we employed strong drivers such as tub-GAL4 and nSyb-GAL4 for SIFaR-RNAi knockdown, we observed that the flies failed to eclose and exhibited a lethal phenotype during the larval stage, which closely resembles the homozygous lethal phenotype seen in SIFaR mutants. This suggests that, in most cases, the effects of SIFaR knockdown can effectively mimic those of SIFaR mutations. To share our methodology and reinforce our findings, we have added clarifying statements in the main text as follows:

"Employment of broad drivers, including the tub-GAL4 and the strong neuronal driver nSyb-GAL4, with HMS00299 line consistently results in 100% embryonic lethality (data not shown). This phenotype mirrors the homozygous lethality observed in the SIFaRB322 mutant."

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Due to the significant lethality phenotype observed, we conducted PCR analyses using a combination of tub-GAL80ts and SIFaR-RNAi. As detailed in Fig. 1E, we reared the flies at 22{degree sign}C to suppress RNAi expression and then shifted the temperature to 29{degree sign}C for just three days prior to performing PCR. While our PCR results indicate a 50% reduction in SIFaR levels, we believe that experiments conducted without the tub-GAL80ts system would likely demonstrate an even greater reduction in SIFaR expression. To clarify this point and provide additional context, we have included the following description in the main text:

"The silencing of SIFaR mRNA was achieved at approximately 50% using the HMS00299 knockdown line in combination with tub-GAL80ts, with RNAi induction lasting for three days (bottom diagram in Fig. 1E). Notably, the same tub-GAL4 driver, when used without the tub-GAL80ts combination, resulted in embryonic lethality while still reducing SIFaR mRNA levels by 50% after three days of RNAi induction. This finding suggests that SIFaR knockdown using the HMS00299 line with GAL4 drivers is likely sufficient to elicit the observed LMD and SMD behaviors. This rationale underscores the effectiveness of our experimental approach and its potential implications for understanding the role of SIFaR in mating behaviors."

We also concur with the reviewer that the absence of a behavioral phenotype associated with CrzR-RNAi may be due to inefficient RNAi knockdown. Consequently, we have included a description of this issue in the main text as follows:

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"It is important to consider that the 50% knockdown of SIFaR and CrzR may be sufficient to disrupt LMD and/or SMD behavior. However, the lack of phenotype with repo-GAL4 or elav-GAL4 could be due to a less efficient knockdown. This possibility highlights the need for cautious interpretation of negative behavioral data."

Comment 3. *Regarding the issue of outcrossing, I am confused by the authors' statement: "To reduce the variation from genetic background, all flies were backcrossed for at least 3 generations to CS strain. For the generation of outcrosses, all GAL4, UAS, and RNAi lines employed as the virgin female stock were backcrossed to the CS genetic background for a minimum of ten generations. Notably, the majority of these lines, which were utilized for LMD assays, have been maintained in a CS backcrossed state for long-term generations subsequent to the initial outcrossing process, exceeding ten backcrosses." It's not clear what this means. Perhaps the authors could definitively state how many times each line was outcrossed. The genetic background is important because of 1) the lack of all controls, and 2) the variability of the behavioral phenotype. Often, the presence or absence of LMD or SMD appears to depend on the behavior of the control flies. When these flies show low mating duration, there is typically not a reduction following sexual experience or group raising. Could these differences derive from genetic background or transgenic insertion effects? *

Answer: We appreciate the reviewer's concern regarding the potential for confusion stemming from our descriptions of the genetic background. As the reviewer noted, we have published multiple papers on LMD and SMD behaviors, and we have conducted our experiments with careful attention to controlling the genetic background [1-3,6-8]. In response to the reviewer's comments about the importance of genetic control and background, we have completed all necessary genetic control experiments and confirmed that all our flies have been backcrossed for more than ten generations to the Canton-S (CS) strain. We believe that we have adequately addressed the reviewer's concerns regarding potential differences arising from genetic background or transgenic insertion effects. To provide readers with more detailed information about our genetic background, we have added a paragraph in the MATERIALS AND METHODS section as follows:

"The CS background was selected as the experimental background due to its well-characterized and consistent LMD and SMD behaviors. To ensure that genetic variation did not confound our results, all GAL4, UAS, and RNAi lines employed in our assays were rigorously backcrossed into the CS strain, often exceeding ten generations of backcrossing. This approach was undertaken to isolate the effects of our genetic manipulations from those of genetic background. We assert that the extensive backcrossing to the CS background, in concert with the internal control in LMD and SMD, provides a stable platform for the accurate interpretation of the LMD and SMD phenotypes observed in our experiments."

Comment 4. *I continue to have substantial concerns about the thresholding method used across many experiments to quantify overlap, and then to claim that this indicates that synaptic connections are being made between different neuronal populations. The degree of overlap will depend on factors including the settings during imaging (was care taken to prevent pixel saturation?). It is also not clear to me from the methods whether analysis was done on single confocal images or on projections. The images shown in the figures look like maximum projections of a confocal stack. Overlap would have to be assessed on individual confocal sections-it is possible that this is what was done for analysis but not clear from the description in the methods. Furthermore, a lot of figure space is dedicated to superfluous information. For example, in Figure 1F-J, there is a massive amount of space dedicated to assessing the agree of overlap between red stinger and CD4GFP, each driven from the same SIFaR2A driver, and further assessing what percentage of the CD4GFP signal overlaps with nc82, with the apparent goal of showing that a lot of the SIFaR signal is at active zones. This information does little to drive the narrative forward, and is quite confusing to read. Finally, the confocal images are generally too small to actually assess. *

   __Answer:__ We appreciate the reviewer's concerns regarding our imaging quantification methods. We recognize the importance of providing a clear and transparent methodology for both readers and the broader scientific community. Instead of using maximum projection of confocal images, we employed a projection method that incorporates the standard deviation function available in ImageJ. Based on our experience, this approach yields more reliable quantification results, allowing for a more accurate assessment of our data. To ensure clarity and reproducibility, we have detailed our methods in the MATERIALS AND METHODS section as follows:

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"The quantification of the overlap was performed using confocal images with projection by standard deviation function provided by ImageJ to ensure precise measurements and avoid pixel saturation artifacts."

We appreciate the reviewer's suggestion regarding the inclusion of image quantification data for overlapping regions, which may not be essential to the logical flow of our narrative and could lead to confusion for readers. In response, we have removed nearly all of the quantification data related to overlapping regions, retaining only those that we consider critical for the paper. Currently, only Fig. S3B-E remains, as it is important for illustrating how SIFa neuronal arborization interacts with SIFaR neurons in the central nervous system.

Additionally, we fully agree with the reviewer that the overall size of the confocal images was too small for effective assessment. To address this concern, we have enlarged all confocal images and increased the spacing in the figures. We believe these improvements will enhance the clarity of our manuscript and facilitate a better understanding of our findings.

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Comment 5. *In general, the figures are still very cluttered, with panels too close together, and the labels are hard to read. *

Answer: We thank the reviewer for their valuable feedback regarding the clarity of our figures. In response to their concern, we have enlarged the figures to enhance readability and ensure that the panels are more distinct. We believe these adjustments will significantly improve the viewer's ability to interpret the data. We appreciate the reviewer's attention to detail, which has helped us to refine the presentation of our findings.

Comment 6. *There are no methodological details on how the VFB was used. The authors have not addressed my concern that they are showing only the neuronal skeleton (rather than the actual site of synapses). They are simply identifying all locations where the neuronal skeleton overlaps an entire brain region, and suggesting that these represent synapses. Many papers use the VFB to denote the actual location of synapses, which should be done in Figures 3B and S4A. *

Answer: We appreciate the reviewer's constructive comments regarding the methodological details of using VFB data. We fully agree that we cannot draw definitive conclusions about SIFa projections to specific regions based solely on neuronal skeleton data, which do not indicate the actual locations of synapses. To address this concern, we have made it clear to readers that the VFB skeleton data serves only as a preliminary indication of potential SIFa projections to GA, FB, and AL.

To confirm the presence of actual synapses from SIFa neurons, we conducted a thorough analysis using FlyWire data, which validated our findings from VFB. By integrating insights from VFB with the detailed synaptic mapping provided by FlyWire, we can confidently assert the functional relevance of these connections within the context of SIFa neuronal activity. This comprehensive approach not only bolsters our conclusions but also enhances our understanding of how SIFa neurons interact within the broader neural circuitry. We believe this rationale highlights the significance of our work in elucidating the complex relationships among these neuronal populations. We have detailed our findings in the main text as follows:

"We utilized the "Virtual Fly Brain (VFB)" platform, an interactive tool designed for exploring neuronal connectivity, to gain insights into the connectivity of SIFa neurons with four other neurons, specifically GA, FB, and AL (Fig. 3B and Fig. S4B) [74]. While VFB provides valuable information, it does not offer precise locations of synapses originating from SIFa neurons. To address this limitation, we incorporated data from the FlyWire connectome, which allowed us to confirm that SIFa projections indeed form actual synapses with GA, AL, FB, and SMP (Fig. S3F and S3G) [75]. This multi-faceted approach enhances the robustness of our findings by integrating different data sources to validate neuronal connections."

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Comment 7. *The changes in GRASP and CaLexA with experience are very interesting, and suggest a substantial rearrangement of synaptic connectivity associated with changes in mating duration following group rearing or female exposure. I am still concerned, however, that the nsyb and tGRASP images look so different. I wouldn't expect them to be identical, but it is puzzling that the nsyb-GRASP data show connections in a few discrete brain areas, while the tGRASP data show connections in a much larger overall brain area, but curiously not in the major regions seen with nsyb-GRASP (ie PI, FB and GA). Shouldn't the tGRASP signal appear in all the places that the nsyb-GRASP does? For CaLexA and GRASP data, the methods should indicate the timing of the dissections and staining relative to the group/sexual experience. *

Answer: We appreciate the reviewer's constructive comments regarding our GRASP data, which indeed reveal an intriguing neural plasticity phenotype, as the reviewer noted. In our previous response, we suggested that the observed differences may be attributed to the distinct SIFa-GAL4 strains utilized, as described in another manuscript focused on SIFa inputs [9]. In that manuscript, we classified the four SIFa neurons into two groups: SIFaDA (dorsal-lateral) and SIFaVP (ventral-posterior). The SIFa2A-GAL4 specifically labels only the SIFaVP neurons, while the SIFa-PT driver labels all four neurons. We acknowledge that we did not clearly communicate this distinction to the reviewer or our readers, and we apologize for any confusion this may have caused. To rectify this oversight, we have added a detailed explanation of these differences in the main text as follows:

"The subtle differences in GRASP signals observed in Fig. 3A may stem from the distinct expression patterns of the SIFa2A-lexA and GAL4SIFa.PT drivers. We would like to emphasize that the SIFa2A driver labels only a subset of SIFa neurons in other regions (Kim 2024)."

We recognize that a clear and transparent methodology is essential for generating reproducible data. In response to the reviewer's suggestion, we have revised our MATERIALS AND METHODS section to include more detailed descriptions of the dissection conditions. This enhancement aims to provide readers with the necessary information to replicate our experiments effectively.

"To ascertain calcium levels and synaptic intensity from microscopic images, we dissected and imaged five-day-old flies of various social conditions and genotypes under uniform conditions. For group reared (naïve) flies, the flies were reared in group condition and dissect right after 5 days of rearing without any further action. For single reared flies, the flies were reared in single condition and dissect at the same time as group reared flies right after 5 days of rearing without any further action. For sexual experienced flies, the flies were reared in group condition after 4 days of rearing and will be given virgins to give them sexual experience for one day, those flies will also be dissected at the same time as group and single reared flies after one day."

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Comment 8. *The calcium imaging data are odd. In most cases, the experimental flies don't actually show an increase in calcium levels but rather a lack of a decrease that is present in the ATR- controls. Also, in the cases where they argue for an excitatory affect of SIF neuron stimulation, the baseline signal intensity appears higher in ATR- controls compared to ATR+ experimental flies (eg Fig 5L, 6O), while it is significantly higher in ATR+ flies compared to ATR- controls when the activation results in decreased calcium signals. Perhaps more details on how these experiments were conducted and whether data were normalized in some way would help to clarify this. *

Answer: Thank you for your valuable feedback. We appreciate your careful analysis of our calcium imaging data and have addressed your concerns below:

In our experiments, we observed that ATR+ flies maintained relatively stable calcium levels, whereas ATR- controls exhibited a gradual decrease. Under confocal imaging, GFP signals typically decrease over time, which we observed in ATR- controls. However, ATR+ flies did not exhibit this decline. To better convey this observation, we have refined the language in the manuscript. Specifically, we now describe this as a tendency to sustain the activity of Crz neurons in the OL and AG regions (Fig. 6K-M, Fig. S6G-I). This is supported by the sustained intracellular calcium activity in ATR+ flies compared to the gradual decline to baseline levels observed in ATR- controls (Fig. 6K-M).

Baseline signal intensity differences: You correctly noted that in some cases, the baseline signal intensity appears higher in ATR- controls compared to ATR+ flies. These differences are likely due to technical factors, such as variations in the distance between the imaged brain and the objective lens. Even minor positional shifts in the brain (forward or backward) can affect the observed signal intensity.

Our analyses focus on relative changes in fluorescence intensity within the same sample, which we present as line graphs to highlight trends rather than absolute values. However, we acknowledge that showing the magnitude of relative values instead of absolute values may have caused some confusion. We have revised the images to better align with our conclusions, ensuring that the adjustments do not affect the observed relative changes.

Normalization and experimental details: The calcium imaging data were normalized to ΔF/F to account for differences in baseline fluorescence intensity. However, we recognize that further clarification of the normalization process and experimental setup is essential. We have expanded the methods section to include detailed descriptions of data acquisition, normalization steps, and statistical analyses.

As the reviewer correctly noted, calcium signals in ATR+ flies are generally higher than those in ATR- flies. However, it appears that the calcium levels exhibit a maintained response rather than a dramatic increase compared to the control ATR- condition, particularly in the case shown in Fig. 6K, which illustrates SIFa-to-Crz signaling. We believe this observation may reflect the actual physiological conditions under which SIFa influences SIFaR neurons to sustain activity during activation. We have included our interpretation of these findings in the main text as follows:

"Upon optogenetic stimulation of SIFa neurons, we observed a tendency to maintain the activity of Crz neurons in OL and AG regions (Fig. 6K-M, Fig. S6H-J), evidenced by a sustained activity in intracellular Ca2+ levels that persisted in a high level compared to control ATR- condition which shows gradual declining to baseline levels (Fig. 6K-M). In contrast to the OL and AG regions, the cells in the upper region of the SIP consistently show a decrease in Ca2+ levels following stimulation of the SIFa neurons (Fig. 6N-P)."

To enhance readers' understanding of our calcium imaging results, we have reformatted our GCaMP data for improved clarity and included additional details in the MATERIALS AND METHODS section regarding the quantification of GCaMP imaging methods. Furthermore, as the reviewer correctly noted, discrepancies in baseline activity were due to our error in presenting the baseline data. We have now corrected this oversight accordingly.

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Comment 9. *The models in Fig 4 J and T show data from Song et al, though I could not find a citation for this. I would omit this part of the model since these data are not discussed at all in the manuscript. *

Answer: We appreciate the reviewer for correctly identifying our oversight in failing to properly cite Song et al.'s paper. This error occurred partly because the preprint was not available at the time we submitted our manuscript. We now have a preprint for Song et al.'s paper, which discusses the contributions of SIFa neurons to various energy balance behaviors, and we plan to submit this paper back-to-back with our current submission to PLOS Biology. We have briefly cited Song et al.'s work in the manuscript; however, we have removed references to it from Fig. 4J and T to avoid any potential confusion for readers.

Comment 10. *The graphs for the SCOPE data (eg Figure 8I-L) are still too small to make sense of. *

Answer: We enlarged the tSNE plot generated from the SCOPE data.

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Comment 11. The rationale behind including the data in Figure 9 is not well explained. I would omit this data to help streamline and focus the manuscript.

Answer: We fully understand and agree with the reviewer's concerns, and we have removed all previous versions of Figure 9 from the manuscript to prevent any confusion regarding the storyline.

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Comment 12. *The single control group is still being duplicated in two different graphs but with different names in each graph. The authors updated figure caption hints at this but does not make it explicit. At the very least, these should be given the same name across all graphs, as is done, for example, in the CaLexA experiments in Figure 4B-C. *

Answer: We concur with the reviewer and have changed the label for all "group" conditions to "naïve" in all figures.

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Comment 13. *Lines 640-641: Moreover, the pacemaker function is essential for the generation of interval timing capabilities (Meck et al, 2012; Matell, 2014; Buhusi & Meck, 2005), with the heart being recognized as the primary pacemaker organ within the animal body". This is an intriguing idea, however, I attempted to look at the cited references and don't see any claim about the heart being involved in interval timing. I could not find a paper matching the citation of Matell 2014. Meck et al 2012 is an introduction to a Frontiers in Integrative Neuroscience Research Topic and does not mention the heart, nor does the Buhusi and Meck 2005 paper. Perhaps there is a more suitable reference to make the assertion that the fly's interval timer would be affected by changes in heart rate. My suggestion would be to simplify the manuscript, focusing on the most robust findings-the behavioral effect of SIFaR knockdown, the GRASP and CaLexA data showing differences following group rearing or female exposure, and the effect of Crz knockdown in SIFaR neurons. Other details could be included but would have to be verified with more rigorous experiments. *

__ Answer:__ We appreciate the reviewer's interest in our exploration of the role of heart function in interval timing. While we found that knocking down CrzR in the heart specifically disrupts LMD behavior, we agree that our manuscript needs to be streamlined for clarity. As a result, we have eliminated all CrzR-RNAi knockdown data except for the oenocyte, neuronal and glial knockdown data presented in Fig. S8C-H. This decision was made to ensure a more focused comparison with the SIFaR knockdown experiments shown in Fig. 1. We are dedicated to further investigating the role of Crz-CrzR in heart function and its influence on interval timing in a future project. This approach allows us to maintain clarity in our current manuscript while laying the groundwork for more comprehensive studies ahead.

In line with the reviewer's suggestions, we have simplified our manuscript by eliminating unnecessary data, such as overlapping image quantification and CrzR-RNAi screening, allowing us to focus on SIFaR knockdown and GRASP, as well as CaLexA with GCaMP imaging. We are grateful to the reviewer for providing us with the opportunity to delineate the role of CrzR in heart function related to LMD as a significant future project. We believe that our manuscript has been greatly improved by the reviewer's constructive feedback.

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Reviewer #2

General Comments:* The authors investigate mating behavior in male fruit flies, Drosophila melanogaster, and test for a role of the SIFamide receptor (SIFaR) in this type of behavior, in particular mating duration in dependence of social isolation and prior mating experience. The anatomy of SIFamide-releasing neurons in comparison with SIFamide receptor-expressing neurons is characterized in a detail-rich manner. Isolating males or exposing them to mating experience modifies the anatomical organization of SIFamidergic axon termini projecting onto SIFamide receptor-expressing neurons. This structural synaptic plasticity is accompanied by changes in calcium influx. Lastly, it is reported that corazonin-releasing neurons are modulated by SIFamide releasing neurons and impact the duration of mating behavior.

Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide, and contributes to an understanding how motivated behavior such as mating is orchestrated by modulatory peptides. The manuscript has some points that are less convincing.*

__ Answer:__ We appreciate the reviewer's positive feedback regarding our investigation into the role of the SIFamide receptor (SIFaR) in mating behavior in male Drosophila melanogaster. We are pleased that the detailed characterization of SIFamide-releasing neurons and their anatomical changes in response to social isolation and mating experience has been recognized as a valuable contribution to the understanding of synaptic plasticity and its impact on behavior. We are also grateful that the reviewer described our manuscript as a "highly interesting study" that advances knowledge about the behavioral roles of SIFamide and contributes to the understanding of how motivated behaviors, such as mating, are orchestrated by modulatory peptides. We sincerely thank the reviewer for these encouraging comments about our work.

We acknowledge the reviewer's concerns about certain aspects of our manuscript that may be less convincing. We are committed to addressing these points thoroughly to strengthen our arguments and enhance the clarity of our findings. In response to the feedback, we have made several revisions throughout the manuscript, including clarifying our methodology, enhancing the presentation of our data, and providing additional context where needed. We believe these changes will improve the overall quality of the manuscript and make our conclusions more compelling. Thank you for your thoughtful review, and we look forward to your further insights.

Comment 1. *It remains unclear why the authors link the differentially motivated duration of mating behavior with the psychological concept of interval timing. This distracts from the actually interesting neurobiology and is not necessary to make the study interesting. The study deals with the modulation of mating behavior by SIFamide. The abstraction that SIFamide plays a role in the neuronal calculation of time intervals for the perception of time sequenc es is not convincing in itself. *

• Answer: We appreciate the reviewer's thoughtful comments regarding our conclusion that links SIFamide to interval timing in mating behavior. We recognize that our data primarily indicate that SIFamide is essential for normal mating duration and influences the motivation-dependent aspects of this behavior. We also acknowledge the need for more robust evidence to establish a clearer connection between these findings and interval timing. Recent research by Crickmore et al. has provided valuable insights into how mating duration in Drosophila *serves as an effective model for examining changes in motivation over time as behavioral goals are achieved. For example, around six minutes into mating, sperm transfer occurs, resulting in a significant shift in the male's nervous system, where he no longer prioritizes continuing the mating at the expense of his own survival. This pivotal change is mediated by four male-specific neurons that release the neuropeptide Corazonin (Crz). When these Crz neurons are inhibited, sperm transfer does not take place, and as a result, the male fails to reduce his motivation, leading to matings that can extend for hours instead of the typical duration of approximately 23 minutes [10].

Recent research conducted by Crickmore et al. has secured NIH R01 funding (Mechanisms of Interval Timing, 1R01GM134222-01) to investigate mating duration and sperm transfer timing in Drosophila as a genetic model for understanding interval timing. Their study emphasizes how fluctuations in motivation over time can affect mating behavior, particularly noting that significant behavioral changes occur during mating. For instance, around six minutes into the mating process, sperm transfer takes place, which corresponds with a notable decrease in the male's motivation to continue mating [10]. These findings indicate that mating duration serves not only as an endpoint for behavior but may also reflect fundamental mechanisms associated with interval timing.

   We believe that by leveraging the robustness and experimental tractability of these findings, along with our own work on SIFamide's role in mating behavior, we can gain deeper insights into the molecular and circuit mechanisms underlying interval timing. We will revise our manuscript to clarify this relationship and emphasize how SIFamide may interact with other neuropeptides and neuronal circuits involved in motivation and timing.

   In addition to the efforts of Crickmore's group to connect mating duration with a straightforward genetic model for interval timing, we have previously published several papers demonstrating that LMD and SMD can serve as effective genetic models for interval timing within the fly research community. For instance, we have successfully connected SMD to an interval timing model in a recently published paper [3], as detailed below:

"We hypothesize that SMD can serve as a straightforward genetic model system through which we can investigate "interval timing," the capacity of animals to distinguish between periods ranging from minutes to hours in duration.....

In summary, we report a novel sensory pathway that controls mating investment related to sexual experiences in Drosophila. Since both LMD and SMD behaviors are involved in controlling male investment by varying the interval of mating, these two behavioral paradigms will provide a new avenue to study how the brain computes the 'interval timing' that allows an animal to subjectively experience the passage of physical time [11-16]."

   Lee, S. G., Sun, D., Miao, H., Wu, Z., Kang, C., Saad, B., ... & Kim, W. J. (2023). Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. *PLoS Genetics*, *19*(5), e1010753.

   We have also successfully linked LMD behavior to an interval timing model and have published several papers on this topic recently [6-8].

   Sun, Y., Zhang, X., Wu, Z., Li, W., & Kim, W. J. (2024). Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. *G3: Genes, Genomes, Genetics*, jkae255.

   Zhang, T., Zhang, X., Sun, D., & Kim, W. J. (2024). Exploring the Asymmetric Body's Influence on Interval Timing Behaviors of Drosophila melanogaster. *Behavior Genetics*, *54*(5), 416-425.

   Huang, Y., Kwan, A., & Kim, W. J. (2024). Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. *Gene Reports*, *36*, 101999.

   Finally, in this context, we have outlined in our INTRODUCTION section below how our LMD and SMD models are related to interval timing, aiming to persuade readers of their relevance. We hope that the reviewer and readers are convinced that mating duration and its associated motivational changes such as LMD and SMD provide a compelling model for studying the genetic basis of interval timing in *Drosophila*.

"The dimension of time is the fundamental basis for an animal's survival. Being able to estimate and control the time between events is crucial for all everyday activities [25]. The perception of time in the seconds-to-hours range, referred to as 'interval timing', is involved in foraging, decision making, and learning via activation of cortico-striatal circuits in mammals [26]. Interval timing requires entirely different neural mechanisms from millisecond or circadian timing [27-29]. There is abundant psychological research on time perception because it is a universal cognitive dimension of experience and behavioral plasticity. Despite decades of research, the genetic and neural substrates of temporal information processing have not been well established except for the molecular bases of circadian timing [30,31]. Thus, a simple genetic model system to study interval timing is required. Considering that the mating duration in fruit flies, which averages approximately 20 minutes, is well within the range addressed by interval timing mechanisms, this behavioral parameter provides a relevant context for examining the neural circuits that modulate the Drosophila's perception of time intervals. Such an investigation necessitates an understanding of the extensive neural and behavioral plasticity underlying interval timing [32-37]."

We would like to highlight that many researchers are currently working to bridge the gap between interval timing as a purely psychological concept and its neurobiological underpinnings, as illustrated in the following articles [15,17-20]. We appreciate the reviewer's concerns regarding the relationship between mating duration and interval timing. However, we believe that our LMD and SMD model can effectively bridge the gap between psychological concepts and neurobiological mechanisms using a straightforward genetic model organism. By employing Drosophila as our model, we aim to elucidate the underlying neural circuits that govern these behaviors, thereby contributing to a deeper understanding of how interval timing is represented in both psychological and biological contexts.

Matell, M. S. Neurobiology of Interval Timing. Adv. Exp. Med. Biol. 209-234 (2014) doi:10.1007/978-1-4939-1782-2_12.

Matell, M. S. & Meck, W. H. Cortico-striatal circuits and interval timing: coincidence detection of oscillatory processes. Cogn. Brain Res. 21, 139-170 (2004).

Merchant, H. & Lafuente, V. de. Introduction to the neurobiology of interval timing. Adv Exp Med Biol 829, 1-13 (2014).

Golombek, D. A., Bussi, I. L. & Agostino, P. V. Minutes, days and years: molecular interactions among different scales of biological timing. Philosophical Transactions Royal Soc B Biological Sci 369, 20120465 (2014).

Balcı, F. & Toda, K. Editorial: Psychological and neurobiological mechanisms of time perception and temporal information processing: insight from novel technical approaches. Front. Behav. Neurosci. 17, 1208794 (2023).

Comment 2. *For all behavioral experiments, genetic controls should always be conducted. That is, both the heterozygous Gal4-line as well as the heterozygous UAS-line should be used as controls. This is laborious, but important and common standard. The authors often report data only for offspring from genetc crosses in which UAS-lines and Gal4-lines are combined (e.g. figure S1). This is not sufficient. *

• *Answer: We are grateful for the reviewer's constructive suggestions regarding the genetic control experiments. In response to similar concerns raised by another reviewer, we have conducted all necessary genetic control experiments and included the results in Supplementary Information 1-2. We hope that this thorough effort will demonstrate to both the reviewer and readers that the LMD and SMD behaviors represent stable and reproducible phenotypes for investigating the genetic components of interval timing.

Comment 3. *There are quite a lot of citations of preprints, including preprints from the authors's own lab. It seems inappropriate to cite non-peer reviewed preprints in order to present the basic principles of the study (interval timing in flies) as recognized knowledge. In general, it is unclear whether the information presented in these multiple preprints will turn out to be credible and acceptable. *

• *Answer: We concur with the reviewer and have removed most of the preprint material, retaining only one preprint that discusses SIFa function, which has been co-submitted with this manuscript.

Comment 4. *Anatomical images are often very small and not informative. For example, figure S1 O, R, S and U shows small images of fly brains and ventral nerve chords that do not convincingly describe the expression of fluorescent proteins. The choice of a threshold to quantify fluorescence seems arbitrary. It is also not clear what the quantification "83% of brain and 71% of VNC SIFaR+ neurons" actually tells us. This quantification does not rely on counting neurons (such as 83% of neurons), but only shows how fluorescence in these neurons overlaps with an immunostaining of an ubiquitous active zone protein. The same is true for figure S2 or S3: overlapping brain areas do not inform you about numbers of cells, as stated in the text. *

Answer: We appreciate the reviewer's concerns regarding our imaging quantification methods. In response to similar questions raised by another reviewer, we have thoroughly reformatted our methods section and eliminated much of the overlapping data that appeared unnecessary for this paper. We recognize the importance of providing a clear and transparent methodology for both readers and the broader scientific community. Instead of using maximum projection of confocal images, we employed a projection method that incorporates the standard deviation function available in ImageJ. Based on our experience, this approach yields more reliable quantification results, allowing for a more accurate assessment of our data. To ensure clarity and reproducibility, we have detailed our methods in the MATERIALS AND METHODS section as follows:

• *

"The quantification of the overlap was performed using confocal images with projection by standard deviation function provided by ImageJ to ensure precise measurements and avoid pixel saturation artifacts."

We appreciate the reviewer's suggestion regarding the inclusion of image quantification data for overlapping regions, which may not be essential to the logical flow of our narrative and could lead to confusion for readers. In response, we have removed nearly all of the quantification data related to overlapping regions, retaining only those that we consider critical for the paper. Currently, only Fig. S3B-E remains, as it is important for illustrating how SIFa neuronal arborization interacts with SIFaR neurons in the central nervous system.

Additionally, we fully agree with the reviewer that the overall size of the confocal images was too small for effective assessment. To address this concern, we have enlarged all confocal images and increased the spacing in the figures. We believe these improvements will enhance the clarity of our manuscript and facilitate a better understanding of our findings.

Comment 5. *The authors have consistently confused the extensive overlap of neuronal processes (dendrites and presynaptic regions) across large brain areas with synaptic connections. One cannot infer functional synaptic connectivity from the overlap of these fluorescent signals. *

Answer: We appreciate the reviewer's feedback and, in light of similar comments from another reviewer, we have removed most of the DenMark and syt.eGFP data, retaining only Fig. 3A. We are grateful for the constructive suggestions, which have significantly enhanced our manuscript. We believe that these revisions have clarified the narrative for readers, allowing for a more focused exploration of SIFaR's role in synaptic plasticity and neuronal orchestration.

Reviewer #3

General Comments: In this revised manuscript, the authors have fully and satisfactorily addressed my comments on the previous version. I recommend publication of this manuscript.

__ Answer:__ We would like to extend our heartfelt thanks for the careful consideration and positive assessment of our revised manuscript. Your insightful feedback has been instrumental in shaping the final version of our work, and we are delighted to hear that our revisions have met your expectations.

Your dedication to ensuring the quality and rigor of the scientific literature is truly commendable, and we are immensely grateful for the time and effort you have devoted to reviewing our paper. Your support for publication is a significant encouragement to us and validates the hard work we have put into addressing the issues you raised.

Please accept our sincere appreciation for your professional and constructive approach throughout the review process. We look forward to the possibility of contributing to the scientific community through the dissemination of our research.

REFERENCES

1. Kim WJ, Jan LY, Jan YN. Contribution of visual and circadian neural circuits to memory for prolonged mating induced by rivals. Nat Neurosci. 2012;15: 876-883. doi:10.1038/nn.3104
2. Kim WJ, Jan LY, Jan YN. A PDF/NPF Neuropeptide Signaling Circuitry of Male Drosophila melanogaster Controls Rival-Induced Prolonged Mating. Neuron. 2013;80: 1190-1205. doi:10.1016/j.neuron.2013.09.034
3. Lee SG, Sun D, Miao H, Wu Z, Kang C, Saad B, et al. Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. PLOS Genet. 2023;19: e1010753. doi:10.1371/journal.pgen.1010753
4. Zhang X, Miao H, Kang D, Sun D, Kim WJ. Male-specific sNPF peptidergic circuits control energy balance for mating duration through neuron-glia interactions. bioRxiv. 2024; 2024.10.17.618859. doi:10.1101/2024.10.17.618859
5. Merchant H, Luciana M, Hooper C, Majestic S, Tuite P. Interval timing and Parkinson's disease: heterogeneity in temporal performance. Exp Brain Res. 2008;184: 233-248. doi:10.1007/s00221-007-1097-7
6. Sun Y, Zhang X, Wu Z, Li W, Kim WJ. Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. G3: Genes, Genomes, Genet. 2024; jkae255. doi:10.1093/g3journal/jkae255
7. Zhang T, Zhang X, Sun D, Kim WJ. Exploring the Asymmetric Body's Influence on Interval Timing Behaviors of Drosophila melanogaster. Behav Genet. 2024; 1-10. doi:10.1007/s10519-024-10193-y
8. Huang Y, Kwan A, Kim WJ. Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. Gene Rep. 2024; 101999. doi:10.1016/j.genrep.2024.101999
9. Kim WJ, Song Y, Zhang T, Zhang X, Ryu TH, Wong KC, et al. Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male Drosophila melanogaster. bioRxiv. 2024; 2024.06.04.597277. doi:10.1101/2024.06.04.597277
10. Thornquist SC, Langer K, Zhang SX, Rogulja D, Crickmore MA. CaMKII Measures the Passage of Time to Coordinate Behavior and Motivational State. Neuron. 2020;105: 334-345.e9. doi:10.1016/j.neuron.2019.10.018
11. Buhusi CV, Meck WH. What makes us tick? Functional and neural mechanisms of interval timing. Nat Rev Neurosci. 2005;6: 755-765. doi:10.1038/nrn1764
12. Merchant H, Harrington DL, Meck WH. Neural Basis of the Perception and Estimation of Time. Annu Rev Neurosci. 2012;36: 313-336. doi:10.1146/annurev-neuro-062012-170349
13. Allman MJ, Teki S, Griffiths TD, Meck WH. Properties of the Internal Clock: First- and Second-Order Principles of Subjective Time. Annu Rev Psychol. 2013;65: 743-771. doi:10.1146/annurev-psych-010213-115117
14. Rammsayer TH, Troche SJ. Neurobiology of Interval Timing. Adv Exp Med Biol. 2014; 33-47. doi:10.1007/978-1-4939-1782-2_3
15. Golombek DA, Bussi IL, Agostino PV. Minutes, days and years: molecular interactions among different scales of biological timing. Philosophical Transactions Royal Soc B Biological Sci. 2014;369: 20120465. doi:10.1098/rstb.2012.0465
16. Jazayeri M, Shadlen MN. A Neural Mechanism for Sensing and Reproducing a Time Interval. Curr Biol. 2015;25: 2599-2609. doi:10.1016/j.cub.2015.08.038
17. Balcı F, Toda K. Editorial: Psychological and neurobiological mechanisms of time perception and temporal information processing: insight from novel technical approaches. Front Behav Neurosci. 2023;17: 1208794. doi:10.3389/fnbeh.2023.1208794
18. Gür E, Duyan YA, Arkan S, Karson A, Balcı F. Interval timing deficits and their neurobiological correlates in aging mice. Neurobiol Aging. 2020;90: 33-42. doi:10.1016/j.neurobiolaging.2020.02.021
19. Merchant H, Lafuente V de. Introduction to the neurobiology of interval timing. Adv Exp Med Biol. 2014;829: 1-13. doi:10.1007/978-1-4939-1782-2_1
20. Matell MS. Neurobiology of Interval Timing. Adv Exp Med Biol. 2014; 209-234. doi:10.1007/978-1-4939-1782-2_12

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Referee #3

Evidence, reproducibility and clarity

Summary

The article investigates the role of the neuropeptide SIFa and its receptor SIFaR in regulating two distinct mating duration behaviors in male Drosophila melanogaster, Longer-Mating-Duration (LMD) and Shorter-Mating-Duration (SMD). The study reveals that SIFaR expression in specific neurons is required for both behaviors. It shows that social context and sexual experience lead to synaptic reorganization between SIFa and SIFaR neurons, altering internal brain states. The SIFa-SIFaR/Crz-CrzR neuropeptide relay pathway is essential for generating these behaviors, with Crz neurons responding to SIFa neuron activity. Furthermore, CrzR expression in non-neuronal cells is critical for regulating LMD and SMD behaviors. The study utilizes neuropeptide RNAi screening, chemoconnectome (CCT) knock-in, and genetic intersectional methods to elucidate these findings.

Major Comments

• Are the key conclusions convincing? The key conclusions are intriguing but require more robust data to be fully convincing. While the study presents compelling evidence for the involvement of SIFa and SIFaR in mating behaviors, additional experiments are needed to firmly establish the proposed mechanisms.
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify certain claims as preliminary or speculative. Specifically, the proposed SIFa-SIFaR/Crz-CrzR neuropeptide relay pathway is only investigated via imaging approach. More experiments using behavioral tests are needed to confirm that Crz relays the SIFa signaling pathway. For example, Crz-Gal4>UAS-SIFaR RNAi should be done to show that SIFaR+ Crz+ cells are necessary for LMD and SMD.
• Would additional experiments be essential to support the claims of the paper? Yes, additional experiments are essential. Detailed molecular and imaging studies are needed to support claims about synaptic reorganization. For example:
  • More controls are needed for RNAi and Gal80ts experiments, such as Gal4-only control, RNAi-only control, etc.
  • Using synaptic markers and high-resolution imaging to observe synaptic changes directly.
  • Electrophysiological recordings from neurons expressing SIFa and SIFaR to analyze their functional connectivity and activity patterns.
• Are the suggested experiments realistic in terms of time and resources? The suggested experiments are realistic but will require considerable time and resources. Detailed molecular interaction studies, imaging synaptic plasticity, and electrophysiological recordings could take several months to over a year, depending on the complexity and availability of necessary equipment and expertise. The cost would be moderate to high, involving expenses for reagents, imaging equipment, and animal husbandry for maintaining Drosophila stocks.
• Are the data and the methods presented in such a way that they can be reproduced? The methods are generally described in detail, allowing for potential reproducibility. However, more precise documentation of certain experimental conditions, such as the timing and conditions of RNAi induction and temperature controls, is necessary. The methods about imaging analysis are too detailed. The exact steps about how to use ImageJ should be removed.
• Are the experiments adequately replicated and statistical analysis adequate? Most figures in the manuscript need to be re-plotted. The right y-axis "Difference between means" is not necessary, if not confusing. The image panels are too small to see, while the quantification of overlapping cells are unnecessarily large. The figures are too crowded with labels and texts, which makes it extremely difficult to comprehend the data.

Minor Comments

• Specific experimental issues that are easily addressable. Clarify the timing of RNAi induction and provide more detailed figure legends for better understanding and reproducibility.
• Are prior studies referenced appropriately? Yes.
• Are the text and figures clear and accurate? The text is generally clear, but the figures need re-work. See comment above.
• Suggestions to improve the presentation of data and conclusions. Use smaller fonts in the bar plots and make the plots smaller. Enlarge the imaging panels and let the pictures tell the story.

Significance

Nature and Significance of the Advance

This study aims to advance understanding of how neuropeptides modulate context-dependent behaviors in Drosophila. It provides novel insights into the role of SIFa and SIFaR in interval timing behaviors, contributing to the broader field of neuropeptide research and behavioral neuroscience. However, the significance of the findings is limited by the preliminary nature of some claims and the need for additional supporting data.

Context in Existing Literature

The work builds on previous studies that identified various roles of neuropeptides in behavior modulation but lacked detailed mechanistic insights. By elucidating the SIFa-SIFaR/Crz-CrzR pathway, this study attempts to fill a gap in the literature, but more robust evidence is required to solidify its contributions.

Interested Audience

The findings will interest neuroscientists, behavioral biologists, and researchers studying neuropeptides and their roles in behavior and neural circuitry. Additionally, this research may have implications for understanding neuropeptidergic systems in other organisms, making it relevant to a broader audience in the fields of neurobiology and physiology.

Field of Expertise

Keywords: Neuropeptides, Drosophila melanogaster, Behavioral Neuroscience. Areas without sufficient expertise: courtship behavior.

Recommendation

I recommend a major revision of this manuscript. The study presents intriguing findings, but several key claims are preliminary and require additional experiments for support. The data is poorly presented and the figures can be significantly improved. Detailed molecular and imaging studies, as well as more rigorous statistical analyses, are necessary to strengthen the conclusions. Addressing these concerns will significantly improve the robustness and impact of the paper.

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Referee #2

Evidence, reproducibility and clarity

Zhang et al., "Long-range neuropeptide relay as a central-peripheral communication mechanism for the context-dependent modulation of interval timing behaviors".

The authors investigate mating behavior in male fruit flies, Drosophila melanogaster, and test for a role of the SIFamide receptor (SIFaR) in this type of behavior, in particular mating duration in dependence of social isolation and prior mating experience. The anatomy of SIFamide-releasing neurons in comparison with SIFamide receptor-expressing neurons is characterized in a detail-rich manner. Isolating males or exposing them to mating experience modifies the anatomical organization of SIFamidergic axon termini projecting onto SIFamide receptor-expressing neurons. This structural synaptic plasticity is accompanied by changes in calcium influx. Lastly, it is shown that corazonin-releasing neurons are modulated by SIFamide releasing neurons and impact the duration of mating behavior.

Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide, and contributes to an understanding how motivated behavior such as mating is orchestrated by modulatory peptides. The approach to take the entire organism, including peripheral tissue, into consideration, is very good and a rather unique point. The manuscript has only some points that are less convincing, and these should be addressed.

Major concerns:

• It is highly interesting that the duration of mating behavior is dependent on external and motivational factors. In fact, that provides an elegant way to study which neuronal mechanisms orchestrate these factors. However, it remains elusive why the authors link the differentially motivated durations of mating behavior to the psychological concept of interval timing. This distracts from the actually interesting neurobiology, and is not necessary to make the study interesting.
• In figure 4 A and 4K, fluorescence microscopy images of brains and ventral nerve chords are shown, one illustrating GRASP experiments, and one showing CaLexA experiments. The extreme difference between the differentially treated flies (bright fluorescence versus almost no fluorescence) is - in its drastic form- surprising. Online access to the original confocal microscopy images (raw data) might help to convince the reader that these illustrations do not reflect the most drastic "representative" examples out of a series of brain stainings.
• In particular for behavioral experiments, genetic controls should always be conducted. That is, both the heterozygous Gal4-line as well as the heterozygous UAS-line should be used as controls. This is laborious, but important.

Minor comments:

• Line 75: word missing ("...including FEEDING-RELATED BEHAVIOR, courtship, ...").
• Line 120: word missing ("SIFaR expression in adult neurons BUT not glia...").
• I find the figures often to be quite overloaded, and anatomical details often very small (e.g., figure 7A).

Significance

Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide, and contributes to an understanding how motivated behavior is orchestrated by modulatory peptides. The approach to take the entire organism, including peripheral tissue, into consideration, is very good and a rather unique point.

Since decades it has been investigated how sensory stimuli are processed and encoded by the brain, and how behavioral actions are executed. Likewise, principles underlying learning and memory, sleep, orentation, circadian rhythms, etc. are subject to intense investigation. However, how motivational factors (sleep pressure, hunger, sexual drive) are actually "encoded", signaled and finally used to orchstrate behavior and guide decision-making is, to a very large degree, unknown - in any species. The model use here (Drosophila and its peptidergic system wit SIFamide as a central hub) represents actually a ideal entry point to study just this question. In this sense, the manuscript is at the forefront of modern, state-of-the-art neurobiology.

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Referee #1

Evidence, reproducibility and clarity

This manuscript from Zhang et al. primarily investigates the contribution of the SIFa neuropeptide receptor (SIFaR) to mating duration in male fruit flies. Through RNAi-mediated downregulation, they show that SIFaR receptor is necessary for previous experience to alter mating duration. Using cell-specific knockdown and rescue of the SIFaR receptor, they identify a population of ~400 neurons that could underlie this effect. This is still a large number of cells but is narrowed from the ~1,200 total SIFaR-expressing neurons. They then use the GRASP synaptic labeling technique to show that SIFa+ neurons form synapses onto the relevant SIFaR-expressing population, and that the area of synaptic contact is systematically altered depending on the fly's past mating history. Finally, they provide evidence to argue that SIFa neurons act through SIFaR neurons that release the neuropeptide corazonin to regulate mating duration. Overall, the authors have used an impressive array of techniques in their attempt to define the neural circuits and molecules involved in changing internal state to modify the duration of mating.

Major Comments:

1. The authors are to be commended for the sheer quantity of data they have generated, but I was often overwhelmed by the figures, which try to pack too much into the space provided. As a result, it is often unclear what components belong to each panel. Providing more space between each panel would really help.
2. The use of three independent RNAi lines to knock down SIFaR expression is experimentally solid, as the common phenotype observed with all 3 lines supports the conclusion that the SIFaR is important for mating duration choice. However, the authors have not tested whether these lines effectively reduce SIFaR expression, nor whether the GAL80 constructs used to delimit knockdown are able to effectively do so. This makes it hard to make definitive conclusions with these manipulations, especially in the face of negative results. A lack of complete knockdown is suggested by the fact that the F24F06 driver rescues lethality when used to express SIFaR in the B322 mutant background, but does not itself produce lethality when used to express SIFaR RNAi. The authors should either conduct experiments to determine knockdown efficiency or explicitly acknowledge this limitation in drawing conclusions from their experiments. A similar concern relates to the CrzR knockdown experiments (eg Figure 7).
3. Most of the behavioral experiments lack traditional controls, for example flies that contain either the GAL4 or UAS elements alone. The authors should explain their decision to omit these control experiments and provide an argument for why they are not necessary to correctly interpret the data. In this vein, the authors have stated in the methods that stocks were outcrossed at least 3x to Canton-S background, but 3 outcrosses is insufficient to fully control for genetic background.
4. Throughout the manuscript, the authors appear to use a single control condition (sexually naïve flies raised in groups) to compare to both males raised singly and males with previous sexual experience. These control conditions are duplicated in two separate graphs, one for long mating duration and one for short mating duration, but they are given different names (group vs naïve) depending on the graph. If these are actually the same flies, then this should be made clear, and they should be given a consistent name across the different "experiments".
5. The authors have consistently conflated overlap of neuronal processes with synaptic connections. Claims of synaptic connectivity deriving solely from overlap of processes should be tempered and qualified.
   • For example, they say (Lines 201-202) "These findings suggest that SIFa neurons and GAL424F06-positive neurons form more synapses in the VNC than in the brain." This is misleading. Overlap of24F06-LexA>CD8GFP and SIFa-GAL4>CD8RFP tells us nothing about synapse number, or even whether actual synapses are being formed.
   • Lines 210-211: "The overlap of DenMark and syt.EGFP signals was highly enriched in both SOG and ProNm regions, indicating that these regions are where GAL424F06 neurons form interconnected networks". This is misleading. Overlap of DenMark and syt.EGFP does not indicate synapses (especially since these molecules can be expressed outside the expected neuronal compartment if driven at high enough levels).
   • Lines 320-322: "Neurons expressing Crz exhibit robust synaptic connections with SIFaR24F06 neurons located in the PRW region of the SOG in the brain (panels of Brain and SOG in Fig. 5A)". This is again misleading. They are not actually measuring synapses here, but instead looking at area of overlap between neuronal processes of Crz and SIFaR cells.
   • In Figs 3B and S4A, they are claiming that all neuronal processes within a given delineated brain area are synapses. The virtual fly brain and hemibrain resource have a way to actually identify synapses. This should be used in addition to the neuron skeleton. Otherwise, it is misleading to label these as synapses.
   • Furthermore, measuring the area of GRASP signal is not the same as quantifying synapses. We don't know if synapse number changes (eg in lines 240-242).
6. In general, the first part of the manuscript (implicating SIFaR in mating duration) is much stronger than the second part, which attempts to demonstrate that SIFa acts through Crz-expressing neurons to induce its effects. The proof that SIFa acts through Crz-expressing neurons to modify mating duration is tenuous. The most direct evidence of this, achieved via knockdown on Crz in SIFaR-expressing cells, is relegated to supplemental figures. The calcium response of the Crz neurons to SIFa neuron activation (Fig. 6) is more of a lack of a decrease that is observed in controls. Also, this is only done in the VNC. Why not look in the brain, because the authors previously stated a hypothesis that the "transmission of signals through SIFaR in Crz-expressing neurons is limited to the brain" (lines 381-382)?

Furthermore, the authors suggest that Crz acts on cells in the heart to regulate mating duration. It would be useful to add a discussion/speculation as to possible mechanisms for heart cells to regulate mating decisions. Is there evidence of CrzR in the heart? The SCope data presented in Fig. 7I-L and S7G-H is hard to read. 7. In several cases, the effects of being raised single are opposite the effects of sexual experience. For example, in Fig. 4T, calcium activity is increased in the AG following sexual experience, but decreased in flies raised singly. Likewise, Crz-neurons in the OL have increased CaLexA signal in singly-raised flies but reduced signals in flies with previous sexual experience. In some cases, manipulations selectively affect LMD or SMD. It would be useful to discuss these differences and consider the mechanistic implications of these differential changes, when they all result in decreased mating duration. This could help to clarify the big picture of the manuscript.

Minor Comments:

1. For CaLexA experiments (eg Fig 7A-D), signal intensity should be quantified in addition to area covered. Increased intensity would indicate greater calcium activity within a particular set of neurons.
2. In Figure 5K: quantification of cell overlap is missing. In the text they state that there are ~100 neurons that co-express SIFaR24F06 and Crz. How was this determined? Is there a graph or numerical summary of this assertion?
3. In lines 709-711: "Our experience suggests that the relative mating duration differences between naïve and experienced condition and singly reared are always consistent; however, both absolute values and the magnitude of the difference in each strain can vary. So, we always include internal controls for each treatment as suggested by previous studies." I had trouble understanding this section of methods. What is done with the data from the internal controls?
4. Could the authors comment on why the brain GRASP signal is so different in Figures 3A and 4A? I realize that different versions of GRASP were used in these experiments, but I would expect broad agreement between the different approaches.

Significance

This study will be most relevant to researchers interested in understanding neuronal control of behavior. The manuscript offers a conceptual advance in identifying cell types and molecules that influence mating duration decisions. The strength of the manuscript is the number of different assays used; however, there is a sense that this has occurred at the cost of providing a cohesive narrative. The first part of the manuscript (detailing the role of SIFaR in LMD and SMD) is relatively stronger and more conclusive.

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Reply to the reviewers

We want to thank both reviewers for their thorough and constructive review of our manuscript. Below, we have re-iterated their comments followed by an explanation of how we have revised the manuscript to address this.

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

This manuscript presented by Segeren et al. applied an interesting HRASG12V inducible cell model to study the mechanism of cellular resistance to replication stress inducing agents. They also employed a novel reversible fixation technique which allows them to FAC sort cells according to their replication stress levels before applying single cell sequencing analysis to the same cell populations. By comparing cells with low levels of replication stress to cells with high levels of replication stress, they found that reduction in gene expression of FOXM1 target genes potentially protects cells against replication stress induced by CHK1i plus gemcitabine combination. Overall, this is a very interesting study. However, the following points should be addressed prior to publication:

Major: 1. Figure 3E and 3F showed two lists of differentially expressed genes in γH2Ax low cells. However, instead of arbitrarily extracting the FOXM1 target genes and TP53 targeted genes, it would be appreciated if the author could perform an unbiased and unsupervised gene set enrichment analysis such as Enrichr.

As recommended, we performed an enrichment analysis using Enrichr to identify transcriptional programs associated with the we used the genes that were downregulated in the γH2AX-low cells. FOXM1 appeared as a prominent hit in different databases (both experimental and computational). We have included the lists of differentially expressed genes as an additional supplemental table (Table S1) and have included the Enrichr results as Table S3 (i.e. CHEA and ENCODE). We have described our results in lines 198-200 of the revised manuscript.

1. At the experiment design stage, the authors also included HRASG12V status as a test condition because they previously found that HRASG12V mutation induces basal level replication stress and they would like to include this condition to study the adaptation to replication stress (line 110). However, the difference in HRASG12V negative and HRASG12V positive cells was not followed up in the later part of the paper. Can they show lists of differentially expressed genes identified under HRASG12V negative conditions as well (in the same format of Figure 3E and 3F) and comment on the differences as well?

In the original manuscript, we included heatmaps of differentially expressed genes in the control cells in Figure S2. For improved clarity, we have modified this figure so that the heatmaps are labeled "Control cells". In the revised manuscript, we have also included Table S2, which lists the differentially expressed genes between yH2AX low and yH2AX high control cells, and Table S3, which lists the Enrichr results obtained based on these gene lists.

We observed FOXM1 target genes in both the control and HRASG12V cells. Thus, the mechanism we identify does not appear to be specific to oncogenic Ras expression. We discuss this in lines 221-225. Because there were no other notable differences between the gene sets, we do not focus on this in the manuscript.

1. In line 194 and in Figure S2B, the authors claimed that ANLN, HMGB2, CENPE, MKI67, and UBE2C demonstrated co-expression, but other genes displaying similar correlation scores were not commented (such as F3, CYR61, CTGF, etc). To avoid being biased at the analysis stage, the authors should define clearly what the cut-off of correlation score is and why only co-expression of ANLN, HMGB2, CENPE, MKI67, and UBE2C were mentioned.

As suggested, we explain now in the revised manuscript that we focused on gene clusters consisting of at least 3 genes, that had a correlation coefficient greater than or equal to 0.4 with at least one other gene within the clusters. This cutoff is typically defined as representing a "moderate to good" correlation in biological data (Overholser, Sowinski, 2008). To make clear which clusters correlating gene sets passed these criteria, we have also highlighted these genes in Figure S3B. This returned the cluster we had already identified as FOXM1 targets, and as well spotted by the reviewer, a larger cluster which included F3, CYR61, CTGF, SERPINE1, ANKRD1, KRTAP2-3, UGCG, and AMOTL. Our Enrichr analysis did not identify any putative transcription factors linking the genes in this larger cluster. We are still interested to identify the putative transcription regulation mechanism linking these genes in future studies, but this is beyond the scope of the current manuscript. We have described these observations in lines 211-218.

1. In line 215, instead of validating CENPE, UBE2C, HMGB2, ANLN, and MKI67 individually, the authors decided to validate FOXM1 instead, because they believe all the aforementioned genes are targets of FOXM1, therefore, validating FOXM1 alone would suffice. Again, this makes the validation process also biased. CENPE, UBE2C, HMGB2, ANLN, and MKI67 should be validated individually because they might sensitize cells to replication stress via different mechanisms. Besides, if all these genes were identified together because they are FOXM1 target genes, why did the authors not identify FOXM1 itself as a differentially expressed gene from the single cell sequencing? The sequencing only analyzed the S/G2/M cells, expression of FOXM1 should be detected easily.

We agree with the reviewer that the omission of individual FOXM1 target genes in the validation process makes a biased impression. Therefore we ordered siRNAs against CENPE, UBE2C, HMGB2, ANLN, and MKI67. Similar to the other DE genes in the original mini-screen we first knocked down these genes using the siRNA Smartpools (pools of 4 individual siRNAs against each genes). Here, we observed a decrease in γH2AX signal compared to drug-treated cells transfected with all 5 Smartpools compared to drug-treated cells transfected with control siRNA. We next moved on to the deconvolution step of the screen, where we transfected cells with 4 individual siRNA against each gene. Here, we observed inconsistent effects of ANLN, CENPE, and HMGB2 when comparing the individual siRNAs, which all produce efficient knockdown of their target genes. But interestingly, for both MKI67 and UBE2C, each of the 4 individual siRNAs similar decreased yH2AX signal, though it was not as strong as the decrease observed when FOXM1 is knocked out. Understanding the exact mechanism of how MKI67 and UBE2C reduce replication stress is beyond the scope of this paper, but we hypothesize that, as with FOXM1, it is likely linked to their role in promoting progression through the cell cycle. These results are shown in Figures S5, and we mention these remarkable findings in the revised abstract and discuss these in the light of the recent literature in the Discussion section (lines 275-286).

Then, we also addressed the comment about FOXM1 not being changed in the single cell RNA-seq analysis. We could indeed readily detect FOXM1 expression our single-cell RNA sequencing data. The difference in expression did not change significantly in cells sorted according to γH2AX level (Figure 4C). Because FOXM1 is highly regulated post-translationally, we hypothesized that an increase in the (active) protein is correlated to increased replication stress rather than transcript levels. This was indeed the case and we further explain our experiment to test this hypothesis in response to Point #6 (results are displayed in Figure 4D and described in lines 201-209).

1. As pointed out by the author in the Discussion, single cell sequencing is not good at differentiating the causes from the consequences. The author tried to validate many of the differentially expressed genes in γH2Ax low cells. However, the fact that only FOXM1 knockdown passed the validation and deconvolution pointed out that the great majority of the identified genes are not the cause of the sensitivity change to replication stress inducing agents but likely the consequences. Therefore, in Figure S2C and S2D, it would be better that the authors could just name the genes as 'downregulated genes' in Figure S2C and 'upregulated genes' in Figure S2D. Taking into consideration that the expression change in the great majority of these genes are just consequences of sensitivity change to replication stress, defining them as 'potentially sensitizing' genes and 'potentially conferring resistance' genes is rather misleading.

We agree that the way we originally labeled these plots may have been misleading. We have renamed then to "Downregulated in yH2AXlow" and "Upregulated in yH2AXlow", as recommended by the reviewer.

1. To better prove that FOXM1 is the leading cause of the sensitivity to CHK1i+Gemcitabine induced replication stress, can the authors show the FOXM1 expression status in the tolerant cell population identified in Figure 1B (lowest panel)? Alternatively, can they plot FOXM1 expression level in the same tSNE plots shown in Figure 3B to 3D to see whether some of the γH2Ax low populations also show reduced FOXM1 expression?

FOXM1 expression levels were not increased with gH2AXhigh versus gH2AXlow HRASG12V cells in the single cell RNA-sequencing data (Figure 4C in revised manuscript). However, as mentioned in our answer to point #4 we performed an additional experiment, which showed a strong positive correlation between phospho-FOXM1 and γH2AX (as measured by flow cytometry) in S-phase cells (Figure 4D). This indicates that the active form of the FOXM1 indeed increases as yH2AX levels increase, consistent with the observed increase in FOXM1 target genes. These results are described in lines 201-209.

1. Clonogenic survival assay in Figure 4D was not quantified properly in Figure 4E. To rule out the siFOXM1 mediated growth/survival defects and to only focus on the siFOXM1 mediated resistance to CHK1i+Gemcitabine, the survival rate (intensity percent in this case) of CHK1i+Gemcitabine treated condition should be normalized against the survival rate of the Vehicle condition. E.g., the intensity percent of the siSCRAMBLE after treatment should be divided by the intensity percent of the untreated siSCRAMBLE; the intensity percent of the si#1 after treatment should be divided by the intensity percent of the untreated si#1, and so on. If the authors would like to show siFOXM1 induced growth/survival defects, they can still present the left part of the Figure 4E (the Vehicle group).

Originally, we chose to show the absolute IntensityPercent for all groups, without normalizing to the untreated group, because we wanted to also highlight the FOXM1-mediated changes in growth. We agree that normalizing the IntensityPercent of the drug-treated group to the vehicle group better highlights the siFOXM1-mediated resistance. We have therefore re-analyzed the data and presented it this way in Figure 5E (described in lines 293-295). We moved our original Figure 4E to a new supplemental figure (Figure S4B) to still point out the effects of siFOXM1 on cell growth in untreated cells.

Minor:

1. In line 176, the author claimed that 'Interestingly, rare cells treated with CHK1i + gemcitabine are located within the untreated cell cluster (Fig. 3C)'. However, it is not as obvious where these cells are in the plot, especially to people who are new to tSNE plots. It would be appreciated if the authors could label these cells by circling them with red lines and make the point stronger.

Rather than circling these points (we thought this would make the plot too "busy"), we have created an inset that zooms in on the region where we see the untreated cells within the untreated cell cluster. Within the inset, we use arrows to point out the cells we are referring to. This can be seen in our updated Figure 3C.

1. In Figure S2B, it will be ideal to label clearly which genes are upregulated genes and which are downregulate.

On the x-axis of the heatmap, we have drawn lines to separate the downregulated and upregulated genes.

1. In line 50, the word 'multifaced' needs to be corrected to 'multifaceted'.

Thank you for catching this, we have fixed it.

1. It is unclear what 'underly drug resistance' means in line 150.

We have reworded this sentence so that is more clear. It is now written as follows: "we aimed to identify gene-expression programs that mediate the low level of RS in a subset of cells, which could potentially mediate drug resistance". This change is in lines 155.

1. It is advised that the phrase 'cell cycle position' could be changed to 'cell cycle phase' or 'cell cycle stage'.

We purposefully used the phrase "cell cycle position" because we wanted to emphasis gradient-like progress through the cell cycle rather than a discrete distinction from one-phase to the next. We have reworded the text slightly to now say "position within S-phase" (lines 163, 187, 191, 208), since all the cells we are interested in are in S phase, but some are further through S phase than others.

1. In line 185, the word 'in' after 'within' can be removed.

Thank you for catching this, we have fixed it.

1. In line 194, 'Among genes downregulated in γH2AXlow cells, the expression of ANLN, HMGB2, CENPE, MKI67 and UBE2C correlated' is missing an 'are' in front of the word 'correlated'.

Thank you for catching this, we have fixed it.

1. In line 239, Fig.SC3 should be Fig. S3C.

Thank you for catching this, we have fixed it.

1. FOXM1 is known as a crucial gene for G2/M transition. Therefore, FOXM1 knockdown cells are expected to be mostly arrested at the G2/M interface. Therefore, in line 244, it is incorrect to say stronger FOXM1 knockdown induced a 'lower proportion of cells in G2 phase'. In fact, as shown in Figure 4C, cells are accumulating in G2 phase (peaking around 11M on the DAPI axis) and depleted from G1 phase (peaking around 7M).

We have reworded this to say that there is "a higher proportion of cells in S-phase and a less distinct G2 peak" (lines 270-271). The DAPI profiles of the scrambled, siFOXM1 #1, and siFOXM1 #2 conditions all show an S-phase "valley" between a G1 and G2 peak (the valley sits at about 8M-9M). In the siFOXM1 #3 and siFOXM1 #4 conditions, we no longer see this valley, therefore we interpret this as cells still in S-phase. If they had progressed from S-phase into G2 phase, we expect that we would again see this "valley" to the left of a clear G2 peak. In the figure below, we overlayed DNA content histograms of the different FOXM1 targeting siRNAs with the scrambled siRNA to demonstrate this point more clearly.

Reviewer #1 (Significance (Required)):

Advance: The study reported a novel reversible fixation technique which can lead to potentially good citations. However, the findings from the single cell sequencing alone fell short in novelty to reach high impact because FOXM1 has been reported to impact on cellular sensitivity to CHK1 inhibition mediated replication stress (PMC7970065). Moreover, the study did not provide mechanistic explanation to the observed phenotype but only validated the finding from the sequencing, and the gene of focus (FOXM1) was not originally identified from the sequencing, slightly undermining the paper's foundation. To make it a better paper. the authors need to be less biased when it comes to data analysis and interpretation.

Audience: People who are interested in basic research in cell cycle, DNA damage, cancer, chemotherapy would be interested.

My expertise: Cancer, DNA damage, cell cycle

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

Summary:

Replication stress activates ATR and CHEK1 kinases as part of the inter S phase DNA damage response. CHEK1 kinase inhibitors (CHK1i) have been shown to induce an accumulation of unresolved replication stress and widespread DNA damage and cell death caused by replication catastrophe, and are therefore under clinical evaluation. At the same time, CHEK1 inhibition results in the activation of CDK1 and FOXM1 and premature expression of G2/M genes (Saldivar et al., 2018 Science). FOXM1-drivent premature mitosis has been shown to be required for the replication catastrophe and CHK1i sensitivity (Branigan et al., 2021 Cell Rep.). In this study, Segeren and colleagues set out to investigate the mechanisms of replication stress tolerance. They used CHK1i inhibitors in combination with the DNA-damaging chemotherapeutic agent Gemcitabine and oncogenic HRASG12V expression to increase replication stress. The authors utilized an intriguing setup of combined immunofluorescence staining followed by single cell RNA-seq analysis to overcome limitations of bulk cell analyses. In particular, the authors sought to identify genes that are differentially regulated in replication stress-tolerant cells compared to sensitive cells. However, even single cell analyses can be confounded by differences in cell cycle distribution. To mitigate this, the authors selected mid S-phase cells for their analysis. While this may not have completely eliminated minor differences in cell cycle progression, the authors identified FOXM1-regulated G2/M cell cycle genes, among others, that were down-regulated in the tolerant cells. When the authors followed up on the effect of these genes on replication stress tolerance, they identified FOXM1 knockdown as the only robust mediator of replication stress tolerance.

Major comments:

The authors observed that cell cycle distribution could be a major confounding factor in their single cell analysis and attempted to reduce this variation by selecting mid S-phase cells based on the DAPI signal. The authors then chose to compare gH2AXlow and gH2AXhigh subpopulations of RPE-HRASG12V cells because their "DAPI signal was comparable" (line 181-184). However, their data show that these subpopulations also show differences in their DAPI signal distribution, with gH2AXlow cells tending to have lower DAPI signals than gH2AXhigh cells (Supplementary Figure 2A). Thus, the major confounding factor that the authors sought to remove seems to have prevailed and it remains possible that the difference in cell cycle gene expression is merely due to differences in cell cycle progression of the individual cells. Given that DAPI information seem to be readily available for the individual cells, the authors should normalize their analysis to the DAPI signal to remove this potential confounding effect or clearly state this potential limitation.

We agree that indeed it is very challenging to fully disentangle the influence of cell cycle distribution on our analysis. And indeed, the γH2AXlow HRASG12V cells have slightly reduced median DNA content compared to γH2AXmid and γH2AXhigh. However, this was not the case in the RPE control cells, and we still found that FOXM1 target genes were strongly enriched in the γH2AXhigh cells (Fig S2C and Table S4). Therefore, it is highly unlikely that bias in S-phase position distributions does not explain our results. Nevertheless, to be transparent about this write in the Results on lines 192-193 the following: "The other groups all showed similar DAPI intensities, although gH2AXlow RPE-HRASG12V cells showed a slight but statistically significant reduction compared to their gH2AXhigh counterparts (Fig. S2A)".

In our subsequent experiments to assess the relationship between phospho-FOXM1 (representing the transcriptionally active protein) and γH2AX, we observed that though there was a strong correlation between pFOXM1 and γH2AX, there was no correlation between phospho-FOXM1 and DAPI (Figure 4D-E). We therefore would like to point out that although our readout for replication stress inevitably increases as cells progress through DNA replication, heterogeneity in phospho-FOXM1 levels cannot be explained by position in S-phase. These results are described in lines 203-209.

Finally, we do not think it would be statistically appropriate to use the DAPI signal (generated by fluorescence intensity as measured by the flow cytometer) as a normalization factor for our gene expression data.

Minor comments:

The findings of Saldivar et al., 2018 Science and Branigan et al., 2021 Cell Rep. should be mentioned in the introduction.

As recommended, we mentioned both these papers in the introduction. In line 62, we cite the Branigan paper as showing that modulation of cell cycle regulators is a strategy used by cancer cells to resist replication stress. In lines 63-65, we reference them as follows: "The RS response is tightly linked with cell cycle progression, as multiple intra S-phase checkpoint kinases play a role in curtailing proteins involved in the S-G2 transition (Branigan et al., 2021, Saldivar et al., 2018)."

The authors conclude that "cell cycle position can be a major confounding factor when evaluating the transcriptomic response to RS." It should be noted that stochastic differences in the cell cycle distribution of bulk cells are perhaps the best-known confounder in single cell analyses (see, for example, Buettner et al., 2015 Nat. Biotechnol.).

We chose to reference the Buettner paper to justify our decision to select only cycling cells in our scRNA seq approach. Our reference to the paper, and to the fact that cell cycle distribution is a major confounder in single cell analysis, is in lines 138-140.

Supplementary Figure 2A: The median should be added to the violin plots.

As suggested, we have added medians to the violin plots. In addition, we added details on statistical analysis.

The statement "Differential expression analysis revealed 19 genes that were significantly downregulated in gH2AXlow RPE-HRASG12V cells, suggesting that elevated levels of these genes are correlated with sensitivity to RS-inducing drugs" refers to Figure 3E and Table S1. However, Table S1 lists the "key resources" and does not seem to be related to this statement. A table showing log2fold-changes and FDR values should be added and referenced here.

We have generated tables with the fold change values of differentially expressed genes between the yH2AX low and yH2AX high cells. These are found in Table S1 (for HRAS G12V cells) and Table S2 (for Control cells) in the supplementary file of the revised manuscript. The "key resources" has been moved to Table S5.

The statement "Remarkably, Braningan and co-workers observed no effect of full FOXM1 deletion on cell cycle progression" seems somewhat inconsistent with what has been stated and assessed in that study. The authors may want to replace "progression" with "distribution". A reduction in proliferation is commonly observed when FOXM1 levels are reduced.

In addition, the authors may want to consider that their addition of HRASG12V and Gemcitabine may contribute to a more substantial S phase checkpoint response.

We agree with the reviewer that a reduction in proliferation is commonly observed when FOXM1 levels are reduced (Barger et al., 2021, Cheng et al., 2022, Yang et al., 2015, Wu et al., 2010), but in Branigan et al., they see no decrease in proliferation with knockout of FOXM1. They state "There were no apparent differences in the growth rate of the LIN54 and FOXM1 KO versus EV cells over 10 days (Figure 1G)". Though they do not elaborate on why they see this unexpected response, we suspect a permanent full knockout of FOXM1 could cause compensatory adaptation in their cell lines. In our experiments, we perform transient knockdowns, so cells may not have the time to adapt to the loss of FOXM1 and obtain compensatory mechanisms that would allow them to continue cycling as rapidly as control cells treated with non-targeting siRNA.

However, we decided to remove this from the Discussion section, as it seemed to interrupt the discussion about the potential mechanisms underlying protection against DNA damage by FOXM1 depletion.

The statement that "the mechanism by which high FOXM1 activity is a prerequisite to accumulate DNA damage in S-phase during CHK1 inhibition remains to be uncovered" seems to neglect that premature mitosis has been suggested as a mechanistic cause (Branigan et al., 2021 Cell Rep.). It would be helpful if the authors could elaborate on this.

In our discussion, we do already emphasize the described role of FOXM1 in promoting premature mitosis (lines 330-337), but we argue that in our experimental conditions we are observing another - previously undescribed- role for FOXM1 in promoting replication stress during S phase. We previously observed with live cell imaging that CHK1i + gemcitabine does not cause premature mitosis in RPE-HRASG12V cells (published in Segeren et al. Oncogene 2022, Figure 5). Instead, these cells typically showed a cell cycle exit from G2. This makes it highly unlikely that premature mitosis is the reason why these cells would accumulate excessive DNA damage. We realize now that it was an important omission not to elaborate on this and have added this clarification to the Discussion (lines 341-345 in revised manuscript). In addition, we have removed a few lines of less important text (about the lack of direct effect of FOXM1 KO in the Branigan paper; see answer to previous point) to improve clarity and readability.

Reviewer #2 (Significance (Required)):

General assessment: The strength of the study is the intriguing methodology of combined immunofluorescence followed by single cell RNA-seq. The limitations are that this methodology does not seem to fully solve the stated problems. In addition, the study is essentially limited to confirming previous findings.

Advance: The study strengthens current knowledge but provides essentially no advance. The authors confirm existing knowledge with an additional approach. While this is not an advance in itself, it is important to the community.

Audience: I felt that the study would appeal to a basic science audience. In particular, the CHK1i and intra S-phase checkpoint areas, with limited interest beyond that.

My relevant expertise lies in transcriptomics, gene regulation and the cell cycle.

Reference list

Barger, C.J., Chee, L., Albahrani, M., Munoz-Trujillo, C., Boghean, L., Branick, C., Odunsi, K., Drapkin, R., Zou, L. & Karpf, A.R. 2021, "Co-regulation and function of FOXM1/RHNO1 bidirectional genes in cancer", eLife, vol. 10, pp. 10.7554/eLife.55070.

Branigan, T.B., Kozono, D., Schade, A.E., Deraska, P., Rivas, H.G., Sambel, L., Reavis, H.D., Shapiro, G.I., D'Andrea, A.D. & DeCaprio, J.A. 2021, "MMB-FOXM1-driven premature mitosis is required for CHK1 inhibitor sensitivity", Cell reports, vol. 34, no. 9, pp. 108808.

Cheng, Y., Sun, F., Thornton, K., Jing, X., Dong, J., Yun, G., Pisano, M., Zhan, F., Kim, S.H., Katzenellenbogen, J.A., Katzenellenbogen, B.S., Hari, P. & Janz, S. 2022, "FOXM1 regulates glycolysis and energy production in multiple myeloma", Oncogene, vol. 41, no. 32, pp. 3899-3911.

Overholser, B.R. & Sowinski, K.M. 2008, "Biostatistics primer: part 2", Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition, vol. 23, no. 1, pp. 76-84.

Saldivar, J.C., Hamperl, S., Bocek, M.J., Chung, M., Bass, T.E., Cisneros-Soberanis, F., Samejima, K., Xie, L., Paulson, J.R., Earnshaw, W.C., Cortez, D., Meyer, T. & Cimprich, K.A. 2018, "An intrinsic S/G(2) checkpoint enforced by ATR", Science (New York, N.Y.), vol. 361, no. 6404, pp. 806-810.

Segeren, H.A., van Liere, E.A., Riemers, F.M., de Bruin, A. & Westendorp, B. 2022, "Oncogenic RAS sensitizes cells to drug-induced replication stress via transcriptional silencing of P53", Oncogene, vol. 41, no. 19, pp. 2719-2733.

Wu, Q., Liu, C., Tai, M., Liu, D., Lei, L., Wang, R., Tian, M. & Lu, Y. 2010, "Knockdown of FoxM1 by siRNA interference decreases cell proliferation, induces cell cycle arrest and inhibits cell invasion in MHCC-97H cells in vitro", Acta Pharmacologica Sinica, vol. 31, no. 3, pp. 361-366.

Yang, K., Jiang, L., Hu, Y., Yu, J., Chen, H., Yao, Y. & Zhu, X. 2015, "Short hairpin RNA- mediated gene knockdown of FOXM1 inhibits the proliferation and metastasis of human colon cancer cells through reversal of epithelial-to-mesenchymal transformation", Journal of experimental & clinical cancer research : CR, vol. 34, no. 1, pp. 40-1.

We want to thank both reviewers for their thorough and constructive review of our manuscript. Below, we have re-iterated their comments followed by an explanation of how we have revised the manuscript to address this.

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:

Replication stress activates ATR and CHEK1 kinases as part of the inter S phase DNA damage response. CHEK1 kinase inhibitors (CHK1i) have been shown to induce an accumulation of unresolved replication stress and widespread DNA damage and cell death caused by replication catastrophe, and are therefore under clinical evaluation. At the same time, CHEK1 inhibition results in the activation of CDK1 and FOXM1 and premature expression of G2/M genes (Saldivar et al., 2018 Science). FOXM1-drivent premature mitosis has been shown to be required for the replication catastrophe and CHK1i sensitivity (Branigan et al., 2021 Cell Rep.). In this study, Segeren and colleagues set out to investigate the mechanisms of replication stress tolerance. They used CHK1i inhibitors in combination with the DNA-damaging chemotherapeutic agent Gemcitabine and oncogenic HRASG12V expression to increase replication stress. The authors utilized an intriguing setup of combined immunofluorescence staining followed by single cell RNA-seq analysis to overcome limitations of bulk cell analyses. In particular, the authors sought to identify genes that are differentially regulated in replication replication stress-tolerant cells compared to sensitive cells. However, even single cell analyses can be confounded by differences in cell cycle distribution. To mitigate this, the authors selected mid S-phase cells for their analysis. While this may not have completely eliminated minor differences in cell cycle progression, the authors identified FOXM1-regulated G2/M cell cycle genes, among others, that were down-regulated in the tolerant cells. When the authors followed up on the effect of these genes on replication stress tolerance, they identified FOXM1 knockdown as the only robust mediator of replication stress tolerance.

Major comments:

The authors observed that cell cycle distribution could be a major confounding factor in their single cell analysis and attempted to reduce this variation by selecting mid S-phase cells based on the DAPI signal. The authors then chose to compare gH2AXlow and gH2AXhigh subpopulations of RPE-HRASG12V cells because their "DAPI signal was comparable" (line 181-184). However, their data show that these subpopulations also show differences in their DAPI signal distribution, with gH2AXlow cells tending to have lower DAPI signals than gH2AXhigh cells (Supplementary Figure 2A). Thus, the major confounding factor that the authors sought to remove seems to have prevailed and it remains possible that the difference in cell cycle gene expression is merely due to differences in cell cycle progression of the individual cells. Given that DAPI information seem to be readily available for the individual cells, the authors should normalize their analysis to the DAPI signal to remove this potential confounding effect or clearly state this potential limitation.

Minor comments:

The findings of Saldivar et al., 2018 Science and Branigan et al., 2021 Cell Rep. should be mentioned in the introduction.

The authors conclude that "cell cycle position can be a major confounding factor when evaluating the transcriptomic response to RS." It should be noted that stochastic differences in the cell cycle distribution of bulk cells are perhaps the best-known confounder in single cell analyses (see, for example, Buettner et al., 2015 Nat. Biotechnol.).

Supplementary Figure 2A: The median should be added to the violin plots.

The statement "Differential expression analysis revealed 19 genes that were significantly downregulated in gH2AXlow RPE-HRASG12V cells, suggesting that elevated levels of these genes are correlated with sensitivity to RS-inducing drugs" refers to Figure 3E and Table S1. However, Table S1 lists the "key resources" and does not seem to be related to this statement. A table showing log2fold-changes and FDR values should be added and referenced here.

The statement "Remarkably, Braningan and co-workers observed no effect of full FOXM1 deletion on cell cycle progression" seems somewhat inconsistent with what has been stated and assessed in that study. The authors may want to replace "progression" with "distribution". A reduction in proliferation is commonly observed when FOXM1 levels are reduced. In addition, the authors may want to consider that their addition of HRASG12V and Gemcitabine may contribute to a more substantial S phase checkpoint response.

The statement that "the mechanism by which high FOXM1 activity is a prerequisite to accumulate DNA damage in S-phase during CHK1 inhibition remains to be uncovered" seems to neglect that premature mitosis has been suggested as a mechanistic cause (Branigan et al., 2021 Cell Rep.). It would be helpful if the authors could elaborate on this.

Significance

General assessment: The strength of the study is the intriguing methodology of combined immunofluorescence followed by single cell RNA-seq. The limitations are that this methodology does not seem to fully solve the stated problems. In addition, the study is essentially limited to confirming previous findings.

Advance: The study strengthens current knowledge but provides essentially no advance. The authors confirm existing knowledge with an additional approach. While this is not an advance in itself, it is important to the community.

Audience: I felt that the study would appeal to a basic science audience. In particular, the CHK1i and intra S-phase checkpoint areas, with limited interest beyond that.

My relevant expertise lies in transcriptomics, gene regulation and the cell cycle.

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

This manuscript presented by Segeren et al. applied an interesting HRASG12V inducible cell model to study the mechanism of cellular resistance to replication stress inducing agents. They also employed a novel reversible fixation technique which allows them to FAC sort cells according to their replication stress levels before applying single cell sequencing analysis to the same cell populations. By comparing cells with low levels of replication stress to cells with high levels of replication stress, they found that reduction in gene expression of FOXM1 target genes potentially protects cells against replication stress induced by CHK1i plus gemcitabine combination.

Overall, this is a very interesting study. However, the following points should be addressed prior to publication:

Major:

1. Figure 3E and 3F showed two lists of differentially expressed genes in γH2Ax low cells. However, instead of arbitrarily extracting the FOXM1 target genes and TP53 targeted genes, it would be appreciated if the author could perform an unbiased and unsupervised gene set enrichment analysis such as Enrichr.
2. At the experiment design stage, the authors also included HRASG12V status as a test condition because they previously found that HRASG12V mutation induces basal level replication stress and they would like to include this condition to study the adaptation to replication stress (line 110). However, the difference in HRASG12V negative and HRASG12V positive cells was not followed up in the later part of the paper. Can they show lists of differentially expressed genes identified under HRASG12V negative conditions as well (in the same format of Figure 3E and 3F) and comment on the differences as well?
3. In line 194 and in Figure S2B, the authors claimed that ANLN, HMGB2, CENPE, MKI67, and UBE2C demonstrated co-expression, but other genes displaying similar correlation scores were not commented (such as F3, CYR61, CTGF, etc). To avoid being biased at the analysis stage, the authors should define clearly what the cut-off of correlation score is and why only co-expression of ANLN, HMGB2, CENPE, MKI67, and UBE2C were mentioned.
4. In line 215, instead of validating CENPE, UBE2C, HMGB2, ANLN, and MKI67 individually, the authors decided to validate FOXM1 instead, because they believe all the aforementioned genes are targets of FOXM1, therefore, validating FOXM1 alone would suffice. Again, this makes the validation process also biased. CENPE, UBE2C, HMGB2, ANLN, and MKI67 should be validated individually because they might sensitize cells to replication stress via different mechanisms. Besides, if all these genes were identified together because they are FOXM1 target genes, why did the authors not identify FOXM1 itself as a differentially expressed gene from the single cell sequencing? The sequencing only analyzed the S/G2/M cells, expression of FOXM1 should be detected easily.
5. As pointed out by the author in the Discussion, single cell sequencing is not good at differentiating the causes from the consequences. The author tried to validate many of the differentially expressed genes in γH2Ax low cells. However, the fact that only FOXM1 knockdown passed the validation and deconvolution pointed out that the great majority of the identified genes are not the cause of the sensitivity change to replication stress inducing agents but likely the consequences. Therefore, in Figure S2C and S2D, it would be better that the authors could just name the genes as 'downregulated genes' in Figure S2C and 'upregulated genes' in Figure S2D. Taking into consideration that the expression change in the great majority of these genes are just consequences of sensitivity change to replication stress, defining them as 'potentially sensitizing' genes and 'potentially conferring resistance' genes is rather misleading.
6. To better prove that FOXM1 is the leading cause of the sensitivity to CHK1i+Gemcitabine induced replication stress, can the authors show the FOXM1 expression status in the tolerant cell population identified in Figure 1B (lowest panel)? Alternatively, can they plot FOXM1 expression level in the same tSNE plots shown in Figure 3B to 3D to see whether some of the γH2Ax low populations also show reduced FOXM1 expression?
7. clonogenic survival assay in Figure 4D was not quantified properly in Figure 4E. To rule out the siFOXM1 mediated growth/survival defects and to only focus on the siFOXM1 mediated resistance to CHK1i+Gemcitabine, the survival rate (intensity percent in this case) of CHK1i+Gemcitabine treated condition should be normalized against the survival rate of the Vehicle condition. E.g., the intensity percent of the siSCRAMBLE after treatment should be divided by the intensity percent of the untreated siSCRAMBLE; the intensity percent of the si#1 after treatment should be divided by the intensity percent of the untreated si#1, and so on. If the authors would like to show siFOXM1 induced growth/survival defects, they can still present the left part of the Figure 4E (the Vehicle group).

Minor:

1. In line 176, the author claimed that 'Interestingly, rare cells treated with CHK1i + gemcitabine are located within the untreated cell cluster (Fig. 3C)'. However, it is not as obvious where these cells are in the plot, especially to people who are new to tSNE plots. It would be appreciated if the authors could label these cells by circling them with red lines and make the point stronger.
2. In Figure S2B, it will be ideal to label clearly which genes are upregulated genes and which are downregulate.
3. In line 50, the word 'multifaced' needs to be corrected to 'multifaceted'.
4. It is unclear what 'underly drug resistance' means in line 150.
5. It is advised that the phrase 'cell cycle position' could be changed to 'cell cycle phase' or 'cell cycle stage'.
6. In line 185, the word 'in' after 'within' can be removed.
7. In line 194, 'Among genes downregulated in γH2AXlow cells, the expression of ANLN, HMGB2, CENPE, MKI67 and UBE2C correlated' is missing an 'are' in front of the word 'correlated'.
8. In line 239, Fig.SC3 should be Fig. S3C.
9. FOXM1 is known as a crucial gene for G2/M transition. Therefore, FOXM1 knockdown cells are expected to be mostly arrested at the G2/M interface. Therefore, in line 244, it is incorrect to say stronger FOXM1 knockdown induced a 'lower proportion of cells in G2 phase'. In fact, as shown in Figure 4C, cells are accumulating in G2 phase (peaking around 11M on the DAPI axis) and depleted from G1 phase (peaking around 7M).

Significance

Advance:

The study reported a novel reversible fixation technique which can lead to potentially good citations. However, the findings from the single cell sequencing alone fell short in novelty to reach high impact because FOXM1 has been reported to impact on cellular sensitivity to CHK1 inhibition mediated replication stress (PMC7970065). Moreover, the study did not provide mechanistic explanation to the observed phenotype but only validated the finding from the sequencing, and the gene of focus (FOXM1) was not originally identified from the sequencing, slightly undermining the paper's foundation. To make it a better paper. the authors need to be less biased when it comes to data analysis and interpretation.

Audience:

People who are interested in basic research in cell cycle, DNA damage, cancer, chemotherapy would be interested.

My expertise:

Cancer, DNA damage, cell cycle

https://www.liberation.fr/international/amerique/pour-assurer-la-domination-energetique-des-etats-unis-sur-le-monde-trump-sappuie-sur-lindustrie-des-fossiles-20241203_DYBODLTQ4BD3XMQUEHNPLS3LIM/

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The work from Petazzi et al. aimed at identifying novel factors supporting the differentiation of human hematopoietic progenitors from induced pluripotent stem cells (iPSCs). The authors developed an inducible CRISPR-mediated activation strategy (iCRISPRa) to test the impact of newly identified candidate factors on the generation of hematopoietic progenitors in vitro. They first compared previously published transcriptomic data of iPSCderived hemato-endothelial populations with cells isolated ex vivo from the aorta-gonadmesonephros (AGM) region of the human embryo and they identified 9 transcription factors expressed in the aortic hemogenic endothelium that were poorly expressed in the in vitro differentiated cells. They then tested the activation of these candidate factors in an iPSCbased culture system supporting the differentiation of hematopoietic progenitors in vitro. They found that the IGF binding protein 2 (IGFBP2) was the most upregulated gene in arterial endothelium after activation and they demonstrated that IGFBP2 promotes the generation of functional hematopoietic progenitors in vitro.

Strengths:

The authors developed an extremely useful doxycycline-inducible system to activate the expression of specific candidate genes in human iPSC. This approach allows us to simultaneously test the impact of 9 different transcription factors on in vitro differentiation of hematopoietic cells, and the system appears to be very versatile and applicable to a broad variety of studies.

The system was extensively validated for the expression of 1 transcription factor (RUNX1) in both HeLa cells and human iPSC, and a detailed characterization of this test experiment was provided.

The authors exhaustively demonstrated the role of IGFBP2 in promoting the generation of functional hematopoietic progenitors in vitro from iPSCs. Even though the use of IGFBP2interacting proteins IGF1 and IGF2 have been previously reported in human iPSC-derived hematopoietic differentiation in vitro (Ditadi and Sturgeon, Methods 2016; Ng et al., Nature Biotechnology 2016), and IGFBP-2 itself has been shown to promote adult HSC expansion ex vivo (Zhang et al., Blood 2008), its role on supporting in vitro hematopoiesis was demonstrated here for the first time.

Weaknesses:

Although the authors performed a very thorough characterization of the system in proof-ofprinciple experiments activating a single transcription factor, the data provided when 9 independent factors were used is not sufficient to fully validate the experimental strategy. Indeed, in the current version of the manuscript, it is not clear whether the results presented in both the scRNAseq analysis and the functional assays are the consequence of the simultaneous activation of all 9 TF or just a subset of them. This is essential to establish whether all the proposed factors play a role during embryonic hematopoiesis, and a more complete analysis of the scRNAseq dataset could help clarify this aspect.

Similarly, the data presented in the manuscript are not sufficient to clarify at what stage of the endothelial-to-hematopoietic transition (EHT) the TF activation has an impact. Indeed, even though the overall increase of functional hematopoietic progenitors is fully demonstrated, the assays proposed in the manuscript do not clarify whether this is due to a specific effect at the endothelial level or to an increased proliferation rate of the generated hematopoietic progenitors. Similar conclusions can be applied to the functional validation of IGFBP2 in vitro.

The overall conclusions are sometimes vague and not always supported by the data. For instance, the authors state that the CRISPR activation strategy resulted in transcriptional remodeling and a steer in cell identity, but they do not specify which cell types are involved and at what level of the EHT process this is happening. In the discussion, the authors also claim that they provided evidence to support that RUNX1T1 could regulate IGFBP2 expression. However, this is exclusively based on the enrichment of RUNX1T1 gRNA in cells expressing higher levels of IGFBP2 and it does not demonstrate any direct or indirect association of the two factors.

We thank the reviewer for the positive comments about the importance of our work and have now addressed the points raised as weaknesses by performing additional analysis and experiments, adding a new schematic of the mechanism, and rewording our claims.

We have clarified the different effects mediated by the activation and the IGFBP2 addition in a summary section at the end of the results and added Figure 6, showing this in visual form. We have also clearly stated the limitations related to the correlation between RUNX1T1 and IGFBP2 in the discussion and toned down our claims regarding this throughout the entire paper. We have also reworded the text to clarify the specific cell types identified in the sequencing data that we refer to.

Reviewer #2 (Public Review):

To enable robust production of hematopoietic progenitors in-vitro, Petazzi et al examined the role of transcription factors in the arterial hemogenic endothelium. They use IGFBP2 as a candidate gene to increase the directed differentiation of iPSCs into hematopoietic progenitors. They have established a novel induced-CRISPR mediated activation strategy to drive the expression of multiple endogenous transcription factors and show enhanced production of hematopoietic progenitors through expansion of the arterial endothelial cells. Further, upregulation of IGFBP2 in the arterial cells facilitates the metabolic switch from glycolysis to oxidative phosphorylation, inducing hematopoietic differentiation. While the overall study and resources generated are good, assertions in the manuscript are not entirely supported by the experimental data and some claims need further experimental validation.

We thank the reviewer for the positive comments, and we have provided new data and analysis to make sure that all our assertations are clearly supported and also reworded those where limitations were identified by the reviewers.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

The assessment could change from "incomplete" to "solid" if the authors: i) improve data analysis (for both scRNAseq and functional assays) by providing additional information that could strengthen their conclusions, as suggested in the specific comments by both reviewers; ii) either provide new functional evidence supporting their mechanistic conclusion or alternatively tone down the claims that are not fully supported by data and acknowledge the limitations raised by reviewers in the discussion; (iii) the issue of paracrine signaling to expand only hematopoietic progenitors needs to be addressed.

We have now improved the data analysis and provided additional functional tests to strengthen our conclusions and toned down those that were identified by the reviewers as not supported enough and included a discussion on these limitations. We have also reworded the section about the paracrine signaling throughout the paper.

Reviewer #1 (Recommendations For The Authors):

Figure 1 contains exclusively published data. It might be more appropriate to use it as a supplementary figure or as part of a more exhaustive figure (maybe combining Figures 1 and 2 together?).

Figure 1 contained novel bioinformatic analyses that represent the base of our research and it has a different content and focus to figure 2, which is already a large figure. We therefore believe it is better to keep it as a separate figure, containing a new panel now too. 

It seems there is an issue with Figure S3 labelling:

• In line 112, Figure S2A-B does not display genomic PCR and sequencing results;

• In line 123, Figure S3D-E does not show viability and proliferation data;

• In line 127, Figure S3G does not show mCherry expression in response to DOX;

We apologies for the confusion with the numbers, we have now correctly labelled the figures.

It would be more informative to include gates and frequency on flow cytometry plots in Figure S3, to be able to evaluate the extent of the reduction in mCherry expression.

We have now included the gating and frequency of mCherry-expressing cells in Supplementary Figure 3D.

It is not clear from the text and figures whether the SB treatment was maintained throughout the hematopoietic differentiation protocol (line 122):

• If so, it would be important to confirm that HDAC treatment does not affect EHT cultures

• If not, can the authors provide some evidence that transgene silencing is not occurring during hematopoietic differentiation?

We have clarified that we decided to treat the cells with SB exclusively in maintenance condihons because HDACs have been shown to be essenhal for the EHT (lines 138-142). We have now also included addihonal data showing the high expression of the mCherry tag reporhng the iSAM expression on day 8 (Supplementary Figure 4F).

Can the authors provide a simple diagram summarizing the experimental strategy for each differentiation experiment in the respective supplementary figure? For instance, at what stage of the protocol was DOX added in Figure 3? Or at what stage IGFBP2 was added in Figure 5? It would be a very useful addition to the interpretation of the results.

We have now included three schemahcs for all the experiments in the manuscript in supplementary figure 4 A-C.

In Figure 3, the authors should provide more detailed information about the data filtering of the scRNAseq experiment, and more specifically:

• How many cells were included in the analysis for each library after QC and filtering?

• How "cells in which the gRNAs expression was detected" were selected? Do they include only cells showing expression of gRNAs for all 9 TF?

This informahon is now included in the method sechon lines 773-781; the detailed code is available on the GitHub link provided in the same sechon. We have filtered the cells expressing one gRNA for the non-targehng gRNA (iSAM_NT) control and more than one for the iSAM_AGM sample. 

In Figure 3A, it is not clear whether the expression of the 9 factors is consistently detected in all cells or just a subset of them, and the heatmap in Figure 3A does not provide this information. It would be more accurate to provide expression on a per-cell basis, for instance, as a violin plot displaying single dots representing each cell. 

We have now included this violin plot in Supplementary Figure 4G as requested. However, this visualisation is difficult to interpret because some of the target genes’ expression seems variable in both experimental and control conditions. We had envisaged that this could have been the case and so this is why we had included the three different controls.  For this reason we chose to show the normalised expression which takes all the different variables into account (Figure 3A). 

In Figure 3B-C, it seems that clusters EHT1 and EHT2 do not express endothelial markers anymore. Are these fully differentiated hematopoietic cells rather than cells undergoing EHT? In general, it would be quite important to provide evidence of expressed marker genes characterizing each cluster (eg. heatmap summarizing top DEG in the supplementary figure?). 

We have now provided a spreadsheet containing the clusters’ markers that we used in

Supplementary Table 1) a heatmap in Figure 3E. Furthermor,e we have now edited Figure 3C to include Pan Endothelial markers (PECAM1 and CDH5). These data show that the EHT1 and EHT2 cluster both express endothelial markers but are progressively downregulated as expected during endothelial to hematopoietic transition. We have also included and discussed this in the manuscript lines 192-195 and a schematic for the mechanism in Figure 6.

In Figure 3E, displaying the proportion of clusters within each sample/library would be a more accurate way of comparing the cell types present in each library (removing potential bias introduced by loading different numbers of cells in each sample).

We have now included the requested data in Supplementary Figure 4I and it confirms again the expansion of arterial cells in the activated cells.    

In Figure 3G, by plating 20,000 total CD34+, the assay does not account for potential differences in sample composition. It is then hard to discriminate between the increased number of progenitors in the input or an enhanced ability of HE to undergo EHT. This is an important aspect to consider to precisely identify at what level the activation of the 9 factors is acting. A proper quantification of flow cytometry data summarizing the % of progenitors, arterial cells, etc. would be useful to interpret these results.

Lines 204-205 reworded. We are very much aware of the fact that the CD34+ cell population consists of a range of cells across the EHT process and this is precisely why we carried out this single cell sequencing analyses.  We purposely tested the effect of the observed changes in composition by colony assays

In Figure 3G, it seems that NT cells w/o DOX have very little CFU potential (if any). Can the authors provide an explanation for this?

We think that the limited CFU potential is due to the extensive genetic manipulation and selection that the cells underwent for the derivation of all the iSAM lines but this did not impede us from observing an effect of gene activation on CFU numbers. This is one of the primary reasons that we then validated our overall findings using the parental iPSC line in control condition and with the addition of IGFBP2. We show that the parental iPSC line gives rise to hematopoietic progenitor, both immunophenotypically (Figure 4D) and functionally, at expected levels (Figure 4B left column).

Figure 4A shows an upregulation of IGFBP2 in arterial cells as a result of TF activation. However, from the data presented here, it is not possible to evaluate whether this is specific to the arterial cluster, or it is a common effect shared by all cell types regardless of their identity. 

Data has now been included in Supplementary Figure 4H, which shows that all the cells show an increase in IGFBP2, but arterial cells show the highest increase. We have now edited the text to reflect this, in lines 228-230.

In Figure 5A-B only a minority of arterial cells express RUNX1 in response to IGFBP2 treatment. Is this sufficient to explain the very significant increase in the generation of functional hematopoietic progenitors described in Figure 4? Quantification and statistical analysis of RUNX1 upregulation would strengthen this conclusion.

We have now provided the statistical analysis showing significant upregulation of RUNX1 upon IGFBP2 addition. The p values are now provided in the figure 5 legend.

In Figure 5 the authors conclude that IGFBP2 remodels the metabolic profile of endothelial cells. However, it is not clear which cell types and clusters were included in the analysis of Figure 5C-G. Is the switch from Glycolysis to Oxidative Phosphorylation specific to endothelial cells? Or it is a more general effect on the entire culture, including hematopoietic cells? 

We based this conclusion on the fact that the single-cell RNAseq allows to verify that the metabolic differences are obtained in the endothelial cells. Given that we sorted the adherent cells, the majority of these are endothelial cells as shown in Figure 5A. The Seahorse pipeline includes a number of washing steps resulting in the analyses being performed on the adherent compartment which we know consists primarily of endothelial cells. We cannot exclude some contamination from non-endothelial cells but we highlight to this reviewer that the initial observation of the metabolic changes was identified in endothelial cells in the single cell sequencing data. Taken together, we believe that this implies that metabolic changes are specific to this population. We have clarified this in the line 317.

In the discussion, the authors conclude that they "provide evidence to support the hypothesis that RUNX1T1 could regulate IGFBP2 expression". To further support this conclusion, the authors could provide a correlation analysis of the expression of the two genes in the cell type of interest. 

Following the observation of the IGFBP2 high expression across clusters, we have now reworded this sentence in lines 382-385  We have tried to perform the correlation analysis but we believe this not to be appropriate due to the detection level of the gRNA, we have now included this as a limitation point in the discussion lines 416-427, and also toned down the conclusion we did draw about RUNX1T1 throughout the whole manuscript.

As mentioned by the authors, IGFBP2 binds IGF1 and IGF2 modulating their function. Both IGF1 (http://dx.doi.org/10.1016/j.ymeth.2015.10.001) and IGF2 (doi:10.1038/nbt.3702) have been used in iPSC differentiation into definitive hematopoietic cells. It would be relevant to discuss/reference this in the discussion.

We have now included the suggested reference in the section where we discuss the role of IGFBP2 in binding IGF1 and IGF2.

Reviewer #2 (Recommendations For The Authors):

(1) Figure 1 compares the transcriptome of human AGM and in-vitro derived hemogenic endothelial cells (HECs). It is not clear why only the genes downregulated in the latter were chosen. Are there any significantly upregulated genes, knockdown/knockout which could also serve a similar purpose? Single-cell transcriptome database analysis is very preliminary. A detailed panel with differences in cluster properties of HECs between the two systems should be provided. A heatmap of all differentially expressed genes between the two samples must be generated, along with a logical explanation for choosing the given set of genes. 

We have now included another panel in figure 1 to better clarify the logic behind the strategy used to identify our target genes (Figure 1A).

(2) Figure 2 - a panel describing the workflow of gRNA design and targeting for the 9 candidate genes, along with lentiviral packaging and transduction would make it easier to follow. 

We have now included three schematics for all the experiments in the manuscript in supplementary figure 4 A-C. 

(3) Figure 3- to assess the effect of arterial cell expansion on the emergence of hematopoietic progenitors, CD34+ Dll4+ cells should be sorted for OP9 co-culture assay.

Using only CD34+ cells does not answer the question raised. Also, the CFU assay performed does not fully support the claim of enhanced hematopoietic differentiation since only CFU-E and CFU-GM colonies are increased in Dox-treated samples, with no effect on other colony types. OP9 co-culture assay with these cells would be required to strengthen this claim. 

We wanted to clarify that the effect on the methylcellulose coming from the activated cells was not limited to CFU-E, as the reviewer reported; instead, it also affected CFU-GM and CFU-M. 

We have now performed additional experiments where we sorted the CD34+ compartment into DLL4- and DLL4+ in Supplementary Figure 5D-E, which we discussed in lines 250-258. 

(4) In Figure 3F, there appears to be a lot of variation in the DLL4% fold change values for

DOX treated iSAM_AGM sample, which weakens the claim of increased arterial expansion.

Can the authors explain the probable reason? It is suggested that the two other controls (iSAM_+DOX and iSAM_-DOX) should be included in this analysis. It is imperative to also show % populations rather than just fold change to gain confidence.

We agree that there is a lot of variability. That is because differentiation happens in 3D in embryoid bodies, which contain many different cell types that differentiate in different proportions across independent experiments. We have now included the raw data in Supplementary Figure 4 D, with additional statistical analysis to show the expansion of arterial cells including also the suggested additional controls.

(5) How does activation of these target genes cause increased arterialization? Is the emergence of non-HE populations suppressed? Or is it specific to the HE? The data on this should be clarified and also discussed. ANTO/Lesley text

We have provided additional data clarifying the connection between increased arterialisation and hemogenic potential. We showed that the activation induces increased arterialisation and that IGFBP2 acts by supporting the acquisition of hemogenic potential. We have discussed this in lines 326-348 and provided a new figure to explain this in detail (figure 6)

(6) Considering that IGFBP2 was chosen from the activated target gene(s) cluster, can the authors explain why the reduced CFU-M phenomenon observed in Figure 3G does not appear in the MethoCult assay for IGFBP2 treated cells (Figure 4B)?

The difference could be explained by the fact that in Figure 3G, the cells underwent activation of multiple genes, while in Figure 4B, they were only exposed to IGFBP2. Our results show that IGFBP2 could at least partially explain the phenotype that we see with the activation, but we believe that during the activation experiments, there might be other signals available that might not be induced by IGFBP2 alone. We have also added a summary section and a figure to clarify the different mechanisms of action of the gene activation and IGFBP2.

(7) Figure 4- while the experiments conducted support the role of IGFBP2 in increasing hematopoietic output, there is no experimental evidence to prove its function through paracrine signalling in HECs. The authors need to provide some evidence of how IGFBP2 supplementation specifically expands only the hematopoietic progenitors. Experimental strategies involving specifically targeting IGFBP2 in hemogenic/arterial endothelial cells are required to prove its cell type specific function. Additionally, assessing the in vivo functional potential of the hematopoietic cells generated in the presence of IGFBP2, by bone-marrow transplantation of CD34+ CD43+ cells, is essential. 

The role of IGFBP2 in the context of HSC production and expansion was not the topic of our research, and we have not claimed that IGFBP2  affects the long-term repopulating capacity of HSPCs. Therefore, we believe that the requested experiments are not required to support the specific claims that we do make. We have now provided more experiments and bioinformatic analysis that support the role of IGFBP2 in inducing the progression of EHT from arterial cells to hemogenic endothelium, and to avoid misunderstandings, we have toned down our claims by editing the text regarding its paracrine effect s. 

(8) Figure 4C-D -It is recommended to plot % populations along with fold change value. As this is a key finding, it is important to perform flow cytometry for additional hematopoietic markers- CD144, CD235a and CD41a to demonstrate whether this strategy can also expand erythroid-megakaryocyte progenitors. Telma

Figure 4C already shows the percentage values; we have now added the percentage for Figure 4D in SF5C. We have also performed additional analysis as requested and added the data obtained to Supplementary Figure 5D.

(9) In Figure 5, analysis showing the frequency of cells constituting different clusters, between untreated and IGFBP2-treated samples in the single-cell transcriptome analysis is essential. Additional experiments are required to validate the function of IGFBP2 through modulation of metabolic activity. Inhibition of oxidative phosphorylation in the IGFBP2treated cells should reduce the hematopoietic output. Authors should consider doing these experiments to provide a stronger mechanistic insight into IGFBP2-mediated regulation of hematopoietic emergence.

We have now included the requested cluster composition in Supplementary Figure 5F. We decided not to include further tests on the metabolic profile of IGFBP2 as we already discussed in other papers that showed, using selective inhibitors, that the EHT coincides with a glycol to OxPhos switch. 

(10) It is very striking to see that IGFBP2 supplementation changes the transcriptional profile of developing hematopoietic cells by increasing transcription of OXPHOS-related genes with concomitant reduction of glycolytic signatures, particularly at Day 13. However, the mitochondrial ATP rate measurements do not seem convincing. The bioenergetic profiles show that when mitochondrial inhibitors are added, both groups exhibit decreased OCR values and, on the other hand, higher ECAR. This indicates that both groups have the capability to utilize OXPHOS or glycolysis and may only differ in their basal respiration rates.

Differences in proliferation rate can cause basal respiration to change. There is no information on how the bioenergetic profile was normalized (cell no./protein amount). Given that IGFBP2 has been shown to increase proliferation, it is very likely that the cells treated with IGFBP2 proliferated faster and therefore have higher OCR. The data needs to be normalized appropriately to negate this possibility.

We have previously tested whether IGFBP2 causes an increase in proliferation by analysing the cell cycle of cells treated with it, as we initially thought this could be a mechanism of action. We have now provided the quantification of the cell cycle in the cells treated with IGFBP2, showing no effect was observed in cell cycle Supplementary Figure 4E. Following this analysis, we decided to plate the same number of cells and test their density under the microscope before running the experiment; each experiment was done in triplicate for each condition. We have now added this info to the method sections lines 806-813.  We did not comment on the basal difference, which we agree might be due to several factors, but we only compared the difference in response to the inhibitors, which isn’t affected by the basal level but exclusively by their D values. We have also included the formulas used to calculate the ATP production rate.

Overall, it appears that IGFBP2 does not seem to primarily cause metabolic changes, but simply accelerates the metabolic dependency on OXPHOS. Hence, the term 'metabolic remodelling' must be avoided unless IGFBP2 depletion/loss of function analysis is shown.

We thank the reviewer for suggesting how to interpret the data about the dependency on OXPHOS. We have now changed the conclusions and claims about the effect of IGFBP2. We have also included a cell cycle analysis of the hematopoietic cells derived upon IGFBP2 addition to show that they don’t show differences in proliferation that could cause the increase in colony formation we observed. Regarding the assay, we have plated the same number of cells for each group to make sure we were comparing the same number of cells, which we also assessed in the microscope before the test, and we eliminated the suspension cells during the washes that preceded the measurement. The review is correct in indicating that there is a basal difference in the value of OCR and ECAR where the IGFBP2 is lower at the start and not higher, which would not conceal higher proliferation. Finally, the ATP production rate is calculated on the variation of OCR and ECAR upon the addition of inhibitors, which normalizes for the basal differences.

eLife Assessment

This study presents useful findings to inform and improve the in vitro differentiation of hematopoietic progenitor cells from human induced pluripotent stem cells. Relying on a well-characterised technical approach, the data analysis is overall solid and reasonably supports the main conclusions.

Reviewer #1 (Public Review):

Summary:

The work from Petazzi et al. aimed at identifying novel factors supporting the differentiation of human hematopoietic progenitors from induced-pluripotent stem cells (iPSCs). The authors developed an inducible CRISPR-mediated activation strategy (iCRISPRa) to test the impact of newly identified candidate factors on the generation of hematopoietic progenitors in vitro. They first compared previously published transcriptomic data of iPSC-derived hemato-endothelial populations with cells isolated ex vivo from the aorta-gonad-mesonephros (AGM) region of the human embryo and they identified 9 transcription factors expressed in the aortic hemogenic endothelium that were poorly expressed in the in vitro differentiated cells. They then tested the activation of these candidate factors in an iPSC-based culture system supporting the differentiation of hematopoietic progenitors in vitro. They found that the IGF binding protein 2 (IGFBP2) was the most upregulated gene in arterial endothelium after activation and they demonstrated that IGFBP2 promotes the generation of functional hematopoietic progenitors in vitro.

Strengths:

The authors developed a very useful doxycycline-inducible system to activate the expression of specific candidate genes in human iPSC. This approach allows us to simultaneously test the impact of 9 different transcription factors on in vitro differentiation of hematopoietic cells, and the system appears to be very versatile and applicable to a broad variety of studies. Using this approach, the authors exhaustively demonstrated the role of IGFBP2 in promoting the generation of functional hematopoietic progenitors in vitro from iPSCs.

Weaknesses:

The authors performed a very thorough characterization of the system in proof-of-principle experiments activating a single transcription factor. However, when 9 independent factors were used, it is not always clear whether the observed results were the consequence of the simultaneous activation of all 9 TF or just a subset of them.

μετρα προσαρμογης απο τους αγροτεσ για τους ελαιωνες και την αυξημενη παραγωγικοτητα τους σε περιοδους ξηρασιας

ουσιαστικα εδω αναφερονται μετρα προσαρμογης της ελληνικης γεωργιασ στην κλιματικη αλλαγη με βαση διεθνησ δεικτεσ, και κινησεις που εχουν γινει πο ΗΕ και ΕΕ. (ειναι προτασεις η πραγματικότητα)? θα μπορουσε να μπει στο κομματι του ρεσιλιενς

concept of resilience: how to minimize the impacts of CC for greek agriculture theory of adaptive capacity framework: what is the framework in which people and institutions are working in, in order to resist cc?

εδω μπορω να βαλω το αρθρο που μου εστειλε ο Ανδρονικοσ για ενα παρασιτο που επηρεαζει την ελαιοπαραγωγη. How does climate change affect agriculture? answers back to the central question.

implications of climate change in greek agriculture

context of the region: why is CC implications so important for greek agriculture? bc of economic activity.

social influence is not just something people do to affect others, but something that can be bought, sold, or traded like a product.

Incentivize means to motivate or encourage someone to take a specific action by offering a reward or benefit.

we as viewers don't always see what infleuncers do to us and when they are sponsered etc, there should be guidlines for this, like #ad. even if an influencer is not sponsered, they should think about what their content can do to the viewer

The phrase implies that brands and marketers should move beyond mere operational efficiency and focus on creativity that aligns with their core values.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

Summary:

In this manuscript, the molecular mechanism of interaction of daptomycin (DAP) with bacterial membrane phospholipids has been explored by fluorescence and CD spectroscopy, mass spectrometry, and RP-HPLC. The mechanism of binding was found to be a two-step process. A fast reversible step of binding to the surface and a slow irreversible step of membrane insertion. Fluorescence-based titrations were performed and analysed to infer that daptomycin bound simultaneously two molecules of PG with nanomolar affinity in the presence of calcium. Conformational change but not membrane insertion was observed for DAP in the presence of cardiolipin and calcium.

Strengths:

The strength of the study is skillful execution of biophysical experiments, especially stoppedflow kinetics that capture the first surface binding event, and careful delineation of the stoichiometry.

Weaknesses:

The weakness of the study is that it does not add substantially to the previously known information and fails to provide additional molecular details. The current study provides incremental information on DAP-PG-calcium association but fails to capture the complex in mass spectrometry. The ITC and NMR studies with G3P are inconclusive. There are no structural models presented. Another aspect missing from the study is the reconciliation between PG in the monomer, micellar, and membrane forms.

Besides the two-stage process, another important finding in the current work is the stable complex that plays a critical role in the drug uptake both in vitro and in B. subtilis. This complex has been shown to be a stable species in HPLC and its binding stoichiometry and affinity have been quantitatively characterized. The complex may not be stable enough in gas phase to be detected in the MS analysis, which was designed to detect the phospholipid and Dap components, not the complex itself. The structural model of this complex is clearly proposed and presented in Figure 6. 

The NMR and ITC studies have a very clear conclusion that Dap has a weak interaction with the PG headgroup alone, which is unable to account for the Dap-PG interaction observed in the fluorescence studies. Thus, the whole PG molecule has to be involved in the interaction, leading to the discovery of the stable complex.  

Reviewer #2 (Recommendations For The Authors):

(1) I appreciate and agree with the comment that there are stages of daptomycin insertion, and these might involve the formation of different complexes with different binding partners (e.g. pre-insertion vs quaternary vs bactericidal). However, it seems like lipid II is an apparent participant in daptomycin membrane dynamics (Grein et al. Nature Communications 2020). It's not clear why this was excluded from analysis by the authors, or what basis there is for the discussion statement that the quaternary complex can shift into the bactericidal complex by exchanging 1 PG for lipid II. 

We agree that lipid II and other isoprenyl lipids may be involved in the uptake and insertion of daptomycin into membrane according to the results of the Nat. Comm. paper. However, these isoprenyl lipids are very small components of the membrane in comparison to PG and their contribution to the drug uptake is thus expected to be much less significant. Nonetheless, we included farnesyl pyrophosphate (FPP) as an analog of bactoprenol pyrophosphate (C55PP), which was reported to have the same promoting effect as lipid II in the previous study, in our study but found no promoting effect in the fluorescence assay (Fig. 2B). In addition, no complex was formed when FPP replaced PG in our preparation and analysis of the drug-lipid complex. In consideration of these negative results and the expected small contribution, other isoprenyl lipids or their analogs were not included in the study.

The statement of forming the proposed bactericidal complex from the identified complex is a speculation that is possible only when lipid II has a higher affinity for Dap than a PG ligand. To avoid confusion, we deleted the sentence’ in the revision. 

(2) The detailed examination of daptomycin dynamics, particularly on the millisecond scale, in this paper is ideal for characterizing the effect of lipid II on daptomycin insertion. It would be helpful to either include lipid II in some analyses (micelle binding, fluorescence shifts, CD) or at least address why it was excluded from the scope of this work.

As mentioned in the response to the first comment, we did not exclude isoprenyl lipids in our study but used some of their analogs in the fluorescence assay. Besides FPP mentioned above, we also tested geranyl pyrophosphate and geranyl monophosphate but obtained the same negative results. Lipid II was not directly used because it is one of the three isoprenyl lipids reported to have the same promoting effects in the Nat. Comm. paper and also because its preparation is not easy. Even if lipid II were different from other isoprenyl lipids in promoting membrane binding, its contribution is likely negligible at the reversible stage compared to the phospholipids because of its minuscule content in bacterial membrane. This is the main reason we did not use the isoprenyl lipids in the fast kinetic study (this stage only involves reversible binding, not insertion). 

(3) Grein et al. 2020 saw that PG did not have a strong effect on daptomycin interaction with membranes. I believe this discrepancy is more likely due to the complex physical parameters of supported bilayers versus micelles/vesicles or some other methodological variable, but if the authors have more insight on this, it would be valuable commentary in the discussion.

We totally agree that the discrepancy is likely due to the different conditions in the assays. It is hard to tell exactly what causes the difference. Thus, we did not attempt to comment on the cause of this difference in the discussion.

(4) Isolation of the daptomycin complex from B. subtilis cells clearly had different traces from the in vitro complex; is it possible that lipid II is present in the B. subtilis complex? If not, a time-course extraction could be useful to support the model that different complexes have different activities. Isolates from early-stage incubation with daptomycin may lack lipid II but isolates from longer incubations may have lipid II present as the complex shifts from insertion to bactericidal.

From the day we isolated the complex from B. subtilis, we have been looking for evidence for the previously proposed lipid complexes containing lipid II or other isoprenyl lipids but have not been successful. We did not see any sign of lipid II or other isoprenyl lipids in the MALDI or ESI mass spectroscopic data. The minute peaks in the HPLC traces are not the expected complexes in separate LC-MS analysis. However, this does not mean that such complexes are not present in the isolated PG-containing complex because: (1) the amount of such complexes may be too small to be detected due to the low content of the isoprenyl lipids; (2) the isoprenyl lipids, particularly lipid II, are not easily ionizable due to their size and unique structure for detection in mass spectrometry. 

We don’t think the drug treatment time is the reason for the failure in detecting lipid II or other isoprenyl lipids. In our reported experiment, the cells were treated with a very high dose of Dap for 2 hours before extraction. In a separate experiment done recently, we treated B. subtilis at 1/3 of the used dose under the same condition and found all treated cells were dead after 1 hour in a titration assay, consistent with the results from reported time-killing assays in the literature. From this result, the proposed bactericidal lipid-containing complex should have been formed in the treated cells used in our extraction and isolated along with the PG-containing complex. It was not detected likely due to the reasons discussed above. To avoid the interference of the PG-containing complex, a large amount of bacterial cells might have to be treated at a low dose to isolate enough amount of the lipid II-containing complex for identification. However, isolation or identification of the lipid II-containing complex is outside the scope of the current investigation and is therefore not pursued. 

(5) Part of the daptomycin mechanism of interacting with bacterial membranes involves the flipping of daptomycin from one leaflet to another. There was some mentioned work on the consistency of results between micelles and vesicles, but the dynamics or existence of a flipping complex in the bilayer system wasn't addressed at all in this paper.

The current investigation makes no attempt to solve all problems in the daptomycin mode of action and is limited to the uptake of the drug, up to the point when Dap is inserted into the membrane. Within this scope, flipping of the complex is not yet involved and is thus irrelevant to the study. How the complex is flipped and used to kill the bacteria is what should be investigated next.  

(6) The authors mention data with phosphatidylethanolamine in the text, but I could not find the data in the main or supplemental figures. I recommend including it in at least one of the figures.

It is much appreciated that this error is identified. The POPE data was lost when the graphic (Fig. 2B) was assembled in Adobe to create Figure 2. We re-draw the graphic and reassemble the figure to solve this problem. Fig. 2B has also been modified to use micromolar for the concentration of the lipids.

(7) Readability point: I'd suggest some consistency in the concentrations mentioned. Making the concentrations either all molar-based or all percentage-based would make comparison across figures easier.

As suggested, we have changed the % into micromolar concentrations in Fig. 2B and also in Fig. 3A. 

(8) The model figure is quite difficult to interpret, particularly the final stage of the tail unfolding. I recommend the authors use a zoomed-in inset for this stage, or at least simplify the diagram by removing the non-participating lipid structures. The figure legend for the model figure should also have a brief description of the events and what the arrows mean, particularly the POPS PG arrow in the final panel of the figure. I am assuming here the authors are implying that daptomycin can transiently interact with one lipid species and move to another, but the arrow here suggests that daptomycin is moving through the lipid headgroup space.

We really appreciate the suggestions. As suggested, we put an inset to show the preinsertion complex more clearly. In addition, we have removed the green arrows originally intended to show the re-organization/movement of the phospholipids. Moreover, the legend is changed to ‘Proposed mechanism for the two-phased uptake of Dap into bacterial membrane. In the first phase, Dap reversibly binds to negative phospholipids with a hidden tail in the headgroup region, where it combines with two PG molecules to form a pre-insertion complex. In the second phase, the hidden tail unfolds and irreversibly inserts into the membrane. The inset shows the headgroup of the pre-insertion complex with the broad arrow showing the direction for the unfolding of the hidden tail. The red dots denote Ca2+.’  

(9) The authors listed the Kd for daptomycin and 2 PG as 7.2 x 10-15 M2. Is this correct? This is an affinity in the femtomolar range.

Please note that this Kd is for the simultaneous binding of two PG molecules, not for the binding of a single ligand that we usually refer to. Assuming that each PG contributes equally to this interaction, the binding affinity for each ligand is then the squared root of 7.2 x 10-15 M2, which equals to 8.5 x 10-8 M. This is equivalent to a nanomolar affinity for PG and is a reasonably high affinity.

Reviewer #3 (Recommendations For The Authors):

(1) The authors reported an increase in daptomycin intensity with the increasing amount of negatively charged DMPG. A similar observation has been reported for GUVs, however, the authors did not refer to this paper in their manuscript: E. Krok, M. Stephan, R. Dimova, L. Piatkowski, Tunable biomimetic bacterial membranes from binary and ternary lipid mixtures and their application in antimicrobial testing, Biochim. Biophys. Acta - Biomembr. 1865 (2023) [1]. This paper is also consistent with the authors' observation that there is negligible fluorescence detected for the membranes composed of PC lipids upon exposure to the Dap treatment.

As suggested, this paper is cited as ref. 29 in the revision by adding the following sentence at the end of the section ‘Dependence of Dap uptake on phosphatidylglycerol.’: ‘PG-dependent increase of the steady-state fluorescence was also observed in giant unilamellar vesicles (GUVs).29’. The numbering is changed accordingly for the remaining references.  

(2) Please include the plot of the steady-state Kyn fluorescence vs the content of POPA (Figure 2C shows traces for DMPG, CL, and POPS). Both POPA and POPS lipids are negatively charged, however, POPS seems to interact with Dap, while POPA does not. In my opinion, this observation is really interesting and might deserve a more thorough discussion. The authors might want to describe what could be the mechanism behind this lipid-specific mode of binding.

As suggested, a plot is now added for POPA in Fig. 2C, which is basically a flat line without significant increase for the Kyn fluorescence. Indeed, the different effect of the negative phospholipids is very interesting, indicating that the reversible binding of Dap to the lipid surface is dependent not only on the Ca2+-mediated ionic interaction but also the structure of the headgroup. In other words, Dap recognizes the phospholipids at the surface binding stage. Considering this headgroup specificity, the last sentence in the second paragraph in “Discussion’ is changed from ‘In addition, due to the low lipid specificity, this reversible binding likely involves Ca2+-mediated ionic interaction between Dap and the phosphoryl moiety of the headgroups.’ to ‘In addition, due to the specificity for negative phospholipids (Fig. 2B and 2C), this reversible binding of Dap likely involves both a nonspecific Ca2+-mediated ionic interaction and a specific interaction with the remaining part of the headgroups.’

(3) The authors write that they propose a novel mechanism for the Ca2+-dependent insertion of Dap to the bacterial membrane, however, they rather ignored the already published findings and hypotheses regarding this process. In fact the role of Ca2+, as well as the proposed conformational changes of Dap, which allow its deeper insertion into the membrane are well known:

The role of Ca2+ ions in the mechanism of binding is actually three-fold: (i) neutralization of daptomycin charge [2], (iii) creating the connection between lipids and daptomycin and (iii) inducing two daptomycin conformational changes. It should be noted that the interactions between calcium ions and daptomycin are 2-3 orders of magnitude stronger than between daptomycin and PG lipids [3,4]. Thus, upon the addition of CaCl2 to the solution, the divalent cations of calcium bind preferentially to the daptomycin, rather than to the negatively charged PG lipids, which results in the decrease of daptomycin net negative charge but also leads to its first conformational change [4]. Upon binding between calcium ions and two aspartate residues, the area of the hydrophobic surface increases, which allows the daptomycin to interact with the negatively charged membrane. In the next step, Ca2+ acts as a bridge connecting daptomycin with the anionic lipids. This event leads to the second conformational change, which enables deeper insertion of daptomycin into the lipid membrane and enables its fluorescence [4]. The overall mechanism has a sequential character, where the binding of daptomycin-Ca2+ complex to the negatively charged PG (or CA) occurs at the end.

The authors should focus on emphasizing the novelty of their manuscript, keeping in mind the already published paper.

We agree with the comments on the three general roles of calcium ion in the Dap interaction with membrane. The current investigation does not ignore the previous findings, which involve many more works than mentioned above, but takes these findings as common knowledge. Actually, the role of calcium ion is not the focus of current work. Instead, the current work focuses on how the drug is taken up and inserted into the membrane in the presence of the ion and how its structure changes in this process. With the known roles of calcium ion in mind, we propose an uptake mechanism (Fig. 6) that shows no conflict with the common knowledge.

We would like to point out that the ‘deeper insertion into the membrane’ in the comment is different from the membrane insertion referred to in our manuscript. This ‘deeper insertion’ still remains in the reversible stage of binding to the membrane surface because all negative phospholipids can do this (causing a conformational change and fluorescence increase, as quantified in Fig.2C) but now we know that only PG can enable irreversible membrane insertion because of our work. In addition, the comment that calcium binding to daptomycin causes first conformational change is not supported by our finding that no conformational change is found for Dap in the presence of calcium in a lipid-free environment (Fig. 3B). One important aspect of novelty and contribution of our work is to clear up some of these ambiguities in the literature. Another contribution of our work is to demonstrate the formation of a stable complex between Dap and PG with a defined stoichiometry and its crucial role in the drug uptake. 

(4) One paragraph in the section "Ca2+- dependent interaction between Dap and DMPG" is devoted to a discussion of the formation of precipitate upon extraction of DMPG-containing micelles, exposed to Dap in the calcium-rich environment. Contrary, in the absence of Dap, no precipitate was detected. The authors did not provide any visual proof for their statement. Please include proper photographs in the supplementary information.

The precipitate formed upon extraction of the DMPG-containing micelles was too little to be visually identifiable but could be collected by centrifugation and detected by fluorescence or HPLC after dissolving in DMSO. For visualization, we show below the precipitate formed using higher amount of Dap and DMPG. The Dap-DMPG-Ca2+ complex (left tube) was formed by mixing 1 mM Dap, 2 mM DMPG and 1 mM Ca2+ and the control (right tube) was a mixture of 2 mM DMPG and 1 mM Ca2+. This is now added as Fig. S7 in the supplementary information (the index is modified accordingly) and cited in the main text.

(5) The authors wrote that it is not clear how many calcium ions are bound to Dap-2PG complex (page 11, Discussion section). There are already reports discussing this issue. I recommend citing the paper discussing that exactly two Ca2+ ions bind to a single Dap molecule: R. Taylor, K. Butt, B. Scott, T. Zhang, J.K. Muraih, E. Mintzer, S. Taylor, M. Palmer, Two successive calcium-dependent transitions mediate membrane binding and oligomerization of daptomycin and the related antibiotic A54145, Biochim. Biophys. Acta - Biomembr. 1858, (2016) 1999-2005 [5]

We were aware of the cited work that shows binding of two Ca2+ but also noted that there are more works showing one Ca2+ in the binding, such as the paper in [Ho, S. W., Jung, D., Calhoun, J. R., Lear, J. D., Okon, M., Scott, W. R. P., Hancock, R. E. W., & Straus, S. K. (2008), Effect of divalent cations on the structure of the antibiotic daptomycin. European Biophysics Journal, 37(4), 421–433.]. That was the reason we said ‘it is not clear how many calcium ions are bound to Dap-2PG complex’. Now, both papers are cited (as Ref. #33, 34) to support this statement.

(6) The authors wrote two contradictory statements:

-  PG cannot be found in mammalian cell membranes:

"Moreover, the complete dependence of the membrane insertion on PG also explains why Dap selectively attacks Gram-positive bacteria without affecting mammalian cells, because PG is present only in bacterial membrane but not in mammalian membrane. " (Page 10, Discussion section, last sentence of the first paragraph)

"However, Dap absorbed on bacterial surface is continuously inserted into the acyl layer via formation of complex with PG in a time scale of minutes, whereas no irreversible insertion of Dap occurs on mammalian membrane due to the absence of PG while the bound Dap is continuously released to the circulation as the drug is depleted by the bacteria." (Page 13, Discussion section)

-  PG in trace amounts is present in mammalian membranes:

"The proposed requirement of the pre-insertion quaternary complex increases the threshold of PG content for the membrane insertion to happen and thus makes it impossible on the surface of mammalian cells even if their plasma membrane contains a trace amount of PG." (Page 13, Discussion section).

In fact, phosphatidylglycerol comprises 1-2 mol% of the mammalian cell membranes. Please, correct this information, which in this form is misleading to the readers.

We appreciate the comments about the PG content in mammalian cells. Changes are made as listed below:

(1) p10, the sentence is changed to ‘Moreover, the complete dependence of the membrane insertion on PG also explains why Dap selectively attacks Gram-positive bacteria without affecting mammalian cells, because PG is a major phospholipid in bacterial membrane but is a minor component in mammalian membrane.’ 

(2) p13, the sentence is changed to ‘However, Dap absorbed on bacterial surface is continuously inserted into the acyl layer via formation of complex with PG in a time scale of minutes, whereas little irreversible insertion of Dap occurs on mammalian membrane due to the low content of PG while the bound Dap is continuously released to the circulation as the drug is depleted by the bacteria.’

(3) p13, another sentence is modified to ‘The proposed requirement of the pre-insertion quaternary complex increases the threshold of PG content for the membrane insertion to happen and thus makes it less likely on the surface of mammalian cells that contain PG at a low level in the membrane.’ 

(7) Please include information that Dap is effective only against Gram-positive bacteria and does not show antimicrobial properties against Gram-negative strains. The authors focused on emphasizing that Dap does not affect mammalian membranes, most likely due to the low PG content, however even membranes of Gram-negative bacteria are not susceptible to the Dap, despite the relatively high content of negatively charged PG in the inner membrane (e.g. inner cell membrane of E. coli has ~20% PG).

The requested information is already included in ‘Introduction’. In this part, Dap is introduced to be only active against Gram-positive bacteria, implicating that it is not active against Gram-negative bacteria. The reason Dap is inactive against E. coli or other Gramnegative bacteria is because the outer membrane prevents the antibiotic from accessing the PG in the inner membrane to cause any harm. When the outer membrane is removed, Dap will also attack the plasma membrane of Gram-negative bacteria. 

Literature cited in the comments:

(1) E. Krok, M. Stephan, R. Dimova, L. Piatkowski, Tunable biomimetic bacterial membranes from binary and ternary lipid mixtures and their application in antimicrobial testing, Biochim. Biophys. Acta - Biomembr. 1865 (2023). https://doi.org/10.1101/2023.02.12.528174.

(2) S.W. Ho, D. Jung, J.R. Calhoun, J.D. Lear, M. Okon, W.R.P. Scott, R.E.W. Hancock, S.K. Straus, Effect of divalent cations on the structure of the antibiotic daptomycin, Eur. Biophys. J. 37 (2008) 421-433. https://doi.org/10.1007/S00249-007-0227-2/METRICS.

(3) A. Pokorny, P.F. Almeida, The Antibiotic Peptide Daptomycin Functions by Reorganizing the Membrane, J. Membr. Biol. 254 (2021) 97-108. https://doi.org/10.1007/s00232-02100175-0.

(4) L. Robbel, M.A. Marahiel, Daptomycin, a bacterial lipopeptide synthesized by a nonribosomal machinery, J. Biol. Chem. 285 (2010) 2750127508. https://doi.org/10.1074/JBC.R110.128181.

(5) R. Taylor, K. Butt, B. Scott, T. Zhang, J.K. Muraih, E. Mintzer, S. Taylor, M. Palmer, Two successive calcium-dependent transitions mediate membrane binding and oligomerization of daptomycin and the related antibiotic A54145, Biochim. Biophys. Acta - Biomembr. 1858 (2016) 1999-2005. https://doi.org/10.1016/J.BBAMEM.2016.05.020.

eLife Assessment

This valuable study describes the molecular mechanism of daptomycin insertion into bacterial membranes. The authors provide solid in vitro evidence for the early events of daptomycin interaction with phospholipid headgroups and stronger, specific interaction with phosphatidylglycerol. This work will be of interest to bacterial membrane biologists and biochemists working in the antimicrobial resistance field.

Reviewer #3 (Public review):

Summary:

Machhua et al. in their work focused on unravelling the molecular mechanism of daptomycin binding and interaction with bacterial cell membranes. Daptomycin (Dap) is an acidic, cyclic lipopeptide composed of 13 amino acids, known for preferential binding to anionic lipids, particularly phosphatidylglycerol (PG), which are prevalent components in the membranes of Gram-positive bacteria. The process of binding and antimicrobial efficacy of Dap are significantly influenced by the ionic composition of the surrounding environment, especially the presence of Ca2+ ions. The authors underscore the presence of significant knowledge gaps in our understanding of daptomycin's mode of action. Several critical questions remain unanswered, including the basis for selective recognition and accumulation in membranes of Gram-positive strains, the specific role of Ca2+ ions in this process, and the mechanisms by which daptomycin binds to and inserts into the cell membrane.

Dap is intrinsically fluorescent due to its kynurenine residue (Kyn-13) and this property allows direct imaging of Dap binding to model cell membranes without the need of additional labeling. Taking advantage of this Dap autofluorescence, authors monitored the emission intensity of micelles, composed of varying DMPG content upon their exposure to Dap and compared it with the kinetics of fluorescence observed for zwitterionic DMPC and other negatively charged lipids such as cardiolipin (CA), POPA and POPS. The authors noted that the linear relationship between DMPG content and Dap fluorescence is strongly lipid-specific, as it was not observed for other anionic lipids. The manuscript sheds light on the specificity of Dap's interaction with CA and DMPG lipids. Through Ca2+ sequestration with EGTA, the authors demonstrated that the binding of Dap with CA is reversible, while its interaction with DMPG results in the irreversible insertion of Dap into the lipid membrane structure, caused by the significant conformational change of this lipopeptide. The formation of a stable DMPG-Dap complex was also verified in bacterial cells isolated from Gram-positive bacteria B. subtilis, where Dap exhibited a permanent binding to PG lipids.

Altogether, the authors endeavored to illuminate novel insights into the molecular basis of Dap binding, interaction, and the mechanism of insertion into bacterial cell membranes. Such understanding holds promise for the development of innovative strategies in combating drug resistance and the emerging of the so-called superbugs.

Strengths:

- The manuscript by Machhua et al. provides a comprehensive analysis of the Dap mechanism of binding and interaction with the membrane. It discusses various aspects of this, only apparently trivial interaction such as the importance of PG presence in the membrane, the impact of Ca2+ ions, and different mechanisms of Dap binding with other negatively charged lipids.<br /> - The authors focused not only on model membranes (micelles) but also extended their research to bacterial cell membranes obtained from B. subtilis<br /> - The research is not only a report of the experimental findings but tries to give potential hypotheses explaining the molecular mechanisms behind the observed results

Weaknesses:

- The authors overestimate their findings, stating that they propose a novel mechanism of Dap interaction with bacterial cell membranes. This research is the extension of the hypotheses that have already been reported.<br /> - The literature study and overall discussion about the mechanism of action of Ca2+ ions or conformational changes of daptomycin could be improved.

This manuscript examines preprint review services and their role in the scholarly communications ecosystem.  It seems quite thorough to me. In Table 1 they list many peer-review services that I was unaware of e.g. SciRate and Sinai Immunology Review Project.

To help elicit critical & confirmatory responses for this peer review report I am trialling Elsevier’s suggested “structured peer review” core questions, and treating this manuscript as a research article.

Introduction

1. Is the background and literature section up to date and appropriate for the topic?

   Yes.

2. Are the primary (and secondary) objectives clearly stated at the end of the introduction?

   No. Instead the authors have chosen to put the two research questions on page 6 in the methods section. I wonder if they ought to be moved into the introduction – the research questions are not methods in themselves. Might it be better to state the research questions first and then detail the methods one uses to address those questions afterwards? [as Elsevier’s structured template seems implicitly to prefer.

Methods

1. Are the study methods (including theory/applicability/modelling) reported in sufficient detail to allow for their replicability or reproducibility?

   I note with approval that the version number of the software they used (ATLAS.ti) was given.

   I note with approval that the underlying data is publicly archived under CC BY at figshare.

   The Atlas.ti report data spreadsheet could do with some small improvement – the column headers are little cryptic e.g. “Nº  ST “ and “ST” which I eventually deduced was Number of Schools of Thought and Schools of Thought (?)   

   Is there a rawer form of the data that could be deposited with which to evidence the work done? The Atlas.ti report spreadsheet seemed like it was downstream output data from Atlas.ti. What was the rawer input data entered into Atlas.ti? Can this be archived somewhere in case researchers want to reanalyse it using other tools and methods.

   I note with disapproval that Atlas.ti is proprietary software which may hinder the reproducibility of this work. Nonetheless I acknowledge that Atlas.ti usage is somewhat ‘accepted’ in social sciences despite this issue.

   I think the qualitative text analysis is a little vague and/or under-described: “Using ATLAS.ti Windows (version 23.0.8.0), we carried out a qualitative analysis of text from the relevant sites, assigning codes covering what they do and why they have chosen to do it that way.” That’s not enough detail. Perhaps an example or two could be given? Was inter-rater reliability performed when ‘assigning codes’ ? How do we know the ‘codes’ were assigned accurately?

2. Are statistical analyses, controls, sampling mechanism, and statistical reporting (e.g., P-values, CIs, effect sizes) appropriate and well described?

   This is a descriptive study (and that’s fine) so there aren’t really any statistics on show here other than simple ‘counts’ (of Schools of Thought) in this manuscript. There are probably some statistical processes going on within the proprietary qualitative analysis of text done in ATLAS.ti but it is under described and so hard for me to evaluate. 

Results

1. Is the results presentation, including the number of tables and figures, appropriate to best present the study findings?

   Yes. However, I think a canonical URL to each service should be given.  A URL is very useful for disambiguation, to confirm e.g. that the authors mean this Hypothesis (www.hypothes.is) and NOT this Hypothesis (www.hyp.io). I know exactly which Hypothesis is the one the authors are referring to but we cannot assume all readers are experts 😊

   Optional suggestion: I wonder if the authors couldn’t present the table data in a slightly more visual and/or compact way? It’s not very visually appealing in its current state. Purely as an optional suggestion, to make the table more compact one could recode the answers given in one or more of the columns 2, 3 and 4 in the table e.g. "all disciplines = ⬤ , biomedical and life sciences = ▲, social sciences =  ‡  , engineering and technology = † ". I note this would give more space in the table to print the URLs for each service that both reviewers have requested.

   ———————————————————————————————

   | Service name | Developed by | Scientific disciplines | Types of outputs |

   | Episciences | Other | ⬤ | blah blah blah. |

   | Faculty Opinions | Individual researcher | ▲ | blah blah blah. |

   | Red Team Market | Individual researcher | ‡ | blah blah blah. |

   ———————————————————————————————

   The "Types of outputs" column might even lend themselves to mini-colour-pictograms (?) which could be more concise and more visually appealing? A table just of text, might be scientifically 'correct' but it is incredibly dull for readers, in my opinion.

2. Are additional sub-analyses or statistical measures needed (e.g., reporting of CIs, effect sizes, sensitivity analyses)?

   No / Not applicable. 

Discussion

1. Is the interpretation of results and study conclusions supported by the data and the study design?

   Yes.

2. Have the authors clearly emphasized the limitations of their study/theory/methods/argument?

   No. Perhaps a discussion of the linguistic/comprehension bias of the authors might be appropriate for this manuscript. What if there are ‘local’ or regional Chinese, Japanese, Indonesian or Arabic language preprint review services out there? Would this authorship team really be able to find them?

Additional points:

• Perhaps the points made in this manuscript about financial sustainability (p24) are a little too pessimistic. I get it, there is merit to this argument, but there is also some significant investment going on there if you know where to look. Perhaps it might be worth citing some recent investments e.g. Gates -> PREreview (2024) https://content.prereview.org/prereview-welcomes-funding/  and Arcadia’s $4 million USD to COAR for the Notify Project which supports a range of preprint review communities including Peer Community In, Episciences, PREreview and Harvard Library.  (source: https://coar-repositories.org/news-updates/coar-welcomes-significant-funding-for-the-notify-project/ ) 

• Although I note they are mentioned, I think more needs to be written about the similarity and overlap between ‘overlay journals’ and preprint review services. Are these arguably not just two different terms for kinda the same thing? If you have Peer Community In which has it’s overlay component in the form of the Peer Community Journal, why not mention other overlay journals like Discrete Analysis and The Open Journal of Astrophysics.   I think Peer Community In (& it’s PCJ) is the go-to example of the thin-ness of the line the separates (or doesn’t!) overlay journals and preprint review services. Some more exposition on this would be useful.

Thank you very much for the opportunity to review the preprint titled “Preprint review services: Disrupting the scholarly communication landscape?” (https://doi.org/10.31235/osf.io/8c6xm) The authors review services that facilitate peer review of preprints, primarily in the STEM (science, technology, engineering, and math) disciplines. They examine how these services operate and their role within the scholarly publishing ecosystem. Additionally, the authors discuss the potential benefits of these preprint peer review services, placing them in the context of tensions in the broader peer review reform movement. The discussions are organized according to four “schools of thought” in peer review reform, as outlined by Waltman et al. (2023), which provides a useful framework for analyzing the services. In terms of methodology, I believe the authors were thorough in their search for preprint review services, especially given that a systematic search might be impractical.

As I see it, the adoption of preprints and reforming peer review are key components of the move towards improving scholarly communication and open research. This article is a useful step along that journey, taking stock of current progress, with a discussion that illuminates possible paths forward. It is also well-structured and easy for me to follow. I believe it is a valuable contribution to the metaresearch literature.

On a high level, I believe the authors have made a reasonable case that preprint review services might make peer review more transparent and rewarding for all involved. Looking forward, I would like to see metaresearch which gathers further evidence that these benefits are truly being realised.

In this review, I will present some general points which merit further discussion or clarification to aid an uninitiated reader. Additionally, I raise one issue regarding how the authors framed the article and categorised preprint review services and the disciplines they serve. In my view, this problem does not fundamentally undermine the robust search, analyses, and discussion in this paper, but it risks putting off some researchers and constrains how broadly one should derive conclusions.

General comments

Some metaresearchers may be aware of preprints, but not all readers will be familiar with them. I suggest briefly defining what they are, how they work, and which types of research have benefited from preprints, similar to how “preprint review service” is clearly defined in the introduction.

Regarding Waltman et al.’s (2023) “Equity & Inclusion” school of thought, does it specifically aim for “balanced” representation by different groups as stated in this article? There is an important difference between “balanced” versus “equitable” representation, and I would like to see it addressed in this text.

Another analysis I would like to see is whether any of the 23 services reviewed present any evidence that their approach has improved research quality. For instance, the discussion on peer review efficiency and incentives states that there is currently “no hard evidence” that journals want to utilise reviews by Rapid Reviews: COVID-19, and that “not all journals are receptive” to partnerships. Are journals skeptical of whether preprint review services could improve research quality? Or might another dynamic be at work?

The authors cite Nguyen et al. (2015) and Okuzaki et al. (2019), stating that peer review is often “overloaded”. I would like to see a clearer explanation by what “overloaded” means in this context so that a reader does not have to read the two cited papers.

To the best of my understanding, one of the major sticking points in peer review reform is whether to anonymise reviewers and/or authors. Consequently, I appreciate the comprehensive discussion about this issue by the authors.

However, I am only partially convinced by the statement that double anonymity is “essentially incompatible” with preprint review. For example, there may be, as yet not fully explored, ways to publish anonymous preprints with (a) a notice that it has been submitted to, or is undergoing, peer review; and (b) that the authors will be revealed once peer review has been performed (e.g. at least one review has been published). This would avoid the issue of publishing only after review is concluded as is the case for Hypothesis and Peer Community In.

Additionally, the authors describe 13 services which aim to “balance transparency and protect reviewers’ interests”. This is a laudable goal, but I am concerned that framing this as a “balance” implies a binary choice, and that to have more of one, we must lose an equal amount of the other. Thinking only in terms of “balance” prevents creative, win-win solutions. Could a case be made for non-anonymity to be complemented by a reputation system for authors and reviewers? For example, major misconduct (e.g. retribution against a critical review) would be recorded in that system and dissuade bad actors. Something similar can already be seen in the reviewer evaluation system of CrowdPeer, which could plausibly be extended or modified to highlight misconduct.

I also note that misconduct and abusive behaviour already occur even in fully or partially anonymised peer review, and they are not limited to the review or preprints. While I am not aware of existing literature on this topic, academics’ fears seem reasonable. For example, there is at least anecdotal testimonies that a reviewer would deliberately reject a paper to retard the progress of a rival research group, while taking the ideas of that paper and beating their competitors to winning a grant. Or, a junior researcher might refrain from giving a negative review out of fear that the senior researcher whose work they are reviewing might retaliate. These fears, real or not, seem to play a part in the debates about if and how peer review should (or should not) be anonymised. I would like to see an exploration of whether de-anonimisation will improve or worsen this behaviour and in what contexts. And if such studies exist, it would be good to discuss them in this paper.

I found it interesting that almost all preprint review services claim to be complementary to, and not compete with, traditional journal-based peer review. The methodology described in this article cannot definitely explain what is going on, but I suspect there may be a connection between this aversion to compete with traditional journals, and (a) the skepticism of journals towards partnering with preprint review services and (b) the dearth of publisher-run options. I hypothesise that there is a power dynamic at play, where traditional publishers have a vested interest in maintaining the power they hold over scholarly communication, and that preprint review services stress their complementarity (instead of competitiveness) as a survival mechanism. This may be an avenue for further metaresearch.

To understand preprints from which fields of research are actually present on the services categorised under “all disciplines,” I used the Random Integer Set Generator by the Random.org true random number service (https://www.random.org/integer-sets/) to select five services for closer examination: Hypothesis, Peeriodicals, PubPeer, Qeios, and Researchers One. Of those, I observed that Hypothesis is an open source web annotation service that allows commenting on and discussion of any web page on the Internet regardless of whether it is research or preprints. Hypothesis has a sub-project named TRiP (Transparent Review in Preprints), which is their preprint review service in collaboration with Cold Spring Harbor Laboratory. It is unclear to me why the authors listed Hypothesis as the service name in Table 1 (and elsewhere) instead of TRiP (or other similar sub-projects). In addition, Hypothesis seems to be framed as a generic web annotation service that is used by some as a preprint review tool. This seems fundamentally different from others who are explicitly set up as preprint review services. This difference seems noteworthy to me.

To aid readers, I also suggest including hyperlinks to the 23 services reviewed in this paper. My comments on disciplinary representation in these services are elaborated further below.

One minor point of curiosity is that several services use an “automated tool” to select reviewers. It would be helpful to describe in this paper exactly what those tools are and how they work, or report situations where services do not explain it.

Lastly, what did the authors mean by “software heritage” in section 6? Are they referring to the organisation named Software Heritage (https://www.softwareheritage.org/) or something else? It is not clear to me how preprint reviews would be deposited in this context.

Respecting disciplinary and epistemic diversity

In the abstract and elsewhere in the article, the authors acknowledge that preprints are gaining momentum “in some fields” as a way to share “scientific” findings. After reading this article, I agree that preprint review services may disrupt publishing for research communities where preprints are in the process of being adopted or already normalised. However, I am less convinced that such disruption is occurring, or could occur, for scholarly publishing more generally.

I am particularly concerned about the casual conflation of “research” and “scientific research” in this article. Right from the start, it mentions how preprints allow sharing “new scientific findings” in the abstract, stating they “make scientific work available rapidly.” It also notes that preprints enable “scientific work to be accessed in a timely way not only by scientists, but also…” This framing implies that all “scholarly communication,” as mentioned in the title, is synonymous with “scientific communication.” Such language excludes researchers who do not typically identify their work as “scientific” research. Another example of this conflation appears in the caption for Figure 1, which outlines potential benefits of preprint review services. Here, “users” are defined as “scientists, policymakers, journalists, and citizens in general.” But what about researchers and scholars who do not see themselves as “scientists”?

Similarly, the authors describe the 23 preprint review services using six categories, one of which is “scientific discipline”. One of those disciplines is called “humanities” in the text, and Table 1 lists it as a discipline for Science Open Reviewed. Do the authors consider “humanities” to be a “scientific” discipline? If so, I think that needs to be justified with very strong evidence.

Additionally, Waltman et al.’s four schools of thought for peer review reform works well with the 23 services analysed. However, at least three out of the four are explicitly described as improving “scientific” research.

Related to the above are how the five “scientific disciplines” are described as the “usual organisation” of the scholarly communication landscape. On what basis should they be considered “usual”? In this formulation, research in literature, history, music, philosophy, and many other subjects would all be lumped together into the “humanities”, which sit at the same hierarchical level as “biomedical and life sciences”, arguably a much more specific discipline. My point is not to argue for a specific organisation of research disciplines, but to highlight a key epistemic assumption underlying the whole paper that comes across as very STEM-centric (science, technology, engineering, and math).

How might this part of the methodology affect the categories presented in Table 1? “Biomedical and life sciences” appear to be overrepresented compared to other “disciplines”. I’d like to see a discussion that examines this pattern, and considers why preprint review services (or maybe even preprints more generally) appear to cover mostly the biomedical or physical sciences.

In addition, there are 12 services described as serving “all disciplines”. I believe this paper can be improved by at least a qualitative assessment of the diversity of disciplines actually represented on those services. Because it is reported that many of these service stress improving the “reproducibility” of research, I suspect most of them serve disciplines which rely on experimental science.

I randomly selected five services for closer examination, as mentioned above. Of those, only Qeios has demonstrated an attempt to at least split “arts and humanities” into subfields. The others either don’t have such categories altogether, or have a clear focus on a few disciplines (e.g. life sciences for Hypothesis/TRiP). In all cases I studied, there is a heavy focus on STEM subjects, especially biology or medical research. However, they are all categorised by the authors as serving “all disciplines”.

If preprint review services originate from, or mostly serve, a narrow range of STEM disciplines (especially experiment-based ones), it would be worth examining why that is the case, and whether preprints and reviews of them could (or could not) serve other disciplines and epistemologies.

It is postulated that preprint review services might “disrupt the scholarly communication landscape in a more radical way”. Considering the problematic language I observed, what about fields of research where peer-reviewed journal publications are not the primary form of communication? Would preprint review services disrupt their scholarly communications?

To be clear, my concern is not just the conflation of language in a linguistic sense but rather inequitable epistemic power. I worry that this conflation would (a) exclude, minoritise, and alienate researchers of diverse disciplines from engaging with metaresearch; and (b) blind us from a clear pattern in these 23 services, that is their strong focus on the life sciences and medical research and a discussion of why that might be the case. Critically, what message are we sending to, for example, a researcher of 18th century French poetry with the language and framing of this paper? I believe the way “disciplines” are currently presented here poses a real risk of devaluing and minoritising certain subject areas and ways of knowing. In its current form, I believe that while this paper is a very valuable contribution, one should not derive from it any conclusions which apply to scholarly publishing as a whole.

The authors have demonstrated inclusive language elsewhere. For example, they have consciously avoided “peer” when discussing preprint review services, clearly contrasting them to “journal-based peer review”. Therefore, I respectfully suggest that similar sensitivity be adopted to avoid treating “scientific research” and “research” as the same thing. A discussion, or reference to existing works, on the disciplinary skew of preprints (and reviews of them) would also add to the intellectual rigour of this already excellent piece.

Overall, I believe this paper is a valuable reflection on the state of preprints and services which review them. Addressing the points I raised, especially the use of more inclusive language with regards to disciplinary diversity, would further elevate its usefulness in the metaresearch discourse. Thank you again for the chance to review.

Signed:

Dr Pen-Yuan Hsing (ORCID ID: 0000-0002-5394-879X)

University of Bristol, United Kingdom

Data availability

I have checked the associated dataset, but still suggest including hyperlinks to the 23 services analysed in the main text of this paper.

Competing interests

No competing interests are declared by me as reviewer.

Henriques, S. O., Rzayeva, N., Pinfield, S., & Waltman, L. (2023, October 13). Preprint review services: Disrupting the scholarly communication landscape?. https://doi.org/10.31235/osf.io/8c6xm

Aug 11, 2024

Nov 20, 2024

Nov 20, 2024

Authors:

• Susana Henriques (Research on Research Institute (RoRI) Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, the Netherlands Scientific Research Department, Azerbaijan University of Architecture and Construction, Baku, Azerbaijan) s.oliveira@cwts.leidenuniv.nl
• Narmin Rzayeva (Research on Research Institute (RoRI) Information School, University of Sheffield, Sheffield, UK) n.rzayeva@cwts.leidenuniv.nl
• Stephen Pinfield (Research on Research Institute (RoRI) Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, the Netherlands) s.pinfield@sheffield.ac.uk
• Ludo Waltman waltmanlr@cwts.leidenuniv.nl

7

10.31235/osf.io/8c6xm

Preprint review services: Disrupting the scholarly communication landscape

"Using behavioral data for tailored advertisements, while profitable, threatens user autonomy. When platforms like Meta use user data without explicit authorization, it seems invasive—I've seen how quickly advertising change depending on a single search or discussion. This type of tracking makes me feel as if my privacy is continually jeopardized, and it underlines the disparity between profit-driven businesses and user control."

"Surveillance capitalism creates severe ethical concerns, particularly regarding the exploitation of 'behavioral surplus.' Reading about Meta's example of permitting harmful targeted ads reminded me of how I've received strangely particular adverts on Instagram, making me wonder how much the platform knows about me. It's distressing to learn how customer data can be abused, and businesses must accept responsibility for protecting against such behaviors."

Explanation:

The annotated text, which is a URL (https://errors.edgesuite.net/18.aa2d3e17.1733239827.ea4e98df), appears to be a reference link that leads to an error page. This indicates that the link provided in the original text is either broken or incorrect.

Given the user question, which asks for an annotation of a Spanish bill with key provisions and related bills, the broken link is significant for several reasons:

1. Access to Information: The broken link prevents access to the actual content of the Spanish bill that the user is interested in. This is crucial because without access to the original document, it is impossible to analyze or annotate the key provisions or related bills.

2. Reliability of Source: The presence of a broken link raises questions about the reliability and accuracy of the source. It suggests that the provided reference might not have been verified, which is important in legal and academic contexts where accuracy is paramount.

3. User Guidance: Highlighting the broken link is essential to inform the user that the provided reference is not functional. This helps in setting the correct expectations and guides the user to seek an alternative source or correct the link if possible.

4. Implications for Research: For anyone conducting research or needing detailed information about the bill, the broken link is a significant hindrance. It implies that further steps need to be taken to locate the correct document, such as contacting the source, searching for the document through official legislative databases, or using other references that might be available.

In summary, the annotated text underscores the importance of having a functional and accurate reference link when dealing with legal documents. It highlights the need for the user to obtain the correct URL to access the Spanish bill in question, which is crucial for providing a comprehensive annotation of its key provisions and related bills.

Explanation:

The annotated text "Reference #18.aa2d3e17.1733239827.ea4e98df" appears to be a reference code or link to an external resource, likely pertaining to the Spanish bill mentioned in the user question. The significance of this annotation lies in its function as a placeholder or identifier for additional information that is not directly included in the provided text. This reference could potentially lead to a detailed document, database, or error page that contains the key provisions of the bill or outlines its relationship to other bills.

The implications of this annotation are multifaceted:

1. Identification and Retrieval: The reference code is crucial for locating the specific document or webpage that contains the relevant details about the Spanish bill. This ensures that users can access comprehensive information that may not be immediately visible in the main text.

2. Contextual Linking: By referencing an external source, the annotation suggests that the full understanding of the bill's provisions and related legislation requires consulting the linked material. This highlights the interconnected nature of legal documents and the importance of cross-referencing for thorough legal analysis.

3. Potential Error: The URL provided in the reference indicates an "errors.edgesuite.net" domain, which might imply that the link is broken or leads to an error page. This could signify issues with accessing the necessary information, suggesting the need for alternative methods to obtain the details about the bill.

In summary, the annotated text serves as a critical reference point for accessing detailed information regarding the Spanish bill. Its significance is rooted in its role as a connector to external resources, which are essential for a full understanding of the bill's key provisions and its relationship to other legislation.

Hey, we can annotate any PDF in the world too.

I don't really like this much sugar.

THIS IS WHERE THE HEAT COMES FROM

Hi Misha. Why pay taxes?

Hi Misha. Wow, books too.

Now we're annotating in a youtube video.

eLife Assessment

This important study provides empirical evidence of the effects of genetic diversity and species diversity on ecosystem functions across multi-trophic levels in an aquatic ecosystem. The support for these findings is solid, but a more nuanced interpretation of the results could strengthen the conclusions. The work will be of interest to ecologists working on multi-trophic relationships and biodiversity.

Reviewer #1 (Public review):

Summary:

This work used a comprehensive dataset to compare the effects of species diversity and genetic diversity within each trophic level and across three trophic levels. The results stated that species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects. Additionally, these effects were observed only within each trophic level and not across the three trophic levels studied. Although the effects of biodiversity, especially genetic diversity across multi-trophic levels, have been shown to be important, there are still very few empirical studies on this topic due to the complex relationships and difficulty in obtaining data. This study collected an excellent dataset to address this question, enhancing our understanding of genetic diversity effects in aquatic ecosystems.

Strengths:

The study collected an extensive dataset that includes species diversity of primary producers (riparian trees), primary consumers (macroinvertebrate shredders), and secondary consumers (fish). It also includes genetic diversity of the dominant species in each trophic level, biomass production, decomposition rates, and environmental data. The writing is logical and easy to follow.

Weaknesses:

The two main conclusions-(1) species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects, and (2) these effects were observed only within each trophic level, not across the three levels-are overly generalized. Analysis of the raw data shows that species and genetic diversity have different effects depending on the ecosystem function. For example, neither affected invertebrate biomass, but species diversity positively influenced fish biomass, while genetic diversity had no effect. Furthermore, Table S2 reveals that only four effect sizes were significant (P < 0.05): one positive genetic effect, one negative genetic effect, and two negative species effects, with two effects within a trophic level and two across trophic levels. Additionally, using a P < 0.2 threshold to omit lines in the SEMs is uncommon and was not adequately justified. A more cautious interpretation of the results, with acknowledgment of the variability observed in the raw data, would strengthen the manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work used a comprehensive dataset to compare the effects of species diversity and genetic diversity within each trophic level and across three trophic levels. The results showed that species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects. These effects were observed only within each trophic level and not across the three trophic levels studied. Although the effects of biodiversity, especially genetic diversity across multi-trophic levels, have been shown to be important, there are still very few empirical studies on this topic due to the complex relationships and difficulty in obtaining data. This study collected an excellent dataset to address this question, enhancing our understanding of genetic diversity effects in aquatic ecosystems.

Strengths:

The study collected an extensive dataset that includes species diversity of primary producers (riparian trees), primary consumers (macroinvertebrate shredders), and secondary consumers (fish). It also includes the genetic diversity of the dominant species at each trophic level, biomass production, decomposition rates, and environmental data.

The conclusions of this paper are mostly well supported by the data and the writing is logical and easy to follow.

Weaknesses:

(1) While the dataset is impressive, the authors conducted analyses more akin to a "meta-analysis," leaving out important basic information about the raw data in the manuscript. Given the complexity of the relationships between different trophic levels and ecosystem functions, it would be beneficial for the authors to show the results of each SEM (structural equation model).

We understand the point raised by the reviewer. We now provide individual SEMs (Figure 3), although we limit causal relationships to those for which the p-value was below 0.2 for the sake of graphical clarity. We also provide the percentage of explained variance for each ecosystem function. We detail the graph in the Results section (see l. 317-328) and discuss them (see l. 387-398). Note that we do not detail each function separately as this would (in our opinion) result in a long descriptive paragraph from which it might be difficult to get some key information. Rather, we summarize the percentage of explained variance for each function and discuss the strength of environmental vs biodiversity effects for some examples. In the Discussion, we explain why environmental effects (on functions and biodiversity) are relatively weak. We mainly attribute this to the sampling scheme that follows an East-West gradient (weak altitudinal range) rather than an upstream-downstream gradient as it is traditionally done in rivers. The reasoning behind this sampling scheme is explained in our companion paper (Fargeot et al. Oikos 2023) to which we now refer more explicitly in the MS. Briefly, using an upstream-downstream gradient would have certainly push up the effects of the environment, but this would have made extremely complex the inference of biodiversity effects due to strong collinearity among environmental and biodiversity parameters.

(2) The main results presented in the manuscript are derived from a "metadata" analysis of effect sizes. However, the methods used to obtain these effect sizes are not sufficiently clarified. By analyzing the effect sizes of species diversity and genetic diversity on these ecosystem functions, the results showed that species diversity had negative effects, while genetic diversity had positive effects on ecosystem functions. The negative effects of species diversity contradict many studies conducted in biodiversity experiments. The authors argue that their study is more relevant because it is based on a natural system, which is closer to reality, but they also acknowledge that natural systems make it harder to detect underlying mechanisms. Providing more results based on the raw data and offering more explanations of the possible mechanisms in the introduction and discussion might help readers understand why and in what context species diversity could have negative effects.

(We now provide more details. However, we are unfortunately not sure that this helped reaching some stronger explanation regarding underlying mechanisms. To be frank, we did not succeed in improving mechanistic inferences based on the outputs of the SEM models. We explored visually some additional relationships (e.g. relationships between the biomass of the focal species and that of other species in the assemblage) that we now discuss a bit more, but again, this did not really help in better understanding processes. We realize this is a limitation of our study and that this can be frustrating for readers. Nonetheless, as said in the Discussion, field-based study must be taken for what they are; observational studies forming the basis for future mechanistic studies. Although we failed to explain mechanisms, we still think that we provide important field-base evidence for the importance of biodiversity (as a whole) for ecosystem functions.

3) Environmental variation was included in the analyses to test if the environment would modulate the effects of biodiversity on ecosystem functions. However, the main results and conclusions did not sufficiently address this aspect.

This is now addressed, see our response to your first comment. We now explain (result section) and discuss environmental effects. As explained in the MS, environmental effects are similar in strength to those of biodiversity and are not that high, which is partly explained by the sampling scheme (see Fargeot et al. 2023). This is a choice we’ve made at the onset of the experiment, as we wanted to focus on biodiversity effects and avoid strong collinearity as it is generally the case in rivers (which impedes any proper and strong statistical inferences).

Reviewer #2 (Public review):

Summary:

Fargeot et al. investigated the relative importance of genetic and species diversity on ecosystem function and examined whether this relationship varies within or between trophic-level responses. To do so, they conducted a well-designed field survey measuring species diversity at 3 trophic levels (primary producers [trees], primary consumers [macroinvertebrate shredders], and secondary consumers [fishes]), genetic diversity in a dominant species within each of these 3 trophic levels and 7 ecosystem functions across 52 riverine sites in southern France. They show that the effect of genetic and species diversity on ecosystem functions are similar in magnitude, but when examining within-trophic level responses, operate in different directions: genetic diversity having a positive effect and species diversity a negative one. This data adds to growing evidence from manipulated experiments that both species and genetic diversity can impact ecosystem function and builds upon this by showing these effects can be observed in nature.

Strengths:

The study design has resulted in a robust dataset to ask questions about the relative importance of genetic and species diversity of ecosystem function across and within trophic levels.

Overall, their data supports their conclusions - at least within the system that they are studying - but as mentioned below, it is unclear from this study how general these conclusions would be.

Weaknesses:

(4) While a robust dataset, the authors only show the data output from the SEM (i.e., effect size for each individual diversity type per trophic level (6) on each ecosystem function (7)), instead of showing much of the individual data. Although the summary SEM results are interesting and informative, I find that a weakness of this approach is that it is unclear how environmental factors (which were included but not discussed in the results) nor levels of diversity were correlated across sites. As species and genetic diversity are often correlated but also can have reciprocal feedbacks on each other (e.g., Vellend 2005), there may be constraints that underpin why the authors observed positive effects of one type of diversity (genetic) when negative effects of the other (species). It may have also been informative to run SEM with links between levels of diversity. By focusing only on the summary of SEM data, the authors may be reducing the strength of their field dataset and ability to draw inferences from multiple questions and understand specific study-system responses.

We have addressed this remark and we ask the reviewers and the readers to refer to our response to comment 1 from reviewer 1. Regarding co-variation among biodiversity estimates (SGDCs according to Vellend’s framework), we have addressed these issues in a companion paper that we now cite and expand further in the MS (Fargeot et al. Oikos, 2023). Given the size of the dataset and its complexity (and associated analyses), we have decided to focus on patterns of species and genetic biodiversity in a first paper (Oikos paper) and then on the link between biodiversity and functions (this paper). As it can be read in the Oikos’s paper, there are no co-variation in term of biodiversity estimates; species diversity is not correlated to genetic diversity, and within facet, there are not co-variation among species. In addition, environmental predictors are highly estimate-specific (i.e. environmental predictors sustaining species and genetic estimates are idiosyncratic). As a result (see the new Figure 3), environmental effects are relatively weak (the same intensity that those of biodiversity) and collinearity among parameters is relatively weak. The second point is important, as this permit to better infer parameters from models, and this allows to discuss direct relationships (as observed in Figure 3, indirect environmental effects are relatively rare). We provide in the Discussion a bit more explanation about the absence of co-variation among biodiversity estimates (see l. 433-440).

(5) My understanding of SEM is it gives outputs of the strength/significance of each pathway/relationship and if so, it isn't clear why this wasn't used and instead, confidence intervals of Z scores to determine which individual BEFs were significant. In addition, an inclusion of the 7 SEM pathway outputs would have been useful to include in an appendix.

We now provide p-values (Table S2) and the seven models (Figure 3).

(6) I don't fully agree with the authors calling this a meta-analysis as it is this a single study of multiple sites within a single region and a specific time point, and not a collection of multiple studies or ecosystems conducted by multiple authors. Moreso, the authors are using meta-analysis summary metrics to evaluate their data. The authors tend to focus on these patterns as general trends, but as the data is all from this riverine system this study could have benefited from focusing on what was going on in this system to underpin these patterns. I'd argue more data is needed to know whether across sites and ecosystems, species diversity and genetic diversity have opposite effects on ecosystem function within trophic levels.

We agree. “Meta-regression” would perhaps be more adequate than “meta-analyses”. We changed the formulation.

Reviewer #3 (Public review):

The manuscript by Fargeot and colleagues assesses the relative effects of species and genetic diversity on ecosystem functioning. This study is very well written and examines the interesting question of whether within-species or among-species diversity correlates with ecosystem functioning, and whether these effects are consistent across trophic levels. The main findings are that genetic diversity appears to have a stronger positive effect on function than species diversity (which appears negative). These results are interesting and have value.

However, I do have some concerns that could influence the interpretation.

(7) Scale: the different measures of diversity and function for the different trophic levels are measured over very different spatial scales, for example, trees along 200 m transects and 15 cm traps. It is not clear whether trees 200 m away are having an effect on small-scale function.

Trees identification and invertebrate (and fish) sampling are done on the same scale. Trees are spread along the river so that their leaves fall directly in the river. Traps have been installed all along the same transect in various micro-habitats. Diversity have been measured at the exact same scale for all organisms. We have modified the MS to make this clear.

(8) Size of diversity gradients: More information is needed on the actual diversity gradients. One of the issues with surveys of natural systems is that they are of species that have already gone through selection filters from a regional pool, and theoretically, if the environments are similar, you should get similar sets of species, without monocultures. So, if the species diversity gradients range from say, 6 to 8 species, but genetic diversity gradients span an order of magnitude more, you can explain much more variance with genetic diversity. Related to this, species diversity effects on function are often asymptotic at high diversity and so if you are only sampling at the high diversity range, we should expect a strong effect.

Fish species number varies from 1 to 11, invertebrate family number varies from 15 to 42 and the tree species number varies from 7 to 20 (see Fargeot et al. 2023 for details). We have added this information in the M&M. The gradients are hence relatively large and do not cover a restricted set of values. There is a variance in species number among sites, even if sites are collected along a relatively weak altitudinal gradient. This is obviously complex to compare to SNP (genomic) diversity. Genetic and species effects are similar in effect sizes (percentage of explained variance), so it does not seem we have biased one of the two gradients of biodiversity.

(9) Ecosystem functions: The functions are largely biomass estimates (expect decomposition), and I fail to see how the biomass of a single species can be construed as an ecosystem function. Aren't you just estimating a selection effect in this case?

The biomass estimated for a certain area represents an estimate of productivity, whatever the number of species being considered. Obviously, productivity of a species can be due to environmental constraints; the biomass is expected to be lower at the niche margin (selection effect). But if these environmental effects are taken into account (which is the case in the SEMs), then the residual variation can be explained by biodiversity effects. We provide an explanation (l. 217-219).

(10) Note that the article claims to be one of the only studies to look at function across trophic levels, but there are several others out there, for example:

Thanks, we now cite some of these studies (Li et al 2020, Moi et al. 2021, Seibold et al. 2018).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Introduction:

The introduction of the manuscript is generally well-structured, and the scientific questions are clearly presented. However, in each paragraph where specific aspects are introduced, the authors do not focus sufficiently on the given points. The current introduction discusses the weaknesses of previous studies extensively but lacks detailed explanations of mechanisms and a clear anticipation of this study's contributions.

For example:

L72-77: The authors mention that "genetic diversity may functionally compensate for a species loss," but this point is not highly relevant to the main analyses of this study, which focus on comparing the relative effects of species diversity and genetic diversity.

Yes true, we understand the point made by the reviewers. We deleted this part of the sentence.

L87-95: As previously noted, "whether environmental variation decreases or enhances the relative influence of genetic and species diversity on ecosystem functions" was not addressed in this study. Additionally, the last sentence seems unnecessary here, as it does not relate to "environmental variation." The phrase "generate insightful knowledge for future mechanistic models" is vague. It would be helpful to specify what kind of knowledge and what types of future mechanistic models are being referred to.

We modified these two sentences. We now posit the prediction that what has been observed under controlled conditions (that genetic and species have effects of similar magnitude) might not be the norm under fluctuating environments (because it has been shown that environmental variation modulates the strength of interspecific BEFS and create huge variance).

L96-116: The use of "for instance" three times in this paragraph makes the structure seem scattered, as only examples are provided. Improving the transition words can help the text focus better on the main point.

We have modified some parts of this section to better reflect predictions

L115-116: Again, it would be beneficial to specify what kind of insightful information can be provided.

We have modified this sentence by making more explicit some of the information that may be gained.

L117-134: Stating clear expectations can help the introduction focus on the mechanisms and assist readers in following the results.

We now provide some predictions. We were reluctant to make predictions in the first version of the MS as we have the feeling that predictions can go on very different direction depending on how we set the scene. We therefore stick to predictions that we think are the most logical (the simplest ones). This illustrates the lack of theoretical papers on these issues.

Methods:

L287-293: The method for estimating the standard effect size is unclear. I assume it was derived from the SEM models? This needs further clarification.

Yes, it is derived from the standardized estimate from each pSEM. This is now explained in the MS.

Results:

As mentioned in the public review, it is very important to show the results of analyzing raw data.

Done, see Figure 3 and Results section.

Table 1: The font and format of the PCA table are different from other tables and appear vague, resembling a picture rather than a table.

Changed.

Table 2 (and supplementary table): "D.f." is not explained in the table legend. Is 1 the numerator df and 30 the denominator df? Is the denominator the residual? Additionally, the table legend mentions "magnitude and direction." ANOVA only tests if the biodiversity effects are significantly different between species or genetic diversity, but not the magnitude. For example, -0.5 and 0.5 are very different, but their effect magnitudes are the same.

This is a mistake; sorry the format of the Table was from a previous version of the MS in which we used linear models rather that linear mixed models (both lead to the same results). The ANOVA used to test the significance of fixed terms in linear mixed model are based on Wald chi-sqare tests, and it should have been read “Chi-value” rather than “F-value” in both tables and the only degree of freedom in this test is the one at the numerator. This has been changed. We have changed the caption of the Table (“ANOVA table for the linear mixed model testing whether the relationships between biodiversity and ecosystem functions measured in a riverine trophic chain differ between the biodiversity facets (species or genetic diversity) and the types of BEF (within- or between-trophic levels)”)

Minor:

There should always be a space between a number and a unit. In the manuscript, spaces are inconsistently used between numbers and units.

Corrected

Reviewer #2 (Recommendations for the authors):

(1) In the introduction, the authors could focus more and build out what they predicted/hypothesized as well as what has been found in the manipulated experiments that examined the role of species and genetic diversity. That would enhance the background information for a more general audience, and highlight expected results and why.

We modified the Introduction according to comments made by reviewer 1 and clarified the predictions as best as we can.

(2) Similarly, the discussion is fairly big picture, but this dataset focused exclusively on this 3-trophic interaction in a riverine system. It could be beneficial to dig into the ecology to find out why the opposite effects of species and genetic diversity are seen within trophic levels in this system.

We have added some explanations based on the specific pSEM (see our responses to the public reviews for details). But as said in the responses to the public reviews, even with mode detailed models, it is hard to tease apart mechanisms. One important point is that genetic and species diversity do not correlate one to each other (they do not co-vary over space), which means the effect of one facet is independent from the other. However, apart from that, we can’t really tell more without more mechanistic approaches. We understand this is frustrating, but this is the nature of field-based data. This does not mean they are useless. On the contrary, they confirm and expand patterns found under controlled conditions (which for ecologists is quite important as nature is our playground), but they are limited in inferences of mechanisms.

(3) It would also be informative if the authors specified what positive and negative Z scores mean. It seems counterintuitive in Figure 3. For example, in the upper left, it's denoted as a larger intraspecific effect - which I'd assume is higher genetic (within species) diversity - but is this not where species diversity effects are higher? In theory this figure could be similar to Figure 1 from Des Roches et al. 2018 - where showing the 1:1 line of where species and genetic diversity effects are similar and then how some are more impacted by SD or GD as that links to the overall question, right?

For example: Figure 3 makes it seem that GD effects are stronger (more positive) for within trophic responses (which is reflected in the text), but in that quadrant, it states that the interspecific effect is larger?

yes, you’re true Figure 3 (now Figure 4) is not ideal. We added an explicit explanation for interpreting Zr in the main text. In addition, we modified the text in the quadrat as this was not correct. Note that it cannot be directly be compared to that of DesRoches et al. In DesRoches et al., there is a single effect size (ES) per situation (which is roughly expressed as “ES = effect of species - effect of genotypes”). Here, there are two ES per situation, one for the species effect, the other for the genetic effect, which makes the biplot more complex (as species and genetic can be similar in magnitude, but opposite in direction, e.g., 0.5 and -0.5). We may have done as DesRoches et al. (“ES = effect of species - effect of genotypes”), but as we don’t have absolute ES (as in DesRoches) the resulting signs of the ES are non sensical…Not easy for us to find a clever solution (or said differently, we were not clever enough to find an easy solution).  Nonetheless, we tried another visualization by including “sub-quadrats” into the four main quadrats. We hope this will be clearer

(4) It's unclear why authors included both a simplified linear mixed model with diversity type and biodiversity facet as fixed factors, and then a second linear model that included trophic level (with those other 2 factors and interactions), but only showed results of trophic level from that more complex model. It is unclear why they include two models when the more complex one would have evaluated all aspects of their research question and shown the same patterns.

You’re true, the more complex model evaluates both aspects. Nonetheless, as the hypotheses were strictly separated, we thought it is simpler to associate one model to one hypothesis. We agree that this duplicates information, but we would like to keep the two models to make the text more gradual.

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-2024-02545

Corresponding author(s): Woo Jae, Kim

1. General Statements

We sincerely appreciate the positive and constructive feedback provided by all three reviewers. Their insightful comments have been invaluable in guiding our revisions. In response, we have made every effort to address their suggestions through additional experiments and by restructuring our manuscript to improve clarity and coherence.

In this revision, we have streamlined the presentation of our data to enhance the narrative flow, ensuring that it is more accessible to a general readership. We believe that these changes not only strengthen our manuscript but also align with the reviewers' recommendations for improvement.

We are hopeful that the revisions we have implemented meet the expectations of the reviewers and contribute to a clearer understanding of our findings. Thank you once again for your thoughtful critiques, which have greatly aided us in refining our work.

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2. Point-by-point description of the revisions

Reviewer #1

General comment: This manuscript by Song et al. investigates the molecular mechanisms underlying changes in mating duration in Drosophila induced by previous experience. As they have shown previously, they find that male flies reared in isolation have shorter mating duration than those reared in groups, and also that male flies with previous mating experience have shorter mating duration than sexually naïve males. They have conducted a myriad of experiments to demonstrate that the neuropeptide SIFa is required for these changes in mating duration. They have further provided evidence that SIFa-expressing neurons undergo changes in synaptic connectivity and neuronal firing as a result of previous mating experience. Finally, they argue that SIFa neurons form reciprocal connections with sNPF-expressing neurons, and that communication within the SIFa-sNPF circuit is required for experience-dependent changes in mating duration. These results are used to assert that SIFa neurons track the internal state of the flies to modulate behavioral choice.

   __Answer:__ We appreciate the reviewer's thoughtful comments and commendations regarding our manuscript. The recognition of our investigation into the molecular mechanisms influencing mating duration in *Drosophila* is greatly valued. In particular, we are grateful for the reviewer's positive remarks about our comprehensive experimental approach to demonstrate the role of the neuropeptide SIFa in these changes. The evidence we provided indicating that SIFa-expressing neurons undergo alterations in synaptic connectivity and neuronal firing due to previous social experiences is crucial for elucidating the underlying neural circuitry involved in experience-dependent behaviors. Finally, we are thankful for the recognition of our assertion that SIFa neurons form reciprocal connections with sNPF-expressing neurons, emphasizing the importance of this circuit in modulating behavioral choices based on internal states. To provide stronger evidence for the interactions between SIFa and sNPF, we conducted detailed GCaMP experiments, which revealed intriguing neural connections between these two neuropeptides. We have included this new data in our main figure. We believe these insights contribute significantly to the existing literature on neuropeptidergic signaling and its implications for understanding complex behaviors in *Drosophila*. We look forward to addressing any further comments and enhancing our manuscript based on your invaluable feedback. Thank you once again for your constructive critique and support.

Major concerns:

Comment 1. The authors are to be commended for the sheer quantity of data they have generated, but I was often overwhelmed by the figures, which try to pack too much into the space provided. As a result, it is often unclear what components belong to each panel. Providing more space between each panel would really help.

   __Answer:__ We sincerely appreciate the reviewer’s commendation regarding the extensive data we have generated in our study. It is gratifying to know that our efforts to provide a comprehensive analysis of the molecular mechanisms underlying changes in mating duration have been recognized. We understand the concern regarding the density of information presented in our figures. We aimed to convey a wealth of data to support our findings, but we acknowledge that this may have led to some confusion regarding the organization and clarity of the panels. We are grateful for your constructive feedback on this matter. In response, we have significantly reduced the density of the main figures and decreased the size of the graphs to improve clarity. We have also increased the spacing between panels to ensure that each component is more easily distinguishable. Further details will be provided in our responses to each comment below.

• *

Comment 2. This is a rare instance where I would recommend paring down the paper to focus on the more novel, clear and relevant results. For example, all of Figure 2 shows the projection pattern of SIFa+ neuron dendrites and axons, which have been reported by multiple previous papers. Figure 7G and J show trans-tango data and SIFaR-GAL4 expression patterns, which were previously reported by Dreyer et al., 2019. These parts could be removed to supplemental figures. Figure 5 details experiments that knock down expression of different neurotransmitter receptors within the SIFa-expressing cells. The results here are less definitive than the SIFa knockdown results, and the SCope data supporting the idea that these receptors are expressed in SIFa-expressing neurons is equivocal. I would recommend removing these data (perhaps they could serve as the basis for another manuscript) or focusing solely on the CCHa1R results, which is the only manipulation that affects both LMD and SMD.

   __Answer:__ We sincerely appreciate the reviewer’s positive feedback regarding the extensive data generated in our study. We also fully agree with the reviewer that the sheer volume of our data made it challenging to support our hypothesis that SIFa neurons serve as a hub for integrating multiple neuropeptide inputs and orchestrating various behaviors related to energy balance, as highlighted in our new Figure 5N.

   In response to the reviewer's suggestions, we have streamlined our manuscript by removing excessive and redundant data to enhance clarity and simplicity. First, we have moved Figure 2 to the supplementary materials as the reviewer noted that the branching patterns of SIFa neurons are well-documented in previous literature. Second, we relocated the trans-tango data from Figure 7G to Figure S7, since this information is also well-established. We retained this data in the supplementary section to illustrate the connection of SIFa to our recent findings regarding SIFaR24F06 neuron connections. Additionally, we have completely removed the neuropeptide receptor input screening data previously included in Figure 5, as well as Figure S8, which presented fly SCope tSNE data. As suggested by the reviewer, we plan to utilize these data for a future paper focused on investigating the underlying mechanisms of SIFa inputs that modulate SIFa activity. Thanks to the reviewer’s constructive suggestions, we believe our manuscript is now more convincing and clearer for readers.

Comment 3. Finally, I would like the authors to spend more time explaining how they think the results tie together. For example, how do the authors think the changes in branching and activity in SIFa-expressing neurons tie to the change in mating duration provoked by previous experience? It would benefit the manuscript to simplify and clarify the message about what the authors think is happening at the mechanistic level. The various schematics (eg. Fig 7N) describe the results but the different parts feel like separate findings rather than a single narrative. (MECHANISMS diagram and explanation)

   __Answer:__ We appreciate the reviewer’s constructive comments, which have significantly improved our manuscript and conclusions for our readers. As the reviewer will see, we have made substantial revisions in line with the suggestions provided. We dedicated additional time to clarify the electrical activities and synaptic plasticity of SIFa neurons in relation to internal states that orchestrate various behaviors. We have summarized our hypothesis regarding the mechanistic role of SIFa neurons in Figure 5N. In brief, we propose that SIFa neurons function as a hub that receives diverse neuropeptidergic signals, which subsequently alters their electrical activity and synaptic branching. This, in turn, leads to different internal states. The internal states of SIFa neurons can then be interpreted by SIFaR-expressing cells, which help orchestrate various behaviors and physiological responses. We aim to address these aspects further in another manuscript that has been co-submitted alongside this one [1].

Comment 4. Most of the experiments lack traditional controls. For example, in experiments in Fig 1C-K, one would typically include genetic controls that contain either the GAL4 or UAS elements alone. The authors should explain their decision to omit these control experiments and provide an argument for why they are not necessary to correctly interpret the data. In this vein, the authors have stated in the methods that stocks were outcrossed at least 3x to Canton-S background, but 3 outcrosses is insufficient to fully control for genetic background.

   __Answer:__ We sincerely thank the reviewer for insightful comments regarding the absence of traditional genetic controls in our study of LMD and SMD behaviors. We acknowledge the importance of such controls and wish to clarify our rationale for not including them in the current investigation. The primary reason for not incorporating all genetic control lines is that we have previously assessed the LMD and SMD behaviors of GAL4/+ and UAS/+ strains in our earlier studies. Our past experiences have consistently shown that 100% of the genetic control flies for both GAL4 and UAS exhibit normal LMD and SMD behaviors. Given these findings, we deemed the inclusion of additional genetic controls to be non-essential for the present study, particularly in the context of extensive screening efforts. We understand the value of providing a clear rationale for our methodology choices. To this end, we have added a detailed explanation in the "MATERIALS AND METHODS" section and the figure legends of Figure 1. This clarification aims to assist readers in understanding our decision to omit traditional controls, as outlined below.

"Mating Duration Assays for Successful Copulation

The mating duration assay in this study has been reported[33,73,93]. To enhance the efficiency of the mating duration assay, we utilized the Df (1)Exel6234 (DF here after) genetic modified fly line in this study, which harbors a deletion of a specific genomic region that includes the sex peptide receptor (SPR)[94,95]. Previous studies have demonstrated that virgin females of this line exhibit increased receptivity to males[95]. We conducted a comparative analysis between the virgin females of this line and the CS virgin females and found that both groups induced SMD. Consequently, we have elected to employ virgin females from this modified line in all subsequent studies. For naïve males, 40 males from the same strain were placed into a vial with food for 5 days. For single reared males, males of the same strain were collected individually and placed into vials with food for 5 days. For experienced males, 40 males from the same strain were placed into a vial with food for 4 days then 80 DF virgin females were introduced into vials for last 1 day before assay. 40 DF virgin females were collected from bottles and placed into a vial for 5 days. These females provide both sexually experienced partners and mating partners for mating duration assays. At the fifth day after eclosion, males of the appropriate strain and DF virgin females were mildly anaesthetized by CO2. After placing a single female in to the mating chamber, we inserted a transparent film then placed a single male to the other side of the film in each chamber. After allowing for 1 h of recovery in the mating chamber in 25℃ incubators, we removed the transparent film and recorded the mating activities. Only those males that succeeded to mate within 1 h were included for analyses. Initiation and completion of copulation were recorded with an accuracy of 10 sec, and total mating duration was calculated for each couple. All assays were performed from noon to 4pm. Genetic controls with GAL4/+ or UAS/+ lines were omitted from supplementary figures, as prior data confirm their consistent exhibition of normal LMD and SMD behaviors [33,73,93,96,97]. Hence, genetic controls for LMD and SMD behaviors were incorporated exclusively when assessing novel fly strains that had not previously been examined. In essence, internal controls were predominantly employed in the experiments, as LMD and SMD behaviors exhibit enhanced statistical significance when internally controlled. Within the LMD assay, both group and single conditions function reciprocally as internal controls. A significant distinction between the naïve and single conditions implies that the experimental manipulation does not affect LMD. Conversely, the lack of a significant discrepancy suggests that the manipulation does influence LMD. In the context of SMD experiments, the naïve condition (equivalent to the group condition in the LMD assay) and sexually experienced males act as mutual internal controls for one another. A statistically significant divergence between naïve and experienced males indicates that the experimental procedure does not alter SMD. Conversely, the absence of a statistically significant difference suggests that the manipulation does impact SMD. Hence, we incorporated supplementary genetic control experiments solely if they deemed indispensable for testing. All assays were performed from noon to 4 PM. We conducted blinded studies for every test[98,99] .

   While we have previously addressed this type of reviewer feedback in our published manuscript [2–7], we appreciate the reviewer’s suggestion to include traditional genetic control experiments. In response, we conducted all feasible combinations of genetic control experiments for LMD/SMD during the revision period. The results are presented in the supplementary figures and are described in the main text.

   We appreciate the reviewer's inquiry regarding the genetic background of our experimental lines. In response to the comments, we would like to clarify the following. All of our GAL4, UAS, or RNAi lines, which were utilized as the virgin female stock for outcrosses, have been backcrossed to the Canton-S (CS) genetic background for over ten generations. The majority of these lines, particularly those employed in LMD assays, have been maintained in a CS backcrossed status for several years, ensuring a consistent genetic background across multiple generations. Our experience has indicated that the genetic background, particularly that of the X chromosome inherited from the female parent, plays a pivotal role in the expression of certain behavioral traits. Therefore, we have consistently employed these fully outcrossed females as virgins for conducting experiments related to LMD and SMD behaviors. It is noteworthy that, in contrast to the significance of genetic background for LMD behaviors, we have previously established in our work [6] that the genetic background does not significantly influence SMD behaviors. This distinction is important for the interpretation of our findings. To provide a comprehensive understanding of our experimental design, we have detailed the genetic background considerations in the __"Materials and Methods"__ section, specifically in the subsection __"Fly Stocks and Husbandry"__ as outlined below.

"To reduce the variation from genetic background, all flies were backcrossed for at least 3 generations to CS strain. For the generation of outcrosses, all GAL4, UAS, and RNAi lines employed as the virgin female stock were backcrossed to the CS genetic background for a minimum of ten generations. Notably, the majority of these lines, which were utilized for LMD assays, have been maintained in a CS backcrossed state for long-term generations subsequent to the initial outcrossing process, exceeding ten backcrosses. Based on our experimental observations, the genetic background of primary significance is that of the X chromosome inherited from the female parent. Consequently, we consistently utilized these fully outcrossed females as virgins for the execution of experiments pertaining to LMD and SMD behaviors. Contrary to the influence on LMD behaviors, we have previously demonstrated that the genetic background exerts negligible influence on SMD behaviors, as reported in our prior publication [6]. All mutants and transgenic lines used here have been described previously."

Comment 5. Throughout the manuscript, the authors appear to use a single control condition (sexually naïve flies raised in groups) to compare to both males raised singly and males with previous sexual experience. These control conditions are duplicated in two separate graphs, one for long mating duration and one for short mating duration, but they are given different names (group vs naïve) depending on the graph. If these are actually the same flies, then this should be made clear, and they should be given a consistent name across the different "experiments".

   __Answer:__ We are grateful to the reviewer for highlighting the potential for confusion among readers regarding the visualization methods used in our figures. In response to this valuable feedback, we have now included a more detailed explanation of the graph visualization techniques in the legends of Figure 1, as detailed below. This additional information should enhance the clarity and understanding of the figure for all readers.

In the mating duration (MD) assays, light grey data points denote males that were group-reared (or sexually naïve), whereas blue (or pink) data points signify males that were singly reared (or sexually experienced). The dot plots represent the MD of each male fly. The mean value and standard error are labeled within the dot plot (black lines). Asterisks represent significant differences, as revealed by the unpaired Student’s t test, and ns represents non-significant differences M.D represent mating duration. DBMs represent the 'difference between means' for the evaluation of estimation statistics (See MATERIALS AND METHODS). Asterisks represent significant differences, as revealed by the Student’s t test (* p

Comment 6. The authors use SCope data to provide evidence for co-expression of SIFa and other neurotransmitters or neuropeptide receptors. The graphs they show are hard to read and it is not clear to what extent the gene expression is actually overlapping. It would be more definitive to show graphs that indicate which percentage of SIFa-expressing cells co-express other neurotransmitter components, and what the actual level of expression of the genes is. The authors should also provide more information on how they identified the SIFa+ cells in the fly atlas dataset. These are important pieces of information to be able to interpret the effects of manipulation of these other neurotransmitter systems within SIFa-expressing cells on mating duration.

__ Answer: We appreciate the reviewer for pointing out the potential for confusion among readers regarding the visualization methods used in our figures, particularly concerning the tSNE plots of scRNA-seq data. As mentioned in our previous response, we have removed most of the tSNE plots related to co-expression data with SIFa and NPRs, which we believe will reduce any confusion for readers interpreting these plots. However, we have retained a few tSNE plots, specifically Figures 2N-O, to confirm the potential co-expression of the ple and Vglut genes in SIFa cells. We understand the reviewer’s concerns about the clarity of the presented data and the necessity for more detailed information regarding the extent of co-expression and the identification of SIFa-expressing cells. To address these concerns, we have included a comprehensive description of our methods in the __MATERIALS AND METHODS section below.

"Single-nucleus RNA-sequencing analyses

The snRNAseq dataset analyzed in this paper is published in [112] and available at the Nextflow pipelines (VSN, https://github.com/vib-singlecell-nf), the availability of raw and processed datasets for users to explore, and the development of a crowd-annotation platform with voting, comments, and references through SCope (https://flycellatlas.org/scope), linked to an online analysis platform in ASAP (https://asap.epfl.ch/fca). For the generation of the tSNE plots, we utilized the Fly SCope website (https://scope.aertslab.org/#/FlyCellAtlas/*/welcome). Within the session interface, we selected the appropriate tissues and configured the parameters as follows: 'Log transform' enabled, 'CPM normalize' enabled, 'Expression-based plotting' enabled, 'Show labels' enabled, 'Dissociate viewers' enabled, and both 'Point size' and 'Point alpha level' set to maximum. For all tissues, we referred to the individual tissue sessions within the '10X Cross-tissue' RNAseq dataset. Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Dashed lines denote the significant overlap of cell populations annotated by the respective genes. Coexpression between genes or annotated tissues is visually represented by differentially colored cell populations. For instance, yellow cells indicate the coexpression of a gene (or annotated tissue) with red color and another gene (or annotated tissue) with green color. Cyan cells signify coexpression between green and blue, purple cells for red and blue, and white cells for the coexpression of all three colors (red, green, and blue). Consistency in the tSNE plot visualization is preserved across all figures.

Single-cell RNA sequencing (scRNA-seq) data from the Drosophila melanogaster were obtained from the Fly Cell Atlas website (https://doi.org/10.1126/science.abk2432). Oenocytes gene expression analysis employed UMI (Unique Molecular Identifier) data extracted from the 10x VSN oenocyte (Stringent) loom and h5ad file, encompassing a total of 506,660 cells. The Seurat (v4.2.2) package (https://doi.org/10.1016/j.cell.2021.04.048) was utilized for data analysis. Violin plots were generated using the “Vlnplot” function, the cell types are split by FCA.

   We have also included detailed descriptions in the figure legends for the initial tSNE plot presented below to help readers clearly understand the significance of this visualization.

"Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and/or bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Consistency in the tSNE plot visualization is preserved across all figures."

Comment 7. I would like to see more information on how the thresholding and normalization was done for immunohistochemistry experiments. Was thresholding applied equally across all datasets? Furthermore, "overlap" of Denmark and Syt-eGFP is taken as evidence for synaptic connectivity, but the latter requires more than just overlap in the location of the axon terminal and dendrite regions of the neuron.

__ Answer: Thank you for your continued engagement with our manuscript and for highlighting the need for further clarification on our methods. Your attention to the details of our immunohistochemistry experiments is commendable, and we agree that providing a clear explanation of our thresholding and normalization procedures is essential for the transparency and reproducibility of our results. We concur that the intensity of these signals is indeed correlated with the area measurements, which is a critical factor to consider. In response to the reviewer's valuable suggestion, we have revised our approach and now present our data based on intensity measurements. Additionally, we have updated the labeling of our Y-axis to "Norm. GFP Int.", which stands for "normalized GFP intensity". This change ensures clarity and consistency in the presentation of our data. We primarily adhered to the established methods outlined by Kayser et al. [8]. To address your first point, we have now included a more detailed description of our thresholding and normalization procedures in the __MATERIALS AND METHODS section as below.

"Quantitative analysis of fluorescence intensity

To ascertain calcium levels and synaptic intensity from microscopic images, we dissected and imaged five-day-old flies of various social conditions and genotypes under uniform conditions. The GFP signal in the brains and VNCs was amplified through immunostaining with chicken anti-GFP primary antibody. Image analysis was conducted using ImageJ software. For the quantification of fluorescence intensities, an investigator, blinded to the fly's genotype, thresholded the sum of all pixel intensities within a sub-stack to optimize the signal-to-noise ratio, following established methods [93]. The total fluorescent area or region of interest (ROI) was then quantified using ImageJ, as previously reported. For CaLexA or TRIC signal quantification, we adhered to protocols detailed by Kayser et al. [94], which involve measuring the ROI's GFP-labeled area by summing pixel values across the image stack. This method assumes that changes in the GFP-labeled area and intensity are indicative of alterations in the CaLexA and TRIC signal, reflecting synaptic activity. ROI intensities were background-corrected by measuring and subtracting the fluorescent intensity from a non-specific adjacent area, as per Kayser et al. [94]. For normalization, nc82 fluorescence is utilized for CaLexA, while RFP signal is employed for TRIC experiments, as the RFP signal from the TRIC reporter is independent of calcium signaling [76]. For the analysis of GRASP or tGRASP signals, a sub-stack encompassing all synaptic puncta was thresholded by a genotype-blinded investigator to achieve the optimal signal-to-noise ratio. The fluorescence area or ROI for each region was quantified using ImageJ, employing a similar approach to that used for CaLexA or TRIC quantification [93]. 'Norm. GFP Int.' refers to the normalized GFP intensity relative to the RFP signal."

Comment 8. None of the RNAi experiments have been validated to demonstrate effective knockdown. In many cases, this would be difficult to do because of a lack of an antibody to quantify in a cell-specific manner; however, this fact should be acknowledged, especially in cases where there was found to be a lack of phenotype, which could result from lack of knockdown. The authors could also look for evidence in the literature of cases where RNAi lines they have used have been previously validated. For SIFa, knockdown can be easily confirmed with the SIFa antibody the authors have used elsewhere in the manuscript.

__ Answer:__ We appreciate the reviewer’s constructive and critical comments regarding the validation of our RNAi experiments through effective knockdown. We understand the reviewer’s concerns about achieving effective knockdown with RNAi; however, we have demonstrated in our unpublished preprint that the neuronal knockdown using independent SIFa-RNAi lines aligns with the SIFa mutant phenotype, which is consistent with our current findings on SIFa knockdown (Wong 2019). In most cases involving RNAi experiments, we have utilized independent RNAi strains to confirm consistent phenotypes and have compared these results with those from mutant phenotypes [1,9]. Therefore, we are confident that our observed SIFa phenotype results from effective RNAi knockdown. Nevertheless, we respect the reviewer’s comments and have conducted additional SIFa knockdown experiments using various GAL4 drivers, followed by immunostaining with SIFa antibodies. As shown in Figure S1B, both neuronal GAL4 drivers and SIFa-GAL4 effectively reduced SIFa immunoreactivity. We believe this indicates that our SIFa knockdown efficiently phenocopies the SIFa mutant phenotype. We also described this result in manuscript as below:

"Using the GAL4SIFa.PT driver and the elavc155 driver, we observed a significant decrease in SIFa immunoreactivity following SIFa-RNAi treatment, thereby confirming the effective knockdown of SIFa in these cells. In contrast, when SIFa-RNAi was expressed under the control of the repo-GAL4 driver, no significant change in SIFa immunoreactivity was detected (Fig. S1B). This control experiment highlights the specificity of the SIFa-RNAi effect and supports the conclusion that the behavioral changes observed in SMD and LMD are likely attributable to the targeted reduction of SIFa in the intended neuronal populations."

Minor comments:

Comment 1. There are quite a lot of citations to preprints, including preprints of the manuscripts under review. It seems inappropriate to cite a preprint of the manuscript you are submitting because it gives a false sense of strengthening the assertions being made in the manuscript.

   __Answer:__ We agree with the reviewer and have omitted all preprints that are currently under review, except for those that are deemed necessary, such as the Zhang et al. 2024 preprint, which is being submitted alongside this manuscript.

Comment 2. It seems that labels are incorrect on a number of the immunohistochemistry figures. For example, in Fig 2N, it labels dendrites as green, but this is sytEGFP, which is the presynaptic terminal.

__ Answer:__ We thoroughly reviewed and corrected the errors in the labels.

Comment ____3. Fig 4N shows grasp between SIFa-LexA and sNPF-R-GAL4, but the authors have argued that these two components should both be expressed in SIFa-expressing cells. This would make grasp signal misleading, because it would appear in the SIFa-expressing cells even without synaptic contacts due to both split GFP molecules being expressed in these cells.

   __Answer:__ We appreciate the reviewer’s critical comments regarding the interpretation of our GRASP experiments. As the reviewer noted, we acknowledge that the GRASP results also indicate synaptic contacts between SIFa cells. We have elaborated on these results in the following sections.

"This indicates that the synapses between SIFa cells expressing sNPF-R become stronger (S5K to S5M Fig)."

   However, we understand that readers may find the interpretation of this GRASP data confusing, so we have included additional explanations below to clarify.

This indicates that the synapses between SIFa cells expressing sNPF-R become stronger (S5K to S5M Fig) since we have found that SIFa cells express sNPF-R (Fig 3M, S5E and S5G)

Comment 4. For quantifying TRIC and CaLexA experiments (eg. Figure 6A-E), intensity of signal should be measured in addition to the area covered by the signal.

__ Answer:__ We concur with the reviewer. Since all of our analyses indicated that the area covered by the signal correlates with the signal intensity, we opted to use normalized intensity rather than area coverage.

Conclusive Comments: This study will be most relevant to researchers interested in understanding neuronal control of behavior. It has provided novel information about the mechanisms underlying mating duration in flies, which is used to delineate how internal state influences behavioral outcomes. This represents a conceptual advance, particularly in identifying a cell type and molecule that influences mating duration decisions. The strength of the manuscript is the number of different assays used to investigate the central question from a number of angles. The limitation is that there is a lack of a big picture tying the different components of the manuscript together. Too much data is presented without providing a framework to understand how the data points fit together.

• Answer: We sincerely appreciate the reviewer’s positive feedback regarding our study and the recognition of its relevance to researchers interested in understanding the neuronal control of behavior. We are grateful for the acknowledgment of our novel insights into the mechanisms underlying mating duration in Drosophila*, particularly in how internal states influence behavioral outcomes. The identification of specific cell types and molecules that affect mating duration decisions indeed represents a significant conceptual advance. We also appreciate the reviewer’s commendation of the diverse array of assays employed in our investigation, which allowed us to approach our central question from multiple perspectives.

  In response to the reviewer’s constructive criticism regarding the lack of a cohesive framework tying the various components of our manuscript together, we have completely restructured our manuscript. We removed redundant data and incorporated additional convincing experiments, such as GCaMP analyses, to enhance clarity and coherence. Furthermore, we have provided a simplified yet comprehensive overview that describes the role of SIFa as a hub for neuropeptidergic signaling. This framework illustrates how SIFa orchestrates multiple behaviors related to energy balance through calcium signaling and synaptic plasticity via SIFaR-expressing cells.

  We believe these revisions address the reviewer’s concerns and provide a clearer understanding of how the different elements of our study fit together, ultimately strengthening the overall impact of our manuscript. Thank you for your valuable feedback, which has guided us in improving our work.

Reviewer #2

General Comments:* In the present study, the authors employ mating behavior in male fruit flies, Drosophila melanogaster, to investigate the behavioral roles of the neuropeptide SIFamide. The duration of mating behavior in these animals varies depending on context, previous experience, and internal metabolic state. The authors use this variability to explore the neuronal mechanisms that control these influences. In an abstraction step, they compare the different mating durations to concepts of neuronal interval timing.

The behavioral functions of the neuropeptide SIFamide have been thoroughly characterized in several studies, particularly in the contexts of circadian rhythm and sleep, courtship behavior, and food uptake. This study adds new data, demonstrating that SIFamide is essential for the proper control of mating behavior, highlighting the interconnection of various state- and motivation-dependent behaviors at the neuronal level. However, the hypothesis that mating behavior is related to interval timing is not convincingly supported.

Experimentally, the authors show that RNAi-mediated downregulation of SIFamide affects mating duration in male flies. They use combinations of RNAi lines under the control of various Gal4 lines to identify additional neurotransmitters, neuropeptides, and receptors involved in this process. This approach is complemented by neuroanatomical staining and single-cell RNA sequencing.*

* Overall, the study advances our knowledge about the behavioral roles of SIFamide, which is certainly important, interesting, and worthy of being reported. However, the manuscript also raises several serious caveats and includes points that remain speculative, are less convincing, or are simply incorrect.*

• Answer: We would like to thank the reviewer for their thoughtful and constructive comments regarding our study. We appreciate the recognition of our investigation into the behavioral roles of the neuropeptide SIFamide in male Drosophila melanogaster*, particularly how we explored the variability in mating duration influenced by context, previous experience, and internal metabolic state. We are grateful for the acknowledgment that our study adds valuable data demonstrating the essential role of SIFamide in regulating mating behavior, highlighting the interconnectedness of various state- and motivation-dependent behaviors at the neuronal level.

  We also appreciate the reviewer's recognition of our experimental approach, which includes RNAi-mediated downregulation of SIFamide, the use of various Gal4 lines to identify additional neurotransmitters, neuropeptides, and receptors involved in this process, as well as our incorporation of neuroanatomical staining and single-cell RNA sequencing.

  In response to the reviewer’s concerns regarding the hypothesis that mating behavior is related to interval timing, we acknowledge that this aspect requires further clarification and support. We have revisited this hypothesis in our manuscript to strengthen its foundation and address any speculative elements. We aim to provide more robust evidence and clearer connections between mating behavior and neuronal interval timing.

  Furthermore, we have taken care to address any points that may have been perceived as less convincing or incorrect. We are committed to refining our manuscript to ensure that all claims are well-supported by our data. Thank you once again for your valuable feedback. We believe that these revisions will enhance the clarity and impact of our study while addressing the concerns raised.

Major concerns:

Comment 1. The authors conclude from their mating experiments that SIFamide controls interval timing. This conclusion is not supported by the data, which only indicate that SIFamide is required for normal mating duration and modulates the motivation-dependent component of this behavior. There is no clear evidence linking this to interval timing.

__ Answer: __We appreciate the reviewer’s insightful comments regarding our conclusion linking SIFamide to interval timing in mating behavior. We acknowledge that our data primarily demonstrate that SIFamide is required for normal mating duration and modulates the motivation-dependent aspects of this behavior, and we recognize the need for clearer evidence connecting these observations to interval timing. Current research by Crickmore et al. has shed light on how mating duration in Drosophila serves as a powerful model for exploring changes in motivation over time as behavioral goals are achieved. For instance, at approximately six minutes into mating, sperm transfer occurs, leading to a significant shift in the male's nervous system: he no longer prioritizes sustaining the mating at the expense of his own survival. This change is driven by the output of four male-specific neurons that produce the neuropeptide Corazonin (Crz). When these Crz neurons are inhibited, sperm transfer does not occur, and the male fails to downregulate his motivation, resulting in matings that can last for hours instead of the typical ~23 minutes [10].

   Recent research by Crickmore et al. has received NIH R01 funding (Mechanisms of Interval Timing, 1R01GM134222-01) to explore mating duration in *Drosophila* as a genetic model for interval timing. Their work highlights how changes in motivation over time can influence mating behavior, particularly noting that significant behavioral shifts occur during mating, such as the transfer of sperm at approximately six minutes, which correlates with a decrease in the male's motivation to continue mating [10]. These findings suggest that mating duration is not only a behavioral endpoint but may also reflect underlying mechanisms related to interval timing.

   We believe that by leveraging the robustness and experimental tractability of these findings, along with our own work on SIFamide's role in mating behavior, we can gain deeper insights into the molecular and circuit mechanisms underlying interval timing. We will revise our manuscript to clarify this relationship and emphasize how SIFamide may interact with other neuropeptides and neuronal circuits involved in motivation and timing.

   In addition to the efforts of Crickmore's group to connect mating duration with a straightforward genetic model for interval timing, we have previously published several papers demonstrating that LMD and SMD can serve as effective genetic models for interval timing within the fly research community. For instance, we have successfully connected SMD to an interval timing model in a recently published paper [6], as detailed below:

"We hypothesize that SMD can serve as a straightforward genetic model system through which we can investigate "interval timing," the capacity of animals to distinguish between periods ranging from minutes to hours in duration.....

In summary, we report a novel sensory pathway that controls mating investment related to sexual experiences in Drosophila. Since both LMD and SMD behaviors are involved in controlling male investment by varying the interval of mating, these two behavioral paradigms will provide a new avenue to study how the brain computes the ‘interval timing’ that allows an animal to subjectively experience the passage of physical time [11–16]."

   Lee, S. G., Sun, D., Miao, H., Wu, Z., Kang, C., Saad, B., ... & Kim, W. J. (2023). Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. *PLoS Genetics*, *19*(5), e1010753.

   We have also successfully linked LMD behavior to an interval timing model and have published several papers on this topic recently [4,5,7].

   Sun, Y., Zhang, X., Wu, Z., Li, W., & Kim, W. J. (2024). Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. *G3: Genes, Genomes, Genetics*, jkae255.

   Zhang, T., Zhang, X., Sun, D., & Kim, W. J. (2024). Exploring the Asymmetric Body’s Influence on Interval Timing Behaviors of Drosophila melanogaster. *Behavior Genetics*, *54*(5), 416-425.

   Huang, Y., Kwan, A., & Kim, W. J. (2024). Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. *Gene Reports*, *36*, 101999.

   Finally, in this context, we have outlined in our INTRODUCTION section below how our LMD and SMD models are related to interval timing, aiming to persuade readers of their relevance. We hope that the reviewer and readers are convinced that mating duration and its associated motivational changes such as LMD and SMD provide a compelling model for studying the genetic basis of interval timing in *Drosophila*.

"The mating duration of male fruit flies is a suitable model for studying interval timing and it could change based on internal states and environmental context. Previous studies by our group[27–30] and others[31,32] have established several frameworks for investigating the mating duration using sophisticated genetic techniques that can analyze and uncover the neural circuits’ principles governing interval timing. In particular, males exhibit LMD behavior when they are exposed to an environment with rivals, which means they prolong their mating duration. Conversely, they display SMD behavior when they are in a sexually saturated condition, meaning they reduce their mating duration[33,34]."

Comment 2. On line 160, the authors state, "The connection between the dendrites and axons of the SIFamide neuronal processes is unknown." This is not entirely correct. State-of-the-art connectome analyses can determine synaptic connectivities between SIFamidergic neurons and pre-/postsynaptic neurons. The authors also overlook the thorough connectivity analysis by Martelli et al. (2017), which includes functional analyses and detailed anatomical descriptions that the current study confirms.

__ Answer:__ We appreciate the reviewer for acknowledging the efforts of Martelli et al. in elucidating the neuronal architecture of SIFa neurons. We recognize that it was an oversight on our part to state that "the connection between the dendrites and axons of SIFa neurons is unknown." This error arose because our manuscript has been in preparation for over ten years, predating the publication of Martelli et al.'s work. That statement likely reflects an outdated section of the manuscript.

We fully acknowledge the findings from previous publications and have removed that sentence entirely from our manuscript. In its place, we have added the following statement:

"The established connections and architecture of SIFa neurons has been described by Martelli et al., which enhances our understanding of their functional roles within the neuronal circuitry [51]. To identify the dendritic and axonal components of SIFa-neuronal processes, we employed a similar approach to that reported by Martelli [51]."

Thank you for your valuable feedback, which has helped us improve the clarity and accuracy of our manuscript.

Comment 3. The mating experiments are overall okay, with sufficiently high sample sizes and appropriate statistical tests. However, many experiments lack genetic controls for the heterozygous parental strains, such as Gal4-ines AND UAS-lines. This is of course of importance and common standard.

__ Answer: __While we have previously addressed this type of reviewer feedback in our published manuscript [2–7] as well as this manuscript by Reviewer #1, we appreciate the reviewer’s suggestion to include traditional genetic control experiments. In response, we conducted all feasible combinations of genetic control experiments for LMD/SMD during the revision period. The results are presented in the supplementary figures and are described in the main text.

Comment 4. *Using a battery of RNAi lines, the authors aim to uncover which neurotransmitters might be co-released from SIFamide neurons to influence mating behavior. However, a behavioral effect of an RNAi construct expressed in SIFamidergic neurons does not demonstrate that the respective transmitter is actually released from these neurons. Alternative methods are needed to show whether glutamate, dopamine, serotonin, octopamine, etc., are present and released from SIFamide neurons. It is particularly challenging to prove that a certain substance acts as a transmitter released by a specific neuron. For example, anti-Tdc2 staining does not actually cover SIFamide neurons, and dopamine has not been described as present in SIFamide neurons. *

__ Answer:__ We appreciate the reviewer’s constructive comments regarding the need to demonstrate the presence of the responsible neurotransmitters in SIFa neurons. While many studies utilize neurotransmitter-synthesizing enzymes such as TH, VGlut, Gad1, and Trhn to assess neurotransmitter effects, we recognize the importance of conclusively establishing that glutamate and dopamine play significant roles in modulating energy balance within SIFa neurons.

   First, the enrichment of tyramine (TA), octopamine (OA), and dopamine (DA) in SIFa neurons was suggested in the study by Croset et al. (2018) [17]. Although we tested Tdc2-RNAi and observed interesting phenotypes, we chose not to publish these findings, as our data on glutamate and dopamine provide a more compelling explanation for how SIFa cotransmission with these neurotransmitters can independently influence various behaviors, including sleep and mating duration.

   To confirm the expression of DA in SIFa neurons, we employed a well-established genetic toolkit for dissecting dopamine circuit function in *Drosophila* [18]. Our findings indicate that TH-C-GAL4 specifically labels SIFa neurons, which have been confirmed as dopaminergic (S4M Fig). Our genetic intersection data, along with Xie et al.'s findings from 2018, confirm that a subset of SIFa neurons is indeed dopaminergic. We have described these new results in the main text as follows:

To further verify the presence of DA neurons within the SIFa neuron population, we utilized a well-established genetic toolkit for dissecting DA circuits and confirmed part of SIFa neurons are dopaminergic (S4M Fig) [58].

    To confirm the glutamatergic characteristics of SIFa neurons, we conducted several experiments that established glutamate as the most critical neurotransmitter for generating interval timing in both SIFa and SIFaR neurons. First, to demonstrate the presence of glutamatergic synaptic vesicles in SIFa neurons, we utilized a conditional glutamatergic synaptic vesicle marker for *Drosophila*, developed by Certel et al. [19]. Our results confirmed that SIFa neurons exhibit strong expression of glutamatergic synaptic vesicles (Fig. 2P and Fig. S4N as a genetic control). We have described these new results in the main text as follows:

“To further verify the presence of DA neurons within the SIFa neuron population, we utilized a well-established genetic toolkit for dissecting DA circuits and confirmed part of SIFa neurons are dopaminergic (S4M Fig) [58]. We also employed a conditional glutamatergic synaptic vesicle marker to confirm the presence of glutamatergic SIFa neurons (Fig 2P and Fig S4N) [59].”

   To further confirm that glutamate release from SIFa neurons influences the function of SIFaR neurons, we tested several RNAi strains targeting glutamate receptors. Our results showed that the knockdown of glutamate receptors in SIFaR-expressing neurons produced phenotypes similar to those observed with VGlut-RNAi knockdown in SIFa neurons (Fig. G-L). We believe that this series of experiments demonstrates that glutamate and dopamine work in conjunction with SIFa to modulate interval timing and other behaviors related to energy balance. We have described these new results in the main text as follows:

"To further substantiate the role of glutamate in SIFa-mediated behaviors. we targeted knockdown of VGlut receptors in SIFaR-expressing neurons. Strikingly, the knockdown of VGlut receptors in these neurons also disrupted SMD behavior, mirroring the phenotype observed upon direct suppression of glutamatergic signaling in SIFa neurons (S4G to S4L Fig). This suggests that glutamate is an essential neurotransmitter for modulating interval timing in SIFa neurons.”

Comment 5. Single-cell RNA sequencing data alone is insufficient to claim multiple transmitter co-release from SIFamide neurons. Figures illustrating single-cell RNA sequencing, such as Figure 3P-R, are not intuitively understandable, and the figure legends lack sufficient information to clarify these panels. As a side note, Tdc2 is not only present in octopaminergic neurons, but also in tyraminergic neurons.

__ Answer:__ We agree with the reviewer that scRNA-seq data alone is insufficient to support claims of multiple transmitter co-release in SIFa neurons. We also appreciate the reviewer for highlighting the potential for confusion among readers regarding the visualization methods used in our figures, particularly the tSNE plots of the scRNA-seq data. As noted in our previous response to Reviewer #1, we have removed most of the tSNE plots related to co-expression data involving SIFa and NPRs, which we believe will help clarify the interpretation for readers. However, we have retained a few tSNE plots, specifically Figures 2N-O, to illustrate the potential co-expression of the ple and Vglut genes in SIFa cells.

   We understand the reviewer’s concerns regarding the clarity of the presented data and the need for more detailed information about the extent of co-expression and the identification of SIFa-expressing cells. To address these concerns, we have provided a comprehensive description of our methods in the __MATERIALS AND METHODS__ section below.

"Single-nucleus RNA-sequencing analyses

The snRNAseq dataset analyzed in this paper is published in [20]and available at the Nextflow pipelines (VSN, https://github.com/vib-singlecell-nf), the availability of raw and processed datasets for users to explore, and the development of a crowd-annotation platform with voting, comments, and references through SCope (https://flycellatlas.org/scope), linked to an online analysis platform in ASAP (https://asap.epfl.ch/fca). For the generation of the tSNE plots, we utilized the Fly SCope website (https://scope.aertslab.org/#/FlyCellAtlas/*/welcome). Within the session interface, we selected the appropriate tissues and configured the parameters as follows: 'Log transform' enabled, 'CPM normalize' enabled, 'Expression-based plotting' enabled, 'Show labels' enabled, 'Dissociate viewers' enabled, and both 'Point size' and 'Point alpha level' set to maximum. For all tissues, we referred to the individual tissue sessions within the '10X Cross-tissue' RNAseq dataset. Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Dashed lines denote the significant overlap of cell populations annotated by the respective genes. Coexpression between genes or annotated tissues is visually represented by differentially colored cell populations. For instance, yellow cells indicate the coexpression of a gene (or annotated tissue) with red color and another gene (or annotated tissue) with green color. Cyan cells signify coexpression between green and blue, purple cells for red and blue, and white cells for the coexpression of all three colors (red, green, and blue). Consistency in the tSNE plot visualization is preserved across all figures.

Single-cell RNA sequencing (scRNA-seq) data from the Drosophila melanogaster were obtained from the Fly Cell Atlas website (https://doi.org/10.1126/science.abk2432). Oenocytes gene expression analysis employed UMI (Unique Molecular Identifier) data extracted from the 10x VSN oenocyte (Stringent) loom and h5ad file, encompassing a total of 506,660 cells. The Seurat (v4.2.2) package (https://doi.org/10.1016/j.cell.2021.04.048) was utilized for data analysis. Violin plots were generated using the “Vlnplot” function, the cell types are split by FCA."

   We have also included detailed descriptions in the figure legends for the initial tSNE plot presented below to help readers clearly understand the significance of this visualization.

"Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and/or bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Consistency in the tSNE plot visualization is preserved across all figures."

   We appreciate the reviewer for acknowledging that Tdc2 is present in both TA and OA neurons. As we mentioned earlier, we have completely removed the Tdc2-related results from this manuscript, as we believe that more detailed experiments are necessary to confirm the roles of TA and OA in SIFa neurons.

Comment 6. The same argument applies to the expression of sNPF receptors in SIFamide neurons. The rather small anatomical stainings shown in figure 4M do not convincingly and unambiguously show that actually sNPF receptors are located on SIFamide neurons.

__ Answer:__ We appreciate the reviewer for pointing out that the co-expression of sNPF-R and SIFa needs further verification, and we agree with this assessment. To confirm the co-expression of SIFa with sNPF-R, we conducted a mini-screen of various sNPF-R driver lines and found that the chemoconnectome (CCT) sNPF-R2A driver which represent the physiological expression patterns of sNPF-R, consistently labels SIFa neurons [21].

   To further establish the functional connection between the SIFa and sNPF systems, we performed GCaMP experiments using SIFa-driven GCaMP in conjunction with sNPF-R neurons expressing P2X2, which can be activated by ATP treatment. As shown in Figures 3N-P, we demonstrated that activation of sNPF-R neurons by ATP significantly increases calcium levels in SIFa neurons. Our results strongly suggest that the sNPF-sNPF-R/SIFa system is functionally present and plays a role in modulating interval timing behaviors.

Comment 7. The authors use the GRASP technique (figure 4N) to determine whether synaptic connections are subject to modulation as a result from the animals' individual experience. The overall extremely bright fluorescence at the dorsal areas of both brain hemispheres (figure 4 N, middle panel) raises doubts whether this signal is actually a specific GRASP fluorescence between two small populations of neurons.

Answer: We appreciate the reviewer for critically highlighting the inadequacies in our presentation of the GRASP data. We agree that one of our previous panels contained excessive background noise, making it difficult for reviewers and readers to discern the different neuronal connections. To address this issue, we have replaced it with a more representative image that clearly illustrates the strengthening of synaptic connections from SIF to sNPF-R in several neurons, including SIFa cells (Fig. S5J). We hope that this updated image will help convince both the reviewer and readers of the validity of our GRASP data.

Comment 8. The authors cite Martelli et al. (2017) with the hypothesis that sNPF-releasing neurons provide input signals to SIFamide neurons to modulate feeding behavior. However, the cited manuscript does not contain such a hypothesis. The authors should review the reference in more detail.

__ Answer:__ We appreciate reviewer to correctly point our misunderstanding of references. We agree with reviewer that Martelli et al.'s paper didn't mention about sNPF signaling transmits hunger and satiety information to SIFa neurons. We removed this sentence and replaced it as below correctly mentioning that sNPF signaling is related to feeding behavior however it's connection to SIFa neurons are not known. We greatly appreciate the reviewer for acknowledging our efforts to accurately cite previous articles that support our rationale and ideas.

" Short neuropeptide F (sNPF) signaling plays a crucial role in regulating feeding behavior in Drosophila melanogaster, influencing food intake and body size [60,66,67]. However, there is currently no direct evidence reported linking sNPF signaling to SIFa neurons."

Comment ____9. In lines 281 ff., the authors state that SIFamide neurons receive inputs from peptidergic neurons but simultaneously claim that "this speculation is based on morphological observations." This is incorrect. The functional co-activation/imaging analyses provided in Martelli et al. (2017) should not be ignored.

* Answer: We fully agree with the reviewer that we misinterpreted Martelli et al.'s analysis. We have removed "this speculation is based on morphological observations." from* the following sentence and finalize as below:

"The SIFa neurons receive inputs from many peptidergic pathways including Crz, dilp2, Dsk, sNPF, MIP, and hugin"

Comment 10. Figure 6: A transcriptional calcium sensor (TRIC) was used to quantify the accumulation GFP induced by calcium influx in SIFamide neurons. However, I could not find any description of the method in the materials and methods section, nor any explanation how the data were acquired or analyzed. What is the RFP expression good for? How exactly are thresholds determined, and why are areas rather than fluorescence intensities quantified? Overall, this part of the manuscript is rather confusing and needs more explanation.

__ Answer: Thank you for your continued engagement with our manuscript and for highlighting the need for further clarification on our methods. Your attention to the details of our immunohistochemistry experiments is commendable, and we agree that providing a clear explanation of our thresholding and normalization procedures is essential for the transparency and reproducibility of our results. We primarily adhered to the established methods outlined by Kayser et al. [8]. To address your first point, we have now included a more detailed description of our thresholding and normalization procedures in the __MATERIALS AND METHODS section as below.

"Quantitative analysis of fluorescence intensity

To ascertain calcium levels and synaptic intensity from microscopic images, we dissected and imaged five-day-old flies of various social conditions and genotypes under uniform conditions. The GFP signal in the brains and VNCs was amplified through immunostaining with chicken anti-GFP, rabbit anti-DsRed, and mouse anti-nc82 primary antibodies. Image analysis was conducted using ImageJ software. For the quantification of fluorescence intensities, an investigator, blinded to the fly's genotype, thresholded the sum of all pixel intensities within a sub-stack to optimize the signal-to-noise ratio, following established methods [100]. The total fluorescent area or region of interest (ROI) was then quantified using ImageJ, as previously reported. For CaLexA or TRIC signal quantification, we adhered to protocols detailed by Kayser et al. [101], which involve measuring the ROI's GFP-labeled area by summing pixel values across the image stack. This method assumes that changes in the GFP-labeled area and intensity are indicative of alterations in the CaLexA and TRIC signal, reflecting synaptic activity. ROI intensities were background-corrected by measuring and subtracting the fluorescent intensity from a non-specific adjacent area, as per Kayser et al. [101]. For normalization, nc82 fluorescence is utilized for CaLexA, while RFP signal is employed for TRIC experiments, as the RFP signal from the TRIC reporter is independent of calcium signaling [72] . For the analysis of GRASP or tGRASP signals, a sub-stack encompassing all synaptic puncta was thresholded by a genotype-blinded investigator to achieve the optimal signal-to-noise ratio. The fluorescence area or ROI for each region was quantified using ImageJ, employing a similar approach to that used for CaLexA or TRIC quantification [100]. 'Norm. GFP Int.' refers to the normalized GFP intensity relative to the RFP signal.

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__Comment 11. __Similarly, it remains unclear how exactly syteGFP fluorescence and DenMark fluorescence were quantified. Why are areas indicated and not fluorescence intensity values? In fact, it appears worrisome that isolation of males should lead to a drastic decline in synaptic terminals (as measure through a vesicle-associated protein) by ~ 30%, or, conversely, keeping animals in groups lead to an respective increase (figure 7D). The technical information how exactly this was quantified is not sufficient.

__ Answer: __Thank you for your ongoing engagement with our manuscript and for emphasizing the need for clarification on our methods. We appreciate your attention to the details of our immunohistochemistry experiments and agree that a clear explanation of our thresholding and normalization procedures is vital for transparency and reproducibility. We acknowledge that signal intensity correlates with area measurements, which is an important consideration. In response to your valuable suggestion, we have revised our approach to present data based on intensity measurements and updated the Y-axis labeling to "Norm. GFP Int." (normalized GFP intensity) for clarity. We primarily followed the established methods from Kayser et al. (2014) [8]. Additionally, we have included a more detailed description of our thresholding and normalization procedures in the "Quantitative analysis of fluorescence intensity" in __MATERIALS AND METHODS __section as we quoted above.

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Minor concerns:

Comment 1. Reference 29 and reference 33 are the same.

   __Answer:__ We removed reference 29.

Comment 2. In figure legends, abbreviations should be explained when used first (e.g., figure 1 A "MD", is explained below for panel C-F), or "CS males". __ __

__Answer: __We have ensured that abbreviations are explained only when they are first used in the figure legends.

Comment 3. Indications for statistical significance must be shown in all figure legends at the end of each figure legend, not only in figure 1. __ __

__ Answer:__ We appreciate the reviewer’s advice. However, we have published all our other manuscripts using the same format for mating duration, stating, "The same notations for statistical significance are used in other figures," in the first figure where we describe our statistical significances. We intend to continue with this approach initially and will then adhere to the journal's policy.

Comment 4. The figures appear overloaded. For example why do you need two different axis designations (mating duration and differences between means)? __ __

__ Answer: __We appreciate the reviewer's suggestion to refine our figures, and we have indeed reformatted them to provide clearer presentation and improved readability. Our decision is based on the fact that our analysis encompasses not only traditional t-tests but also incorporates estimation statistics, which have been demonstrated to be effective for biological data analysis [22]. The inclusion of DBMs is essential for the accurate interpretation of these estimation statistics, ensuring a comprehensive representation of our findings. This is the primary area where we present two different axis designations.

Comment 5. Line: 1154: Typo: gluttaminergic should be glutamatergic.

   __Answer:__ We fixed all.

Comment 6. The authors frequently write "system" when referring to transmitter types, e.g., "glutaminergic system", "octopaminergic system", etc. It I not clear what the term "system" actually refers to. If the authors claim that SIFamide neurons release these transmitters in addition to SIFamide, they should state that precisely and then add experiments to show that this is the case.

   __Answer:__ We agree with reviewer and removed the word 'system' after the name of neurotransmitter's name.

Comment 7. Figure S6: It is not explained in the figure legend what fly strain "UAS-ctrl" actually is. Does "ctrl" mean control? And what genotype is hat control? __ __

__Answer: __It was wild-type strain. We fixed it as "+".

Comment 8. Figure legend S6, line 1371: The authors indicate experiments using UAS-OrkDeltaC. I could not find these data in the figure. __ __

__Answer: __It's now in Fig.S6U-W.

Comment 9. Line 470: "...reduced branching of SIFa axons at the postsynaptic level" should perhaps be "presynaptic level"?

Answer: Reviewer is correct. We fixed it.

Conclusive Comments:* Overall, the study advances our knowledge about the behavioral roles of SIFamide, which is certainly important, interesting, and worthy of being reported. However, the manuscript also raises several serious caveats and includes points that remain speculative and are less convincing.

Overall, the neuronal basis of action selection based on motivational factors (metabolic state, mating experience, sleep/wake status, etc.) is not well understood. The analysis of SIFamide function in insects might provide a way to address the question how different motivational signals are integrated to orchestrate behavior.*

• *Answer: Thank you for your thoughtful review and for recognizing the significance of our study in advancing knowledge about the behavioral roles of SIFamide. We appreciate your acknowledgment that our work is important, interesting, and worthy of publication.

We understand your concerns regarding the caveats and speculative points raised in the manuscript. We agree that the neuronal basis of action selection influenced by motivational factors—such as metabolic state, mating experience, and sleep/wake status—remains poorly understood. We believe that our analysis of SIFamide function in insects offers valuable insights into how various motivational signals are integrated to orchestrate behavior.

In response to your comments, we have made revisions to clarify our findings and address the concerns raised. We aim to strengthen the arguments presented in the manuscript and provide a more robust discussion of the implications of our results. Thank you once again for your constructive feedback, which has been instrumental in improving the clarity and impact of our work.

• *

* *

Reviewer #3

General Comments:* The Manuscript Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male Drosophila melanogaster by Yuton Song and colleagues addresses the question how SIFamidergic neurons coordinate behavioral responses in a context-dependent manner. In this context the authors investigate how SIFa neurons receive information about the physiological state of the animal and integrate this information into the processing of external stimuli. The authors show that SIFamidergic neurons and sNPPF expressing neurons form a feedback loop in the ventral nerve cord that modulate long mating (LMD) and shorter mating duration (SMD).

The manuscript is well written and very detailed and provides an enormous amount of data corroborating the claims of the authors. However, before publication the authors may want to address some points of concern that warrant some deeper explanation.*

• *__Answer: __Thank you for your positive feedback on our manuscript. We appreciate your recognition of the importance of our study in investigating how SIFa neurons integrate information about the physiological state of the animal with external stimuli, as well as your acknowledgment of the substantial data we provide to support our claims. We understand your concerns regarding certain points that require deeper explanation, and we are committed to addressing these issues to enhance the clarity and robustness of our findings. Your insights into the neuronal basis of action selection influenced by motivational factors are invaluable, and we believe that our exploration of SIFamide function in insects contributes significantly to understanding how various motivational signals orchestrate behavior. Thank you once again for your constructive comments, which will help us improve our manuscript before publication.

Major concerns:

Comment 1. On page 6 line 110 the authors describe that knocking-down SIFamide in glia cell does not change LMD or SMD and say that SIFa expression in glia does not contribute to interval timing behavior. However, the authors do not provide any information why they investigate the role of SIFa expression in glia. Is there any SIFa-expression in glia? The authors should somehow demonstrate using antibody labelling against SIFamide whether any glia specific expression of this peptide is to be expected. If they cannot provide this data - the take home message of the experiment cannot be that glia knockdown of SIFamide does not affect the behavior because you cannot knockdown anything that is not there.

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• In the latter case the experiment could be considered as a nice negative control for the elav-Gal4 pan-neuronal knockdown of SIFamide. The authors provide some Figure supplement where they use repo-Gal80 to partially answer this question. However, the authors should keep in mind that Gal4-drivers are not always complete in the expression pattern. Accordingly, the result should be corroborated with immune-labelling against SIFamide directly.*

__ Answer: __We appreciate the reviewer's constructive and critical comments regarding the use of our glial cell drivers. As the reviewer rightly pointed out, we believe that glial control is not essential for our manuscript, given that the expression of SIFa is well established in only four neurons. Therefore, we have removed the data related to glial drivers from this manuscript.

Comment 2. At this point I would like to directly comment on the figure quality. The figures are so crowded that the described anatomical details are hardly visible. In my opinion the manuscript would profit from less data in the main part and more stringent description of the core of the biological problem the authors want to address. The authors may want to reduce data from the main text and provide additional data that are not directly related to the main story as supplementary information.

__ Answer: __We agree with the reviewer. As another reviewer also suggested that we streamline our figures and data, we have completely restructured our figures and their presentation. In response, we have significantly reduced the density of the main figures and decreased the size of the graphs to enhance clarity. Additionally, we have increased the spacing between panels to ensure that each component is more easily distinguishable. Further details will be provided in our responses to each comment below.

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Comment 3. On page 8 starting with line 140 the authors describe the architecture of SIFamidergic neurons using several anatomical markers e.g., Denmark and further state that they have discovered that the dendrites of SIFa neurons span just the central brain area. Seeing that these data have been published in Martelli et al., 2017 the authors should tune down the claim that this was discovered in their work but rather corroborated earlier results.

__ Answer: __We acknowledge this error, as another reviewer also raised this issue. We have corrected our manuscript as follows:

"The established connections and architecture of SIFa neurons has been described by Martelli et al., which enhances our understanding of their functional roles within the neuronal circuitry [51]. To identify the dendritic and axonal components of SIFa-neuronal processes, we employed a similar approach to that reported by Martelli [51]."

Comment 4. In the next chapter, the authors aim at identifying the presynaptic inputs from SIFa positive neurons that may influence interval timing behavior and make a broad RNAi knock-down screen targeting a majority of neuromodulators. The authors claim that glutaminergic and dopaminergic signaling is necessary for interval timing behavior. I guess the authors mean "glutamatergic" instead of "glutaminergic" as glutamine is the precursor but not the neurotransmitter.

__ Answer: __The reviewer is correct. We have corrected this error and changed all instances to "glutamatergic."

Comment 5____. Furthermore, the authors show that the knock down of Tdc2 with RNAi has comparable effects on SMD than Glutamate and dopamine but appear to not further discuss this in the main text. To me it is not clear why the authors exclude Tdc2 from their resume. The authors should explain this in detail.

   __Answer:__ We appreciate the reviewer’s constructive comments regarding the need for a more detailed demonstration of the role of Tdc2 data. While we did test Tdc2-RNAi and observed interesting phenotypes, we decided not to include these findings in our publication, as our data on glutamate and dopamine offer a more compelling explanation for how SIFa cotransmission with these neurotransmitters can independently influence various behaviors, such as sleep and mating duration. Consequently, we have removed all data related to Tdc2. We believe that further evaluation is necessary to better understand the roles of the tyramine and octopamine systems in SIFa neurons.

Comment 6. The authors base their assumptions that the tested neurotransmitters are expressed in SIFamidergic neurons on Scope database analysis. But a transcript does not necessarily mean that it will be translated too. To my knowledge there is no available data in the literature showing that tyrosine hydroxylase is expressed in SIFamidergic neurons (see e.g., Mao and Davis, 2010). To show that ple or Tdc2 are indeed expressed and translated into functional enzymes in SIFamidergic neurons the authors should provide the according antibody labelling corroborating the result from the transcriptome analysis.

__ Answer:__ We appreciate the reviewer’s constructive comments regarding the role of neurotransmitters in conjunction with SIFa in modulating interval timing behaviors. To confirm the expression of dopamine (DA) in SIFa neurons, we utilized a well-established genetic toolkit for dissecting dopamine circuit function in Drosophila [18]. Our findings demonstrate that TH-C-GAL4 specifically labels SIFa neurons, which have been confirmed to be dopaminergic (Fig. S4M). This aligns with the genetic intersection data and the findings from Xie et al. (2018), confirming that a subset of SIFa neurons is indeed dopaminergic. We have included these new results in the main text as follows:

" To further verify the presence of DA neurons within the SIFa neuron population, we utilized a well-established genetic toolkit for dissecting DA circuits and confirmed part of SIFa neurons are dopaminergic (S4M Fig) [58]."

   To confirm the glutamatergic characteristics of SIFa neurons, we conducted several experiments that established glutamate as the most critical neurotransmitter for generating interval timing in both SIFa and SIFaR neurons. First, to demonstrate the presence of glutamatergic synaptic vesicles in SIFa neurons, we utilized a conditional glutamatergic synaptic vesicle marker for *Drosophila*, developed by Certel et al. [19]. Our results confirmed that SIFa neurons exhibit strong expression of glutamatergic synaptic vesicles (Fig. 2P and Fig. S4N as a genetic control). We have described these new results in the main text as follows:

"To further substantiate the role of glutamate in SIFa-mediated behaviors. we targeted the expression of VGlut receptor in neurons that carry the SIFaR. Strikingly, the knockdown of VGlut receptor in these neurons also disrupted SMD behavior, mirroring the phenotype observed upon direct suppression of glutamatergic signaling in SIFa neurons (S4O-L Fig)."

   To further confirm that glutamate release from SIFa neurons influences the function of SIFaR neurons, we tested several RNAi strains targeting glutamate receptors. Our results showed that the knockdown of glutamate receptors in SIFaR-expressing neurons produced phenotypes similar to those observed with VGlut-RNAi knockdown in SIFa neurons (Fig. S4I-N). We believe that this series of experiments demonstrates that glutamate and dopamine work in conjunction with SIFa to modulate interval timing and other behaviors related to energy balance. We have described these new results in the main text as follows:

"We also further verified that the knockdown of glutamate receptors in SIFaR-expressing neurons produces phenotypes similar to those resulting from VGlut knockdown in SIFa neurons (S4G to S4L Fig). This suggests that glutamate is an essential neurotransmitter for modulating interval timing in SIFa neurons."

Comment 7. The authors compare the LMD and SMD behavior of the animals with reduced expression with "heterozygous control animals" the authors should describe in detail what these are - are these controls the driver lines or the effector lines or a mix of both? The authors should provide the data for heterozygous driver line controls as well as heterozygous effector line controls to exclude any genetic background influence on the measured behavior. Accordingly, the authors should provide the data for the same controls for the sleep experiment in figure 3O and all the other behavioral experiments in the following parts of the manuscript.

__ Answer: __We sincerely thank the reviewer for insightful comments regarding the absence of traditional genetic controls in our study of LMD and SMD behaviors. We acknowledge the importance of such controls and wish to clarify our rationale for not including them in the current investigation. The primary reason for not incorporating all genetic control lines is that we have previously assessed the LMD and SMD behaviors of GAL4/+ and UAS/+ strains in our earlier studies. Our past experiences have consistently shown that 100% of the genetic control flies for both GAL4 and UAS exhibit normal LMD and SMD behaviors. Given these findings, we deemed the inclusion of additional genetic controls to be non-essential for the present study, particularly in the context of extensive screening efforts. We understand the value of providing a clear rationale for our methodology choices. To this end, we have added a detailed explanation in the "MATERIALS AND METHODS" section and the figure legends of Figure 1. This clarification aims to assist readers in understanding our decision to omit traditional controls, as outlined below.

"Mating Duration Assays for Successful Copulation

The mating duration assay in this study has been reported [33,73,93]. To enhance the efficiency of the mating duration assay, we utilized the Df (1) Exel6234 (DF here after) genetic modified fly line in this study, which harbors a deletion of a specific genomic region that includes the sex peptide receptor (SPR)[94,95]. Previous studies have demonstrated that virgin females of this line exhibit increased receptivity to males [95]. We conducted a comparative analysis between the virgin females of this line and the CS virgin females and found that both groups induced SMD. Consequently, we have elected to employ virgin females from this modified line in all subsequent studies. For naïve males, 40 males from the same strain were placed into a vial with food for 5 days. For single reared males, males of the same strain were collected individually and placed into vials with food for 5 days. For experienced males, 40 males from the same strain were placed into a vial with food for 4 days then 80 DF virgin females were introduced into vials for last 1 day before assay. 40 DF virgin females were collected from bottles and placed into a vial for 5 days. These females provide both sexually experienced partners and mating partners for mating duration assays. At the fifth day after eclosion, males of the appropriate strain and DF virgin females were mildly anaesthetized by CO2. After placing a single female in to the mating chamber, we inserted a transparent film then placed a single male to the other side of the film in each chamber. After allowing for 1 h of recovery in the mating chamber in 25℃ incubators, we removed the transparent film and recorded the mating activities. Only those males that succeeded to mate within 1 h were included for analyses. Initiation and completion of copulation were recorded with an accuracy of 10 sec, and total mating duration was calculated for each couple. All assays were performed from noon to 4pm. Genetic controls with GAL4/+ or UAS/+ lines were omitted from supplementary figures, as prior data confirm their consistent exhibition of normal LMD and SMD behaviors [33,73,93,96,97]. Hence, genetic controls for LMD and SMD behaviors were incorporated exclusively when assessing novel fly strains that had not previously been examined. In essence, internal controls were predominantly employed in the experiments, as LMD and SMD behaviors exhibit enhanced statistical significance when internally controlled. Within the LMD assay, both group and single conditions function reciprocally as internal controls. A significant distinction between the naïve and single conditions implies that the experimental manipulation does not affect LMD. Conversely, the lack of a significant discrepancy suggests that the manipulation does influence LMD. In the context of SMD experiments, the naïve condition (equivalent to the group condition in the LMD assay) and sexually experienced males act as mutual internal controls for one another. A statistically significant divergence between naïve and experienced males indicates that the experimental procedure does not alter SMD. Conversely, the absence of a statistically significant difference suggests that the manipulation does impact SMD. Hence, we incorporated supplementary genetic control experiments solely if they deemed indispensable for testing. All assays were performed from noon to 4 PM. We conducted blinded studies for every test[98,99] .

   While we have previously addressed this type of reviewer feedback in our published manuscript [2–7], we appreciate the reviewer’s suggestion to include traditional genetic control experiments. In response, we conducted all feasible combinations of genetic control experiments for LMD/SMD during the revision period. The results are presented in the supplementary figures and are described in the main text.

__Comment 8. __On page 11 line 231 to page 12 line 233 the authors claim that "sNPF signaling transmits hunger and satiety information to SIFa neurons in order to control food search and feeding" and cite Martelli et al., 2017. Could the authors explain more in detail how the Martelli paper somehow proposes this idea? I do not find the link between sNPF signaling hunger and SIFamide in this precise paper.

__ Answer:__ We appreciate the reviewer for accurately pointing out our misunderstanding of the references. We agree that Martelli et al.'s paper does not mention that sNPF signaling transmits hunger and satiety information to SIFa neurons. Consequently, we have removed the relevant sentence and replaced it with a statement correctly indicating that while sNPF signaling is related to feeding behavior, its connection to SIFa neurons remains unknown. We are grateful to the reviewer for acknowledging our efforts to accurately cite previous articles that support our rationale and ideas.

" Short neuropeptide F (sNPF) signaling plays a crucial role in regulating feeding behavior in Drosophila melanogaster, influencing food intake and body size [60,66,67] . However, there is currently no direct evidence reported linking sNPF signaling to SIFa neurons."

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Comment 9. On page 15 line 302 - 303 the authors write that "except for PK2-R2, all other genes coexpress with SIFa in SCope data, indicating that hugin inputs to SIFa may not be transmitted through peptidergic signaling" - if SIFamidergic neurons do not express hugin-receptors how do the authors explain the inverted effect of PK2-R2-RNAi on single housed male courtship index when compared to heterozygous SIFaPT Gal4 control that show a reduction under comparable conditions.

__ Answer:__ We appreciate the reviewer’s constructive comments. In line with another reviewer’s suggestion, we have completely removed results of other neuropeptidergic inputs, focusing instead on how sNPF inputs modulate SIFa-mediated behavioral modulation using more advanced techniques such as GCaMP (Fig 3N). Consequently, the phenotypes resulting from various knockdowns of neuropeptide receptors are currently under investigation for a separate manuscript that we are preparing. We hope to successfully address how different neuropeptidergic inputs regulate SIFa neuron activity through various strategies.

Comment 10. On page 17 line 350 - 351 the authors write that "Stimulation of SIFa neurons resulted in an elevation in food consumption. Further, the authors write that "deactivation of SIFa neurons leads to a decrease in food consumption in male flies". From the way this is formulated it is not visible that the role of SIFamide in feeding control was published by Martelli and colleagues before. As the authors do not discuss the finding further in their discussion but cite the concerned paper in other aspects it appears as the authors intentionally want to omit this information to the reader. The authors may add a note that this has been shown before for female flies by Martelli and colleagues.

__ Answer:__ We appreciate reviewer's concern for properly mention previous Martelli et al.'s results about female feeding behavior modulated by SIFa neurons' activity. We agree with reviewer and added sentence as below in main text.

"Nevertheless, the temporary deactivation of SIFa neurons leads to a decrease in food consumption in male flies (Fig 4N and S6F to S6H) as previously described by Martelli et al.'s report in female flies [43]."

Comment 11. SIFamide receptor and GnIHR are discussed as descendants from a common ancestor and the authors nicely demonstrate that SIFamide does not only control homeostatic behavior as shown by Martelli and colleagues but also controls reproductive behavior. The evolution of such behavior control mechanisms may be integrated in the discussion too.

Answer: We appreciate the reviewer’s constructive comments, which enhance the evolutionary significance of our study. We agree with the reviewer and have added the following paragraph to the DISCUSSION section:

"The relationship between SIFamide receptors (SIFaR) and gonadotropin inhibitory hormone receptors (GnIHR) [89] highlights an intriguing evolutionary connection, as both are believed to have descended from a common ancestor [90,91]. This study expands on previous findings by Martelli et al., demonstrating that SIFamide not only regulates homeostatic behaviors but also plays a significant role in reproductive behavior [43]. GnIHR regulates food intake and reproductive behavior in opposing directions, thereby prioritizing feeding behavior over other behavioral tasks during times of metabolic need [92]. The evolution of these behavioral control mechanisms suggests a complex interplay between neuropeptides that modulate both physiological states and reproductive strategies. As SIFamide influences various behaviors, including feeding and sexual activity, it may be integral to understanding how organisms adapt their reproductive strategies in response to environmental and internal cues. This integration of behavioral modulation underscores the evolutionary significance of SIFamide signaling in coordinating essential life functions in Drosophila melanogaster and potentially other species, revealing pathways through which neuropeptides can shape behavior across different contexts."

Conclusive Comments: The manuscript by Song and colleagues is very interesting and may attract a broad readership. However, the authors miss to make clear what was already known and published on the role of SIFamide in homeostatic behavior control before their own study. Seen that the receptors for SIFamide and GnRHI derive from a common ancestor and apparently both GnRHI and SIFamide share similar roles in behavioral control this might indeed suggests that the basic function of this SIFaR/GnIHR-signaling pathway is conserved. This more broad evolutionary aspect is missing in the discussion of the manuscript.

• *Answer: We wholeheartedly agree with the reviewer regarding the evolutionary significance of SIFaR's function in relation to GnIHR, and we have expanded the DISCUSSION section to emphasize this important aspect.

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"The relationship between SIFamide receptors (SIFaR) and gonadotropin inhibitory hormone receptors (GnIHR) [89] highlights an intriguing evolutionary connection, as both are believed to have descended from a common ancestor [90,91]. This study expands on previous findings by Martelli et al., demonstrating that SIFamide not only regulates homeostatic behaviors but also plays a significant role in reproductive behavior [43]. GnIHR regulates food intake and reproductive behavior in opposing directions, thereby prioritizing feeding behavior over other behavioral tasks during times of metabolic need [92]. The evolution of these behavioral control mechanisms suggests a complex interplay between neuropeptides that modulate both physiological states and reproductive strategies. As SIFamide influences various behaviors, including feeding and sexual activity, it may be integral to understanding how organisms adapt their reproductive strategies in response to environmental and internal cues. This integration of behavioral modulation underscores the evolutionary significance of SIFamide signaling in coordinating essential life functions in Drosophila melanogaster and potentially other species, revealing pathways through which neuropeptides can shape behavior across different contexts."

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Reference

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2. Kim WJ, Jan LY, Jan YN. A PDF/NPF Neuropeptide Signaling Circuitry of Male Drosophila melanogaster Controls Rival-Induced Prolonged Mating. Neuron. 2013;80: 1190–1205. doi:10.1016/j.neuron.2013.09.034
3. Kim WJ, Jan LY, Jan YN. Contribution of visual and circadian neural circuits to memory for prolonged mating induced by rivals. Nat Neurosci. 2012;15: 876–883. doi:10.1038/nn.3104
4. Zhang T, Zhang X, Sun D, Kim WJ. Exploring the Asymmetric Body’s Influence on Interval Timing Behaviors of Drosophila melanogaster. Behav Genet. 2024; 1–10. doi:10.1007/s10519-024-10193-y
5. Sun Y, Zhang X, Wu Z, Li W, Kim WJ. Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. G3: Genes, Genomes, Genet. 2024; jkae255. doi:10.1093/g3journal/jkae255
6. Lee SG, Sun D, Miao H, Wu Z, Kang C, Saad B, et al. Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. PLOS Genet. 2023;19: e1010753. doi:10.1371/journal.pgen.1010753
7. Huang Y, Kwan A, Kim WJ. Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. Gene Rep. 2024; 101999. doi:10.1016/j.genrep.2024.101999
8. Kayser MS, Yue Z, Sehgal A. A Critical Period of Sleep for Development of Courtship Circuitry and Behavior in Drosophila. Science. 2014;344: 269–274. doi:10.1126/science.1250553
9. Wong K, Schweizer J, Nguyen K-NH, Atieh S, Kim WJ. Neuropeptide relay between SIFa signaling controls the experience-dependent mating duration of male Drosophila. Biorxiv. 2019; 819045. doi:10.1101/819045
10. Thornquist SC, Langer K, Zhang SX, Rogulja D, Crickmore MA. CaMKII Measures the Passage of Time to Coordinate Behavior and Motivational State. Neuron. 2020;105: 334-345.e9. doi:10.1016/j.neuron.2019.10.018
11. Buhusi CV, Meck WH. What makes us tick? Functional and neural mechanisms of interval timing. Nat Rev Neurosci. 2005;6: 755–765. doi:10.1038/nrn1764
12. Merchant H, Harrington DL, Meck WH. Neural Basis of the Perception and Estimation of Time. Annu Rev Neurosci. 2012;36: 313–336. doi:10.1146/annurev-neuro-062012-170349
13. Allman MJ, Teki S, Griffiths TD, Meck WH. Properties of the Internal Clock: First- and Second-Order Principles of Subjective Time. Annu Rev Psychol. 2013;65: 743–771. doi:10.1146/annurev-psych-010213-115117
14. Rammsayer TH, Troche SJ. Neurobiology of Interval Timing. Adv Exp Med Biol. 2014; 33–47. doi:10.1007/978-1-4939-1782-2_3
15. Golombek DA, Bussi IL, Agostino PV. Minutes, days and years: molecular interactions among different scales of biological timing. Philosophical Transactions Royal Soc B Biological Sci. 2014;369: 20120465. doi:10.1098/rstb.2012.0465
16. Jazayeri M, Shadlen MN. A Neural Mechanism for Sensing and Reproducing a Time Interval. Curr Biol. 2015;25: 2599–2609. doi:10.1016/j.cub.2015.08.038
17. Croset V, Treiber CD, Waddell S. Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics. eLife. 2018;7: e34550. doi:10.7554/elife.34550
18. Xie T, Ho MCW, Liu Q, Horiuchi W, Lin C-C, Task D, et al. A Genetic Toolkit for Dissecting Dopamine Circuit Function in Drosophila. Cell Reports. 2018;23: 652–665. doi:10.1016/j.celrep.2018.03.068
19. Certel SJ, Ruchti E, McCabe BD, Stowers RS. A conditional glutamatergic synaptic vesicle marker for Drosophila. G3. 2022;12: jkab453. doi:10.1093/g3journal/jkab453
20. Li H, Janssens J, Waegeneer MD, Kolluru SS, Davie K, Gardeux V, et al. Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly. Science. 2022;375: eabk2432. doi:10.1126/science.abk2432
21. Deng B, Li Q, Liu X, Cao Y, Li B, Qian Y, et al. Chemoconnectomics: Mapping Chemical Transmission in Drosophila. Neuron. 2019;101: 876-893.e4. doi:10.1016/j.neuron.2019.01.045
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Referee #3

Evidence, reproducibility and clarity

Review on the manscurpit "Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male Drosophila melanogaster." By Yuton Song and colleagues.

The Manuscript Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male Drosophila melanogaster by Yuton Song and colleagues addresses the question how SIFamidergic neurons coordinate behavioral responses in a context-dependent manner. In this context the authors investigate how SIFa neurons receive information about the physiological state of the animal and integrate this information into the processing of external stimuli. The authors show that SIFamidergic neurons and sNPPF expressing neurons form a feedback loop in the ventral nerve cord that modulate long mating (LMD) and shorter mating duration (SMD).

The manuscript is well written and very detailed and provides an enormous amount of data corroborating the claims of the authors. However, before publication the authors may want to address some points of concern that warrant some deeper explanation.

On page 6 line 110 the authors describe that knocking-down SIFamide in glia cell does not change LMD or SMD and say that SIFa expression in glia does not contribute to interval timing behavior. However, the authors do not provide any information why they investigate the role of SIFa expression in glia. Is there any SIFa-expression in glia? The authors should somehow demonstrate using antibody labelling against SIFamide whether any glia specific expression of this peptide is to be expected. If they cannot provide this data - the take home message of the experiment cannot be that glia knockdown of SIFamide does not affect the behavior because you cannot knockdown anything that is not there. In the latter case the experiment could be considered as a nice negative control for the elav-Gal4 pan-neuronal knockdown of SIFamide. The authors provide some Figure supplement where they use repo-Gal80 to partially answer this question. However, the authors should keep in mind that Gal4-drivers are not always complete in the expression pattern. Accordingly, the result should be corroborated with immune-labelling against SIFamide directly.

At this point I would like to directly comment on the figure quality. The figures are so crowded that the described anatomical details are hardly visible. In my opinion the manuscript would profit from less data in the main part and more stringent description of the core of the biological problem the authors want to address. The authors may want to reduce data from the main text and provide additional data that are not directly related to the main story as supplementary information. On page 8 starting with line 140 the authors describe the architecture of SIFamidergic neurons using several anatomical markers e.g., Denmark and further state that they have discovered that the dendrites of SIFa neurons span just the central brain area. Seeing that these data have been published in Martelli et al., 2017 the authors should tune down the claim that this was discovered in their work but rather corroborated earlier results.

In the next chapter, the authors aim at identifying the presynaptic inputs from SIFa positive neurons that may influence interval timing behavior and make a broad RNAi knock-down screen targeting a majority of neuromodulators. The authors claim that glutaminergic and dopaminergic signaling is necessary for interval timing behavior. I guess the authors mean "glutamatergic" instead of "glutaminergic" as glutamine is the precursor but not the neurotransmitter. Furthermore, the authors show that the knock down of Tdc2 with RNAi has comparable effects on SMD than Glutamate and dopamine but appear to not further discuss this in the main text. To me it is not clear why the authors exclude Tdc2 from their resume. The authors should explain this in detail. The authors base their assumptions that the tested neurotransmitters are expressed in SIFamidergic neurons on Scope database analysis. But a transcript does not necessarily mean that it will be translated too. To my knowledge there is no available data in the literature showing that tyrosine hydroxylase is expressed in SIFamidergic neurons (see e.g., Mao and Davis, 2010). To show that ple or Tdc2 are indeed expressed and translated into functional enzymes in SIFamidergic neurons the authors should provide the according antibody labelling corroborating the result from the transcriptome analysis. The authors compare the LMD and SMD behavior of the animals with reduced expression with "heterozygous control animals" the authors should describe in detail what these are - are these controls the driver lines or the effector lines or a mix of both? The authors should provide the data for heterozygous driver line controls as well as heterozygous effector line controls to exclude any genetic background influence on the measured behavior. Accordingly, the authors should provide the data for the same controls for the sleep experiment in figure 3O and all the other behavioral experiments in the following parts of the manuscript.

On page 11 line 231 to page 12 line 233 the authors claim that "sNPF signaling transmits hunger and satiety information to SIFa neurons in order to control food search and feeding" and cite Martelli et al., 2017. Could the authors explain more in detail how the Martelli paper somehow proposes this idea? I do not find the link between sNPF signaling hunger and SIFamide in this precise paper. On page 15 line 302 - 303 the authors write that "except for PK2-R2, all other genes coexpress with SIFa in SCope data, indicating that hugin inputs to SIFa may not be transmitted through peptidergic signaling" - if SIFamidergic neurons do not express hugin-receptors how do the authors explain the inverted effect of PK2-R2-RNAi on single housed male courtship index when compared to heterozygous SIFaPT Gal4 control that show a reduction under comparable conditions.

On page 17 line 350 - 351 the authors write that "Stimulation of SIFa neurons resulted in an elevation in food consumption. Further, the authors write that "deactivation of SIFa neurons leads to a decrease in food consumption in male flies". From the way this is formulated it is not visible that the role of SIFamide in feeding control was published by Martelli and colleagues before. As the authors do not discuss the finding further in their discussion but cite the concerned paper in other aspects it appears as the authors intentionally want to omit this information to the reader. The authors may add a note that this has been shown before for female flies by Martelli and colleagues. SIFamide receptor and GnIHR are discussed as descendants from a common ancestor and the authors nicely demonstrate that SIFamide does not only control homeostatic behavior as shown by Martelli and colleagues but also controls reproductive behavior. The evolution of such behavior control mechanisms may be integrated in the discussion too.

Significance

The manuscript by Song and colleagues is very interesting and may attract a broad readership. However, the authors miss to make clear what was already known and published on the role of SIFamide in homeostatic behavior control before their own study. Seen that the receptors for SIFamide and GnRHI derive from a common ancestor and apparently both GnRHI and SIFamide share similar roles in behavioral control this might indeed suggests that the basic function of this SIFaR/GnIHR-signaling pathway is conserved. This more broad evolutionary aspect is missing in the discussion of the manuscript.

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Referee #2

Evidence, reproducibility and clarity

In the present study, the authors employ mating behavior in male fruit flies, Drosophila melanogaster, to investigate the behavioral roles of the neuropeptide SIFamide. The duration of mating behavior in these animals varies depending on context, previous experience, and internal metabolic state. The authors use this variability to explore the neuronal mechanisms that control these influences. In an abstraction step, they compare the different mating durations to concepts of neuronal interval timing.

The behavioral functions of the neuropeptide SIFamide have been thoroughly characterized in several studies, particularly in the contexts of circadian rhythm and sleep, courtship behavior, and food uptake. This study adds new data, demonstrating that SIFamide is essential for the proper control of mating behavior, highlighting the interconnection of various state- and motivation-dependent behaviors at the neuronal level. However, the hypothesis that mating behavior is related to interval timing is not convincingly supported.

Experimentally, the authors show that RNAi-mediated downregulation of SIFamide affects mating duration in male flies. They use combinations of RNAi lines under the control of various Gal4 lines to identify additional neurotransmitters, neuropeptides, and receptors involved in this process. This approach is complemented by neuroanatomical staining and single-cell RNA sequencing. Overall, the study advances our knowledge about the behavioral roles of SIFamide, which is certainly important, interesting, and worthy of being reported. However, the manuscript also raises several serious caveats and includes points that remain speculative, are less convincing, or are simply incorrect.

Major concerns:

• The authors conclude from their mating experiments that SIFamide controls interval timing. This conclusion is not supported by the data, which only indicate that SIFamide is required for normal mating duration and modulates the motivation-dependent component of this behavior. There is no clear evidence linking this to interval timing.
• On line 160, the authors state, "The connection between the dendrites and axons of the SIFamide neuronal processes is unknown." This is not entirely correct. State-of-the-art connectome analyses can determine synaptic connectivities between SIFamidergic neurons and pre-/postsynaptic neurons. The authors also overlook the thorough connectivity analysis by Martelli et al. (2017), which includes functional analyses and detailed anatomical descriptions that the current study confirms.
• The mating experiments are overall okay, with sufficiently high sample sizes and appropriate statistical tests. However, many experiments lack genetic controls for the heterozygous parental strains, such as Gal4-ines AND UAS-lines. This is of course of importance and common standard.
• Using a battery of RNAi lines, the authors aim to uncover which neurotransmitters might be co-released from SIFamide neurons to influence mating behavior. However, a behavioral effect of an RNAi construct expressed in SIFamidergic neurons does not demonstrate that the respective transmitter is actually released from these neurons. Alternative methods are needed to show whether glutamate, dopamine, serotonin, octopamine, etc., are present and released from SIFamide neurons. It is particularly challenging to prove that a certain substance acts as a transmitter released by a specific neuron. For example, anti-Tdc2 staining does not actually cover SIFamide neurons, and dopamine has not been described as present in SIFamide neurons. Single-cell RNA sequencing data alone is insufficient to claim multiple transmitter co-release from SIFamide neurons. Figures illustrating single-cell RNA sequencing, such as Figure 3P-R, are not intuitively understandable, and the figure legends lack sufficient information to clarify these panels. As a side note, Tdc2 is not only present in octopaminergic neurons, but also in tyraminergic neurons.
• The same argument applies to the expression of sNPF receptors in SIFamide neurons. The rather small anatomical stainings shown in figure 4M do not convincingly and unambiguously show that actually sNPF receptors are located on SIFamide neurons.
• The authors use the GRASP technique (figure 4N) to determine whether synaptic connections are subject to modulation as a result from the animals' individual experience. The overall extremely bright fluorescence at the dorsal areas of both brain hemispheres (figure 4 N, middle panel) raises doubts whether this signal is actually a specific GRASP fluorescence between two small populations of neurons.
• The authors cite Martelli et al. (2017) with the hypothesis that sNPF-releasing neurons provide input signals to SIFamide neurons to modulate feeding behavior. However, the cited manuscript does not contain such a hypothesis. The authors should review the reference in more detail.
• In lines 281 ff., the authors state that SIFamide neurons receive inputs from peptidergic neurons but simultaneously claim that "this speculation is based on morphological observations." This is incorrect. The functional co-activation/imaging analyses provided in Martelli et al. (2017) should not be ignored.
• Figure 6: A transcriptional calcium sensor (TRIC) was used to quantify the accumulation GFP induced by calcium influx in SIFamide neurons. However, I could not find any description of the method in the materials and methods section, nor any explanation how the data were acquired or analyzed. What is the RFP expression good for? How exactly are thresholds determined, and why are areas rather than fluorescence intensities quantified? Overall, this part of the manuscript is rather confusing and needs more explanation.
• Similarly, it remains unclear how exactly syteGFP fluorescence and DenMark fluorescence were quantified. Why are areas indicated and not fluorescence intensity values? In fact, it appears worrisome that isolation of males should lead to a drastic decline in synaptic terminals (as measure through a vesicle-associated protein) by ~ 30%, or, conversely, keeping animals in groups lead to an respective increase (figure 7D). The technical information how exactly this was quantified is not sufficient.

Minor comments:

• Reference 29 and reference 33 are the same.
• In figure legends, abbreviations should be explained when used first (e.g., figure 1 A "MD", is explained below for panel C-F), or "CS males".
• Indications for statistical significance must be shown in all figure legends at the end of each figure legend, not only in figure 1.
• The figures appear overloaded. For example why do you need two different axis designations (mating duration and differences between means)?
• Line: 1154: Typo: gluttaminergic should be glutamatergic.
• The authors frequently write "system" when referring to transmitter types, e.g., "glutaminergic system", "octopaminergic system", etc. It I not clear what the term "system" actually refers to. If the authors claim that SIFamide neurons release these transmitters in addition to SIFamide, they should state that precisely and then add experiments to show that this is the case.
• Figure S6: It is not explained I the figure legend what fly strain "UAS-ctrl" actually is. Does "ctrl" mean control? And what genotype is hat control?
• Figure legend S6, line 1371: The authors indicate experiments using UAS-OrkDeltaC. I could not find these data in the figure.
• Line 470: "...reduced branching of SIFa axons at the postsynaptic level" should perhaps be "presynaptic level"?

Significance

Overall, the study advances our knowledge about the behavioral roles of SIFamide, which is certainly important, interesting, and worthy of being reported. However, the manuscript also raises several serious caveats and includes points that remain speculative and are less convincing.

Overall, the neuronal basis of action selection based on motivational factors (metabolic state, mating experience, sleep/wake status, etc.) is not well understood. The analysis of SIFamide function in insects might provide a way to address the question how different motivational signals are integrated to orchestrate behavior.

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Referee #1

Evidence, reproducibility and clarity

This manuscript by Song et al. investigates the molecular mechanisms underlying changes in mating duration in Drosophila induced by previous experience. As they have shown previously, they find that male flies reared in isolation have shorter mating duration than those reared in groups, and also that male flies with previous mating experience have shorter mating duration than sexually naïve males. They have conducted a myriad of experiments to demonstrate that the neuropeptide SIFa is required for these changes in mating duration. They have further provided evidence that SIFa-expressing neurons undergo changes in synaptic connectivity and neuronal firing as a result of previous mating experience. Finally, they argue that SIFa neurons form reciprocal connections with sNPF-expressing neurons, and that communication within the SIFa-sNPF circuit is required for experience-dependent changes in mating duration. These results are used to assert that SIFa neurons track the internal state of the flies to modulate behavioral choice.

Major Comments:

1. The authors are to be commended for the sheer quantity of data they have generated, but I was often overwhelmed by the figures, which try to pack too much into the space provided. As a result, it is often unclear what components belong to each panel. Providing more space between each panel would really help.

This is a rare instance where I would recommend paring down the paper to focus on the more novel, clear and relevant results. For example, all of Figure 2 shows the projection pattern of SIFa+ neuron dendrites and axons, which have been reported by multiple previous papers. Figure 7G and J show trans-tango data and SIFaR-GAL4 expression patterns, which were previously reported by Dreyer et al., 2019. These parts could be removed to supplemental figures. Figure 5 details experiments that knock down expression of different neurotransmitter receptors within the SIFa-expressing cells. The results here are less definitive than the SIFa knockdown results, and the SCope data supporting the idea that these receptors are expressed in SIFa-expressing neurons is equivocal. I would recommend removing these data (perhaps they could serve as the basis for another manuscript) or focusing solely on the CCHa1R results, which is the only manipulation that affects both LMD and SMD.

Finally, I would like the authors to spend more time explaining how they think the results tie together. For example, how do the authors think the changes in branching and activity in SIFa-expressing neurons tie to the change in mating duration provoked by previous experience? It would benefit the manuscript to simplify and clarify the message about what the authors think is happening at the mechanistic level. The various schematics (eg Fig 7N) describe the results but the different parts feel like separate findings rather than a single narrative. 2. Most of the experiments lack traditional controls. For example, in experiments in Fig 1C-K, one would typically include genetic controls that contain either the GAL4 or UAS elements alone. The authors should explain their decision to omit these control experiments and provide an argument for why they are not necessary to correctly interpret the data. In this vein, the authors have stated in the methods that stocks were outcrossed at least 3x to Canton-S background, but 3 outcrosses is insufficient to fully control for genetic background. 3. Throughout the manuscript, the authors appear to use a single control condition (sexually naïve flies raised in groups) to compare to both males raised singly and males with previous sexual experience. These control conditions are duplicated in two separate graphs, one for long mating duration and one for short mating duration, but they are given different names (group vs naïve) depending on the graph. If these are actually the same flies, then this should be made clear, and they should be given a consistent name across the different "experiments". 4. The authors use SCope data to provide evidence for co-expression of SIFa and other neurotransmitters or neuropeptide receptors. The graphs they show are hard to read and it is not clear to what extent the gene expression is actually overlapping. It would be more definitive to show graphs that indicate which percentage of SIFa-expressing cells co-express other neurotransmitter components, and what the actual level of expression of the genes is. The authors should also provide more information on how they identified the SIFa+ cells in the fly atlas dataset. These are important pieces of information to be able to interpret the effects of manipulation of these other neurotransmitter systems within SIFa-expressing cells on mating duration. 5. I would like to see more information on how the thresholding and normalization was done for immunohistochemistry experiments. Was thresholding applied equally across all datasets? Furthermore, "overlap" of Denmark and Syt-eGFP is taken as evidence for synaptic connectivity, but the latter requires more than just overlap in the location of the axon terminal and dendrite regions of the neuron. 6. None of the RNAi experiments have been validated to demonstrate effective knockdown. In many cases, this would be difficult to do because of a lack of an antibody to quantify in a cell-specific manner; however, this fact should be acknowledged, especially in cases where there was found to be a lack of phenotype, which could result from lack of knockdown. The authors could also look for evidence in the literature of cases where RNAi lines they have used have been previously validated. For SIFa, knockdown can be easily confirmed with the SIFa antibody the authors have used elsewhere in the manuscript.

Minor Comments:

1. There are quite a lot of citations to preprints, including preprints of the manuscripts under review. It seems inappropriate to cite a preprint of the manuscript you are submitting because it gives a false sense of strengthening the assertions being made in the manuscript.
2. It seems that labels are incorrect on a number of the immunohistochemistry figures. For example, in Fig 2N, it labels dendrites as green, but this is sytEGFP, which is the presynaptic terminal.
3. Fig 4N shows grasp between SIFa-LexA and sNPF-R-GAL4, but the authors have argued that these two components should both be expressed in SIFa-expressing cells. This would make grasp signal misleading, because it would appear in the SIFa-expressing cells even without synaptic contacts due to both split GFP molecules being expressed in these cells.
4. For quantifying TRIC and CaLexA experiments (eg Figure 6A-E), intensity of signal should be measured in addition to the area covered by the signal.

Significance

This study will be most relevant to researchers interested in understanding neuronal control of behavior. It has provided novel information about the mechanisms underlying mating duration in flies, which is used to delineate how internal state influences behavioral outcomes. This represents a conceptual advance, particularly in identifying a cell type and molecule that influences mating duration decisions. The strength of the manuscript is the number of different assays used to investigate the central question from a number of angles. The limitation is that there is a lack of a big picture tying the different components of the manuscript together. Too much data is presented without providing a framework to understand how the data points fit together.

Hi Misha

Hi Misha.

eLife Assessment

This valuable work provides novel insights into the substrate binding mechanism of a tripartite ATP-independent periplasmic (TRAP) transporter, which may be helpful for the development of specific inhibitors. The structural analysis is convincing, but additional work will be required to establish the transport mechanism as well as well as binding sites for all ligands. This study will be of interest to the membrane transport and bacterial biochemistry communities.

Reviewer #1 (Public review):

Summary:

This manuscript reports the substrate-bound structure of SiaQM from F. nucleatum, which is the membrane component of a Neu5Ac-specific Tripartite ATP-dependent Periplasmic (TRAP) transporter. Until recently, there was no experimentally derived structural information regarding the membrane components of TRAP transporter, limiting our understanding of the transport mechanism. Since 2022, there have been 3 different studies reporting the structures of the membrane components of Neu5Ac-specific TRAP transporters. While it was possible to narrow down the binding site location by comparing the structures to proteins of the same fold, a structure with substrate bound has been missing. In this work, the authors report the Na+-bound state and the Na+ plus Neu5Ac state of FnSiaQM, revealing information regarding substrate coordination. In previous studies, 2 Na+ ion sites were identified. Here, the authors also tentatively assign a 3rd Na+ site. The authors reconstitute the transporter to assess the effects of mutating the binding site residues they identified in their structures. Of the 2 positions tested, only one of them appears to be critical to substrate binding.

Strengths:

The main strength of this work is the capture of the substrate bound state of SiaQM, which provides insight into an important part of the transport cycle.

Weaknesses:

The main weakness is the lack of experimental validation of the structural findings. The authors identified the Neu5Ac binding site, but only test 2 residues for their involvement in substrate interactions, which is quite limited. However, comparison with previous mutagenesis studies on homologues supports the location of the Neu5Ac binding site. The authors tentatively identified a 3rd Na+ binding site, which if true would be an impactful finding, but this site was not sufficiently experimentally tested for its contribution to Na+ dependent transport. This lack of experimental validation prevents the authors from unequivocally assigning this site as a Na+ binding site. However, the reporting of these new data is important as it will facilitate follow up studies by the authors or other researchers.

Comments on revisions:

Overall, the authors have done a good job of addressing the reviewers' comments. It's good to know that the authors are working on the characterisation of the potential metal binding site mutants - characterising just a few of these will provide much needed experimental support for this potential Na+ site.<br /> The new MD simulations provide some additional support for the new Na+ site and could be included. However, as the authors know, direct experimental characterisation of mutants is the ideal evidence of the Na+ site.

Aside from the characterisation of mutants, which seems to be held up by technical issues, the only remaining issue is the comparison of the Na+- and Na+/Neu5Ac-bound states with ASCT2.<br /> It still does not make sense to me why the authors are not directly comparing their Na+ only and Na+/Neu5Ac states with the structures of VcINDY in the Na+-only and Na+/succinate bound states. These VcINDY structures also revealed no conformational changes in the HP loops upon binding succinate, as the authors see for SiaQM. Therefore, this comparison is very supportive. It is understood that the similarity to the DASS structure is mentioned on p.17, but it is also interesting and useful to note that TRAP and DASS transporters also share a lack of substrate-induced local conformational changes, to the extent these things have been measured.

Reviewer #3 (Public review):

The manuscript by Goyal et al report substrate-bound and substrate-free structures of a tripartite ATP independent periplasmic (TRAP) transporter from a previously uncharacterized homolog, F. nucleatum. This is one of most mechanistically fascinating transporter families, by means of its QM domain (the domain reported in his manuscript) operating as a monomeric 'elevator', and its P domain functioning as a substrate-binding 'operator' that is required to deliver the substrate to the QM domain; together, this is termed an 'elevator with an operator' mechanism. Remarkably, previous structures had not demonstrated the substrate Neu5Ac bound. In addition, they confirm the previously reported Na+ binding sites, and report a new metal binding site in the transporter, which seems to be mechanistically relevant. Finally, they mutate the substrate binding site and use proteoliposomal uptake assays to show the mechanistic relevance of the proposed substrate binding residues.

Strengths:

The structures are of good quality, the presentation of the structural data has improved, the functional data is robust, the text is well-written, and the authors are appropriately careful with their interpretations. Determination of a substrate bound structure is an important achievement and fills an important gap in the 'elevator with an operator' mechanism.

Weaknesses:

Although the possibility of the third metal site is compelling, I do not feel it is appropriate to model in a publicly deposited PDB structure without directly confirming experimentally. The authors do not extensively test the binding sites due to technical limitations of producing relevant mutants; however, their model is consistent with genetic assays of previously characterized orthologs, which will be of benefit to the field. Finally, some clarifications of EM processing would be useful to readers, and it would be nice to have a figure visualizing the unmodeled lipid densities - this would be important to contextualize to their proposed mechanism.

Author response:

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

Reviewer #1 (Public Review):

This manuscript reports the substrate-bound structure of SiaQM from F. nucleatum, which is the membrane component of a Neu5Ac-specific Tripartite ATP-dependent Periplasmic (TRAP) transporter. Until recently, there was no experimentally derived structural information regarding the membrane components of the TRAP transporter, limiting our understanding of the transport mechanism. Since 2022, there have been 3 different studies reporting the structures of the membrane components of Neu5Ac-specific TRAP transporters. While it was possible to narrow down the binding site location by comparing the structures to proteins of the same fold, a structure with substrate bound has been missing. In this work, the authors report the Na+-bound state and the Na+ plus Neu5Ac state of FnSiaQM, revealing information regarding substrate coordination. In previous studies, 2 Na+ ion sites were identified. Here, the authors also tentatively assign a 3rd Na+ site. The authors reconstitute the transporter to assess the effects of mutating the binding site residues they identified in their structures. Of the 2 positions tested, only one of them appears to be critical to substrate binding.

Strengths:

The main strength of this work is the capture of the substrate-bound state of SiaQM, which provides insight into an important part of the transport cycle.

Weaknesses:

The main weakness is the lack of experimental validation of the structural findings. The authors identified the Neu5Ac binding site, but only tested 2 residues for their involvement in substrate interactions, which was very limited. The authors tentatively identified a 3rd Na+ binding site, which if true would be an impactful finding, but this site was not tested for its contribution to Na+ dependent transport, and the authors themselves report that the structural evidence is not wholly convincing. This lack of experimental validation undermines the confidence of the findings. However, the reporting of these new data is important as it will facilitate follow-up studies by the authors or other researchers.

The main concern, also mentioned by other reviewers, is the lack of mutational data and functional studies on the identified binding sites. Two other structures of TRAP transporters have been determined, one from Haemophilus influenzae (Hi) and the other from Photobacterium profundum (Pp). We will refer to the references in this paper as [1], Peter et al. as [2], and Davies et al. as [3]. The table below lists all the mutations made in the Neu5Ac binding site, including direct polar interactions between Neu5Ac and the side chains, as well as the newly identified metal sites.

The structure of Fusobacterium nucleatum (Fn) that we have reported shows a significant sequence identity with the previously reported Hi structure. When we superimpose the Pp and Fn structures, we observe that nearly all the residues that bind to the Neu5Ac and the third metal site are conserved. This suggests that mutagenesis and functional studies from other research can be related to the structure presented in our work.

The table below shows that all three residues that directly interact with Neu5Ac have been tested by site-directed mutagenesis for their role in Neu5Ac transport. Both D521 and S300 are critical for transport, while S345 is not. We do not believe that a mutation of D521A in Fn, followed by transport studies, will provide any new information.

However, Peter et al. have mutated only one of the 5 residues near the newly identified metal binding site, which resulted in no transport. The rest of the residues have not been functionally tested. We propose to mutate these residues into Ala, express and purify the proteins, and then carry out transport assays on those that show expression. We will include this information in the revised manuscript.

Author response table 1.

Reviewer #2 (Public Review):

In this exciting new paper from the Ramaswamy group at Purdue, the authors provide a new structure of the membrane domains of a tripartite ATP-independent periplasmic (TRAP) transporter for the important sugar acid, N-acetylneuraminic acid or sialic acid (Neu5Ac). While there have been a number of other structures in the last couple of years (the first for any TRAP-T) this is the first to trap the structure with Neu5Ac bound to the membrane domains. This is an important breakthrough as in this system the ligand is delivered by a substrate-binding protein (SBP), in this case, called SiaP, where Neu5Ac binding is well studied but the 'hand over' to the membrane component is not clear. The structure of the membrane domains, SiaQM, revealed strong similarities to other SBP-independent Na+-dependent carriers that use an elevator mechanism and have defined Na+ and ligand binding sites. Here they solve the cryo-EM structure of the protein from the bacterial oral pathogen Fusobacterium nucleatum and identify a potential third (and theoretically predicted) Na+ binding site but also locate for the first time the Neu5Ac binding site. While this sits in a region of the protein that one might expect it to sit, based on comparison to other transporters like VcINDY, it provides the first molecular details of the binding site architecture and identifies a key role for Ser300 in the transport process, which their structure suggests coordinates the carboxylate group of Neu5Ac. The work also uses biochemical methods to confirm the transporter from F. nucleatum is active and similar to those used by selected other human and animal pathogens and now provides a framework for the design of inhibitors of these systems.

The strengths of the paper lie in the locating of Neu5Ac bound to SiaQM, providing important new information on how TRAP transporters function. The complementary biochemical analysis also confirms that this is not an atypical system and that the results are likely true for all sialic acid-specific TRAP systems.

The main weakness is the lack of follow-up on the identified binding site in terms of structure-function analysis. While Ser300 is shown to be important, only one other residue is mutated and a much more extensive analysis of the newly identified binding site would have been useful.

Please see the comments above.

Reviewer #3 (Public Review):

The manuscript by Goyal et al reports substrate-bound and substrate-free structures of a tripartite ATP-independent periplasmic (TRAP) transporter from a previously uncharacterized homolog, F. nucleatum. This is one of the most mechanistically fascinating transporter families, by means of its QM domain (the domain reported in his manuscript) operating as a monomeric 'elevator', and its P domain functioning as a substrate-binding 'operator' that is required to deliver the substrate to the QM domain; together, this is termed an 'elevator with an operator' mechanism. Remarkably, previous structures had not demonstrated the substrate Neu5Ac bound. In addition, they confirm the previously reported Na+ binding sites and report a new metal binding site in the transporter, which seems to be mechanistically relevant. Finally, they mutate the substrate binding site and use proteoliposomal uptake assays to show the mechanistic relevance of the proposed substrate binding residues.

The structures are of good quality, the functional data is robust, the text is well-written, and the authors are appropriately careful with their interpretations. Determination of a substrate-bound structure is an important achievement and fills an important gap in the 'elevator with an operator' mechanism. Nevertheless, I have concerns with the data presentation, which in its current state does not intuitively demonstrate the discussed findings. Furthermore, the structural analysis appears limited, and even slight improvements in data processing and resulting resolution would greatly improve the authors' claims. I have several suggestions to hopefully improve the clarity and quality of the manuscript.

We appreciate your feedback and will make the necessary modifications to the manuscript incorporating most of the suggestions. We will submit the revised version once the experiments are completed. We are also working on improving the quality of the figures and have made several attempts to enhance the resolution using CryoSPARC or RELION, but without success. We will continue to explore newer methods in an effort to achieve higher resolution and to model more lipids, particularly in the binding pocket.

Reviewing Editor (Recommendations for the Authors):

After discussing the reviews, the reviewers and reviewing editor have agreed on a list of the most important suggested revisions for the authors, which, if satisfactorily addressed, would improve the assessment of the work. These suggested revisions are listed below. We also include the full Recommendations For The Authors from each of the individual reviewers.

(1) The authors tentatively identified a 3rd Na+ binding site, which if true would be an impactful finding, but this site was not tested for its contribution to Na+ dependent transport, and the authors themselves report that the structural evidence is not wholly convincing. Additional mutagenesis and activity experiments to test the contribution of this site to transport would strengthen the manuscript. Measuring Na+ concentration-response relations and calculating Hill slopes in WT vs. an M site mutant would be a good experiment. Given the lack of functional data and poor density, it does not seem appropriate to build the M site sodium in the PDB model.

The density is well defined to suggest a metal bound (waters would not be clearly defined at this resolution).  While our modeling of the site as a Na+ is arbitrary, this was done to satisfy the refinement programs where we have a known scatterer modeled.  We could model this density with other metals, but unlike crystallographic refinement, real-space refinement of cryoEM maps does not produce a difference map that might allow us to identify the metal but not conclusively.   The density of the maps is good (we have added better figures to demonstrate this).  We tried making multiple mutations to test for activity – unfortunately, we are still struggling to express proteins with mutations in this site in sufficient quantities to carry out transport assays.

In the absence of being able to do the experiments, we did MD simulations (carried out by Senwei Quan and Jane Allison at University of Auckland).  Our results are shown below – we are not certain without further studies that these should be included in the current paper (we will add them as authors if the editor feels that this evidence is critical).

Author response table 2.

We are showing this for review to suggest that K+, Ca2+, and Na+ were tried, and only Na+ stays stably in the binding pocket. The rest of the results will also have to be explained, which would change the focus of the paper.

We also provided the sequence to Alphafold3 and asked it to identify the possible metal binding sites—when the input was Na+, it found all three binding sites. 

Summary:  Both our experimental data and computational studies suggest the observed metal binding site is real but at the moment, it is not possible to refine the structure and put an unidentified metal.  Computational studies suggest that this is a high-probability Na+ site. 

Demonstration of cooperativity between the Na+ site and transport require carrying out these experiments with mutations in these sites in a concentration-dependent manner. Unfortunately, our inability to produce well-expressed and purified proteins with mutations in a short time frame failed. 

(2) The authors identified the Neu5Ac binding site but only tested 2 residues for their involvement in substrate interactions, which was very limited. Given that the major highlight of this paper is the identification of the Neu5Ac binding site, it would strengthen the manuscript if the authors provided a more extensive series of mutagenesis experiments - testing at least the effect of D521A would be important. One inconsistency is Ser345 mutagenesis not affecting transport, and the authors should further discuss in the text why they think that is.

D521A has been tested in H. influenzae, and this mutation results in loss of transport.  This residue is highly conserved and occupies the same position. We expect the result to remain the same. 

We have added a few extra lines to discuss Serine 345: “Ser 345 OG is 3.5Å away from the C1-carboxylate oxygen – a distance that would result in a weak interaction between the two groups. It is, therefore, not surprising that the mutation into Ala did not affect transport. The space created by the mutation can be occupied by a water molecule.”

(3) The purification and assessment of the stability of the protein are described in text alone with no accompanying data. It would be beneficial to include these data (e.g. in the Supplementary info) as it allows the reader to evaluate the protein quality.

This is now added as Supplementary Figure 2.

(4) The structural figures throughout the paper could benefit from more clarity to better support the conclusions. Specific critiques are listed below:

- Figure 1: since the unbound map has a similar reported resolution, displaying the unbound structure's substrate binding site with the same contour would clearly demonstrate that the appearance of this density is substrate-dependent.

- Figure 1: the atomic fit of the ligand to the density, and the suggested coordination by side chain and backbone residues, would be useful in this figure.

- Figure 1: I think it would be more intuitive to compare apo and bound structures with the same local resolution scale.

We have remade Figure 1 “Architecture of FnSiaQM with nanobody. (A and B) Cryo-EM maps of FnSiaQM unliganded and sialic acid bound at 3.2 and 3.17 Å, respectively. The TM domain of FnSiaQM is colored using the rainbow model (N-terminus in blue and C-terminus in red). The nanobody density is colored in purely in red. The density for modeled lipids is colored in tan and the unmodelled density in gray. The figures were made with Chimera at thresholds of 1.2 and 1.3 for the unliganded and sialic acid-bound maps. (C and D) The cytoplasmic view of apo and sialic acid bound FnSiaQM, respectively. Color coding is the same as in panels A and B. The density corresponding to sialic acid and sodium ions are in purple. The substrate binding sites of apo and sialic acid bound FnSiaQM are shown with key residues labeled. The density (blue mesh) around these atoms was made in Pymol with 2 and 1.5 s for the apo and the sialic acid, respectively, with a carve radius of 2 Å.”

The local resolution maps have been moved to Supplementary Figure 3.

- Figure 3, Figure 5a: The mesh structures throughout the manuscript are blocky and very difficult to look at and interpret, especially for the ion binding sites, which are currently suggestive of but not definitively ion densities. Either using transparent surfaces, higher triangle counts, or smoothing the surface might help this.

We have made Figure 3 again with higher triangle counts.  We tried all three suggestions and this provided the best figure. We have replaced Figure 5A with density for Neu5Ac and residues around it.

- Figure 5A: It would be important to show the densities of the entire binding pocket, especially coordinating side chains, to show the reader what is and isn't demonstrated by this structure.

- It's not clear how Figure 5D is supposed to show that the cavity can accommodate Neu5Gc, as suggested by the text - please make the discussed cavity clearer in the Figure.

We have now marked with an arrow the Methyl Carbon where the hydroxyl group is added.  We have mentioned that in the legend.  It is open to the periplasmic side of the cavity.

- Supplementary Figure 4: Please label coordinating residue sites.

Labels have been added to Supplementary Figure 6 which was earlier Supplementary Figure 4.

(5) Intro section: the authors should introduce the work on HiSiaP around the role of the R147 residue in high-affinity Neu5Ac binding, which coordinates the carboxylate of Neu5Ac, and which is a generally conserved mechanism for organic acid binding in other TRAP transporters. This context will help magnify their discovery later that in the membrane domains, it is a key serine and not an arginine that coordinates the carboxylate group (probably as the local concentration of Neu5Ac is high and tight binding site is not desirable for rapid transport, which is mentioned in the discussion).

Thank you for pointing this out. We have added a new sentence to the introduction.

“All the SiaP structures show the presence of a conserved Arginine that binds to the C1-carboxylate of Neu5Ac, and this Arg residue is critical as the high electrostatic affinity may be important to have a strong binding affinity that sequesters the small amounts that reach the bacterial periplasmic space  (Glaenzer et al., 2017).”

(6) TRAP transporters exist for many organic compounds and not just sialic acid, which might be nice to make the reader aware of.

We initially did not do this as this is an advance paper and this was discussed in the earlier paper (Currie et. al., 2024). However, we have now added a sentence to the introduction. “Additionally, amino acids, C4-dicarboxylates, aromatic substrates and alpha-keto acids are also transported by TRAP transporters (Vetting et al., 2015). “

(7) On p. 12, the authors describe the Neu5Ac binding site as a large solvent-exposed vestibule, having previously described the substrate-bound state as occluded. These descriptions should be adjusted to make clear which structure is being referenced. The clarity of this would be substantially improved if the authors included a figure that showed this occlusion - currently none of the structure figures clearly demonstrate what the authors are referring to. There are several conspicuous unmodeled densities proximal to the substrate, reminiscent of lipids (in between transport and scaffold domain) and possibly waters/ions. Given this, it is really surprising that the substrate binding site is described as "solvent-exposed" since the larger molecules seem to occlude the pocket. The authors should further process their dataset and discuss the implications of these surrounding densities.

We have processed the data sets carefully both with cryosparc and relion and the resolution described here is same with both software with the cryosparc maps slightly better in terms of interpretability of peripheral helices and described in the manuscript. The current sample (FnTRAP) with the nanobody is a relatively stable sample (in our experience with other similar proteins) as evident from the number of images and particles to achieve a decent resolution and thus the workflow is straightforward and simple.  There are number of non-protein densities, which in principle can be modelled but we have chosen a conservative approach not to model these extra densities (except for the two lipids, few ions) due to limit of the resolution. It is possible that increasing the number of particles will result in an increase in resolution but from the estimated B-factor (125 or 135 Å2 for unliganded and liganded), this will certainly require lot of more images with no guarantee of increased resolution.

The question of outward open Vs outward occluded is a valid point. We have now modified this in the manuscript. “The Neu5Ac binding site has a large solvent-exposed vestibule towards the cytoplasmic side, while its periplasmic side is sealed off. Cryo-EM map shows the presence of multiple densities that could be modeled as lipids, possibly preventing the substrate from leaving the transporter. However, the densities are not well defined to model them as specific lipids, hence they have not been modeled.  We describe this as the “inward-facing open state” with the substrate-bound.”

(8) On p.15, the activity of FnSiaPQM in liposomes is reported, although the impetus for this study is not clear. Presumably, the reason for its inclusion is to ensure that the structurally characterized protein is active. It would be useful to say this at the start of the section if this is the case. This study nicely shows that the energetics and requirements of transport are identical to all the previous studies on Neu5Ac TRAP transporters - it would be good to acknowledge this somewhere in this section as well.

These changes have been incorporated.  We have added a line to say why we did this and added as the last line that this is similar to other SiaPQM’s characterized.

(9) Figure 5C. The authors show the transport activity with and without valinomycin. The authors do not explain the rationale for testing and reporting both conditions for these mutants; an explanation is required, or the data should be simplified. The expected membrane potential induced by valinomycin should be mentioned in the legend.

We have simplified Figure 5C and added the expected membrane potential value.

(10) The authors state that the S300A mutant is inactive. However, unless the authors also measured the background binding/transport of radiolabelled substrate in the absence of protein, then the accuracy of this statement is not clear because Figure 5C does indicate some activity for S300A, albeit much lower than WT. This is an important point in light of the authors' suggestion that the membrane protein does not need a binding site of high affinity or stringent selectivity.

We thank the reviewer for pointing this out we have now added a line in the experimental protocols “The experimental values were corrected by subtracting the control, i.e. the radioactivity taken up in liposomes reconstituted in the absence of protein. The radioactivity associated with the control samples, i.e. empty liposomes was less than 10% with respect to proteoliposomes.”.

(11) There are several issues and important omissions in the work cited:

- It is not normal practice to cite a reference in the abstract and the citation is only to the second structure of HiSiaQM, which does not fairly reflect previous work in the field by only referring to their own work. Also throughout the article, it is normal practice with in-text citations to order them chronologically, i.e. earliest first. Please update this.

This article was submitted as an “Research advance article”.  The instructions specifically say that “Research advance article should cite the article in eLife this paper advances.  Hence the citation of the “second structure of HiSiaQM”.  In fact, in the manuscript we explicitly say “The first structure of _Hi_SiaQM (4.7 Å resolution) demonstrated that it is composed of 15 transmembrane helices and two helical hairpins.”   We are following the policy laid out.  

Zotero organizes multiple references in alphabetical order, we did not choose to do it that way – the suggestion of bias is not true. The final version of the accepted paper will have numbers, and this argument will automatically be corrected.

- Intro: please cite the primary papers discovering other families of sialic acid transporters.

- Intro: When introducing information on the binding site, dissociation constant of Neu5Ac, and thermodynamics of ligand binding to SiaP, the authors should also include references to the work done by others in addition to their own work.

The Setty et al. paper was the first to demonstrate that the two-component systems are distinct, and that the binding protein of the TRAP system binds enthalpically while the binding protein of the ABC system binds entropically (SiaP vs SatA). As the reviewer points out, this is significant because it highlights how the Arg binding to the carboxylate, which is the enthalpic driver in this case and contributes to the difference between sugar binding to SiaP and SatA. Many studies have published binding affinities of molecules to SiaP, but this paper offers valuable insight into the differences between these systems. We have cited a number of the SiaP papers from other groups, including acknowledging the first structure of SiaP from H. influenzae by Muller et al., in 2006.

- p.5 "TRAP transporters are postulated to employ an elevator-type mechanism...". This postulation has been experimentally tested and published, so should be discussed and referenced (Peter et al. 2024. https://doi.org/10.1038/s41467-023-44327-3).

We have now corrected this error. We removed “are postulated to” and added the reference.

- p.5 "Notably, the transport of Neu5Ac by TRAP transporters requires at least two sodium ions (Davies et al., 2023)." The requirement for at least 2 Na+ ions for Neu5Ac transport was first demonstrated in Mulligan et al. PNAS 2009, so should also be cited (for completion, so should Mulligan et al. JBC. 2012 and Currie et al. elife 2023, which have also shown this requirement is a commonality amongst all Neu5Ac TRAP transporters).

Added.

- P.12, Mulligan et al, JBC, 2012 should be added to the citations in the first sentence.

Added.

- p.19 "Interestingly, even the dicarboxylate transporter from V. cholerae (VcINDY) binds to its ligand via electrostatic interactions with both carboxylate groups". Other references are more appropriate than the one used to support this statement.

Also added references for Mancusso et. al, 2012, Nie et.al, 2017 and Sauer et.al., 2022 here.

- p.19. "The structure of the protein in the outward-facing conformation is unknown". The authors do not discuss the mechanistic findings from Peter et al 2024 Nat Comm here. The work described in that paper revealed an experimentally verified model of the OFS of HiSiaQM, so really needs to be included.

This is not an experimentally determined 3D structure. They have shown the possible existence of this by microscopy, but the structure is not determined. The work mentioned is a wonderful piece of work, but it does not report the three-dimensional structure of the protein in the outward-facing conformation to allow us to understand the nature of the molecular interactions. 

- The reference to Kinz-Thompson et al 2022 on p. 6 is not appropriate - neither the HiSiaQM papers nor the PpSiaQM paper makes reference to this work when identifying the binding site. More suitable references are used, for example, Mancusso et al 2012, Nie et al 2017 and Sauer et al 2022; this should be reported accurately.

Added the suggested references.  We think the paper (Kinz-Thomposin et al 2022) is relevant and have also kept that reference.

- Garaeva et al report the opposite of what the authors mention - "In the human neutral amino acid transporter (ASCT2), which also uses the elevator mechanism, the HP1 and HP2 loops have been proposed to undergo conformational changes to enable substrate binding and release (Garaeva et al., 2019)." In fact, this paper suggested a one-gate model of transport (HP2), where HP1 seems uninvolved in gating.

The Reviewer is correct.  We were wrong and not clear.  The entire paragraph has been rewritten.

“While, both the HP1 and HP2 loops have been hypothesized to be involved in gating, in the human neutral amino acid transporter (ASCT2), (which also uses the elevator mechanism), only the HP2 loops have been shown to undergo conformational changes to enable substrate binding and release (Garaeva et al., 2019). Hence, it is suggested that there is a single gate that controls substrate binding. Superposition of the _Pp_SiaQM and _Hi_SiaQM structures do not reveal any change in these loop structures upon substrate binding. For TRAP transporters, the substrate is delivered to the QM protein by the P protein; hence, these loop changes may not play a role in ligand binding or release. This may support the idea that there is minimal substrate specificity within SiaQM and that it will transport the cargo delivered by SiaP, which is more selective.”

- p.19 "suggesting that SSS transporters have probably evolved to transport nine-carbon sugars such as Neu5Ac (Wahlgren et al, 2018)." Surely this goes without saying since Wahlgren et al 2018 demonstrated that SiaT, an SSS, could transport sialic acid? It's unclear why this was included here - perhaps it needs to be rewritten to make the point more clearly, but as it stands, this statement appears self-evident. Furthermore, these proteins can transport all kinds of molecules (see TCDB 2.A.21). This statement needs to be clarified. 

This was a comparison to other Neu5Ac binding sites in other Neu5Ac transporters. We have modified the sentence. “The polar groups bind to both the C1-caboxylate side of the molecule and the C8-C9 carbonyls, suggesting that Proteus mirabilis Neu5Ac transporter (SSS type) evolved specifically to transport nine-carbon sugars such as Neu5Ac (Wahlgren et al., 2018)”.  These were arguments we were making to suggest that the lack of tight binding could also mean reduced specificity.

- The authors reconstitute the FnSiaQM and measure transport with SiaP, which resembles closely what is known for both HiSiaPQM, VcSiaPQM, which is not cited (https://doi.org/10.1074/jbc.M111.281030).

- Regarding lipids between transport and scaffold domains: there is precedent for such lipids in the elevator transporter GltPh, Wang, and Boudker (eLife 2020) proposed similar displacements during transport and would be appropriate to cite here.

We have now cited the reference to the Mulligan et al., 2012 paper.  We also added a sentence on the findings of GltPh paper by Wang and Boudker.  Thank you for pointing this out.

(12) p.9 "TRAP transporters, as their name suggests, comprise three units: a substrate-binding protein (SiaP) and two membrane-embedded transporter units (SiaQ and SiaM) (Severi et al., 2007)." This is somewhat odd phrasing because the existence of fused membrane components has been well-documented for a long time. The addition of "Many" at the start of the sentence fixes this.

Added Many.

(13) On p.12 the authors compare the ligand-induced conformational changes of FnSiaQM with ASCT2, citing Garaeva et al, 2019. This comparison does not make sense considering TRAP transporters and ASCT2 do not share a common fold. A far superior comparison is with DASS transporters, which actually do have the same fold as TRAP transporters. And, importantly, the Na+ and substrate-induced conformational changes have been investigated for DASS transporters revealing a unique mechanism likely shared by TRAP transporters (Sauer et al, Nat Comm, 2022). The text on p.12 should be adjusted to replace the ASCT comparison with a VcINDY comparison.

The purpose of citing the ASCT2 paper was only concerning the HP1 and HP2 gates.  The authors show that HP2 changes conformation only.  Comparing the two FnSiaQM structures – with and without ligand, we see no change in either the HP1 or the HP2 loops.  On Page 17, when we describe the structure, we do specifically mention that the overall architecture is similar to VcINDY and the DASS transporters.

(14) p.12 "For TRAP transporters, the substrate is delivered to the QM protein by the SiaP" protein;" "SiaP protein" should be "P protein"

Corrected.

(15) p.18. "periplasmic membrane" should be "cytoplasmic membrane".

Corrected.

(16) p.19. "This prevents Neu5Ac from binding..." There is no evidence for this so this needs to be softened, e.g. "This likely prevents Neu5Ac from...".

Agree – Modified.

(17) Figure 2B is rather small, cramped, and difficult to see. We suggest that the authors make that panel larger, or include it as a stand-alone supplementary figure.

We have moved this figure into a supplementary figure as suggested by the reviewer.

(18) The authors describe the Neu5Ac binding site in SiaQM. It would be helpful if the authors provided a figure in support of the statement that the Neu5Ac binding site architecture is similar to dicarboxylate in VcINDY (especially as Neu5Ac is a monocarboxylate).

The Neu5Ac binding site is NOT similar to the VcINDY binding site. But, we understand the origin of the comment. We have now changed the sentence: “The overall architecture of the Neu5Ac binding site is similar to that of citrate/malate/fumarate in the di/tricarboxylate transporter of V. cholerae (Vc_INDY), but the residues involved in providing specificity are different (Kinz-Thompson _et al., 2022; Mancusso et al., 2012; Nie et al., 2017; Sauer et al., 2022). Neu5Ac binds to the transport domain without direct interactions with the residues in the scaffold domain. The majority of the interactions are with residues in the HP1 and HP2 loops of the transport domain (Figure 5B). Asp521 (HP2), Ser300 (HP1), and Ser345 (helix 5) interact with the substrate through their side chains, except for one interaction between the main chain amino group of residue 301 and the C1-carboxylate oxygen of Neu5Ac. Mutation of the residue equivalent to Asp521 has been shown to result in loss of transport (Peter et al., 2022). To evaluate the role of residues Ser-300 and Ser-345, we mutated them to alanine and performed the transport assays.”  

(19) When comparing the binding modes of Neu5Ac to different proteins in Figure 6, it would be helpful to include the structure in this paper as well.

The Neu5Ac binding site is present in figure 5. We would prefer not to show it again in Figure 6.

Additionally, there is a clear binding mode of Neu5Ac in Figure 1 as well.

(20) The manuscript would benefit from a more detailed comparison between Na+-bound (described as apo) and Na+/Neu5Ac structures, especially the prospective gates. If this transporter behaves anything like the archetypical ion-coupled glutamate transporters, some structural changes in the gates might be expected to facilitate transport domain movement when the substrate is loaded, but not when only Na+ is bound. It would be important to discuss and visualize these changes.

We have described in the manuscript that there is NO change in the HP1 and HP2 gates between the unliganded structure and the Neu5Ac bound structure. The major difference we observe is the ordering of the third metal binding site.

A figure comparing the substrate binding pockets between the different high-resolution structures would also be informative. Do the bonding distances between ligands and side chains significantly change between homologs?

This is the only Neu5Ac bound structure.  Since the specificity to the substrate comes from the variability of the residues that interact it, we do not believe that this figure would not add much value.  

(21) A supplementary figure (or an inset to Figure 2) showing pairwise percent identity between different characterized QM transporters would be useful.

We have now added a Supplementary Figure 4 showing the comparison of the three QM sequences whose structures have been determined.

(22) There is relatively minimal EM processing. More rigorous processing would require relatively little effort and could boost resolution, making this a vastly improved manuscript with a much more confident interpretation of structures.

We described the overall workflow. The processing was rigorous. After obtaining the first maps, we created templates with the structure and did template-based picking.  We then did several rounds of 2D classification followed by homogenous refinement, Non-Uniform Refinement.  We then made masks and carried out local refinement.  We then got the best maps and did a 3D classification. Refined the 3D classes independently.  Then, we regrouped them based on how similar they were. We then went back and picked particles again (we used different methods of particle picking, but template-based picking resulted in the final set of particles used) and went through the whole process again.  At the end of the refinement, we carried out global and local CTF refinement followed by reference-based motion correction. The final refinement was then done with the Bayesian polished particles.  The final refinement was local refinement with a mask over only the transporter and the nano-body. After the reviews came, we tried multi-body refinement in Relion5.  It did not improve resolution. We have expanded the legend to supplementary Figure 2 (without listing all the different things we tried). The best resolution we obtained for the structure was 3.1 Å. However, it is important to note that the local resolution of the map around the ligand is good. 

We realized this is not easy to depict in a local resolution map.  So, we wrote a script to take every atom, then take a radius of 5 Å (again we tried different radii and used the optimal one; we are preparing a manuscript to describe this), take all the local resolution values within the 5 Å spere and average it and add it as B-factor that atom. We have moved the local resolution map figure to the supplement and replaced Figure 1 with a Cartoon, where the color represents the local resolution in which the atom is. 

(23) Calling the structure without Neu5Ac bound an "apo" structure is confusing since it indeed has the ligand Na+ present and bound. "Na+" and "Na+/Neu5Ac" structures would be more appropriate.

Changed all “apo” to “unliganded”.

2 phần quan trọng của meta-prompts: - Mô tả bài toán tối ưu: Phần đầu tiên là mô tả bằng văn bản của bài toán tối ưu, bao gồm hàm mục tiêu và ràng buộc giải pháp. Ví dụ, đối với việc tối ưu hóa prompt, LLM có thể được hướng dẫn để "tạo sinh ra 1 chỉ dẫn mới giúp đạt được điểm accuracy cao hơn". và các chỉ dẫn như vậy trong các meta-prompt được gọi là meta-instructions.

Kiến trúc tổng thể của OPRO. Ở mỗi bước tối ưu, LLM sẽ đưa ra các giải pháp tiềm năng cho bài toán tối ưu dựa trên mô tả bài toán tối ưu hóa và các prompt đã được đánh giá từ trước trong meta-prompt. Sau đó các giải pháp mới sẽ được đánh giá và đưa vào meta-prompt cho quá trình tối ưu kế tiếp. Qúa trình tối ưu kết thúc khi LLM không thể đưa ra các giải pháp mới với điểm tối ưu cao hơn hoặc số bước tối ưu chạm ngưỡng.

Khác với các nghiên cứu gần đây sử dụng LLM cho việc sinh prompt tự động, mỗi bước tối ưu hóa sẽ tạo sinh các prompt mới có mục tiêu tăng điểm accuracy dựa trên lân cận của các prompt đã được tạo sinh trước đó, thay vì chỉnh sửa một prompt đầu vào dựa trên phản hồi ngôn ngữ tự nhiên hoặc yêu cầu prompt mới phải có ý nghĩa ngữ cảnh tương tự.

Meta-prompt (prompt dùng cho LLM để làm trình tối ưu) có 2 phần thông tin chính. - Thông tin chính đầu tiên là các prompt đã được tạo sinh từ trước cùng với điểm accuracy tương ứng. - Thông tin chính thứ 2 là mô tả bài toán tối ưu hóa chứa một vài mẫu ví dụ được lấy ngẫu nhiên từ tập huấn luyện để minh họa cho bài toán.

Purity of intentions

Important

how one is raised is what they later project onto the world

HUH?

Symbolic practices can make self-interested actions depersonalized

Denial that practices are anything but a justified course of actions- that there exists motives within them

!!! Power is legitimated by symbolism- the power itself is not symbolic Distinct from Marx

symbolic power requires both dominant and dominated cooperation

Diction: violence symbolic violence is ability to maintain power by claiming a legitimacy and naturalness to political structures

Power relations maintained by status quo or belief that there is a rightness to the way things are

symbolic power reproduced by its own fullfillment

Power of symbols null without the reinforcing social structure Power not in words or symbols themselves

symbolic systems are INSTRUMENT OF DOMINATION !!!

symbolic systems hold structure

Unlike Marx- Bourdieu emphasizes symbolic power in social reproduction

!!!!!!!!

conversion is difficult- cultural, social, and economic capital all interchangeable but not equally possible

still considers economic capital as the root of all other capitals

capital as an "energy of social physics"

use of institutions a key factor in social inequality

third form of cultural capital- the institution

second state of cultural capital- objects requiring cultural knowledge to use

first state of cultural capital- cultural nonmaterial goods

Cultural capital suggests that capital itself can become the resource

bourdieu believes there is capital beyond monetary means (social, cultural, or symbolic capital)

objectivist scientists forget how they are working with ideal types

reason without understanding of practicality cannot explain social structures

mix of qual and quan data

reject objectivism- that one should aim for perfectly objective assessments of others

all activity within structures that differ from formed understandings (common sense)

must leave behind own conception of the world

In his epistemology- Bourdieu distances himself with notions of common sense and taking human characterization of their experiences at face value

combines subjective/objective dichotomy into the "general science of practices"

symbolic power = to make sacred legitimates things and strengthens boundaries

Bourdieu follows Durkheims observation of a historical shift undifferentiated --> differentiated

adopts Durkheim's observation of experience

Bourdieu adopted Durkheim's scientific approach to Sociology

Bourdieu works a bit with ideal types

Weber's types of religious leaders allows Bourdieu to organize cultural producers and their cultural fields

symbolic labor plays a central role in social strat

continued use of value rational vs rational distinction to observe religious vs cultural capital

acknowledge distinction of value-rational motives

borrowing Weber's analysis of the material withing the symbolic (routinization of religion)

doesn't distinguish social infrastructure from superstructure

eLife Assessment

This valuable study reports a potential connection between the seminal microbiome and sperm quality/male fertility. The data are generally convincing. This study will be of interest to clinicians and biomedical researchers who work on microbiome and male fertility.

Reviewer #1 (Public review):

Summary:

The authors analyzed the bacterial colonization of human sperm using 16S rRNA profiling. Patterns of microbiota colonization were subsequently correlated with clinical data, such as spermiogram analysis, presence of reactive oxygen species (ROS), and DNA fragmentation. The authors identified three main clusters dominated by Streptococcus, Prevotella, and Lactobacillus & Gardnerella, respectively, which aligns with previous observations. Specific associations were observed for certain bacterial genera, such as Flavobacterium and semen quality. Overall, it is a well-conducted study that further supports the importance of the seminal microbiota.

Strengths:

- The authors performed the analysis on 223 samples, which is the largest dataset in semen microbiota analysis so far<br /> - Inclusion of negative controls to control contaminations.<br /> - Inclusion of a positive control group consisting of men with proven fertility.

Weaknesses:

- The manuscript needs comprehensive proofreading for language and formatting. In many instances spaces are missing or not required.<br /> - Could the authors explore correlation network analyses to get additional insights in the structure of different clusters?<br /> - The github link is not correct.<br /> - It is not possible to access the dataset on ENA.<br /> - Add the graphs obtained with decontam analysis as a supplementary figure.<br /> - There is nothing about the RPL group in the results section, while the authors discuss this issue in the introduction. What about the controls with proven fertility?<br /> - While correctly stated in the title, the term microbiota should be used throughout the manuscript instead of "microbiome"

Comments on revised version:

Discussion: Could the authors discuss more the findings about Flavobacterium? Has it ever been associated with the urogenital tract? What is the relative abundance in the present study: this type of bacterium has been previously associated with contaminations (PMID: 25387460, 30497919).

Figure 1: Increase the size of panel A.

Figure 3: Can the authors indicate the relative abundance of each genus/species by the size of the node?

Supplementary data: I don't see anywhere the decontam plots.

Author response:

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

Reviewer 1:

- The manuscript needs comprehensive proofreading for language and formatting. In many instances, spaces are missing or not required.

Thank you for your comments. The manuscript has been thoroughly proofread for errors in language and formatting.

- Could the authors explore correlation network analyses to get additional insights into the structure of different clusters? 

We have added a co-occurrence analysis (at species taxonomic level) based on SparCC to the manuscript (Figure 2).

This is described on Page 9 line 141-148

- The GitHub link is not correct. 

The github repository has now been made public.

- It is not possible to access the dataset on ENA. 

We have changed the ENA study PRJEB57401 status to open.

- Add the graphs obtained with decontam analysis as a supplementary figure. 

We have added the outputs of decontam (.csv files with feature lists of ASVs that were filtered based on the prevalence and frequency tests) to the github repository.

- There is nothing about the RPL group in the results section, while the authors discuss this issue in the introduction. What about the controls with proven fertility? 

Thank you. We have amended the manuscript to compare characteristics between the RPL, unexplained subfertility and controls groups.

Line 1279-130 page 8:  

“The study group represented 85% of samples with high sperm DNA fragmentation, 85% of samples with elevated ROS and 79% of samples with oligospermia. Rates of abnormal seminal parameters including low sperm concentration, reduced progressive motility and ROS concentrations were found to be highest in the MFI group (Supplementary Figure 1). Baseline characteristics between the RPL, unexplained subfertility and controls groups were similar.

Line 150-154 Page 9: 

“Bacterial richness, diversity and load were similar between all patient groups examined in the study (Supplementary Figure 4).

- While correctly stated in the title, the term microbiota should be used throughout the manuscript instead of "microbiome" 

Thank you. This misnomer has been amended throughout the manuscript.

Minor corrections:

Line 25: provoke is not a good term here. 

Thank you. The term ‘provoke’ has been removed

Line 26: why does semen culture have a limited scope? 

Thank you. Line 40-41 Page 3 has been amended:

“It is therefore plausible that asymptomatic seminal infections may be associated with impaired reproductive function in some men. Since semen culture has a limited scope for studying the seminal microbiota due to its inability to identify all present microbiota next generation sequencing (NGS) approaches have been reported recently by a growing number of investigators (13, 14, 15, 16, 17, 18, 19)”.

Line 68: write μl correctly

Thank you. This has been corrected

Line 131: several organisms at the genus level. 

Thank you. This has been corrected

Line 136: what are the relative abundances of these genera? Is this relevant? 

The mean relative abundances for the key taxa mention in each cluster are all above 20%. This information has been added to the manuscript text on page 9, line 153.

Line 173: Molina et al. 

Thank you. This has been corrected

Line 173: the contaminations are referred to the low biomass nature of testicular samples. If present, bacteria of accessory gland secretions are an integral part of the seminal microbiota itself. Please review these sentences. 

Thank you. This had been reworked to highlight the important of urethral contamination, which you later allude to as a limitation of our study is the failure to provide paired urine and semen samples.

Page 11 line 194-196

“Molina et al report that 50%-70% of detected bacterial reads may be environmental contaminants in a sample from extracted testicular spermatozoa (35); with the addition of passage along the urethra it is likely that contamination of ejaculated semen would be much higher.”

Table 1: remove results interpretation from table caption. 

Thank you this has been acted upon.

Table 1: why in some cases, like in DNA fragmentation index, the total is not equal to n=223? 

This is due to missing data/ analysis not possible for some men due to the requirement of a minimum number of sperm in the ejaculate to perform DNA fragmentation testing.

Table 1: "frag" is not defined. 

Thank you, this has been amended

Tables 2, 3 & 4: bacterial genera in italics. 

Thank you, this has been amended

Figure 1A: add the fertility status information above the cluster colors. 

Thank you, this has been amended in Figure 1.

Figure 1C: the color code is confusing. Use different colors for each cluster. 

Figure 1 legend: bacterial genera in italics. 

Figures 1 & 2: the authors should use similar chart formatting in the two tables. 

Thank you, this has been amended

Reviewer 2:

(1) The patient groups have different diagnoses and should be handled as different groups, and not fused into one 'patient' group in analyses. <br /> Why are the data in tables presented as controls and cases? I would consider men from couples with recurrent pregnancy loss, unexplained infertility, and male factor infertility to have different seminal parameters (not to fuse them into one group). This means, that the statistical analyses should be performed considering each group separately, and not to fuse 3 different infertility diagnoses into one patient group. 

We have conducted detailed analyses, requested by the reviewer, comparing seminal DNA, ROS and microbiota characteristics between each individual patient groups (Supplimental figures 1 and 4). No specific taxa (at either genera or species-level) were found to differ in relative abundance between the diagnostic groups. However, we expect associations between parameters such as reactive oxygen species, or DNA fragmentation, and relative abundance of bacterial species, to be general and not restricted to or specific to each diagnostic group. Therefore, we also conducted further analyses aggregating data from all patient groups to investigate relationships common to these different forms of male reproductive dysfunction.

(2) Were any covariables included in the statistical analyses, e.g. age, BMI, smoking, time of sexual abstinence, etc? 

Covariates were not included in the statistical analyses. This has been added in the manuscript to the limitations.

Page 14 line 267-268

“Additionally, we did not have other covariables such as smoking status with which to include in further analyses”.

(3) Furthermore, it is known that 16S rRNA gene analysis does not provide sensitive enough detection of bacteria on the species level. How much do the authors trust their results on the species level? 

The limitations of taxonomic assignment using 16S rRNA gene metataxonomics are well documented. However, the capacity to assign sequence amplicons at species level depends on the sequence variability of the 16S rRNA gene for each of the taxa reported and the specific gene region chosen. In this study, amplification of the V1-V2 region was performed using a mixed 28f primer set (see methods for details) that enables resolution and assignment of several bacterial species highly relevant to the reproductive tract including Lactobacillus spp., such as L. crispatus and L. iners, (e.g. https://doi.org/10.3389/fcell.2021.641921, https://doi.org/10.1128/msystems.01039-23, https://doi.org/10.1186/s12915-023-01702-2). In this study, we report the presence of L. iners, but not L. crispatus in semen samples, and we have also identified a specific association/co-occurrence between Gardnerella vaginalis and Lactobacillus iners, similar to that observed in vaginal bacterial communities.

(4) Were the analyses of bacterial genera and species abundances with seminal quality parameters controlled for diagnosis and other confounders? 

As stated in point 2, no adjustment was made for co-variates. No differences in microbiome composition were observed among the three diagnostic groups, so no adjustments were made to our analysis.

(5) The authors stress that their study is the biggest on the microbiome in semen. However, when considering that the study consists of 4 groups (with n=46-63), it does not stand out from previous studies. 

Our study is overall the largest investigating interactions between the seminal microbiome and male reproductive dysfunction. Other studies have included greater numbers of men with infertility.

(6) Weaknesses: There is a lack of paired seminal/urinal samples. 

Thank you. This limitation has been added.

Page 14 line 266-267

“A further limitation of this study, and others, is the lack of reciprocal genital tract microbiota testing of the female partners, or paired seminal and urinary samples from male participants”.

Recommendation for authors to consider:

Including previous classical reviews in the introduction: DOI:10.1097/MOU.0000000000000742 <br /> DOI: 10.1038/s41585-019-0250-y 

Thank you. This has been added.

Mentioning in the M&M section that there is a supplementary text with a more detailed M&M part. 

Thank you. This has been added. Further methodological detail can be found in supplementary text.

Revising the use of 'microbiota' and 'microbiome', they are not synonyms. When talking of 16S rRNA gene analysis, we consider 'microbiome' analysis. 

Thank you. This misnomer has been amended throughout the manuscript.

Revising the text, there are several erratas (e.g. verb missing, etc). 

Thank you for your comments. The manuscript has been thoroughly proofread for errors in language and formatting.

how does climate change affect agriculture and local livelihoods in peloponnese?

implications of cc in the region of Peloponnese

again, lets put it in the local context/what is happening on the ground, what are the local policies, how do people react (if they do)

what the problem in the region is, what needs to be adressed and solved

what is happening on the ground today & implications of climate change

how does climate change affect the region..

socio historical context

when i see this part in the text, i will use it for the contextual framework

goes to the context of the region

Description of Director of novices

Very important

In essence, the absence of their workforce (slaves and slave labor) became problematic for new beginning and well more specifically correction

Flag: suggested answer (don't read if don't want to see a (possibly incorrect) attempt:

Update - realise some bi-modal continuous distribution may be better (but potentially difficult to perform the update)

Attempt: we model the parameter pi in a Bayesian way: we put a distribution on pi (0.7 w.p 1/2, 0.2 w.p 1/2) then we weight the 1/2 with the likelihood of the observations, given that parameter (i.e. what is the likleihood when pi = 0.7, multiply that by 1/2 then divide by the normalizing constant to get our new probability for pi = 0.7 (do the same for pi = 0.2, the normalizing constant is the sum of the 'scores' for 0.7 and 0.2 i.e. 1/2 * likelihood so we can't 'divide by the normalising constant until we have the score for both 0.2 and 0.7)

Flag - suggested answer (don't read if don't want to see a (possibly incorrect) attempt:

Grateful for comments here as I am not very certain on the situations that the MLE approach is better vs situations where Bayesian approach is better

Suggested answer:

c(i) Is frequentist approach where we have one parameter estimate (the MLE) c(ii) bayesian approach - distribution over parameters and we update our prior belief based on observations If we have no prior belief - c(i) may be a better estimate (i.e. in (my version of) c(ii) we are constraining the parameters to be 0.7 or 0.2 and updating our relative convictions about these - which is a strong prior asssumption (we can never have 0.5 for instance) If we do have prior belief and also want to incorporate uncertainty estimations in our parameters, I think c(ii) is better If the MLE is 0.7 then we will have c(i) giving 0.7 and c(ii) giving 0.7 with a very high probability and 0/2 with a very low probability to the methods will perform similarly

Flag: suggested answer (don't read if don't want to see a )(possibly incorrect) attempt:

attempt: MLE = k/3

Flag: Don't read if don't want to see a (possibly incorrect) attempt of an answer: (Grateful for any comments/disagreements, further points to add)

Attempted answer: Assumption is that, given a class, features are independent. We could relax this by using 2-d gaussians for our class distributions that have non-zero covariance (off-diagonal) terms so that we have dependencies between features (currently we have these set to zero for independence)

J'ajouterais une phrase à la fin pour expliquer que ce choc est estimé sur la base de l'incertitude du 3eme trimestre et que si une marche supplémentaire sur l'incertitude était franchie avec la censure alors l'impact sur 2025 pourrait être plus négatif

et la très possible censure du gouvernement

qui n'est pas retombée avec

-0,3 point sur le PIB en moyenne en 2025 mais -0,2 point sur la croissance ?

nationale

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review):

Summary: 

In the manuscript entitled "Magnesium modulates phospholipid metabolism to promote bacterial phenotypic resistance to antibiotics", Li et al demonstrated the role of magnesium in promoting phenotypic resistance in V. alginolyticus. Using standard microbiological and metabolomic techniques, the authors have shown the significance of fatty acid biosynthesis pathway behind the resistance mechanism. This study is significant as it sheds light on the role of an exogenous factor in altering membrane composition, polarization, and fluidity which ultimately leads to antimicrobial resistance. 

Strengths: 

(1) The experiments were carried out methodically and logically. 

(2) An adequate number of replicates were used for the experiments. 

Weaknesses: 

(1) The introduction section needs to be more informative and to the point.  

Thank you so much for your suggestion. We have revised the introduction to make it more informative and to the point as following:

“Non-inheritable antibiotic or phenotypic resistance represents a serious challenge for treating bacterial infections. Phenotypic resistance does not involve genetic mutations Phenotypic resistance does not involve genetic mutations and is transient, allowing bacteria to resume normal growth. Biofilm and bacterial persisters are two phenotypic resistance types that have been extensively studied (Brandis et al., 2023; Corona & Martinez, 2013). Biofilms have complex structures, containing elements that impede antibiotic diffusion, sequestering and inhibiting their activity (Ciofu et al., 2022). Biofilm-forming bacteria and persisters also have distinct metabolic states that significantly reduce their antibiotic susceptibility (Yan & Bassler, 2019). These two types of phenotypic resistance share the common feature in their retarded or even cease of growth in the presence of antibiotics (Corona & Martinez, 2013). However, specific factors that promote phenotypic resistance and allow bacteria to proliferate in the presence of antibiotics remain poorly defined.

Metal ions have a diverse impact on the chemical, physical, and physiological processes of antibiotic resistance  (Booth et al, 2011; Lu et al, 2020; Poole, 2017). This includes genetic elements that confer resistance to metals and antibiotics (Poole, 2017) and metal cations that directly hinder (or enhance) the activity of specific antibiotic drugs (Zhang et al., 2014). The metabolic environment can also impact the sensitivity of bacteria to antibiotics (Jiang et al., 2023; Lee & Collins, 2012; Peng et al., 2015; Zhang et al., 2020; Zhao et al., 2021). Light metal ions, such as magnesium, sodium, and potassium, can behave as cofactors for different enzymes (Du et al., 2016) and influence drug efficacy. Heavy metal ions, including Cu2+ and Zn2+, confer resistance to antibiotics (Yazdankhah et al., 2014; Zhang et al., 2018). Recent reports suggest that sodium negatively regulates redox states to promote the antibiotic resistance of Vibrio alginolyticus (Yang et al., 2018), while actively growing Bacillus subtilis cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al, 2019). In Gram-negative bacteria, by contrast, zinc enhances antibiotic efficacy by potentiating carbapenem, fluoroquinolone, and β-lactam-mediated killing (Isaei et al., 2016; Zhang et al., 2014). Magnesium influences bacterial structure, cell motility, enzyme function, cell signaling, and pathogenesis (Wang et al., 2019). This mineral also modulates microbiota to harvest energy from the diet (Garcia-Legorreta et al., 2020), allowing Bacillus subtilis to cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al., 2019). However, the role of magnesium in promoting phenotypic resistance is less well understood.

Vibrios inhabit seawater, estuaries, bays, and coastal waters, regions full of metal ions such as magnesium (Kumarage et al., 2022). Magnesium is the second most dissolved element in seawater after sodium. At a salinity of 3.5% seawater, the magnesium concentration is about 54 mM (Potis, 1968), and in deep seawater, can be as high as 2,500 mM (Wang et al., 2024). Vibrio parahaemolyticus and V. alginilyticus are two representative Vibrio pathogens that infect humans and aquatic animals, resulting in illness and economic loss, respectively (Grimes, 2020). (Fluoro)quinolones such as balofloxacin are used to treat Vibrio infection, however, resistance has emerged due to overuse (Suyamud et al., 2024). Indeed, (fluoro)quinolones are one of China's two primary residual chemicals associated with aquaculture (Liu et al., 2017). Vibrio can develop quinolone resistance through mutations in the DNA gyrase gene or through plasmid-mediated mechanisms (Dutta et al., 2021). Thus, the use of V. parahaemolyticus and V. alginilyticus as bacterial representatives, and balofloxacin as a quinolone-based antibacterial representative, can help to define novel magnesiumdependent phenotypic resistance mechanisms of pathogenic Vibrio species. 

The current study evaluated whether magnesium induces phenotypic resistance in Vibrio species and defined the molecular/genetic basis for this resistance. Genetic approaches, GC-MS analysis of metabolite and membrane remodeling upon antibiotic exposure, membrane physiology, and extensive antimicrobial susceptibility testing were used for the evaluations.”

(2) The weakest point of this paper is in the logistics through the results section. The way authors represented the figures and interpreted them in the results section (or the figure legends) does not match. The figures are difficult to interpret and are not at all self-explanatory. 

Thank you so much for your suggestion. We have followed your suggestion to check the match between result and figures. They are now revised. 

(3) There are too many mislabeling of the figure panels in the main text which makes it difficult to find out which figures the authors are explaining. There should be more explanation on why and how they did the experiments and how the results were interpreted. 

Thank you so much for your suggestion. We have checked the figures and main text to ensure that we make every figure clearly stated.  

Reviewer #2 (Public Review): 

Summary: 

In this study, the authors aimed to identify if and how magnesium affects the ability of two particular bacteria species to resist the action of antibiotics. In my view, the authors succeeded in their goals and presented a compelling study that will have important implications for the antibiotic resistance research community. Since metals like magnesium are present in all lab media compositions and are present in the host, the data presented in this study certainly will inspire additional research by the community. These could include research into whether other types of metals also induce multi-drug resistance, whether this phenomenon can be observed in other bacterial species, especially pathogenic species that cause clinical disease, and whether the underlying molecular determinants (i.e. enzymes) of metal-induced phenotypic resistance could be new antimicrobial drug targets themselves. 

Strengths: 

This study's strengths include that the authors used a variety of methodologies, all of which point to a clear effect of exogenous Mg2+ on drug resistance in the targeted species. I also commend the authors for carrying out a comprehensive study, spanning evaluation of whole cell phenotypes, metabolic pathways, genetic manipulation, to enzyme activity level evaluation. The fact that the authors uncovered a molecular mechanism underlying Mg2+-induced phenotypic resistance is particularly important as the key proteins should be studied further.

Weaknesses: 

I believe there are weaknesses in the manuscript, however. The authors take for granted that the reader is familiar with all the assays utilized, and do not properly explain some experiments, and thus I highly suggest that the authors add a brief statement in each situation describing the rationale for each selected methodology (more details are in the private review to the authors). The Results section is also quite long and bogs down at times, and I suggest that the authors reduce its length by 10 to 20%. In contrast, the Introduction is sparse and lacks key aspects, for example, there should be mention of the study's main purpose and approaches, plus an introduction to the authors' choice of species and their known drug resistance properties, as well as the drug of choice (balofloxacin). Another notable weakness is that the authors evaluated Mg2+-induced phenotypic resistance only against two closely related species, and thus the generalizability of this mechanism of drug resistance is not known. The paper would be strengthened if the authors could demonstrate this type of phenotypic resistance in at least one more Gram-negative species and at least one Gram-positive species (antimicrobial susceptibility evaluations would suffice), each of which should be pathogenic to humans. Demonstrating magnesium-induced phenotypic drug resistance in the WHO Priority Bacterial Pathogens would be particularly important. 

In general, the conclusions drawn by the authors are justified by the data, except for the interpretation of some experiments. Importantly, this paper has discovered new antimicrobial resistance mechanisms and has also pointed to potential new targets for antimicrobials. 

Thank you so much for your suggestion! We followed your idea the revise the manuscript as following:

(1) We added a brief statement in the situation to explain the result and methodology according to your suggestion in the private review.

(2) To make the streamline of the story more logic, we moved the whole second result to supplementary text and supplementary figure. 

(3) We revised the introduction part by adding additional information to make it informative and to the point as following:

“Non-inheritable antibiotic or phenotypic resistance represents a serious challenge for treating bacterial infections. Phenotypic resistance does not involve genetic mutations Phenotypic resistance does not involve genetic mutations and is transient, allowing bacteria to resume normal growth. Biofilm and bacterial persisters are two phenotypic resistance types that have been extensively studied (Brandis et al., 2023; Corona & Martinez, 2013). Biofilms have complex structures, containing elements that impede antibiotic diffusion, sequestering and inhibiting their activity (Ciofu et al., 2022). Biofilm-forming bacteria and persisters also have distinct metabolic states that significantly reduce their antibiotic susceptibility (Yan & Bassler, 2019). These two types of phenotypic resistance share the common feature in their retarded or even cease of growth in the presence of antibiotics (Corona & Martinez, 2013). However, specific factors that promote phenotypic resistance and allow bacteria to proliferate in the presence of antibiotics remain poorly defined.

Metal ions have a diverse impact on the chemical, physical, and physiological processes of antibiotic resistance  (Booth et al, 2011; Lu et al, 2020; Poole, 2017). This includes genetic elements that confer resistance to metals and antibiotics (Poole, 2017) and metal cations that directly hinder (or enhance) the activity of specific antibiotic drugs (Zhang et al., 2014). The metabolic environment can also impact the sensitivity of bacteria to antibiotics (Jiang et al., 2023; Lee & Collins, 2012; Peng et al., 2015; Zhang et al., 2020; Zhao et al., 2021). Light metal ions, such as magnesium, sodium, and potassium, can behave as cofactors for different enzymes (Du et al., 2016) and influence drug efficacy. Heavy metal ions, including Cu2+ and Zn2+, confer resistance to antibiotics (Yazdankhah et al., 2014; Zhang et al., 2018). Recent reports suggest that sodium negatively regulates redox states to promote the antibiotic resistance of Vibrio alginolyticus (Yang et al., 2018), while actively growing Bacillus subtilis cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al, 2019). In Gram-negative bacteria, by contrast, zinc enhances antibiotic efficacy by potentiating carbapenem, fluoroquinolone, and β-lactam-mediated killing (Isaei et al., 2016; Zhang et al., 2014). Magnesium influences bacterial structure, cell motility, enzyme function, cell signaling, and pathogenesis (Wang et al., 2019). This mineral also modulates microbiota to harvest energy from the diet (Garcia-Legorreta et al., 2020), allowing Bacillus subtilis to cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al., 2019). However, the role of magnesium in promoting phenotypic resistance is less well understood.

Vibrios inhabit seawater, estuaries, bays, and coastal waters, regions full of metal ions such as magnesium (Kumarage et al., 2022). Magnesium is the second most dissolved element in seawater after sodium. At a salinity of 3.5% seawater, the magnesium concentration is about 54 mM (Potis, 1968), and in deep seawater, can be as high as 2,500 mM (Wang et al., 2024). Vibrio parahaemolyticus and V. alginilyticus are two representative Vibrio pathogens that infect humans and aquatic animals, resulting in illness and economic loss, respectively (Grimes, 2020). (Fluoro)quinolones such as balofloxacin are used to treat Vibrio infection, however, resistance has emerged due to overuse (Suyamud et al., 2024). Indeed, (fluoro)quinolones are one of China's two primary residual chemicals associated with aquaculture (Liu et al., 2017). Vibrio can develop quinolone resistance through mutations in the DNA gyrase gene or through plasmid-mediated mechanisms (Dutta et al., 2021). Thus, the use of V. parahaemolyticus and V. alginilyticus as bacterial representatives, and balofloxacin as a quinolone-based antibacterial representative, can help to define novel magnesiumdependent phenotypic resistance mechanisms of pathogenic Vibrio species. 

The current study evaluated whether magnesium induces phenotypic resistance in Vibrio species and defined the molecular/genetic basis for this resistance. Genetic approaches, GC-MS analysis of metabolite and membrane remodeling upon antibiotic exposure, membrane physiology, and extensive antimicrobial susceptibility testing were used for the evaluations.”

(4) We examined the effect of magnesium in WHO listed priority strains, which confirmed the results as following:

“Importantly, exogenous MgCl2 also increased MICs of clinic isolates, carbapenemresistant Escherichia coli, carbapenem-resistant Klebsiella pneumoniae, carbapenemresistant Pseudomonas aeruginosa and carbapenem-resistant Acinetobacter baumannii to balofloxacin (Fig 1G).”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

(1) There are many grammatical mistakes to point out. The manuscript needs proofreading and editing.

We appreciate this comment! The manuscript has been revised by a native speaker.

(2) The introduction could be more informative. A little more description of magnesium - such as what it does to antibiotics and how it's known to affect the microbiome - might be helpful for the general readers. The question remains why out of all the metal ions that might affect antibiotic resistance (many of them are less explored), authors particularly decided to work on the effect of magnesium. The introduction should cover the rationale of their hypothesis. Also, the authors might want to briefly talk about the model organisms (V. algonolyticus and V. parahemolyticus) describing how threatening they are and how they are becoming resistant to antibiotics. 

We appreciate this comment! We revise the introduction by providing additional information as following:

“In Gram-negative bacteria, by contrast, zinc enhances antibiotic efficacy by potentiating carbapenem, fluoroquinolone, and β-lactam-mediated killing (Isaei et al., 2016; Zhang et al., 2014). Magnesium influences bacterial structure, cell motility, enzyme function, cell signaling, and pathogenesis (Wang et al., 2019). This mineral also modulates microbiota to harvest energy from the diet (Garcia-Legorreta et al., 2020), allowing Bacillus subtilis to cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al., 2019). However, the role of magnesium in promoting phenotypic resistance is less well understood.

Vibrios inhabit seawater, estuaries, bays, and coastal waters, regions full of metal ions such as magnesium (Kumarage et al., 2022). Magnesium is the second most dissolved element in seawater after sodium. At a salinity of 3.5% seawater, the magnesium concentration is about 54 mM (Potis, 1968), and in deep seawater, can be as high as 2,500 mM (Wang et al., 2024). Vibrio parahaemolyticus and V. alginilyticus are two representative Vibrio pathogens that infect humans and aquatic animals, resulting in illness and economic loss, respectively (Grimes, 2020). (Fluoro)quinolones such as balofloxacin are used to treat Vibrio infection, however, resistance has emerged due to overuse (Suyamud et al., 2024). Indeed, (fluoro)quinolones are one of China's two primary residual chemicals associated with aquaculture (Liu et al., 2017). Vibrio can develop quinolone resistance through mutations in the DNA gyrase gene or through plasmid-mediated mechanisms (Dutta et al., 2021). Thus, the use of V. parahaemolyticus and V. alginilyticus as bacterial representatives, and balofloxacin as a quinolone-based antibacterial representative, can help to define novel magnesiumdependent phenotypic resistance mechanisms of pathogenic Vibrio species. 

The current study evaluated whether magnesium induces phenotypic resistance in Vibrio species and defined the molecular/genetic basis for this resistance. Genetic approaches, GC-MS analysis of metabolite and membrane remodeling upon antibiotic exposure, membrane physiology, and extensive antimicrobial susceptibility testing were used for the evaluations. ”

(3) Figure 1C is mislabeled as 1B (line 100). Line 101: The sentence is not clear and very confusing. What is meant by 15.6mM - 62.4 mM? Are they talking about the concentration of BLFX (though in the figure the concentration was shown in µg)? Please rewrite the sentence in a simplified way. Also, the zone of inhibition was decreased with increasing MgCl2, not increased. 

We appreciate this comment! These have been revised, including that Fig 1B is now corrected as Fig. 1C. Line 101, which is now Line 122. The sentence was revised as following:

“At balofloxacin doses of 1.56, 3.125, 6.25, and 12.5 µg, the zone of inhibition decreased with increasing MgCl2 (Fig 1D)”

(4) In the western blot images, it would be nice to indicate the MW of the protein bands shown. The loading control used for the experiments should be clearly mentioned in the figure legends. 

We appreciate this comment! The MWs are indicated in the western-blot image throughout the manuscript. 

The loading control is clearly stated in the figure legend as following:

“Whole cell lysates resolved by SDS-PAGE gel was stained with Coomassie brilliant blue as loading control.”. 

(5) Figures 2 B and C: the figure legend does not explain what the authors wanted to show. It's not clear how they plotted the inhibitory curve, or the binding efficacy. These panels need an explanation of how the analysis was done.

We appreciate this comment! The figure 2 is now removed to Suppl. Fig 2, and the description of figure 2 is moved to Suppl. Text. We revise the description of the result as following, which is in Suppl. Text:

“Prior studies suggest that the chelation of antibiotics by magnesium ions inhibits antibiotic uptake (Deitchman et al., 2018; Lunestad and Goksøyr, 1990). To investigate whether magnesium binds to balofloxacin, balofloxacin was pre-incubated with magnesium, and zone of inhibition (ZOI) analysis was conducted. Six different concentrations of balofloxacin were separately incubated with six different concentrations of MgCl2, and then spotted on filter paper so that a defined amount of balofloxacin could be used for ZOI. While lower concentrations of MgCl2, (0.78, 3.125, or 12.5 mM) did not alter the ZOI, higher concentrations, including 50 and 200 mM MgCl2, decreased the ZOI (Suppl. Fig 2A), suggesting that even high doses of magnesium had only a partial effect on balofloxacin through direct binding. For example, at 200 mM MgCl2 and 5 or 10 μg/mL balofloxacin, the balofloxacin ZOI was 53.2 and 70.3% of the ZOI at 0 mM MgCl2, suggesting that  50% of the antibiotics were still functional. Intracellular BLFX also decreased with increasing MgCl2 (Suppl. Fig 2B), while exogenous Mg2+ increased intracellular Mg2+ levels in a dose-dependent manner. For example, exogenous 50 and 200 mM MgCl2 increased intracellular Mg2+ levels to 1.21 and 1.31 mM, respectively (Suppl. Fig 2C). The relationship between TolC, an efflux pump that transports quinolones from bacterial cells, and Mg2+ was also assessed (Kobylka et al., 2020; Song et al., 2020). The expression of TolC/tolC was unaffected by Mg2+ (Suppl. Fig 2D). Magnesium is critical for LPS stability. LPS levels increased at 200 mM Mg2+ (Suppl. Fig 2E), however, the loss of waaF, lpxA, and lpxC, three key genes involved in LPS biosynthesis, did not influence balofloxacin sensitivity/resistance in the presence of Mg2+ (Suppl. Fig 2F). These findings suggest that magnesium-induced LPS biosynthesis does not contribute directly to BLFX resistance and demonstrate that Mg2+ influx is involved in balofloxacin resistance.”

(6) For the metabolomics results, it will help immensely if the authors provide a volcano plot of the identified metabolites and plot the heat map according to the -log2 metabolite intensities. In Figure 3A, it's not clear what information is conveyed through Euclidean distance calculations of the heat map. In Figure 3 B, the authors mentioned that the OPLS-DA test was conducted, although the figure shows a PCA plot, so it's not clear how these two are connected. Figure 3 E: the figure legend says scattered plot, but the panel represents color-coded numerical values, not a scattered plot. Also, it's not clear how they got those values. 

We appreciate this comment! We quite agree with you that if the differential metabolites could be shown as volcano plot. However, we didn’t adopt volcano plot in this study because this is a magnesium concentration-dependent metabolomes that includes 6 groups in parallel. Volcano plots may give a complex view of the comparison among different groups. We also tried to plot the heat map according to the -log2 metabolite intensities. Although this analysis cluster 200 mM and 50 mM groups better, the data of low magnesium concentrations was not consistent, which may be due to the minor metabolic change of low concentrations magnesium. Thank you for your understanding. 

For Euclidean distance calculations, we explain in the figure legend as following:

“Euclidean distance calculations were used to generate a heatmap that shows clustering of the biological and technical replicates of each treatment.” 

In Figure 2B, which was Figure 3B in previous version, it has been replaced with OPLS-DA analysis in the revised version. 

In Figure 2E, which was Figure 3E in previous version, it is revised as following:

“E. Areas of the peaks of palmitic acid and stearic acid generated by GC-MS analysis.” 

(7) In Figure 4, the figure legends (as well as the in the text) are not properly referred to. Please make sure to refer to the correct panel. 

We appreciate this comment! The figure legends have been corrected to match the panel and text. 

Figure 4F: how was the synergy analysis done? In the methods section, the authors described the antibiotic bactericidal assay protocol, but there was no clear indication of how they generated the isobologram. 

We appreciate this comment! We provide additional information in the Figure 3F legend, which was Figure 4F in previous version,  as following: 

“Synergy analysis for BFLX with palmitic acid for V. alginolyticus. Synergy was performed by comparing the dose needed for 50% inhibition of the synergistic agents (white) and non-synergistic (i.e., additive) agents (purple).”

(8) Figure 5 A: the scatter plot is plotted according to the area along the Y axis: which "area" is represented here? There is absolutely no explanation, neither in the results nor in the figure legends. Using box plots might be a better option than using a scattered plot.

We appreciate this comment! “Area” has been noted in the revised manuscript as following:

“The area indicates the area of the peak of the metabolite in total ion chromatography of GC-MS.” 

(9) In Figure 6 A, the heat map is plotted according to the column Z scores. What is meant by "column Z score"? The corresponding figure legend says, "heat map showing differential abundance of lipid". Z scores do not represent an abundance of a variable, so the conclusion might not be appropriate here. 

We appreciate this comment! In Figure 5A, which was Figure 6A in previous version, column Z score shows the abundance of metabolites analyzed, which is automatically generated in the heat map analysis to give a sign of these metabolites tested. The legend has been revised as following: 

“Heatmap showing changes in differential lipid levels at the indicated concentration of MgCl2.”  

(10) Line 313-314: it should be Figure EV6C.  

We appreciate this comment! The citation has been corrected.

(11) The authors have shown that Mg+2 does not alter the LPS transport system, however, there was some significant increase in LPS expression at 200mM MgCl2. It would be interesting if the authors could also check if Mg+2 has any effect on the outer membrane protein (OMP) integrity (by checking OMP components BamA and LptD).  

We appreciate this comment!  We have carefully examined the membrane permeability in Figure 7. We thus didn’t perform additional experiment here to see the change of BamA and LptD. Thank you very much for your understanding.

(12) I wonder if the authors could check the effect of extracellular Mg+2 during the co-treatment of palmitic acid, linoleic acid, and balofloxacin. Will there still be the antagonistic effect or the presence of Mg+2 could change the phenotype? 

We appreciate this comment! Additional experiments is performed as following:

“Furthermore, magnesium had a minimal effect on the antagonistic effect of palmitic acid, linolenic acid, and balofloxacin (Fig 4G), suggesting that this mineral functions through lipid metabolism.” 

Reviewer #2 (Recommendations For The Authors): 

(1) As mentioned in the Public Review, I strongly believe that the impact of this study will be more significant if magnesium-induced phenotypic drug resistance could be demonstrated in at least one other Gram-negative and one other Grampositive species, both of which should be human pathogens. The full suite of experiments would not be necessary for this suggestion; evaluation of the effect of Mg concentration in growth media on the drug resistance of other species, testing the different antibiotic types used in this study, would be sufficient. 

We appreciate this comment! Additional experiments have performed to test this idea. Mg2+ has the similar effect on carbapenem-resistant Escherichia coli, carbapenem-resistant Klebsiella pneumoniae, carbapenem-resistant Pseudomonas aeruginosa and carbapenem-resistant Acinetobacter baumannii as the similar as on the Vibrio species in shown in Figure 1G. These have been described following as

“Importantly, exogenous MgCl2 also increased MICs of clinic isolates, carbapenemresistant Escherichia coli, carbapenem-resistant Klebsiella pneumoniae, carbapenemresistant Pseudomonas aeruginosa and carbapenem-resistant Acinetobacter baumannii to balofloxacin (Fig 1G).”

(2) I recommend that the Introduction section be expanded. I recommend one or two sentences introducing the two Vibrio species selected for study. I.e. why did the authors choose these two species? What is known about their phenotypic drug resistance in the literature? Why did the authors select balofloxacin for their studies, is it a common antimicrobial used vs Vibrios? As well, the end of the Introduction section ends abruptly with no transition to the present study itself. The end of the introduction should include one or two sentences introducing the main purpose of the study, its approach, and the techniques undertaken. For example, "In this study, we evaluated whether magnesium induces phenotypic resistance in Vibrio species and the molecular/genetic basis for such resistance. We used genetic approaches, GC-MS analysis of metabolite and membrane remodeling upon antibiotic exposure, membrane physiology, and extensive antimicrobial susceptibility evaluations." 

We appreciate this comment! We revise the introduction by providing additional information as following:

“In Gram-negative bacteria, by contrast, zinc enhances antibiotic efficacy by potentiating carbapenem, fluoroquinolone, and β-lactam-mediated killing (Isaei et al., 2016; Zhang et al., 2014). Magnesium influences bacterial structure, cell motility, enzyme function, cell signaling, and pathogenesis (Wang et al., 2019). This mineral also modulates microbiota to harvest energy from the diet (Garcia-Legorreta et al., 2020), allowing Bacillus subtilis to cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al., 2019). However, the role of magnesium in promoting phenotypic resistance is less well understood.

Vibrios inhabit seawater, estuaries, bays, and coastal waters, regions full of metal ions such as magnesium (Kumarage et al., 2022). Magnesium is the second most dissolved element in seawater after sodium. At a salinity of 3.5% seawater, the magnesium concentration is about 54 mM (Potis, 1968), and in deep seawater, can be as high as 2,500 mM (Wang et al., 2024). Vibrio parahaemolyticus and V. alginilyticus are two representative Vibrio pathogens that infect humans and aquatic animals, resulting in illness and economic loss, respectively (Grimes, 2020). (Fluoro)quinolones such as balofloxacin are used to treat Vibrio infection, however, resistance has emerged due to overuse (Suyamud et al., 2024). Indeed, (fluoro)quinolones are one of China's two primary residual chemicals associated with aquaculture (Liu et al., 2017). Vibrio can develop quinolone resistance through mutations in the DNA gyrase gene or through plasmid-mediated mechanisms (Dutta et al., 2021). Thus, the use of V. parahaemolyticus and V. alginilyticus as bacterial representatives, and balofloxacin as a quinolone-based antibacterial representative, can help to define novel magnesiumdependent phenotypic resistance mechanisms of pathogenic Vibrio species. 

The current study evaluated whether magnesium induces phenotypic resistance in Vibrio species and defined the molecular/genetic basis for this resistance. Genetic approaches, GC-MS analysis of metabolite and membrane remodeling upon antibiotic exposure, membrane physiology, and extensive antimicrobial susceptibility testing were used for the evaluations. ”

(3) The authors introduce the acronym AWST but never use it again in the paper, instead they use SWT. The authors should introduce SWT only for consistency. 

We appreciate this comment! We have corrected all the “SWT” to “ASWT”

(4) Line 76 is not clear: what is meant by "some of which could influence drug efficacy" - the enzymes that utilize light metal ions are co-factors? Or the metals directly?  

We appreciate this comment! The information we wanted to deliver is that light metal ions can serve as cofactors to catalyze biochemical reaction. Such chemical reaction would alter the drug efficacy, e.g. the Fe-S cluster are metallocofactor for proteins which regulates redox chemistry including antibioticinduced redox change. However, this information is not appropriate for this manuscript, so we delete this sentence. 

(5) Line 90: add a reference corroborating that this chemical composition is a mimic of marine water. The NaCl concentration used in particular looks quite low. 

We appreciate this comment! It was a typo error. The NaCl concentration was 210 mM as shown in Suppl. Table 1. We also provide details of the chemical composition of the marine water as following:

“Marine environments and agriculture, where antibiotics are commonly used, are rich in magnesium. To investigate whether this mineral impacts antibiotic activity, the minimal inhibitory concentration (MIC) of V. alginolyticus ATCC33787 and V. parahaemolyticus VP01, which we referred as ATCC33787 and VP01 afterwards, isolated from marine aquaculture, to balofloxacin (BLFX) in Luria-Bertani medium

(LB medium) plus 3% NaCl as LBS medium and “artificial seawater” (ASWT) medium that included the major ion species in marine water (Wilson, 1975) (LB medium plus 210 mM NaCl, 35 mM Mg2SO4, 7 mM KCl, and 7 mM CaCl2) were assessed”

(6) Line 98 and Figure 1B. M9 is indicated in the text but does not appear in the figure, the figure only shows SWT. This should be checked. Line 99: based on Figure 1C, the authors are adding MgCl2 to SWT, SWT should be mentioned in this line. Line 100: I believe this is referring to Figure 1C, which should be checked. 

We appreciate this comment! 

Line 98, which is now Line 118: We have corrected M9 to ASWT as following:

“However, the MIC for BLFX was higher in ASWT medium supplemented with Mg2SO4 or MgCl2 than in LB medium (Fig 1B).”

Line 99, which is now Line 133: the sentence is corrected as following:

“The MIC for BLFX increased at higher concentrations of MgCl2 in ASWT”

Line 100, which is now Line 135: we have corrected Fig 1B to Fig. 1C.

(7) Line 101: text and Figure 1D are not consistent, as Figure 1D does not show this level of precision in added MgCl2 as indicated in the text (15.6 - 62.4 mM).  

We appreciate this comment! The sentence has been corrected as following: “At balofloxacin doses of 1.56, 3.125, 6.25, and 12.5 µg, the zone of inhibition decreased with increasing MgCl2 (Fig 1D)””.  

(8) MgCl2 clearly induces increasing levels of BLFX resistance, and to high levels, but not for every antibiotic. For example, the level of increased resistance to blactams is low (ceftriaxone) and plateaus (ceftazidime). As well, resistance to gentamicin plateaus at a lower level than the other aminoglycosides. These observations do not take away from the conclusion that Mg induces multi-drug resistance, but since the behaviour of the MICs for these drugs is different than the other drugs, they should be mentioned. Also, Figure 1F - tetracyclines (plural) is used for vertical axis label - does this refer to the tetracycline itself or the class itself, and if the class, which one was tested? 

We appreciate this comment! We revise the description as following: “Notably, magnesium had a reduced effect on ceftriaxone and gentamicin than other antibiotics.”

The tetracyclines is labeled as “Oxytetracycline” in the revised manuscript. 

- The magnesium chelation experiments presented in Figure 2 are not clear. The authors should briefly mention how this was done around line 128, and what data underlies the values in Figure 2C. Figure 2B is also not clear to me at all. Similarly, how the authors measured intracellular balofloxacin and Mg2+ is not clear and should be mentioned briefly around lines 130-132. 

We appreciate this comment! These have been rewritten following as  “To investigate whether magnesium binds to balofloxacin, balofloxacin was preincubated with magnesium, and zone of inhibition (ZOI) analysis was conducted. Six different concentrations of balofloxacin were separately incubated with six different concentrations of MgCl2, and then spotted on filter paper so that a defined amount of balofloxacin could be used for ZOI. While lower concentrations of MgCl2, (0.78, 3.125, or 12.5 mM) did not alter the ZOI, higher concentrations, including 50 and 200 mM MgCl2, decreased the ZOI (Suppl. Fig 2A), suggesting that even high doses of magnesium had only a partial effect on balofloxacin through direct binding. For example, at 200 mM MgCl2 and 5 or 10 μg/mL balofloxacin, the balofloxacin ZOI was 53.2 and 70.3% of the ZOI at 0 mM MgCl2, suggesting that  50% of the antibiotics were still functional. Intracellular BLFX also decreased with increasing MgCl2 (Suppl. Fig 2B), while exogenous Mg2+ increased intracellular Mg2+ levels in a dose-dependent manner. For example, exogenous 50 and 200 mM MgCl2 increased intracellular Mg2+ levels to 1.21 and 1.31 mM, respectively (Suppl. Fig 2C). The relationship between TolC, an efflux pump that transports quinolones from bacterial cells, and Mg2+ was also assessed (Kobylka et al., 2020; Song et al., 2020). The expression of TolC/tolC was unaffected by Mg2+ (Suppl. Fig 2D). Magnesium is critical for LPS stability. LPS levels increased at 200 mM Mg2+ (Suppl. Fig 2E), however, the loss of waaF, lpxA, and lpxC, three key genes involved in LPS biosynthesis, did not influence balofloxacin sensitivity/resistance in the presence of Mg2+ (Suppl. Fig 2F). These findings suggest that magnesium-induced LPS biosynthesis does not contribute directly to BLFX resistance and demonstrate that Mg2+ influx is involved in balofloxacin resistance.”

- Line 135: LPS cannot be "expressed", as the authors word it here. This should be corrected. Also, the inspection of Figure 2G actually shows the levels of LPS increase with increased Mg2+. The authors should re-evaluate these results and change their description around this area of the Results. 

We appreciate this comment! We have removed the whole Figure 2 to Supplementary Text and Supplementary Figure 2. We rewrite this part as following: “The relationship between TolC, an efflux pump that transports quinolones from bacterial cells, and Mg2+ was also assessed (Kobylka et al., 2020; Song et al., 2020). The expression of TolC/tolC was unaffected by Mg2+ (Suppl. Fig 2D). Magnesium is critical for LPS stability. LPS levels increased at 200 mM Mg2+ (Suppl. Fig 2E), however, the loss of waaF, lpxA, and lpxC, three key genes involved in LPS biosynthesis, did not influence balofloxacin sensitivity/resistance in the presence of Mg2+ (Suppl. Fig 2F). These findings suggest that magnesium-induced LPS biosynthesis does not contribute directly to BLFX resistance and demonstrate that Mg2+ influx is involved in balofloxacin resistance.”

- Section: MgCl2 affects bacterial metabolism. Authors switched to M9 medium - why? This contrasts with other sections using SWT and should be explained. Also, I cannot evaluate whether the statistical analysis of the data here was performed correctly and was appropriate for this type of experiment. I advise the authors to move the details in lines 166-169 to the Materials and Methods and replace this section instead with a more accessible description of the statistical analysis that a non-expert would be able to appreciate. Furthermore, analysis of Figure 3A indicates that the levels of asparagine, 4-hydroxybutyric acid, uracil, cystathionine, fumaric acid, and aminoethanol have significantly changed at high MgCl2, but these are not mentioned in the text. I suggest the authors mention these if they are relevant to the 12 enriched pathways, especially the biosynthesis of fatty acids. 

We appreciate this comment! 

We indicate the reason we use M9 medium as following:

“To better understand how magnesium affects bacterial metabolism” for explaining why the M9 medium was used.”

The information lines 166-169 indicated has been removed to M &M. 

We have carefully examined the abundance of the metabolites and the enriched pathway. Among the listed metabolites, only fumarate is within the enriched pathways. We mention this point in our revised manuscript as following:

“The increase in fatty acid biosynthesis could be partially explained by an imbalanced pyruvate cycle/TCA cycle, in which fumarate levels increased at higher Mg2+ while succinate levels increased at lower Mg2+ (Suppl. Fig 5B). These findings indicated that glycolysis fluxes into fatty acid biosynthesis rather than the pyruvate cycle/TCA cycle. The relevance of fatty acids and BLFX was demonstrated by the observation that exogenous palmitic acid increased bacterial resistance to balofloxacin (Fig 2F). These results suggest that fatty acid metabolism may be critical to magnesium-based phenotypic resistance.”

- Line 211 appears to refer to Figure 4F and should be checked. Similarly in line 216 - appears this should be Figure 4H, and line 218 should be Figure 4H. Line 226: add a reference to Fig 4I (after arcA was decreased). Line 227: what are genes N646_1004 and N646_1885? Based on Fig 4J these are crp - authors should add to line 227. Line 228 appears to refer to Figure 4J, not Figure 4I. Line 229 - should be Figure 4K, not Figure 4I. Line 231 - should be 4L, not 4K. Line 239 - should be 4M.

We appreciate this comment! The text and figure is now matched. 

- Line 312: the descriptions of "11 lipids, 32 lipids, and 53", and then "26 lipids, 52 lipids, and 107 lipids" are not clear at all and should be corrected. 

We appreciate this comment! The sentence is revised as following:

“The abundance of 11, 32, and 53 lipids was increased in 3.125, 50, and 200 mM MgCl2-treated bacteria, respectively, while the abundance of 26, 52, and 107 lipids was decreased in 3.125, 50, and 200 mM MgCl2-treated bacteria, respectively (Suppl. Fig 7C)”

- Line 340. What is the assay the authors are using to measure the levels of the PGS and PSS enzymes? This is not mentioned or clear in this part of the Results.  

We appreciate this comment!  We provide the information in the manuscript as following:

“Levels of PGS and PSS were quantified by ELISA kits according to manufacture’s instruction (Shanghai Fusheng Industrial Co., Ltd., China)”

- Line 372: What is the assay for measuring membrane depolarization? This is not mentioned and I suggest it should be. Line 374: Figure 7B does not show time dependence, only dose dependence, this should be corrected, it is assumed the authors are referring to Fig 7C for the time dependence data. 

We appreciate this comment! We provide the information in the result as following:  

“The voltage-sensitive dye, DiBAC4(3) showed that 12.5–200 mM MgCl2 promoted membrane depolarization in a dose-dependent manner (Fig 6A)”

We also explain how DiBAC4(3) can be used to measure membrane depolarization in the Materials and Methods section as following:

“DiBAC4(3) is a s voltage-sensitive probe that penetrates depolarized cells, binding intracellular proteins or membranes exhibiting enhanced fluorescence and red spectral shift.”

To make it clear the specific figure, we revise the sentence as following:

“Meanwhile, MgCl2 had a dose-dependent (Fig 6B) and time-dependent (Fig 6C) effect on proton motive force (PMF).”

- Line 384: mention how FM5-95 measures membrane permeability. The authors should also clarify how this reagent is used to measure membrane fluidity, and it is not clear if the data for this is presented in Figure 7 - please clarify. Regarding SYTO9 dye experiment: the authors should briefly explain the experimental design - how SYTO9 dye operates and why FACS was chosen. What is labeled with FITC?  

We appreciate this comment! We clarify the reason we use FM5-95 in the Methods and Materials section as following:

“Measurement of fluidity by fluorescence microscopy

Measurement of membrane fluidity is performed as previously described (Wen et al., 2022). Briefly, ATCC33787 were cultured in medium with indicated concentrations of MgCl2, collected and then adjusted to OD 0.6. Aliquot of 100 μL bacteria cells of each sample were diluted to 1 mL and 10 μL (10 mg/mL) FM5-95 (Thermo Fisher

Scientific, USA) was added. FM5-95 is a lipophilic styryl dye that insert into the outer leaflet of bacterial membrane and become fluorescence. This dye preferentially bind to the microdomains with high membrane fluidity(Wen et al., 2022). After incubated for 20 min at 30 ℃ at vibration without light, the sample was centrifuged for 10 min at 12,000 rpm. The pellets were resuspended with 20 μL of 3% NaCI. Aliquot of 2 μL sample was dropped on the agarose slide, and take photos under the inverted fluorescence microscope.”

This data is presented as micrographs in Fig. 6D, which shows the decreased FM5-95 staining with increasing concentrations of MgCl2. We make this description clear with the following revision:

“FM5-95 staining decreased with increasing concentrations of Mg2+, and no staining was observed in the presence of 200 mM Mg2+ (Fig 6D).”

We explain the reason why we use SYTO9 as following:

“SYTO9, a green fluorescent dye that binds to nucleic acid, enters and stains bacteria cells when there is an increase in membrane permeability (Lehtinen et al., 2004; McGoverin et al., 2020). Staining decreased with increasing MgCl2, indicating that bacterial membrane permeability declined in an Mg2+ dose-dependent manner (Fig 6E).”

We didn’t use FACS in this study, while we only analyze the fluorescence distribution with the equipment. To make it clear, we revise the sentence as following:

“After incubated for 15 min at 30 ℃ at vibration without light, the mixtures were filtered and measured by flow cytometry (BD FACSCalibur, USA).”

- Lines 391-397. The statement that palmitic acid shifts the peaks in Figure 7F is not supported by the data. There is essential no change in the major peak position within each MgCl2 concentration set with increasing palmitic acid. For the linolenic acid data, it is clear that linolenic acid increases permeability only at 50 mM MgCl2-this should be mentioned in the text. 

We appreciate this comment! We revise the sentence as following:

“Exogenous palmitic acid also shifted the fluorescence signal peaks to the left in an MgCl2-dependent manner while palmitic acid only slightly shifted the peaks (Fig 6F). In contrast, exogenous linolenic acid shifted the peak to the right in a dose-dependent manner at 50 mM MgCl2 (Fig 6G).” 

- Line 404-405 - as mentioned earlier, the assay for the update of BLFX should be mentioned (if it is done so earlier in the text, then it does not need to be here).  

We appreciate this comment! It has been mentioned in the introduction.  

- Discussion: CpxA/R-OmprF pathway is mentioned here for the first time. Is this one of the pathways modified by MgCl2 as determined during the course of the study? If so, this should be reworded to mention that. If not, the relevance of this particular pathway as it relates to light metals and phenotypic resistance should be discussed.

We appreciate this comment! Since it is not relevant to the discussion of Mg2+ and fatty acid biosynthesis, we delete this sentence in the revised manuscript.  

-The following grammatical errors should be corrected:

-line 55 change to: "genetic mutations; instead, this type of resistance is transient, and bacteria resume normal growth"

-line 57: change to "resistance types are biofilm" 

-line 61: change to "states that significantly" 

-line 63: change to "resistance share the common feature in they retard or even cease in the presence" 

-line 65: change to "resistance that allow bacteria to proliferate" 

-line 81: change "But whether" to "Whether" 

-line 178: change to "may be critical to the Mg-based phenotypic resistance"

-line 86: change to "Marine environments and agriculture are rich in magnesium, where..." 

-line 93: change in to vs

-line 154: insert space after metabolism 

-line 158: change 'identified" to "focused on the levels of" 

-line 160: change "The levels of forty-one metabolites" 

-line 198: change shared to share 

-line 310: increased is duplicated, delete one 

-line 451: add "the" before ratio 

-line 453: gram should be capitalized 

-line 462: "the regulation" should be reworded to "More importantly, the effect of exogenous MgCl targets the..." 

-line 469: add dash between Mg2+ and limited

-line 478: change "the crucial" to "a crucial" 

-there are numerous locations in the manuscript where the word "magnetism" is used when clearly the word is supposed to be magnesium - this should be corrected

We appreciate this comment! These have been corrected or revised. 

Editors comments:

Page 2 line 27; Page 25 line number 426; page 27 line number 481: In the abstract and discussion, only Vibrio alginolyticus was mentioned, even though two Vibrio species were used in the study. It would be helpful to understand the rationale behind the focus on this particular species.

We appreciate this comment! We have revised the introduction to provide additional information as following:

“Vibrios inhabit seawater, estuaries, bays, and coastal waters, regions full of metal ions such as magnesium (Kumarage et al., 2022). Magnesium is the second most dissolved element in seawater after sodium. At a salinity of 3.5% seawater, the magnesium concentration is about 54 mM (Potis, 1968), and in deep seawater, can be as high as 2,500 mM (Wang et al., 2024). Vibrio parahaemolyticus and V. alginilyticus are two representative Vibrio pathogens that infect humans and aquatic animals, resulting in illness and economic loss, respectively (Grimes, 2020). (Fluoro)quinolones such as balofloxacin are used to treat Vibrio infection, however, resistance has emerged due to overuse (Suyamud et al., 2024). Indeed, (fluoro)quinolones are one of China's two primary residual chemicals associated with aquaculture (Liu et al., 2017). Vibrio can develop quinolone resistance through mutations in the DNA gyrase gene or through plasmid-mediated mechanisms (Dutta et al., 2021). Thus, the use of V. parahaemolyticus and V. alginilyticus as bacterial representatives, and balofloxacin as a quinolone-based antibacterial representative, can help to define novel magnesium-dependent phenotypic resistance mechanisms of pathogenic Vibrio species.”

On Page 2, line 34: The abstract contains some undefined abbreviations, such as 'PE' and 'PG', which should be explained. 

We appreciate this comment! We explain the PE and PG in the revised abstract as following:

“phosphatidylethanolamine (PE) biosynthesis is reduced and phosphatidylglycerol (PG)”

On Page 2, line 31-32: For the statement "Exogenous supplementation of fatty acids confirm the role of fatty acids in antibiotic resistance…" it would be beneficial to specify whether the fatty acids were saturated or unsaturated. 

Response, We appreciate this comment! We revise the sentence as following:

“Exogenous supplementation of unsaturated and saturated fatty acids increased and decreased bacterial susceptibility to antibiotics, respectively, confirming the role of fatty acids in antibiotic resistance.”

The potential effects of the specific ions (SO4 and Cl2) present in the Mg2SO4 and MgCl2 compounds used in the study were not discussed. It would be useful to understand if these ions had any influence on the observed outcomes.

We appreciate this comment! We revise the sentence as following:

“However, the MIC for BLFX was higher in ASWT medium supplemented with Mg2SO4 or MgCl2 than in LB medium (Fig 1B). And Mg2SO4 or MgCl2 had no

difference on MIC, suggesting it is Mg2+ not other ions contribute to the MIC change.”

On Page 8, line 141: The heading of Figure 2, "Mg2+ elevates intracellular Mg2+," seems redundant and could be revised for clarity or modified. 

We appreciate this comment! Figure 2 is now moved to supplementary figure as Suppl. Fig 2. The title is revised as following:

“Figure 2. Mg2+ decreases balofloxacin uptake.”

On Page 4, line 91: some terms/abbreviations, such as 'LB' and 'M9,' require expansion or definition to ensure the reader's understanding.

We appreciate this comment! We include the expansion for LB and M9 in the  revised manuscript as following:

“Luria-Bertani medium (LB medium)” and “M9 minimal medium (M9 medium)”

Page 4, line 92: The real seawater composition used in the experiments should be supported by a reference.

We appreciate this comment! We provide the reference in the revised manuscript as following:

““artificial seawater” (ASWT) medium that included the major ion species in marine water (Wilson, 1975) (LB medium plus 210 mM NaCl, 35 mM Mg2SO4, 7 mM KCl, and 7 mM CaCl2)”

Page 4 line, number 93: the he full names of the bacterial strains (e.g., ATCC33787 and VP01) should be provided instead of just the strain numbers.

We appreciate this comment! We revised the sentence as following:

“To investigate whether this mineral impacts antibiotic activity, the minimal inhibitory concentration (MIC) of V. alginolyticus ATCC33787 and V. parahaemolyticus VP01, which we referred as ATCC33787 and VP01 afterwards,”

Finally, there appears to be a potential contradiction between the statements on page 12, lines 211-212 and 214-216, regarding the effects of Mg2+ on the synthesis of unsaturated fatty acids. Further explanation may be needed to reconcile these seemingly contradictory points.

We appreciate this comment! For line 221-226, which was previously line 211-212, is about the gene expression for fatty acid biosynthesis. While, Line 228 and 233, which was previously line 214-216 is about the gene expression for fatty acid degradation. We agree that the previous description is a little bit confuse. We revise the sentence to emphasize that we focus on fatty acid degradation so that the readers can tell them apart. 

In the text, we revised it as following:

“In addition, we also quantified gene expression during fatty acid degradation to determine whether Mg2+ affects this process”  In the figure legend, we also indicate that 

“H. qRT-PCR for the expression of genes encoding fatty acid degradation in the absence or presence of the indicated concentrations of MgCl2”

eLife Assessment

The study explored the influence of magnesium on phenotypic antibiotic resistance in two strains of Vibrios: V. alginolyticus ATCC33787 and V. parahaemolyticus VP01. This research is fundamental for revealing the phenotypic antibiotic resistance mechanism utilized by the specified model bacteria in elevated levels of magnesium. The study produced convincing evidence indicating that in high concentrations of magnesium, the efficacy of selected antibiotics was diminished due to decreased biosynthesis of unsaturated fatty acids and PE, along with an increase in the biosynthesis of PG.

Reviewer #1 (Public review):

Summary:

In the manuscript entitled "Magnesium modulates phospholipid metabolism to promote bacterial phenotypic resistance to antibiotics", Li et al demonstrated the role of magnesium in promoting phenotypic resistance in V. alginolyticus. Using standard microbiological and metabolomic techniques, the authors have shown the significance of fatty acid biosynthesis pathway behind the resistance mechanism. This study is significant as it sheds light on the role of an exogenous factor in altering membrane composition, polarization and fluidity which ultimately leads to antimicrobial resistance.

Strengths:

Authors have used different approaches to demonstrate the effect of Mg+2 on drug resistance in Vibrio alginolyticus. The revised version of the manuscript is much improved, with a very informative introduction and a variety of methodologies with clear explanation of the experiments performed. Also, additional experiments were performed as suggested by the reviewers which certainly enhanced the quality of the paper. I believe the findings of this study will be of high impact in the bacterial community.

Weaknesses:

There are a few grammatical mistakes.

Comments on revisions:

The authors have done a comprehensive job of addressing all my concerns in their revised version.

Reviewer #2 (Public review):

Summary:

In this study, the authors aimed to identify if and how magnesium affects the ability of two particular bacteria species to resist the action of antibiotics. In my view, the authors succeeded in their goals and present a compelling study that will have important implications for the antibiotic resistance research community. Since metals like magnesium are present in all lab media compositions and are present in the host, the data presented in this study certainly will inspire additional research by the community. These could include research into whether other types of metals also induce multi-drug resistance, whether this phenomenon can be observed in other bacterial species, especially pathogenic species that cause clinical disease, and whether the underlying molecular determinants (i.e. enzymes) of metal-induced phenotypic resistance could be new antimicrobial drug targets themselves.

Strengths:

This study's strengths include that the authors used a variety of methodologies, all of which point to a clear effect of exogenous Mg2+ on drug resistance in the targeted species. I also comment the authors for carrying out a comprehensive study, spanning evaluation of whole cell phenotypes, metabolic pathways, genetic manipulation, to enzyme activity level evaluation. The fact that the authors uncovered a molecular mechanism underlying Mg2+-induced phenotypic resistance is particularly important as the key proteins should be studied further.

Weaknesses:

I thank the authors for improving their manuscript based on my previous suggestions. I still believe the Results section is long and bogs down at times.

In general, the conclusions drawn by the authors are justified by the data, except for the interpretation of some experiments. Importantly, this paper has discovered new antimicrobial resistance mechanisms and has also pointed to potential new targets for antimicrobials.

Comments on revisions:

I just wanted to thank the authors for addressing most of my previous comments.

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Reply to the reviewers

Manuscript number: RC-2024-02648

Corresponding author(s): Kevin Berthenet (kevin.berthenet@lyon.unicancer.fr) and Gabriel Ichim (gabriel.ichim@lyon.unicancer.fr)

1. General Statements

We thank all the reviewers for their time and their constructive criticism, based on which we propose the revision plan detailed bellow. All our responses are indicated in italics font. When is the case, the figures for the reviewers are included just below the answer. Only where indicated they have been included in the manuscript. The line numbers indicated here refer to those in original manuscript.

The two reviews are listed in full at the end of the document.

2. Description of the planned revisions

Reviewer #1

In this manuscript, the authors report a non-apoptotic role for caspase 3 in promoting cell migration. RNA sequencing revealed a "gene signature" associated with caspase 3 knockdown in a melanoma cell line, although there is no investigation of the connection between caspase 3 expression and the regulation of gene expression. Mass spectrometry-based experiments (AP-MS and BioID) identified numerous interacting proteins, with coronin 1B being the most extensively characterized. Data provided indicates that there is a direct interaction between caspase 3 and coronin 1B, and that caspase 3 influences coronin 1B phosphorylation basally and following ligand stimulation. Both proteins are required for efficient cell migration in scratch wound assays. Data is provided indicating that the actions of caspase 3 are independent of proteolytic activity, although the pharmacological inhibition of caspase activity is not complete, nor is the knockdown of BAX/BAK, making these conclusions poorly substantiated. Evaluation of pathways regulating caspase 3 expression implicates the SP1 transcription factor.

Response: We thank the reviewer for their supportive comment. Regarding specific pharmacological inhibition of caspase-3, work is under way to complement the results obtained with a pan-caspase inhibitor (qVD-OPh). We will use specific effector caspases inhibitors, complemented by several other approaches: complete KO of BAX and BAK proteins to prevent all eventual mitochondrial permeabilization and low-level effector caspase activation, overexpression (OE) of the anti-apoptotic protein BCL-xL to also prevent residual mitochondrial permeabilization, while also OE XIAP, a potent caspase inhibitor. The promising preliminary data using two effector caspases specific inhibitors (Ac-DEVD-CHO and Ac-DNLD-CHO) in two different melanoma cells, during wound healing migration, is shown below, with no effect on melanoma cell migration.

Line 129 - The data in Sup. Fig. 1H-L are technical, but where are the mass spectrometry results from the BioID2 experiments? These technical figures are really only relevant if the BioID2 system has been used for protein pull-downs, not for the IF analysis in Fig. 2B.

Response: We apologize for lack of precision in the article logical flow, we will now incorporate the MS data based on the BioID2 experiment earlier in the manuscript.

Line 143 - Figure 2C - it is not entirely convincing that caspase 7 is not associated with the cytoskeleton, there is a visible band in lysates from both cell lines, in contrast with GAPDH which is convincingly cytoplasmic. This is particularly true in the WM852 cell lines, in which the Caspase 3 band is almost the same as Caspase 7. These results would also be more convincing if there was IF of Caspase 7 and actin to show whether it is or is not enriched in regions of higher F-actin levels.

Response: Indeed, our data points towards an enrichment of caspase-3 at the cell cortex. Since generally caspase-7 protein levels are lower, we detected it less in the cytosolic fraction. As suggested, now we performed more sensitive IF colocalization confocal imaging between caspase-7 and F-actin and find it also partially localized to the cortical cytoskeleton (see below). However, this effector caspase is not involved in melanoma cell migration (see wound healing assay below, with two different siRNAs for CASP7 and the positive control of siRNA CASP3).

Figure 2D - knockdowns with only a single siRNA are insufficient, this should be replicated with additional siRNAs. In addition to the effect on actin anisotropy, it appears as though cells are smaller, is this and any other morphological changes reproducible?

Response: We plan to strengthen the data shown in Fig.2D with additional siRNAs, as shown below. In addition, high-content screening (HCS) microscopy will provide several other cell morphology descriptors.

Figure 2D-E. Is it cytochalasin B or D used in these experiments? The text and figures don't agree with each other. 5. Figure 2F-G, same comments above for 2D-E (i.e. comments 3 & 4).

Response: The experimental conditions will be better detailed in the revised manuscript.

Figure 2F-G, it appears as though the fewer focal adhesions in the Caspase 3 knockdown cells are bigger per focal adhesion, is this a consistent result? If so, what is the explanation?

Response: In addition to number, we also plan to quantify the size of focal adhesions.

Figure 2H - it's not clear how this RNAseq data is relevant to the manuscript. There are some genes in the heat map, but it's not clear which ones are changed in their expression in the caspase 3 knockdown cells, nor is it clear how this is relevant to the proposed mechanisms of Caspase 3 interacting with and influencing the phosphorylation of coronin 1B. If there is no connection, then these data can be removed.* *

Response: As suggested by the reviewer, the RNAseq data presented in Figure 2H will be removed from the revised manuscript since it is not very relevant.

Supp. Figure 3 - given that there is data from multiple siRNAs for the incucyte migration data, it should be in the primary figures. And since there are multiple siRNAs and CRISPR/Cas9 KO cells, there should be nothing limiting the replication of the other data presented from only a single siRNA.

Response: Several siRNA are now used for replicating key results as shown above.

Figure 3A - how was cell adhesion measured? The methods section says "cell adhesion was determined through cell shape analysis and scoring" But this is very vague.

Response: We thank the reviewer for spotting out this ambiguity, in the revised manuscript we will be more precise in Material and Methods section.

Figure 3L - was the Casp7 knockdown experiments done with multiple siRNAs? Both melanoma cell lines? Why is this figure only shown out to 24 hours, whereas the other Incucyte experiment run out to 48 hours? Where is the western blot confirming the caspase 7 knockdown? This is important to establish a clear lack of effect.

Response*: We apologize for lacking more details, we now provide several siRNA for caspase-7, all showing no or minimal effect of melanoma cell migration (see answer to point 2). *

Line 190 - it is not true to say that in the presence of QVD there is no longer any caspase activity induced by actinomycin D/ABT263 in supplemental Figures 3J-K. The way that the Y axis has been broken diminishes the difference between untreated and treated cells. In fact, there is apparently over 3-4 times more caspase activity in the actinomycin D/ABT263 treated cells in the presence of QVD relative to basal caspase activity. As a result, it cannot be concluded that there is no residual caspase activity.

Response: We were not precise enough in describing the data in S3J-K. In the revised manuscript we will clearly say that since treatment with a pan-caspase inhibitor does not have the effect of lowering any basal caspase activity (column 1 versus 2), we conclude that in melanoma cells (WM793 and WM852) there is no basal caspase activation that could drive cell motility. The ActD/ABT263 treatment was used as positive control for bona fide induction of effector caspase activation. These results will be complemented by BAX/BAK DKO and BCLxL OE.

Line 192 - Does the knockout of BAX/BAK (which apparently reduced but did not eliminate BAX/BAK protein levels in Supp. Fig. 3L) actually "completely block" caspase activity via the mitochondrial pathway? This has not been demonstrated.

Response: We now provide a fluorometric effector caspases assay showing abrogation of caspase activity in BAX/BAK DKO cells (see below, caspase activating treatment is ActinomycinD plus ABT263). In addition, we will improve the DKO efficacy.

Line 217 - coronin 1B was a hit from which assays? IP-MS and/or BioID2? I see that this is shown in Figure 5A but not referenced in this sentence.

Line 218 - the reference to Figure 5A should be in the previous sentence. Line 220 - Can it really be said that the interaction is specific since there is a coronin 1B band in the GFP "negative" control?__ __

Response*: The revised manuscript will address these inadequacies. *

Line 222 - it is a good control to show that siRNA-knockdown of Caspase 3 reduced the PLA signal in Figure 5C, but the reciprocal experiment of looking at what happens with Coronin 1B knockdown should be included. How does the PLA signal relate to phalloidin-stained F-actin?

Response: The proximity ligation assay (PLA) is now complemented by KD of Coronin 1B (see below) and we will try to also add the phalloidin staining for F-actin, if compatible with the PLA protocol.

Line 224 - looking at the line scans, is the lack of recruitment of coronin 1B to the F-actin at the edge of the protrusion in the Caspase 3 knockdown cells reproducible? Is the point that caspase 3 recruits Coronin 1B? There is an obvious difference in the F-actin at the cell edge, but if the F-actin were as dense in the Caspase 3 knockdown cells as they are for the control, would the same lack of coronin 1B be apparent?

Response: This aspect will be better addressed/discussed in the revised manuscript.

Line 227 - where is the western blot showing the effectiveness of the coronin 1B knockdown to accompany Figure 5F.

Response: The efficacy of coronin 1B KD will be added in the revised manuscript.

Figure 5G - the blots indicate that there is no change in phospho-PKCalpha in the caspase 3 knockdown cells, although phospho-coronin 1B does decrease. This has not been commented upon in the text. Is the implication that there is a non-PKCalpha mediated mechanism for coronin 1B phosphorylation that is dependent on caspase 3?

Figure 5H - following from the previous point, there is no phospho-PKCalpha blot that would be a positive control for the effect of PDGF stimulation on PKC activation, in control and caspase 3 knockdown cells, to evaluate whether the effect on coronin 1B phosphorylation was upstream or downstream of PKCalpha. This is also true for Supp. Fig. 4H.

Response*: Since there are several PKC isoforms that might be co-expressed in melanoma cells, it is possible that PKCalpha is not the one responsible for phosphorylating Coronin 1B. We will be more precise in our investigations by using a pan-phospho-PKC antibody. *

Does phosphorylation of coronin 1B affect its interaction with caspase 3?

Response: We will assess by Co-IP the interaction of caspase-3 with both non-phosphorylated and phosphorylated Coronin 1B.

Figure 6 - as before, only a single siRNA to knockdown SP1 is insufficient to robustly support the conclusions.

Response: As shown below, we addressed this helpful comment by using several siRNAs to assess the role of SP1 in melanoma cell motility, in two different melanoma cell lines.

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Reviewer #2

In this manuscript, the authors provide substantial amounts of experimental evidence that caspase-3, more precisely pro-caspase-3, might be involved in promoting melanoma cell migration and invasion. As such, this function, which might stem from scaffolding roles independent of proteolytic activity (yet not shown entirely convincingly), could possibly be similar to those attributed to other caspases, yet the latter omitted experiments testing for the necessity of enzyme activity. The data are novel and interesting and obviously deserve publication. Yet, a number of criticisms need to be listed.

Response*: We thank the reviewer for upholding the novelty of our study. As also rightfully pointed by R1, we will strive in a revised manuscript to definitely show that caspase-3 participate to melanoma cell motility independently of its pro-apoptotic protease role: we will use two effector caspases specific inhibitors (Ac-DEVD-CHO and Ac-DNLD-CHO, as shown above) complemented by several other approaches: complete KO of BAX and BAK protein to prevent all eventual mitochondrial permeabilization and low-level effector caspase activation, OE of the anti-apoptotic protein BCL-xL to also prevent residual mitochondrial permeabilization, while also OE XIAP, a potent caspase inhibitor. *

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• First and foremost, I don't seem to find ethical approval information on the animal experiments. While I do not work with zebrafish myself, I am also somewhat concerned by the size of tumours seen in some of the depicted fish. It is highly important that appropriate information in this direction, including possible endpoints, is provided. Response*: We completely agree with the reviewer, yet the ethical approval is already provided in the manuscript (line 588) and will be complemented by adding the endpoints. *

The second major issue lies in figure 1. The figure as a whole seems to be very much forced to support or motivate later experimental findings. The authors lack sufficient clarity on some of the approaches and seem to judge on the data to a good bit as they see fit. (…)

I´d suggest to largely take out Fig1 in its current form, spend time on properly describing any analysis of public data, carefully interpret these and move them probably to the end of the results. Currently, it just leaves the impression that the data were pushed as hard as possible to promote the good work that follows.

Response*: We will carefully consider the reviewer’s comments and rework the bioinformatics analysis presented in Figure 1 (and associated supplementary figure), making sure we will present certain data as correlation (and not causality) and go into more details on the physio-pathological features of melanoma patients with low/high caspase-3 expression. *

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The text on line 129ff seems to have omitted any outcomes from the Suppl. Fig1H-L. What was found and what are we supposed to learn from this?

Response: We apologize for lack of precision in the article logical flow, we will now incorporate the MS data based on the BioID2 experiment earlier in the manuscript.* *

Lines 146/147 state similar effects upon CASP3 depletion and cytochalasin D. I cannot make that out from Fig.2D. Can you be more specific or visualize this better?

Response: We will fix this by including zoomed and detailed images of individual cells.

• Is it possible to state whether effects such as in Fig.3B are general rather than showing just 1 cell?

Response: The defects in cell adhesion for caspase-3-depleted cells are quantified in Figure 3A. Moreover, we will add representative images.

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It is unclear how the genes in Fig.2H were defined and why would all of these differ (unless this was an inclusion criterion for the panel). Are these considered to be downstream of CASP3 somehow? I don't fully get the message here. Is this panel even required here?

Response: As it brings little information, panel 2H will be excluded from the revised manuscript.

To fully prove independence of caspase-3 activity, it would be appropriate to k/o caspase-3 to then reconstitute the cells with inactive caspase-3.

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Response: We will try our best of addressing this comment in the revised manuscript.

Fig.4C and associated text: Statements on changes in tumor size cannot be made from data on tumor free survival.

Response: We apologize for the misleading data interpretation; this will be tuned down in a revised manuscript.